Feeds:
Posts
Comments

Archive for the ‘Pharmaceutical Drug Discovery’ Category

From High-Throughput Assay to Systems Biology: New Tools for Drug Discovery

Curator: Stephen J. Williams, PhD

Marc W. Kirschner*

Department of Systems Biology
Harvard Medical School

Boston, Massachusetts 02115

With the new excitement about systems biology, there is understandable interest in a definition. This has proven somewhat difficult. Scientific fields, like spe­cies, arise by descent with modification, so in their ear­liest forms even the founders of great dynasties are only marginally different than their sister fields and spe­cies. It is only in retrospect that we can recognize the significant founding events. Before embarking on a def­inition of systems biology, it may be worth remember­ing that confusion and controversy surrounded the in­troduction of the term “molecular biology,” with claims that it hardly differed from biochemistry. Yet in retro­spect molecular biology was new and different. It intro­duced both new subject matter and new technological approaches, in addition to a new style.

As a point of departure for systems biology, consider the quintessential experiment in the founding of molec­ular biology, the one gene one enzyme hypothesis of Beadle and Tatum. This experiment first connected the genotype directly to the phenotype on a molecular level, although efforts in that direction can certainly be found in the work of Archibald Garrod, Sewell Wright, and others. Here a protein (in this case an enzyme) is seen to be a product of a single gene, and a single function; the completion of a specific step in amino acid biosynthesis is the direct result. It took the next 30 years to fill in the gaps in this process. Yet the one gene one enzyme hypothesis looks very different to us today. What is the function of tubulin, of PI-3 kinase or of rac? Could we accurately predict the phenotype of a nonle­thal mutation in these genes in a multicellular organ­ism? Although we can connect structure to the gene, we can no longer infer its larger purpose in the cell or in the organism. There are too many purposes; what the protein does is defined by context. The context also includes a history, either developmental or physiologi­cal. Thus the behavior of the Wnt signaling pathway depends on the previous lineage, the “where and when” questions of embryonic development. Similarly the behavior of the immune system depends on previ­ous experience in a variable environment. All of these features stress how inadequate an explanation for function we can achieve solely by trying to identify genes (by annotating them!) and characterizing their transcriptional control circuits.

That we are at a crossroads in how to explore biology is not at all clear to many. Biology is hardly in its dotage; the process of discovery seems to have been per­fected, accelerated, and made universally applicable to all fields of biology. With the completion of the human genome and the genomes of other species, we have a glimpse of many more genes than we ever had before to study. We are like naturalists discovering a new con­tinent, enthralled with the diversity itself. But we have also at the same time glimpsed the finiteness of this list of genes, a disturbingly small list. We have seen that the diversity of genes cannot approximate the diversity of functions within an organism. In response, we have argued that combinatorial use of small numbers of components can generate all the diversity that is needed. This has had its recent incarnation in the sim­plistic view that the rules of cis-regulatory control on DNA can directly lead to an understanding of organ­isms and their evolution. Yet this assumes that the gene products can be linked together in arbitrary combina­tions, something that is not assured in chemistry. It also downplays the significant regulatory features that in­volve interactions between gene products, their local­ization, binding, posttranslational modification, degra­dation, etc. The big question to understand in biology is not regulatory linkage but the nature of biological systems that allows them to be linked together in many nonlethal and even useful combinations. More and more we come to realize that understanding the con­served genes and their conserved circuits will require an understanding of their special properties that allow them to function together to generate different pheno­types in different tissues of metazoan organisms. These circuits may have certain robustness, but more impor­tant they have adaptability and versatility. The ease of putting conserved processes under regulatory control is an inherent design feature of the processes them­selves. Among other things it loads the deck in evolu­tionary variation and makes it more feasible to generate useful phenotypes upon which selection can act.

Systems biology offers an opportunity to study how the phenotype is generated from the genotype and with it a glimpse of how evolution has crafted the pheno­type. One aspect of systems biology is the develop­ment of techniques to examine broadly the level of pro­tein, RNA, and DNA on a gene by gene basis and even the posttranslational modification and localization of proteins. In a very short time we have witnessed the development of high-throughput biology, forcing us to consider cellular processes in toto. Even though much of the data is noisy and today partially inconsistent and incomplete, this has been a radical shift in the way we tear apart problems one interaction at a time. When coupled with gene deletions by RNAi and classical methods, and with the use of chemical tools tailored to proteins and protein domains, these high-throughput techniques become still more powerful.

High-throughput biology has opened up another im­portant area of systems biology: it has brought us out into the field again or at least made us aware that there is a world outside our laboratories. Our model systems have been chosen intentionally to be of limited genetic diversity and examined in a highly controlled and repro­ducible environment. The real world of ecology, evolu­tion, and human disease is a very different place. When genetics separated from the rest of biology in the early part of the 20th century, most geneticists sought to understand heredity and chose to study traits in the organism that could be easily scored and could be used to reveal genetic mechanisms. This was later ex­tended to powerful effect to use genetics to study cell biological and developmental mechanisms. Some ge­neticists, including a large school in Russia in the early 20th century, continued to study the genetics of natural populations, focusing on traits important for survival. That branch of genetics is coming back strongly with the power of phenotypic assays on the RNA and pro­tein level. As human beings we are most concerned not with using our genetic misfortunes to unravel biology’s complexity (important as that is) but with the role of our genetics in our individual survival. The context for understanding this is still not available, even though the data are now coming in torrents, for many of the genes that will contribute to our survival will have small quan­titative effects, partially masked or accentuated by other genetic and environmental conditions. To under­stand the genetic basis of disease will require not just mapping these genes but an understanding of how the phenotype is created in the first place and the messy interactions between genetic variation and environ­mental variation.

Extracts and explants are relatively accessible to syn­thetic manipulation. Next there is the explicit recon­struction of circuits within cells or the deliberate modifi­cation of those circuits. This has occurred for a while in biology, but the difference is that now we wish to construct or intervene with the explicit purpose of de­scribing the dynamical features of these synthetic or partially synthetic systems. There are more and more tools to intervene and more and more tools to measure. Although these fall short of total descriptions of cells and organisms, the detailed information will give us a sense of the special life-like processes of circuits, pro­teins, cells in tissues, and whole organisms in their en­vironment. This meso-scale systems biology will help establish the correspondence between molecules and large-scale physiology.

You are probably running out of patience for some definition of systems biology. In any case, I do not think the explicit definition of systems biology should come from me but should await the words of the first great modern systems biologist. She or he is probably among us now. However, if forced to provide some kind of label for systems biology, I would simply say that systems biology is the study of the behavior of complex biologi­cal organization and processes in terms of the molecu­lar constituents. It is built on molecular biology in its special concern for information transfer, on physiology for its special concern with adaptive states of the cell and organism, on developmental biology for the impor­tance of defining a succession of physiological states in that process, and on evolutionary biology and ecol­ogy for the appreciation that all aspects of the organ­ism are products of selection, a selection we rarely understand on a molecular level. Systems biology attempts all of this through quantitative measurement, modeling, reconstruction, and theory. Systems biology is not a branch of physics but differs from physics in that the primary task is to understand how biology gen­erates variation. No such imperative to create variation exists in the physical world. It is a new principle that Darwin understood and upon which all of life hinges. That sounds different enough for me to justify a new field and a new name. Furthermore, the success of sys­tems biology is essential if we are to understand life; its success is far from assured—a good field for those seeking risk and adventure.

Source: “Meaning of Systems Biology” Cell, Vol. 121, 503–504, May 20, 2005, DOI 10.1016/j.cell.2005.05.005

Old High-throughput Screening, Once the Gold Standard in Drug Development, Gets a Systems Biology Facelift

From Phenotypic Hit to Chemical Probe: Chemical Biology Approaches to Elucidate Small Molecule Action in Complex Biological Systems

Quentin T. L. Pasquer, Ioannis A. Tsakoumagkos and Sascha Hoogendoorn 

Molecules 202025(23), 5702; https://doi.org/10.3390/molecules25235702

Abstract

Biologically active small molecules have a central role in drug development, and as chemical probes and tool compounds to perturb and elucidate biological processes. Small molecules can be rationally designed for a given target, or a library of molecules can be screened against a target or phenotype of interest. Especially in the case of phenotypic screening approaches, a major challenge is to translate the compound-induced phenotype into a well-defined cellular target and mode of action of the hit compound. There is no “one size fits all” approach, and recent years have seen an increase in available target deconvolution strategies, rooted in organic chemistry, proteomics, and genetics. This review provides an overview of advances in target identification and mechanism of action studies, describes the strengths and weaknesses of the different approaches, and illustrates the need for chemical biologists to integrate and expand the existing tools to increase the probability of evolving screen hits to robust chemical probes.

5.1.5. Large-Scale Proteomics

While FITExP is based on protein expression regulation during apoptosis, a study of Ruprecht et al. showed that proteomic changes are induced both by cytotoxic and non-cytotoxic compounds, which can be detected by mass spectrometry to give information on a compound’s mechanism of action. They developed a large-scale proteome-wide mass spectrometry analysis platform for MOA studies, profiling five lung cancer cell lines with over 50 drugs. Aggregation analysis over the different cell lines and the different compounds showed that one-quarter of the drugs changed the abundance of their protein target. This approach allowed target confirmation of molecular degraders such as PROTACs or molecular glues. Finally, this method yielded unexpected off-target mechanisms for the MAP2K1/2 inhibitor PD184352 and the ALK inhibitor ceritinib [97]. While such a mapping approach clearly provides a wealth of information, it might not be easily attainable for groups that are not equipped for high-throughput endeavors.

All-in-all, mass spectrometry methods have gained a lot of traction in recent years and have been successfully applied for target deconvolution and MOA studies of small molecules. As with all high-throughput methods, challenges lie in the accessibility of the instruments (both from a time and cost perspective) and data analysis of complex and extensive data sets.

5.2. Genetic Approaches

Both label-based and mass spectrometry proteomic approaches are based on the physical interaction between a small molecule and a protein target, and focus on the proteome for target deconvolution. It has been long realized that genetics provides an alternative avenue to understand a compound’s action, either through precise modification of protein levels, or by inducing protein mutations. First realized in yeast as a genetically tractable organism over 20 years ago, recent advances in genetic manipulation of mammalian cells have opened up important opportunities for target identification and MOA studies through genetic screening in relevant cell types [98]. Genetic approaches can be roughly divided into two main areas, with the first centering on the identification of mutations that confer compound resistance (Figure 3a), and the second on genome-wide perturbation of gene function and the concomitant changes in sensitivity to the compound (Figure 3b). While both methods can be used to identify or confirm drug targets, the latter category often provides many additional insights in the compound’s mode of action.

Figure 3. Genetic methods for target identification and mode of action studies. Schematic representations of (a) resistance cloning, and (b) chemogenetic interaction screens.

5.2.1. Resistance Cloning

The “gold standard” in drug target confirmation is to identify mutations in the presumed target protein that render it insensitive to drug treatment. Conversely, different groups have sought to use this principle as a target identification method based on the concept that cells grown in the presence of a cytotoxic drug will either die or develop mutations that will make them resistant to the compound. With recent advances in deep sequencing it is now possible to then scan the transcriptome [99] or genome [100] of the cells for resistance-inducing mutations. Genes that are mutated are then hypothesized to encode the protein target. For this approach to be successful, there are two initial requirements: (1) the compound needs to be cytotoxic for resistant clones to arise, and (2) the cell line needs to be genetically unstable for mutations to occur in a reasonable timeframe.

In 2012, the Kapoor group demonstrated in a proof-of-concept study that resistance cloning in mammalian cells, coupled to transcriptome sequencing (RNA-seq), yields the known polo-like kinase 1 (PLK1) target of the small molecule BI 2536. For this, they used the cancer cell line HCT-116, which is deficient in mismatch repair and consequently prone to mutations. They generated and sequenced multiple resistant clones, and clustered the clones based on similarity. PLK1 was the only gene that was mutated in multiple groups. Of note, one of the groups did not contain PLK1 mutations, but rather developed resistance through upregulation of ABCBA1, a drug efflux transporter, which is a general and non-specific resistance mechanism [101]. In a following study, they optimized their pipeline “DrugTargetSeqR”, by counter-screening for these types of multidrug resistance mechanisms so that these clones were excluded from further analysis (Figure 3a). Furthermore, they used CRISPR/Cas9-mediated gene editing to determine which mutations were sufficient to confer drug resistance, and as independent validation of the biochemical relevance of the obtained hits [102].

While HCT-116 cells are a useful model cell line for resistance cloning because of their genomic instability, they may not always be the cell line of choice, depending on the compound and process that is studied. Povedana et al. used CRISPR/Cas9 to engineer mismatch repair deficiencies in Ewing sarcoma cells and small cell lung cancer cells. They found that deletion of MSH2 results in hypermutations in these normally mutationally silent cells, resulting in the formation of resistant clones in the presence of bortezomib, MLN4924, and CD437, which are all cytotoxic compounds [103]. Recently, Neggers et al. reasoned that CRISPR/Cas9-induced non-homologous end-joining repair could be a viable strategy to create a wide variety of functional mutants of essential genes through in-frame mutations. Using a tiled sgRNA library targeting 75 target genes of investigational neoplastic drugs in HAP1 and K562 cells, they generated several KPT-9274 (an anticancer agent with unknown target)-resistant clones, and subsequent deep sequencing showed that the resistant clones were enriched in NAMPT sgRNAs. Direct target engagement was confirmed by co-crystallizing the compound with NAMPT [104]. In addition to these genetic mutation strategies, an alternative method is to grow the cells in the presence of a mutagenic chemical to induce higher mutagenesis rates [105,106].

When there is already a hypothesis on the pathway involved in compound action, the resistance cloning methodology can be extended to non-cytotoxic compounds. Sekine et al. developed a fluorescent reporter model for the integrated stress response, and used this cell line for target deconvolution of a small molecule inhibitor towards this pathway (ISRIB). Reporter cells were chemically mutagenized, and ISRIB-resistant clones were isolated by flow cytometry, yielding clones with various mutations in the delta subunit of guanine nucleotide exchange factor eIF2B [107].

While there are certainly successful examples of resistance cloning yielding a compound’s direct target as discussed above, resistance could also be caused by mutations or copy number alterations in downstream components of a signaling pathway. This is illustrated by clinical examples of acquired resistance to small molecules, nature’s way of “resistance cloning”. For example, resistance mechanisms in Hedgehog pathway-driven cancers towards the Smoothened inhibitor vismodegib include compound-resistant mutations in Smoothened, but also copy number changes in downstream activators SUFU and GLI2 [108]. It is, therefore, essential to conduct follow-up studies to confirm a direct interaction between a compound and the hit protein, as well as a lack of interaction with the mutated protein.

5.2.3. “Chemogenomics”: Examples of Gene-Drug Interaction Screens

When genetic perturbations are combined with small molecule drugs in a chemogenetic interaction screen, the effect of a gene’s perturbation on compound action is studied. Gene perturbation can render the cells resistant to the compound (suppressor interaction), or conversely, result in hypersensitivity and enhanced compound potency (synergistic interaction) [5,117,121]. Typically, cells are treated with the compound at a sublethal dose, to ascertain that both types of interactions can be found in the final dataset, and often it is necessary to use a variety of compound doses (i.e., LD20, LD30, LD50) and timepoints to obtain reliable insights (Figure 3b).

An early example of successful coupling of a phenotypic screen and downstream genetic screening for target identification is the study of Matheny et al. They identified STF-118804 as a compound with antileukemic properties. Treatment of MV411 cells, stably transduced with a high complexity, genome-wide shRNA library, with STF-118804 (4 rounds of increasing concentration) or DMSO control resulted in a marked depletion of cells containing shRNAs against nicotinamide phosphoribosyl transferase (NAMPT) [122].

The Bassik lab subsequently directly compared the performance of shRNA-mediated knockdown versus CRISPR/Cas9-knockout screens for the target elucidation of the antiviral drug GSK983. The data coming out of both screens were complementary, with the shRNA screen resulting in hits leading to the direct compound target and the CRISPR screen giving information on cellular mechanisms of action of the compound. A reason for this is likely the level of protein depletion that is reached by these methods: shRNAs lead to decreased protein levels, which is advantageous when studying essential genes. However, knockdown may not result in a phenotype for non-essential genes, in which case a full CRISPR-mediated knockout is necessary to observe effects [123].

Another NAMPT inhibitor was identified in a CRISPR/Cas9 “haplo-insufficiency (HIP)”-like approach [124]. Haploinsuffiency profiling is a well-established system in yeast which is performed in a ~50% protein background by heterozygous deletions [125]. As there is no control over CRISPR-mediated loss of alleles, compound treatment was performed at several timepoints after addition of the sgRNA library to HCT116 cells stably expressing Cas9, in the hope that editing would be incomplete at early timepoints, resulting in residual protein levels. Indeed, NAMPT was found to be the target of phenotypic hit LB-60-OF61, especially at earlier timepoints, confirming the hypothesis that some level of protein needs to be present to identify a compound’s direct target [124]. This approach was confirmed in another study, thereby showing that direct target identification through CRISPR-knockout screens is indeed possible [126].

An alternative strategy was employed by the Weissman lab, where they combined genome-wide CRISPR-interference and -activation screens to identify the target of the phase 3 drug rigosertib. They focused on hits that had opposite action in both screens, as in sensitizing in one but protective in the other, which were related to microtubule stability. In a next step, they created chemical-genetic profiles of a variety of microtubule destabilizing agents, rationalizing that compounds with the same target will have similar drug-gene interactions. For this, they made a focused library of sgRNAs, based on the most high-ranking hits in the rigosertib genome-wide CRISPRi screen, and compared the focused screen results of the different compounds. The profile for rigosertib clustered well with that of ABT-571, and rigorous target validation studies confirmed rigosertib binding to the colchicine binding site of tubulin—the same site as occupied by ABT-571 [127].

From the above examples, it is clear that genetic screens hold a lot of promise for target identification and MOA studies for small molecules. The CRISPR screening field is rapidly evolving, sgRNA libraries are continuously improving and increasingly commercially available, and new tools for data analysis are being developed [128]. The challenge lies in applying these screens to study compounds that are not cytotoxic, where finding the right dosage regimen will not be trivial.

SYSTEMS BIOLOGY AND CANCER RESEARCH & DRUG DISCOVERY

Integrative Analysis of Next-Generation Sequencing for Next-Generation Cancer Research toward Artificial Intelligence

Youngjun Park, Dominik Heider and Anne-Christin Hauschild. Cancers 202113(13), 3148; https://doi.org/10.3390/cancers13133148

Abstract

The rapid improvement of next-generation sequencing (NGS) technologies and their application in large-scale cohorts in cancer research led to common challenges of big data. It opened a new research area incorporating systems biology and machine learning. As large-scale NGS data accumulated, sophisticated data analysis methods became indispensable. In addition, NGS data have been integrated with systems biology to build better predictive models to determine the characteristics of tumors and tumor subtypes. Therefore, various machine learning algorithms were introduced to identify underlying biological mechanisms. In this work, we review novel technologies developed for NGS data analysis, and we describe how these computational methodologies integrate systems biology and omics data. Subsequently, we discuss how deep neural networks outperform other approaches, the potential of graph neural networks (GNN) in systems biology, and the limitations in NGS biomedical research. To reflect on the various challenges and corresponding computational solutions, we will discuss the following three topics: (i) molecular characteristics, (ii) tumor heterogeneity, and (iii) drug discovery. We conclude that machine learning and network-based approaches can add valuable insights and build highly accurate models. However, a well-informed choice of learning algorithm and biological network information is crucial for the success of each specific research question

1. Introduction

The development and widespread use of high-throughput technologies founded the era of big data in biology and medicine. In particular, it led to an accumulation of large-scale data sets that opened a vast amount of possible applications for data-driven methodologies. In cancer, these applications range from fundamental research to clinical applications: molecular characteristics of tumors, tumor heterogeneity, drug discovery and potential treatments strategy. Therefore, data-driven bioinformatics research areas have tailored data mining technologies such as systems biology, machine learning, and deep learning, elaborated in this review paper (see Figure 1 and Figure 2). For example, in systems biology, data-driven approaches are applied to identify vital signaling pathways [1]. This pathway-centric analysis is particularly crucial in cancer research to understand the characteristics and heterogeneity of the tumor and tumor subtypes. Consequently, this high-throughput data-based analysis enables us to explore characteristics of cancers with a systems biology and a systems medicine point of view [2].Combining high-throughput techniques, especially next-generation sequencing (NGS), with appropriate analytical tools has allowed researchers to gain a deeper systematic understanding of cancer at various biological levels, most importantly genomics, transcriptomics, and epigenetics [3,4]. Furthermore, more sophisticated analysis tools based on computational modeling are introduced to decipher underlying molecular mechanisms in various cancer types. The increasing size and complexity of the data required the adaptation of bioinformatics processing pipelines for higher efficiency and sophisticated data mining methodologies, particularly for large-scale, NGS datasets [5]. Nowadays, more and more NGS studies integrate a systems biology approach and combine sequencing data with other types of information, for instance, protein family information, pathway, or protein–protein interaction (PPI) networks, in an integrative analysis. Experimentally validated knowledge in systems biology may enhance analysis models and guides them to uncover novel findings. Such integrated analyses have been useful to extract essential information from high-dimensional NGS data [6,7]. In order to deal with the increasing size and complexity, the application of machine learning, and specifically deep learning methodologies, have become state-of-the-art in NGS data analysis.

Figure 1. Next-generation sequencing data can originate from various experimental and technological conditions. Depending on the purpose of the experiment, one or more of the depicted omics types (Genomics, Transcriptomics, Epigenomics, or Single-Cell Omics) are analyzed. These approaches led to an accumulation of large-scale NGS datasets to solve various challenges of cancer research, molecular characterization, tumor heterogeneity, and drug target discovery. For instance, The Cancer Genome Atlas (TCGA) dataset contains multi-omics data from ten-thousands of patients. This dataset facilitates a variety of cancer researches for decades. Additionally, there are also independent tumor datasets, and, frequently, they are analyzed and compared with the TCGA dataset. As the large scale of omics data accumulated, various machine learning techniques are applied, e.g., graph algorithms and deep neural networks, for dimensionality reduction, clustering, or classification. (Created with BioRender.com.)

Figure 2. (a) A multitude of different types of data is produced by next-generation sequencing, for instance, in the fields of genomics, transcriptomics, and epigenomics. (b) Biological networks for biomarker validation: The in vivo or in vitro experiment results are considered ground truth. Statistical analysis on next-generation sequencing data produces candidate genes. Biological networks can validate these candidate genes and highlight the underlying biological mechanisms (Section 2.1). (c) De novo construction of Biological Networks: Machine learning models that aim to reconstruct biological networks can incorporate prior knowledge from different omics data. Subsequently, the model will predict new unknown interactions based on new omics information (Section 2.2). (d) Network-based machine learning: Machine learning models integrating biological networks as prior knowledge to improve predictive performance when applied to different NGS data (Section 2.3). (Created with BioRender.com).

Therefore, a large number of studies integrate NGS data with machine learning and propose a novel data-driven methodology in systems biology [8]. In particular, many network-based machine learning models have been developed to analyze cancer data and help to understand novel mechanisms in cancer development [9,10]. Moreover, deep neural networks (DNN) applied for large-scale data analysis improved the accuracy of computational models for mutation prediction [11,12], molecular subtyping [13,14], and drug repurposing [15,16]. 

2. Systems Biology in Cancer Research

Genes and their functions have been classified into gene sets based on experimental data. Our understandings of cancer concentrated into cancer hallmarks that define the characteristics of a tumor. This collective knowledge is used for the functional analysis of unseen data.. Furthermore, the regulatory relationships among genes were investigated, and, based on that, a pathway can be composed. In this manner, the accumulation of public high-throughput sequencing data raised many big-data challenges and opened new opportunities and areas of application for computer science. Two of the most vibrantly evolving areas are systems biology and machine learning which tackle different tasks such as understanding the cancer pathways [9], finding crucial genes in pathways [22,53], or predicting functions of unidentified or understudied genes [54]. Essentially, those models include prior knowledge to develop an analysis and enhance interpretability for high-dimensional data [2]. In addition to understanding cancer pathways with in silico analysis, pathway activity analysis incorporating two different types of data, pathways and omics data, is developed to understand heterogeneous characteristics of the tumor and cancer molecular subtyping. Due to its advantage in interpretability, various pathway-oriented methods are introduced and become a useful tool to understand a complex diseases such as cancer [55,56,57].

In this section, we will discuss how two related research fields, namely, systems biology and machine learning, can be integrated with three different approaches (see Figure 2), namely, biological network analysis for biomarker validation, the use of machine learning with systems biology, and network-based models.

2.1. Biological Network Analysis for Biomarker Validation

The detection of potential biomarkers indicative of specific cancer types or subtypes is a frequent goal of NGS data analysis in cancer research. For instance, a variety of bioinformatics tools and machine learning models aim at identify lists of genes that are significantly altered on a genomic, transcriptomic, or epigenomic level in cancer cells. Typically, statistical and machine learning methods are employed to find an optimal set of biomarkers, such as single nucleotide polymorphisms (SNPs), mutations, or differentially expressed genes crucial in cancer progression. Traditionally, resource-intensive in vitro analysis was required to discover or validate those markers. Therefore, systems biology offers in silico solutions to validate such findings using biological pathways or gene ontology information (Figure 2b) [58]. Subsequently, gene set enrichment analysis (GSEA) [50] or gene set analysis (GSA) [59] can be used to evaluate whether these lists of genes are significantly associated with cancer types and their specific characteristics. GSA, for instance, is available via web services like DAVID [60] and g:Profiler [61]. Moreover, other applications use gene ontology directly [62,63]. In addition to gene-set-based analysis, there are other methods that focuse on the topology of biological networks. These approaches evaluate various network structure parameters and analyze the connectivity of two genes or the size and interconnection of their neighbors [64,65]. According to the underlying idea, the mutated gene will show dysfunction and can affect its neighboring genes. Thus, the goal is to find abnormalities in a specific set of genes linked with an edge in a biological network. For instance, KeyPathwayMiner can extract informative network modules in various omics data [66]. In summary, these approaches aim at predicting the effect of dysfunctional genes among neighbors according to their connectivity or distances from specific genes such as hubs [67,68]. During the past few decades, the focus of cancer systems biology extended towards the analysis of cancer-related pathways since those pathways tend to carry more information than a gene set. Such analysis is called Pathway Enrichment Analysis (PEA) [69,70]. The use of PEA incorporates the topology of biological networks. However, simultaneously, the lack of coverage issue in pathway data needs to be considered. Because pathway data does not cover all known genes yet, an integration analysis on omics data can significantly drop in genes when incorporated with pathways. Genes that can not be mapped to any pathway are called ‘pathway orphan.’ In this manner, Rahmati et al. introduced a possible solution to overcome the ‘pathway orphan’ issue [71]. At the bottom line, regardless of whether researchers consider gene-set or pathway-based enrichment analysis, the performance and accuracy of both methods are highly dependent on the quality of the external gene-set and pathway data [72].

2.2. De Novo Construction of Biological Networks

While the known fraction of existing biological networks barely scratches the surface of the whole system of mechanisms occurring in each organism, machine learning models can improve on known network structures and can guide potential new findings [73,74]. This area of research is called de novo network construction (Figure 2c), and its predictive models can accelerate experimental validation by lowering time costs [75,76]. This interplay between in silico biological networks building and mining contributes to expanding our knowledge in a biological system. For instance, a gene co-expression network helps discover gene modules having similar functions [77]. Because gene co-expression networks are based on expressional changes under specific conditions, commonly, inferring a co-expression network requires many samples. The WGCNA package implements a representative model using weighted correlation for network construction that leads the development of the network biology field [78]. Due to NGS developments, the analysis of gene co-expression networks subsequently moved from microarray-based to RNA-seq based experimental data [79]. However, integration of these two types of data remains tricky. Ballouz et al. compared microarray and NGS-based co-expression networks and found the existence of a bias originating from batch effects between the two technologies [80]. Nevertheless, such approaches are suited to find disease-specific co-expressional gene modules. Thus, various studies based on the TCGA cancer co-expression network discovered characteristics of prognostic genes in the network [81]. Accordingly, a gene co-expression network is a condition-specific network rather than a general network for an organism. Gene regulatory networks can be inferred from the gene co-expression network when various data from different conditions in the same organism are available. Additionally, with various NGS applications, we can obtain multi-modal datasets about regulatory elements and their effects, such as epigenomic mechanisms on transcription and chromatin structure. Consequently, a gene regulatory network can consist of solely protein-coding genes or different regulatory node types such as transcription factors, inhibitors, promoter interactions, DNA methylations, and histone modifications affecting the gene expression system [82,83]. More recently, researchers were able to build networks based on a particular experimental setup. For instance, functional genomics or CRISPR technology enables the high-resolution regulatory networks in an organism [84]. Other than gene co-expression or regulatory networks, drug target, and drug repurposing studies are active research areas focusing on the de novo construction of drug-to-target networks to allow the potential repurposing of drugs [76,85].

2.3. Network Based Machine Learning

A network-based machine learning model directly integrates the insights of biological networks within the algorithm (Figure 2d) to ultimately improve predictive performance concerning cancer subtyping or susceptibility to therapy. Following the establishment of high-quality biological networks based on NGS technologies, these biological networks were suited to be integrated into advanced predictive models. In this manner, Zhang et al., categorized network-based machine learning approaches upon their usage into three groups: (i) model-based integration, (ii) pre-processing integration, and (iii) post-analysis integration [7]. Network-based models map the omics data onto a biological network, and proper algorithms travel the network while considering both values of nodes and edges and network topology. In the pre-processing integration, pathway or other network information is commonly processed based on its topological importance. Meanwhile, in the post-analysis integration, omics data is processed solely before integration with a network. Subsequently, omics data and networks are merged and interpreted. The network-based model has advantages in multi-omics integrative analysis. Due to the different sensitivity and coverage of various omics data types, a multi-omics integrative analysis is challenging. However, focusing on gene-level or protein-level information enables a straightforward integration [86,87]. Consequently, when different machine learning approaches tried to integrate two or more different data types to find novel biological insights, one of the solutions is reducing the search space to gene or protein level and integrated heterogeneous datatypes [25,88].

In summary, using network information opens new possibilities for interpretation. However, as mentioned earlier, several challenges remain, such as the coverage issue. Current databases for biological networks do not cover the entire set of genes, transcripts, and interactions. Therefore, the use of networks can lead to loss of information for gene or transcript orphans. The following section will focus on network-based machine learning models and their application in cancer genomics. We will put network-based machine learning into the perspective of the three main areas of application, namely, molecular characterization, tumor heterogeneity analysis, and cancer drug discovery.

3. Network-Based Learning in Cancer Research

As introduced previously, the integration of machine learning with the insights of biological networks (Figure 2d) ultimately aims at improving predictive performance and interpretability concerning cancer subtyping or treatment susceptibility.

3.1. Molecular Characterization with Network Information

Various network-based algorithms are used in genomics and focus on quantifying the impact of genomic alteration. By employing prior knowledge in biological network algorithms, performance compared to non-network models can be improved. A prominent example is HotNet. The algorithm uses a thermodynamics model on a biological network and identifies driver genes, or prognostic genes, in pan-cancer data [89]. Another study introduced a network-based stratification method to integrate somatic alterations and expression signatures with network information [90]. These approaches use network topology and network-propagation-like algorithms. Network propagation presumes that genomic alterations can affect the function of neighboring genes. Two genes will show an exclusive pattern if two genes complement each other, and the function carried by those two genes is essential to an organism [91]. This unique exclusive pattern among genomic alteration is further investigated in cancer-related pathways. Recently, Ku et al. developed network-centric approaches and tackled robustness issues while studying synthetic lethality [92]. Although synthetic lethality was initially discovered in model organisms of genetics, it helps us to understand cancer-specific mutations and their functions in tumor characteristics [91].

Furthermore, in transcriptome research, network information is used to measure pathway activity and its application in cancer subtyping. For instance, when comparing the data of two or more conditions such as cancer types, GSEA as introduced in Section 2 is a useful approach to get an overview of systematic changes [50]. It is typically used at the beginning of a data evaluation [93]. An experimentally validated gene set can provide information about how different conditions affect molecular systems in an organism. In addition to the gene sets, different approaches integrate complex interaction information into GSEA and build network-based models [70]. In contrast to GSEA, pathway activity analysis considers transcriptome data and other omics data and structural information of a biological network. For example, PARADIGM uses pathway topology and integrates various omics in the analysis to infer a patient-specific status of pathways [94]. A benchmark study with pan-cancer data recently reveals that using network structure can show better performance [57]. In conclusion, while the loss of data is due to the incompleteness of biological networks, their integration improved performance and increased interpretability in many cases.

3.2. Tumor Heterogeneity Study with Network Information

The tumor heterogeneity can originate from two directions, clonal heterogeneity and tumor impurity. Clonal heterogeneity covers genomic alterations within the tumor [95]. While de novo mutations accumulate, the tumor obtains genomic alterations with an exclusive pattern. When these genomic alterations are projected on the pathway, it is possible to observe exclusive relationships among disease-related genes. For instance, the CoMEt and MEMo algorithms examine mutual exclusivity on protein–protein interaction networks [96,97]. Moreover, the relationship between genes can be essential for an organism. Therefore, models analyzing such alterations integrate network-based analysis [98].

In contrast, tumor purity is dependent on the tumor microenvironment, including immune-cell infiltration and stromal cells [99]. In tumor microenvironment studies, network-based models are applied, for instance, to find immune-related gene modules. Although the importance of the interaction between tumors and immune cells is well known, detailed mechanisms are still unclear. Thus, many recent NGS studies employ network-based models to investigate the underlying mechanism in tumor and immune reactions. For example, McGrail et al. identified a relationship between the DNA damage response protein and immune cell infiltration in cancer. The analysis is based on curated interaction pairs in a protein–protein interaction network [100]. Most recently, Darzi et al. discovered a prognostic gene module related to immune cell infiltration by using network-centric approaches [101]. Tu et al. presented a network-centric model for mining subnetworks of genes other than immune cell infiltration by considering tumor purity [102].

3.3. Drug Target Identification with Network Information

In drug target studies, network biology is integrated into pharmacology [103]. For instance, Yamanishi et al. developed novel computational methods to investigate the pharmacological space by integrating a drug-target protein network with genomics and chemical information. The proposed approaches investigated such drug-target network information to identify potential novel drug targets [104]. Since then, the field has continued to develop methods to study drug target and drug response integrating networks with chemical and multi-omic datasets. In a recent survey study by Chen et al., the authors compared 13 computational methods for drug response prediction. It turned out that gene expression profiles are crucial information for drug response prediction [105].

Moreover, drug-target studies are often extended to drug-repurposing studies. In cancer research, drug-repurposing studies aim to find novel interactions between non-cancer drugs and molecular features in cancer. Drug-repurposing (or repositioning) studies apply computational approaches and pathway-based models and aim at discovering potential new cancer drugs with a higher probability than de novo drug design [16,106]. Specifically, drug-repurposing studies can consider various areas of cancer research, such as tumor heterogeneity and synthetic lethality. As an example, Lee et al. found clinically relevant synthetic lethality interactions by integrating multiple screening NGS datasets [107]. This synthetic lethality and related-drug datasets can be integrated for an effective combination of anticancer therapeutic strategy with non-cancer drug repurposing.

4. Deep Learning in Cancer Research

DNN models develop rapidly and become more sophisticated. They have been frequently used in all areas of biomedical research. Initially, its development was facilitated by large-scale imaging and video data. While most data sets in the biomedical field would not typically be considered big data, the rapid data accumulation enabled by NGS made it suitable for the application of DNN models requiring a large amount of training data [108]. For instance, in 2019, Samiei et al. used TCGA-based large-scale cancer data as benchmark datasets for bioinformatics machine learning research such as Image-Net in the computer vision field [109]. Subsequently, large-scale public cancer data sets such as TCGA encouraged the wide usage of DNNs in the cancer domain [110]. Over the last decade, these state-of-the-art machine learning methods have been incorporated in many different biological questions [111].

In addition to public cancer databases such as TCGA, the genetic information of normal tissues is stored in well-curated databases such as GTEx [112] and 1000Genomes [113]. These databases are frequently used as control or baseline training data for deep learning [114]. Moreover, other non-curated large-scale data sources such as GEO (https://www.ncbi.nlm.nih.gov/geo/, accessed on 20 May 2021) can be leveraged to tackle critical aspects in cancer research. They store a large-scale of biological data produced under various experimental setups (Figure 1). Therefore, an integration of GEO data and other data requires careful preprocessing. Overall, an increasing amount of datasets facilitate the development of current deep learning in bioinformatics research [115].

4.1. Challenges for Deep Learning in Cancer Research

Many studies in biology and medicine used NGS and produced large amounts of data during the past few decades, moving the field to the big data era. Nevertheless, researchers still face a lack of data in particular when investigating rare diseases or disease states. Researchers have developed a manifold of potential solutions to overcome this lack of data challenges, such as imputation, augmentation, and transfer learning (Figure 3b). Data imputation aims at handling data sets with missing values [116]. It has been studied on various NGS omics data types to recover missing information [117]. It is known that gene expression levels can be altered by different regulatory elements, such as DNA-binding proteins, epigenomic modifications, and post-transcriptional modifications. Therefore, various models integrating such regulatory schemes have been introduced to impute missing omics data [118,119]. Some DNN-based models aim to predict gene expression changes based on genomics or epigenomics alteration. For instance, TDimpute aims at generating missing RNA-seq data by training a DNN on methylation data. They used TCGA and TARGET (https://ocg.cancer.gov/programs/target/data-matrix, accessed on 20 May 2021) data as proof of concept of the applicability of DNN for data imputation in a multi-omics integration study [120]. Because this integrative model can exploit information in different levels of regulatory mechanisms, it can build a more detailed model and achieve better performance than a model build on a single-omics dataset [117,121]. The generative adversarial network (GAN) is a DNN structure for generating simulated data that is different from the original data but shows the same characteristics [122]. GANs can impute missing omics data from other multi-omics sources. Recently, the GAN algorithm is getting more attention in single-cell transcriptomics because it has been recognized as a complementary technique to overcome the limitation of scRNA-seq [123]. In contrast to data imputation and generation, other machine learning approaches aim to cope with a limited dataset in different ways. Transfer learning or few-shot learning, for instance, aims to reduce the search space with similar but unrelated datasets and guide the model to solve a specific set of problems [124]. These approaches train models with data of similar characteristics and types but different data to the problem set. After pre-training the model, it can be fine-tuned with the dataset of interest [125,126]. Thus, researchers are trying to introduce few-shot learning models and meta-learning approaches to omics and translational medicine. For example, Select-ProtoNet applied the ProtoTypical Network [127] model to TCGA transcriptome data and classified patients into two groups according to their clinical status [128]. AffinityNet predicts kidney and uterus cancer subtypes with gene expression profiles [129].

Figure 3. (a) In various studies, NGS data transformed into different forms. The 2-D transformed form is for the convolution layer. Omics data is transformed into pathway level, GO enrichment score, or Functional spectra. (b) DNN application on different ways to handle lack of data. Imputation for missing data in multi-omics datasets. GAN for data imputation and in silico data simulation. Transfer learning pre-trained the model with other datasets and fine-tune. (c) Various types of information in biology. (d) Graph neural network examples. GCN is applied to aggregate neighbor information. (Created with BioRender.com).

4.2. Molecular Charactization with Network and DNN Model

DNNs have been applied in multiple areas of cancer research. For instance, a DNN model trained on TCGA cancer data can aid molecular characterization by identifying cancer driver genes. At the very early stage, Yuan et al. build DeepGene, a cancer-type classifier. They implemented data sparsity reduction methods and trained the DNN model with somatic point mutations [130]. Lyu et al. [131] and DeepGx [132] embedded a 1-D gene expression profile to a 2-D array by chromosome order to implement the convolution layer (Figure 3a). Other algorithms, such as the deepDriver, use k-nearest neighbors for the convolution layer. A predefined number of neighboring gene mutation profiles was the input for the convolution layer. It employed this convolution layer in a DNN by aggregating mutation information of the k-nearest neighboring genes [11]. Instead of embedding to a 2-D image, DeepCC transformed gene expression data into functional spectra. The resulting model was able to capture molecular characteristics by training cancer subtypes [14].

Another DNN model was trained to infer the origin of tissue from single-nucleotide variant (SNV) information of metastatic tumor. The authors built a model by using the TCGA/ICGC data and analyzed SNV patterns and corresponding pathways to predict the origin of cancer. They discovered that metastatic tumors retained their original cancer’s signature mutation pattern. In this context, their DNN model obtained even better accuracy than a random forest model [133] and, even more important, better accuracy than human pathologists [12].

4.3. Tumor Heterogeneity with Network and DNN Model

As described in Section 4.1, there are several issues because of cancer heterogeneity, e.g., tumor microenvironment. Thus, there are only a few applications of DNN in intratumoral heterogeneity research. For instance, Menden et al. developed ’Scaden’ to deconvolve cell types in bulk-cell sequencing data. ’Scaden’ is a DNN model for the investigation of intratumor heterogeneity. To overcome the lack of training datasets, researchers need to generate in silico simulated bulk-cell sequencing data based on single-cell sequencing data [134]. It is presumed that deconvolving cell types can be achieved by knowing all possible expressional profiles of the cell [36]. However, this information is typically not available. Recently, to tackle this problem, single-cell sequencing-based studies were conducted. Because of technical limitations, we need to handle lots of missing data, noises, and batch effects in single-cell sequencing data [135]. Thus, various machine learning methods were developed to process single-cell sequencing data. They aim at mapping single-cell data onto the latent space. For example, scDeepCluster implemented an autoencoder and trained it on gene-expression levels from single-cell sequencing. During the training phase, the encoder and decoder work as denoiser. At the same time, they can embed high-dimensional gene-expression profiles to lower-dimensional vectors [136]. This autoencoder-based method can produce biologically meaningful feature vectors in various contexts, from tissue cell types [137] to different cancer types [138,139].

4.4. Drug Target Identification with Networks and DNN Models

In addition to NGS datasets, large-scale anticancer drug assays enabled the training train of DNNs. Moreover, non-cancer drug response assay datasets can also be incorporated with cancer genomic data. In cancer research, a multidisciplinary approach was widely applied for repurposing non-oncology drugs to cancer treatment. This drug repurposing is faster than de novo drug discovery. Furthermore, combination therapy with a non-oncology drug can be beneficial to overcome the heterogeneous properties of tumors [85]. The deepDR algorithm integrated ten drug-related networks and trained deep autoencoders. It used a random-walk-based algorithm to represent graph information into feature vectors. This approach integrated network analysis with a DNN model validated with an independent drug-disease dataset [15].

The authors of CDRscan did an integrative analysis of cell-line-based assay datasets and other drug and genomics datasets. It shows that DNN models can enhance the computational model for improved drug sensitivity predictions [140]. Additionally, similar to previous network-based models, the multi-omics application of drug-targeted DNN studies can show higher prediction accuracy than the single-omics method. MOLI integrated genomic data and transcriptomic data to predict the drug responses of TCGA patients [141].

4.5. Graph Neural Network Model

In general, the advantage of using a biological network is that it can produce more comprehensive and interpretable results from high-dimensional omics data. Furthermore, in an integrative multi-omics data analysis, network-based integration can improve interpretability over traditional approaches. Instead of pre-/post-integration of a network, recently developed graph neural networks use biological networks as the base structure for the learning network itself. For instance, various pathways or interactome information can be integrated as a learning structure of a DNN and can be aggregated as heterogeneous information. In a GNN study, a convolution process can be done on the provided network structure of data. Therefore, the convolution on a biological network made it possible for the GNN to focus on the relationship among neighbor genes. In the graph convolution layer, the convolution process integrates information of neighbor genes and learns topological information (Figure 3d). Consequently, this model can aggregate information from far-distant neighbors, and thus can outperform other machine learning models [142].

In the context of the inference problem of gene expression, the main question is whether the gene expression level can be explained by aggregating the neighboring genes. A single gene inference study by Dutil et al. showed that the GNN model outperformed other DNN models [143]. Moreover, in cancer research, such GNN models can identify cancer-related genes with better performance than other network-based models, such as HotNet2 and MutSigCV [144]. A recent GNN study with a multi-omics integrative analysis identified 165 new cancer genes as an interactive partner for known cancer genes [145]. Additionally, in the synthetic lethality area, dual-dropout GNN outperformed previous bioinformatics tools for predicting synthetic lethality in tumors [146]. GNNs were also able to classify cancer subtypes based on pathway activity measures with RNA-seq data. Lee et al. implemented a GNN for cancer subtyping and tested five cancer types. Thus, the informative pathway was selected and used for subtype classification [147]. Furthermore, GNNs are also getting more attention in drug repositioning studies. As described in Section 3.3, drug discovery requires integrating various networks in both chemical and genomic spaces (Figure 3d). Chemical structures, protein structures, pathways, and other multi-omics data were used in drug-target identification and repurposing studies (Figure 3c). Each of the proposed applications has a specialty in the different purposes of drug-related tasks. Sun et al. summarized GNN-based drug discovery studies and categorized them into four classes: molecular property and activity prediction, interaction prediction, synthesis prediction, and de novo drug design. The authors also point out four challenges in the GNN-mediated drug discovery. At first, as we described before, there is a lack of drug-related datasets. Secondly, the current GNN models can not fully represent 3-D structures of chemical molecules and protein structures. The third challenge is integrating heterogeneous network information. Drug discovery usually requires a multi-modal integrative analysis with various networks, and GNNs can improve this integrative analysis. Lastly, although GNNs use graphs, stacked layers still make it hard to interpret the model [148].

4.6. Shortcomings in AI and Revisiting Validity of Biological Networks as Prior Knowledge

The previous sections reviewed a variety of DNN-based approaches that present a good performance on numerous applications. However, it is hardly a panacea for all research questions. In the following, we will discuss potential limitations of the DNN models. In general, DNN models with NGS data have two significant issues: (i) data requirements and (ii) interpretability. Usually, deep learning needs a large proportion of training data for reasonable performance which is more difficult to achieve in biomedical omics data compared to, for instance, image data. Today, there are not many NGS datasets that are well-curated and -annotated for deep learning. This can be an answer to the question of why most DNN studies are in cancer research [110,149]. Moreover, the deep learning models are hard to interpret and are typically considered as black-boxes. Highly stacked layers in the deep learning model make it hard to interpret its decision-making rationale. Although the methodology to understand and interpret deep learning models has been improved, the ambiguity in the DNN models’ decision-making hindered the transition between the deep learning model and translational medicine [149,150].

As described before, biological networks are employed in various computational analyses for cancer research. The studies applying DNNs demonstrated many different approaches to use prior knowledge for systematic analyses. Before discussing GNN application, the validity of biological networks in a DNN model needs to be shown. The LINCS program analyzed data of ’The Connectivity Map (CMap) project’ to understand the regulatory mechanism in gene expression by inferring the whole gene expression profiles from a small set of genes (https://lincsproject.org/, accessed on 20 May 2021) [151,152]. This LINCS program found that the gene expression level is inferrable with only nearly 1000 genes. They called this gene list ’landmark genes’. Subsequently, Chen et al. started with these 978 landmark genes and tried to predict other gene expression levels with DNN models. Integrating public large-scale NGS data showed better performance than the linear regression model. The authors conclude that the performance advantage originates from the DNN’s ability to model non-linear relationships between genes [153].

Following this study, Beltin et al. extensively investigated various biological networks in the same context of the inference of gene expression level. They set up a simplified representation of gene expression status and tried to solve a binary classification task. To show the relevance of a biological network, they compared various gene expression levels inferred from a different set of genes, neighboring genes in PPI, random genes, and all genes. However, in the study incorporating TCGA and GTEx datasets, the random network model outperformed the model build on a known biological network, such as StringDB [154]. While network-based approaches can add valuable insights to analysis, this study shows that it cannot be seen as the panacea, and a careful evaluation is required for each data set and task. In particular, this result may not represent biological complexity because of the oversimplified problem setup, which did not consider the relative gene-expressional changes. Additionally, the incorporated biological networks may not be suitable for inferring gene expression profiles because they consist of expression-regulating interactions, non-expression-regulating interactions, and various in vivo and in vitro interactions.

“ However, although recently sophisticated applications of deep learning showed improved accuracy, it does not reflect a general advancement. Depending on the type of NGS data, the experimental design, and the question to be answered, a proper approach and specific deep learning algorithms need to be considered. Deep learning is not a panacea. In general, to employ machine learning and systems biology methodology for a specific type of NGS data, a certain experimental design, a particular research question, the technology, and network data have to be chosen carefully.”

References

  1. Janes, K.A.; Yaffe, M.B. Data-driven modelling of signal-transduction networks. Nat. Rev. Mol. Cell Biol. 20067, 820–828. [Google Scholar] [CrossRef] [PubMed]
  2. Kreeger, P.K.; Lauffenburger, D.A. Cancer systems biology: A network modeling perspective. Carcinogenesis 201031, 2–8. [Google Scholar] [CrossRef] [PubMed]
  3. Vucic, E.A.; Thu, K.L.; Robison, K.; Rybaczyk, L.A.; Chari, R.; Alvarez, C.E.; Lam, W.L. Translating cancer ‘omics’ to improved outcomes. Genome Res. 201222, 188–195. [Google Scholar] [CrossRef]
  4. Hoadley, K.A.; Yau, C.; Wolf, D.M.; Cherniack, A.D.; Tamborero, D.; Ng, S.; Leiserson, M.D.; Niu, B.; McLellan, M.D.; Uzunangelov, V.; et al. Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin. Cell 2014158, 929–944. [Google Scholar] [CrossRef] [PubMed]
  5. Hutter, C.; Zenklusen, J.C. The cancer genome atlas: Creating lasting value beyond its data. Cell 2018173, 283–285. [Google Scholar] [CrossRef]
  6. Chuang, H.Y.; Lee, E.; Liu, Y.T.; Lee, D.; Ideker, T. Network-based classification of breast cancer metastasis. Mol. Syst. Biol. 20073, 140. [Google Scholar] [CrossRef]
  7. Zhang, W.; Chien, J.; Yong, J.; Kuang, R. Network-based machine learning and graph theory algorithms for precision oncology. NPJ Precis. Oncol. 20171, 25. [Google Scholar] [CrossRef] [PubMed]
  8. Ngiam, K.Y.; Khor, W. Big data and machine learning algorithms for health-care delivery. Lancet Oncol. 201920, e262–e273. [Google Scholar] [CrossRef]
  9. Creixell, P.; Reimand, J.; Haider, S.; Wu, G.; Shibata, T.; Vazquez, M.; Mustonen, V.; Gonzalez-Perez, A.; Pearson, J.; Sander, C.; et al. Pathway and network analysis of cancer genomes. Nat. Methods 201512, 615. [Google Scholar]
  10. Reyna, M.A.; Haan, D.; Paczkowska, M.; Verbeke, L.P.; Vazquez, M.; Kahraman, A.; Pulido-Tamayo, S.; Barenboim, J.; Wadi, L.; Dhingra, P.; et al. Pathway and network analysis of more than 2500 whole cancer genomes. Nat. Commun. 202011, 729. [Google Scholar] [CrossRef]
  11. Luo, P.; Ding, Y.; Lei, X.; Wu, F.X. deepDriver: Predicting cancer driver genes based on somatic mutations using deep convolutional neural networks. Front. Genet. 201910, 13. [Google Scholar] [CrossRef]
  12. Jiao, W.; Atwal, G.; Polak, P.; Karlic, R.; Cuppen, E.; Danyi, A.; De Ridder, J.; van Herpen, C.; Lolkema, M.P.; Steeghs, N.; et al. A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns. Nat. Commun. 202011, 728. [Google Scholar] [CrossRef]
  13. Chaudhary, K.; Poirion, O.B.; Lu, L.; Garmire, L.X. Deep learning–based multi-omics integration robustly predicts survival in liver cancer. Clin. Cancer Res. 201824, 1248–1259. [Google Scholar] [CrossRef]
  14. Gao, F.; Wang, W.; Tan, M.; Zhu, L.; Zhang, Y.; Fessler, E.; Vermeulen, L.; Wang, X. DeepCC: A novel deep learning-based framework for cancer molecular subtype classification. Oncogenesis 20198, 44. [Google Scholar] [CrossRef]
  15. Zeng, X.; Zhu, S.; Liu, X.; Zhou, Y.; Nussinov, R.; Cheng, F. deepDR: A network-based deep learning approach to in silico drug repositioning. Bioinformatics 201935, 5191–5198. [Google Scholar] [CrossRef]
  16. Issa, N.T.; Stathias, V.; Schürer, S.; Dakshanamurthy, S. Machine and deep learning approaches for cancer drug repurposing. In Seminars in Cancer Biology; Elsevier: Amsterdam, The Netherlands, 2020. [Google Scholar]
  17. Weinstein, J.N.; Collisson, E.A.; Mills, G.B.; Shaw, K.R.M.; Ozenberger, B.A.; Ellrott, K.; Shmulevich, I.; Sander, C.; Stuart, J.M.; Network, C.G.A.R.; et al. The cancer genome atlas pan-cancer analysis project. Nat. Genet. 201345, 1113. [Google Scholar] [CrossRef]
  18. The ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium. Pan-cancer analysis of whole genomes. Nature 2020578, 82. [Google Scholar] [CrossRef] [PubMed]
  19. King, M.C.; Marks, J.H.; Mandell, J.B. Breast and ovarian cancer risks due to inherited mutations in BRCA1 and BRCA2. Science 2003302, 643–646. [Google Scholar] [CrossRef] [PubMed]
  20. Courtney, K.D.; Corcoran, R.B.; Engelman, J.A. The PI3K pathway as drug target in human cancer. J. Clin. Oncol. 201028, 1075. [Google Scholar] [CrossRef] [PubMed]
  21. Parker, J.S.; Mullins, M.; Cheang, M.C.; Leung, S.; Voduc, D.; Vickery, T.; Davies, S.; Fauron, C.; He, X.; Hu, Z.; et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. J. Clin. Oncol. 200927, 1160. [Google Scholar] [CrossRef]
  22. Yersal, O.; Barutca, S. Biological subtypes of breast cancer: Prognostic and therapeutic implications. World J. Clin. Oncol. 20145, 412. [Google Scholar] [CrossRef] [PubMed]
  23. Zhao, L.; Lee, V.H.; Ng, M.K.; Yan, H.; Bijlsma, M.F. Molecular subtyping of cancer: Current status and moving toward clinical applications. Brief. Bioinform. 201920, 572–584. [Google Scholar] [CrossRef] [PubMed]
  24. Jones, P.A.; Issa, J.P.J.; Baylin, S. Targeting the cancer epigenome for therapy. Nat. Rev. Genet. 201617, 630. [Google Scholar] [CrossRef] [PubMed]
  25. Huang, S.; Chaudhary, K.; Garmire, L.X. More is better: Recent progress in multi-omics data integration methods. Front. Genet. 20178, 84. [Google Scholar] [CrossRef]
  26. Chin, L.; Andersen, J.N.; Futreal, P.A. Cancer genomics: From discovery science to personalized medicine. Nat. Med. 201117, 297. [Google Scholar] [CrossRef] [PubMed]

Use of Systems Biology in Anti-Microbial Drug Development

Genomics, Computational Biology and Drug Discovery for Mycobacterial Infections: Fighting the Emergence of Resistance. Asma Munir, Sundeep Chaitanya Vedithi, Amanda K. Chaplin and Tom L. Blundell. Front. Genet., 04 September 2020 | https://doi.org/10.3389/fgene.2020.00965

In an earlier review article (Waman et al., 2019), we discussed various computational approaches and experimental strategies for drug target identification and structure-guided drug discovery. In this review we discuss the impact of the era of precision medicine, where the genome sequences of pathogens can give clues about the choice of existing drugs, and repurposing of others. Our focus is directed toward combatting antimicrobial drug resistance with emphasis on tuberculosis and leprosy. We describe structure-guided approaches to understanding the impacts of mutations that give rise to antimycobacterial resistance and the use of this information in the design of new medicines.

Genome Sequences and Proteomic Structural Databases

In recent years, there have been many focused efforts to define the amino-acid sequences of the M. tuberculosis pan-genome and then to define the three-dimensional structures and functional interactions of these gene products. This work has led to essential genes of the bacteria being revealed and to a better understanding of the genetic diversity in different strains that might lead to a selective advantage (Coll et al., 2018). This will help with our understanding of the mode of antibiotic resistance within these strains and aid structure-guided drug discovery. However, only ∼10% of the ∼4128 proteins have structures determined experimentally.

Several databases have been developed to integrate the genomic and/or structural information linked to drug resistance in Mycobacteria (Table 1). These invaluable resources can contribute to better understanding of molecular mechanisms involved in drug resistance and improvement in the selection of potential drug targets.

There is a dearth of information related to structural aspects of proteins from M. leprae and their oligomeric and hetero-oligomeric organization, which has limited the understanding of physiological processes of the bacillus. The structures of only 12 proteins have been solved and deposited in the protein data bank (PDB). However, the high sequence similarity in protein coding genes between M. leprae and M. tuberculosis allows computational methods to be used for comparative modeling of the proteins of M. leprae. Mainly monomeric models using single template modeling have been defined and deposited in the Swiss Model repository (Bienert et al., 2017), in Modbase (Pieper et al., 2014), and in a collection with other infectious disease agents (Sosa et al., 2018). There is a need for multi-template modeling and building homo- and hetero-oligomeric complexes to better understand the interfaces, druggability and impacts of mutations.

We are now exploiting Vivace, a multi-template modeling pipeline developed in our lab for modeling the proteomes of M. tuberculosis (CHOPIN, see above) and M. abscessus [Mabellini Database (Skwark et al., 2019)], to model the proteome of M. leprae. We emphasize the need for understanding the protein interfaces that are critical to function. An example of this is that of the RNA-polymerase holoenzyme complex from M. leprae. We first modeled the structure of this hetero-hexamer complex and later deciphered the binding patterns of rifampin (Vedithi et al., 2018Figures 1A,B). Rifampin is a known drug to treat tuberculosis and leprosy. Owing to high rifampin resistance in tuberculosis and emerging resistance in leprosy, we used an approach known as “Computational Saturation Mutagenesis”, to identify sites on the protein that are less impacted by mutations. In this study, we were able to understand the association between predicted impacts of mutations on the structure and phenotypic rifampin-resistance outcomes in leprosy.

FIGURE 2

Figure 2. (A) Stability changes predicted by mCSM for systematic mutations in the ß-subunit of RNA polymerase in M. leprae. The maximum destabilizing effect from among all 19 possible mutations at each residue position is considered as a weighting factor for the color map that gradients from red (high destabilizing effects) to white (neutral to stabilizing effects) (Vedithi et al., 2020). (B) One of the known mutations in the ß-subunit of RNA polymerase, the S437H substitution which resulted in a maximum destabilizing effect [-1.701 kcal/mol (mCSM)] among all 19 possibilities this position. In the mutant, histidine (residue in green) forms hydrogen bonds with S434 and Q438, aromatic interactions with F431, and other ring-ring and π interactions with the surrounding residues which can impact the shape of the rifampin binding pocket and rifampin affinity to the ß-subunit [-0.826 log(affinity fold change) (mCSM-lig)]. Orange dotted lines represent weak hydrogen bond interactions. Ring-ring and intergroup interactions are depicted in cyan. Aromatic interactions are represented in sky-blue and carbonyl interactions in pink dotted lines. Green dotted lines represent hydrophobic interactions (Vedithi et al., 2020).

Examples of Understanding and Combatting Resistance

The availability of whole genome sequences in the present era has greatly enhanced the understanding of emergence of drug resistance in infectious diseases like tuberculosis. The data generated by the whole genome sequencing of clinical isolates can be screened for the presence of drug-resistant mutations. A preliminary in silico analysis of mutations can then be used to prioritize experimental work to identify the nature of these mutations.

FIGURE 3

Figure 3. (A) Mechanism of isoniazid activation and INH-NAD adduct formation. (B) Mutations mapped (Munir et al., 2019) on the structure of KatG (PDB ID:1SJ2; Bertrand et al., 2004).

Other articles related to Computational Biology, Systems Biology, and Bioinformatics on this online journal include:

20th Anniversary and the Evolution of Computational Biology – International Society for Computational Biology

Featuring Computational and Systems Biology Program at Memorial Sloan Kettering Cancer Center, Sloan Kettering Institute (SKI), The Dana Pe’er Lab

Quantum Biology And Computational Medicine

Systems Biology Analysis of Transcription Networks, Artificial Intelligence, and High-End Computing Coming to Fruition in Personalized Oncology

Read Full Post »

The NIH-funded adjuvant improves the efficacy of India’s COVID-19 vaccine.

Curator and Reporter: Dr. Premalata Pati, Ph.D., Postdoc

Anthony S. Fauci, Director of the National Institute of Allergy and Infectious Diseases (NIAID), Part of National Institute of Health (NIH) said,

Ending a global pandemic demands a global response. I am thrilled that a novel vaccine adjuvant developed in the United States with NIAID support is now included in an effective COVID-19 vaccine that is available to individuals in India.”

Adjuvants are components that are created as part of a vaccine to improve immune responses and increase the efficiency of the vaccine. COVAXIN was developed and is manufactured in India, which is currently experiencing a terrible health catastrophe as a result of COVID-19. An adjuvant designed with NIH funding has contributed to the success of the extremely effective COVAXIN-COVID-19 vaccine, which has been administered to about 25 million individuals in India and internationally.

Alhydroxiquim-II is the adjuvant utilized in COVAXIN, was discovered and validated in the laboratory by the biotech company ViroVax LLC of Lawrence, Kansas, with funding provided solely by the NIAID Adjuvant Development Program. The adjuvant is formed of a small molecule that is uniquely bonded to Alhydrogel, often known as alum and the most regularly used adjuvant in human vaccines. Alhydroxiquim-II enters lymph nodes, where it detaches from alum and triggers two cellular receptors. TLR7 and TLR8 receptors are essential in the immunological response to viruses. Alhydroxiquim-II is the first adjuvant to activate TLR7 and TLR8 in an approved vaccine against an infectious disease. Additionally, the alum in Alhydroxiquim-II activates the immune system to look for an infiltrating pathogen.

Although molecules that activate TLR receptors strongly stimulate the immune system, the adverse effects of Alhydroxiquim-II are modest. This is due to the fact that after COVAXIN is injected, the adjuvant travels directly to adjacent lymph nodes, which contain white blood cells that are crucial in recognizing pathogens and combating infections. As a result, just a minimal amount of Alhydroxiquim-II is required in each vaccination dosage, and the adjuvant does not circulate throughout the body, avoiding more widespread inflammation and unwanted side effects.

This scanning electron microscope image shows SARS-CoV-2 (round gold particles) emerging from the surface of a cell cultured in the lab. SARS-CoV-2, also known as 2019-nCoV, is the virus that causes COVID-19. Image Source: NIAID

COVAXIN is made up of a crippled version of SARS-CoV-2 that cannot replicate but yet encourages the immune system to produce antibodies against the virus. The NIH stated that COVAXIN is “safe and well tolerated,” citing the results of a phase 2 clinical investigation. COVAXIN safety results from a Phase 3 trial with 25,800 participants in India will be released later this year. Meanwhile, unpublished interim data from the Phase 3 trial show that the vaccine is 78% effective against symptomatic sickness, 100% effective against severe COVID-19, including hospitalization, and 70% effective against asymptomatic infection with SARS-CoV-2, the virus that causes COVID-19. Two tests of blood serum from persons who had received COVAXIN suggest that the vaccine creates antibodies that efficiently neutralize the SARS-CoV-2 B.1.1.7 (Alpha) and B.1.617 (Delta) variants (1) and (2), which were originally identified in the United Kingdom and India, respectively.

Since 2009, the NIAID Adjuvant Program has supported the research of ViroVax’s founder and CEO, Sunil David, M.D., Ph.D. His research has focused on the emergence of new compounds that activate innate immune receptors and their application as vaccination adjuvants.

Dr. David’s engagement with Bharat Biotech International Ltd. of Hyderabad, which manufactures COVAXIN, began during a 2019 meeting in India organized by the NIAID Office of Global Research under the auspices of the NIAID’s Indo-US Vaccine Action Program. Five NIAID-funded adjuvant investigators, including Dr. David, two representatives of the NIAID Division of Allergy, Immunology, and Transplantation, and the NIAID India representative, visited 4 top biotechnology companies to learn about their work and discuss future collaborations. The delegation also attended a consultation in New Delhi, which was co-organized by the NIAID and India’s Department of Biotechnology and hosted by the National Institute of Immunology.

Among the scientific collaborations spawned by these endeavors was a licensing deal between Bharat Biotech and Dr. David to use Alhydroxiquim-II in their candidate vaccines. During the COVID-19 outbreak, this license was expanded to cover COVAXIN, which has Emergency Use Authorization in India and more than a dozen additional countries. COVAXIN was developed by Bharat Biotech in partnership with the Indian Council of Medical Research’s National Institute of Virology. The company conducted thorough safety research on Alhydroxiquim-II and undertook the arduous process of scaling up production of the adjuvant in accordance with Good Manufacturing Practice standards. Bharat Biotech aims to generate 700 million doses of COVAXIN by the end of 2021.

NIAID conducts and supports research at the National Institutes of Health, across the United States, and across the world to better understand the causes of infectious and immune-mediated diseases and to develop better methods of preventing, detecting, and treating these illnesses. The NIAID website contains news releases, info sheets, and other NIAID-related materials.

Main Source:

https://www.miragenews.com/adjuvant-developed-with-nih-funding-enhances-587090/

References

  1. https://academic.oup.com/cid/advance-article-abstract/doi/10.1093/cid/ciab411/6271524?redirectedFrom=fulltext
  2. https://academic.oup.com/jtm/article/28/4/taab051/6193609

Other Related Articles published in this Open Access Online Scientific Journal include the following:

Comparing COVID-19 Vaccine Schedule Combinations, or “Com-COV” – First-of-its-Kind Study will explore the Impact of using eight different Combinations of Doses and Dosing Intervals for Different COVID-19 Vaccines

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2021/02/08/comparing-covid-19-vaccine-schedule-combinations-or-com-cov-first-of-its-kind-study-will-explore-the-impact-of-using-eight-different-combinations-of-doses-and-dosing-intervals-for-diffe/

Thriving Vaccines and Research: Weizmann Institute Coronavirus Research Development

Reporter:Amandeep Kaur, B.Sc., M.Sc.

https://pharmaceuticalintelligence.com/2021/05/04/thriving-vaccines-and-research-weizmann-coronavirus-research-development/

National Public Radio interview with Dr. Anthony Fauci on his optimism on a COVID-19 vaccine by early 2021

Reporter: Stephen J. Williams, PhD

https://pharmaceuticalintelligence.com/2020/07/19/national-public-radio-interview-with-dr-anthony-fauci-on-his-optimism-on-a-covid-19-vaccine-by-early-2021/

Cryo-EM disclosed how the D614G mutation changes SARS-CoV-2 spike protein structure

Reporter: Dr. Premalata Pati, Ph.D., Postdoc

https://pharmaceuticalintelligence.com/2021/04/10/cryo-em-disclosed-how-the-d614g-mutation-changes-sars-cov-2-spike-protein-structure/

Updates on the Oxford, AstraZeneca COVID-19 Vaccine

Reporter: Stephen J. Williams, PhD

https://pharmaceuticalintelligence.com/2020/06/16/updates-on-the-oxford-astrazeneca-covid-19-vaccine/

Read Full Post »

Despite heated discussion over whether it works, the FDA has approved Aduhelm, bringing a new ray of hope to the Alzheimer’s patients.

Curator and Reporter: Dr. Premalata Pati, Ph.D., Postdoc

On Monday, 7th June 2021, a controversial new Alzheimer’s Disease treatment was licensed in the United States for the first time in nearly 20 years, sparking calls for it to be made available worldwide despite conflicting evidence about its usefulness. The drug was designed for people with mild cognitive impairment, not severe dementia, and it was designed to delay the progression of Alzheimer’s disease rather than only alleviate symptoms.

Vhttps://youtu.be/atAhUI6OMnsII

The Controversies

The route to FDA clearance for Aducanumab has been bumpy – and contentious.

Though doctors, patients, and the organizations that assist them are in desperate need of therapies that can delay mental decline, scientists question the efficacy of the new medicine, Aducanumab or Aduhelm. In March 2019, two trials were halted because the medications looked to be ineffective. “The futility analysis revealed that the studies were most likely to fail,” said Isaacson of Weill Cornell Medicine and NewYork-Presbyterian. Biogen, the drug’s manufacturer revealed several months later that a fresh analysis with more participants found that individuals who got high doses of Aducanumab exhibited a reduction in clinical decline in one experiment. Patients treated with high-dose Aducanumab had 22% reduced clinical impairment in their cognitive health at 18 months, indicating that the advancement of their early Alzheimer’s disease was halted, according to FDA briefing documents from last year.

When the FDA’s members were split on the merits of the application in November, it was rejected. Three of its advisers went public, claiming that there was insufficient evidence that it worked in a scientific journal. They were concerned that if the medicine was approved, it might reduce the threshold for future approvals, owing to the scarcity of Alzheimer’s treatments.

Dr. Caleb Alexander, a drug safety and effectiveness expert at the Johns Hopkins Bloomberg School of Public Health, was one of the FDA advisers who was concerned that the data presented to the agency was a reanalysis after the experiment was stopped. It was “like the Texas sharpshooter fallacy,” he told the New York Times, “where the sharpshooter blows up a barn and then goes and paints a bullseye around the cluster of holes he loves.”

Some organizations, such as the non-profit Public Citizen’s Health Research Group, claimed that the FDA should not approve Aducanumab for the treatment of Alzheimer’s disease because there is insufficient proof of its efficacy.

The drug is a monoclonal antibody that inhibits the formation of amyloid protein plaques in the brain, which are thought to be the cause of Alzheimer’s disease. The majority of Alzheimer’s medications have attempted to erase these plaques.

Aducanumab appears to do this in some patients, but only when the disease is in its early stages. This means that people must be checked to see if they have the disease. Many persons with memory loss are hesitant to undergo testing because there is now no treatment available.

The few Alzheimer’s medications available appear to have limited effectiveness. When Aricept, also known as Donepezil, was approved more than 20 years ago, there was a major battle to get it. It was heralded as a breakthrough at the time – partly due to the lack of anything else. It has become obvious that it slows mental decline for a few months but makes little effect in the long run.

The findings of another trial for some patients backed up those conclusions.

Biogen submitted a Biologics License Application to the FDA in July 2020, requesting approval of the medicine.

The FDA’s decision has been awaited by Alzheimer’s disease researchers, clinicians, and patients since then.

Support for approval of the drug

Other groups, such as the Alzheimer’s Association, have supported the drug’s approval.

The Alzheimer’s Association‘s website stated on Friday, “This is a critical time, regardless of the FDA’s final judgment. We’ve never been this close to approving an Alzheimer’s drug that could affect the disease’s development rather than just the symptoms. We can keep working together to achieve our goal of a world free of Alzheimer’s disease and other dementias.”

The drug has gotten so much attention that the Knight Alzheimer Disease Research Center at Washington University in St. Louis issued a statement on Friday stating that even if it is approved, “it will still likely take several months for the medication to pass other regulatory steps and become available to patients.”

Biogen officials told KGO-TV on Monday that the medicine will be ready to ship in about two weeks and that they have identified more than 900 facilities across the United States that they feel will be medically and commercially suitable.

Officials stated the corporation will also provide financial support to qualifying patients so that their out-of-pocket payments are as low as possible. Biogen has also pledged not to raise the price for at least the next four years.

Most Medicare customers with supplemental plans, according to the firm, will have a limited or capped co-pay.

Case studies connected to the Drug Approval

Case 1

Ann Lange, one of several Chicago-area clinical trial volunteers who received the breakthrough Alzheimer’s treatment, said,

It really offers us so much hope for a long, healthy life.

Lange, 60, has Alzheimer’s disease, which she was diagnosed with five years ago. Her memory has improved as a result of the monthly infusions, she claims.

She said,

I’d forget what I’d done in the shower, so I’d scribble ‘shampoo, conditioner, face, body’ on the door. Otherwise, I’d lose track of what I’m doing “Lange remarked. “I’m not required to do that any longer.

Case 2

Jenny Knap, 69, has been receiving infusions of the Aducanumab medication for about a year as part of two six-month research trials. She told CNN that she had been receiving treatment for roughly six months before the trial was halted in 2019, and that she had recently resumed treatment.

Knap said,

I can’t say I noticed it on a daily basis, but I do think I’m doing a lot better in terms of checking for where my glasses are and stuff like that.

When Knap was diagnosed with mild cognitive impairment, a clinical precursor to Alzheimer’s disease, in 2015, the symptoms were slight but there.

Her glasses were frequently misplaced, and she would repeat herself, forgetting previous talks, according to her husband, Joe Knap.

Joe added,

We were aware that things were starting to fall between the cracks as these instances got more often

Jenny went to the Lou Ruvo Center for Brain Health at the Cleveland Clinic in Ohio for testing and obtained her diagnosis. Jenny found she was qualified to join in clinical trials for the Biogen medicine Aducanumab at the Cleveland Clinic a few years later, in early 2017. She volunteered and has been a part of the trial ever since.

It turns out that Jenny was in the placebo category for the first year and a half, Joe explained, meaning she didn’t get the treatment.

They didn’t realize she was in the placebo group until lately because the trial was blind. Joe stated she was given the medicine around August 2018 and continued until February 2019 as the trial progressed. The trial was halted by Biogen in March 2019, but it was restarted last October, when Jenny resumed getting infusions.

Jenny now receives Aducanumab infusions every four weeks at the Cleveland Clinic, which is roughly a half-hour drive from their house, with Joe by her side. Jenny added that, despite the fact that she has only recently begun therapy, she believes it is benefiting her, combined with a balanced diet and regular exercise (she runs four miles).

The hope of Aducanumab is to halt the progression of the disease rather than to improve cognition. We didn’t appreciate any significant reduction in her condition, Jenny’s doctor, Dr. Babak Tousi, who headed Aducanumab clinical studies at the Cleveland Clinic, wrote to CNN in an email.

This treatment is unlike anything we’ve ever received before. There has never been a drug that has slowed the growth of Alzheimer’s disease, he stated, Right now, existing medications like donepezil and memantine aid with symptoms but do not slow the disease’s progression.

Jenny claims that the medicine has had no significant negative effects on her.

There was signs of some very minor bleeding in the brain at one point, which was quite some time ago. It was at very low levels, in fact, Joe expressed concern about Jenny, but added that the physicians were unconcerned.

According to Tousi, with repeated therapy, “blood vessels may become leaky, allowing fluid and red blood cells to flow out to the surrounding area,” and “micro hemorrhages have been documented in 19.1% of trial participants who got” the maximal dose of therapy”.

Jenny and Joe’s attitude on the future has improved as a result of the infusions and keeping a healthy lifestyle, according to Joe. They were also delighted to take part in the trial, which they saw as an opportunity to make a positive influence in other people’s lives.

There was this apprehension of what was ahead before we went into the clinical trial, Joe recalled. “The medical aspect of the infusion gives us reason to be optimistic. However, doing the activity on a daily basis provides us with immediate benefits.”

The drug’s final commercialization announcement

Aducanumab, which will be marketed as Aduhelm, is a monthly intravenous infusion that is designed to halt cognitive decline in patients with mild memory and thinking issues. It is the first FDA-approved medication for Alzheimer’s disease that targets the disease process rather than just the symptoms.

The manufacturer, Biogen, stated Monday afternoon that the annual list price will be $56,000. In addition, diagnostic tests and brain imaging will very certainly cost tens of thousands of dollars.

The FDA approved approval for the medicine to be used but ordered Biogen to conduct a new clinical trial, recognizing that prior trials of the medicine had offered insufficient evidence to indicate effectiveness.

Biogen Inc said on Tuesday that it expects to start shipping Aduhelm, a newly licensed Alzheimer’s medicine, in approximately two weeks and that it has prepared over 900 healthcare facilities for the intravenous infusion treatment.

Other Relevant Articles

Gene Therapy could be a Boon to Alzheimer’s disease (AD): A first-in-human clinical trial proposed

Reporter: Dr. Premalata Pati, Ph.D., Postdoc

https://pharmaceuticalintelligence.com/2021/03/22/gene-therapy-could-be-a-boon-to-alzheimers-disease-ad-a-first-in-human-clinical-trial-proposed/

Alzheimer’s Disease – tau art thou, or amyloid

Curator: Larry H. Bernstein, MD, FCAP

https://pharmaceuticalintelligence.com/2016/02/15/alzheimers-disease-tau-art-thou-or-amyloid/

Connecting the Immune Response to Amyloid-β Aggregation in Alzheimer’s Disease via IFITM3

Reporter : Irina Robu, PhD

https://pharmaceuticalintelligence.com/2020/10/13/connecting-the-immune-response-to-amyloid-%ce%b2-aggregation-in-alzheimers-disease-via-ifitm3/

Ustekinumab New Drug Therapy for Cognitive Decline resulting from Neuroinflammatory Cytokine Signaling and Alzheimer’s Disease

Curator: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2013/02/27/ustekinumab-new-drug-therapy-for-cognitive-decline-resulting-from-neuroinflammatory-cytokine-signaling-and-alzheimers-disease/

Alnylam Announces First-Ever FDA Approval of an RNAi Therapeutic, ONPATTRO™ (patisiran) for the Treatment of the Polyneuropathy of Hereditary Transthyretin-Mediated Amyloidosis in Adults

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2018/08/13/alnylam-announces-first-ever-fda-approval-of-an-rnai-therapeutic-onpattro-patisiran-for-the-treatment-of-the-polyneuropathy-of-hereditary-transthyretin-mediated-amyloidosis-in-adults/

Recent progress in neurodegenerative diseases and gliomas

Curator: Larry H. Bernstein, MD, FCAP

https://pharmaceuticalintelligence.com/2016/05/28/recent-progress-in-neurodegenerative-diseases-and-gliomas/

Read Full Post »

Thriving Vaccines and Research: Weizmann Institute Coronavirus Research Development

Reporter: Amandeep Kaur, B.Sc., M.Sc.

In early February, Prof. Eran Segal updated in one of his tweets and mentioned that “We say with caution, the magic has started.”

The article reported that this statement by Prof. Segal was due to decreasing cases of COVID-19, severe infection cases and hospitalization of patients by rapid vaccination process throughout Israel. Prof. Segal emphasizes in another tweet to remain cautious over the country and informed that there is a long way to cover and searching for scientific solutions.

A daylong webinar entitled “COVID-19: The epidemic that rattles the world” was a great initiative by Weizmann Institute to share their scientific knowledge about the infection among the Israeli institutions and scientists. Prof. Gideon Schreiber and Dr. Ron Diskin organized the event with the support of the Weizmann Coronavirus Response Fund and Israel Society for Biochemistry and Molecular Biology. The speakers were invited from the Hebrew University of Jerusalem, Tel-Aviv University, the Israel Institute for Biological Research (IIBR), and Kaplan Medical Center who addressed the molecular structure and infection biology of the virus, treatments and medications for COVID-19, and the positive and negative effect of the pandemic.

The article reported that with the emergence of pandemic, the scientists at Weizmann started more than 60 projects to explore the virus from different range of perspectives. With the help of funds raised by communities worldwide for the Weizmann Coronavirus Response Fund supported scientists and investigators to elucidate the chemistry, physics and biology behind SARS-CoV-2 infection.

Prof. Avi Levy, the coordinator of the Weizmann Institute’s coronavirus research efforts, mentioned “The vaccines are here, and they will drastically reduce infection rates. But the coronavirus can mutate, and there are many similar infectious diseases out there to be dealt with. All of this research is critical to understanding all sorts of viruses and to preempting any future pandemics.”

The following are few important projects with recent updates reported in the article.

Mapping a hijacker’s methods

Dr. Noam Stern-Ginossar studied the virus invading strategies into the healthy cells and hijack the cell’s systems to divide and reproduce. The article reported that viruses take over the genetic translation system and mainly the ribosomes to produce viral proteins. Dr. Noam used a novel approach known as ‘ribosome profiling’ as her research objective and create a map to locate the translational events taking place inside the viral genome, which further maps the full repertoire of viral proteins produced inside the host.

She and her team members grouped together with the Weizmann’s de Botton Institute and researchers at IIBR for Protein Profiling and understanding the hijacking instructions of coronavirus and developing tools for treatment and therapies. Scientists generated a high-resolution map of the coding regions in the SARS-CoV-2 genome using ribosome-profiling techniques, which allowed researchers to quantify the expression of vital zones along the virus genome that regulates the translation of viral proteins. The study published in Nature in January, explains the hijacking process and reported that virus produces more instruction in the form of viral mRNA than the host and thus dominates the translation process of the host cell. Researchers also clarified that it is the misconception that virus forced the host cell to translate its viral mRNA more efficiently than the host’s own translation, rather high level of viral translation instructions causes hijacking. This study provides valuable insights for the development of effective vaccines and drugs against the COVID-19 infection.

Like chutzpah, some things don’t translate

Prof. Igor Ulitsky and his team worked on untranslated region of viral genome. The article reported that “Not all the parts of viral transcript is translated into protein- rather play some important role in protein production and infection which is unknown.” This region may affect the molecular environment of the translated zones. The Ulitsky group researched to characterize that how the genetic sequence of regions that do not translate into proteins directly or indirectly affect the stability and efficiency of the translating sequences.

Initially, scientists created the library of about 6,000 regions of untranslated sequences to further study their functions. In collaboration with Dr. Noam Stern-Ginossar’s lab, the researchers of Ulitsky’s team worked on Nsp1 protein and focused on the mechanism that how such regions affect the Nsp1 protein production which in turn enhances the virulence. The researchers generated a new alternative and more authentic protocol after solving some technical difficulties which included infecting cells with variants from initial library. Within few months, the researchers are expecting to obtain a more detailed map of how the stability of Nsp1 protein production is getting affected by specific sequences of the untranslated regions.

The landscape of elimination

The article reported that the body’s immune system consists of two main factors- HLA (Human Leukocyte antigen) molecules and T cells for identifying and fighting infections. HLA molecules are protein molecules present on the cell surface and bring fragments of peptide to the surface from inside the infected cell. These peptide fragments are recognized and destroyed by the T cells of the immune system. Samuels’ group tried to find out the answer to the question that how does the body’s surveillance system recognizes the appropriate peptide derived from virus and destroy it. They isolated and analyzed the ‘HLA peptidome’- the complete set of peptides bound to the HLA proteins from inside the SARS-CoV-2 infected cells.

After the analysis of infected cells, they found 26 class-I and 36 class-II HLA peptides, which are present in 99% of the population around the world. Two peptides from HLA class-I were commonly present on the cell surface and two other peptides were derived from coronavirus rare proteins- which mean that these specific coronavirus peptides were marked for easy detection. Among the identified peptides, two peptides were novel discoveries and seven others were shown to induce an immune response earlier. These results from the study will help to develop new vaccines against new coronavirus mutation variants.

Gearing up ‘chain terminators’ to battle the coronavirus

Prof. Rotem Sorek and his lab discovered a family of enzymes within bacteria that produce novel antiviral molecules. These small molecules manufactured by bacteria act as ‘chain terminators’ to fight against the virus invading the bacteria. The study published in Nature in January which reported that these molecules cause a chemical reaction that halts the virus’s replication ability. These new molecules are modified derivates of nucleotide which integrates at the molecular level in the virus and obstruct the works.

Prof. Sorek and his group hypothesize that these new particles could serve as a potential antiviral drug based on the mechanism of chain termination utilized in antiviral drugs used recently in the clinical treatments. Yeda Research and Development has certified these small novel molecules to a company for testing its antiviral mechanism against SARS-CoV-2 infection. Such novel discoveries provide evidences that bacterial immune system is a potential repository of many natural antiviral particles.

Resolving borderline diagnoses

Currently, Real-time Polymerase chain reaction (RT-PCR) is the only choice and extensively used for diagnosis of COVID-19 patients around the globe. Beside its benefits, there are problems associated with RT-PCR, false negative and false positive results and its limitation in detecting new mutations in the virus and emerging variants in the population worldwide. Prof. Eran Elinavs’ lab and Prof. Ido Amits’ lab are working collaboratively to develop a massively parallel, next-generation sequencing technique that tests more effectively and precisely as compared to RT-PCR. This technique can characterize the emerging mutations in SARS-CoV-2, co-occurring viral, bacterial and fungal infections and response patterns in human.

The scientists identified viral variants and distinctive host signatures that help to differentiate infected individuals from non-infected individuals and patients with mild symptoms and severe symptoms.

In Hadassah-Hebrew University Medical Center, Profs. Elinav and Amit are performing trails of the pipeline to test the accuracy in borderline cases, where RT-PCR shows ambiguous or incorrect results. For proper diagnosis and patient stratification, researchers calibrated their severity-prediction matrix. Collectively, scientists are putting efforts to develop a reliable system that resolves borderline cases of RT-PCR and identify new virus variants with known and new mutations, and uses data from human host to classify patients who are needed of close observation and extensive treatment from those who have mild complications and can be managed conservatively.

Moon shot consortium refining drug options

The ‘Moon shot’ consortium was launched almost a year ago with an initiative to develop a novel antiviral drug against SARS-CoV-2 and was led by Dr. Nir London of the Department of Chemical and Structural Biology at Weizmann, Prof. Frank von Delft of Oxford University and the UK’s Diamond Light Source synchroton facility.

To advance the series of novel molecules from conception to evidence of antiviral activity, the scientists have gathered support, guidance, expertise and resources from researchers around the world within a year. The article reported that researchers have built an alternative template for drug-discovery, full transparency process, which avoids the hindrance of intellectual property and red tape.

The new molecules discovered by scientists inhibit a protease, a SARS-CoV-2 protein playing important role in virus replication. The team collaborated with the Israel Institute of Biological Research and other several labs across the globe to demonstrate the efficacy of molecules not only in-vitro as well as in analysis against live virus.

Further research is performed including assaying of safety and efficacy of these potential drugs in living models. The first trial on mice has been started in March. Beside this, additional drugs are optimized and nominated for preclinical testing as candidate drug.

Source: https://www.weizmann.ac.il/WeizmannCompass/sections/features/the-vaccines-are-here-and-research-abounds

Other related articles were published in this Open Access Online Scientific Journal, including the following:

Identification of Novel genes in human that fight COVID-19 infection

Reporter: Amandeep Kaur, B.Sc., M.Sc. (ept. 5/2021)

https://pharmaceuticalintelligence.com/2021/04/19/identification-of-novel-genes-in-human-that-fight-covid-19-infection/

Fighting Chaos with Care, community trust, engagement must be cornerstones of pandemic response

Reporter: Amandeep Kaur, B.Sc., M.Sc. (ept. 5/2021)

https://pharmaceuticalintelligence.com/2021/04/13/fighting-chaos-with-care/

T cells recognize recent SARS-CoV-2 variants

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2021/03/30/t-cells-recognize-recent-sars-cov-2-variants/

Need for Global Response to SARS-CoV-2 Viral Variants

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2021/02/12/need-for-global-response-to-sars-cov-2-viral-variants/

Mechanistic link between SARS-CoV-2 infection and increased risk of stroke using 3D printed models and human endothelial cells

Reporter: Adina Hazan, PhD

https://pharmaceuticalintelligence.com/2020/12/28/mechanistic-link-between-sars-cov-2-infection-and-increased-risk-of-stroke-using-3d-printed-models-and-human-endothelial-cells/

Read Full Post »

Cryo-EM disclosed how the D614G mutation changes SARS-CoV-2 spike protein structure.

Reporter: Dr. Premalata Pati, Ph.D., Postdoc

SARS-CoV-2, the virus that causes COVID-19, has had a major impact on human health globally; infecting a massive quantity of people around 136,046,262 (John Hopkins University); causing severe disease and associated long-term health sequelae; resulting in death and excess mortality, especially among older and prone populations; altering routine healthcare services; disruptions to travel, trade, education, and many other societal functions; and more broadly having a negative impact on peoples physical and mental health.

It’s need of the hour to answer the questions like what allows the variants of SARS-CoV-2 first detected in the UK, South Africa, and Brazil to spread so quickly? How can current COVID-19 vaccines better protect against them?

Scientists from the Harvard Medical School and the Boston Children’s Hospital help answer these urgent questions. The team reports its findings in the journal “Science a paper entitled Structural impact on SARS-CoV-2 spike protein by D614G substitution. The mutation rate of the SARS-CoV-2 virus has rapidly evolved over the past few months, especially at the Spike (S) protein region of the virus, where the maximum number of mutations have been observed by the virologists.

Bing Chen, HMS professor of pediatrics at Boston Children’s, and colleagues analyzed the changes in the structure of the spike proteins with the genetic change by D614G mutation by all three variants. Hence they assessed the structure of the coronavirus spike protein down to the atomic level and revealed the reason for the quick spreading of these variants.


This model shows the structure of the spike protein in its closed configuration, in its original D614 form (left) and its mutant form (G614). In the mutant spike protein, the 630 loop (in red) stabilizes the spike, preventing it from flipping open prematurely and rendering SARS-CoV-2 more infectious.

Fig. 1. Cryo-EM structures of the full-length SARS-CoV-2 S protein carrying G614.

(A) Three structures of the G614 S trimer, representing a closed, three RBD-down conformation, an RBD-intermediate conformation and a one RBD-up conformation, were modeled based on corresponding cryo-EM density maps at 3.1-3.5Å resolution. Three protomers (a, b, c) are colored in red, blue and green, respectively. RBD locations are indicated. (B) Top views of superposition of three structures of the G614 S in (A) in ribbon representation with the structure of the prefusion trimer of the D614 S (PDB ID: 6XR8), shown in yellow. NTD and RBD of each protomer are indicated. Side views of the superposition are shown in fig. S8.

IMAGE SOURCE: Bing Chen, Ph.D., Boston Children’s Hospital, https://science.sciencemag.org/content/early/2021/03/16/science.abf2303

The work

The mutant spikes were imaged by Cryo-Electron microscopy (cryo-EM), which has resolution down to the atomic level. They found that the D614G mutation (substitution of in a single amino acid “letter” in the genetic code for the spike protein) makes the spike more stable as compared with the original SARS-CoV-2 virus. As a result, more functional spikes are available to bind to our cells’ ACE2 receptors, making the virus more contagious.


Fig. 2. Cryo-EM revealed how the D614G mutation changes SARS-CoV-2 spike protein structure.

IMAGE SOURCE:  Zhang J, et al., Science

Say the original virus has 100 spikes,” Chen explained. “Because of the shape instability, you may have just 50 percent of them functional. In the G614 variants, you may have 90 percent that is functional. So even though they don’t bind as well, the chances are greater and you will have an infection

Forthcoming directions by Bing Chen and Team

The findings suggest the current approved COVID-19 vaccines and any vaccines in the works should include the genetic code for this mutation. Chen has quoted:

Since most of the vaccines so far—including the Moderna, Pfizer–BioNTech, Johnson & Johnson, and AstraZeneca vaccines are based on the original spike protein, adding the D614G mutation could make the vaccines better able to elicit protective neutralizing antibodies against the viral variants

Chen proposes that redesigned vaccines incorporate the code for this mutant spike protein. He believes the more stable spike shape should make any vaccine based on the spike more likely to elicit protective antibodies. Chen also has his sights set on therapeutics. He and his colleagues are further applying structural biology to better understand how SARS-CoV-2 binds to the ACE2 receptor. That could point the way to drugs that would block the virus from gaining entry to our cells.

In January, the team showed that a structurally engineered “decoy” ACE2 protein binds to SARS-CoV-2 200 times more strongly than the body’s own ACE2. The decoy potently inhibited the virus in cell culture, suggesting it could be an anti-COVID-19 treatment. Chen is now working to advance this research into animal models.

Main Source:

Abstract

Substitution for aspartic acid by glycine at position 614 in the spike (S) protein of severe acute respiratory syndrome coronavirus 2 appears to facilitate rapid viral spread. The G614 strain and its recent variants are now the dominant circulating forms. We report here cryo-EM structures of a full-length G614 S trimer, which adopts three distinct prefusion conformations differing primarily by the position of one receptor-binding domain. A loop disordered in the D614 S trimer wedges between domains within a protomer in the G614 spike. This added interaction appears to prevent premature dissociation of the G614 trimer, effectively increasing the number of functional spikes and enhancing infectivity, and to modulate structural rearrangements for membrane fusion. These findings extend our understanding of viral entry and suggest an improved immunogen for vaccine development.

https://science.sciencemag.org/content/early/2021/03/16/science.abf2303?rss=1

Other Related Articles published in this Open Access Online Scientific Journal include the following:

COVID-19-vaccine rollout risks and challenges

Reporter : Irina Robu, PhD

https://pharmaceuticalintelligence.com/2021/02/17/covid-19-vaccine-rollout-risks-and-challenges/

COVID-19 Sequel: Neurological Impact of Social isolation been linked to poorer physical and mental health

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2021/03/30/covid-19-sequel-neurological-impact-of-social-isolation-been-linked-to-poorer-physical-and-mental-health/

Comparing COVID-19 Vaccine Schedule Combinations, or “Com-COV” – First-of-its-Kind Study will explore the Impact of using eight different Combinations of Doses and Dosing Intervals for Different COVID-19 Vaccines

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2021/02/08/comparing-covid-19-vaccine-schedule-combinations-or-com-cov-first-of-its-kind-study-will-explore-the-impact-of-using-eight-different-combinations-of-doses-and-dosing-intervals-for-diffe/

COVID-19 T-cell immune response map, immunoSEQ T-MAP COVID for research of T-cell response to SARS-CoV-2 infection

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2020/11/20/covid-19-t-cell-immune-response-map-immunoseq-t-map-covid-for-research-of-t-cell-response-to-sars-cov-2-infection/

Tiny biologic drug to fight COVID-19 show promise in animal models

Reporter : Irina Robu, PhD

https://pharmaceuticalintelligence.com/2020/10/11/tiny-biologic-drug-to-fight-covid-19-show-promise-in-animal-models/

Miniproteins against the COVID-19 Spike protein may be therapeutic

Reporter: Stephen J. Williams, PhD

https://pharmaceuticalintelligence.com/2020/09/30/miniproteins-against-the-covid-19-spike-protein-may-be-therapeutic/

Read Full Post »

A Platform called VirtualFlow: Discovery of Pan-coronavirus Drugs help prepare the US for the Next Coronavirus Pandemic

Reporter: Aviva Lev-Ari, PhD, RN

 

ARTICLE|ONLINE NOW, 102021

A multi-pronged approach targeting SARS-CoV-2 proteins using ultra-large virtual screening

Open AccessPublished:January 04, 2021DOI:https://doi.org/10.1016/j.isci.2020.102021

 

The work was made possible in large part by about $1 million in cloud computing hours awarded by Google through a COVID-19 research grant program.

The work reported, below was sponsored by

  • a Google Cloud COVID-19 research grant. Funding was also provided by the
  • Fondation Aclon,
  • National Institutes of Health (GM136859),
  • Claudia Adams Barr Program for Innovative Basic Cancer Research,
  • Math+ Berlin Mathematics Research Center,
  • Templeton Religion Trust (TRT 0159),
  • U.S. Army Research Office (W911NF1910302), and
  • Chleck Family Foundation

 

Harvard University, AbbVie form research alliance to address emergent viral diseases

This article is part of Harvard Medical School’s continuing coverage of medicine, biomedical research, medical education and policy related to the SARS-CoV-2 pandemic and the disease COVID-19.

Harvard University and AbbVie today announced a $30 million collaborative research alliance, launching a multi-pronged effort at Harvard Medical School to study and develop therapies against emergent viral infections, with a focus on those caused by coronaviruses and by viruses that lead to hemorrhagic fever.

The collaboration aims to rapidly integrate fundamental biology into the preclinical and clinical development of new therapies for viral diseases that address a variety of therapeutic modalities. HMS has led several large-scale, coordinated research efforts launched at the beginning of the COVID-19 pandemic.

“A key element of having a strong R&D organization is collaboration with top academic institutions, like Harvard Medical School, to develop therapies for patients who need them most,” said Michael Severino, vice chairman and president of AbbVie. “There is much to learn about viral diseases and the best way to treat them. By harnessing the power of collaboration, we can develop new therapeutics sooner to ensure the world is better prepared for future potential outbreaks.”

“The cataclysmic nature of the COVID-19 pandemic reminds us how vital it is to be prepared for the next public health crisis and how critical collaboration is on every level—across disciplines, across institutions and across national boundaries,” said George Q. Daley, dean of Harvard Medical School. “Harvard Medical School, as the nucleus of an ecosystem of fundamental discovery and therapeutic translation, is uniquely positioned to propel this transformative research alongside allies like AbbVie.”

AbbVie will provide $30 million over three years and additional in-kind support leveraging AbbVie’s scientists, expertise and facilities to advance collaborative research and early-stage development efforts across five program areas that address a variety of therapeutic modalities:

  • Immunity and immunopathology—Study of the fundamental processes that impact the body’s critical immune responses to viruses and identification of opportunities for therapeutic intervention.

Led by Ulirich Von Andrian, the Edward Mallinckrodt Jr. Professor of Immunopathology in the Blavatnik Institute at HMS and program leader of basic immunology at the Ragon Institute of MGH, MIT and Harvard, and Jochen Salfeld, vice president of immunology and virology discovery at AbbVie.

  • Host targeting for antiviral therapies—Development of approaches that modulate host proteins in an effort to disrupt the life cycle of emergent viral pathogens.

Led by Pamela Silver, the Elliot T. and Onie H. Adams Professor of Biochemistry and Systems Biology in the Blavatnik Institute at HMS, and Steve Elmore, vice president of drug discovery science and technology at AbbVie.

  • Antibody therapeutics—Rapid development of therapeutic antibodies or biologics against emergent pathogens, including SARS-CoV-2, to a preclinical or early clinical stage.

Led by Jonathan Abraham, assistant professor of microbiology in the Blavatnik Institute at HMS, and by Jochen Salfeld, vice president of immunology and virology discovery at AbbVie.

  • Small molecules—Discovery and early-stage development of small-molecule drugs that would act to prevent replication of known coronaviruses and emergent pathogens.

Led by Mark Namchuk, executive director of therapeutics translation at HMS and senior lecturer on biological chemistry and molecular pharmacology in the Blavatnik Institute at HMS, and Steve Elmore, vice president of drug discovery science and technology at AbbVie.

  • Translational development—Preclinical validation, pharmacological testing, and optimization of leading approaches, in collaboration with Harvard-affiliated hospitals, with program leads to be determined.

SOURCE

https://hms.harvard.edu/news/joining-forces

 

 

A Screen Door Opens

Virtual screen finds compounds that could combat SARS-CoV-2

This article is part of Harvard Medical School’s continuing coverage of medicine, biomedical research, medical education, and policy related to the SARS-CoV-2 pandemic and the disease COVID-19.

Less than a year ago, Harvard Medical School researchers and international colleagues unveiled a platform called VirtualFlow that could swiftly sift through more than 1 billion chemical compounds and identify those with the greatest promise to become disease-specific treatments, providing researchers with invaluable guidance before they embark on expensive and time-consuming lab experiments and clinical trials.

Propelled by the urgent needs of the pandemic, the team has now pushed VirtualFlow even further, conducting 45 screens of more than 1 billion compounds each and ranking the compounds with the greatest potential for fighting COVID-19—including some that are already approved by the FDA for other diseases.

“This was the largest virtual screening effort ever done,” said VirtualFlow co-developer Christoph Gorgulla, research fellow in biological chemistry and molecular pharmacology in the labs of Haribabu Arthanari and Gerhard Wagner in the Blavatnik Institute at HMS.

The results were published in January in the open-access journal iScience.

The team searched for compounds that bind to any of 15 proteins on SARS-CoV-2 or two human proteins, ACE2 and TMPRSS2, known to interact with the virus and enable infection.

Researchers can now explore on an interactive website the 1,000 most promising compounds from each screen and start testing in the lab any ones they choose.

The urgency of the pandemic and the sheer number of candidate compounds inspired the team to release the early results to the scientific community.

“No one group can validate all the compounds as quickly as the pandemic demands,” said Gorgulla, who is also an associate of the Department of Physics at Harvard University. “We hope that our colleagues can collectively use our results to identify potent inhibitors of SARS-CoV-2.

In most cases, it will take years to find out whether a compound is safe and effective in humans. For some of the compounds, however, researchers have a head start.

Hundreds of the most promising compounds that VirtualFlow flagged are already FDA approved or being studied in clinical or preclinical trials for other diseases. If researchers find that one of those compounds proves effective against SARS-CoV-2 in lab experiments, the data their colleagues have already collected could save time establishing safety in humans.

Other compounds among VirtualFlow’s top hits are currently being assessed in clinical trials for COVID-19, including several drugs in the steroid family. In those cases, researchers could build on the software findings to investigate how those drug candidates work at the molecular level—something that’s not always clear even when a drug works well.

It shows what we’re capable of computationally during a pandemic.

Hari Arthanari

SOURCE

https://hms.harvard.edu/news/screen-door-opens?utm_source=Silverpop&utm_medium=email&utm_term=field_news_item_1&utm_content=HMNews02012021

Read Full Post »

Danny Bar-Zohar, MD –  New R&D Leader for new pipelines at Merck KGaA as Luciano Rossetti steps out

Reporter: Aviva Lev-Ari, PhD, RN

 

Danny Bar-Zohar, MD – A Pharmaceutical Executive Profile in R&D: Ex-Novastis, Ex-Teva

Experience

Education

SOURCE

https://www.linkedin.com/in/danny-bar-zohar-513904a/

 

Novartis vet Danny Bar-Zohar leaps back into R&D, taking over the development team at Merck KGaA as Luciano Rossetti steps out

John Carroll
Editor & Founder

After a brief stint as a biotech investor at Syncona, Novartis vet Danny Bar-Zohar is back in R&D, and he’s taking the lead position at Merck KGaA’s drug division.

Bar-Zohar had led late-stage clinical development across a variety of areas — neuroscience, immunology, oncology and ophthalmology, among others — before joining the migration of talent out of the Basel-based multinational. He had been at Novartis for 7 years, which followed an earlier chapter in research at Teva.

Luciano Rossetti
The scientist is taking the lead on development at Merck KGaA, in place of Luciano Rossetti, who had a mixed record in R&D that nevertheless marked a big improvement over the dismal run the company had endured earlier. Joern-Peter Halle will continue on as global head of research. Rossetti is retiring after 6 years of running the research group, which has extensive operations in Germany as well as Massachusetts.

Their PD-L1 Bavencio — allied with Pfizer — has had a few successes, and a whole slate of failures. Sprifermin was touted as a big potential advance in osteoarthritis, but Merck KGaA is now auctioning off that part of the portfolio. One of the few late-stage bright spots has been their MET inhibitor tepotinib, which won breakthrough status and now is under priority review. That drug faces a rival at Novartis — capmatinib — that won an accelerated OK at the FDA in May.

advertisement

advertisement
There’s also a BTK inhibitor, evobrutinib, that’s being developed for MS. But that’s a very crowded field, and Sanofi has been bullish about its prospects in the same research niche after buying out Principia.

Moving back into mid-stage development, there’s a major program underway for bintrafusp alfa, a bifunctional fusion protein targeting TGF-β and PD-L1, which Merck KGaA has high hopes for.

That all marks some bright, though limited, prospects for Merck KGaA, highlighting the need to find something new to beef up the pipeline. Bar-Zohar will get a say in that.

AUTHOR
John Carroll

SOURCE

https://endpts.com/novartis-vet-danny-bar-zohar-leaps-back-into-rd-taking-over-the-team-at-merck-kgaa-as-luciano-rossetti-steps-out/

Read Full Post »

Double Mutant PI3KA Found to Lead to Higher Oncogenic Signaling in Cancer Cells

Curator: Stephen J. Williams, PhD

PIK3CA (Phosphatidylinsitol 4,5-bisphosphate (PIP2) 3-kinase catalytic subunit α) is one of the most frequently mutated oncogenes in various tumor types ([1] and http://www.sanger.ac.uk/genetics/CGP/cosmic). Oncogenic mutations leading to the overactivation of PIK3CA, especially in context in of inactivating PTEN mutations, result in overtly high signaling activity and associated with the malignant phenotype.

In a Perspective article (Double trouble for cancer gene: Double mutations in an oncogene enhance tumor growth) in the journal Science[2], Dr. Alex Toker discusses the recent results of Vasan et al. in the same issue of Science[3] on the finding that double mutations in the same allele of PIK3CA are more frequent in cancer genomes than previously identified and these double mutations lead to increased PI3K pathway activation, increased tumor growth, and increased sensitivity to PI3K inhibitors in human breast cancer.

 

 

From Dr. Melvin Crasto blog NewDrugApprovals.org

Alpelisib: PIK3CA inhibitor:

Alpelisib: New PIK3CA inhibitor approved for HER2 negative metastatic breast cancer

 

FDA approves first PI3K inhibitor for breast cancer

syn https://newdrugapprovals.org/2018/06/25/alpelisib-byl-719/

Today, the U.S. Food and Drug Administration approved Piqray (alpelisib) tablets, to be used in combination with the FDA-approved endocrine therapy fulvestrant, to treat postmenopausal women, and men, with hormone receptor (HR)-positive, human epidermal growth factor receptor 2 (HER2)-negative, PIK3CA-mutated, advanced or metastatic breast cancer (as detected by an FDA-approved test) following progression on or after an endocrine-based regimen.

The FDA also approved the companion diagnostic test, therascreen PIK3CA RGQ PCR Kit, to detect the PIK3CA mutation in a tissue and/or a liquid biopsy. Patients who are negative by

May 24, 2019

Today, the U.S. Food and Drug Administration approved Piqray (alpelisib) tablets, to be used in combination with the FDA-approved endocrine therapy fulvestrant, to treat postmenopausal women, and men, with hormone receptor (HR)-positive, human epidermal growth factor receptor 2 (HER2)-negative, PIK3CA-mutated, advanced or metastatic breast cancer (as detected by an FDA-approved test) following progression on or after an endocrine-based regimen.

The FDA also approved the companion diagnostic test, therascreen PIK3CA RGQ PCR Kit, to detect the PIK3CA mutation in a tissue and/or a liquid biopsy. Patients who are negative by the therascreen test using the liquid biopsy should undergo tumor biopsy for PIK3CA mutation testing.

“Piqray is the first PI3K inhibitor to demonstrate a clinically meaningful benefit in treating patients with this type of breast cancer. The ability to target treatment to a patient’s specific genetic mutation or biomarker is becoming increasingly common in cancer treatment, and companion diagnostic tests assist oncologists in selecting patients who may benefit from these targeted treatments,” said Richard Pazdur, M.D., director of the FDA’s Oncology Center of Excellence and acting director of the Office of Hematology and Oncology Products in the FDA’s Center for Drug Evaluation and Research. “For this approval, we employed some of our newer regulatory tools to streamline reviews without compromising the quality of our assessment. This drug is the first novel drug approved under the Real-Time Oncology Review pilot program. We also used the updated Assessment Aid, a multidisciplinary review template that helps focus our written review on critical thinking and consistency and reduces time spent on administrative tasks.”

Metastatic breast cancer is breast cancer that has spread beyond the breast to other organs in the body (most often the bones, lungs, liver or brain). When breast cancer is hormone-receptor positive, patients may be treated with anti-hormonal treatment (also called endocrine therapy), alone or in combination with other medicines, or chemotherapy.

The efficacy of Piqray was studied in the SOLAR-1 trial, a randomized trial of 572 postmenopausal women and men with HR-positive, HER2-negative, advanced or metastatic breast cancer whose cancer had progressed while on or after receiving an aromatase inhibitor. Results from the trial showed the addition of Piqray to fulvestrant significantly prolonged progression- free survival (median of 11 months vs. 5.7 months) in patients whose tumors had a PIK3CA mutation.

Common side effects of Piqray are high blood sugar levels, increase in creatinine, diarrhea, rash, decrease in lymphocyte count in the blood, elevated liver enzymes, nausea, fatigue, low red blood cell count, increase in lipase (enzymes released by the pancreas), decreased appetite, stomatitis, vomiting, weight loss, low calcium levels, aPTT prolonged (blood clotting taking longer to occur than it should), and hair loss.

Health care professionals are advised to monitor patients taking Piqray for severe hypersensitivity reactions (intolerance). Patients are warned of potentially severe skin reactions (rashes that may result in peeling and blistering of skin or mucous membranes like the lips and gums). Health care professionals are advised not to initiate treatment in patients with a history of severe skin reactions such as Stevens-Johnson Syndrome, erythema multiforme, or toxic epidermal necrolysis. Patients on Piqray have reported severe hyperglycemia (high blood sugar), and the safety of Piqray in patients with Type 1 or uncontrolled Type 2 diabetes has not been established. Before initiating treatment with Piqray, health care professionals are advised to check fasting glucose and HbA1c, and to optimize glycemic control. Patients should be monitored for pneumonitis/interstitial lung disease (inflammation of lung tissue) and diarrhea during treatment. Piqray must be dispensed with a patient Medication Guide that describes important information about the drug’s uses and risks.

Piqray is the first new drug application (NDA) for a new molecular entity approved under the Real-Time Oncology Review (RTOR) pilot program, which permits the FDA to begin analyzing key efficacy and safety datasets prior to the official submission of an application, allowing the review team to begin their review and communicate with the applicant earlier. Piqray also used the updated Assessment Aid (AAid), a multidisciplinary review template intended to focus the FDA’s written review on critical thinking and consistency and reduce time spent on administrative tasks. With these two pilot programs, today’s approval of Piqray comes approximately three months ahead of the Prescription Drug User Fee Act (PDUFA) VI deadline of August 18, 2019.

The FDA granted this application Priority Review designation. The FDA granted approval of Piqray to Novartis. The FDA granted approval of the therascreen PIK3CA RGQ PCR Kit to QIAGEN Manchester, Ltd.

https://www.fda.gov/news-events/press-announcements/fda-approves-first-pi3k-inhibitor-breast-cancer?utm_campaign=052419_PR_FDA%20approves%20first%20PI3K%20inhibitor%20for%20breast%20cancer&utm_medium=email&utm_source=Eloqua

 

Alpelisib

(2S)-1-N-[4-methyl-5-[2-(1,1,1-trifluoro-2-methylpropan-2-yl)pyridin-4-yl]-1,3-thiazol-2-yl]pyrrolidine-1,2-dicarboxamide

PDT PAT WO 2010/029082

CHEMICAL NAMES: Alpelisib; CAS 1217486-61-7; BYL-719; BYL719; UNII-08W5N2C97Q; BYL 719
MOLECULAR FORMULA: C19H22F3N5O2S
MOLECULAR WEIGHT: 441.473 g/mol
  1. alpelisib
  2. 1217486-61-7
  3. BYL-719
  4. BYL719
  5. UNII-08W5N2C97Q
  6. BYL 719
  7. Alpelisib (BYL719)
  8. (S)-N1-(4-Methyl-5-(2-(1,1,1-trifluoro-2-methylpropan-2-yl)pyridin-4-yl)thiazol-2-yl)pyrrolidine-1,2-dicarboxamide
  9. NVP-BYL719

Alpelisib is an orally bioavailable phosphatidylinositol 3-kinase (PI3K) inhibitor with potential antineoplastic activity. Alpelisib specifically inhibits PI3K in the PI3K/AKT kinase (or protein kinase B) signaling pathway, thereby inhibiting the activation of the PI3K signaling pathway. This may result in inhibition of tumor cell growth and survival in susceptible tumor cell populations. Activation of the PI3K signaling pathway is frequently associated with tumorigenesis. Dysregulated PI3K signaling may contribute to tumor resistance to a variety of antineoplastic agents.

Alpelisib has been used in trials studying the treatment and basic science of Neoplasms, Solid Tumors, BREAST CANCER, 3rd Line GIST, and Rectal Cancer, among others.

 

SYN 2

POLYMORPHS

https://patents.google.com/patent/WO2012175522A1/en

(S)-pyrrolidine-l,2-dicarboxylic acid 2-amide l-(4-methyl-5-[2-(2,2,2-trifluoro-l,l- dimethyl-ethyl)-pyridin-4-yl]-thiazol-2-yl)-amidei hereafter referred to as compound I,

is an alpha-selective phosphatidylinositol 3 -kinase (PI3K) inhibitor. Compound I was originally described in WO 2010/029082, wherein the synthesis of its free base form was described. There is a need for additional solid forms of compound I, for use in drug substance and drug product development. It has been found that new solid forms of compound I can be prepared as one or more polymorph forms, including solvate forms. These polymorph forms exhibit new physical properties that may be exploited in order to obtain new pharmacological properties, and that may be utilized in drug substance and drug product development. Summary of the Invention

In one aspect, provided herein is a crystalline form of the compound of formula I, or a solvate of the crystalline form of the compound of formula I, or a salt of the crystalline form of the compound of formula I, or a solvate of a salt of the crystalline form of the compound of formula I. In one embodiment, the crystalline form of the compound of formula I has the polymorph form SA, SB, Sc, or SD.

In another aspect, provided herein is a pharmaceutical composition comprising a crystalline compound of formula I. In one embodiment of the pharmaceutical composition, the crystalline compound of formula I has the polymorph form SA, SB,Sc, or So.

In another aspect, provided herein is a method for the treatment of disorders mediated by PI3K, comprising administering to a patient in need of such treatment an effective amount of a crystalline compound of formula I, particularly SA, SB, SC,or SD .

In yet another aspect, provided herein is the use of a crystalline compound of formula I, particularly SA, SB, SC, or SD, for the preparation of a medicament for the treatment of disorders mediated by PI3K.

 

Source: https://newdrugapprovals.org/?s=alpelisib&submit=

 

Pharmacology and Toxicology from drugbank.ca

Indication

Alpelisib is indicated in combination with fulvestrant to treat postmenopausal women, and men, with advanced or metastatic breast cancer.Label This cancer must be hormone receptor (HR)-positive, human epidermal growth factor receptor 2 (HER2)-negative, and PIK3CA­ mutated.Label The cancer must be detected by an FDA-approved test following progression on or after an endocrine-based regimen.Label

Associated Conditions

Contraindications & Blackbox Warnings

Learn about our commercial Contraindications & Blackbox Warnings data.

LEARN MORE

 

Pharmacodynamics

Alpelisib does not prolong the QTcF interval.Label Patients taking alpelisib experience a dose dependent benefit from treatment with a 51% advantage of a 200mg daily dose over a 100mg dose and a 22% advantage of 300mg once daily over 150mg twice daily.6 This suggests patients requiring a lower dose may benefit from twice daily dosing.6

Mechanism of action

Phosphatidylinositol-3-kinase-α (PI3Kα) is responsible for cell proliferation in response to growth factor-tyrosine kinase pathway activation.3 In some cancers PI3Kα’s p110α catalytic subunit is mutated making it hyperactive.3 Alpelisib inhibits (PI3K), with the highest specificity for PI3Kα.Label

TARGET ACTIONS ORGANISM
APhosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit alpha isoform inhibitor Humans

Absorption

Alpelisib reached a peak concentration in plasma of 1320±912ng/mL after 2 hours.4 Alpelisib has an AUClast of 11,100±3760h ng/mL and an AUCINF of 11,100±3770h ng/mL.4 A large, high fat meal increases the AUC by 73% and Cmax by 84% while a small, low fat meal increases the AUC by 77% and Cmax by 145%.Label

Volume of distribution

The apparent volume of distribution at steady state is 114L.Label

Protein binding

Alpelisib is 89% protein bound.Label

Metabolism

Alpelisib is metabolized by hydrolysis reactions to form the primary metabolite.Label It is also metabolized by CYP3A4.Label The full metabolism of Alpelisib has yet to be determined but a series of reactions have been proposed.4,5 The main metabolic reaction is the substitution of an amine group on alpelisib for a hydroxyl group to form a metabolite known as M44,5 or BZG791.Label Alpelisib can also be glucuronidated to form the M1 and M12 metabolites.4,5

Hover over products below to view reaction partners

Route of elimination

36% of an oral dose is eliminated as unchanged drug in the feces and 32% as the primary metabolite BZG791 in the feces.Label 2% of an oral dose is eliminated in the urine as unchanged drug and 7.1% as the primary metabolite BZG791.Label In total 81% of an oral dose is eliminated in the feces and 14% is eliminated in the urine.Label

Half-life

The mean half life of alprelisib is 8 to 9 hours.Label

Clearance

The mean apparent oral clearance was 39.0L/h.4 The predicted clearance is 9.2L/hr under fed conditions.Label

Adverse Effects

Learn about our commercial Adverse Effects data.

LEARN MORE

 

Toxicity

LD50 and Overdose

Patients experiencing an overdose may present with hyperglycemia, nausea, asthenia, and rash.Label There is no antidote for an overdose of alpelisib so patients should be treated symptomatically.Label Data regarding an LD50 is not readily available.MSDS In clinical trials, patients were given doses of up to 450mg once daily.Label

Pregnancy, Lactation, and Fertility

Following administration in rats and rabbits during organogenesis, adverse effects on the reproductive system, such as embryo-fetal mortality, reduced fetal weights, and increased incidences of fetal malformations, were observed.Label Based on these findings of animals studies and its mechanism of action, it is proposed that alpelisib may cause embryo-fetal toxicity when administered to pregnant patients.Label There is no data available regarding the presence of alpelisib in breast milk so breast feeding mothers are advised not to breastfeed while taking this medication and for 1 week after their last dose.Label Based on animal studies, alpelisib may impair fertility of humans.Label

Carcinogenicity and Mutagenicity

Studies of carcinogenicity have yet to be performed.Label Alpelisib has not been found to be mutagenic in the Ames test.Label It is not aneugenic, clastogenic, or genotoxic in further assays.Label

Affected organisms

Not Available

Pathways

Not Available

Pharmacogenomic Effects/ADRs 

 

Not Available

 

Source: https://www.drugbank.ca/drugs/DB12015

References

  1. Yuan TL, Cantley LC: PI3K pathway alterations in cancer: variations on a theme. Oncogene 2008, 27(41):5497-5510.
  2. Toker A: Double trouble for cancer gene. Science 2019, 366(6466):685-686.
  3. Vasan N, Razavi P, Johnson JL, Shao H, Shah H, Antoine A, Ladewig E, Gorelick A, Lin TY, Toska E et al: Double PIK3CA mutations in cis increase oncogenicity and sensitivity to PI3Kalpha inhibitors. Science 2019, 366(6466):714-723.

 

 

Read Full Post »

Tweet Collection by @pharma_BI and @AVIVA1950 and Re-Tweets for e-Proceedings 14th Annual BioPharma & Healthcare Summit, Friday, September 4, 2020, 8 AM EST to 3-30 PM EST – Virtual Edition

Real Time Press Coverage: Aviva Lev-Ari, PhD, RN

 

e-Proceedings 14th Annual BioPharma & Healthcare Summit, Friday, September 4, 2020, 8 AM EST to 3-30 PM EST – Virtual Edition

Real Time Press Coverage: Aviva Lev-Ari, PhD, RN

Founder & Director, LPBI Group

https://pharmaceuticalintelligence.com/2020/07/28/14th-annual-biopharma-healthcare-summit-friday-september-4-2020-8-am-est-to-3-30-pm-est-virtual-edition/

 

Aviva Lev-Ari
@AVIVA1950

#USAIC20 Dr. Hal Barron, Chief Scientific Officer and President R&D, GlaxoSmithKline GWAS not easy to find which gene drives the association  Functional Genomics gene by gene with phenotypes using machine learning significant help

Aviva Lev-Ari
@AVIVA1950

#USAIC20 Dr. Hal Barron, Chief Scientific Officer and President R&D, GSK GWAS not easy to find which gene drives the association  Functional Genomics gene by gene with phenotypes using machine learning significant help

Srihari Gopal
@sgopal2

Enjoyed hearing enthusiasm for Neuroscience R&D by Roy Vagelos at #USAIC20. Wonderful interview by Mathai Mammen

Image

1
2
Show this thread

Aviva Lev-Ari
@AVIVA1950

#USAIC20 Nina Kjellson, General Partner, Canaan Data science is a winner in Healthcare Women – Data Science is an excellent match

Aviva Lev-Ari
@AVIVA1950

#USAIC20 Arpa Garay, President, Global Pharmaceuticals, Commercial Analytics, Merck & Co. Data on Patients and identification who will benefit fro which therapy  cultural bias risk aversion

Aviva Lev-Ari
@AVIVA1950

#USAIC20 Dr. Najat Khan, Chief Operating Officer, Janssen R&D Data Sciences, Johnson & Johnson Data Validation  Deployment of algorithms embed data by type early on in the crisis to understand the disease

Aviva Lev-Ari
@AVIVA1950

#USAIC20 Sastry Chilukuri, President, Acorn AI- Medidata Opportunities in Data Science in Paharma COVID-19 and Data Science

Aviva Lev-Ari
@AVIVA1950

#USAIC20 Dr. Maya Said, Chief Executive Officer, Outcomes4Me Cancer patients taking change of their care Digital Health – consumerization of Health, patient demand to be part of the decision, part the information FDA launched a Program Project Patient Voice

USAIC
@USAIC

We’re taking a quick break at #USAIC20 before our next panel on rare diseases starts at 12:20pm EDT. USAIC would like to thank our Sponsors and Partners for supporting this year’s digital event.

Image

Aviva Lev-Ari
@AVIVA1950

#USAIC20 Dr. Roy Vagelos, Chairman of the Board, Regeneron HIV-AIDS: reverse transcriptase converted a lethal disease to a chronic disease, tried hard to make vaccine – the science was not there

Aviva Lev-Ari
@AVIVA1950

#USAIC20 Dr. Roy Vagelos, Chairman of the Board, Regeneron Pharmaceuticals Congratulates Big Pharma for taking the challenge on COVID-19 Vaccine, Antibody and anti-viral Government funding Merck was independent from Government – to be able to set the price

1

Dr Kapil Khambholja
@kapilmk

Christopher Viehbacher, Gurnet Point Capital touches very sensitive topic at #USAIC20 He claims that we are never going to have real innovation out of big pharma! Well this isn’t new but not entirely true either… any more thoughts?
1
1
Show this thread

Aviva Lev-Ari
@AVIVA1950

#USAIC20 Daphne Zohar, Founder & CEO, PureTech Health Disease focus, best science is the decision factors

1

Aviva Lev-Ari
@AVIVA1950

#USAIC20 Christopher Viehbacher, Managing Partner, Gurnet Point Capital Dream of every Biotech – get Big Pharma coming to acquire and pay a lot Morph and adapt

anju ghangurde
@scripanjug

Biogen’s chair Papadopoulos big co mergers is an attempt to solve problems; typically driven by patent expirations.. #usaic20

2

anju ghangurde
@scripanjug

Chris Viehbacher/Gurnet Point Capital on US election: industry will work with whoever wins; we’ll have to ‘morph & adapt’ #usaic20

1

Dr Kapil Khambholja
@kapilmk

of

talks about various philosophies and key reasons why certain projects/molecules are killed early. My counter questions- What are chances of losing hope little early? Do small #biopharma publish negative results to aid to the knowledge pool? #USAIC20

Image

2
2
Show this thread

Aviva Lev-Ari
@AVIVA1950

#USAIC20 Dr. Laurie Glimcher, President & CEO, Dana-Farber Cancer Institute DNA repair and epignetics are the future of medicine

Aviva Lev-Ari
@AVIVA1950

#USAIC20 Dr. Laurie Glimcher, President & CEO, Dana-Farber Cancer Institute COlonorectal cancer is increasing immuno therapy 5 drugs marketed 30% cancer patients are treated early detection key vs metastatic 10% of cancer are inherited treatment early

Aviva Lev-Ari
@AVIVA1950

#USAIC20 Rehan Verjee, President, EMD Serono Charities funding cancer research – were impacted and resources will come later and in decreased amount New opportunities support access to Medicine improve investment across the board

Aviva Lev-Ari
@AVIVA1950

#USAIC20 Dr. Philip Larsen, Global Head of Research, Bayer AG Repurposing drugs as antiviral from drug screening innovating methods Cytokine storm in OCVID-19 – kinase inhibitors may be antiviral data of tested positive allows research of pathway in new ways

Aviva Lev-Ari
@AVIVA1950

#USAIC20 Dr. Laurie Glimcher, President & CEO, Dana-Farber 3,000 Telemedicine session in the first week of the Pandemic vs 300 before – patient come back visits patient happy with Telemedicine team virtually need be reimbursed same rate working remotely

Aviva Lev-Ari
@AVIVA1950

#USAIC20 Dr. Raju Kucherlapati, Professor of Genetics, Harvard Medical School New normal as a result of the pandemic role of personalized medicine

Aviva Lev-Ari
@AVIVA1950

#USAIC20 Rehan Verjee, President, EMD Serono entire volume of clinical trials at Roche went down same at EMD delay of 6 month, some were to be initiated but was put on hold Charities funding cancer research were impacted and resources will come later smaller

Aviva Lev-Ari
@AVIVA1950

#USAIC20 Dr. Laurie Glimcher, President & CEO, Dana-Farber Cancer Institute Dana Farber saw impact of COVID-19 on immunosuppressed patients coming in for Cancer Tx – switch from IV Tx to Oral 96% decrease in screenings due to Pandemic – increase with Cancer

Aviva Lev-Ari
@AVIVA1950

#USAIC20 Kenneth Frazier, Chairman of the Board and Chief Executive Officer, Merck & Co. Pharma’s obligation for next generations requires investment in R&D vs Politicians running for 4 years Patients must come first vs shareholders vs R&D investment in 2011

Aviva Lev-Ari
@AVIVA1950

#USAIC20 Kenneth Frazier, Chairman of the Board and Chief Executive Officer, Merck & Co. Antibiotic research at Merck – no market incentives on pricing for Merck to invest in antibiotics people will die from bacterial resistance next pandemic be bacterial

Aviva Lev-Ari
@AVIVA1950

#USAIC20 Kenneth Frazier, Chairman of the Board and Chief Executive Officer, Merck & Co. Strategies of Merck = “Medicine is for the People not for Profit” – Ketruda in India is not reembureable in India and million are in need it Partnership are encouraged

Dr Kapil Khambholja
@kapilmk

Chairman Stelios Papadopoulos asks #KennethFrazier if wealthy nations will try to secure large proportion of #COVID19 drugs/vaccines. #KennethFrazie rightly mentions: pharma industry’s responsibility to balance the access to diff countries during pandemic. #USAIC20

1
3
Show this thread

Dr Kapil Khambholja
@kapilmk

Almost 60% participants at #USAIC20 feel that MNCs are more likely to run their #clinicalTrials in #INDIA seeing changing environment here, reveals the poll. Exciting time ahead for scientific fraternity as this can substantially increase the speed of #DrugDevelopment globally

Clapping hands sign

Image

1
1
Show this thread

Aviva Lev-Ari
@AVIVA1950

#USAIC20 Dr. Barry Bloom, Professor & former Dean, Harvard School of Public Health Vaccine in clinical trials, public need to return for 2nd shot, hesitancy Who will get the Vaccine first in the US  most vulnerable of those causing transmission Pharma’s risk

4

Aviva Lev-Ari
@AVIVA1950

#USAIC20 Dr. Barry Bloom, Professor & former Dean, Harvard School of Public Health Testing – PCR expensive does not enable quick testing is expensive result come transmission occurred Antibody testing CRISPR test based Vaccine in clinical trials

1

Aviva Lev-Ari
@AVIVA1950

#USAIC20 Dr Andrew Plump, President of R&D, Takeda Pharmaceuticals COllaboration effort around the Globe in the Pandemic therapy solutions including Vaccines

Read Full Post »

14th Annual BioPharma & Healthcare Summit, Friday, September 4, 2020, 8 AM EST to 3-30 PM EST – Virtual Edition

Real Time Press Coverage: Aviva Lev-Ari, PhD, RN

Founder & Director, LPBI Group

 

Tweet Collection by @pharma_BI and @AVIVA1950 and Re-Tweets for e-Proceedings 14th Annual BioPharma & Healthcare Summit, Friday, September 4, 2020, 8 AM EST to 3-30 PM EST – Virtual Edition

Real Time Press Coverage: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2020/09/04/tweet-collection-by-pharma_bi-and-aviva1950-and-re-tweets-for-e-proceedings-14th-annual-biopharma-healthcare-summit-friday-september-4-2020-8-am-est-to-3-30-pm-est-virtual-editio/

 

 

 

http://www.usaindiachamber.org

 

 2021 summit- June 22. Marriott Cambridge, Massachusetts, USA

 

LPBI’s 2020 VISION

@pharma_BI

@AVIVA1950

#USAIC20

 

 

USAIC has created an ecosystem committed to driving a global dialogue on BioPharma & Healthcare innovation, attracting a diverse mix of senior industry professionals and catalyzing partnerships, new ideas, networks and regulatory reform. This unique platform creates mutually beneficial opportunities and relationships for the global Life Sciences & Healthcare industry.

14th Annual BioPharma & Healthcare Summit, Friday, September 4, 2020, 8 AM EST to 3-30 PM EST – Virtual Edition

 

Speakers


Kenneth Frazier
Chairman of the Board & CEO
Merck & Co.

Dr. Andrew Plump
President of R&D
Takeda Pharmaceuticals

Dr. Laurie Glimcher
President & CEO
Dana-Farber Cancer Institute

Dr. Roy Vagelos
Chairman of the Board
Regeneron

Dr. Stelios Papadopoulos
Chairman of the Board
Biogen

Dr. Mathai Mammen
Global Head of Janssen R&D
Johnson & Johnson

Christopher Viehbacher
Managing Partner
Gurnet Point Capital

Hari Bhartia
Founder & Co-Chairman
Jubilant Bhartia Group

Dr. Hal Barron
President, R&D and CSO
GlaxoSmithKline

Prof. K. Vijay Raghavan
Principal Scientific Advisor
Government of India

Sanat Chattopadhyay
President- Merck Manufacturing Division
Merck & Co.

Dr. George Yancopoulos
Co-Founder, President & CSO
Regeneron

Kiran Mazumdar Shaw
Executive Chairperson
Biocon

Dr. Elias Zerhouni
Professor Emeritus
Johns Hopkins University

Dr. David Reese
Executive Vice President- R&D
Amgen

Dr. Alfred Sandrock
Executive Vice President, R&D
Biogen

Dr. Naresh Trehan
Chairman
Medanta – the Medicity

Dr. Najat Khan
Chief Operating Officer, Data Sciences
Janssen- Johnson & Johnson

Dr. Richard Hatchett
Chief Executive Officer
CEPI

Amitabh Kant
Chief Executive Officer
NITI Aayog

Dr. Martin Mackay
Co-Founder
Rallybio

Dr. Daniel Curran
Head of the Rare Diseases TA
Takeda Pharmaceuticals

Daphne Zohar
Founder & CEO
PureTech Health

Dr. David Meeker
Chairman & CEO
Rhythm Pharmaceuticals

Dr. John Orloff
EVP and Head of R&D
Alexion

Dr. Mandeep Bhandari
Joint Secretary
Ministry of Health, India

Dr. Barry Bloom
Professor & former Dean
Harvard School of Public Health

Dr. Anne Heatherington
Head of Data Sciences Institute
Takeda Pharmaceuticals

Dr. Philip Larsen
Global Head of Research
Bayer AG

Dr. Timothy Yu
Assistant Professor in Pediatrics
Harvard Medical School

Rehan Verjee
President
EMD Serono

Sastry Chilukuri
Executive Vice President
Medidata

Arpa Garay
President, Commercial Analytics
Merck & Co.

Dr. William Chin
Professor of Medicine, Emeritus
Harvard Medical School

Dr. V G Somani
Drugs Controller General of India
Government of India

Dr. Rajeev Venkayya
President-Global Vaccines
Takeda

Dr. Steve Uden
Co-Founder
Rallybio

Muna Bhanji
SVP, Global Market Access
Merck & Co.

Dr. Maya Said
Chief Executive Officer
Outcomes4Me

Dr. Raju Kucherlapati
Professor of Genetics
Harvard Medical School

Dr. Tony Ho
Head of R&D
CRISPR Therapeutics

Dr. Sanjeev Sinha
Professor of Medicine
All India Institute of Medical Sciences

Nina Kjellson
General Partner
Canaan

Dr. Michael Rosenblatt
Chief Medical Officer
Flagship Pioneering

Dr. Shiv Kumar Sarin
Director
Institute of Liver & Biliary Sciences

Matt Wilsey
Co-Founder & Chairman
Grace Science Foundation

Dr. Samuel Waksal
Founder
Meira GTx

Dr. Alise Reicin
Former President, Global Clinical Dev.
Celgene

Dr. Toni Choueiri
Director
Lank Center for Genitourinary Oncology
Dana-Farber Cancer Institute

Dr. Dhaval Patel
EVP & Chief Scientific Officer
UCB

Dr. Nirmal Kumar Ganguly
Former Director General
Indian Council of Medical Research

Dr. Peter Mueller
President
The Muller Health Foundation

Dr. Timothy Clackson
President & CTO
Xilio Therapeutics
 

 

14th Annual BioPharma & Healthcare Summit, Friday, September 4, 2020,

8 AM EST to 3-30 PM EST – Virtual Edition

 

Chair and Master of Ceremonies (Emcee)– Dr. Andrew Plump, President of R&D, Takeda Pharmaceuticals

Timings are Eastern Standard Time (EST)

Time Topic
8 AM – 8-10 AM Welcome addressKarun Rishi, President, USAIC

  • COVID-19 Pandemic is a Global crisis
  • India can play a special role in R&D and in Manufacturing including Vaccine development

Opening commentsDr Andrew Plump, President of R&D, Takeda Pharmaceuticals

  • Global Summit around the World – JP Morgan of the East as we were called – it is Now a Global Conference vs East Coast
  • Record number of Drugs approved as New Drugs with special quality
  • explosion of modality of therapies to include Gene Therapy
  • Billion underserved vs N-of-One drug
  • India’s President Modi allow healthcare access to 1/2Billion
  • collaboration across the World COVID Alliance in vaccine development
  • Global effort, China recovery is remarkable
  • India battle the infection and it is growing – Public Health
  • Remarkable Speakers
8-10 AM – 8-50 AM Panel Discussion- COVID-19: Where are we now? Where are we going?

Panelists:
Dr. Barry Bloom, Professor & former Dean, Harvard School of Public Health

  • Testing – PCR expensive does not enable quick testing is expensive result come transmission occurred
  • Antibody testing
  • CRISPR test based
  • Vaccine in clinical trials, public need to return for 2nd shot, hesitancy
  • Who will get the Vaccine first? in the US  most vulnerable of those causing transmission
  • Pharma takes risk when efficacious level is unknown
    Dr. George Yancopoulos, Co-Founder, President & CSO, Regeneron
  • Repurpose – be careful
  • Ebola vaccine development approach is been REUSED for COVID-19
  • Existential threat by Disease – preparedness is ridiculous as size of investment – far where we need to be
  • Untreatable disease burden COVID-19 cost of healthcare calls massive increases as a society and Private sector Moderna invested in new technology from Academe to the Industry
  • Universal HealthCare will cripple the the healthcare systems
    Kiran Mazumdar-Shaw, Executive Chairperson, Biocon
  • Safety in proof of concept
  • Children focus for emergency use
  • validation of repurpose drugs
  • oral vaccine involve sequential processing, approval and TRUST,
  • concerns about risks
  • accelerate the process is the opportunity
    Dr. Rajeev Venkayya, President of the Global Vaccine Business Unit, Takeda
  • Public confidence in COVID-19 Vaccine
  • The Group with concerns at present is larger than 15 years ago due to the accelerate process od the development process
  • political influences on CDC emergency authorization given prior to election
  • hesitancy – influence of social media, conspiracies
  • Transparency by Pharma and by Regulatory Agencies
  • Independent reviews
    Dr. Richard Hatchett, CEO, Coalition for Epidemic Preparedness Innovations (CEPI)
  • 78 countries ready to participate, Healthcare workers priority to be ready end of next year

 

Moderator:
Dr. William Chin, Professor of Medicine, Emeritus, Harvard Medical School

8-50 AM – 8-55 AM Break + Polling
8-55 AM – 9-10 AM India Regulatory update

Dr. Mandeep Bhandari, Joint Secretary, Ministry of Health & Family Welfare, India

  • COVID related – support for Clinical Trials support to the Industry, innovators, processes and infrastructure is in place

Dr. V G Somani, Drug Controller General of India, Central Drug Control Organization

  • partnership, time line, transparency
  • interaction online with regulators
  • 30 days approval pre and post approval – progress achieved
  • Online presubmission very useful to both sides
  • Ecosystems on early development: Gene therapy

Moderator:
Muna Bhanji, Senior Vice President,  Merck & Co.

  • India’s preparedness
9-10 AM – 9-15 AM Break + Polling
9-15 AM – 9-55 AM Fireside Chat

Kenneth Frazier, Chairman of the Board and Chief Executive Officer, Merck & Co.

Strategies of Merck = “Medicine is for the People not for Profit”

  • AntiViral – nucleocide – orally bioavailable
  • Vaccine in early development – BSV Vaccine used in EBOLA – attenuated virus vector platform experience – 1 single doze, deployed Globally
  • Vaccine modified Measles Vaccine, novel platform – out patient and Hospital
  • Antibiotic research at Merck – no market incentives on pricing for Merck to invest in antibiotics
  • people will die from bacterial resistance infection and next pandemic will be bacterial not viral

Moderator:
Dr. Stelios Papadopoulos, Chairman of the Board, Biogen

  • Most important comments on urgency in investment in drug development by multiple constituencies made by
  • Dr. George Yancopoulos, Co-Founder, President & CSO, Regeneron
  • Access to therapy
9-55 AM – 10 AM Break + Polling
10 AM – 10-40 AM India Innovation Landscape

Panelists:
Amitabh Kant, Chief Executive Officer, National Institution for Transforming India (NITI)

  • Innovation in drug discovery collaboration for clinical trial infrastructure
  • BioEconomy BioSimilar the largest number approved anywhere
  • Incentives for size and scale
  • Ingredients manufacturing to become India’s priority
  • Investment in R&D and Human Capital in the BioEconomy

Hari Bhartia, Founder & Co-Chairman, Jubilant Bhartia Group

  • US history of innovations cluster and infrastructure: Academe, VC, small medium Biopharma, Government involvement
  • India: Contract research – 20 years history, lagging the ability to take risk
  • Changing, pricing of drug increased, innovating drug for local consumption, and it can be taken to US for a better price
  • Cancer immunology in India under development
  • India was Leading Chemistry Research – China’s government invested and took the market
  • Indian companies bigger in size – free on requirement imposed on China
  • India will be a great supplier to US Market to build high capacity raw materials

Dr. K. Vijay Raghavan, Principal Scientific Advisor, Government of India

Resources are necessary 30% from Industry vs Government and Academe with great students and labs

Indian context – Personalized Medicine – Telemedicine and IT infrastructure allowing innovation in a 1Billion Population- sheer volume of quality professional

Dr. Naresh Trehan, Chairman, Medanta – the Medicity

  • Ecosystem ready for Government to promote innovations to conduct clinical trial with global acceptance standard
  • diverse gene pool in population to innovate for new molecule to market
  • Vaccine under development on Phase 1,2,3 – regulatory mechanism is in place
  • genetic drugs, BioSimilar dominance in the market – biotech can do clinical trials in India vs abroad

Moderator:
Sanat Chattopadhyay, President, Merck Manufacturing Division; Merck & Co.

  • Largest producer of generic drugs
  • antiretroviral drug produced by Indian Pharma
  • Biotech innovations growing middle class – how innovation , infrastructure and shift to research
  • Diversify and become self reliance
10-40 AM – 10-45 AM Break + Polling
10-45 AM – 11-25 AM Panel Discussion- Oncology: Changing landscape- COVID learnings and the promise of new technologies

Panelists:
Dr. Alise Reicin, Former President, Global Clinical Development, Celgene

  • Clinical trial were impacted by association of patients to trials
  • anti bacterial resistance requires investment – needs will be greater for antibiotics in the future
  • Cancer mutation next therapy biomarkers for mutations to be developed

Dr. Laurie Glimcher, President & CEO, Dana-Farber Cancer Institute

  • Dana Farber saw impact of COVID-19 on immunosuppressant population of patients coming in for Cancer Tx – switch from IV Tx to Oral
  • 96% decrease in screenings due to Pandemic – increase with Cancer diagnosis in coming years
  • No clinical Trials in Cancer were suspended – all continued
  • Telemedicine and working at home very efficient
  • Genomics of COVID-19 studies at Dana Farber same pathway identifies
  • safety and efficacy must be achieved – not to approve drugs without phase I & Phase II endpoints

Dr. Philip Larsen, Global Head of Research, Bayer AG

  • Repurposing drugs as antiviral from drug screening innovating methods
  • Cytokine storm in OCVID-19 – kinase inhibitors may be antiviral  – dat of tested positive allows research of pathway in new ways
  • Regulatory agencies in US and Europe for types of drugs vs single patient drugs

Rehan Verjee, President, EMD Serono

  • entire volume of clinical trials at Roche went down same at EMD
  • delay of 6 month, some were to be initiated but was put on hold
  • Charities funding cancer research – were impacted and resources will come later and in decreased amount
  • New opportunities support access to Medicine
  • improve investment across the board
  • Antibody cytotoxic with precision

Dr. Tony Ho, Head of Research and Development, CRISPR Therapeutics

  • challenges overcome by testing at home

Moderator:
Dr. Raju Kucherlapati, Professor of Genetics, Harvard Medical School

  • New normal as a result of the pandemic role of personalized medicine
  • Cancer cure – what are the prospects
11-25 AM – 11-30 AM Break + Polling
11-30 AM – 12-10 PM Panel Discussion- Industry & Investment Outlook

Panelists:
Christopher Viehbacher, Managing Partner, Gurnet Point Capital

  • IPOs can have advantages in Pandemics – Travel curtails all deals done virtually in greater efficiency
  • Drug pricing is a target by White house
  • Dream of every Biotech – get Big Pharma coming to acquire and pay a lot
  • Morph and adapt

Daphne Zohar, Founder & CEO, PureTech Health

  • kill project early financial incentive not in line in the industry
  • incentive to move resources among project and kill early project experiments to find which project to kill
  • Innovations – pattern recognition, fast followers academic translation
  • Disease focus, best science is the decision factors

Dr. Elias Zerhouni, Professor Emeritus, Johns Hopkins University

  • Digital Health
  • CVS opens clinics
  • R&D – Capital is low
  • Network of global innovation hubs vc investor channel like in the past
  • Value of company driven by hits blockbusters

 

Dr. Stelios Papadopoulos, Chairman, Biogen

  • Worst pandemic in our lifetime
  • stock market if hot – in balance in supply and demand, interest rates low, excess supply of equities in entertainment, Travel, hospitality
  • Healthcare was defensive therapeutics needed – opportunity to innovate in HC – shift money from entertainment, Travel hospitality to HC
  • Recovery will shift money away from Healthcare
  • IP Protection and patent expiration – biotech are cases not trends

Moderator:

Dr. Andrew Plump,

President of Research & Development, Takeda Pharmaceuticals

Moderator Presenter: Dr. Michael Rosenblatt, CEO

12-10 PM – 12-20 PM Break + Polling
12-20 PM – 1 PM Panel Discussion- Rare Diseases: No longer forgotten; but more to be achieved

ROI is not there, regulatory requirements reduced, Registry

Panelists:
Dr. Alfred Sandrock, Executive Vice President, Research & Development, Biogen

  • Multiple Sclerosis therapy
  • cost effectiveness is not there vs save a life
  • Appeal opportunity is there and regulators are people

Dr. Daniel Curran, Head of the Rare Diseases Therapeutic Area Unit, Takeda

  • Takeda collaborates with Grace Science Foundation

Dr. David Meeker, Chairman & CEO , Rhythm Pharmaceuticals

  • Cystic Fibrosis 

Dr. John Orloff, Head of Research & Development, Alexion

  • ALS
  • Duchenne Muscular Destrophy
  • HUS
  • ASO
  • gene therapy – one time therapy: Valuation for the industry of long term therapy: US (long term non existence) vs Europe and Japan (much appreciated

Matt Wilsey, Co-Founder & Chairman, Grace Science Foundation

  • Ultra-rare (500 Patients) vs Ultra Ultra-rare (50 Patients)
  • 70 patients in the World, Grace disease, Parent drive the search for drug
  • Manufacturing cost comes down
  • Price is dynamic

Moderator:
Dr. Steve Uden, Co-Founder, Rallybio

  • Regulators are people

 

1 PM – 1-05 PM Break + Polling
1-05 PM – 1-50 PM Fireside Chat

Dr. Roy Vagelos, Chairman of the Board, Regeneron Pharmaceuticals

  • Congratulate Big Pharma for taking the challenge on COVID-19
  • Vaccine, Antibody and anti-viral
  • Government funding
  • Merck was independent from Government – to be independent and be able to set the price
  • HIV-AIDS: reverse transcriptase converted a lethal disease to a chronic disease, tried hard to make vaccine – the science was not there
  • Industry role: Competition of drug discovery capacity is been built, global needs, price need be low for global reach
  • Government is a already a player hoping without a control on pricing
  • 300Million people were treated FREE by Merck’s Family Program HepC
  • 9% in China immunize the newborn with HepB 1994 100% babies immunized – no profit to Merck – eradication of HepB in China
  • Neuro degeneration – science supports drug development
  • Role of R&D Scientists in Drug discovery?

Moderator:
Dr. Mathai Mammen, Global Head of Janssen Research & Development, Johnson & Johnson

  • COVID-19 drug development: Response by Big Pharma
  • Industry role in Access to medicines, biologics, antibodies, vaccines
  • Role of R&D Scientists in Drug discovery?
  • PAHTN – use Machine Learning on top of data collected routinely,

 

1-50 PM – 1-55 PM Break + Polling
1-55 PM – 2-35 PM Panel Discussion- Digital & Data Science in Healthcare: Pragmatic Insights from the Real-World

Panelists:
Dr. Anne Heatherington, Head of Data Sciences Institute, Takeda Pharmaceuticals

  • Reliance on Data – AI and Data in Pharma alliance with MIT
  • collaboration of Data for COVID-19
  • Women need education in STEM and in Data Science

Arpa Garay, President, Global Pharmaceuticals, Commercial Analytics, Merck & Co.

  • Data on Patients and identification who will benefit fro which therapy
  •  cultural bias risk aversion
  • Invest early on in STEM

Dr. Maya Said, Chief Executive Officer, Outcomes4Me

  • Cancer patients taking change of their care
  • Digital Health – consumerization of Health, patient demand to be part of the decision, part of the information
  • FDA launched a Program Project Patient Voice

https://www.fda.gov/about-fda/oncology-center-excellence/project-patient-voice

  • Women should not undersell themselves

Dr. Najat Khan, Chief Operating Officer, Janssen R&D Data Sciences, Johnson & Johnson

  • Validation
  • Deployment of algorithms
  • embed data by type early on in the crisis to understand the disease
  • Compare the Big IT-Data and Pharma where are the barriers?
  • STEM and Women in Pharma – the opportunity must be right

Nina Kjellson, General Partner, Canaan

  • Data science is a winner in Healthcare
  • Women – Data Science is an excellent match

Moderator:
Sastry Chilukuri, President, Acorn AI- Medidata

  • Opportunities in Data Science in Pharma
  • COVID-19 and Data Science
  • STEM and Women in Pharma

 

2-35 PM – 2-40 PM Break + Polling
2-40 PM – 3-20 PM Panel Discussion- R&D Strategies and Trends: Innovation – The Big I

Panelists:
Dr. Andrew Plump, President of Research & Development, Takeda Pharmaceuticals

  • Enter for Plasma and for manufacturing vs discovery
  • Change how pharma behaved inefficiently in the past – with COVID-19 new behaviors in the industry
  • End of Century most diseases could be cured

Dr. David Reese, Executive Vice President, Research and Development, Amgen

  • Interaction with regulator was most favorable

Dr. Hal Barron, Chief Scientific Officer and President R&D, GlaxoSmithKline

  • Cytokine storm – few approaches
  • Control molecule GSK owned
  • GWAS not easy to find which gene drives the association
  • Functional Genomics gene by gene with phenotypes using machine learning significant help

Dr. Mathai Mammen, Global Head of Janssen Research & Development, Johnson & Johnson

  • Neuro-modulation: Symptomology Outcomes – no correlation
  • Vaccine platform used in the past for several vaccines: Selection process from several candidates, cell line enter Clinical waiting for data
  • Using same platform with several proteins – great communality in the development
  • Regulator deepen trust relationship which will carry for the future
  • Pulmonologists and cardiologist in the COVIS-19 Patients – remove drugs monitoring on drugs

Moderator:
Duval Patel presented the Moderator

Moderator:

Martin Mackay, Co-Founder, RallyBio

 

3-20 PM – 3-30 PM Closing Remarks

  • Every year it is getting better
  • India – innovate and make drugs for every country and for India
  • Diversity and inclusion
  • Leadership in Pharma Industry in all Panels
  • Massive impact can be made

 

Poll Questions for September 4

Polling Time (EST) Polling Topic
8-50 AM COVID-19 PanelQuestion 1: What do you foresee as the most likely outcome of the race to develop a vaccine?

  • Heightened international tensions due to inequities in distribution
  • Use of the vaccine as an instrument of geopolitics
  • Collaboration between governments to use vaccine to end the pandemic
  • All of the above

Question 2: What minimum criteria would you like to see for approval of COVID19 vaccines, assuming adequate efficacy?

  • Immune response in people over 60 years
  • Durability of response
  • Antibody plus T-cell response
  • Emergency Use Authorization with caveats followed by final approval
9-10 AM India Regulatory UpdateHow will MNCs respond to the recent regulatory changes for BioPharmas in India? They are _____ to run clinical trials there:

  • More likely
  • Less likely
  • Equally likely
9-55 AM Fireside Chat: Ken Frazier

The BioPharma industry this year has publicly committed itself to greater diversity. What specific measures do you expect to see?

  • Increasing diversity in clinical trials
  • Increasing diversity at the C-suite and board level
  • Increasing diversity throughout the company
  • All of the above
  • None of the above
10-40 AM India Innovation LandscapeWhat is the most important step India could take to become a global leader in life sciences innovation?”

  • Implement government policies to incentivize innovative drug development
  • Increase availability of financing for BioPharmas
  • Improve clinical trial infrastructure
  • Increase IP protection
11-25 AM Oncology PanelQuestion 1:

Changes in policy and reimbursement over the next five years will impact innovation in cancer therapeutics

  • Not at all
  • Slightly
  • Moderately
  • Significantly

Question 2: What therapeutic innovation do you think will have the biggest impact on cancer in the next five years?

  • Cell-based immunotherapies
  • Antibody-based immunotherapies
  • Bispecific / multi-specific antibodies
  • Antibody drug conjugates
12-10 PM Industry & Investment Outlook PanelMore and more funding has been going into preclinical companies — do you expect this trend to continue?

  • Yes
  • No

R&D Strategies and Trends Panel

COVID-19 has led to an unprecedented level of collaboration among stakeholders in the biopharma industry. Where do you expect to see the biggest increase in collaborations post-pandemic?

  • Discovery/preclinical research
  • Clinical development
  • Manufacturing
  • Commercialization
1 PM Rare Diseases PanelWhat is the biggest barrier to access to Orphan drugs in low-income countries?

  • Price, Access and Availability
  • Disease recognition and diagnosis
  • Lack of patient education regarding new therapies
  • Ultra-rarity of certain diseases creates barriers for BioPharma companies to pursue therapeutic
1-50 PM Fireside Chat: Roy VagelosQuestion 1:

Will pharma’s reputation continue its positive trend or return to negative base line beyond the pandemic

  • Yes
  • No

Question 2:

COVID-19 has put the spotlight on BioPharma as an essential player in the return to normalcy. What primary action do you think the industry needs to take to maintain a positive reputation beyond the pandemic?

  • Continue developing innovative drug pricing models
  • Increase drug pricing transparency
  • Increase data sharing and transparency
  • Improving availability and access in low income countries
2-35 PM Digital & Data Sciences PanelWhere has COVID-19 had the biggest impact on your adoption and use of digital health technologies?

  • Remote clinical trials and patient monitoring
  • Real-world data collection and analysis
  • Virtual drug launches

 

@@@@

In these unprecedented times due to COVID-19, USAIC is offering Free Registration for its annual summit.

Click for free registration

 

AGENDA & SPEAKERS

Chair and Master of Ceremonies (Emcee)– Dr. Andrew Plump, President of R&D, Takeda Pharmaceuticals
Summit Theme: “From N of One to N of a Billion”

  • Moderated Fireside Chat- Kenneth Frazier, Chairman of the Board and Chief Executive Officer, Merck & Co. and Stelios Papadopoulos, Chairman of the Board, Biogen
  • Moderated Fireside Chat- Roy Vagelos, Chairman of the Board, Regeneron Pharmaceuticals and Mathai Mammen, Global Head of R&D, Janssen Pharmaceutical Companies of Johnson & Johnson
  • Moderated Fireside Chat- K. VijayRaghavan, Principal Scientific Advisor, Government of India and Amitabh Kant, CEO, National Institution for Transforming India (NITI)

Panel Discussions:

  • Covid-19: Where are we now? Where are we going?
  • Oncology: A never ending tunnel?
  • Rare Diseases: Breaking Barriers for a Healthy Brain
  • Digital & Data Sciences: Leveraging data and digital to achieve healthcare solutions
  • Industry & Investment Outlook
  • R&D Strategies and Trends: Innovation – The Big I

Program and speakers subject to change*

14th Annual BioPharma & Healthcare Summit, Friday, September 4, 2020, 8 AM EST to 3-30 PM EST – Virtual Edition

Speakers


Kenneth Frazier
Chairman of the Board & CEO
Merck & Co.

Dr. Andrew Plump
President of R&D
Takeda Pharmaceuticals

Dr. Laurie Glimcher
President & CEO
Dana-Farber Cancer Institute

Dr. Roy Vagelos
Chairman of the Board
Regeneron

Dr. Stelios Papadopoulos
Chairman of the Board
Biogen

Christopher Viehbacher
Managing Partner
Gurnet Point Capital

Dr. Mathai Mammen
Global Head of R&D
Janssen- Johnson & Johnson

Kiran Mazumdar Shaw
Chairperson & Managing Director
Biocon

Dr. Hal Barron
President, R&D and CSO
GlaxoSmithKline

Prof. K. Vijay Raghavan
Principal Scientific Advisor
Government of India

Dr. George Yancopoulos
Co-Founder, President & CSO
Regeneron

Dr. Elias Zerhouni
Professor Emeritus
Johns Hopkins University

Daphne Zohar
Founder & CEO
PureTech Health

Sanat Chattopadhyay
President- Merck Manufacturing Division
Merck & Co.

Dr. David Reese
Executive Vice President- R&D
Amgen

Hari Bhartia
Founder & Co-Chairman
Jubilant Bhartia Group

Dr. Alfred Sandrock
Exe Vice President R&D & CMO
Biogen

Dr. Najat Khan
Chief Operating Officer, Data Sciences
Janssen- Johnson & Johnson

Dr. Richard Hatchett
Chief Executive Officer
CEPI

Amitabh Kant
Chief Executive Officer
NITI Aayog

Dr. Martin Mackay
Co-Founder
Rallybio

Dr. Daniel Curran
Head of the Rare Diseases TA
Takeda Pharmaceuticals

Dr. Alise Reicin
Former President, Global Clinical Dev.
Celgene

Dr. David Meeker
Chairman & CEO
Rhythm Pharmaceuticals

Dr. John Orloff
EVP and Head of R&D
Alexion

Dr. Barry Bloom
Professor & former Dean
Harvard School of Public Health

Dr. Mandeep Bhandari
Joint Secretary
Ministry of Health, India

Arpa Garay
President, Commercial Analytics
Merck & Co.

Dr. Steve Uden
Co-Founder
Rallybio

Dr. Philip Larsen
Global Head of Research
Bayer AG

Sastry Chilukuri
Executive Vice President
Medidata

Dr. William Chin
Professor of Medicine, Emeritus
Harvard Medical School

Dr. Anne Heatherington
Head of Data Sciences Institute
Takeda Pharmaceuticals

Dr. V G Somani
Drugs Controller General of India
Government of India

Dr. Rajeev Venkayya
President-Global Vaccines
Takeda

Dr. Raju Kucherlapati
Professor of Genetics
Harvard Medical School

Matt Wilsey
Co-Founder & Chairman
Grace Science Foundation

Muna Bhanji
SVP, Global Market Access
Merck & Co.

Dr. Maya Said
Chief Executive Officer
Outcomes4Me

Rehan Verjee
President
EMD Serono
Pharmasia News Biospectrum India Online

SOURCE:

https://usaindiachamber.org/speaker.php

Read Full Post »

Older Posts »