UPDATED on 9/11/2023
Please see the following PAGES on this Open Access Scientific Journal of OpenAI and ChatGPT Tools that have been developed which may aid in the evaluation of the literature on Galectins and Cancer
ChatGPT applied to Cancer & Oncology
This page highlights the recent advances in OpenAI with regard to Oncology, both treatment, diagnosis, and cancer drug discovery. Of note are one tools, BioGPT, a new collaboration of MicroSoft and the Broad Institute for the specific Large Language Model OpenAI (GPT) for biomedical literature. An evaluation is also given of this tool.
Two other tools recently developed are GeneGPT and ChemGPT, which may be useful for the following endeavors in drug development against Galectins as a onco-therapeutic target.
The Use of ChatGPT in the World of BioInformatics and Cancer Research and Development of BioGPT by MIT
ChatGPT Chemistry Assistant for Text Mining and the Prediction of metal–organic framework (MOF) synthesis
SEE BELOW THE UPDATE ON 4/11/2023 on potential application of ChatGPT for Text Analysis of 74 articles on Galectin
UPDATED on 4/11/2022
Why Use Mathematical Modeling to create a model of Galectin binding to receptors?
We are also considering the alternate approach other than a PROTAC strategy to alter cooperative binding characteristics of Galectins for disease states such as cardiovascular where the goal will be to augment galectin binding to receptors and increase galectin signaling. This will first entail a two step approach
- model galectin binding to the CBD of galectin receptors
- model intracellular signaling pathways of galectins 3 and 9
First Step #1
Included in this folder is a word file which combines all Galectin papers which need to be analyzed by text analysis and NLP. Each individual selected paper was concatenated into one Word document. The final document contains
72 papers
554 pages
46,785 lines of text
389,940 words
The paper selection was as follows:
A Pubmed search was performed on the criteria of: Galectin-3 OR Galectin-9 AND model AND binding AND receptor
174 papers were retrieved and further reanalyzed on the above criteria for fit as well as galectin binding parameters while reviews were discarded. This left 72 papers which full text and figures were combined for further text analysis
A wordcloud by WordItOut was generated from the finalized curated 72 paper full abstracts to determine if proper selection of papers were made. As shown in the wordcloud we have curated a substantial and appropriate number of relevant papers for this purpose of generating a NLP based algorithm for insertion into Copasi mathematical modeling software in order to generate a mathematical model of Galectin3 and 9 binding to CBD receptors. A similar approach will be used to model galectin signaling pathways.
The following files are located on LPBI DropBox folder (by invitation only)
The following links can be used for the Word format file and same file in PDF format
Combine of all galectin papers
Word Combine of all galectin papers
I also want to highlight another effort we are undertaking and ask question how we can combine the technology of AI with our curated database WITH REGARD TO OUR EFFORT OF DRUG DISCOVERY IN GALECTINS AND GALECTIN SIGNALING HOWEVER I will refer the reader to our new PAGE Chat GPT + Wolfram Plugin
A brief illustration is given below to describe the potential power of combining these two AI and NLP algorithms together
The important points is that ChatGPT and Wolfram use two different conceptual views of language: a mathematical view and a structural semantic view. Combining these may be more powerful in extracting mathematical relationships from medical text.
UPDATED on 12/12/2022
Mission 4: Use of Systems Biology for Design of inhibitor of Galectins as Cancer Therapeutic – Strategy and Software
UPDATED on 10/10/2022
Endoglin Protein Interactome Profiling Identifies TRIM21 and Galectin-3 as New Binding Partners
UPDATED on 9/1/2022
The drug efflux pump MDR1 promotes intrinsic and acquired resistance to PROTACs in cancer cells
Accelerating PROTAC drug discovery: Establishing a relationship between ubiquitination and target protein degradation
UPDATED on 7/12/2022
UPDATED on 7/12/2022
Molecule Raises $12.7 Million in Seed Funding for Biotech
The organization intends to use NFTs for important things.
Switzerland, Jun 30, 2022 – Molecule, a comprehensive funding and incubation ecosystem for early-stage biopharma research, announced this week they raised $12.7 million in seed funding. The round was led by Northpond Ventures with participation from Shine Capital, 1kx, Fifty Years, KdT Ventures, BACKED VC, Inflection VC, Chris Leiter, Balaji Srinivasan, Zee Prime Capital, The LAO, L1 Digital, Boom Capital, Compound VC, Koji Capital, Pillar VC, Seedclub Ventures, Speedinvest, Healthspan Capital, BoxGroup and many others.
While the biopharma industry has come a long way, many challenges remain, such as difficulty in aligning incentives of key stakeholders. Additionally, while many therapeutics are discovered in academic labs, funding in academia is preferentially allocated to established researchers and research fields, to the detriment of early career researchers and higher risk projects. The result is an ecosystem where potentially impactful research goes unfunded, and incentives for collaboration are often missing.
Molecule aims to address these incentive problems and funding gaps. They are creating an open ecosystem that assists researchers in raising funds for impactful research projects in a community-driven way. With Molecule, diverse communities (including patient groups, researchers, VCs and pharma companies) can fund, own and govern therapeutic intellectual property. This provides novel incentives for collaboration, investment, and risk-sharing. The result is that communities, such as patient groups, can choose which projects get funded, and later own the therapeutics that treat their disease.
The Molecule ecosystem has three components:
1) Communities: A community of physicians, scientists, and patients collaborate to form funding groups in specific therapeutic areas e.g. rare diseases and Alzheimer’s disease.
2) The IP-NFT framework: this allows researchers to fundraise without needing to patent early or create a startup.
3) A marketplace: Molecule is building a marketplace for biotech IP. Researchers at any career stage submit projects for funding, increasing discoverability of neglected research areas, and addressing key funding gaps.
“Many of the most impactful therapeutics are discovered in academic labs, yet development and translation of these assets toward the clinic remains difficult. Molecule’s marketplace and IP-NFT framework will address many of the pain points inherent in the funding and commercialization of early-stage biotech IP, ultimately benefiting the most important stakeholder in the Molecule ecosystem – the patient. Northpond is excited to partner with Molecule and lead their seed round.” – Patrick Malone, MD PhD, Associate at Northpond Ventures.
Molecule: what’s to come
The capital raised will be used to grow their marketplace with hundreds of research projects, expand their team, build out their protocol and technology layer into an open infrastructure, and provide grants to patient-centric biotech DAOs to expand the DeSci ecosystem. They also hope to out-license the first NFT-based IP to industry, demonstrating for the first time patient-led translational drug development.
“Many of us are tired of hearing about NFT monkeys selling for millions. Molecule is using the NFT framework for something truly important: helping incredible scientific research transition from the lab bench to the benefit of all. Molecule’s work will help more life-saving drugs get to patients. That’s an application of web3 that’s easy to get excited about.”
Seth Bannon, Founding Partner at Fifty Years.
To truly decentralize adoption, they are also building accelerator frameworks for the friction-less launch of biotech DAOs targeting specific diseases of high unmet need, such as rare diseases and mental health. They see a future where scientists raise funds from incentive-aligned communities that wish to support them, and where patients themselves have governance over how therapeutics are researched, developed and accessed.
Their journey since inception
Since their pre-seed in early 2020, their team has grown to 22 members spread across the US, South-Africa and Europe, operating in a semi-remote fashion. Over these past few years, they have funded a first group of projects, and helped build the first biotech DAO – VitaDAO – which has deployed $2m+ to fund longevity research to date, including research at the Scheibye-Knudsen Lab (University of Copenhagen), Viktor Korolchuk’s Lab (University of Newcastle) and the Evandro Fang Lab (University of Oslo) all using Molecule’s IP-NFT framework.
“We believe that Molecule is a category defining project which will inspire a wave of innovators bringing the concept of collective ownership and liquid IP markets into various areas of R&D. We’re grateful for having been part of Molecule’s journey since inception and couldn’t be more excited about what’s to come.” – Alexander Lange, Founding GP at Inflection VC
Investors in this round:
Northpond Ventures, Shine Capital, 1kx, Fifty Years, The LAO, BACKED VC, ZeePrime Capital, L1 Digital, Seed Club Ventures, IDTheory, Andrew Keys, Compound VC, Pillar VC, Inflection VC, Protocol Labs, BoxGroup, KdT Ventures, IDTheory, Boom Capital, Gaingels, Cherry Ventures, Amino Collective, Chorus One, OrangeDAO, BeakerDAO, Kindergarten Ventures, Speedinvest, Bool Capital, Koji Capital, Healthspan Capital, Breyer Capital.
Chris Leiter, Zen Chu, Allison Duettman, Pamir Gelenbe, Katelyn Donnelly, Steve Wiggins, Balaji Srinivasan, Piers Kicks, Qiao Wang, Brian Fabian Crain, Scott Moore, Philipp Banhardt, Meher Roy, Ajay Rayasam, Angelo Tagliabue, Collin Myers, Jonas Keller, Garret MacDonald, Luis Cuende, Theodor Walker, Ronjon Nag, Alok Tayi, Laurence Ion, Todd White, Tim Peterson, Jahed Momand, Laurens De Poorter, Andrew Steinwold, Tim Schlidt, Justin Olshavsky & some who remain anonymous.
Discover Molecule:
Join the conversation on their Discord
Discover research projects on Molecule Discovery
Visit their website
Media enquiries:
Heinrich Tessendorf: pr@molecule.to
UPDATED on 6/20/2022
Targeting galectin-1 inhibits pancreatic cancer progression by modulating tumor–stroma crosstalk
Galectins in Cancer and the Microenvironment: Functional Roles, Therapeutic Developments, and Perspectives
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8465754/
The emerging role of galectins in cardiovascular disease
UPDATED on 6/12/2022
Startup to Strengthen Synthetic Biology and Regenerative Medicine Industries with Cutting Edge Cell Products
Synthetic Biology: On Advanced Genome Interpretation for Gene Variants and Pathways: What is the Genetic Base of Atherosclerosis and Loss of Arterial Elasticity with Aging
Synthesizing Synthetic Biology: PLOS Collections
http://pharmaceuticalintelligence.com/2012/08/17/synthesizing-synthetic-biology-plos-collections/
Capturing ten-color ultrasharp images of synthetic DNA structures resembling numerals 0 to 9
Silencing Cancers with Synthetic siRNAs
http://pharmaceuticalintelligence.com/2013/12/09/silencing-cancers-with-synthetic-sirnas/
Mission #4 for LPBI USA is Mission #2 for LPBI India:
UPDATED on 11/8/2021
Synthesis among four contributing factors
This is A
The Nobel Prize in Chemistry 2021
pharmaceuticalintelligence.com/2021/10/06/the-nobel-prize-in-chemistry-2021/
This is B
The Map of human proteins drawn by artificial intelligence and PROTAC (proteolysis targeting chimeras) Technology for Drug Discovery
This is C
Synthetic Biology in Drug Discovery – @MIT
http://pharmaceuticalintelligence.com/synthetic-biology-in-drug-discovery/
This is D – Access to software
PRosettaC: Rosetta Based Modeling of PROTAC Mediated Ternary Complexes
PRosettaC: Rosetta Based Modeling of PROTAC Mediated Ternary Complexes
- Daniel Zaidman
- Jaime Prilusky
- Nir London*
Cite this: J. Chem. Inf. Model. 2020, 60, 10, 4894–4903Publication Date:September 25, 2020 https://doi.org/10.1021/acs.jcim.0c00589
Copyright © 2020 American Chemical Society
PRosettaC, a holistic protocol for modeling PROTAC mediated ternary complexes. It combines global docking with PatchDock (33,34) under PROTAC derived distance constraints and local docking with RosettaDock, (35) followed by modeling of the PROTAC into the ternary complex. (36) The protocol was able to accurately recover published structures of ternary complexes to near atomic resolution and recapitulate experimental trends for two model systems. This general protocol should be useful in the design of new PROTACs for a wide variety of targets. To enable wide access to this protocol, we have made available both the code (https://github.com/LondonLab/PRosettaC) as well as a Web server for running PRosettaC (https://prosettac.weizmann.ac.il/).
https://pubs.acs.org/doi/10.1021/acs.jcim.0c00589#
UPDATED on 11/5/2021
How knowledge graphs will transform drug discovery
It’s time to get serious about knowledge graphs in drug discovery. If depressing projections are to be believed (and there is strong support that they are), the pharmaceutical industry is in a terminal decline, with return on investment (ROI) projected to hit zero by 2020. Pharmaceutical companies have made some heroic efforts to plug the holes in the hull of the Titanic, but the fact remains that the returns on new drugs that do get to market do not justify the massive investments that Pharma currently puts into R&D.
Yet, there is much needing to be done in drug discovery. Despite some recent successes in immuno-oncology and gene therapy, our treatments for the most prevalent, devastating diseases like cancer and diabetes are still not that great, and there are many more conditions like Alzheimer’s that have no good treatments at all, not to mention the hundreds of rare and neglected diseases that need treatments. As we used to say when I worked at Parke-Davis, “the patient is waiting”.
So how do we save drug discovery and get back to helping the patient? Well, as has been said many times before, we have to find a way to make drug discovery much leaner, faster, and effective, and that means radically rethinking the processes, assumptions and technologies of drug discovery, and most importantly how we use data and knowledge effectively. Current drug discovery processes are predicated on letting a sequence of expert scientists (chemists, biologists, toxicologists, and so on) use their deep knowledge to drive experimental investigations through a well-trodden process of target identification, lead compound identification, ADME/toxicology, animal studies, and so on. Most of these efforts fail, but the investigations result in mountains of experimental databases, papers, documents, spreadsheets, with modern experimental technologies such as high throughput screening and genomics delivering millions of data points for every project. Key decisions, insights, and observations are neatly organized into PowerPoints and then forgotten. Then there are the petabytes of experimental and patient data being made available in public or commercial data sources, as well as the well over a million life sciences publications that come out each year. It is not too extreme to say that if pharma companies are going to survive, they need to stop being drug companies and start being data science companies.
What if we could bring all that knowledge, data, insight, and prior decision-making together and use it to accelerate the discovery of new drugs? What if we could encode the millions of known relationships between potential new (or old) drugs, protein targets, genomics, biological processes, and disease mechanisms, and then use all this together to get new insights into disease and treatments? What if we could encode scientific decisions? What if we could even map translational relationships that bring all the scientific molecular data together with data from patients and clinical trials? What if we could partner this huge map of knowledge with powerful AI and machine learning algorithms that can prioritize insights and connections for the expert human scientists to assess? What if we could actually do data-driven drug discovery that gets drugs to patients quicker and faster?
Well we can do this.
Since 2008, I and my colleagues at Indiana University have been researching ways to link together massive amounts of heterogeneous drug discovery data and knowledge into computable graph structures, which we now call knowledge graphs, and we have designed new powerful algorithms to run on top of these knowledge graphs. We have already done some pretty exciting things like predicting the biological activities of drugs, mining patterns to explain side effects, and identifying new patterns of relationships between diseases. In 2010, the OpenPHACTS consortium brought together the knowledge and insight from pharmaceutical companies and academia to demonstrate how drug companies and academia can collaborate to combine knowledge into a linked, searchable network. Our partner and the successor to the consortium, the OpenPHACTS Foundation, will soon be ready to release a highly accessible, interoperable, sustainable knowledge graph of public drug discovery data that can be harvested and reused in many ways. In 2012, we launched Data2Discovery, one of the earliest AI for drug discovery startups. Data2Discovery is, with customers and partners, building knowledge graphs that transcend the boundaries of traditional public/proprietary data silos and which power completely new AI-driven applications. We are able to improve drug discovery now as well as demonstrating new fast-cycle AI-driven processes that will have a revolutionary impact on drug discovery if fully implemented. We have had some dramatic successes, but we are just starting to discover the impact that data, knowledge graphs, AI and machine learning can together have on drug discovery.
We need all the expertise of academics, consortia, AI companies and pharma to make his happen, and it’s going to require some serious investment, and a big change of thinking. But the opportunity to get drug discovery out of the death spiral and framed for data-driven success is too important to pass up. The patient is waiting.
SOURCE
UPDATED on 10/3/2021
Geometric deep learning of RNA structure
RAPHAEL J. L. TOWNSHENDHTTPS://ORCID.ORG/0000-0001-6362-1451STEPHAN EISMANNANDREW M. WATKINSHTTPS://ORCID.ORG/0000-0003-1617-1720RAMYA RANGANHTTPS://ORCID.ORG/0000-0002-0960-0825MARIA KARELINAHTTPS://ORCID.ORG/0000-0003-1880-4536RHIJU DASHTTPS://ORCID.ORG/0000-0001-7497-0972 AND RON O. DROR HTTPS://ORCID.ORG/0000-0002-6418-2793Authors Info & Affiliations
SCIENCE•27 Aug 2021•Vol 373, Issue 6558•pp. 1047-1051•DOI: 10.1126/science.abe56508,9611
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Machine learning solves RNA puzzles
RNA molecules fold into complex three-dimensional shapes that are difficult to determine experimentally or predict computationally. Understanding these structures may aid in the discovery of drugs for currently untreatable diseases. Townshend et al. introduced a machine-learning method that significantly improves prediction of RNA structures (see the Perspective by Weeks). Most other recent advances in deep learning have required a tremendous amount of data for training. The fact that this method succeeds given very little training data suggests that related methods could address unsolved problems in many fields where data are scarce. —DJ
Abstract
RNA molecules adopt three-dimensional structures that are critical to their function and of interest in drug discovery. Few RNA structures are known, however, and predicting them computationally has proven challenging. We introduce a machine learning approach that enables identification of accurate structural models without assumptions about their defining characteristics, despite being trained with only 18 known RNA structures. The resulting scoring function, the Atomic Rotationally Equivariant Scorer (ARES), substantially outperforms previous methods and consistently produces the best results in community-wide blind RNA structure prediction challenges. By learning effectively even from a small amount of data, our approach overcomes a major limitation of standard deep neural networks. Because it uses only atomic coordinates as inputs and incorporates no RNA-specific information, this approach is applicable to diverse problems in structural biology, chemistry, materials science, and beyond.
SOURCE
https://www.science.org/doi/10.1126/science.abe5650
UPDATED on 8/20/2021
Below are four great reviews explaining synthetic biology and some of the requirements.
UPDATED on 8/20/2021
Below is a great article and graphics on Drug Discovery Process in the 21st Century by Charles River
Source: https://www.drugdiscoverytrends.com/the-new-age-of-drug-development/
The New Age of Drug Development
By Martin O’Rourke, PhD, Jane Hughes, PhD, Charles River Laboratories | July 19, 2018
Over the last 20 years, the landscape of drug discovery has changed dramatically. Historically, research and development took place within large pharmaceutical companies with a collection of smaller biotechnology companies doing the innovating, and eventually being acquired by larger companies. However, given the ever-increasing drive to deliver value quickly and more cost-effectively, the environment is now filled with a dynamic mixture of large pharma, biotechs and virtual organizations.
The timelines for delivery of a candidate compound, irrespective of the therapeutic area, has consistently averaged between two and a half and four years from the initial hit identification effort (see image above). The number of U.S. Food and Drug Administration (FDA) approvals is lower today compared to 1996, which has led to an increased quest for better validation of targets, and higher quality candidates with a greater chance of success.
Pressure is mounting on pharmaceutical companies to both fill their internal pipelines as well as deliver value to shareholders. The trend toward a reduction in internal R&D spend within pharma worldwide coupled with increased outsourcing of research has enabled pharma to reduce its fixed cost-base and allow increased capital flexibility. Increasingly, the R&D market has observed significant increases in the number of small virtual companies with limited budgets being expected to progress compounds to the point of candidate nomination under the shortest timeline possible. This paradigm shift towards virtual research has also resulted in an increase in the number of experienced drug discovery scientists now working for contract research organizations (CRO).
The main aim of the integrated CRO is to act as an extension of the sponsor company and drive projects to the point of nomination. Toward this end, Charles River Laboratories (Charles River) has established a discovery division focused solely on delivering pre-clinical candidates across multiple therapeutic areas. One unique aspect of Charles River’s strategy is to robustly validate the target at an early stage of the project, confirming expression and link to disease, as well as identification of a response biomarker. Their track record of delivering clinical candidates has been analyzed internally and compares favorably with industry standards. In the last 15 years Charles River has delivered 79 preclinical candidates, with over 30 of these molecules progressing to Phase I and beyond (see chart below).
Charles River has achieved these high delivery metrics by pioneering and implementing a translational integrated drug discovery platform which allows their partners to effectively increase their lab size by contracting a complete drug discovery team, including medicinal and computational chemists, in vitro and structural biologists, DMPK scientists and in vivo pharmacologists. These dynamic teams, managed by an experienced project lead—usually a drug discoverer who has delivered preclinical candidates coupled with a specialist in the particular therapeutic area—strive to rapidly transition projects through the phases of drug discovery.
Note: In above diagram LPBI India is focused on the BLUE areas of drug discovery process.
The project leader is responsible for all elements of project management, operating a single point of contact policy with his or her counterpart in the partner company. This ensures clear communication, efficient delivery of objectives and prompt data sharing through Charles River-curated data portals and shared electronic laboratory books. An additional benefit is that an integrated CRO can coordinate all activities under a single Master Service Agreement, thereby streamlining the contracting process for potential partners.
The relationship between the partner and Charles River is absolutely key, and trust is developed through regular face-to-face meetings, supplemented with regular teleconferences and email communication. The shared vision for the project is paramount as it allows dynamic decision-making, and combined input on direction and agreement on the key parameters required to transition the project through the various phases of drug discovery.
Charles River also recognizes that flexibility is crucial to the delivery of preclinical compounds and has adjusted their integrated offering to support this. Charles River will happily contribute one or two disciplines to the project and form part of a project team with the partner organization. For example, Charles River could provide the chemistry and structural biologist for the project, working closely with the partner’s biology and DMPK team. Charles River is equally comfortable working in a collaborative network, interacting with multiple touch-points which could include the partner organization, academic collaborators or a charitable foundation. Charles River firmly believes this flexible working arrangement allows access to our expertise and knowledge – regardless of budget – and focuses on the specific project needs.
As drug discovery evolves, Charles River is focused on embracing new technologies through strategic partnerships and internal development, to allow our partners access to the most up-to-date information and technology with the aim of shortening delivery times of preclinical candidates.
It is becoming clearer within the industry that robust validation of the target expression in the human disease state and the tractability of the target are key to the delivery of successful projects. Charles River can provide due diligence on the project by evaluating the expression of the data in diseased tissues, assessing the current literature, proposing chemistry strategy based on assessment of the target crystal structure, and virtual screening over three million ligands. Investing time in these activities at the initiation of the project has resulted in shorter timelines to reach candidate nomination. In addition, ensuring early identification of target engagement and efficacy biomarkers early in the process will ensure a seamless transition from preclinical to clinical development and ensure a clear line of sight between compound exposure and effect. Charles River is dedicated to continually reviewing the working practices of an integrated project to identify areas of white space which could be removed. A recent initiative reviewing the timelines for compound synthesis to testing in vivo during a lead optimization project resulted in a six-week timeline from synthesis to PK study; this truncation of timeline lead to the successful achievement of project transition ahead of the agreed timeline.
The creation of the integrated drug discovery model has allowed Charles River to offer partners internal expertise, project management skills and the seamless integration of multiple disciplines into a translational project package fully enabled with a biomarker strategy across species, thereby reducing the partner’s delivery timelines for transition of compounds and potentially reducing the cost of delivering a preclinical candidate.
Martin O’Rourke, PhD, is a senior director of Oncology In Vitro Biosciences, Integrated Drug Discovery at Charles River Laboratories and Jane Hughes, PhD, is a senior director of In Vitro CNS Research, Integrated Drug Discovery at Charles River Laboratories
UPDATED on 8/14/2021
Synthetic Biology: On Advanced Genome Interpretation for Gene Variants and Pathways: What is the Genetic Base of Atherosclerosis and Loss of Arterial Elasticity with Aging
Aviva Lev-Ari, PhD, RN
UPDATED on 8/7/2021
This is the voice of Dr. Larry H. Bernstein in
Series E: VOLUME TWO
Medical Scientific Discoveries for the 21st Century
&
Interviews with Scientific Leaders
Author, Curator and Editor:
Larry H Bernstein, MD, FCAP
Available on Kindle Store @ Amazon.com since 12/9/2017
https://www.amazon.com/dp/B078313281
Chapter 14: Synthetic Medicinal Chemistry
Introduction
This is a chapter that identifies major work in organic, inorganic, and medicinal chemistry.
14.1 Insights in Biological and Synthetic Medicinal Chemistry
Author & Curator: Larry H. Bernstein, MD, FCAP
14.2 Breakthrough work in cancer
Larry H. Bernstein, MD, FCAP, Curator
Summary to Part Two
The second part of this volume is more directed at the growth of remarkable discoveries in the health related sciences since the late-20th century. There have been observations that discovery has slowed. I cannot share such a view. It has become difficult to follow the rapid progress that is achieved in a step-by-step order. There is always reference to “serendipidous” discoveries, but for such an event it requires a prepared mind. A problem that has existed in the reliance on large funding for so much of research and the implementation using mathematics, computers, and advanced spectroscopy for discovery, is that there is a significant skew to the direction of research, and in the current state of expenditure it is more difficult for young investigators to enter with new ideas. Perhaps it has always been that way, but the scope of the work needed is much larger than ever before.
Volume Summary and Conclusions
This series of articles follows an earlier volume on scientific discoveries of the late 19th and 20th centuries. However, it is noteworthy to consider that the lifetime achievements recognized in the first decade of the 21st millenium reach back to an accumulation of half a century of sustained research. These articles provide a significant amount of insight and background to understanding the motivations of the contributors over a productive lifetime, and also confirms the repeated observation of influential exposures to other innovators. The material contains interviews with investigators, peer comments about their mentoring of young investigators, snd also some very interesting background on childhood experiences. The biographical content is as interesting as the discoveries and honors discussed.
EPILOGUE
The second volume of the discoveries in medical and biological sciences series identifies many heroes of our time, just as it describes a trend of development in the related or supporting professions. The relationships have not always been clear, but a more integrated structure is emerging. The future of medical research and the practice of medicine with be more highly dependent on integrated work of professionals who have complementary skills. This is a challenge for current and future education.
UPDATED on 8/3/2021
Mission #4: Synthetic Biology: Drug Discovery targeting Galectins
- LPBI will appoint a PhD level leader for Mission #4 based in the USA and/or in India.
The intent is to:
- LPBI USA and LPBI India will develop IP on targeting 12 Galectins as therapeutics using Synthetic Biology Software in Drug Discovery 💡
- Testing the molecules by Dr. Nir in ABI Lab & SBH Sciences, Inc. wet labs
- Give Dr. Nir Right of Use
- Hosting the molecules in Blockchain Knowledge Graph Data Base
- Open up the molecules for licensing in a cyber secured confidential Auction
- Host molecules inventories from Technology Transfer Offices in several Academic centers around the Globe. We have relations to three Academic institutions in Israel, and three in the US
Applications of Synthetic Biology Software to Galectins for design of new drug molecules.
- Collaboration with Dr. Raphael Nir of ABI Lab and SBH, Inc in Natick, MA. We will have in 4Q’2021 a dedicated meeting on Mission #2.
- LPBI India will play a role in these tasks, please review:
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5877766/
- LPBI USA has interest in Synthetic Biology Software for Drug Discovery and has two scientists with expertise in Galectins as therapeutics targets
- Scientists love Synthetic Biology – designing drug molecules using Software – igem.org
A repository of open source software tools.
Community driven data exchange standards.
Specific software frameworks built explicitly for synthetic biology.
- LPBI USA will have a DropBox Folder with all the Synthetic Biology Software accessible to LPBI India
- Mentorship and training will be offered by LPBI USA
At LPBI India, been considered for the Leadership function for Mission #2: TBA
STRATEGIC PLAN for Mission #2
- We will have in 4Q’2021 a JOINT meeting between LPBI USA, LPBI India with Dr. Raphael Nir of ABI Lab and SBH, Inc in Natick, MA on Mission #2.
- A collaboration between LPBI’s Lead on Mission #2 and Dr. Nir is expected
- LPBI India Mission #2 is OPEN for all Interns of LPBI USA
- LPBI USA will grant RIGHT TO USE for the molecules developed by B2B contracts to 3rd parties
- Library of molecules will be on LPBI’s Blockchain Transactions Network
- All INTERNS will work 50% on NLP and 50% on Synthetic Biology
- Training will be offered
- Protocol will be developed
- Software applications will be selected.
- Mid September we will have an Internal Meeting on Mission #2, LPBI India before the Meeting with Dr. Nir
- All Interns need to complete at least ½ an e-Book Ten-Step Workflow Protocol for NLP before starting the Synthetic Biology SW Training
- This is the Ten-Step WORKFLOW Protocol for Medical Text Analysis using NLP at LPBI:
- https://pharmaceuticalintelligence.com/2021/07/15/workflow-for-a-ten-steps-medical-text-analysis-operation-using-nlp-on-lpbi-medical-and-life-sciences-content/
- Each INTERN Completing the 50% assignment on NLP will need to submit this table for his/hers NLP Book Assignment with a Check off mark for each article in each Chapter in the Book the intern was assigned for
- This Table filled in serves as INPUT for QA of the work of the INTERN. Verification is needed for Internship completion for Certification purposes
| NLP | Step 1 | Step 2 | Step 3 | Step 4 | Step 5 | Step 6 | Step 7 | Step 8 | Step 9 | Step 10 |
| Chapter 1, Article 1 | ||||||||||
| Chapter 1, Article n | ||||||||||
| Chapter 2 Article 1 | ||||||||||
| Chapter 2, Article n | ||||||||||
| Chapter 3, Article 1 | ||||||||||
| Chapter 3, Article n | ||||||||||
| Chapter 4 Article 1 | ||||||||||
| Chapter 4, Article n | ||||||||||
| Chapter 5, Article 1 | ||||||||||
| Chapter 5, Article n | ||||||||||
| Chapter n Article 1 | ||||||||||
| Chapter n Article n |
Author: Aviva Lev-Ari, PhD, RN, 7/23/2021
This Table is the supporting evidence for:
- (a) obtaining the Completion Certification of the NLP Internship at LPBI, start date, completion date, two signatures
- (b) Handing off of the completed assignment for additional QA Verification
- (c) Submission of PERSONAL PAGE nested under Medical Text Analysis using NLP https://pharmaceuticalintelligence.com/2021-medical-text-analysis-nlp/
- (c) Getting New Assignment in Synthetic Biology in Drug Discovery
- (d) Each Intern assigned to (c) will have a PERSONAL PAGE for recording all the milestones by date, recent at the top. This personal page will be nested under https://pharmaceuticalintelligence.com/synthetic-biology-in-drug-discovery/
We will have a Team working on a NEW WEBSITE for
- 2.0 LPBI
- Spanish BioMed e-Books
- LPBI India – nested under 2.0 LPBI
PRESENTATIONS Delivered on 7/19/2021: LPBI Launch of LPBI India
- Prof. Stephen J. Williams, LPBI USA, Chief Scientific Officer and Board Member is with LPBI USA since 9/2012 had presented
From High-Throughput Assay to Systems Biology: New Tools for Drug Discovery
Curator: Stephen J. Williams, PhD
https://pharmaceuticalintelligence.com/2021/07/19/from-high-throughput-assay-to-systems-biology-new-tools-for-drug-discovery/
- Aviva Lev-Ari, PhD, RN had presented
Engineering Life: A Review of Synthetic Biology
Author and Article Information
Artificial Life (2020) 26 (2): 260–273.
https://doi.org/10.1162/artl_a_00318
Galectins as Molecular Targets for Therapeutic Intervention
Ruud P. M. Dings,1 Michelle C. Miller,2 Robert J. Griffin,1 and Kevin H. Mayo2,*
Int J Mol Sci. 2018 Mar; 19(3): 905.
Published online 2018 Mar 19. doi: 10.3390/ijms19030905
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5877766/
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5877766/
Image Source:
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5877766/I
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5877766/I
Table of Human Galectins [edit]
Table Source
https://en.wikipedia.org/wiki/Galectin#Table_of_Human_Galectins
| Human galectin | Location | Function | Implication in disease |
| Galectin-1 | Secreted by immune cells such as by T helper cells in the thymus or by stromal cells surrounding B cells [7]Also found in abundance in muscle, neurons and kidney[1] | Negatively regulate B cell receptor activationActivate apoptosis in T cells[6] Suppression of Th1 and Th17 immune responses[7] Contributes to nuclear splicing of pre-mRNA[23] | Can enhance HIV infectionFound upregulated in tumour cells |
| Galectin-2 | Gastrointestinal tract [24] | Binds selectively to β-galactosides of T cells to induce apoptosis[24] | None found |
| Galectin-3 | Wide distribution | Can be pro- or anti-apoptotic (cell dependent)Regulation of some genes including JNK1 [7] Contributes to nuclear splicing of pre-mRNA[23] Crosslinking and adhesive properties In the cytoplasm, helps form the ULK1-Beclin-1-ATG16L1-TRIM16 complex following endomembrane damage[13] | Upregulation occurs in some cancers, including breast cancer, gives increased metastatic potentialImplicated in tuberculosis defense[13] |
| Galectin-4 | Intestine and stomach | Binds with high affinity to lipid rafts suggesting a role in protein delivery to cells[7] | Inflammatory bowel disease (IBD)[7] |
| Galectin-7 | Stratified squamous epithelium[7] | Differentiation of keratinocytesMay have a role in apoptosis and cellular repair mediated by p53[7] | Implications in cancer |
| Galectin-8 | Wide distribution | Binds to integrins of the extracellular matrix.[3] In the cytoplasm, alternatively binds to mTOR or forms the GALTOR complex with SLC38A9, LAMTOR1, and RagA/B[12] | Downregulation in some cancersImplicated in tuberculosis defense |
| Galectin-9 | KidneyThymus[6] Synovial fluid Macrophages | Functions as a urate transporter in the kidney [25]Induces apoptosis of thymocytes and Th1 cells[6][7] Enhances maturation of dendritic cells to secrete inflammatory cytokines. In the cytoplasm, associates upon lysosomal damage with AMPK and activates it[12] | Rheumatoid arthritisImplicated in tuberculosis defense[12][26] |
| Galectin-10 | Expressed in eosinophils and basophils | Essential role in immune system by suppression of T cell proliferation | Found in Charcot Leyden crystals in asthma |
| Galectin-12 | Adipose tissue | Stimulates apoptosis of adipocytesInvolved in adipocyte differentiation[7] | None found |
https://en.wikipedia.org/wiki/Galectin#Table_of_Human_Galectins
The Team involved in Developing Mission #2
Team Lead – TBA
Members by Geographic Locations
| Member Name | Geographical region in India Or Place of Birth in India if now outside of India | Distance from New Delhi | LANGUAGES |
| Dr (Lt Col) Vivek Lal | Ambuja Nagar, Kodinar (Sasan-Gir) Gujarat. | 1300 kms | English, Hindi |
| Yash Choudhary ???? |
Dewas (Madhya Pradesh) | 800 kms | English, Hindi |
| Dr. Premalata Pati | Bhubaneswar, Odisha | 1764 kms | English, Hindi, Odia |
| Dr. Sudipta Saha ???? | Kolkata, West Bengal | 1500 kms | English, Hindi, Bengali |
| Komal Ingle | |||
| Amandeep Kaur | Kolkata, West Bengal | 1500 kms | English, Hindi, Punjabi, Bengali |
| Vaishnavee Joshi | |||
| Madison Davis LPBI USA ???? |
|||
| Srinivas Sriram ???? |
Place of Birth: Chennai, Tamil Nadu | 2,187 kms | English, Tamil |
| Abhisar Anand ???? |
Place of Birth: Muzaffarpur, Bihar | 1,042 kms | English, Hindi |
| Danielle Smolyar LPBI USA ???? |
|||
| Ethan Coomber LPBI USA ???? |
Curator: Amandeep Kaur
India’s Map & Distribution of Team Members for Mission #2
Map Curators: Srinivas Sriram and Abhisar Anand
INSERT MAP HERE
Team Expertise with Pharmaceutics
Pharmacology:
Dr. Stephen J. Williams, PhD
Pharmacology and Clinical Trials:
Dr. Vivek Lal, MD
Glycobiology:
Dr. Ofer Markman, PhD
Molecular Cardiology:
Dr. Vivek Lal, MD
Dr. Aviva Lev-Ari, PhD, RN
Alumni of Big Pharma:
Dr. John McCarthy, MD, PhD, ex-Pfizer, External Scientific Relations to LPBI
Dr. Raphael Nir, PhD, ex-Schering-Plough, External Scientific Relations to LPBI
Drug Repurposing and Clinical Trials:
Dr. Ajay Gupta, MD, External Scientific Relations to LPBI
Prof. Saul Yedgar, PhD, External Scientific Relations to LPBI
Dr. Yigal Blum, PhD, External Scientific Relations to LPBI
List Source:
Curator: Aviva Lev-Ari, PhD, RN
2012pharmaceutical
Amandeep Kaur
Aashir Awan, Phd
Abhisar Anand
Adina Hazan
Alan F. Kaul, PharmD., MS, MBA, FCCP
alexcrystal6
anamikasarkar
apreconasia
aptubman
Arav Gandhi
aviralvatsa
David Orchard-Webb, PhD
danutdaagmailcom
Demet Sag, Ph.D., CRA, GCP
Dror Nir
dsmolyar
Ethan Coomber
evelinacohn
Gail S Thornton
Irina Robu
jayzmit48
jdpmd
jshertok
kellyperlman
Ed Kislauskis
larryhbern
Madison Davis
marzankhan
megbaker58
ofermar2020
Dr. Pati
pkandala
ritusaxena
Rick Mandahl
sjwilliamspa
Srinivas Sriram
stuartlpbi
Dr. Sudipta Saha
tildabarliya
zraviv06
zs22