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FDA Moves Forward With Its Guidance Framework for Rare Disease Trials: 2026

Posted in Artificial Intelligence in Medicine - Applications in Therapeutics, children, Clinical & Translational, Drug Development Process, FDA, FDA Regulatory Affairs, New Drug Approval, tagged CBER, clinical trial design, drug approvals, FDA, FDA guidance, pediatric, pediatric diseases, Rare diseases, regulatory agenicies on August 3, 2026| Leave a Comment »

FDA Moves Forward With Its Guidance Framework for Rare Disease Trials: 2026

Reporter: Stephen J. Williams, Ph,D.

In 2025, the Trump Administration had determined it wants to streamline the FDA and clinical trials in order to expedite much needed drugs for various terminal diseases such as cancer and rare childhood diseases. In 2026, under the guidance of the new FDA commissioner, Dr. Makary, the FDA initiated guidance for the proposed changes in February and available for comments at that time. This has been a growing discussion aver the years and the FDA wanted, for many years, to figure out they could speed bringing new therapies to market, especially for terminally ill patients. Given the progress of biomarkers development early in the drug discovery process and new computational technologies, the time seems to be ready for such changes in trial design and even for the long drug development process. In addition the FDA up to this point was focused on other matters so it wasn’t really at the forefront of their to do list.

From: https://www.fda.gov/news-events/press-announcements/fda-launches-framework-accelerating-development-individualized-therapies-ultra-rare-diseases

FDA Launches Framework for Accelerating Development of Individualized Therapies for Ultra-Rare Diseases

For Immediate Release:: February 23, 2026

The U.S. Food and Drug Administration today issued draft guidance for sponsors seeking approval for targeted individualized therapies by generating substantial evidence of effectiveness and safety when randomized controlled trials are not feasible due to small patient populations.

The draft guidance, issued by the Center for Biologics Evaluation and Research and Center for Drug Evaluation and Research, specifically discusses genome editing and RNA-based therapies such as antisense oligonucleotides but leaves open the potential that this framework may apply to additional tailored therapeutics provided they directly address the underlying specific cause of the disease.

“President Trump promised to accelerate cures for American families — and we are delivering, especially for children with ultra-rare diseases who cannot afford to wait,” said Health and Human Services Secretary Robert F. Kennedy, Jr. “We are cutting unnecessary red tape, aligning regulation with modern biology, and clearing a path for breakthrough treatments to reach the patients who need them most.”

“This guidance is a critical step the FDA is taking to tailor our regulatory approach to patients with ultra-rare conditions,” said FDA Commissioner Marty Makary, MD, MPH. “It is our priority to remove barriers and exercise regulatory flexibility to encourage scientific advances and deliver more cures and meaningful treatments for patients suffering from rare diseases.”

The draft guidance focuses on therapies that target a specific genetic, cellular or molecular abnormality and are designed to correct or modify the underlying cause of disease. Key criteria include:

Identifying the disease-causing abnormality.
Demonstrating the therapy targets the root cause or proximate biological pathway.
Relying on well-characterized natural history data in untreated patients.
Confirming successful target drugging or editing.
For traditional approval, therapies should demonstrate improvement in clinical outcomes, disease course, or biomarkers if they are established to predict clinical benefit.

“Designing treatments unique to individual patients has always been the promised goal of personalized medicine,” said Chief Medical and Scientific Officer and Center for Biologics Evaluation and Research Director Vinay Prasad, MD, MPH. “After 25 years the FDA has, for the first time, outlined a framework to facilitate these approvals. The Plausible Mechanism Framework is a revolutionary advance in regulatory science.”

“The Plausible Mechanism draft guidance creates a novel framework through which cutting-edge treatments tailor-made for patients with ultra-rare diseases can be used as a basis for FDA approval,” said Center for Drug Evaluation and Research Acting Director Tracy Beth Høeg, MD, Ph.D. “We anticipate our Plausible Mechanism draft guidance will inspire industry to place increased focus on individualized therapies, thereby driving innovation, improving safety, lowering costs and offering more patients with ultra-rare diseases a unique shot at a life-saving treatment.”

Because genome editing technologies are designed to be highly specific to unique DNA sequences, a product targeting different mutations in a single gene could be included in a single product application and potentially evaluated through the use of master protocols that evaluate these product variations in a single trial. A highly supported “plausible” mechanism of action may then be used to support the addition of other such genome editing product variants, intended to treat patients with mutations that were not included in the clinical trial used to support the original approval.

The FDA recognizes that an adequate and well-controlled clinical investigation in this context will include a small sample size, therefore, investigation results should be sufficiently robust to exclude chance findings. When determining effectiveness, the FDA considers the specific disease, the strength of the evidence and the challenges of conducting clinical investigations for individualized therapies.

In June of 2026, the FDA formalized their discussion into guidelines for discussion so it appears the following are not set into register of regulations as of yet.

From https://www.fda.gov/drugs/guidances-drugs/guidance-documents-rare-disease-drug-development

Guidance Documents for Rare Disease Drug Development

In general, FDA’s guidance documents do not establish legally enforceable responsibilities. Instead, guidances describe the agency’s current thinking on a topic and should be viewed only as recommendations, unless specific regulatory or statutory requirements are cited. The use of the word should in agency guidances means that something is suggested or recommended, but not required.

Below are selected guidances that are relevant to rare disease drug development, organized by topic. This list does not include all FDA guidances on or relevant to rare disease drug development but represents our most commonly used guidances. This list may be updated periodically.

I have kept the original text in order for reference and to provide the background for these changes “in their words”.

However there are a few themes in these guideline changes and discussions including:

definitions of rare diseases
reduction of complexities and simplification of trial design and requirements (the FDA wants to go to a more ONE trial with very well designed controls than the two trial design for INDs (I will discuss this further in a near future post)
accelerated approval which will entail streamlining both the submission and approval process for rare conditions
heavy reliance on biomarkers during drug development and clinical trials (which is already in frequent use in pharma)
early communication with the FDA
more reliance on plausible mechanism of action to reduce number of studies needed

Rare Disease

Considerations for the use of the Plausible Mechanism Framework to Develop Individualized Therapies that Target Specific Genetic Conditions with Known Biological Cause
The purpose of this guidance is to describe considerations for generating substantial evidence of effectiveness and evidence of safety for individualized therapies based on a plausible mechanism framework.

Rare Diseases: Considerations for the Development of Drugs and Biological Products
This guidance clarifies FDA’s thinking on important considerations in rare disease drug development to ultimately assist rare disease drug and biologic product developers in conducting successful drug development programs.

Rare Diseases: Natural History Studies for Drug Development: Draft Guidance for Industry
FDA is publishing this draft guidance to help inform the design and implementation of natural history studies that can be used to support the development of safe and effective drugs and biological products for rare diseases. A natural history study collects information about the natural history of a disease in the absence of an intervention, from the disease’s onset until either its resolution or the individual’s death. Although knowledge of a disease’s natural history can benefit drug development for many disorders and conditions, natural history information is usually not available or is incomplete for most rare diseases; therefore, natural history information is particularly needed for these diseases.

Rare Pediatric Disease Priority Review Vouchers
This guidance provides information on the implementation of section 908 of the Food and Drug Administration Safety and Innovation Act (FDASIA), which added section 529 to the Federal Food, Drug, and Cosmetic Act (the FD&C Act). Under section 529, FDA will award priority review vouchers to sponsors of certain rare pediatric disease product applications that meet the criteria specified in that section.

Rare Diseases: Early Drug Development and the Role of Pre-IND Meetings : Draft Guidance for Industry
The purpose of this draft guidance is to assist sponsors of drug and biological products for the treatment of rare diseases in planning and conducting more efficient and productive pre-investigational new drug application (pre-IND) meetings. Drug development for rare diseases has many challenges related to the nature of these diseases. This draft guidance is intended to advance and facilitate the development of drugs and biological products for the treatment of rare diseases.

Slowly Progressive, Low-Prevalence Rare Diseases with Substrate Deposition That Results from Single Enzyme Defects: Providing Evidence of Effectiveness for Replacement or Corrective Therapies : Guidance for Industry
This document provides guidance to sponsors on the evidence necessary to demonstrate the effectiveness of investigational new drugs or new drug uses intended for slowly progressive, low-prevalence rare diseases that are associated with substrate deposition and are caused by single enzyme defects. This guidance applies only to those low-prevalence rare diseases with well-characterized pathophysiology, and in which changes in substrate deposition can be readily measured in relevant tissue or tissues.

Pediatric Rare Diseases–A Collaborative Approach for Drug Development Using Gaucher Disease as a Model : Draft Guidance for Industry
The purpose of this guidance is to facilitate drug development in pediatric rare diseases. In particular, it discusses a new possible approach to enhance the efficiency of drug development in pediatric rare diseases using Gaucher disease as an example.

Inborn Errors of Metabolism That Use Dietary Management: Considerations for Optimizing and Standardizing Diet in Clinical Trials for Drug Product Development: Guidance for Industry
This guidance describes the Food and Drug Administration’s (FDA’s) current recommendations regarding how to optimize and standardize dietary management in clinical trials for the development of drugs that treat inborn errors of metabolism (IEM) for which dietary management is a key component of patients’ metabolic control. Optimizing dietary management in these patients before entry into and during clinical trials is essential to providing an accurate evaluation of the efficacy of new drug products.

Accelerated Approval

Accelerated Approval and Considerations for Determining Whether a Confirmatory Trial is Underway
For drugs granted accelerated approval, sponsors have been required to conduct confirmatory studies postapproval to verify and describe the anticipated effect on irreversible morbidity or mortality or other clinical benefit. In the Consolidated Appropriations Act, 2023 (CAA), Congress amended section 506(c) of the FD&C Act (21 U.S.C. 356(c)), to provide additional authorities to help ensure timely completion of such trials, including that FDA “may require, as appropriate, a study or studies to be underway prior to approval, or within a specified time period after the date of approval, of the applicable product.” This draft guidance, when finalized, will describe FDA’s interpretation of the term “underway” and policies for implementing this requirement, including factors FDA intends to consider when determining whether a confirmatory trial is underway prior to an accelerated approval action.

Accelerated Approval – Expedited Program for Serious Conditions
Accelerated approval is one of FDA’s expedited programs intended to facilitate and expedite development and review of new drugs to address an unmet medical need in the treatment of a serious or life-threatening condition. The purpose of this guidance is to provide information on FDA’s policies and procedures for accelerated approval as well as threshold criteria generally applicable to concluding that a drug is a candidate for accelerated approval. This guidance also describes the procedures for expedited withdrawal of approval of a product approved under accelerated approval and the revisions Congress made through the Consolidated Appropriations Act, 2023 (Public Law 117-328). Additional programs to expedite product development and review are covered in other guidances.

Benefit-Risk

Benefit-Risk Assessment for New Drug and Biological Products
The intent of this guidance is to clarify for drug sponsors and other stakeholders how considerations about a drug’s benefits, risks, and risk management options factor into certain premarket and postmarket regulatory decisions that the Food and Drug Administration (FDA or Agency) makes about new drug applications (NDAs) submitted under section 505(c) of the Federal Food, Drug, and Cosmetic Act (FD&C Act) as well as biologics license applications (BLAs) submitted under section 351(a) of the Public Health Service Act (PHS Act).

Biomarkers

For general information on Biomarkers, please see About Biomarkers and Qualification

Biomarker Qualification: Evidentiary Framework
This draft guidance provides recommendations on general considerations to address when developing a biomarker for qualification under the 21st Century Cures Act (Cures Act), enacted on December 13, 2016, that added a new section to the Federal Food, Drug, and Cosmetic Act (FD&C Act). Qualification of a biomarker is a determination that within the stated context of use, the biomarker can be relied on to have a specific interpretation and application in drug development and regulatory review.

Qualification Process for Drug Development Tools
This guidance describes the qualification process for drug development tools (DDTs) intended for potential use, over time, in multiple drug development programs.

Clinical Outcome Assessments (COAs) and Endpoints

For information on the COA Qualification Program, please see Clinical Outcome Assessment (COA) Qualification Program

Patient-Focused Drug Development: Selecting, Developing, or Modifying Fit-for-Purpose Clinical Outcome Assessments
This guidance (Guidance 3) is the third in a series of four methodological patient-focused drug development (PFDD) guidance documents that describe how stakeholders (patients, caregivers, researchers, medical product developers, and others) can collect and submit patient experience data and other relevant information from patients and caregivers to be used for medical product development and regulatory decision-making.

Patient-Focused Drug Development: Incorporating Clinical Outcome Assessments Into Endpoints for Regulatory Decision-Making
This guidance (Guidance 4) is the fourth in a series of four methodological patient-focused drug development (PFDD) guidance documents that describe how stakeholders (patients, caregivers, researchers, medical product developers, and others) can collect and submit patient experience data and other relevant information from patients and caregivers to be used for medical product development and regulatory decision-making.

Multiple Endpoints in Clinical Trials Guidance for Industry
This guidance provides sponsors and review staff with the Agency’s thinking about the problems posed by multiple endpoints in the analysis and interpretation of study results and how these problems can be managed in clinical trials for human drugs, including drugs subject to licensing as biological products.

Clinical Pharmacology

Exposure-Response Relationships — Study Design, Data Analysis, and Regulatory Applications
This document provides recommendations for sponsors of investigational new drugs (INDs) and applicants submitting new drug applications (NDAs) or biologics license applications (BLAs) on the use of exposure-response information in the development of drugs, including therapeutic biologics. It can be considered along with the International Conference on Harmonisation (ICH) E4 guidance on Dose-Response Information to Support Drug Registration and other pertinent guidances (see Appendix A).

Bioavailability Studies Submitted in NDAs or INDs – General Considerations
This guidance provides recommendations to sponsors and applicants submitting bioavailability (BA) information for drug products in investigational new drug applications (INDs), new drug applications (NDAs), and NDA supplements. This guidance contains recommendations on how to meet the BA requirements set forth in 21 CFR part 320 as they apply to dosage forms intended for oral administration.

General Clinical Pharmacology Considerations for Pediatric Studies of Drugs, Including Biological Products
This guidance assists sponsors of investigational new drug applications (INDs) and applicants of new drug applications (NDAs) under section 505 of the Federal Food, Drug, and Cosmetic Act (the FD&C Act), biologics license applications (BLAs) under section 351(a) of the Public Health Service Act (PHS Act), and supplements to such applications who are planning to conduct clinical studies in pediatric populations.

General Clinical Pharmacology Considerations for Neonatal Studies for Drugs and Biological Products Guidance for Industry
This guidance is intended to assist sponsors of investigational new drug applications (INDs) and applicants of new drug applications (NDAs), biologics license applications (BLAs), and supplements to such applications who are planning to conduct clinical studies in neonatal populations. This guidance provides recommendations for neonatal clinical pharmacology studies, whether the studies are conducted pursuant to section 505A of the Federal Food, Drug, and Cosmetic Act (FD&C Act), section 505B of the FD&C Act, or neither.

Assessing the Effects of Food on Drugs in INDs and NDAs – Clinical Pharmacology Considerations
This guidance provides recommendations to sponsors planning to conduct food-effect (FE) studies for orally administered drug products under investigational new drug applications (INDs) to support new drug applications (NDAs) and supplements to these applications for drugs being developed under section 505 of the Federal Food, Drug, and Cosmetic Act (21 U.S.C. 355).

Population Pharmacokinetics
This guidance is intended to assist sponsors and applicants of new drug applications (NDAs), biologics license applications (BLAs), abbreviated new drug applications (ANDAs), and investigational new drugs (IND) applications in the application of population pharmacokinetic (PK) analysis.

Clinical Pharmacology Considerations for Antibody-Drug Conjugates Guidance for Industry
This guidance provides recommendations to assist industry and other parties involved in the development of antibody-drug conjugates (ADCs) with a cytotoxic small molecule drug or payload. Specifically, this guidance addresses the FDA’s current thinking regarding clinical pharmacology considerations and recommendations for ADC development programs, including bioanalytical methods, dosing strategies, dose- and exposure-response analysis, intrinsic factors, QTc assessments, immunogenicity, and drug-drug interactions (DDIs).

Drug-Drug Interaction Assessment for Therapeutic Proteins Guidance for Industry
The purpose of this guidance is to help sponsors of investigational new drug applications (INDs) and applicants of biologic license applications (BLAs) determine the need for drug-drug interaction (DDI) studies for a therapeutic protein (TP) by providing a systematic, risk-based approach.

Developing Targeted Therapies in Low-Frequency Molecular Subsets of a Disease
The pharmacological effect of a targeted therapy is often related to a particular molecular alteration, and many diseases are caused by a range of different molecular alterations (some of which may be rare). Therefore, a targeted therapy may have differential effects among patients with the same disease who have different molecular alterations. The purpose of this guidance is to describe general approaches to evaluating the benefits and risks of targeted therapeutics within a clinically defined disease where some molecular alterations may occur at low frequencies.

Clinical Pharmacogenomics: Premarket Evaluation in Early-Phase Clinical Studies and Recommendations for Labeling
This guidance is intended to assist the pharmaceutical industry and other investigators engaged in new drug development in evaluating how variations in the human genome, specifically DNA sequence variants, could affect a drug’s pharmacokinetics (PK), pharmacodynamics (PD), efficacy, or safety. The guidance provides recommendations on when and how genomic information should be considered to address questions arising during drug development and regulatory review.

Clinical Trials

All clinical trials guidances are listed here.

Enhancing Participation in Clinical Trials — Eligibility Criteria, Enrollment Practices, and Trial Designs
This guidance recommends approaches that sponsors of clinical trials intended to support a new drug application or a biologics license application can take to increase enrollment of a representative population in their clinical trials. This guidance considers both demographic characteristics of study populations (e.g., sex, race, ethnicity, age, location of residency) and non-demographic characteristics of populations (e.g., patients with organ dysfunction, comorbid conditions, disabilities, those at the extremes of the weight range, and populations with diseases or conditions with low prevalence). Enrolling participants with a wide range of baseline characteristics may create a study population that more accurately reflects the patients likely to take the drug if it is approved and allow assessment of the impact of those characteristics on the safety and effectiveness of the study drug.

E8(R1) General Considerations for Clinical Studies
This guidance describes internationally accepted principles and practices in the design and conduct of clinical studies of drug and biological products. The guidance is intended to assist sponsors and other parties that design clinical studies, and to promote the quality of the studies submitted to regulatory authorities, while allowing for flexibility.

Multiple Endpoints in Clinical Trials Guidance for Industry
The purpose of this guidance is to describe various strategies for grouping and ordering endpoints for analysis and applying some well-recognized statistical methods for managing multiplicity within a study in order to control the chance of making erroneous conclusions about a drug’s effects. Basing a conclusion on an analysis where the risk of false conclusions has not been appropriately controlled can lead to false or misleading representations regarding a drug’s effects.

E17 General Principles for Planning and Design of Multi-Regional Clinical Trial
With the increasing globalization of drug development, it has become important that data from multiregional clinical trials (MRCTs) can be accepted by regulatory authorities across regions and countries as the primary source of evidence to support marketing approval of drugs (medicinal products). The purpose of this guidance is to describe general principles for the planning and design of MRCTs with the aim of increasing the acceptability of MRCTs in global regulatory submissions.

Decentralized Clinical Trials for Drugs, Biological Products, and Devices
This draft guidance provides recommendations for sponsors, investigators, and other stakeholders regarding the implementation of decentralized clinical trials (DCTs) for drugs, biological products, and devices. In this guidance, a DCT refers to a clinical trial where some or all of the trial-related activities occur at locations other than traditional clinical trial sites.

Enrichment Strategies for Clinical Trials to Support Approval of Human Drugs and Biological Products: Guidance for Industry
The purpose of this guidance is to assist industry in developing enrichment strategies that can be used in clinical investigations intended to demonstrate effectiveness (and in some cases safety) of human drugs and biological products. This guidance defines several types of enrichment strategies, provides examples of potential clinical trial designs, and discusses potential regulatory considerations when using enrichment strategies in clinical trials.

Ethical Considerations for Clinical Investigations of Medical Products Involving Children
Clinical investigations in children are essential for obtaining data on the safety and effectiveness of drugs, biological products, and medical devices in children and to protect children from the risks associated with exposure to medical products that may be unsafe or ineffective. Children are a vulnerable population who cannot consent for themselves and who therefore are afforded additional safeguards when participating in a clinical investigation. Such safeguards are an essential requirement for the initiation and conduct of pediatric investigations as part of a medical product development program.

Master Protocols for Drug and Biological Product Development
This guidance document provides recommendations on the design and analysis of trials conducted under a master protocol as well as guidance on the submission of documentation to support regulatory review.

There are some other Guidances which were issued and I will discuss them in another post. These include Guidances on minimizing use of animals for preclinical toxicology, Innovative clinical trial design especially for gene and cell therapies, and use of AI in designing of clinical trials.

For more information go to the FDA website at : https://www.fda.gov/drugs/guidances-drugs/guidance-documents-rare-disease-drug-development

Other Articles on FDA Guidances on this Open Access Scientific Journal Include:

FDA Guidance on Use of Xenotransplanted Products in Human: Implications in 3D Printing

FDA Guidance Documents Update Nov. 2015 on Devices, Animal Studies, Gene Therapy, Liposomes

FDA Cellular & Gene Therapy Guidances: Implications for CRSPR/Cas9 Trials

New FDA Draft Guidance On Homologous Use of Human Cells, Tissues, and Cellular and Tissue-Based Products – Implications for 3D BioPrinting of Regenerative Tissue

FDA Guidelines For Developmental and Reproductive Toxicology (DART) Studies for Small Molecules

Read Full Post »

Companies Actively Engaging Artificial Intellegence for Drug Design: 2026

Posted in Artificial Intelligence Applications in Health Care, Artificial Intelligence in CANCER, Artificial Intelligence in Medicine - Applications in Therapeutics, Deep Learning, Drug Development Process, Machine Learning, Pharmaceutical Drug Discovery, Pharmaceutical Industry Competitive Intelligence, tagged Artificial intelligence, artificial intelligence in drug design, biotech, drug design, In Silico Medicine, pharma, recursion pharmaceuticals on July 31, 2026| Leave a Comment »

Companies Actively Engaging Artificial Intellegence for Drug Design: 2026

Curator: Stephen J. Williams, Ph.D.

Work-in-Progress

This is not an exhaustive list of all AI companies involved in the drug discovery process. Some like Chai Discovery will be featured in separate posts and this will be constantly updated as companies are acquired and formed.

I want to start with a Great interview and white paper by McKinsey where they interview key opinion leaders on the business intelligence on AI firms in the drug discovery world and what to look for in companies that are in this space.

Source: https://www.mckinsey.com/industries/life-sciences/our-insights/how-ai-could-revolutionize-drug-discovery

How AI could revolutionize drug discovery

November 16, 2022 | Video

Watch VIDEO

https://www.mckinsey.com/Videos/video?vid=6313209549112&plyrid=IzQolWCsY

Artificial intelligence could help scientists develop better medicines faster—and thus improve millions of people’s lives. But for that to happen, companies will need to change the way they work.

Below is a Mckinsey report on How AI will Change the Future of Biotech

https://www.mckinsey.com/featured-insights/the-next-normal/biotech

CHARTING THE FUTURE

AI will be embedded into everyday research

The AI-driven drug discovery industry continues to grow, fueled by new entrants in the market, significant capital investment, and technology maturation. We’ve identified more than 250 companies working in the industry. More than half of them are based in the United States, but key hubs are emerging in Western Europe and Southeast Asia as well. The best of these companies will fully integrate AI into research workflows, as the exhibit shows. By putting AI at the center of the research engine, companies can transform research at scale—and bring about dramatic improvements in patient outcomes. For the full article, see:

Parts of a high-throughput screening (HTS) process embedded with AI technology

1. High-throughput screen commenced with diverse compound sets Scientist selects diverse compound sets (a set of chemical compounds with a wide range of chemical structures) as first high-throughput screen

In silico/ on-the-chip simulations

In-vitro/ ‘wet lab’ experiments

2. Automated compound selection and transfer Using HTS machinery, individual compounds are transferred to individual wells of cells under experimental conditions

3. Computer-vision-based hit selection Cell response to each compound is measured using microscope analysis (eg, through computer vision techniques); promising compounds are labeled “hits”

4. Automated machine learning (ML) model training from screen outcomes Information from HTS for first few plates is automatically transferred into an ML pipeline, which “learns” how cells respond to each kind of chemical structure

5. Compound library inferencing and prioritization ML algorithm then scans the remainder of the library compounds and predicts which plates should be prioritized to identify the highest number of hits in the next screen

6. Automated compound selection based on ML recommendations ML recommendations are automatically queued and used in the next round of HTS. The cycle continues, with the algorithm continuously learningfrom “real world” outputs. Recommendations trigger scientists to explore new chemical space and begin downstream screening processes more quickly. These recommendations feed into the selection of chemical compounds in step 1

DOWNLOADS

Article (3 pages)

Human bodies are incredibly complex. It takes many years to discover even just one new medicine to successfully treat a disease. Could artificial intelligence help speed up that process? McKinsey experts believe so. (The following transcript has been edited for clarity.)

Video

How AI could revolutionize drug discovery

Faster and better

Lydia The: What excites me about AI and drug discovery is the convergence between technology, drug development, and biology, which is going to lead to better drugs being developed faster—using all of the capabilities that Silicon Valley and the tech ecosystem have developed—to help us have even greater impact on patients.

Christoph Sandler: Today, to discover and develop a drug takes more than ten years.

Alex Devereson: We might be able to have drugs in one-tenth of the time, from being discovered to being able to treat patients. Today, many diseases simply have no treatments whatsoever. I think, and I hope, we’re going to see a world where we can generate therapies that can treat those patients very effectively. Fundamentally, we will have life-changing, game-changing drugs—on a scale and at a pace that we’ve never seen before—getting to the right patient at the right time.

The promise of personalized medicine

Christoph Sandler: In the not-so-distant future, we might collect health data across different inputs: from wearables, from our electronic medical records, or from clinical or academic research. And we will have the opportunity, on a voluntary basis, to upload these data into a central, secure, trusted data storage system.

Lydia The: You could imagine using the data to figure out not just what drug might work for you but exactly what drug would work for you at what time, in what sequence, in what dose—really personalized to you.

Will AI replace scientists?

Lydia The: What we’ve found is that technology doesn’t supplant the people. Rather, it will enable scientists to do things faster and better—and potentially develop insights that humans would not be able to develop at all.

Alex Devereson: Scientists will be able to discover things with machine learning that they could never have thought of by themselves, generate entirely new ideas, and move at a pace at which one person can do what it would have taken 100 to do before.

Lydia The: While in previous generations, scientists would spend a lot of their time—maybe even the majority of their time—on manual efforts such as pipetting from one tray to another tray or manually curating and cleaning data, I think AI will help us do all of those things in a more automated and quick way, and develop hypotheses that can then lead a scientist to think through, “What would the next experiment be? What are the implications of the data?”

What companies should do today

Alex Devereson: I think the challenge a lot of companies have is that they want to explore new ideas but, for good reason, they are reluctant to commit until they’ve seen some results and some tangible impact. So they explore a lot of pilots.

Lydia The: We have a term for that. We call it “pilot purgatory”: companies focus on one pilot, and they see the returns in a single pilot, but they don’t establish that approach and way of operating across their organization.

Define a ‘North Star’

Lydia The: One of the most important things for companies to escape pilot purgatory is a real mindset shift, from the top end all the way through the rest of the organization.

Christoph Sandler: It is important for the organization to define what a North Star for them should be, so that the data and analytics transformation of the R&D function can be targeted toward that North Star.

Identify—and solve—the biggest pain points

Alex Devereson: Truly understand what your biggest scientific and operational pain points are. For the lab scientists, for the patients who are in your clinical trials, for the patients who might get the drug: What is the biggest unsolved problem today?

Embed analytics into decision making

Christoph Sandler: Bring the organization on board and help them understand the potential of data and analytics.

Alex Devereson: You need to really, truly redesign a process that embeds analytics, where it’s not something on the side; it’s truly a part of the decision making.

Christoph Sandler: And you need to establish trust in the data and in these models. Equally important as the technical part is the human part.

Deliver value quickly

Alex Devereson: Don’t set up a project that delivers only after five years. Have a view on how you can deliver value in three months—and on what it takes in terms of analytics, data, and technology—with a relentless laser focus on value for the patient and for the scientific process.

Source: https://www.statnews.com/2026/06/03/alnylam-partner-with-inceptive-nucleics-ai-foundation-models/?utm_campaign=the_readout&utm_medium=email&_hsenc=p2ANqtz-_DOWjLTkMjWQ-ewilam07aNlz79w35ktjIKopXav_x3PpkOIfQIqnnMJlxdznIvjv-lGeo3tI4BUwIuxqEclEu4fHA9Q&_hsmi=422199873&utm_content=422199873&utm_source=hs_email

By Meghana Keshavan

Biotech Correspondent

The inventor of some of AI’s technical underpinnings is shifting focus to an RNA startup. Also, Rick Pazdur has some ideas on how to determine whether Revolution Medicines’ pancreatic cancer drug can work in a first-line setting. And an oncologist explains why she believes the failed Grail trial matters.

artificial intelligence

The AI architect taking aim at RNAi

Jakob Uszkoreit helped create the transformer architecture — the “T” in ChatGPT — that sparked the generative AI boom. Now he’s trying to do something just as ambitious in drug development, STAT’s Brittany Trang writes. His startup, Inceptive Nucleics, is building biological foundation models that can be applied across a wide range of sequence-based medicines, from RNA interference therapies to mRNA and antisense drugs.

That vision caught the attention of Alnylam, which yesterday announced that it had struck a three-year partnership worth up to $2 billion in potential milestone payments and royalties, along with a $30 million upfront investment. The idea is that AI could perhaps do more than analyze biological data — and instead design the molecules themselves.

Top 10 Leading AI Drug Discovery Companies Transforming the Market: Trends, Technologies, Growth Outlook & Competitive Landscape (2026-2034)

Source: https://www.linkedin.com/pulse/top-10-leading-ai-drug-discovery-companies-transforming-fsc5e/

IntelMarketResearch

MARKET OVERVIEW:

Artificial Intelligence (AI) is redefining the future of pharmaceutical research, making drug development faster, smarter, and more cost-effective than ever before. What once took more than a decade and billions of dollars can now be significantly accelerated through advanced machine learning algorithms, generative AI, predictive analytics, and computational biology.

AI Drug Discovery Market is projected to grow from USD 3.41 billion in 2026 to USD 10.45 billion by 2034, registering a robust 15.7% CAGR. This remarkable expansion reflects the pharmaceutical industry’s increasing reliance on AI-powered platforms to improve research efficiency, reduce clinical failures, and identify breakthrough therapies for complex diseases.

As healthcare systems worldwide demand faster innovation and precision medicine continues to evolve, AI is becoming an essential component of modern drug discovery rather than an experimental technology.

Key Technologies Powering AI Drug Discovery

The industry’s rapid growth is supported by several advanced technologies that work together throughout the drug development pipeline.

Machine Learning helps identify disease targets, recognize biological patterns, and predict therapeutic outcomes using historical datasets.

Deep Learning improves molecular analysis by recognizing highly complex biological relationships that conventional computational methods may overlook.

Generative AI creates entirely new molecular structures designed for specific therapeutic applications, enabling scientists to explore millions of virtual compounds before physical synthesis.

Predictive Modeling estimates toxicity, efficacy, pharmacokinetics, and safety profiles early in development, reducing unnecessary experimentation.

Molecular Docking Simulations evaluate how drug molecules interact with proteins, helping researchers prioritize candidates with the highest therapeutic potential.

AI-Driven Clinical Trial Optimization assists pharmaceutical companies in selecting suitable patient populations, improving trial design, and increasing the probability of successful outcomes.

Top 10 Leading AI Drug Discovery Companies Driving Innovation

The competitive landscape is rapidly evolving, with established technology providers and specialized biotechnology companies competing to develop next-generation AI platforms.

1. Insilico Medicine

Insilico Medicine has become one of the industry’s most recognized innovators through its end-to-end AI drug discovery platform. The company combines generative AI with biological research to accelerate target identification and therapeutic development.

2. Exscientia

Exscientia focuses on AI-designed precision medicines and has established multiple collaborations with leading pharmaceutical organizations to improve drug candidate selection and optimization. Exscientia is a pioneering artificial intelligence-driven pharmatech company that was acquired by Recursion Pharmaceuticals in November 2024

3. Atomwise

Known for its deep learning platform, Atomwise uses artificial intelligence to identify promising small-molecule therapies across oncology, infectious diseases, and rare disorders.

4. Recursion Pharmaceuticals

Recursion Pharmaceuticals integrates high-content imaging, automation, and AI to analyze millions of biological experiments, enabling large-scale phenotypic drug discovery.

5. BERG

BERG (now operating or known as BPGbio) specializes in biology-driven AI by combining multi-omics datasets with artificial intelligence to identify biomarkers and novel therapeutic opportunities.

6. Cyclica

Cyclica’s AI platform focuses on polypharmacology, helping researchers understand complex drug-protein interactions while improving drug design efficiency. , focusing on MatchMaker and POEM technology platforms before being acquired by Recursion for $40 million in May 2023.

7. GNS Healthcare

GNS Healthcare applies causal AI and patient-level analytics to support personalized medicine and identify drug repurposing opportunities.

8. DeepCure

DeepCure develops generative chemistry platforms capable of designing optimized molecules while reducing early-stage research complexity.

9. Schrödinger

Schrödinger combines computational chemistry, molecular simulation, and AI technologies to improve molecular modeling and accelerate pharmaceutical innovation.

10. IBM Watson Health & DeepMind Health

These technology pioneers continue expanding AI capabilities across healthcare by leveraging advanced analytics, large-scale biological data processing, and intelligent research platforms that support drug development initiatives. https://www.ibm.com/products/watsonx

There are many other companies that are using AI in Innovative ways to support drug discovery. Here is another list from

11 Innovative Companies Using AI for Drug Discovery

Contributing Writer

By Sherri Gordon

Published December 15, 2025

Overview

A look at 11 companies using AI-driven platforms to reshape drug discovery, including generative models, computational chemistry, and data-centric biology.

For more articles on AI and healthcare on this Open Access Scientific Journals please see our Portals at

Medicine with GPT-4 & Chat GPT

AGI, generativeAI, Grok, DeepSeek & Expert Models in Healthcare

Artificial Intelligence: Genomics & Cancer

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AI-Native Drug Discovery Landscape 2026 – How LPBI Group Differentiates in the New Era of Foundation Models

Posted in Artificial Intelligence - Breakthroughs in Theories and Technologies, Artificial intelligence applications for cardiology, Artificial Intelligence Applications in Health Care, Artificial Intelligence in CANCER, Artificial Intelligence in Health Care - Tools & Innovations, Artificial Intelligence in Medicine - Application for Diagnosis, Artificial Intelligence in Medicine - Applications in Therapeutics, Drug Development Process, Drug Discovery Chemistry, Intellectual Property, Intellectual Property, Innovations, Commercialization, Investment in technological breakthrough, International Global Work in Pharmaceutical, Machine Learning, Personalized and Precision Medicine & Genomic Research, Pharmaceutical Discovery, Pharmaceutical Drug Discovery, Pharmaceutical Industry Competitive Intelligence, Pharmaceutical R&D Informatics, Pharmaceutical R&D Investment, Pharmacogenomics, Precision Cancer Medicine on July 19, 2026| Leave a Comment »

AI-Native Drug Discovery Landscape 2026 – How LPBI Group Differentiates in the New Era of Foundation Models

Curators: Aviva Lev-Ari, PhD, RN with Grok Assistence

As the race to build powerful biology foundation models intensifies, several well-funded AI-native companies have emerged with ambitious platforms for protein design, small-molecule generation, and multimodal drug discovery. While these companies bring strong technical capabilities, LPBI Group occupies a distinct and complementary position in the ecosystem.

Key AI-Native Players (2026)

Competitive Landscape Table for AI-Native in Drug Discovery

Company	Focus	Stage	LPBI Differentiation
Isomorphic Labs (DeepMind)	Protein/small molecule design using AI	Advanced (AlphaFold3 based)	LPBI offers curated multimodal training data + COM methodology (they need high-quality data to train/validate)
EvolutionaryScale	Protein design (ESM models)	Early commercial	LPBI’s strength is in clinical/therapeutic context + mechanism-of-action curation across full disease spectrum
Chai Discovery	Multimodal AI for drug discovery	Early	LPBI provides the upstream high-provenance corpus + ontology they would need for better results
Recursion Pharma	Phenotypic screening + AI	Clinical stage	LPBI’s expert-curated literature + images + COM complements their wet-lab focus
Insilico Medicine	Generative AI for drug design	Clinical stage	LPBI’s causal reasoning framework + AJAUS offers continuous refresh they lack
AlignedHQ.ai	Generative AI for protein design & therapeutic optimization	Early-stage	LPBI supplies the high-provenance, expert-curated multimodal corpus and COM Tool Factory that significantly enhances model accuracy and reduces failure rates in downstream development

How LPBI Group Differentiates

LPBI Group does not compete directly in building foundation models. Instead, we provide the critical upstream layer these companies and hyperscalers urgently need:

A 9 GB private multimodal corpus of expert-curated scientific content (6,290+ articles, 48 e-Books, 7,500+ images, 300+ podcasts)
The 17-part Composition of Methods (COM) Tool Factory, including AJAUS (autonomous 24/7 refresh) and Rosetta Stone Ontology (causal mapping)
15 Subject Matter Small Language Models (SLMs) ready for concatenation into proprietary LLMs and MFMH
Proven track record of 4–5×+ uplift in novel causal relationship extraction when combined with frontier models

Strategic Positioning

While AI-native startups excel at model architecture and computation, they still face the persistent bottleneck of high-quality, causally structured, provenance-rich training data. LPBI Group’s vertically integrated assets and methodology offer a defensible moat and a true “own-both” advantage when partnered with hyperscalers or pharma companies.

This complementary role positions LPBI Group as the ideal upstream partner for the next generation of domain-aware AI in Health.

AlignedHQ.ai as Partner vs. Insilico Medicine

AlignedHQ.ai is a strong potential partner.

Why?

They are AI-native focused on protein design and generative models — directly complementary to LPBI’s strengths in curated biomedical literature, mechanism-of-action, and multimodal data.

They would benefit enormously from access to LPBI’s high-quality training data and COM methodology.

Partnership model: They use LPBI data (licensed) + Grok 5 / SpaceXAI compute and frontier models → Co-develop specific therapeutic pipelines.

Insilico Medicine is also a good candidate but slightly less ideal than AlignedHQ for early partnership because:

Insilico is more advanced clinically (has candidates in trials) and may want more control.

AlignedHQ appears earlier-stage and more open to collaboration.

Recommendation: Start with AlignedHQ.ai as a proof-of-concept partner (easier entry, high complementarity). Use success there to approach Insilico and others from a position of strength.

Where does AlignedHQ.ai’s Domain Knowledge in Medicine Come From?

From public information:

Primarily from public + licensed datasets (PDB, UniProt, scientific literature, clinical trial data, etc.).

They rely heavily on large-scale public biomedical databases and pre-trained models (e.g., AlphaFold derivatives).

Like most AI-native drug discovery companies, they have limited proprietary clinical/therapeutic context compared to LPBI’s expert-curated, mechanism-rich corpus.

Their domain knowledge is model-derived rather than expert-curated at source.

This is LPBI’s Core Delta: AlignedHQ (and similar companies) excel at model architecture and generation but lack the deep, traceable, expert-validated biomedical knowledge that LPBI has built over 14+ years. This is why access to LPBI’s portfolio would be highly valuable to them.

Grok, 7/19/2026

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In Memoriam: In Remembrance of Cancer Researchers who passed in 2026

Posted in Cancer Prevention: Research & Programs, Drug Discovery Chemistry, Gene Regulation, Li-fraumeni syndrome., Mourning the Loss of a Scientific Leader, Patents, TP53 - Germline mutations, tagged AACR, breast cancer, cancer risk, Li-fraumeni syndrome., novartis, nuclear receptors, Purdue University, retinol binding protein, steroid receptor, TP53 on July 13, 2026| Leave a Comment »

In Memoriam: In Remembrance of Cancer Researchers who passed in 2026

Reporter: Stephen J. Williams, Ph.D.

Source: https://www.aacr.org/professionals/membership/in-memoriam/

The following remembrances of American Association of Cancer Research (AACR) prominent member who have recently passed in 2026 is given below. Each have contributed seminal research and discovery in the field of cancer biology and cancer risk. In many cases, their discoveries transformed the way we understand and treat cancer. A separate In Memoriam for Nobel Leaureatte Dr. J. Michael Bishop will be given in a separate post.

Joseph F. Fraumeni, Jr., MD, FAACR (04/01/1933 – 06/22/2026)

Headshot of Joseph Fraumeni

Joseph F. Fraumeni, Jr., MD, FAACR, a renowned cancer epidemiologist, a Fellow of the AACR Academy, and a former member of the AACR Board of Directors, died June 22, 2026, at the age of 93. A career researcher and leader at the National Cancer Institute, Fraumeni was a co-discoverer of the genetic condition now known as the Li-Fraumeni syndrome and launched the U.S. Atlas of Cancer Mortality, which mapped geographic variations in cancer.Born April 1, 1933, in Boston, Fraumeni earned a bachelor’s degree from Harvard College, a medical degree from Duke University School of Medicine, and a master of science in epidemiology from the Harvard University School of Public Health. He completed medical residencies at Johns Hopkins Hospital and the Memorial Sloan-Kettering Cancer Center. A member of the AACR since 1968, Fraumeni served on the AACR’s Board of Directors from 1983 to 1986. He also served the AACR as an assistant editor, senior editor, and editorial board member for Cancer Epidemiology, Biomarkers & Prevention and an assistant editor for Cancer Research. The AACR recognized him with the AACR-American Cancer Society Award for Research Excellence in Epidemiology and Prevention in 1993 and the AACR Award for Lifetime Achievement in Cancer Research in 2009. He was inducted as a member of the inaugural class of Fellows of the AACR Academy in 2013. Fraumeni was a fellow of the American College of Physicians, the American Association for the Advancement of Science, and the American Academy of Arts and Sciences, and a member of the Institute of Medicine, the Association of American Physicians, and the National Academy of Sciences.

In 1962, Fraumeni joined the Epidemiology Branch of the National Cancer Institute (NCI) as a commissioned officer in the U.S. Public Health Service (USPHS). He went on to hold several leadership positions at the NCI, including posts as head of the Ecology Studies Section, chief of the Environmental Epidemiology Branch, director of the Epidemiology and Biostatistics Program, and founding director of the Division of Cancer Epidemiology and GeneticsHe retired from the USPHS in 1999 with the rank of rear admiral and assistant surgeon general. When he retired from NCI in 2017, he was named Scientist Emeritus. He authored or co-authored more than 900 scientific publications.

His research focused the epidemiology of high cancer risk populations and, in 1969, led him to discover a familial syndrome of early-onset cancers of the breast, brain, and other malignancies known as Li-Fraumeni Syndrome. Li-Fraumeni Sydrome is characterized by inherited mutations in the p53 tumor suppressor gene.

Li-Fraumeni Syndrome

from Cleveland Clinic: Li-Fraumeni syndrome is a rare genetic disorder that increases the risk you and your family members will develop cancer. Everyone with this condition has a 90% chance of developing one or more types of cancer by age 60. About half develop cancer before they turn 40. Females with Li-Fraumeni syndrome almost always develop breast cancer.

Below is the original reference published with his colleague the late Dr, Federick Li. Thier work together over the years helped develop the discovery of cancer susceptiblitiy genes and the importance of mutations of these genes linked to increased risk of developing cancer.

Li FP, Fraumeni JF Jr. 1969. Soft-tissue sarcomas, breast cancer, and other neoplasms. A familial syndrome? Ann Intern Med 71: 747–752.

Four families were identified in which a pair of children had soft-tissue sarcomas: three sets of sibs and one set of cousins. One parent of each affected child developed cancer; carcinoma of the breast occurred in three mothers under 30 years of age. Other young adults in these families had a high frequency of cancer, with no evidence of underlying genetic disorders known to carry a high risk of neoplasia. The increased familial susceptibility to cancer was manifested not only by the large number of members affected but by a seeming excess of multiple primary neoplasms.

It wasn’t until the 1990’s that Malkin et al. that germline mutations in TP53 were associated with this disease

Malkin D, Li FP, Strong LC, Fraumeni JF Jr, Nelson CE, Kim DH, Kassel J, Gryka MA, Bischoff FZ, Tainsky MA, et al. 1990. Germ line p53 mutations in a familial syndrome of breast cancer, sarcomas, and other neoplasms. Science 250: 1233–1238.

A similar syndrome named Lynch syndrome also gave rise to early increased risk of multiple cancers but due to germline mutations in mismatch repair genes like MLH1, MSH2, MSH6, or PMS2.

Lynch HT, Mulcahy GM, Harris RE, Guirgis HA, Lynch JF. 1978. Genetic and pathologic findings in a kindred with hereditary sarcoma, breast cancer, brain tumors, leukemia, lung, laryngeal, and adrenal cortical carcinoma. Cancer 41: 2055–2064.

Pierre Chambon, MD, FAACR, (02/07/1931 – 05/05/2026)

Pierre Chambon, MD, FAACR, a Fellow of the AACR Academy who was a pioneer in the structure and expression of genes, died May 5, 2026, at the age of 95. Chambon’s early work contributed to the discovery of PolyADPribose, the discovery of multiple RNA polymerases, major contributions to the elucidation of chromatin structure, and the discovery of animal split genes. Later work included the discovery of multiple promoter elements and their cognate factors. His research on nuclear receptors has had a marked influence on the understanding of signal transduction and endocrinology in vertebrates.

Born February 7, 1931, in Mulhouse, France, Chambon received his medical degree from the University of Strasbourg in 1958. He joined the university as a research associate, becoming an associate professor in 1962 and professor of biochemistry in 1968. He founded the Institute for Genetics and Cellular and Molecular Biology in 1994 and served as its director until 2002. He then founded the Mouse Clinical Institute and served as director until 2006. He held the chair of molecular genetics at the Collège de France from 1993 to 2003 and served as chair of molecular genetics and biology at the University of Strasbourg Institute for Advanced Study from 2012 to 2021. Chambon was elected to the French Academy of Sciences in1985, the same year in which he was elected a foreign member of both the U.S. National Academy of Sciences and the American Academy of Arts and Sciences.

Juliet M. Daniel, PhD

Juliet M. Daniel, PhD, a cell biologist who was a distinguished university professor at McMaster University in Hamilton, Ontario, and member of AACR since 2002, died April 28, 2026. She was 61 years of age. Noted for her work on genetic risk factors for breast cancer, Daniel discovered and gave the name “Kaiso” to a gene associated with triple negative breast cancer in women of African descent. Born in Barbados in 1964, Daniel obtained a bachelor’s degree in life sciences from Queen’s University in Kingston, Ontario, in 1987 and a doctorate in microbiology from University of British Columbia in Vancouver in 1993. She conducted postdoctoral research at St. Jude Children’s Research Hospital in Memphis and Vanderbilt University in Nashville. She joined McMaster as an assistant professor in 1999, the first black woman to become a member of the Faculty of Science. She was promoted to associate professor in 2005 and professor in 2012. Daniel was appointed associate dean of research and external relations for the Faculty of Science on an acting basis in 2020 and permanently in 2021. She was named strategic advisor to the university president for the Canada-Caribbean Institute (CCI) at McMaster in 2024. She was named a distinguished university professor, the highest faculty honor, in 2025. Among many other honors, she was elected a fellow of the Canadian Academy of Health Sciences in 2025, received the inaugural Canadian Cancer Society Inclusive Excellence Prize in Cancer Research in 2020, and was awarded an honorary doctorate in science by the University of the West Indies in 2021.

Philip S. Low, PhD

Philip S. Low, PhD, the Ralph C. Corley distinguished professor of chemistry at Purdue University, an inventor and entrepreneur with more than 100 patents to his credit, and an emeritus member of AACR, died March 4, 2026, at the age of 78. He also served as Purdue’s Presidential Scholar for Drug Discovery and was for a time as director of the university’s Center for Drug Discovery. Low held more than 100 U.S.-issued patents through Purdue Innovates and is listed on 600 U.S. and international patents and 145 invention disclosures. He founded seven companies based on based on work conducted at Purdue, one of which, Endocyte Inc., was sold to Novartis in 2018. Born in Ames, Iowa, in 1947, Low earned a bachelor’s degree in chemistry from Brigham Young University in 1971 and a doctorate in biochemistry from the University of California, San Diego, in 1975. He joined the Purdue University faculty in 1976. An AACR member since 2005, Low received the AACR Award for Outstanding Achievement in Chemistry in Cancer Research in 2015 in recognition of his research on low molecular weight ligand-targeted therapeutic and imaging agents. In the same year, he also received the American Chemical Society (ACS) George & Christine Sosnovsky Award for Cancer Research and was elected to the National Academy of Inventors. In August 2025, Low was named the recipient of the ACS Alfred Burger Award in Medicinal Chemistry for 2026. He also received the Order of the Griffin and the Morrill Award from Purdue.

For more remebrances of past AACR members please visit: https://www.aacr.org/professionals/membership/in-memoriam/

Other recent In Memoriam on this Open Access Scientific Journal Include:

News from AACR; In Memoriam: Nobel Leaureate David Baltimore, Ph.D

In Memoriam: Professor Yitzhak Apeloig, President and Distinguised Professor of the Technion

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Protected: KOL on Competitive Dynamics: LPBI Group’s Founder’s Radar Screens on AI-in-Health, Life Sciences & BioPharma

Posted in Artificial Intelligence - Breakthroughs in Theories and Technologies, Artificial Intelligence - General, Artificial Intelligence Applications in Health Care, Artificial Intelligence in Medicine - Application for Diagnosis, Artificial Intelligence in Medicine - Applications in Therapeutics, BioTechnology - Venture Creation, BioTechnology - Venture Creation, Venture Capital, Disease Biology, Small Molecules in Development of Therapeutic Drugs, Drug Delivery Platform Technology, Drug Development Process, Drug Discovery Chemistry, drug repurposing, Genetics & Pharmaceutical, Global Market of Medical Devices Technology, Global Partnering & Biotech Investment, HealthCare IT, Personalized and Precision Medicine & Genomic Research, Pharmaceutical Discovery, Pharmaceutical Drug Discovery, Pharmaceutical Industry Competitive Intelligence, Pharmaceutical R&D Investment, Pharmacogenomics, Precision Cancer Medicine, Rapid automation of plasma protein pools on June 15, 2026|

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In Memoriam: Professor Yitzhak Apeloig, President and Distinguised Professor of the Technion

Posted in Drug Delivery Platform Technology, Drug Discovery Chemistry, Mourning the Loss of a Scientific Leader, tagged computational chemistry, Israel Chemical Society Gold Medal, Memoriam, silicon chemistry, Technion, The ACS Frederic Stanley Kipping Award in Silicon Chemistry, The Schrödinger Medal, The Wacker Silicone Award on June 4, 2026| Leave a Comment »

In Memoriam: Professor Yitzhak Apeloig, President and Distinguised Professor of the Technion

Reporters: Aviva Lev-Ari, PhD, RN and Stephen J. Williams, Ph.D.

From the Technion:

The Technion community mourns the passing of Distinguished Professor Yitzhak Apeloig (1944–2026), president of the Technion from 2001 to 2009 and one of Israel’s most distinguished chemists.
A pioneer in computational chemistry and organosilicon compounds, Prof. Apeloig made groundbreaking scientific contributions while mentoring generations of researchers and helping position the Technion as a global leader in science and technology.
During his presidency, he expanded interdisciplinary research, strengthened international partnerships, increased investment in research infrastructure and scholarships, and advanced collaboration between engineering, medicine, and the humanities.“Prof. Apeloig led the Technion with quiet confidence and steadfast leadership,” said Technion President Prof. Uri Sivan. “His years in office were marked by exceptional academic development and a profound impact on the State of Israel and beyond.”
The Technion was his home and family. He will be deeply missed. May his memory be a blessing.

Distinguished Professor Yitzhak Apeloig (1944–2026), president of the Technion from 2001 to 2009 and one of Israel’s most distinguished chemists

1. Pioneering Computational Quantum Chemistry

In the 1970s, Apeloig was one of the first experimental chemists in the world to recognize the immense potential of using quantum theory and computational models to predict molecular characteristics.

The “First Calculate, Then Synthesize” Philosophy: He pioneered a paradigm shift in organic chemistry by using quantum-mechanical calculations to predict whether a novel molecule could exist and what its lifespan would be before attempting physical synthesis in the laboratory.
He was unique for his time in requiring his students to master both computational modeling and physical laboratory experimentation rather than separating the disciplines.

2. Breakthroughs in Organosilicon Chemistry

Before Apeloig’s work, much of the scientific community believed that silicon could not easily form stable multiple bonds (such as $Si=Si$ or $Si=C$ double bonds) in the way carbon does. Apeloig revolutionized the field of organosilicon chemistry:

Low-Coordination Silicon Compounds: He successfully used computational tools combined with experimental testing to predict, isolate, and understand highly reactive, low-coordination silicon species. This included landmark research into multiply-bonded silicon compounds, silyl radicals, silyl anions, silylenes, and metallosilane reagents.
Silicone and Materials Science: His structural insights fundamentally advanced the molecular understanding of silicon polymers and siloxanes, which heavily influenced the development of advanced moulding materials, rubbers, and photosensitive materials used in lithography.

3. Discovery of Reactive Intermediates

During his early career and PhD research, he focused heavily on physical organic chemistry and organic reaction mechanisms:

He was the first to experimentally demonstrate that a vinyl cation holds a linear geometry around its carbocationic center.
His extensive work mapping out short-lived, highly reactive intermediates (like carbenium ions and strained molecules) provided the foundational mechanics for how complex organic reactions occur.

Major Recognitions

For these definitive contributions to computational and silicon chemistry, Apeloig was awarded some of the highest honors in the discipline:

The Schrödinger Medal (2021): Awarded by the World Association of Theoretical and Computational Chemists (WATOC) for his seminal contributions to theoretical chemistry.
The ACS Frederic Stanley Kipping Award in Silicon Chemistry (2010): The premier international award recognizing advancements in the study of silicon.
The Wacker Silicone Award (2007) and the Israel Chemical Society Gold Medal (2017).

The seminal publications that define his academic footprint include:

1. Foundational Computational and Structural Chemistry

During the mid-1970s and 1980s, Apeloig co-authored several massive, highly cited studies establishing the rules of computational molecular architecture, specifically challenging traditional rules of carbon and silicon bonding.

“Stabilization of planar tetracoordinate carbon”
- Journal of the American Chemical Society (1976)
- Co-authors: J. B. Collins, J. D. Dill, E. D. Jemmis, P. v. R. Schleyer, R. Seeger, J. A. Pople
- Impact: A true milestone in structural chemistry that theoretically demonstrated how specific substitution patterns could stabilize a planar geometry around a carbon atom, defying the standard tetrahedral configuration.
“A theoretical survey of unsaturated or multiply bonded and divalent silicon compounds. Comparison with carbon analogs”
- Journal of the American Chemical Society (1986)
- Co-authors: B. T. Luke, J. A. Pople, M. B. Krogh-Jespersen, M. Karni, J. Chandrasekhar, P. v. R. Schleyer
- Impact: A definitive ab initio survey that comprehensively mapped out the differences between carbon and silicon multiple bonds, predicting the stability and reaction behaviors of transient silicon chemical species.

2. High-Impact Silicon and Stable Carbene Analogs

In the 1990s and 2000s, Apeloig focused on predicting and identifying highly sought-after reactive intermediates—particularly “impossible” double bonds and carbenes.

“On the Question of Stability, Conjugation, and ‘Aromaticity’ in Imidazol-2-ylidenes and Their Silicon Analogs”
- Journal of the American Chemical Society (1996)
- Co-authors: C. Heinemann, T. Müller, H. Schwarz
- Impact: Heavily cited paper evaluating the electronic properties, structural stability, and aromaticity of N-heterocyclic carbenes (NHCs) versus their heavier silicon counterparts (silylenes).
“Substituent effects on the geometries and energies of the silicon-silicon double bond”
- Journal of the American Chemical Society (1990)
- Co-author: M. Karni
- Impact: This study mapped how changing the attached chemical groups altered the trans-bending and bond lengths of $Si=Si$ double bonds, establishing a predictive guide for experimentalists trying to isolate stable disilenes.

3. Definitive Academic Reviews and Reference Books

Beyond standalone journal entries, Apeloig is globally recognized for editing the foundational texts that summarized the state of organosilicon chemistry for generations of scientists.

“The Chemistry of Organic Silicon Compounds” (Volumes 1, 2, and 3)
- Co-edited with: Zvi Rappoport (Published by John Wiley & Sons, beginning in 1989)
- Impact: Apeloig authored critical chapters, such as “Theoretical Aspects of Organosilicon Compounds,” within these volumes. This multi-book compendium serves as the literal “bible” for researchers studying silicon polymers, reactive silicon intermediates, and silicon-based material sciences.

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The Health Care Dossier on Clarivate PLC: How Cortellis Is Changing the Life Sciences Industry

Posted in Artificial Intelligence in Medicine - Applications in Therapeutics, Big Data, BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceutical R&D Informatics, Clinical Genomics, Cancer Informatics, Drug Development Process, Intelligent Information Systems, Natural Language Processing (NLP), Pharmaceutical Drug Discovery, Pharmaceutical Industry Competitive Intelligence, Pharmaceutical R&D Informatics, tagged AI in drug discovery, Artificial intelligence, artificial intelligence in drug design, bioinformatic tools, Biotechnology and Pharmaceuticals, Clarivate, Cortellis, Mergers and acquisitions, pharmaceutical intelligence on April 16, 2024| Leave a Comment »

The Health Care Dossier on Clarivate PLC: How Cortellis Is Changing the Life Sciences Industry

Curator: Stephen J. Williams, Ph.D.

Source: https://en.wikipedia.org/wiki/Clarivate

Clarivate Plc is a British-American publicly traded analytics company that operates a collection of subscription-based services, in the areas of bibliometrics and scientometrics; business / market intelligence, and competitive profiling for pharmacy and biotech, patents, and regulatory compliance; trademark protection, and domain and brand protection. In the academy and the scientific community, Clarivate is known for being the company that calculates the impact factor,^[4] using data from its Web of Science product family, that also includes services/applications such as Publons, EndNote, EndNote Click, and ScholarOne. Its other product families are Cortellis, DRG, CPA Global, Derwent, MarkMonitor, CompuMark, and Darts-ip, ^[3] and also the various ProQuest products and services.

Clarivate was formed in 2016, following the acquisition of Thomson Reuters‘ Intellectual Property and Science business by Onex Corporation and Baring Private Equity Asia. Clarivate has acquired various companies since then, including, notably, ProQuest in 2021.

Further information: Thomson Scientific

Clarivate (formerly CPA Global) was formerly the Intellectual Property and Science division of Thomson Reuters. Before 2008, it was known as Thomson Scientific. In 2016, Thomson Reuters struck a $3.55 billion deal in which they spun it off as an independent company, and sold it to private-equity firms Onex Corporation and Baring Private Equity Asia.

In May 2019, Clarivate merged with the Churchill Capital Corp SPAC to obtain a public listing on the New York Stock Exchange (NYSE) It currently trades with symbol NYSE:CLVT.

Acquisitions

June 1, 2017: Publons, a platform for researchers to share recognition for peer review.
April 10, 2018: Kopernio, AI-tech startup providing ability to search for full-text versions of selected scientific journal articles.
October 30, 2018: TrademarkVision, provider of Artificial Intelligence (AI) trademark research applications.
September 9, 2019: SequenceBase, provider of patent sequence information and search technology to the biotech, pharmaceutical and chemical industries.
December 2, 2019: Darts-ip, provider of case law data and analytics for intellectual property (IP) professionals.
January 17, 2020: Decision Resources Group (DRG), a leading healthcare research and consulting company, providing high-value healthcare industry analysis and insights.
June 22, 2020: CustomersFirst Now, in intellectual property (“IP”) software and tech-enabled services.
October 1, 2020: CPA Global, intellectual property (“IP”) software and tech-enabled services.
December 1, 2021: ProQuest, software, data and analytics provider to academic, research and national institutions.^[27]It was acquired for $5.3 billion from Cambridge Information Group in what was described as a “huge deal in the library and information publishing world”. The company said that the operational concept behind the acquisition was integrating ProQuest’s products and applications with Web of Science. Chairman of ProQuest Andy Snyder became the vice chairman of Clarivate. The Scholarly Publishing and Academic Resources Coalition, an advocacy group for open access to scholarship, voiced antitrust concerns. The acquisition had been delayed mid-year due to a Federal Trade Commission antitrust probe.

Divestments

November 2020: Clarivate sold its Techstreet division to a for-profit subsidiary of the American Society of Mechanical Engineers.
November 2022: Clarivate divested MarkMonitorto Newfold Digital. MarkMonitor registers the domains of Google, Microsoft, Amazon, Tencent, YouTube, Wikipedia, and eBay, among other companies. They provide domain management, brand protection, anti-piracy, and anti-fraud services.

How Clarivate Has Changed Since 2019

2019 Strategy

From 2019 Manager Discussion Yearly Report

We are a leading global information services and analytics company serving the scientific research, intellectual property and life sciences end-markets. We provide structured information and analytics to facilitate the discovery, protection and commercialization of scientific research, innovations and brands. Our product porfolio includes well-established market-leading brands such as Web of Science, Derwent Innovation, Life Sciences, CompuMark and MarkMonitor (which they later divested). We believe that the stron balue proposition of our content, user interfaces, visualization and analytical tools, combined with the integration of our products and services into customers’ daily workflows, leads to our substantial customer loyalty as evidenced by their willingness to renew subscriptions with us.

Our structure, enabling a sharp focus on cross-selling opportunities within markets, is comprised of two product groups:

Science Group: consists of Web of Science and Life Science Product Lines
Intellectual Property Group: consists of Derwent, CompuMark and MarkMonitor

Corporations, government agencies, universities, law firms depend on our high-value curated content, analytics and services. Unstructured data has grown exponentially over the last decade. The trend has resulted in a critical need for unstructured data to be meaningfully filtered, analyzed and curated into relvent information that facilitates key operational and strategic decision making. Our highly curated, proprietary information created through our sourcing, aggregation, verification, translation, and categorization (ONTOLOGY) of data has resulted in our solutions being embedded in our customers’ workflow and decision-making processes.

Overview of Clarivate PLC five year strategy in 2019. Note that in 2019 the Science Group accounted for 56.2% of revenue! This was driven by their product Cortellis!

Figure. Overview of Clarivate PLC five year strategy in 2019. Note that in 2019 the Science Group accounted for 56.2% of revenue! This was driven by their product Cortellis!

Also Note nowhere in the M&A Discussion in years before 2023 was anything mentioned concerning AI or Large Language Models.

The Clarivate of Today: Built for Life Sciences with Cortellis

Clarivate PLC has integrated multiple platforms into their offering Cortellis, which integrated AI and LLM into the structured knowledge bases (see more at https://clarivate.com/products/cortellis-family/)

“Life sciences organizations are tasked, now more than ever, to discover and develop treatments that challenge the status quo, increase ROI, and improve patient lives. However, its become increasingly difficult to find, integrate and analyze the key data your teams need to make critical decisions and get your Cortellis products to patients faster.

The Cortellis solutions help research and development, portfolio strategy and business development, and regulatory and compliance professionals gather and assess the information you need to discover innovative drugs, differentiate your treatments, and increase chances of successful regulatory approval.

Some of Cortellis solutions include:

Cortellis Competitive Intelligence: maximize ROI and improve patient outcomes
Cortellis Deals Intelligence: Portfolio Strategy and Business Development (find best deal)
Cortellis Clinical Intelligence: Clinical Trial Support and Regulatory
Cortellis Digital Health Intelligence: understand digital health ecosystem
Cortellis Drug Discovery: improve drug development speed and efficiency
MetaBase and MetaCore: integrated omics knowledge bases for drug discovery
Cortellis Regulatory: help with filings
Cortellis HTA: health tech compliance (HIPAA)
CMC Intelligence: new drug marketing
Generics Intelligence
Drug Safety Intelligence: both preclinical safety and post marketing pharmacovigilence

Watch Videos on Cortellis for Drug Discovery

Watch Video on Qiagen Site to see how Cortellis Integrates with Qiagen Omics Platform IPA with Clarivate Meta Core to gain more insights into genomic and proteomic data

https://digitalinsights.qiagen.com/products-overview/discovery-insights-portfolio/analysis-and-visualization/qiagen-ipa/?cmpid=QDI_GA_Comp&gad_source=2&gclid=EAIaIQobChMIwu6HtvHGhQMVnZ9aBR1iCgHTEAEYASAAEgJiWPD_BwE

From the Qiagen website on Ingenuity Pathway Analysis: https://digitalinsights.qiagen.com/products-overview/discovery-insights-portfolio/analysis-and-visualization/qiagen-ipa/

Understand complex ‘omics data to accelerate your research

Discover why QIAGEN Ingenuity Pathway Analysis (IPA) is the leading pathway analysis application among the life science research community and is cited in tens of thousands of articles for the analysis, integration and interpretation of data derived from ‘omics experiments. Such experiments include:

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Articles Relevant to Drug Development, Natural Language Processing in Drug Development, and Clarivate on this Open Access Scientific Journal Include:

The Use of ChatGPT in the World of BioInformatics and Cancer Research and Development of BioGPT by MIT

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

Medical Startups – Artificial Intelligence (AI) Startups in Healthcare

New York Academy of Sciences Symposium: The New Wave of AI in Healthcare 2024. May 1-2, 2024 New York City, NY

Clarivate Analytics – a Powerhouse in IP assets and in Pharmaceuticals Informercials

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Live Notes from JP Morgan Healthcare Conference Virtual Endpoints Preview: January 8-9 2024

Posted in Artificial Intelligence - Breakthroughs in Theories and Technologies, Artificial Intelligence - General, Artificial Intelligence Applications in Health Care, Artificial Intelligence in CANCER, Artificial Intelligence in Medicine - Applications in Therapeutics, Big Data, Big Data & Analytics, BioTechnology - Venture Creation, Cancer and Current Therapeutics, Conference Coverage with Social Media, Data Science, Drug Development Process, Drug Discovery Chemistry, Institutional Capital Raised by Female Founders, Longevity, Machine Learning, New Drug Approval, Personalized and Precision Medicine & Genomic Research, Pharmaceutical Discovery, Pharmaceutical Drug Discovery, RNA Biology, Cancer and Therapeutics, Technology Transfer: Biotech and Pharmaceutical, Translational Science, US and Global Economy: Trends and Findings, Venture Capital, tagged #JPM24, antibody-drug conjugate, antibody-drug conjugates, antiobesity, bioinvestment, Biotech Investment and Venture Growth: The Franchising of Intellectual Property as a Business Model, Cancer immunotherapy, click chemistry, GLP-1, JPM, nobel leureates on January 16, 2024| Leave a Comment »

Live Notes from JP Morgan Healthcare Conference Virtual Endpoints Preview: January 8-9 2024

Reporter: Stephen J. Williams, Ph.D.

Endpoints at #JPM24 | Primed to unlock biopharma’s next dealmaking wave

Endpoints at JP Morgan Healthcare Conference

January 8-9 | San Francisco, CA80 Mission St, San Francisco, CA

An oasis has emerged in the biopharma money desert as backers look to replenish capital — still, uncertainty remains on whether it’s a mirage or the much needed dealmaking bump the industry needs. Yet spirits run high as JPM24 marks the triumphant return of inking strategic alliances and peering into the industry crystal ball — while keeping an eye out for some major M&A.

We’re back live from San Francisco for JPM Monday and Tuesday — our calendar of can’t-miss panels and fireside chats will feature prominent biopharma leaders to watch. The Endpoints Hub provides the ultimate coworking space with everything you need — 1:1 and group meeting spots plus guest pass capabilities and more. Join us in-person at the Endpoints Hub or watch online to stay plugged into all the action.

8 JAN

Welcome remarks

8:05 AM – 8:25 AM PST

Pfizer vet Mikael Dolsten has some thoughts on Big Pharma R&D

Endpoints News founding editor John Carroll will sit down with longtime Pfizer CSO Mikael Dolsten to talk about Pfizer’s pipeline, what he’s learned on the job about preclinical research and development and what’s ahead for the pharma giant in drug development and deals.

Mikael Dolsten

Chief Scientific Officer, President, Pfizer Research & Development

Pfizer

Pfizer Mikael Dolsten: Pfizer produced a series of AI generated molecules with new properties. Sees rapid adoption of AI in the area of drug discovery and molecular design.

8:25 AM – 9:05 AM PST

What pharma wants: The industry’s dealmakers look ahead at 2024

The drug industry’s appetite for new assets hasn’t slowed down. Top business development execs will give their outlook on the year, what they’re looking for and how they see the market.

Glenn Hunzinger

Pharmaceutical & Life Sciences Consulting Solutions Leader

PwC US

Rachna Khosla

SVP, Head of Business Development

Amgen

James Sabry

Global Head of Pharma Partnering

Roche

Devang Bhuva

SVP, Corporate Development

Gilead Sciences, Inc.

Endpoints News

Dealmaking panel

Glenn Hunzinger: if you do not have a GLP1 will have a tough time getting a good market price for your company; capital markets are not where they want to be; sees a tough deal making climate like last year. The problem with many biotech companies are they are coming earlier to the venture capital because of greater funding needs and so it is imperative that they articulate the potential of their company in scientific detail

Rachna Khosla: Make sure your investors are not just CAPITAL PARTNERS but use their expertise and involve them in development issues you may have, especially ones that a young firm will face. The problem is most investments assume what the future looks like (for example how antibody drug conjugates, once a field left for dead, has been rejuvenated because of advances in chemistry).

James Sabry: noted that cardiac and metabolic drugs are now at the focus of many investors, especially with the new anti-obesity drugs on market

Devang Bhuva: Most deals we see start as collaborations or partnerships. You want to involve an alliance management team early in the deal making process. This process could take years.

9:05 AM – 9:20 AM PST

The IPO: How Apogee Therapeutics went public in the most challenging market in years

Not many biotechs went public in 2023. And of those that did, not many have had a great time of it. Apogee is the exception and our panel will offer a behind-the-scenes look at their decision to enter the market and what life is like as a young public company.

Michael Henderson

CEO

Apogee Therapeutics

Kyle LaHucik

MODERATOR

Senior Reporter

Endpoints News

Michael Henderson: Not many biotech IPOs deals happened in 2023. Michael feels it is because too many biotechs focused on building platforms, which was a hard sell in 2023. He felt not many biotechs had clear milestones and investors wanted a clear primary validated target. He said many biotech startups are in a funding crunch and most need at least $440M on their balance sheet to get to 2026.

9:50 AM – 10:10 AM PST

Top predictions for biotech in 2024

Catalent CEO Alessandro Maselli will be back at the big JPM healthcare confab to talk with Endpoints News founder John Carroll about their top predictions of what’s coming up for the biotech industry in 2024. The stakes couldn’t be higher as the industry grapples with headwinds and new opportunities in a gale of market forces. Two top observers share their thoughts on the year ahead.

Alessandro Maselli

President & CEO

Catalent

10:15 AM – 10:35 AM PST

Innovation at a crossroads: Keys to unlocking the value of science and technology

The industry has long discussed the promise of technology and the acceleration it provides in scientific advancement and across the industry value chain. However, the promise of its impact has yet to fully be realized. This discussion will outline the keys to unleashing this promise and the implications and actions to be taken by the biopharmaceutical companies across the industry.

Ray Pressburger

North America Life Sciences Industry Lead & Global Life Sciences Strategy Lead

Accenture

10:35 AM – 11:05 AM PST

Activism and Investing: In conversation with Elliott Investment Management’s Marc Steinberg

Elliott has been behind many of 2023’s highest-profile healthcare investments, including multiple activist engagements and taking Syneos Health private. What has made large healthcare companies such interesting investment opportunities for firms like Elliott? What’s Elliott’s investing strategy in healthcare? And what should companies expect when an activist calls?

Marc Steinberg

Senior Portfolio Manager

Elliott Investment Management

Andrew Dunn

MODERATOR

Biopharma Correspondent

Endpoints News

11:05 AM – 11:35 AM PST

Creating ROI from AI

AI is predicted to transform the way drugs are made, from discovery to clinical trials to market. But beyond the initial hype and early adoption, where has AI made meaningful contributions to R&D? How does it help drug developers advance science? Endpoints publisher Arsalan Arif is convening a panel of leading experts to discuss the state of AI in the pharmaceutical landscape and the outlook for 2024. How does AI impact the drug pipeline, from the early steps of discovery to reducing trial failure rate?

Thomas Clozel

Co-Founder & CEO

Owkin

Venkat Sethuraman

SVP, Global Biometrics & Data Sciences

Bristol Myers Squibb

Frank O. Nestle

Global Head of Research & Chief Scientific Officer

Sanofi

Matthias Evers

Chief Business Officer

Evotec

Arsalan Arif

MODERATOR

Founder & Publisher

Endpoints News

11:35 AM – 12:00 PM PST

Biopharma’s dealmaker: Behind the scenes with Centerview Partners co-president Eric Tokat

Almost every major biopharma deal in 2023 had Centerview’s name attached to it. And much of the time, Eric Tokat was the banker making those deals happen. Hear his outlook for 2024, how transactions are getting done and what’s placed his firm at the center of so much action.

E. Eric Tokat

Co-President, Investment Banking

Centerview Partners

CenterView Partners Eric Tokat feels dealmaking will improve in 2024, given the recent flurry of dealmaking at end of last year and right before main JPM Healthcare Conference. He says Centerview wants to help the biotechs they invest in on their strategic path. This may translate into buyers more actively involved (more than startups want) and buyers now are in the drivers seat as far as the timeline of deals and development.

Is the megamerger dead for this year? He says it is very hard to see two major mergers happening but there will be many smaller and mid size biotech deals happening, but these deals will be more speculative in nature.. The focus for large pharma is top line growth. Most of the buyers have an infrastructure and value is more of buying and dropping it in their business so there is now a huge emphasis on due diligence on whether synergies exist or not

12:00 PM – 12:30 PM PST

Founder, legend, leader: In conversation with Nobel laureate Carolyn Bertozzi

Carolyn Bertozzi’s discoveries around bioorthogonal chemistry won the Nobel Prize in Chemistry in 2022 and are at the heart of new therapies being tested in patients. Join us as we discuss what inspires her and where she sees the next big advances.

Carolyn Bertozzi

Prof. of Chemistry, Stanford University and Baker Family Director of Sarafan ChEM-H

Stanford University

Nicole DeFeudis

MODERATOR

Editor

Endpoints News

Bioorthogonal chemistry: class of high yielding chemical reactions that proceed rapidly and selectively in biological environments without side reactions toward endogenous functions. This is also a type of ‘click chemistry’ in biological system where only specifically alter the biomolecule of interest.

Orthogonal: two chemicals not interacting with each other

Dr. Bertozzi noted she has started a new Antibody-Drug-Conjugate (ADC) company which involves designing with biorthogonal chemistry to make new functional molecules with varying properties

She noted hardly any biologists knew anything about glycobiology when she first started. However now she feels pharma and academia are working very well with each other

Bioorthogonal and Click Chemistry
Curated by Prof. Carolyn R. Bertozzi, 2022 winner of the Nobel Prize in Chemistry

Source: https://pubs.acs.org/page/vi/bioorthogonal-click-chemistry

The 2022 Nobel Prize in Chemistry has been awarded jointly to ACS Central Science Editor-in-Chief, Carolyn R. Bertozzi of Stanford University, Morten Meldal of the University of Copenhagen, and K. Barry Sharpless of Scripps Research, for the development of click chemistry and bioorthogonal chemistry.

To celebrate this remarkable achievement, 2022 Nobel Prize winner Professor Carolyn R. Bertozzi has curated this Bioorthogonal and Click Chemistry Virtual Issue, highlighting papers published across ACS journals that have built upon the foundational work in this exciting area of chemistry.

From Mechanism to Mouse: A Tale of Two Bioorthogonal Reactions

Ellen M. Sletten and Carolyn R. Bertozzi* Acc. Chem. Res. 2011, 44, 9, 666-676 August 15, 2011

Abstract

Bioorthogonal reactions are chemical reactions that neither interact with nor interfere with a biological system. The participating functional groups must be inert to biological moieties, must selectively reactive with each other under biocompatible conditions, and, for in vivo applications, must be nontoxic to cells and organisms. Additionally, it is helpful if one reactive group is small and therefore minimally perturbing of a biomolecule into which it has been introduced either chemically or biosynthetically. Examples from the past decade suggest that a promising strategy for bioorthogonal reaction development begins with an analysis of functional group and reactivity space outside those defined by nature. Issues such as stability of reactants and products (particularly in water), kinetics, and unwanted side reactivity with biofunctionalities must be addressed, ideally guided by detailed mechanistic studies. Finally, the reaction must be tested in a variety of environments, escalating from aqueous media to biomolecule solutions to cultured cells and, for the most optimized transformations, to live organisms.

9 JAN

9:40 AM – 10:10 AM PST

Biotech downturn survival school

Our panelists have seen the worst, and made it through to the other side. Join us for downturn survival school as our panelists talk about what sets apart the ones who make it through tough times.

These panalists think it will be specialist capital year to shine while the general capital is still sitting on the sidelines

JJ Kang

CEO

Appia Bio

“2023 was a tough year while 2020 was a boon year to start a company. We will continue to see these cycles; many of these new CEOs have never seen a biotech downturn yet and may not know how to preserve capital for the downturn”.

“Doing a partnership with Kite Pharmaceuticals early in our startp allowed us to get work done without risking a lot of capital, even if it means equity and asset dilution. That makes sense. However even if you are small insist on being an equal partner.”

“There are many investors we talk to who do not want to invest in cell therapy. Too risky now”

Carl Gordon

Managing Partner

OrbiMed Advisors

There are many macroeconomic factors affecting investment and capital today which will carry on through 2024. Not raising money when you do not need money is a bad philosophy. Always bbe raising captial. This is especially true when you have to rely on hedge funds. Parnerships howeve are sometimes the only way for small biotechs to leverage their strengths.

Joshua Boger

Executive Chair

Alkeus Pharmaceuticals, Inc.

Boger: Expect volatility for 2024. This environment feels very different than past downturns.

Even in downturns there is still lots of capital; remember access to human capital is better in a downturn and is easier to access; however it has become harder to get drug approvals

The panelists agree that access to capital and funding will be as tricky in 2024 than 2023. They did

suggest that a new funding avenue, private credit, may be a source of capital. This is discussed below:

When thinking about a private alternative investment asset class, the first thing that springs to mind is private equity. But there’s one more asset class with the word private in its name that has recently gained much attention. We’re talking about private credit.

Indeed, this once little-known investment strategy is now growing rapidly in popularity, offering private investors worldwide an exciting opportunity to diversify their portfolio with, in theory, less risky investments that yield significant returns.

Private credit investments refer to investors lending money to companies who then repay the loan at a given interest rate within the predetermined period.
The private credit market has grown significantly over the past years, rising from $875 million in 2020 to $1.4 trillion at the beginning of 2023.

Please WATCH VIDEO BY GOLDMAN SACHS ON PRIVATE CREDIT

https://www.gsam.com/content/dam/gsam/video/common/en/2023/TheRoadAheadForPrivateCredit.mp4

10:50 AM – 11:20 AM PST

The New Molecule: How breakthrough technologies are actually changing pharma R&D

Join us for a look at how AI, machine learning and generative technologies are actually being applied inside drugmakers’ labs. We’ll explore how new technologies are being used, their implications, how they intersect with regulatory and IP issues and how this fast-changing field is likely to evolve.

Kailash Swarna

Managing Director & Global Life Sciences Clinical Development Lead

Accenture

Artificial Intelligence is making impact in a grand way on biology in three aspects:

Speeding up target validation: now we can get through 300 molecules a day
Predicition like AlphaFold is doing; molecular simulations
Document submission especially with regulatory and IND submissions

Pamela Carroll

COO

Isomorphic Labs formerly of AlphaFold

We were first with Novartis at last year JPM and was one year old but parnering with them in that initial year was very important for sealing the deal.

They are looking now at neurologic diseases like ALS. She wondered whether ALS is actually multiple diseases and we need to stratify patients like we do in oncology trials. Their main competion is the whole tech world like Amazon, Google and other Machine Learning companies so being a tech player in the biotech world means you are not just competing with other biotechs but large tech companies as well.

Jorge Conde

General Partner

Andreessen Horowitz

Need is still great for drug discovery; early adopters show AI tools can be used in big pharma. There are lots of applications of AI in managing care; a lot of back office applications including patient triaging. He does not see big AI mergers with pharma companies – this will be mainly partnerships not M&A deals

Alicyn Campbell

Chief Scientific Officer

Evinova, a Healthtech Subsidiary of the AstraZeneca Group

There is a need to turn AI for real world example. For example AI tools were used in clinical trials to determine patient cohorts with pneumonitis. At Evinova they are determining how AI can hel[p show clinical benefit with respect to efficacy and safety

Joshua Boger at #JPM24 (Brian Benton Photography)

January 12, 2024 09:06 AM ESTUpdated 10:00 AM PeopleStartups

Vertex founder Joshua Boger on surviving downturns, ‘painful’ partnerships, and the importance of culture: #JPM24

Andrew Dunn

Biopharma Correspondent

Source: https://endpts.com/jpm24-vertex-founder-joshua-boger-on-surviving-downturns-painful-partnerships-and-the-importance-of-culture/

While the JP Morgan Healthcare Conference was full of voices of measured optimism, rooting for the market to bounce back in 2024, one longtime biotech leader warned against setting any firm expectations.

Instead of predicting when the downturn may end, Vertex Pharmaceuticals founder Joshua Boger said he advises biotech leaders to expect — and plan for — volatility. Speaking Tuesday on an Endpoints News panel alongside OrbiMed’s Carl Gordon and Appia Bio CEO JJ Kang, Boger shared lessons learned on surviving downturns, striking pharma deals, and the importance of keeping a company’s culture based on his two decades of founding and leading Vertex as CEO from 1989 to 2009. The 72-year-old is now serving as executive chairman of Alkeus Pharmaceuticals, a startup developing a rare disease drug.

“I never experienced a straight line up,” Boger said. “Everything had its cycles, and it was how you respond to the cycle, not by predicting when the end is going to be, but just by responding to the present situation.”

At Boger’s first appearance at the JP Morgan conference in 1991, he said the conference’s theme was the end of biotech financing. Just a few months later, Regeneron successfully went public, rapidly changing the outlook for the whole field.

“We had no idea we were ever going to take public money,” he said. “When Regeneron did their IPO, we went, ‘Whoa, there’s something happening here,’ and we pivoted quickly.”

Vertex went public later that year. Throughout his 20-year tenure, Boger said no pharma company ever made an acquisition offer for Vertex, which now commands a market value of $110 billion and recently won the first FDA approval for a CRISPR gene editing therapy.

“We had an uber corporate policy to always make ourselves more expensive than anyone would stomach,” Boger said.

However, Vertex did strike a range of partnerships with Big Pharmas, which Boger described as a painful but necessary part of running a biotech startup.

“It’s impossible for a partnership not to slow you down,” he said. “You can and should try as hard as you can not to do that, but just count on it. They’ll slow you down.”

Boger said startups should insist on being equal partners in pharma deals, at least making sure they have a seat at a partner’s development meetings.

“Realize they’re going to be painful, it’s going to be horrible, and you need to do it,” Boger said.

While Vertex suffered through layoffs, stock price plunges, and trial failures, Boger credited a focus on culture as key to its long-term success.

“It’s the most important ingredient for a successful company,” he said. “Technology is acquirable. Culture is not acquirable. There are 10 companies that will fail because of culture for every one that succeeds, and the successful companies in retrospect will almost always have special cultural aspects that kept them through those downtimes.”

JPM24 opens with ADCs the hottest ticket in San Francisco

By Annalee ArmstrongJan 8, 2024 6:30am

Source: https://www.fiercebiotech.com/biotech/jpm24-opens-adcs-hottest-ticket-san-francisco

The overall deal flow in biopharma tapered off in 2023 but the big companies sure know what they want (what they really, really want), according to a new report from J.P. Morgan.

And that’s antibody-drug conjugates, which drove a fourth-quarter spike in licensing deal proceeds and provided a glimmer of hope to an industry battered by outside forces and grim financing prospects.

J.P. Morgan’s annual 2023 Biopharma Licensing and Venture Report arrived on the eve of the firm’s famous conference, which is set to welcome thousands of attendees in San Francisco today—East Coast weather permitting.

2023 was tough, but clinical biotechs still had a lot of opportunities to wheel and deal, according to J.P. Morgan. While licensing deals, venture investments, M&A and IPOs were down overall in the fourth quarter, deal values stayed fairly high thanks to a flurry of late-stage tie ups.

Follow the Fierce team’s coverage of the 2024 J.P. Morgan Healthcare Conference here.

Biopharma licensing partnerships accounted for $63 billion in total value during the fourth quarter from 108 deals. Just one deal—Merck’s ADC partnership with Daiichi Sankyo—accounted for $22 billion of that. Another huge one was another ADC bet, with Bristol Myers Squibb signing on to work with SystImmune for a total value of $8.4 billion. If you exclude the Merck deal, the total value of these partnerships is still higher than the previous quarter, which ended with $32.1 billion.

The total number of licensing deals compares to 149 in the same quarter a year earlier, 195 for Q4 2021 and 223 for Q4 2022.

As for venture investments, the year closed out with $17 billion total across 250 rounds, thanks to $3.5 billion earned through 79 rounds in the last quarter. Aiolos Bio snagged the title of largest venture round of the quarter with $245 million, which also proved to be the largest series A, too.

There was just one IPO in all of the fourth quarter—Cargo Therapeutics making the plunge for $300 million—and 13 overall for the year. It’s a far cry from the heyday of 2021 and experts are still unsure what 2024 will hold. J.P. Morgan reported $2.5 billion raised from 12 completed biopharma IPOs for the year on Nasdaq and NYSE. Nine out of the 12 companies had clinical programs when they took the leap to the public markets. As of December 13, five of the companies were trading above their IPO price.

As for M&A, December saw a rush of Big Pharmas snapping up companies around Christmas. J.P. Morgan tallied the fourth quarter at $37.6 billion and $128.8 billion across 112 total acquisitions for all of 2023.

AbbVie was the top buyer of the quarter with the two largest acquisitions thanks to the $10 billion outlay for ImmunoGen and $8.7 billion buy of Cerevel Therapeutics.

All of this adds up to 270 total deals in the fourth quarter total, which is lower than the third quarter which exceeded 300.

J.P. Morgan sees some big potential for smaller biopharmas looking for licensing partners, as Big Pharmas have been handing out larger upfront payments for the deals they really want.

Cancer was once again the most in-demand therapeutic areas, reaching a new height of $86.1 billion in 2023. Followed by $21.1 billion for neurological disorders.

For More Articles on Real Time Conference Coverage in this Open Access Scientific Journal see:

Part One: The Process of Real Time Coverage using Social Media

Part Two: List of BioTech Conferences 2013 to Present

https://worldmedicalinnovation.org/

https://pharmaceuticalintelligence.com/2022/05/01/2022-world-medical-innovation-forum-gene-cell-therapy-may-2-4-2022-boston-in-person/

https://event.technologyreview.com/emtech-digital-2022/agenda-overview

Read Full Post »

Eight Subcellular Pathologies driving Chronic Metabolic Diseases – Methods for Mapping Bioelectronic Adjustable Measurements as potential new Therapeutics: Impact on Pharmaceuticals in Use

Posted in Acute Myocardial Infarction, Adaptive Immune Response to Biomaterials and Tissue Repair, Advanced Drug Manufacturing Technology, Amino acids, Apoptosis, Apoptosis, Artificial intelligence applications for cardiology, Artificial Intelligence in Medicine - Application for Diagnosis, Artificial Intelligence in Medicine - Applications in Therapeutics, Atherogenic Processes & Pathology, Autoimmune Inflammatory DIseases, Autologous Cell Therapy, Automated Cell Processing, Autophagosome, Autophagy, Autophagy-Modulating Proteins, “Antibody–enzyme conjugates”, Bio Instrumentation in Experimental Life Sciences Research, Biochemical pathways, Bioengineering & reverse engineering design, Biological Engineering, Biomarkers & Medical Diagnostics, Biomedical Measurement Science, Ca2+ triggered activation, Calcium Signaling, Calmodulin, Calmodulin Kinase and Contraction, Cancer - General, Cardiac muscle regeneration, Cardiovascular Pharmacogenomics, Cell Biology, Signaling & Cell Circuits, 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Systemic Inflammatory Response Related Disorders, Technology Transfer: Biotech and Pharmaceutical, therapeutics, Tissue Engineering and Regenerative Medicine, Tissue Microenvironment, Tolerance-inducing Autoimmune Disease Therapeutics, Transcriptomics, Transformative Technologies in Healthcare, Translational Research, tyrosine decarboxylases, Ubiquitin, Ubiquitinylation, Unfolded Protein Response (UPR), Universal Immune Cell Therapies (uICT), Variation in human protein-coding regions, Vascular Diseases, Water Transporters on August 19, 2023| Leave a Comment »

Eight Subcellular Pathologies driving Chronic Metabolic Diseases – Methods for Mapping Bioelectronic Adjustable Measurements as potential new Therapeutics: Impact on Pharmaceuticals in Use

Curators:

Cardiovascular Expertise – Dr. Justin D. Pearlman, MD, PhD, FACC
Pharmacology and Oncology Expertise – Dr. Stephen J. Williams, PhD
Principal Investigator, initiator of the project – Aviva Lev-Ari, PhD, RN

THE VOICE of Aviva Lev-Ari, PhD, RN

In this curation we wish to present two breaking through goals:

Goal 1:

Exposition of a new direction of research leading to a more comprehensive understanding of Metabolic Dysfunctional Diseases that are implicated in effecting the emergence of the two leading causes of human mortality in the World in 2023: (a) Cardiovascular Diseases, and (b) Cancer

Goal 2:

Development of Methods for Mapping Bioelectronic Adjustable Measurements as potential new Therapeutics for these eight subcellular causes of chronic metabolic diseases. It is anticipated that it will have a potential impact on the future of Pharmaceuticals to be used, a change from the present time current treatment protocols for Metabolic Dysfunctional Diseases.

According to Dr. Robert Lustig, M.D, an American pediatric endocrinologist. He is Professor emeritus of Pediatrics in the Division of Endocrinology at the University of California, San Francisco, where he specialized in neuroendocrinology and childhood obesity, there are eight subcellular pathologies that drive chronic metabolic diseases.

These eight subcellular pathologies can’t be measured at present time.

In this curation we will attempt to explore methods of measurement for each of these eight pathologies by harnessing the promise of the emerging field known as Bioelectronics.

Unmeasurable eight subcellular pathologies that drive chronic metabolic diseases

Glycation
Oxidative Stress
Mitochondrial dysfunction [beta-oxidation Ac CoA malonyl fatty acid]
Insulin resistance/sensitive [more important than BMI], known as a driver to cancer development
Membrane instability
Inflammation in the gut [mucin layer and tight junctions]
Epigenetics/Methylation
Autophagy [AMPKbeta1 improvement in health span]

Diseases that are not Diseases: no drugs for them, only diet modification will help

Image source

Robert Lustig, M.D. on the Subcellular Processes That Belie Chronic Disease

https://www.youtube.com/watch?v=Ee_uoxuQo0I

Exercise will not undo Unhealthy Diet

Image source

Robert Lustig, M.D. on the Subcellular Processes That Belie Chronic Disease

https://www.youtube.com/watch?v=Ee_uoxuQo0I

These eight Subcellular Pathologies driving Chronic Metabolic Diseases are becoming our focus for exploration of the promise of Bioelectronics for two pursuits:

Will Bioelectronics be deemed helpful in measurement of each of the eight pathological processes that underlie and that drive the chronic metabolic syndrome(s) and disease(s)?
IF we will be able to suggest new measurements to currently unmeasurable health harming processes THEN we will attempt to conceptualize new therapeutic targets and new modalities for therapeutics delivery – WE ARE HOPEFUL

In the Bioelecronics domain we are inspired by the work of the following three research sources:

Biological and Biomedical Electrical Engineering (B2E2) at Cornell University, School of Engineering https://www.engineering.cornell.edu/bio-electrical-engineering-0
Bioelectronics Group at MIT https://bioelectronics.mit.edu/
The work of Michael Levin @Tufts, The Levin Lab

Michael Levin is an American developmental and synthetic biologist at Tufts University, where he is the Vannevar Bush Distinguished Professor. Levin is a director of the Allen Discovery Center at Tufts University and Tufts Center for Regenerative and Developmental Biology. Wikipedia

Born: 1969 (age 54 years), Moscow, Russia

Education: Harvard University (1992–1996), Tufts University (1988–1992)

Affiliation: University of Cape Town

Research interests: Allergy, Immunology, Cross Cultural Communication

Awards: Cozzarelli prize (2020)

Doctoral advisor: Clifford Tabin

Fields: Developmental biology, synthetic biology

SOURCE

https://www.google.com/search?q=Michael+Levin+%40Tufts&oq=Michael+Levin+%40Tufts&aqs=chrome..69i57j69i58.894128314j0j15&sourceid=chrome&ie=UTF-8

Most recent 20 Publications by Michael Levin, PhD

SOURCE

https://facultyprofiles.tufts.edu/michael-levin-1/publications

SCHOLARLY ARTICLE

The nonlinearity of regulation in biological networks

1 Dec 2023npj Systems Biology and Applications9(1)

Co-authorsManicka S, Johnson K, Levin M…

VIEW PDF

10.1038/S41540-023-00273-W

SCHOLARLY ARTICLE

Toward an ethics of autopoietic technology: Stress, care, and intelligence

1 Sep 2023BioSystems231

Co-authorsWitkowski O, Doctor T, Solomonova E…

VIEW PDF

10.1016/J.BIOSYSTEMS.2023.104964

SCHOLARLY ARTICLE

Closing the Loop on Morphogenesis: A Mathematical Model of Morphogenesis by Closed-Loop Reaction-Diffusion

14 Aug 2023Frontiers in Cell and Developmental Biology11:1087650

Co-authorsGrodstein J, McMillen P, Levin M

VIEW PDF

10.3389/FCELL.2023.1087650

SCHOLARLY ARTICLE

Correcting instructive electric potential patterns in multicellular systems: External actions and endogenous processes.

30 Jul 2023Biochim Biophys Acta Gen Subj1867(10):130440

Co-authorsCervera J, Levin M, Mafe S

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10.1016/J.BBAGEN.2023.130440

SCHOLARLY ARTICLE

Regulative development as a model for origin of life and artificial life studies

1 Jul 2023BioSystems229

Co-authorsFields C, Levin M

10.1016/J.BIOSYSTEMS.2023.104927

SCHOLARLY ARTICLE

The Yin and Yang of Breast Cancer: Ion Channels as Determinants of Left–Right Functional Differences

1 Jul 2023International Journal of Molecular Sciences24(13)

Co-authorsMasuelli S, Real S, McMillen P…

VIEW PDF

10.3390/IJMS241311121

SCHOLARLY ARTICLE

Bioelectricidad en agregados multicelulares de células no excitables- modelos biofísicos

Jun 2023Revista Española de Física32(2)

Co-authorsCervera J, Levin M, Mafé S

SCHOLARLY ARTICLE

Bioelectricity: A Multifaceted Discipline, and a Multifaceted Issue!

1 Jun 2023Bioelectricity5(2):75

Co-authorsDjamgoz MBA, Levin M

10.1089/BIOE.2023.0022.EDITORIAL

SCHOLARLY ARTICLE

Control Flow in Active Inference Systems – Part I: Classical and Quantum Formulations of Active Inference

1 Jun 2023IEEE Transactions on Molecular, Biological, and Multi-Scale Communications9(2):235-245

Co-authorsFields C, Fabrocini F, Friston K…

10.1109/TMBMC.2023.3272150

SCHOLARLY ARTICLE

Control Flow in Active Inference Systems – Part II: Tensor Networks as General Models of Control Flow

1 Jun 2023IEEE Transactions on Molecular, Biological, and Multi-Scale Communications9(2):246-256

Co-authorsFields C, Fabrocini F, Friston K…

10.1109/TMBMC.2023.3272158

SCHOLARLY ARTICLE

Darwin’s agential materials: evolutionary implications of multiscale competency in developmental biology

1 Jun 2023Cellular and Molecular Life Sciences80(6)

Co-authorsLevin M

VIEW PDF

10.1007/S00018-023-04790-Z

SCHOLARLY ARTICLE

Morphoceuticals: Perspectives for discovery of drugs targeting anatomical control mechanisms in regenerative medicine, cancer and aging

1 Jun 2023Drug Discovery Today28(6)

Co-authorsPio-Lopez L, Levin M

VIEW PDF

10.1016/J.DRUDIS.2023.103585

SCHOLARLY ARTICLE

Morphoceuticals: Perspectives for discovery of drugs targeting anatomical control mechanisms in regenerative medicine, cancer and aging

Jun 2023DRUG DISCOVERY TODAY28(6):1-13

Co-authorsPio-Lopez L, Levin M

VIEW MORE INFO

10.1016/J.DRUDIS.2023.103585THIS

SCHOLARLY ARTICLE

Cellular signaling pathways as plastic, proto-cognitive systems: Implications for biomedicine

12 May 2023Patterns4(5)

Co-authorsMathews J, Chang A, Devlin L…

VIEW PDF

10.1016/J.PATTER.2023.100737

SCHOLARLY ARTICLE

Making and breaking symmetries in mind and life

14 Apr 2023Interface Focus13(3)

Co-authorsSafron A, Sakthivadivel DAR, Sheikhbahaee Z…

VIEW PDF

10.1098/RSFS.2023.0015

SCHOLARLY ARTICLE

The scaling of goals from cellular to anatomical homeostasis: an evolutionary simulation, experiment and analysis

14 Apr 2023Interface Focus13(3)

Co-authorsPio-Lopez L, Bischof J, LaPalme JV…

VIEW PDF

10.1098/RSFS.2022.0072

SCHOLARLY ARTICLE

The collective intelligence of evolution and development

Apr 2023Collective Intelligence2(2):263391372311683SAGE Publications

Co-authorsWatson R, Levin M

VIEW PDF

10.1177/26339137231168355

SCHOLARLY ARTICLE

Bioelectricity of non-excitable cells and multicellular pattern memories: Biophysical modeling

13 Mar 2023Physics Reports1004:1-31

Co-authorsCervera J, Levin M, Mafe S

10.1016/J.PHYSREP.2022.12.003

SCHOLARLY ARTICLE

There’s Plenty of Room Right Here: Biological Systems as Evolved, Overloaded, Multi-Scale Machines

1 Mar 2023Biomimetics8(1)

Co-authorsBongard J, Levin M

VIEW PDF

10.3390/BIOMIMETICS8010110

SCHOLARLY ARTICLE

Transplantation of fragments from different planaria: A bioelectrical model for head regeneration

7 Feb 2023Journal of Theoretical Biology558

Co-authorsCervera J, Manzanares JA, Levin M…

VIEW PDF

10.1016/J.JTBI.2022.111356

SCHOLARLY ARTICLE

Bioelectric networks: the cognitive glue enabling evolutionary scaling from physiology to mind

1 Jan 2023Animal Cognition

Co-authorsLevin M

VIEW PDF

10.1007/S10071-023-01780-3

SCHOLARLY ARTICLE

Biological Robots: Perspectives on an Emerging Interdisciplinary Field

1 Jan 2023Soft Robotics

Co-authorsBlackiston D, Kriegman S, Bongard J…

10.1089/SORO.2022.0142

SCHOLARLY ARTICLE

Cellular Competency during Development Alters Evolutionary Dynamics in an Artificial Embryogeny Model

1 Jan 2023Entropy25(1)

Co-authorsShreesha L, Levin M

VIEW PDF

10.3390/E25010131

SCHOLARLY ARTICLE

Endless forms most beautiful 2.0: teleonomy and the bioengineering of chimaeric and synthetic organisms (July, blac073, 2022)

1 Jan 2023BIOLOGICAL JOURNAL OF THE LINNEAN SOCIETY138(1):141

Co-authorsClawson WP, Levin M

VIEW PDF

VIEW MORE INFO

10.1093/BIOLINNEAN/BLAC116

SCHOLARLY ARTICLE

Future medicine: from molecular pathways to the collective intelligence of the body

1 Jan 2023Trends in Molecular Medicine

Co-authorsLagasse E, Levin M

10.1016/J.MOLMED.2023.06.007

THE VOICE of Dr. Justin D. Pearlman, MD, PhD, FACC

PENDING

THE VOICE of Stephen J. Williams, PhD

Ten TakeAway Points of Dr. Lustig’s talk on role of diet on the incidence of Type II Diabetes

25% of US children have fatty liver
Type II diabetes can be manifested from fatty live with 151 million people worldwide affected moving up to 568 million in 7 years
A common myth is diabetes due to overweight condition driving the metabolic disease
There is a trend of ‘lean’ diabetes or diabetes in lean people, therefore body mass index not a reliable biomarker for risk for diabetes
Thirty percent of ‘obese’ people just have high subcutaneous fat. the visceral fat is more problematic
there are people who are ‘fat’ but insulin sensitive while have growth hormone receptor defects. Points to other issues related to metabolic state other than insulin and potentially the insulin like growth factors
At any BMI some patients are insulin sensitive while some resistant
Visceral fat accumulation may be more due to chronic stress condition
Fructose can decrease liver mitochondrial function
A methionine and choline deficient diet can lead to rapid NASH development

Read Full Post »

Artificial Intelligence (AI) Used to Successfully Determine Most Likely Repurposed Antibiotic Against Deadly Superbug Acinetobacter baumanni

Posted in Antibiotic resistance, Artificial Intelligence in Health Care - Tools & Innovations, Artificial Intelligence in Medicine - Applications in Therapeutics, Drug Development Process, drug repurposing, Evidence-based decision-making, Infectious Disease & New Antibiotic Targets, Inflammatory Pathophysiology, Pharmaceutical Drug Discovery, Transformative Technologies in Healthcare, tagged Acinetobacter baumanni, AI, Antibiotic resistance, Artificial intelligence, Artificial Intelligence ( AI), artificial intelligence in drug design, drug screens, in silico, rational drug design, superbug, WHO on May 26, 2023| Leave a Comment »

Artificial Intelligence (AI) Used to Successfully Determine Most Likely Repurposed Antibiotic Against Deadly Superbug Acinetobacter baumanni

Reporter: Stephen J. Williams, Ph.D.

The World Health Organization has identified 3 superbugs, or infective micororganisms displaying resistance to common antibiotics and multidrug resistance, as threats to humanity:

Three bacteria were listed as critical:

Acinetobacter baumannii bacteria that are resistant to important antibiotics called carbapenems. Acinetobacter baumannii are highly-drug resistant bacteria that can cause a range of infections for hospitalized patients, including pneumonia, wound, or blood infections.
Pseudomonas aeruginosa, which are resistant to carbapenems. Pseudomonas aeruginosa can cause skin rashes and ear infectious in healthy people but also severe blood infections and pneumonia when contracted by sick people in the hospital.
Enterobacteriaceae — a family of bacteria that live in the human gut — that are resistant to both carbepenems and another class of antibiotics, cephalosporins.

It has been designated critical need for development of antibiotics to these pathogens. Now researchers at Mcmaster University and others in the US had used artificial intelligence (AI) to screen libraries of over 7,000 chemicals to find a drug that could be repurposed to kill off the pathogen.

Liu et. Al. (1) published their results of an AI screen to narrow down potential chemicals that could work against Acinetobacter baumanii in Nature Chemical Biology recently.

Abstract

Acinetobacter baumannii is a nosocomial Gram-negative pathogen that often displays multidrug resistance. Discovering new antibiotics against A. baumannii has proven challenging through conventional screening approaches. Fortunately, machine learning methods allow for the rapid exploration of chemical space, increasing the probability of discovering new antibacterial molecules. Here we screened ~7,500 molecules for those that inhibited the growth of A. baumannii in vitro. We trained a neural network with this growth inhibition dataset and performed in silico predictions for structurally new molecules with activity against A. baumannii. Through this approach, we discovered abaucin, an antibacterial compound with narrow-spectrum activity against A. baumannii. Further investigations revealed that abaucin perturbs lipoprotein trafficking through a mechanism involving LolE. Moreover, abaucin could control an A. baumannii infection in a mouse wound model. This work highlights the utility of machine learning in antibiotic discovery and describes a promising lead with targeted activity against a challenging Gram-negative pathogen.

Schematic workflow for incorporation of AI for antibiotic drug discovery for A. baumannii from 1. Liu, G., Catacutan, D.B., Rathod, K. et al. Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumannii. Nat Chem Biol (2023). https://doi.org/10.1038/s41589-023-01349-8

Figure source: https://www.nature.com/articles/s41589-023-01349-8

Article Source: https://www.nature.com/articles/s41589-023-01349-8

Liu, G., Catacutan, D.B., Rathod, K. et al.Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumannii. Nat Chem Biol (2023). https://doi.org/10.1038/s41589-023-01349-8

For reference to WHO and lists of most pathogenic superbugs see https://www.scientificamerican.com/article/who-releases-list-of-worlds-most-dangerous-superbugs/

The finding was first reported by the BBC.

Source: https://www.bbc.com/news/health-65709834

By James Gallagher

Health and science correspondent

Scientists have used artificial intelligence (AI) to discover a new antibiotic that can kill a deadly species of superbug.

The AI helped narrow down thousands of potential chemicals to a handful that could be tested in the laboratory.

The result was a potent, experimental antibiotic called abaucin, which will need further tests before being used.

The researchers in Canada and the US say AI has the power to massively accelerate the discovery of new drugs.

It is the latest example of how the tools of artificial intelligence can be a revolutionary force in science and medicine.

AI breakthrough could spark medical revolution

Stopping the superbugs

Antibiotics kill bacteria. However, there has been a lack of new drugs for decades and bacteria are becoming harder to treat, as they evolve resistance to the ones we have.

More than a million people a year are estimated to die from infections that resist treatment with antibiotics.The researchers focused on one of the most problematic species of bacteria – Acinetobacter baumannii, which can infect wounds and cause pneumonia.

You may not have heard of it, but it is one of the three superbugs the World Health Organization has identified as a “critical” threat.

It is often able to shrug off multiple antibiotics and is a problem in hospitals and care homes, where it can survive on surfaces and medical equipment.

Dr Jonathan Stokes, from McMaster University, describes the bug as “public enemy number one” as it’s “really common” to find cases where it is “resistant to nearly every antibiotic”.

Artificial intelligence

To find a new antibiotic, the researchers first had to train the AI. They took thousands of drugs where the precise chemical structure was known, and manually tested them on Acinetobacter baumannii to see which could slow it down or kill it.

This information was fed into the AI so it could learn the chemical features of drugs that could attack the problematic bacterium.

The AI was then unleashed on a list of 6,680 compounds whose effectiveness was unknown. The results – published in Nature Chemical Biology – showed it took the AI an hour and a half to produce a shortlist.

The researchers tested 240 in the laboratory, and found nine potential antibiotics. One of them was the incredibly potent antibiotic abaucin.

Laboratory experiments showed it could treat infected wounds in mice and was able to kill A. baumannii samples from patients.

However, Dr Stokes told me: “This is when the work starts.”

The next step is to perfect the drug in the laboratory and then perform clinical trials. He expects the first AI antibiotics could take until 2030 until they are available to be prescribed.

Curiously, this experimental antibiotic had no effect on other species of bacteria, and works only on A. baumannii.

Many antibiotics kill bacteria indiscriminately. The researchers believe the precision of abaucin will make it harder for drug-resistance to emerge, and could lead to fewer side-effects.

In principle, the AI could screen tens of millions of potential compounds – something that would be impractical to do manually.

“AI enhances the rate, and in a perfect world decreases the cost, with which we can discover these new classes of antibiotic that we desperately need,” Dr Stokes told me.

The researchers tested the principles of AI-aided antibiotic discovery in E. coli in 2020, but have now used that knowledge to focus on the big nasties. They plan to look at Staphylococcus aureus and Pseudomonas aeruginosa next.

“This finding further supports the premise that AI can significantly accelerate and expand our search for novel antibiotics,” said Prof James Collins, from the Massachusetts Institute of Technology.

He added: “I’m excited that this work shows that we can use AI to help combat problematic pathogens such as A. baumannii.”

Prof Dame Sally Davies, the former chief medical officer for England and government envoy on anti-microbial resistance, told Radio 4’s The World Tonight: “We’re onto a winner.”

She said the idea of using AI was “a big game-changer, I’m thrilled to see the work he (Dr Stokes) is doing, it will save lives”.

Series D: e-Books on BioMedicine – Metabolomics, Immunology, Infectious Diseases, Reproductive Genomic Endocrinology

(3 book series: Volume 1, 2&3, 4)

https://www.amazon.com/gp/product/B08VVWTNR4?ref_=dbs_p_pwh_rwt_anx_b_lnk&storeType=ebooks

The Immune System, Stress Signaling, Infectious Diseases and Therapeutic Implications:

Series D, VOLUME 2

Infectious Diseases and Therapeutics

and

Series D, VOLUME 3

The Immune System and Therapeutics

(Series D: BioMedicine & Immunology) Kindle Edition.

On Amazon.com since September 4, 2017

(English Edition) Kindle Edition – as one Book

https://www.amazon.com/dp/B075CXHY1B $115

Bacterial multidrug resistance problem solved by a broad-spectrum synthetic antibiotic

The Journey of Antibiotic Discovery

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Artificial Intelligence: Genomics & Cancer

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Companies Actively Engaging Artificial Intellegence for Drug Design: 2026

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In Memoriam: In Remembrance of Cancer Researchers who passed in 2026

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In Memoriam: Professor Yitzhak Apeloig, President and Distinguised Professor of the Technion

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Major Recognitions

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The Health Care Dossier on Clarivate PLC: How Cortellis Is Changing the Life Sciences Industry

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Live Notes from JP Morgan Healthcare Conference Virtual Endpoints Preview: January 8-9 2024

Endpoints at #JPM24 | Primed to unlock biopharma’s next dealmaking wave

Endpoints at JP Morgan Healthcare Conference

January 8-9 | San Francisco, CA80 Mission St, San Francisco, CA

8 JAN

Welcome remarks

8:05 AM – 8:25 AM PST

Pfizer vet Mikael Dolsten has some thoughts on Big Pharma R&D

8:25 AM – 9:05 AM PST

What pharma wants: The industry’s dealmakers look ahead at 2024

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Bioorthogonal and Click Chemistry
Curated by Prof. Carolyn R. Bertozzi, 2022 winner of the Nobel Prize in Chemistry