Healthcare analytics, AI solutions for biological big data, providing an AI platform for the biotech, life sciences, medical and pharmaceutical industries, as well as for related technological approaches, i.e., curation and text analysis with machine learning and other activities related to AI applications to these industries.
AI Initiatives in Big Pharma @Grok prompt & Proprietary Training Data and Inference by LPBI Group’s IP Asset Class I: PharmaceuticalIntelligence.com Journal, 2.5MM Views, 6,250 Scientific articles and Live Ontology
Curator: Aviva Lev-Ari, PhD, RN
We had researched the topic of AI Initiatives in Big Pharma in the following article:
Authentic Relevance of LPBI Group’s Portfolio of IP as Proprietary Training Data Corpus for AI Initiatives at Big Pharma
We are publishing a Series of Five articles that demonstrate the Authentic Relevance of Five of the Ten Digital IP Asset Classes in LPBI Group’s Portfolio of IP for AI Initiatives at Big Pharma.
For the Ten IP Asset Classes in LPBI Group’s Portfolio, See
This Corpus comprises of Live Repository of Domain Knowledge Expert-Written Clinical Interpretationsof Scientific Findings codified in the following five Digital IP ASSETS CLASSES:
• IP Asset Class V: 7,500 Biological Images in our Digital Art Media Gallery, as prior art. The Media Gallery resides in WordPress.com Cloud of LPBI Group’s Web site
BECAUSE THE ABOVE ASSETS ARE DIGITAL ASSETS they are ready for use as Proprietary TRAINING DATA and INFERENCE for AI Foundation Models in HealthCare.
Expert‑curated healthcare corpus mapped to a living ontology, already packaged for immediate model ingestion and suitable for safe pre-training, evals, fine‑tuning and inference. If healthcare domain data is on your roadmap, this is a rare, defensible asset.
The article TITLE of each of the five Digital IP Asset Classes matched to AI Initiatives in Big Pharma, an article per IP Asset Class are:
AI Initiatives in Big Pharma @Grok prompt & Proprietary Training Data and Inference by LPBI Group’s IP Asset Class I: PharmaceuticalIntelligence.comJournal, 2.5MM Views, 6,250 Scientific articles and Live Ontology
The article concludes that LPBI’s assets are primed for Big Pharma adoption, enabling AI to evolve from tools to “knowledge amplifiers.” It hints at licensing opportunities without explicit calls to action. Technical Implications: Improves AI precision in complex pharma tasks like rare disease modeling. Business Implications: Accelerates ROI on AI investments (e.g., Pfizer’s $500M push) while ensuring defensible IP. Unique Insight: In an era of open-source AI, curated expert data like this is a “rare moat” for competitive edge.Overall, the piece is insightful for AI-pharma intersections, blending real-world examples with promotional asset positioning. It’s dense with links to LPBI resources (e.g., e-books, podcasts) for deeper dives.
AI Initiatives in Big Pharma @Grok prompt & Proprietary Training Data and Inference by LPBI Group’s IP Asset Class II: 48 e-Books: English Edition & Spanish Edition. 152,000 pages downloaded under pay-per-view
AI Initiatives in Big Pharma @Grok prompt & Proprietary Training Data and Inference by LPBI Group’s IP Asset Class III: 100 e-Proceedings and 50 Tweet Collections of Top Biotech and Medical Global Conferences, 2013-2025
AI Initiatives in Big Pharma @Grok prompt & Proprietary Training Data and Inference by LPBI Group’s IP Asset Class V: 7,500 Biological Images in LPBI Group’s Digital Art Media Gallery, as Prior Art
AI Initiatives in Big Pharma @Grok prompt & Proprietary Training Data and Inference by LPBI Group’s IP Asset Class X: +300 Audio Podcasts Library: Interviews with Scientific Leaders
In the series of five articles, as above, we are presenting the key AI Initiatives in Big Pharma as it was created by our prompt to @Grok on 11/18/2025:
Generative AI tools that save scientists up to 16,000 hours annually in literature searches and data analysis.
Drug Discovery and Development Acceleration Pfizer uses AI, supercomputing, and ML to streamline R&D timelines
Clinical Trials and Regulatory Efficiency AI:
-Predictive Regulatory Tools
-Decentralize Trials
-inventory management
Disease Detection and Diagnostics:
– ATTR-CM Initiative
– Rare diseases
Generative AI and Operational Tools:
– Charlie Platform
– Scientific Data Cloud AWS powered ML on centralized data
– Amazon’s SageMaker /Bedrock for Manufacturing efficiency
– Global Health Grants:
Pfizer Foundation’s AI Learning Lab for equitable access to care and tools for community care
Partnerships and Education
– Collaborations: IMI Big Picture for 3M – sample disease database
– AI in Pharma AIPM Symposium: Drug discovery and Precision Medicine
– Webinars of AI for biomedical data integration
– Webinar on AI in Manufacturing
Strategic Focus:
– $500M R&D reinvestment by 2026 targets AI for Productivity
– Part of $7.7B cost savings
– Ethical AI, diverse DBs
– Global biotech advances: China’s AI in CRISPR
AI Initiatives in Big Pharma @Grok prompt & Proprietary Training Data and Inference by LPBI Group’s IP Asset Class I: PharmaceuticalIntelligence.comJournal, 2.5MM Views, 6,250 Scientific articles and Live Ontology
Generative AI tools that save scientists up to 16,000 hours annually in literature searches and data analysis.
Generative AI tools searching LPBI’s Proprietary data in addition to Public Domain data sources
Journal ONTOLOGY used to optimize context classification selected for search
Drug Discovery and Development Acceleration Pfizer uses AI, supercomputing, and ML to streamline R&D timelines
– Run prompts by category of research on three dyads
– Run ML across categories of research for these three dyads
-Gene-disease
-Disease-drug
-Gene-drug
Clinical Trials and Regulatory Efficiency AI:
-Predictive Regulatory Tools
-Decentralize Trials
-inventory management
content creation across the drug lifecycle, from lab data to regulatory docs
Disease Detection and Diagnostics:
– ATTR-CM Initiative
– Rare diseases
Heart Failure Diagnosis by Early detection of transthyretin amyloid cardiomyopathy
– Journal published ~30 curations by Dr. Larry on this subject
– Run NLP on this Corpus
Rare disease:
Journal published 560 articles on Rare diseases
– Run ML on this Corpus
Generative AI and Operational Tools:
– Charlie Platform
– Scientific Data Cloud AWS powered ML on centralized data
– Amazon’s SageMaker /Bedrock for Manufacturing efficiency
– Global Health Grants:
Pfizer Foundation’s AI Learning Lab for equitable access to care and tools for community care
Content generated in the Journal can become “generated compliant content” if run by the
Charlie Platform.
– For REUSE content in context
Entire Corpus of 9 Giga bytes can be ingested to Pfizer Foundation’s AI Learning Lab
– Rum prompts against it
– Journal’s Content to be used for Internal staff expertise development
– Journal’s Content for Leadership development
Partnerships and Education
– Collaborations: IMI Big Picture for 3M – sample disease database
– AI in Pharma AIPM Symposium: Drug discovery and Precision Medicine
– Webinars of AI for biomedical data integration
– Webinar on AI in Manufacturing
The Journal had published 547 articles in Precision Medicine
The Journal had published 1,114 articles in Drug Discovery
The Journal had published 701 articles in Drug Delivery
The Journal had published 3,615 articles on subject matter “Disease”
The Journal had published 738 articles on Biomedical topic
The Journal had published 425 articles in Artificial Intelligence
Strategic Focus:
– $500M R&D reinvestment by 2026 targets AI for Productivity
– Part of $7.7B cost savings
– Ethical AI, diverse DBs
– Global biotech advances: China’s AI in CRISPR
The Journal had published 432 articles on CRISPR
Productivity derived from Journal’s content:
– As a result of the fact that ~70% of the Journal’s articles are curations written by Domain Knowledge Experts subjectively expressing theirs clinical interpretations of basic and primary research – the productivity of the knowledge workers at any Big Pharma would increase vastly.
– If Grok and Claude would run on LPBI Group’s Corpus – A scientific revolution will emerge
– It is not combinatorics applied to molecules with 98% futile results!!! – it is the IQ of Gifted HUMANS, of domain knowledge experts generating content using individual CREATIVITY no Quantum or Super Intelligence which is not in existence YET.
– Foundation Models in Healthcare depends on the OUTPUT of the human creative mind. AI takes keyword (classic search) and concepts (semantic search) and run frequency of occurrence, predict the nest word, one after the next one.
AI Initiatives in Big Pharma @Grok prompt & Proprietary Training Data and Inference by LPBI Group’s IP Asset Class I: PharmaceuticalIntelligence.com Journal, 2.5MM Views, 6,250 Scientific articles and Live Ontology
Overview: This article, part of a series on LPBI Group’s digital IP assets, explores how the group’s proprietary content—particularly IP Asset Class I (PharmaceuticalIntelligence.com Journal)—can serve as high-value training data for AI models in the pharmaceutical industry. It uses a Grok AI prompt to detail Pfizer’s AI efforts and maps them to the journal’s expert-curated content, emphasizing the shift from generic AI to domain-specific, human-enhanced models for drug discovery, clinical trials, and beyond. The piece positions LPBI’s assets as “defensible” resources for pre-training, fine-tuning, and inference in healthcare foundation models, contrasting them with public-domain data.Main Thesis and Key Arguments
Core Idea: Traditional AI in pharma often relies on combinatorial predictions from vast but uncurated data, leading to inefficiencies. LPBI’s corpus, with 70% expert interpretations (curations and opinions), injects human creativity and clinical insights, enabling more productive AI outputs. This could spark a “scientific revolution” by powering models like Grok or Claude, boosting knowledge worker efficiency and reducing R&D costs.
Value Proposition: The journal’s 6,250 articles (30% scientific reports, 70% expert analyses) form a 9GB digital repository with a live ontology for semantic mapping. Valued at ~$50MM (based on $30/article download equivalent), it supports AI applications across gene-disease-drug dyads, NLP for rare diseases, and regulatory content generation.
Broader Context: Part of a five-article series showcasing LPBI’s ten IP classes (e.g., e-books, podcasts, images) as ready-to-ingest assets for Big Pharma’s AI strategies.
AI Initiatives in Big Pharma (Focus on Pfizer)The article leverages a November 18, 2025, Grok prompt (“What are PFIZER’s AI INITIATIVES?”) to outline Pfizer’s efforts, sourced from real-time AI generation. Key highlights include:
Initiative Category
Description
Key Metrics/Examples
Generative AI Tools
Automates literature searches, data analysis, and report writing.
Saves 16,000 hours annually; integrates with Pfizer’s Charlie Platform for compliant content.
Drug Discovery Acceleration
Uses ML, supercomputing, and AI to identify targets and optimize molecules.
Part of $500M R&D reinvestment by 2026; contributes to $7.7B in cost savings.
Clinical Trials & Regulatory Efficiency
Predictive modeling for patient recruitment, decentralized trials, and inventory management.
Partnerships like IMI Big Picture; AI for faster FDA submissions.
Disease Detection & Diagnostics
Focus on rare diseases (e.g., ATTR-CM via transthyretin amyloid cardiomyopathy initiative).
NLP and ML for early detection; equitable care via AI Learning Lab.
Operational & Strategic Tools
Scientific Data Cloud with AWS ML/SageMaker; ethical AI with diverse datasets.
Webinars for education; global expansions (e.g., China’s CRISPR AI).
No other pharma companies are deeply covered, but the framework implies applicability to peers like those in prior LPBI articles.Mapping to LPBI’s Proprietary DataA central table links Pfizer’s initiatives to the journal’s content, demonstrating ingestion potential:
Pfizer AI Initiative
LPBI Journal Alignment
Productivity Boost
Generative AI for searches
Ontology-optimized semantic searches across 6,250 articles.
Enhances accuracy in 425 AI-related articles.
Drug Discovery ML
1,114 articles on discovery; dyad mapping (e.g., gene-drug).
Supports ML training on precision medicine (547 articles).
Clinical Trials Efficiency
3,615 “Disease” articles; NLP for trial simulations.
Reduces time via expert-curated protocols.
Rare Disease Diagnostics
Content on ATTR-CM and similar (e.g., 432 CRISPR articles).
Fine-tuning for targeted inference.
Operational Platforms
Full corpus ingestion for Charlie-like labs.
2.5MM views indicate high-engagement data.
This shows how the live ontology enables context classification, outperforming frequency-based AI.Methodologies and Frameworks
AI Training Pipeline: Ingest corpus for pre-training (broad knowledge), fine-tuning (domain-specific), and inference (real-time queries). Use prompts by research categories; apply ML across dyads; generate lifecycle content.
Productivity Model: 70% expert curations amplify human-AI synergy, avoiding “futile” combinatorial results. Ethical focus: Diverse, compliant data for global equity.
Insights: Quotes Dr. Stephen J. Williams: Foundation models thrive on “gifted humans’ creativity,” not just scale. Predicts vast productivity gains (e.g., beyond Pfizer’s 16,000 hours saved).
Conclusions and Implications
The article concludes that LPBI’s assets are primed for Big Pharma adoption, enabling AI to evolve from tools to “knowledge amplifiers.” It hints at licensing opportunities without explicit calls to action. Technical Implications: Improves AI precision in complex pharma tasks like rare disease modeling. Business Implications: Accelerates ROI on AI investments (e.g., Pfizer’s $500M push) while ensuring defensible IP. Unique Insight: In an era of open-source AI, curated expert data like this is a “rare moat” for competitive edge.Overall, the piece is insightful for AI-pharma intersections, blending real-world examples with promotional asset positioning. It’s dense with links to LPBI resources (e.g., e-books, podcasts) for deeper dives.
If you’d like a full review (e.g., strengths/weaknesses, comparisons to industry trends), or analysis of specific sections, let me know!
Authentic Relevance of LPBI Group’s Portfolio of IP as Proprietary Training Data Corpus for AI Initiatives at Big Pharma
Curators: Aviva Lev-Ari, PhD, RN and Stephen J. Williams, PhD
Updated on 11/29/2025
The Voice of Stephen J. Williams, PhD
Since the formation of the LPBI group in 2012, we have set as a goal to digitally curate and ontologize the biomedical literature. These aspects of our mission, although not understandable to most at that time of our inception, was at the forefront of many academic efforts which became the springboard and initial conceptualization of today’s large language models. In select universities like University of Illinois, University of Delaware, and the Technion, academics and researchers at Microsoft and Google, were laying the framework for a semantic web, or Web 3.0. Another company at the forefront of this idea was a company springing forth from Mathematica, Wolfram Research. This idea for a semantic web would entail the processes of curation, digitization, and ontology creation. Their belief, such as ours, was a precursor and much needed key to the puzzle of moving language from machine learning algorithms to the generative transformers used by artificial intelligence.
However as most efforts in this genre focused on general language, our efforts at LPBI Group were focused on the biomedical field, as we understood, from 2012, that the biomedical literature was unique, and so a unique strategy had to be developed to semantically understand biomedical text, even though at the time of 2012 GPTs were not even a concept. However the potential for doing biomedical text analysis was there, and LPBI Group responded by developing a methodology of scientific curation which involved a multimodal strategy to curate, digitize, and ontologize biomedical findings and text.
It was about at the time of 2012 that other groups, mainly focused of drug development applications (for example at University of Indiana) recognized that new computational power of machine learning algorthims could be useful in analyzing complex biological questions. Please see our Synthetic Biology in Drug Discovery section of our Journal for more information on this. For instance, an early adopter of this strategy, a company called Data2Discovery, one of the earliest AI for drug discovery startups, stated
We are able to improve drug discovery now as well as demonstrating new fast-cycle AI-driven processes that will have a revolutionary impact on drug discovery if fully implemented. We have had some dramatic successes, but we are just starting to discover the impact that data, knowledge graphs, AI and machine learning can together have on drug discovery.
We need all the expertise of academics, consortia, AI companies and pharma to make his happen, and it’s going to require some serious investment, and a big change of thinking. But the opportunity to get drug discovery out of the death spiral and framed for data-driven success is too important to pass up.
However the LPBI Group was cognizant of these changes occuring and pivoted to the developing natural language processing arena as well as ideas for the developing Blockchain technology. This was more of a natural progression for the LPBI Group than a pivot (please read here).
This would be our Vision 2.0, to make biomedical text amenable for Natural Language Processing. We utilized a few strategies in this regard, partnering with a company who was developing NLP for biomedical text analysis, and developing in house machine learning and NLP methods using the Wolfram language environment. Our focus on structuring biomedical text (versus the highly structured genomics and omics data found in many omics related databanks) was prescient for the time. As NLP and machine learning efforts realized, biomedical text needs to have a structure much like genes, proteins and other molecular databases had been organized. Therefore it was realized that structured data was imperative for efficient NLP analysis, a crux for the new GPT which was being developed (and in this mind still is a crux for current GPT and LLM models when it comes to biomedical text analysis).
Our strategy using our scientific curation methodology (as described below in links form our founder Dr. Aviva Lev-Ari, was proven to be highly efficient and amenable to NLP analysis, as a pilot with an NLP company noticed. Most of the data they were using was unstructured and their first step involved annotation and structuring the text, as we had already performed for years. This was critical as our text was able to pull out more concepts, relationships, in a faster time than NLP on sources such as PubMed available text. We had also developed our own in house algorithms for NLP on our material, which is shown in some of our book offerrings and individual articles.
However with the advent of GPT it was thought all this was unnecessary. However this idea that our strategy was outdated or irrelevent in the era of GPT was wholly incorrect to the advocates of a sole GPT strategy to analyze biomedical text and data. It is now understood that structure is needed as some of biomedical-centric GPT projects would find out, such as BioGPT. We have many articles which attest to the lack of accuracy and efficiency of these GPT architectures (seen here). These include failure rates in many areas of healthcare and biomedicine by sole reliance on GPT,
It was realized by many in the biomedical arena, especially those involved in NLP efforts, that there was much value in the semantic web 3.0 idea, and this was readily picked up by those spearheading effort to incorporate knowledge graphs with the new generative AI or GPT technology. We have shown a clear example our scientific methodology of curation with ontology has better inference when combined with knowledge graphs and GPT than reliance on GPT alone
This update was performed by the following methods:
A. GPT 5 Text analysis and Reasoning
B. Insertion of Knowledge Graph on topic Curation of Genomic Analysis from Non Small Cell Lung Cancer Studies from Nodus Labs using InfraNodus software
C. Domain Knowledge Expert evaluation of the Update outcomes
This article has the following Structure:
Part A: Introduction to LLM, Knowledge Graph software InfraNodus, ChatGPT5 and Background Information on curated material for Test Case
Part B: InfraNodus Analysis of manual curation and Knowledge Graph Creation
Part C: Chat GPT 5 Analysis of Manually Curated Material
Part D: Curation entitled Multiple Lung Cancer Genomic Projects Suggest New Targets, Research Directions for Non-Small Cell Lung Cancer originally published on 09/05/2014
Results of Article Update with GPT 5
1. GPT5 alone was not able to understand the goal of the article, namely to determine knowledge gaps in a particular research area involving 5 genomic studies on lung cancer patients
2. GPT5 alone was not able to group concepts or comonalities between biological pathways unless supplied with a manually curated list of KEGG pathways from a list of mutated genes. However this precluded any effect that fusion proteins had on the analysis and so GPT5 would only concentrate on mutated genes commonly found in literature
3. GPT was not able to access some of the open Access databases like NCBI Gene Ontology database
Results of Article Update with KnowledgeGraph presentation to GPT 5
4. As the Knowledge Graph understood the importance of fusion proteins and transversions, the knowledgegraph augmented the GPT analysis and so enriched the known pathways as well as could correctly identify the less represented pathways in the knowledge graph
5. This led to the identification of many novel signaling pathways not identified in the original analysis, and was able to perform this task with ease and speed
6. GPT with InfraNodus Analysis was able to propose pertinent questions for future research (the goal of the original curation) such as:
How does the interaction between [[EGFR]] mutations and sex-specific gene alterations, including [[RBM10]], influence treatment outcomes in lung adenocarcinoma?
How does the intersection of mutational patterns from smoking influence pathway activation in NSCLC, and can identifying these interactions improve targeted therapy development?
Novelty in comparison to Original article published on 09/05/2014
7. it appears that manual curation is necessary to assist in the building of relevant knowledge graphs in the biomedical fields to augment generative AI analysis
8. by itself, generative AI is not optimized for inference of higher concepts from biomedical text, and therefore, at this point, requires the input from human curators developing domain-specific knowledge graphs
9. The combination of ChatGPT5 and Knowledge graphs of this manually curated biomedical text added a further layer of complexity of gaps of knowledge not seen in the original curations including the need to study noncanonical signaling pathways like WNT and Hedgehog in smoker versus nonsmoker cohorts of lung cancer patients
The Voice of Aviva Lev-Ari, PhD, RN
LPBI Group’s Portfolio of Digital IP Assets as Proprietary Training Data Corpus for AI in Medicine, in Life Sciences, in Pharmaceutical and in Health Care Applications
LPBI Group is offering transfer of ownership, in full, a privately held, multimodal healthcare training corpus leveraging propriety unique data set curated by domain experts and mapped to a living ontology for GenAI creating defensibility.
The Portfolio of IP spans:
6,250+ articles (~2.5MM views),
48 e‑books (EN/ES) (+152,000 page downloads),
100+ e‑Proceedings with +50 Tweet collections,
7,500+ biological images with expert context, and
300+ Audio podcasts on Life Sciences breakthroughs.
Each asset (Use Case: Scientific Article) has timestamps, author/role labels, crosslinks, and view histories.
Metadata export exists; full text and media transfer via WordPress/Amazon account control for immediate ingestion.
Rights are centrally assigned with explicit model‑training data by domain-aware for model implementation for Small Language Models or Large Language Models.
Strategic acquirers in Big Pharma of Vertical AI startups (i.e., LPBI Group) with data‑moat strategies
Pharma strategics Acquire LPBI’s end‑to‑end, rights‑clean healthcare knowledge base to accelerate R&D, medical affairs, and safety. Ideal for and with acceleration of R&D, medical affairs, and safety. Emphasize compliant internal copilots and evidence synthesis enabled by expert curation and living ontology. Close with rapid onboarding under NDA Metadata export plus full text/media transfer for rapid onboarding. Full acquisition only.
Subject: Buy the moat: full acquisition of expert healthcare corpus with clean rights
We’re selling the entire asset: a privately held, multimodal healthcare corpus with centralized training rights and an exportable ontology, validated on gene–disease–drug extraction. It’s ingest‑ready and transfers cleanly via account control plus a metadata export. If owning differentiated data is critical for your agent or workflow, we can provide a diligence preview under NDA.
compliant internal copilots and evidence synthesis enabled by expert curation and living ontology. Close with rapid onboarding under NDA
Five Examples of Domain-aware for model implementation for Small Language Models – English Edition & Spanish Edition
Series A: Cardiovascular Diseases ($515) – Six Volumes
The electronic books are collections of curated articles in biomedical science. The electronic Tables of Contents (eTOCs) of each volume was designed by a senior editor with expertise in the subjects covered in that volume. The curations use as sources published research findings in peer-reviewed scientific journals together with expert added interpretations.
The e-books are designed to make the latest research in the Five Bilingual BioMed e-Series – 37 volumes accessible to practicing health care professionals. These five e-Series cover the following medical specialties:
Cardiovascular diseases and therapies,
Genomics,
Cancer etiology and oncological therapies,
Immunology, and
Patient-centered precision medicine.
The material in these volumes can greatly enhance medical education and provide a resource for continued updating and education for health care professionals. In addition to the 37 e-books, LPBI has published more than 6,000 articles in its online scientific journal “PharmaceuticalIntelligence.com”, which has received 2.5 million views since its launch in 4/2012, Top articles had more than 18,000 views.
The Portfolio is:
rights‑clean,
expert‑curated healthcare corpus
mapped to a living Ontology,
already packaged for immediate model ingestion and
suitable for safe pre-training, evals, and fine‑tuning.
If healthcare domain data is on your roadmap, this is a rare, defensible asset worth a preview.
LPBI Group is offering transfer of ownership, in full, a privately held, multimodal healthcare training corpus leveraging propriety unique data set curated by domain experts and mapped to a living ontology for GenAI creating defensibility. It spans 6,250+ articles (~2.5MM views), 48 e‑books (EN/ES) (+151,000 page downloads), 100+ e‑proceedings with +50 tweet collections, 7,500+ biological images with expert context, and 300+ Audio podcasts on Life Sciences breakthroughs. Each asset has timestamps, author/role labels, crosslinks, and view histories. Rights are centrally assigned with explicit model‑training data by domain-aware for model implementation for Small LMs or LLMs. Metadata export exists; full text and media transfer via WordPress/Amazon account control for immediate ingestion.
For Recognitions and a DETAILED BIO by Perplexity.ai and Gemini 2.5 Pro (experimental), see
“Show Thinking” by AI Deep Research: The AI-generated Biography Description of Aviva Lev-Ari, PhD, RN, Founder of PharmaceuticalIntelligence.com by Perplexity.ai and by Google’s Gemini 2.5 Pro (experimental)
Pfizer, a global pharmaceutical leader, has integrated artificial intelligence (AI) and machine learning (ML) across its operations to accelerate drug discovery, enhance clinical trials, improve manufacturing, and drive patient-centric innovations. These efforts aim to reduce development timelines, boost efficiency, and address challenges like rare disease detection. As of late 2025, Pfizer’s AI strategy emphasizes internal expertise, cloud-based tools (primarily with AWS), and cross-industry collaborations, with a focus on multimodal data analysis and generative AI. Below is a breakdown of key initiatives, drawn from recent developments.
1. Pfizer AI Cloud Transformation (PACT) Program Launched in 2021 with AWS, PACT is a prototyping accelerator for AI/ML applications in lab, clinical manufacturing, and supply chain efforts. It has pursued 14 projects, with five now in production. Key outcomes include:
Generative AI tools that save scientists up to 16,000 hours annually in literature searches and data analysis.
55% reduction in infrastructure costs through cloud optimization.
Exploration of virtual/augmented reality for training, with rapid prototyping to test feasibility. This program fosters “fail-fast” innovation, enabling quick iteration on ideas like AI-driven predictive modeling.
2. Drug Discovery and Development Acceleration Pfizer uses AI, supercomputing, and ML to streamline R&D, cutting years off traditional timelines (e.g., from 8–10 years to months for certain candidates).
COVID-19 Initiatives: AI optimized molecular searches for PAXLOVID (oral antiviral), enabling intravenous-to-oral pivots. During vaccine development, the Safety Data Quality (SDQ) tool—a custom AI for clinical trial data management—saved a full month of analysis via the Breakthrough Change Accelerator sandbox.
Precision Medicine and Subpopulation Identification: Advanced ML (classical and deep learning) analyzes molecular datasets from trials to predict patient responses, informing trial design, first-in-human studies, and oncology/inflammatory disease targeting. This has doubled clinical success rates to ~20% (vs. industry 7–10%).
ML Research Hub: Pfizer’s Cambridge, MA-based Biomedical Data Science group builds internal talent for AI in genome sciences, predicting disease outcomes and treatment efficacy using techniques from tech giants like Google.
3. Clinical Trials and Regulatory Efficiency AI automates content creation across the drug lifecycle, from lab data to regulatory docs and patient booklets, ensuring quality and speed.
Predictive Regulatory Tools: ML models forecast regulator queries, proactively addressing safety concerns to build trust and shorten approvals.
Decentralized Trials: The “Clinical Trial Anywhere” model uses AI to integrate remote data (e.g., home health visits), improving recruitment/retention and generating real-world evidence.
Inventory and Supply Chain: AI predicts vaccine storage issues, pioneered during COVID distribution.
4. Disease Detection and Diagnostics
ATTR-CM Initiative: Through the ASPIRE collaboration (with Amyloidosis Research Consortium), AI analyzes EHRs, ECGs, and echocardiograms to detect transthyretin amyloid cardiomyopathy earlier, targeting underdiagnosed heart failure cases.
Rare Diseases: ML models identify symptoms in large datasets, mapping the immune system for better predictions.
5. Generative AI and Operational Tools
Charlie Platform: Rolled out in 2024 for marketing, this gen AI tool (named after co-founder Charles Pfizer) generates compliant content like emails, presentations, and medical article drafts. Trained on approved product data, it streamlines physician targeting and customer insights.
Scientific Data Cloud: AWS-powered centralization of data/ML capabilities, enabling faster research and treating 1.3 billion patients in 2022. It supports gen AI via Amazon SageMaker/Bedrock for manufacturing efficiency.
Global Health Grants: Through the Pfizer Foundation’s AI Learning Lab, funding supports AI for equitable healthcare access, e.g., biometric tools for community care.
6. Partnerships and Education
Collaborations: With AWS for cloud AI; IMI Big Picture for a 3M-sample disease database; IIT Madras for the 2025 AIPM Symposium on AI in pharma (focusing on drug discovery and precision medicine).
Talent and Events: Hosting webinars on AI in manufacturing (e.g., predictive maintenance, regulatory submissions) and engaging leaders like Dr. Subha Madhavan on AI for biomedical data integration.
Strategic Focus: $500M R&D reinvestment by 2026 targets AI for productivity, part of $7.7B cost savings. Emphasis on ethical AI, diverse datasets, and competing with global biotech advances (e.g., China’s AI in CRISPR).
Pfizer’s approach balances operational efficiency with transformative R&D, positioning AI as a core enabler rather than a standalone focus. Ongoing investments, like cloud migrations saving $47M annually and reducing CO2 emissions, underscore sustainability. For the latest, monitor Pfizer’s investor updates or events like AWS re:Invent.
Responsiveness to Big Pharma AI Initiatives by LPBI Group’s Digital IP Portfolio:
Scientific articles
e‑Books in Medicine
e‑Proceedings,
Biological images
Podcasts
AI Initiative at Big Pharma
i.e., Pfizer
Composed @Grok
LPBI Group’s Digital IP Asset:
Journal articles
N = 6,250
Composed by Aviva Lev-Ari
Generative AI tools
that save scientists up to 16,000 hours annually in literature searches and data analysis.
Generative AI tools searching LPBI’s Proprietary data in addition to Public Domain data sources
Journal ONTOLOGY used to optimize context classification selected for search
Drug Discovery and Development Acceleration
Pfizer uses AI, supercomputing, and ML to streamline R&D timelines
– Run prompts by category of research on the following three dyads
– Run ML across categories of research for these three dyads
-Gene-disease
-Disease-drug
-Gene-drug
Clinical Trials and Regulatory Efficiency AI:
-Predictive Regulatory Tools
-Decentralize Trials
-inventory management
Curation competences:
content creation across the drug lifecycle, from lab data to regulatory docs
Disease Detection and Diagnostics:
– ATTR-CM Initiative
– Rare diseases
Heart Failure Diagnosis by Early detection of transthyretin amyloid cardiomyopathy
– Journal published ~30 curations by Dr. Larry on this subject ATTR-CM
– Run NLP on this Corpus
Rare diseases:
Journal published 560 articles on Rare diseases
– Run ML on this Corpus
Generative AI and Operational Tools:
– Charlie Platform
– Scientific Data Cloud AWS powered ML on centralized data
– Amazon’s SageMaker /Bedrock for Manufacturing efficiency
– Global Health Grants:
Pfizer Foundation’s AI Learning Lab for equitable access to care and tools for community care
Content generated in the Journal can become “generated compliant content” if run on the Charlie Platform.
– For REUSE content in context
Entire Corpus of 9 Giga bytes can be ingested to Pfizer Foundation’s AI Learning Lab
– Run prompts against it
– Journal’s Content to be used for Internal staff expertise development
– Journal’s Content for Leadership development
Partnerships and Education
– Collaborations: IMI Big Picture for 3M – sample disease database
– AI in Pharma AIPM Symposium: Drug discovery and Precision Medicine
– Webinars of AI for biomedical data integration
– Webinar on AI in Manufacturing
The Journal had published 547 articles in Precision Medicine
The Journal had published 1,114 articles in Drug Discovery
The Journal had published 701 articles in Drug Delivery
The Journal had published 3,615 articles on subject matter “Disease”
The Journal had published 738 articles on Biomedical topics
The Journal had published 425 articles on Artificial Intelligence (AI)
Strategic Focus:
– $500M R&D reinvestment by 2026 targets AI for Productivity
– Part of $7.7B cost savings
– Ethical AI, diverse DBs
– Global biotech advances: China’s AI in CRISPR
The Journal had published 432 articles on CRISPR
Productivity derived from Journal’s content:
– As a result of the fact that ~70% of the Journal’s articles are curations written by Domain Knowledge Experts subjectively expressing theirs clinical interpretations of basic and primary research – the productivity of the knowledge workers at any Big Pharma would increase vastly.
– If Grok and Claude would run on LPBI Group’s Digital IP Corpus, a scientific revolution will emerge
– It is not combinatorics applied to molecules with 98% futile results!!!
it is the IQ of Gifted HUMANS, of domain knowledge experts generating content using individual CREATIVITY no Quantum or Super Intelligence which is not in existence, YET.
– Foundation Models in Healthcare depends on the OUTPUT of the human creative mind. AI takes keyword (classic search) and concepts (semantic search) and run frequency of occurrence and predict the nest word, one word after the next one.
@@@@@@@
AI Initiative at Big Pharma
i.e., Pfizer
LPBI Group’s Digital IP Asset:
e-Books
Domain-aware Editorials and Curations
Generative AI tools that save scientists up to 16,000 hours annually in literature searches and data analysis.
The electronic Table of Contents of every e-book is a CONCEPTUAL MASTER PIECE of one unique occurrence in Nature generated by the Editor, or the Editors that had
– Commissioned articles for the e-Book
– Had selected articles from collections of Categories of Research created by domain knowledge experts
– Had reviewed the TOTALITY of the Journal’s Ontology and found new concept to cover in the e-Book not originally planned
Had incorporated Highlights of Lectures given at 100 Conferences LPBI Group’s Dr. Lev-Ari and Dr. Willians had cover in Real Real, by invitation, only as PRESS.
– The vision of the Editor-in-Chief of the BioMed e-Series reflects the BIG PICTURE of Patient care delivery.
– UC, Berkeley PhD’83
– Knowledge student and Knowledge worker, 10/1970 to Present
– Conceptual pioneer of 26 algorithms in Decision Science of Operations Management decision support systems
– 2005 to Present in the Healthcare field.
– 2005-2012: Clinical Nurse Manager in Post-acute SNF settings and Long-term Acute care Hospital Supervisor – had developed a unique view on Diagnosis, Therapeutics and Patient care delivery
– The BioMed e-Series is the EPITOM of human CREATIVITY in Healthcare an OPUS MAGNUM created by collaboration of top Scientists, Physicians and MD/PhDs
– The 48 e-Books Published by LPBI Group – represent the ONLY one Publisher on Amazon.com with +151,000 pages downloaded since the 1st e-book published on 6/2013 and since Pay-per-View was launched by Amazon.com in 2016.
Drug Discovery and Development Acceleration Pfizer uses AI, supercomputing, and ML to streamline R&D timelines
Two volumes on the BioMed e-Series were subjected to Medical Text Analysis with AI, ML, Natural Language Processing (NLP).
– Cancer, Volume 1 (In English, part of the Spanish Edition, Series C)
– Genomics, Volume 2 (In English, part of the Spanish Edition, Series B)
– GPT capabilities are warranted to attempt to subject to ML Analytics every book of the MUTUALLY EXCLUSIVE 48 URLs provided by Amazon.com to LPBI Group, the Publisher.
– 5 URLs for 5 Bundles in The English Edition: Series A,B,C,D,E – English Edition
– All books in each series – 5 Corpuses for domain-aware Small Language Model in English
– All books in each series – 5 Corpuses for domain-aware Small Language Model in Spanish
– 5 URLs for 5 Bundles in The Spanish Edition: Series A,B,C,D,E –Spanish Edition
Clinical Trials and Regulatory Efficiency AI:
-Predictive Regulatory Tools
-Decentralize Trials
-inventory management
Disease Detection and Diagnostics:
– ATTR-CM Initiative
– Rare diseases
– No one had attempted ML on every book, only two books were analyzed by ML.
– No one had attempted ML on all the Volumes in any of the 5 Series.
– No one had attempted ML on all the 48 books
– WHEN that will be done – a REVOLUTION on Disease Detection and Diagnostics will be seen for the first time because the totality of these 48 books represent the Brains of Human Experts
Generative AI and Operational Tools:
– Charlie Platform
– Scientific Data Cloud AWS powered ML on centralized data
– Amazon’s SageMaker/Bedrock for Manufacturing efficiency
– Global Health Grants:
Pfizer Foundation’s AI Learning Lab for equitable access to care and tools for community care
Add the content of all the Books to Charlie Platform
Partnerships and Education
Collaborations: IMI Big Picture for 3M – sample disease database
AI in Pharma AIPM Symposium: Drug discovery and Precision Medicine
Webinars of AI for biomedical data integration
Webinard on Ai in Manufacturing
e-Books are the SOURCE for Education
– Offer the books as Partnership sustenance
Strategic Focus:
– $500M R&D reinvestment by 2026 targets AI for Productivity
– Part of $7.7B cost savings
– Ethical AI, diverse DBs
– Global biotech advances: China’s AI in CRISPR
URLs for the English-language Edition by e-Series:
In these conferences the Frontier of Science was presented, ofter BEFORE publication findings were revealed. These Proceedings are the ONLY written record of the events. They are privately-held, now for the first time available for Transfer of Ownership
The Tweet Collection are QUOTES of speakers on record. NOT ELSEWHERE available by name of speaker and affiliation
Clinical Trials and Regulatory Efficiency AI:
-Predictive Regulatory Tools
-Decentralize Trials
-inventory management
Disease Detection and Diagnostics:
– ATTR-CM Initiative
– Rare diseases
Generative AI and Operational Tools:
– Charlie Platform
– Scientific Data Cloud AWS powered ML on centralized data
– Amazon’s SageMaker/Bedrock for Manufacturing efficiency
– Global Health Grants:
Pfizer Foundation’s AI Learning Lab for equitable access to care and tools for community care
Ingest to Charlie Platform ALL e-Proceedings of ALL Conferences
Apply GPT:
Training Data:
– One conference at a time
– All Conference on ONE subject matter, i.e., Immunotherapy, Oncolytic Virus Immunotherapy, Immune Oncology
Partnerships and Education
– Collaborations: IMI Big Picture for 3M – sample disease database
– AI in Pharma AIPM Symposium: Drug discovery and Precision Medicine
– Webinars of AI for biomedical data integration
– Webinar on Ai in Manufacturing
Use Past Conference Agendas to build Future Conference Agendas
Use Speakers Lists to invite speakers/consultants to your events
Use topics covered in Conferences for Employee training & and in-house Leadership development
Strategic Focus:
– $500M R&D reinvestment by 2026 targets AI for Productivity
– Part of $7.7B cost savings
– Ethical AI, diverse DBs
– Global biotech advances: China’s AI in CRISPR
Having access to +100 e-Proceedings vs Not having access to this resource is a make or break in fine-tuning Corporate Branding: All your competitors attended and had sent Speakers
LPBI Group’s e-Proceedings is the only record in one URL
@@@@@@
AI Initiative at Big Pharmas
i.e., Pfizer
LPBI Group’s Digital IP Asset:
Biological Images selected by Experts embedded in original Text (Prior Art)
Generative AI tools that save scientists up to 16,000 hours annually in literature searches and data analysis.
Drug Discovery and Development Acceleration Pfizer uses AI, supercomputing, and ML to streamline R&D timelines
Gallery of ~8,000 Biological images and captions is a Treasure TROVE for scientific article writing, Presentation preparations. This Media Gallery is an Art collection of top Scholars in Medicine and Biology
Clinical Trials and Regulatory Efficiency AI:
-Predictive Regulatory Tools
-Decentralize Trials
-inventory management
Disease Detection and Diagnostics:
– ATTR-CM Initiative
– Rare diseases
Gallery of ~8,000 Biological images and captions is a Treasure TROVE for Disease Detection and Diagnostics
Generative AI and Operational Tools:
– Charlie Platform
– Scientific Data Cloud AWS powered ML on centralized data
– Amazon’s SageMaker/Bedrock for Manufacturing efficiency
– Global Health Grants:
Pfizer Foundation’s AI Learning Lab for equitable access to care and tools for community care
Ingest into Charlie Platform the Media Gallery for generation of Medical article drafts
Partnerships and Education
– Collaborations: IMI Big Picture for 3M – sample disease database
– AI in Pharma AIPM Symposium: Drug discovery and Precision Medicine
– Webinars of AI for biomedical data integration
– Webinar on Ai in Manufacturing
Strategic Focus:
– $500M R&D reinvestment by 2026 targets AI for Productivity
– Part of $7.7B cost savings
– Ethical AI, diverse DBs
– Global biotech advances: China’s AI in CRISPR
@@@@@@
AI Initiative at Big Pharma
i.e., Pfizer
LPBI Group’s Digital IP Asset:
Library of Audio and Video Podcasts
N = +300
Generative AI tools that save scientists up to 16,000 hours annually in literature searches and data analysis.
Drug Discovery and Development Acceleration Pfizer uses AI, supercomputing, and ML to streamline R&D timelines
Review ALL SCIENTIFIC BREAKTHROUGHS
Two criteria for Classifications used by Prof. Marcus W. Feldman and by Dr. Stephen J. Williams to generate the two classifications
– $500M R&D reinvestment by 2026 targets AI for Productivity
– Part of $7.7B cost savings
– Ethical AI, diverse DBs
– Global biotech advances: China’s AI in CRISPR
Dr. Lev-Ari Prompted @Grok on 11/18/2025:
AI-Driven Cost Savings in Big Pharma
@Grok responded:
AI-Driven Cost Savings in Big PharmaArtificial intelligence (AI) applications across the pharmaceutical value chain—particularly in drug discovery, clinical trials, and operations—have the potential to generate substantial savings for major pharmaceutical companies (often referred to as “Big Pharma”). Based on recent industry reports and analyses from 2024-2025, these savings vary by application but could collectively reach tens of billions annually by the late 2020s. Projections indicate AI could add up to 11% in value relative to revenue for biopharma firms over the next five years through productivity gains and cost reductions. Below, I break down the estimated savings levels by key areas, drawing from credible sources like McKinsey, Deloitte, IQVIA, and others.
Clinical Trials
Annual Industry-Wide Savings: By 2030, AI integration into 60-70% of clinical trials could save the pharmaceutical industry $20-30 billion per year through faster timelines, better patient matching, and reduced administrative costs.
Per-Trial Cost Reduction: AI can cut trial costs by up to 70% and shorten durations by 80%, enabling quicker regulatory submissions (40% faster) with 50% improved cost efficiency. For example, AstraZeneca reported up to 25% shorter trial durations in cancer drug studies using AI for dose optimization and biomarker identification.
Specific Efficiency Gains: Feasibility surveys can see 90% time reductions using AI/ML tools.
Drug Discovery and Development
R&D Cost Drop: AI could reduce drug discovery costs by up to 40%, with overall development timelines shrinking from 5 years to 12-18 months (an 80% cut in some cases). From discovery to preclinical candidate stage, timelines can decrease by up to 50%.
Total Clinical Development Savings: Projected at $25 billion across the sector, driven by AI-accelerated pipelines.
Operations and Broader Impacts
Value Relative to Revenue: Biopharma AI investments may yield up to 11% additional value (e.g., via cost efficiencies and revenue growth) over five years. For related medtech segments, savings could hit 12% of total revenue within 2-3 years.
Operating Profits Boost: By 2030, AI could add $254 billion in annual operating profits industry-wide through productivity jumps (e.g., 50-100% in quality control) and lead time reductions (60-70%).
Time-to-Market: Up to 10% faster overall, with production KPIs improving revenue by 25%.
These estimates assume scaled adoption, with global AI spending in pharma rising from ~$3 billion in 2025 to $22-25 billion by 2027-2030. Challenges like high initial costs and data infrastructure may temper short-term gains, but early adopters (e.g., Pfizer, Novartis) are already seeing ROI. For context, the global pharma market is projected at $1.6 trillion by 2025, so even modest percentages translate to billions in savings. If you need details on a specific AI use case or company, let me know!
Overview of Pfizer and Key CompetitorsPfizer is one of the world’s largest pharmaceutical companies, with a strong focus on innovative medicines and vaccines. Its primary competitors include Merck & Co., Johnson & Johnson (J&J), Roche, Novartis, and now AstraZeneca, based on revenue leadership and market overlap in areas like oncology, immunology, and vaccines. These companies dominate the global pharmaceutical industry, which is projected to reach $1.21 trillion in revenue by 2025. Below, I compare them across size (measured by 2024 revenue and employees), market share (approximate global pharma share based on 2024 revenues), product lines (key therapeutic areas and top products), and investments in AI technologies. Data reflects the latest available figures as of late 2025.
Here are the same comparisons with more concise tables (2024 data, latest available as of Nov 2025):
Size (2024 Revenue & Employees)
Company
Revenue (USD B)
Employees
Merck & Co.
64.2
70,000
Pfizer
63.6
88,000
Roche
~58.3
103,000
J&J (pharma only)
57.8
138,000
AstraZeneca
54.1
94,300
Novartis
~50.0
76,000
Approx. Global Pharma Market Share
Company
Share %
Merck & Co.
4.3
Pfizer
4.2
J&J / Roche
3.9
AstraZeneca
3.6
Novartis
3.3
Key Product Lines & Top Sellers (2024 revenue, USD B)
Company
Main Areas
Top Products (revenue)
Pfizer
Vaccines, cardiology, oncology
Eliquis (7.3), Prevnar (6.4), Vyndaqel (5.4)
Merck & Co.
Oncology, vaccines
Keytruda (25.0), Gardasil (8.6)
J&J
Immunology, oncology
Stelara (10.9), Darzalex (9.7)
AstraZeneca
Oncology, CVRM, respiratory
Farxiga (7.7), Tagrisso (6.6), Enhertu (4.0)
Roche
Oncology, neurology, hemophilia
Ocrevus (5.7), Hemlibra (4.0)
Novartis
Cardiology, immunology
Entresto (6.6), Cosentyx (5.2)
AI Investment Snapshot (2025)
Company
AI Highlights (2025)
Scale/Impact
Pfizer
AI cuts discovery from years → 30 days; heavy partnerships
Leader in adoption
AstraZeneca
Evinova AI trials unit; $2.5B AI Beijing hub; 12k+ staff upskilled
Very aggressive (>85% productivity gain)
Merck & Co.
Gen-AI for protocols; 20+ AI “blockbusters” in pipeline
>$1.2B spend
J&J
900+ AI projects → $500M savings; surgical AI training
Broad internal rollout
Roche
AI diagnostics + drug discovery; $55M Manifold Bio deal
Searched for “(“AI” OR “artificial intelligence”) (“biotech” OR “biotechnology”) startup since:2025-01-01″
10 results
Show All (2)
AI in Biotech Startups:
2025 LandscapeArtificial Intelligence (AI) is revolutionizing biotech startups by accelerating drug discovery, optimizing clinical trials, and enabling personalized medicine. In 2025, AI applications like generative models for molecule design and predictive analytics for protein structures are slashing R&D timelines from years to months, reducing costs by up to 30%, and boosting success rates. The sector blends biology’s complexity with AI’s data-crunching power, drawing massive VC interest amid ethical debates on gene editing and biosecurity.Key Trends
Explosive Funding Growth: U.S. healthcare VC hit $23B in 2024, with ~30% ($5.6B) targeting AI-biotech—nearly triple 2023 levels. Biopharma startups raised $24.2B across 568 deals, up from $10.1B in 2023. Q3 2025 saw $97B global VC (up 38% YoY), half to AI firms. Mega-rounds ($100M+) surged, focusing on drug discovery and rare diseases.
Market Expansion: Global AI-biotech market: $4.6B in 2025, projected to $11.4B by 2030 (20% CAGR). Biotech overall to $5.85T by 2034 (13.6% CAGR), driven by AI in gene editing, microbiome therapeutics, and automation.
Compute Demands: Biotech AI requires massive GPUs; Nvidia’s AI sales hit $41.1B in one 2025 quarter (56% YoY). Startups lean on cloud partnerships (e.g., AWS-Recursion) or in-house clusters, with hyperscalers eyeing $3-4T infrastructure by 2030.
Regional Hotspots: North America leads (42% share) via FDA support and hubs like Boston/SF; Europe grows via funding/regulatory advances; Asia (e.g., China) via collaborations.
Challenges & Ethics: Selective funding favors clinically validated firms; rising M&A (big pharma acquiring AI startups); ethical scrutiny on AI gene editing (e.g., CRISPR enhancements).
Notable Startups & Recent Funding
Here’s a snapshot of standout AI-biotech startups, highlighting innovations and 2024-2025 funding:
Startup
Focus Area
Key Innovation
Recent Funding (2025)
Insilico Medicine
Drug Discovery
Generative AI for novel molecules
$100M+ Series D (ongoing)
Recursion Pharmaceuticals
Phenomics & Drug Repurposing
AI mapping diseases via cell images
$50M partnership w/ Nvidia
Absci Corp.
Protein Design
AI-generated antibodies/proteins
$20M from AMD (Jan 2025)
TandemAI
Drug Discovery
ML for molecular simulation
$22M Series A extension
Red Queen Bio
Biosecurity
AI for pathogen detection/design
$15M Seed led by OpenAI
Antiverse
Antibody Design
AI predicting antibody structures
Deal w/ Nxera Pharma (2024)
Deep Genomics
Genomics
AI extracting insights from DNA data
VC round undisclosed
Aizen Therapeutics
Molecule Generation
Proprietary AI engines for trials
Early-stage VC
These firms exemplify AI’s shift from hype to pipeline impact—e.g., Insilico’s AI-discovered drug entered Phase II trials in record time.Emerging ApplicationsAI’s top uses in biotech startups:
Drug Discovery: ML predicts drug-target interactions (e.g., AlphaFold-inspired tools cut screening costs 70%).
Genomics & Diagnostics: AI analyzes vast datasets for rare variants, powering precision oncology.
2025 signals a “quality over quantity” pivot: Investors prioritize AI startups with real-world data and partnerships (e.g., Y Combinator, Google for Startups). M&A will rise as big pharma (Pfizer, Merck) snaps up talent. Yet, compute shortages and regs (e.g., FDA AI guidelines) pose hurdles. For founders, blending AI with wet-lab validation is key to unlocking the next wave of breakthroughs—potentially curing diseases once deemed untreatable. If you’re building or investing, focus on
Key Products and Focus AreasPfizer’s portfolio spans immunology, oncology, cardiology, endocrinology, neurology, and vaccines. In 2024, its top revenue generators included:
Eliquis (apixaban, blood thinner): $7.3 billion (11% of total revenue).
Prevnar family (pneumococcal vaccines): $6.4 billion (10%).
Paxlovid (COVID-19 antiviral): $5.7 billion (9%).
Vyndaqel (tafamidis, for rare heart disease): $5.4 billion (8%).
Comirnaty (COVID-19 vaccine): $5.3 billion (8%).
Ibrance (palbociclib, breast cancer drug): $4.3 billion (6%).
The company is investing heavily in AI-driven drug discovery (e.g., 2024 partnerships with NVIDIA and Saama) and next-gen therapies like RSV vaccines, gene therapies for muscular dystrophy, and oral GLP-1 drugs for obesity/diabetes. Challenges include patent expirations (e.g., Eliquis in 2026-2028) and biosimilar competition, but Pfizer’s diversified pipeline positions it for mid-single-digit growth in 2025.Pfizer’s Main CompetitorsPfizer operates in a highly competitive $1.5 trillion global pharma market, where rivals vie for dominance in oncology, vaccines, immunology, and emerging areas like obesity and gene therapy. Its primary competitors are fellow “Big Pharma” giants, often overlapping in therapeutic areas. Based on revenue, market share, and direct product rivalries, here are the top ones (ranked roughly by 2024 revenue for context):
Competitor
Headquarters
2024 Revenue (Pharma Segment)
Key Strengths & Overlaps with Pfizer
Notable Products
Johnson & Johnson (J&J)
New Brunswick, NJ, USA
$88.8 billion (total; pharma ~$57.8 billion)
Diversified (pharma, devices, consumer); strong in oncology, immunology, vaccines. Competes directly in blood thinners and cancer therapies.
Oncology and immunology; Opdivo directly competes with Pfizer’s Bavencio in checkpoint inhibitors.
Opdivo (immunotherapy), Eliquis (co-developed with Pfizer, but BMS handles ex-US).
Sanofi S.A.
Paris, France
~$46 billion
Vaccines and rare diseases; Dupixent challenges in immunology; rivals Prevnar in pneumococcal vaccines.
Dupixent (atopic dermatitis), Lantus (diabetes).
These competitors collectively hold over 50% of the global pharma market share. Emerging threats include biotech upstarts like Moderna (mRNA vaccines, directly rivaling Comirnaty) and generics firms like Teva (eroding post-patent revenues). In 2025, competition intensifies in obesity (Novo/Lilly lead) and oncology (Merck/Roche dominate), with tariffs and pricing pressures (e.g., US executive orders) affecting all.Pfizer’s edge comes from its scale and partnerships (e.g., BioNTech for mRNA), but it must navigate revenue dips from COVID fade-out and biosimilars. For 2025 projections, analysts expect 6-9% growth across the sector, with Pfizer targeting modest gains through oncology expansions. If you’d like deeper dives on specific rivals, financials, or products, let me know!
2d • 2 days ago • Visible to anyone on or off LinkedIn
AI in Pharma: From Tool to Value Creator 🚀
Over the past 5 years, pharma’s AI footprint has expanded rapidly and the investment trends tell a clear story.
💡 ~60% of collaborations and acquisitions now focus on TargetDiscovery and Validation — pharma’s main frontier to shorten early-stage R&D timelines and boost success rates through PrecisionBiology.
⚗️ The second-largest area (~25%) centers on AI-driven DrugDesign and MoleculeOptimization, where algorithms accelerate CandidateSelection, refine molecular properties, and enable NewModalities such as Biologics and CellAndGeneTherapies.
🏆 Among top players, Eli Lilly and Company stands out as the most active investor — making AI a true StrategicCapability, not just another research tool.
The takeaway?
AI isn’t just supporting pharma anymore, it’s reshaping it.
“Authentic Relevance of LPBI Group’s Portfolio of IP as Proprietary Training Data Corpus for AI Initiatives at Big Pharma”
This November 15, 2025, article on PharmaceuticalIntelligence.com (curated by LPBI Group) makes a compelling case for leveraging LPBI’s decade-plus of expert-curated digital IP as a high-fidelity, rights-clean training corpus to supercharge AI-driven innovation in Big Pharma. It contrasts the limitations of generic LLMs (like GPT) with the need for domain-specific, ontologized biomedical data, positioning LPBI’s assets as a “defensible moat” for accelerating R&D in drug discovery, precision medicine, and beyond. The piece blends market analysis of pharma giants with a deep dive into LPBI’s portfolio, emphasizing its multimodal, metadata-rich structure for NLP, knowledge graphs, and fine-tuning SLMs/LLMs.IntroductionThe thesis: In an era where AI investments in pharma (e.g., Eli Lilly’s aggressive funding) focus on slashing R&D timelines by 60%+ in target discovery and 25% in drug design, LPBI’s proprietary corpus—built since 2012 via human curation, digitization, and ontology mapping—offers unmatched authenticity over web-scraped data. It addresses GPT’s biomedical shortcomings (e.g., failing to infer fusion proteins or noncanonical pathways without curated inputs) and highlights LPBI’s foresight in text analysis for generative AI augmentation. A key quote from Data2Discovery: “We are able to improve drug discovery now as well as demonstrating new fast-cycle AI-driven processes that will have a revolutionary impact on drug discovery if fully implemented.”Portfolio OverviewLPBI’s ~9 GB, debt-free, multimodal corpus is privately held, expert-curated (e.g., by Prof. Marcus W. Feldman and Dr. Stephen J. Williams), and ingest-ready for AI pre-training/evaluations. It spans five key asset classes, each with metadata exports, timestamps, crosslinks, and centralized rights for model training:
Asset Class
Description & Size
Unique Value Proposition
I: Scientific Articles
6,250+ articles on PharmaceuticalIntelligence.com (~2.5M views); covers genomics, oncology, immunology, etc.
Live ontology, author/role labels, view histories; enables temporal NLP for trend analysis.
II: e-Books
48 bilingual (English/Spanish) volumes in 5 BioMed e-Series (e.g., Series A: Cardiovascular, 6 vols., $515 total; Series E: Patient-Centered, 4 vols., $274); 151,000+ page downloads; 2,728 articles.
Peer-reviewed, senior-editor TOCs; pay-per-view model proves demand; ideal for entity-relationship extraction.
III: e-Proceedings
100+ from biotech/genomics conferences (2013–2025); +50 tweet collections as speaker quotes with affiliations.
Real-time event curation; captures emerging insights for knowledge graph augmentation.
V: Biological Images
7,500+ images in Digital Art Media Gallery; embedded as prior art in texts.
Expert-contextualized visuals; supports multimodal AI for image-text pairing in diagnostics.
X: Audio Podcasts
300+ interviews with scientific leaders (e.g., Nobel laureates like Jennifer Doudna); classified by themes like CRISPR, mRNA vaccines.
Transcripts + NLP WordClouds; adds auditory/verbal depth for voice-enabled AI copilots.
The portfolio’s “living ontology” allows seamless integration into tools like InfraNodus for concept mapping.AI Training RelevanceUnlike PubMed’s unstructured dumps, LPBI’s assets are pre-annotated for concept extraction (e.g., gene-disease-drug dyads), reducing hallucinations and bias in LLMs. A case study integrates curation with ChatGPT-5: Manual ontology + knowledge graphs uncovered novel WNT/Hedgehog interactions in lung cancer, generating research questions like: “How does the interaction between [[EGFR]] mutations and sex-specific gene alterations, including [[RBM10]], influence treatment outcomes in lung adenocarcinoma?” This hybrid approach outperforms solo GPT, proving the corpus’s role in trustworthy biomedical inference.Applications
Drug Discovery: ML prompts on dyads to mimic Pfizer’s AI (e.g., reducing discovery from years to months); supports biologics/cell therapies.
Precision Medicine: 547 articles for patient subtyping; 3,615 disease-focused pieces for predictive modeling.
Diagnostics & Rare Diseases: 560 rare disease articles + 30 on ATTR-CM for early detection (e.g., via Pfizer’s ASPIRE AI).
Clinical/Regulatory: Streamlines trials, evidence synthesis, and content generation (e.g., via Pfizer’s “Charlie” copilot, saving hours on lit reviews).
Generative Tools: Fine-tune for compliant, domain-aware outputs in medical affairs/safety.
Challenges & Solutions
Data Quality/Bias: Unstructured text leads to GPT errors; solved by expert curation + ontology for accurate, diverse inputs.
Regulatory Compliance: Rights fragmentation; addressed via LPBI’s centralized assignment for NDA-secure licensing.
Scalability: Generic data overload; LPBI’s focused, high-signal corpus enables fast-cycle AI without noise.
Recommendations
Urge Big Pharma (e.g., Pfizer, Merck—table highlights Merck’s $64.2B revenue, Keytruda focus) to acquire the full portfolio for end-to-end knowledge bases, or license under NDA for SLM/LLM training (English/Spanish).
Emphasizes “buy the moat” for competitive edge in 2025’s 6–9% sector growth (obesity/oncology hotspots).
Contact: avivalev-ari@alum.berkeley.edu.
Conclusion
LPBI’s corpus isn’t just data—it’s a “scientific revolution enabler,” blending human creativity with AI for transformative pharma impact. Validated on extraction tasks, it’s primed for partnerships, outshining combinatorial predictions with authentic, curated depth. This article builds on prior LPBI mappings (e.g., our podcast TOC summary), reinforcing the portfolio’s strategic fit for AI.
For deeper analysis or related assets, let me know!
Advances in Liver Transplantation: New Frontiers in Organ Regeneration and Immunomodulation
Curator: Dr. Sudipta Saha, Ph. D.
Recent research in the field of liver transplantation has been marked by significant advancements in organ preservation, immune tolerance, and regenerative medicine. Efforts have been made to address the critical shortage of donor organs and reduce long-term complications associated with immunosuppressive therapy.
Normothermic machine perfusion (NMP) techniques have been employed to preserve and assess donor livers outside the body. This method has allowed marginal or extended criteria livers to be reconditioned, increasing the usable donor pool. The viability of these organs has been improved through real-time functional monitoring during perfusion.
Immunological tolerance has been targeted through cell-based therapies and gene editing strategies. Regulatory T-cell therapies and tolerogenic dendritic cells have been investigated to reduce the reliance on lifelong immunosuppression. CRISPR-based gene editing is also being explored to modify donor tissues before transplantation to evade host immune responses.
In parallel, liver organoids and bioengineered tissue scaffolds have been studied for their potential in partial transplantation or functional support in acute liver failure. Although clinical application remains at an early stage, these developments have suggested future directions for transplant alternatives or bridge-to-transplant therapies.
Artificial intelligence has been integrated into transplant decision-making, predicting post-transplant outcomes and optimizing donor-recipient matching. These models are being trained on large datasets to improve prognostic accuracy.
Ethical concerns surrounding organ allocation equity and experimental treatments continue to be actively discussed. However, these advancements have collectively pushed the boundaries of transplant medicine toward safer, more personalized, and more sustainable outcomes.
Tirzepatide Outperforms Semaglutide in Diabetes Control
Curator: Dr. Sudipta Saha, Ph. D.
In a recent clinical study published in The New England Journal of Medicine, the effectiveness of tirzepatide was compared with that of semaglutide in patients with type 2 diabetes. The trial was conducted to evaluate which of the two medications offers better glycemic control and weight loss benefits when combined with standard care.
It was found that participants treated with tirzepatide achieved significantly greater reductions in both HbA1c levels and body weight than those who received semaglutide. A once-weekly administration of tirzepatide was shown to be more effective across multiple dosages. These findings were consistent even in patients with longstanding diabetes and those previously treated with insulin or oral agents.
Gastrointestinal side effects were commonly observed in both groups, including nausea and diarrhoea, but were generally mild to moderate in severity. No new safety concerns were identified during the study period.
The enhanced dual agonist mechanism of tirzepatide, which targets both GIP and GLP-1 receptors, is believed to have contributed to its superior efficacy. While semaglutide acts only on the GLP-1 pathway, tirzepatide’s dual action is thought to improve insulin sensitivity, promote satiety, and reduce appetite more robustly.
This trial represents a significant advancement in diabetes care and suggests that tirzepatide may become a preferred treatment option in clinical practice. It has been proposed that future studies investigate its long-term cardiovascular effects, impact on diabetic complications, and cost-effectiveness in diverse populations.
Nearly half of the global population—and 80 percent of patients in therapeutic areas such as immunology—are women. Yet, treatments are frequently developed without tailored insights for female patients, often ignoring critical biological differences such as hormonal impacts, genetic factors, and cellular sex. Historically, women’s health has been narrowly defined through the lens of reproductive organs, while for non-reproductive conditions, women were treated as “small men.” This lack of focus on sex-specific biology has contributed to significant gaps in healthcare.
A recent analysis found that women spend 25 percent more of their lives in poor health compared with men due to the absence of sex-based treatments. Addressing this disparity could not only improve women’s quality of life but also unlock over $1 trillion in annual global GDP by 2040.
Four key factors contribute to the women’s health gap: limited understanding of sex-based biological differences, healthcare systems designed around male physiology, incomplete data that underestimates women’s disease burden, and chronic underfunding of female-focused research. For instance, despite women representing 78 percent of U.S. rheumatoid arthritis patients, only 7 percent of related NIH funding in 2019 targeted female-specific studies.
However, change is happening. Companies have demonstrated how targeted R&D can drive better outcomes for women. These therapies achieved expanded FDA approvals after clinical trials revealed their unique benefits for female patients. Similarly, addressing sex-based treatment gaps in asthma, atrial fibrillation, and tuberculosis could prevent millions of disability-adjusted life years.
By closing the women’s health gap, biopharma companies can drive innovation, improve therapeutic outcomes, and build high-growth markets while addressing long-standing inequities. This untapped opportunity holds the potential to transform global health outcomes for women and create a more equitable future.
SNU-BioTalk 2025: Symphony of Cellular Signals in Metabolism and Immune Response – International Conference at Sister Nivedita University, Kolkata, India on 16 & 17 January 2025
Joint Convenor: Dr. Sudipta Saha (Member of LPBI since 2012)
About the Conference:
The International Conference on ‘Symphony of Cellular Signals in Metabolism and Immune Response’ focuses on the complex signalling pathways governing cellular functions in health and disease. It will explore the cellular mechanisms that regulate metabolism, immune responses, and survival, highlighting advances in medical science and biotechnology. Bringing together leading experts and emerging researchers, the conference will feature keynote lectures, panel discussions, research presentations, and interactive sessions, all designed to foster collaboration and innovation. By promoting an exchange of ideas, the event aims to drive transformative insights and solutions that impact human health and sustainable healthcare practices.
The conference will also be livestreamed on YouTube and Facebook
This programme will also host I-STEM: Indian Science, Technology and Engineering facilities Map (I-STEM) is a dynamic and interactive national portal for research cooperation.
Thrust areas:
Intracellular signalling processes of cellular metabolism
Signalling pathways in physiological and pathological processes
“This milestone makes AISAP the first company in the world to secure FDA clearance in the CADx pathway for the comprehensive diagnosis of structural heart diseases using POCUS,”
Cardio is a cloud-based platform that includes four modules for the computer-assist diagnosis (CADx) of valvular pathologies and eight key cardiac measurements. Its advanced AI algorithms can evaluate a patient’s left ventricle ejection fraction, right and left ventricular dimensions, right ventricular fractional area change, atrial areas, ascending aorta diameter and inferior vena cava diameter in addition to identifying aortic stenosis or mitral, tricuspid or aortic regurgitation.
The platform, trained on more than 24 million echocardiography clips, was designed to help even inexperienced users scan and diagnose a majority of common heart issues within minutes without leaving the patient’s side. In addition, it can communicate with equipment manufactured by a variety of vendors, directing data to a physician’s electronic health record or PACS system as needed.
Ehud Raanani, MD, co-founder of AISAP and director of the Leviev Cardiovascular and Thoracic Center at Sheba Medical Center, said in a statement. “It marks a big step in our goal of delivering point-of-care assisted diagnosis, or POCAD, with unparalleled scalability and accessibility—from the largest academic centers to the most remote rural locations.”
Smadar Kort, MD, system director of noninvasive cardiac imaging at Stony Brook Medicine, who has experience with the platform
said:
“We know that structural heart disease and heart failure are the leading causes of hospitalization and morbidity in the U.S. Enabling a wide variety of qualified physicians to quickly and accurately diagnose these conditions at the bedside could lead to earlier detection and treatment, and better patient outcomes, as well as greater efficiencies and cost savings to health systems, while ultimately saving countless lives.”
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100+ Mass General Brigham Leading Experts Identify
Top Unmet Needs in Healthcare
Project from Harvard Medical School-affiliated clinicians and scientists in the Mass General Brigham healthcare system stimulates new consideration, urgency regarding
innovation in life sciences, healthcare
Top 10 List Announced at World Medical Innovation Forum
BOSTON, MA September 25, 2024 – Some of the most vexing challenges and transformational opportunities in healthcare are included in a new list, “Top Unmet Needs in Healthcare” released by leading experts at Mass General Brigham. Identified by more than 100 Harvard Medical School faculty at Mass General Brigham, the findings range from the need to expand and accelerate rare disease treatment, to the coming “gray tsunami” of aging patients and the implications for patient care, delivery, and technology. The project, revealed at the 10th annual World Medical Innovation Forum, is meant to stimulate new consideration and urgency regarding solving and advancing these issues for improved patient care.
Views from Leading Clinicians, Researchers, and Practitioners in Academic Medicine
The Top Unmet Needs emerge from structured one-on-one discussions with more than 100 Harvard faculty who practice medicine and conduct research at Mass General Brigham, the largest hospital system-based research enterprise in the U.S., with an annual research budget exceeding $2 billion, and five of the nation’s top hospitals according to US News & World Report.
Through one-on-one discussions with these key opinion leaders from diverse clinical and research fields, and subsequent analyses by internal teams of experts, Mass General Brigham has identified the following top 10 unmet clinical needs:
#1. Preparing for the ‘Gray Tsunami’
The need for better tools and therapies aimed at caring for geriatric populations and maintaining geriatric independence, with a particular focus on expanded hospital-at-home capabilities, and the need to better understand the pathways that lead to chronic and acute disease in geriatric patients to enable better and more proactive treatment.
#2. Defining and Maintaining Brain Health
The need for a model of brain health and neurological care that clearly defines not only what brain health is but also integrates our current understanding of the mechanisms and phases of neuroinflammatory and neurodegenerative diseases; enables better and earlier diagnoses and treatment; and propels the development of therapies that target these mechanisms and phases.
#3. A Paradigm Shift in Cancer Treatment
The need for a new framework for therapeutic development in cancer that is focused on improving curability as opposed to an exclusive focus on the development of drugs for metastatic disease. This
framework also requires effective tools for early-stage cancer detection across the board in all cancers, but especially in lung, ovarian, pancreatic, and GI cancers (esophagus, stomach and colon).
#4. Targeting Fibrosis, a Shared Culprit in Disease
The need for therapeutics that target fibrosis (tissue scarring), which is responsible for a significant percentage of deaths worldwide, representing diseases of the lung, liver, kidney, heart, and skin.
#5. New Approaches for Infectious Disease in a Changing World
The need for novel strategies for the rapid diagnoses, treatment, and even prevention of antibiotic-resistant infections, and the need for the next generation of globally deployable vaccines to enable pandemic preparedness.
#6. Striving for Equity in Healthcare
The need to radically rethink how, when, and where patients interact with healthcare services to optimize healthcare access and efficiency without diminishing its effectiveness, and to proactively meet the needs of currently underserved populations.
#7. Riding the Wave of Clinical Data
The need to expand the scope of available clinical data to include historically understudied populations (including women) and to model and implement a cohesive, dynamic data “stream,” which flows as patients do between the different phases of health and clinical care, enabling comparisons of patients to their previously healthy selves and the development of AI/ML approaches to harness these data to improve diagnosis, prognosis, and treatment.
#8. A Systems-Level View of Human Disease
The need to rethink how we understand and treat disease — not only from an organ-specific standpoint but from a whole-body, systems-level view — and to fully elucidate the roles that inflammation and immune pathways play in autoimmune and infectious diseases and their effects on chronic and acute diseases in diverse human systems, such as the cardiovascular/circulatory and nervous systems.
#9. A New Approach to Psychiatric Disease
The need for novel treatments for psychiatric disease, improved biomarkers and minimally invasive and ambulatory ways of measuring them, and more productive interactions with industry to advance new therapies to the clinic. This includes hybrid therapies (therapies that combine elements such as talk therapy, novel biomarkers, and pharmacological treatments) as well as new diagnostic and treatment modalities, such as psychedelic therapeutics and precision psychiatry.
#10. Charting a Course in Rare Disease Treatment
The need for viable treatments for the 7,000 identified rare diseases, especially the roughly 70% of such diseases that are genetic and the effects of which are first observed in early childhood.
The Unmet Needs list also include the following honorable mentions which rose to significant rankings in the analysis:
Driving Innovation in Chronic Disease: Improved Diagnosis, Treatment, and Prevention
A New Era of Obesity Medicine
A New Generation of Pain Treatments
Unlocking Novel Treatments for the Skin
Overarching Themes
Addressing unmet clinical needs involves solving a number of common challenges, including commercialization hurdles, regulatory considerations, and funding. The Mass General Brigham project identified overarching themes to help address these challenges and support innovation across multiple sectors. These include:
Taking a systems view of human disease and the practice of system-medicine
Developing a global view of infectious disease, including antimicrobial resistance
An expansion in high-quality, real-world data that closes gaps in current data (particularly for women and other underserved populations) and ensures that data sets are sufficiently enabling for AI/ML
Improving health and healthcare across key populations, including geriatrics and rare genetic disease
Addressing major diseases of the brain, including both neurodegenerative and neuropsychiatric conditions; these include Alzheimer’s disease, Parkinson’s disease, ALS, as well as psychiatric and mental health disorders
Opening an era of precision medicine across disease areas that includes early diagnosis, treating staged disease, and biomarker discovery and utilization
Panel co-chairs José Florez, Physician-in-Chief and Co-Chair of the MGB Department of Medicine and the Jackson Professor of Clinical Medicine at Harvard Medical School, and Bruce Levy, Physician-In-Chief and Co-Chair of the MGB Department of Medicine and the Parker B. Francis Professor of Medicine at Harvard Medical School, noted how the observations of a broad and representative set of faculty help illuminate the innovation landscape ahead.
“As a leader in patient care and healthcare innovation, our goal is to build on the legacy of research and discovery that has shaped the hospitals of the Mass General Brigham healthcare system for more than a hundred years, and continue to bring breakthroughs forward that can help solve pressing needs,” said Dr. Florez.
Dr. Levy added that “This is a roadmap for the future that can inform discussions happening throughout the healthcare and investment ecosystem regarding the future of medicine.”
More than 2000 decision-makers from healthcare, industry, finance and government attended the World Medical Innovation Forum this week in Boston. A premier global event, the Forum highlights leading innovations in medicine and transformative advancements in patient care.
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About Mass General Brigham
Mass General Brigham is an integrated academic health care system, uniting great minds to solve the hardest problems in medicine for our communities and the world. Mass General Brigham connects a full continuum of care across a system of academic medical centers, community and specialty hospitals, a health insurance plan, physician networks, community health centers, home care, and long-term care services. Mass General Brigham is a nonprofit organization committed to patient care, research, teaching, and service to the community. In addition, Mass General Brigham is one of the nation’s leading biomedical research organizations with several Harvard Medical School teaching hospitals. For more information, please visit massgeneralbrigham.org.
Contact: Tracy Doyle Mass General Brigham Innovation
(262) 227-5514
Tdoyle5@mgb.org
SOURCE
From: “Doyle, Tracy” <tdoyle5@mgb.org> Date: Thursday, September 26, 2024 at 10:19 AM Cc: “Card, Matthew” <matthew.card@bofa.com> Subject: Unmet Needs in Healthcare — Press Release and link to panel
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Invitation as MEDIA
From: “Doyle, Tracy” <tdoyle5@mgb.org> Date: Wednesday, August 14, 2024 at 4:04 PM Cc: “Doyle, Tracy” <tdoyle5@mgb.org>, “Card, Matthew” <matthew.card@bofa.com> Subject: Media Invite: World Medical Innovation Forum, Sept. 23-25, Boston — Hundreds of clinical experts, industry, investment leaders
Media Invite: World Medical Innovation Forum: Monday, Sept. 23—Wednesday, Sept. 25, Boston
At the intersection of innovation and investment in healthcare
Join Us!
Register Now: WMIF24 Media Registration
Mass General Brigham, one of the nation’s leading academic medical centers, is pleased to invite reporters to the 10th annual World Medical Innovation Forum (WMIF) Monday, Sept. 23–Wednesday, Sept. 25 at the Encore Boston Harbor in Boston. The event features expert discussions of scientific and investment trends for some of the hottest areas in healthcare, including
GLP-1s,
the cancer care revolution,
generative AI-enabled care paths,
xenotransplant,
community health,
hospital at home, and
therapeutic psychedelics, among many others.
The agenda includes nearly 175 executive speakers from healthcare, pharma, venture, start-ups, and the front lines of care, including many of Mass General Brigham’s Harvard Medical School-affiliated researchers and clinicians who this year will host 20+ focused sessions. Bank of America, presenting sponsor of the Forum, will provide additional expert insights on the investment landscape associated with healthcare innovation.
Forum highlights include:
1:1 and panel interviews with leading CEOs and government officials including:
Stéphane Bancel, CEO, Moderna
Albert Bourla, PhD, CEO, Pfizer
Marc Casper, CEO, Thermo Fisher
Deepak Chopra, MD, Founder, The Chopra Foundation
Scott Gottlieb, MD, PhD, Former Commissioner, FDA (2017-2019)
Maura Healey, Governor, Commonwealth of Massachusetts
David Hyman, MD, CMO, Eli Lilly
Haim Israel, Head of Global Thematic Investing Research, BofA Global Research
Reshma Kewalramani, MD, CEO, Vertex
Anne Klibanski, MD, President and CEO, Mass General Brigham
Peter Marks, MD, PhD, Director, Center for Biologics Evaluation and Research, FDA
Tadaaki Taniguchi, MD, PhD, Chief Medical Officer, Astellas Pharma
Christophe Weber, CEO, Takeda
Renee Wegrzyn, PhD, Director, ARPA-H
Expert panels including:
Oncology’s New Paradigm
Gene Therapies for Rare Diseases
Future of Metabolic Therapies
Digital Transformation
Biologic Revolution in Radiotherapies
Cell Therapies for Autoimmune Diseases
Hospital Venture Funds
Leading biotech and venture speakers from companies including:
Abata Therapeutics
Atlas Venture
Be Biopharma
Everly Health
Flagship Pioneering
Fractyl Health
MindMed
Mirador Therapeutics
Regor Therapeutics
RH Capital
Transcend Therapeutics
Exclusive programming:
First Look – 15 rapid-fire presentations on the latest research from leading Mass General Brigham scientists
Un-Met Clinical Needs – 100+ key opinion leaders in healthcare weigh in on the top un-met clinical needs in medicine today
Emerging Tech Zone – Hands-on exploration of some of the latest digital and AI-based healthcare technologies
Liz Everett Krisberg, Head of Bank of America Institute
Record attendance this year
Introduction to Haim
Panelist
Haim Israel
Head of Global Thematic Investing Research, BofA Global Research
Concept of the Future and for the Future: Short-term and long-term
Humanity achievements in Ten Year: Data, Processing power and BRAIN – Long-term becomes Short-term – Last 10 years: 2012, 2014 solar system, 2015 medicine, 2019 blackhole, 2023 core of sun – star was created hotter than core sun
2022, 2024 – galaxy picture of the universe
Volume of data created every month in terrabyts every 18 month data is duplicating itself.
Olny 1% is used – imagine 2% or 3%
Processing power since Apollo 11 [one trillion] – getting cheaper – cost for calculation went down 16,000 fold since 1995
AMMOUNT of DATA goes up and Cost of COMPUTATION goes down – price per giga byte
Projections for the next 100 years
Negative for people and Negative for Companies who are concerned with quarterly financial data
Companies: Walmart, Alphabet, Home Depot – DATA larger that COuntries
Living in defining moment: started by iPhone revolution and 2023 by AI revolution – 6x outpaced Moore’s Law by GPT by 3000x
18 months into AI revolution – GPT in use
The next 10 years:
Aging population
2024 – birth rate low in US, Japan, CHina, S. Korea – Pension system will decline in size
2.2 millions new material were created by DeepMind at Alphabet by simulation of AI on molecule
Microsoft in 80 hours identified 18 materials winners for Batteries using AI from 32 million material candidates
AI- weather calculations in minutes 1,000x faster, cheaper and more accurate
2025 – GPT-6 AI surpass Human Brain
China is a big player in AI
Cyber CRIME is the 3rd largest economy in the World. Hackers are using ChatGPT to create fake pictures leading to ZERO privacy
PRIVACY: Deepfakes up 62x, social media
2024 – Global Grid – needs much more energy because AI consumes so much energy
Metals shortages: Nickel, Copper,
Scarcity of water for 2/3 of the planet
data centers consume water more than Japan
2025 – Genomics Data sequencing bigger that X.com or Youtube
2027 – Peak oil demand: needed to be scalable, cheaper 25%
2028 – 5G networks reaches full capacity, 6G will be needed
2029 – 25x more satellites in Orbit than today
2029 – Personalized AI medicines and treatments will manipulate death and revive LONGEVITY – AI will generate drugs and all treatments
2030 – Generative AI: re-skill 1 Billion people
2035 – Fusion energy, known technology since the atomic bomb, how to keep it stable in plasma state of material – not yet achieved, it is clean, cheap: to Power the World – equivalent of 11 barrels of oil
Large cities: Cable diameter 17cm wide to power a large city
AI will change scarcity into abundance
2037 – Artifitial SUPER Intelligence – AI to outsmart Life
Quantum computer – Consortium of NASA and other governmental agencies and Google on quantum computer design
David Brown, MD, President, Academic Medical Centers, Mass General Brigham; Mass General Trustees Professor of Emergency Medicine, Harvard Medical School
Hoe do you balance Private medicine with Public not for profit HealthCare
Healthcare delivery system can achieve that much in Human health
Resources for Equity: housing and services: Capacity and COst
Evolution of care close to home catalyst of the Pandemic – How government think about the right patient for the right care level
MGB 40-60 In-patients at Home – Largest Program in the State – product needs to scale across all population though some do not have food security at home
Panelist
Kate Walsh, Secretary of Health and Human Services, State of Massachusetts
Stuart Bankrupcy – pstioents and providers involvement – structure challenges
Race and ethnicity – disparities, access and equity
Identify the challenge for Race and ethnicity
Focus to identify resources
Medicare & Medicaid – Human needs equity involve housing, food and home care – Public and Private sector cooperation
Pay for Performance
MA vs NYC – resources for welcoming new populations to the State of MA
Help finding Housing vs Shelter people
MA is the only State in the Union that is a Shelter State
People in our COuntry LEGALLY are in and out of shelters, new arrivals of skilled labor – temporary assistance to get jobs that we can’t find people to fill: CNA as example
MA has a community of shelters and medical center in the communities
Services for people that are at risk due to past life in home countries
Support for kids that do not speak English
Care and location: Keep care at home or SNF at home or in the community
David Hyman, MD, Chief Medical Officer, Eli Lilly and Company
Cardio-metabolic – medicines redefining disease by medicines benefit to patients
Investment in manufacturing medicines for Obesity, demand continue to expand
Oral small molecule and scaling focus on Sleep apnea, half of the population have metabolic disease and heart failure
Extension Program with sustained weigh loss in pre-diabetes progressing into maintained weigh loss
Invest in R&D in the cardio-metabolic
Listed to community feedback on experience how the drugs in AD affected patients in the Community – learning about challenges in delivery innovation in AD – irreversible neurodegenerative diseases – prevent not to loose the patients entirely – brain function
Targeted therapies, genetic therapies
Past life Oncologist – delivered innovations into Cancer patients – genetic medicines
AD medicines are not accessible even to people of means, Drug delivery using PET spinal injections
Ten years horizons at Eli Lilly is common
Obligation to provide scientific evidence from clinical trials
Inventory of patients qualification to participate in Clinical trials
Oncology: Interactions in biologics, cell therapies, conjucate compounds
Renewal of Targeting antigens
In Oncology: Proportions of patients get long term disease control by molecules developed in Academic Centers.
Eli Lilly acquired a BioPharma with manufacturing capabilities
Innovations are core vs discount cash-flow, strategy is to look at the science due to capacity to develop innovations
Alec Stranahan, PhD, SMid-Cap Biotech Analyst, BofA Global Research
Caroline Apovian, MD, MGH, HMS
Last ten years, from metabolic lessons of Bariatric patients
Treat obesity before surgery
product composition
multidisciplinary approach to obesity needs to be like in Oncology – multiple dsciplines
Bariatric and weigh regain like stent stenosis after surgery
Obesity dysfunction inflammation Gut-Brain transfer of hormones from the gut do not reach the brain to carb hunger socieaty is not signaled in the Brain and eating continued to mitigate hunger
Insurance must cover
Obesity Medicine – training 25 new practitioners to treat Obesity – Standards of Care, life style change
Primary care providers do not have resources to treat Life style component of
To reduce mortality by 20% by Bariatric surgery – No reduce of mortality by stenting – THAT I DISAGREE with
Panelists
David Hyman, MD, Chief Medical Officer, Eli Lilly and Company
non-peptide agonist, bariatric level for obesity
peptide injecting device
hormones and peptids activan inhibitor
hundred of million of people – scaling up
Adolescence with obesity will develop CVD, NASH
Epidemic of obesity the medicines are combating the epidemic
Vials, differential pricing, orals vs injectables
Productivity of work force, coverage by employers health insurance vs Government to handle coverage
10 additional drug
Xiayang Qiu, PhD, CEO, Regor Therapeutics
six years ago, great opportunity peptide and biologics for lifetime disease of obesity
cardiovascular favorably = affected by reduction in weigh
Medicines that works start early at age 35
Harith Rajagopalan, MD, PhD, CEO & Co-Founder, Fractyl Health
Diet & Life Style
Eli Lilly and Novo Nordik – have great drugs
Patients stop using them before they see the benefit
durable long term of mentainance long-tern to stay on the drug
Past life coronary cardiologist: PCI vs surgery choice of care angioplasty vs open heart surgery
Bariatric surgery vs great medicines
may be angioplasty for Bariatric patients
Obesity is different than CVD
BC-BS coverage of obesity drugs because weight is gained back vs Statins – continual use control cholestrol
maintenance drugs in the field of Obesity are needed
cost of drugs will come down
more evidence on obesity drugs will affect Formulary
Jason Zemansky, PhD, SMid-Cap Biotech Analyst, BofA Global Research
Patrick Ellinor, MD, PhD, MGH, HMS
Panelists
Craig Basson, MD, PhD, Chief Medical Officer, Bitterroot Bio
17,000 patients obese no DM
prior CVD followed 3 yrs of treatment 6% mortality during the Trial
Death from CVD endpoint
weight at joining the trial, loss during the trial, benefir from the drug’
improve CVD not weigh loss
mechanism of Inflammation – drug, reduced atherosclerosis and reduced plaque and cytokins and inflammation improve CVD status
combination of life style and drugs GI axis systemic
cardiac artery disease: cholesterol, inhibit inflammatory signals plaque build on top of itself – approaches to remove debris macrophages in the plaque for artherosclerosis mechanism as CVD risk
Joshua Cohen, Co-CEO, Amylyx Pharmaceuticals
Bariatric surgery lower obesity
genetics, eating habits,
GLP-1 agonist developed
Punit Dhillon, CEO, Skye Bioscience
Phase II study combination therapy CVD and Obesity
optimize body composition – more productive on the body periphery
subtypes metabolic gains
Pharmacotherapy for obesity: mechanisms complementary life style change is a must have for long-term benefits
weight loss as a start before obesity treatment
co-morbidities of obesity
Justin Klee, Co-CEO, Amylyx Pharmaceuticals
Parkinson’s CNS peripheral Brain access therapies
revolution in metabolic disease treatment options, more studies for pathways to target the right patients for the right treatment
GLP-1 is energy regulator, Hypoglycemia is very dangerous
Rohan Palekar, CEO, 89bio
applications to obesity – data support
bariatric surgery intervention is not enough, NASH will not be impacted only by the surgery
NASH is a disease taking 25 years to develop
risk of fibrosis to set in Cirrhosis which is not curable
Liz Kwo, MD, Chief Commercial Officer, Everly Health
Infrastructure
AI used for
Panelists
Anna Åsberg, Vice President, AstraZeneca Pharmaceuticals
Massive data bases organize
AI to augment intelligence inside the data
Tyler Bryson, Corporate Vice President, US Health & Public Sector Industries, Microsoft Corporation
Do we have platforms to serve new problem
Regulatory changes require visiting use cases
Pharma has the research data, providers have EMR – Microsoft builds new models using that data
Tumor imaging data was processed and new pattern recognition done on data of these tumors. New patterns are now a subject for research, just identified inside the data
Trust in Healthcare
NYC and Microsoft developed a System for small businesses to access city resources
Works with Academic institutions: Programs at Harvard and Princeton to train students by Microsoft employees on MIcrosoft AI technologies that as they graduate there will be trained new AI-trained employees
collaborations
Aditya Bhasin, BofA
AI in Banking: Bias, security
AI virtual system analytics to provide insight for scaling
Jane Moran, MGH
Network, Data structure needs updates
technology to help clinicians
care team to work with Generative AI to assist in e-mail reading and problem solving
Healthcare equity – avoid Bias
AI is not an answer to every problem
innovate at scale: using Epic and Microsoft
Clinical data structure for LLM, AI to renovate administrative processes inside MGH
John Bishai, PhD, Global Healthcare Investment Banking, BofA Securities
Umar Mahmood, MD, PhD, MGH, HMS
Panelists
Amos Hedt, Chief Business Strategy Officer, Perspective Therapeutics
imaging used to deliver the therapeutics before the drug touch the patient to calculate toxicity
PL-1 combined with radiotherapy synergistics results
immunogenic combination therapy, in presence of these agents, immune response reaction in the immune cells
Matthew Roden, PhD, President & CEO, Aktis Oncology
Conjugates – delivery direct to tumors
Opportunity two targets: (1) SSTA2 marker (2) xx
WHen agent inside the tumor, shrinkage and no emergence of cell nascent
optimization design
Treatment break for patients and families
Philip Kantoff, MD, Co-Founder & CEO, Convergent Therapeutics
Radio-pharmeceutics : 10 days half-life carrier not a target for small molecules Data on 120 patient, namo robust response synergy of antibody and molecule
image alphas
durable responses
Matt Vincent, PhD, AdvanCell Isotopes
ROS species generated in the tumor
peptides, protein binders
paradigm shift in delivery of oncology therapeutics directly to tumors
Lena Janes, PhD, Abdera Therapeutics
isotope will deliver the payload without damaging the DNA and healthy tissue
target different types of tumors, different half-life
Radiation therapy using isotopes id one of two modalities: tumor in and tumor out approach
screen for patient for the translational therapy
Next generation of products will come, now it is the beginning of these agents
Michael Ryskin, Life Science Tools & Diagnostics Analyst, BofA Global Research
Precision Medicine was it a paradigm shift??
Acquisition of manufacturing capabilities
research, manufacturinf line blurred
WHat excites you the most
Panelist
Marc Casper, Chairman, President & CEO, Thermo Fisher Scientific
Enabling Life sceinces, Pharmaceutical industries $1.5Billion internal investment annually
AI increasing knowledge
How is Precision Medicine applied? Sequencing in Cancer accelerated the Genomics information in use for 24 hours response of the sequence – adopted around the World.
at MGH lung cancers are treated with genomic sequencing
identification of the patients suitability for a targeted treatment
treatment during pregnacy at home vs hospitalization
History of company: Tools first: Mass spectrometry, one year for one sequence, protein identification and carrying to Mass spectrometry
Interactions need understanding acquiring electro spectrometry allowing analytical chemistry on proteins
Broad range of products: Clinical research to meet regulatory requirements entry into Reagents products.
Clinical Trials made effective by Thermo Scientific Products
Capabilities in registries, patient safety in psoriasis
Large role in experimental medicine drives efficiency in LABS
SIze of customers: small Biotech and large Pharma
Manufacture medicines: work with partnersbuilt by acquisitions small molecules,
100 engagements research, supply chain making medicines available at sites
Role for AI at Thermo Scientific:
Productivity – Cost effective for processes in use by 120,000 employees
Super customer interaction perfected by interogations with internal manuals to provide answers quickly
Improvement of products
Excitement Points: Responsiveness to COVID pandemic
Tazeen Ahmad, SMid-Cap Biotech Analyst, BofA Global Research
Are you using AI
Neuroinflammation
Cynthia Lemere, PhD, BWH, HMS
What systems are primarily impacted by the Immunes system
Drug delivery for inflammation huge area
Getting antibodies to the Brain
Precision medicine, genetics,specific person with specific immune disease
Panelists
Jo Viney, PhD, Cofounder, President & CEO, Seismic Therapeutic
Pandemics highlighted the impact of the immune system
Targeting cytokines in specific locations – hew approach
Modalities on hand: protein degradation mediation by bringing two cells together
AI is used for Patient stratification
AI to be used in Pathways involved in disease process to identify Biologics, PROTAC,
AI and ML for training models from interaction between proteins
ChatGPT to predict interactions among proteins
Immune disease and remission bust the immune system to improve quality of life of patient undergoing interventions
T-cell engaggers – in cases of refractory – great approach for boosting the immune system: removal of antibidies, recycling antibodies,
Two ends: Cell depletion vs Early detection
Therapy is every 6 months, cell depletion takes 3 months to come back.
Target immune system in the periphery,
Immune system in neurodegenerative diseases: Parkinson’s local modulation to penetrate neurological system
Markers to cross the BBB or not cross in neurological diseases
Immune disease is POLYGENIC multiple o=etiologies, mutation, genetics, which cell and which pathway to target a therapeutics: Biologics
Patient stratification is key for Precision Medicine at the cell level
T-cell, B-cell, Cytokines and antibodies mediated disease
ADGs degradation
9:45 AM – 10:10 AM
Picasso Ballroom
H. Jeffrey Wilkins, MD, Abcuro
Inflammation play a role in activating the immune system
zin the days of Medical School: inhibition of cytokines
Today: specificity to target cells for depletion
Specific biomarkers for response to therapies
cell types by mutations and physiology and causality in the inflammation area: we know why they have inflammation we need to learn interventions for inflammation
Asthma in the 40s as an inflammatory disease
assess treatment of inflammation
Neuro-inflammation – not well understood
What is the cause that drive the disease: understanding encephalitis?
NiranJana Nagarajan, PhD, MGB Ventures
Biology is the driver not AI
depletion of cells in a certain stage
Translation from disease to other diseases in the case of cell therapy potential – active area companies are trying solutions
Daniel Kuritzkes, MD, Chief, Division of Infectious Diseases, Brigham and Women’s Hospital; Harriet Ryan Albee Professor of Medicine, Harvard Medical School
Pathways in vaccine design
How to educate population on Vaccines
other approaches than vaccines
Alec Stranahan, PhD, SMid-Cap Biotech Analyst, BofA Global Research
Vaccine approval
Next generation vaccines
Panelist
Stéphane Bancel, CEO, Moderna
Vaccine design: long term vaccines weakens in aged population
data on role of AVV in Multiple Sclerosis
working on in the US vs France, Netherland in Europe different approaches
Vaccine for HIV
Vaccine was approved last year for children, pharmacies shortage
Season of FLu three times more vaccines in use
Employees run vaccine clinics on site
Vaccines not related to COVID
Misinformation from COVID vaccine
5% of COVID hospitalized were on the booster
Combination vaccines for high risk populations
Healthcare providers need to be involved in Education, many do not have an interest in the education on vaccines
Local stories from Vaccine manufectures and developer to be used in education in the communities
Individual DNA cancer celll signature of the cancer – data over time for development of vaccine to cancer many more tumor types are needed
Checkpoints in early disease
biopsy are too expensive
Side effect studies going on
mono-therapy vs immunotherapy costs involved
Naive virus to get into the Liver two diseases – cassets for sose management
Recombinant antibodies technology from the 70s
PD-1
COVID – was nto in the plan for development – design in silicon in two weeks – no change after this design
Large/SMid-Cap Biotech and Major Pharma Analyst, BofA Global Research
TCM
CAR-T
advantages of each cell type
Angele Shen, MGB Innovations
CAR-T
What would be a quick breakthrough?
Panelists
Jeff Bluestone, PhD, CEO & President, Sonoma Biotherapeutics
Cell therapy for cell depletion elimination of B-cells like its role in Multiple Sclerosis
Working with regulatory T-cells
Population of cells to study: T-cells master regulator in multiple ways – produce metabolic factors, infection tone in activation of other cells
Biology of cell: RNA, DNA
TCR – target antigens in tissues they are in in immune suppression
FInding the right peptide bindes to a certain MAC
CAR-T – recornize the cells in the local milieu like in patients with RA as an autoimmune disease
Clinical models ascertain cell types involvement leading to clinical trial insights then to therapies on a decision tree
recent data on CAR-T immune response in allogeneic for potential use in neurodegenerative diseases
patients and companies over react on immune therapy: Patients and Science vs hype
next generation: POC,
Gene therapy specificities vs Cell therapies – each approach will develop a different drug
FDA and NIH has in 11/2023 a meeting on Regulation of Cell therapy on stability and their approach to immune disease where there are already several drugs
approvals challenges companies
Price, too expensive a treatment is cell therapy
Chad Cowan, PhD, Executive Advisor, Century Therapeutics
use Natural Killer cells to elicit long-term immune response, T-cells,
active Beta cells]Regulatory monitoring use
DM – regulatory cells made from Stem cells
mission durable response
Clinical issues – not easy way for treatment wiht a cell line and bioreactors and modalities less similar to autologoous celles
CAR-T in oncology lessons now are transferred to Immune disease
Cell therapy requires technologies to mature multiple modalities and multiple drugs not one cell therapy for all immune diseases
Stability of the therapy vs rejection by immune system
FDA making cells is not as making drugs – higher level of scrutiny for cell therapy
SYNTHETIC BIOLOGY on B-cells for future breakthrough
Samantha Singer, President & CEO, Abata Therapeutics
Immune response involve many cell types in many diseases
Oncology the use of T-cells as tissue residents staying in tissue long time
Specific biology of the disease and regulatory cells receptors optimizing TCR presentation in pathology of tissue residents phyno types
activate in nervous system or in pancreas – intersection of cell biology with disease biology
Market feasibility – scaling, biology, pathology for reimbursement
antibody therapy may be appropriate than cell therapy is only a novel option
Cell manufacturing requires optimization of process, companies commercializing across all cell types
comprehensive approach for systemic immune suppression
: healthy tissue vs diseased tissue with cell theray implanted cells as residents in tissue
clinical data on product performance and on the biology reactions
Jose Florez, MD, PhD, Physician-in-Chief and Chair, Department of Medicine, Massachusetts General Hospital; Professor, Harvard Medical School
40 minutes to deal with big needs collected from 100 faculties at Harvard Medical School
The ten issues on one slide
How could we use compute to distill data
Bruce Levy, MD, Physician-In-Chief and Co-Chair, Department of Medicine, Brigham and Women’s Hospital; Parker B. Francis Professor of Medicine, Harvard Medical School
Transformation from the Present to the Future
identifying the needs
Infectious diseases: Rapid diagnostics need
resistance to antibiotics and metabolic reactions endogenous
Pandemics globally of diseases erradicated in the past: Pox, polio
Improving health in Geriatrics, not population growing but geriatric population growing. Beyong age 60 a citizen will use 1 or 2 physicians each
7,000 diseases, Genetic diseases requires integration and innovations in therapy
Innovations in Home devices
Panelists
Rox Anderson, MD, Lancer Endowed Chair of Dermatology;, Director, Wellman Center for Photomedicine, MGH; Professor of Dermatology, HMS
Access to data across institutions
Nicole Davis, PhD, Biomedical Communications
We asked 104 expert practitioners, content collected was analyzed
detection early
keeping the Human brain healthy
geriatrics Medicine, aging and compound effects on health system with aging and Health equity
Bias in Data
Jean-François Formela, MD, Partner, Atlas Venture
genetic information used in therapeutics design
Steven Greenberg, MD, Neurologist, Brigham and Women’s Hospital; Professor of Neurology, Harvard Medical School
Human genome completed in 1999, human genetic diseases were discovered learn about the disease at the tissue level with genomics and a system approach
Pathogenic drivers, systme integration by therapeutics approaches to pathways multiple cytokines in allergic reactions Pfizer had two biomarkers and therapies for systemic biology of disease
Pediatrics has its own challenges
Imaging medicine
Living longer at a lower cost – HOW TO ACHIEVE THAT?
growth abnormality in children: Body growth and Skull shrink
John Lepore, MD, CEO, ProFound Therapeutics;, CEO-Partner, Flagship Pioneering
Pathway, targeting therapy to patients in a System biological approach
Database of systme biology has missing components not included in the Human genome project – completion of the Data
Definition of End points needs revisiting
Identifying specific populations vs getting quickly to market
Diseases of aging: Muscles diseases – how to promote improvement in muscle mass
CONCLUSIONS
Gray Tsunami
Brain health
Cancer treatment paradigm shift
Fibrosis in many diseases
infectious disease in changing World
Equity in HC
Clinical Data is VAST
Systemic view of Human disease
New approaches to Psychaitry
Rare disease treatment needs a charter
In addition,
new generation of pain treatment
skin treatment new drugs
Chronic disease: improve treatment and prevention.
Tazeen Ahmad, SMid-Cap Biotech Analyst, BofA Global Research
FDA sets criteria – How is that done?
Autoimmune disease therapies – What is in the horizon?
Paul Anderson, MD, PhD, Chief Academic Officer, Mass General Brigham;
drug development
drug pricing in Europe
New book
RA needs more medicines
UNCONTROLLED SPREAD
In Uncontrolled Spread, a New York Times Best Seller, Dr. Scott Gottlieb identifies the reasons why the US was caught unprepared for the pandemic and how the country can improve its strategic planning to prepare for future viral threats.
Panelist
Scott Gottlieb, MD, Physician; Former Commissioner, Food and Drug Administration (2017-2019)
FDA approval 1st gene therapy in his tenure
Price of drugs: efficatious vs time to deveop
competitors in the marketplace are there for market share
New Book: Episodes in the FDA, appproval process at FDA, Gene therapy 1st in class approved – a special moment. Back in 1980s era translated to antibodies, to T-cell pioneering work.
Publisher worried it will not sell very well
FDA had concerns about manufacturing aspects
In 2024 we understand Biologics on novel platforms
Worries that Medicare will not reimbursement and cover the new therapies: Cell therapy
Statins approval had a known very large market vs Cell therapy not known which Cancer patients will benefit???
Black box involved in Autoimmune, studies bring exciting results
In 2018 – needs arise for early approved of drugs in AD, amyloid plaque – change in thinking and is controversial
In early 2020, change in settings of clinical trials, placido no more the only way for Randomized trials
Approval for AD drug vs othe indication – the process is difference (DMD a case to think about)
AI & NLP: Train on data of 10,000 lesions
FDA choose not to regulate AI the physician is in the Middle
Who is wrong: CHatGPT or the clinician ?
Data set on gene may represents NEW biologies that Physicians had not seen before
Data validation on medical devices and their approval after regulating them
Diagnostics tests: Validation Panels are involved
Regulated on input data vs Output data and validate the input data
Platforms are needed for regulation of AI involvement in the drug discovery and the drug approval process
investment in this platforms will be done by Whom?? It will come
Framework for AI at FDA: Regulatory gray data for applications and standards for output – not a novel regulatory concept
If AI will be applied widely, I/O accuracy is a must have
may be achievable soon?
FDA is evolutionary organization in its decision process NOT a REVOLUTIONARY organization. Simulation work started in 2003, 40 people doing that then.
Recently, new team in Agency working of Safety with tools and technologies that are common in Science – Approvals to drug labels and off labels that 20 years ago would not have happened
Tolerance for higher prices is to support Private sector that brings the innovating drugs to market
Chief Medical & Digital Officer, UC San Diego Health
Kevin Mahoney
CEO, University of Pennsylvania Health System
Niall Martin, PhD
CEO, Artios Pharma
James Mawson
CEO, Global Corporate Venturing
Mark McKenna
Chairman & CEO, Mirador Therapeutics
Jane Moran
Chief Information and Digital Officer, Mass General Brigham
William Morris, MD
Chief Medical Information Officer, Google Cloud
Rohan Palekar
CEO, 89bio
Raju Prasad, PhD
Chief Financial Officer, CRISPR Therapeutics
Xiayang Qiu, PhD
CEO, Regor Therapeutics
Harith Rajagopalan MD, PhD
CEO & Co-Founder, Fractyl Health
Shiv Rao, MD
CEO & Founder, Abridge
Kerry Ressler, MD, PhD
Chief Scientific Officer, McLean Hospital; Professor of Psychiatry, Harvard Medical School
Matthew Roden, PhD
President & CEO, Aktis Oncology
Sandi See Tai, MD
Chief Development Officer, Lexeo Therapeutics
Samantha Singer
President & CEO, Abata Therapeutics
Joanne Smith-Farrell, PhD
CEO & Director, Be Biopharma
Emma Somers-Roy
Chief Investment Officer, Mass General Brigham
Adam Steensberg, MD
President & CEO, Zealand Pharma
Tadaaki Taniguchi, MD, PhD
Chief Medical Officer, Astellas Pharma
Elsie Taveras, MD
Chief Community Health & Health Equity Officer, Mass General Brigham; Conrad Taff Endowed Chair and Professor of Pediatrics, Harvard Medical School
Jo Viney, PhD
Cofounder, President & CEO, Seismic Therapeutic
Ron Walls, MD
Chief Operating Officer, Mass General Brigham; Neskey Family Professor of Emergency Medicine, Harvard Medical School
Christophe Weber
President & CEO, Takeda
Fraser Wright, PhD
Chief Gene Therapy Officer, Kriya Therapeutics
Speakers
Anna Åsberg
Vice President, AstraZeneca Pharmaceuticals
Tazeen Ahmad
SMid-Cap Biotech Analyst, BofA Global Research
Jessica Allegretti, MD
Director, Crohn’s and Colitis Center, Brigham and Women’s Hospital; Associate Professor of Medicine, Harvard Medical School
Rox Anderson, MD
Lancer Endowed Chair of Dermatology; Director, Wellman Center for Photomedicine, MGH; Professor of Dermatology, HMS
Katherine Andriole, PhD
Director of Academic Research and Education, Mass General Brigham Data Science Office; Associate Professor, Harvard Medical School
Caroline Apovian, MD
Co-Director, Center for Weight Management and Wellness, Brigham and Women’s Hospital; Professor of Medicine, Harvard Medical School
Vanita Aroda, MD
Director, Diabetes Clinical Research, Brigham and Women’s Hospital; Associate Professor, Harvard Medical School
Natalie Artzi, PhD
Associate Professor of Medicine, Brigham and Women’s Hospital & Harvard Medical School
John Bishai, PhD
Global Healthcare Investment Banking, BofA Securities
David Blumenthal, MD
Professor of Practice of Public Health and Health Policy, Harvard TH Chan School of Public Health; Research Fellow, Harvard Kennedy School of Government; Samuel O. Thier Professor of Medicine, Emeritus, Harvard Medical School
Giles Boland, MD
President, Brigham and Women’s Hospital and Brigham and Women’s Physicians Organization; Philip H. Cook Distinguished Professor of Radiology, Harvard Medical School
Andrew Bressler
Washington Healthcare Policy Analyst, BofA Global Research
James Brink, MD
Enterprise Chief, Radiology, Mass General Brigham; Juan M. Taveras Professor of Radiology, Harvard Medical School
David Brown, MD
President, Academic Medical Centers, Mass General Brigham; Mass General Trustees Professor of Emergency Medicine, Harvard Medical School
Tyler Bryson
Corporate Vice President, US Health & Public Sector Industries, Microsoft Corporation
Jonathan Carlson, MD, PhD
Director of Chemistry, Center for Systems Biology, Massachusetts General Hospital; Assistant Professor of Medicine, Harvard Medical School
Miceal Chamberlain
President of Massachusetts, Bank of America
Moitreyee Chatterjee-Kishore, PhD
Head of Development, Immuno-Oncology and Cancer Cell Therapy, Astellas Pharma Inc.
Dong Feng Chen, MD, PhD
Associate Scientist, Massachusetts Eye and Ear; Associate Professor, Harvard Medical School
Jasmeer Chhatwal, MD, PhD
Associate Neurologist, Massachusetts General Hospital; Associate Professor of Neurology, Harvard Medical School
E. Antonio Chiocca, MD, PhD
Chair, Department of Neurosurgery, Brigham and Women’s Hospital; Harvey W. Cushing Professor of Neurosurgery, Harvard Medical School
Bryan Choi, MD, PhD
Associate Director, Center for Brain Tumor Immunology and Immunotherapy, Massachusetts General Hospital; Assistant Professor of Neurosurgery, Harvard Medical School
Deepak Chopra, MD
Founder, The Chopra Foundation
Yolonda Colson, MD, PhD
Chief, Division of Thoracic Surgery, Massachusetts General Hospital; Hermes C. Grillo Professor of Surgery, Harvard Medical School
Chad Cowan, PhD
Executive Advisor, Century Therapeutics
Cristina Cusin, MD
Director, MGH Ketamine Clinic and Psychiatrist, Depression Clinical and Research Program, Massachusetts General Hospital; Associate Professor in Psychiatry, Harvard Medical School
Nicole Davis, PhD
Biomedical Communications
Marcela del Carmen, MD
President, Massachusetts General Hospital and Massachusetts General Physicians Organization (MGPO); Executive Vice President, Mass General Brigham; Professor of Obstetrics, Gynecology and Reproductive Biology, Harvard Medical School
Gerard Doherty, MD
Surgeon-in-Chief, Mass General Brigham Cancer; Surgeon-in-Chief, Brigham and Women’s Hospital; Moseley Professor of Surgery, Harvard Medical School
Liz Everett Krisberg
Head of Bank of America Institute
Maurizio Fava, MD
Chair, Department of Psychiatry, Massachusetts General Hospital; Slater Family Professor of Psychiatry, Harvard Medical School
Keith Flaherty, MD
Director of Clinical Research, Mass General Cancer Center; Professor of Medicine, Harvard Medical School
Jose Florez, MD, PhD
Physician-in-Chief and Chair, Department of Medicine, Massachusetts General Hospital; Professor, Harvard Medical School
Jean-François Formela, MD
Partner, Atlas Venture
Fritz François, MD
Executive Vice President and Vice Dean, Chief of Hospital Operations, NYU Langone Health
Joanna Gajuk
Health Care Facilities and Managed Care Analyst, BofA Global Research
Jason Gerberry
Specialty Pharma and SMid-Cap Biotech Analyst, BofA Global Research
Gad Getz, PhD
Director of Bioinformatics, Krantz Center for Cancer Research and Department of Pathology; Paul C. Zamecnik Chair in Cancer Research, Mass General Cancer Center; Professor of Pathology, Harvard Medical School
Alexandra Golby, MD
Neurosurgeon; Director of Image-guided Neurosurgery, Brigham and Women’s Hospital; Professor of Neurosurgery, Professor of Radiology, Harvard Medical School
Allan Goldstein, MD
Chief of Pediatric Surgery, Massachusetts General Hospital; Surgeon-in-Chief, Mass General for Children; Marshall K. Bartlett Professor of Surgery, Harvard Medical School
Scott Gottlieb, MD
Physician; Former Commissioner, Food and Drug Administration (2017-2019)
David Grayzel, MD
Partner, Atlas Venture
Steven Greenberg, MD
Neurologist, Brigham and Women’s Hospital; Professor of Neurology, Harvard Medical School
Steven Grinspoon, MD
Chief, Metabolism Unit, Massachusetts General Hospital; Professor of Medicine, Harvard Medical School
Daphne Haas-Kogan, MD
Chief, Enterprise Radiation Oncology, Mass General Brigham; Professor, Harvard Medical School
Roger Hajjar, MD
Director, Gene & Cell Therapy Institute, Mass General Brigham
John Hanna, MD, PhD
Associate Professor, Brigham and Women’s Hospital & Harvard Medical School
Yvonne Hao
Secretary of Economic Development, Commonwealth of Massachusetts
Nobuhiko Hata PhD
Director, Surgical Navigation and Robotics Laboratory, Brigham and Women’s Hospital; Professor of Radiology, Harvard Medical School
Maura Healey
Governor of the Commonwealth of Massachusetts
Elizabeth Henske, MD
Director, Center for LAM Research and Clinical Care, Brigham and Women’s Hospital; Professor of Medicine, Harvard Medical School
Leigh Hochberg MD, PhD
Director of Neurotechnology and Neurorecovery, Massachusetts General Hospital; Senior Lecturer on Neurology, Harvard Medical School
Daphne Holt, MD, PhD
Director of the Resilience and Prevention Program, Massachusetts General Hospital; Associate Professor of Psychiatry, Harvard Medical School
Susan Huang, MD
EVP, Chief Executive, Providence Clinical Network, Providence Southern CA
Keith Isaacson, MD
Director of Minimally Invasive Gynecologic Surgery and Infertility, Newton Wellesley Hospital; Associate Professor of Obstetrics, Gynecology and Reproductive Biology, Harvard Medical School
Ole Isacson, MD-PhD
Founding Director, Neuroregeneration Research Institute, McLean Hospital; Professor of Neurology and Neuroscience, Harvard Medical School
Haim Israel
Head of Global Thematic Investing Research, BofA Global Research
Farouc Jaffer, MD, PhD
Director, Coronary Intervention, Massachusetts General Hospital; Associate Professor of Medicine, Harvard Medical School
Russell Jenkins, MD, PhD
Krantz Family Center for Cancer Research, Massachusetts General Hospital; Mass General Cancer Center, Center for Melanoma; Assistant Professor of Medicine, Harvard Medical School
Hadine Joffe, MD
Executive Director of the Connors Center for Women’s Health and Gender Biology; Interim Chair, Department of Psychiatry, Brigham and Women’s Hospital; Paula A. Johnson Professor of Psychiatry in the Field of Women’s Health, Harvard Medical School
Benjamin Kann, MD
Assistant Professor, Brigham and Women’s Hospital & Harvard Medical School
Tatsuo Kawai, MD, PhD
Director of the Legorreta Center for Clinical Transplantation Tolerance, A.Benedict Cosimi Chair in Transplant Surgery, Massachusetts General Hospital; Professor of Surgery, Harvard Medical School
Albert Kim, MD
Assistant Physician, Mass General Cancer Center; Assistant Professor, Harvard Medical School
Roger Kitterman
Senior Vice President, Ventures and Business Development & Licensing, Mass General Brigham Managing Partner, Mass General Brigham Ventures
Lotte Bjerre Knudsen, DMSc
Chief Scientific Advisor, Novo Nordisk
Vesela Kovacheva, MD, PhD
Director of Translational and Clinical Research, Mass General Brigham; Assistant Professor of Anesthesia, Harvard Medical School
Jonathan Kraft
President, The Kraft Group; Board Chair, Massachusetts General Hospital
John Krystal, MD
Chair, Department of Psychiatry, Yale School of Medicine
Daniel Kuritzkes, MD
Chief, Division of Infectious Diseases, Brigham and Women’s Hospital; Harriet Ryan Albee Professor of Medicine, Harvard Medical School
Bruce Levy, MD
Physician-In-Chief and Co-Chair, Department of Medicine, Brigham and Women’s Hospital; Parker B. Francis Professor of Medicine, Harvard Medical School
Katherine Liao, MD
Associate Physician, Department of Rheumatology, Inflammation, and Immunity, Brigham and Women’s Hospital; Associate Professor of Medicine and Biomedical Informatics, Harvard Medical School
David Louis, MD
Enterprise Chief, Pathology, Mass General Brigham Benjamin Castleman Professor of Pathology, Harvard Medical School
Tim Luker, PhD
VP, Ventures & West Coast Head, Eli Lilly
Andrew Luster, MD, PhD
Chief, Division of Rheumatology, Allergy and Immunology; Director, Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital; Persis, Cyrus and Marlow B. Harrison Professor of Medicine, Harvard Medical School
Allen Lutz
Health Care Services Analyst, BofA Global Research
Calum MacRae MD, PhD
Vice Chair for Scientific Innovation, Department of Medicine, Brigham and Women’s Hospital; Professor of Medicine, Harvard Medical School
Joren Madsen, MD, PhD
Director, MGH Transplant Center; Paul S. Russell/Warner-Lambert Professor of Surgery, Harvard Medical School
Faisal Mahmood, PhD
Associate Professor, Brigham and Women’s Hospital & Harvard Medical School
Peter Marks, MD, PhD
Director, Center for Biologics Evaluation and Research, FDA
Marcela Maus, MD, PhD
Director of Cellular Therapy and Paula O’Keeffe Chair in Cancer Research, Krantz Family Center for Cancer Research and Mass General Cancer Center; Associate Director, Gene and Cell Therapy Institute, Mass General Brigham; Associate Professor, Harvard Medical School
Thorsten Mempel, MD, PhD
Associate Director, Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital; Professor of Medicine, Harvard Medical School
Rebecca Mishuris, MD
Chief Medical Information Officer, Mass General Brigham; Member of the Faculty, Harvard Medical School
Pradeep Natarajan, MD
Director of Preventive Cardiology, Paul & Phyllis Fireman Endowed Chair in Vascular Medicine, Massachusetts General Hospital; Associate Professor of Medicine, Harvard Medical School
Nawal Nour, MD
Chair, Department of Obstetrics and Gynecology, Brigham and Women’s Hospital; Associate Professor, Kate Macy Ladd Professorship, Harvard Medical School
Heather O’Sullivan, MS, RN, AGNP
President, Mass General Brigham Healthcare at Home
Anne Oxrider
Senior Vice President, Benefits Executive, Bank of America
Claire-Cecile Pierre, MD
Vice President, Community Health Programs, Mass General Brigham; Instructor in Medicine, Harvard Medical School
Richard Pierson III, MD
Scientific Director, Center for Transplantation Sciences, Massachusetts General Hospital; Professor of Surgery, Harvard Medical School
Mark Poznansky, MD, PhD
Director, Vaccine and Immunotherapy Center, Massachusetts General Hospital; Steve and Deborah Gorlin MGH Research Scholar; Professor of Medicine, Harvard Medical School
Yakeel Quiroz, PhD
Director, Familial Dementia Neuroimaging Lab and Director, Multicultural Alzheimer’s Prevention Program, Massachusetts General Hospital; Paul B. and Sandra M. Edgerley MGH Research Scholar; Associate Professor, Harvard Medical School
Heidi Rehm, PhD
Chief Genomics Officer, Massachusetts General Hospital; Professor of Pathology, Harvard Medical School
Leonardo Riella, MD, PhD
Medical Director of Kidney Transplantation, Massachusetts General Hospital; Harold and Ellen Danser Endowed Chair in Transplantation, Harvard Medical School
Jorge Rodriguez, MD
Clinician-investigator, Brigham and Women’s Hospital; Assistant Professor, Harvard Medical School
Adam Ron
Health Care Facilities and Managed Care Analyst, BofA Global Research
David Ryan, MD
Physician-in-Chief, Mass General Brigham Cancer; Professor of Medicine, Harvard Medical School
Michael Ryskin
Life Science Tools & Diagnostics Analyst, BofA Global Research
Alkesh Shah
Head of US Equity Software Research, BofA Global Research
Angela Shen, MD
Vice President, Strategic Innovation Leaders, Mass General Brigham Innovation
Gregory Simon
President, Simonovation
Prabhjot Singh, MD, PhD
Senior Advisor, Strategic Initiatives Peterson Health Technology Institute
Brendan Singleton
Healthcare Equity Capital Markets, BofA Securities
Caroline Sokol, MD, PhD
Assistant Physician, Massachusetts General Hospital; Assistant Professor, Harvard Medical School
Daniel Solomon, MD
Matthew H. Liang Distinguished Chair in Arthritis and Population Health, Brigham and Women’s Hospital; Professor of Medicine, Harvard Medical School
Scott Solomon, MD
Director, Clinical Trials Outcomes Center; Edward D. Frohlich Distinguished Chair in Cardiovascular Pathophysiology, Brigham and Women’s Hospital; Professor of Medicine, Harvard Medical School
Fatima Cody Stanford, MD
Obesity Medicine Physician Scientist, Massachusetts General Hospital; Associate Professor of Medicine and Pediatrics, Harvard Medical School
Shannon Stott, PhD
Associate Investigator, Krantz Family Center for Cancer Research and Mass General Cancer Center; d’Arbeloff Research Scholar, Massachusetts General Hospital; Associate Investigator, Krantz Family Center for Cancer Research Harvard Medical School
Alec Stranahan, PhD
SMid-Cap Biotech Analyst, BofA Global Research
Marc Succi, MD
Executive Director, Mass General Brigham MESH Incubator; Associate Chair of Innovation & Commercialization, Mass General Brigham Radiology; Assistant Professor, Harvard Medical School
Guillermo Tearney, MD, PhD
Principal Investigator, Wellman Center for Photomedicine, Massachusetts General Hospital; Remondi Family Endowed MGH Research Institute Chair; Professor of Pathology, Harvard Medical School
David Ting, MD
Associate Clinical Director for Innovation, Mass General Cancer Center; Associate Professor of Medicine, Harvard Medical School
Raul Uppot, MD
Interventional Radiologist, Massachusetts General Hospital; Associate Professor, Harvard Medical School
Chris Varma, PhD
Co-founder, Chairman & CEO, Frontier Medicines
Kaveeta Vasisht, MD, PharmD
Associate Commissioner, Women’s Health, U.S. Food and Drug Administration
Alexandra-Chloé Villani PhD
Investigator, Massachusetts General Hospital; Assistant Professor, Harvard Medical School
Kate Walsh
Secretary of Health and Human Services, State of Massachusetts
David Walt, PhD
Professor of Pathology, Brigham and Women’s Hospital; Hansjörg Wyss Professor of Biologically Inspired Engineering, Harvard Medical School
With the advent of AI in the last 5-7 years in our fields: Pharmaceutical, Life Sciences and Medicine, LPBI Group had launched several initiatives to advance the frontier of knowledge by using our own contents repositories of +8 giga bytes for experimenting with Machine Learning (ML) technologies for Medical Text Analysis.
These AI Technologies include
Natural Language Processing (NLP): Statistical ML and Deep Learning ML
ChatGPT and GPT-4
Generative AI
A quote by Brad Power, Co-founder and CEO, Cancer Patient Lab made in January 2024
LPBI Group is in the admirable position of sitting on a treasure trove of medical literature that would be useful input in the current environment of customized ChatGPTs looking for reliable medical content.
In the Drug Development (DD) field, AI technologies are been employed chiefly, for these tasks:
(a) Generation of molecular information libraries
(b) Explorations and combinatorial experiments on protein structures, and
(c) measurements of biochemical interactions
The A.I. learns from patterns in the data to suggest possible useful drug candidates, as if matching chemical keys to the right protein locks.
Because A.I. for drug development is powered by precise scientific data, toxic “hallucinations” are far less likely than with more broadly trained chatbots. And any potential drug must undergo extensive testing in labs and in clinical trials before it is approved for patients.
“Generative A.I. is transforming the field, but the drug-development process is messy and very human,” said David Baker, a biochemist and director of the Institute for Protein Design at the University of Washington.
As of December 2023,
24 AI-discovered molecules had completed Phase I trials, with 21 of them being successful. This success rate of 80–90% is higher than the historical industry average of 40–65%.
In Phase II trials, the success rate is around 40%, which is similar to the historical average.
A drug that blocks the activity of an enzyme called aromatase, which the body uses to make estrogen in the ovaries and other tissues. Blocking aromatase lowers the amount of estrogen made by the body, which may stop the growth of cancer cells that need estrogen to grow. AI drugs are used to treat some types of breast cancer or to keep it from coming back. They may also be used to help prevent breast cancer in some women who are at a high risk of developing it. Examples of AI drugs are anastrozole, letrozole, and exemestane. AI drugs are a type of hormone therapy. Also called aromatase inhibitor.
Examples of AI drugs are anastrozole, letrozole, and exemestane. AI drugs are a type of hormone therapy. Also called aromatase inhibitor.
More examples of AI Drugs, Drugs developed with AI technologies
#1:
INS018_055
Developed by Insilico Medicine, a Hong Kong-based biotech startup, to treat idiopathic pulmonary fibrosis (IPF). IPF is a chronic lung disease that causes scarring and can be fatal if left untreated. In January 2023, Insilico Medicine announced positive results from a Phase I safety trial of INS018_055. In February 2023, the FDA granted breakthrough status to a small molecule inhibitor identified by Insilico Medicine’s AI platforms for the drug. As of November 2023, INS018_055 was in mid-stage trials in the US and China, with some results expected in early 2025.
The first fully A.I. -generated drug enters clinical trials in human patients. Insilico Medicine, a Hong Kong-based biotech startup with more than $400 million in funding, created the drug as a treatment for idiopathic pulmonary fibrosis, a chronic lung disease.Jun 29, 2023
Uses predictive modeling to optimize the molecular structure of drugs. AI is expected to integrate more advanced simulation techniques, such as quantum computing, to more accurately predict molecular behavior.
Drug discovery software developer Schrodinger Inc. (NASDAQ: SDGR) stock has been trying to recover after plummeting over 80% off its all-time high of $117 in January 2021. Schrodinger’s artificial intelligence (AI) powered software technology platform utilizes physics-based modeling and sophisticated machine learning algorithms to help clients identify the suitable molecules to treat the desired ailments. Its programs can help predict the behavior of molecules and potential outcomes.
This entails finding suitable molecules that effectively target specific cells and proteins, transcend through cell walls, are absorbed and dissolved well without interfering with other drugs or producing bad reactions to other drugs, and are scalable.
In high-tech labs, workers are generating data to train A.I. algorithms to design better medicine, faster. But the transformation is just getting underway.
By Steve Lohr, Terray Therapeutics campus in Monrovia, Calif., June 17, 2024
According to Grand View Research, the global AI in drug discovery market size was valued at $1.1 billion in 2022, and is expected to expand at a compound annual growth rate (CAGR) of 29.6% from 2023 to 2030. The report states that the growing demand for the discovery and development of novel drug therapies and increasing manufacturing capacities of the life science industry are driving the demand for AI-empowered solutions in the drug discovery processes.
As this report suggests, AI for drug discovery is clearly a growing field within the biopharma industry. Inevitably, as it grows even larger, we will see more companies come to the forefront of the field, hoping to change the face of drug discovery – and also the biopharma industry as a whole – so that the entire drug development process can become faster, more consistent, more accurate, and more scalable.
The Table of Contents of these two book can be found in our
Spanish-language Edition, as well
Serie E, Volumen 4
Bioimpresión médica en 3D: la revolución de la medicina: Tecnologías para una medicina centrada en el paciente: de la I+D en agentes biológicos a los nuevos … en el paciente nº 4) (Spanish Edition) 2023
Tratamientos contra el cáncer: Metabólicos, genómicos, intervencionistas, inmunoterapia y nanotecnología para la administración de tratamientos (Serie … y la oncología nº 2) 2022
The Use of ChatGPT in the World of BioInformatics and Cancer Research and Development of BioGPT by MIT
Curator: Stephen J. Williams, Ph.D.
Chatbots are being used in multiple interdisciplanary areas in research and medicine and so it was a natural progression to incorporate artificial intelligence (AI), natural language processing (NLP), and chatbot technology like ChatGPT into bioinformatic analysis.
“In domains like informatics, management and marketing, media and communication science, languages and philosophy, psychology and sociology, engineering, design, and human-computer interaction, the fast expanding body of chatbot study is clearly interdisciplinary.
The field of bioinformatics is a natural fit for incorporting this technology. The curated information below shows some use examples of this technology in bioinformatics related to cancer research.
How ChatGPT can be used in bioinformatics research?
ChatGPT is a variant of the GPT (Generative Pre-trained Transformer) language model that was designed to generate human-like text in a conversational setting. It is not directly related to bioinformatics, which is the field of study that deals with the storage, retrieval, and analysis of biological data, particularly in the context of genetics and genomics. However, ChatGPT could potentially be used in a bioinformatics context as a tool for generating natural language descriptions of genetic or genomic data, or for generating responses to queries about such data.
For example, a researcher could use ChatGPT to generate descriptions of genetic variants or gene expression patterns based on input data. This could be useful for generating summaries of results for reports or papers, or for generating explanations of complex concepts for non-expert audiences. ChatGPT could also be used to generate responses to queries about genetic or genomic data, such as “What is the function of gene X?” or “What are the potential consequences of variant Y?”
It’s worth noting that ChatGPT is just one of many tools and techniques that can be used in bioinformatics, and it is unlikely to be the most suitable or effective option in every situation. There are many specialized tools and resources available for working with biological data, and it is often necessary to use a combination of these tools to fully analyze and understand the data.
Generating descriptions of genetic or genomic data: ChatGPT could be used to generate natural language descriptions of genetic or genomic data based on input data. For example, suppose a researcher has a dataset containing information about gene expression levels in different tissues. The researcher could use ChatGPT to generate a description of the data, such as:
“Gene X is highly expressed in the liver and kidney, with moderate expression in the brain and heart. Gene Y, on the other hand, shows low expression in all tissues except for the lung, where it is highly expressed.”
Thereby ChatGPT, at its simplest level, could be used to ask general questions like “What is the function of gene product X?” and a ChatGPT could give a reasonable response without the scientist having to browse through even highly curated databases lie GeneCards or UniProt or GenBank. Or even “What are potential interactors of Gene X, validated by yeast two hybrid?” without even going to the curated InterActome databases or using expensive software like Genie.
Summarizing results: ChatGPT could be used to generate summaries of results from genetic or genomic studies. For example, a researcher might use ChatGPT to generate a summary of a study that found a association between a particular genetic variant and a particular disease. The summary might look something like this:
“Our study found that individuals with the variant form of gene X are more likely to develop disease Y. Further analysis revealed that this variant is associated with changes in gene expression that may contribute to the development of the disease.”
It’s worth noting that ChatGPT is just one tool that could potentially be used in these types of applications, and it is likely to be most effective when used in combination with other bioinformatics tools and resources. For example, a researcher might use ChatGPT to generate a summary of results, but would also need to use other tools to analyze the data and confirm the findings.
ChatGPT is a variant of the GPT (Generative Pre-training Transformer) language model that is designed for open-domain conversation. It is not specifically designed for generating descriptions of genetic variants or gene expression patterns, but it can potentially be used for this purpose if you provide it with a sufficient amount of relevant training data and fine-tune it appropriately.
To use ChatGPT to generate descriptions of genetic variants or gene expression patterns, you would first need to obtain a large dataset of examples of descriptions of genetic variants or gene expression patterns. You could use this dataset to fine-tune the ChatGPT model on the task of generating descriptions of genetic variants or gene expression patterns.
Here’s an example of how you might use ChatGPT to generate a description of a genetic variant:
First, you would need to pre-process your dataset of descriptions of genetic variants to prepare it for use with ChatGPT. This might involve splitting the descriptions into individual sentences or phrases, and encoding them using a suitable natural language processing (NLP) library or tool.
Next, you would need to fine-tune the ChatGPT model on the task of generating descriptions of genetic variants. This could involve using a tool like Hugging Face’s Transformers library to load the ChatGPT model and your pre-processed dataset, and then training the model on the task of generating descriptions of genetic variants using an appropriate optimization algorithm.
Once the model has been fine-tuned, you can use it to generate descriptions of genetic variants by providing it with a prompt or seed text and asking it to generate a response. For example, you might provide the model with the prompt “Generate a description of a genetic variant associated with increased risk of breast cancer,” and ask it to generate a response. The model should then generate a description of a genetic variant that is associated with increased risk of breast cancer.
It’s worth noting that generating high-quality descriptions of genetic variants or gene expression patterns is a challenging task, and it may be difficult to achieve good results using a language model like ChatGPT without a large amount of relevant training data and careful fine-tuning.
To train a language model like chatGPT to extract information about specific genes or diseases from research papers, you would need to follow these steps:
Gather a large dataset of research papers that contain information about the specific genes or diseases you are interested in. This dataset should be diverse and representative of the types of papers you want the model to be able to extract information from.
Preprocess the text data in the research papers by tokenizing the text and creating a vocabulary. You may also want to consider lemmatizing or stemming the text to reduce the dimensionality of the dataset.
Train the language model on the preprocessed text data. You may want to fine-tune a pre-trained model such as chatGPT on your specific dataset, or you can train a new model from scratch.
ChatGPT could also be useful for sequence analysis
A few examples of sequence analysis a ChatGPT could be useful include:
Protein structure
Identifying functional regions of a protein
Predicting protein-protein interactions
Identifying protein homologs
Generating Protein alignments
All this could be done without having access to UNIX servers or proprietary software or knowing GCG coding
ChatGPT in biomedical research
There are several potential ways that ChatGPT or other natural language processing (NLP) models could be applied in biomedical research:
Text summarization: ChatGPT or other NLP models could be used to summarize large amounts of text, such as research papers or clinical notes, in order to extract key information and insights more quickly.
Data extraction: ChatGPT or other NLP models could be used to extract structured data from unstructured text sources, such as research papers or clinical notes. For example, the model could be trained to extract information about specific genes or diseases from research papers, and then used to create a database of this information for further analysis.
Literature review: ChatGPT or other NLP models could be used to assist with literature review tasks, such as identifying relevant papers, extracting key information from papers, or summarizing the main findings of a group of papers.
Predictive modeling: ChatGPT or other NLP models could be used to build predictive models based on large amounts of text data, such as electronic health records or research papers. For example, the model could be trained to predict the likelihood of a patient developing a particular disease based on their medical history and other factors.
It’s worth noting that while NLP models like ChatGPT have the potential to be useful tools in biomedical research, they are only as good as the data they are trained on, and it is important to carefully evaluate the quality and reliability of any results generated by these models.
ChatGPT in text mining of biomedical data
ChatGPT could potentially be used for text mining in the biomedical field in a number of ways. Here are a few examples:
Extracting information from scientific papers: ChatGPT could be trained on a large dataset of scientific papers in the biomedical field, and then used to extract specific pieces of information from these papers, such as the names of compounds, their structures, and their potential uses.
Generating summaries of scientific papers: ChatGPT could be used to generate concise summaries of scientific papers in the biomedical field, highlighting the main findings and implications of the research.
Identifying trends and patterns in scientific literature: ChatGPT could be used to analyze large datasets of scientific papers in the biomedical field and identify trends and patterns in the data, such as emerging areas of research or common themes among different papers.
Generating questions for further research: ChatGPT could be used to suggest questions for further research in the biomedical field based on existing scientific literature, by identifying gaps in current knowledge or areas where further investigation is needed.
Generating hypotheses for scientific experiments: ChatGPT could be used to generate hypotheses for scientific experiments in the biomedical field based on existing scientific literature and data, by identifying potential relationships or associations that could be tested in future research.
PLEASE WATCH VIDEO
In this video, a bioinformatician describes the ways he uses ChatGPT to increase his productivity in writing bioinformatic code and conducting bioinformatic analyses.
He describes a series of uses of ChatGPT in his day to day work as a bioinformatian:
Using ChatGPT as a search engine: He finds more useful and relevant search results than a standard Google or Yahoo search. This saves time as one does not have to pour through multiple pages to find information. However, a caveat is ChatGPT does NOT return sources, as highlighted in previous postings on this page. This feature of ChatGPT is probably why Microsoft bought OpenAI in order to incorporate ChatGPT in their Bing search engine, as well as Office Suite programs
ChatGPT to help with coding projects: Bioinformaticians will spend multiple hours searching for and altering open access available code in order to run certain function like determining the G/C content of DNA (although there are many UNIX based code that has already been established for these purposes). One can use ChatGPT to find such a code and then assist in debugging that code for any flaws
ChatGPT to document and add coding comments: When writing code it is useful to add comments periodically to assist other users to determine how the code works and also how the program flow works as well, including returned variables.
One of the comments was interesting and directed one to use BIOGPT instead of ChatGPT
0:54 oh dear. You cannot use chatgpt like that in Bioinformatics as it is rn without double checking the info from it. You should be using biogpt instead for paper summarisation. ChatGPT goes for human-like responses over precise information recal. It is quite good for debugging though and automating boring awkward scripts
The BioGPT model was proposed in BioGPT: generative pre-trained transformer for biomedical text generation and mining by Renqian Luo, Liai Sun, Yingce Xia, Tao Qin, Sheng Zhang, Hoifung Poon and Tie-Yan Liu. BioGPT is a domain-specific generative pre-trained Transformer language model for biomedical text generation and mining. BioGPT follows the Transformer language model backbone, and is pre-trained on 15M PubMed abstracts from scratch.
The abstract from the paper is the following:
Pre-trained language models have attracted increasing attention in the biomedical domain, inspired by their great success in the general natural language domain. Among the two main branches of pre-trained language models in the general language domain, i.e. BERT (and its variants) and GPT (and its variants), the first one has been extensively studied in the biomedical domain, such as BioBERT and PubMedBERT. While they have achieved great success on a variety of discriminative downstream biomedical tasks, the lack of generation ability constrains their application scope. In this paper, we propose BioGPT, a domain-specific generative Transformer language model pre-trained on large-scale biomedical literature. We evaluate BioGPT on six biomedical natural language processing tasks and demonstrate that our model outperforms previous models on most tasks. Especially, we get 44.98%, 38.42% and 40.76% F1 score on BC5CDR, KD-DTI and DDI end-to-end relation extraction tasks, respectively, and 78.2% accuracy on PubMedQA, creating a new record. Our case study on text generation further demonstrates the advantage of BioGPT on biomedical literature to generate fluent descriptions for biomedical terms.
Tips:
BioGPT is a model with absolute position embeddings so it’s usually advised to pad the inputs on the right rather than the left.
BioGPT was trained with a causal language modeling (CLM) objective and is therefore powerful at predicting the next token in a sequence. Leveraging this feature allows BioGPT to generate syntactically coherent text as it can be observed in the run_generation.py example script.
The model can take the past_key_values (for PyTorch) as input, which is the previously computed key/value attention pairs. Using this (past_key_values or past) value prevents the model from re-computing pre-computed values in the context of text generation. For PyTorch, see past_key_values argument of the BioGptForCausalLM.forward() method for more information on its usage.
This model was contributed by kamalkraj. The original code can be found here.
BioGPT is MIT-licensed. The license applies to the pre-trained models as well.
Contributing
This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.
As of right now this does not seem Open Access, however a sign up is required!
We provide our pre-trained BioGPT model checkpoints along with fine-tuned checkpoints for downstream tasks, available both through URL download as well as through the Hugging Face 🤗 Hub.
Luo R, Sun L, Xia Y, Qin T, Zhang S, Poon H, Liu TY.Brief Bioinform. 2022 Nov 19;23(6):bbac409. doi: 10.1093/bib/bbac409.PMID: 36156661
We evaluate BioGPT on six biomedical natural language processing tasks and demonstrate that our model outperforms previous models on most tasks. …Our case study on text generation further demonstrates the advantage of BioGPT on biomedical literature to generate fl …
Here they show the advantages of BioGPT over BioBERT and ChatGPT in an example of text extraction and question answer
Table 7. Example of generated text
Input Model Text
Bicalutamide GPT-2 Bicalutamide, a synthetic bicalutamide derivative, was found to increase the cellular proliferation
of C. elegans cells. This effect was not observed with the other synthetic bicalutamide derivatives. This is in line with our previous work which showed that bicalutamide increases the proliferation of C. elegans cells but not in the presence of synthetic bicalutamide.
BioGPT Bicalutamide (Casodex) is an androgen receptor (AR) antagonist approved for the treatment of metastatic castration-resistant prostate cancer (mCRPC) in patients who have progressed on or are ineligible for docetaxel chemotherapy, as well as for the treatment of early-stage prostate cancer in men who have not progressed on or are ineligible for docetaxel chemotherapy, as well as for the treatment of metastatic castration-sensitive prostate cancer (mCSPC) in men who have not progressed on or are ineligible for docetaxel chemotherapy.
Janus kinase 3 (JAK-3) GPT-2 Janus kinase 3 (JAK-3) mediates the conversion of glucose to glycogen in skeletal muscle. The
increase in glucose uptake by skeletal muscle is believed to be associated with a reduction in muscle protein breakdown and with the release of ketone bodies, which contribute to the metabolism of glucose.
BioGPT Janus kinase 3 (JAK-3) is a member of the Janus kinase (JAK) family of non-receptor tyrosine kinases and plays an important role in the regulation of cell proliferation, differentiation, survival, migration and angiogenesis.
Apricitabine GPT-2 Apricitabine, duloxetine and citalopram) were investigated in patients with a history of
treatment-resistant depression (n = 10). We found that treatment with the three drugs significantly decreased depressive symptoms, but not depressive symptoms over the next 12 weeks. We conclude that antidepressant drugs that reduce depressive symptoms and reduce depressive symptoms over time may be beneficial in patients with recurrent depression.
BioGPT Apricitabine is an oral prodrug of 5-aza-2 ’-deoxycytidine (5-aza-CdR), a DNA methyltransferase (DNMT) inhibitor, which has been approved by the US Food and Drug Administration (FDA) for the treatment of myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML) in combination with low-dose cytarabine (Ara-C) and granulocyte colony-stimulating factor (G-CSF) for patients with intermediate-2 or high-risk MDS or AML.
Xylazine GPT-2 Xylazine, the ‘active ingredient’ in ‘bath salts’ (see above) has been linked to numerous deaths,
including those of former University of California, Berkeley, professor Andrew Anglin, who posted
a video of himself having a bath filled with it.
BioGPT Xylazine is an alpha 2-adrenoceptor agonist which has been used as a sedative and analgesic in veterinary medicine for many years, but its effects on the cardiovascular system have not been extensively studied in the dog, and its effects on the central nervous system (CNS) have not been well characterized in the dog, despite the fact that xylazine has been widely used as a sedative and analgesic in veterinary medicine for more than 30 years.
Psoralen GPT-2 Psoralen, the city of the spiritualist, and had been given to him by God, who wished to show him
the way of salvation, and to draw him back to the true life of Christ, and to bring him into communion with his fellow-man.
BioGPT Psoralen photochemotherapy (PUVA) is a well-established treatment for psoriasis, but its use is limited by the risk of skin cancer, particularly squamous cell carcinoma (SCC) of the head and neck (H & N), which is the most common site of skin cancer in the United States (US)
CP-673451 GPT-2 CP-673451) and 4-5-6-3-1-2-3-4-6-7-8-9-10-11-12-13-14-15-16-17-18-19-20-21-22-23-24-25-26-27-
BioGPT CP-673451 is a potent, selective, and orally active inhibitor of human neutrophil elastase (HNE) and human cathepsin G (CatG) with in vitro and in vivo anti-inflammatory activity in a variety of animal models of inflammation and in a model of acute lung injury (ALI) in the rat induced by intratracheal instillation of lipopolysaccharide (LPS) and tumor necrosis factor-alpha (TNF-alpha), a model of acute lung injury (ALI) in which neutrophils play an important role.
BIIB-021 GPT-2 BIIB-021A-2-1.pdf. Wright, C. (2002). ‘The War on Terror: A Legal Theory of Terrorist Attacks,’ in
L.E.R. Wright, S.R. Warrick and J.H. Rothman (Eds.), The War on Terror: Essays in Honor of Michael T. Klare (New York: Oxford University Press), 143-57.
BioGPT BIIB-021 is a novel, orally active, non-peptide bradykinin B2 receptor antagonist with potent and long-lasting anti-inflammatory activity in animal models of acute and chronic inflammation and in a rat model of adjuvant-induced arthritis (AIA), an animal model of rheumatoid arthritis (RA) and in a rat model of collagen-induced arthritis (CIA), an animal model of collagen-induced arthritis (CIA), in which arthritis is induced by immunization with bovine type II collagen (CII).
Huang L, Lin J, Li X, Song L, Zheng Z, Wong KC.Brief Bioinform. 2022 Jan 17;23(1):bbab451. doi: 10.1093/bib/bbab451.PMID: 34791012
The rapid growth in literature accumulates diverse and yet comprehensive biomedical knowledge hidden to be mined such as drug interactions. However, it is difficult to extract the heterogeneous knowledge to retrieve or even discover the latest and novel knowledge in an efficient manner. To address such a problem, we propose EGFI for extracting and consolidating drug interactions from large-scale medical literature text data. Specifically, EGFI consists of two parts: classification and generation. In the classification part, EGFI encompasses the language model BioBERT which has been comprehensively pretrained on biomedical corpus. In particular, we propose the multihead self-attention mechanism and packed BiGRU to fuse multiple semantic information for rigorous context modeling. In the generation part, EGFI utilizes another pretrained language model BioGPT-2 where the generation sentences are selected based on filtering rules.
Results: We evaluated the classification part on ‘DDIs 2013’ dataset and ‘DTIs’ dataset, achieving the F1 scores of 0.842 and 0.720 respectively. Moreover, we applied the classification part to distinguish high-quality generated sentences and verified with the existing growth truth to confirm the filtered sentences. The generated sentences that are not recorded in DrugBank and DDIs 2013 dataset demonstrated the potential of EGFI to identify novel drug relationships.
Jin Q, Yang Y, Chen Q, Lu Z.ArXiv. 2023 May 16:arXiv:2304.09667v3. Preprint.PMID: 37131884 Free PMC article.
While large language models (LLMs) have been successfully applied to various tasks, they still face challenges with hallucinations. Augmenting LLMs with domain-specific tools such as database utilities can facilitate easier and more precise access to specialized knowledge. In this paper, we present GeneGPT, a novel method for teaching LLMs to use the Web APIs of the National Center for Biotechnology Information (NCBI) for answering genomics questions. Specifically, we prompt Codex to solve the GeneTuring tests with NCBI Web APIs by in-context learning and an augmented decoding algorithm that can detect and execute API calls. Experimental results show that GeneGPT achieves state-of-the-art performance on eight tasks in the GeneTuring benchmark with an average score of 0.83, largely surpassing retrieval-augmented LLMs such as the new Bing (0.44), biomedical LLMs such as BioMedLM (0.08) and BioGPT (0.04), as well as GPT-3 (0.16) and ChatGPT (0.12). Our further analyses suggest that: (1) API demonstrations have good cross-task generalizability and are more useful than documentations for in-context learning; (2) GeneGPT can generalize to longer chains of API calls and answer multi-hop questions in GeneHop, a novel dataset introduced in this work; (3) Different types of errors are enriched in different tasks, providing valuable insights for future improvements.
PLEASE WATCH THE FOLLOWING VIDEOS ON BIOGPT
This one entitled
Microsoft’s BioGPT Shows Promise as the Best Biomedical NLP
gives a good general description of this new MIT/Microsoft project and its usefullness in scanning 15 million articles on PubMed while returning ChatGPT like answers.
Please note one of the comments which is VERY IMPORTANT
bioGPT is difficult for non-developers to use, and Microsoft researchers seem to default that all users are proficient in Python and ML.
Much like Microsoft Azure it seems this BioGPT is meant for developers who have advanced programming skill. Seems odd then to be paying programmers multiK salaries when one or two Key Opinion Leaders from the medical field might suffice but I would be sure Microsoft will figure this out.
ALSO VIEW VIDEO
This is a talk from Microsoft on BioGPT
Other Relevant Articles on Natural Language Processing in BioInformatics, Healthcare and ChatGPT for Medicine on this Open Access Scientific Journal Include
2012pharmaceutical
sjwilliamspa
AI in Pharma: From Tool to Value Creator 🚀
Over the past 5 years, pharma’s AI footprint has expanded rapidly and the investment trends tell a clear story.
💡 ~60% of collaborations and acquisitions now focus on TargetDiscovery and Validation — pharma’s main frontier to shorten early-stage R&D timelines and boost success rates through PrecisionBiology.
⚗️ The second-largest area (~25%) centers on AI-driven DrugDesign and MoleculeOptimization, where algorithms accelerate CandidateSelection, refine molecular properties, and enable NewModalities such as Biologics and CellAndGeneTherapies.
🏆 Among top players, Eli Lilly and Company stands out as the most active investor — making AI a true StrategicCapability, not just another research tool.
The takeaway?
AI isn’t just supporting pharma anymore, it’s reshaping it.
Source: Bita Sehat, PhD, MBA
hashtag#ai hashtag#lifesciences hashtag#pharmaceuticals hashtag#techbio hashtag#biotech