Drug Development Process been Revolutionized by Artificial Intelligence (AI) Technologies
Curators: Stephen J. Williams, PhD and Aviva Lev-Ari, PhD, RN
The Voice of Stephen J. Williams, PhD
LPBI Group, CSO
PENDING
The Voice of Aviva Lev-Ari, PhD, RN
1.0 LPBI Group and 2.0 LPBI Group, Founder
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
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.
SOURCE
AI Overview
Examples of drugs developed by AI technologies:
NCI definition of AI Drugs:
AI drug
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.
SOURCE
https://www.cancer.gov/publications/dictionaries/cancer-terms/def/ai-drug
More examples of AI Drugs, Drugs developed with AI technologies
#1:
INS018_055Developed 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
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Schrödinger’s AI-driven platformUses 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.
Big Name Pharma Customers of Schrödinger, Inc.
Its technology platform allows for the faster and cheaper discovery of novel molecules with a higher success rate than traditional methods. Its clients include the top 20 pharmaceutical companies in the world, including Pfizer Inc. (NYSE: PFE), Merck & Co. Inc. (NYSE: MRK), Takeda, AstraZeneca PLC (NYSE: AZN), and GlaxoSmithKline plc (NYSE: GSK). It closed new agreements with Eli Lilly & Co. (NYSE: LLY) and Otsuka Pharmaceuticals out of Tokyo, Japan.
SOURCE
Schrodinger is an AI-Powered Drug Discovery Developer to Watch
https://www.nasdaq.com/articles/schrodinger-is-an-ai-powered-drug-discovery-developer-to-watch
Schrodinger’s Pipelines include:
- SGR-1505 (MALT1)
Hematologic Malignancies
- SGR-2921 (CDC7)
AML/MDS
- SGR-3515 (Wee1/Myt1)
Solid Tumors
- SOS1
Oncology
- PRMT5-MTA
Oncology
- EGFRC797S
Oncology
- NLRP3
Immunology
- LRRK2
Neurology
- Undisclosed Programs
Multiple Areas
SOURCE
How A.I. Is Revolutionizing Drug Development
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.
Terray Therapeutics campus in Monrovia, Calif., June 17, 2024
Five AI drug discovery companies you should know about
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.
SOURCE
At LPBI Group, Of Note is our Journal PharmaceuticalIntelligence.com
it represents our commitment to AI technologies in the following research categories and How many articles have been written in each of these topics:
-
A total of x articles have been categorized 511 times among the following Artificial Intelligence research categories
Artificial Intelligence – General
113
An executive’s guide to AI
9
Artificial Intelligence – Breakthroughs in Theories and Technologies
94
Artificial Intelligence Applications in Health Care
81
Artificial Intelligence in CANCER
29
Artificial Intelligence in Health Care – Tools & Innovations
55
Artificial Intelligence in Medicine – Application for Diagnosis
44
Artificial intelligence applications for cardiology
21
AI-assisted Cardiac MRI
9
Artificial Intelligence in Psychiatry
5
Artificial Intelligence in Medicine – Applications in Therapeutics
50
LPBI Group’s involvement in Conceptual Drug Development covers the following two areas:
DrugDiscovery @LPBI Group, 2016 – 2018
Synthetic Biology in Drug Discovery, 2021 – Present
Applications of Artificial Intelligence to Medicine
Artificial Intelligence: Genomics & Cancer, 2021 – Present
Medicine with GPT-4 & ChatGPT, 2023 – Present
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ChatGPT applied to Cardiovascular diseases: Diagnosis and Management
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ChatGPT and AI Algorithms applied to Medical Imaging & Radiology
LPBI Group commitment to Medical Text Analysis using Machine Learning
2021-2025 Medical Text Analysis (NLP), 2020 – Present
ChatGPT + Wolfram PlugIn, 2023 – Present
LPBI Group Team members published two books on Drug Delivery Technologies
We had covered drug delivery technologies in two of our books. See all the Books:
https://www.amazon.com/s?k=Aviva+Lev-Ari&i=digital-text&rh=n%3A133140011&ref=nb_sb_noss
- Series E, Volume Four
Medical 3D BioPrinting – The Revolution in Medicine, Technologies for Patient-centered Medicine: From R&D in Biologics to New Medical Devices.
https://www.amazon.com/dp/B078QVDV2W
and
- Series C, Volume Two
Cancer Therapies: Metabolic, Genomics, Interventional, Immunotherapy and Nanotechnology in Therapy Delivery (Series C Book 2).
http://www.amazon.com/dp/B071VQ6YYK
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
(Spanish Edition) Kindle Edition
https://www.amazon.com/dp/B0BRNVDB1P $56
- Serie C, Volumen 2
Tratamientos contra el cáncer: Metabólicos, genómicos, intervencionistas, inmunoterapia y nanotecnología para la administración de tratamientos (Serie … y la oncología nº 2) 2022
(Spanish Edition) Kindle Edition
http://www.amazon.com/dp/B0BQTM44SM $75
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