What is the role of AI in drug discovery?
Background
Drug development is a long and extremely expensive process, taking on average 10 years and requiring a series of medical trials to evaluate the effectiveness and side effects of any proposed new treatment. Only around 1 in 1000 possible drug treatments progress from preclinical testing to full clinical trials and of those only 1 in ten make it to the approval stage.
AI algorithms are used in the early stages of the drug development process to help to reduce the initial number of compounds considered, including predicting likely adverse reactions.
Drug repurposing
One of the success areas in the use of Machine Learning algorithms has been in the repurposing of drugs already approved for use in a particular condition. An example of this is the repurposing of a drug used in treating rheumatoid arthritis to alleviate the severe symptoms that can occur in some patients with COVID 19. The usefulness of AI in pattern matching as well as in discovering patterns, or connections, that are difficult for humans to spot easily, make it a powerful tool in learning from and searching millions of published texts and trials data.
Vaccine development
In vaccine development, AI is being used along with systems biology to screen candidates and predict immune system response, through simulation, significantly cutting down the time normally taken in the development of a new vaccine. Very high levels of safety are required in vaccine development as they are being given to a very large population of healthy patients. It normally takes many years of rigorous development and testing of different vaccine candidates on animals, and then humans, before a safe and effective vaccine is licensed.
Machine Learning has also been used for taxonomic and hierarchical classification of COVID strains. Google owned DeepMind have used their deep learning algorithms in a program called Alphfold, to identify protein structure linked to COVID 19 that might be valuable for vaccine formulation.
Caution needed
AI algorithms are not a panacea for drug development but can speed up the process. Some have expressed concerns about the limitations of AI in candidate selection and screening as well as response prediction. Rigerous trials should help to alleviate these concerns.
There are deep worries that a drug could prove ineffective or worse, dangerous, when used on populations that have a different response to the biomarker that was used to develop or validate that drug.
References
Ramamoorthy, A., Pacanowski, M. A., Bull, J., & Zhang, L. (2015). Racial/ethnic differences in drug disposition and response: review of recently approved drugs. Clinical Pharmacology & Therapeutics, 97(3), 263-273.