How Can Pharmaceutical Comapnies Use AI?
Introduction
Artificial Intelligence and Machine learning (a subfield of artificial intelligence) are very beneficial to the pharmaceutical industry. AI can be considered a significant way of improving pharmaceutical companies, ranging from helping manufacturing and marketing to other more beneficial uses such as the discovery of drugs with improved efficiency, success rate and cost-effectiveness. AI also provides lots of support in health protection through diagnostics, as shall be discussed in later chains. This article will be useful to you because it identifies how a developing technology can have such a strong medicinal impact on the world and help cure existing and future diseases.
Chain 1 – The importance of drug discovery and its development in the Pharmaceutical Industry using AI
AI and Machine Learning in the Pharmaceutical Industry are effectively able to transform drug discovery and development. Due to AI’s capability of predicting compound bioactivity, AI is able to accelerate research and create a timeline as to when drugs are able to develop.
This is done by identifying when drugs will enter clinical trials and when they can actually be put into the market for sale. AI can also, more importantly, re-purpose drugs that have been used in different instances. Take for example drugs that have been used to cure the illness COVID-19, used by organisations such as Pfizer, GSK and AstraZeneca, which are now less used and failed some clinical trials. AI can identify the clinical trials that have failed and redirect the drug to perform a function to cure a different disease. As mentioned in the introduction, AI does this process with low cost and more efficiency, with its efficiency stemming from the fact that it causes minimal errors and has quicker algorithms than humans. A practical example, in which OCD was tested using AI neatly summarises these points. The drug development carried out by British firm Exiscienta and Japanese firm Sumitomo Dainippon Pharma was tested and took 12 months instead of 5 years. The molecule DSP-1181 was created using AI algorithms, and successfully acts as an aid for OCD, which was said to be a “key milestone in drug discovery”.
Chain 2 - How AI uses its functions for optimised drug discovery and development
The following image describes all the functions in AI drug discovery and development.
Chain 3 – Artificial Intelligence and Biomarkers in Diagnostics
A biomarker can be defined as “a measurable indicator of some biological state or condition”. Biomarkers are an essential tool for understanding the profile of response during clinical research. It provides a more accurate decision than a human as to whether these medicines should be approved and used and produces personalised medicine. Specific biomarkers will recognise early-stage diseases and how to combat them effectively so that such diseases can have proper medical treatment. With human identification, many biomarkers are missed by error. Due to the AI algorithm having a minimal error rate, it is able to identify all the biomarker information. The information ignored by human review has the ability to make more accurate predictions on how likely a response can be carried out for specific treatment with a drug (predictive biomarkers). This is used a lot in the study of pathology, in illnesses to do with the prostate tissue, the lung tissue and the breast tissue.
Chain 4 - The Future of AI in the Pharmaceutical Industry
The important question arises: what is the future of this revolutionary technology in the Pharmaceutical Industry? The simple answer is only improvements. It is important to recognise that “with 50% of global healthcare companies planning to implement AI strategies, there is no sign of AI adoption slowing down any time soon”. The major plan for pharmaceutical companies is to continue improving the diagnostic function of AI to identify more complex diseases. Another future function of AI is to discover new drugs for chronic diseases which are currently incurable such as cancer and diabetes. With further development, AI can possess the ability to extract data from MRI scans, which will no doubt help diagnostics and help pharmaceutical companies find the right drug for the job.
Conclusion
In conclusion, AI is revolutionary in the Pharmaceutical Industry for a wide range of reasons and has a bright future, which will positively impact the world of medicine. Not only does it provide better results and is more cost-effective, but also has a wide range of ability to discover and tackle new diseases, as well as diagnose them for specialised treatment. AI also has the strong capability of proofreading with predictive biomarkers to see if a response is able to be carried out.
Sources :
Machine learning, explained | MIT Sloan
Artificial Intelligence in Pharmaceutical Industry: 8 Exciting Applications in 2023 | upGrad blog
AI in the Pharma Industry: Current Uses, Best Cases, Digital Future (pharmanewsintel.com)
Artificial intelligence-created medicine to be used on humans for first time - BBC News
[AI and drug discovery - ScienceDirect](https://www.sciencedirect.com/science/article/pii/S2666386422004532#:~:text=Research studies during the last few years have,potential to accelerate discovery timelines and reduce costs.)
