Role of Artificial Intelligence and Machine Learning in Pharmaceuticals
Artificial Intelligence (AI) is comprised of two words ie, “Artificial” (meaning man-made) and “Intelligence” (meaning the ability to understand or think). However, machine learning (ML) is an application of artificial intelligence that provides machines the ability to understand automatically from accessed data and improve with time. Artificial intelligence is one of the most enormously existed digital healthcare technologies that offer transformational progression in the pharmaceutical sector. Similarly, advancement in ML algorithms increased the software’s ability to solve the highly focused problems of the healthcare sector. Earlier, the pharmaceutical industry spent around 15 years and approximately billions dollar in the drug development methods to get one drug approval from the Food and Drug Administration (FDA). Currently, the pharmaceutical industry has started developing AI and ML technology tools in the drug development process which has helped discover new drugs more efficiently and quickly than already present methods.
How Pharmaceutical Industries Implement AI or ML into Drug Discovery?
New technologies of AI and ML are transforming the aspects of data quality, Medical Dictionary for Regulatory Activities (MedDRA) coding, and World health organization (WHO) drug coding in the drug development process. Following given well-known pharmaceutical industries are using AI or ML into drug discovery:
- Roche Company: Partnered with Owkin (a French data science company) to enhance the drug discovery process and also purchased the Flatiron Company to accelerate cancer research.
- GlaxoSmithKline: Partnered with AI companies such as BERG, Exscientia, Insilico Medicine, and Cloud Pharmaceuticals for enhancing drug development.
- Bayer: Started using AI-augmented & cloud-based platforms of the Cyclica company (Toronto-based Biotechnology Company) for advancing the peptide drugs discovery.
- AstraZeneca: Partnered with Berg Health (AI-powered Biotechnology Company) to detect the therapeutic targets for neurological diseases and also partnered with Alibaba to enhance patient diagnosis and treatment.
- Berg: This US-based company is applying AI in diagnostics and therapeutics in the fields of oncology, endocrinology, and neurology. They have developed Interrogative Biology to differentiate between healthy and diseased environments.
How AI Impact the MedDRA coding?
Drug developing companies are moving towards using effective strategy for extraction of information about Adverse Drug Reactions (ADR). For this, MagiCoder (an efficient Natural Language Processing algorithm) is created which is able to derive terminologies of MedDRA from freely available ADR description sources. Pharmacologists only review and authenticate the MedDRA terms which are suggested by MagiCoder tool, instead of picking the correct terms from the 70 thousand terms of MedDRA. MagiCoder decreased the encoding time in the report of ADR. In this way, it improves the work efficiency of pharmacologists and provides quality data.
Current Utility of AI and ML Technology in the Pharmaceutical Sector
In the pharmaceutical industries, AI and ML technology mainly explore into three divisions: drug discovery, drug development, and drug commercialization. It helps to provide an accurate and quick diagnosis when diagnostic techniques combined with AI, which also save time and cost. Artificial intelligence assists to design the treatment plan. For doing this, the doctor collects the information of the patients and records them in the software, and then the machine automatically provides diagnosis, test, prescriptions as well as cost information. Artificial intelligence also works as a Virtual health assistant (VHA) that reminds the dementia patients to take prescribed medicine on time and also gives suggestion for taking treatments for a common medical condition.
Even after all these applications of AI and ML in the pharmaceutical industry, their implementation is slower here than in other industries. However, this scenario is observed to be changing now. Artificial intelligence and ML are developing speedily and the pharmaceutical industry will need to adapt this for affirming their presence.