IMPROVISING DRUG DEVELOPMENT PROCESS USING AI/ML TOOLS AND METHOD: A REVIEW PAPER

Abstract

We cannot simply accept that testing new drugs will continue to be a slow and expensive process. AI has the potential to disrupt the current approach to clinical trials — from patient recruitment to adherence monitoring and data collection – and it is time to seize these opportunities.

future of clinical development is on the verge of a major transformation due to convergence of large new digital data sources, computing power to identify clinically meaningful patterns in the data using efficient artificial intelligence and machine-learning algorithms, and regulators embracing this change through new collaborations. This perspective summarizes insights, recent developments, and recommendations for infusing actionable computational evidence into clinical development and health care from academy, biotechnology industry, nonprofit foundations, regulators, and technology corporations. Analysis and learning from publicly available biomedical and clinical trial data sets, real-world evidence from sensors, and health records by machine-learning architectures are discussed.

The need for new medical treatments and drugs has never been greater. But before pharmaceutical companies can go to market with a breakthrough drug, they need to ensure safety and efficacy through clinical trials. While this process is essential, it’s also slow, expensive and unpredictable. Pharma R&D teams are solving this problem by leveraging the power of artificial intelligence (AI) in clinical trials to save time and money.

According to the US Food and Drug Administration (FDA)^[8], approximately 33 percent of drugs move from Phase II to III, while around 25 to 30 percent move from Phase III to the next phase 

Drug development is already a difficult endeavour, with the vast majority of R&D efforts failing to produce a market-worthy product. Even reaching the clinical trial phase offers no guarantees, as only 12% of such drugs receive U.S. Food and Drug Administration approval)^[2]. Pharma companies need tools like AI that can reliably improve this percentage without jeopardizing safety.

With the power of AI, companies can rapidly digitize clinical trial processes so they can complete studies faster. That means life-saving medicines and treatments can get to patients more quickly—and life sciences companies can gain a competitive edge.

“The big delay areas are always patient recruitment, site start-up, querying, data review, and data cleaning,” explains Scott Clark, chief commercial officer a Taimei