Pharma Industry Steps Towards a Patient-Centred PV System

A shift in focus from solely collecting and analysing adverse event data to a more patient-centric approach

What is the role of AI in Signal Detection?

I am no AI expert, but being a Research physician my interactions with AI pros, have certainly fascinated me, about what we are going to experience in the field of research. The FDA has demonstrated a positive intent with the recent approval of AI/machine learning-based devices for COVID-19 screening, which integrates the power of AI with the healthcare system. As a result, we can anticipate the emergence of a new era of development of AI-based activities aimed at providing greater efficiency in healthcare provision.

Various AI tools can be used for signal detection in pharmacovigilance and clinical trials such as Natural Language Processing, Machine Learning and Bayesian Networks. These can help identify potential safety signals more quickly and effectively.

There have been notable advancements in the development of novel analytics in pharmacovigilance studies through post-marketing data mining. By combining adverse event information with machine learning molecular descriptors, AI can provide an information-driven approach for characterising post-marketing or clinical trial observations with a plausible causal basis.

How could AI affect the conduct of exploratory trials from safety perspective?

Safety input utilising AI could have a productive contribution and a constructive output in the conduct of exploratory trials. Early in the drug discovery process, AI can assist in predicting the ADME and toxicity of new drug candidates by scanning through millions of data points, thus providing safety input early in the development phase. It can suggest near to accurate, if not spot on inclusion and exclusion criteria to select appropriate study participants. It can sketch out the details of safety monitoring and risk minimisation measures within a trial, and provide process improvements and checkpoint inputs during a clinical development program. Having said that, human oversight is essential to regulate the use of AI in clinical trials and to ensure the safety and well-being of study participants.

How should the pharma industry move in the direction of designing a patient-centred pharmacovigilance system?

Designing a patient-centred pharmacovigilance system requires a shift in focus from solely collecting and analysing adverse event data to a more patient-centric approach that involves understanding and addressing the patient’s needs and experiences. Pharma industry has already started taking steps in this direction, but there is a scope for improvement.

Here are some examples of patient-centred activities:

• Patient-reported outcome measures can provide useful information on patients’ lives and can be used to identify adverse events and inform pharmacovigilance efforts
• Patient engagement in pharmacovigilance process can provide valuable insights into their experiences and adverse events and the pattern of medication
• Well conducted Drug utilisation studies in target population are great pharmacovigilance tools which support Regulatory decision-making and can be used by Regulatory authorities to inform decisions on drug approval, labelling, and risk management.
• Patient advocacy groups represent patients with specific medical conditions or diseases, and can play an important role in advocating pharmacovigilance activities
• At the moment there is underreporting of AEs by the patients. Encouraging and educating patients about the importance of reporting adverse events can increase patient engagement in the pharmacovigilance process

Amit Jadhav is a Director, Global Patient Safety Lead at Regeneron. He is a physician by background and has over a decade of experience in the pharmaceutical industry, having worked in Pharmacovigilance, Early and Late Phase Clinical Development, and Medical Affairs. Amit holds the position of Director Global Safety Lead at Regeneron Pharmaceuticals, where he is currently involved in Exploratory Clinical Development projects, including First in Human (FIH) clinical trials.

Amit’s professional experience includes providing strategic safety input within clinical development programs, contributing to Marketing Authorisation Application (MAA) submissions, formulating and implementing risk minimisation measures by collaborating with global affiliates, conducting signal surveillance activities, performing effective Due diligence, leading quality improvement projects, working closely with institutes for PASS studies, KOL engagements, product launches and collaborating with alliance and co-development partners.