Data Collection and Management for AI-Based Pharmaceutical Formulation Development: A Comprehensive Review
Pritam Kayal, Priyam Manna, Ramit Rahaman, Mohan Kumar Ramar, Abimanyu Sugumaran, Natarajan JawaharIntroduction:
Traditional pharmacovigilance relies on slow clinical trials and post-marketing studies with limited coverage. This review synthesizes evidence on Real-World Data (RWD) integration with Artificial Intelligence (AI) for enhanced Adverse Drug Reaction (ADR) detection, evaluates generative AI like ChatGPT-4 and LLaMA-2 in Substance Use Disorder (SUD) scenarios, discusses current applications, and outlines future directions. The objective is to guide researchers, clinicians, and regulators in this evolving field.
Methods:
Literature was reviewed on RWD sources (EHRs, claims, registries, wearables), AI algorithms (supervised/ unsupervised learning, NLP, deep learning), and regulatory frameworks. Generative AI performance was assessed via clinician-blind evaluation of responses to Reddit-sourced SUD queries from r/stopdrinking, r/leaves, and r/OpiatesRecovery, with fact-checking against SAMHSA/FDA guidelines and consistency testing. Data included tables comparing RWD, algorithms, and AI models.
Results:
AI enables real-time ADR signals via RWD-AI in CCM, improving diagnostics, personalization, and drug discovery. ChatGPT-4 suggested unsafe opioid microdosing; LLaMA-2 referenced nonexistent resources and improper Xanax sharing, both showing severe inaccuracies in SUD contexts. Tables highlight RWD applications, algorithm uses, and AI limitations like bias and inconsistency.
Discussion:
RWD-AI transforms pharmacovigilance but faces bias, transparency, and validation challenges. FHIR/DLT enhance secure exchange; generative AIs require oversight. Implications include equitable safety monitoring via bias mitigation and regulatory compliance.
Conclusion:
AI-RWD integration advances ADR detection and personalized safety, despite generative AI risks in SUD management. Future success demands validated LLMs, FHIR/blockchain infrastructure, and clinician collaboration for comprehensive, equitable pharmacovigilance.