AI-Enabled Pharmacovigilance and Neuropsychiatric Drug Safety
Drug safety surveillance systems in the US, the challenges posed by neuropsychiatric drugs, and the role of artificial intelligence technologies in this field are examined.
The widespread use of psychiatric medications and next-generation treatments in the US create significant surveillance gaps in traditional pharmacovigilance systems.
Safety Profile of Neuropsychiatric Drugs
The widespread and long-term use of psychotropic medications leads to even the smallest differences in their safety profiles resulting in major societal consequences.
Next-Generation Treatments and Complex Risks
Clinical trials of NMDA receptor modulators such as ketamine and esketamine, alongside psychedelics like psilocybin, harbor complex risks that limited premarket studies fail to capture.
These novel treatments bring various risks such as dissociation, sedation, abuse potential, and increased blood pressure.
Traditional Passive Surveillance Systems
Traditional pharmacovigilance relies on passive systems like the FDA Adverse Event Reporting System, where underreporting rates remain very high.
Active Surveillance and Unique Obstacles
Although active structures like the FDA Sentinel System query electronic health records, side effects from neuropsychiatric medications can easily be confused with underlying conditions.
Subjective changes in mood, cognition, or behavior present unique challenges in tracking the safety of these drugs.
Artificial Intelligence and Natural Language Processing Solutions
Machine learning, natural language processing, and large language models offer advanced capabilities to analyze large datasets and unstructured clinical texts.
Regulatory Decision-Making Processes and the Future
These data-driven approaches support drug safety surveillance within the US healthcare context and significantly improve regulatory decision-making.