Artificial Intelligence for Drug Toxicity and Safety.

Area of Science:

• Pharmacology
• Drug Safety
• Computational Science

Background:

• Interventional pharmacology offers potent disease treatments but carries risks of adverse drug reactions (ADRs).
• Traditional pharmacovigilance methods, including preclinical studies, clinical trials, and postmarketing surveillance, face limitations with increasing polypharmacy and patient diversity.
• Massive data generation necessitates advanced analytical tools for effective drug safety monitoring.

Purpose of the Study:

• To explore recent advances in artificial intelligence (AI) and machine learning (ML) for improving drug safety science.
• To highlight the application of AI and ML in preclinical drug safety assessment and postmarketing surveillance.
• To focus on machine learning (ML) and deep learning (DL) approaches in pharmacovigilance.

Main Methods:

• Review of recent advances in AI and ML applied to drug safety.
• Focus on machine learning and deep learning methodologies.
• Examination of applications in preclinical and postmarketing surveillance.

Main Results:

• AI and ML demonstrate significant potential to enhance the detection and prevention of adverse drug reactions (ADRs).
• Machine learning and deep learning approaches offer novel solutions for analyzing large datasets in drug safety.
• These computational methods can improve the efficiency and accuracy of pharmacovigilance.

Conclusions:

• Artificial intelligence and machine learning are poised to revolutionize drug safety science.
• Implementing AI and ML in pharmacovigilance can address challenges posed by polypharmacy and patient diversity.
• Future drug safety efforts will likely integrate advanced computational techniques for more robust monitoring and risk assessment.