Data mining methodologies for pharmacovigilance

Abstract

Medicines are designed to cure, treat, or prevent diseases; however, there are also risks in taking any medicine - particularly short term or long term adverse drug reactions (ADRs) can cause serious harm to patients. Adverse drug events have been estimated to cause over 700,000 emergency department visits each year in the United States. Thus, for medication safety, ADR monitoring is required for each drug throughout its life cycle, including early stages of drug design, different phases of clinical trials, and postmarketing surveillance. Pharmacovigilance (PhV) is the science that concerns with the detection, assessment, understanding and prevention of ADRs. In the pre-marketing stages of a drug, PhV primarily focuses on predicting potential ADRs using preclinical characteristics of the compounds (e.g., drug targets, chemical structure) or screening data (e.g., bioassay data). In the postmarketing stage, PhV has traditionally involved in mining spontaneous reports submitted to national surveillance systems. The research focus is currently shifting toward the use of data generated from platforms outside the conventional framework such as electronic medical records (EMRs), biomedical literature, and patient-reported data in online health forums. The emerging trend of PhV is to link preclinical data from the experimental platform with human safety information observed in the postmarketing phase. This article provides a general overview of the current computational methodologies applied for PhV at different stages of drug development and concludes with future directions and challenges.