A Review of In Silico and Chemoinformatic Models in Pharmacovigilance Studies.
C. K. Olanike, Sylvester Aghahowa, Michael E Aghahowa · Journal of Basic and Social Pharmacy Research · 2023
Background: Following an increase in the reports of adverse drug reactions, it is pertinent that in silico and cheminformatic models are adopted as most pharmacovigilance activities to enhance drug safety during drug utilization. Objectives: The study therefore assessed in silico and chemoinformatic models in literature that would help in the furtherance of pharmacovigilance activities. Methods Extraction of data was carried out based on pre-defined inclusion criteria and such data were evaluated for bias. Novel method based on Drug Interaction Profile Fingerprints (DIPFs) with successful application to Drug-Drug Interaction (DDI) detection was the approach adopted. Results: The main database search identified 56,675 articles, among which 143 articles were excluded based on titles and duplicates. Out of those that were included, 18 articles had relevance to the topic being studied. Out of those excluded, 95 articles had full-text incompletion and 48 articles were identified in the grey literature. The reference lists for the included articles identified 5 additional articles. Relevant articles published after completing the search were also included. Ten different articles or sets of data were described as novel methodologies using Quantitative- Structure-Activity Research (QSAR) to predict DDI, and Stevens-Johnson Syndrome (SJS). Databases were also identified. Conclusion: Findings in this study have shown that some researchers are working to enhance and develop easier models for pharmacovigilance studies. Therefore keen interest in in silico and chemoinformatic models should be adopted to enhance pharmacovigilance activities.