In-silico evaluation of Malawi essential medicines and reactive metabolites for potential drug-induced toxicities

• Main Authors: Ibrahim Chikowe (Author), Alfred Chipanda Phiri (Author), Kirios Patrick Mbewe (Author), Dunstan Matekenya (Author)
• Format: Book
• Published: BMC, 2021-06-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 10.1186/s40360-021-00499-6; 2050-6511
• Keywords: Screening; Drug-induced toxicity; Computer-aided; Therapeutics. Pharmacology; RM1-950; Toxicology. Poisons; RA1190-1270
• Source: BMC Pharmacology and Toxicology, Vol 22, Iss 1, Pp 1-10 (2021)

Abstract Background Drug-induced toxicity is one of the problems that have negatively impacted on the well-being of populations throughout the world, including Malawi. It results in unnecessary hospitalizations, retarding the development of the country. This study assessed the Malawi Essential Medicines List (MEML) for structural alerts and reactive metabolites with the potential for drug-induced toxicities. Methods This in-silico screening study used StopTox, ToxAlerts and LD-50 values toxicity models to assess the MEML drugs. A total of 296 drugs qualified for the analysis (those that had defined chemical structures) and were screened in each software programme. Each model had its own toxicity endpoints and the models were compared for consensus of their results. Results In the StopTox model, 86% of the drugs had potential to cause at least one toxicity including 55% that had the potential of causing eye irritation and corrosion. In ToxAlerts, 90% of the drugs had the potential of causing at least one toxicity and 72% were found to be potentially reactive, unstable and toxic. In LD-50, 70% of the drugs were potentially toxic. Model consensus evaluation results showed that the highest consensus was observed between ToxAlerts and StopTox (80%). The overall consensus amongst the three models was 57% and statistically significant (p < 0.05). Conclusions A large number of drugs had the potential to cause various systemic toxicities. But the results need to be interpreted cautiously since the clinical translation of QSAR-based predictions depends on many factors. In addition, inconsistencies have been reported between screening results amongst different models.