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

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 Lis...

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Autores principales: Chikowe, Ibrahim, Phiri, Alfred Chipanda, Mbewe, Kirios Patrick, Matekenya, Dunstan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8207713/
https://www.ncbi.nlm.nih.gov/pubmed/34134770
http://dx.doi.org/10.1186/s40360-021-00499-6
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author Chikowe, Ibrahim
Phiri, Alfred Chipanda
Mbewe, Kirios Patrick
Matekenya, Dunstan
author_facet Chikowe, Ibrahim
Phiri, Alfred Chipanda
Mbewe, Kirios Patrick
Matekenya, Dunstan
author_sort Chikowe, Ibrahim
collection PubMed
description 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40360-021-00499-6.
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spelling pubmed-82077132021-06-16 In-silico evaluation of Malawi essential medicines and reactive metabolites for potential drug-induced toxicities Chikowe, Ibrahim Phiri, Alfred Chipanda Mbewe, Kirios Patrick Matekenya, Dunstan BMC Pharmacol Toxicol Research 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40360-021-00499-6. BioMed Central 2021-06-16 /pmc/articles/PMC8207713/ /pubmed/34134770 http://dx.doi.org/10.1186/s40360-021-00499-6 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Chikowe, Ibrahim
Phiri, Alfred Chipanda
Mbewe, Kirios Patrick
Matekenya, Dunstan
In-silico evaluation of Malawi essential medicines and reactive metabolites for potential drug-induced toxicities
title In-silico evaluation of Malawi essential medicines and reactive metabolites for potential drug-induced toxicities
title_full In-silico evaluation of Malawi essential medicines and reactive metabolites for potential drug-induced toxicities
title_fullStr In-silico evaluation of Malawi essential medicines and reactive metabolites for potential drug-induced toxicities
title_full_unstemmed In-silico evaluation of Malawi essential medicines and reactive metabolites for potential drug-induced toxicities
title_short In-silico evaluation of Malawi essential medicines and reactive metabolites for potential drug-induced toxicities
title_sort in-silico evaluation of malawi essential medicines and reactive metabolites for potential drug-induced toxicities
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8207713/
https://www.ncbi.nlm.nih.gov/pubmed/34134770
http://dx.doi.org/10.1186/s40360-021-00499-6
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