Lennert Verboven, Steven Callens, John Black, Gary Maartens, Kelly E. Dooley, Samantha Potgieter, Ruben Cartuyvels, SMARTT team, Kris Laukens, Robin M. Warren, Annelies Van Rie
Abstract
Rifampicin resistant tuberculosis remains a global health problem with almost half a million new cases annually. In high-income countries patients empirically start a standardized treatment regimen, followed by an individualized regimen guided by drug susceptibility test (DST) results.
Introduction
Tuberculosis (TB) continues to be a global public health problem with about 10 million new cases and 1.4 million TB deaths annually [1]. The ‘End TB Strategy’ of the World Health Organization (WHO) aims to reduce new TB cases by 90% and TB deaths by 95% by 2035.
Methods
The research was performed in accordance with the Declaration of Helsinki. Collection of the clinical strains and the determination of the genotypic drug resistance profile was obtained from the Human Research Ethics Committee of the University of the Witwatersrand in South Africa.
Results
The fluoroquinolones, moxifloxacin and levofloxacin are oral drugs that inhibit DNA gyrase of Mtb [24]. Fluoroquinolone toxicity was classified as low (score 1 for levofloxacin, 1.25 for moxifloxacin) given the low (1.2%) incidence of serious adverse effects (SAE) in RR-TB patients receiving fluoroquinolones [25].
Discussion
When a patient is infected with an Mtb strain that is resistant to one or more of the drugs included in the standardized treatment regimen, has a contra-indication to one of the drugs, or experiences a side effect that requires one of the drugs to be stopped either temporarily or permanently, an individualized treatment should be initiated.
Acknowledgments
Gavin Churchyard10, Salome Charalambous10, Noriah Maraba10, Felex Ndebele10, Zandile Sibeko10, Pulane Segwaba10, S’thabiso Bohlela10, Anneke Van der Spoel Van Dijk11, Ayodeji Emmanuel Ogunbayo12, Mhlambi Nomadlozi12, Emilyn Costa Conceicao9, Felicia Wells9, Astrid Paulse9, Fanampe Boitumelo13, Tim Heupink1, Trang Tu1.
Citation: Verboven L, Callens S, Black J, Maartens G, Dooley KE, Potgieter S, et al. (2024) A machine-learning based model for automated recommendation of individualized treatment of rifampicin-resistant tuberculosis. PLoS ONE 19(9): e0306101. https://doi.org/10.1371/journal.pone.0306101
Editor: Meredith Blair Brooks, Boston University School of Public Health, UNITED STATES OF AMERICA
Received: December 16, 2023; Accepted: June 11, 2024; Published: September 6, 2024
Copyright: © 2024 Verboven et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data generated or analyzed during this study are within the paper and its Supporting Information files. The treatment recommender and the webapp are available from the GitHub repositories (https://github.com/LennertVerboven/treatment_recommender) and (https://github.com/LennertVerboven/treatment_recommender_webap).
Funding: This research was funded by the Research Foundation Flanders (FWO) strategic basic research grant 1SB4519N, (personal PhD funding for L.V.), the FWO Odysseus grant G0F8316N (A.V.R), the FWO applied biomedical research with a primary social finality T001018N (A.V.R), and National Institute on Allergy and Infectious Diseases / National Institutes of Health (NIAID/NIH) K24AI150349 (K.E.D). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Source: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0306101#abstract0
