AIMC Topic: Antitubercular Agents
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Small Molecule Inhibitors of Topoisomerase I Identified by Machine Learning and In Vitro Assays.
International journal of molecular sciences
Nov 15, 2024
Tuberculosis (TB) caused by is a leading infectious cause of death globally. The treatment of patients becomes much more difficult for the increasingly common multi-drug resistant TB. Topoisomerase I is essential for the viability of and has been v...
Leveraging large-scale Mycobacterium tuberculosis whole genome sequence data to characterise drug-resistant mutations using machine learning and statistical approaches.
Scientific reports
Nov 7, 2024
Tuberculosis disease (TB), caused by Mycobacterium tuberculosis (Mtb), is a major global public health problem, resulting in > 1 million deaths each year. Drug resistance (DR), including the multi-drug form (MDR-TB), is challenging control of the dis...
Predicting Treatment Outcomes in Patients with Drug-Resistant Tuberculosis and Human Immunodeficiency Virus Coinfection, Using Supervised Machine Learning Algorithm.
Pathogens (Basel, Switzerland)
Oct 24, 2024
Drug-resistant tuberculosis (DR-TB) and HIV coinfection present a conundrum to public health globally and the achievement of the global END TB strategy in 2035. A descriptive, retrospective review of medical records of patients, who were diagnosed wi...
Machine learning algorithms to predict treatment success for patients with pulmonary tuberculosis.
PloS one
Oct 16, 2024
Despite advancements in detection and treatment, tuberculosis (TB), an infectious illness caused by the Mycobacterium TB bacteria, continues to pose a serious threat to world health. The TB diagnosis phase includes a patient's medical history, physic...
A machine-learning based model for automated recommendation of individualized treatment of rifampicin-resistant tuberculosis.
PloS one
Sep 6, 2024
BACKGROUND: 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 g...
Identification of mycobacterial Thymidylate kinase inhibitors: a comprehensive pharmacophore, machine learning, molecular docking, and molecular dynamics simulation studies.
Molecular diversity
Aug 16, 2024
Thymidylate kinase (TMK) is a pivotal enzyme in Mycobacterium tuberculosis (Mtb), crucial for phosphorylating thymidine monophosphate (dTMP) to thymidine diphosphate (dTDP), thereby playing a critical role in DNA biosynthesis. Dysregulation or inhibi...
Prediction of Mycobacterium tuberculosis cell wall permeability using machine learning methods.
Molecular diversity
Aug 12, 2024
Tuberculosis (TB) caused by the bacteria Mycobacterium tuberculosis (M. tb), continues to pose a significant worldwide health threat. The advent of drug-resistant strains of the disease highlights the critical need for novel treatments. The unique ce...
Quantitative drug susceptibility testing for Mycobacterium tuberculosis using unassembled sequencing data and machine learning.
PLoS computational biology
Aug 5, 2024
There remains a clinical need for better approaches to rapid drug susceptibility testing in view of the increasing burden of multidrug resistant tuberculosis. Binary susceptibility phenotypes only capture changes in minimum inhibitory concentration w...
Machine learning investigation of tuberculosis with medicine immunity impact.
Diagnostic microbiology and infectious disease
Aug 4, 2024
Tuberculosis (T.B.) remains a prominent global cause of health challenges and death, exacerbated by drug-resistant strains such as multidrug-resistant tuberculosis MDR-TB and extensively drug-resistant tuberculosis XDR-TB. For an effective disease ma...
Machine Learning Approach in Dosage Individualization of Isoniazid for Tuberculosis.
Clinical pharmacokinetics
Jul 11, 2024
INTRODUCTION: Isoniazid is a first-line antituberculosis agent with high variability, which would profit from individualized dosing. Concentrations of isoniazid at 2 h (C), as an indicator of safety and efficacy, are important for optimizing therapy.
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