Professor Timothy Walker
Contact information
Research groups
Timothy Walker
Professor of Infectious Diseases
- Clinicial Research Fellow
 
Tuberculosis
Although 95% of the global population of patients with TB still qualify for first line therapy, namely treatment with isoniazid, rifampicin, ethambutol and pyrazinamide, the number of patients with multi-drug resistant TB (MDR-TB) has been growing as a proportion of total incidence.
Tim is conducting a series of observational studies based in Ho Chi Minh City to understanding how we best preserve the efficacy of what is still our most effective and shortest treatment regimen for any kind of TB. Seeking to understand how patients get MDR-TB, Tim is on the one hand performing pathogen whole genome sequencing for a comparative genomic analysis of the burden of MDR-TB transmission, and is on the other hand seeking to understand the contribution of different factors related to the de novo emergence of resistance to first line drugs. To this end, two case control studies are assessing potential selection pressures including undiagnosed resistance to other drugs, pharmacogenomic and pharmacokinetic factors.
These studies will help us understanding how we best preserve the efficacy of what is still our most effective and shortest treatment regimen for any kind of TB. Depending on the findings, potential interventions will include broader ranging and more precise diagnostic approaches, more personalised drug dosing, or more focus on community based public health measures. 
Recent publications
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                All parts of the WHO <i>Mycobacterium tuberculosis</i> mutation catalog need to be applied when evaluating its performance.
Laurent S. et al, (2025), Microbiology spectrum
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                Tuberculosis preventive therapy: scientific and ethical considerations for trials of ultra-short regimens
Walker TM. et al, (2025), The Lancet Infectious Diseases
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                A modified decontamination and storage method for sputum from patients with tuberculosis
Le Quang N. et al, (2025), Wellcome Open Research, 8, 166 - 166
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                Deep learning-based framework for Mycobacterium tuberculosis bacterial growth detection for antimicrobial susceptibility testing
Vo H-AT. et al, (2025), Computational and Structural Biotechnology Journal, 27, 2208 - 2218
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                Regression for accurate and sensitive grading of mutations diagnostic of antibiotic resistance inMycobacterium tuberculosis
Kulkarni SG. et al, (2024)