Unravelling the Mysteries of Tuberculosis Treatment

Unravelling the Mysteries of Tuberculosis Treatment

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Tuberculosis (TB) remains a formidable challenge in developing countries, and the complexities of its treatment continue to baffle researchers. Dr. Monique Opperman (née Combrink), a post-doctoral fellow at the NWU Centre for Human Metabolomics, has been at the forefront of unravelling these mysteries through her work in the field of metabolomics.

In her doctoral research, Dr. Opperman employed a multi-platform metabolomics approach to study the host’s response to standard TB treatment. The focus was on the urinary metabolome profiles of 46 culture-confirmed TB patients, investigating both eventually cured and treatment failure outcomes. The findings were nothing short of insightful.

The intensive phase of standard TB treatment was found to induce significant variations in the host metabolome of cured patients. Time-dependent alterations in oxidative stress levels, enzyme activity, urea cycle, and insulin production were observed. This ground-breaking study marked the first time such changes were detected.

Dr. Opperman then compared amino acid and acylcarnitine profiles between eventually cured and treatment failure patient groups. Surprisingly, both groups exhibited similar metabolite fluctuations, but with delayed patterns in the cured group. This hinted at potential vitamin B6 deficiency and variations in drug metabolism enzymes contributing to the observed trends.

Further investigations into specific TB drug metabolites using a 1H-NMR approach revealed intriguing differences. Isoniazid (INH) metabolites displayed overall elevated concentrations in treatment failure patients, suggesting that environmental factors and individual variations in INH pathways might influence the variation in treatment outcome.

These comprehensive findings shed light on the impact and mechanism of TB treatment on the urine metabolome of patients, providing insights into the mechanisms of TB treatment failure. The research emphasizes the need for personalised treatment regimens, considering the individual variations in TB drug metabolism.

Dr. Opperman’s published papers, including “Chronological Metabolic Response to Intensive Phase TB Therapy” and “Time-Dependent Changes in Urinary Metabolome,” have made significant contributions to the field. Her review paper on factors contributing to TB treatment lost to follow-up adds a crucial layer to understanding the challenges faced in developing countries.

In her current role as a post-doctoral fellow, Dr. Opperman is taking her research further. With funding from the National Research Foundation, she is assessing the suitability of the guinea pig animal model for TB metabolomics research. If proven suitable, this model could offer a more controlled experimental environment, potentially advancing diagnostics and treatment strategies.

Dr. Monique Opperman’s work stands as a beacon of hope in the quest to understand and combat TB. Her dedication to unravelling the complexities of TB treatment showcases the power of metabolomics in providing valuable insights for a brighter, healthier future.


1. Combrink, M., et al. 2019. Time-Dependent Changes in Urinary Metabolome Before and After Intensive Phase Tuberculosis Therapy: A Pharmacometabolomics Study. OMICS: A Journal of Integrative Biology, 23(11):560-572. 

2.Opperman, M., et al., 2021. Chronological Metabolic Response to Intensive Phase TB Therapy in Patients with Cured and Failed Treatment Outcomes. ACS Infectious Diseases, 7(6)1859-1869.

3. Opperman, M., du Preez, I. 2022. Factors contributing to pulmonary tuberculosis treatment lost to follow-up in developing countries: An overview. African Journal of Infectious Diseases. 17:60–73.

4. Combrink, M., Loots, DT, du Preez, I. 2020. Metabolomics describes previously unknown toxicity mechanisms of isoniazid and rifampicin. Toxicology Letters, 322:104-110.

The graphical abstract for this website article was partially generated using BioRender.com

GCxGC-TOFMS Untargeted

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Spectra identified via comparison with library spectra

NMR Untargeted

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Spectra identified via comparison with library spectra

LDL Cholesterol Subfractions

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The LDL subfraction test measures up to twelve lipoprotein fractions and subfractions (VLDL, mid-bands A-C and LDL 1 through 7)

HDL Cholesterol Subfractions

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Separates and quantifies up to 10 HDL subfractions, classified from large buoyant HDL lipoproteins (HDL-L) to small-dense HDL lipoproteins (HDL-S).