Role of pharmacogenomics in the treatment of tuberculosis: a review

Abstract

Background: Tuberculosis is one of the major public health problems worldwide. Modern antituberculous treatment can cure most patients; cure rates > 95% are achieved with standard short-course chemotherapy regimens containing isoniazid, rifampicin, pyrazinamide, and ethambutol among patients with drug-susceptible strains of tuberculosis; however, a small proportion do not respond to treatment or develop serious adverse events. Pharmacogenomic studies of drugs used in the treatment of tuberculosis could help us understand intersubject variations in treatment response. In this review, we compiled pharmacogenomic data on antituberculous drugs that were available from different settings that would give a better insight into the role of pharmacogenomics in the treatment of tuberculosis, thereby enhancing the efficacy and limiting the toxicity of existing antituberculosis medications.

Methods: The PubMed database was searched from 1960 to the present using the keywords "tuberculosis", "antituberculosis treatment", "isoniazid", "rifampicin", "pyrazinamide", "ethambutol", "pharmacogenomics", and "polymorphism". Abstracts from meetings and review articles were included.

Conclusion: Studies conducted in different settings suggest that pharmacogenomics plays a significant role in isoniazid metabolism, and impacts both treatment efficacy and frequency of adverse reactions. Single nucleotide polymorphisms influencing plasma rifampicin concentrations are also reported. No data are available regarding other first-line drugs, ie, ethambutol and pyrazinamide. There is a need to incorporate pharmacogenomics into clinical trials of tuberculosis in order to understand the factors impacting therapeutic success and occurrence of adverse drug effects.

Keywords: antituberculous treatment; drug metabolism; pharmacogenomics; polymorphism; tuberculosis.

Figure 1

Suggested metabolic pathways of isoniazid and metabolites via NAT2, CYP2E1, and GSTM1. © Copyright Future Medicine. Reprinted with permission from Roy PD, Majumder M, Roy B. Pharmacogenomics of anti-TB drug-related hepatotoxicity. Pharmacogenomics. 2008.

Figure 2

Bimodal variation in isoniazid exposure. Note: Plasma isoniazid concentrations were determined in subjects at six hours after isoniazid administration. Abbreviation: INH, isoniazid.

Figure 3

Individual clearance of isoniazid in relation to the number of high-activity NAT2*4 alleles. © Copyright American Society for Microbiology. Reprinted with permission from Kinzig-Schippers M, Tomalik-Scharte D, Jetter A, et al. Should we use N-acetyl transferase type 2 genotyping to personalize isoniazid doses? Antimicrob Agents Chemother. 2005;49(5):1733–1738.

Figure 4

Plasma rifampin concentrations (μg/mL) versus time (hours) in patients grouped by SLCO1B1 c.463CA polymorphisms (▾) versus SLCO1B1 c.463CC genotypes (Δ). © Copyright American Society for Microbiology. Reprinted with permission from Weiner M, Peloquin CA, Burman W, et al. Effects of tuberculosis, race, and human gene SLCO1B1 polymorphisms on rifampin concentrations. Antimicrob Agents Chemother. 2010;54(10):4192–4200. Note: Arithmetic mean and standard error shown with vertical bars.