Effects of genetic variability on rifampicin and isoniazid pharmacokinetics in South African patients with recurrent tuberculosis

Anushka Naidoo, Maxwell Chirehwa, Veron Ramsuran, Helen McIlleron, Kogieleum Naidoo, Nonhlanhla Yende-Zuma, Ravesh Singh, Sinaye Ncgapu, John Adamson, Katya Govender, Paolo Denti, Nesri Padayatchi, Anushka Naidoo, Maxwell Chirehwa, Veron Ramsuran, Helen McIlleron, Kogieleum Naidoo, Nonhlanhla Yende-Zuma, Ravesh Singh, Sinaye Ncgapu, John Adamson, Katya Govender, Paolo Denti, Nesri Padayatchi

Abstract

Aim: We report the prevalence and effect of genetic variability on pharmacokinetic parameters of isoniazid and rifampicin.

Materials & methods: Genotypes for SLCO1B1, NAT2, PXR, ABCB1 and UGT1A genes were determined using a TaqMan® Genotyping OpenArray™. Nonlinear mixed-effects models were used to describe drug pharmacokinetics.

Results: Among 172 patients, 18, 43 and 34% were classified as rapid, intermediate and slow NAT2 acetylators, respectively. Of the 58 patients contributing drug concentrations, rapid and intermediate acetylators had 2.3- and 1.6-times faster isoniazid clearance than slow acetylators. No association was observed between rifampicin pharmacokinetics and SLCO1B1, ABCB1, UGT1A or PXR genotypes.

Conclusion: Clinical relevance of the effects of genetic variation on isoniazid concentrations and low first-line tuberculosis drug exposures observed require further investigation.

Trial registration: ClinicalTrials.gov NCT02114684.

Keywords: isoniazid; pharmacogenetics; pharmacokinetics; pyrazinamide; rifampicin; tuberculosis.

Conflict of interest statement

Financial & competing interests disclosure

This publication was made possible by grant number 5R24TW008863from the Office of Global AIDS Coordinator and the US Department of Health and Human Services, National Institutes of Health (NIH OAR and NIH OWAR – grant number 5R24TW008863). Research reported in this publication was supported by the European & Developing Countries Clinical Trials Partnership (EDCTP) (TA.2011.40200.044), the South African Medical Research Council CAPRISA HIV-TB Pathogenesis and Treatment Research Unit the South African Medical Research Council under a Self-Initiated Research Grant. Study drug was donated by Bayer Pharmaceuticals. A Naidoo was the recipient of the University of KwaZulu-Natal College of Health Sciences Scholarship.The sponsor of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report.

H McIlleron is supported in part by theWellcome Trust (206379/Z/17/Z). K Naidoo and N Padayatchi were supported by the Columbia University – South Africa Fogarty AIDS260 International Training and Research Program (AITRP, grant number D43TW000231). V Ramsuran was supported in part by a research Flagship grant from the South African Medical Research Council (MRC-RFA-UFSP-01-2013/UKZN HIVEPI) and by the Sub-Saharan African Network for TB/HIV Research Excellence (SANTHE) – a DELTAS Africa Initiative programme (grant number DEL-15-006).

N Padayatchi is the principal studyinvestigator, on the Improving Retreatment Success Trial (IMPRESS) (NCT02114684). Bayer Pharmaceuticals donated the study drug (moxifloxacin) used during the trial. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

No writing assistance was utilized in the production of this manuscript.

Figures

Figure 1. . Visual predictive check for…
Figure 1.. Visual predictive check for rifampicin, pyrazinamide and isoniazid.
Visual predictive check for pharmacokinetics of rifampicin (A), pyrazinamide (B) and isoniazid stratified by NAT2 acetylator status (C). The dashed and solid lines are the 5th percentile, median and 95th percentile of the observed concentrations, while the shaded regions represent the corresponding 95% CIs for the same percentiles. The subplot in each panel or stratum shows the same VPC with a logarithmic transformation applied to the y-axis. VPC: Visual predictive check.
Figure 2. . Model-predicted individual exposures (AUC…
Figure 2..  Model-predicted individual exposures (AUC0–24 and Cmax).
Model-predicted individual exposures (AUC0–24 and Cmax) of rifampicin (A), pyrazinamide (B) and isoniazid (C). For isoniazid, the values are stratified by NAT2 acetylator status. Each point represents the average AUC or Cmax per individual. AUC: Area under the curve.

Source: PubMed

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