Effect of single nucleotide polymorphisms in cytochrome P450 isoenzyme and N-acetyltransferase 2 genes on the metabolism of artemisinin-based combination therapies in malaria patients from Cambodia and Tanzania.

The pharmacogenetics of antimalarial agents are poorly known, although the application of pharmacogenetics might be critical in optimizing treatment. This population pharmacokinetic-pharmacogenetic study aimed at assessing the effects of single nucleotide polymorphisms (SNPs) in cytochrome P450 isoenzyme genes (CYP, namely, CYP2A6, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP3A4, and CYP3A5) and the N-acetyltransferase 2 gene (NAT2) on the pharmacokinetics of artemisinin-based combination therapies in 150 Tanzanian patients treated with artemether-lumefantrine, 64 Cambodian patients treated with artesunate-mefloquine, and 61 Cambodian patients treated with dihydroartemisinin-piperaquine. The frequency of SNPs varied with the enzyme and the population. Higher frequencies of mutant alleles were found in Cambodians than Tanzanians for CYP2C9*3, CYP2D6*10 (100C → T), CYP3A5*3, NAT2*6, and NAT2*7. In contrast, higher frequencies of mutant alleles were found in Tanzanians for CYP2D6*17 (1023C → T and 2850C → T), CYP3A4*1B, NAT2*5, and NAT2*14. For 8 SNPs, no significant differences in frequencies were observed. In the genetic-based population pharmacokinetic analyses, none of the SNPs improved model fit. This suggests that pharmacogenetic data need not be included in appropriate first-line treatments with the current artemisinin derivatives and quinolines for uncomplicated malaria in specific populations. However, it cannot be ruled out that our results represent isolated findings, and therefore more studies in different populations, ideally with the same artemisinin-based combination therapies, are needed to evaluate the influence of pharmacogenetic factors on the clearance of antimalarials.

Residual antimalarials in malaria patients from Tanzania--implications on drug efficacy assessment and spread of parasite resistance.

Repeated antimalarial treatment for febrile episodes and self-treatment are common in malaria-endemic areas. The intake of antimalarials prior to participating in an in vivo study may alter treatment outcome and affect the interpretation of both efficacy and safety outcomes. We report the findings from baseline plasma sampling of malaria patients prior to inclusion into an in vivo study in Tanzania and discuss the implications of residual concentrations of antimalarials in this setting. In an in vivo study conducted in a rural area of Tanzania in 2008, baseline plasma samples from patients reporting no antimalarial intake within the last 28 days were screened for the presence of 14 antimalarials (parent drugs or metabolites) using liquid chromatography-tandem mass spectrometry. Among the 148 patients enrolled, 110 (74.3%) had at least one antimalarial in their plasma: 80 (54.1%) had lumefantrine above the lower limit of calibration (LLC = 4 ng/mL), 7 (4.7%) desbutyl-lumefantrine (4 ng/mL), 77 (52.0%) sulfadoxine (0.5 ng/mL), 15 (10.1%) pyrimethamine (0.5 ng/mL), 16 (10.8%) quinine (2.5 ng/mL) and none chloroquine (2.5 ng/mL). The proportion of patients with detectable antimalarial drug levels prior to enrollment into the study is worrying. Indeed artemether-lumefantrine was supposed to be available only at government health facilities. Although sulfadoxine-pyrimethamine is only recommended for intermittent preventive treatment in pregnancy (IPTp), it was still widely used in public and private health facilities and sold in drug shops. Self-reporting of previous drug intake is unreliable and thus screening for the presence of antimalarial drug levels should be considered in future in vivo studies to allow for accurate assessment of treatment outcome. Furthermore, persisting sub-therapeutic drug levels of antimalarials in a population could promote the spread of drug resistance. The knowledge on drug pressure in a given population is important to monitor standard treatment policy implementation.

Transient hyperglycemia in patients with tuberculosis in Tanzania: implications for diabetes screening algorithms.

BACKGROUND: Diabetes mellitus (DM) increases tuberculosis risk while tuberculosis, as an infectious disease, leads to hyperglycemia. We compared hyperglycemia screening strategies in controls and patients with tuberculosis in Dar es Salaam, Tanzania. METHODS: Consecutive adults with tuberculosis and sex- and age-matched volunteers were included in a case-control study between July 2012 and June 2014. All underwent DM screening tests (fasting capillary glucose [FCG] level, 2-hour CG [2-hCG] level, and glycated hemoglobin A1c [HbA1c] level) at enrollment, and cases were tested again after receipt of tuberculosis treatment. Association of tuberculosis and its outcome with hyperglycemia was assessed using logistic regression analysis adjusted for sex, age, body mass index, human immunodeficiency virus infection status, and socioeconomic status. Patients with tuberculosis and newly diagnosed DM were not treated for hyperglycemia. RESULTS: At enrollment, DM prevalence was significantly higher among patients with tuberculosis (n = 539; FCG level > 7 mmol/L, 4.5% of patients, 2-hCG level > 11 mmol/L, 6.8%; and HbA1c level > 6.5%, 9.3%), compared with controls (n = 496; 1.2%, 3.1%, and 2.2%, respectively). The association between hyperglycemia and tuberculosis disappeared after tuberculosis treatment (adjusted odds ratio [aOR] for the FCG level: 9.6 [95% confidence interval {CI}, 3.7-24.7] at enrollment vs 2.4 [95% CI, .7-8.7] at follow-up; aOR for the 2-hCG level: 6.6 [95% CI, 4.0-11.1] vs 1.6 [95% CI, .8-2.9]; and aOR for the HbA1c level, 4.2 [95% CI, 2.9-6.0] vs 1.4 [95% CI, .9-2.0]). Hyperglycemia, based on the FCG level, at enrollment was associated with tuberculosis treatment failure or death (aOR, 3.3; 95% CI, 1.2-9.3). CONCLUSIONS: Transient hyperglycemia is frequent during tuberculosis, and DM needs confirmation after tuberculosis treatment. Performance of DM screening at tuberculosis diagnosis gives the opportunity to detect patients at risk of adverse outcome.