Non-compartmental and population pharmacokinetic analysis of dapsone in healthy NIGERIANS: A pilot study.

Dapsone is employed for both non-dermatological and dermatological indications but with non-existent population pharmacokinetics (popPK) data in Nigerians. This study was therefore designed to develop a popPK model in Nigerians. Non-compartmental analysis and nonlinear mixed effects modelling were utilized for data analysis. Eleven participants administered 50 mg dapsone tablet were included in the analysis. Derived pharmacokinetic parameters were: Cmax = 1.16 ± 0.32 µg/mL, Tmax = 3.77 ± 2.40 h, and t1/2z = 30.23 ± 11.76 h. PopPK model parameter estimates with inter-individual variability were Tlag = 0.40 h (10.0%, fixed); ka = 1.78 h-1 (75.9%); V/F = 89.25 L (21.6%); and Cl/F = 1.32 Lh-1 (27.7%). Sex was significantly associated with Cl/F, and body weight with V/F. Best popPK model was one-compartment with lag time, and first-order absorption and elimination. Sex and body weight significantly influenced the clearance and distribution volume of dapsone respectively.

Comparison of efavirenz levels in blood and hair with pharmacy refills as measures of adherence and predictors of viral suppression among people living with HIV in Nigeria.

BACKGROUND: Strategies to support adherence are constrained by the lack of tools to objectively monitor medication intake in low-resource settings. Pharmacologic measures are objective, but pharmacy refill data is more accessible and cost-efficient. This study compared short-term and long-term efavirenz (EFV) drug levels with pharmacy refill adherence data (PRA) and evaluated their ability to predict viral suppression among people living with HIV in Nigeria. METHODS: Paired hair and dried blood spot (DBS) samples were obtained from 91 adults living with HIV receiving 600 mg EFV-based antiretroviral therapy (ART) and EFV concentrations were measured via validated methods using liquid-chromatography-mass-spectrometry. PRA was estimated from pharmacy records, based on the number of days a patient collected medication before or after the scheduled pick-up date. PRA was categorized into ≤ 74%, 75-94% and ≥ 95%, defined as poor, medium and high adherence, respectively. HIV viral loads closest to the hair sampling time (within 6 months) were also abstracted. Receiver Operating Characteristics (ROC) curve analyses compared the ability of adherence metrics to predict viral suppression. RESULTS: Based on PRA, 81% of participants had high adherence while 11% and 8% had medium and poor adherence, respectively. The median (IQR) EFV concentrations were 6.85 ng/mg (4.56-10.93) for hair and 1495.6 ng/ml (1050.7-2365.8) for DBS. Of the three measures of adherence, hair EFV concentration had the highest Area Under Curve (AUC) to predict viral suppression. Correlations between EFV concentrations in DBS and hair with PRA were positive (r = 0.12, P = 0.27 and r = 0.21, P = 0.05, respectively) but not strong. CONCLUSIONS: EFV concentrations in hair were the strongest predictor of viral suppression and only weakly correlated with pharmacy refill adherence data in Nigeria. This study suggests that resource-limited settings may benefit from objective adherence metrics to monitor and support adherence.

N-acetyltransferase 2 enzyme genotype-phenotype discordances in both HIV-negative and HIV-positive Nigerians.

BACKGROUND: The N-acetyltransferase 2 (NAT2) enzyme has been understudied in Nigerians including genotype-phenotype association studies. OBJECTIVE: The aim of this study was NAT2 haplotype identification and genotype-phenotype investigations in HIV-positive and HIV-negative Nigerians. PATIENTS AND METHODS: Phenotypes included self-reported sulphonamide hypersensitivity survey, experimental and computational NAT2 phenotyping. The NAT2 gene was amplified by PCR. Gene sequencing used ABI 3730 and Haploview 4.2 for haplotype reconstruction. Genotype-phenotype analyses used the χ P-value and odds ratio with a 95% confidence interval. RESULTS: Self-reported sulphonamide hypersensitivity showed a prevalence of 3.1 and 12.4% in HIV-positive and HIV-negative Nigerians, respectively. NAT2 genetic variants 191G>A, 282C>T, 341T>C, 481C>T, 590G>A, 803A>G and 857G>A were not significantly different between both groups (odds ratio=0.87; 95% confidence interval: 0.54-1.38, P=0.55). Nine haplotypes: NAT2*4, NAT2*12A, NAT2*13A, NAT2*5B, NAT2*6A, NAT2*7B, NAT2*5C, NAT2*14B and NAT2*14A had frequencies more than 1%, whereas NAT2*12B had 1.1% in the HIV-positive and 0.4% in the HIV-negative group. Overall, slow acetylator haplotypes made up 68%. The NAT2*12 signature single-nucleotide polymorphism was in high linkage disequilibrium with signature single-nucleotide polymorphism for NAT2*13 (D'=0.97, r=0.61) and NAT2*5 (D'=0.98, r=0.64). Genotype-phenotype association analysis showed haplotypes NAT2*13A, NAT2*5C, NAT2*7B and NAT2*14A to be associated strongly with the slow metabolic phenotype (P=0.002, 0.029, 0.032 and 0.050, respectively). Computational phenotypes were similar, with 30.9, 66 and 3.1% for slow, intermediate and rapid acetylators, respectively, among HIV-positive Nigerians and 31.2, 66.3 and 2.5% among the HIV-negative group. Overall, slow phenotypes made up 31%. CONCLUSION: NAT2 haplotype frequencies are similar in Nigerians, irrespective of HIV status, but genotype-phenotype discordances exist.

Quinoline Antimalarials Increase the Antibacterial Activity of Ampicillin.

Bacterial and malaria co-infections are common in malaria endemic countries and thus necessitate co-administration of antibiotics and antimalarials. There have long been anecdotal clinical reports of interactions between penicillins and antimalarial agents, but the nature and mechanisms of these interactions remain to be investigated. In this study, we employed antimicrobial interaction testing methods to study the effect of two antimalarials on the antibacterial activity of ampicillin in vitro. Paper strip diffusion, a modified disc diffusion and checkerboard methods were used to determine the nature of interactions between ampicillin and quinoline antimalarials, chloroquine and quinine, against Gram-positive and Gram-negative bacteria. The impact of antimalarials and ampicillin-antimalarial drug combinations on cell integrity of test bacteria were determined by measuring potassium release. The tested antimalarials did not show substantial antibacterial activity but quinine was bactericidal at high concentrations. Chloroquine and quinine increased ampicillin activity, with increasing concentrations extending the antibacterial's inhibition zones by 2.7-4.4 mm and from 1.1 to over 60 mm, respectively. Observed interactions were largely additive with Fractional Inhibitory Concentration Indices of >0.5-1 for all ampicillin-antimalarial combinations. Quinine and, to a lesser extent, chloroquine increase the activity of ampicillin and potentially other ß-lactams, which has implications for combined clinical use.