Background: HIV infection, anti-tuberculosis and efavirenz therapy are associated with neuropsychological effects. We evaluated the influence of rifampicin cotreatment, efavirenz pharmacokinetics and pharmacogenetics on neuropsychiatric disorders in Ugandan HIV patients with or without tuberculosis coinfection.Methods: 197 treatment naïve Ugandan HIV patients, of whom 138 were TB co-infected, enrolled prospectively and received efavirenz based HAART. TB-HIV confected patients received concomitant rifampicin based anti-TB therapy. Genotypes forCYP2B6(*6,*11),CYP3A5(*3,*6,*7), ABCB1 (c.3435C>T and c.4036 A/G rs3842),CYP2A6(*9, *17) andNR1I3rs3003596 T/C were determined. Efavirenz plasma concentrations were serially quantified at 3rd day, 1st, 2nd, 4th, 6th, 8th and 12th weeks during therapy. Efavirenz neuropsychiatric symptoms were evaluated in terms of sleep disorders, hallucinations and cognitive effects at baseline, at two and twelve weeks of efavirenz treatment using a modified Mini Mental State Examination (MMSE) score.Results: During the first twelve weeks of ART, 73.6% of the patients experienced at least one efavirenz related neuropsychiatric symptom. Commonest symptoms experienced were sleep disorders 60.5% (n=124) and hallucination 30.7% (n=63). Neuropsychiatric symptoms during HAART were significantly predicted by efavirenz plasma concentrations consistently. Rifampicin cotreatment reduced plasma efavirenz concentrations significantly only during the first week but not afterwards. There was no significant difference in the incidence of neuropsychiatric symptoms between patients receiving efavirenz with or without rifampicin cotreatment.CYP2B6*6and ABCB1 c.4036 A/G genotype significantly predicted efavirenz concentrations. The tendency ofCYP2B6*6genotype association with higher incidence of having vivid dream (p=0.05), insomnia (p=0.19) and tactile hallucination (p=0.09) was observed mainly at week-2.Conclusions: Efavirenz related neuropsychiatric symptoms are common among Ugandan HIV patients receiving ART and is mainly predicted by higher efavirenz plasma concentrations andCYP2B6genotype but not by rifampicin based anti-TB co-treatment.

Peer reviewed article authored by (Mukonzo JK, Namuwenge PM, Okure G, Mwesige B, Namusisi OK, Mukanga D). Read article or submit your manuscript for publishing.

Pharmacogenetics contributes to inter-individual variability in pharmacokinetics (PK) of efavirenz (EFV), leading to variations in both efficacy and toxicity. The purpose of this study was to assess the effect of genetic factors on EFV pharmacokinetics, treatment outcomes and genotype based EFV dose recommendations for adult HIV-1 infected Ugandans. In total, 556 steady-state plasma EFV concentrations from 99 HIV infected patients (64 female) treated with EFV/lamivudine/zidovidine were analyzed. Patient genotypes for CYP2B6 (*6 & *11), CYP3A5 (*3,*6 & *7) and ABCB1 c.4046A>G, baseline biochemistries and CD4 and viral load change from baseline were determined. A one-compartment population PK model with first-order absorption (NONMEM) was used to estimate genotype effects on EFV pharmacokinetics. PK simulations were performed based upon population genotype frequencies. Predicted AUCs were compared between the product label and simulations for doses of 300 mg, 450 mg, and 600 mg. EFV apparent clearance (CL/F) was 2.2 and 1.74 fold higher in CYP2B6*6 (*1/*1) and CYP2B6*6 (*1/*6) compared CYP2B6*6 (*6/*6) carriers, while a 22% increase in F1 was observed for carriers of ABCB1 c.4046A>G variant allele. Higher mean AUC was attained in CYP2B6 *6/*6 genotypes compared to CYP2B6 *1/*1 (p40 copies/mL after 84 days of treatment. No trend with exposure was noted for these six patients. Results of this study suggest that daily doses of 450 mg and 300 mg might meet the EFV treatment needs of HIV-1 infected Ugandans in general and individuals homozygous for CYP2B6*6 mutation, respectively.

Treatment with effective antiretroviral therapy (ART) lowers morbidity and mortality among HIV positive individuals. Effective highly active antiretroviral therapy (HAART) should lead to undetectable viral load within 6 months of initiation of therapy. Failure to achieve and maintain viral suppression may lead to development of resistance and increase the risk of viral transmission. In this paper three logistic regression based machine learning approaches are developed to predict early virological outcomes using easily measurable baseline demographic and clinical variables (age, body weight, sex, TB disease status, ART regimen, viral load, CD4 count). The predictive performance and generalizability of the approaches are compared. The multitask temporal logistic regression (MTLR), patient specific survival prediction (PSSP) and simple logistic regression (SLR) models were developed and validated using the IDI research cohort data and predictive performance tested on an external dataset from the EFV cohort. The model calibration and discrimination plots, discriminatory measures (AUROC, F1) and overall predictive performance (brier score) were assessed. The MTLR model outperformed the PSSP and SLR models in terms of goodness of fit (RMSE = 0.053, 0.1, and 0.14 respectively), discrimination (AUROC = 0.92, 0.75 and 0.53 respectively) and general predictive performance (Brier score= 0.08, 0.19, 0.11 respectively). The predictive importance of variables varied with time after initiation of ART. The final MTLR model accurately (accuracy = 92.9%) predicted outcomes in the external (EFV cohort) dataset with satisfactory discrimination (0.878) and a low (6.9%) false positive rate. Multitask Logistic regression based models are capable of accurately predicting early virological suppression using readily available baseline demographic and clinical variables and could be used to derive a risk score for use in resource limited settings.

HIV-positive TB co-infected patients are at increased risk of multidrug-resistant (MDR)-TB compared to HIV-negative patients. Co-treatment of MDR-TB and HIV is common particularly in Sub-Saharan Africa where the co-morbidity is endemic. We discuss potential cellular metabolic pathway-mediated drug-drug interactions and the possible effect on HIV treatment outcomes of commonly prescribed antiretroviral therapy.

Purpose: Plasma albumin, a biomarker for hepatic function, is reported to correspondingly decrease in concentration as disease severity increases in chronic infections including HIV and TB. Our objective was to develop a semi-mechanistic disease progression model to quantify plasma albumin concentration changes during TB and HIV therapy and identify the associated covariate factors. Methods: Plasma albumin concentration data was collected at specified times for 3 months from 262 HIV participants receiving efavirenz based anti retroviral therapy. Of these, 158 were TB co-infected and on Rifampicin based anti –tuberculosis co-treatment. An indirect response model with zero order albumin production and first order elimination was developed in NONMEM version 7.2 to describe our data. Genotype (CYP2B6*6 and 11, CYP3A5, ABCB1c.3435C>T and ABCB1rs), TB disease status, baseline age, body weight, plasma creatinine, alanine transaminase enzyme and CD4⁺ count were the potential model covariates tested. Results: The proposed model successfully described plasma albumin concentration changes in the study population. There was a 10.9% and 48.6% increase in albumin production rates in HIV only and TB co-infected participants respectively. Participants co-infected with TB showed a 44.2% lower baseline albumin secretion rate than those without TB while ABCB1c.3435C>T mutation was associated with a 16% higher steady state albumin secretion rate following treatment. Conclusion: A semi-mechanistic model describes plasma albumin concentration changes in HIV patients on ART. Further work is required to establish the utility of the model in monitoring disease progression and predicting prognosis in HIV and TB co-infected patients in absence of or during treatment.