A comparative study of logistic regression based machine learning techniques for prediction of early virological suppression in antiretroviral initiating HIV patients

Abstract Background 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. Methods 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. Results 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. Conclusion 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

Expanding regulatory science: Regulatory complementarity and reliance

Abstract Drug regulatory institutions, infrastructures, and systems are becoming increasingly interconnected across national boundaries and increasingly global in outlook. This process is reflected in the broadening and deepening application of the principles and practice of Regulatory Reliance, and parallel initiatives to strengthen the capacities of regulatory institutions in low‐ and middle‐income countries (LMICs). Although these developments are important and constructive, they have tended to be framed in terms of the transfer of systems, knowledge, and skills from relatively “mature” regulatory agencies in high‐income countries (HICs) to less‐well‐resourced regulatory agencies in LMICs. This framing recognizes and foregrounds the considerable practical challenges that many LMIC regulatory agencies face, but in doing so, also backgrounds and underestimates the significance of the different contextual insights that LMIC health researchers and regulators can bring to the regulatory deliberations of their HIC counterparts. This position paper argues that the systematic pursuit, identification, and sharing of these different contextual insights—a dimension of regulatory science that we term “Regulatory Complementarity”—can augment the current practice and goals of Regulatory Reliance, and further invigorate the emerging global regulatory ecosystem

Pharmacogenetic-based efavirenz dose modification: suggestions for an African population and the different CYP2B6 genotypes.

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 (p<0.0001). Simulation based AUCs for 600 mg doses were 1.25 and 2.10 times the product label mean AUC for the Ugandan population in general and CYP2B6*6/*6 genotypes respectively. Simulated exposures for EFV daily doses of 300 mg and 450 mg are comparable to the product label. Viral load fell precipitously on treatment, with only six patients having HIV RNA >40 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

Final model pharmacokinetic parameters.

<p>Ka = Mean population absorption rate constant, V = Mean population Volume of distribution, CL = Mean population clearance, F1 = Bioavailability fraction, IIV CL = inter-individual variability on Clearance in the population, RV = residual variability.</p

The individually weighted residuals (WRES) are plotted <i>vs.</i> time.

<p>The dashed line is the zero reference line while the solid line is a smooth nonparametric regression line. The plot demonstrates a good fit of all time point concentration data by the model.</p

Distribution of estimated patient AUC values by CYP2B6 genotype.

<p>CYP2B6*1/*1, CYP2B6 *1/*6, and CYP2B6 *6/*6. Dotted line = the mean AUC value in the product label.</p

Baseline characteristics and <i>CYP2B6*6</i> and <i>ABCB1</i> (<i>c.4046A>G)</i> genotypes of the study participants (n = 99).

<p>Missing was assigned to individuals whose genotypes were not determined. Data presented in the table are the assignments as analyzed.</p