Point-of-Care Detection of Nonadherence to Antiretroviral Treatment for HIV-1 in Resource-Limited Settings Using Drug Level Testing for Efavirenz, Lopinavir, and Dolutegravir: A Validation and Pharmacokinetic Simulation Study.

BACKGROUND: Virological failure during antiretroviral treatment (ART) may indicate the presence of drug resistance, but may also originate from nonadherence. Qualitative detection of ART components using drug level testing may be used to differentiate between these scenarios. We aimed to validate and implement qualitative point-of-care drug level tests for efavirenz (EFV), lopinavir (LPV), and dolutegravir (DTG) in rural South Africa. METHODS: Qualitative performance of immunoassays for EFV, LPV, and DTG was assessed by calculating limit of detection (LoD), region of uncertainty, and qualitative agreement with a reference test. Minimum duration of nonadherence resulting in a negative drug level test was assessed by simulation of treatment cessation using validated population pharmacokinetic models. RESULTS: LoD was 0.05 mg/L for EFV, 0.06 mg/L for LPV, and 0.02 mg/L for DTG. Region of uncertainty was 0.01-0.06 mg/L for EFV, 0.01-0.07 mg/L for LPV, and 0.01-0.02 mg/L for DTG. Qualitative agreement with reference testing at the LoD in patient samples was 95.2% (79/83) for EFV, 99.3% (140/141) for LPV, and 100% (118/118) for DTG. After simulated treatment cessation, median time to undetectability below LoD was 7 days [interquartile range (IQR) 4-13] for EFV, 30 hours (IQR 24-36) for LPV, and 6 days (IQR 4-7) for DTG. CONCLUSIONS: We demonstrate that qualitative ART drug level testing using immunoassays is feasible in a rural resource-limited setting. Implementation of this technology enables reliable detection of recent nonadherence and may allow for rapid and cost-effective differentiation between patients in need for adherence counseling and patients who require drug resistance testing or alternative treatment.

2021 update to HIV-TRePS: a highly flexible and accurate system for the prediction of treatment response from incomplete baseline information in different healthcare settings.

OBJECTIVES: With the goal of facilitating the use of HIV-TRePS to optimize therapy in settings with limited healthcare resources, we aimed to develop computational models to predict treatment responses accurately in the absence of commonly used baseline data. METHODS: Twelve sets of random forest models were trained using very large, global datasets to predict either the probability of virological response (classifier models) or the absolute change in viral load in response to a new regimen (absolute models) following virological failure. Two 'standard' models were developed with all baseline variables present and 10 others developed without HIV genotype, time on therapy, CD4 count or any combination of the above. RESULTS: The standard classifier models achieved an AUC of 0.89 in cross-validation and independent testing. Models with missing variables achieved AUC values of 0.78-0.90. The standard absolute models made predictions that correlated significantly with observed changes in viral load with a mean absolute error of 0.65 log10 copies HIV RNA/mL in cross-validation and 0.69 log10 copies HIV RNA/mL in independent testing. Models with missing variables achieved values of 0.65-0.75 log10 copies HIV RNA/mL. All models identified alternative regimens that were predicted to be effective for the vast majority of cases where the new regimen prescribed in the clinic failed. All models were significantly better predictors of treatment response than genotyping with rules-based interpretation. CONCLUSIONS: These latest models that predict treatment responses accurately, even when a number of baseline variables are not available, are a major advance with greatly enhanced potential benefit, particularly in resource-limited settings. The only obstacle to realizing this potential is the willingness of healthcare professions to use the system.

Heart Rate Variability, HIV and the Risk of Cardiovascular Diseases in Rural South Africa.

Background: Antiretroviral therapy (ART) transformed human immunodeficiency virus (HIV) infection into a chronic disease. Possible HIV-associated complications have emerged including cardiovascular diseases (CVD). Objectives: This study aims to determine the heart rate variability (HRV) distribution and association between HRV and HIV treated with ART in a rural African population. Methods: This cross-sectional study included 325 participants of the Ndlovu Cohort Study, South Africa. HRV was measured using a standardized five-minute resting ECG and assessed by the standard deviation of normal RR intervals (SDNN), root of mean squares of successive RR differences (RMSSD), percentage of RR intervals greater than 50 milliseconds different from its predecessor (pNN50), total-, low- and high-frequency power. CVD risk factors were assessed using measurements (blood pressure, anthropometry, cholesterol) and questionnaires (e.g. socio-demographics, alcohol, smoking, physical activity, age, diabetes). We used a Wilcoxon rank test to assess differences in medians between HIV-infected and HIV-uninfected participants and multivariable linear regression to investigate associations between HRV and HIV treated with ART. Conclusions: Of the participants, 196 (61.4%) were HIV-infected treated with ART and 123 (38.6%) were HIV-uninfected. HIV-infected consumed less alcohol, 52% versus 35%, smoked less, were less physically active, more often attained lower education, 26% versus 14%, and had lower systolic blood pressure, 134 mmHg versus 140 mmHg, compared to HIV-uninfected. Medians of all HRV parameters were lower for HIV-infected participants. The model fully adjusted for CVD risk factors showed a significant inverse association between HIV treated with ART and log RMSSD (-0.16) and log pnn50 (-0.61). Although HIV-infected participants treated with ART presented with less CVD risk factors they had a lower HRV indicating an increased risk of CVD. Highlights: - African HIV-infected participants on ART had less conventional CVD risk factors than HIV-uninfected.- However, HIV-infected participants had lower HRV than HIV-uninfected participants.- Lower HRV of the HIV-infected participants indicates that they are at a higher risk for CVD.

Predicting Virological Response to HIV Treatment Over Time: A Tool for Settings With Different Definitions of Virological Response.

OBJECTIVE: Definitions of virological response vary from <50 up to 1000 copies of HIV-RNA/mL. Our previous models estimate the probability of HIV drug combinations reducing the viral load to <50 copies/mL, with no indication of whether higher thresholds of response may be achieved. Here, we describe the development of models that predict absolute viral load over time. METHODS: Two sets of random forest models were developed using 50,270 treatment change episodes from more than 20 countries. The models estimated viral load at different time points following the introduction of a new regimen from variables including baseline viral load, CD4 count, and treatment history. One set also used genotypes in their predictions. Independent data sets were used for evaluation. RESULTS: Both models achieved highly significant correlations between predicted and actual viral load changes (r = 0.67-0.68, mean absolute error of 0.73-0.74 log10 copies/mL). The models produced curves of virological response over time. Using failure definitions of <100, 400, or 1000 copies/mL, but not 50 copies/mL, both models were able to identify alternative regimens they predicted to be effective for the majority of cases where the new regimen prescribed in the clinic failed. CONCLUSIONS: These models could be useful for selecting the optimum combination therapy for patients requiring a change in therapy in settings using any definition of virological response. They also give an idea of the likely response curve over time. Given that genotypes are not required, these models could be a useful addition to the HIV-TRePS system for those in resource-limited settings.

An update to the HIV-TRePS system: the development of new computational models that do not require a genotype to predict HIV treatment outcomes.

OBJECTIVES: The optimal individualized selection of antiretroviral drugs in resource-limited settings is challenging because of the limited availability of drugs and genotyping. Here we describe the development of the latest computational models to predict the response to combination antiretroviral therapy without a genotype, for potential use in such settings. METHODS: Random forest models were trained to predict the probability of a virological response to therapy (<50 copies HIV RNA/mL) following virological failure using the following data from 22,567 treatment-change episodes including 1090 from southern Africa: baseline viral load and CD4 cell count, treatment history, drugs in the new regimen, time to follow-up and follow-up viral load. The models were assessed during cross-validation and with an independent global test set of 1000 cases including 100 from southern Africa. The models' accuracy [area under the receiver-operating characteristic curve (AUC)] was evaluated and compared with genotyping using rules-based interpretation systems for those cases with genotypes available. RESULTS: The models achieved AUCs of 0.79-0.84 (mean 0.82) during cross-validation, 0.80 with the global test set and 0.78 with the southern African subset. The AUCs were significantly lower (0.56-0.57) for genotyping. CONCLUSIONS: The models predicted virological response to HIV therapy without a genotype as accurately as previous models that included a genotype. They were accurate for cases from southern Africa and significantly more accurate than genotyping. These models will be accessible via the online treatment support tool HIV-TRePS and have the potential to help optimize antiretroviral therapy in resource-limited settings where genotyping is not generally available.

HPV Type Distribution and Cervical Cytology among HIV-Positive Tanzanian and South African Women.

Background. There are limited data on high-risk human papillomavirus (hr-HPV) genotypes among HIV-positive women in Africa, and little is known about their relationship with cervical cytology in these populations. Methods. We conducted a cross-sectional study among 194 HIV-positive women (143 from Tanzania, and 51 from South Africa) to evaluate HPV genotypes among HIV-positive women with normal and abnormal cytology. Cervical samples were genotyped for HPV types, and slides were evaluated for atypical squamous cell changes according to the Bethesda classification system. Results. Prevalence of high grade squamous intraepithelial dysplasia (HSIL) was 9%. Overall, more than half (56%) of women were infected with an hr-HPV type; 94% of women with HSIL (n = 16), 90% of women with LSIL (n = 35), and 42% of women within normal limits (WNL) (n = 58) tested positive for hr-HPV. Overall, the most prevalent hr-HPV subtypes were HPV16 (26%) and HPV52 (30%). Regional differences in the prevalence of HPV18 and HPV35 were found. Conclusion. Regional differences in HPV genotypes among African women warrant the need to consider different monitoring programmes for cervical preneoplasia. HPV-based screening tests for cervical preneoplasia would be highly inefficient unless coupled with cytology screening of the HPV-positive sample, especially in HIV-positive women.

PCR-based identification of eight Lactobacillus species and 18 hr-HPV genotypes in fixed cervical samples of South African women at risk of HIV and BV.

Vaginal lactobacilli assessed by PCR-based microarray and PCR-based genotyping of HPV in South African women at risk for HIV and BV. Vaginal lactobacilli can be defined by microarray techniques in fixed cervical samples of South African women. Cervical brush samples suspended in the coagulant fixative BoonFix of one hundred women attending a health centre for HIV testing in South Africa were available for this study. In the Ndlovu Medical Centre in Elandsdoorn, South Africa, identification of 18 hr-HPV genotypes was done using the INNO-LiPA method. An inventory of lactobacilli organisms was performed using microarray technology. On the basis of the Lactobacillus and Lactobacillus biofilm scoring, the cases were identified as Leiden bacterial vaginosis (BV) negative (BV-; n = 41), Leiden BV intermediate (BV±; n = 25), and Leiden BV positive (BV+; n = 34). Fifty-one women were HIV positive and 49 HIV negative. Out of the 51 HIV positive women, 35 were HPV infected. These 51 HIV positive women were frequently infected with HPV16 and HPV18. In addition, HPV35, HPV52, HPV33, and HPV66 were often detected in these samples. Lactobacillus salivarius and Lactobacillus iners were the most prevalent lactobacilli as established by the microarray technique. In women with HPV infection, the prevalence of Lactobacillus crispatus was significantly reduced. In both HIV and HPV infection, a similar (but not identical) shift in the composition of the lactobacillus flora was observed. We conclude that there is a shift in the composition of vaginal lactobacilli in HIV-infected women. Because of the prominence of HPV35, HPV52, HPV33, and HPV66, vaccination for exclusively HPV16 and HPV18 might be insufficient in South African HIV+ women.