Stanford HIV Database Research Articles

HIV-1 pretreatment and acquired antiretroviral drug resistance before tenofovir/ lamivudine /dolutegravir (TLD) roll-out in Mozambique

BackgroundThe World Health Organization (WHO) recommends that HIV treatment scale-up is accompanied by a robust assessment of drug resistance emergence and transmission. The WHO HIV Drug Resistance (HIVDR) monitoring and surveillance strategy includes HIVDR testing in adults both initiating and receiving antiretroviral therapy (ART). Due to limited information about HIVDR in Mozambique, we conducted two nationally representative surveys of adults initiating and receiving first-line ART regimes to better inform the HIV program.MethodsWe carried out a cross-sectional study between March 2017 and December 2019. Adults (older than 15 years) living with HIV (PLHIV) initiating ART or receiving first-line ART for between 9-15 months at 25 health facilities across all eleven provinces in Mozambique were included. Genotypic HIVDR was assessed on dried blood spots (DBS) when viral loads were ≥ 1000 copies/ml. Genotypic resistance for non-nucleoside reverse transcriptase inhibitors (NNRTIs), nucleoside reverse transcriptase inhibitors (NRTIs), and protease inhibitors (PIs) was determined using the Stanford HIV database algorithm 9.5 and calibrated population resistance tool 8.1.ResultsOf 828 participants -enrolled, viral load (VL) testing was performed on 408 initiators and 409 ART experienced. Unsuppressed VL was found in 68.1% 419 initiators and 18.8% (77/409) of the ART experienced. Of the 278 initiators and 70 ART experienced who underwent sequencing, 51.7% (144/278) and 75.7% (53/70) were sequenced successfully. Among the new initiators, pretreatment drug resistance (PDR) for NNRTI and PI was found in 16.0% (23/144) and 1.4% (2/144) of the participants, respectively. Acquired drug resistance (ADR) was found in 56.5% (30/53) of the ART-experienced participants of whom 24.5% (13/53) were resistant to both NRTI and NNRTI.ConclusionHigh rates of PDR and ADR for NNRTI and ADR for NRTI were observed in our study. These findings support the replacement of NNRTIs with dolutegravir (DTG) but high levels of NRTI resistance in highly treatment-experienced individuals still require attention when transitioning to new regimens. Moreover, the study underlines the need for routine VL testing and HIVDR surveillance to improve treatment management strategies.

Transmitted drug resistance and transmission clusters among ART-naïve HIV-1-infected individuals from 2019 to 2021 in Nanjing, China.

Transmitted drug resistance (TDR) is an increasingly prevalent problem worldwide, which will significantly compromise the effectiveness of HIV treatments. However, in Nanjing, China, there is still a dearth of research on the prevalence and transmission of TDR among ART-naïve HIV-1-infected individuals. This study aimed to understand the prevalence and transmission of TDR in Nanjing. A total of 1,393 participants who were newly diagnosed with HIV-1 and had not received ART between January 2019 and December 2021 were enrolled in this study. HIV-1 pol gene sequence was obtained by viral RNA extraction and nested PCR amplification. Genotypes, TDR and transmission cluster analyses were conducted using phylogenetic tree, Stanford HIV database algorithm and HIV-TRACE, respectively. Univariate and multivariate logistic regression analyses were performed to identify the factors associated with TDR. A total of 1,161 sequences were successfully sequenced, of which CRF07_BC (40.6%), CRF01_AE (38.4%) and CRF105_0107 (6.3%) were the main HIV-1 genotypes. The overall prevalence of TDR was 7.8%, with 2.0% to PIs, 1.0% to NRTIs, and 4.8% to NNRTIs. No sequence showed double-class resistance. Multivariate logistic regression analysis revealed that compared with CRF01_AE, subtype B (OR = 2.869, 95%CI: 1.093-7.420) and female (OR = 2.359, 95%CI: 1.182-4.707) were risk factors for TDR. Q58E was the most prevalent detected protease inhibitor (PI) -associated mutation, and V179E was the most frequently detected non-nucleoside reverse transcriptase inhibitor (NNRTI) -associated mutation. A total of 613 (52.8%) sequences were segregated into 137 clusters, ranging from 2 to 74 sequences. Among 44 individuals with TDR (48.4%) within 21 clusters, K103N/KN was the most frequent TDR-associated mutation (31.8%), followed by Q58E/QE (20.5%) and G190A (15.9%). Individuals with the same TDR-associated mutations were usually cross-linked in transmission clusters. Moreover, we identified 9 clusters in which there was a transmission relationship between drug-resistant individuals, and 4 clusters in which drug-resistant cases increased during the study period. The overall prevalence of TDR in Nanjing was at a moderate level during the past 3 years. However, nearly half of TDR individuals were included in the transmission clusters, and some drug-resistant individuals have transmitted in the clusters. Therefore, HIV drug-resistance prevention, monitoring and response efforts should be sustained and expanded to reduce the prevalence and transmission of TDR in Nanjing.

HIV virologic response, patterns of drug resistance mutations and correlates among adolescents and young adults: A cross-sectional study in Tanzania.

The emergence of HIV drug resistance mutations (DRMs) is of significant threat to achieving viral suppression (VS) in the quest to achieve global elimination targets. We hereby report virologic outcomes and patterns of acquired DRMs and its associated factors among adolescents and young adults (AYA) from a broader HIV drug resistance surveillance conducted in Tanzania. Data of AYA was extracted from a cross-sectional study conducted in 36 selected facilities using a two-stage cluster sampling design. Dried blood spot (DBS) samples were collected and samples with a viral load (VL) ≥1000 copies/mL underwent genotyping for the HIV-1 pol gene. Stanford HIV database algorithm predicted acquired DRMs, Fisher's exact test and multivariable logistic regression assessed factors associated with DRMs and VS, respectively. We analyzed data of 578 AYA on antiretroviral therapy (ART) for 9-15 and ≥ 36 months; among them, 91.5% and 88.2% had VS (VL<1000copies/mL) at early and late time points, respectively. Genotyping of 64 participants (11.2%) who had VL ≥1000 copies/ml detected 71.9% of any DRM. Clinically relevant DRMs were K103N, M184V, M41L, T215Y/F, L210W/L, K70R, D67N, L89V/T, G118R, E138K, T66A, T97A and unexpectedly absent K65R. Participants on a protease inhibitor (PI) based regimen were twice as likely to not achieve VS compared to those on integrase strand transfer inhibitors (INSTI). The initial VL done 6 months after ART initiation of ≥1000copies/mL was the primary factor associated with detecting DRMs (p = .019). VS amongst AYA is lower than the third UNAIDs target. Additionally, a high prevalence of ADR and high levels of circulating clinically relevant DRMs may compromise the long-term VS in AYA. Furthermore, the first VL result of ≥1000copies/ml after ART initiation is a significant risk factor for developing DRMs. Thus, strict VL monitoring for early identification of treatment failure and genotypic testing during any ART switch is recommended to improve treatment outcomes for AYA.

Molecular transmission network of pretreatment drug resistance among human immunodeficiency virus-positive individuals and the impact of virological failure on those who received antiretroviral therapy in China.

ObjectivesWe investigated the prevalence of pretreatment drug resistance (PDR), the molecular transmission network among HIV-positive individuals, and the impact of virological failure on those who received antiretroviral therapy (ART) in China.MethodsBased on the World Health Organization (WHO) surveillance guidelines for PDR, a baseline survey and follow-up were conducted in 2018 and 2021, respectively. Demographic information and plasma samples were obtained from all participants. HIV pol gene region sequences were used to analyze the PDR and molecular transmission networks using the Stanford HIV database algorithm and HIV-TRACE, respectively. This study assessed the odds ratios (OR) of PDR to virological failure (viral load ≥ 50 copies/mL) after 3 years of ART using multivariable logistic regression.ResultsOf the 4,084 individuals, 370 (9.1%) had PDR. The prevalence of PDR to non-nucleoside reverse transcriptase inhibitors (5.2%) was notably higher than that to nucleoside reverse transcriptase inhibitors (0.7%, p < 0.001), protease inhibitors (3.0%, p < 0.001), and multidrug resistance (0.3%, p < 0.001). A total of 1,339 (32.8%) individuals from 361 clusters were enrolled in the molecular transmission network. Of the 361 clusters, 22 included two or more individuals with PDR. The prevalence of virological failure among HIV-positive individuals after 3 years of ART without PDR, those with PDR to Chinese listed drugs, and those with PDR to other drugs was 7.9, 14.3, and 12.6%, respectively. Compared with that in HIV-positive individuals without PDR, virological failure after 3 years of ART was significantly higher (OR: 2.02, 95% confidence interval (CI): 1.25–3.27) and not significantly different (OR: 1.72, 95% CI: 0.87–3.43) in individuals with PDR to Chinese listed drugs and those with PDR to other drugs, respectively. Missed doses in the past month were significantly associated with virological failure (OR, 2.82; 95% CI: 4.08–5.89).ConclusionThe overall prevalence of PDR was close to a high level and had an impact on virological failure after 3 years of ART. Moreover, HIV drug-resistant strains were transmitted in the molecular transmission network. These results illustrate the importance of monitoring PDR and ensuring virological suppression through drug adherence.

A Partially Multiplexed HIV Drug Resistance (HIVDR) Assay for Monitoring HIVDR Mutations of the Protease, Reverse-Transcriptase (PRRT), and Integrase (INT).

ABSTRACTAs dolutegravir (DTG)-containing HIV regimens are scaled up globally, monitoring for HIV drug resistance (HIVDR) will become increasingly important. We designed a partially multiplexed HIVDR assay using Sanger sequencing technology to monitor HIVDR mutations in the protease, reverse-transcriptase (PRRT), and integrase (INT). A total of 213 clinical and analytical plasma and dried blood spot (DBS) samples were used in the evaluation. The assay detected a wide range of known HIV-1 subtypes and circulating recombinant forms (CRFs) of group M from 139 samples. INT accuracy showed that the average nucleotide (nt) sequence concordance was 99.8% for 75 plasma samples and 99.5% for 11 DBS samples compared with the reference sequences. The PRRT accuracy also demonstrated the average nucleotide sequence concordance was 99.5% for 57 plasma samples and 99.2% for 33 DBS samples. The major PRRT and INT DR mutations of all samples tested were concordant with those of the reference sequences using the Stanford HIV database (db). Amplification sensitivity of samples with viral load (VL) >5000 copies/mL showed plasma exceeded 95% of positivity, and DBS exceeded 90% for PRRT and INT. Samples with VL (1000 to 5000 copies/mL) showed plasma exceeded 90%, and DBS reached 88% positivity for PRRT and INT. Assay precision and reproducibility showed >99% nucleotide sequence concordance in each set of replicates for PRRT and INT. In conclusion, this HIVDR assay met WHO HIVDR assay performance criteria for surveillance, worked for plasma and DBS, used minimal sample volume, was sensitive, and was a potentially cost-effective tool to monitor HIVDR mutations in PRRT and INT.IMPORTANCE This HIVDR genotyping assay works for both plasma and DBS samples, requires low sample input, and is sensitive. This assay has the potential to be a user-friendly and cost-effective HIVDR assay because of its partially multiplexed design. Application of this genotyping assay will help HIVDR monitoring in HIV high-burdened countries using a DGT-based HIV drug regimen recommended by the U.S. President's Emergency Plan for AIDS Relief and the WHO.

An Android-Based Mobile App (ARVPredictor) for the Detection of HIV Drug-Resistance Mutations and Treatment at the Point of Care: Development Study.

BackgroundHIV/AIDS remains one of the major global human health challenges, especially in resource-limited environments. By 2017, over 77.3 million people were infected with the disease, and approximately 35.4 million individuals had already died from AIDS-related illnesses. Approximately 21.7 million people were accessing ART with significant clinical outcomes. However, numerous challenges are experienced in the delivery and accurate interpretation of data on patients with HIV data by various health care providers at different care levels. Mobile health (mHealth) technology is progressively making inroads into the health sector as well as medical research. Different mobile devices have become common in health care settings, leading to rapid growth in the development of downloadable software specifically designed to fulfill particular health-related purposes.ObjectiveWe developed a mobile-based app called ARVPredictor and demonstrated that it can accurately define HIV-1 drug-resistance mutations in the HIV pol gene for use at the point of care.MethodsARVPredictor was designed using Android Studio with Java as the programming language and is compatible with both Android and iOS. The app system is hosted on Nginx Server, and network calls are built on PHP’s Laravel framework handled by the Retrofit Library. The DigitalOcean offers a high-performance and stable cloud computing platform for ARVPredictor. This mobile app is enlisted in the Google Play Store as an “ARVPredictor” and the source code is available under MIT permissive license at a GitHub repository. To test for agreement between the ARVPredictor and Stanford HIV Database in detecting HIV subtype and NNRT and NRTI mutations, a total of 100 known HIV sequences were evaluated.ResultsThe mobile-based app (ARVPredictor) takes in a set of sequences or known mutations (protease, reverse transcriptase and integrase). It then returns inferred levels of resistance to selected nucleoside, nonnucleoside protease, and integrase inhibitors for accurate HIV/AIDS management at the point of care. The ARVPredictor identified similar HIV subtypes in 98/100 sequences compared with the Stanford HIV Database (κ=0.98, indicating near perfect agreement). There were 89/100 major NNRTI and NRTI mutations identified by ARVPredictor, similar to the Stanford HIV Database (κ=0.89, indicating near perfect agreement). Eight mutations classified as major by the Stanford HIV Database were classified as others by ARVPredictor.ConclusionsThe ARVPredictor largely agrees with the Stanford HIV Database in identifying both major and minor proteases, reverse transcriptase, and integrase mutations. The app can be conveniently used robustly at the point of care by HIV/AIDS care providers to improve the management of HIV infection.

Survey of pretreatment HIV drug resistance and the genetic transmission networks among HIV-positive individuals in southwestern China, 2014\u20132020

BackgroundPretreatment drug resistance (PDR) can limit the effectiveness of HIV antiretroviral therapy (ART). The aim of this study was to assess the prevalence of PDR among HIV-positive individuals that initiated antiretroviral therapy in 2014–2020 in southwestern China.MethodsConsecutive cross-sectional surveys were conducted in Qinzhou, Guangxi. We obtained blood samples from individuals who were newly diagnosed with HIV in 2014–2020. PDR and genetic networks analyses were performed by HIV-1 pol sequences using the Stanford HIV-database algorithm and HIV-TRACE, respectively. Univariate and multivariate logistic regression models were used to explore the potential factors associated with PDR.ResultsIn total, 3236 eligible HIV-positive individuals were included. The overall prevalence of PDR was 6.0% (194/3236). The PDR frequency to NNRTI (3.3%) was much higher than that of NRTI (1.7%, p < 0.001) and PI (1.2%, p < 0.001). A multivariate logistic regression analysis revealed that PDR was significantly higher among individuals aged 18–29 (adjusted odds ratio (aOR): 1.79, 95% CI 1.28–2.50) or 30–49 (aOR: 2.82, 95% CI 1.73–4.82), and harboring CRF08_BC (aOR: 3.23, 95% CI 1.58–6.59). A total of 1429 (43.8%) sequences were linked forming transmission clusters ranging in size from 2 to 119 individuals. Twenty-two individuals in 10 clusters had the same drug resistant mutations (DRMs), mostly to NNRTIs (50%, 5/10).ConclusionsThe overall prevalence of PDR was medium, numerous cases of the same DRMs among genetically linked individuals in networks further illustrated the importance of surveillance studies for mitigating PDR.

'Geno-to-pheno' SARS-CoV-2 genome-COVID-19 association studies

'Geno-to-pheno' SARS-CoV-2 genome-COVID-19 association studies

Predictors of first-line antiretroviral therapy failure among adults and adolescents living with HIV/AIDS in a large prevention and treatment program in Nigeria

BackgroundA substantial number of persons living with HIV (PLWH) in Nigeria do not experience durable viral suppression on first-line antiretroviral therapy (ART). Understanding risk factors for first-line treatment failure informs patient monitoring practices and distribution of limited resources for second-line regimens. We determined predictors of immunologic and virologic failures in a large ART delivery program in Abuja, Nigeria.MethodsA retrospective cohort study was conducted at the University of Abuja Teaching Hospital, a tertiary health care facility, using data from February 2005 to December 2014 in Abuja, Nigeria. All PLWH aged ≥ 15 years who initiated ART with at least 6-month follow-up and one CD4 measurement were included. Immunologic failure was defined as a CD4 decrease to or below pre-ART level or persistent CD4 < 100 cells per mm3 after 6 months on ART. Virologic failure (VF) was defined as two consecutive HIV-1 RNA levels > 1000 copies/mL after at least 6 months of ART and enhanced adherence counselling. HIV drug resistance (Sanger sequences) was analyzed using the Stanford HIV database algorithm and scored for resistance to common nucleoside reverse transcriptase inhibitors (NRTIs) and non-nucleoside reverse transcriptase inhibitors (NNRTIs). Univariate and multivariate log binomial regression models were used to estimate relative risks (RRs) and 95% confidence intervals (CIs).ResultsOf 12,452 patients followed, a total of 5928 initiated ART with at least 6 months of follow-up and one CD4 measurement. The entry point for 3924 (66.2%) was through the program’s own voluntary counseling and testing (VCT) center, while 1310 (22.1%) were referred from an outside clinic/program, 332 (5.6%) in-patients, and 373 (6.3%) through other entry points including prevention of mother to child transmission (PMTCT) and transferred from other programs. The mean CD4 at enrollment in care was 268 ± 23.7 cells per mm3, and the mean HIV-1 RNA was 3.3 ± 1.3.log10 copies/mL. A total of 3468 (80.5%) received nevirapine (NVP) and 2260 (19.5%) received efavirenz (EFV)—based regimens. A total of 2140 (36.1%) received tenofovir (TDF); 2662 (44.9%) zidovudine (AZT); and 1126 (19.0%) stavudine (d4T). Among those receiving TDF, 45.0% also received emtricitabine (FTC). In a multivariate model, immunologic failure was more common among PLWH with female gender as compared to male [RR (95% CI) 1.22 (1.07–1.40)] and less common among those who entered care at the program’s VCT center as compared to other entry points [0.79 (0.64–0.91)], WHO stage 3/4 as compared to 1/2 [0.19 (0.16–0.22)], or CD4 200 + cells per mm3 as compared to lower [0.19 (0.16–0.22)]. Virologic failure was more common among PLWH who entered care at the program’s VCT center as compared to other entry points [RR (95% CI) 1.45 (1.11–1.91) and those with CD4 < 200 cells per mm3 at entry into care as compared to higher [1.71 (1.36–2.16)]. Of 198 patient-derived samples sequenced during virologic failure, 42 (21%) were wild-type; 145 (73%) carried NNRTI drug resistance mutations; 151 (76.3%) M184I/V; 29 (14.6%) had ≥ 3 TAMs, and 37 (18.7%) had K65R, of whom all were on TDF-containing first-line regimens.ConclusionsIn this cohort of Nigerian PLWH followed for a period of 9 years, immunologic criteria poorly predicted virologic failure. Furthermore, a subset of samples showed that patients failing ART for extended periods of time had HIV-1 strains harboring drug resistance mutations.

Short Communication: HIV-DRLink: A Tool for Reporting Linked HIV-1 Drug Resistance Mutations in Large Single-Genome Data Sets Using the Stanford HIV Database.

The prevalence of HIV-1 drug resistance is increasing worldwide and monitoring its emergence is important for the successful management of populations receiving combination antiretroviral therapy. It is likely that pre-existing drug resistance mutations linked on the same viral genomes are predictive of treatment failure. Because of the large number of sequences generated by ultrasensitive single-genome sequencing (uSGS) and other similar next-generation sequencing methods, it is difficult to assess each sequence individually for linked drug resistance mutations. Several software/programs exist to report the frequencies of individual mutations in large data sets, but they provide no information on linkage of resistance mutations. In this study, we report the HIV-DRLink program, a research tool that provides resistance mutation frequencies as well as their genetic linkage by parsing and summarizing the Sierra output from the Stanford HIV Database. The HIV-DRLink program should only be used on data sets generated by methods that eliminate artifacts due to polymerase chain reaction recombination, for example, standard single-genome sequencing or uSGS. HIV-DRLink is exclusively a research tool and is not intended to inform clinical decisions.

Modelling drugs interaction in treatment-experienced patients on antiretroviral therapy

Understanding pharmacology and drug resistance patterns plus appropriate use of laboratory testing is vital for managing treatment-experienced patients with new agents. While we acknowledge that patients with extensive drug resistance now have multiple options for suppressive therapy, and expert care is essential to avoid the rapid emergence of resistance to these new agents, clinicians are unaware of the inherent (hidden) patterns created by combined drug regimens that could trigger adverse drug reactions. This paper proposes a novel hybrid system framework that combines soft computing techniques, for drugs interaction modelling and precise patient response optimisation. A Fuzzy Logic system was developed to address the uncertainty in treatment change episodes (TCEs). A weighted least-squares cost function was then employed to auto-tune hyperparameters for training the neural network. After acceptable tuning, the final hyperparameters served the neural network—to efficiently learn the ensuing patterns for precice drug interaction classification. The proposed framework was experimented with clinical data of TCEs from two disparate sources: a publicly available HIV database (the Stanford HIV database: https://hivdb.stanford.edu ), and clinical data collected from 13 health centers managing HIV cases in Akwa Ibom State of Nigeria (the Akwa-Ibom HIV database). In both databases, a correlation of prognostic markers suggests strong association between first line CD4 and follow-up CD4 counts; while a moderately weak association was observed for first line and follow-up viral loads. Correlation of physiological feature gave very strong association between first line and follow-up body mass index in Akwa-Ibom database. Analysis of the patients progress explains the decreased potency of CD4 count and body mass index as HIV predictors. The root mean square error (RMSE) and classification accuracy were used as performance metrics for measuring the precision of our hybrid framework. Results obtained showed improved RMSE and classification accuracy for both databases, when compared with existing works.

From Sequence Data to Patient Result: A Solution for HIV Drug Resistance Genotyping With Exatype, End to End Software for Pol-HIV-1 Sanger Based Sequence Analysis and Patient HIV Drug Resistance Result Generation.

Introduction:With the rapid scale-up of antiretroviral therapy (ART) to treat HIV infection, there are ongoing concerns regarding probable emergence and transmission of HIV drug resistance (HIVDR) mutations. This scale-up has to lead to an increased need for routine HIVDR testing to inform the clinical decision on a regimen switch. Although the majority of wet laboratory processes are standardized, slow, labor-intensive data transfer and subjective manual sequence interpretation steps are still required to finalize and release patient results. We thus set out to validate the applicability of a software package to generate HIVDR patient results from raw sequence data independently.Methods:We assessed the performance characteristics of Hyrax Bioscience’s Exatype (a sequence data to patient result, fully automated sequence analysis software, which consolidates RECall, MEGA X and the Stanford HIV database) against the standard method (RECall and Stanford database). Exatype is a web-based HIV Drug resistance bioinformatic pipeline available at sanger.exatype.com. To validate the exatype, we used a test set of 135 remnant HIV viral load samples at the National HIV Reference Laboratory (NHRL).Result:We analyzed, and successfully generated results of 126 sequences out of 135 specimens by both Standard and Exatype software. Result production using Exatype required minimal hands-on time in comparison to the Standard (6 computation-hours using the standard method versus 1.5 Exatype computation-hours). Concordance between the 2 systems was 99.8% for 311,227 bases compared. 99.7% of the 0.2% discordant bases, were attributed to nucleotide mixtures as a result of the sequence editing in Recall. Both methods identified similar (99.1%) critical antiretroviral resistance-associated mutations resulting in a 99.2% concordance of resistance susceptibility interpretations. The Base-calling comparison between the 2 methods had Cohen’s kappa (0.97 to 0.99), implying an almost perfect agreement with minimal base calling variation. On a predefined dataset, RECall editing displayed the highest probability to score mixtures accurately 1 vs. 0.71 and the lowest chance to inaccurately assign mixtures to pure nucleotides (0.002–0.0008). This advantage is attributable to the manual sequence editing in RECall.Conclusion:The reduction in hands-on time needed is a benefit when using the Exatype HIV DR sequence analysis platform and result generation tool. There is a minimal difference in base calling between Exatype and standard methods. Although the discrepancy has minimal impact on drug resistance interpretation, allowance of sequence editing in Exatype as RECall can significantly improve its performance.

Genetic diversity and antiretroviral resistance-associated mutation profile of treated and naive HIV-1 infected patients from the Northwest and Southwest regions of Cameroon.

BackgroundAntiretroviral therapy (ART) has improved the survival of HIV infected persons. However, rapid scale-up of ART and the high HIV-1 genetic variability, has greatly influenced the emergence of drug-resistant strains. This constitutes a potential threat to achieving the UNAIDS’ 90-90-90 goals by 2020. We investigated the prevalent HIV-1 genotypes, drug resistance-associated mutations and assessed some predictors of the occurrence of these mutations.MethodsThis was a hospital-based cross-sectional study conducted between October 2010 and June 2012. Participants were consecutively enrolled from selected HIV treatment centers of the Southwest and Northwest regions of Cameroon. Viral load was determined with the automated Abbott Real-time HIV-1 m2000rt System. HIV genotyping and antiretroviral resistance mutations analysis were performed using Bayer’s HIV-1 TRUGENE™ Genotyping Kit and OpenGene DNA Sequencing system. The drug resistance mutation was interpreted with the Stanford HIV database. Epidemiological data were obtained using pre-tested semi-structured questionnaires.ResultsOf the 387 participants, 239 were successfully genotyped. The median age of these participants was 33 years (interquartile range, IQR: 28–40 years), and a majority (65.7%) were female. A total of 29.3% of the participants were receiving ART. The median duration of ART was 10.5 months (IQR: 4–17.25 months). The median CD4 count and log10 viral load of study participants were 353.5 cells/ml (IQR:145–471) and 4.89 copies/ml (IQR: 3.91–5.55) respectively. CRF02 (A/G) (69%) was the most prevalent subtype followed by G (8.2%) and F (6.7%). Overall, resistance mutations were present in 37.1% of ART-experienced and 10.7% of ART-naive patients. Nucleoside reverse transcriptase inhibitors (NRTI) mutations occurred in 30% of ART-experienced and 2.4% of ART-naïve patients, while non-nucleoside reverse transcriptase inhibitors (NNRTI) mutations occurred in 34.2% of ART-experienced and 10.1% of -naïve patients. M184V (8.4%, 20/239) and K103N (5.4%, 13/239) were the most prevalent mutations. Major protease inhibitor mutations occurred in 3 (1.3%) out of the 239 sequences. The duration of ART independently predicted the occurrence of resistance mutation among ART-experienced patients.ConclusionThe high resistance to NNRTIs, which are the main support to the backbone (NRTIs) first-line antiretroviral regimen in Cameroon, has prompted the need to rollout an integrase strand transfer inhibitor regimen (containing Dolutegravir) with a higher genetic barrier to resistance as the preferred first line regimen.

Epistasis and entrenchment of drug resistance in HIV-1 subtype B.

The development of drug resistance in HIV is the result of primary mutations whose effects on viral fitness depend on the entire genetic background, a phenomenon called 'epistasis'. Based on protein sequences derived from drug-experienced patients in the Stanford HIV database, we use a co-evolutionary (Potts) Hamiltonian model to provide direct confirmation of epistasis involving many simultaneous mutations. Building on earlier work, we show that primary mutations leading to drug resistance can become highly favored (or entrenched) by the complex mutation patterns arising in response to drug therapy despite being disfavored in the wild-type background, and provide the first confirmation of entrenchment for all three drug-target proteins: protease, reverse transcriptase, and integrase; a comparative analysis reveals that NNRTI-induced mutations behave differently from the others. We further show that the likelihood of resistance mutations can vary widely in patient populations, and from the population average compared to specific molecular clones.

Fuzzy-multidimensional deep learning for efficient prediction of patient response to antiretroviral therapy

Drug component interactions are most likely to trigger unexpected pharmacological effects with unknown causal mechanisms, hence, demanding the discovery of patterns to establish suitable and effective regimens. This paper proposes a novel framework that embeds machine learning (ML) and multidimensional scaling (MDS) techniques, for efficient prediction of patient response to antiretroviral therapy (ART). To achieve this, experiment databases were created from two independent sources: a publicly available HIV domain datasets of patients with failed treatment – hosted by the Stanford University, hereinafter referred to as the Stanford HIV database, and locally sourced datasets gathered from 13 prominent healthcare facilities treating HIV patients in Akwa Ibom State of Nigeria, hereinafter referred to as the Akwa-Ibom HIV database: with 5,780 and 3,168 individual treatment change episodes (TCEs) of HIV treatment indicators (baseline CD4 count (BCD4), followup CD4 count (FCD4), baseline viral load (BRNA), followup viral load (FRNA), and drug type combination (DType)), observed from 1,521 and 1,301 unique patient records, respectively. A hybridised (two-stage) classification system consuming the Interval Type-2 Fuzzy Logic (IT2FL) and Deep Neural Network (DNN) was employed to model and optimise patients’ response to ART with appreciable error pruning achieved through MDS. Visualisation of the experiment databases showed remarkable immunological changes in the Akwa-Ibom HIV database, as the FCD4 of TCEs clustered far above the BCD4, compared to the Stanford HIV database, where over 40% of FCD4 clustered below the BCD4. Similar changes were noticed for the RNA, as more FRNA copies clustered below the BRNA for the Akwa-Ibom datasets, compared to the Stamford datasets. DNN classification results for both databases showed best performance metrics for the Levenberg-Marquardt algorithm when compared with the resilient backpropagation algorithm, with improved drug pattern predictions for experiment with MDS. This paper is most likely to evolve an avenue that triggers interesting combination(s) for optimum patient response, while ensuring minimal side effects, as further findings revealed the superiority of the proposed approach over existing approaches.

Piloting a surveillance system for HIV drug resistance in the European Union.

BackgroundA steady increase in HIV drug resistance (HIVDR) has been demonstrated globally in individuals initiating first-line antiretroviral therapy (ART). To support effective use of ART and prevent spread of HIVDR, monitoring is essential.AimWe piloted a surveillance system for transmitted HIVDR to assess the feasibility of implementation at the European level.MethodAll 31 countries in the European Union and European Economic Area were invited to retrospectively submit data on individuals newly diagnosed with HIV in 2015 who were tested for antiviral susceptibility before ART, either as case-based or as aggregate data. We used the Stanford HIV database algorithm to translate genetic sequences into levels of drug resistance.ResultsNine countries participated, with six reporting case-based data on 1,680 individuals and four reporting aggregated data on 1,402 cases. Sequence data were available for 1,417 cases: 14.5% of individuals (n = 244) showed resistance to at least one antiretroviral drug. In case-based surveillance, the highest levels of transmitted HIVDR were observed for non-nucleoside reverse-transcriptase inhibitors (NNRTIs) with resistance detected in 8.6% (n = 145), followed by resistance to nucleoside reverse-transcriptase inhibitors (NRTI) (5.1%; n = 85) and protease inhibitors (2.0%; n = 34).ConclusionWe conclude that standard reporting of HIVDR data was feasible in the participating countries. Legal barriers for data sharing, consensus on definitions and standardisation of interpretation algorithms should be clarified in the process of enhancing European-wide HIV surveillance with drug resistance information.

Antiretroviral drug resistance mutations among patients failing first-line treatment in Hanoi, Vietnam.

Objectives: To study the prevalence of drug resistance and genotype testing for HIV drug resistance on HIV/AIDS patients with first-line antiretroviral treatment failure at Dong Da Hospital, Hanoi, Vietnam.Patients and methods: Forty-seven patients in Dong Da Hospital, Hanoi, with confirmation of first-line antiretroviral therapy (ART) failure were enrolled in this study from June 2006 to December 2016. Both the protease and reverse transcriptase genes were amplified and sequenced using Trugene® HIV-1 Genotyping Kit and OpenGene® DNA system at the biomolecular laboratory of the National Institute of Hygiene and Epidemiology, Vietnam. The Stanford HIV database algorithm was used for interpretation of resistance data and genotyping.Results: Drug resistance mutations were 90.7% in patients with first-line treatment failure. Amongst patients with drug resistance mutation, 97.7% resisted to non-nucleoside reverse transcriptase inhibitors (NNRTIs), followed by nucleoside reverse transcriptase inhibitors (NRTIs, 95.3%) and protease inhibitors (PIs, 11.6%). Amongst the genetic mutations resistant to NNRTIs, G190S mutation was the highest (51.2%), K101HQ mutation was 39.5% and Y181I mutation was 34.9%. In genetic mutations to NRTIs, M184V mutation was 88.4%. In thymidine analogue mutations, K70R mutation was the most common (37.2%), followed by D67N, T215F and T69N mutations (27.9%, 27.9% and 25.6%, respectively). In genetic mutations in PIs, M36I and K20R mutations made up 9.3%. In NNRTIs, the prevalence of nevirapine resistance was 55.8%, and that of efavirenz resistance was 4.7%. In NRTIs, the ratio of lamivudine resistance was 93.0%, and that of zidovudine resistance was 9.3%. No lopinavir/ritonavir resistance was recorded.Conclusions: Drug resistance mutations in patients with first-line ART failure had a high prevalence of NNRTI and NRTI resistance but still susceptible to PIs.

HIV-1 Molecular Epidemiology, Transmission Clusters and Transmitted Drug Resistance Mutations in Central Brazil

We aimed to characterize HIV-1 molecular epidemiology and transmission clusters among heterosexual (HET) and men who have sex with men (MSM) individuals, as well as transmitted drug resistance mutations (TDRM) in Central-Western Brazil. This cross-sectional survey was conducted among 190 antiretroviral naïve HIV-1 infected individuals. Proviral DNA was extracted, and nested PCR amplified partial polymerase gene (PR/RT). After sequencing, subtypes were assigned, and the sequences were analyzed for the occurrence of possible transmission networks. Calibrated Population Resistance (CPR) tool from Stanford HIV Database was used to investigate the presence of TDRM. Among 150 individuals whose samples were successfully sequenced, the most prevalent HIV-1 subtype was B, followed by recombinant forms. The occurrence of twenty transmission clusters composed by at least two sequences was verified, suggesting the existence of transmission clusters among individuals from the same or distinct sexual orientations. Intermediate level of TDRM (12%) was found in the study population, and almost half of the subjects with TDRM had more than one resistance mutation. No correlations between sexual orientation and the presence of TDRM, HIV-1 subtypes/recombinants forms were verified. Taken together, the necessity of the continuous monitoring of the TDRM to verify the importance of pre-genotyping and to delineate future strategies in primary antiretroviral therapy. Likewise, the knowledge of the HIV-1 transmission networks in Brazil would allow the implementation of effective HIV-1 prevention strategies in local settings.

Emergence of HIV-1 drug resistance mutations in mothers on treatment with a history of prophylaxis in Ghana

BackgroundAntiretrovirals have been available in Ghana since 2003 for HIV-1 positive pregnant women for prevention of mother-to-child transmission (PMTCT). Suboptimal responses to treatment observed post-PMTCT interventions necessitated the need to investigate the profile of viral mutations generated. This study investigated HIV-1 drug resistance profiles in mothers in selected centres in Ghana on treatment with a history of prophylaxis.MethodsGenotypic Drug Resistance Testing for HIV-1 was carried out. Subtyping was done by phylogenetic analysis and Stanford HIV Database programme was used for drug resistance analysis and interpretation. To compare the significance between the different groups and the emergence of drug resistance mutations, p values were used.ResultsParticipants who had prophylaxis before treatment, those who had treatment without prophylaxis and those yet to initiate PMTCT showed 32% (8), 5% (3) and 15% (4) HIV-1 drug resistance associated mutations respectively. The differences were significant with p value < 0.05. Resistance Associated Mutations (RAMs) were seen in 14 participants (35%) to nucleoside reverse transcriptase inhibitors (NRTIs) and non-nucleoside reverse transcriptase inhibitors (NNRTIs). The most common NRTI mutation found was M184 V; K103 N and A98G were the most common NNRTI mutations seen. Thymidine Analogue Mutations (TAMs) such as M41 L, K70R and T215Y were found in all the groups; the most common of the TAMs found were M41 L and T215Y. Majority of the subtypes were CRF02_AG (82%).ConclusionIn Ghana initiation of uninterrupted treatment upon diagnosis, coupled with drug resistance testing, would produce a better treatment outcome for HIV-1 positive pregnant women.

Sanger and Next Generation Sequencing Approaches to Evaluate HIV-1 Virus in Blood Compartments

The implementation of antiretroviral treatment combined with the monitoring of drug resistance mutations improves the quality of life of HIV-1 positive patients. The drug resistance mutation patterns and viral genotypes are currently analyzed by DNA sequencing of the virus in the plasma of patients. However, the virus compartmentalizes, and different T cell subsets may harbor distinct viral subsets. In this study, we compared the patterns of HIV distribution in cell-free (blood plasma) and cell-associated viruses (peripheral blood mononuclear cells, PBMCs) derived from ART-treated patients by using Sanger sequencing- and Next-Generation sequencing-based HIV assay. CD4+CD45RA−RO+ memory T-cells were isolated from PBMCs using a BD FACSAria instrument. HIV pol (protease and reverse transcriptase) was RT-PCR or PCR amplified from the plasma and the T-cell subset, respectively. Sequences were obtained using Sanger sequencing and Next-Generation Sequencing (NGS). Sanger sequences were aligned and edited using RECall software (beta v3.03). The Stanford HIV database was used to evaluate drug resistance mutations. Illumina MiSeq platform and HyDRA Web were used to generate and analyze NGS data, respectively. Our results show a high correlation between Sanger sequencing and NGS results. However, some major and minor drug resistance mutations were only observed by NGS, albeit at different frequencies. Analysis of low-frequency drugs resistance mutations and virus distribution in the blood compartments may provide information to allow a more sustainable response to therapy and better disease management.