Personalized HIV therapy to control drug resistance

Navigating the Antiretroviral Therapy Switch Conundrum: Unveiling the Dilemma of Drug Resistance and Disease Progression in HIV/AIDS

There is a need to establish consensus for harmonization in antiretroviral (ARV) therapy (ART) switch treatment strategy and address the dilemma that exists in terms of subpar immune response to therapy or an immunologic deterioration while on therapy. The purpose of this review is to identify the factors that contribute to ARV treatment failure, such as insufficient dosage, drug interactions, poor adherence, drug resistance, and poor medication absorption. It is crucial to adopt a more efficient strategy to address this challenging dilemma. After ARV treatment failure, the aim of therapy is virologic suppression, which targets plasma viral load below the limits of detection as assessed by very sensitive tests with lower limits of quantification of 20 to 75 RNA copies/ml. The therapeutic objectives when complete virologic suppression is not possible, should be to maintain or restore immunologic function, stop the progression of the clinical illness, and minimize the emergence of new drug resistance that could further restrict the options for ARV drugs. Treatment history and drug-resistance testing, including the findings of previous and ongoing resistance tests, should be considered while selecting ARV regimens. Hence, the treatment approach post-ARV failure can be personalized based on clinical, immunologic, virologic, or as a mix of the three domains on a case-to-case basis. The evaluation of projected ARV activity should be based on treatment history and previous resistance test findings.

Evaluation of Circulating and Archived HIV-1 Integrase Drug-Resistance Variants among Patients on Third-Line ART in Cameroon: Implications for Dolutegravir-Containing Regimens in Resource-Limited Settings

To ensure the long-term efficacy of dolutegravir (DTG), we evaluated the genotypic profile in viral reservoirs among patients on third-line (3L) antiretroviral therapy (ART) in Cameroon, according to prior exposure to raltegravir (RAL). A facility-based study was conducted from May through December 2021 among patients on 3L ART from HIV treatment centers in Yaoundé and Douala. Viral load was measured, and genotyping was performed on plasma RNA and proviral DNA. HIV-1 drug resistance mutations were interpreted using HIVdb.v9.1 and phylogeny analysis was performed using MEGA.v7, with P < 0.05 considered significant. Of the 12,093 patients on ART, 53 fully met our inclusion criteria. The median (IQR) age was 51 years (40 to 55 years), and the male/female ratio was 4/5. The median duration on integrase strand-transfer inhibitors (INSTI)-containing regimens was 18 months (12 to 32 months), and 15.09% (8/53) were exposed to RAL. The most administered 3L ART was TDF+3TC+DTG+DRV/r (33.96%, 18/53). Only 5.66% (3/53) had unsuppressed viremia (>1000 copies/mL). Resistance testing in proviral DNA was successful for 18/22 participants and revealed 1/18 patients (5.56%, in the RAL-arm) with archived mutations at major resistance positions (G140R and G163R). Five subtypes were identified, CRF02_AG (12/18), CRF22_01AE (3/18), A1 (1/18), G (1/18), and F2 (1/18). In Cameroon, 3L-experienced patients had a good virological response with a low level of archived mutations in the integrase. This finding underscored the use of DTG-containing ART for heavily treated patients in similar programmatic settings. However, patients with prior exposure to RAL should be closely monitored following a stratified or personalized approach to mitigate risks of INSTI-resistance, alongside pharmacovigilance. IMPORTANCE We described the analysis of the genotypes of the population within third-line antiviral therapy in Cameroon, with a focus on defining the effects of prior raltegravir (RAL) treatment and resistance mutations for current dolutegravir (DTG) treatment. While supporting the current transition to DTG-containing ART in resource-limited settings toward the achievement of the UNAIDS' goal of HIV elimination by 2030, our findings suggested that RAL-exposed patients may need a specific monitoring approach either in a stratified or personalized model of third-line ART to ensure the long-term success of DTG-containing regimens.

High performance of integrase genotyping on diverse HIV-1 clades circulating in Cameroon: toward a successful transition to dolutegravir-based regimens in low and middle-income countries

A successful transition to dolutegravir-based regimens in low and middle-income countries (LMICs) requires an integrase genotyping assay effective on diverse HIV-1 clades. We herein developed and validated an in-house integrase genotyping protocol on plasma samples from 195 HIV-infected patients in Cameroon. Median [IQR] viremia was 23,574 (518-109,235) copies/mL; 128/195 participants had ≥1000copies/mL (i.e., WHO-threshold for genotypic resistance testing in LMICs). A total of 18 viral clades were detected: 72(51.1%) CRF02_AG, 38(26.9%) pure subtypes and 31(22.0%) other recombinants. Following WHO-threshold (≥1000copies/ml), sequencing performance was 82.81%(106/128). Regarding viremia, performance was 85.00%(68/80) with ≥100,000copies/mL versus 76.67%(23/30) with 10,000 to 99,999copies/mL (P = 0.22); 83.33%(15/18) with 1,000 to 99,999copies/mL (P = 0.55); 73.68%(14/19) with 500 to 999copies/mL (P = 0.19); 50%(13/26) for 200 to 499copies/mL (P = 0.0005) and 36.36%(8/22) for <200copies/mL (P < 0.0001). The developed in-house integrase-genotyping is highly effective on both pure and recombinant viral clades, even at low-level viremia. This performance underscores its usefulness in monitoring integrase-resistance mutations and supporting the scale-up of dolutegravir-based regimens in LMICs.

Chemical exchange saturation transfer for detection of antiretroviral drugs in brain tissue

OBJECTIVE

Antiretroviral drug theranostics facilitates the monitoring of biodistribution and efficacy of therapies designed to target HIV type-1 (HIV-1) reservoirs. To this end, we have now deployed intrinsic drug chemical exchange saturation transfer (CEST) contrasts to detect antiretroviral drugs within the central nervous system (CNS).

DESIGN AND METHODS

CEST effects for lamivudine (3TC) and emtricitabine (FTC) were measured by asymmetric magnetization transfer ratio analyses. The biodistribution of 3TC in different brain sub-regions of C57BL/6 mice treated with lipopolysaccharides was determined using MRI. CEST effects of 3TC protons were quantitated by Lorentzian fitting analysis. 3TC levels in plasma and brain regions were measured using ultraperformance liquid chromatography tandem mass spectrometry to affirm the CEST test results.

RESULTS

CEST effects of the hydroxyl and amino protons in 3TC and FTC linearly correlated to drug concentrations. 3TC was successfully detected in vivo in brain sub-regions by MRI. The imaging results were validated by measurements of CNS drug concentrations.

CONCLUSION

CEST contrasts can be used to detect antiretroviral drugs using MRI. Such detection can be used to assess spatial--temporal drug biodistribution. This is most notable within the CNS where drug biodistribution may be more limited with the final goal of better understanding antiretroviral drug-associated efficacy and potential toxicity.

Ranking evidence in substance use and addiction

Evidence-based medicine has consistently prized the epistemological value of randomized-controlled trials (RCTs) owing to their methodological advantages over alternative designs such as observational studies. However, there are limitations to RCTs that hinder their ability to study chronic and dynamic conditions such as substance use and addiction. For these conditions, observational studies may provide superior evidence based on methodological and practical strengths. Assuming epistemic superiority of RCTs has led to an inappropriate devaluation of other study designs and the findings they support, including support for harm reduction services, especially needle exchange programs and supervised injection facilities. The value offered by observational studies should be reflected in evidence-based medicine by allowing more flexibility in evidence hierarchies that presume methodological superiority of RCTs. Despite the popularity of evidence ranking systems and hierarchies, nothing should replace critical appraisal of study methodology and examining the suitability of applying a given study design to a specific research question.

Estimating the association between antibiotic exposure and colonization with extended-spectrum β-lactamase-producing Gram-negative bacteria using machine learning methods: a multicentre, prospective cohort study

OBJECTIVES

The aim of the study was to measure the impact of antibiotic exposure on the acquisition of colonization with extended-spectrum β-lactamase-producing Gram-negative bacteria (ESBL-GNB) accounting for individual- and group-level confounding using machine-learning methods.

METHODS

Patients hospitalized between September 2010 and June 2013 at six medical and six surgical wards in Italy, Serbia and Romania were screened for ESBL-GNB at hospital admission, discharge, antibiotic start, and after 3, 7, 15 and 30 days. Primary outcomes were the incidence rate and predictive factors of new ESBL-GNB colonization. Random forest algorithm was used to rank antibiotics according to the risk of selection of ESBL-GNB colonization in patients not colonized before starting antibiotics.

RESULTS

We screened 10 034 patients collecting 28 322 rectal swab samples. New ESBL-GNB colonization incidence with and without antibiotic treatment was 22/1000 and 9/1000 exposure-days, respectively. In the adjusted regression analyses, antibiotic exposure (hazard ratio (HR) 2.38; 95% CI 1.29-4.40), age 60-69 years (HR 1.19; 95% CI 1.05-1.34), and spring season (HR 1.25; 95% CI 1.14-1.38) were independently associated with new colonization. Monotherapy ranked higher als combination therapy in promoting ESBL-GNB colonization. Among monotherapy, cephalosporins ranked first followed by tetracycline (second), macrolide (fourth) and cotrimoxazole (seventh). Overall the ranking of cephalosporins was lower when used in combination. Among combinations not including cephalosporins, quinolones plus carbapenems ranked highest (eighth). Among sequential therapies, quinolones ranked highest (tenth) when prescribed within 30 days of therapy with cephalosporins.

CONCLUSIONS

Impact of antibiotics on selecting ESBL-GNB at intestinal level varies if used in monotherapy or combination and according to previous antibiotic exposure. These finding should be explored in future clinical trials on antibiotic stewardship interventions.

CLINICAL TRIAL REGISTRATION

NCT01208519.

The dawn of precision medicine in HIV: state of the art of pharmacotherapy

INTRODUCTION

Combination antiretroviral therapy (ART) reduces viral load to under the limit of detection, successfully decreasing HIV-related morbidity and mortality. Due to viral mutations, complex drug combinations and different patient response, there is an increasing demand for individualized treatment options for patients.

AREAS COVERED

This review first summarizes the pharmacokinetic and pharmacodynamic profile of clinical first-line drugs, which serves as guidance for antiretroviral precision medicine. Factors which have influential effects on drug efficacy and thus precision medicine are discussed: patients' pharmacogenetic information, virus mutations, comorbidities, and immune recovery. Furthermore, strategies to improve the application of precision medicine are discussed.

EXPERT OPINION

Precision medicine for ART requires comprehensive information on the drug, virus, and clinical data from the patients. The clinically available genetic tests are a good starting point. To better apply precision medicine, deeper knowledge of drug concentrations, HIV reservoirs, and efficacy associated genes, such as polymorphisms of drug transporters and metabolizing enzymes, are required. With advanced computer-based prediction systems which integrate more comprehensive information on pharmacokinetics, pharmacodynamics, pharmacogenomics, and the clinically relevant information of the patients, precision medicine will lead to better treatment choices and improved disease outcomes.

Precision medicine for HIV: where are we?

To date, antiretroviral therapy is highly effective in HIV-affected patients, but the individualization of such a life-long therapy may be advised. This review briefly summarizes the main factors involved in the potential personalization of antiretroviral treatment. Relevant articles in English were identified by PubMed and recent congresses’ abstracts. Foremost influences concerning pharmacodynamics, therapeutic drug monitoring, pharmacogenetics, comorbidities, immune recovery and viral characteristics affecting the healthcare of HIV-positive patients are listed here. Furthermore, pharmacoeconomic aspects are mentioned. Applying pharmacokinetic and pharmacogenetic knowledge may be informative and guide the better choice of treatment in order to achieve long-term efficacy and avoid adverse events. Randomized investigations of the clinical relevance of tailored antiretroviral regimens are needed in order to obtain a better management of HIV/AIDS-affected patients.

Prediction of HIV-1 sensitivity to broadly neutralizing antibodies shows a trend towards resistance over time

Treatment with broadly neutralizing antibodies (bNAbs) has proven effective against HIV-1 infections in humanized mice, non-human primates, and humans. Due to the high mutation rate of HIV-1, resistance testing of the patient’s viral strains to the bNAbs is still inevitable. So far, bNAb resistance can only be tested in expensive and time-consuming neutralization experiments. Here, we introduce well-performing computational models that predict the neutralization response of HIV-1 to bNAbs given only the envelope sequence of the virus. Using non-linear support vector machines based on a string kernel, the models learnt even the important binding sites of bNAbs with more complex epitopes, i.e., the CD4 binding site targeting bNAbs, proving thereby the biological relevance of the models. To increase the interpretability of the models, we additionally provide a new kind of motif logo for each query sequence, visualizing those residues of the test sequence that influenced the prediction outcome the most. Moreover, we predicted the neutralization sensitivity of around 34,000 HIV-1 samples from different time points to a broad range of bNAbs, enabling the first analysis of HIV resistance to bNAbs on a global scale. The analysis showed for many of the bNAbs a trend towards antibody resistance over time, which had previously only been discovered for a small non-representative subset of the global HIV-1 population.

Several sequence-based approaches exist to predict the epitope of broadly neutralizing antibodies (bNAbs) against HIV based on the correlation between variation in the viral sequence and neutralization response to the antibody. Though the potential epitope sites can be used to predict the neutralization response, the methods are not optimized for the task, using additional structural information, additional preselection steps to identify the epitope sites, and assuming independence and/or only linear relationship between the potential sites and the neutralization response. To model also the neutralization response to bNAbs with more complex binding sites, including for example several non-consecutive residues or accompanying conformational changes, we used non-linear, multivariate machine learning techniques. Though we used only the viral sequence information, the models learnt the corresponding binding sites of the bNAbs. In general only few residues were learnt to be responsible for a change in neutralization response, which can additionally reduce the sequencing cost for application in clinical routine. We propose our tailored models to aid the patient selection process for current clinical trials for bNAb immunotherapy, but also as a basis to predict the best combinations of bNAbs, which will be required for routine clinical practice in the future.

Decoding HIV resistance: from genotype to therapy

Genetic variation in HIV poses a major challenge for prevention and treatment of the AIDS pandemic. Resistance occurs by mutations in the target proteins that lower affinity for the drug or alter the protein dynamics, thereby enabling viral replication in the presence of the drug. Due to the prevalence of drug-resistant strains, monitoring the genotype of the infecting virus is recommended. Computational approaches for predicting resistance from genotype data and guiding therapy are discussed. Many prediction methods rely on rules derived from known resistance-associated mutations, however, statistical or machine learning can improve the classification accuracy and assess unknown mutations. Adding classifiers such as information on the atomic structure of the protein can further enhance the predictions.

Towards a systems approach for chronic diseases, based on health state modeling

Rising pressure from chronic diseases means that we need to learn how to deal with challenges at a different level, including the use of systems approaches that better connect across fragments, such as disciplines, stakeholders, institutions, and technologies. By learning from progress in leading areas of health innovation (including oncology and AIDS), as well as complementary indications (Alzheimer's disease), I try to extract the most enabling innovation paradigms, and discuss their extension to additional areas of application within a systems approach. To facilitate such work, a Precision, P4 or Systems Medicine platform is proposed, which is centered on the representation of health states that enable the definition of time in the vision to provide the right intervention for the right patient at the right time and dose. Modeling of such health states should allow iterative optimization, as longitudinal human data accumulate. This platform is designed to facilitate the discovery of links between opportunities related to a) the modernization of diagnosis, including the increased use of omics profiling, b) patient-centric approaches enabled by technology convergence, including digital health and connected devices, c) increasing understanding of the pathobiological, clinical and health economic aspects of disease progression stages, d) design of new interventions, including therapies as well as preventive measures, including sequential intervention approaches. Probabilistic Markov models of health states, e.g. those used for health economic analysis, are discussed as a simple starting point for the platform. A path towards extension into other indications, data types and uses is discussed, with a focus on regenerative medicine and relevant pathobiology.

A Comprehensive Analysis of Primer IDs to Study Heterogeneous HIV-1 Populations

Determining the composition of viral populations is becoming increasingly important in the field of medical virology. While recently developed computational tools for viral haplotype analysis allow for correcting sequencing errors, they do not always allow for the removal of errors occurring in the upstream experimental protocol, such as PCR errors. Primer IDs (pIDs) are one method to address this problem by harnessing redundant template resampling for error correction. By using a reference mixture of five HIV-1 strains, we show how pIDs can be useful for estimating key experimental parameters, such as the substitution rate of the PCR process and the reverse transcription (RT) error rate. In addition, we introduce a hidden Markov model for determining the recombination rate of the RT PCR process. We found no strong sequence-specific bias in pID abundances (the same RT efficiencies as compared to commonly used short, specific RT primers) and no effects of pIDs on the estimated distribution of the references viruses.