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

Prevalence of resistance-associated viral variants to the HIV-specific broadly neutralising antibody 10-1074 in a UK bNAb-naïve population

Broadly neutralising antibodies (bNAbs) targeting HIV show promise for both prevention of infection and treatment. Among these, 10-1074 has shown potential in neutralising a wide range of HIV strains. However, resistant viruses may limit the clinical efficacy of 10-1074. The prevalence of both de novo and emergent 10-1074 resistance will determine its use at a population level both to protect against HIV transmission and as an option for treatment. To help understand this further, we report the prevalence of pre-existing mutations associated with 10-1074 resistance in a bNAb-naive population of 157 individuals presenting to UK HIV centres with primary HIV infection, predominantly B clade, receiving antiretroviral treatment. Single genome analysis of HIV proviral envelope sequences showed that 29% of participants' viruses tested had at least one sequence with 10-1074 resistance-associated mutations. Mutations interfering with the glycan binding site at HIV Env position 332 accounted for 95% of all observed mutations. Subsequent analysis of a larger historic dataset of 2425 B-clade envelope sequences sampled from 1983 to 2019 revealed an increase of these mutations within the population over time. Clinical studies have shown that the presence of pre-existing bNAb mutations may predict diminished therapeutic effectiveness of 10-1074. Therefore, we emphasise the importance of screening for these mutations before initiating 10-1074 therapy, and to consider the implications of pre-existing resistance when designing prevention strategies.

Predicting neutralization susceptibility to combination HIV-1 monoclonal broadly neutralizing antibody regimens

Combination monoclonal broadly neutralizing antibodies (bnAbs) are currently being developed for preventing HIV-1 infection. Recent work has focused on predicting in vitro neutralization potency of both individual bnAbs and combination regimens against HIV-1 pseudoviruses using Env sequence features. To predict in vitro combination regimen neutralization potency against a given HIV-1 pseudovirus, previous approaches have applied mathematical models to combine individual-bnAb neutralization and have predicted this combined neutralization value; we call this the combine-then-predict (CP) approach. However, prediction performance for some individual bnAbs has exceeded that for the combination, leading to another possibility: combining the individual-bnAb predicted values and using these to predict combination regimen neutralization; we call this the predict-then-combine (PC) approach. We explore both approaches in both simulated data and data from the Los Alamos National Laboratory's Compile, Neutralize, and Tally NAb Panels repository. The CP approach is superior to the PC approach when the neutralization outcome of interest is binary (e.g., neutralization susceptibility, defined as inhibitory concentration < 1 μg/mL. For continuous outcomes, the CP approach performs at least as well as the PC approach, and is superior to the PC approach when the individual-bnAb prediction algorithms have poor performance. This knowledge may be used when building prediction models for novel antibody combinations in the absence of in vitro neutralization data for the antibody combination; this, in turn, will aid in the evaluation and down-selection of these antibody combinations into prevention efficacy trials.

Application of the SLAPNAP statistical learning tool to broadly neutralizing antibody HIV prevention research

Combination monoclonal broadly neutralizing antibody (bnAb) regimens are in clinical development for HIV prevention, necessitating additional knowledge of bnAb neutralization potency/breadth against circulating viruses. Williamson et al. (2021) described a software tool, Super LeArner Prediction of NAb Panels (SLAPNAP), with application to any HIV bnAb regimen with sufficient neutralization data against a set of viruses in the Los Alamos National Laboratory's Compile, Neutralize, and Tally Nab Panels repository. SLAPNAP produces a proteomic antibody resistance (PAR) score for Env sequences based on predicted neutralization resistance and estimates variable importance of Env amino acid features. We apply SLAPNAP to compare HIV bnAb regimens undergoing clinical testing, finding improved power for downstream sieve analyses and increased precision for comparing neutralization potency/breadth of bnAb regimens due to the inclusion of PAR scores of Env sequences with much larger sample sizes available than for neutralization outcomes. SLAPNAP substantially improves bnAb regimen characterization, ranking, and down-selection.

The applications of machine learning in HIV neutralizing antibodies research-A systematic review

Machine learning algorithms play an essential role in bioinformatics and allow exploring the vast and noisy biological data in unrivaled ways. This paper is a systematic review of the applications of machine learning in the study of HIV neutralizing antibodies. This significant and vast research domain can pave the way to novel treatments and to a vaccine. We selected the relevant papers by investigating the available literature from the Web of Science and PubMed databases in the last decade. The computational methods are applied in neutralization potency prediction, neutralization span prediction against multiple viral strains, antibody-virus binding sites detection, enhanced antibodies design, and the study of the antibody-induced immune response. These methods are viewed from multiple angles spanning data processing, model description, feature selection, evaluation, and sometimes paper comparisons. The algorithms are diverse and include supervised, unsupervised, and generative types. Both classical machine learning and modern deep learning were taken into account. The review ends with our ideas regarding future research directions and challenges.

A calculated risk: Evaluating HIV resistance to the broadly neutralising antibodies10-1074 and 3BNC117

PURPOSE OF THIS REVIEW

Broadly neutralising antibodies (bNAbs) are a promising new therapy for the treatment of HIV infection. However, the effective use of bNAbs is impacted by the presence of preexisting virological resistance and the potential to develop new resistance during treatment. With several bNAb clinical trials underway, sensitive and scalable assays are needed to screen for resistance. This review summarises the data on resistance from published clinical trials using the bNAbs 10-1074 and 3BNC117 and evaluates current approaches for detecting bNAb sensitivity as well as their limitations.

RECENT FINDINGS

Analyses of samples from clinical trials of 10-1074 and 3BNC117 reveal viral mutations that emerge on therapy which may result in bNAb resistance. These mutations are also found in some potential study participants prior to bNAb exposure. These clinical data are further informed by ex-vivo neutralisation assays which offer an alternative measure of resistance and allow more detailed interrogation of specific viral mutations. However, the limited amount of publicly available data and the need for better understanding of other viral features that may affect bNAb binding mean there is no widely accepted approach to measuring bNAb resistance.

SUMMARY

Resistance to the bNAbs 10-1074 and 3BNC117 may significantly impact clinical outcome following their therapeutic administration. Predicting bNAb resistance may help to lower the risk of treatment failure and therefore a robust methodology to screen for bNAb sensitivity is needed.

Prediction of HIV sensitivity to monoclonal antibodies using aminoacid sequences and deep learning

MOTIVATION

Knowing the sensitivity of a viral strain versus a monoclonal antibody is of interest for HIV vaccine development and therapy. The HIV strains vary in their resistance to antibodies, and the accurate prediction of virus-antibody sensitivity can be used to find potent antibody combinations that broadly neutralize multiple and diverse HIV strains. Sensitivity prediction can be combined with other methods such as generative algorithms to design novel antibodies in silico or with feature selection to uncover the sites of interest in the sequence. However, these tools are limited in the absence of in silico accurate prediction methods.

RESULTS

Our method leverages the CATNAP dataset, probably the most comprehensive collection of HIV-antibodies assays, and predicts the antibody-virus sensitivity in the form of binary classification. The methods proposed by others focus primarily on analyzing the virus sequences. However, our article demonstrates the advantages gained by modeling the antibody-virus sensitivity as a function of both virus and antibody sequences. The input is formed by the virus envelope and the antibody variable region aminoacid sequences. No structural features are required, which makes our system very practical, given that sequence data is more common than structures. We compare with two other state-of-the-art methods that leverage the same dataset and use sequence data only. Our approach, based on neuronal networks and transfer learning, measures increased predictive performance as measured on a set of 31 specific broadly neutralizing antibodies.

AVAILABILITY AND IMPLEMENTATION

https://github.com/vlad-danaila/deep_hiv_ab_pred/tree/fc-att-fix.

Navigating the complexity of chronic HIV-1 associated immune dysregulation

Despite successful viral suppression with antiretroviral therapy, chronic HIV-1 infection is associated with ongoing immune dysfunction. Investigation of the complex immune response in treated and untreated individuals with chronic HIV-1 infection is warranted. Immune alterations such as monocyte phenotype and Th-17/Treg ratios often persist years after the reduction in viraemia and predispose many individuals to long-term comorbidities such as cardiovascular disease or cancer. Furthermore, while there has been extensive research on the latent reservoir of treated patients with chronic HIV-1, which prevents the discontinuation of treatment, the mechanism behind this remains elusive and needs further investigation. In this review, we assist in navigating the recent research on these groups of individuals and provide a basis for further investigation.

Antibodies for Human Immunodeficiency Virus-1 Cure Strategies

Human immunodeficiency virus (HIV) infection leads to the establishment of a long-lived latent cellular reservoir. One strategy to eliminate quiescent reservoir cells is to reactivate virus replication to induce HIV envelope glycoprotein (Env) expression on the cell surface exposing them to subsequent antibody targeting. Via the interactions between the antibody Fc domain and Fc-γ receptors (FcγRs) that are expressed on innate effector cells, such as natural killer cells, monocytes, and neutrophils, antibodies can mediate the elimination of infected cells. Over the last decade, a multitude of human monoclonal antibodies that are broadly neutralizing across many HIV-1 subtypes have been identified and are currently being explored for HIV eradication strategies. Antibody development also includes novel Fc engineering approaches to increase engagement of effector cells and optimize antireservoir efficacy. In this review, we discuss the usefulness of antibodies for HIV eradication approaches specifically focusing on antibody-mediated strategies to target latently infected cells and options to increase antibody efficacy.

Broadly Neutralizing Antibodies for HIV-1 Prevention

Given the absence of an effective vaccine for protection against HIV-1 infection, passive immunization strategies that utilize potent broadly neutralizing antibodies (bnAbs) to block acquisition of HIV-1 are being rigorously pursued in the clinical setting. bnAbs have demonstrated robust protection in preclinical animal models, and several leading bnAb candidates have shown favorable safety and pharmacokinetic profiles when tested individually or in combinations in early phase human clinical trials. Furthermore, passive administration of bnAbs in HIV-1 infected individuals has resulted in prolonged suppression of viral rebound following interruption of combination antiretroviral therapy, and robust antiviral activity when administered to viremic individuals. Recent results from the first efficacy trials testing repeated intravenous administrations of the anti-CD4 binding site bnAb VRC01 have demonstrated positive proof of concept that bnAb passive immunization can confer protection against HIV-1 infection in humans, but have also highlighted the considerable barriers that remain for such strategies to effectively contribute to control of the epidemic. In this review, we discuss the current status of clinical studies evaluating bnAbs for HIV-1 prevention, highlight lessons learned from the recent Antibody Mediated Prevention (AMP) efficacy trials, and provide an overview of strategies being employed to improve the breadth, potency, and durability of antiviral protection.

Geospatial HIV-1 subtype C gp120 sequence diversity and its predicted impact on broadly neutralizing antibody sensitivity

Evolving diversity in globally circulating HIV-1 subtypes presents a formidable challenge in defining and developing neutralizing antibodies for prevention and treatment. HIV-1 subtype C is responsible for majority of global HIV-1 infections. In the present study, we examined the diversity in genetic signatures and attributes that differentiate region-specific HIV-1 subtype C gp120 sequences associated with virus neutralization outcomes to key bnAbs having distinct epitope specificities. A total of 1814 full length HIV-1 subtype C gp120 sequence from 37 countries were retrieved from Los Alamos National Laboratory HIV database (www.hiv.lanl.gov). The amino acid sequences were assessed for their phylogenetic association, variable loop lengths and prevalence of potential N-linked glycosylation sites (pNLGS). Responses of these sequences to bnAbs were predicted with a machine learning algorithm ‘bNAb-ReP’ and compared with those reported in the CATNAP database. Subtype C sequences from Asian countries including India differed phylogenetically when compared with that from African countries. Variable loop lengths and charges within Indian and African clusters were also found to be distinct from each other, specifically for V1, V2 and V4 loops. Pairwise analyses at each of the 25 pNLG sites indicated distinct country specific profiles. Highly significant differences (p<0.001***) were observed in prevalence of four pNLGS (N130, N295, N392 and N448) between South Africa and India, having most disease burden associated with subtype C. Our findings highlight that distinctly evolving clusters within global intra-subtype C gp120 sequences are likely to influence the disparate region-specific sensitivity of circulating HIV-1 subtype C to bnAbs.

Super LeArner Prediction of NAb Panels (SLAPNAP): A Contain-erized Tool for Predicting Combination Monoclonal Broadly Neu-tralizing Antibody Sensitivity

MOTIVATION

A single monoclonal broadly neutralizing antibody (bnAb) regimen was recently evaluated in two randomized trials for prevention efficacy against HIV-1 infection. Subsequent trials will evaluate combination bnAb regimens (e.g., cocktails, multi-specific antibodies), which demonstrate higher potency and breadth in vitro compared to single bnAbs. Given the large number of potential regimens, methods for down-selecting these regimens into efficacy trials are of great interest.

RESULTS

We developed Super LeArner Prediction of NAb Panels (SLAPNAP), a software tool for training and evaluating machine learning models that predict in vitro neutralization sensitivity of HIV Envelope (Env) pseudoviruses to a given single or combination bnAb regimen, based on Env amino acid sequence features. SLAPNAP also provides measures of variable importance of sequence features. By predicting bnAb coverage of circulating sequences, SLAPNAP can improve ranking of bnAb regimens by their potential prevention efficacy. In addition, SLAPNAP can improve sieve analysis by defining sequence features that impact bnAb prevention efficacy.

AVAILABILITY

SLAPNAP is a freely available docker image that can be downloaded from DockerHub (https://hub.docker.com/r/slapnap/slapnap). Source code and documentation are available at GitHub (respectively, https://github.com/benkeser/slapnap and https://benkeser.github.io/slapnap/).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Broadly neutralizing antibodies for HIV-1 prevention and therapy

Despite immense progress in our ability to prevent and treat HIV-1 infection, HIV-1 remains an incurable disease and a highly efficacious HIV-1 vaccine is not yet available. Additional tools to prevent and treat HIV-1 are therefore necessary. The identification of potent and broadly neutralizing antibodies (bNAbs) against HIV-1 has revolutionized the field and may prove clinically useful. Significant advances have been made in identifying broader and more potent antibodies, characterizing antibodies in preclinical animal models, engineering antibodies to extend half-life and expand breadth and functionality, and evaluating the efficacy of single bNAbs and bNAb combinations in people with and without HIV-1. Here, we review recent progress in developing bNAbs for the prevention and treatment of HIV-1.

Neutralizing Activity of Broadly Neutralizing anti-HIV-1 Antibodies against Primary African Isolates

Novel therapeutic and preventive strategies are needed to contain the HIV-1 epidemic. Broadly neutralizing human antibodies (bNAbs) with exceptional activity against HIV-1 are currently being tested in HIV-1 prevention trials. The selection of anti-HIV-1 bNAbs for clinical development was primarily guided by their in vitro neutralizing activity against HIV-1 Env pseudotyped viruses. Here we report on the neutralizing activity of 9 anti-HIV-1 bNAbs now in clinical development against 126 Clade A, C, D PBMC-derived primary African isolates. The neutralizing potency and breadth of the bNAbs tested was significantly reduced compared to pseudotyped viruses panels. The difference in sensitivity between pseudotyped viruses and primary isolates varied from 3- to nearly 100-fold depending on the bNAb and the HIV-1 clade. Thus, the neutralizing activity of bNAbs against primary African isolates differs from their activity against pseudovirus panels. The data have significant implications for interpreting the results of ongoing HIV-1 prevention trials.IMPORTANCE HIV remains a major public health problem worldwide, and new therapies and preventive strategies are necessary for controlling the epidemic. Broadly neutralizing antibodies (bNAbs) have been developed in the past decade to fill this gap. The neutralizing activity of these antibodies against diverse HIV strains has mostly been measured using Env-pseudotyped viruses, which overestimate bNAb coverage and potency. In this study we measured the neutralizing activity of nine bNAbs against clade A, C, and D HIV isolates derived from cells of African patients living with HIV and produced in peripheral blood mononuclear cells. We found that the coverage and potency of bNAbs were often significantly lower than what was predicted by Env-psuedotyped viruses, and that this decrease was related to the bNAb biding site class. This data is important for the planning and analysis of clinical trials that seek to evaluate bNAbs for the treatment and prevention of HIV infection in Africa.

HIV-1 Subtype C Tier 3 Viruses Have Increased Infectivity Compared to Tier 2 Viruses

A primary concern of an antibody-based HIV-1 therapy is the virus' ability to rapidly escape antibody responses. Therefore, we investigated the relationships between antibody neutralization sensitivity, viral phenotype, and infectivity in 13 subtype C viruses using a HeLa transfectant-based assay. We observed that the seven tier 3 viruses exhibited higher infectivity than the tier 2 viruses, suggesting that higher neutralization resistance did not have a substantial entry cost. There was no relationship between neutralization resistance and susceptibility to entry inhibitors Maraviroc, PSC RANTES, or the fusion inhibitor T20, indicating that neutralization resistance may not alter these inhibitor target sites. By analyzing glycosylation patterns in 82 subtype C viruses, we found that the presence of an N-linked glycan motif at position N413 and its absence at N332 were the most important predictors of neutralization resistance. In a set of 200 subtype C viruses, tier 3 strains were more resistant than tier 2 or 1B viruses to several broadly neutralizing monoclonal antibodies targeting three different epitopes. This suggests that it is unlikely that resistance to antibodies targeting a single epitope drives overall resistance. In the context of an antibody-based intervention, highly resistant viruses with increased infectivity, circulating in the population, could hinder HIV-1 control since entry of tier 3 viruses is not always selected against. Therefore, for any long-term antibody-based intervention to be globally relevant, it must elicit responses that limit the occurrence of resistance.

Predicting Antibody Neutralization Efficacy in Hypermutated Epitopes Using Monte Carlo Simulations

Human Immunodeficiency Virus 1 (HIV-1) evades adaptive immunity by means of its extremely high mutation rate, which allows the HIV envelope glycoprotein to continuously escape from the action of antibodies. However, some broadly neutralizing antibodies (bNAbs) targeting specific viral regions show the ability to block the infectivity of a large number of viral variants. The discovery of these antibodies opens new avenues in anti-HIV therapy; however, they are still suboptimal tools as their amplitude of action ranges between 50% and 90% of viral variants. In this context, being able to discriminate between sensitive and resistant strains to an antibody would be of great interest for the design of optimal clinical antibody treatments and to engineer potent bNAbs for clinical use. Here, we describe a hierarchical procedure to predict the antibody neutralization efficacy of multiple viral isolates to three well-known anti-CD4bs bNAbs: VRC01, NIH45-46 and 3BNC117. Our method consists of simulating the three-dimensional binding process between the gp120 and the antibody by using Protein Energy Landscape Exploration (PELE), a Monte Carlo stochastic approach. Our results clearly indicate that the binding profiles of sensitive and resistant strains to a bNAb behave differently, showing the latter’s weaker binding profiles, that can be exploited for predicting antibody neutralization efficacy in hypermutated HIV-1 strains.

Improved killing of HIV-infected cells using three neutralizing and non-neutralizing antibodies

The correlation of HIV-specific antibody-dependent cellular cytotoxicity (ADCC) responses with protection from and delayed progression of HIV-1 infection provides a rationale to leverage ADCC-mediating antibodies for treatment purposes. We evaluated ADCC mediated by different combinations of 2 to 6 neutralizing and non-neutralizing anti-HIV-1 Envelope (Env) mAbs, using concentrations ≤ 1 μg/mL, to identify combinations effective at targeting latent reservoir HIV-1 viruses from 10 individuals. We found that within 2 hours, combinations of 3 mAbs mediated more than 30% killing of HIV-infected primary CD4+ T cells in the presence of autologous NK cells, with the combination of A32 (C1C2), DH511.2K3 (MPER), and PGT121 (V3) mAbs being the most effective. Increasing the incubation of target and effector cells in the presence of mAb combinations from 2 to 24 hours resulted in increased specific killing of infected cells, even with neutralization-resistant viruses. The same combination eliminated reactivated latently HIV-1-infected cells in an ex vivo quantitative viral outgrowth assay. Therefore, administration of a combination of 3 mAbs should be considered in planning in vivo studies seeking to eliminate persistently HIV-1-infected cells.

Prevalence of Different Genotypes of HIV-1 in Injection Drug Users in China: A Systematic Review and Meta-Analysis

BACKGROUND

Since 1981, an increasing trend in HIV has been observed for transmission via injection drug users (IDUs), sexual transmission and mother-to-child transmission. The IDUs are blamed for early increases in HIV-positive cases in China.

OBJECTIVE

HIV genotypes of IDUs were comprehensively analysed to trace the source and relationships of the AIDS epidemic in China.

METHODS

Relevant databases written in English and Chinese were searched. Overall, 7,149 publications were identified in six databases. After screening 7,104 articles according to the inclusion and exclusion criteria, 45 studies consisting of 2,765 cases were finally identified. A meta-analysis was conducted using R MATLAB software, RevMan and SPSS. Subgroup analyses focused on time frame, region, and location of different genotypes of IDUs in China.

RESULTS

There were five dominant HIV-1 genotypes among the 2,765 IDU cases. The proportions of CRF07_BC, CRF01_AE, CRF08_BC, subtype B/B', and subtype C were 45.18% (95% CI: 33.55-57.08%), 16.00% (95% CI: 9.39-23.82%), 13.43% (95% CI: 7.32-20.84%), 3.58% (95% CI: 1.52-6.24%), and 0.90% (95% CI: 0.04-2.43%), respectively. HIV genotypes transmitted among IDUs in China are primarily CRF07-BC, followed by CRF01-AE and CRF08-BC. Across the different time frames and regions, CRF07_BC was the most prevalent HIV-1 genotype among IDUs, while CRF08_BC was the most prevalent genotype in the southwest region.

CONCLUSION

Our study reveals that CRF07-BC was the dominant prevalent strain among IDUs from 1991 to 2015 in China, while CRF08-BC was the dominant prevalent strain among IDUs in southwestern China. This systematic review and meta-analysis shows evidence of the comprehensive prevalence of different genotypes, data and characteristics of HIV among IDUs in China.

Accurate Prediction for Antibody Resistance of Clinical HIV-1 Isolates

Broadly neutralizing antibodies (bNAbs) targeting the HIV-1 envelope glycoprotein (Env) have promising utility in prevention and treatment of HIV-1 infection, and several are currently undergoing clinical trials. Due to the high sequence diversity and mutation rate of HIV-1, viral isolates are often resistant to specific bNAbs. Currently, resistant isolates are commonly identified by time-consuming and expensive in vitro neutralization assays. Here, we report machine learning classifiers that accurately predict resistance of HIV-1 isolates to 33 bNAbs. Notably, our classifiers achieved an overall prediction accuracy of 96% for 212 clinical isolates from patients enrolled in four different clinical trials. Moreover, use of gradient boosting machine – a tree-based machine learning method – enabled us to identify critical features, which had high accordance with epitope residues that distinguished between antibody resistance and sensitivity. The availability of an in silico antibody resistance predictor should facilitate informed decisions of antibody usage and sequence-based monitoring of viral escape in clinical settings.

Predicting the broadly neutralizing antibody susceptibility of the HIV reservoir

Broadly neutralizing antibodies (bNAbs) against HIV-1 are under evaluation for both prevention and therapy. HIV-1 sequence diversity observed in most HIV-infected individuals and archived variations in critical bNAb epitopes present a major challenge for the clinical application of bNAbs, as preexistent resistant viral strains can emerge, resulting in bNAb failure to control HIV. In order to identify viral resistance in patients prior to antibody therapy and to guide the selection of effective bNAb combination regimens, we developed what we believe to be a novel Bayesian machine-learning model that uses HIV-1 envelope protein sequences and foremost approximated glycan occupancy information as variables to quantitatively predict the half-maximal inhibitory concentrations (IC50) of 126 neutralizing antibodies against a variety of cross clade viruses. We then applied this model to peripheral blood mononuclear cell-derived proviral Env sequences from 25 HIV-1-infected individuals mapping the landscape of neutralization resistance within each individual's reservoir and determined the predicted ideal bNAb combination to achieve 100% neutralization at IC50 values <1 μg/ml. Furthermore, predicted cellular viral reservoir neutralization signatures of individuals before an analytical antiretroviral treatment interruption were consistent with the measured neutralization susceptibilities of the respective plasma rebound viruses, validating our model as a potentially novel tool to facilitate the advancement of bNAbs into the clinic.

Estimation of the breadth of CD4bs targeting HIV antibodies by molecular modeling and machine learning

HIV is a highly mutable virus for which all attempts to develop a vaccine have been unsuccessful. Nevertheless, few long-infected patients develop antibodies, called broadly neutralizing antibodies (bnAbs), that have a high breadth and can neutralize multiple variants of the virus. This suggests that a universal HIV vaccine should be possible. A measure of the efficacy of a HIV vaccine is the neutralization breadth of the antibodies it generates. The breadth is defined as the fraction of viruses in the Seaman panel that are neutralized by the antibody. Experimentally the neutralization ability is measured as the half maximal inhibitory concentration of the antibody (IC50). To avoid such time-consuming experimental measurements, we developed a computational approach to estimate the IC50 and use it to determine the antibody breadth. Given that no direct method exists for calculating IC50 values, we resort to a combination of atomistic modeling and machine learning. For each antibody/virus complex, an all-atoms model is built using the amino acid sequence and a known structure of a related complex. Then a series of descriptors are derived from the atomistic models, and these are used to train a Multi-Layer Perceptron (an Artificial Neural Network) to predict the value of the IC50 (by regression), or if the antibody binds or not to the virus (by classification). The neural networks are trained by use of experimental IC50 values collected in the CATNAP database. The computed breadths obtained by regression and classification are reported and the importance of having some related information in the data set for obtaining accurate predictions is analyzed. This approach is expected to prove useful for the design of HIV bnAbs, where the computation of the potency must be accompanied by a computation of the breadth, and for evaluating the efficiency of potential vaccination schemes developed through modeling and simulation.

Prediction of VRC01 neutralization sensitivity by HIV-1 gp160 sequence features

The broadly neutralizing antibody (bnAb) VRC01 is being evaluated for its efficacy to prevent HIV-1 infection in the Antibody Mediated Prevention (AMP) trials. A secondary objective of AMP utilizes sieve analysis to investigate how VRC01 prevention efficacy (PE) varies with HIV-1 envelope (Env) amino acid (AA) sequence features. An exhaustive analysis that tests how PE depends on every AA feature with sufficient variation would have low statistical power. To design an adequately powered primary sieve analysis for AMP, we modeled VRC01 neutralization as a function of Env AA sequence features of 611 HIV-1 gp160 pseudoviruses from the CATNAP database, with objectives: (1) to develop models that best predict the neutralization readouts; and (2) to rank AA features by their predictive importance with classification and regression methods. The dataset was split in half, and machine learning algorithms were applied to each half, each analyzed separately using cross-validation and hold-out validation. We selected Super Learner, a nonparametric ensemble-based cross-validated learning method, for advancement to the primary sieve analysis. This method predicted the dichotomous resistance outcome of whether the IC50 neutralization titer of VRC01 for a given Env pseudovirus is right-censored (indicating resistance) with an average validated AUC of 0.868 across the two hold-out datasets. Quantitative log IC50 was predicted with an average validated R2 of 0.355. Features predicting neutralization sensitivity or resistance included 26 surface-accessible residues in the VRC01 and CD4 binding footprints, the length of gp120, the length of Env, the number of cysteines in gp120, the number of cysteines in Env, and 4 potential N-linked glycosylation sites; the top features will be advanced to the primary sieve analysis. This modeling framework may also inform the study of VRC01 in the treatment of HIV-infected persons.

Susceptibility to Neutralization by Broadly Neutralizing Antibodies Generally Correlates with Infected Cell Binding for a Panel of Clade B HIV Reactivated from Latent Reservoirs

Efforts to cure human immunodeficiency virus (HIV) infection are obstructed by reservoirs of latently infected CD4+ T cells that can reestablish viremia. HIV-specific broadly neutralizing antibodies (bNAbs), defined by unusually wide neutralization breadths against globally diverse viruses, may contribute to the elimination of these reservoirs by binding to reactivated cells, thus targeting them for immune clearance. However, the relationship between neutralization of reservoir isolates and binding to corresponding infected primary CD4+ T cells has not been determined. Thus, the extent to which neutralization breadths and potencies can be used to infer the corresponding parameters of infected cell binding is currently unknown. We assessed the breadths and potencies of bNAbs against 36 viruses reactivated from peripheral blood CD4+ T cells from antiretroviral (ARV)-treated HIV-infected individuals by using paired neutralization and infected cell binding assays. Single-antibody breadths ranged from 0 to 64% for neutralization (80% inhibitory concentration [IC80] of ≤10 μg/ml) and from 0 to 89% for binding, with two-antibody combinations (results for antibody combinations are theoretical/predicted) reaching levels of 0 to 83% and 50 to 100%, respectively. Infected cell binding correlated with virus neutralization for 10 of 14 antibodies (e.g., for 3BNC117, r = 0.82 and P < 0.0001). Heterogeneity was observed, however, with a lack of significant correlation for 2G12, CAP256.VRC26.25, 2F5, and 4E10. Our results provide guidance on the selection of bNAbs for interventional cure studies, both by providing a direct assessment of intra- and interindividual variabilities in neutralization and infected cell binding in a novel cohort and by defining the relationships between these parameters for a panel of bNAbs.IMPORTANCE Although antiretroviral therapies have improved the lives of people who are living with HIV, they do not cure infection. Efforts are being directed towards harnessing the immune system to eliminate the virus that persists, potentially resulting in virus-free remission without medication. HIV-specific antibodies hold promise for such therapies owing to their ability to both prevent the infection of new cells (neutralization) and direct the killing of infected cells. We isolated 36 HIV strains from individuals whose virus was suppressed by medication and tested 14 different antibodies for neutralization of these viruses and for binding to cells infected with the same viruses (critical for engaging natural killer cells). For both neutralization and infected cell binding, we observed variation both between individuals and amongst different viruses within an individual. For most antibodies, neutralization activity correlated with infected cell binding. These data provide guidance on the selection of antibodies for clinical trials.

Antibody-mediated prevention and treatment of HIV-1 infection

Novel broadly neutralizing antibodies targeting HIV-1 hold promise for their use in the prevention and treatment of HIV-1 infection. Pre-clinical results have encouraged the evaluation of these antibodies in healthy and HIV-1-infected humans. In first clinical trials, highly potent broadly neutralizing antibodies have demonstrated their safety and significant antiviral activity by reducing viremia and delaying the time to viral rebound in individuals interrupting antiretroviral therapy. While emerging antibody-resistant viral variants have indicated limitations of antibody monotherapy, strategies to enhance the efficacy of broadly neutralizing antibodies in humans are under investigation. These include the use of antibody combinations to prevent viral escape, antibody modifications to increase the half-life and the co-administration of latency-reversing agents to target the cellular reservoir of HIV-1. We provide an overview of the results of pre-clinical and clinical studies of broadly HIV-1 neutralizing antibodies, discuss their implications and highlight approaches for the ongoing advancement into humans.

Antibody-Mediated Therapy against HIV/AIDS: Where Are We Standing Now?

Acquired immunodeficiency syndrome (AIDS) cases are on the rise globally. To date, there is still no effective measure to eradicate the causative agent, human immunodeficiency virus (HIV). Highly active antiretroviral therapy (HAART) is being used in HIV/AIDS management, but it results in long-term medication and has major drawbacks such as multiple side effects, high cost, and increasing the generation rate of escape mutants. In addition, HAART does not control HIV-related complications, and hence more medications and further management are required. With this, other alternatives are urgently needed. In the past, small-molecule inhibitors have shown potent antiviral effects, and some of them are now being evaluated in clinical trials. The challenges in developing these small molecules for clinical use include the off-target effect, poor stability, and low bioavailability. On the other hand, antibody-mediated therapy has emerged as an important therapeutic modality for anti-HIV therapeutics development. Many antiviral antibodies, namely, broad neutralizing antibodies (bnAbs) against multiple strains of HIV, have shown promising effects in vitro and in animal studies; further studies are ongoing in clinical trials to evaluate their uses in clinical applications. This short review aims to discuss the current development of therapeutic antibodies against HIV and the challenges in adopting them for clinical use.