Super LeArner Prediction of NAb Panels (SLAPNAP): a containerized tool for predicting combination monoclonal broadly neutralizing antibody sensitivity

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 acquisition. 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 80% concentration < 1 μg/mL). For continuous outcomes, the CP approach performs nearly as well as the PC approach when the individual-bnAb prediction algorithms have strong performance, 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.

Broadly neutralizing antibodies for HIV prevention: a comprehensive review and future perspectives

SUMMARYThe human immunodeficiency virus (HIV) epidemic remains a formidable global health concern, with 39 million people living with the virus and 1.3 million new infections reported in 2022. Despite anti-retroviral therapy's effectiveness in pre-exposure prophylaxis, its global adoption is limited. Broadly neutralizing antibodies (bNAbs) offer an alternative strategy for HIV prevention through passive immunization. Historically, passive immunization has been efficacious in the treatment of various diseases ranging from oncology to infectious diseases. Early clinical trials suggest bNAbs are safe, tolerable, and capable of reducing HIV RNA levels. Although challenges such as bNAb resistance have been noted in phase I trials, ongoing research aims to assess the additive or synergistic benefits of combining multiple bNAbs. Researchers are exploring bispecific and trispecific antibodies, and fragment crystallizable region modifications to augment antibody efficacy and half-life. Moreover, the potential of other antibody isotypes like IgG3 and IgA is under investigation. While promising, the application of bNAbs faces economic and logistical barriers. High manufacturing costs, particularly in resource-limited settings, and logistical challenges like cold-chain requirements pose obstacles. Preliminary studies suggest cost-effectiveness, although this is contingent on various factors like efficacy and distribution. Technological advancements and strategic partnerships may mitigate some challenges, but issues like molecular aggregation remain. The World Health Organization has provided preferred product characteristics for bNAbs, focusing on optimizing their efficacy, safety, and accessibility. The integration of bNAbs in HIV prophylaxis necessitates a multi-faceted approach, considering economic, logistical, and scientific variables. This review comprehensively covers the historical context, current advancements, and future avenues of bNAbs in HIV prevention.

Prevention efficacy of the broadly neutralizing antibody VRC01 depends on HIV-1 envelope sequence features

In the Antibody Mediated Prevention (AMP) trials (HVTN 704/HPTN 085 and HVTN 703/HPTN 081), prevention efficacy (PE) of the monoclonal broadly neutralizing antibody (bnAb) VRC01 (vs. placebo) against HIV-1 acquisition diagnosis varied according to the HIV-1 Envelope (Env) neutralization sensitivity to VRC01, as measured by 80% inhibitory concentration (IC80). Here, we performed a genotypic sieve analysis, a complementary approach to gaining insight into correlates of protection that assesses how PE varies with HIV-1 sequence features. We analyzed HIV-1 Env amino acid (AA) sequences from the earliest available HIV-1 RNA-positive plasma samples from AMP participants diagnosed with HIV-1 and identified Env sequence features that associated with PE. The strongest Env AA sequence correlate in both trials was VRC01 epitope distance that quantifies the divergence of the VRC01 epitope in an acquired HIV-1 isolate from the VRC01 epitope of reference HIV-1 strains that were most sensitive to VRC01-mediated neutralization. In HVTN 704/HPTN 085, the Env sequence-based predicted probability that VRC01 IC80 against the acquired isolate exceeded 1 µg/mL also significantly associated with PE. In HVTN 703/HPTN 081, a physicochemical-weighted Hamming distance across 50 VRC01 binding-associated Env AA positions of the acquired isolate from the most VRC01-sensitive HIV-1 strain significantly associated with PE. These results suggest that incorporating mutation scoring by BLOSUM62 and weighting by the strength of interactions at AA positions in the epitope:VRC01 interface can optimize performance of an Env sequence-based biomarker of VRC01 prevention efficacy. Future work could determine whether these results extend to other bnAbs and bnAb combinations.

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.

Development of screening assays for use of broadly neutralizing antibodies in people with HIV

PURPOSE OF REVIEW

Treatment with combinations of complementary broadly neutralizing antibodies (bnAbs) has increased the proportion of participants for whom bnAbs can maintain virus suppression upon cessation of antiretroviral therapy (ART). There remains, however, a population of trial participants who experience virus rebound despite high plasma concentrations of bnAbs. Thus, baseline resistance remains a critical barrier to the efficacy of bnAbs for use in the treatment and cure of HIV, and the development of a screening assay to guide bnAb selection is a high priority.

RECENT FINDINGS

There are two conceptual approaches to assess the putative rebound-competent HIV-1 reservoir for bnAb sensitivity: to assess neutralization sensitivity of reactivated virus in outgrowth assays and sequence-based approaches that include a selection for intact genomes and assessment of known resistance mutations within the env gene. Currently, the only phenotypic assay for bnAb screening that is clinical laboratory improvement amendments certified (CLIA certified) and available for clinical trial use is Monogram Biosciences' PhenoSense HIV Neutralizing Antibody Assay.

SUMMARY

Several new approaches for screening are currently under development and future screening methods must address three issues. First, complete sampling of the reservoir may be impossible, and determination of the relevance of partial sampling is needed. Second, multiple lines of evidence indicate that in vitro neutralization measures are at least one correlate of in vivo bnAb activity that should be included in screening, but more research is needed on how to use in vitro neutralization assays and other measures of antibody functions and measures of other antibody features. Third, the feasibility of screening assays must be a priority. A feasible, predictive bnAb screening assay will remain relevant until a time when bnAb combinations are substantially more broad and potent.

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.

Optimizing clinical dosing of combination broadly neutralizing antibodies for HIV prevention

Broadly neutralizing antibodies (bNAbs) are promising agents to prevent HIV infection and achieve HIV remission without antiretroviral therapy (ART). As with ART, bNAb combinations are likely needed to cover HIV's extensive diversity. Not all bNAbs are identical in terms of their breadth, potency, and in vivo longevity (half-life). Given these differences, it is important to optimally select the composition, or dose ratio, of combination bNAb therapies for future clinical studies. We developed a model that synthesizes 1) pharmacokinetics, 2) potency against a wide HIV diversity, 3) interaction models for how drugs work together, and 4) correlates that translate in vitro potency to clinical protection. We found optimization requires drug-specific balances between potency, longevity, and interaction type. As an example, tradeoffs between longevity and potency are shown by comparing a combination therapy to a bi-specific antibody (a single protein merging both bNAbs) that takes the better potency but the worse longevity of the two components. Then, we illustrate a realistic dose ratio optimization of a triple combination of VRC07, 3BNC117, and 10-1074 bNAbs. We apply protection estimates derived from both a non-human primate (NHP) challenge study meta-analysis and the human antibody mediated prevention (AMP) trials. In both cases, we find a 2:1:1 dose emphasizing VRC07 is nearly optimal. Our approach can be immediately applied to optimize the next generation of combination antibody prevention and cure studies.