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Meijers M, Ruchnewitz D, Eberhardt J, Karmakar M, Łuksza M, Lässig M. Concepts and methods for predicting viral evolution. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.19.585703. [PMID: 38746108 PMCID: PMC11092427 DOI: 10.1101/2024.03.19.585703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
The seasonal human influenza virus undergoes rapid evolution, leading to significant changes in circulating viral strains from year to year. These changes are typically driven by adaptive mutations, particularly in the antigenic epitopes, the regions of the viral surface protein haemagglutinin targeted by human antibodies. Here we describe a consistent set of methods for data-driven predictive analysis of viral evolution. Our pipeline integrates four types of data: (1) sequence data of viral isolates collected on a worldwide scale, (2) epidemiological data on incidences, (3) antigenic characterization of circulating viruses, and (4) intrinsic viral phenotypes. From the combined analysis of these data, we obtain estimates of relative fitness for circulating strains and predictions of clade frequencies for periods of up to one year. Furthermore, we obtain comparative estimates of protection against future viral populations for candidate vaccine strains, providing a basis for pre-emptive vaccine strain selection. Continuously updated predictions obtained from the prediction pipeline for influenza and SARS-CoV-2 are available on the website previr.app .
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Meijers M, Ruchnewitz D, Eberhardt J, Karmakar M, Łuksza M, Lässig M. Concepts and methods for predicting viral evolution. ARXIV 2024:arXiv:2403.12684v2. [PMID: 38745695 PMCID: PMC11092678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
The seasonal human influenza virus undergoes rapid evolution, leading to significant changes in circulating viral strains from year to year. These changes are typically driven by adaptive mutations, particularly in the antigenic epitopes, the regions of the viral surface protein haemagglutinin targeted by human antibodies. Here we describe a consistent set of methods for data-driven predictive analysis of viral evolution. Our pipeline integrates four types of data: (1) sequence data of viral isolates collected on a worldwide scale, (2) epidemiological data on incidences, (3) antigenic characterization of circulating viruses, and (4) intrinsic viral phenotypes. From the combined analysis of these data, we obtain estimates of relative fitness for circulating strains and predictions of clade frequencies for periods of up to one year. Furthermore, we obtain comparative estimates of protection against future viral populations for candidate vaccine strains, providing a basis for pre-emptive vaccine strain selection. Continuously updated predictions obtained from the prediction pipeline for influenza and SARS-CoV-2 are available on the website previr.app.
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Affiliation(s)
- Matthijs Meijers
- Institute for Biological Physics, University of Cologne, Zülpicherstr. 77, 50937, Köln, Germany
| | - Denis Ruchnewitz
- Institute for Biological Physics, University of Cologne, Zülpicherstr. 77, 50937, Köln, Germany
| | - Jan Eberhardt
- Institute for Biological Physics, University of Cologne, Zülpicherstr. 77, 50937, Köln, Germany
| | - Malancha Karmakar
- Institute for Biological Physics, University of Cologne, Zülpicherstr. 77, 50937, Köln, Germany
| | - Marta Łuksza
- Tisch Cancer Institute, Departments of Oncological Sciences and Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Michael Lässig
- Institute for Biological Physics, University of Cologne, Zülpicherstr. 77, 50937, Köln, Germany
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Cassidy T, Stephenson KE, Barouch DH, Perelson AS. Modeling resistance to the broadly neutralizing antibody PGT121 in people living with HIV-1. PLoS Comput Biol 2024; 20:e1011518. [PMID: 38551976 PMCID: PMC11006161 DOI: 10.1371/journal.pcbi.1011518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Revised: 04/10/2024] [Accepted: 03/14/2024] [Indexed: 04/11/2024] Open
Abstract
PGT121 is a broadly neutralizing antibody in clinical development for the treatment and prevention of HIV-1 infection via passive administration. PGT121 targets the HIV-1 V3-glycan and demonstrated potent antiviral activity in a phase I clinical trial. Resistance to PGT121 monotherapy rapidly occurred in the majority of participants in this trial with the sampled rebound viruses being entirely resistant to PGT121 mediated neutralization. However, two individuals experienced long-term ART-free viral suppression following antibody infusion and retained sensitivity to PGT121 upon viral rebound. Here, we develop mathematical models of the HIV-1 dynamics during this phase I clinical trial. We utilize these models to understand the dynamics leading to PGT121 resistance and to identify the mechanisms driving the observed long-term viral control. Our modeling highlights the importance of the relative fitness difference between PGT121 sensitive and resistant subpopulations prior to treatment. Specifically, by fitting our models to data, we identify the treatment-induced competitive advantage of previously existing or newly generated resistant population as a primary driver of resistance. Finally, our modeling emphasizes the high neutralization ability of PGT121 in both participants who exhibited long-term viral control.
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Affiliation(s)
- Tyler Cassidy
- School of Mathematics, University of Leeds, Leeds, United Kingdom
| | - Kathryn E. Stephenson
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States of America
- Division of Infectious Diseases, Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States of America
- Ragon Institute of MGH, MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Dan H. Barouch
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States of America
- Division of Infectious Diseases, Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States of America
- Ragon Institute of MGH, MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Alan S. Perelson
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
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Lässig M, Mustonen V, Nourmohammad A. Steering and controlling evolution - from bioengineering to fighting pathogens. Nat Rev Genet 2023; 24:851-867. [PMID: 37400577 PMCID: PMC11137064 DOI: 10.1038/s41576-023-00623-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/30/2023] [Indexed: 07/05/2023]
Abstract
Control interventions steer the evolution of molecules, viruses, microorganisms or other cells towards a desired outcome. Applications range from engineering biomolecules and synthetic organisms to drug, therapy and vaccine design against pathogens and cancer. In all these instances, a control system alters the eco-evolutionary trajectory of a target system, inducing new functions or suppressing escape evolution. Here, we synthesize the objectives, mechanisms and dynamics of eco-evolutionary control in different biological systems. We discuss how the control system learns and processes information about the target system by sensing or measuring, through adaptive evolution or computational prediction of future trajectories. This information flow distinguishes pre-emptive control strategies by humans from feedback control in biotic systems. We establish a cost-benefit calculus to gauge and optimize control protocols, highlighting the fundamental link between predictability of evolution and efficacy of pre-emptive control.
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Affiliation(s)
- Michael Lässig
- Institute for Biological Physics, University of Cologne, Cologne, Germany.
| | - Ville Mustonen
- Organismal and Evolutionary Biology Research Programme, Department of Computer Science, Institute of Biotechnology, University of Helsinki, Helsinki, Finland.
| | - Armita Nourmohammad
- Department of Physics, University of Washington, Seattle, WA, USA.
- Department of Applied Mathematics, University of Washington, Seattle, WA, USA.
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA.
- Herbold Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, USA.
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Meijers M, Ruchnewitz D, Eberhardt J, Łuksza M, Lässig M. Population immunity predicts evolutionary trajectories of SARS-CoV-2. Cell 2023; 186:5151-5164.e13. [PMID: 37875109 PMCID: PMC10964984 DOI: 10.1016/j.cell.2023.09.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 08/26/2023] [Accepted: 09/21/2023] [Indexed: 10/26/2023]
Abstract
The large-scale evolution of the SARS-CoV-2 virus has been marked by rapid turnover of genetic clades. New variants show intrinsic changes, notably increased transmissibility, and antigenic changes that reduce cross-immunity induced by previous infections or vaccinations. How this functional variation shapes global evolution has remained unclear. Here, we establish a predictive fitness model for SARS-CoV-2 that integrates antigenic and intrinsic selection. The model is informed by tracking of time-resolved sequence data, epidemiological records, and cross-neutralization data of viral variants. Our inference shows that immune pressure, including contributions of vaccinations and previous infections, has become the dominant force driving the recent evolution of SARS-CoV-2. The fitness model can serve continued surveillance in two ways. First, it successfully predicts the short-term evolution of circulating strains and flags emerging variants likely to displace the previously predominant variant. Second, it predicts likely antigenic profiles of successful escape variants prior to their emergence.
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Affiliation(s)
- Matthijs Meijers
- Institute for Biological Physics, University of Cologne, Zülpicherstr. 77, 50937 Köln, Germany
| | - Denis Ruchnewitz
- Institute for Biological Physics, University of Cologne, Zülpicherstr. 77, 50937 Köln, Germany
| | - Jan Eberhardt
- Institute for Biological Physics, University of Cologne, Zülpicherstr. 77, 50937 Köln, Germany
| | - Marta Łuksza
- Tisch Cancer Institute, Departments of Oncological Sciences and Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Michael Lässig
- Institute for Biological Physics, University of Cologne, Zülpicherstr. 77, 50937 Köln, Germany.
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Bjorgen JC, Dick JK, Cromarty R, Hart GT, Rhein J. NK cell subsets and dysfunction during viral infection: a new avenue for therapeutics? Front Immunol 2023; 14:1267774. [PMID: 37928543 PMCID: PMC10620977 DOI: 10.3389/fimmu.2023.1267774] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 09/25/2023] [Indexed: 11/07/2023] Open
Abstract
In the setting of viral challenge, natural killer (NK) cells play an important role as an early immune responder against infection. During this response, significant changes in the NK cell population occur, particularly in terms of their frequency, location, and subtype prevalence. In this review, changes in the NK cell repertoire associated with several pathogenic viral infections are summarized, with a particular focus placed on changes that contribute to NK cell dysregulation in these settings. This dysregulation, in turn, can contribute to host pathology either by causing NK cells to be hyperresponsive or hyporesponsive. Hyperresponsive NK cells mediate significant host cell death and contribute to generating a hyperinflammatory environment. Hyporesponsive NK cell populations shift toward exhaustion and often fail to limit viral pathogenesis, possibly enabling viral persistence. Several emerging therapeutic approaches aimed at addressing NK cell dysregulation have arisen in the last three decades in the setting of cancer and may prove to hold promise in treating viral diseases. However, the application of such therapeutics to treat viral infections remains critically underexplored. This review briefly explores several therapeutic approaches, including the administration of TGF-β inhibitors, immune checkpoint inhibitors, adoptive NK cell therapies, CAR NK cells, and NK cell engagers among other therapeutics.
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Affiliation(s)
- Jacob C. Bjorgen
- Division of Infectious Diseases and International Medicine, Department of Medicine, University of Minnesota, Minneapolis, MN, United States
| | - Jenna K. Dick
- Division of Infectious Diseases and International Medicine, Department of Medicine, University of Minnesota, Minneapolis, MN, United States
- Masonic Cancer Center, University of Minnesota, Minneapolis, MN, United States
- Center for Immunology, University of Minnesota, Minneapolis, MN, United States
| | - Ross Cromarty
- Masonic Cancer Center, University of Minnesota, Minneapolis, MN, United States
| | - Geoffrey T. Hart
- Division of Infectious Diseases and International Medicine, Department of Medicine, University of Minnesota, Minneapolis, MN, United States
- Masonic Cancer Center, University of Minnesota, Minneapolis, MN, United States
- Center for Immunology, University of Minnesota, Minneapolis, MN, United States
| | - Joshua Rhein
- Division of Infectious Diseases and International Medicine, Department of Medicine, University of Minnesota, Minneapolis, MN, United States
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Fili M, Hu G, Han C, Kort A, Trettin J, Haim H. A classification algorithm based on dynamic ensemble selection to predict mutational patterns of the envelope protein in HIV-infected patients. Algorithms Mol Biol 2023; 18:4. [PMID: 37337202 DOI: 10.1186/s13015-023-00228-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 06/04/2023] [Indexed: 06/21/2023] Open
Abstract
BACKGROUND Therapeutics against the envelope (Env) proteins of human immunodeficiency virus type 1 (HIV-1) effectively reduce viral loads in patients. However, due to mutations, new therapy-resistant Env variants frequently emerge. The sites of mutations on Env that appear in each patient are considered random and unpredictable. Here we developed an algorithm to estimate for each patient the mutational state of each position based on the mutational state of adjacent positions on the three-dimensional structure of the protein. METHODS We developed a dynamic ensemble selection algorithm designated k-best classifiers. It identifies the best classifiers within the neighborhood of a new observation and applies them to predict the variability state of each observation. To evaluate the algorithm, we applied amino acid sequences of Envs from 300 HIV-1-infected individuals (at least six sequences per patient). For each patient, amino acid variability values at all Env positions were mapped onto the three-dimensional structure of the protein. Then, the variability state of each position was estimated by the variability at adjacent positions of the protein. RESULTS The proposed algorithm showed higher performance than the base learner and a panel of classification algorithms. The mutational state of positions in the high-mannose patch and CD4-binding site of Env, which are targeted by multiple therapeutics, was predicted well. Importantly, the algorithm outperformed other classification techniques for predicting the variability state at multi-position footprints of therapeutics on Env. CONCLUSIONS The proposed algorithm applies a dynamic classifier-scoring approach that increases its performance relative to other classification methods. Better understanding of the spatiotemporal patterns of variability across Env may lead to new treatment strategies that are tailored to the unique mutational patterns of each patient. More generally, we propose the algorithm as a new high-performance dynamic ensemble selection technique.
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Affiliation(s)
- Mohammad Fili
- Department of Industrial and Manufacturing Systems Engineering, Iowa State University, 3014 Black Engineering, 2529 Union Drive, Ames, IA, 50011, USA
| | - Guiping Hu
- Department of Industrial and Manufacturing Systems Engineering, Iowa State University, 3014 Black Engineering, 2529 Union Drive, Ames, IA, 50011, USA.
| | - Changze Han
- Department of Microbiology and Immunology, Carver College of Medicine, University of Iowa, 51 Newton Rd, 3-770 BSB, Iowa City, IA, 52242, USA
| | - Alexa Kort
- Department of Microbiology and Immunology, Carver College of Medicine, University of Iowa, 51 Newton Rd, 3-770 BSB, Iowa City, IA, 52242, USA
| | - John Trettin
- Department of Industrial and Manufacturing Systems Engineering, Iowa State University, 3014 Black Engineering, 2529 Union Drive, Ames, IA, 50011, USA
| | - Hillel Haim
- Department of Microbiology and Immunology, Carver College of Medicine, University of Iowa, 51 Newton Rd, 3-770 BSB, Iowa City, IA, 52242, USA.
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Qerqez AN, Silva RP, Maynard JA. Outsmarting Pathogens with Antibody Engineering. Annu Rev Chem Biomol Eng 2023; 14:217-241. [PMID: 36917814 PMCID: PMC10330301 DOI: 10.1146/annurev-chembioeng-101121-084508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/16/2023]
Abstract
There is growing interest in identifying antibodies that protect against infectious diseases, especially for high-risk individuals and pathogens for which no vaccine is yet available. However, pathogens that manifest as opportunistic or latent infections express complex arrays of virulence-associated proteins and are adept at avoiding immune responses. Some pathogens have developed strategies to selectively destroy antibodies, whereas others create decoy epitopes that trick the host immune system into generating antibodies that are at best nonprotective and at worst enhance pathogenesis. Antibody engineering strategies can thwart these efforts by accessing conserved neutralizing epitopes, generating Fc domains that resist capture or degradation and even accessing pathogens hidden inside cells. Design of pathogen-resistant antibodies can enhance protection and guide development of vaccine immunogens against these complex pathogens. Here, we discuss general strategies for design of antibodies resistant to specific pathogen defense mechanisms.
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Affiliation(s)
- Ahlam N Qerqez
- Department of Chemical Engineering, The University of Texas, Austin, Texas, USA;
| | - Rui P Silva
- Department of Molecular Biosciences, The University of Texas, Austin, Texas, USA
| | - Jennifer A Maynard
- Department of Chemical Engineering, The University of Texas, Austin, Texas, USA;
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Alves E, Al-Kaabi M, Keane NM, Leary S, Almeida CAM, Deshpande P, Currenti J, Chopra A, Smith R, Castley A, Mallal S, Kalams SA, Gaudieri S, John M. Adaptation to HLA-associated immune pressure over the course of HIV infection and in circulating HIV-1 strains. PLoS Pathog 2022; 18:e1010965. [PMID: 36525463 PMCID: PMC9803285 DOI: 10.1371/journal.ppat.1010965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 12/30/2022] [Accepted: 11/01/2022] [Indexed: 12/23/2022] Open
Abstract
Adaptation to human leukocyte antigen (HLA)-associated immune pressure represents a major driver of human immunodeficiency virus (HIV) evolution at both the individual and population level. To date, there has been limited exploration of the impact of the initial cellular immune response in driving viral adaptation, the dynamics of these changes during infection and their effect on circulating transmitting viruses at the population level. Capturing detailed virological and immunological data from acute and early HIV infection is challenging as this commonly precedes the diagnosis of HIV infection, potentially by many years. In addition, rapid initiation of antiretroviral treatment following a diagnosis is the standard of care, and central to global efforts towards HIV elimination. Yet, acute untreated infection is the critical period in which the diversity of proviral reservoirs is first established within individuals, and associated with greater risk of onward transmissions in a population. Characterizing the viral adaptations evident in the earliest phases of infection, coinciding with the initial cellular immune responses is therefore relevant to understanding which changes are of greatest impact to HIV evolution at the population level. In this study, we utilized three separate cohorts to examine the initial CD8+ T cell immune response to HIV (cross-sectional acute infection cohort), track HIV evolution in response to CD8+ T cell-mediated immunity over time (longitudinal chronic infection cohort) and translate the impact of HLA-driven HIV evolution to the population level (cross-sectional HIV sequence data spanning 30 years). Using next generation viral sequencing and enzyme-linked immunospot interferon-gamma recall responses to peptides representing HLA class I-specific HIV T cell targets, we observed that CD8+ T cell responses can select viral adaptations prior to full antibody seroconversion. Using the longitudinal cohort, we uncover that viral adaptations have the propensity to be retained over time in a non-selective immune environment, which reflects the increasing proportion of pre-adapted HIV strains within the Western Australian population over an approximate 30-year period.
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Affiliation(s)
- Eric Alves
- School of Human Sciences, The University of Western Australia, Perth, Western Australia, Australia
| | - Marwah Al-Kaabi
- School of Human Sciences, The University of Western Australia, Perth, Western Australia, Australia
| | - Niamh M. Keane
- Institute for Immunology and Infectious Diseases, Murdoch University, Perth, Western Australia, Australia
| | - Shay Leary
- Institute for Immunology and Infectious Diseases, Murdoch University, Perth, Western Australia, Australia
| | - Coral-Ann M. Almeida
- Institute for Immunology and Infectious Diseases, Murdoch University, Perth, Western Australia, Australia
| | - Pooja Deshpande
- School of Human Sciences, The University of Western Australia, Perth, Western Australia, Australia
- Institute for Immunology and Infectious Diseases, Murdoch University, Perth, Western Australia, Australia
| | - Jennifer Currenti
- School of Human Sciences, The University of Western Australia, Perth, Western Australia, Australia
| | - Abha Chopra
- Institute for Immunology and Infectious Diseases, Murdoch University, Perth, Western Australia, Australia
| | - Rita Smith
- Division of Infectious Diseases, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Alison Castley
- Department of Clinical Immunology, Royal Perth Hospital, Perth, Western Australia, Australia
| | - Simon Mallal
- Institute for Immunology and Infectious Diseases, Murdoch University, Perth, Western Australia, Australia
- Division of Infectious Diseases, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Spyros A. Kalams
- Division of Infectious Diseases, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Silvana Gaudieri
- School of Human Sciences, The University of Western Australia, Perth, Western Australia, Australia
- Institute for Immunology and Infectious Diseases, Murdoch University, Perth, Western Australia, Australia
- Division of Infectious Diseases, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Mina John
- Institute for Immunology and Infectious Diseases, Murdoch University, Perth, Western Australia, Australia
- Department of Clinical Immunology, Royal Perth Hospital, Perth, Western Australia, Australia
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Zacharopoulou P, Ansari MA, Frater J. A calculated risk: Evaluating HIV resistance to the broadly neutralising antibodies10-1074 and 3BNC117. Curr Opin HIV AIDS 2022; 17:352-358. [PMID: 36178770 PMCID: PMC9594129 DOI: 10.1097/coh.0000000000000764] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
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.
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Affiliation(s)
- Panagiota Zacharopoulou
- Peter Medawar Building for Pathogen Research, Nuffield Department of Medicine, University of Oxford
| | - M. Azim Ansari
- Peter Medawar Building for Pathogen Research, Nuffield Department of Medicine, University of Oxford
| | - John Frater
- Peter Medawar Building for Pathogen Research, Nuffield Department of Medicine, University of Oxford
- NIHR Oxford Biomedical Research Centre, Oxford, UK
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LaMont C, Otwinowski J, Vanshylla K, Gruell H, Klein F, Nourmohammad A. Design of an optimal combination therapy with broadly neutralizing antibodies to suppress HIV-1. eLife 2022; 11:76004. [PMID: 35852143 PMCID: PMC9467514 DOI: 10.7554/elife.76004] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 07/04/2022] [Indexed: 11/13/2022] Open
Abstract
Infusion of broadly neutralizing antibodies (bNAbs) has shown promise as an alternative to anti-retroviral therapy against HIV. A key challenge is to suppress viral escape, which is more effectively achieved with a combination of bNAbs. Here, we propose a computational approach to predict the efficacy of a bNAb therapy based on the population genetics of HIV escape, which we parametrize using high-throughput HIV sequence data from bNAb-naive patients. By quantifying the mutational target size and the fitness cost of HIV-1 escape from bNAbs, we predict the distribution of rebound times in three clinical trials. We show that a cocktail of three bNAbs is necessary to effectively suppress viral escape, and predict the optimal composition of such bNAb cocktail. Our results offer a rational therapy design for HIV, and show how genetic data can be used to predict treatment outcomes and design new approaches to pathogenic control.
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Affiliation(s)
- Colin LaMont
- Max Planck Institute for Dynamics and Self-Organization
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Parikh UM, Mellors JW. How could HIV-1 drug resistance impact preexposure prophylaxis for HIV prevention? Curr Opin HIV AIDS 2022; 17:213-221. [PMID: 35762376 PMCID: PMC9245149 DOI: 10.1097/coh.0000000000000746] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
PURPOSE OF REVIEW To review current laboratory and clinical data on the frequency and relative risk of drug resistance and range of mutations selected from approved and investigational antiretroviral agents used for preexposure prophylaxis (PrEP) of HIV-1 infection, including tenofovir disproxil fumarate (TDF)-based oral PrEP, dapivirine ring, injectable cabotegravir (CAB), islatravir, lenacapavir and broadly neutralizing antibodies (bNAbs). RECENT FINDINGS The greatest risk of HIV-1 resistance from PrEP with oral TDF/emtricitabine (FTC) or injectable CAB is from starting or continuing PrEP after undiagnosed acute HIV infection. By contrast, the dapivirine intravaginal ring does not appear to select nonnucleoside reverse transcriptase inhibitor resistance in clinical trial settings. Investigational inhibitors including islatravir, lenacapavir, and bNAbs are promising for use as PrEP due to their potential for sustained delivery and low risk of cross-resistance to currently used antiretrovirals, but surveillance for emergence of resistance mutations in more HIV-1 gene regions (gag, env) will be important as the same drugs are being developed for HIV therapy. SUMMARY PrEP is highly effective in preventing HIV infection. Although HIV drug resistance from PrEP use could impact future options in individuals who seroconvert on PrEP, the current risk is low and continued monitoring for the emergence of resistance and cross-resistance during product development, clinical studies, and product roll-out is advised to preserve antiretroviral efficacy for both treatment and prevention.
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Affiliation(s)
- Urvi M Parikh
- Division of Infectious Diseases, Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
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