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Mathematical modeling to reveal breakthrough mechanisms in the HIV Antibody Mediated Prevention (AMP) trials
- Source :
- PLoS Computational Biology, Vol 16, Iss 2, p e1007626 (2020), PLoS Computational Biology
- Publication Year :
- 2020
- Publisher :
- Public Library of Science (PLoS), 2020.
-
Abstract
- The ongoing Antibody Mediated Prevention (AMP) trials will uncover whether passive infusion of the broadly neutralizing antibody (bNAb) VRC01 can protect against HIV acquisition. Previous statistical simulations indicate these trials may be partially protective. In that case, it will be crucial to identify the mechanism of breakthrough infections. To that end, we developed a mathematical modeling framework to simulate the AMP trials and infer the breakthrough mechanisms using measurable trial outcomes. This framework combines viral dynamics with antibody pharmacokinetics and pharmacodynamics, and will be generally applicable to forthcoming bNAb prevention trials. We fit our model to human viral load data (RV217). Then, we incorporated VRC01 neutralization using serum pharmacokinetics (HVTN 104) and in vitro pharmacodynamics (LANL CATNAP database). We systematically explored trial outcomes by reducing in vivo potency and varying the distribution of sensitivity to VRC01 in circulating strains. We found trial outcomes could be used in a clinical trial regression model (CTRM) to reveal whether partially protective trials were caused by large fractions of VRC01-resistant (IC50>50 μg/mL) circulating strains or rather a global reduction in VRC01 potency against all strains. The former mechanism suggests the need to enhance neutralizing antibody breadth; the latter suggests the need to enhance VRC01 delivery and/or in vivo binding. We will apply the clinical trial regression model to data from the completed trials to help optimize future approaches for passive delivery of anti-HIV neutralizing antibodies.<br />Author summary Infusions of broadly neutralizing antibodies are currently being tested as a novel HIV prevention modality. To help interpret the results of these antibody mediated prevention (AMP) studies we developed a mathematical modeling framework. The approach combines antibody potency and drug levels with models of HIV viral dynamics, which will be generally applicable to future studies. Through simulating these clinical trials, we found trial outcomes can be used in combination to infer whether breakthrough infections are caused by large fractions of antibody-resistant circulating strains or some reduction in potency against all strains. This distinction helps to focus future trials on enhancing neutralizing antibody breadth or antibody delivery and/or in vivo binding.
- Subjects :
- RNA viruses
0301 basic medicine
Physiology
HIV Infections
HIV Antibodies
Pathology and Laboratory Medicine
Biochemistry
White Blood Cells
0302 clinical medicine
Immunodeficiency Viruses
Animal Cells
Immune Physiology
Medicine and Health Sciences
Medicine
Biology (General)
Neutralizing antibody
Clinical Trials as Topic
Immune System Proteins
Cell Death
Ecology
biology
T Cells
Simulation and Modeling
Viral Load
3. Good health
Computational Theory and Mathematics
Cell Processes
Medical Microbiology
Viral Pathogens
Modeling and Simulation
Viruses
Infectious diseases
Pathogens
Cellular Types
Viral load
Research Article
QH301-705.5
Immune Cells
Immunology
Viral diseases
Research and Analysis Methods
Microbiology
Antibodies
03 medical and health sciences
Cellular and Molecular Neuroscience
Pharmacokinetics
In vivo
Virology
Retroviruses
Genetics
Humans
Distribution (pharmacology)
Microbial Pathogens
Molecular Biology
Ecology, Evolution, Behavior and Systematics
Blood Cells
Mechanism (biology)
business.industry
Lentivirus
Organisms
Biology and Life Sciences
Proteins
HIV
Cell Biology
Models, Theoretical
Acquired Immune System
Clinical trial
030104 developmental biology
Immune System
Pharmacodynamics
biology.protein
business
Viral Transmission and Infection
Broadly Neutralizing Antibodies
030217 neurology & neurosurgery
Subjects
Details
- Language :
- English
- ISSN :
- 15537358
- Volume :
- 16
- Issue :
- 2
- Database :
- OpenAIRE
- Journal :
- PLoS Computational Biology
- Accession number :
- edsair.doi.dedup.....829503738ff96942eb9867cda57de26b