Design of an optimal combination therapy with broadly neutralizing antibodies to suppress HIV-1.
Colin LaMontJakub OtwinowskiKanika VanshyllaHenning GruellFlorian KleinArmita NourmohammadPublished in: eLife (2022)
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.
Keyphrases
- antiretroviral therapy
- hiv infected
- hiv positive
- hiv testing
- human immunodeficiency virus
- hepatitis c virus
- hiv aids
- men who have sex with men
- clinical trial
- combination therapy
- high throughput
- sars cov
- end stage renal disease
- physical activity
- chronic kidney disease
- electronic health record
- gene expression
- ejection fraction
- low dose
- stem cells
- newly diagnosed
- mesenchymal stem cells
- artificial intelligence
- open label
- dengue virus
- cell therapy
- zika virus
- patient reported outcomes
- phase ii
- deep learning