Speaker
Description
Due to their long circulating half-life, high neutralization potency, and large breadth of coverage, broadly neutralizing antibodies are increasingly studied for the treatment of HIV-1 infection. Recent phase I clinical studies of antibody treatment have demonstrated robust and durable antiviral effects in viremic participants and in participants undergoing analytical treatment interruption. Consequently, these antibodies are an attractive novel treatment candidate for long-term, antiretroviral therapy-free, viral control. However, these early-stage trials are small, time consuming, and expensive. I'll show how mechanistic mathematical modelling can identify clinically actionable determinants of treatment response and uncover the evolutionary dynamics driving viral rebound. Further, I’ll show how combining mechanistic modelling and virtual population approaches can predict the duration of viral suppression in both monotherapy and combination clinical trial results. The resulting virtual population platform provides mechanistic insight that identifies clinically relevant biomarkers that are predictive of bnAb response and individualize bnAb combination therapy against HIV-1 infection. Further, this computational approach can quantify the increased antiviral effect of recently developed bnAb variants.
