Harnessing Artificial Intelligence to Optimize Long‐Term Maintenance Dosing for Antiretroviral‐Naive Adults with HIV‐1 Infection

Abstract

AbstractAntiretroviral therapy (ART) serves as a mainstay in treating human immunodeficiency virus (HIV) infection. An HIV patient is traditionally administered the same ART regimen for life, even if his/her viral load has been reduced by several orders of magnitude from the initial viral load. Dose reduction in ART has been clinically explored in a trial‐and‐error manner to reduce side effects and improve ART sustainability. Using artificial intelligence (AI), we have discovered that drugs and doses inputs can be related to viral load reduction through a Parabolic Response Surface (PRS). The AI‐PRS platform can rationally guide a clinically‐actionable approach to identify optimized population‐wide and personalized dosing. In this prospective pilot clinical trial, a combination regimen of tenofovir (TDF), efavirenz (EFV) and lamivudine (3TC) is administered to ten patients. Using AI‐PRS, a 33% reduction in the long‐term TDF maintenance dose (200 mg) is identified compared to standard regimens (300 mg). This regimen keeps the HIV viral load below 40 copies/mL with no relapse during a 144‐week observation period. This study demonstrates that AI‐PRS can potentially serve as a scalable approach to optimize and sustain the long‐term management of HIV as well as a broad spectrum of other indications.