Speaker
Description
Dual IL-10/PD-1 blockade in ART-treated, SIVmac239-infected rhesus macaques (RMs) produced durable control of viral rebound after analytical treatment interruption (ATI). We analyzed the longitudinal data from 28 RMs randomized to vehicle (n=8), anti–IL-10 (n=10), or anti–IL-10+anti–PD-1 (n=10): plasma viremia, cell-associated vRNA/vDNA, intact proviral DNA (IPDA), and multiple immune markers were assayed. We compared different model classes by the corrected Bayesian Information Criteria. We used in-silico cohorts to simulate trial-level outcomes (controller frequency, viremia, CA-DNA/IPDA). Machine learning analyses of simulated trajectories identified a minimal predictive signature and linked pre-ATI immune markers to key parameters. Our models captured plasma viremia and CA-vRNA trajectories and predicted CA-DNA and IPDA dynamics across all RMs. Anti–IL-10 increased infected-cell loss rates by 29–80%, while anti–PD-1 enhanced effector cell exhaustion reversal by 2.5–4.7-fold. Parameter distributions showed that viremia control was mainly driven by treatment effects rather than baseline differences. A four-parameter classifier learned from the in-silico cohorts achieved 90% accuracy in predicting controller status. Mechanistic modeling indicates IL-10/PD-1 co-blockade synergistically enhances effector-cell function and reduces rebound risk, explaining high controller frequency after ATI.
