Virtual patients inspired by multiomics predict the efficacy of an anti-IFNα mAb in cutaneous lupus
Highlights • QSP modeling of lupus can be used to predict the efficacy of drug candidates. • A cohort of virtual lupus patients was created from profiling data of actual patients. • Virtual patient simulations predicted distinct anti IFN treatment responses. • Machine learning found biomarkers to differentiate responders from non-responders.
QSP modeling of OPB-171775, a phosphodiesterase 3A–Schlafen 12 molecular glue, to predict clinical responses in patients with gastrointestinal stromal tumors
Presented by Otsuka Pharmaceutical Co. Ltd. and Rosa at PAGE 2025 in Thessaloniki, Greece
Accurate modeling of ACTH and cortisol dynamics for Cushing’s disease treatment
Presented by Rosa in collaboration with Lundbeck at ACoP 2025 in Denver, CO, USA.
Evaluation of Parallel Tempering for Efficient Generation of Virtual Populations
Presented by Rosa at ACoP 2025 in Denver, CO, USA.
Adaptable PBPK-QSP gene therapy model applied to i.v. administration of AAV-2 across different species
Presented by Rosa in collaboration with Sanofi at ACoP 2025 in Denver, CO, USA.
In Silico Hypothesis Testing in Drug Discovery: Using Quantitative Systems Pharmacology Modeling to Evaluate the Therapeutic Value of Proinsulin Conversion to Insulin Therapy for Type 2 Diabetes Mellitus
Highlights • Uses a quantitative systems pharmacology (QSP) model of glucose homeostasis extended to include detailed proinsulin biology. • Explores a hypothetical therapy that converts circulating proinsulin to insulin using virtual patients with diverse Type 2 Diabetes (T2D) phenotypes. • Finds that proinsulin conversion yields only about a 0.2 percent reduction in HbA1c, below commonly accepted thresholds for meaningful clinical benefit in Type 2 Diabetes (T2D). • Shows that achieving a ≥0.5 percent HbA1c reduction would require proinsulin to insulin ratios above reported physiological ranges. • Concludes that proinsulin conversion is unlikely to justify a drug development program in Type 2 Diabetes (T2D) despite its biological appeal. • Demonstrates how in silico hypothesis testing can help Type 2 Diabetes (T2D) and obesity teams prioritize mechanisms with realistic clinical headroom.
