MBMA and traditional MA are statistical techniques that leverage summary-level data to make predictions [1] and provide inference [2], and can be used to inform oncology drug development decisions. MBMA, an extension of network MA, leverages pharmacology principles using mathematical models. Oncology trials are prone to variability from varying trial designs, diverse patients with numerous treatment options, and different prior therapies. Here, we share our experience utilizing MBMA and MA of immune checkpoint inhibitors to inform decisions on development plans in mNSCLC. An MBMA with mixed-effects logistic regression quantified effects on ORR. MBMA with semi-parametric longitudinal mixed-effects models quantified PFS and OS Kaplan–Meier curves as a function of observed ORR and other factors. Model-based head-to-head trial simulations predicted hazard ratios (HR) for PD-1 vs PD-L1 treatments. MA-based matched indirect treatment comparison (ITC) evaluated PFS HR and OS HR for PD-1 vs PD-L1 treatments. This approach first matches identified studies of different drugs by important trial-level characteristics for a fair comparison, then compares efficacy or safety outcomes of the two drugs using Bucher's approach [2]. From MBMA, correlations between ORR and OS and between ORR and PFS were established for each treatment type (i.e. PD-(L)1 monotherapy, chemotherapy, etc.), supporting use of ORR data to predict survival. The analyses found numerical trends in historical and simulated PFS HR and OS HR favoring PD-1 over PD-L1 inhibitors, alone or in combination. The MBMA- and MA-based matched ITC provided a comprehensive and consistent assessment of the relative effect for PD-1 vs PD-L1 treatments in mNSCLC. Results were used as prior knowledge to support oncology drug development under the quantitative decision-making framework at GSK. 1. Turner et al., 2023 https://doi.org/10.1002/psp4.1... 2. Bucher et al., 1997 https://doi.org/10.1016/s0895-... | 
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