Incorporating Placebo Response in Quantitative Systems Pharmacology Models

Current practices for QSP model assessment: an IQ consortium survey

“…An opportunity exists for statistical considerations to be applied more robustly as the QSP community moves towards standardized approaches for constructing and evaluating virtual patient populations. As an example, incorporation of placebo response in QSP models has benefitted from statistical approaches [24]. Documentation of QSP models was revealed to be a surprisingly simplistic and informal process by the survey, with many practitioners using separate spreadsheets and PowerPoint presentations, despite recent publications suggesting the need for standardized approaches to model reporting [25].…”

Model-Based Meta-Analysis to Optimize Staphylococcus aureus‒Targeted Therapies for Atopic Dermatitis

“…We described the normalized S. aureus level, for the j -th treatment at time t by where the first term corresponds to the net effects of the treatment defined by the difference of the change in S. aureus levels (log 10 scale) at time t from baseline, between the j -th treatment ( ) and the corresponding placebo groups ( ). This term adjusts for different placebo effects across clinical studies that may differ in the study participants’ background, concomitant drugs, and the study sites ( Wang et al., 2019 ). The remaining two terms describe the change in S. aureus levels at time t from baseline, , in the placebo group in the dupilumab clinical trial that evaluated efficacies for the longest period among the trials evaluated in this study and the baseline level of S. aureus, , in the Sh A9 clinical trial that is the only one reporting the levels of both S. aureus and coagulase-negative Staphylococcus (CoNS) among the trials evaluated in this study.…”

“…We described normalized percentage-improved EASI, for the j -th treatment at t by the following: where the first term corresponds to the net effects of the treatment defined by the difference of the efficacy (percentage-improved EASI) between the j -th treatment ( ) and the corresponding placebo groups ( ). This term adjusts for different efficacies in the placebo group across the clinical studies owing to differences in study participants’ background, concomitant drugs, and sites of study ( Wang et al., 2019 ). The second term corresponds to the placebo effects defined by the efficacy in the placebo group in the dupilumab clinical trial ( ).…”

American Society for Clinical Pharmacology and Therapeutics 2019 Annual Meeting Pre‐Conferences

“…Conducting virtual clinical trials with cohorts of virtual patients derived from QSP models has enabled applications of QSP to late‐stage drug development, such as the development of immune‐oncology combination therapy; however, several challenges remain. One challenge, for instance, is modeling placebo responses by way of parameterizing a virtual placebo group with a QSP model, whereas another challenge is reducing model uncertainty in the context of capturing complex human biology.…”