A consortium has been launched by Certara for the development of a quantitative systems pharmacology (QSP) immune-oncology simulator to test combination cancer therapies and different dose regimens and biomarkers in computer-generated, virtual patients.
Consortium
This new consortium will bring together biopharmaceutical companies to cooperatively develop a QSP Immuno-oncology Simulator that can model clinical populations of cancer patients. The aim will be to capture the pertinent biology, pharmacology and variation between individuals in sufficient detail so that the Simulator can guide and improve clinical development of immuno-oncology therapies.
“We believe that the key to developing novel, new immuno-oncology therapies will be selecting optimal combination therapies, dose regimens and biomarkers, tailored to specific cancers and patient populations. But there are so many possible therapy combinations, and the biological and pathological processes involved are so complex, that quantitative, mechanistic models of the interactions between the tumour, immune system and therapies have to be created first to guide development decisions. That is where the QSP Immuno-oncology Simulator will come in,” explained Certara’s Simcyp president and managing director Steve Toon, PhD.
“The QSP Immuno-oncology Simulator will allow researchers to explore different therapeutic combinations, even drugs using different modalities, within a virtual population. It will enable sponsors to answer a lot of ‘what-if’ questions, providing input and guidance for clinical development,” said Professor Piet van der Graaf, PharmD, PhD, Certara vice president, QSP.
“We anticipate that QSP modelling will follow a similar adoption curve to physiologically-based pharmacokinetic (PBPK) modelling, which is now standard practice within the drug development process and an expected component of regulatory submissions,” he added. “In fact, QSP has been identified as a promising technology and incorporated into the US Food and Drug Administration (FDA) model-informed drug development roadmap.”