Category: Preclinical Development
Purpose: Syngeneic mouse models have been widely employed in preclinical discovery of checkpoint inhibitors as they enable study of drug impact on the intact immune system (Lechner, 2013; Murphy, 2015). However, the interpretation of such studies remains challenging partly due to the large variability in individual animal responses to drug treatment.
Methods: In this work, we describe the generation of a model platform that captures essential aspects of the pharmacokinetics, cellular and tumor growth effects of murine surrogates of two checkpoint therapeutic antibodies, anti-PD1 and anti-CTLA4, in the CT26 syngeneic tumor model. The model describes individual animal responses with regard to drug exposure, key intra-tumoral cell kinetics and tumor volume changes and provides biologically plausible explanations for the observed differences between good and poor responders to treatment with anti-PD1 or anti-CTLA4.
Results: We used the model to predict the antibody dose-response relationships for individual animals and to identify dose thresholds above which complete tumor elimination can be achieved in good responders. In contrast, our models predict that poor responders would not achieve complete response even with much higher drug doses. The parameters in our model that impact the response in poor responders are not drug-related. This finding suggests that immune-cell related barriers have to be crossed in order to achieve a therapeutic response in these animals - possibly via combination therapy.
In addition, we identified the net tumor cell doubling rate, one potential parameter that contributes to individual variability in response to treatment, as the most sensitive biological parameter determining tumor volume changes upon treatment with anti-PD1 or anti-CTLA4. Measuring individual animal tumor cell growth characteristics may help with the experimental design and qualification of animals for studies (in addition to absolute tumor volume), and thereby reduce inter-animal variability and enhance the interpretability of study results, especially in combination with a model such as the one presented here.
Conclusion: This model platform can be adapted to capture and compare checkpoint drug effects in different syngeneic tumor models. Moreover, it can be expanded to add additional drug mechanisms and can serve as a tool to inform the experimental design of mouse studies.
John Burke– CEO, President, Co-founder, Applied BioMath, Concord, Massachusetts
Alison Betts– Concord, Massachusetts
Lin Lin– Applied BioMath, Concord, Massachusetts
Carissa Young– Cambridge, Massachusetts
Jatin Narula– Cambridge, Massachusetts
Wendy Qiao– Cambridge, Massachusetts
Peter O'Brien– Cambridge, Massachusetts
Derek Bartlett– Pfizer, Inc, Cambridge, Massachusetts
Andrea Hooper– Associate Director, Pfizer Inc., Pearl River, New York
Jason Williams– Cambridge, Massachusetts
Joshua Apgar– CSO & Co-founder, Applied BioMath, Concord, Massachusetts
Fei Hua– Applied BioMath, Concord, Massachusetts
Lore Gruenbaum– New York, New York