A QSP model for predicting efficacy assessment of combined treatment of radiotherapy and anti-PD-1 for NSCLC patients through virtual clinical trials.
Miriam Schirru (1), Hamza Charef (1), Andy Tan (1), Frédérique Fenneteau (1), Didier Zugaj (2), Pierre-Olivier Tremblay (2), Fahima Nekka (1), (3), (4 )
(1) Laboratoire de recherche en pharmacométrie, Faculté de pharmacie, Université de Montréal, Montréal, Canada (2)Syneos Health, Clinical Pharmacology, Québec, Canada (3)Centre de recherches mathématiques, Université de Montréal, Montréal, Canada (4)Centre for Applied Mathematics in Bioscience and Medicine (CAMBAM), McGill University, Montréal, Canada
Introduction: Immunotherapy approaches represent a promising paradigm for cancer treatment. This led to an explosion in the number of therapeutic targets and treatment combinations [1], [2], adding pressure on the drug development system with the need to launch a large number of clinical trials, which are not only costly, suffer from the shortage of participants [3], but also fail to demonstrate the improvement of combined therapies compared to standard ones [4]. This is mainly attributed to the lack of our understanding of the complex relationship between the tumor microenvironment and the immunological system. Therefore, recourse to Quantitative Systems Pharmacology (QSP) becomes paramount to shed additional lights on the underlying mechanisms. QSP is now adapted in the drug development as the means par excellence to complement clinical research and personalize therapy.
Objectives:
- identify effective treatments in non-small cancer lungs cells (NSCLC) through virtual clinical trials
- develop a platform as a clinical decision tool.
Methods: We enriched a previously developed QSP modular platform for immuno-oncology, which integrates mechanisms of the tumor microenvironment and the cancer immunity cycle [5], translated into ordinary differential equations [6]. A radiotherapy (RT) module, adapted from [7], was added to anti-PD1 treatment (Nivolumab). Model customization is done using MATLAB. The model was previously used to investigate the potential of dynamical systems analysis of immune-oncology models for the evaluation of drug regimens [8],[9]. In this study, a series of virtual clinical trials was conducted giving Nivolumab or RT as monotherapy or in combination. Various drug regimens are tested.
To conduct virtual clinical trials, a virtual patients’ (VP) cohort was generated by taking into account the between-patients variability with the consideration of a reasonable range of physiological parameters using a Latin Hypercube Sampling.
In order to assess response to treatment options and compare it among model predictions, clinical endpoints, (as the Time to Progression, the Duration of Response, and the Best overall Response), based on RECIST [10], were estimated. A Global Sensitivity Analysis (GSA) was performed with Partial Rank Correlation Coefficients (PRCC) method to evaluate VP’s intrinsic parameters' effect on the percentage change in tumor diameter, at different time points. Subpopulations were identified based on the efficacy assessment of the clinical endpoints.
Moreover, to provide a solution to the increasing complexity of the model and to present information in a well-designed and detailed way, postprocessing functionalities such as the generation simulation reporting document were also integrated into the platform.
Results: We have shown that simulations of combined therapy of immune-oncology with radiotherapy provide better outcomes. We have identified the most promising combinations and schedules, that can be personalized according to patients’ characteristics. Intrinsic parameters directly related to the ability of the immune system to fight cancer cells or to the growth of cancer cells were found to be significatively involved in the differentiation between responders and non-responders.
Conclusion: The developed modular platform can be a valuable tool for testing drug combination and drug development and clinical decision. While refinement of the involved model and parameters remain a continuous challenge, our platform constitutes an inexpensive and relatively fast manner to sustain clinical effort and help develop personalized treatments.
References:
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