Implementation of a knowledge-based mechanistic model of EGFR-mutant lung adenocarcinoma that considers tumor heterogeneity and metastases to reproduce disease progression upon EGFR-TKI administration.

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e21190 Background: 16,4% of lung adenocarcinomas (LUAD) are presenting a mutation in the epidermal growth factor receptor (EGFR), resulting in its constitutive activation and leading to uncontrolled cell proliferation [1]. Tyrosine kinase inhibitors (TKI) have been developed to inhibit EGFR activity but the presence of metastases and resistance mutations explain the lack of durable response to the treatment [2]. Knowledge-based mechanistic models reproducing existing clinical trials, based on population characteristics, can be used to help the design of future clinical trials. In particular, they can inform on the best responders to given treatments. Methods: We developed a physiologically based pharmacokinetic model of osimertinib, a 3rd generation TKI, to account for the distribution of the drug in the primary tumor and metastases after oral administration. This model was then combined to a pathophysiological mechanistic model of EGFR-mutant LUAD to represent the impact of osimertinib on the signals arising from EGFR activation. The combined model outputs the evolution of the primary tumor and each metastasis to allow the evaluation of the patient progression according to the RECIST criteria. Furthermore, each tumor in the model is composed of several subclones each possessing their own set of mutations and therefore responding differently to the treatment. Data from the clinical trials FLAURA and AURA3, in which osimertinib was given respectively as first and second line, were used to calibrate the model. Visual predictive checks as well as statistical tests were performed to ensure the proper behavior of the model. Results: The model successfully reproduced the time to progression in an EGFR mutant LUAD population treated with osimertinib as first line or as second line. In addition, the model reproduced the causes of progression according to the RECIST criteria and the sites of apparition of new metastases (in lung, brain, liver and bone). Conclusions: Reproduction of real world data brings credibility to the model. This is essential to use the model as an investigational tool to provide relevant insights, potentially on the best responders to osimertinib. After validation with additional clinical patient level data, the model could be used to create synthetic control arms in upcoming clinical trials. This would grant an improved analysis of covariate relationships with the comparison of an investigational treatment to the standard of care osimertinib administered as first or second line. It would also reduce the number of patients needed in the trial. References: [1] DOI: 10.1007/s11523-021-00848-9 [2] DOI: 10.3389/fonc.2020.602762 Acknowledgments: The authors would like to thank the novadiscovery team associated with this project and Janssen-Cilag France for their support.

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