P27.06 Multiscale NSCLC Tumor Growth Knowledge-Based Model Reproduces Tumor-Non-Progression under Gefitinib

Non-small cell lung cancer (NSCLC) is the leading form of and adenocarcinoma (LUAD) its most common histotype. Tumor size, part TNM-staging, important for prognosis treatment guidance. Response to Tyrosine Kinase Inhibitors, e.g. Gefitinib, altered with certain epidermal growth factor receptor (EGFR+) gene mutations making time-to-progression (TTP) predictions difficult. We therefore developed an in silico EGFR+LUAD model characterize tumor size TTP advanced-stage patients (IIIb or higher) EGFR (exon19-deletion (E19+) exon21-L858R-substitution (E21+)). The a mechanistic representation evolution upon Gefitinib administration, including heterogeneity, age, gender, initial clinical stage smoking status as covariates. Three-step development: 1. Model Building using Knowledge Computational (CM): Pathophysiology was characterized seven sub-models: mutational burden, downstream pathways, PK/PD, treatment-induced resistance outcome. For each sub-model, relevant biological entities their functional relationships were extracted from scientific papers translated into ordinary differential equations (ODEs). CM has 43 variables, 170 parameters 18 83 ODEs reflecting intra-tumor heterogeneity. 2. Calibration information literature: Spheroids, xenografts results used stepwise calibration. 3. Validation against published data: Patients[1] (n=159) had E19+ E21+ treated (250mg/day orally). A Virtual Population equivalent baseline characteristics simulated calibrated CM. Kaplan-Meier curves show non-progression over time. metrics: (1) “Raw-Data-Coverage” expressing fit 95% prediction interval (PI) curve (computed by bootstrapping), (2) Comparison bootstrapped survival log-rank tests (LR) statistical significance (α=0.05). Coverage simulations raw data 95.92%. Proportion non-significant LR comparing observed 88.43% (Figure 1). reproduced trial Gefitinib. created modular, reusable, multiscale Knowledge-Based adaptable also test efficacy other treatments drug combinations on tumor-non-progression. This enables us identify best responders optimize designs silico. References [1]: Paz-Ares L, et al. Ann Oncol. 2017.