Integration of heterogeneous biological data in multiscale mechanistic model calibration: application to lung adenocarcinoma

Abstract

Mechanistic models are built using knowledge as the primary information source, with well-established biological and physical laws determining the causal relationships within the model. Once the causal structure of the model is determined, parameters must be defined in order to accurately reproduce relevant data. Determining parameters and their values is particularly challenging in the case of models of pathophysiology, for which data for calibration is sparse. Multiple data sources might be required, and data may not be in a uniform or desirable format. We describe a calibration strategy to overcome the challenges of scarcity and heterogeneity of calibration data. Our strategy focuses on parameters where initial values cannot be easily derived from the literature and where final values are estimated via calibration with constraints set by relevant data. When combined with a covariance matrix adaptation evolution strategy (CMA-ES), this step-by-step approach can be applied to a wide range of biological models. We describe a stepwise, integrative and iterative approach to multiscale mechanistic model calibration, and provide an example of calibrating a pathophysiological lung adenocarcinoma model. Using the approach described here we illustrate the successful calibration of a complex knowledge-based mechanistic model using only the limited heterogeneous datasets publicly available in the literature.

Empirical methods for the validation of Time-To-Event mathematical models taking into account uncertainty and variability: Application to EGFR+ Lung Adenocarcinoma

“…Raw coverage 52 . It should be noted that this model is not designed to predict mortality from any cause, but rather developed to predict time to tumor progression (TTP), which was deduced from progression-free and overall survival curves.…”

“…It includes shrinkage in response to the administration of a first generation tyrosine kinase (TKI) drug called gefitinib. This model was calibrated with publicly available data 46 47 48 49 50 51 , and details regarding the calibration of tumor growth are given in a paper published by Palgen et al 52 . It should be noted that this model is not designed to predict mortality from any cause, but rather developed to predict time to tumor progression (TTP), which was deduced from progression-free and overall survival curves.…”

“…It includes shrinkage in response to administration of a first generation tyrosine kinase (TKI) drug called gefitinib. This model was calibrated with publicly available data46 47 48 49 50 51 , and details regarding the calibration of tumor growth are given in a paper published by Palgen et al52 . It should be noted that this model is not designed to predict mortality from any cause, but rather developed to predict time to tumor progression (TTP), which was deduced from progression-free and overall survival curves.NEJ002, took place in Japan and gefitinib was used as the first-line treatment.…”

Solving the Evidence Interpretability Crisis in Health Technology Assessment: A Role for Mechanistic Models?

“…These features can provide biological plausibility to those models by design and thus be used to rationalize, explain, and translate representative or individual clinical findings based on the (often large) body of mechanistic knowledge used in the model. Where parameters cannot informed by knowledge and remain unknown, heterogenous ( in vitro , preclinical, omics, clinical) data can be used for (algorithmic) calibration ( 38 ).…”