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 address the challenges of scarcity and heterogeneity of calibration data. Our strategy focuses on parameters whose initial values cannot be easily derived from the literature, and our goal is to determine the values of these parameters 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.

Comparing the Efficacy of Two Generations of EGFR-TKIs: An Integrated Drug–Disease Mechanistic Model Approach in EGFR-Mutated Lung Adenocarcinoma

“…After calibration, the model was also able to reproduce the tumor volume evolution after gefitinib or osimertinib treatment in mice transplanted with xenografts bearing an EGFR exon 19 deletion ( Figure 3 ) and also with xenografts bearing both an EGFR exon 19 deletion and a PIK3CA mutation (Palgen et al [ 35 ]). The model also reproduced the dynamics of the tumor without treatment (Palgen et al [ 35 ]), ensuring that the treatment was indeed responsible for the shrinkage of the tumor when administered. Table 6 summarizes the calibration results in mice.…”

“…As the available data were heterogeneous (from the Ki presented in Table 2 to the evolution of tumor size in xenograft mice), we applied a calibration strategy detailed by Palgen et al [ 35 ]. This step-by-step approach minimizes an objective function to calibrate complex mechanistic models focusing on adjusting parameters that are challenging to derive from the literature.…”

Multi-strain modeling of influenza vaccine effectiveness in older adults and its dependence on antigenic distance

“…Calibration is an automatic numerical procedure, in which a priori unknown parameter values are concomitantly estimated and refined to reproduce desired model behavior, usually via an iterative process. Calibration constrains the dynamic behavior of the model by finding a set of parameter values that allows the model to represent biological behaviors consistent with literature [40]. All raw data used in the study was extracted from scientific publications and public CDC reports, no administrative permission was required for the access.…”

Knowledge-based mechanistic modeling accurately predicts disease progression with gefinitib in EGFR-mutant lung adenocarcinoma

“…Since calibration steps are executed sequentially, the first calibration steps are prerequisites for the following steps. They take into account both quantitative and qualitative constraints, to consider and reproduce the heterogeneous and multi-scale data extracted from literature Palgen et al (2022). details the two first steps of the process, where we aligned the ISELA model with:1. published in vitro dynamics to calibrate EGFR / cellular mesenchymal epithelial transition (cMET) associated pathways and tumor growth in vitro…”

“…The observational data corresponds to both maximal phosphorylation rate (dashed line) and time of that maximum following the indicated stimulation; (c) evolution of xenografted tumor size; the observational data corresponds to the mean tumor radius overtime with estimated error bars (taken as maximum between 2 standard deviations and 20% of the mean) d) maximal depth at which living tumor cells can be found (viable depth) in xenografted mice the observational data corresponds to the viable depth observed on in vitro spheroids; (e-h). Adapted fromPalgen et al (2022). The observational data corresponds to the mean tumor volume overtime, with estimated error bars (taken as maximum between 2 standard deviations and 20% of the mean).…”