Marija Kekic1, Andrzej Nowojewski2, Oleg Stepanov3, Sam Richardson2, Itziar Irurzun3, Diansong Zhou4, Weifeng Tang4, Richard Dearden2

1Imaging & Data Analytics, Clinical Pharmacology & Safety Sciences, R&D BioPharmaceuticals, AstraZeneca, Barcelona, Spain

2Imaging & Data Analytics, Clinical Pharmacology & Safety Sciences, R&D BioPharmaceuticals, AstraZeneca, Cambridge, UK

3Clinical Pharmacology & Quantitative Pharmacology​, Clinical Pharmacology & Safety Sciences, R&D BioPharmaceuticals, AstraZeneca, Cambridge, UK

4Clinical Pharmacology & Quantitative Pharmacology​, Clinical Pharmacology & Safety Sciences, R&D BioPharmaceuticals, AstraZeneca, Gaithersburg, MD, USA

 

Abstract:
Patients’ covariates can significantly influence drug absorption, distribution, and elimination processes, making their identification a crucial step in pharmacometrics model development. Stepwise covariate selection (SCM) is the commonly used tool to automatically identify influential covariates. However, this procedure has several drawbacks, e.g., the requirement to invoke the parameter estimation repeatedly, leading to extended computation times.

Supervised Machine Learning has emerged as a promising alternative for covariate selection.
In this study, we employ Neural Networks with Stochastic Gate layer for automatic feature selection. We evaluate the model performance on simulated data with a known covariate structure. Initially, we establish the proof of concept using a simple one-compartment pharmacokinetic (PK) model, where we vary the number of relevant covariates, their effect sizes, and the correlation coefficient between them.

Finally, we present the performance of this approach on data simulated for a study related to the development of a PK model for a specific compound.