Go beyond the limits of genetic algorithm in daily covariate selection practice

• Published in: Journal of pharmacokinetics and pharmacodynamics Vol. 51; no. 2; pp. 109 - 121
• Main Authors: Ronchi, D., Tosca, E. M., Bartolucci, R., Magni, P.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.04.2024; Springer Nature B.V
• Subjects: Algorithms; Biochemistry; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Computer applications; Convergence; Exercise; Genetic algorithms; Original Paper; Pharmacodynamics; Pharmacokinetics; Pharmacology/Toxicology; Pharmacy; Problem solving; Remifentanil; Veterinary Medicine/Veterinary Science; Automatic model building; Population PK/PD model; Covariate selection; Genetic algorithm; Artificial intelligence; Machine learning
• ISSN: 1567-567X; 1573-8744; 1573-8744
• DOI: 10.1007/s10928-023-09875-7

Covariate identification is an important step in the development of a population pharmacokinetic/pharmacodynamic model. Among the different available approaches, the stepwise covariate model (SCM) is the most used. However, SCM is based on a local search strategy, in which the model-building process iteratively tests the addition or elimination of a single covariate at a time given all the others. This introduces a heuristic to limit the searching space and then the computational complexity, but, at the same time, can lead to a suboptimal solution. The application of genetic algorithms (GAs) for covariate selection has been proposed as a possible solution to overcome these limitations. However, their actual use during model building is limited by the extremely high computational costs and convergence issues, both related to the number of models being tested. In this paper, we proposed a new GA for covariate selection to address these challenges. The GA was first developed on a simulated case study where the heuristics introduced to overcome the limitations affecting currently available GA approaches resulted able to limit the selection of redundant covariates, increase replicability of results and reduce convergence times. Then, we tested the proposed GA on a real-world problem related to remifentanil. It obtained good results both in terms of selected covariates and fitness optimization, outperforming the SCM.