Population pharmacokinetic model selection assisted by machine learning

• Published in: Journal of pharmacokinetics and pharmacodynamics Vol. 49; no. 2; pp. 257 - 270
• Main Authors: Sibieude, Emeric, Khandelwal, Akash, Girard, Pascal, Hesthaven, Jan S., Terranova, Nadia
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.04.2022; Springer Nature B.V
• Subjects: Algorithms; Biochemistry; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Classification; Computer applications; Entropy; Genetic algorithms; Learning algorithms; Machine Learning; Mathematical models; Models, Statistical; Neural networks; Neural Networks, Computer; Original Paper; Pharmacokinetics; Pharmacology/Toxicology; Pharmacy; Veterinary Medicine/Veterinary Science; Deep learning; Pharmacometrics; Neural network; Genetic algorithm; Model-informed drug discovery and development; Population PK/PD
• ISSN: 1567-567X; 1573-8744; 1573-8744
• DOI: 10.1007/s10928-021-09793-6

A fit-for-purpose structural and statistical model is the first major requirement in population pharmacometric model development. In this manuscript we discuss how this complex and computationally intensive task could benefit from supervised machine learning algorithms. We compared the classical pharmacometric approach with two machine learning methods, genetic algorithm and neural networks, in different scenarios based on simulated pharmacokinetic data. Genetic algorithm performance was assessed using a fitness function based on log-likelihood, whilst neural networks were trained using mean square error or binary cross-entropy loss. Machine learning provided a selection based only on statistical rules and achieved accurate selection. The minimization process of genetic algorithm was successful at allowing the algorithm to select plausible models. Neural network classification tasks achieved the most accurate results. Neural network regression tasks were less precise than neural network classification and genetic algorithm methods. The computational gain obtained by using machine learning was substantial, especially in the case of neural networks. We demonstrated that machine learning methods can greatly increase the efficiency of pharmacokinetic population model selection in case of large datasets or complex models requiring long run-times. Our results suggest that machine learning approaches can achieve a first fast selection of models which can be followed by more conventional pharmacometric approaches.