Computer methods and programs in biomedicine · 2026
Comprehensive comparison of six optimization algorithms for automated population pharmacokinetic model selection via two simulation studies and eleven real case studies.
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Pierre Matthias, Mihaljevic Frano, Celliere Geraldine, Bertrand Julie
Аннотация
BACKGROUND AND OBJECTIVES: Prior efforts to automate population pharmacokinetic structural and statistical model selection have explored various algorithms with mixed results. This study is conducting a comprehensive comparison of six distinct optimization algorithms for selecting pharmacokinetic and target-mediated drug disposition models. Our objective is to determine which algorithms to use preferentially for different model search space sizes.
METHODS: We implemented six algorithms in Monolix R API: (i) Decision Tree, (ii) Genetic Algorithm, (iii) Simulated Annealing, (iv) Particle Swarm Optimization, (v) Ant Colony Optimization, and (vi) a Tournament Algorithm. Each algorithm's performance was evaluated on a first simulation involving a simple structural pharmacokinetic model, a second simulation focused on a more complex target-mediated drug disposition model and applied to 11 case studies representing diverse real-life situations.
RESULTS: In the first simulation with a small search space, Ant Colony Optimization, Particle Swarm Optimization, Genetic Algorithm, and Tournament Algorithm achieved 90% concordance with the gold standard. A Decision Tree algorithm reached the same performance, but three times faster. In the larger target-mediated drug disposition search space, Ant Colony Optimization consistently identified the best models, though with high computational costs. Tournament Algorithm balanced speed and performance. When applied to real case studies, the top-performing algorithms selected models with better cost function values than those previously published in 10 out of 11 cases.
CONCLUSIONS: Optimization algorithms offer a robust and effective approach to model selection automation. To explore simple population pharmacokinetic models, we advise using a Decision Tree algorithm, while using Ant Colony Optimization for exploring more complex problems.
METHODS: We implemented six algorithms in Monolix R API: (i) Decision Tree, (ii) Genetic Algorithm, (iii) Simulated Annealing, (iv) Particle Swarm Optimization, (v) Ant Colony Optimization, and (vi) a Tournament Algorithm. Each algorithm's performance was evaluated on a first simulation involving a simple structural pharmacokinetic model, a second simulation focused on a more complex target-mediated drug disposition model and applied to 11 case studies representing diverse real-life situations.
RESULTS: In the first simulation with a small search space, Ant Colony Optimization, Particle Swarm Optimization, Genetic Algorithm, and Tournament Algorithm achieved 90% concordance with the gold standard. A Decision Tree algorithm reached the same performance, but three times faster. In the larger target-mediated drug disposition search space, Ant Colony Optimization consistently identified the best models, though with high computational costs. Tournament Algorithm balanced speed and performance. When applied to real case studies, the top-performing algorithms selected models with better cost function values than those previously published in 10 out of 11 cases.
CONCLUSIONS: Optimization algorithms offer a robust and effective approach to model selection automation. To explore simple population pharmacokinetic models, we advise using a Decision Tree algorithm, while using Ant Colony Optimization for exploring more complex problems.