ArticleJournal of pharmacokinetics and pharmacodynamics2022
Development of a genetic algorithm and NONMEM workbench for automating and improving population pharmacokinetic/pharmacodynamic model selection.
Article in Journal of pharmacokinetics and pharmacodynamics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Covariate Model Selection Approaches for Population Pharmacokinetics: A Systematic Review of Existing Methods, From SCM to AI.CPT: pharmacometrics & systems pharmacology · 2025Pooled it
- Automated Pharmacometric Model Development by Leveraging Low-Dimensional Neural ODEs and LASSO Regression.CPT: pharmacometrics & systems pharmacology · 2026Article
- PKGPT: Expert-Orchestrated Recursive LLM Agent for Automated NONMEM PopPK Modeling with Human Benchmarking.Pharmaceutics · 2026Article
- Hybrid mechanistic-machine learning PK/PD models with digital biomarkers: from cage to clinic.Frontiers in pharmacology · 2026Review
- An automated pipeline to generate initial estimates for population Pharmacokinetic base models.Journal of pharmacokinetics and pharmacodynamics · 2025Article
- Subgroup-based model selection to improve the prediction of vancomycin concentrations.Antimicrobial agents and chemotherapy · 2025Article
- Recommended approaches for integration of population pharmacokinetic modelling with precision dosing in clinical practice.British journal of clinical pharmacology · 2025Review
- pyDarwin machine learning algorithms application and comparison in nonlinear mixed-effect model selection and optimization.Journal of pharmacokinetics and pharmacodynamics · 2024Article
- A fully automatic tool for development of population pharmacokinetic models.CPT: pharmacometrics & systems pharmacology · 2024Article
- Simulation-based evaluation of the Pharmpy Automatic Model Development tool for population pharmacokinetic modeling in early clinical drug development.CPT: pharmacometrics & systems pharmacology · 2024Article
- Parameter grouping and co-estimation in physiologically based kinetic models using genetic algorithms.Toxicological sciences : an official journal of the Society of Toxicology · 2024Article
- Subgroup identification-based model selection to improve the predictive performance of individualized dosing.Journal of pharmacokinetics and pharmacodynamics · 2024Article
- Machine Learning and Artificial Intelligence in PK-PD Modeling: Fad, Friend, or Foe?Clinical pharmacology and therapeutics · 2024Article
- Go beyond the limits of genetic algorithm in daily covariate selection practice.Journal of pharmacokinetics and pharmacodynamics · 2024Article
- Advancing Precision Medicine: A Review of Innovative In Silico Approaches for Drug Development, Clinical Pharmacology and Personalized Healthcare.Pharmaceutics · 2024Review
- Bridging the Worlds of Pharmacometrics and Machine Learning.Clinical pharmacokinetics · 2023Review
- Adoption of Machine Learning in Pharmacometrics: An Overview of Recent Implementations and Their Considerations.Pharmaceutics · 2022Article
- Editor's note on the themed issue: integration of machine learning and quantitative systems pharmacology.Journal of pharmacokinetics and pharmacodynamics · 2022Article
- Smart Home Product Layout Design Method Based on Real-Number Coding Genetic Algorithm.Computational intelligence and neuroscience · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The current approach to selection of a population PK/PD model is inherently flawed as it fails to account for interactions between structural, covariate, and statistical parameters. Further, the current approach requires significant manual and redundant model modifications that heavily lend themselves to automation. Within the discipline of numerical optimization it falls into the "local search" category. Genetic algorithms are a class of algorithms inspired by the mathematics of evolution. GAs are general, powerful, robust algorithms and can be used to find global optimal solutions for difficult problems even in the presence of non-differentiable functions, as is the case in the discrete nature of including/excluding model components in search of the best performing mixed-effects PK/PD model. A genetic algorithm implemented in an R-based NONMEM workbench for identification of near optimal models is presented. In addition to the GA capabilities, the workbench supports modeling efforts by: (1) Organizing and displaying models in tabular format, allowing the user to sort, filter, edit, create, and delete models seamlessly, (2) displaying run results, parameter estimates and precisions, (3) integrating xpose4 and PsN to facilitate generation of model diagnostic plots and run PsN scripts, (4) running regression models between post-hoc parameter estimates and covariates. This approach will further facilitate the scientist to shift efforts to focus on model evaluation, hypotheses generation, and interpretation and applications of resulting models.
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34604941What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.