ArticlePharmaceutics2022
Adoption of Machine Learning in Pharmacometrics: An Overview of Recent Implementations and Their Considerations.
Article in Pharmaceutics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
29 citing papers in PubMed.
- Pharmacokinetics of Clofazimine Tablet Formulation in Children with Rifampicin-Resistant Tuberculosis in CATALYST, a Multi-Site International Trial.Journal of the Pediatric Infectious Diseases Society · 2026Article
- The evolution of nonlinear mixed effects modeling in pharmacometrics: toward AI-based variational autoencoders.Journal of pharmacokinetics and pharmacodynamics · 2026Article
- Bridging the operational gap in population pharmacokinetic-pharmacodynamic analysis: an international perspective on the 2025 Chinese group standard.Journal of pharmacokinetics and pharmacodynamics · 2026Review
- Global Trends in Integrating Machine Learning (ML) with Model-Informed Drug Development (MIDD): A Bibliometric and Systematic Review (2015-2025).Pharmaceutics · 2026Review
- A structure-preserving hybrid learning framework for compartmental pharmacokinetic models.Journal of pharmacokinetics and pharmacodynamics · 2026Article
- Improving Population Pharmacokinetic Modelling with Artificial Patients using Generative Artificial Intelligence.Pharmacology research & perspectives · 2026Article
- Pioneering pharmacometrics practice in Korea: an eight-year retrospective analysis of 192 projects from the first dedicated service organization (2016-2024).Translational and clinical pharmacology · 2026Review
- Rising Role of Artificial Intelligence in Clinical Pharmacometrics and Model-Informed Precision Dosing in Pediatrics.The journal of pediatric pharmacology and therapeutics : JPPT : the official journal of PPAG · 2026Article
- Digital health in clinical pharmacy practice: transforming precision medicine and pharmacometrics in Africa.Frontiers in pharmacology · 2026Review
- A Comparison of AI and Population PK Models to Predict the Concentrations of Antiepileptic Drugs Using Therapeutic Drug Monitoring Records.Clinical and translational science · 2025Article
- Pharmacometrics in the Age of Large Language Models: A Vision of the Future.Pharmaceutics · 2025Article
- Machine Learning Prediction and Validation of Plasma Concentration-Time Profiles.Molecular pharmaceutics · 2025Article
- Review
- Comparing Scientific Machine Learning With Population Pharmacokinetic and Classical Machine Learning Approaches for Prediction of Drug Concentrations.CPT: pharmacometrics & systems pharmacology · 2025Article
- The Role of Cross-Institutional and Interdisciplinary Collaboration in Defining and Executing a Quantitative Systems Pharmacology Strategy.Handbook of experimental pharmacology · 2025Review
- Artificial Intelligence and Machine Learning Applications to Pharmacokinetic Modeling and Dose Prediction of Antibiotics: A Scoping Review.Antibiotics (Basel, Switzerland) · 2024Article
- Mixed effect estimation in deep compartment models: Variational methods outperform first-order approximations.Journal of pharmacokinetics and pharmacodynamics · 2024Article
- Simulating realistic patient profiles from pharmacokinetic models by a machine learning postprocessing correction of residual variability.CPT: pharmacometrics & systems pharmacology · 2024Article
- On inductive biases for the robust and interpretable prediction of drug concentrations using deep compartment models.Journal of pharmacokinetics and pharmacodynamics · 2024Article
- Covariate modeling in pharmacometrics: General points for consideration.CPT: pharmacometrics & systems pharmacology · 2024Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Pharmacometrics is a multidisciplinary field utilizing mathematical models of physiology, pharmacology, and disease to describe and quantify the interactions between medication and patient. As these models become more and more advanced, the need for advanced data analysis tools grows. Recently, there has been much interest in the adoption of machine learning (ML) algorithms. These algorithms offer strong function approximation capabilities and might reduce the time spent on model development. However, ML tools are not yet an integral part of the pharmacometrics workflow. The goal of this work is to discuss how ML algorithms have been applied in four stages of the pharmacometrics pipeline: data preparation, hypothesis generation, predictive modelling, and model validation. We will also discuss considerations before the use of ML algorithms with respect to each topic. We conclude by summarizing applications that hold potential for adoption by pharmacometricians.
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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.