ReviewClinical pharmacokinetics2023
Bridging the Worlds of Pharmacometrics and Machine Learning.
Review in Clinical pharmacokinetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Model-Informed Precision Dosing in Pediatric Patients: Current Software Tools and Bedside Guided Application.Paediatric drugs · 2026Pooled it
- Exploring Influencing Factors Including CYP2C19 Genotypes, and Developing a Machine Learning-Based Predictive Model for Clopidogrel Resistance in Chinese Patients with Ischemic Stroke.International journal of general medicine · 2026Article
- A hybrid ML-PBPK digital twin framework for clinically interpretable readmission risk and drug exposure stratification in diabetes.Frontiers in digital health · 2026Article
- Artificial intelligence applications in oxaliplatin-based chemotherapy for colon cancer: advancing prognosis, toxicity prediction, and dose personalization.Frontiers in pharmacology · 2026Review
- Beyond Algorithms: Machine Learning and Clinical Determinants of Voriconazole Plasma Levels in Therapeutic Drug Monitoring.European journal of drug metabolism and pharmacokinetics · 2026Article
- Hybrid Population Pharmacokinetic-Machine Learning Modeling to Predict Infliximab Pharmacokinetics in Pediatric and Young Adult Patients with Crohn's Disease.Clinical pharmacokinetics · 2025Article
- Optimizing Mycophenolate Therapy in Renal Transplant Patients Using Machine Learning and Population Pharmacokinetic Modeling.Medical sciences (Basel, Switzerland) · 2025Article
- Machine Learning-Based Model Selection and Averaging Outperform Single-Model Approaches for a Priori Vancomycin Precision Dosing.CPT: pharmacometrics & systems pharmacology · 2025Article
- Methodological Techniques Used in Machine Learning to Support Individualized Drug Dosing Regimens Based on Pharmacokinetic Data: A Scoping Review.Clinical pharmacokinetics · 2025Article
- Leveraging Neural ODEs for Population Pharmacokinetics of Dalbavancin in Sparse Clinical Data.Entropy (Basel, Switzerland) · 2025Article
- Hybrid Population PK-Machine Learning Modeling to Predict Infliximab Pharmacokinetics in Pediatric and Young Adult Patients with Crohn's Disease.bioRxiv : the preprint server for biology · 2025Article
- Comparing Scientific Machine Learning With Population Pharmacokinetic and Classical Machine Learning Approaches for Prediction of Drug Concentrations.CPT: pharmacometrics & systems pharmacology · 2025Article
- Recommended approaches for integration of population pharmacokinetic modelling with precision dosing in clinical practice.British journal of clinical pharmacology · 2025Review
- Leveraging machine learning in limited sampling strategies for efficient estimation of the area under the curve in pharmacokinetic analysis: a review.European journal of clinical pharmacology · 2025Review
- To be or not to be, when synthetic data meet clinical pharmacology: A focused study on pharmacogenetics.CPT: pharmacometrics & systems pharmacology · 2025Article
- Concentration monitoring and dose optimization for infliximab in Crohn's disease patients: a machine learning-based covariate ensemble model.Frontiers in immunology · 2025Article
- Artificial Intelligence and Machine Learning Applications to Pharmacokinetic Modeling and Dose Prediction of Antibiotics: A Scoping Review.Antibiotics (Basel, Switzerland) · 2024Article
- The Role of Pharmacometrics in Advancing the Therapies for Autoimmune Diseases.Pharmaceutics · 2024Review
- Mixed effect estimation in deep compartment models: Variational methods outperform first-order approximations.Journal of pharmacokinetics and pharmacodynamics · 2024Article
- Dose Individualisation of Antimicrobials from a Pharmacometric Standpoint: The Current Landscape.Drugs · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Precision medicine requires individualized modeling of disease and drug dynamics, with machine learning-based computational techniques gaining increasing popularity. The complexity of either field, however, makes current pharmacological problems opaque to machine learning practitioners, and state-of-the-art machine learning methods inaccessible to pharmacometricians. To help bridge the two worlds, we provide an introduction to current problems and techniques in pharmacometrics that ranges from pharmacokinetic and pharmacodynamic modeling to pharmacometric simulations, model-informed precision dosing, and systems pharmacology, and review some of the machine learning approaches to address them. We hope this would facilitate collaboration between experts, with complementary strengths of principled pharmacometric modeling and flexibility of machine learning leading to synergistic effects in pharmacological applications.
Indexed as
Identifiers
37803104What 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.