ArticlePharmaceutics2022
Machine Learning and Pharmacometrics for Prediction of Pharmacokinetic Data: Differences, Similarities and Challenges Illustrated with Rifampicin.
Article in Pharmaceutics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 2 of them syntheses 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
51 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Model-Informed Precision Dosing in Pediatric Patients: Current Software Tools and Bedside Guided Application.Paediatric drugs · 2026Pooled it
- Predicting Pharmacokinetics of Drugs Using Artificial Intelligence Tools: A Systematic Review.European journal of drug metabolism and pharmacokinetics · 2024Pooled it
- Comparing the Predictive Performance of Machine Learning Models with a Newly Developed Population Pharmacokinetic Model for Busulfan in Paediatric Patients.Clinical pharmacokinetics · 2026Article
- Machine Learning-Driven Prediction of Dose-Linear Pharmacokinetics: Utilizing Molecular Descriptors to Guide Formulation Strategy.ACS omega · 2026Article
- Machine Learning Enables Rapid Prediction of Acid-Reducing Agent Drug Interactions: A Streamlined Complement to PBPK Modeling.CPT: pharmacometrics & systems pharmacology · 2026Article
- Development and validation of an artificial intelligence-based model for predicting teicoplanin plasma concentrations in intensive care unit patients with pulmonary infections: a retrospective study.International journal of clinical pharmacy · 2026Article
- Predicting Ketamine Exposure in Pediatric Status Epilepticus: A Pharmacokinetic-Machine Learning Approach.European journal of drug metabolism and pharmacokinetics · 2026Article
- Development and validation of a machine learning model for individualised meropenem dosing in CRRT patients.Scientific reports · 2026Article
- 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
- Enhancing Severe Neutropenia Prediction: PKPD-Informed Labeling for Machine Learning Models Trained on Real-World Data.Clinical pharmacology and therapeutics · 2026Article
- Building Hybrid Pharmacometric-Machine Learning Models in Oncology Drug Development: Current State and Recommendations.CPT: pharmacometrics & systems pharmacology · 2026Review
- Prediction of infliximab and anti-drug antibody concentrations in patients with inflammatory bowel disease using machine learning models with real-world data from a prospective cohort study.Frontiers in pharmacology · 2026Article
- Exposure prediction and dose optimization of polymyxin B based on bayesian and machine learning.Frontiers in pharmacology · 2026Article
- Machine learning combined with population pharmacokinetics: a hybrid model for predicting the plasma concentration of linezolid in critically ill pediatric patients.Frontiers in pharmacology · 2026Article
- Machine learning to predict food effects during drug development: a comprehensive review.Journal of cheminformatics · 2025Review
- Cross-cohort integrative multi-omics analysis of cancer essential gene ATP6V0B to dissect functional characteristics and clinical implications in breast cancer.World journal of surgical oncology · 2025Article
- Opportunities for AI-based Model-informed Drug Development: A Comparative Analysis of NONMEM and AI-based Models for Population Pharmacokinetic Prediction.The AAPS journal · 2025Article
- QSP-Copilot: An AI-Augmented Platform for Accelerating Quantitative Systems Pharmacology Model Development.CPT: pharmacometrics & systems pharmacology · 2025Article
- Artificial Intelligence-Driven Nanoarchitectonics for Smart Targeted Drug Delivery.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Machine Learning-Based Model Selection and Averaging Outperform Single-Model Approaches for a Priori Vancomycin Precision Dosing.CPT: pharmacometrics & systems pharmacology · 2025Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Pharmacometrics (PM) and machine learning (ML) are both valuable for drug development to characterize pharmacokinetics (PK) and pharmacodynamics (PD). Pharmacokinetic/pharmacodynamic (PKPD) analysis using PM provides mechanistic insight into biological processes but is time- and labor-intensive. In contrast, ML models are much quicker trained, but offer less mechanistic insights. The opportunity of using ML predictions of drug PK as input for a PKPD model could strongly accelerate analysis efforts. Here exemplified by rifampicin, a widely used antibiotic, we explore the ability of different ML algorithms to predict drug PK. Based on simulated data, we trained linear regressions (LASSO), Gradient Boosting Machines, XGBoost and Random Forest to predict the plasma concentration-time series and rifampicin area under the concentration-versus-time curve from 0-24 h (AUC
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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.