ReviewCPT: pharmacometrics & systems pharmacology2022
Comparing the applications of machine learning, PBPK, and population pharmacokinetic models in pharmacokinetic drug-drug interaction prediction.
Review in CPT: pharmacometrics & systems pharmacology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Computational approaches for drug-drug interaction prediction: a systematic review of data sources, modeling strategies, and evaluation frameworks.Frontiers in pharmacology · 2026Pooled it
- Molecular Mechanisms of Acute Drug Toxicity in Polypharmacy: Analgesic-Psychotropic Interactions.International journal of molecular sciences · 2026Review
- Advances in Machine Learning-Enhanced PBPK Models for Brain-Targeted Drug Delivery via Nanocarriers: A Comprehensive Review.Journal of functional biomaterials · 2026Review
- Advancing Maternal Health with Long-Acting Therapeutics: Priorities, Efficacy and Safety Considerations, and Emerging Technologies.Clinical pharmacology and therapeutics · 2026Review
- PKRxiv: A Best Practice Model for Advancing Pharmacoequity Through Open Pharmacokinetic Data Sharing.Clinical pharmacology and therapeutics · 2026Article
- Integration of Machine Learning With PBPK and QSAR Modeling Approaches to Facilitate Drug Discovery and Development.CPT: pharmacometrics & systems pharmacology · 2026Review
- Monitoring of dual-drug combination therapy in pediatric epilepsy patients: a machine learning model for simultaneous VPA-LEV concentration-dose prediction.Journal of translational medicine · 2026Article
- The Evolution and Future Directions of PBPK Modeling in FDA Regulatory Review.Pharmaceutics · 2025Review
- ADME Properties in Drug Delivery.Pharmaceutics · 2025Article
- Drug-Drug Interactions and Initial Dosage Optimization of Quetiapine in Patients with Depression: A Real-World Study.Drug design, development and therapy · 2025Article
- Estimation of linezolid exposure in patients with hepatic impairment using machine learning based on a population pharmacokinetic model.European journal of clinical pharmacology · 2024Article
- Insights into Inhalation Drug Disposition: The Roles of Pulmonary Drug-Metabolizing Enzymes and Transporters.International journal of molecular sciences · 2024Review
- Multimodal CNN-DDI: using multimodal CNN for drug to drug interaction associated events.Scientific reports · 2024Article
- Model-Informed Dose Selection for a Novel Human Immunoglobulin G4 Derived Monoclonal Antibody Targeting Proprotein Convertase Kwashiorkor Type 9: Insights from Population Pharmacokinetics-Pharmacodynamics and Systems Pharmacology.ACS pharmacology & translational science · 2024Article
- An insight into pharmacokinetics and dose optimization of antimicrobials agents in elderly patients.Frontiers in pharmacology · 2024Review
- Comparing the applications of machine learning, PBPK, and population pharmacokinetic models in pharmacokinetic drug-drug interaction prediction.CPT: pharmacometrics & systems pharmacology · 2022Review
- AI-Based Computational Methods in Early Drug Discovery and Post Market Drug Assessment: A Survey.IEEE transactions on computational biology and bioinformaticsReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The gold-standard approach for modeling pharmacokinetic mediated drug-drug interactions is the use of physiologically-based pharmacokinetic modeling and population pharmacokinetics. However, these models require extensive amounts of drug-specific data generated from a wide variety of in vitro and in vivo models, which are later refined with clinical data and system-specific parameters. Machine learning has the potential to be utilized for the prediction of drug-drug interactions much earlier in the drug discovery cycle, using inputs derived from, among others, chemical structure. This could lead to refined chemical designs in early drug discovery. Machine-learning models have many advantages, such as the capacity to automate learning (increasing the speed and scalability of predictions), improved generalizability by learning from multicase historical data, and highlighting statistical and potentially clinically significant relationships between input variables. In contrast, the routinely used mechanistic models (physiologically-based pharmacokinetic models and population pharmacokinetics) are currently considered more interpretable, reliable, and require a smaller sample size of data, although insights differ on a case-by-case basis. Therefore, they may be appropriate for later stages of drug-drug interaction assessment when more in vivo and clinical data are available. A combined approach of using mechanistic models to highlight features that can be used for training machine-learning models may also be exploitable in the future to improve the performance of machine learning. In this review, we provide concepts, strategic considerations, and compare machine learning to mechanistic modeling for drug-drug interaction risk assessment across the stages of drug discovery and development.
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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.