ArticlePharmaceutical research2023
A Hybrid Algorithm Combining Population Pharmacokinetic and Machine Learning for Isavuconazole Exposure Prediction.
Article in Pharmaceutical research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07695571 (Hybrid Population Pharmacokinetic,Machine Learning and Deep Learning Modelling to Predict Dosing for the Individualization of Cyclosporine Therapy in Transplant Recipients), which is not on this map. Cited by 17 papers, 2 of them syntheses that pooled it.
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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.
Hybrid Population Pharmacokinetic,Machine Learning and Deep Learning Modelling to Predict Dosing for the Individualization of Cyclosporine Therapy in Transplant Recipients
Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Factors affecting voriconazole pharmacokinetic variability in critically ill patients: a systematic review.Critical care (London, England) · 2025Pooled it
- Predicting Pharmacokinetics of Drugs Using Artificial Intelligence Tools: A Systematic Review.European journal of drug metabolism and pharmacokinetics · 2024Pooled it
- Model-Informed Precision Dosing of Protein Kinase Inhibitors: Evaluation of Model-Averaging and Flattened Priors Methods.Clinical pharmacokinetics · 2026Trial
- External Evaluation of Population Pharmacokinetic Models of Cabotegravir, During Its Oral and Intramuscular Administration in HIV-Infected Patients.CPT: pharmacometrics & systems pharmacology · 2026Observational
- Development and validation of a machine learning model for individualised meropenem dosing in CRRT patients.Scientific reports · 2026Article
- A microdose cocktail and population pharmacokinetic study suggests potentially reduced CYP3A and P-gp activities in type 2 diabetes.Frontiers in pharmacology · 2026Article
- Hybrid Population Pharmacokinetic-Machine Learning Modeling to Predict Infliximab Pharmacokinetics in Pediatric and Young Adult Patients with Crohn's Disease.Clinical pharmacokinetics · 2025Article
- Methodological Techniques Used in Machine Learning to Support Individualized Drug Dosing Regimens Based on Pharmacokinetic Data: A Scoping Review.Clinical pharmacokinetics · 2025Article
- Estimation of Ganciclovir Exposure in Adults Transplant Patients by Machine Learning.The AAPS journal · 2025Article
- 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
- A Real-Time Plasma Concentration Prediction Model for Voriconazole in Elderly Patients via Machine Learning Combined with Population Pharmacokinetics.Drug design, development and therapy · 2025Article
- Dose Individualisation of Antimicrobials from a Pharmacometric Standpoint: The Current Landscape.Drugs · 2024Review
- Modeling Pharmacokinetics in Individual Patients Using Therapeutic Drug Monitoring and Artificial Population Quasi-Models: A Study with Piperacillin.Pharmaceutics · 2024Article
- Improving prediction of tacrolimus concentration using a combination of population pharmacokinetic modeling and machine learning in chinese renal transplant recipients.Frontiers in pharmacology · 2024Article
- Application of machine learning combined with population pharmacokinetics to improve individual prediction of vancomycin clearance in simulated adult patients.Frontiers in pharmacology · 2024Article
- Clinical decision support for chemotherapy-induced neutropenia using a hybrid pharmacodynamic/machine learning model.CPT: pharmacometrics & systems pharmacology · 2023Article
- AI-Based Computational Methods in Early Drug Discovery and Post Market Drug Assessment: A Survey.IEEE transactions on computational biology and bioinformaticsReview
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesMaximum a posteriori Bayesian estimation (MAP-BE) based on a limited sampling strategy and a population pharmacokinetic (POPPK) model is used to estimate individual pharmacokinetic parameters. Recently, we proposed a methodology that combined population pharmacokinetic and machine learning (ML) to decrease the bias and imprecision in individual iohexol clearance prediction. The aim of this study was to confirm the previous results by developing a hybrid algorithm combining POPPK, MAP-BE and ML that accurately predicts isavuconazole clearance.
methodsA total of 1727 isavuconazole rich PK profiles were simulated using a POPPK model from the literature, and MAP-BE was used to estimate the clearance based on: (i) the full PK profiles (refCL); and (ii) C24h only (C24h-CL). Xgboost was trained to correct the error between refCL and C24h-CL in the training dataset (75%). C24h-CL as well as ML-corrected C24h-CL were evaluated in a testing dataset (25%) and then in a set of PK profiles simulated using another published POPPK model.
resultsA strong decrease in mean predictive error (MPE%), imprecision (RMSE%) and the number of profiles outside ± 20% MPE% (n-out20%) was observed with the hybrid algorithm (decreased in MPE% by 95.8% and 85.6%; RMSE% by 69.5% and 69.0%; n-out20% by 97.4% and 100% in the training and testing sets, respectively. In the external validation set, the hybrid algorithm decreased MPE% by 96%, RMSE% by 68% and n-out20% by 100%.
conclusionThe hybrid model proposed significantly improved isavuconazole AUC estimation over MAP-BE based on the sole C24h and may improve dose adjustment.
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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.