A model based on machine learning for the prediction of cyclosporin A trough concentration in Chinese allo-HSCT patients

Song, Lin; Huang, Chenrong; Pan, Shi-zheng; Zhu, Jiaqian; Cheng, Zong-qi; Xia, Yu; Xue, Liang; Xia, Fan; Zhang, Jinyuan; Wu, Depei; Miao, Liyan

doi:10.1080/17512433.2023.2142561

Cited by 6 publications

(2 citation statements)

References 54 publications

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“…After removing duplicated articles, 3,346 studies were screened by the title and/or abstract, 3,175 irrelevant studies were excluded and 171 articles were included for full‐text review. Finally, 64 articles related to precision dosing using ML were included for analysis 11–74 . The PRISMA flow diagram representing the study selection process and review results is presented in Figure .…”

Section: Resultsmentioning

confidence: 99%

Machine Learning: A New Approach for Dose Individualization

Li,

Tang,

et al. 2023

Clin Pharma and Therapeutics

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The application of machine learning (ML) has shown promising results in precision medicine due to its exceptional performance in dealing with complex multidimensional data. However, using ML for individualized dosing of medicines is still in its early stage, meriting further exploration. A systematic review of study designs and modeling details of using ML for individualized dosing of different drugs was performed. We have summarized the status of the study populations, predictive targets, and data sources for ML modeling, the selection of ML algorithms and features, and the evaluation and validation of their predictive performance. We also used the Prediction model Risk of Bias Assessment Tool (PROBAST) to assess the risk of bias of included studies. Currently, ML can be used for both a priori and a posteriori dose selection and optimization, and it can also assist the implementation of therapeutic drug monitoring. However, studies are mainly focused on drugs with narrow therapeutic windows, predominantly immunosuppressants (N=23, 35.9%) and anti‐infectives (N=21, 32.8%), and there is currently only very limited attention for special populations such as children (N=22, 34.4%). Most studies showed poor methodological quality and a high risk of bias. The lack of external validation and clinical utility evaluation currently limits the further clinical implementation of ML for dose individualization. We therefore have proposed several ways to improve the clinical relevance of the studies and facilitate the translation of ML models into clinical practice.

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Section: Resultsmentioning

confidence: 99%

Machine Learning: A New Approach for Dose Individualization

Li,

Tang,

et al. 2023

Clin Pharma and Therapeutics

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“…This method has been used to estimate the TAC concentration or dosage based on various factors, including body weight, age, pathophysiological status, concomitant drugs, and genetic polymorphisms of drug-metabolizing enzymes or transporters ( Woillard et al, 2021 ). The ML method is suitable for predicting targets affected by many variables and sometimes shows stronger generalization and better accuracy ( Huang et al, 2022 ; Song et al, 2023 ). Despite the higher accuracy of ML algorithms, there are some limitations to this strategy, such as inexplicable results ( Destere et al, 2023 ).…”

Section: Introductionmentioning

confidence: 99%

Improving prediction of tacrolimus concentration using a combination of population pharmacokinetic modeling and machine learning in chinese renal transplant recipients

Wang,

Lu,

Shao

et al. 2024

Front. Pharmacol.

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AimsThe population pharmacokinetic (PPK) model-based machine learning (ML) approach offers a novel perspective on individual concentration prediction. This study aimed to establish a PPK-based ML model for predicting tacrolimus (TAC) concentrations in Chinese renal transplant recipients.MethodsConventional TAC monitoring data from 127 Chinese renal transplant patients were divided into training (80%) and testing (20%) datasets. A PPK model was developed using the training group data. ML models were then established based on individual pharmacokinetic data derived from the PPK basic model. The prediction performances of the PPK-based ML model and Bayesian forecasting approach were compared using data from the test group.ResultsThe final PPK model, incorporating hematocrit and CYP3A5 genotypes as covariates, was successfully established. Individual predictions of TAC using the PPK basic model, postoperative date, CYP3A5 genotype, and hematocrit showed improved rankings in ML model construction. XGBoost, based on the TAC PPK, exhibited the best prediction performance.ConclusionThe PPK-based machine learning approach emerges as a superior option for predicting TAC concentrations in Chinese renal transplant recipients.

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Intelligent Pharmacy's applications of cyclosporine

Zhang

Zhou

2023

Intelligent Pharmacy

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A model based on machine learning for the prediction of cyclosporin A trough concentration in Chinese allo-HSCT patients

Cited by 6 publications

References 54 publications

Machine Learning: A New Approach for Dose Individualization

Machine Learning: A New Approach for Dose Individualization

Improving prediction of tacrolimus concentration using a combination of population pharmacokinetic modeling and machine learning in chinese renal transplant recipients

Intelligent Pharmacy's applications of cyclosporine

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