Drug Clearance in Neonates: A Combination of Population Pharmacokinetic Modelling and Machine Learning Approaches to Improve Individual Prediction

Tang, Bo; Zheng, Guoyan; Allegaert, Karel; Wu, Yue‐E; Manolis, Efthymios; Leroux, Stéphanie; Yao, Bu‐Fan; Shi, Haiyan; Li, Xiao; Huang, Xin; Wang, Wenqi; Shen, Adong; Wang, Xiaoling; Wang, Tianyou; Kou, Chen; Xu, Huilin; Zhou, Yue; Zheng, Yi; Hao, Guo‐Xiang; Xu, Bin; Thomson, Alison; Capparelli, Edmund V.; Biran, Valérie; Simon, Nicolas; Lo, Yu-Lung; Marqués, Remedios; Peris, José-Esteban; Lutsar, Irja; Saito, Jumpei; Burggraaf, Jacobus; Jacqz-Aigrain, Évelyne; Anker, John van den; Zhao, Wei

doi:10.1007/s40262-021-01033-x

Cited by 27 publications

(19 citation statements)

References 49 publications

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“…In addition, the mathematics underlying each method is different. To increase model predictability and interpretability, a combination of ML and pharmacometrics models may be necessary ( Koch et al, 2020 ; Tang et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

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Applying machine learning to the pharmacokinetic modeling of cyclosporine in adult renal transplant recipients: a multi-method comparison

Mao

Chen²,

et al. 2022

Front. Pharmacol.

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Objective: The aim of this study was to identify the important factors affecting cyclosporine (CsA) blood concentration and estimate CsA concentration using seven different machine learning (ML) algorithms. We also assessed the predictability of established ML models and previously built population pharmacokinetic (popPK) model. Finally, the most suitable ML model and popPK model to guide precision dosing were determined.Methods: In total, 3,407 whole-blood trough and peak concentrations of CsA were obtained from 183 patients who underwent initial renal transplantation. These samples were divided into model-building and evaluation sets. The model-building set was analyzed using seven different ML algorithms. The effects of potential covariates were evaluated using the least absolute shrinkage and selection operator algorithms. A separate evaluation set was used to assess the ability of all models to predict CsA blood concentration. R squared (R2) scores, median prediction error (MDPE), median absolute prediction error (MAPE), and the percentages of PE within 20% (F20) and 30% (F30) were calculated to assess the predictive performance of these models. In addition, previously built popPK model was included for comparison.Results: Sixteen variables were selected as important covariates. Among ML models, the predictive performance of nonlinear-based ML models was superior to that of linear regression (MDPE: 3.27%, MAPE: 34.21%, F20: 30.63%, F30: 45.03%, R2 score: 0.68). The ML model built with the artificial neural network algorithm was considered the most suitable (MDPE: −0.039%, MAPE: 25.60%, F20: 39.35%, F30: 56.46%, R2 score: 0.75). Its performance was superior to that of the previously built popPK model (MDPE: 5.26%, MAPE: 29.22%, F20: 33.94%, F30: 51.22%, R2 score: 0.68). Furthermore, the application of the most suitable model and the popPK model in clinic showed that most dose regimen recommendations were reasonable.Conclusion: The performance of these ML models indicate that a nonlinear relationship for covariates may help to improve model predictability. These results might facilitate the application of ML models in clinic, especially for patients with unstable status or during initial dose optimization.

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Section: Discussionmentioning

confidence: 99%

“…This approach was also used to predict the exposure of tacrolimus ( Woillard et al, 2021b ) and mycophenolic acid ( Woillard et al, 2021a ). Moreover, Tang et al combined popPK and ML models to improve the prediction of individual clearance of renally cleared drugs in neonates ( Tang et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Applying machine learning to the pharmacokinetic modeling of cyclosporine in adult renal transplant recipients: a multi-method comparison

Mao

Chen²,

et al. 2022

Front. Pharmacol.

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“…Therefore, PPK combined with machine learning to accurately predict pharmacokinetics may be a better approach (van Gelder and Vinks, 2021;Yang et al, 2022). Tang et al (2021) developed an individual clearance prediction model for neonatal renal clearance of drugs and successfully validated that PPK combined with machine learning can improve the prediction accuracy of drug clearance. In this study, we first obtained variables significantly associated with TAC clearance (age, combined use of Wuzhi capsules, CYP3A5*3 rs776746 and CTLA4 rs4553808) by PPK approach, and then further developed a machine learning model for TAC clearance, and the final model had a good predictive performance with an R 2 value of 0.42, similar to our previous tacrolimus dose/weightadjusted trough concentration prediction model (R 2 = 0.44), but superior to the groups of whether CYP3A5 was expressed (Mo et al, 2022); and the Lasso algorithm outperformed other machine learning algorithms such as XGBoost, RF, and Extra-Trees.…”

Section: Discussionmentioning

confidence: 99%

Tacrolimus pharmacokinetics in pediatric nephrotic syndrome: A combination of population pharmacokinetic modelling and machine learning approaches to improve individual prediction

et al. 2022

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Background and Aim: Tacrolimus (TAC) is a first-line immunosuppressant for the treatment of refractory nephrotic syndrome (RNS), but the pharmacokinetics of TAC varies widely among individuals, and there is still no accurate model to predict the pharmacokinetics of TAC in RNS. Therefore, this study aimed to combine population pharmacokinetic (PPK) model and machine learning algorithms to develop a simple and accurate prediction model for TAC.Methods: 139 children with RNS from August 2013 to December 2018 were included, and blood samples of TAC trough and partial peak concentrations were collected. The blood concentration of TAC was determined by enzyme immunoassay; CYP3A5 was genotyped by polymerase chain reaction-restriction fragment length polymorphism method; MYH9, LAMB2, ACTN4 and other genotypes were determined by MALDI-TOF MS method; PPK model was established by nonlinear mixed-effects method. Based on this, six machine learning algorithms, including eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Extra-Trees, Gradient Boosting Decision Tree (GBDT), Adaptive boosting (AdaBoost) and Lasso, were used to establish the machine learning model of TAC clearance.Results: A one-compartment model of first-order absorption and elimination adequately described the pharmacokinetics of TAC. Age, co-administration of Wuzhi capsules, CYP3A5 *3/*3 genotype and CTLA4 rs4553808 genotype were significantly affecting the clearance of TAC. Among the six machine learning models, the Lasso algorithm model performed the best (R2 = 0.42).Conclusion: For the first time, a clearance prediction model of TAC in pediatric patients with RNS was established using PPK combined with machine learning, by which the individual clearance of TAC can be predicted more accurately, and the initial dose of administration can be optimized to achieve the goal of individualized treatment.

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“…Fortunately, research collaborations among experts in different fields are advancing the integration of these approaches. Tang et al (2021) reported a combined population pharmacokinetic (popPK) and ML approach, which had more accurate predictions of individual clearances of renally eliminated drugs in neonates and could be used to individualize the initial dosing regimen. Bououda et al (2022) also suggested that ML could be used in combination with standard popPK approaches to increase confidence in the predictions of vancomycin exposure.…”

Section: Introductionmentioning

confidence: 99%

Machine learning advances the integration of covariates in population pharmacokinetic models: Valproic acid as an example

et al. 2022

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Background and Aim: Many studies associated with the combination of machine learning (ML) and pharmacometrics have appeared in recent years. ML can be used as an initial step for fast screening of covariates in population pharmacokinetic (popPK) models. The present study aimed to integrate covariates derived from different popPK models using ML.Methods: Two published popPK models of valproic acid (VPA) in Chinese epileptic patients were used, where the population parameters were influenced by some covariates. Based on the covariates and a one-compartment model that describes the pharmacokinetics of VPA, a dataset was constructed using Monte Carlo simulation, to develop an XGBoost model to estimate the steady-state concentrations (Css) of VPA. We utilized SHapley Additive exPlanation (SHAP) values to interpret the prediction model, and calculated estimates of VPA exposure in four assumed scenarios involving different combinations of CYP2C19 genotypes and co-administered antiepileptic drugs. To develop an easy-to-use model in the clinic, we built a simplified model by using CYP2C19 genotypes and some noninvasive clinical parameters, and omitting several features that were infrequently measured or whose clinically available values were inaccurate, and verified it on our independent external dataset.Results: After data preprocessing, the finally generated combined dataset was divided into a derivation cohort and a validation cohort (8:2). The XGBoost model was developed in the derivation cohort and yielded excellent performance in the validation cohort with a mean absolute error of 2.4 mg/L, root-mean-squared error of 3.3 mg/L, mean relative error of 0%, and percentages within ±20% of actual values of 98.85%. The SHAP analysis revealed that daily dose, time, CYP2C19*2 and/or *3 variants, albumin, body weight, single dose, and CYP2C19*1*1 genotype were the top seven confounding factors influencing the Css of VPA. Under the simulated dosage regimen of 500 mg/bid, the VPA exposure in patients who had CYP2C19*2 and/or *3 variants and no carbamazepine, phenytoin, or phenobarbital treatment, was approximately 1.74-fold compared to those with CYP2C19*1/*1 genotype and co-administered carbamazepine + phenytoin + phenobarbital. The feasibility of the simplified model was fully illustrated by its performance in our external dataset. Conclusion: This study highlighted the bridging role of ML in big data and pharmacometrics, by integrating covariates derived from different popPK models.

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Drug Clearance in Neonates: A Combination of Population Pharmacokinetic Modelling and Machine Learning Approaches to Improve Individual Prediction

Cited by 27 publications

References 49 publications

Applying machine learning to the pharmacokinetic modeling of cyclosporine in adult renal transplant recipients: a multi-method comparison

Applying machine learning to the pharmacokinetic modeling of cyclosporine in adult renal transplant recipients: a multi-method comparison

Tacrolimus pharmacokinetics in pediatric nephrotic syndrome: A combination of population pharmacokinetic modelling and machine learning approaches to improve individual prediction

Machine learning advances the integration of covariates in population pharmacokinetic models: Valproic acid as an example

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