A Machine Learning Approach to Predict Interdose Vancomycin Exposure

Bououda, Mehdi; Uster, David W; Sidorov, Egor; Labriffe, Marc; Marquet, Pierre; Wicha, Sebastian G.; Woillard, Jean‐Baptiste

doi:10.1007/s11095-022-03252-8

Cited by 28 publications

(27 citation statements)

References 18 publications

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“…The best performing model was trained using 5016 simulated PK profiles and resulted in a model that was able to accurately predict the everolimus AUC for an external validation set ( n = 114, R 2 = 0.956, root mean squared error [RMSE] = 10.3%) 20 . Similar approaches have been developed for other drugs 21–23 . However, by design, these models will always have the same error margin as the TDM measurements that were used to train the model.…”

Section: Drug Treatment Optimizationmentioning

confidence: 99%

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Artificial intelligence in pharmacology research and practice

Lee

Swen

2022

Clinical Translational Sci

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In recent years, the use of artificial intelligence (AI) in health care has risen steadily, including a wide range of applications in the field of pharmacology. AI is now used throughout the entire continuum of pharmacology research and clinical practice and from early drug discovery to real‐world datamining. The types of AI models used range from unsupervised clustering of drugs or patients aimed at identifying potential drug compounds or suitable patient populations, to supervised machine learning approaches to improve therapeutic drug monitoring. Additionally, natural language processing is increasingly used to mine electronic health records to obtain real‐world data. In this mini‐review, we discuss the basics of AI followed by an outline of its application in pharmacology research and clinical practice.

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Section: Drug Treatment Optimizationmentioning

confidence: 99%

“… 20 Similar approaches have been developed for other drugs. 21 , 22 , 23 However, by design, these models will always have the same error margin as the TDM measurements that were used to train the model. Meaning that an AI model can never be more accurate than the outcome on which it was trained.…”

Section: Drug Treatment Optimizationmentioning

confidence: 99%

Artificial intelligence in pharmacology research and practice

Lee

Swen

2022

Clinical Translational Sci

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“… 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. Ogami et al (2021) developed a model by applying artificial neural networks for predicting the time-series pharmacokinetics of cyclosporine A, which showed higher prediction accuracy than the conventional popPK model.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, it can simultaneously reduce the model bias and variance ( Cao et al, 2010 ). This state-of-the-art ML algorithm has been gradually applied to deal with predictions of therapeutic drug monitoring (TDM) values, drug dose, and drug exposure to specific medications ( Huang et al, 2021a ; Guo et al, 2021 ; Bououda et al, 2022 ). The details of the differences between the XGBoost and GBDT algorithms are given in the section titled “An introduction to XGBoost algorithm.”…”

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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“…In previous studies, machine learning was used to predict AKI after cardiac surgery, in pediatric intensive care and cancer patients ( Tseng et al, 2020 ; Dong et al, 2021 ; Scanlon et al, 2021 ). In addition, Kim et al have developed a single-center vancomycin-associated AKI risk scoring system ( Kim et al, 2022 ), which estimated of vancomycin area under the curve after vancomycin administration based on machine learning ( Bououda et al, 2022 ). However, despite these advancements, all of these models were globally, which could not analyse and evaluate vancomycin-associated AKI in different underlying diseases specifically.…”

Section: Introductionmentioning

confidence: 99%

Analysis of a machine learning–based risk stratification scheme for acute kidney injury in vancomycin

Tang

Guo

et al. 2022

Front. Pharmacol.

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Vancomycin-associated acute kidney injury (AKI) continues to pose a major challenge to both patients and healthcare providers. The purpose of this study is to construct a machine learning framework for stratified predicting and interpreting vancomycin-associated AKI. Our study is a retrospective analysis of medical records of 724 patients who have received vancomycin therapy from 1 January 2015 through 30 September 2020. The basic clinical information, vancomycin dosage and days, comorbidities and medication, laboratory indicators of the patients were recorded. Machine learning algorithm of XGBoost was used to construct a series risk prediction model for vancomycin-associated AKI in different underlying diseases. The vast majority of sub-model performed best on the corresponding sub-dataset. Additionally, the aim of this study was to explain each model and to explore the influence of clinical variables on prediction. As the results of the analysis showed that in addition to the common indicators (serum creatinine and creatinine clearance rate), some other underappreciated indicators such as serum cystatin and cumulative days of vancomycin administration, weight and age, neutrophils and hemoglobin were the risk factors for cancer, diabetes mellitus, heptic insufficiency respectively. Stratified analysis of the comorbidities in patients with vancomycin-associated AKI further confirmed the necessity for different patient populations to be studied.

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A Machine Learning Approach to Predict Interdose Vancomycin Exposure

Cited by 28 publications

References 18 publications

Artificial intelligence in pharmacology research and practice

Artificial intelligence in pharmacology research and practice

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

Analysis of a machine learning–based risk stratification scheme for acute kidney injury in vancomycin

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