Deep Learning Methods Applied to Drug Concentration Prediction of Olanzapine

Khusial, Richard Darien; Bies, Robert R.; Akil, Ayman

doi:10.3390/pharmaceutics15041139

Cited by 7 publications

(2 citation statements)

References 47 publications

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“…Because of the numerous issues that PM and ML encounter, research in this field remains in its exploratory phase, underscoring the need for further investigation and validation. The fusion of PK and ML holds the potential to yield precise estimations of drug exposure by simulating rich concentration-versus-time profiles, by exploring and learning the relationships within all the patient covariates [62] or by using faster models and performing faster analyses [63]. For instance, the ML approach has been shown to confer advantages over traditional approaches, including increased accuracy and reduced variance [64].…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Models for Predicting Personalized Tacrolimus Stable Dosages in Pediatric Renal Transplant Patients

Sánchez-Herrero,

Calvet,

Juan

2023

BioMedInformatics

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Tacrolimus, characterized by a narrow therapeutic index, significant toxicity, adverse effects, and interindividual variability, necessitates frequent therapeutic drug monitoring and dose adjustments in renal transplant recipients. This study aimed to compare machine learning (ML) models utilizing pharmacokinetic data to predict tacrolimus blood concentration. This prediction underpins crucial dose adjustments, emphasizing patient safety. The investigation focuses on a pediatric cohort. A subset served as the derivation cohort, creating the dose-prediction algorithm, while the remaining data formed the validation cohort. The study employed various ML models, including artificial neural network, RandomForestRegressor, LGBMRegressor, XGBRegressor, AdaBoostRegressor, BaggingRegressor, ExtraTreesRegressor, KNeighborsRegressor, and support vector regression, and their performances were compared. Although all models yielded favorable fit outcomes, the ExtraTreesRegressor (ETR) exhibited superior performance. It achieved measures of −0.161 for MPE, 0.995 for AFE, 1.063 for AAFE, and 0.8 for R2, indicating accurate predictions and meeting regulatory standards. The findings underscore ML’s predictive potential, despite the limited number of samples available. To address this issue, resampling was utilized, offering a viable solution within medical datasets for developing this pioneering study to predict tacrolimus trough concentration in pediatric transplant recipients.

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Section: Discussionmentioning

confidence: 99%

Machine Learning Models for Predicting Personalized Tacrolimus Stable Dosages in Pediatric Renal Transplant Patients

Sánchez-Herrero,

Calvet,

Juan

2023

BioMedInformatics

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“…For instance, a DL model was applied to predict olanzapine drug concentrations with comparable performance to a traditional PK model using NONMEM. 17 Alongside, the neural ordinary differential equation (neural‐ODE) approach has enabled efficient PK forecasting across different dosing regimens. 18 Moreover, Janssen et al.…”

Section: Introductionmentioning

confidence: 99%

Deep‐NCA: A deep learning methodology for performing noncompartmental analysis of pharmacokinetic data

Liu,

Brooks,

Canty

et al. 2024

CPT Pharmacom & Syst Pharma

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Noncompartmental analysis (NCA) is a model‐independent approach for assessing pharmacokinetics (PKs). Although the existing NCA algorithms are very well‐established and widely utilized, they suffer from low accuracies in the setting of sparse PK samples. In response, we developed Deep‐NCA, a deep learning (DL) model to improve the prediction of key noncompartmental PK parameters. Our methodology utilizes synthetic PK data for model training and uses an innovative patient‐specific normalization method for data preprocessing. Deep‐NCA demonstrated adequate performance across six previously unseen simulated drugs under multiple dosing, showcasing effective generalization. Compared to traditional NCA, Deep‐NCA exhibited superior performance for sparse PK data. This study advances the application of DL to PK studies and introduces an effective method for handling sparse PK data. With further validation and refinement, Deep‐NCA could significantly enhance the efficiency of drug development by providing more accurate NCA estimates while requiring fewer PK samples.

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Simulating realistic patient profiles from pharmacokinetic models by a machine learning postprocessing correction of residual variability

Kaikousidis,

Bies,

Dokoumetzidis

2024

CPT Pharmacom & Syst Pharma

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We address the problem of model misspecification in population pharmacokinetics (PopPK), by modeling residual unexplained variability (RUV) by machine learning (ML) methods in a postprocessing step after conventional model building. The practical purpose of the method is the generation of realistic virtual patient profiles and the quantification of the extent of model misspecification, by introducing an appropriate metric, to be used as an additional diagnostic of model quality. The proposed methodology consists of the following steps: After developing a PopPK model, the individual residual errors IRES = DV–IPRED, are computed, where DV are the observations and IPRED the individual predictions and are modeled by ML to obtain IRESML. Correction of the IPREDs can then be carried out as DVML = IPRED + IRESML. The methodology was tested in a PK study of ropinirole, for which a PopPK model was developed while a second deliberately misspecified model was also considered. Various supervised ML algorithms were tested, among which Random Forest gave the best results. The ML model was able to correct individual predictions as inspected in diagnostic plots and most importantly it simulated realistic profiles that resembled the real data and canceled out the artifacts introduced by the elevated RUV, even in the case of the heavily misspecified model. Furthermore, a metric to quantify the extent of model misspecification was introduced based on the R2 between IRES and IRESML, following the rationale that the greater the extent of variability explained by the ML model, the higher the degree of model misspecification present in the original model.

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Deep Learning Methods Applied to Drug Concentration Prediction of Olanzapine

Cited by 7 publications

References 47 publications

Machine Learning Models for Predicting Personalized Tacrolimus Stable Dosages in Pediatric Renal Transplant Patients

Machine Learning Models for Predicting Personalized Tacrolimus Stable Dosages in Pediatric Renal Transplant Patients

Deep‐NCA: A deep learning methodology for performing noncompartmental analysis of pharmacokinetic data

Simulating realistic patient profiles from pharmacokinetic models by a machine learning postprocessing correction of residual variability

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