The Application of Virtual Therapeutic Drug Monitoring to Assess the Pharmacokinetics of Imatinib in a Chinese Cancer Population Group

He, Yu; Badhan, Raj

doi:10.1016/j.xphs.2022.09.028

Cited by 2 publications

(3 citation statements)

References 96 publications

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“…IM is an anti-cancer drug administered primarily to outpatients because blood samples are not always available at the end of the administration interval. Thus, IM C min is the most widely used pharmacokinetic proxy for predicting clinical outcomes [ 7 , 37 ], and C min is naturally used as a focus for TDM [ 38 ]. TDM for IM may reassure patients and physicians about full exposure to the drug and improve long-term adherence to this chronic treatment, which may be a promising approach for fine-tuning the IM dosage for better tolerability and optimal clinical outcomes in patients with GISTs [ 7 , 37 ].…”

Section: Discussionmentioning

confidence: 99%

“…Thus, IM C min is the most widely used pharmacokinetic proxy for predicting clinical outcomes [ 7 , 37 ], and C min is naturally used as a focus for TDM [ 38 ]. TDM for IM may reassure patients and physicians about full exposure to the drug and improve long-term adherence to this chronic treatment, which may be a promising approach for fine-tuning the IM dosage for better tolerability and optimal clinical outcomes in patients with GISTs [ 7 , 37 ]. It is widely known that high IM C min increases the risk of adverse effects and toxicity, which can reduce medication adherence rates and quality of life.…”

Section: Discussionmentioning

confidence: 99%

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Advanced gastrointestinal stromal tumor: reliable classification of imatinib plasma trough concentration via machine learning

Ran,

Tan,

et al. 2024

BMC Cancer

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Aim Patients with advanced gastrointestinal stromal tumors (GISTs) exhibiting an imatinib plasma trough concentration (IM Cmin) under 1100 ng/ml may show a reduced drug response rate, leading to the suggestion of monitoring for IM Cmin. Consequently, the objective of this research was to create a customized IM Cmin classification model for patients with advanced GISTs from China. Methods Initial data and laboratory indicators from patients with advanced GISTs were gathered, and the above information was segmented into a training set, validation set, and testing set in a 6:2:2 ratio. Key variables associated with IM Cmin were identified to construct the classification model using the least absolute shrinkage and selection operator (LASSO) regression and forward stepwise binary logistic regression. Within the training and validation sets, nine ML classification models were constructed via the resampling method and underwent comparison through the Brier scores, the areas under the receiver-operating characteristic curve (AUROC), the decision curve, and the precision-recall (AUPR) curve to determine the most suitable model for this dataset. Two methods of internal validation were used to assess the most suitable model's classification performance: tenfold cross-validation and random split-sample validation (test set), and the value of the test set AUROC was used to evaluate the model's classification performance. Results Six key variables (gender, daily IM dose, metastatic site, red blood cell count, platelet count, and percentage of neutrophils) were ultimately selected to construct the classification model. In the validation set, it is found by comparison that the Extreme Gradient Boosting (XGBoost) model has the largest AUROC, the lowest Brier score, the largest area under the decision curve, and the largest AUPR value. Furthermore, as evaluated via internal verification, it also performed well in the test set (AUROC = 0.725). Conclusion For patients with advanced GISTs who receive IM, initial data and laboratory indicators could be used to accurately estimate whether the IM Cmin is below 1100 ng/ml. The XGBoost model may stand a chance to assist clinicians in directing the administration of IM.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Advanced gastrointestinal stromal tumor: reliable classification of imatinib plasma trough concentration via machine learning

Ran,

Tan,

et al. 2024

BMC Cancer

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“…A range of software companies offers PBPK modelling platforms [ 25 ], including paediatrics, of which the Simcyp™ Simulator is widely used to predict drug performance from virtual population groups, including paediatrics [ 26 , 27 ], pregnancy [ 28 , 29 , 30 ] and specialised disease states such as oncology [ 31 , 32 ].…”

Section: Introductionmentioning

confidence: 99%

Virtual Clinical Trials Guided Design of an Age-Appropriate Formulation and Dosing Strategy of Nifedipine for Paediatric Use

et al. 2023

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The rapid onset of action of nifedipine causes a precipitous reduction in blood pressure leading to adverse effects associated with reflex sympathetic nervous system (SNS) activation, including tachycardia and worsening myocardial and cerebrovascular ischemia. As a result, short acting nifedipine preparations are not recommended. However, importantly, there are no modified release preparations of nifedipine authorised for paediatric use, and hence a paucity of clinical studies reporting pharmacokinetics data in paediatrics. Pharmacokinetic parameters may differ significantly between children and adults due to anatomical and physiological differences, often resulting in sub therapeutic and/or toxic plasma concentrations of medication. However, in the field of paediatric pharmacokinetics, the use of pharmacokinetic modelling, particularly physiological-based pharmacokinetics (PBPK), has revolutionised the ability to extrapolate drug pharmacokinetics across age groups, allowing for pragmatic determination of paediatric plasma concentrations to support drug licensing and clinical dosing. In order to pragmatically assess the translation of resultant dissolution profiles to the paediatric populations, virtual clinical trials simulations were conducted. In the context of formulation development, the use of PBPK modelling allowed the determination of optimised formulations that achieved plasma concentrations within the target therapeutic window throughout the dosing strategy. A 5 mg sustained release mini-tablet was successfully developed with the duration of release extending over 24 h and an informed optimised dosing strategy of 450 µg/kg twice daily. The resulting formulation provides flexible dosing opportunities, improves patient adherence by reducing frequent administration burden and enhances patient safety profiles by maintaining efficacious levels of consistent drug plasma levels over a sustained period of time.

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The Application of Virtual Therapeutic Drug Monitoring to Assess the Pharmacokinetics of Imatinib in a Chinese Cancer Population Group

Cited by 2 publications

References 96 publications

Advanced gastrointestinal stromal tumor: reliable classification of imatinib plasma trough concentration via machine learning

Advanced gastrointestinal stromal tumor: reliable classification of imatinib plasma trough concentration via machine learning

Virtual Clinical Trials Guided Design of an Age-Appropriate Formulation and Dosing Strategy of Nifedipine for Paediatric Use

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