Mechanistic Modelling Identifies and Addresses the Risks of Empiric Concentration-Guided Sorafenib Dosing

Development and Evaluation of a Prototype Mobile Application for Intravenous Drug Dose Calculation in Overweight and Obese Thai Children: Precision Dosing in Practice

Background: Medication dosing in overweight and obese children often involves complex weight-based calculations, leading to higher dosing errors, particularly with intravenous drugs. Currently, tools to aid in dosage calculations are lacking for these patients, especially in Thai population. Objective: This study aimed to develop a mobile application with the intent of utilizing it as a tool to enhance the efficiency and accuracy of dosing calculations required for obese and overweight Thai children. Methods: The performance of the application was assessed in 3 key aspects using a sample of 30 healthcare professionals. These key aspects included: 1) the accuracy of dosage calculations, assessed through pre- and posttests comparing manual calculations to app-based calculations using a 10-item questionnaire, 2) the time taken for calculations before and after app usage, 3) user satisfaction, which was measured through a questionnaire. Results: The integration of applications into the calculation demonstrated a significant improvement when compared to the manual calculation in both accuracy (6.10 vs 9.33 out of 10, P < .001) and efficiency (10.40 vs 8.53 minutes per 10 questions, P = .008). Also, the application elicited high levels of satisfaction among users, as reflected by an overall mean satisfaction score of 4.57 on a 5-point scale. Conclusion: The integration of this application to assist in dosage calculations for overweight and obese pediatric Thai patients has yielded favorable outcomes concerning accuracy, efficiency, and user satisfaction. Further development should be pursued within a larger cohort, with an emphasis on real-world implementation in clinical settings.

Modeling the complexity of drug-drug interactions: A physiologically-based pharmacokinetic study of Lenvatinib with Schisantherin A/Schisandrin A

BACKGROUND

Lenvatinib's efficacy as a frontline targeted therapy for radioactive iodine-refractory thyroid carcinoma and advanced hepatocellular carcinoma owes to its inhibition of multiple tyrosine kinases. However, as a CYP3A4 substrate, lenvatinib bears susceptibility to pharmacokinetic modulation by co-administered agents. Schisantherin A (STA) and schisandrin A (SIA) - bioactive lignans abundant in the traditional Chinese medicinal Wuzhi Capsule - act as CYP3A4 inhibitors, engendering the potential for drug-drug interactions (DDIs) with lenvatinib.

METHODS

To explore potential DDIs between lenvatinib and STA/SIA, we developed a physiologically-based pharmacokinetic (PBPK) model for lenvatinib and used it to construct a DDI model for lenvatinib and STA/SIA. The model was validated with clinical trial data and used to predict changes in lenvatinib exposure with combined treatment.

RESULTS

Following single-dose administration, the predicted area under the plasma concentration-time curve (AUC) and maximum plasma concentrations (Cmax) of lenvatinib increased 1.00- to 1.03-fold and 1.00- to 1.01-fold, respectively, in the presence of STA/SIA. Simulations of multiple-dose regimens revealed slightly greater interactions, with lenvatinib AUC0-t and Cmax increasing up to 1.09-fold and 1.02-fold, respectively.

CONCLUSION

Our study developed the first PBPK and DDI models for lenvatinib as a victim drug. STA and SIA slightly increased lenvatinib exposure in simulations, providing clinically valuable information on the safety of concurrent use. Given the minimal pharmacokinetic changes, STA/SIA are unlikely to interact with lenvatinib through pharmacokinetic alterations synergistically but rather may enhance efficacy through inherent anti-cancer efficacy of STA/ SIA.

Application of physiologically based pharmacokinetics modeling in the research of small-molecule targeted anti-cancer drugs

INTRODUCTION

Physiologically based pharmacokinetics (PBPK) models are increasingly used in the drug research and development, especially in anti-cancer drugs. Between 2001 and 2020, a total of 89 small-molecule targeted antitumor drugs were approved in China and the United States, some of which already included PBPK modeling in their application or approval packages. This article intended to review the prevalence and application of PBPK model in these drugs.

METHOD

Article search was performed in the PubMed to collect English research articles on small-molecule targeted anti-cancer drugs using PBPK modeling. The selected articles were classified into nine categorizes according to the application areas and further analyzed.

RESULT

From 2001 to 2020, more than 60% of small-molecule targeted anti-cancer drugs (54/89) were studied using PBPK model with a wide range of application. Ninety research articles were included, of which 48 involved enzyme-mediated drug-drug interaction (DDI). Of these retrieved articles, Simcyp, GastroPlus, and PK-Sim were the most widely model building platforms, which account for 63.8%, 15.2%, and 8.6%, respectively.

CONCLUSION

PBPK modeling is commonly and widely used to research small-molecule targeted anti-cancer drugs.

A Physiologically Based Pharmacokinetic Model to Predict Determinants of Variability in Epirubicin Exposure and Tissue Distribution

BACKGROUND

Epirubicin is an anthracycline antineoplastic drug that is primarily used in combination therapies for the treatment of breast, gastric, lung and ovarian cancers and lymphomas. Epirubicin is administered intravenously (IV) over 3 to 5 min once every 21 days with dosing based on body surface area (BSA; mg/m²). Despite accounting for BSA, marked inter-subject variability in circulating epirubicin plasma concentration has been reported.

METHODS

In vitro experiments were conducted to determine the kinetics of epirubicin glucuronidation by human liver microsomes in the presence and absence of validated UGT2B7 inhibitors. A full physiologically based pharmacokinetic model was built and validated using Simcyp^® (version 19.1, Certara, Princeton, NJ, USA). The model was used to simulate epirubicin exposure in 2000 Sim-Cancer subjects over 158 h following a single intravenous dose of epirubicin. A multivariable linear regression model was built using simulated demographic and enzyme abundance data to determine the key drivers of variability in systemic epirubicin exposure.

RESULTS

Multivariable linear regression modelling demonstrated that variability in simulated systemic epirubicin exposure following intravenous injection was primarily driven by differences in hepatic and renal UGT2B7 expression, plasma albumin concentration, age, BSA, GFR, haematocrit and sex. By accounting for these factors, it was possible to explain 87% of the variability in epirubicin in a simulated cohort of 2000 oncology patients.

CONCLUSIONS

The present study describes the development and evaluation of a full-body PBPK model to assess systemic and individual organ exposure to epirubicin. Variability in epirubicin exposure was primarily driven by hepatic and renal UGT2B7 expression, plasma albumin concentration, age, BSA, GFR, haematocrit and sex.

Understanding the Risk of Drug Interactions Between Ritonavir-Containing COVID-19 Therapies and Small-Molecule Kinase Inhibitors in Patients With Cancer

PURPOSE

The introduction of COVID-19 therapies containing ritonavir has markedly expanded the scope of use for this medicine. As a strong cytochrome P450 3A4 inhibitor, the use of ritonavir is associated with a high drug interaction risk. There are currently no data to inform clinician regarding the likely magnitude and duration of interaction between ritonavir-containing COVID-19 therapies and small-molecule kinase inhibitors (KIs) in patients with cancer.

METHODS

Physiologically based pharmacokinetic modeling was used to conduct virtual clinical trials with a parallel group study design in the presence and absence of ritonavir (100 mg twice daily for 5 days). The magnitude and time course of changes in KI exposure when coadministered with ritonavir was evaluated as the primary outcome.

RESULTS

Dosing of ritonavir resulted in a > 2-fold increase in steady-state area under the plasma concentration-time curve and maximal concentration for six of the 10 KIs. When the KI was coadministered with ritonavir, dose reductions to between 10% and 75% of the original dose were required to achieve an area under the plasma concentration-time curve within 1.25-fold of the value in the absence of ritonavir.

CONCLUSION

To our knowledge, this study provides the first data to assist clinicians' understanding of the drug interaction risk associated with administering ritonavir-containing COVID-19 therapies to patients with cancer who are currently being treated with KIs. These data may support clinicians to make more informed dosing decisions for patients with cancer undergoing treatment with KIs who require treatment with ritonavir-containing COVID-19 antiviral therapies.

Evaluating the utility of therapeutic drug monitoring in the clinical use of small molecule kinase inhibitors: a review of the literature

Introduction: Orally administered small molecule kinase inhibitors (KI) are a key class of targeted anti-cancer medicines that have contributed substantially to improved survival outcomes in patients with advanced disease. Since the introduction of KIs in 2001, there has been a building body of evidence that the benefit derived from these drugs may be further enhanced by individualizing dosing on the basis of concentration.Areas covered: This review considers the rationale for individualized KI dosing and the requirements for robust therapeutic drug monitoring (TDM). Current evidence supporting TDM-guided KI dosing is presented and critically evaluated, and finally potential approaches to address translational challenges for TDM-guided KI dosing and alternate approaches to support individualization of KI dosing are discussed.Expert opinion: Intuitively, the individualization of KI dosing through an approach such as TDM-guided dosing has great potential to enhance the effectiveness and tolerability of these drugs. However, based on current literature evidence it is unrealistic to propose that TDM-guided KI dosing should be routinely implemented into clinical practice.