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

Docetaxel, cyclophosphamide, and epirubicin: application of PBPK modeling to gain new insights for drug-drug interactions

The new adjuvant chemotherapy of docetaxel, epirubicin, and cyclophosphamide has been recommended for treating breast cancer. It is necessary to investigate the potential drug-drug Interactions (DDIs) since they have a narrow therapeutic window in which slight differences in exposure might result in significant differences in treatment efficacy and tolerability. To guide clinical rational drug use, this study aimed to evaluate the DDI potentials of docetaxel, cyclophosphamide, and epirubicin in cancer patients using physiologically based pharmacokinetic (PBPK) models. The GastroPlus™ was used to develop the PBPK models, which were refined and validated with observed data. The established PBPK models accurately described the pharmacokinetics (PKs) of three drugs in cancer patients, and the predicted-to-observed ratios of all the PK parameters met the acceptance criterion. The PBPK model predicted no significant changes in plasma concentrations of these drugs during co-administration, which was consistent with the observed clinical phenomenon. Besides, the verified PBPK models were then used to predict the effect of other Cytochrome P450 3A4 (CYP3A4) inhibitors/inducers on these drug exposures. In the DDI simulation, strong CYP3A4 modulators changed the exposure of three drugs by 0.71-1.61 fold. Therefore, patients receiving these drugs in combination with strong CYP3A4 inhibitors should be monitored regularly to prevent adverse reactions. Furthermore, co-administration of docetaxel, cyclophosphamide, or epirubicin with strong CYP3A4 inducers should be avoided. In conclusion, the PBPK models can be used to further investigate the DDI potential of each drug and to develop dosage recommendations for concurrent usage by additional perpetrators or victims.

Population Pharmacokinetics in Oncology and Its Clinical Applications

Most traditional cytotoxic drugs are characterized by steep dose-response relationships and narrow therapeutic windows [...].

Preliminary Investigation of a Rapid and Feasible Therapeutic Drug Monitoring Method for the Real-Time Estimation of Blood Pazopanib Concentrations

Pazopanib is a multi-kinase inhibitor used to treat advanced/metastatic renal cell carcinoma and advanced soft tissue tumors; however, side effects such as diarrhea and hypertension have been reported, and dosage adjustment based on drug concentration in the blood is necessary. However, measuring pazopanib concentrations in blood using the existing methods is time-consuming; and current dosage adjustments are made using the results of blood samples taken at the patient's previous hospital visit (approximately a month prior). If the concentration of pazopanib could be measured during the waiting period for a doctor's examination at the hospital (in approximately 30 min), the dosage could be adjusted according to the patient's condition on that day. Therefore, we aimed to develop a method for rapidly measuring blood pazopanib concentrations (in approximately 25 min) using common analytical devices (a tabletop centrifuge and a spectrometer). This method allowed for pazopanib quantification in the therapeutic concentration range (25-50 μg/mL). Additionally, eight popular concomitant medications taken simultaneously with pazopanib did not interfere with the measurements. We used the developed method to measure blood concentration in two patients and obtained similar results to those measured using the previously reported HPLC method. By integrating it with the point of care and sample collection by finger pick, this method can be used for measurements in pharmacies and patients' homes. This method can maximize the therapeutic effects of pazopanib by dose adjustment to control adverse events.

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

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.

Ultrasensitive detection of imatinib in human serum using a gold-based paper sensor

Imatinib is the tyrosine kinase inhibitor of choice for the treatment of chronic myeloid leukemia and gastrointestinal stromal tumors. However, imatinib has drawbacks such as drug resistance and significant differences in pharmacokinetics within patients. Therefore, a colloidal gold-based immunochromatographic assay (CG-IA) was developed for measuring and monitoring imatinib in human serum. An imatinib derivative containing carboxyl groups was used for the synthesis of the immunogen, and 4-(4-methyl-1-piperazinylmethyl) benzoic acid was selected as the hapten for the heterologous coating antigen. Next, a highly sensitive and specific monoclonal antibody (mAb), 2F7 was screened for the construction of a CG-IA, with an IC50 value of 0.091 ng/mL. For the qualification of imatinib in human serum, the visual limit of detection (vLOD) and cut-off values of the CG-IA were 2 and 20 ng/mL, respectively. For quantitative detection, the calculated LOD value of the CG-IA was 0.068 ng/mL, with a linearity range of 1.004 and 23.087 ng/mL. The recovery rate of spiked serum samples was between 88.24 % and 104.75 %. In addition, the concentration of imatinib in the serum samples from 10 patients was detected by CG-IA and revealed a good correlation with those from LC-MS/MS. These results indicated that the developed gold-based paper sensor could become an effective tool for the rapid monitoring of imatinib in human serum samples.

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.

Therapeutic Drug Monitoring of Protein Kinase Inhibitors in the Treatment of Non-small Cell Lung Cancer

Targeted therapy with protein kinase inhibitors (PKIs) represents one of the important treatment options for non-small cell lung cancer (NSCLC). It has contributed to improve patients' survival and quality of life significantly. These anticancer drugs are administrated orally in flat-fixed doses despite the well-known large interpatient pharmacokinetic variability and the possible need for dose individualization. To optimize and individualize dosing of PKIs, and thereby increasing the effectiveness and safety of the treatment, therapeutic drug monitoring (TDM) is the most frequently mentioned method. Unlike other areas of medicine, TDM has been rather exceptional in oncological practise since there is a little evidence or no data for concentration-effect relationships of PKIs. Therefore, the aim of this review is to summarize the pharmacokinetic characteristics of PKIs and provide the evidence supporting the use of TDM for personalised treatment of patients with NSCLC.

Translational proteomics and phosphoproteomics: Tissue to extracellular vesicles

We are currently experiencing a rapidly developing era in terms of translational and clinical medical sciences. The relatively mature state of nucleic acid examination has significantly improved our understanding of disease mechanism and therapeutic potential of personalized treatment, but misses a large portion of phenotypic disease information. Proteins, in particular phosphorylation events that regulates many cellular functions, could provide real-time information for disease onset, progression and treatment efficacy. The technical advances in liquid chromatography and mass spectrometry have realized large-scale and unbiased proteome and phosphoproteome analyses with disease relevant samples such as tissues. However, tissue biopsy still has multiple shortcomings, such as invasiveness of sample collection, potential health risk for patients, difficulty in protein preservation and extreme heterogeneity. Recently, extracellular vesicles (EVs) have offered a great promise as a unique source of protein biomarkers for non-invasive liquid biopsy. Membranous EVs provide stable preservation of internal proteins and especially labile phosphoproteins, which is essential for effective routine biomarker detection. To aid efficient EV proteomic and phosphoproteomic analyses, recent developments showcase clinically-friendly EV techniques, facilitating diagnostic and therapeutic applications. Ultimately, we envision that with streamlined sample preparation from tissues and EVs proteomics and phosphoproteomics analysis will become routine in clinical settings.

Dose Individualization of Oral Multi-Kinase Inhibitors for the Implementation of Therapeutic Drug Monitoring

Oral multi-kinase inhibitors have transformed the treatment landscape for various cancer types and provided significant improvements in clinical outcomes. These agents are mainly approved at fixed doses, but the large inter-individual variability in pharmacokinetics and pharmacodynamics (efficacy and safety) has been an unsolved clinical issue. For example, certain patients treated with oral multi-kinase inhibitors at standard doses have severe adverse effects and require dose reduction and discontinuation, yet other patients have a suboptimal response to these drugs. Consequently, optimizing the dosing of oral multi-kinase inhibitors is important to prevent over-dosing or under-dosing. To date, multiple studies on the exposure-efficacy/toxicity relationship of molecular targeted therapy have been attempted for the implementation of therapeutic drug monitoring (TDM) strategies. In this milieu, we recently conducted research on several multi-kinase inhibitors, such as sunitinib, pazopanib, sorafenib, and lenvatinib, with the aim to optimize their treatment efficacy using a pharmacokinetic/pharmacodynamic approach. Among them, sunitinib use is an example of successful TDM implementation. Sunitinib demonstrated a significant correlation between drug exposure and treatment efficacy or toxicities. As a result, TDM services for sunitinib has been covered by the National Health Insurance program in Japan since April 2018. Additionally, other multi-kinase targeted anticancer drugs have promising data regarding the exposure-efficacy/toxicity relationship, suggesting the possibility of personalization of drug dosage. In this review, we provide a comprehensive summary of the clinical evidence for dose individualization of multi-kinase inhibitors and discuss the utility of TDM of multi-kinase inhibitors, especially sunitinib, pazopanib, sorafenib, and lenvatinib.