Relevance of Therapeutic Drug Monitoring of Tyrosine Kinase Inhibitors in Routine Clinical Practice: A Pilot Study

Model-Informed Dose Optimization of Pazopanib in Real-World Patients with Cancer

BACKGROUND AND OBJECTIVES

Pazopanib is approved for metastatic renal cell carcinoma (mRCC) and soft tissue sarcoma (STS) in a dose of 800 mg once daily (QD) taken under fasted conditions. In clinical practice, approximately 60% of patients require dose reductions due to toxicity, with severe liver toxicity necessitating treatment interruptions in over 10% of cases. While a trough concentration (Cmin,ss) target of ≥ 20.5 mg/L has been established for mRCC efficacy, no specific threshold exists for liver toxicity. The objectives of this study were to develop a population pharmacokinetic (POPPK), an exposure-liver toxicity, and an exposure-tumor size dynamics model to optimize pazopanib initial dose in real-world patients.

METHODS

In total, 135 patients were included and treated with a median starting dose of 800 mg (interquartile range, IQR: 600-800 mg) QD pazopanib fasted with a median follow-up of 120 (IQR 63-372) days. A population pharmacokinetic model was developed using 460 concentration measurements from 135 patients. Exposure-liver toxicity was evaluated using time-to-event modeling, and exposure-tumor size dynamics was evaluated using tumor growth modelling.

RESULTS

The liver toxicity model, with 27 cases of grade ≥ 2 liver toxicity out of 135 patients (20%), identified a Cmin,ss threshold of > 34 mg/L associated with a 3.35-fold increased toxicity risk (P < 0.01). Model simulations showed that an initial dose of 600 mg QD significantly reduced liver toxicity risk (P < 0.001) while maintaining Cmin,ss ≥ 20.5 mg/L for 76% of the simulated individuals. Tumor size dynamics were analyzed using baseline and posttreatment tumor size measurements from 111 patients. The introduction of primary resistance by using a mixture model improved the model fit significantly. Tumor growth and decay rates differed between mRCC and STS but showed no pazopanib exposure dependency across the studied range, suggesting maximal tumor inhibition at current exposure levels.

CONCLUSIONS

These findings suggest that an initial pazopanib dose of 600 mg fasted, followed by model-informed precision dosing to maintain Cmin,ss between 20 and 34 mg/L, may improve efficacy-toxicity balance and mitigate treatment interruptions.

From Prospective Evaluation to Practice: Model-Informed Dose Optimization in Oncology

One dose does not fit all, especially in oncolytic drugs, where side effects and therapy failures highlight the need for personalized dosing approaches. In recent years, the quest to apply model-informed precision dosing to oncology drugs has gained significant momentum, reflecting its potential to revolutionize patient care by tailoring treatments to individual pharmacokinetic profiles. Despite this progress, model-informed precision dosing has not (yet) become widely integrated into routine clinical care. We aimed to explain model-informed precision dosing from a clinical viewpoint while addressing all prospective model-informed precision dosing implementation and validation studies in the field of oncology. We identified 16 different drugs for which prospective model-informed precision dosing validation/implementation has been performed. Although these studies are mostly focused on attaining adequate drug exposures and reducing inter-individual variability, improved clinical outcomes after performing model-informed precision dosing were shown for busulfan, and high-dose methotrexate. Toxicities were significantly reduced for busulfan and cyclophosphamide treatment. In contrast, for carboplatin, for which model-informed precision dosing has been used in the Calvert formula, no prospective validation on outcomes was deemed necessary as the therapeutic window had been extensively validated. Model-informed precision dosing has shown to be of added value in oncology and is expected to significantly change dosing regimens in the future.

Body mass index affects imatinib exposure: Real-world evidence from TDM with adaptive dosing

BACKGROUND

Imatinib is the treatment of elderly or frail patients with chronic myeloid leukemia (CML). Trough levels of around 1000 ng/ml are considered as the target exposure.

OBJECTIVES

We searched for baseline parameters associated with imatinib pharmacokinetics, and studied the clinical impact of subsequent adaptive dosing.

METHODS

We present data from 60 adult CML patients upon imatinib with therapeutic drug monitoring (TDM) and adaptive dosing.

RESULTS

Mean trough levels after treatment initiation were 994.2 ± 560.6 ng/ml with 56% inter-patient variability). Only 29% of patients were in the therapeutic range. Body weight, height, body surface area, body mass index (BMI), and age were associated with imatinib plasma levels on univariate analysis. Age and BMI remained the only parameters associated with imatinib trough levels on multivariate analysis. As severe toxicities have been previously reported in patients with low BMI treated with standard imatinib, we evaluated the extent to which low BMI may lead to plasma overexposure. We found a statistically significant difference in trough imatinib levels in patients with BMI < 18.5 kg/m2, with exposure +61.5% higher than in patients with 18.5 < BMI ≤  24.9 and +76.3% higher than in patients with BMI ≥ 25. After TDM with adaptive dosing, a statistically significant difference in dosing between patients was observed, with doses ranging from 200 to 700 mg. No difference in toxicity or efficacy was observed regardless of BMI after adaptive dosing.

CONCLUSION

Our data suggest that low BMI has a significant impact on imatinib exposure but that pharmacokinetically-guided dosing limits its clinical impact in patients.

LC-MS/MS method for quantification of 23 TKIs in Plasma: Assessing the relationship between anlotinib trough concentration and toxicities

OBJECTIVES

To develop a simple, rapid, and sensitive LC-MS/MS method for quantifying 23 tyrosine kinase inhibitors (TKIs) in plasma samples, and evaluate the relationship between the trough concentration of anlotinib(ANL) and its toxicities.

METHODS

The method was developed in Agilent 1290-6460 UHPLC-MS/MS system. This study prospectively enrolled 55 cancer patients undergoing ANL treatment. Plasma samples were collected at steady-state trough concentration and subsequently analyzed using the method. Patients were recorded for the occurrence of toxicities. Statistical analysis was performed to assess the association of the toxicities with ANL exposure level and patients' characteristics.

RESULTS

The LC-MS/MS method was developed and validated for all items required by pharmacopoeia. The results revealed a positive association between the trough concentration of ANL and the incidence of toxicities. The exposure level 17.655 ng/mL (AUC 0.82, p = 0.010) was identified as a predictive threshold value for grade ≥ 3 overall toxicities. In addition, lower platelet count (PLT count < 179 × 109 g/L) was significantly associated with higher occurrence of grade ≥ 3 toxicities (AUC 0.75, p = 0.049). A logistic model incorporating these two factors demonstrated improved diagnostic capacity for predicting ≥ 3 overall toxicities (AUC = 0.90, p = 0.001).

CONCLUSIONS

This study successfully developed and validated a simple, rapid, and sensitive LC-MS/MS method for quantifying 23 TKIs in plasma samples. Besides, this study found that both Ctrough of ANL and PLT count as independent predictors for ANL-induced ≥ 3 overall toxicities. Moreover, a logistic model including these two factors presents better prediction capacity for ≥ 3 overall toxicities.

Model-informed precision dosing: State of the art and future perspectives

Model-informed precision dosing (MIPD) stands as a significant development in personalized medicine to tailor drug dosing to individual patient characteristics. MIPD moves beyond traditional therapeutic drug monitoring (TDM) by integrating mathematical predictions of dosing and considering patient-specific factors (patient characteristics, drug measurements) as well as different sources of variability. For this purpose, rigorous model qualification is required for the application of MIPD in patients. This review delves into new methods in model selection and validation, also highlighting the role of machine learning in improving MIPD, the utilization of biosensors for real-time monitoring, as well as the potential of models integrating biomarkers for efficacy or toxicity for precision dosing. The clinical evidence of TDM and MIPD is discussed for various medical fields including infection medicine, oncology, transplant medicine, and inflammatory bowel diseases, thereby underscoring the role of pharmacokinetics/pharmacodynamics and specific biomarkers. Further research, particularly randomized clinical trials, is warranted to corroborate the value of MIPD in enhancing patient outcomes and advancing personalized medicine.

Phenotype versus genotype to optimize cancer dosing in the clinical setting-focus on 5-fluorouracil and tyrosine kinase inhibitors

Cancer medicines often have narrow therapeutic windows; toxicity can be severe and sometimes fatal, but inadequate dose intensity reduces efficacy and survival. Determining the optimal dose for each patient is difficult, with body-surface area used most commonly for chemotherapy and flat dosing for tyrosine kinase inhibitors, despite accumulating evidence of a wide range of exposures in individual patients with many receiving a suboptimal dose with these strategies. Therapeutic drug monitoring (measuring the drug concentration in a biological fluid, usually plasma) (TDM) is an accepted and well validated method to guide dose adjustments for individual patients to improve this. However, implementing TDM in routine care has been difficult outside a research context. The development of genotyping of various proteins involved in drug elimination and activity has gained prominence, with several but not all Guideline groups recommending dose reductions for particular variant genotypes. However, there is increasing concern that dosing recommendations are based on limited data sets and may lead to unnecessary underdosing and increased cancer mortality. This Review discusses the evidence surrounding genotyping and TDM to guide decisions around best practice.

Validation of Liquid Chromatography Coupled with Tandem Mass Spectrometry for the Determination of 12 Tyrosine Kinase Inhibitors (TKIs) and Their Application to Therapeutic Drug Monitoring in Adult and Pediatric Populations

Tyrosine kinase inhibitors (TKIs) are used as targeted cancer therapies in adults and have an off-label pediatric application for the treatment of Langerhans cell histiocytosis. A multitarget LC-MS/MS method was developed and validated for the determination of alectinib, alectinib-M4, binimetinib, cobimetinib, crizotinib, dabrafenib, encorafenib, imatinib, lorlatinib, osimertinib, AZ5104, and trametinib. A total of 150 µL of internal standard methanolic solution was added to 50 µL of plasma sample to precipitate proteins. After centrifugation, 10 µL of the supernatant was injected into the chromatographic system. The chromatographic separation was conducted on a Kinetex C18 Polar column with a gradient of 2 mM ammonium formate in 0.1% formic acid and acetonitrile over 5 min. Limits of detection and quantification, linearity, accuracy, precision, selectivity, carryover, matrix effect, recovery, and stability were evaluated and satisfied EMA guidelines on bioanalytical methods. This method has been successfully applied to the therapeutic drug monitoring (TDM) of adults with melanoma and lung cancer, as well as children with histiocytosis, to improve the pharmacokinetic data for these drugs, with the aim of enhancing the therapeutic management and follow-up of patients. Blood concentrations of trametinib and binimetinib were different in the two groups, highlighting the age-related inter-individual variability of these molecules and the need for TDM.

Enhanced In Vitro and In Vivo Anticancer Activity Through the Development of Sunitinib-Loaded Nanoniosomes with Controlled Release and Improved Uptake

This study aims to develop sunitinib niosomal formulations and assess their in-vitro anti-cancer efficiency against lung cancer cell line, A549. Sunitinib, a highly effective anticancer drug, was loaded in the niosome with high encapsulation efficiency. Collagen was coated on the surface of the niosome for enhanced cellular uptake and prolonged circulation time. Different formulations were produced, while response surface methodology was utilized to optimize the formulations. The stability of the formulations was evaluated over a 2-month period, revealing the importance of collagen coating. MTT assay demonstrated dose-dependent cytotoxicity for all formulations against lung cancer cells. Scratch assay test suggested antiproliferative efficacy of the formulations. The flow cytometry data confirmed the improved cytotoxicity with enhanced apoptosis rate when different formulations used. The 2D fluorescent images proved the presence of drug-containing niosomes in the tumor cells. The activation of the apoptotic pathway leading to protein synthesis was confirmed using an ELISA assay, which specifically evaluated the presence of cas3 and cas7. The results of this study indicated the antiproliferative efficacy of optimized niosomal formulations and their mechanism of action. Therefore, niosomes could be utilized as a suitable carrier for delivering sunitinib into lung cancer cells, paving the way for future clinical studies.

Perspectives of Therapeutic Drug Monitoring of Biological Agents in Non-Infectious Uveitis Treatment: A Review

Biological drugs, especially those targeting anti-tumour necrosis factor α (TNFα) molecule, have revolutionized the treatment of patients with non-infectious uveitis (NIU), a sight-threatening condition characterized by ocular inflammation that can lead to severe vision threatening and blindness. Adalimumab (ADA) and infliximab (IFX), the most widely used anti-TNFα drugs, have led to greater clinical benefits, but a significant fraction of patients with NIU do not respond to these drugs. The therapeutic outcome is closely related to systemic drug levels, which are influenced by several factors such as immunogenicity, concomitant treatment with immunomodulators, and genetic factors. Therapeutic drug monitoring (TDM) of drug and anti-drug antibody (ADAbs) levels is emerging as a resource to optimise biologic therapy by personalising treatment to bring and maintain drug concentration within the therapeutic range, especially in those patients where a clinical response is less than expected. Furthermore, some studies have described different genetic polymorphisms that may act as predictors of response to treatment with anti-TNFα agents in immune-mediated diseases and could be useful in personalising biologic treatment selection. This review is a compilation of the published evidence in NIU and in other immune-mediated diseases that support the usefulness of TDM and pharmacogenetics as a tool to guide clinicians’ treatment decisions leading to better clinical outcomes. In addition, findings from preclinical and clinical studies, assessing the safety and efficacy of intravitreal administration of anti-TNFα agents in NIU are discussed.

Model-Informed Precision Dosing (MIPD)

Model-informed precision dosing (MIPD) is an advanced quantitative approach focusing on individualized dosage optimization, integrating complex mathematical and statistical models of drugs and disease combined with individual demographic and clinical patient characteristics [...].