Model-Based Biomarker Selection for Dose Individualization of Tyrosine-Kinase Inhibitors

Risk-benefit trade-offs and precision utilities in phase I-II clinical trials

BACKGROUND

Identifying optimal doses in early-phase clinical trials is critically important. Therapies administered at doses that are either unsafe or biologically ineffective are unlikely to be successful in subsequent clinical trials or to obtain regulatory approval. Identifying appropriate doses for new agents is a complex process that involves balancing the risks and benefits of outcomes such as biological efficacy, toxicity, and patient quality of life.

PURPOSE

While conventional phase I trials rely solely on toxicity to determine doses, phase I-II trials explicitly account for both efficacy and toxicity, which enables them to identify doses that provide the most favorable risk-benefit trade-offs. It is also important to account for patient covariates, since one-size-fits-all treatment decisions are likely to be suboptimal within subgroups determined by prognostic variables or biomarkers. Notably, the selection of estimands can influence our conclusions based on the prognostic subgroup studied. For example, assuming monotonicity of the probability of response, higher treatment doses may yield more pronounced efficacy in favorable prognosis compared to poor prognosis subgroups when the estimand is mean or median survival. Conversely, when the estimand is the 3-month survival probability, higher treatment doses produce more pronounced efficacy in poor prognosis compared to favorable prognosis subgroups.

METHODS AND CONCLUSIONS

Herein, we first describe why it is essential to consider clinical practice when designing a clinical trial and outline a stepwise process for doing this. We then review a precision phase I-II design based on utilities tailored to prognostic subgroups that characterize efficacy-toxicity risk-benefit trade-offs. The design chooses each patient's dose to optimize their expected utility and allows patients in different prognostic subgroups to have different optimal doses. We illustrate the design with a dose-finding trial of a new therapeutic agent for metastatic clear cell renal cell carcinoma.

Optimization of blood pressure measurement practices for pharmacodynamic analyses of tyrosine-kinase inhibitors

Blood pressure measurements form a critical component of adverse event monitoring for tyrosine kinase inhibitors, but might also serve as a biomarker for dose titrations. This study explored the impact of various sources of within-individual variation on blood pressure readings to improve measurement practices and evaluated the utility for individual- and population-level dose selection. A pharmacokinetic-pharmacodynamic modeling framework was created to describe circadian blood pressure changes, inter- and intra-day variability, changes from dipper to non-dipper profiles, and the relationship between drug exposure and blood pressure changes over time. The framework was used to quantitatively evaluate the influence of physiological and pharmacological aspects on blood pressure measurements, as well as to compare measurement techniques, including office-based, home-based, and ambulatory 24-h blood pressure readings. Circadian changes, as well as random intra-day and inter-day variability, were found to be the largest sources of within-individual variation in blood pressure. Office-based and ambulatory 24-h measurements gave rise to potential bias (>5 mmHg), which was mitigated by model-based estimations. Our findings suggest that 5-8 consecutive, home-based, measurements taken at a consistent time around noon, or alternatively within a limited time frame (e.g., 8.00 a.m. to 12.00 p.m. or 12.00 p.m. to 5.00 p.m.), will give rise to the most consistent blood pressure estimates. Blood pressure measurements likely do not represent a sufficiently accurate method for individual-level dose selection, but may be valuable for population-level dose identification. A user-friendly tool has been made available to allow for interactive blood pressure simulations and estimations for the investigated scenarios.

Adaptive dosing of sunitinib in a metastatic renal cell carcinoma patient: when in silico modeling helps to go quicker to the point

PURPOSE

Adaptive dosing strategy with oral targeted therapies in oncology is mostly based upon clinical signs. Using pharmacokinetics (PK) models to customize dosing could help saving time, i.e., by predicting clinical outcome through early monitoring of drug levels.

CASE REPORT

We present the case of a metastatic renal cell carcinoma patient treated with standard Sunitinib dosing (i.e., 50 mg QD). Clinical signs suggested lack of efficacy. Therapeutic Drug Monitoring (TDM) confirmed that exposure was below the expected target exposure. PK modeling suggested that dosing could be increased safely to 75 mg QD. Sunitinib dosing was instead changed empirically to 62.5 mg only, increasing drug exposure to the lower part of the therapeutic window. Resolution of bone pains plus Stable Disease were observed. Even though further modeling suggested to increase Sunitinib dosing to 75 mg again, the intermediate dosing was maintained for the subsequent cycles to preserve the safety. Unfortunately, severe pains plus degradation of the general state were reported and imaging showed Progressive Disease. The patient was finally switched to alternative therapy, without being treated at the 75 mg level of Suntitinib.

CONCLUSIONS AND DISCUSSION

This case suggests that model-based adaptive dosing could have allowed to reach quicker the best dosing with Sunitinib, thus possibly ensuring a better management of this patient. Model-informed dosing should be used instead of empirical search for the most appropriate dosing to ensure a good benefit/risk ratio with Sunitinib, especially in the context of such aggressive disease.

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.