Exposure-response relationship of cabozantinib in patients with metastatic renal cell carcinoma treated in routine care.

BACKGROUND: Interindividual pharmacokinetic variability may influence the clinical benefit or toxicity of cabozantinib in metastatic renal cell carcinoma (mRCC). We aimed to investigate the exposure-toxicity and exposure-response relationship of cabozantinib in unselected mRCC patients treated in routine care. METHODS: This ambispective multicenter study enrolled consecutive patients receiving cabozantinib in monotherapy. Steady-state trough concentration (Cmin,ss) within the first 3 months after treatment initiation was used for the PK/PD analysis with dose-limiting toxicity (DLT) and survival outcomes. Logistic regression and Cox proportional-hazards models were used to identify the risk factors of DLT and inefficacy in patients, respectively. RESULTS: Seventy-eight mRCC patients were eligible for the statistical analysis. Fifty-two patients (67%) experienced DLT with a median onset of 2.1 months (95%CI 0.7-8.2). In multivariate analysis, Cmin,ss was identified as an independent risk factor of DLT (OR 1.46, 95%CI [1.04-2.04]; p = 0.029). PFS and OS were not statistically associated with the starting dose (p = 0.81 and p = 0.98, respectively). In the multivariate analysis of PFS, Cmin, ss > 336 ng/mL resulted in a hazard ratio of 0.28 (95%CI, 0.10-0.77, p = 0.014). By contrast, Cmin, ss > 336 ng/mL was not statistically associated with longer OS. CONCLUSION: Early plasma drug monitoring may be useful to optimise cabozantinib treatment in mRCC patients treated in monotherapy, especially in frail patients starting at a lower than standard dose.

Relation between Plasma Trough Concentration of Pazopanib and Progression-Free Survival in Metastatic Soft Tissue Sarcoma Patients.

Background: Pazopanib (PAZ) is an oral angiogenesis inhibitor approved to treat soft tissue sarcoma (STS) but associated with a large interpatient pharmacokinetic (PK) variability and narrow therapeutic index. We aimed to define the specific threshold of PAZ trough concentration (Cmin) associated with better progression-free survival (PFS) in STS patients. Methods: In this observational study, PAZ Cmin was monitored over the treatment course. For the primary endpoint, the 3-month PFS in STS was analyzed with logistic regression. Second, we performed exposure−overall survival (OS) (Cox model plus Kaplan−Meier analysis/log-rank test) and exposure−toxicity analyses. Results: Ninety-five STS patients were eligible for pharmacokinetic/pharmacodynamic (PK/PD) assessment. In the multivariable analysis, PAZ Cmin < 27 mg/L was independently associated with a risk of progression at 3 months (odds ratio (OR) 4.21, 95% confidence interval (CI) (1.47−12.12), p = 0.008). A higher average of PAZ Cmin over the first 3 months was associated with a higher risk of grade 3−4 toxicities according to the NCI-CTCAE version 5.0 (OR 1.07 per 1 mg/L increase, CI95 (1.02−1.13), p = 0.007). Conclusion: PAZ Cmin ≥ 27 mg/L was independently associated with improved 3-month PFS in STS patients. Pharmacokinetically-guided dosing could be helpful to optimize the clinical management of STS patients in daily clinical practice.

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.

Clinical-Based vs. Model-Based Adaptive Dosing Strategy: Retrospective Comparison in Real-World mRCC Patients Treated with Sunitinib.

Different target exposures with sunitinib have been proposed in metastatic renal cell carcinoma (mRCC) patients, such as trough concentrations or AUCs. However, most of the time, rather than therapeutic drug monitoring (TDM), clinical evidence is preferred to tailor dosing, i.e., by reducing the dose when treatment-related toxicities show, or increasing dosing if no signs of efficacy are observed. Here, we compared such empirical dose adjustment of sunitinib in mRCC patients, with the parallel dosing proposals of a PK/PD model with TDM support. In 31 evaluable patients treated with sunitinib, 53.8% had an empirical change in dosing after treatment started (i.e., 46.2% decrease in dosing, 7.6% increase in dosing). Clinical benefit was observed in 54.1% patients, including 8.3% with complete response. Overall, 58.1% of patients experienced treatment discontinuation eventually, either because of toxicities or progressive disease. When choosing 50-100 ng/mL trough concentrations as a target exposure (i.e., sunitinib + active metabolite N-desethyl sunitinib), 45% patients were adequately exposed. When considering 1200-2150 ng/mL.h as a target AUC (i.e., sunitinib + active metabolite N-desethyl sunitinib), only 26% patients were in the desired therapeutic window. TDM with retrospective PK/PD modeling would have suggested decreasing sunitinib dosing in a much larger number of patients as compared with empirical dose adjustment. Indeed, when using target trough concentrations, the model proposed reducing dosing for 61% patients, and up to 84% patients based upon target AUC. Conversely, the model proposed increasing dosing in 9.7% of patients when using target trough concentrations and in 6.5% patients when using target AUC. Overall, TDM with adaptive dosing would have led to tailoring sunitinib dosing in a larger number of patients (i.e., 53.8% vs. 71-91%, depending on the chosen metrics for target exposure) than a clinical-based decision. Interestingly, sunitinib dosing was empirically reduced in 41% patients who displayed early-onset severe toxicities, whereas model-based recommendations would have immediately proposed to reduce dosing in more than 80% of those patients. This observation suggests that early treatment-related toxicities could have been partly avoided using prospective PK/PD modeling with adaptive dosing. Conversely, the possible impact of model-based adapted dosing on efficacy could not be fully evaluated because no clear relationship was found between baseline exposure levels and sunitinib efficacy measured at 3 months.

Estradiol Regulates mRNA Levels of Estrogen Receptor Beta 4 and Beta 5 Isoforms and Modulates Human Granulosa Cell Apoptosis.

Estrogen receptor beta (ERß) plays a critical role in granulosa cell (GC) functions. The existence of four human ERß splice isoforms in the ovary suggests their differential implication in 17ß-estradiol (E2) actions on GC apoptosis causing follicular atresia. In this study, we investigated whether E2 can regulate ERß isoforms expression to fine tune its apoptotic activities in human GC. For this purpose, we measured by RT-qPCR the expression of ERß isoforms in primary culture of human granulosa cells (hGCs) collected from patients undergoing in vitro fertilization, before and after E2 exposure. Besides, we assessed the potential role of ERß isoforms on cell growth and apoptosis after their overexpression in a human GC line (HGrC1 cells). We confirmed that ERß1, ERß2, ERß4, and ERß5 isoform mRNAs were predominant over that of ERα in hGCs, and found that E2 selectively regulates mRNA levels of ERß4 and ERß5 isoforms in these cells. In addition, we demonstrated that overexpression of ERß1 and ERß4 in HGrC1 cells increased cell apoptosis by 225% while ERß5 or ERß2 had no effect. Altogether, our study revealed that E2 may influence GC fate by specifically regulating the relative abundance of ERß isoforms mRNA to modulate the balance between pro-apoptotic and non-apoptotic ERß isoforms.

A novel method based on unbiased correlations tests for covariate selection in nonlinear mixed effects models: The COSSAC approach.

Building a covariate model is a crucial task in population pharmacokinetics and pharmacodynamics in order to understand the determinants of the interindividual variability. Identifying a good covariate model usually requires many runs. Several procedures have been proposed in the past to automatize this task. The most commonly used is Stepwise Covariate Modeling (SCM). Here, we present a novel stepwise method based on statistical tests between individual parameters sampled from their conditional distribution and the covariates. This strategy, called the COnditional Sampling use for Stepwise Approach based on Correlation tests (COSSAC), makes use of the information contained in the current model to choose which parameter-covariate relationship to try next. This strategy greatly reduces the number of covariate models tested, while retaining on its search path the models improving the log-likelihood (LL). In this article, we detail the COSSAC method and its implementation in Monolix, and evaluate its performance. The performance was assessed by comparing COSSAC to the traditional SCM method on 17 representative data sets. For the large majority of cases (15 out of 17), the final covariate model is identical (11 cases) or very similar (4 cases with LL differences less than 3.84) with both procedures. Yet, COSSAC requires between 2 to 20 times fewer runs than SCM. This represents a decisive speed up, especially for models that take long to run and would not be tractable using the SCM method.

Efficient Pharmacokinetic Modeling Workflow With the MonolixSuite: A Case Study of Remifentanil.

MonolixSuite is a software widely used for model-based drug development. It contains interconnected applications for data visualization, noncompartmental analysis, nonlinear mixed effect modeling, and clinical trial simulations. Its main assets are ease of use via an interactive graphical interface, computation speed, and efficient parameter estimation even for complex models. This tutorial presents a step-by-step pharmacokinetic (PK) modeling workflow using MonolixSuite, including how to visualize the data, set up a population PK model, estimate parameters, and diagnose and improve the model incrementally.