Population pharmacokinetic/pharmacodynamic modeling for the time course of tumor shrinkage by motesanib in thyroid cancer patients

Population Pharmacokinetic Analysis of Tucatinib in Healthy Participants and Patients with Breast Cancer or Colorectal Cancer

BACKGROUND AND OBJECTIVE

Tucatinib is a highly selective, oral, reversible, human epidermal growth factor receptor 2 (HER2)-specific tyrosine kinase inhibitor. Tucatinib is approved at a 300-mg twice-daily dose in adults in combination with trastuzumab and capecitabine for advanced HER2-postitive (HER2+) unresectable or metastatic breast cancer and in combination with trastuzumab for RAS wild-type HER2+ unresectable or metastatic colorectal cancer. This study sought to characterize the pharmacokinetics (PK) and assess sources of PK variability of tucatinib in healthy volunteers and in patients with HER2+ metastatic breast or colorectal cancers.

METHODS

A population pharmacokinetic model was developed based on data from four healthy participant studies and three studies in patients with either HER2+ metastatic breast cancer or metastatic colorectal cancer using a nonlinear mixed-effects modeling approach. Clinically relevant covariates were evaluated to assess their impact on exposure, and overall model performance was evaluated by prediction-corrected visual predictive checks.

RESULTS

A two-compartment pharmacokinetic model with linear elimination and first-order absorption preceded by a lag time adequately described tucatinib pharmacokinetic profiles in 151 healthy participants and 132 patients. Tumor type was identified as a significant covariate affecting tucatinib bioavailability and clearance, resulting in a 1.2-fold and 2.1-fold increase in tucatinib steady-state exposure (area under the concentration-time curve) in HER2+ metastatic colorectal cancer and HER2+ metastatic breast cancer, respectively, compared with healthy participants. No other covariates, including mild renal or hepatic impairment, had an impact on tucatinib pharmacokinetics.

CONCLUSIONS

The impact of statistically significant covariates identified was not considered clinically meaningful. No tucatinib dose adjustments are required based on the covariates tested in the final population pharmacokinetic model.

CLINICAL TRIAL REGISTRATION

NCT03723395, NCT03914755, NCT03826602, NCT03043313, NCT01983501, NCT02025192.

Enhancing targeted tumor treatment: A novel fuzzy logic framework for precision drug delivery strategy selection

OBJECTIVE

This study aims to address the challenge of selecting optimal drug delivery strategies for tumor patients by introducing a novel theoretical framework.

METHODS

We propose a fuzzy logic-based framework for quantitatively assessing Health States (HS) in tumor patients. This framework integrates quantified HS assessments with causality strength analyses, offering a comprehensive understanding of various drug delivery schemes' effectiveness from pharmacokinetic and pharmacodynamic perspectives.

RESULTS

The efficacy of our approach is demonstrated through a series of real-world patient case studies, highlighting its potential to enhance the evaluation and selection of targeted drug delivery strategies.

CONCLUSION

Our work contributes to the field by showcasing practical applications of fuzzy logic in targeted drug delivery systems (TDDs) and establishing a new benchmark for precision in drug delivery strategy selection.

SIGNIFICANCE

This study has significant implications for developing personalized medical treatments, potentially revolutionizing the field with a more nuanced and scientifically rigorous method for evaluating and selecting drug delivery protocols.

CONTRIBUTIONS

Development of a fuzzy logic framework for precise quantification of health states in tumor patients. Innovative integration of a causal system for comprehensively evaluating targeted drug delivery strategies.

Population pharmacokinetic-pharmacodynamic modeling of serum biomarkers as predictors of tumor dynamics following lenvatinib treatment in patients with radioiodine-refractory differentiated thyroid cancer (RR-DTC)

Lenvatinib is a receptor tyrosine kinase (RTK) inhibitor targeting vascular endothelial growth factor (VEGF) receptors 1-3, fibroblast growth factor (FGF) receptors 1-4, platelet-derived growth factor receptor-α (PDGFRα), KIT, and RET that have been implicated in pathogenic angiogenesis, tumor growth, and cancer. The primary objective of this work was to evaluate, by establishing quantitative relationships, whether lenvatinib exposure and longitudinal serum biomarker data (VEGF, Ang-2, Tie-2, and FGF-23) are predictors for change in longitudinal tumor size which was assessed based on data from 558 patients with radioiodine-refractory differentiated thyroid cancer (RR-DTC) receiving either lenvatinib or placebo treatment. Lenvatinib PK was best described by a 3-compartment model with simultaneous first- and zero-order absorption and linear elimination from the central compartment with significant covariates (body weight, albumin <30 g/dL, ALP>ULN, RR-DTC, RCC, HCC subjects, and concomitant CYP3A inhibitors). Except for body weight, none of the covariates have any clinically meaningful effect on exposure to lenvatinib. Longitudinal biomarker measurements over time were reasonably well defined by a PK/PD model with common EC50, Emax, and a slope for disease progression for all biomarkers. Longitudinal tumor measurements over time were reasonably well defined by a tumor growth inhibition Emax model, which in addition to lenvatinib exposure, included model-predicted relative changes from baseline over time for Tie-2 and Ang-2 as having significant association with tumor response. The developed PK/PD models pave the way for dose optimization and potential prediction of clinical response.

Tumor growth inhibition modeling to support the starting dose for dacomitinib

Dacomitinib is a second-generation, irreversible EGFR tyrosine kinase inhibitor for first-line treatment of patients with metastatic non-small cell lung cancer and EGFR-activating mutations. A high rate of dose reductions in the pivotal trial led to an observed inverse exposure-response (ER) relationship with the primary end points. Three ER models were developed to determine if the starting dose from the pivotal trial, 45 mg once daily (q.d.) dose, is appropriate: a longitudinal logistic regression model for adverse event-related dose changes, a Claret tumor growth inhibition (TGI) model, and a Cox model for progression-free survival (PFS) based on the TGI model predictions. This analysis included 266 patients taking dacomitinib with a starting dose of 45 mg (N = 250) or 30 mg (N = 16) q.d. The ER relationships with the time-varying exposure metrics, most recent maximum plasma concentration (C_max ) and average concentration (C_avg ) from the first dose, were established for the dose reduction and TGI models, respectively. The TGI model characterized the tumor inhibition over time with constant growth rate (k_L  = 0.0012 years^-1 ) and highly variable kill rate (k_D  = 1.002 years^-1 /[μg/L]^θcavg , coefficient of variation [CV] = 89%) and drug resistance (λ = 14.47 years^-1 , CV = 96%) leading to prolonged tumor shrinkage. The ER relationship was characterized using an exposure parameter with a power parameterization (θcavg = 0.454, p < 0.0001). The Cox model found that baseline tumor size (p = 0.0166) and week 8 tumor shrinkage rate (p = 0.0726) were the best predictors of PFS. Simulations of dose reductions and drug interruptions on tumor shrinkage over time showed greater and more prolonged tumor shrinkage with a starting dose of 45 mg q.d.

Exposure-response analysis and simulation of lenvatinib safety and efficacy in patients with radioiodine-refractory differentiated thyroid cancer

Purpose

Once-daily lenvatinib 24 mg is the approved dose for radioiodine-refractory differentiated thyroid cancer. In a phase 3 trial with lenvatinib, the starting dose of 24 mg was associated with a relatively high incidence of adverse events that required dose reductions. We used an exposure–response model to investigate the risk–benefit of different dosing regimens for lenvatinib.

Methods

A population pharmacokinetics/pharmacodynamics modeling analysis was used to simulate the potential benefit of lower starting doses to retain efficacy with improved safety. The seven lenvatinib regimens tested were: 24 mg; and 20 mg, 18 mg, and 14 mg, all with or without up-titration to 24 mg. Exposure–response models for efficacy and safety were created using a 24-week time course.

Results

The approved dose of lenvatinib at 24 mg, predicted the best efficacy. However, the lenvatinib dosing regimens of 14 mg with up-titration or 18 mg without up-titration potentially provides comparable efficacy (objective response rate at 24 weeks) and a better safety profile.

Conclusions

Treatment with lenvatinib at starting doses lower than the approved once-daily 24 mg dose could provide comparable antitumor efficacy and a similar or better safety profile. Based on the results from this modeling and simulation study, a comparator dose of lenvatinib 18 mg without up-titration was selected for evaluation in a clinical trial.

Electronic supplementary material

The online version of this article (10.1007/s00280-018-3687-4) contains supplementary material, which is available to authorized users.

A population pharmacokinetic model of cabozantinib in healthy volunteers and patients with various cancer types

Purpose

An integrated population pharmacokinetic (popPK) model was developed to describe the pharmacokinetics (PK) of tyrosine kinase inhibitor cabozantinib in healthy volunteers (HVs) and patients with various cancer types and to identify any differences in cabozantinib PK across these populations.

Methods

Plasma concentration data used to develop the popPK model were obtained from nine clinical trials (8072 concentrations from 1534 HVs or patients) of cabozantinib in HVs and patients with renal cell carcinoma (RCC), medullary thyroid carcinoma (MTC), glioblastoma multiforme, castration-resistant prostate cancer, or other advanced malignancies.

Results

PK data across studies were adequately characterized by a two-compartment disposition model with dual first- and zero-order absorption processes and first-order elimination. Baseline demographic covariates (age, weight, gender, race, and cancer type) were generally predicted to have a small-to-moderate impact on apparent clearance (CL/F). However, MTC cancer type did show an approximately 93% higher CL/F relative to HVs following chronic dosing, resulting in approximately 40–50% lower predicted steady-state cabozantinib plasma concentrations.

Conclusion

This popPK analysis showed cabozantinib CL/F values to be higher for patients with MTC and may account for the higher dosage required in this patient population (140-mg) to achieve plasma exposures comparable to those in patients with RCC and other tumor types administered a 60-mg cabozantinib tablet dose. Possible factors that may underlie the higher cabozantinib clearance observed in MTC patients are discussed.

Electronic supplementary material

The online version of this article (10.1007/s00280-018-3581-0) contains supplementary material, which is available to authorized users.

Aggressive differentiated thyroid cancer

Differentiated thyroid cancer is characteristically associated with an innocuous clinical course, but a minority of cases may manifest surprisingly aggressive behaviour. Such aggressive DTC are directly responsible for the majority of thyroid cancer related deaths. Moreover, they contribute indirectly to increased DTC-related morbidity, because our inability to differentiate these tumours from innocuous DTC at an early stage fuels a significant degree of DTC overtreatment around the globe. In the present paper we describe how improved understanding of the clinicopathological thyroid tumour progression model and optimization of clinical staging systems continues to improve our ability to diagnose and treat aggressive DTC. Early recognition of aggressive DTC allows instillation of an aggressive management strategy which is based upon surgical-oncologic completeness, and minimization of treatment-related sequelae through continued development of reconstructive options and focussed delivery of adjuvant treatments.

Effects of a Multikinase Inhibitor Motesanib (AMG 706) Alone and Combined with the Selective DuP-697 COX-2 Inhibitor on Colorectal Cancer Cells

In the present study, we investigated the effects of motesanib (AMG 706), a multikinase inhibitor alone and in combination with DuP-697, an irreversible selective inhibitor of COX-2, on cell proliferation, angiogenesis, and apoptosis induction in a human colorectal cancer cell line (HT29). Real time cell analysis (RTCA, Xcelligence system) was used to determine the effects on colorectal cancer cell proliferation. Apoptosis was assessed with annexin V staining and angiogenesis was determined with chorioallantoic membrane model. We found that motesanib alone exerted antiproliferative, antiangiogenic and apoptotic effects on HT29 colorectal cancer cells. Combination with DUP-697 increased the antiproliferative, antiangiogenic and apoptotic effects. Results of this study indicate that motesanib may be a good choice in treatment of colorectal tumors. In addition, the increased effects of combination of motesanib with DuP-697 raise the possibility of using lower doses of these drugs and therefore avoid/minimize the dose-dependent side effects generally observed.

Models for change in tumour size, appearance of new lesions and survival probability in patients with advanced epithelial ovarian cancer

AIMS

The aims of this study were (i) to develop a modelling framework linking change in tumour size during treatment to survival probability in metastatic ovarian cancer; and (ii) to model the appearance of new lesions and investigate their relationship with survival and disease characteristics.

METHODS

Data from a randomized Phase III clinical trial comparing carboplatin monotherapy to gemcitabine plus carboplatin combotherapy in 336 patients with metastatic ovarian cancer were used. A population model describing change in tumour size based on drug treatment information was established and its relationship with time to appearance of new lesions and survival were investigated with time to event models.

RESULTS

The tumour size profiles were well characterized as evaluated by visual predictive checks. Metastasis in the liver at enrolment and change in tumour size up to week 12 were predictors of time to appearance of new lesions. Survival was predicted based on the patient tumour size and ECOG performance status at enrolment and on appearance of new lesions during treatment and change in tumour size up to week 12. Tumour size and survival data from a separate study were adequately predicted.

CONCLUSIONS

The proposed models simulate tumour dynamics following treatment and provide a link to the probability of developing new lesions as well as to survival. The models have potential to be used for optimizing the design of late phase clinical trials in metastatic ovarian cancer based on early phase clinical study results and simulation.

Population pharmacokinetic modeling of motesanib and its active metabolite, M4, in cancer patients

Motesanib is a small molecule and potent multikinase inhibitor with antiangiogenic and antitumor activity. Population pharmacokinetic (POPPK) modeling of motesanib and M4, an active metabolite, was performed to assess sources of variability in cancer patients. The analysis included data collected from 451 patients from 8 clinical trials with oral doses of motesanib ranging from 25 to 175 mg, either once daily or twice daily. The POPPK analyses were performed using nonlinear mixed-effect models with a sequential approach. Covariate effects of demographics and other baseline characteristics were assessed with stepwise covariate modeling. A 2-compartment model with food effect on absorption parameters fitted the PK data of motesanib well. The effects albumin and sex on apparent clearance (CL/F) of motesanib were statistically significant. The albumin effect was more important but remained below a 25% difference. A 1-compartment model fitted PK data of M4 well. Effects of race (Asian vs non-Asian) and dosing frequency were identified as statistically significant covariates on the CL/F of M4. The maximum effect of albumin would result in less than 25% change in motesanib CL/F and as such would not warrant any dosing adjustment. However, faster elimination of M4 in Asian patients requires further investigation.

Population pharmacokinetic-pharmacodynamic modelling in oncology: a tool for predicting clinical response

In oncology trials, overall survival (OS) is considered the most reliable and preferred endpoint to evaluate the benefit of drug treatment. Other relevant variables are also collected from patients for a given drug and its indication, and it is important to characterize the dynamic effects and links between these variables in order to improve the speed and efficiency of clinical oncology drug development. However, the drug-induced effects and causal relationships are often difficult to interpret because of temporal differences. To address this, population pharmacokinetic–pharmacodynamic (PKPD) modelling and parametric time-to-event (TTE) models are becoming more frequently applied. Population PKPD and TTE models allow for exploration towards describing the data, understanding the disease and drug action over time, investigating relevance of biomarkers, quantifying patient variability and in designing successful trials. In addition, development of models characterizing both desired and adverse effects in a modelling framework support exploration of risk-benefit of different dosing schedules. In this review, we have summarized population PKPD modelling analyses describing tumour, tumour marker and biomarker responses, as well as adverse effects, from anticancer drug treatment data. Various model-based metrics used to drive PD response and predict OS for oncology drugs and their indications are also discussed.

Rilotumumab exposure-response relationship in patients with advanced or metastatic gastric cancer

PURPOSE

Rilotumumab is an investigational, fully human monoclonal antibody to hepatocyte growth factor. In a randomized phase II study, trends toward improved survival were observed with rilotumumab (7.5 or 15 mg/kg) plus epirubicin, cisplatin, and capecitabine (ECX) versus placebo plus ECX in gastric/gastroesophageal junction (GEJ) cancer patients, especially in MET-positive patients. Here, we quantitatively characterized the longitudinal exposure-response [tumor growth (TG) and overall survival (OS)] relationship for rilotumumab.

EXPERIMENTAL DESIGN

Rilotumumab concentrations, tumor sizes, and survival time from the phase II study were pooled to develop a longitudinal exposure versus TG model and parametric OS model that explored predictive/prognostic/treatment effects (MET expression, rilotumumab exposure, relative tumor size). Model evaluation included visual predictive checks, nonparametric bootstrap, and normalized prediction distribution errors. Simulations were undertaken to predict the relationship between rilotumumab dose and OS.

RESULTS

Rilotumumab exhibited linear time-independent pharmacokinetics not affected by MET expression. The TG model adequately described tumor size across arms. A Weibull distribution best described OS. Rilotumumab exposure and change in tumor size from baseline at week 24 were predictive of OS. MET-positive patients showed shorter survival and responded better to rilotumumab than MET-negative patients. Simulations predicted a median (95% confidence interval) HR of 0.38 (0.18-0.60) in MET-positive patients treated with 15 mg/kg rilotumumab Q3W.

CONCLUSIONS

Rilotumumab plus ECX demonstrated concentration-dependent effects on OS, influenced by MET expression, and tumor size in gastric/GEJ cancer patients. These findings support the phase II testing of rilotumumab 15 mg/kg every 3 weeks in MET-positive gastric/GEJ cancer (RILOMET-1; NCT01697072).

A Phase II trial of axitinib in patients with various histologic subtypes of advanced thyroid cancer: long-term outcomes and pharmacokinetic/pharmacodynamic analyses

PURPOSE

Axitinib, a potent and selective second-generation inhibitor of vascular endothelial growth factor receptors, has shown activity in advanced thyroid cancer in a Phase II study. We report updated overall survival and pharmacokinetic/pharmacodynamic (PK/PD) analyses from the study.

METHODS

Patients (N = 60) with advanced thyroid cancer of any histology for whom iodine-131 ((131)I) failed to control the disease or (131)I was not appropriate therapy were administered axitinib 5 mg twice daily. Objective response rate (primary endpoint), duration of response, progression-free survival, overall survival, safety, and PK/PD relationships were assessed.

RESULTS

Objective response rate was 38 % [23 partial responses; 95 % confidence interval (CI) 26-52], and 18 (30 %) patients had stable disease lasting ≥16 weeks. Responses occurred in all histologic subtypes. With median follow-up of 34 months (95 % CI 32-37), median overall survival was 35 months (95 % CI 19-not estimable), median progression-free survival was 15 months (95 % CI 10-20), and median duration of response was 21 months (95 % CI 13-46). Most common Grade 3/4 treatment-related adverse events included hypertension (13 %), proteinuria (8 %), diarrhea (7 %), weight decrease (7 %), and fatigue (5 %). PK/PD analyses revealed trends toward greater tumor size reduction and response probability with higher axitinib plasma exposures.

CONCLUSIONS

Axitinib appears active and well tolerated in patients with various histologic subtypes of advanced thyroid cancer, demonstrating durable responses and long overall survival. Axitinib may be useful for the treatment of advanced thyroid cancer.

Population pharmacometric analyses of eribulin in patients with locally advanced or metastatic breast cancer previously treated with anthracyclines and taxanes

Pharmacometric investigation of eribulin was undertaken in patients with metastatic breast cancer (MBC) and other advanced solid tumors. A population pharmacokinetic (PK) model used data combined from seven phase 1 studies (advanced solid tumors; n = 129), and one phase 2 (MBC; n = 211), and one phase 3 study (MBC; n = 173). Phase 3 data were also used in a PK/pharmacodynamic (PD) model of efficacy and tumor response (sum of longest diameters of target lesions). All analyses used NONMEM 7.2. Eribulin PK, described by a dose-independent, three-compartment model with allometric relationship for body weight, was similar for all tumor types. Inter-individual variability (IIV) was 52% for both exposure and clearance. Liver function markers (albumin, alkaline phosphatase, bilirubin) significantly influenced eribulin PK (7.3% of IIV in clearance). Tumor shrinkage correlated with eribulin exposure; a 36% decrease in tumor size from baseline was modeled at week 36. No patient/disease factors significantly predicted eribulin's effect on tumor size. At week 6, a decrease in tumor size was associated with longer survival than an increase (P = .0055), suggesting survival may relate indirectly to eribulin exposure. These pharmacometric analyses provide a detailed overview of eribulin exposure-efficacy relationships to inform physicians treating patients with MBC.

PKPD Modeling of VEGF, sVEGFR-2, sVEGFR-3, and sKIT as Predictors of Tumor Dynamics and Overall Survival Following Sunitinib Treatment in GIST

The predictive value of longitudinal biomarker data (vascular endothelial growth factor (VEGF), soluble VEGF receptor (sVEGFR)-2, sVEGFR-3, and soluble stem cell factor receptor (sKIT)) for tumor response and survival was assessed based on data from 303 patients with imatinib-resistant gastrointestinal stromal tumors (GIST) receiving sunitinib and/or placebo treatment. The longitudinal tumor size data were well characterized by a tumor growth inhibition model, which included, as significant descriptors of tumor size change, the model-predicted relative changes from baseline over time for sKIT (most significant) and sVEGFR-3, in addition to sunitinib exposure. Survival time was best described by a parametric time-to-event model with baseline tumor size and relative change in sVEGFR-3 over time as predictive factors. Based on the proposed modeling framework to link longitudinal biomarker data with overall survival using pharmacokinetic-pharmacodynamic models, sVEGFR-3 demonstrated the greatest predictive potential for overall survival following sunitinib treatment in GIST.CPT: Pharmacometrics & Systems Pharmacology (2013) 2, e84; doi:10.1038/psp.2013.61; advance online publication 20 November 2013.

Mathematical model approach to describe tumour response in mice after vaccine administration and its applicability to immune-stimulatory cytokine-based strategies

Immunotherapy is a growing therapeutic strategy in oncology based on the stimulation of innate and adaptive immune systems to induce the death of tumour cells. In this paper, we have developed a population semi-mechanistic model able to characterize the mechanisms implied in tumour growth dynamic after the administration of CyaA-E7, a vaccine able to target antigen to dendritic cells, thus triggering a potent immune response. The mathematical model developed presented the following main components: (1) tumour progression in the animals without treatment was described with a linear model, (2) vaccine effects were modelled assuming that vaccine triggers a non-instantaneous immune response inducing cell death. Delayed response was described with a series of two transit compartments, (3) a resistance effect decreasing vaccine efficiency was also incorporated through a regulator compartment dependent upon tumour size, and (4) a mixture model at the level of the elimination of the induced signal vaccine (k 2) to model tumour relapse after treatment, observed in a small percentage of animals (15.6%). The proposed model structure was successfully applied to describe antitumor effect of IL-12, suggesting its applicability to different immune-stimulatory therapies. In addition, a simulation exercise to evaluate in silico the impact on tumour size of possible combination therapies has been shown. This type of mathematical approaches may be helpful to maximize the information obtained from experiments in mice, reducing the number of animals and the cost of developing new antitumor immunotherapies.

Mathematical modeling of tumor growth and tumor growth inhibition in oncology drug development

INTRODUCTION

Approaches aiming to model the time course of tumor growth and tumor growth inhibition following a therapeutic intervention have recently been proposed for supporting decision making in oncology drug development. When considered in a comprehensive model-based approach, tumor growth can be included in the cascade of quantitative and causally related markers that lead to the prediction of survival, the final clinical response.

AREAS COVERED

The authors examine articles dealing with the modeling of tumor growth and tumor growth inhibition in both preclinical and clinical settings. In addition, the authors review models describing how pharmacological markers can be used to predict tumor growth and models describing how tumor growth can be linked to survival endpoints.

EXPERT OPINION

Approaches and success stories of application of model-based drug development centered on tumor growth modeling are growing. It is also apparent that these approaches can answer practical questions on drug development more effectively than that in the past. For modeling purposes, some improvements are still needed related to study design and data quality. Further efforts are needed to encourage the mind shift from a simple description of data to the prediction of untested conditions that modeling approaches allow.

Exposure-response relationship of AMG 386 in combination with weekly paclitaxel in recurrent ovarian cancer and its implication for dose selection

Purpose

To characterize exposure–response relationships of AMG 386 in a phase 2 study in advanced ovarian cancer for the facilitation of dose selection in future studies.

Methods

A population pharmacokinetic model of AMG 386 (N = 141) was developed and applied in an exposure–response analysis using data from patients (N = 160) with recurrent ovarian cancer who received paclitaxel plus AMG 386 (3 or 10 mg/kg once weekly) or placebo. Reduction in the risk of progression or death with increasing exposure (steady-state area under the concentration-versus-time curve [AUC_ss]) was assessed using Cox regression analyses. Confounding factors were tested in multivariate analysis. Alternative AMG 386 doses were explored with Monte Carlo simulations using population pharmacokinetic and parametric survival models.

Results

There was a trend toward increased PFS with increased AUC_ss (hazard ratio [HR] for each one-unit increment in AUC_ss, 0.97; P = 0.097), suggesting that the maximum effect on prolonging PFS was not achieved at the highest dose tested (10 mg/kg). Among patients with AUC_ss ≥ 9.6 mg h/mL, PFS was 8.1 months versus 5.7 months for AUC_ss < 9.6 mg h/mL and 4.6 months for placebo. No relationship between AUC_ss and grade ≥3 adverse events was observed. Simulations predicted that AMG 386 15 mg/kg once weekly would result in an AUC_ss ≥ 9.6 mg h/mL in >90% of patients with median PFS of 8.2 months versus 5.0 months for placebo (HR [15 mg/kg vs. placebo], 0.56).

Conclusions

Increased exposure to AMG 386 was associated with improved clinical outcomes in recurrent ovarian cancer, supporting the evaluation of a higher dose in future studies.

Electronic supplementary material

The online version of this article (doi:10.1007/s00280-011-1787-5) contains supplementary material, which is available to authorized users.

Targeted therapy in refractory thyroid cancer: current achievements and limitations

Thyroid cancer refractory to conventional treatments lacks effective treatment. Targeted therapy is an emerging therapeutic strategy for these cancers, based on preliminary promising results. Tyrosine kinase inhibitors target both specific oncogenic pathways involved in thyroid cancer progression and aspecific mechanisms such as neoangiogenesis. They are generally well tolerated and most adverse events have low-to-moderate severity. Other classes of drugs have been tested, alone or in combination with tyrosine kinase inhibitors, but so far the results have been limited. The aim of this article is to describe the benefits and limitations of innovative drugs that are currently under investigation in patients with refractory thyroid cancer.

Development of a modeling framework to simulate efficacy endpoints for motesanib in patients with thyroid cancer

PURPOSE

To develop a modeling framework that simulates clinical endpoints (objective response rate and progression-free survival) to support development of motesanib. The framework was evaluated using results from a phase 2 study of motesanib in thyroid cancer.

METHODS

Models of probability and duration of dose modifications and overall survival were developed using data from 93 patients with differentiated thyroid cancer and 91 patients with medullary thyroid cancer, who received motesanib 125 mg once daily. The models, combined with previously developed population pharmacokinetic and tumor growth inhibition models, were assessed in predicting dose intensity, tumor size over time, objective response rate, and progression-free survival. Dose-response simulations were performed in patients with differentiated thyroid cancer.

RESULTS

The predicted objective response rate and median progression-free survival in patients with differentiated thyroid cancer was 15.0% (95% prediction interval, 7.5%-23.7%) and 40 weeks (95% prediction interval, 32-49 weeks), respectively, compared with the observed objective response rate of 14.0% and median progression-free survival of 40 weeks. The simulated median objective response rate increased with motesanib starting dose from 13.5% at 100 mg once daily to 38.0% at 250 mg once daily. However, simulated median progression-free survival was independent of starting dose, ranging from 40.5 weeks (95% prediction interval, 38.6-46.9 weeks) at 100 mg once daily to 40.0 weeks (95% prediction interval, 38.6-46.8 weeks) at 250 mg once daily.

CONCLUSIONS

Dose-response simulations confirmed the appropriateness of 125-mg once-daily dosing; no clinically relevant improvement in progression-free survival would be obtained by dose intensification. This modeling framework represents an important tool to simulate clinical response and support clinical development decisions.