Nonlinear Mixed-Effect Models for Prostate-Specific Antigen Kinetics and Link with Survival in the Context of Metastatic Prostate Cancer: a Comparison by Simulation of Two-Stage and Joint Approaches

In metastatic castration-resistant prostate cancer (mCRPC) clinical trials, the assessment of treatment efficacy essentially relies on the time-to-death and the kinetics of prostate-specific antigen (PSA). Joint modelling has been increasingly used to characterize the relationship between a time-to-event and a biomarker kinetics but numerical difficulties often limit this approach to linear models. Here we evaluated by simulation the capability of a new feature of the Stochastic Approximation Expectation-Maximization algorithm in Monolix to estimate the parameters of a joint model where PSA kinetics was defined by a mechanistic nonlinear mixed-effect model. The design of the study and the parameter values were inspired from one arm of a clinical trial. Increasingly high levels of association between PSA and survival were considered and results were compared with those found using two simplified alternatives to joint model, a two-stage and a joint sequential model. We found that joint model allowed for a precise estimation of all longitudinal and survival parameters. In particular the effect of PSA kinetics on survival could be precisely estimated, regardless of the strength of the association. In contrast, both simplified approaches led to bias on longitudinal parameters and two-stage model systematically underestimated the effect of PSA kinetics on survival. In summary we showed that joint model can be used to characterize the relationship between a nonlinear kinetics and survival. This opens the way for the use of more complex and physiological models to improve treatment evaluation and prediction in oncology.Comment: The AAPS Journal, 2015, pp.1550-741

L'analyse de pharmacocinétique de population dans le développement des anticancéreux (modélisation d'une molécule à métabolite actif dans trois essais cliniques de phase 1)

POITIERS-BU Médecine pharmacie (861942103) / SudocSudocFranceF

Population pharmacokinetic analysis of free and bound aflibercept in patients with advanced solid tumors.

International audienceOBJECTIVE: Aflibercept (Zaltrap®) is a novel antiangiogenic agent that binds to vascular endothelial growth factor (VEGF) and inhibits VEGF-dependent tumor growth. We aimed to characterize the population pharmacokinetics (PK) of free and bound aflibercept in patients with solid tumors to examine the influence of covariates on their PK and to evaluate the proposed dosing regimens by simulation. METHODS: Data from 9 clinical trials with 1,506 cancer patients receiving aflibercept (2-9 mg/kg every 2 or 3 weeks; 1 h IV infusion) as a monotherapy or in combination with various chemotherapies were included. Free and bound aflibercept concentrations were analyzed using a non-linear mixed-effects modeling approach with MONOLIX 4.1.2. RESULTS: An approximation of a target-mediated drug disposition model with irreversible binding of free aflibercept to VEGF adequately described the PK of free and bound aflibercept. The typical estimated clearances for free (CL(f)) and bound aflibercept (CL(b)) were 0.88 and 0.19 L/day, respectively. The volumes of distribution for free (V(p)) and bound (V(b)) aflibercept were similar (~4 L). CL f and V(p) increased with body weight and were lower in women. Patients with low albumin (ALB) or high alkaline phosphatase (ALK) had faster CL(f) compared to a typical patient. Pancreatic cancer may be associated with changes in binding of aflibercept to VEGF. Simulations of different dosing regimens showed that adequate saturation of circulating VEGF was achieved with a dose of 4 mg/kg every 2 weeks. CONCLUSIONS: Aflibercept kinetics was most affected by gender, body weight, ALB, ALK and pancreatic cancer. Simulations supported the rationale for the recommended dose of 4 mg/kg every 2 weeks for aflibercept

Nonlinear joint models for individual dynamic prediction of risk of death using Hamiltonian Monte Carlo: application to metastatic prostate cancer

International audienceAbstractBackgroundJoint models of longitudinal and time-to-event data are increasingly used to perform individual dynamic prediction of a risk of event. However the difficulty to perform inference in nonlinear models and to calculate the distribution of individual parameters has long limited this approach to linear mixed-effect models for the longitudinal part. Here we use a Bayesian algorithm and a nonlinear joint model to calculate individual dynamic predictions. We apply this approach to predict the risk of death in metastatic castration-resistant prostate cancer (mCRPC) patients with frequent Prostate-Specific Antigen (PSA) measurements.MethodsA joint model is built using a large population of 400 mCRPC patients where PSA kinetics is described by a biexponential function and the hazard function is a PSA-dependent function. Using Hamiltonian Monte Carlo algorithm implemented in Stan software and the estimated population parameters in this population as priors, the a posteriori distribution of the hazard function is computed for a new patient knowing his PSA measurements until a given landmark time. Time-dependent area under the ROC curve (AUC) and Brier score are derived to assess discrimination and calibration of the model predictions, first on 200 simulated patients and then on 196 real patients that are not included to build the model.ResultsSatisfying coverage probabilities of Monte Carlo prediction intervals are obtained for longitudinal and hazard functions. Individual dynamic predictions provide good predictive performances for landmark times larger than 12 months and horizon time of up to 18 months for both simulated and real data.ConclusionsAs nonlinear joint models can characterize the kinetics of biomarkers and their link with a time-to-event, this approach could be useful to improve patient’s follow-up and the early detection of most at risk patients

Using the SAEM algorithm for mechanistic joint models characterizing the relationship between nonlinear PSA kinetics and survival in prostate cancer patients

International audienceJoint modelling is increasingly popular for investigating the relationship between longitudinal and time-to-event data. However numerical complexity often restricts this approach to linear models for the longitudinal part. Here we use a novel development of the Stochastic-Approximation Expectation Maximization algorithm that allows joint models defined by nonlinear mixed-effect models. In the context of chemotherapy in metastatic prostate cancer, we show that a variety of patterns for the Prostate Specific Antigen (PSA) kinetics can be captured by using a mechanistic model defined by nonlinear ordinary differential equations. The use of a mechanistic model predicts that biological quantities that cannot be observed, such as treatment-sensitive and treatment-resistant cells, may have a larger impact than PSA value on survival. This suggests that mechanistic joint models could constitute a relevant approach to evaluate the efficacy of treatment and to improve the prediction of survival in patients

Evaluation of bootstrap methods for estimating uncertainty of parameters in nonlinear mixed-effects models: a simulation study in population pharmacokinetics

International audienceBootstrap methods are used in many disciplines to estimate the uncertainty of parameters, in-cluding multi-level or linear mixed-effects models. Residual-based bootstrap methods which resample both random effects and residuals are an alternative approach to case bootstrap, which resamples the individuals. Most PKPD applications use the case bootstrap, for which software is available. In this study, we evaluated the performance of three bootstrap methods (case bootstrap, nonparametric residual bootstrap and para-metric bootstrap) by a simulation study and compared them to that of an asymptotic method in estimating uncertainty of parameters in nonlinear mixed-effects models (NLMEM) with heteroscedastic error. This simulation was conducted using as an example of the PK model for aflibercept, an anti-angiogenic drug. As expected, we found that the bootstrap methods provided better estimates of uncertainty for parameters in NLMEM with high nonlinearity and having balanced designs compared to the asymptotic method, as implemented in MONOLIX. Overall, the parametric bootstrap performed better than the case bootstrap as the true model and variance distribution were used. However, the case bootstrap is faster and simpler as it makes no assumptions on the model and preserves both between subject and residual variability in one resampling step. The performance of the nonparametric residual bootstrap was found to be limited when ap-plying to NLMEM due to its failure to reflate the variance before resampling in unbalanced designs where the asymptotic method and the parametric bootstrap performed well and better than case bootstrap even with stratification

A mechanism-based model for the population pharmacokinetics of free and bound aflibercept in healthy subjects.

International audienceAIM: Aflibercept (VEGF-Trap), a novel anti-angiogenic agent that binds to VEGF, has been investigated for the treatment of cancer. The aim of this study was to develop a mechanism-based pharmacokinetic (PK) model for aflibercept to characterize its binding to VEGF and its PK properties in healthy subjects. METHODS: Data from two phase I clinical studies with aflibercept administered as a single intravenous infusion were included in the analysis. Free and bound aflibercept concentration-time data were analysed using a nonlinear mixed-effects modelling approach with MONOLIX 3.1. RESULTS: The best structural model involved two compartments for free aflibercept and one for bound aflibercept, with a Michaelis-Menten type binding of free aflibercept to VEGF from the peripheral compartment. The typical estimated clearances for free and bound aflibercept were 0.88 l day(-1) and 0.14 l day(-1), respectively. The central volume of distribution of free aflibercept was 4.94 l. The maximum binding capacity was 0.99 mg day(-1) and the concentration of aflibercept corresponding to half of maximum binding capacity was 2.91 µg ml(-1). Interindividual variability of model parameters was moderate, ranging from 13.6% (V(max) ) to 49.8% (Q). CONCLUSION: The present PK model for aflibercept adequately characterizes the underlying mechanism of disposition of aflibercept and its nonlinear binding to VEGF

Joint modeling of tumor dynamics and progression‐free survival in advanced breast cancer: Leveraging data from amcenestrant early phase I–II trials

International audienceA joint modeling framework was developed using data from 75 patients of early amcenestrant phase I–II AMEERA‐1‐2 dose escalation and expansion cohorts. A semi‐mechanistic tumor growth inhibition (TGI) model was developed. It accounts for the dynamics of sensitive and resistant tumor cells, an exposure‐driven effect on tumor proliferation of sensitive cells, and a delay in the initiation of treatment effect to describe the time course of target lesion tumor size (TS) data. Individual treatment exposure overtime was introduced in the model using concentrations predicted by a population pharmacokinetic model of amcenestrant. This joint modeling framework integrated complex RECISTv1.1 criteria information, linked TS metrics to progression‐free survival (PFS), and was externally evaluated using the randomized phase II trial AMEERA‐3. We demonstrated that the instantaneous rate of change in TS (TS slope) was an important predictor of PFS and the developed joint model was able to predict well the PFS of amcenestrant phase II monotherapy trial using only early phase I–II data. This provides a good modeling and simulation tool to inform early development decisions