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

A 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.

Model-based simulation to support the approval of isatuximab alone or with dexamethasone for the treatment of relapsed/refractory multiple myeloma in Japanese patients.

This study aimed to support dosing regimen selection for isatuximab as a single agent or in combination with dexamethasone for Japanese patients with relapsed/refractory multiple myeloma (RRMM). A joint model characterizing the dynamics of serum M-protein kinetics and its association with progression-free survival (PFS) was developed using data from 201 evaluable Japanese and non-Japanese patients with RRMM enrolled in two monotherapy phase I/II trials, where Japanese patients (n = 31) received isatuximab at 10 or 20 mg/kg once weekly (qw) for 4 weeks then every 2 weeks (q2w) in subsequent cycles (10 or 20 mg/kg qw-q2w). Among non-Japanese patients, 38 received isatuximab 20 mg/kg qw-q2w in combination with dexamethasone. Trial simulations were then performed to evaluate the effect of the isatuximab dosing regimens on both serum M-protein and PFS with and without dexamethasone. The model identified instantaneous changes in serum M-protein as the best on-treatment predictor for PFS. Trial simulations demonstrated that 20 mg/kg qw-q2w induced a greater decrease (30% vs. 22%) of serum M-protein at week 8 and prolonged median PFS by 2.4 weeks compared with 10 mg/kg qw-q2w. Although Japanese patients did not receive isatuximab plus dexamethasone in the phase I/II trial, simulations predicted that isatuximab 20 mg/kg qw-q2w plus dexamethasone would induce a greater decrease (67% vs. 43%) of serum M-protein and a prolonged median PFS by 7.2 weeks compared with isatuximab alone. Trial simulations support the approved isatuximab 20 mg/kg qw-q2w regimen when administered as a single agent and in combination with dexamethasone in Japanese patients.

Exposure-response analyses for selection/confirmation of optimal isatuximab dosing regimen in combination with pomalidomide/dexamethasone treatment in patients with multiple myeloma.

Isatuximab is an approved anti-CD38 monoclonal antibody with multiple antitumor modes of action. An exposure-response (E-R) analysis using data from patients with relapsed/refractory multiple myeloma (RRMM) enrolled in a phase Ib clinical study who received isatuximab at doses from 5 to 20 mg/kg weekly for 1 cycle (4 weeks) followed by every 2 weeks thereafter (qw/q2w) in combination with pomalidomide/dexamethasone (n = 44) was first used to determine the optimal dose/schedule for the phase III ICARIA-MM study. It was complemented by an E-R analysis from a second phase Ib study of patients who received isatuximab at doses from 3 to 10 mg/kg q2w or 10 or 20 mg/kg qw/q2w in combination with lenalidomide/dexamethasone (n = 52). Plasma trough concentration at week 4 (CT4W) was the best predictor for response, and the benefit of the initial 4-weekly administration was confirmed. Although the predicted overall response rate (ORR) was higher at 20 mg/kg vs. 10 mg/kg, the 95% confidence intervals were overlapping. Considering the high probability of success to reach the targeted ORR of greater than or equal to 60%, 10 mg/kg qw/q2w was selected. Results of the E-R analysis from the lenalidomide/dexamethasone study and published disease modeling using data from both phase Ib clinical studies reinforced 10 mg/kg qw/q2w as the optimal dose/schedule for the phase III ICARIA-MM study. E-R analysis showed that higher CT4W was associated with higher ORR. Developed models supported the phase III isatuximab dosing regimen selection/confirmation of 10 mg/kg qw/q2w for use in combination with pomalidomide/dexamethasone in patients with RRMM.

Joint modelling and simulation of M-protein dynamics and progression-free survival for alternative isatuximab dosing with pomalidomide/dexamethasone.

AIMS: Addition of isatuximab (Isa) to pomalidomide/dexamethasone (Pd) significantly improved progression-free survival (PFS) in patients with relapsed/refractory multiple myeloma (RRMM). We aimed to characterize the relationship between serum M-protein kinetics and PFS in the phase 3 ICARIA-MM trial (NCT02990338), and to evaluate an alternative dosing regimen of Isa by simulation. METHODS: Data from the ICARIA-MM trial comparing Isa 10 mg/kg weekly for 4 weeks then every 2 weeks (QW-Q2W) in combination with Pd versus Pd in 256 evaluable RRMM patients were used. A joint model of serum M-protein dynamics and PFS was developed. Trial simulations were then performed to evaluate whether efficacy is maintained after switching to a monthly dosing regimen. RESULTS: The model identified instantaneous changes (slope) in serum M-protein as the best on-treatment predictor for PFS and baseline patient characteristics impacting serum M-protein kinetics (albumin and ß2-microglobulin on baseline levels, non-IgG type on growth rate) and PFS (presence of plasmacytomas). Trial simulations demonstrated that switching to a monthly Isa regimen at 6 months would shorten median PFS by 2.3 weeks and induce 42.3% patients to progress earlier. CONCLUSIONS: Trial simulations supported selection of the approved Isa 10 mg/kg QW-Q2W regimen and showed that switching to a monthly regimen after 6 months may reduce clinical benefit in the overall population. However, patients with good prognostic characteristics and with a stable, very good partial response may switch to a monthly regimen after 6 months without compromising the risk of disease progression. This hypothesis will be tested in a prospective clinical trial.

PK/PD modeling analysis for dosing regimen selection of isatuximab as single agent and in combination therapy in patients with multiple myeloma.

This analysis describes the pharmacokinetic/pharmacodynamic (PK/PD) modeling framework that supported selection of the isatuximab (anti-CD38 monoclonal antibody) dosing regimen alongside its early clinical development in patients with relapsed/refractory multiple myeloma (RRMM). The PK/PD mathematical model characterized the variations of patient serum M-protein concentrations, the primary marker of tumor burden in multiple myeloma (MM). Three separate PK/PD models were built sequentially as data became available from phase I clinical trials. The primary PK/PD analysis was initiated using monotherapy phase I study data (n = 122), followed by analysis of data collected from phase Ib combination studies with lenalidomide and dexamethasone (Rd, n = 40) and then with pomalidomide and dexamethasone (Pd, n = 31). Using the PK/PD model, abnormal "myeloma" protein (M-protein) profiles under different isatuximab dosing regimens were simulated. Overall, simulations revealed that regimens which included a loading period of four weekly administrations followed by administration every 2 weeks thereafter (QW4-Q2W), reduced M-protein levels more than a Q2W regimen without a loading period. For isatuximab monotherapy, a 20 mg/kg dose induced greater reduction in serum M-protein levels compared with doses equal or lower than 10 mg/kg. For isatuximab in combination with either Rd or Pd, simulations yielded no substantial benefit in terms of M-protein reduction between isatuximab 10 mg/kg and 20 mg/kg. These PK/PD analyses supported the use of isatuximab 10 mg/kg QW4-Q2W in combination with Pd in the phase III trial.

Accelerating diagnosis of Parkinson's disease through risk prediction.

BACKGROUND: Characterization of prediagnostic Parkinson's Disease (PD) and early prediction of subsequent development are critical for preventive interventions, risk stratification and understanding of disease pathology. This study aims to characterize the role of the prediagnostic period in PD and, using selected features from this period as novel interception points, construct a prediction model to accelerate the diagnosis in a real-world setting. METHODS: We constructed two sets of machine learning models: a retrospective approach highlighting exposures up to 5 years prior to PD diagnosis, and an alternative model that prospectively predicted future PD diagnosis from all individuals at their first diagnosis of a gait or tremor disorder, these being features that appeared to represent the initiation of a differential diagnostic window. RESULTS: We found many novel features captured by the retrospective models; however, the high accuracy was primarily driven from surrogate diagnoses for PD, such as gait and tremor disorders, suggesting the presence of a distinctive differential diagnostic period when the clinician already suspected PD. The model utilizing a gait/tremor diagnosis as the interception point, achieved a validation AUC of 0.874 with potential time compression to a future PD diagnosis of more than 300 days. Comparisons of predictive diagnoses between the prospective and prediagnostic cohorts suggest the presence of distinctive trajectories of PD progression based on comorbidity profiles. CONCLUSIONS: Overall, our machine learning approach allows for both guiding clinical decisions such as the initiation of neuroprotective interventions and importantly, the possibility of earlier diagnosis for clinical trials for disease modifying therapies.

Bayesian Individual Dynamic Predictions with Uncertainty of Longitudinal Biomarkers and Risks of Survival Events in a Joint Modelling Framework: a Comparison Between Stan, Monolix, and NONMEM.

Given a joint model and its parameters, Bayesian individual dynamic prediction (IDP) of biomarkers and risk of event can be performed for new patients at different landmark times using observed biomarker values. The aim of the present study was to compare IDP, with uncertainty, using Stan 2.18, Monolix 2018R2 and NONMEM 7.4. Simulations of biomarker and survival were performed using a nonlinear joint model of prostate-specific antigen (PSA) kinetics and survival in metastatic prostate cancer. Several scenarios were evaluated, according to the strength of the association between PSA and survival. For various landmark times, a posteriori distribution of PSA kinetic individual parameters was estimated, given individual observations, with each software. Samples of individual parameters were drawn from the posterior distribution. Bias and imprecision of individual parameters as well as coverage of 95% credibility interval for PSA and risk of death were evaluated. All software performed equally well with small biases on individual parameters. Imprecision on individual parameters was comparable across software and showed marked improvements with increasing landmark time. In terms of coverage, results were also comparable and all software were able to well predict PSA kinetics and survival. As for computing time, Stan was faster than Monolix and NONMEM to obtain individual parameters. Stan 2.18, Monolix 2018R2 and NONMEM 7.4 are able to characterize IDP of biomarkers and risk of event in a nonlinear joint modelling framework with correct uncertainty and hence could be used in the context of individualized medicine.

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

BACKGROUND: Joint 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. METHODS: A 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. RESULTS: Satisfying 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. CONCLUSIONS: As 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.

Translational Model-Based Strategy to Guide the Choice of Clinical Doses for Antibody-Drug Conjugates.

This work proposes a model-based approach to help select the phase 1 dosing regimen for the antibody-drug conjugate (ADC) SAR408701 leveraging the available data for 2 other ADCs of the same construct: SAR3419 and SAR566658. First, monkey and human pharmacokinetic (PK) data of SAR566658 and SAR3419 were used to establish the appropriate allometric approach to be applied to SAR408701 monkey PK data. Second, a population pharmacokinetics-pharmacodynamics (PK-PD) model was developed to describe tumor volume evolution following SAR408701 injection in mice. Third, allometric approaches identified for SAR566658 and SAR3419 were applied to SAR408701 monkey PK data to predict the human PK profile. Both SAR566658 and SAR3419 human and monkey PK were best described by a 2-compartment linear model. The relative difference was less than 10% between predicted and observed clearance using allometric exponents of 0.75 and 1, respectively. Tumor volume evolution following SAR408701 injection was best described by a full Simeoni model with a plasma concentration threshold of 4.6 µg/mL for eradication in mice. Both allometric exponents were used to predict SAR408701 PK in human from PK in monkey and to identify the potential effective dosing regimens. This translational strategy may be a valuable tool to design future clinical studies for ADCs, to support selection of the most appropriate dosing regimen, and to estimate the minimal dose required to assure antitumor activity, according to the schedule used.

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

Joint modeling 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.

International randomized phase 2 study on the addition of docetaxel to the combination of cisplatin and 5-fluorouracil in the induction treatment for nasopharyngeal carcinoma in children and adolescents.

PURPOSE: Nasopharyngeal carcinoma (NPC) is a rare but aggressive malignancy in children and adolescents. An international, randomized phase 2 trial was conducted to compare induction chemotherapy with docetaxel plus cisplatin and 5-fluorouracil (TPF) with cisplatin and 5-fluorouracil (PF) in NPC patients under the age of 21. METHODS: Patients with stage IIB-IV NPC were randomly assigned, in a 2:1 ratio, to receive TPF or PF 3-weekly for three cycles, followed by chemoradiotherapy. The primary endpoint was the complete response rate achieved with TPF or PF. Docetaxel pharmacokinetics was also evaluated. RESULTS: Seventy-five patients (median 16 years old) were randomized, with 50 assigned to the TPF group and 25 to the PF group. Overall response was assessed after induction treatment: one patient in the TPF group and none in the PF group had a complete response. Partial response was achieved in 76.0 and 80.0 % in the TPF and PF groups, respectively. The overall safety profile was consistent with findings in adults. The estimated 3-year overall survival rate was 78.0 % for the PF group and 85.7 % for the TPF group (median follow-up 3.3 years). Mean docetaxel area under the curve was 3.41 µg h/mL, compared with 3.51 µg h/mL seen in adult patients. CONCLUSION: This study demonstrated the feasibility of prospective randomized protocols, even for such rare tumors as pediatric NPC. Overall, there were no differences between the two treatment arms in terms of efficacy and toxicity. The pharmacokinetics of docetaxel in pediatric patients at 75 mg/m(2) was similar to those observed in adults.

Optimizing pharmacokinetic bridging studies in paediatric oncology using physiologically-based pharmacokinetic modelling: application to docetaxel.

AIM: Applying physiologically-based pharmacokinetic (PBPK) modelling in paediatric cancer drug development is still challenging. We aimed to demonstrate how PBPK modelling can be applied to optimize dose and sampling times for a paediatric pharmacokinetic (PK) bridging study in oncology and to compare with the allometric scaling population PK (AS-popPK) approach, using docetaxel as an example. METHODS: A PBPK model for docetaxel was first developed for adult cancer patients using Simcyp® and subsequently used to predict its PK profiles in children by accounting for age-dependent physiological differences. Dose (mg m(-2) ) requirements for children aged 0-18 years were calculated to achieve targeted exposure in adults. Simulated data were then analyzed using population PK modelling with MONOLIX® in order to perform design optimization with the population Fisher information matrix (PFIM). In parallel, the AS-popPK approach was performed for the comparison. RESULTS: The PBPK model developed for docetaxel adequately predicted its PK profiles in both adult and paediatric cancer patients (predicted clearance and volume of distribution within 1.5 fold of observed data). The revised dose of docetaxel for a child over 1.5 years old was higher than the adult dose. Considering clinical constraints, the optimal design contained two groups of 15 patients, having three or four sampling times and had good predicted relative standard errors (RSE<30%) for almost all parameters. The AS-popPK approach performed reasonably well but could not predict for very young children. CONCLUSION: This research shows the clinical utility of PBPK modelling in combination with population PK modelling and optimal design to support paediatric oncology development.

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 modeling 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.

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

Bootstrap methods are used in many disciplines to estimate the uncertainty of parameters, including 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 parametric bootstrap) by a simulation study and compared them to that of an asymptotic method (Asym) 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 Asym, 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 applying to NLMEM due to its failure to reflate the variance before resampling in unbalanced designs where the Asym and the parametric bootstrap performed well and better than case bootstrap even with stratification.

Phase I safety, pharmacokinetic and pharmacodynamic evaluation of the vascular disrupting agent ombrabulin (AVE8062) in patients with advanced solid tumors.

PURPOSE: The vascular disrupting agent ombrabulin rapidly reduces tumor blood flow and causes necrosis in vivo. A phase I dose-escalation study was designed to determine the recommended phase II dose (RP2D) of single-agent ombrabulin administered once every three weeks in patients with advanced solid malignancies. EXPERIMENTAL DESIGN: Ombrabulin (30-minute infusion) was escalated from 6 to 60 mg/m2, with RP2D cohort expansion. Safety, tumor response, pharmacokinetics, and pharmacodynamic biomarkers were evaluated. RESULTS: Eleven dose levels were evaluated in 105 patients. Two patients had dose-limiting toxicities in cycle 1 during escalation: grade 3 abdominal pain at 50 mg/m2, grade 3 tumor pain/grade 3 hypertension at 60 mg/m2, and the RP2D was 50 mg/m2 (39 patients). Common toxicities were headache, asthenia, abdominal pain, nausea, diarrhea, transient hypertension, anemia, and lymphopenia. No clinically significant QTc prolongations or left ventricular ejection fraction (LVEF) decreases occurred. Ombrabulin was rapidly converted to its active metabolite RPR258063 (half-life 17 minutes and 8.7 hours, respectively), both having dose-proportional exposure. Weak inhibition of CYP2C19-mediated metabolism occurred at the clinical doses used and there was no effect on CYP1A2 and CYP3A4. A patient with rectal cancer had a partial response and eight patients had stable disease lasting four months or more. Circulating endothelial cells (CEC), VEGF, and matrix metalloproteinase (MMP)-9 levels increased significantly six to 10 hours postinfusion in a subset of patients. CONCLUSIONS: The recommended schedule for single-agent ombrabulin is 50 mg/m2 every 3 weeks. CECs, VEGF, and MMP-9 are potential biomarkers of ombrabulin activity.

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

OBJECTIVE: 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.

A comparison of bootstrap approaches for estimating uncertainty of parameters in linear mixed-effects models.

A version of the nonparametric bootstrap, which resamples the entire subjects from original data, called the case bootstrap, has been increasingly used for estimating uncertainty of parameters in mixed-effects models. It is usually applied to obtain more robust estimates of the parameters and more realistic confidence intervals (CIs). Alternative bootstrap methods, such as residual bootstrap and parametric bootstrap that resample both random effects and residuals, have been proposed to better take into account the hierarchical structure of multi-level and longitudinal data. However, few studies have been performed to compare these different approaches. In this study, we used simulation to evaluate bootstrap methods proposed for linear mixed-effect models. We also compared the results obtained by maximum likelihood (ML) and restricted maximum likelihood (REML). Our simulation studies evidenced the good performance of the case bootstrap as well as the bootstraps of both random effects and residuals. On the other hand, the bootstrap methods that resample only the residuals and the bootstraps combining case and residuals performed poorly. REML and ML provided similar bootstrap estimates of uncertainty, but there was slightly more bias and poorer coverage rate for variance parameters with ML in the sparse design. We applied the proposed methods to a real dataset from a study investigating the natural evolution of Parkinson's disease and were able to confirm that the methods provide plausible estimates of uncertainty. Given that most real-life datasets tend to exhibit heterogeneity in sampling schedules, the residual bootstraps would be expected to perform better than the case bootstrap.

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

AIM: 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.

Population pharmacokinetics and pharmacodynamics of enoxaparin in unstable angina and non-ST-segment elevation myocardial infarction.

AIMS: A major concern with any antithrombotic therapy is an increase in the risk of haemorrhage. The aim of this study was to analyse population pharmacokinetics and pharmacokinetic/pharmacodynamic (PK/PD) relationships for enoxaparin in patients with unstable angina (UA) and non-ST-segment elevation myocardial infarction (NSTEMI), which may help predict risk of haemorrhage. METHODS: Anti-factor Xa (anti-Xa) activity was measured as marker of enoxaparin concentration in 448 patients receiving the drug as a single 30-mg intravenous bolus followed by 1.0 or 1.25 mg kg(-1) subcutaneously twice a day. A population pharmacokinetic analysis was conducted and individual estimates of enoxaparin clearance and area under the curve were tested as prognostic factors for the occurrence of haemorrhagic episodes. RESULTS: Basic population PK parameters were an enoxaparin clearance of 0.733 l h(-1)[95% confidence interval (CI) 0.698, 0.738], a distribution volume of 5.24 l (95% CI 4.20, 6.28) and an elimination half-life of 5.0 h. Enoxaparin clearance was significantly related to patient weight and creatinine clearance, and was the only independent predictor of experiencing both all (10.7%, P = 0.0013) and major (2.2%, P = 0.0004) haemorrhagic events. A creatinine clearance of 30 ml min(-1) was associated with a decrease in enoxaparin clearance of 27% compared with that in a patient with a median creatinine clearance of 88 ml min-1, and was related to a 1.5- and 3.8-fold increase in the risk of 'all' and 'major' haemorrhagic episodes, respectively. CONCLUSIONS: Enoxaparin clearance depends on body weight, and, therefore, weight-adjusted dosing is recommended to minimize interpatient variability in drug exposure and the risk of haemorrhage. The importance of an increased risk of haemorrhage with decreasing renal function must be weighed against the benefit of treatment with enoxaparin in patients with UA and NSTEMI.