Relationship between factor VIII activity, bleeds and individual characteristics in severe hemophilia A patients.

Pharmacokinetic-based prophylaxis of replacement factor VIII (FVIII) products has been encouraged in recent years, but the relationship between exposure (factor VIII activity) and response (bleeding frequency) remains unclear. The aim of this study was to characterize the relationship between FVIII dose, plasma FVIII activity, and bleeding patterns and individual characteristics in severe hemophilia A patients. Pooled pharmacokinetic and bleeding data during prophylactic treatment with BAY 81-8973 (octocog alfa) were obtained from the three LEOPOLD trials. The population pharmacokinetics of FVIII activity and longitudinal bleeding frequency, as well as bleeding severity, were described using non-linear mixed effects modeling in NONMEM. In total, 183 patients [median age 22 years (range, 1-61); weight 60 kg (11-124)] contributed with 1,535 plasma FVIII activity observations, 633 bleeds and 11 patient/study characteristics [median observation period 12 months (3.1-13.1)]. A parametric repeated time-to-categorical bleed model, guided by plasma FVIII activity from a 2-compartment population pharmacokinetic model, described the time to the occurrence of bleeds and their severity. Bleeding probability decreased with time of study, and a bleed was not found to affect the time of the next bleed. Several covariate effects were identified, including the bleeding history in the 12-month pre-study period increasing the bleeding hazard. However, unexplained inter-patient variability in the phenotypic bleeding pattern remained large (111%CV). Further studies to translate the model into a tool for dose individualization that considers the individual bleeding risk are required. Research was based on a post-hoc analysis of the LEOPOLD studies registered at clinicaltrials.gov identifiers: 01029340, 01233258 and 01311648.

Handling interoccasion variability in model-based dose individualization using therapeutic drug monitoring data.

AIMS: This study aims to assess approaches to handle interoccasion variability (IOV) in a model-based therapeutic drug monitoring (TDM) context, using a population pharmacokinetic model of coagulation factor VIII as example. METHODS: We assessed 5 model-based TDM approaches: empirical Bayes estimates (EBEs) from a model including IOV, with individualized doses calculated based on individual parameters either (i) including or (ii) excluding variability related to IOV; and EBEs from a model excluding IOV by (iii) setting IOV to zero, (iv) summing variances of interindividual variability (IIV) and IOV into a single IIV term, or (v) re-estimating the model without IOV. The impact of varying IOV magnitudes (0-50%) and number of occasions/observations was explored. The approaches were compared with conventional weight-based dosing. Predictive performance was assessed with the prediction error percentiles. RESULTS: When IOV was lower than IIV, the accuracy was good for all approaches (50th percentile of the prediction error [P50] <7.4%), but the precision varied substantially between IOV magnitudes (P97.5 61-528%). Approach (ii) was the most precise forecasting method across a wide range of scenarios, particularly in case of sparse sampling or high magnitudes of IOV. Weight-based dosing led to less precise predictions than the model-based TDM approaches in most scenarios. CONCLUSIONS: Based on the studied scenarios and theoretical expectations, the best approach to handle IOV in model-based dose individualization is to include IOV in the generation of the EBEs but exclude the portion of unexplained variability related to IOV in the individual parameters used to calculate the future dose.

Treatment with subcutaneous and transdermal fentanyl: results from a population pharmacokinetic study in cancer patients.

PURPOSE: Transdermal fentanyl is effective for the treatment of moderate to severe cancer-related pain but is unsuitable for fast titration. In this setting, continuous subcutaneous fentanyl may be used. As data on the pharmacokinetics of continuous subcutaneous fentanyl are lacking, we studied the pharmacokinetics of subcutaneous and transdermal fentanyl. Furthermore, we evaluated rotations from the subcutaneous to the transdermal route. METHODS: Fifty-two patients treated with subcutaneous and/or transdermal fentanyl for moderate to severe cancer-related pain participated. A population pharmacokinetic model was developed and evaluated using non-linear mixed-effects modelling. For rotations from subcutaneous to transdermal fentanyl, a 1:1 dose conversion ratio was used while the subcutaneous infusion was continued for 12 h (with a 50 % tapering after 6 h). A 6-h scheme with 50 % tapering after 3 h was simulated using the final model. RESULTS: A one-compartment model with first-order elimination and separate first-order absorption processes for each route adequately described the data. The estimated apparent clearance of fentanyl was 49.6 L/h; the absorption rate constant for subcutaneous and transdermal fentanyl was 0.0358 and 0.0135 h(-1), respectively. Moderate to large inter-individual and inter-occasion variability was found. Around rotation from subcutaneous to transdermal fentanyl, measured and simulated plasma fentanyl concentrations rose and increasing side effects were observed. CONCLUSIONS: We describe the pharmacokinetics of subcutaneous and transdermal fentanyl in one patient cohort and report several findings that are relevant for clinical practice. Further research is warranted to study the optimal scheme for rotations from the subcutaneous to the transdermal route.

Simulation-based evaluation of the Pharmpy Automatic Model Development tool for population pharmacokinetic modeling in early clinical drug development.

The Pharmpy Automatic Model Development (AMD) tool automates the building of population pharmacokinetic (popPK) models by utilizing a systematic stepwise process. In this study, the performance of the AMD tool was assessed using simulated datasets. Ten true models mimicking classical popPK models were created. From each true model, dataset replicates were simulated assuming a typical phase I study design-single and multiple ascending doses with/without dichotomous food effect, with rich PK sampling. For every dataset replicate, the AMD tool automatically built an AMD model utilizing NONMEM for parameter estimation. The AMD models were compared to the true and reference models (true model fitted to simulated datasets) based on their model components, predicted population and individual secondary PK parameters (SP) (AUC0-24, cmax, ctrough), and model quality metrics (e.g., model convergence, parameter relative standard errors (RSEs), Bayesian Information Criterion (BIC)). The models selected by the AMD tool closely resembled the true models, particularly in terms of distribution and elimination, although differences were observed in absorption and inter-individual variability components. Bias associated with the derived SP was low. In general, discrepancies between AMD and true SP were also observed for reference models and therefore were attributed to the inherent stochasticity in simulations. In summary, the AMD tool was found to be a valuable asset in automating repetitive modeling tasks, yielding reliable PK models in the scenarios assessed. This tool has the potential to save time during early clinical drug development that can be invested in more complex modeling activities within model-informed drug development.

A fully automatic tool for development of population pharmacokinetic models.

Population pharmacokinetic (PK) models are widely used to inform drug development by pharmaceutical companies and facilitate drug evaluation by regulatory agencies. Developing a population PK model is a multi-step, challenging, and time-consuming process involving iterative manual model fitting and evaluation. A tool for fully automatic model development (AMD) of common population PK models is presented here. The AMD tool is implemented in Pharmpy, a versatile open-source library for pharmacometrics. It consists of different modules responsible for developing the different components of population PK models, including the structural model, the inter-individual variability (IIV) model, the inter-occasional variability (IOV) model, the residual unexplained variability (RUV) model, the covariate model, and the allometry model. The AMD tool was evaluated using 10 real PK datasets involving the structural, IIV, and RUV modules in three sequences. The different sequences yielded generally consistent structural models; however, there were variations in the results of the IIV and RUV models. The final models of the AMD tool showed lower Bayesian Information Criterion (BIC) values and similar visual predictive check plots compared with the available published models, indicating reasonable quality, in addition to reasonable run time. A similar conclusion was also drawn in a simulation study. The developed AMD tool serves as a promising tool for fast and fully automatic population PK model building with the potential to facilitate the use of modeling and simulation in drug development.

Bayesian Forecasting Utilizing Bleeding Information to Support Dose Individualization of Factor VIII.

Bayesian forecasting for dose individualization of prophylactic factor VIII replacement therapy using pharmacokinetic samples is challenged by large interindividual variability in the bleeding risk. A pharmacokinetic-repeated time-to-event model-based forecasting approach was developed to contrast the ability to predict the future occurrence of bleeds based on individual (i) pharmacokinetic, (ii) bleeding, and (iii) pharmacokinetic, bleeding and covariate information using observed data from the Long-Term Efficacy Open-Label Program in Severe Hemophilia A Disease (LEOPOLD) clinical trials (172 severe hemophilia A patients taking prophylactic treatment). The predictive performance assessed by the area under receiver operating characteristic (ROC) curves was 0.67 (95% confidence interval (CI), 0.65-0.69), 0.78 (95% CI, 0.76-0.80), and 0.79 (95% CI, 0.77-0.81) for patients ≥ 12 years when using pharmacokinetics, bleeds, and all data, respectively, suggesting that individual bleed information adds value to the optimization of prophylactic dosing regimens in severe hemophilia A. Further steps to optimize the proposed tool for factor VIII dose adaptation in the clinic are required.

A Prospective Population Pharmacokinetic Study on Morphine Metabolism in Cancer Patients.

BACKGROUND: Oral and subcutaneous morphine is widely used for the treatment of cancer-related pain; however, solid pharmacokinetic data on this practice are lacking. Furthermore, it is largely unknown which factors contribute to the variability in clearances of morphine and its metabolites and whether morphine clearance is related to treatment outcome. METHODS: Blood samples from 49 cancer patients treated with oral and/or subcutaneous morphine were prospectively collected and were used to develop a population pharmacokinetic model for morphine, morphine-3-glucuronide (M3G) and morphine-6-glucuronide (M6G). The influence of age, gender, renal function and several polymorphisms possibly related to the pharmacokinetics of the three compounds was investigated. In addition, the relation between treatment failure and morphine and metabolite clearances was explored. RESULTS: A one-compartment model including an extensive first-pass effect adequately described the data of morphine and its metabolites. Estimated mean area under the plasma concentration-time curve (AUC) ratios following oral versus subcutaneous administration were: M3G/morphine 29.7:1 vs. 11.1:1; M6G/morphine 5.26:1 vs. 1.95:1; and M3G/M6G 5.65:1 vs. 5.70:1. Renal function was significantly correlated with clearance of the metabolites, which increased 0.602 L/h per every 10 mL/min/1.73 m2 increase of estimated glomerular filtration rate (eGFR), reaching a plateau for eGFR >90 mL/min/1.73 m2. The clearance of morphine or its metabolites was not found to be correlated with treatment failure. CONCLUSION: The influence of age-, gender- and pharmacokinetic-related polymorphisms was not identified on the pharmacokinetics of morphine. Clearance of morphine or its metabolites was not found to explain treatment outcome; however, large variations in plasma concentrations of morphine, M3G and M6G support further studies on the relation between plasma concentrations and treatment outcome. Dutch Trial Register ID: NTR4369.