Optimizing Dose Selection with Modeling and Simulation: Application to the Vasopeptidase Inhibitor M100240

Guidance to develop individual dose recommendations for patients on chronic hemodialysis

INTRODUCTION

In addition to tailored clinical trials in patients on chronic hemodialysis (HD) during drug development, clinician-initiated post-marketing studies and case reports on individual pharmacokinetic (PK) assessments provide an important source of information about drug dialysability and individualized dose recommendations in this vulnerable population. Areas covered: First, factors that may alter drug exposure in HD patients are explained. Second, available regulatory and methodological guidelines for PK assessments in this population are summarized. Third, a 4-step approach is proposed to develop individual dose recommendations for HD patients receiving drugs without data from a PK study: (1) literature search, (2) model-based dosage decisions, (3) validation and refinement through concentration monitoring, and (4) publication of relevant observations. Fourth, clinician-initiated PK assessments and case reports to evaluate and individualize use of drugs in HD patients are reviewed, and recommendations to enhance their quality are discussed. Expert commentary: Guidance on collecting and reporting PK information in individual HD patients is warranted to ensure completeness and consistency of such PK studies. A checklist and template for easy-to-implement PK calculations and pharmacometric modeling is provided to facilitate evaluation and individualization of dosing strategies in these patients.

Quantification of apixaban's therapeutic utility in prevention of venous thromboembolism: selection of phase III trial dose

A model-based approach was used to integrate data from a phase II study in order to provide a quantitative rationale for selecting the apixaban dosage regimen for a phase III trial. The exposure-response models demonstrated that an increase in daily steady-state area under the plasma concentration-vs.-time curve (AUC(ss)) of 1 microg x h/ml would increase the odds ratio for major bleeding by 0.118 and decrease the odds ratio for venous thromboembolism (VTE) by 0.0499. The therapeutic utility index (TUI) was used to integrate the efficacy and safety predictions to quantify apixaban's efficacy/safety balance as a function of AUC(ss). Of the apixaban dosage regimens tested in phase II, the 2.5 mg twice-daily (b.i.d.) dosage regimen had the highest TUI (86.2%). This was also higher than the TUI for either 30 mg b.i.d. enoxaparin (82.5%) or for warfarin (71.8%). Subjects with moderate renal impairment are expected to have a 43% increase in apixaban exposure; however, apixaban's TUI suggests that dose adjustment is not needed in these subjects with renal impairment.

Concepts and challenges in quantitative pharmacology and model-based drug development

Model-based drug development (MBDD) has been recognized as a concept to improve the efficiency of drug development. The acceptance of MBDD from regulatory agencies, industry, and academia has been growing, yet today's drug development practice is still distinctly distant from MBDD. This manuscript is aimed at clarifying the concept of MBDD and proposing practical approaches for implementing MBDD in the pharmaceutical industry. The following concepts are defined and distinguished: PK-PD modeling, exposure-response modeling, pharmacometrics, quantitative pharmacology, and MBDD. MBDD is viewed as a paradigm and a mindset in which models constitute the instruments and aims of drug development efforts. MBDD covers the whole spectrum of the drug development process instead of being limited to a certain type of modeling technique or application area. The implementation of MBDD requires pharmaceutical companies to foster innovation and make changes at three levels: (1) to establish mindsets that are willing to get acquainted with MBDD, (2) to align processes that are adaptive to the requirements of MBDD, and (3) to create a closely collaborating organization in which all members play a role in MBDD. Pharmaceutical companies that are able to embrace the changes MBDD poses will likely be able to improve their success rate in drug development, and the beneficiaries will ultimately be the patients in need.

Importance of characterizing determinants of variability in exposure: application to dasatinib in subjects with chronic myeloid leukemia

Characterizing the key determinants of variability in the exposure of orally administered drugs may be important in understanding the implications of exposure variability on clinical responses. In particular, partitioning overall variability into interoccasion variability (IOV) and interindividual variability (IIV) allows a better assessment of the clinical importance of exposure variability. The IOV characterizes the dose-to-dose variability in exposure within a subject and is likely to be less clinically relevant than IIV for chronically administered drugs as the effect of IOV averages out over repeated dosing. The main aims of this model-based analysis were (1) to characterize the IOV and IIV of dasatinib, a novel, orally administered, multitargeted kinase inhibitor of BCR-ABL and SRC family kinases that is indicated for the treatment of chronic myeloid leukemia and Philadelphia-positive acute lymphoblastic leukemia and (2) to demonstrate using simulated data that it is possible to estimate IIV and IOV in relative bioavailability (F(R)) of an orally administered drug, given an adequate sampling scheme. Variability in dasatinib exposure was estimated to be mainly due to IOV in F(R) (44% coefficient of variation [CV]) and, to a lesser extent, due to IIV in F(R) and IIV in clearance (32% and 25% CV, respectively). The IIV is expected to be more clinically relevant than IOV for chronically administered oral drugs such as dasatinib, as the overall variability in cumulative exposure will be mainly due to IIV. The analysis of simulated data demonstrated that models ignoring either IIV or IOV in F(R) resulted in upwardly biased estimates of interindividual or residual variability. Thus, it may be important to account for both IIV and IOV in F(R), particularly for orally administered agents that exhibit absorption-related variability in exposure.

Are population pharmacokinetic and/or pharmacodynamic models adequately evaluated? A survey of the literature from 2002 to 2004

Model evaluation is an important issue in population analyses. We aimed to perform a systematic review of all population pharmacokinetic and/or pharmacodynamic analyses published between 2002 and 2004 to survey the current methods used to evaluate models and to assess whether those models were adequately evaluated. We selected 324 articles in MEDLINE using defined key words and built a data abstraction form composed of a checklist of items to extract the relevant information from these articles with respect to model evaluation. In the data abstraction form, evaluation methods were divided into three subsections: basic internal methods (goodness-of-fit [GOF] plots, uncertainty in parameter estimates and model sensitivity), advanced internal methods (data splitting, resampling techniques and Monte Carlo simulations) and external model evaluation. Basic internal evaluation was the most frequently described method in the reports: 65% of the models involved GOF evaluation. Standard errors or confidence intervals were reported for 50% of fixed effects but only for 22% of random effects. Advanced internal methods were used in approximately 25% of models: data splitting was more often used than bootstrap and cross-validation; simulations were used in 6% of models to evaluate models by a visual predictive check or by a posterior predictive check. External evaluation was performed in only 7% of models. Using the subjective synthesis of model evaluation for each article, we judged the models to be adequately evaluated in 28% of pharmacokinetic models and 26% of pharmacodynamic models. Basic internal evaluation was preferred to more advanced methods, probably because the former is performed easily with most software. We also noticed that when the aim of modelling was predictive, advanced internal methods or more stringent methods were more often used.