Pharmacometrics 2020

Disease progression modeling with temporal realignment: An emerging approach to deepen knowledge on chronic diseases

The recent development of the first disease-modifying drug for Alzheimer's disease represents a major advancement in dementia treatment. Behind this breakthrough is a quarter century of research efforts to understand the disease not by a particular symptom at a given moment, but by long-term sequential changes in multiple biomarkers. Disease progression modeling with temporal realignment (DPM-TR) is an emerging computational approach proposed with this biomarker-based disease concept. By integrating short-term clinical observations of multiple disease biomarkers in a data-driven manner, DPM-TR provides a way to understand the progression of chronic diseases over decades and predict individual disease stages more accurately. DPM-TR has been developed primarily in the area of neurodegenerative diseases but has recently been extended to non-neurodegenerative diseases, including chronic obstructive pulmonary, autoimmune, and ophthalmologic diseases. This review focuses on opportunities for DPM-TR in clinical practice and drug development and discusses its current status and challenges.

Combined translational pharmacometrics approach to support the design and conduct of the first-in-human study of DWP16001

AIMS

The objective of this study was to characterize the pharmacokinetics (PK)/pharmacodynamics (PD) of DWP16001, a novel sodium-glucose cotransporter 2 inhibitor, and predict efficacious doses for the first-in-human study using various translational approaches.

METHODS

A mechanistic PK/PD model was developed for DWP16001 using nonlinear mixed-effect modelling to describe animal PK/PD properties. Using allometry and in silico physiologically based equations, human PK parameters were predicted. Human PD parameters were scaled by applying interspecies difference and in vitro drug-specific factors. Human parameters were refined using early clinical data. Model-predicted PK and PD outcomes were compared to observations before and after parameter refinement.

RESULTS

The PK/PD model of DWP16001 was developed using a 2-compartment model with first-order absorption and indirect response. Efficacious doses of 0.3 and 2 mg of DWP16001 were predicted using human half-maximal inhibitory concentration values translated from Zucker Diabetic Fatty rats and normal rats, respectively. After parameter refinement, doses of 0.2 and 1 mg were predicted to be efficacious for each disease model, which improved the prediction results to within a 1.2-fold difference between the model prediction and observation.

CONCLUSIONS

This study predicted efficacious human doses of DWP16001 using population PK/PD modelling and a combined translational pharmacometrics approach. Early clinical data allowed the methods used to translate in vitro and in vivo findings to clinical PK/PD values for DWP16001 to be optimized. This study has shown that a refinement step can be readily applied to improve model prediction and further support the study design and conduct of a first-in-human study.

mvLognCorrEst: an R package for sampling from multivariate lognormal distributions and estimating correlations from uncomplete correlation matrix

BACKGROUND AND OBJECTIVE

Pharmacometrics (PMX) is a quantitative discipline which supports decision-making processes in all stages of drug development. PMX leverages Modeling and Simulations (M&S), which represents a powerful tool to characterize and predict the behavior and the effect of a drug. M&S-based methods, such as Sensitivity Analysis (SA) and Global Sensitivity Analysis (GSA), are gaining interest in PMX as they allow the evaluation of model-informed inference quality. Simulations should be correctly designed to obtain reliable results. Neglecting correlations between model parameters can significantly alter the results of simulations. However, the introduction of a correlation structure between model parameters can cause some issues. Sampling from a multivariate lognormal distribution, which is the typically distribution assumed for PMX model parameters, is not straightforward when a correlation structure is introduced. Indeed, correlations need to respect some constraints which depend by the CVs (i.e., coefficients of variation) of lognormal variables. In addition, when correlation matrices have some unspecified values, they should be properly fixed preserving the positive semi-definiteness of the correlation structure. In this paper, we present mvLognCorrEst, an R package developed to address these issues.

METHODS

The proposed sampling strategy was based on reconducting the extraction from the multivariate lognormal distribution of interest to the underlying Normal distribution. However, with high lognormal CVs, a positive semi-definite Normal covariance matrix cannot be obtained due to the violation of some theoretical constraints. In these cases, the Normal covariance matrix was approximated to its nearest positive definite matrix using Frobenius norm as matrix distance. For the estimation of unknown correlations terms, the graph theory was used to represent the correlation structure as weighed undirected graph. Plausible value ranges for the unspecified correlations were derived considering the paths between variables. Then, their estimation was performed by solving a constrained optimization problem.

RESULTS

Package functions are presented and applied on a real case study, that is the GSA of a PMX model that has been recently developed to support preclinical oncological studies.

CONCLUSIONS

mvLognCorrEst package is an R tool to support simulation-based analysis for which sampling from multivariate lognormal distributions with correlated variables and/or estimation of partially defined correlation matrix are required.

Association between simulated ketamine exposures and oxygen saturations in children

Aim

We performed a real-world data analysis to evaluate the relationship between simulated ketamine exposures and oxygen desaturation in children.

Materials & methods

A previously developed population pharmacokinetic model was used to simulate exposures and evaluate target attainment, as well as the association with oxygen desaturation in children ≤17 years treated with intravenous ketamine.

Results

In 2022 children, there was no significant association between simulated plasma ketamine concentrations and oxygen saturation; however, a higher cumulative area under the curve was associated with increased odds of progression to significant desaturation (<85%), though magnitude of effect was small.

Conclusion

By leveraging a population pharmacokinetic model and real-world data, we confirmed there is no relationship between simulated ketamine plasma concentration and oxygen desaturation.

Pharmacometrics: A New Era of Pharmacotherapy and Drug Development in Low- and Middle-Income Countries

Pharmacotherapy, in many cases, is practiced at a suboptimal level of performance in low- and middle-income countries (LMICs) although stupendous amounts of data are available regularly. The process of drug development is time-consuming, costly, and is also associated with loads of hurdles related to the safety concerns of the compounds. This review was conducted with the objective to emphasize the role of pharmacometrics in pharmacotherapy and the drug development process in LMICs for rational drug therapy. Pharmacometrics is widely applied for the rational clinical pharmacokinetic (PK) practice through the population pharmacokinetic (popPK) modeling and physiologically based pharmacokinetic (PBPK) modeling approach. The scope of pharmacometrics practice is getting wider day by day with the untiring efforts of pharmacometricians. The basis for pharmacometrics analysis is the computer-based modeling and simulation of pharmacokinetics/pharmacodynamics (PK/PD) data supplemented by characterization of important aspects of drug safety and efficacy. Pharmacometrics can be considered an invaluable tool not only for new drug development with maximum safety and efficacy but also for dose optimization in clinical settings. Due to the convenience of using sparse and routine patient data, a significant advantage exists in this regard for LMICs which would otherwise lag behind in clinical trials.

Knowledge dissemination and central indexing of resources in pharmacometrics: an ISOP education working group initiative

Pharmacometrics is a constantly evolving field that plays a major role in decision making in drug development and clinical monitoring. Scientists in Pharmacometrics, especially in their early phases of career, are often faced with the challenge of identifying adequate resources for self-training and education. Hence, the ISoP Education Committee through its working group dedicated to Central Indexing and knowledge Dissemination has built a database of worldwide educational programs and most common references in Pharmacometrics.

Hospital Pharmacometrics for Optimal Individual Administration of Antimicrobial Agents for Anti-methicillin-resistant Staphylococcus aureus Infected Patients

Therapeutic drug monitoring and target concentration intervention based on population pharmacokinetic and pharmacodynamic models has been strongly recommended for anti-methicillin-resistant Staphylococcus aureus (MRSA) agents in order to provide appropriate antimicrobial chemotherapy to each individual patient, and pharmacokinetic and pharmacodynamic analyses in hospitalized patients have been actively conducted, as evidenced with vancomycin. Teicoplanin, daptomycin, and linezolid have been the most studied antibiotics, using population pharmacokinetics of patients with MRSA. Infections caused by MRSA have higher severity and fatality rates than other antimicrobial-susceptible infections. Therefore, many medical facilities have been implementing infection control programs based on antimicrobial stewardship to prevent nosocomial infections and drug-resistant strains. Studies detailing pharmacometrics for these antibiotics have been reported to elucidate the pharmacokinetic and pharmacodynamic properties, to determine significant factors influencing variabilities between individuals, and to develop target concentration interventions and dosing regimens for adults, the elderly, patients with renal insufficiency including those on continuous renal replacement therapies, patients with low body weight, obese patients, and pediatric patients. This review presents the details of our recent research on the optimal dosing design of antimicrobial agents for the treatment of MRSA infection based on hospital pharmacometrics. In addition, the prospect of using modeling and simulation has shown major advantages in supporting dosing regimen selection.

Concept and utility of population pharmacokinetic and pharmacokinetic/pharmacodynamic models in drug development and clinical practice

Due to frequent clinical trial failures and consequently fewer new drug approvals, the need for improvement in drug development has, to a certain extent, been met using model-based drug development. Pharmacometrics is a part of pharmacology that quantifies drug behaviour, treatment response and disease progression based on different models (pharmacokinetic - PK, pharmacodynamic - PD, PK/PD models, etc.) and simulations. Regulatory bodies (European Medicines Agency, Food and Drug Administration) encourage the use of modelling and simulations to facilitate decision-making throughout all drug development phases. Moreover, the identification of factors that contribute to variability provides a basis for dose individualisation in routine clinical practice. This review summarises current knowledge regarding the application of pharmacometrics in drug development and clinical practice with emphasis on the population modelling approach.

A review of three years' experience of the first pharmacometrics company in Korea

As the pharmaceutical industry in Korea is reaching the golden era of drug discovery due to increased investments in research and development and government funds, the need for a more efficient tool for the quantitative analysis has emerged. Therefore, the demand for pharmacometrics (PMx) consultancy services increased. Higher quality service suitable for regulatory submission and out-licensing deals were desired. In this analysis, we compiled and summarized 3 years of experiences of Q-fitter, the first PMx consultancy service company providing PMx analysis to the pharmaceutical industry in Korea. The projects were organized by companies, company types, indications, therapeutic areas, drug development stages, purposes, and scope of services. Within each category, we subcategorized the sections and assessed proportions and a year-over-year trend. As a result, we observed an increase in the number of projects in an average of ~170% per year, with the most frequent types of companies collaborated being the domestic pharmaceutical companies. Among the projects, ~72% involved modeling and simulation using population pharmacokinetic (PK) models, and the other included non-compartmental analysis (NCA), drug-drug interaction (DDI) prediction, and interpretation of the modeling results. The most sought-after purpose in PMx analysis was first-in-human (FIH) dose prediction followed by PK analysis, next clinical trial prediction, and scenario-based simulation. Oncology has been the top therapeutic area of interest every year consisting of ~38% of total projects, followed by Neurology (~13%). From this review, we were able to characterize the PMx service needs and spot the trend of current PMx practices in Korea.

Translation Between Two Models; Application with Integrated Glucose Homeostasis Models

PURPOSE

For some biological systems, there exist several models with somewhat different features and perspectives. We propose an evaluation method for NLME models by analyzing real and simulated data from the model of main interest using a structurally different, but similar, NLME model. We showcase this method using the Integrated Glucose Insulin (IGI) model and the Integrated Minimal Model (IMM). Additionally, we try to map parameters carrying similar information between the two models.

METHODS

A bootstrap of real data and simulated datasets from both the IMM and IGI models were analyzed with the two models. Important parameters of the IMM were mapped to IGI parameters using a large IMM simulated dataset analyzed under the IGI model.

RESULTS

Comparison of the parameters estimated from real data and data simulated with the IMM and analyzed with the IGI model demonstrated differences between real and IMM-simulated data. Comparison of the parameters estimated from real data and data simulated with the IGI model and analyzed with the IMM also demonstrated differences but to a lower extent. The strongest parameter correlations were found for: insulin-dependent glucose clearance (IGI) ~ insulin sensitivity (IMM); insulin-independent glucose clearance (IGI) ~ glucose effectiveness (IMM); and insulin effect parameter (IGI) ~ insulin action (IMM).

CONCLUSIONS

We demonstrated a new approach to investigate models' ability to simulate real-life-like data, and the information captured in each model in comparison to real data, and the IMM clinically used parameters were successfully mapped to their corresponding IGI parameters.

Variability Attribution for Automated Model Building

We investigated the possible advantages of using linearization to evaluate models of residual unexplained variability (RUV) for automated model building in a similar fashion to the recently developed method “residual modeling.” Residual modeling, although fast and easy to automate, cannot identify the impact of implementing the needed RUV model on the imprecision of the rest of model parameters. We used six RUV models to be tested with 12 real data examples. Each example was first linearized; then, we assessed the agreement in improvement of fit between the base model and its extended models for linearization and conventional analysis, in comparison to residual modeling performance. Afterward, we compared the estimates of parameters’ variabilities and their uncertainties obtained by linearization to conventional analysis. Linearization accurately identified and quantified the nature and magnitude of RUV model misspecification similar to residual modeling. In addition, linearization identified the direction of change and quantified the magnitude of this change in variability parameters and their uncertainties. This method is implemented in the software package PsN for automated model building/evaluation with continuous data.

Sirolimus exposure and the occurrence of cytomegalovirus DNAemia after allogeneic hematopoietic stem cell transplantation

Sirolimus appears to protect against cytomegalovirus (CMV) in organ transplant recipients. The effect of this drug in allogeneic hematopoietic stem cell transplantation recipients remains unexplored. By means of multivariate continuous-time Markov model analyses, we identified 3 independent covariates that significantly impacted the risk of CMV DNAemia: recipient/donor CMV serostatus, tacrolimus exposure, and sirolimus exposure. CMV-seropositive recipients with CMV-seronegative donors had a significantly higher probability of having detectable CMV DNAemia. Increasing the tacrolimus trough concentration from 0 to 16 ng/mL increased the probability of patients having detectable CMV DNAemia by 40% (from 40% to 80%), whereas this probability decreased by 25% (from 40% to 15%) when trough concentrations of sirolimus increased from 0 to 16 ng/mL. Sensitivity analysis showed that sirolimus exposure between 0 and 6 ng/mL has no or negligible effect on CMV DNAemia, but levels >8 ng/mL significantly decreased the number of detectable CMV DNAemia cases (the risk ratios decreased from 0.68 to 0.21 when whole blood sirolimus concentrations changed from 8 to 18 ng/mL, P < .01). In conclusion, we used a pharmacometric statistical tool to provide the first clinical evidence that fewer CMV DNAemia events become detectable as sirolimus exposure increases.

Model-Based Residual Post-Processing for Residual Model Identification

The purpose of this study was to investigate if model-based post-processing of common diagnostics can be used as a diagnostic tool to quantitatively identify model misspecifications and rectifying actions. The main investigated diagnostic is conditional weighted residuals (CWRES). We have selected to showcase this principle with residual unexplained variability (RUV) models, where the new diagnostic tool is used to scan extended RUV models and assess in a fast and robust way whether, and what, extensions are expected to provide a superior description of data. The extended RUV models evaluated were autocorrelated errors, dynamic transform both sides, inter-individual variability on RUV, power error model, t-distributed errors, and time-varying error magnitude. The agreement in improvement in goodness-of-fit between implementing these extended RUV models on the original model and implementing these extended RUV models on CWRES was evaluated in real and simulated data examples. Real data exercise was applied to three other diagnostics: conditional weighted residuals with interaction (CWRESI), individual weighted residuals (IWRES), and normalized prediction distribution errors (NPDE). CWRES modeling typically predicted (i) the nature of model misspecifications, (ii) the magnitude of the expected improvement in fit in terms of difference in objective function value (ΔOFV), and (iii) the parameter estimates associated with the model extension. Alternative metrics (CWRESI, IWRES, and NPDE) also provided valuable information, but with a lower predictive performance of ΔOFV compared to CWRES. This method is a fast and easily automated diagnostic tool for RUV model development/evaluation process; it is already implemented in the software package PsN.

Lack of Clinical Pharmacokinetic Studies to Optimize the Treatment of Neglected Tropical Diseases: A Systematic Review

INTRODUCTION

Neglected tropical diseases (NTDs) affect more than one billion people, mainly living in developing countries. For most of these NTDs, treatment is suboptimal. To optimize treatment regimens, clinical pharmacokinetic studies are required where they have not been previously conducted to enable the use of pharmacometric modeling and simulation techniques in their application, which can provide substantial advantages.

OBJECTIVES

Our aim was to provide a systematic overview and summary of all clinical pharmacokinetic studies in NTDs and to assess the use of pharmacometrics in these studies, as well as to identify which of the NTDs or which treatments have not been sufficiently studied.

METHODS

PubMed was systematically searched for all clinical trials and case reports until the end of 2015 that described the pharmacokinetics of a drug in the context of treating any of the NTDs in patients or healthy volunteers.

RESULTS

Eighty-two pharmacokinetic studies were identified. Most studies included small patient numbers (only five studies included >50 subjects) and only nine (11 %) studies included pediatric patients. A large part of the studies was not very recent; 56 % of studies were published before 2000. Most studies applied non-compartmental analysis methods for pharmacokinetic analysis (62 %). Twelve studies used population-based compartmental analysis (15 %) and eight (10 %) additionally performed simulations or extrapolation. For ten out of the 17 NTDs, none or only very few pharmacokinetic studies could be identified.

CONCLUSIONS

For most NTDs, adequate pharmacokinetic studies are lacking and population-based modeling and simulation techniques have not generally been applied. Pharmacokinetic clinical trials that enable population pharmacokinetic modeling are needed to make better use of the available data. Simulation-based studies should be employed to enable the design of improved dosing regimens and more optimally use the limited resources to effectively provide therapy in this neglected area.

Magnitude of Increased Infliximab Clearance Imposed by Anti-infliximab Antibodies in Crohn's Disease Is Determined by Their Concentration

Antibodies (Abs) against infliximab (IFX) increase IFX clearance and can result in treatment failure and acute hypersensitivity reactions. However, interpretation of their clinical value is complicated by individual differences in Ab responses and methods used for quantification. The increase in IFX clearance imposed by anti-IFX Abs has generally been evaluated using a binary classification, i.e., positive or negative. This analysis aimed to investigate if anti-IFX Ab concentrations provide a more adequate prediction of alterations in clearance. Data originated from a clinical trial on Crohn's disease patients with IFX treatment failure. The trial was not originally designed for pharmacokinetic analysis. Therefore, published pharmacokinetic models were utilized as priors to enable covariate investigation. The impact of anti-IFX Abs on clearance was assessed using different mathematical relationships and exploiting information from two different quantification assays, measuring semi-quantitative "total" or "unbound neutralizing" concentrations of anti-IFX Ab, respectively. Inclusion of anti-IFX Ab status/concentration improved the model's performance for all investigated relationships. The anti-IFX Ab concentrations were superior to the binary classifications, indicating that the magnitude of increase in IFX clearance imposed by anti-IFX Abs closely relates to their concentration. Furthermore, total anti-IFX Ab concentrations appeared superior to the unbound neutralizing fraction in identifying high clearance individuals. Simulations showed that even at low concentrations, anti-IFX Abs lead to sub-therapeutic IFX concentrations, supporting a need of treatment interventions in all anti-IFX Ab positive patients. The developed model can serve as a basis for further investigations to refine treatment recommendations for patients with anti-IFX Abs.

Communicating to Influence Drug Development and Regulatory Decisions: A Tutorial

Pharmacometricians require three skills to be influential: technical, business (e.g., drug development), and soft skills (e.g., communication). Effective communication is required to translate technical and often complicated quantitative findings to interdisciplinary team members in order to influence drug development or regulatory decisions. In this tutorial, we highlight important aspects related to communicating pharmacometric analysis to influence decisions.

A model qualification method for mechanistic physiological QSP models to support model-informed drug development

Mechanistic physiological modeling is a scientific method that combines available data with scientific knowledge and engineering approaches to facilitate better understanding of biological systems, improve decision‐making, reduce risk, and increase efficiency in drug discovery and development. It is a type of quantitative systems pharmacology (QSP) approach that places drug‐specific properties in the context of disease biology. This tutorial provides a broadly applicable model qualification method (MQM) to ensure that mechanistic physiological models are fit for their intended purposes.

Visualization and communication of pharmacometric models with berkeley madonna

Population or other pharmacometric models are a useful means to describe, succinctly, the relationships between drug administration, exposure (concentration), and downstream changes in pharmacodynamic (PD) biomarkers and clinical endpoints, including the mixed effects of patient factors and random interpatient variation (fixed and random effects). However, showing a set of covariate equations to a drug development team is perhaps not the best way to get a message across. Visualization of the consequences of the knowledge encapsulated within the model is the key component. Yet in many instances, it can take hours, perhaps days, to collect ideas from teams, write scripts, and run simulations before presenting the results-by which time they have moved on. How much better, then, to seize the moment and work interactively to decide on a course of action, guided by the model. We exemplify here the visualization of pharmacometric models using the Berkeley Madonna software with a particular focus on interactive sessions. The examples are provided as Supplementary Material.

Use of a linearization approximation facilitating stochastic model building

The objective of this work was to facilitate the development of nonlinear mixed effects models by establishing a diagnostic method for evaluation of stochastic model components. The random effects investigated were between subject, between occasion and residual variability. The method was based on a first-order conditional estimates linear approximation and evaluated on three real datasets with previously developed population pharmacokinetic models. The results were assessed based on the agreement in difference in objective function value between a basic model and extended models for the standard nonlinear and linearized approach respectively. The linearization was found to accurately identify significant extensions of the model's stochastic components with notably decreased runtimes as compared to the standard nonlinear analysis. The observed gain in runtimes varied between four to more than 50-fold and the largest gains were seen for models with originally long runtimes. This method may be especially useful as a screening tool to detect correlations between random effects since it substantially quickens the estimation of large variance-covariance blocks. To expedite the application of this diagnostic tool, the linearization procedure has been automated and implemented in the software package PsN.

White paper: landscape on technical and conceptual requirements and competence framework in drug/disease modeling and simulation

Pharmaceutical sciences experts and regulators acknowledge that pharmaceutical development as well as drug usage requires more than scientific advancements to cope with current attrition rates/therapeutic failures. Drug disease modeling and simulation (DDM&S) creates a paradigm to enable an integrated and higher-level understanding of drugs, (diseased)systems, and their interactions (systems pharmacology) through mathematical/statistical models (pharmacometrics)¹—hence facilitating decision making during drug development and therapeutic usage of medicines. To identify gaps and challenges in DDM&S, an inventory of skills and competencies currently available in academia, industry, and clinical practice was obtained through survey. The survey outcomes revealed benefits, weaknesses, and hurdles for the implementation of DDM&S. In addition, the survey indicated that no consensus exists about the knowledge, skills, and attributes required to perform DDM&S activities effectively. Hence, a landscape of technical and conceptual requirements for DDM&S was identified and serves as a basis for developing a framework of competencies to guide future education and training in DDM&S.

Role of modelling and simulation: a European regulatory perspective

Modelling and simulation (M&S) of clinical data, e.g. pharmacokinetic, pharmacodynamic and clinical endpoints, is a useful approach for more efficient interpretation of collected data and for extrapolation of knowledge to the entire target population. This type of documentation is included in the majority of marketing authorization applications for new medicinal products. This article summarizes the current status of regulatory review with respect to the role of M&S in Europe from the perspective of the Swedish Medical Products Agency. At present, regulatory bodies in Europe encourage the application of the M&S approach during drug development. However, there is a lack of consensus and transparent guidance documents. The main regulatory usage is in the evaluation of dose choices in sub-populations and as support for the dosing regimen in general. The regulatory review of conestat alfa illustrates how the dose recommendation was revised during the approval procedure based on M&S information. A survey of marketing authorization applications for new medicinal products approved in 2010 revealed that the use of the information gained from M&S documentation varies with respect to both regulatory review and the applicants' presentation of the data in the submitted dossier. Increased utilization and broadened application of M&S is anticipated in pharmaceutical development, where one area of focus is medicines for paediatric patients. Accordingly, the regulatory agencies will need to increase their capability to assess and utilize this type of information, and an interactive process among regulatory agencies is warranted to provide more unified regulatory assessment and guidance. Moreover, applicants are encouraged to expand on the usage of exposure-response models to map the systemic exposure range that yields safe and efficacious treatment and to improve the presentation of the gained knowledge in summary documents of the marketing authorization applications.

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.

Model-based drug development survey finds pharmacometrics impacting decision making in the pharmaceutical industry

During the past decade, the pharmaceutical industry has seen the increasing application of pharmacometrics approaches in drug development. However, the full potential of incorporating model-based approaches in drug development and its impact on decision making has not been fully realized to date. In 2009, a survey on model-based drug development (MBDD) was conducted (1) to further understand the current state of MBDD in the pharmaceutical industry and (2) to identify opportunities to realize the full potential of MBDD. Ten large and mid-sized pharmaceutical companies provided responses to this survey. The results indicate that MBDD is achieving broad application in early and late development and is positively affecting both internal and regulatory decisions. Senior leadership (vice president and higher) within the companies indicated widely accepted utility for dose selection and gaining acceptance for study design and regulatory interactions but limited acceptance in discovery and commercial/pipeline decisions. Mounting appreciation for the impact of MBDD on internal and regulatory decision-making bodes well for the future of the pharmacometric discipline and the growth of opportunities to realize the full potential of MBDD.

Drug Development for Pediatric Populations: Regulatory Aspects

Pediatric aspects are nowadays integrated early in the development process of a new drug. The stronger enforcement to obtain pediatric information by the regulatory agencies in recent years resulted in an increased number of trials in children. Specific guidelines and requirements from, in particular, the European Medicines Agency (EMA) and the Food and Drug Administration (FDA) form the regulatory framework. This review summarizes the regulatory requirements and strategies for pediatric drug development from an industry perspective. It covers pediatric study planning and conduct, considerations for first dose in children, appropriate sampling strategies, and different methods for data generation and analysis to generate knowledge about the pharmacokinetics (PK) and pharmacodynamics (PD) of a drug in children. The role of Modeling and Simulation (M&S) in pediatrics is highlighted-including the regulatory basis-and examples of the use of M&S are illustrated to support pediatric drug development.

Quantitative clinical pharmacology is transforming drug regulation

Prior to 1970s, development and regulation of new drugs was devoid of a fully quantitative, pathophysiological conceptual foundation. Malcolm Rowland pioneered, in collaboration with colleagues and friends, our modern understanding of drug clearance concepts, and equipped drug development and regulatory scientists with key investigative tools such as physiologically-based pharmacokinetic (PBPK) modeling, standardized approaches to characterizing drug metabolism, and microdosing. From the 1970s to the present, Malcolm Rowland has contributed to key advances in pharmacokinetics that have had transformational impacts on drug regulatory science. These advances include concepts that have led to the fundamental understanding that mechanistically derived, quantitative variations in drug concentrations, rather than assigned dosage alone, drive pharmacodynamic effects (PKPD)-including disease biomarkers and clinical outcomes. This body of knowledge has transformed drug development and regulatory science theory and practice from naïve empiricism to a mechanism/model-based, quantitative scientific discipline. As a result, it is now possible to incorporate pre-clinical in vitro data on drug physico-chemical properties, metabolizing enzymes, transporters and permeability properties into PBPK-based simulations of expected PK distributions and drug-drug interactions in human populations. The most comprehensive application of PK-PD is in the modeling and simulation of clinical trials in the context of model-based drug development and regulation, imbedded in the "learn-confirm paradigm". Regulatory agencies have embraced these advances and incorporated them into regulatory requirements, approval acceleration pathways and regulatory decisions. These developments are reviewed here, with emphasis on key contributions of Malcolm Rowland that facilitated this transformation.