Bundling arrows: improving translational CNS drug development by integrated PK/PD-metabolomics.

INTRODUCTION: Diseases of the Central Nervous System (CNS) affect millions of people worldwide, with the number of people affected quickly growing. Unfortunately, the successful development of CNS-acting drugs is less than 10%, and this is attributed to the complexity of the CNS, unexpected side effects, difficulties in penetrating the blood-brain barrier and lack of biomarkers. Areas covered: Herein, the authors first review how pharmacokinetic/pharmacodynamic (PK/PD) models are designed to predict the dose-dependent time course of effect, and how they are used to translate drug effects from animal to man. Then, the authors discuss how pharmacometabolomics gives insight into system-wide pharmacological effects and why it is a promising method to study interspecies differences. Finally, the authors advocate the application of PK/PD-metabolomics modeling to advance translational CNS drug development by discussing its opportunities and challenges. Expert opinion: It is envisioned that PK/PD-metabolomics will increase our understanding of CNS drug effects and improve translational CNS drug development, thereby increasing success rates. The successful future development of this concept will require multi-level and longitudinal biomarker evaluation over a large dose range, multi-tissue biomarker evaluation, and the generation of a proof of principle by application to multiple CNS drugs in multiple species.

Multivariate pharmacokinetic/pharmacodynamic (PKPD) analysis with metabolomics shows multiple effects of remoxipride in rats.

The study of central nervous system (CNS) pharmacology is limited by a lack of drug effect biomarkers. Pharmacometabolomics is a promising new tool to identify multiple molecular responses upon drug treatment. However, the pharmacodynamics is typically not evaluated in metabolomics studies, although being important properties of biomarkers. In this study we integrated pharmacometabolomics with pharmacokinetic/pharmacodynamic (PKPD) modeling to identify and quantify the multiple endogenous metabolite dose-response relations for the dopamine D2 antagonist remoxipride. Remoxipride (vehicle, 0.7 or 3.5mg/kg) was administered to rats. Endogenous metabolites were analyzed in plasma using a biogenic amine platform and PKPD models were derived for each single metabolite. These models were clustered on basis of proximity between their PKPD parameter estimates, and PKPD models were subsequently fitted for the individual clusters. Finally, the metabolites were evaluated for being significantly affected by remoxipride. In total 44 metabolites were detected in plasma, many of them showing a dose dependent decrease from baseline. We identified 6 different clusters with different time and dose dependent responses and 18 metabolites were revealed as potential biomarker. The glycine, serine and threonine pathway was associated with remoxipride pharmacology, as well as the brain uptake of the dopamine and serotonin precursors. This is the first time that pharmacometabolomics and PKPD modeling were integrated. The resulting PKPD cluster model described diverse pharmacometabolomics responses and provided a further understanding of remoxipride pharmacodynamics. Future research should focus on the simultaneous pharmacometabolomics analysis in brain and plasma to increase the interpretability of these responses.

Advanced Methods for Dose and Regimen Finding During Drug Development: Summary of the EMA/EFPIA Workshop on Dose Finding (London 4-5 December 2014).

Inadequate dose selection for confirmatory trials is currently still one of the most challenging issues in drug development, as illustrated by high rates of late-stage attritions in clinical development and postmarketing commitments required by regulatory institutions. In an effort to shift the current paradigm in dose and regimen selection and highlight the availability and usefulness of well-established and regulatory-acceptable methods, the European Medicines Agency (EMA) in collaboration with the European Federation of Pharmaceutical Industries Association (EFPIA) hosted a multistakeholder workshop on dose finding (London 4-5 December 2014). Some methodologies that could constitute a toolkit for drug developers and regulators were presented. These methods are described in the present report: they include five advanced methods for data analysis (empirical regression models, pharmacometrics models, quantitative systems pharmacology models, MCP-Mod, and model averaging) and three methods for study design optimization (Fisher information matrix (FIM)-based methods, clinical trial simulations, and adaptive studies). Pairwise comparisons were also discussed during the workshop; however, mostly for historical reasons. This paper discusses the added value and limitations of these methods as well as challenges for their implementation. Some applications in different therapeutic areas are also summarized, in line with the discussions at the workshop. There was agreement at the workshop on the fact that selection of dose for phase III is an estimation problem and should not be addressed via hypothesis testing. Dose selection for phase III trials should be informed by well-designed dose-finding studies; however, the specific choice of method(s) will depend on several aspects and it is not possible to recommend a generalized decision tree. There are many valuable methods available, the methods are not mutually exclusive, and they should be used in conjunction to ensure a scientifically rigorous understanding of the dosing rationale.

Methodologies for Quantitative Systems Pharmacology (QSP) Models: Design and Estimation.

With the increased interest in the application of quantitative systems pharmacology (QSP) models within medicine research and development, there is an increasing need to formalize model development and verification aspects. In February 2016, a workshop was held at Roche Pharma Research and Early Development to focus discussions on two critical methodological aspects of QSP model development: optimal structural granularity and parameter estimation. We here report in a perspective article a summary of presentations and discussions.

The influence of drug distribution and drug-target binding on target occupancy: The rate-limiting step approximation.

The influence of drug-target binding kinetics on target occupancy can be influenced by drug distribution and diffusion around the target, often referred to as "rebinding" or "diffusion-limited binding". This gives rise to a decreased decline of the drug-target complex concentration as a result of a locally higher drug concentration that arises around the target, which leads to prolonged target exposure to the drug. This phenomenon has been approximated by the steady-state approximation, assuming a steady-state concentration around the target. Recently, a rate-limiting step approximation of drug distribution and drug-target binding has been published. However, a comparison between both approaches has not been made so far. In this study, the rate-limiting step approximation has been rewritten into the same mathematical format as the steady-state approximation in order to compare the performance of both approaches for the investigation of the influence of drug-target binding kinetics on target occupancy. While both approximations clearly indicated the importance of kon and high target concentrations, it was shown that the rate-limiting step approximation is more accurate than the steady-state approximation, especially when dissociation is fast compared to association and distribution out of the binding compartment. It is therefore concluded that the new rate-limiting step approximation is to be preferred for assessing the influence of binding kinetics on local target site concentrations and target occupancy.

Understanding the Behavior of Systems Pharmacology Models Using Mathematical Analysis of Differential Equations: Prolactin Modeling as a Case Study.

In this tutorial, we introduce basic concepts in dynamical systems analysis, such as phase-planes, stability, and bifurcation theory, useful for dissecting the behavior of complex and nonlinear models. A precursor-pool model with positive feedback is used to demonstrate the power of mathematical analysis. This model is nonlinear and exhibits multiple steady states, the stability of which is analyzed. The analysis offers insight into model behavior and suggests useful parameter regions, which simulations alone could not.

A Six-Stage Workflow for Robust Application of Systems Pharmacology.

Quantitative and systems pharmacology (QSP) is increasingly being applied in pharmaceutical research and development. One factor critical to the ultimate success of QSP is the establishment of commonly accepted language, technical criteria, and workflows. We propose an integrated workflow that bridges conceptual objectives with underlying technical detail to support the execution, communication, and evaluation of QSP projects.

A Tutorial on Target-Mediated Drug Disposition (TMDD) Models.

Target-mediated drug disposition (TMDD) is the phenomenon in which a drug binds with high affinity to its pharmacological target site (such as a receptor) to such an extent that this affects its pharmacokinetic characteristics.1 The aim of this Tutorial is to provide an introductory guide to the mathematical aspects of TMDD models for pharmaceutical researchers. Examples of Berkeley Madonna2 code for some models discussed in this Tutorial are provided in the Supplementary Materials.

Inter-study variability of preclinical in vivo safety studies and translational exposure-QTc relationships--a PKPD meta-analysis.

BACKGROUND AND PURPOSE: Preclinical cardiovascular safety studies (CVS) have been compared between facilities with respect to their sensitivity to detect drug-induced QTc prolongation (ΔQTc). Little is known about the consistency of quantitative ΔQTc predictions that are relevant for translation to humans. EXPERIMENTAL APPROACH: We derived typical ΔQTc predictions at therapeutic exposure (ΔQTcTHER ) with 95% confidence intervals (95%CI) for 3 Kv 11.1 (hERG) channel blockers (moxifloxacin, dofetilide and sotalol) from a total of 14 CVS with variable designs in the conscious dog. Population pharmacokinetic-pharmacodynamic (PKPD) analysis of each study was followed by a meta-analysis (pooling 2-6 studies including 10-32 dogs per compound) to derive meta-predictions of typical ΔQTcTHER . Meta-predictions were used as a reference to evaluate the consistency of study predictions and to relate results to those found in the clinical literature. KEY RESULTS: The 95%CIs of study-predicted ΔQTcTHER comprised in 13 out of 14 cases the meta-prediction. Overall inter-study variability (mean deviation from meta-prediction at upper level of therapeutic exposure) was 30% (range: 1-69%). Meta-ΔQTcTHER predictions for moxifloxacin, dofetilide and sotalol overlapped with reported clinical QTc prolongation when expressed as %-prolongation from baseline. CONCLUSIONS AND IMPLICATIONS: Consistent exposure-ΔQTc predictions were obtained from single preclinical dog studies of highly variable designs by systematic PKPD analysis, which is suitable for translational purposes. The good preclinical-clinical pharmacodynamic correlations obtained suggest that such an analysis should be more routinely applied to increase the informative and predictive value of results obtained from animal experiments.

Systems Pharmacology of the NGF Signaling Through p75 and TrkA Receptors.

The nerve growth factor (NGF) pathway has been shown to play a key role in pain treatment. Recently, a systems pharmacology model has been proposed that can aid in the identification and validation of drug targets in the NGF pathway. However, this model did not include the role of the p75 receptor, which modulates the signaling of NGF through the tropomyosin receptor kinase A (TrkA). The precise mechanism of the interaction between these two receptors has not been completely elucidated, and we therefore adopted a systems pharmacology modeling approach to gain understanding of the effect of p75 on the dynamics of NGF signal transduction. Specifically, models were developed for the so-called heterodimer and for the ligand-passing hypotheses. We used the model to compare the effect of inhibition of NGF and TrkA and its implication for drug discovery and development for pain treatment.

A systems pharmacology perspective on the clinical development of Fatty Acid amide hydrolase inhibitors for pain.

The level of the endocannabinoid anandamide is controlled by fatty acid amide hydrolase (FAAH). In 2011, PF-04457845, an irreversible inhibitor of FAAH, was progressed to phase II clinical trials for osteoarthritic pain. This article discusses a prospective, integrated systems pharmacology model evaluation of FAAH as a target for pain in humans, using physiologically based pharmacokinetic and systems biology approaches. The model integrated physiological compartments; endocannabinoid production, degradation, and disposition data; PF-04457845 pharmacokinetics and pharmacodynamics, and cannabinoid receptor CB1-binding kinetics. The modeling identified clear gaps in our understanding and highlighted key risks going forward, in particular relating to whether methods are in place to demonstrate target engagement and pharmacological effect. The value of this modeling exercise will be discussed in detail and in the context of the clinical phase II data, together with recommendations to enable optimal future evaluation of FAAH inhibitors.CPT: Pharmacometrics Systems Pharmacology (2014) 3, e91; doi:10.1038/psp.2013.72; published online 15 January 2014.

Pharmacometrics and Systems Pharmacology Software Tutorials and Use: Comments and Guidelines for PSP Contributions.

In addition to methodological Tutorials,(1) CPT:PSP has recently started to publish software Tutorials.(2,3) Our readership and authors may be wondering what kind of format or product is expected, and the review of submissions we have already received prompted several discussions within the PSP Editorial Team. This editorial reflects on these discussions and summarizes their salient points. It aims at providing some details about the current vision of CPT:PSP for software tutorial articles. In addition, it brings some clarity on the topic of what role commercial software tutorials can have in CPT:PSP and how CPT:PSP tutorials differ from publications which describe the software itself, as those which can be found in other computer science journals. Finally, the discussion includes reproducibility considerations and the general use of commercial and noncommercial software in CPT:PSP publications. We hope our thoughts, and especially a stated requirement to publish user input to the software to aid in reproducibility, will help in guiding our authors and will stimulate healthy debate among our readers about the evolving nature of our science, how it can be facilitated using software and associated databases as a conduit, and what role this journal can play in fostering both the best modeling and simulation practices and the best scientific approaches to computational modeling, to bring the advantages of modeling and simulation to all regular practitioners, and not to just a (self) selected few.

Systems pharmacology models can be used to understand complex pharmacokinetic-pharmacodynamic behavior: an example using 5-lipoxygenase inhibitors.

Zileuton, a 5-lipoxygenase (5LO) inhibitor, displays complex pharmaokinetic (PK)-pharmacodynamic (PD) behavior. Available clinical data indicate a lack of dose-bronchodilatory response during initial treatment, with a dose response developing after ~1-2 weeks. We developed a quantitative systems pharmacology (QSP) model to understand the mechanism behind this phenomenon. The model described the release, maturation, and trafficking of eosinophils into the airways, leukotriene synthesis by the 5LO enzyme, leukotriene signaling and bronchodilation, and the PK of zileuton. The model provided a plausible explanation for the two-phase bronchodilatory effect of zileuton-the short-term bronchodilation was due to leukotriene inhibition and the long-term bronchodilation was due to inflammatory cell infiltration blockade. The model also indicated that the theoretical maximum bronchodilation of both 5LO inhibition and leukotriene receptor blockade is likely similar. QSP modeling provided interesting insights into the effects of leukotriene modulation.CPT: Pharmacometrics & Systems Pharmacology (2013) 2, e74; doi:10.1038/psp.2013.49; advance online publication 11 September 2013.

Model-based drug development: a rational approach to efficiently accelerate drug development.

The pharmaceutical industry continues to face significant challenges. Very few compounds that enter development reach the marketplace, and the investment required for each success can surpass $1.8 billion. Despite attempts to improve efficiency and increase productivity, total investment continues to rise whereas the output of new medicines declines. With costs increasing exponentially through each development phase, it is failure in phase II and phase III that is most wasteful. In today's development paradigm, late-stage failure is principally a result of insufficient efficacy. This is manifested as either a failure to differentiate sufficiently from placebo (shown for both novel and precedented mechanisms) or a failure to demonstrate sufficient differentiation from existing compounds. Set in this context, this article will discuss the role model-based drug development (MBDD) approaches can and do play in accelerating and optimizing compound development strategies through a series of illustrative examples.

Systems pharmacology for drug discovery and development: paradigm shift or flash in the pan?

Systems pharmacology is an emerging approach that sets out to use quantitative concepts rooted in the synergy between modeling and simulation on one hand and large-scale data collection and analysis on the other to investigate biological systems and design therapeutic interventions. Interest in this field has recently increased substantially, but so have perceived challenges and misunderstandings, especially related to implementation. This Commentary explores the opportunities and challenges in the rapidly evolving area of systems pharmacology from a drug discovery and development perspective.

Integrated pharmacometrics and systems pharmacology model-based analyses to guide GnRH receptor modulator development for management of endometriosis.

Endometriosis is a gynecological condition resulting from proliferation of endometrial-like tissue outside the endometrial cavity. Estrogen suppression therapies, mediated through gonadotropin-releasing hormone (GnRH) modulation, decrease endometriotic implants and diminish associated pain albeit at the expense of bone mineral density (BMD) loss. Our goal was to provide model-based guidance for GnRH-modulating clinical programs intended for endometriosis management. This included developing an estrogen suppression target expected to provide symptomatic relief with minimal BMD loss and to evaluate end points and study durations supportive of efficient development decisions. An existing multiscale model of calcium and bone was adapted to include systematic estrogen pharmacologic effects to describe estrogen concentration-related effects on BMD. A logistic regression fit to patient-level data from three clinical GnRH agonist (nafarelin) studies described the relationship of estrogen with endometrial-related pain. Targeting estradiol between 20 and 40 pg/ml was predicted to provide efficacious endometrial pain response while minimizing BMD effects.CPT: Pharmacometrics & Systems Pharmacology (2012) 1, e11; doi:10.1038/psp.2012.10; advance online publication 17 October 2012.