Translational PK–PD modeling in pain

The current gap between animal research and clinical development of analgesic drugs presents a challenge for the application of translational PK–PD modeling and simulation. First, animal pain models lack predictive and construct validity to accurately reflect human pain etiologies and, secondly, clinical pain is a multidimensional sensory experience that can’t always be captured by objective and robust measures. These challenges complicate the use of translational PK–PD modeling to project PK–PD data generated in preclinical species to a plausible range of clinical doses. To date only a few drug targets identified in animal studies have shown to be successful in the clinic. PK–PD modeling of biomarkers collected during the early phase of clinical development can bridge animal and clinical pain research. For drugs with novel mechanism of actions understanding of the target pharmacology is essential in order to increase the success of clinical development. There is a specific interest in the application of human pain models that can mimic different aspects of acute/chronic pain symptoms and serves as link between animal and clinical pain research. In early clinical development the main objective of PK–PD modeling is to characterize the relationship between target site binding and downstream biomarkers that have a potential link to the clinical endpoint (e.g. readouts from the human pain models) so as to facilitate the selection of doses for proof of concept studies. In patient studies, the role of PK–PD modeling and simulation is to characterize and confirm patient populations in terms of responder profiles with the aim to find the right dose for the right patient.     

Pharmacokinetic-pharmacodynamic modelling of opioids in healthy human volunteers. a minireview

Abstract: Pain is characterized by its multi‐dimensional nature, explaining in part why the pharmacokinetic/pharmacodynamic (PK/PD) relationships are not straightforward for analgesics. The first part of this MiniReview gives an overview of PK, PD and PK/PD models, as well as of population approach used in analgesic studies. The second part updates the state‐of‐the‐art in the PK/PD relationship of opioids, focusing on data obtained on experimental human pain models, a useful tool to characterize the PD of analgesics. For the so‐called weak opioids such as codeine, experimental human studies showed that analgesia relies mainly upon biotransformation into morphine. However, the time‐course of plasma concentrations of morphine did not always reflect the time‐course of effects, the major site of action being the central nervous system. For tramadol, a correlation has been observed between the analgesic response and the PK of the (+)R‐O‐demethyl‐tramadol metabolite. For ‘stronger’ opioids such as oxycodone, studies assessing the PK/PD of oxycodone suggested that active metabolite oxymorphone also strongly contributes to the analgesia and that analgesia may also be partially related through an action to peripherally located κ‐opioid receptors. Different models have been proposed to describe the time‐course of buprenorphine. An effect‐compartment model was adopted to describe the PK/PD of morphine and its active metabolite, morphine‐6‐glucuronide (M6G). A longer blood‐effect site equilibration half‐life t_1/2k_e0 was observed for M6G, suggesting a longer onset of action. The studies assessing the PK/PD of fentanyl and its derivatives showed a short t_1/2k_e0 for analgesia, between 0.2 and 9 min., reflecting a short onset of effect. In conclusion, depending on the speed of transfer between the plasma and the effect site as well as the participation of active metabolites, the time‐course of the analgesic effects can be close to the plasma concentrations (alfentanil and derivates) or observed with a prolonged delay (codeine, buprenorphine, morphine). These PK/PD data can be used to better characterize the differences between opioids, and partly explain the important observed variability among opioids in experimental conditions and should be systematically evaluated during drug development to better predict their selection in specific clinical conditions.     

Pharmacodynamic-pharmacokinetic integration as a guide to medicinal chemistry

A primary objective of pharmacokinetic-pharmacodynamic (PKPD) reasoning is to identify key in vivo drug and system proper?ties, enabling prediction of the magnitude and time course of drug responses under physiological and pathological conditions in animals and man. Since the pharmacological response generated by a drug is highly dependent on the actual system used to study its action, knowledge about its potency and efficacy at a given concentration or dose is insufficient to obtain a proper understanding of its pharmacodynamic profile. Hence, the output of PKPD activities extends beyond the provision of quantitative measures (models) of results, to the design of future protocols. Furthermore, because PKPD integrates DMPK (e.g. clearance) and pharmacology (e.g. potency),it provides an anchor point for compound selection, and, as such, should be viewed as an important weapon in medicinal chemistry. Here we outline key PK concepts relevant to PD, and then consider real-life experiments to illustrate the importance to the medicinal chemist of data obtained by PKPD. Useful assumptions and potential pitfalls are described, providing a holistic view of the plethora of determinants behind in vitro-in vivo correlations. By condensing complexity to simplicity, there are not only consequences for experimental design, and for the ranking and design of compounds, but it is also possible to make important predictions such as the impact of changes in drug potency and kinetics. In short, by using quantitative methods to tease apart pharmacodynamic complexities such as temporal differences and changes in plasma protein binding, it is possible to target the changes necessary for improving a compound's profile.     

Evaluation of IPPSE, an alternative method for sequential population PKPD analysis

The aim of this study is to present and evaluate an alternative sequential method to perform population pharmacokinetic-pharmacodynamic (PKPD) analysis. Simultaneous PKPD analysis (SIM) is generally considered the reference method but may be computationally burdensome and time consuming. Evaluation of alternative approaches aims at speeding up the computation time and stabilizing the estimation of the models, while estimating the model parameters with good enough precision. The IPPSE method presented here uses the individual PK parameter estimates and their uncertainty (SE) to propagate the PK information to the PD estimation step, while the IPP method uses the individual PK parameters only and the PPP&D method utilizes the PK data. Data sets (n = 200) with various study designs were simulated according to a one-compartment PK model and a direct Emax PD model. The study design of each dataset was randomly selected. The same PK and PD models were fitted to the simulated observations using the SIM, IPP, PPP&D and IPPSE methods. The performances of the methods were compared with respect to estimation precision and bias, and computation time. Estimated precision and bias for the IPPSE method were similar to that of SIM and PPP&D, while IPP had higher bias and imprecision. Compared with the SIM method, IPPSE saved more computation time (61%) than PPP&D (39%), while IPP remained the fastest method (86% run time saved). The IPPSE method is a promising alternative for PKPD analysis, combining the advantages of the SIM (higher precision and lower bias of parameter estimates) and the IPP (shorter run time) methods.     

Pharmacokinetic/pharmacodynamic integration in drug development and dosage-regimen optimization for veterinary medicine

Pharmacokinetic (PK)/pharmacodynamic (PD) modeling is a scientific tool to help developers select a rational dosage regimen for confirmatory clinical testing. This article describes some of the limitations associated with traditional dose-titration designs (parallel and crossover designs) for determining an appropriate dosage regimen. It also explains how a PK/PD model integrates the PK model (describing the relationship between dose, systemic drug concentrations, and time) with the PD model (describing the relationship between systemic drug concentration and the effect vs time profile) and a statistical model (particularly, the intra- and interindividual variability of PK and/or PD origin). Of equal importance is the utility of these models for promoting rational drug selection on the basis of effectiveness and selectivity. PK/PD modeling can be executed using various approaches, such as direct versus indirect response models and parametric versus nonparametric models. PK/PD concepts can be applied to individual dose optimization. Examples of the application of PK/PD approaches in veterinary drug development are provided, with particular emphasis given to nonsteroidal anti-inflammatory drugs. The limits of PK/PD approaches include the development of appropriate models, the validity of surrogate endpoints, and the acceptance of these models in a regulatory environment.     

Pharmacokinetic considerations for pediatric patients receiving analgesia in the intensive care unit; targeting postoperative,ECMO and hypothermia patients

Introduction: Adequate postoperative analgesia in pediatric patients in the intensive care unit (ICU) matters, since untreated pain is associated with negative outcomes. Compared to routine postoperative patients, children undergoing hypothermia (HT) or extracorporeal membrane oxygenation (ECMO), or recovering after cardiac surgery likely display non-maturational differences in pharmacokinetics (PK) and pharmacodynamics (PD). These differences warrant additional dosing recommendations to optimize pain treatment.

Areas covered: Specific populations within the ICU will be discussed with respect to expected variations in PK and PD for various analgesics. We hereby move beyond maturational changes and focus on why PK/PD may be different in children undergoing HT, ECMO or cardiac surgery. We provide a stepwise manner to develop PK-based dosing regimens using population PK approaches in these populations.

Expert opinion: A one-dose to size-fits-all for analgesia is suboptimal, but for several commonly used analgesics the impact of HT, ECMO or cardiac surgery on average PK parameters in children is not yet sufficiently known. Parameters considering both maturational and non-maturational covariates are important to develop population PK-based dosing advices as part of a strategy to optimize pain treatment.     

Preclinical Pharmacokinetic/Pharmacodynamic Modeling and Simulation in the Pharmaceutical Industry: An IQ Consortium Survey Examining the Current Landscape

The application of modeling and simulation techniques is increasingly common in preclinical stages of the drug discovery and development process. A survey focusing on preclinical pharmacokinetic/pharmacodynamics (PK/PD) analysis was conducted across pharmaceutical companies that are members of the International Consortium for Quality and Innovation in Pharmaceutical Development. Based on survey responses, ~68% of companies use preclinical PK/PD analysis in all therapeutic areas indicating its broad application. An important goal of preclinical PK/PD analysis in all pharmaceutical companies is for the selection/optimization of doses and/or dose regimens, including prediction of human efficacious doses. Oncology was the therapeutic area with the most PK/PD analysis support and where it showed the most impact. Consistent use of more complex systems pharmacology models and hybrid physiologically based pharmacokinetic models with PK/PD components was less common compared to traditional PK/PD models. Preclinical PK/PD analysis is increasingly being included in regulatory submissions with ~73% of companies including these data to some degree. Most companies (~86%) have seen impact of preclinical PK/PD analyses in drug development. Finally, ~59% of pharmaceutical companies have plans to expand their PK/PD modeling groups over the next 2 years indicating continued growth. The growth of preclinical PK/PD modeling groups in pharmaceutical industry is necessary to establish required resources and skills to further expand use of preclinical PK/PD modeling in a meaningful and impactful manner.KEY WORDS: pharmaceutical industry, preclinical pharmacokinetic/pharmacodynamic modeling, simulation     

Utilizing brain imaging for analgesic drug development

Analgesia is defined as loss of pain sensation without loss of consciousness; pain may be acute or chronic. Acute pain is well understood and can be controlled with currently available analgesics. Chronic pain, however, is not effectively controlled with current analgesics and side effect profiles often limit the use of these agents. Currently there are: (i) no objective methods for defining pain or analgesia in humans; (ii) no objective methods for correlating efficacy of analgesics in animal testing with human testing; and (iii) no objective method of evaluating the efficacy of analgesics in painful conditions, including neuropathic pain in which adaptive or maladaptive changes evolve with time. A technological revolution in functional brain imaging in humans and animals offers new approaches to objective evaluation of analgesics and of clinical pain states. These approaches hold great promise for revolutionizing drug development at preclinical and clinical stages.     

Modeling delayed drug effect using discrete-time nonlinear autoregressive models: a connection with indirect response models

Indirect response (IDR) models have been widely applied to pharmacodynamic (PD) modeling, particularly when delayed response (hysteresis) is present. This paper proposes a class of nonlinear discrete-time autoregressive (AR) models with drug concentrations acting as a time-varying covariate on the asymptote parameter or the autocorrelation parameter of the AR models as an alternative modeling approach for delayed response data. The mathematical derivations revealed the inherent connection between IDR models and nonlinear AR models, and showed that the nonlinear AR models approximate the IDR models when the time interval between response data is small. Simulations demonstrate that the IDR models and the corresponding AR models produce similar temporal response profiles, and as the time interval decreased (i.e., more intensive sampling designs), the AR model based parameter estimates were more comparable to those estimated from the IDR models. In conjunction with mixed-effects modeling, the nonlinear AR models have been shown to well describe a set of simulated longitudinal PK/PD data for a clinical study. Further extensions of the proposed nonlinear AR models are warranted to model irregular and sparse PK/PD data.     

Methods and software tools for design evaluation in population pharmacokinetics–pharmacodynamics studies

Population pharmacokinetic (PK)–pharmacodynamic (PKPD) models are increasingly used in drug development and in academic research; hence, designing efficient studies is an important task. Following the first theoretical work on optimal design for nonlinear mixed-effects models, this research theme has grown rapidly. There are now several different software tools that implement an evaluation of the Fisher information matrix for population PKPD. We compared and evaluated the following five software tools: PFIM, PkStaMp, PopDes, PopED and POPT. The comparisons were performed using two models, a simple-one compartment warfarin PK model and a more complex PKPD model for pegylated interferon, with data on both concentration and response of viral load of hepatitis C virus. The results of the software were compared in terms of the standard error (SE) values of the parameters predicted from the software and the empirical SE values obtained via replicated clinical trial simulation and estimation. For the warfarin PK model and the pegylated interferon PKPD model, all software gave similar results. Interestingly, it was seen, for all software, that the simpler approximation to the Fisher information matrix, using the block diagonal matrix, provided predicted SE values that were closer to the empirical SE values than when the more complicated approximation was used (the full matrix). For most PKPD models, using any of the available software tools will provide meaningful results, avoiding cumbersome simulation and allowing design optimization.     

A non-parametric method to analyse time-course of effect in the absence of pharmacokinetic data: Application to inhaled bronchodilators

In spite of the extensive use of long-acting beta(2)-agonist (LABA) bronchodilators in asthma, the actual mechanism of their in vivo duration of action is not well understood, primarily due to limitations of standard pharmacokinetic-pharmacodynamic (PKPD) analysis methodologies. We have developed a novel method of analysing lung efficacy vs. time profiles for LABAs that can be used to provide comparative information on the lung PK. We hypothesised that for compounds that do not differ in their PK at the site of PD action, but differ in their in vivo potencies, the relationship between the area under the effect curve (AUEC) and the observed maximum effect (OME) at different doses is described by the same sigmoid curve. We have illustrated this property for standard PKPD models by obtaining analytical solution and through simulations. Anaesthetised dog in vivo effect vs. time profiles were gathered for six inhaled LABA candidates that differ in their in vitro potencies. Neither lung nor systemic PK was available for any compound. Analysis of the AUEC vs. OME data, derived from the efficacy profiles, using nonlinear mixed effects modelling indicated that for four compounds, the observed differences in in vivo duration of action was due to differences in their in vivo potencies and not because of lung PK differences. Therefore, it was concluded that for these compounds, characterisation of lung PK was unlikely to differentiate their PKPD characteristics. Thus, the proposed approach helped focus resources during translational research leading to lead candidate selection.     

Pharmacokinetic–pharmacodynamic modeling of the influence of chronic phenytoin therapy on the rocuronium bromide response in patients undergoing brain surgery

BACKGROUND: Antiepileptic drugs decrease the intensity of the effect of neuromuscular blocking agents. The objective of this study was to evaluate the influence of chronic phenytoin therapy (CPT) on the pharmacokinetics (PK) and pharmacodynamics (PD) of rocuronium. METHODS: A total of 21 patients undergoing intracranial surgery were enrolled in the study. Ten of these were under CPT. Rocuronium was administered intravenously. Arterial blood samples were drawn, and the T1% (percentage change from the response to the supramaximal stimulus) derived from electromyogram was continuously recorded. NONMEM: software was used to construct, evaluate and validate the PKPD models. RESULTS: The PKPD of rocuronium was described using a three-compartment PK model and effect compartment model. The CPT therapy was found to increase the total plasma clearance from 0.26 to 0.75 L min(-1). The PD model parameter estimates were k(e0)= 0.073 min(-1), IC(50) (the steady-state plasma concentration eliciting half of the maximum response) = 836 ng mL(-1) and gamma = 3.13. CONCLUSIONS: Chronic phenytoin therapy increases the clearance of rocuronium from 0.26 to 0.75 L min(-1) but has no effect on the k(e0), IC(50) or gamma parameters.     

How can biologically-based modeling of arsenic kinetics and dynamics inform the risk assessment process? - A workshop review

Quantitative biologically-based models describing key events in the continuum from arsenic exposure to the development of adverse health effects provide a framework to integrate information obtained across diverse research areas. For example, genetic polymorphisms in arsenic metabolizing enzymes can lead to differences in target tissue dosimetry for key metabolites causative in toxic and carcinogenic response. This type of variation can be quantitatively incorporated into pharmacokinetic (PK) models and used together with population-based modeling approaches to evaluate the impact of genetic variation in methylation capacity on dose of key metabolites to target tissue. The PK model is an essential bridge to the pharmacodynamic (PD) models. A particular benefit of PD modeling for arsenic is that alternative models can be constructed for multiple proposed modes of action for arsenicals. Genomics data will prove useful for identifying the key pathways involved in particular responses and aid in determining other types of data needed for quantitative modeling. These models, when linked with PK models, can be used to better understand and explain dose- and time-response behaviors. This in turn assists in prioritizing modes of action with respect to their risk assessment relevance and future research. This type of integrated modeling approach can form the basis for a highly informative mode-of-action directed risk assessment for inorganic arsenic (iAs). This paper will address both practical and theoretical aspects of integrating PK and PD data in a modeling framework, including practical barriers to its application.     

Using pharmacokinetic/pharmacodynamic modelling in safety pharmacology to better define safety margins: a regional workshop of the Safety Pharmacology Society

This meeting was convened to encourage the incorporation of empirical and mechanism-based pharmacokinetic/pharmacodynamic (PK/PD) modelling into safety pharmacology to improve the predictability of nonclinical investigations for human outcomes. These technologies make use of mathematical expressions relating measured variables to derive essential parameters for describing responses and predicting the behaviour of biological systems to a drug. Hence, empirical PK/PD modelling is intended to define the in vivo interrelationship between three basic entities: time; drug concentrations; and drug effects. The most widely applied equation relating drug bioresponses to plasma concentrations is the Hill sigmoidal E(max) model, which allows the calculation of drug potency (EC(50)) and intrinsic activity (E(max)). However, since the latter parameters depend on attributes of the drug and on the biological system itself, this approach can fail to accurately foretell drug concentration-effect behaviour, particularly between species. A particular phenomenon of PK/PD analysis is hysteresis, which refers to the delay of the bioresponse time-course with respect to exposure time-course, as this provides valuable information on the direct or indirect nature of the drug mechanism of action. The application of these concepts to the examination of the QT interval prolongation produced by dofetilide was discussed. A development surmounting the limitations of empirical PK/PD models is mechanism-based PK/PD modelling because its toolkits integrate specific mathematical expressions replicating the drug (e.g., affinity, intrinsic efficacy), and the physiological system (e.g., nonlinear, time-dependent, transduction processes), properties that play a crucial role in the cascade of biological events culminating in bioresponses. The usefulness of this approach was illustrated by a thorough analysis of nonclinical respiratory depressant and antinociceptive data on buprenorphine and fentanyl for successfully predicting the human safety and efficacy of these analgesic agents. Thus, PK/PD models can be viewed as in silico clones of drug and biological system activities that provide high-level knowledge that can avoid inappropriate attrition, and hasten the progress, of novel drugs, along the entire critical path of pharmaceutical development.     

Joint Population Pharmacokinetic/Pharmacodynamic Model for the Heart Rate Effects at Rest and at the End of Exercise for Cilobradine

Purpose

To develop a semi-mechanistic population pharmacokinetic/pharmacodynamic (PKPD) model for the selective bradycardic agent cilobradine describing simultaneously the heart rate (HR) measured at rest and just after the end of exercise sharing the same set of PKPD parameters.

Methods

Healthy subjects received cilobradine orally once daily over 2 weeks at 0.25–5 mg doses or placebo. Plasma drug concentrations and HR were measured at rest and following 3 min of exercise over the entire study period. PK and HR data were analyzed using the population approach with NONMEM VII.

Results

Plasma disposition of cilobradine was described with a three compartment model. Cilobradine showed dose proportional and time independent pharmacokinetics. HR response was drug concentration dependent and appeared with a significant delay with respect to PK profiles, a phenomenon modeled using two transit compartments. Perturbation in HR at rest as a consequence of exercise was described assuming that physiological processes controlling cardiac frequency were constantly increased over the period of exercise only.

Conclusions

The selected model provides a useful modeling tool for cases where the PD response measured is the result of a temporal experimental induced perturbation.     

CCR2拮抗剂研究进展

细胞表面趋化因子受体2(CCR2)属于G蛋白偶联受体(GPCR)超家族成员,并且是单核细胞趋化蛋白1～4(MCP1～4)的受体.MCP1～4是促炎症反应的化学诱导物,CCR2和MCP-1在人类侵蚀性疾病模型如动脉粥状硬化、多发性硬化症中均起显著作用.大量研究证明CCR2和MCP-1拮抗剂可以减少临床炎症模型的发病率,这些化学拮抗剂的结构多样,主要包括γ-氨基丁酰胺类、甘胺酰胺类、噻唑类、吲哚类、二取代双哌啶醇类、季铵盐类和不饱和杂环类等,它们表现出不同的药理活性.CCR2拮抗剂对各种与趋化因子相关的疾病具有较好的疗效,部分药物已经进入临床试验阶段,综述CCR2及其受体拮抗剂的研究进展.     

Fractional dynamics pharmacokinetics–pharmacodynamic models

While an increasing number of fractional order integrals and differential equations applications have been reported in the physics, signal processing, engineering and bioengineering literatures, little attention has been paid to this class of models in the pharmacokinetics–pharmacodynamic (PKPD) literature. One of the reasons is computational: while the analytical solution of fractional differential equations is available in special cases, it this turns out that even the simplest PKPD models that can be constructed using fractional calculus do not allow an analytical solution. In this paper, we first introduce new families of PKPD models incorporating fractional order integrals and differential equations, and, second, exemplify and investigate their qualitative behavior. The families represent extensions of frequently used PK link and PD direct and indirect action models, using the tools of fractional calculus. In addition the PD models can be a function of a variable, the active drug, which can smoothly transition from concentration to exposure, to hyper-exposure, according to a fractional integral transformation. To investigate the behavior of the models we propose, we implement numerical algorithms for fractional integration and for the numerical solution of a system of fractional differential equations. For simplicity, in our investigation we concentrate on the pharmacodynamic side of the models, assuming standard (integer order) pharmacokinetics.