Utilisation of pharmacokinetic-pharmacodynamic modelling and simulation in regulatory decision-making

Use of a PK/PD Model to Select Cetagliptin Dosages for Patients with Type 2 Diabetes in Phase 3 Trials

BACKGROUND

Cetagliptin is a novel dipeptidyl peptidase-4 (DPP-4) inhibitor developed for the treatment of patients with type 2 diabetes (T2D). Several phase 1 studies have been conducted in China. Modelling and simulation were used to obtain cetagliptin dose for phase 3 trials in T2D patients.

METHODS

A pharmacokinetic (PK)/pharmacodynamic (PD) model and model-based analysis of the relationship between hemoglobin A1c (HbA1c) and dosage was explored to guide dose selection of cetagliptin for phase 3 trials. The PK/PD data were derived from four phase 1 clinical studies, and sitagliptin 100 mg was employed as a positive control in studies 1, 3, and 4.

RESULTS

The PK profiles of cetagliptin were well described by a two-compartment model with first-order absorption, saturated efflux, and first-order elimination. The final PD model was a sigmoid maximum inhibitory efficacy (Emax) model with the Hill coefficient. The final model accurately captured cetagliptin PK/PD, demonstrated by goodness-of-fit plots. Based on weighted average inhibition (WAI), the relationship between HbA1c and dose was well displayed. Cetagliptin 50 mg once daily or above as monotherapy or as add-on therapy appeared more effective in HbA1c reduction than sitagliptin 100 mg. Cetagliptin 50 mg or 100 mg once daily was selected as the dose for phase 3 trials of cetagliptin in T2D patients.

CONCLUSIONS

The PK/PD model supports dose selection of cetagliptin for phase 3 trials. A model‑informed approach can be used to replace a dose-finding trial and accelerate cetagliptin's development.

DN-ODE: Data-driven neural-ODE modeling for breast cancer tumor dynamics and progression-free survivals

Pharmacokinetic/Pharmacodynamic (PK/PD) modeling is crucial in the development of new drugs. However, traditional population-based PK/PD models encounter challenges when modeling for individual patients. We aim to explore the potential of constructing a pharmacodynamic model for individual breast cancer pharmacodynamics leveraging only limited data from early clinical trial phases. While previous studies on Neural Ordinary Differential Equations (ODEs) suggest promising results in clinical trial practices, they primarily focused on theoretical applications or independent PK/PD modeling. PD modeling from complex and irregular clinical trial data, especially when interacting with PK parameters, is still unclear. To achieve that, we introduce a Data-driven Neural Ordinary Differential Equation (DN-ODE) modeling for breast cancer tumor dynamics and progression-free survival data. To validate this approach, experiments are conducted with early-phase clinical trial data from the Amcenestrant (an oral treatment for breast cancer) dataset (AMEERA 1-2), aiming to predict pharmacodynamics in the later phase (AMEERA 3). DN-ODE model achieves RMSE scores of 8.78 and 0.21 in tumor size and progression-free survival, respectively, with R2 scores over 0.9 for each task. Compared to PK/PD methodologies, DN-ODE is able to predict robust individual tumor dynamics with only limited cycle data. We also introduce Principal Component Analysis visualizations for encoder results, demonstrating the DN-ODE's capability to discern individual distributions and diverse tumor growth patterns. Therefore, DN-ODE facilitates comprehensive drug efficacy assessments, pinpoints potential responders, and aids in trial design.

In Silico Clinical Trials: Is It Possible?

Modeling and simulation (M&S), including in silico (clinical) trials, helps accelerate drug research and development and reduce costs and have coined the term "model-informed drug development (MIDD)." Data-driven, inferential approaches are now becoming increasingly complemented by emerging complex physiologically and knowledge-based disease (and drug) models, but differ in setup, bottlenecks, data requirements, and applications (also reminiscent of the different scientific communities they arose from). At the same time, and within the MIDD landscape, regulators and drug developers start to embrace in silico trials as a potential tool to refine, reduce, and ultimately replace clinical trials. Effectively, silos between the historically distinct modeling approaches start to break down. Widespread adoption of in silico trials still needs more collaboration between different stakeholders and established precedence use cases in key applications, which is currently impeded by a shattered collection of tools and practices. In order to address these key challenges, efforts to establish best practice workflows need to be undertaken and new collaborative M&S tools devised, and an attempt to provide a coherent set of solutions is provided in this chapter. First, a dedicated workflow for in silico clinical trial (development) life cycle is provided, which takes up general ideas from the systems biology and quantitative systems pharmacology space and which implements specific steps toward regulatory qualification. Then, key characteristics of an in silico trial software platform implementation are given on the example of jinkō.ai (nova's end-to-end in silico clinical trial platform). Considering these enabling scientific and technological advances, future applications of in silico trials to refine, reduce, and replace clinical research are indicated, ranging from synthetic control strategies and digital twins, which overall shows promise to begin a new era of more efficient drug development.

A compatibility evaluation between the physiologically based pharmacokinetic (PBPK) model and the compartmental PK model using the lumping method with real cases

Pharmacokinetic (PK) modeling is a useful method for investigating drug absorption, distribution, metabolism, and excretion. The most commonly used mathematical models in PK modeling are the compartment model and physiologically based pharmacokinetic (PBPK) model. Although the theoretical characteristics of each model are well known, there have been few comparative studies of the compatibility of the models. Therefore, we evaluated the compatibility of PBPK and compartment models using the lumping method with 20 model compounds. The PBPK model was theoretically reduced to the lumped model using the principle of grouping tissues and organs that show similar kinetic behaviors. The area under the concentration-time curve (AUC) based on the simulated concentration and PK parameters (drug clearance [CL], central volume of distribution [Vc], peripheral volume of distribution [Vp]) in each model were compared, assuming administration to humans. The AUC and PK parameters in the PBPK model were similar to those in the lumped model within the 2-fold range for 17 of 20 model compounds (85%). In addition, the relationship of the calculated Vd/fu (volume of distribution [Vd], drug-unbound fraction [fu]) and the accuracy of AUC between the lumped model and compartment model confirmed their compatibility. Accordingly, the compatibility between PBPK and compartment models was confirmed by the lumping method. This method can be applied depending on the requirement of compatibility between the two models.

When Less May Be Enough: Dose Selection Strategies for Immune Checkpoint Inhibitors Focusing on AntiPD-(L)1 Agents

Early clinical trials investigating antiPD(L)-1 agents rarely reached a maximum tolerated dose (MTD), and efficacy signals were observed even at the lowest dose levels. Most extended treatment intervals investigated indicated that these drugs do not follow a direct dose-toxicity or dose-efficacy relationship. Within this context and considering the high cost of antiPD(L)-1 agents, there is a significant debate on whether lower doses or the administration of such agents at an extended interval should be prospectively evaluated in already-approved agents, or at least be considered in novel combination trials involving antiPD(L)-1 drugs. Herein, we review the dosing, overall response rates, and incidence of treatment-related adverse events of antiPD(L)-1 agents in early dose-escalation trials and discuss the appropriateness of recommended Phase 2 dose selection as well as the final regulatory approved doses of such agents. Efficacy and safety data from randomized dose-range Phase 2 trials and real-world data (RWD) on the usage of lower doses and/or non-standard extended treatment intervals are also examined. As the accumulating evidence suggests lower doses or extended dosing intervals of antiPD(L)-1 may achieve a similar clinical benefit in comparison to the currently approved doses, we address the clinical and financial toxicity implications of using potentially higher doses than necessary. Last, we discuss ways to resolve the current dosing conundrum of antiPD-(L)1 agents such as performing near-equivalence studies and propose a framework for future development of immunotherapeutics to find the lowest efficacious dose instead of MTD.

OptiDose: Computing the Individualized Optimal Drug Dosing Regimen Using Optimal Control

Providing the optimal dosing strategy of a drug for an individual patient is an important task in pharmaceutical sciences and daily clinical application. We developed and validated an optimal dosing algorithm (OptiDose) that computes the optimal individualized dosing regimen for pharmacokinetic-pharmacodynamic models in substantially different scenarios with various routes of administration by solving an optimal control problem. The aim is to compute a control that brings the underlying system as closely as possible to a desired reference function by minimizing a cost functional. In pharmacokinetic-pharmacodynamic modeling, the controls are the administered doses and the reference function can be the disease progression. Drug administration at certain time points provides a finite number of discrete controls, the drug doses, determining the drug concentration and its effect on the disease progression. Consequently, rewriting the cost functional gives a finite-dimensional optimal control problem depending only on the doses. Adjoint techniques allow to compute the gradient of the cost functional efficiently. This admits to solve the optimal control problem with robust algorithms such as quasi-Newton methods from finite-dimensional optimization. OptiDose is applied to three relevant but substantially different pharmacokinetic-pharmacodynamic examples.

Pharmacokinetics and Pharmacodynamics Models of Tumor Growth and Anticancer Effects in Discrete Time

We study the h-discrete and h-discrete fractional representation of a pharmacokinetics-pharmacodynamics (PK-PD) model describing tumor growth and anticancer effects in continuous time considering a time scale h𝕅0, where h > 0. Since the measurements of the drug concentration in plasma were taken hourly, we consider h = 1/24 and obtain the model in discrete time (i.e. hourly). We then continue with fractionalizing the h-discrete nabla operator in the h-discrete model to obtain the model as a system of nabla h-fractional difference equations. In order to solve the fractional h-discrete system analytically we state and prove some theorems in the theory of discrete fractional calculus. After estimating and getting confidence intervals of the model parameters, we compare residual squared sum values of the models in one table. Our study shows that the new introduced models provide fitting as good as the existing models in continuous time.

Transitioning from Basic toward Systems Pharmacodynamic Models: Lessons from Corticosteroids

Technology in bioanalysis, -omics, and computation have evolved over the past half century to allow for comprehensive assessments of the molecular to whole body pharmacology of diverse corticosteroids. Such studies have advanced pharmacokinetic and pharmacodynamic (PK/PD) concepts and models that often generalize across various classes of drugs. These models encompass the "pillars" of pharmacology, namely PK and target drug exposure, the mass-law interactions of drugs with receptors/targets, and the consequent turnover and homeostatic control of genes, biomarkers, physiologic responses, and disease symptoms. Pharmacokinetic methodology utilizes noncompartmental, compartmental, reversible, physiologic [full physiologically based pharmacokinetic (PBPK) and minimal PBPK], and target-mediated drug disposition models using a growing array of pharmacometric considerations and software. Basic PK/PD models have emerged (simple direct, biophase, slow receptor binding, indirect response, irreversible, turnover with inactivation, and transduction models) that place emphasis on parsimony, are mechanistic in nature, and serve as highly useful "top-down" methods of quantitating the actions of diverse drugs. These are often components of more complex quantitative systems pharmacology (QSP) models that explain the array of responses to various drugs, including corticosteroids. Progressively deeper mechanistic appreciation of PBPK, drug-target interactions, and systems physiology from the molecular (genomic, proteomic, metabolomic) to cellular to whole body levels provides the foundation for enhanced PK/PD to comprehensive QSP models. Our research based on cell, animal, clinical, and theoretical studies with corticosteroids have provided ideas and quantitative methods that have broadly advanced the fields of PK/PD and QSP modeling and illustrates the transition toward a global, systems understanding of actions of diverse drugs. SIGNIFICANCE STATEMENT: Over the past half century, pharmacokinetics (PK) and pharmacokinetics/pharmacodynamics (PK/PD) have evolved to provide an array of mechanism-based models that help quantitate the disposition and actions of most drugs. We describe how many basic PK and PK/PD model components were identified and often applied to the diverse properties of corticosteroids (CS). The CS have complications in disposition and a wide array of simple receptor-to complex gene-mediated actions in multiple organs. Continued assessments of such complexities have offered opportunities to develop models ranging from simple PK to enhanced PK/PD to quantitative systems pharmacology (QSP) that help explain therapeutic and adverse CS effects. Concurrent development of state-of-the-art PK, PK/PD, and QSP models are described alongside experimental studies that revealed diverse CS actions.

A study on discrete and discrete fractional pharmacokinetics-pharmacodynamics models for tumor growth and anti-cancer effects

We study the discrete and discrete fractional representation of a pharmacokinetics - pharmacodynamics (PK-PD) model describing tumor growth and anti-cancer effects in continuous time considering a time scale h ℕ 0 h $h\mathbb{N}_0^h$ , where h > 0. Since the measurements of the tumor volume in mice were taken daily, we consider h = 1 and obtain the model in discrete time (i.e. daily). We then continue with fractionalizing the discrete nabla operator to obtain the model as a system of nabla fractional difference equations. The nabla fractional difference operator is considered in the sense of Riemann-Liouville definition of the fractional derivative. In order to solve the fractional discrete system analytically we state and prove some theorems in the theory of discrete fractional calculus. For the data fitting purpose, we use a new developed method which is known as an improved version of the partial sum method to estimate the parameters for discrete and discrete fractional models. Sensitivity analysis is conducted to incorporate uncertainty/noise into the model. We employ both frequentist approach and Bayesian method to construct 90 percent confidence intervals for the parameters. Lastly, for the purpose of practicality, we test the discrete models for their efficiency and illustrate their current limitations for application.

Early-drug development in the era of immuno-oncology: are we ready to face the challenges?

The classical development of drugs has progressively faded away, and we are currently in an era of seamless drug-development, where first-in-human trials include unusually big expansion cohorts in the search for early signs of activity and rapid regulatory approval. The fierce competition between different pharmaceutical companies and the hype for immune combinations obliges us to question the current way in which we are evaluating these drugs. In this review, we discuss critical issues and caveats in immunotherapy development. A particular emphasis is put on the limitations of pre-clinical toxicology studies, where both murine models and cynomolgus monkeys have underpredicted toxicity in humans. Moreover, relevant issues surrounding dose determination during phase I trials, such as dose-escalation methods or flat versus body-weight dosing, are discussed. A proposal of how to face these different challenges is offered, in order to achieve maximum efficacy with minimum toxicity for our patients.

Pharmacokinetic/Pharmacodynamic Modeling for Drug Development in Oncology

High drug attrition rates remain a critical issue in oncology drug development. A series of steps during drug development must be addressed to better understand the pharmacokinetic (PK) and pharmacodynamic (PD) properties of novel agents and, thus, increase their probability of success. As available data continues to expand in both volume and complexity, comprehensive integration of PK and PD information into a robust mathematical model represents a very useful tool throughout all stages of drug development. During the discovery phase, PK/PD models can be used to identify and select the best drug candidates, which helps characterize the mechanism of action and disease behavior of a given drug, to predict clinical response in humans, and to facilitate a better understanding about the potential clinical relevance of preclinical efficacy data. During early drug development, PK/PD modeling can optimize the design of clinical trials, guide the dose and regimen that should be tested further, help evaluate proof of mechanism in humans, anticipate the effect in certain subpopulations, and better predict drug-drug interactions; all of these effects could lead to a more efficient drug development process. Because of certain peculiarities of immunotherapies, such as PK and PD characteristics, PK/PD modeling could be particularly relevant and thus have an important impact on decision making during the development of these agents.

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.

The role of concentration-effect relationships in the assessment of QTc interval prolongation

Population pharmacokinetic and pharmacokinetic-pharmacodynamic (PKPD) modelling has been widely used in clinical research. Yet, its application in the evaluation of cardiovascular safety remains limited, particularly in the evaluation of pro-arrhythmic effects. Here we discuss the advantages of disadvantages of population PKPD modelling and simulation, a paradigm built around the knowledge of the concentration-effect relationship as the basis for decision making in drug development and its utility as a guide to drug safety. A wide-ranging review of the literature was performed on the experimental protocols currently used to characterize the potential for QT interval prolongation, both pre-clinically and clinically. Focus was given to the role of modelling and simulation for design optimization and subsequent analysis and interpretation of the data, discriminating drug from system specific properties. Cardiovascular safety remains one of the major sources of attrition in drug development with stringent regulatory requirements. However, despite the myriad of tests, data are not integrated systematically to ensure accurate translation of the observed drug effects in clinically relevant conditions. The thorough QT study addresses a critical regulatory question but does not necessarily reflect knowledge of the underlying pharmacology and has limitations in its ability to address fundamental clinical questions. It is also prone to issues of multiplicity. Population approaches offer a paradigm for the evaluation of drug safety built around the knowledge of the concentration-effect relationship. It enables quantitative assessment of the probability of QTc interval prolongation in patients, providing better guidance to regulatory labelling and understanding of benefit/risk in specific populations.

Not-in-trial simulation I: Bridging cardiovascular risk from clinical trials to real-life conditions

AIMS

The assessment of heart rate-corrected QT (QTc) interval prolongation relies on the evidence of drug effects in healthy subjects. This study demonstrates the relevance of pharmacokinetic-pharmacodynamic (PKPD) relationships to characterize drug-induced QTc interval prolongation and explore the discrepancies between clinical trials and real-life conditions.

METHODS

d,l-Sotalol data from healthy subjects and from the Rotterdam Study cohort were used to assess treatment response in a phase I setting and in a real-life conditions, respectively. Using modelling and simulation, drug effects at therapeutic doses were predicted in both populations.

RESULTS

Inclusion criteria were shown to restrict the representativeness of the trial population in comparison to real-life conditions. A significant part of the typical patient population was excluded from trials due to weight and baseline QTc interval criteria. Relative risk was significantly different between sotalol users with and without heart failure, hypertension, diabetes and myocardial infarction (P < 0.01). Although drug effects do cause an increase in the relative risk of QTc interval prolongation, the presence of diabetes represented an increase from 4.0 [95% confidence interval (CI) 2.7-5.8] to 6.5 (95% CI 1.6-27.1), whilst for myocardial infarction it increased from 3.4 (95% CI 2.3-5.13) to 15.5 (95% CI 4.9-49.3).

CONCLUSIONS

Our findings show that drug effects on QTc interval do not explain the observed QTc values in the population. The prevalence of high QTc values in the real-life population can be assigned to co-morbidities and concomitant medications. These findings substantiate the need to account for these factors when evaluating the cardiovascular risk of medicinal products.

Pharmacometric Analyses to Support Early Development Decisions for LY2878735: A Novel Serotonin Norepinephrine Reuptake Inhibitor

LY2878735 is a novel dual serotonin (5-hydroxytryptamine (5-HT)) and norepinephrine (NE) reuptake inhibitor (SNRI) in development for chronic pain indications. In vitro profile suggests a more balanced profile as compared with other SNRI's, which is expected to confer superior clinical efficacy. LY2878735 is metabolized partly by the genetically polymorphic cytochrome P450 (CYP) 2D6 pathway, raising pharmacokinetic (PK) variability concerns. Phase 1 PK and biomarker data were analyzed by pharmacometric methods to characterize the balance between dual-target engagement and adverse effects on heart rate (HR) and blood pressure (BP). A narrow range of plasma LY2878735 levels was associated with an acceptable balance. As compared with poor metabolizers (PM), CYP2D6 extensive metabolizers (EM) have 21- and threefold higher clearance and distribution volume, respectively. Even with a CYP2D6-based dosing paradigm, a superior therapeutic index comparable to duloxetine, a widely used SNRI, was not achievable and LY2878735 development was thus terminated. Model-based approach effectively synthesizes PK-pharmacodynamic (PD) relationships, enabling efficient early development decisions.

Formalizing an integrative, multidisciplinary cancer therapy discovery workflow

Although many clinicians and researchers work to understand cancer, there has been limited success to effectively combine forces and collaborate over time, distance, data, and budget constraints. Here we present a workflow template for multidisciplinary cancer therapy that was developed during the 2nd Annual Workshop on Cancer Systems Biology sponsored by Tufts University, Boston, Massachusetts, in July 2012. The template was applied to the development of a metronomic therapy backbone for neuroblastoma. Three primary groups were identified: clinicians, biologists, and quantitative scientists (mathematicians, computer scientists, and engineers). The workflow described their integrative interactions; parallel or sequential processes; data sources and computational tools at different stages as well as the iterative nature of therapeutic development from clinical observations to in vitro, in vivo, and clinical trials. We found that theoreticians in dialog with experimentalists could develop calibrated and parameterized predictive models that inform and formalize sets of testable hypotheses, thus speeding up discovery and validation while reducing laboratory resources and costs. The developed template outlines an interdisciplinary collaboration workflow designed to systematically investigate the mechanistic underpinnings of a new therapy and validate that therapy to advance development and clinical acceptance.

Population pharmacokinetics and pharmacodynamics of bivalirudin in young healthy Chinese volunteers

AIM

To investigate the population pharmacokinetics (PK) and pharmacodynamics (PD) of bivalirudin, a synthetic bivalent direct thrombin inhibitor, in young healthy Chinese subjects.

METHODS

Thirty-six young healthy volunteers were randomly assigned into 4 groups received bivalirudin 0.5 mg/kg, 0.75 mg/kg, and 1.05 mg/kg intravenous bolus, 0.75 mg/kg intravenous bolus followed by 1.75 mg/kg intravenous infusion per hour for 4 h. Blood samples were collected to measure bivalirudin plasma concentration and activated clotting time (ACT). Population PK-PD analysis was performed using the nonlinear mixed-effects model software NONMEM. The final models were validated with bootstrap and prediction-corrected visual predictive check (pcVPC) approaches.

RESULTS

The final PK model was a two-compartment model without covariates. The typical PK population values of clearance (CL), apparent distribution volume of the central-compartment (V(1)), inter-compartmental clearance (Q) and apparent distribution volume of the peripheral compartment (V(2)) were 0.323 L·h(-1)·kg(-1), 0.086 L/kg, 0.0957 L·h(-1)·kg(-1), and 0.0554 L/kg, respectively. The inter-individual variabilities of these parameters were 14.8%, 24.2%, fixed to 0% and 15.6%, respectively. The final PK-PD model was a sigmoid E(max) model without the Hill coefficient. In this model, a covariate, red blood cell count (RBC(*)), had a significant effect on the EC(50) value. The typical PD population values of maximum effect (E(max)), EC(50), baseline ACT value (E(0)) and the coefficient of RBC(*) on EC(50) were 318 s, 2.44 mg/L, 134 s and 1.70, respectively. The inter-individual variabilities of E(max), EC(50), and E(0) were 6.80%, 46.4%, and 4.10%, respectively.

CONCLUSION

Population PK-PD models of bivalirudin in healthy young Chinese subjects have been developed, which may provide a reference for future use of bivalirudin in China.

Mechanisms underlying food-drug interactions: inhibition of intestinal metabolism and transport

Food-drug interaction studies are critical to evaluate appropriate dosing, timing, and formulation of new drug candidates. These interactions often reflect prandial-associated changes in the extent and/or rate of systemic drug exposure. Physiologic and physicochemical mechanisms underlying food effects on drug disposition are well-characterized. However, biochemical mechanisms involving drug metabolizing enzymes and transport proteins remain underexplored. Several plant-derived beverages have been shown to modulate enzymes and transporters in the intestine, leading to altered pharmacokinetic (PK) and potentially negative pharmacodynamic (PD) outcomes. Commonly consumed fruit juices, teas, and alcoholic drinks contain phytochemicals that inhibit intestinal cytochrome P450 and phase II conjugation enzymes, as well as uptake and efflux transport proteins. Whereas myriad phytochemicals have been shown to inhibit these processes in vitro, translation to the clinic has been deemed insignificant or undetermined. An overlooked prerequisite for elucidating food effects on drug PK is thorough knowledge of causative bioactive ingredients. Substantial variability in bioactive ingredient composition and activity of a given dietary substance poses a challenge in conducting robust food-drug interaction studies. This confounding factor can be addressed by identifying and characterizing specific components, which could be used as marker compounds to improve clinical trial design and quantitatively predict food effects. Interpretation and integration of data from in vitro, in vivo, and in silico studies require collaborative expertise from multiple disciplines, from botany to clinical pharmacology (i.e., plant to patient). Development of more systematic methods and guidelines is needed to address the general lack of information on examining drug-dietary substance interactions prospectively.

Clinical trials in the era of personalized oncology

The rapid pace of discoveries in tumor biology, imaging technology, and human genetics hold promise for an era of personalized oncology care. The successful development of a handful of new targeted agents has generated much hope and hype about the delivery of safer and more effective new treatments for cancer. The design and conduct of clinical trials has not yet adjusted to a new era of personalized oncology and so we are more in transition to that era than in it. With the development of treatments for breast cancer as a model, we review the approaches to clinical trials and the development of novel therapeutics in the prior era of population oncology, the current transitional era, and the future era of personalized oncology.

Incorporating receptor theory in mechanism-based pharmacokinetic-pharmacodynamic (PK-PD) modeling

Pharmacokinetic-Pharmacodynamic (PK-PD) modeling helps to better understand drug efficacy and safety and has, therefore, become a powerful tool in the learning-confirming cycles of drug-development. In translational drug research, mechanism-based PK-PD modeling has been recognized as a tool for bringing forward early insights in drug efficacy and safety into the clinical development. These models differ from descriptive PK-PD models in that they quantitatively characterize specific processes in the causal chain between drug administration and effect. This includes target site distribution, binding and activation, pharmacodynamic interactions, transduction and homeostatic feedback mechanisms. Compared to descriptive models mechanism-based PK-PD models that utilize receptor theory concepts for characterization of target binding and target activation processes have improved properties for extrapolation and prediction. In this respect, receptor theory constitutes the basis for 1) prediction of in vivo drug concentration-effect relationships and 2) characterization of target association-dissociation kinetics as determinants of hysteresis in the time course of the drug effect. This approach intrinsically distinguishes drug- and system specific parameters explicitly, allowing accurate extrapolation from in vitro to in vivo and across species. This review provides an overview of recent developments in incorporating receptor theory in PK-PD modeling with a specific focus on the identifiability of these models.

Simulated drug administration: an emerging tool for teaching clinical pharmacology during anesthesiology training

A thorough understanding of the dose-response relationship is required for optimizing the efficacy of anesthetics while minimizing adverse drug effects. Nowadays, except for the inhaled anesthetics (for which end-tidal concentrations can be measured online), most of the drugs used in clinical anesthesia are administered using standard dosing guidelines, without giving due consideration to their pharmacokinetics and dynamics in guiding their administration. Various studies have found that introducing pharmacokinetics and pharmacodynamics as part of the inputs in clinical anesthesiology could lead to better patient care. With this in mind, it is extremely important that clinicians understand and apply the principles of clinical pharmacology that determine the time course of a drug's disposition and effect. Clinical pharmacology is one of the most challenging topics to teach in anesthesiology. The development of simulators to illustrate the time course of a drug's disposition and effect provides online visualization of pharmacokinetic-pharmacodynamic information during the clinical use of anesthetics. The aim of this review is to discuss the importance of simulation as a clinical pharmacology teaching tool for trainees in anesthesiology.

PK/PD modelling and simulations: utility in drug development

Pharmacokinetic/pharmacodynamic (PK/PD) modelling and simulation can be used as an 'applied science' tool to provide answers on efficacy and safety of new drugs faster and at a lower cost. PK/PD modelling can be used from the preclinical phase through all clinical phases of drug development. Optimal use of PK/PD modelling and simulation will lead to fewer failed compounds, fewer study failures and smaller numbers of studies needed for registration. For PK/PD modelling to fulfil its potential in drug development, it needs to be embraced across the industry and regulatory agencies, and more education on this topic is required.

Integrated pharmacokinetics and pharmacodynamics in drug development

Drug development is a complex, lengthy and expensive process. Pharmaceutical companies and regulatory authorities have recognised that the drug development process needs optimisation for efficiency in view of the return on investments. Pharmacokinetics and pharmacodynamics are the two main principles determining the relationship between dose and response. This article provides an update on integrated approaches towards drug development by linking pharmacokinetics, pharmacodynamics and disease aspects into mathematical models. Gradually, a transition is taking place from a rather empirical approach towards a modelling- and simulation-based approach to drug development. The main learning phases should be phases 0, I and II, whereas phase III studies should merely have a confirmatory purpose. In model-based drug development, mechanism-based mathematical models, which are iteratively refined along the path of development, incorporate the accumulating knowledge of the investigational drug, the disease and their mutual interference in different subsets of the target population. These models facilitate the design of the next study and improve the probability of achieving the projected efficacy and safety endpoints. In this article, several theoretical and practical aspects of an integrated approach towards drug development are discussed, together with some case studies from different therapeutic areas illustrating the application of pharmacokinetic/pharmacodynamic disease models at different stages of drug development.

Overview of model-building strategies in population PK/PD analyses: 2002-2004 literature survey

AIMS

A descriptive survey of published population pharmacokinetic and/or pharmacodynamic (PK/PD) analyses from 2002 to 2004 was conducted and an evaluation made of how model building was performed and reported.

METHODS

We selected 324 articles in Pubmed using defined keywords. A data abstraction form (DAF) was then built comprising two parts: general characteristics including article identification, context of the analysis, description of clinical studies from which the data arose, and model building, including description of the processes of modelling. The papers were examined by two readers, who extracted the relevant information and transmitted it directly to a MySQL database, from which descriptive statistical analysis was performed.

RESULTS

Most published papers concerned patients with severe pathology and therapeutic classes suffering from narrow therapeutic index and/or high PK/PD variability. Most of the time, modelling was performed for descriptive purposes, with rich rather than sparse data and using NONMEM software. PK and PD models were rarely complex (one or two compartments for PK; E(max) for PD models). Covariate testing was frequently performed and essentially based on the likelihood ratio test. Based on a minimal list of items that should systematically be found in a population PK-PD analysis, it was found that only 39% and 8.5% of the PK and PD analyses, respectively, published from 2002 to 2004 provided sufficient detail to support the model-building methodology.

CONCLUSIONS

This survey allowed an efficient description of recent published population analyses, but also revealed deficiencies in reporting information on model building.

Pharmacometrics at FDA: evolution and impact on decisions

Drug development and regulatory decisions are driven by information that is compiled primarily from clinical trials and other supportive experiments, but also through clinical experience in the post-market period. The wisdom of these decisions determines the efficiency of drug development, the decision to approve the drug, and the resultant drug product quality including guidance on how to use the product known as the label. Although the decisions are usually simple in nature (e.g., trial design and project progression at the company, product and labeling approval at the Food and Drug Administration (FDA)), the information informing the decision is complex and diverse.

Paving the critical path: how can clinical pharmacology help achieve the vision?

It has been almost 3 years since the launch of the FDA critical path initiative following the publication of the paper "Innovation or Stagnation: Challenges and Opportunities on the Critical Path of New Medical Product Development." The initiative was intended to create an urgency with the drug development enterprise to address the so-called "productivity problem" in modern drug development. Clinical pharmacologists are strategically aligned with solutions designed to reduce late phase clinical trial failures to show adequate efficacy and/or safety. This article reviews some of the ways that clinical pharmacologists can lead and implement change in the drug development process. It includes a discussion of model-based, semi-mechanistic drug development, drug/disease models that facilitate informed clinical trial designs and optimal dosing, the qualification process and criteria for new biomarkers and surrogate endpoints, approaches to streamlining clinical trials and new types of interaction between industry and FDA such as the end-of-phase 2A and voluntary genomic data submission meetings respectively.

Investigation of the effects of herbal medicines on warfarin response in healthy subjects: a population pharmacokinetic-pharmacodynamic modeling approach

Systematic evidence regarding herb-drug interactions is lacking. This study investigated herb-drug interactions with warfarin. S-warfarin concentration and response (prothrombin complex activity) data from healthy subjects (n = 24) who received a single warfarin dose (25 mg) and either St John's wort, Asian ginseng, Ginkgo biloba, or ginger were analyzed using a population pharmacokinetic-pharmacodynamic modeling approach. The ratio of S-warfarin apparent clearance (CL/F) compared to control was 1.39 +/- 0.06 and 1.14 +/- 0.04 after St John's wort and Asian ginseng pretreatment, respectively. Other pharmacokinetic and pharmacodynamic parameters were unaffected. Coadministration of St John's wort significantly increased S-warfarin CL/F, whereas treatment with Asian ginseng produced only a moderate increase in CL/F. Ginkgo and ginger did not affect the pharmacokinetics of warfarin in healthy subjects. None of the herbs studied had a direct effect on warfarin pharmacodynamics. Studies in anticoagulated patients are warranted to assess the clinical significance of these herb-drug interactions.

Translational research in the pharmaceutical industry: from bench to bedside

Current developments in basic discovery sciences have not been mirrored by the same level of progress in understanding the clinical basis of disease and ultimately the development of novel effective therapies. This can be improved by applying translational research throughout the late-stage discovery and exploratory development stages of drug development. A bi-directional dialogue between research scientists and clinicians concerning the biology of mechanism of action and clinical basis for disease will deliver biomarkers that enable drug development decisions to be made earlier and with increased confidence. Thus, we can better exploit the many targets that have been discovered through the mapping of the genome and other breakthroughs in medical sciences, such as the polyomic technologies.

Model-based drug development: the road to quantitative pharmacology

High development costs and low success rates in bringing new medicines to the market demand more efficient and effective approaches. Identified by the FDA as a valuable prognostic tool for fulfilling such a demand, model-based drug development is a mathematical and statistical approach that constructs, validates, and utilizes disease models, drug exposure-response models, and pharmacometric models to facilitate drug development. Quantitative pharmacology is a discipline that learns and confirms the key characteristics of new molecular entities in a quantitative manner, with goal of providing explicit, reproducible, and predictive evidence for optimizing drug development plans and enabling critical decision making. Model-based drug development serves as an integral part of quantitative pharmacology. This work reviews the general concept, basic elements, and evolving role of model-based drug development in quantitative pharmacology. Two case studies are presented to illustrate how the model-based drug development approach can facilitate knowledge management and decision making during drug development. The case studies also highlight the organizational learning that comes through implementation of quantitative pharmacology as a discipline. Finally, the prospects of quantitative pharmacology as an emerging discipline are discussed. Advances in this discipline will require continued collaboration between academia, industry and regulatory agencies.

Development and Evaluation of a Generic Physiologically Based Pharmacokinetic Model for Children

BACKGROUND

Clinical trials in children are being encouraged by regulatory authorities in light of the immense off-label and unlicensed use of drugs in the paediatric population. The use of in silico techniques for pharmacokinetic prediction will aid in the development of paediatric clinical trials by guiding dosing regimens, ensuring efficient blood sampling times, maximising therapeutic effect and potentially reducing the number of children required for the study. The goal of this study was to extend an existing physiologically based pharmacokinetic (PBPK) model for adults to reflect the age-related physiological changes in children from birth to 18 years of age and, in conjunction with a previously developed age-specific clearance model, to evaluate the accuracy of the paediatric PBPK model to predict paediatric plasma profiles.

METHODS

The age-dependence of bodyweight, height, organ weights, blood flows, interstitial space and vascular space were taken from the literature. Physiological parameters that were used in the PBPK model were checked against literature values to ensure consistency. These included cardiac output, portal vein flow, extracellular water, total body water, lipid and protein. Five model compounds (paracetamol [acetaminophen], alfentanil, morphine, theophylline and levofloxacin) were then examined by gathering the plasma concentration-time profiles, volumes of distribution and elimination half-lives from different ages of children and adults. First, the adult data were used to ensure accurate prediction of pharmacokinetic profiles. The model was then scaled to the specific age of children in the study, including the scaling of clearance, and the generated plasma concentration profiles, volumes of distribution and elimination half-lives were compared with literature values.

RESULTS

Physiological scaling produced highly age-dependent cardiac output, portal vein flow, extracellular water, total body water, lipid and protein values that well represented literature data. The pharmacokinetic profiles in children for the five compounds were well predicted and the trends associated with age were evident. Thus, young neonates had plasma concentrations greater than the adults and older children had concentrations less than the adults. Eighty-three percent, 97% and 87% of the predicted plasma concentrations, volumes of distribution and elimination half-lives, respectively, were within 50% of the study reported values. There was no age-dependent bias for term neonates to 18 years of age when examining volumes of distribution and elimination half-lives.

CONCLUSION

This study suggests that the developed paediatric PBPK model can be used to scale pharmacokinetics from adults. The accurate prediction of pharmacokinetic parameters in children will aid in the development of dosing regimens and sampling times, thus increasing the efficiency of paediatric clinical trials.

A new equivalence based metric for predictive check to qualify mixed-effects models

The main objective of any modeling exercise is to provide a rationale for effective decision making during drug development. The aim of the current simulation experiment was to evaluate the properties of predictive check as a covariate model qualification technique and, more importantly, to introduce and evaluate alternative criteria to qualify models.Original concentration-time profiles (yod) were simulated using a 1-compartment model for an intravenous drug administered to 25 men and 25 women. The typical clearance for male subjects (TVCLm) was assumed to be 5-fold higher than that for female subjects (TVCLf). Fifty such trials under the same design were generated randomly. Predictive check was used as the model qualification tool to study predictive performance of true (males not equal females) and false (males = females) models in the context of maximum likelihood estimation. For each yod, 200 replications were generated to study the properties of a discrepancy variable, a statistic that depends on the model, and a test statistic, a statistic that does not depend on the model. Several qualification criteria were evaluated in assessing predictive performance, such as, predictive p-value (Pp), probability of equivalence (peqv), and probability of rejecting the null hypothesis (data = model) using the Kolmogorov-Smirnov test (pks). The Pp value was calculated using sum of squared errors as a discrepancy variable. For both of the models, the Pp values uniformly ranged between 0 and 1. The pattern of Pp values suggests that qualification of the false model is unlikely. For both of the models, the range of peqv is about 0.95 to 1.0 for concentration at 0.5 hours. However, this is not the case for the concentration at 4 hours, which is primarily dependent on the clearance. The false model (0.35 to 0.50) has poor predictive performance compared with the true model (0.65 to 0.80) using peqv. The pks suggests no difference in the distributions of replicated and original concentrations at all of the time points for both of the models. Discrepancy variables cannot aid in rejecting false models, whereas the use of a test statistic can aid in rejecting false models. However, selection of an informative test statistic is challenging. As far as the qualification criteria are considered, the equivalence-based comparison of a test statistic is more informative than a significance-based comparison. No convincing evidence exists in the literature demonstrating the added advantages of predictive check as a routine model qualification tool over the existing tools, such as diagnostic plots or mechanistic reasoning. However, when a model is to be used for designing a trial, it should at least be able to regenerate the data used to build the model. In such cases, predictive check might offer insights into potential inconsistencies.

Estimation of dosing strategies aiming at maximizing utility or responder probability, using oxybutynin as an example drug

Methods for optimizing dosing strategies for individualization with a limited number of discrete doses, in terms of maximizing the expected utility of treatment or responder probability, are presented. The optimality criteria require models for both beneficial and adverse effects that are part of the utility definition and published population models describing those effects for oxybutynin (urge urinary incontinence episodes per week and severity of dry mouth, respectively) were used for illustration. Dosing strategies with two dosing categories were defined in terms of sizes of the daily doses (low and high dose) and the proportion of patients that can be expected to be preferentially treated at the low dose level. Utility and responder definitions were varied to investigate the influence on the resulting dosing strategy. By minimizing a risk function, describing the seriousness of deviations from the predefined target, optimal dosing strategies were estimated using mixture models in NONMEM. The estimated dose ranges for oxybutynin were similar to those recommended. The optimal individualization conditions were dependent on the definitions of responder and utility. The predicted gain of individualization given utility and responder definitions used was greater, when a responder criteria was maximized compared with maximizing utility.

Strategies for designing clinical trials for oligonucleotide therapeutics

Recent Phase III clinical trials for oligonucleotide therapeutics have yielded disappointing results. There is growing evidence that trial designs that consider the specific mode of action of these compounds are of crucial importance for their clinical testing. Early trials for oligonucleotide therapeutics should consider additional endpoints for the definition of a biologically active dose rather than focusing on the traditional concept of maximal tolerated dose. In later phases, alternative clinical endpoints and enriching sensitive study populations through innovative trial designs could improve the efficiency of clinical trials for oligonucleotide therapeutics.

Pharmacokinetic-pharmacodynamic guided trial design in oncology

The application of pharmacokinetic (PK) and pharmacodynamic (PD) modeling in drug development has emerged during the past decades and it is has been suggested that the investigation of PK-PD relationships during drug development may facilitate and optimize the design of subsequent clinical development. Especially in oncology, well designed PK-PD modeling could be extremely useful as anticancer agents usually have a very narrow therapeutic index. This paper describes the application of the current insights in the use of PK-PD modeling to the design of clinical trials in oncology. The application of PK-PD modeling in each separate stage of (pre)clinical drug development of anticancer agents is discussed. The implementation of this approach is illustrated with the clinical development of docetaxel.

Application of resampling techniques to estimate exact significance levels for covariate selection during nonlinear mixed effects model building: some inferences

PURPOSE

One of the main objectives of the nonlinear mixed effects modeling is to provide rational individualized dosing strategies by explaining the interindividual variability using intrinsic and/or extrinsic factors (covariates). The aim of the current study was to evaluate, using computer simulations and real data, methods for estimating the exact significance level for including or excluding a covariate during model building.

METHODS

Original data were simulated using a simple one-compartment pharmacokinetic model with (full model) or without (null model) covariates (one or two). The covariate values in the original data were resampled (using either permutations or parametric bootstrap methods) to generate data under the null hypothesis that there is no covariate effect. The original and permuted data were fitted to null and full models, using first-order and first-order condition estimation (with or without interaction) methods in NONMEM, to compare the asymptotic and conditional p-value. Target log-likelihood ratio cutoffs for assessing covariate effects were derived.

RESULTS

The simulations showed that for sparse as well as dense data, the first-order condition estimation methods yielded the best results while the first-order method performs somewhat better for sparse data. Depending on the modeling objective, the appropriate asymptotic p-value can be substituted for the conditional significance level. Target log-likelihood ratio cutoffs should be determined separately for each covariate when exact p-values are important.

CONCLUSIONS

Resampling methods can be employed to estimate the exact significance level for including a covariate during nonlinear mixed effects model building. Some reasonable inferences can be drawn for potential application to design future population analyses.