Model-based Drug Development

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

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

Drug and disease model resources: a consortium to create standards and tools to enhance model-based drug development

Model-based drug development (MBDD) is accepted as a vital approach in understanding patients' drug-related benefit and risk by integrating quantitative information integration from diverse sources collected throughout drug development.¹ This perspective introduces the activities of the Drug and Disease Model Resources (DDMoRe) consortium, founded in 2011 through the Innovative Medicines Initiative Joint Undertaking (IMI-JU)² as a European public–private partnership to address a lack of common tools, languages, and standards for modeling and simulation (M&S) to improve model-based knowledge integration.

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.

Optimal Adaptive Design in Clinical Drug Development: A Simulation Example

The objective of this article is to demonstrate optimal adaptive design as a methodology for improving the performance of phase II dose-response studies. Optimal adaptive design uses both information prior to the study and data accrued during the study to continuously update and refine the study design. Dose-response models include linear, log-linear, 4-parameter sigmoidal E(max), and exponential models. Where the response has both a placebo effect and plateau at higher doses, only the 4-parameter sigmoidal E(max) model behaves acceptably and hence is used to illustrate the methodology. Across 13 hypothetical dose-response scenarios considered, it was shown that the capability of the adaptive designs to "learn" the true dose response resulted in performances up to 180% more efficient than the best fixed optimal designs. This work exposes the common misconception that adaptive designs are somehow "risky." As shown in this simple simulation example, the converse is true. Adaptive designs perform extremely well both when prior information is accurate and inaccurate. This leads to improved dose-response models and dose selection in phase III. This benefits sponsors, regulators, and subjects alike by reducing sample size, increasing information, and providing better dose guidance.

Modeling and simulation in clinical pharmacology and dose finding

The breakout session 2 of the European Medicines Agency/European Federation of Pharmaceutical Industries and Associations Modeling and Simulation (M&S) workshop focused on two topics: when and how M&S should be used and would be accepted by the authorities for the dose-regimen selection; and when and how M&S can be applied to register a dosing regimen without the need for a specific study. Each topic was introduced by an industry and regulatory perspective, followed by case examples for illustration (Table 1).

The pharmacokinetic-pharmacodynamic model of azithromycin for lipopolysaccharide-induced depressive-like behavior in mice

A mechanism-based model was developed to describe the time course of lipopolysaccharide-induced depressive-like behavior and azithromycin pharmacodynamics in mice. The lipopolysaccharide-induced disease progression was monitored by lipopolysaccharide, proinflammatory cytokines, and kynrenine concentration in plasma. The depressive-like behavior was investigated by forced swimming test and tail suspension test. Azithromycin was selected to inhibit the surge of proinflammatory cytokines induced by lipopolysaccharide. Disease progression model and azithromycin pharmacodynamics were constructed from transduction and indirect response models. A delay in the onset of increased proinflammatory cytokines, kynrenine, and behavior test compared to lipopolysaccharide was successfully characterized by series transduction models. The inhibition of azithromycin on proinflammatory cytokines was described by an indirect response model. After lipopolysaccharide challenging, the proinflammatory cytokines, kynrenine and behavior tests would peak approximately at 3, 12, and 24 h respectively, and then the time courses slowly declined toward a baseline state after peak response. During azithromycin administration, the peak levels of proinflammatory cytokines, kynrenine and behavior indexes decreased. Model parameters indicated that azithromycin significantly inhibited the proinflammatory cytokines level in plasma and improved the depressive-like behavior induced by inflammation. The integrated model for disease progression and drug intervention captures turnovers of proinflammatory cytokines, kynrenine and the behavior results in the different time phases and conditions.

Comparisons of Analysis Methods for Proof-of-Concept Trials

Drug development struggles with high costs and time consuming processes. Hence, a need for new strategies has been accentuated by many stakeholders in drug development. This study proposes the use of pharmacometric models to rationalize drug development. Two simulated examples, within the therapeutic areas of acute stroke and type 2 diabetes, are utilized to compare a pharmacometric model-based analysis to a t-test with respect to study power of proof-of-concept (POC) trials. In all investigated examples and scenarios, the conventional statistical analysis resulted in several fold larger study sizes to achieve 80% power. For a scenario with a parallel design of one placebo group and one active dose arm, the difference between the conventional and pharmacometric approach was 4.3- and 8.4-fold, for the stroke and diabetes example, respectively. Although the model-based power depend on the model assumptions, in these scenarios, the pharmacometric model-based approach was demonstrated to permit drastic streamlining of POC trials.CPT: Pharmacometrics & Systems Pharmacology (2013) 2, e23; doi:10.1038/psp.2012.24; advance online publication 16 January 2013.

Predicting feasibility and characterizing performance of extended-release formulations using physiologically based pharmacokinetic modeling

This review presents nine case studies where physiologically based pharmacokinetic modeling has been used in the design and development of extended-release formulations. While the approaches for creating the models were similar, in each case a product-development or drug-delivery problem unique to each compound was solved so that the drug-release rate could be optimized to achieve the best clinical performance. Examples presented include understanding the relationship between colonic absorption and efflux, effect of drug release and gastric emptying on maximum achieved drug concentration in plasma and area under the plasma concentration-time curve for a Biopharmaceutics Classification System class 3 compound, feasibility of an extended-release product for a prodrug, feasibility of an extended-release product for a biopharmaceutics classification system class 4 compound and predicting the pharmacokinetics in humans based on a primate model and coupling the physiologically-based pharmacokinetic model with a pharmacodynamic model so that the clinical efficacy of the formulations could be predicted based on the simulated plasma concentrations. The use of physiologically based pharmacokinetic models in the development of extended-release formulations is rapidly becoming an acceptable part of the knowledge management and design space components of a quality by design approach to product development. As the use of these in silico tools increase and examples become available through scientific presentations and literature, the inclusion of this approach will become a necessary part of the development process rather than the exception.

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.

Application of pharmacokinetics-pharmacodynamics/clinical response modeling and simulation for biologics drug development

Biologics, specifically monoclonal antibody (mAb) drugs, have unique pharmacokinetic (PK) and pharmacodynamic (PD) characteristics as opposed to small molecules. Under the paradigm of model-based drug development, PK-PD/clinical response models offer critical insight in guiding biologics development at various stages. On the basis of the molecular structure and corresponding properties of biologics, typical mechanism-based [target-mediated drug disposition (TMDD)], physiologically based PK, PK-PD, and dose-response meta-analysis models are summarized. Examples of using TMDD, PK-PD, and meta-analysis in helping starting dose determination in first-in-human studies and dosing regimen optimization in phase II/III trials are discussed. Instead of covering the entirety of model-based biologics development, this review focuses on the guiding principles and the core mathematical descriptions underlying the PK or PK-PD models most used.

Basic concepts in population modeling, simulation, and model-based drug development

Modeling is an important tool in drug development; population modeling is a complex process requiring robust underlying procedures for ensuring clean data, appropriate computing platforms, adequate resources, and effective communication. Although requiring an investment in resources, it can save time and money by providing a platform for integrating all information gathered on new therapeutic agents. This article provides a brief overview of aspects of modeling and simulation as applied to many areas in drug development.

CPT: Pharmacometrics and Systems Pharmacology

Welcome to the first issue of CPT: Pharmacometrics and Systems Pharmacology (CPT:PSP), a new journal from the American Society for Clinical Pharmacology and Therapeutics. CPT:PSP is a cross-disciplinary journal devoted to publishing advances in quantitative, model-based approaches as applied in pharmacology, (patho)physiology, and disease to aid the discovery, development, and utilization of human therapeutics. The emphasis of CPT:PSP will be on the application of modeling and simulation and the impact of Pharmacometrics and Systems Pharmacology on the discovery and development of innovative therapies.

Individualization of drug therapy: history, present state, and opportunities for the future

Individualization of drug therapy, described as tailoring drug selection and drug dosing to a given patient, has been an objective of physicians and other health-care providers for centuries. An understanding of the pathogenesis of the disease, the mechanism of action of the drug, and exposure-response relationships provides the framework for individualization. There are many approaches to individualization: selecting an antibiotic based on minimum effective concentrations and bacterial sensitivity, population (sparse sample) pharmacokinetics, therapeutic drug monitoring and, more recently, pharmacogenomics. The goal of individualization is to optimize the efficacy of a drug, minimize its toxicity, or both. With the growth of technology and databases, drug-disease-trial models and simulation have become useful for integrating information from many different domains. Physiology-based pharmacokinetic (PBPK) models have provided a mechanistic approach to individualization, and clinical trial designs such as those involving enrichment have also enabled individualization. In the future, "-omics" technologies, vaccines, ex vivo gene therapy, and the so-called "diseases-in-a-dish" will provide additional strategies to achieve individualization.

Longitudinal model-based meta-analysis in rheumatoid arthritis: an application toward model-based drug development

Summary-level longitudinal data on the clinical efficacy of drugs for rheumatoid arthritis (RA) are available in the literature. This information can be used to optimize the clinical development of new drugs for RA. The aim of this study was twofold: first, to quantify the time course of the ACR20 score across approved drugs and patient populations, and second, to apply this knowledge in the decision-making process for a specific compound, canakinumab. The integrated analysis included data from 37 phase II-III studies describing 13,474 patients. It showed that, with the tested doses/regimens of canakinumab, there was only a low probability that this drug would be better than the most effective current treatments. This finding supported the decision not to continue with clinical development of canakinumab in RA. This paper presents the first longitudinal model-based meta-analysis of ACR20. The framework can be applied to any other compound targeting RA, thereby supporting internal and external decision making at all clinical development stages.

Use of physiologically based pharmacokinetic modeling for assessment of drug-drug interactions

Interactions between co-administered medicines can reduce efficacy or lead to adverse effects. Understanding and managing such interactions is essential in bringing safe and effective medicines to the market. Ideally, interaction potential should be recognized early and minimized in compounds that reach late stages of drug development. Physiologically based pharmacokinetic models combine knowledge of physiological factors with compound-specific properties to simulate how a drug behaves in the human body. These software tools are increasingly used during drug discovery and development and, when integrating relevant in vitro data, can simulate drug interaction potential. This article provides some background and presents illustrative examples. Physiologically based models are an integral tool in the discovery and development of drugs, and can significantly aid our understanding and prediction of drug interactions.

Mathematical modeling of tumor growth and tumor growth inhibition in oncology drug development

INTRODUCTION

Approaches aiming to model the time course of tumor growth and tumor growth inhibition following a therapeutic intervention have recently been proposed for supporting decision making in oncology drug development. When considered in a comprehensive model-based approach, tumor growth can be included in the cascade of quantitative and causally related markers that lead to the prediction of survival, the final clinical response.

AREAS COVERED

The authors examine articles dealing with the modeling of tumor growth and tumor growth inhibition in both preclinical and clinical settings. In addition, the authors review models describing how pharmacological markers can be used to predict tumor growth and models describing how tumor growth can be linked to survival endpoints.

EXPERT OPINION

Approaches and success stories of application of model-based drug development centered on tumor growth modeling are growing. It is also apparent that these approaches can answer practical questions on drug development more effectively than that in the past. For modeling purposes, some improvements are still needed related to study design and data quality. Further efforts are needed to encourage the mind shift from a simple description of data to the prediction of untested conditions that modeling approaches allow.

Role of modelling and simulation: a European regulatory perspective

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

Rapid sample size calculations for a defined likelihood ratio test-based power in mixed-effects models

Efficient power calculation methods have previously been suggested for Wald test-based inference in mixed-effects models but the only available alternative for Likelihood ratio test-based hypothesis testing has been to perform computer-intensive multiple simulations and re-estimations. The proposed Monte Carlo Mapped Power (MCMP) method is based on the use of the difference in individual objective function values (ΔiOFV) derived from a large dataset simulated from a full model and subsequently re-estimated with the full and reduced models. The ΔiOFV is sampled and summed (∑ΔiOFVs) for each study at each sample size of interest to study, and the percentage of ∑ΔiOFVs greater than the significance criterion is taken as the power. The power versus sample size relationship established via the MCMP method was compared to traditional assessment of model-based power for six different pharmacokinetic and pharmacodynamic models and designs. In each case, 1,000 simulated datasets were analysed with the full and reduced models. There was concordance in power between the traditional and MCMP methods such that for 90% power, the difference in required sample size was in most investigated cases less than 10%. The MCMP method was able to provide relevant power information for a representative pharmacometric model at less than 1% of the run-time of an SSE. The suggested MCMP method provides a fast and accurate prediction of the power and sample size relationship.

A semiphysiologically based pharmacokinetic modeling approach to predict the dose-exposure relationship of an antiparasitic prodrug/active metabolite pair

Dose selection during antiparasitic drug development in animal models and humans traditionally has relied on correlations between plasma concentrations obtained at or below maximally tolerated doses that are efficacious. The objective of this study was to improve the understanding of the relationship between dose and plasma/tissue exposure of the model antiparasitic agent, pafuramidine, using a semiphysiologically based pharmacokinetic (semi-PBPK) modeling approach. Preclinical and clinical data generated during the development of pafuramidine, a prodrug of the active metabolite, furamidine, were used. A whole-body semi-PBPK model for rats was developed based on a whole-liver PBPK model using rat isolated perfused liver data. A whole-body semi-PBPK model for humans was developed on the basis of the whole-body rat model. Scaling factors were calculated using metabolic and transport clearance data generated from rat and human sandwich-cultured hepatocytes. Both whole-body models described pafuramidine and furamidine disposition in plasma and predicted furamidine tissue (liver and kidney) exposure and excretion profiles (biliary and renal). The whole-body models predicted that the intestine contributes significantly (30-40%) to presystemic furamidine formation in both rats and humans. The predicted terminal elimination half-life of furamidine in plasma was 3- to 4-fold longer than that of pafuramidine in rats (170 versus 47 h) and humans (64 versus 19 h). The dose-plasma/tissue exposure relationship for the prodrug/active metabolite pair was determined using the whole-body models. The human model proposed a dose regimen of pafuramidine (40 mg once daily) based on a predefined efficacy-safety index. A similar approach could be used to guide dose-ranging studies in humans for next-in-class compounds.

Quantitative model of the relationship between dipeptidyl peptidase-4 (DPP-4) inhibition and response: meta-analysis of alogliptin, saxagliptin, sitagliptin, and vildagliptin efficacy results

Dipeptidyl peptidase-4 (DPP-4) inhibition is a well- characterized treatment for type 2 diabetes mellitus (T2DM). The objective of this model-based meta-analysis was to describe the time course of HbA1c response after dosing with alogliptin (ALOG), saxagliptin (SAXA), sitagliptin (SITA), or vildagliptin (VILD). Publicly available data involving late-stage or marketed DPP-4 inhibitors were leveraged for the analysis. Nonlinear mixed-effects modeling was performed to describe the relationship between DPP-4 inhibition and mean response over time. Plots of the relationship between metrics of DPP-4 inhibition (ie, weighted average inhibition [WAI], time above 80% inhibition, and trough inhibition) and response after 12 weeks of daily dosing were evaluated. The WAI was most closely related to outcome, although other metrics performed well. A model was constructed that included fixed effects for placebo and drug and random effects for intertrial variability and residual error. The relationship between WAI and outcome was nonlinear, with an increasing response up to 98% WAI. Response to DPP-4 inhibitors could be described with a single drug effect. The WAI appears to be a useful index of DPP-4 inhibition related to HbA1c. Biomarker to response relationships informed by model-based meta-analysis can be leveraged to support study designs including optimization of dose, duration of therapy, and patient population.

Therapeutic index of anticoagulants for prevention of venous thromboembolism following orthopedic surgery: a dose-response meta-analysis

Information on the comparative effectiveness of drugs is crucial for drug development decisions, in addition to being needed by regulators, prescribers, and payers. We have carried out a dose-response meta-analysis of three end points each for efficacy and bleeding for various anticoagulants evaluated for the prevention of venous thromboembolism (VTE) following orthopedic surgery to assess the comparative efficacy and safety of various classes of agents. Data obtained from 89 randomized controlled trials of 23 anticoagulants representing seven drug classes were analyzed. The analysis showed significant differences in the therapeutic index (TI), the ratio of the dose with an acceptable bleeding risk to the dose with a relevant risk reduction for VTE, across the drug classes but not for drugs within a class. The direct inhibitors of FXa, the activated form of factor X--also known as prothrombinase--were found to have a significantly higher TI than that of any other class of anticoagulants, including enoxaparin, suggesting that this mechanism of action provides the best safety-to-efficacy margin.

In vivo-in vitro-in silico pharmacokinetic modelling in drug development: current status and future directions

Although clinical drug trials are indispensable in providing an appropriate background for dosage recommendations, they can provide mechanistic pharmacokinetic information only indirectly with the help of certain biomarkers for pathological, physiological and pharmacological determinants. Thus, to provide such mechanistic information of clinical value, various in vitro and in silico tests and approaches are increasingly employed in drug discovery and development. Integration of the results of these primarily preclinical studies has been made possible by various computational models, such as in vitro-in vivo extrapolation of hepatic clearance or physiologically based pharmacokinetic modelling. In this article, the current status of these modelling approaches is surveyed and some examples are given, highlighting advantages and disadvantages in applying them at various phases of drug development. A new paradigm of model-based drug development is briefly described, and the importance of the approach of integrating all of the information coming from different investigations at all levels--be it in vivo, in vitro or in silico--is emphasized.

Population pharmacokinetic-pharmacodynamic-disease progression model for effects of anakinra in Lewis rats with collagen-induced arthritis

A population pharmacokinetic-pharmacodynamic-disease progression (PK/PD/DIS) model was developed to characterize the effects of anakinra in collagen-induced arthritic (CIA) rats and explore the role of interleukin-1β (IL-1β) in rheumatoid arthritis. The CIA rats received either vehicle, or anakinra at 100 mg/kg for about 33 h, 100 mg/kg for about 188 h, or 10 mg/kg for about 188 h by subcutaneous infusion. Plasma concentrations of anakinra were assayed by enzyme-linked immunosorbent assay. Swelling of rat hind paws was measured. Population PK/PD/DIS parameters were computed for the various groups using non-linear mixed-effects modeling software (NONMEM® Version VI). The final model was assessed using visual predictive checks and nonparameter stratified bootstrapping. A two-compartment PK model with two sequential absorption processes and linear elimination was used to capture PK profiles of anakinra. A transduction-based feedback model incorporating logistic growth rate captured disease progression and indirect response model I captured drug effects. The PK and paw swelling versus time profiles in CIA rats were fitted well. Anakinra has modest effects (I ( max ) = 0.28) on paw edema in CIA rats. The profiles are well-described by our PK/PD/DIS model which provides a basis for future mechanism-based assessment of anakinra dynamics in rheumatoid arthritis.

Interpreting population pharmacokinetic-pharmacodynamic analyses - a clinical viewpoint

The population analysis approach is an important tool for clinical pharmacology in aiding the dose individualization of medicines. However, due to their statistical complexity the clinical utility of population analyses is often overlooked. One of the key reasons to conduct a population analysis is to investigate the potential benefits of individualization of drug dosing based on patient characteristics (termed covariate identification). The purpose of this review is to provide a tool to interpret and extract information from publications that describe population analysis. The target audience is those readers who are aware of population analyses but have not conducted the technical aspects of an analysis themselves. Initially we introduce the general framework of population analysis and work through a simple example with visual plots. We then follow-up with specific details on how to interpret population analyses for the purpose of identifying covariates and how to interpret their likely importance for dose individualization.

Pharmacokinetic/pharmacodynamic (PK/PD) indices of antibiotics predicted by a semimechanistic PKPD model: a step toward model-based dose optimization

A pharmacokinetic-pharmacodynamic (PKPD) model that characterizes the full time course of in vitro time-kill curve experiments of antibacterial drugs was here evaluated in its capacity to predict the previously determined PK/PD indices. Six drugs (benzylpenicillin, cefuroxime, erythromycin, gentamicin, moxifloxacin, and vancomycin), representing a broad selection of mechanisms of action and PK and PD characteristics, were investigated. For each drug, a dose fractionation study was simulated, using a wide range of total daily doses given as intermittent doses (dosing intervals of 4, 8, 12, or 24 h) or as a constant drug exposure. The time course of the drug concentration (PK model) as well as the bacterial response to drug exposure (in vitro PKPD model) was predicted. Nonlinear least-squares regression analyses determined the PK/PD index (the maximal unbound drug concentration [fC(max)]/MIC, the area under the unbound drug concentration-time curve [fAUC]/MIC, or the percentage of a 24-h time period that the unbound drug concentration exceeds the MIC [fT(>MIC)]) that was most predictive of the effect. The in silico predictions based on the in vitro PKPD model identified the previously determined PK/PD indices, with fT(>MIC) being the best predictor of the effect for β-lactams and fAUC/MIC being the best predictor for the four remaining evaluated drugs. The selection and magnitude of the PK/PD index were, however, shown to be sensitive to differences in PK in subpopulations, uncertainty in MICs, and investigated dosing intervals. In comparison with the use of the PK/PD indices, a model-based approach, where the full time course of effect can be predicted, has a lower sensitivity to study design and allows for PK differences in subpopulations to be considered directly. This study supports the use of PKPD models built from in vitro time-kill curves in the development of optimal dosing regimens for antibacterial drugs.

Mechanism-based pharmacokinetic-pharmacodynamic modeling of the estrogen-like effect of ginsenoside Rb1 on neural 5-HT in ovariectomized mice

We sought to develop a mechanism-based pharmacokinetic-pharmacodynamic (PK-PD) model to characterize the effects of ginsenoside Rb1 (Rb1) and estradiol (E(2)) on neural 5-hydroxytryptamine (5-HT) concentration in ovariectomized mice. PK data of Rb1 and E(2) were obtained in plasma and brain. Brain levels of 5-HT, tryptophan (TRP), 5-hydroxytryptophan (5-HTP), and 5-hydroxyindoleacetic acid (5-HIAA) were determined after a single intravenous injection of Rb1 (20mg/kg) and E(2) (0.2mg/kg) in ovariectomized mice. The activities of tryptophan hydroxylase (TPH), aromatic amino acid decarboxylase (AAAD), and monoamine oxidase (MAO) were also evaluated. Rb1 and E(2) elevated neural 5-HT levels via TPH activation and MAO inhibition, respectively. Effects were well described by the mechanism-based PK-PD model. The net effect of increased 5-HT induced by MAO inhibition is greater than TPH activation. The increased brain levels of 5-HT induced by Rb1 and E(2) were well described by the present PK-PD model, suggesting the use and further development of this mechanism-based model for the effects of ginsenoside on brain 5-HT levels.

Physiologically-based pharmacokinetics in drug development and regulatory science

The application of physiologically-based pharmacokinetic (PBPK) modeling is coming of age in drug development and regulation, reflecting significant advances over the past 10 years in the predictability of key pharmacokinetic (PK) parameters from human in vitro data and in the availability of dedicated software platforms and associated databases. Specific advances and contemporary challenges with respect to predicting the processes of drug clearance, distribution, and absorption are reviewed, together with the ability to anticipate the quantitative extent of PK-based drug-drug interactions and the impact of age, genetics, disease, and formulation. The value of this capability in selecting and designing appropriate clinical studies, its implications for resource-sparing techniques, and a more holistic view of the application of PK across the preclinical/clinical divide are considered. Finally, some attention is given to the positioning of PBPK within the drug development and approval paradigm and its future application in truly personalized medicine.

Applications of 'decisionable' biomarkers in cardiovascular drug development

Biomarkers are now increasingly employed in drug development for decision-making. New targets and candidate drugs should not only have drug-like properties (i.e., be 'drugable'), but the supporting biomarker platform should be 'decisionable'. For example, biomarkers for target engagement have supported the biologic plausibility for novel mechanisms and have aided in accelerated proof of concept. In many other circumstances, biomarkers have aided in the elucidation of mechanisms of action and disease progression. In this article, decisonable biomarker principles that aid in decision-making within the realm of early discovery through to clinical proof of concept are discussed. Case studies of applications of both target engagement and disease-related biomarkers are illustrated in the field of cardiovascular drug discovery and translational development. We propose that biomarkers, if prospectively implemented in an early development program, have the potential to accelerate drug development, facilitate the design of informative trials and dose selection for accelerated development, and establish an overall increase in probability of developmental success and efficiency.

Discovering Molecular Targets in Cancer with Multiscale Modeling

Multiscale modeling is increasingly being recognized as a promising research area in computational cancer systems biology. Here, exemplified by two pioneering studies, we attempt to explain why and how such a multiscale approach paired with an innovative cross-scale analytical technique can be useful in identifying high-value molecular therapeutic targets. This novel, integrated approach has the potential to offer a more effective in silico framework for target discovery and represents an important technical step towards systems medicine.

Pharmacometrics 2020

This special issue of the Journal of Clinical Pharmacology is dedicated to pharmacometrics, covering topics related to methodological research, application to decisions, standardization, PhRMA survey, and growth strategy. Innovative methodological and technological advances in analyzing disease, drug, and trial data have equipped pharmacometricians with the know-how to influence high-level decisions, which in turn creates more pharmacometric opportunities. Pharmacometrics is revolutionizing drug development and regulatory decision making. To sustain the success and growth of this field, we need to up the ante. Strategic goals for pharmacometric groups in industry, regulatory agencies, and academia are proposed in this report. These goals should be of significance to all stakeholders who have a vested interest in drug development and therapeutics. The future of pharmacometrics depends on how well we all can deliver on the strategic goals.

Interactive visualization and communication for increased impact of pharmacometrics

The art of pharmacometric activities (also called modeling and simulation) is in developing the appropriate model to describe the data at hand. In a subsequent step, outputs from the model are frequently used for quantitative decision making: what is the appropriate dose and dosing regimen, should the dose be individualized, what percentage of patients can be expected to reach therapeutic levels of exposure, and more. However, a good model does not automatically lead to a good decision-making process, which implies clinical team decisions on the population to be treated, the clinical end point, the dose, and the dosing regimen. The authors argue that seeing is believing: interactive visualization helps the communication process of clinical teams substantially. A flow of arguments guided by visualization of the model-predicted consequences of choosing a particular setup makes the discussion transparent and enhances quantitative decision making. The use of interactive visualization tools (such as the Berkeley Madonna software system) for pharmacometric results facilitates effective communication, enhanced quantitative decision making, and thus increases the impact of pharmacometrics in drug development.

Modelling and simulation as research tools in paediatric drug development

Purpose

Although practical and ethical constraints impose special requirements for the evaluation of treatment safety and efficacy in children, the main issue remains the empirical basis for patient stratification and dose selection at the early stage of the development of new chemical and biological entities. The aim of this review is to highlight the advantages and limitations of modelling and simulation (M&S) in supporting decision making during paediatric drug development.

Methods

A literature search on Pubmed’s database Medical Subject Headings (MeSH) has been performed to retrieve relevant publications on the use of model-based approaches in paediatric drug development and therapeutics.

Results

M&S enable the assessment of the impact of different regimens as well as of different populations on a drug’s safety and efficacy profile. It has been widely used in the last two decades to support pre-clinical and early clinical drug development. In fact, M&S have been applied to drug development as decision tools, as study optimization tools and as data analysis tools. In particular, this approach can be used to support dose adjustment in specific subgroups of a population. M&S may therefore allow the individualisation of drug therapy in children, improving the risk–benefit ratio in this population.

Conclusions

The lack of consensus on how to assess the impact of developmental factors on pharmacokinetics, pharmacodynamics, efficacy and safety has so far prevented a broader use of M&S. This problem is compounded by the limited collaboration between stakeholders, which prevents data sharing in this field. In this article, we emphasise the need for a concerted effort to promote the effective use of this technology in paediatric drug development and avoid unnecessary exposure of children to clinical trials.

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

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

Model-based drug development: strengths, weaknesses, opportunities, and threats for broad application of pharmacometrics in drug development

Systematic implementation of model-based drug development (MBDD) to drug discovery and development has the potential to significantly increase the rate of medical breakthroughs and make available new and better treatments to patients. An analysis of the strengths, weaknesses, opportunities, and threats (ie, SWOT) was conducted through focus group discussions that included 24 members representing 8 pharmaceutical companies to systematically assess the challenges to implementing MBDD into the drug development decision-making process. The application of the SWOT analysis to the successful implementation of MBDD yielded 19 strengths, 27 weaknesses, 34 opportunities, and 22 threats, which support the following conclusions. The shift from empirical drug development to MBDD requires a question-based mentality; early, proactive planning; dynamic access to multisource data; quantitative knowledge integration; multidisciplinary collaboration; effective communication and leadership skills; and innovative, impactful application of pharmacometrics focused on enhancing quantitative decision making. The ultimate goal of MBDD is to streamline discovery and development of innovative medicines to benefit patients.

Quantifying pain relief following administration of a novel formulation of paracetamol (acetaminophen)

This article describes how a model-based analysis was used to aid development of a novel formulation technology. Paracetamol (acetaminophen) was used as the motivating example with 4 different formulations (2 developmental and 2 commercial) compared using stochastic (Monte Carlo) pharmacokinetic (PK)-pharmacodynamic (PD) simulations to explore potential differences in pharmacodynamic outcomes. PK models were developed from data collected during an intensively sampled, 4-arm crossover trial in 25 fasted healthy subjects, administered 1 g of paracetamol in 4 different formulations. The PK models were linked to a previously published PD model that quantified pain relief over time following tonsillectomy. The number needed to treat (NNT) was the primary numeric used to compare effectiveness. The developmental formulations were likely to produce faster and greater analgesia with an NNT (compared with placebo) to reduce pain by 50% over a 45-minute interval post dose of 2.75 and 2.88 compared with 4.31 and 3.2 for the commercial products. Over the course of 1 hour, all formulations were comparable. The stochastic simulations provided support that the novel formulation technology was likely to provide a clinically meaningful advantage and should be developed further.