Model-based Drug Development

At the bench: the key role of PK-PD modelling in enabling the early discovery of biologic therapies

Pharmacokinetic-pharmacodynamic (PK-PD) modelling is already used extensively in pre-clinical and clinical drug development to characterize drug candidates quantitatively, aid go/no-go decisions and to inform future trial design and optimal dosing regimens. Less well known, although arguably as powerful, is its application at the earliest stages of drug development, at target selection and lead selection, where these same techniques can be used to predict and so bring forward drug candidates with the necessary characteristics or, for unachievable requirements, allow the abandonment of the programme for the minimum spend of time and cost. We consider three examples that illustrate the power of the application of modelling at this early stage. We start with the simple case of determining the optimal characteristics for a monoclonal antibody against a soluble ligand with its application to the investment decision for the development of best-in-class compounds. This is extended to the more complex situation of the target protein having an endogenous, inhibitory binding protein. We then illustrate how using physiologically-based pharmacokinetic modelling enables the appropriate engineering and testing of biological therapeutics for optimal PK-PD characteristics. These examples illustrate how a minimal investment in modelling achieves orders of magnitude better returns in choosing the correct targets, mechanism of action and candidate characteristics to progress to clinical trials, streamlining drug development and delivering better medicines to patients.

Developing Exposure/Response Models for Anticancer Drug Treatment: Special Considerations

Anticancer agents often have a narrow therapeutic index (TI), requiring precise dosing to ensure sufficient exposure for clinical activity while minimizing toxicity. These agents frequently have complex pharmacology, and combination therapy may cause schedule-specific effects and interactions. We review anticancer drug development, showing how integration of modeling and simulation throughout development can inform anticancer dose selection, potentially improving the late-phase success rate. This article has a companion article in Clinical Pharmacology & Therapeutics with practical examples.

Simulations of site-specific target-mediated pharmacokinetic models for guiding the development of bispecific antibodies

Bispecific antibodies (BAbs) are novel constructs that are under development and show promise as new therapeutic modalities for cancer and autoimmune disorders. The aim of this study is to develop a semi-mechanistic modeling approach to elucidate the disposition of BAbs in plasma and possible sites of action in humans. Here we present two case studies that showcase the use of modeling to guide BAb development. In case one, a BAb is directed against a soluble and a membrane-bound ligand for treating systemic lupus erythematosus, and in case two, a BAb targets two soluble ligands as a potential treatment for ulcerative colitis and asthma. Model simulations revealed important differences between plasma and tissues, when evaluated for drug disposition and target suppression. Target concentrations at tissue sites and type (soluble vs membrane-bound), tissue-site binding, and binding affinity are all major determinants of BAb disposition and subsequently target suppression. For the presented case studies, higher doses and/or frequent dosing regimens are required to achieve 80 % target suppression in site specific tissue (the more relevant matrix) as compared to plasma. Site-specific target-mediated models may serve to guide the selection of first-in-human doses for new BAbs.

Optimizing oncology therapeutics through quantitative translational and clinical pharmacology: challenges and opportunities

Despite advances in biomedical research that have deepened our understanding of cancer hallmarks, resulting in the discovery and development of targeted therapies, the success rates of oncology drug development remain low. Opportunities remain for objective dose selection informed by exposure-response understanding to optimize the benefit-risk balance of novel therapies for cancer patients. This review article discusses the principles and applications of modeling and simulation approaches across the lifecycle of development of oncology therapeutics. Illustrative examples are used to convey the value gained from integration of quantitative clinical pharmacology strategies from the preclinical-translational phase through confirmatory clinical evaluation of efficacy and safety.

Osteoarthritis disease progression model using six year follow-up data from the osteoarthritis initiative

The objective was to develop a quantitative model of disease progression of knee osteoarthritis over 6 years using the total WOMAC score from patients enrolled into the Osteoarthritis Initiative (OAI) study. The analysis was performed using data from the Osteoarthritis Initiative database. The time course of the total WOMAC score of patients enrolled into the progression cohort was characterized using non-linear mixed effect modeling in NONMEM. The effect of covariates on the status of the disease and the progression rate was investigated. The final model provided a good description of the experimental data using a linear progression model with a common baseline (19 units of the total WOMAC score). The WOMAC score decreased by 1.77 units/year in 89% of the population or increased by 1.74 units/year in 11% of the population. Multiple covariates were found to affect the baseline and the rate of progression, including BMI, sex, race, the use of pain medications, and the limitation in activity due to symptoms. A mathematical model to describe the disease progression of osteoarthritis in the studied population was developed. The model identified two sub-populations with increasing or decreasing total WOMAC score over time, and the effect of important covariates was quantified.

Linking the population pharmacokinetics of tenofovir and its metabolites with its cellular uptake and metabolism

Empirical pharmacokinetic models are used to explain the pharmacokinetics of the antiviral drug tenofovir (TFV) and its metabolite TFV diphosphate (TFV-DP) in peripheral blood mononuclear cells. These empirical models lack the ability to explain differences between the disposition of TFV-DP in HIV-infected patients vs. healthy individuals. Such differences may lie in the mechanisms of TFV transport and phosphorylation. Therefore, we developed an exploratory model based on mechanistic mass transport principles and enzyme kinetics to examine the uptake and phosphorylation kinetics of TFV. TFV-DP median C_max from the model was 38.5 fmol/10⁶ cells, which is bracketed by two reported healthy volunteer studies (38 and 51 fmol/10⁶ cells). The model presented provides a foundation for exploration of TFV uptake and phosphorylation kinetics for various routes of TFV administration and can be updated as more is known on actual mechanisms of cellular transport of TFV.

Virtual Systems Pharmacology (ViSP) software for simulation from mechanistic systems-level models

Multiple software programs are available for designing and running large scale system-level pharmacology models used in the drug development process. Depending on the problem, scientists may be forced to use several modeling tools that could increase model development time, IT costs and so on. Therefore, it is desirable to have a single platform that allows setting up and running large-scale simulations for the models that have been developed with different modeling tools. We developed a workflow and a software platform in which a model file is compiled into a self-contained executable that is no longer dependent on the software that was used to create the model. At the same time the full model specifics is preserved by presenting all model parameters as input parameters for the executable. This platform was implemented as a model agnostic, therapeutic area agnostic and web-based application with a database back-end that can be used to configure, manage and execute large-scale simulations for multiple models by multiple users. The user interface is designed to be easily configurable to reflect the specifics of the model and the user's particular needs and the back-end database has been implemented to store and manage all aspects of the systems, such as Models, Virtual Patients, User Interface Settings, and Results. The platform can be adapted and deployed on an existing cluster or cloud computing environment. Its use was demonstrated with a metabolic disease systems pharmacology model that simulates the effects of two antidiabetic drugs, metformin and fasiglifam, in type 2 diabetes mellitus patients.

Implementation of quantitative and systems pharmacology in large pharma

Quantitative and systems pharmacology concepts and tools are the foundation of the model-informed drug development paradigm at Merck for integrating knowledge, enabling decisions, and enhancing submissions. Rigorous prioritization of modeling and simulation activities has enabled key drug development decisions and led to a high return on investment through significant cost avoidance. Critical factors for the successful implementation, examples on impact on decision making with associated return of investment, and drivers for continued success are discussed.

The use of biomarkers in human pharmacology (Phase I) studies

The development of a new medicine is a risky and costly undertaking that requires careful planning. This planning is largely applied to the operational aspects of the development and less so to the scientific objectives and methodology. The drugs that will be developed in the future will increasingly affect pathophysiological pathways that have been largely unexplored. Such drug prototypes cannot be immediately introduced in large clinical trials. The effects of the drug on normal physiology, pathophysiology, and eventually the desired clinical effects will need to be evaluated in a structured approach, based on the definition of drug development as providing answers to important questions by appropriate clinical studies. This review describes the selection process for biomarkers that are fit-for-purpose for the stage of drug development in which they are used. This structured and practical approach is widely applicable and particularly useful for the early stages of innovative drug development.

Research designs for proof-of-concept chronic pain clinical trials: IMMPACT recommendations

Proof-of-concept (POC) clinical trials play an important role in developing novel treatments and determining whether existing treatments may be efficacious in broader populations of patients. The goal of most POC trials is to determine whether a treatment is likely to be efficacious for a given indication and thus whether it is worth investing the financial resources and participant exposure necessary for a confirmatory trial of that intervention. A challenge in designing POC trials is obtaining sufficient information to make this important go/no-go decision in a cost-effective manner. An IMMPACT consensus meeting was convened to discuss design considerations for POC trials in analgesia, with a focus on maximizing power with limited resources and participants. We present general design aspects to consider including patient population, active comparators and placebos, study power, pharmacokinetic-pharmacodynamic relationships, and minimization of missing data. Efficiency of single-dose studies for treatments with rapid onset is discussed. The trade-off between parallel-group and crossover designs with respect to overall sample sizes, trial duration, and applicability is summarized. The advantages and disadvantages of more recent trial designs, including N-of-1 designs, enriched designs, adaptive designs, and sequential parallel comparison designs, are summarized, and recommendations for consideration are provided. More attention to identifying efficient yet powerful designs for POC clinical trials of chronic pain treatments may increase the percentage of truly efficacious pain treatments that are advanced to confirmatory trials while decreasing the percentage of ineffective treatments that continue to be evaluated rather than abandoned.

Dashboard systems: implementing pharmacometrics from bench to bedside

In recent years, there has been increasing interest in the development of medical decision-support tools, including dashboard systems. Dashboard systems are software packages that integrate information and calculations about therapeutics from multiple components into a single interface for use in the clinical environment. Given the high cost of medical care, and the increasing need to demonstrate positive clinical outcomes for reimbursement, dashboard systems may become an important tool for improving patient outcome, improving clinical efficiency and containing healthcare costs. Similarly the costs associated with drug development are also rising. The use of model-based drug development (MBDD) has been proposed as a tool to streamline this process, facilitating the selection of appropriate doses and making informed go/no-go decisions. However, complete implementation of MBDD has not always been successful owing to a variety of factors, including the resources required to provide timely modeling and simulation updates. The application of dashboard systems in drug development reduces the resource requirement and may expedite updating models as new data are collected, allowing modeling results to be available in a timely fashion. In this paper, we present some background information on dashboard systems and propose the use of these systems both in the clinic and during drug development.

Prediction and modeling of effects on the QTc interval for clinical safety margin assessment, based on single-ascending-dose study data with AZD3839

Corrected QT interval (QTc) prolongation in humans is usually predictable based on results from preclinical findings. This study confirms the signal from preclinical cardiac repolarization models (human ether-a-go-go-related gene, guinea pig monophasic action potential, and dog telemetry) on the clinical effects on the QTc interval. A thorough QT/QTc study is generally required for bioavailable pharmaceutical compounds to determine whether or not a drug shows a QTc effect above a threshold of regulatory interest. However, as demonstrated in this AZD3839 [(S)-1-(2-(difluoromethyl)pyridin-4-yl)-4-fluoro-1-(3-(pyrimidin-5-yl)phenyl)-1H-isoindol-3-amine hemifumarate] single-ascending-dose (SAD) study, high-resolution digital electrocardiogram data, in combination with adequate efficacy biomarker and pharmacokinetic data and nonlinear mixed effects modeling, can provide the basis to safely explore the margins to allow for robust modeling of clinical effect versus the electrophysiological risk marker. We also conclude that a carefully conducted SAD study may provide reliable data for effective early strategic decision making ahead of the thorough QT/QTc study.

Implementation of pharmacokinetic and pharmacodynamic strategies in early research phases of drug discovery and development at Novartis Institute of Biomedical Research

Characterizing the relationship between the pharmacokinetics (PK, concentration vs. time) and pharmacodynamics (PD, effect vs. time) is an important tool in the discovery and development of new drugs in the pharmaceutical industry. The purpose of this publication is to serve as a guide for drug discovery scientists toward optimal design and conduct of PK/PD studies in the research phase. This review is a result of the collaborative efforts of DMPK scientists from various Metabolism and Pharmacokinetic (MAP) departments of the global organization Novartis Institute of Biomedical Research (NIBR). We recommend that PK/PD strategies be implemented in early research phases of drug discovery projects to enable successful transition to drug development. Effective PK/PD study design, analysis, and interpretation can help scientists elucidate the relationship between PK and PD, understand the mechanism of drug action, and identify PK properties for further improvement and optimal compound design. Additionally, PK/PD modeling can help increase the translation of in vitro compound potency to the in vivo setting, reduce the number of in vivo animal studies, and improve translation of findings from preclinical species into the clinical setting. This review focuses on three important elements of successful PK/PD studies, namely partnership among key scientists involved in the study execution; parameters that influence study designs; and data analysis and interpretation. Specific examples and case studies are highlighted to help demonstrate key points for consideration. The intent is to provide a broad PK/PD foundation for colleagues in the pharmaceutical industry and serve as a tool to promote appropriate discussions on early research project teams with key scientists involved in PK/PD studies.

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.

Preclinical optimization of MDM2 antagonist scheduling for cancer treatment by using a model-based approach

PURPOSE

Antitumor clinical activity has been demonstrated for the MDM2 antagonist RG7112, but patient tolerability for the necessary daily dosing was poor. Here, utilizing RG7388, a second-generation nutlin with superior selectivity and potency, we determine the feasibility of intermittent dosing to guide the selection of initial phase I scheduling regimens.

EXPERIMENTAL DESIGN

A pharmacokinetic-pharmacodynamic (PKPD) model was developed on the basis of preclinical data to determine alternative dosing schedule requirements for optimal RG7388-induced antitumor activity. This PKPD model was used to investigate the pharmacokinetics of RG7388 linked to the time-course of the antitumor effect in an osteosarcoma xenograft model in mice. These data were used to prospectively predict intermittent and continuous dosing regimens, resulting in tumor stasis in the same model system.

RESULTS

RG7388-induced apoptosis was delayed relative to drug exposure with continuous treatment not required. In initial efficacy testing, daily dosing at 30 mg/kg and twice a week dosing at 50 mg/kg of RG7388 were statistically equivalent in our tumor model. In addition, weekly dosing of 50 mg/kg was equivalent to 10 mg/kg given daily. The implementation of modeling and simulation on these data suggested several possible intermittent clinical dosing schedules. Further preclinical analyses confirmed these schedules as viable options.

CONCLUSION

Besides chronic administration, antitumor activity can be achieved with intermittent schedules of RG7388, as predicted through modeling and simulation. These alternative regimens may potentially ameliorate tolerability issues seen with chronic administration of RG7112, while providing clinical benefit. Thus, both weekly (qw) and daily for five days (5 d on/23 off, qd) schedules were selected for RG7388 clinical testing.

Modeling of human tumor xenografts and dose rationale in oncology

Xenograft models are commonly used in oncology drug development. Although there are discussions about their ability to generate meaningful data for the translation from animal to humans, it appears that better data quality and better design of the preclinical experiments, together with appropriate data analysis approaches could make these data more informative for clinical development. An approach based on mathematical modeling is necessary to derive experiment-independent parameters which can be linked with clinically relevant endpoints. Moreover, the inclusion of biomarkers as predictors of efficacy is a key step towards a more general mechanism-based strategy.

Pharmacokinetic-pharmacodynamic model of the antihypertensive interaction between telmisartan and hydrochlorothiazide in spontaneously hypertensive rats

OBJECTIVES

The goal of this study was to establish an integrated indirect response pharmacokinetic-pharmacodynamic model between telmisartan and hydrochlorothiazide to describe the antihypertensive interaction of these two drugs in spontaneously hypertensive rats.

METHODS

The blood pressure and plasma concentrations were measured by the tail-cuff test and high performance liquid chromatography-mass spectrometry, respectively, in spontaneously hypertensive rats. The current pharmacokinetic-pharmacodynamic model was based on the non-competitive pharmacodynamic interaction of two drugs acting on different physiological processes.

KEY FINDINGS

This model was able to acquire the temporal changes in drug concentration and blood pressure after administration of telmisartan or hydrochlorothiazide. The noncompetitive pharmacodynamic interaction assumed that the decreased blood pressure was attributed to the inhibitory function of telmisartan and stimulatory function of hydrochlorothiazide after administration of these two drugs. There was no significant pharmacokinetic change of telmisartan and hydrochlorothiazide in the different groups tested. The model predicted a synergistic pharmacodynamic interaction when telmisartan was administered in combination with hydrochlorothiazide, which was notably stronger than if the effects were additive.

CONCLUSION

The results showed that the presented pharmacokinetic-pharmacodynamic model was suitable for describing the antihypertensive interaction between telmisartan and hydrochlorothiazide.

Automated analysis of routinely generated preclinical pharmacokinetic and pharmacodynamic data

Model-based analysis of routinely generated pharmacokinetic and pharmacodynamic (PK-PD) data is a key component of preclinical drug discovery. The work process of such analyses can be automated by properly designed computer programs that reduce the number of manual steps, resulting in time saving and significantly fewer errors. Critical decisions can still be made by modelers. Using concrete animal data examples this paper illustrates when, and demonstrates how, automated PK-PD approaches can be used and what benefits they offer to the modeling and simulation community. Specifically, we describe two compound optimization case studies from drug discovery projects, and also demonstrate how a subsequent optimization step to predict the human dose can be coupled to an automated approach.

A semi-mechanistic model to characterize the pharmacokinetics and pharmacodynamics of brodalumab in healthy volunteers and subjects with psoriasis in a first-in-human single ascending dose study

Pharmacokinetic-pharmacodynamic (PK-PD) modeling can provide a framework for quantitative "learning and confirming" from studies in all phases of drug development. Brodalumab is a human monoclonal antibody (IgG2 ) targeting the IL-17 receptor A that blocks signaling by cytokines thought to play a central role in the pathogenesis of psoriasis (IL-17A, IL-17F, and IL-17A/F). We used semi-mechanistic modeling of single dose, first-in-human data to characterize the exposure-response relationship between brodalumab and the Psoriasis Area and Severity Index (PASI) in a Phase 1 clinical trial. Fifty-seven healthy volunteers and 25 subjects with moderate to severe psoriasis received single intravenous or subcutaneous administration of placebo or brodalumab (7-700 mg). A two-compartment model with parallel linear and nonlinear (Michaelis-Menten) elimination pathways described brodalumab PK. The PK-PASI relationship was characterized by linking a signaling compartment with an indirect response model of psoriatic plaques, where signaling suppressed plaque formation. The concentration of half-maximal inhibition IC50 was 2.86 µg/mL (SE: 50%). The endogenous psoriatic plaque formation rate of 0.862 (SE: 40%) PASI units/day was comparable with literature precedent. Despite the small sample size and single administration data, this semi-mechanistic modeling approach provided a quantitative framework to inform design of dose-ranging Phase 2 studies of brodalumab in psoriasis.

A linearization approach for the model-based analysis of combined aggregate and individual patient data

The application of model-based meta-analysis in drug development has gained prominence recently, particularly for characterizing dose-response relationships and quantifying treatment effect sizes of competitor drugs. The models are typically nonlinear in nature and involve covariates to explain the heterogeneity in summary-level literature (or aggregate data (AD)). Inferring individual patient-level relationships from these nonlinear meta-analysis models leads to aggregation bias. Individual patient-level data (IPD) are indeed required to characterize patient-level relationships but too often this information is limited. Since combined analyses of AD and IPD allow advantage of the information they share to be taken, the models developed for AD must be derived from IPD models; in the case of linear models, the solution is a closed form, while for nonlinear models, closed form solutions do not exist. Here, we propose a linearization method based on a second order Taylor series approximation for fitting models to AD alone or combined AD and IPD. The application of this method is illustrated by an analysis of a continuous landmark endpoint, i.e., change from baseline in HbA1c at week 12, from 18 clinical trials evaluating the effects of DPP-4 inhibitors on hyperglycemia in diabetic patients. The performance of this method is demonstrated by a simulation study where the effects of varying the degree of nonlinearity and of heterogeneity in covariates (as assessed by the ratio of between-trial to within-trial variability) were studied. A dose-response relationship using an Emax model with linear and nonlinear effects of covariates on the emax parameter was used to simulate data. The simulation results showed that when an IPD model is simply used for modeling AD, the bias in the emax parameter estimate increased noticeably with an increasing degree of nonlinearity in the model, with respect to covariates. When using an appropriately derived AD model, the linearization method adequately corrected for bias. It was also noted that the bias in the model parameter estimates decreased as the ratio of between-trial to within-trial variability in covariate distribution increased. Taken together, the proposed linearization approach allows addressing the issue of aggregation bias in the particular case of nonlinear models of aggregate data.

The paradox of scientific excellence and the search for productivity in pharmaceutical research and development

Scientific advances in specialty areas are proceeding at a rapid rate, but the research and development enterprise seems unable to take full advantage. Harnessing the steady stream of knowledge and inventions from different disciplines is the critical management issue of our time. This article suggests a framework for a management-directed effort to improve productivity by enhancing interdisciplinary collaboration.

Modeling of tumor growth in dendritic cell-based immunotherapy using artificial neural networks

Exposure-response modeling and simulation is especially useful in oncology as it permits to predict and design un-experimented clinical trials as well as dose selection. Dendritic cells (DC) are the most effective immune cells in the regulation of immune system. To activate immune system, DCs may be matured by many factors like bacterial CpG-DNA, Lipopolysaccharaide (LPS) and other microbial products. In this paper, a model based on artificial neural network (ANN) is presented for analyzing the dynamics of antitumor vaccines using empirical data obtained from the experimentations of different groups of mice treated with DCs matured by bacterial CpG-DNA, LPS and whole lysate of a Gram-positive bacteria Listeria monocytogenes. Also, tumor lysate was added to DCs followed by addition of maturation factors. Simulations show that the proposed model can interpret the important features of empirical data. Owing to the nonlinearity properties, the proposed ANN model has been able not only to describe the contradictory empirical results, but also to predict new vaccination patterns for controlling the tumor growth. For example, the proposed model predicts an exponentially increasing pattern of CpG-matured DC to be effective in suppressing the tumor growth.

A drug and disease model for lixisenatide, a GLP-1 receptor agonist in type 2 diabetes

Incretin hormone analogs such as glucagon-like peptide-1 (GLP-1) receptor agonists have emerged as promising new options for the treatment of type 2 diabetes mellitus (T2DM), targeting several of its pathophysiological traits, including reduced insulin sensitivity, inadequate insulin secretion, and loss of β-cell mass (BCM). This article describes the semi-mechanistic modeling of lixisenatide dose-response over time using fasting plasma glucose (FPG), fasting serum insulin (FSI) and glycated hemoglobin (HbA1c) data from two Phase II and four Phase III clinical trials, for a total of 2470 T2DM patients. Previously published models for FPG, FSI, and BCM as well as HbA1c were adapted and expanded to describe the available data. The model incorporated aspects describing disease progression, standard-of-care, FPG-dependent and -independent HbA1c synthesis, and covariate effects of body size, race, and sex. The final model described lixisenatide effects on β-cell responsiveness, insulin sensitivity and FPG-independent HbA1c synthesis, was able to describe the observed FPG, FSI, and HbA1c data accurately, and was successful in predicting data from an unseen Phase III clinical study.

Clinical trial simulation to assist in COPD trial planning and design with a biomarker-based diagnostic: when to pull the trigger?

Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous disease with a wide range of clinical phenotypes that vary from predominantly airway disease (chronic bronchitis) to predominantly parenchymal disease (emphysema). Current advances for the treatment of COPD are increasingly focused on targeted treatments and development of novel biomarker-based diagnostics (Dx)'s to select the patients most likely to benefit. Clinical trial planning and design with biomarkers includes additional considerations beyond those for conventional trials in un-selected populations, e.g., the heterogeneity of COPD phenotypes in the population, the ability of a biomarker to predict clinically meaningful phenotypes that are differentially associated with the response to a targeted treatment, and the data needed to make Go/No Go decisions during clinical development. We developed the Clinical Trial Object Oriented Research Application (CTOORA), a computer-aided clinical trial simulator of COPD patient outcomes, to inform COPD trial planning with biomarkers. CTOORA provides serial projections of trial success for a range of hypothetical and plausible scenarios of interest. In the absence of data, CTOORA can identify characteristics of a biomarker-based Dx needed to provide a meaningful advantage when used in a clinical trial. We present a case study in which CTOORA is used to identify the scenarios for which a biomarker may be used successfully in clinical development. CTOORA is a tool for robust clinical trial planning with biomarkers, to guide early-to-late stage development that is positioned for success.

Leveraging systems biology approaches in clinical pharmacology

Computational modeling has been adopted in all aspects of drug research and development, from the early phases of target identification and drug discovery to the late-stage clinical trials. The different questions addressed during each stage of drug R&D has led to the emergence of different modeling methodologies. In the research phase, systems biology couples experimental data with elaborate computational modeling techniques to capture lifecycle and effector cellular functions (e.g. metabolism, signaling, transcription regulation, protein synthesis and interaction) and integrates them in quantitative models. These models are subsequently used in various ways, i.e. to identify new targets, generate testable hypotheses, gain insights on the drug's mode of action (MOA), translate preclinical findings, and assess the potential of clinical drug efficacy and toxicity. In the development phase, pharmacokinetic/pharmacodynamic (PK/PD) modeling is the established way to determine safe and efficacious doses for testing at increasingly larger, and more pertinent to the target indication, cohorts of subjects. First, the relationship between drug input and its concentration in plasma is established. Second, the relationship between this concentration and desired or undesired PD responses is ascertained. Recognizing that the interface of systems biology with PK/PD will facilitate drug development, systems pharmacology came into existence, combining methods from PK/PD modeling and systems engineering explicitly to account for the implicated mechanisms of the target system in the study of drug–target interactions. Herein, a number of popular system biology methodologies are discussed, which could be leveraged within a systems pharmacology framework to address major issues in drug development.

Applications of pharmacometrics in the clinical development and pharmacotherapy of anti-infectives

With the increased emergence of anti-infective resistance in recent years, much focus has recently been drawn to the development of new anti-infectives and the optimization of treatment regimens and combination therapies for established antimicrobials. In this context, the field of pharmacometrics using quantitative numerical modeling and simulation techniques has in recent years emerged as an invaluable tool in the pharmaceutical industry, academia and regulatory agencies to facilitate the integration of preclinical and clinical development data and to provide a scientifically based framework for rational dosage regimen design and treatment optimization. This review highlights the usefulness of pharmacometric analyses in anti-infective drug development and applied pharmacotherapy with select examples.

Establishing in vitro to clinical correlations in the evaluation of cardiovascular safety pharmacology

Preclinical studies are vital in establishing the efficacy and safety of a new chemical entity (NCE) in humans. To deliver meaningful information, experiments have to be well defined and provide outcome that is relevant and translatable to humans. This review briefly surveys the various preclinical experiments that are frequently conducted to assess drug effects on cardiac conductivity in early drug development. We examine the different approaches used to establish correlations between non-clinical and clinical settings and discuss their value in the evaluation of cardiovascular risk.

Integration of biostatistics and pharmacometrics computing platforms for efficient and reproducible PK/PD analysis: a case study

Results of pharmacometric analyses influence high-level decisions such as clinical trial design, drug approval, and labeling. Key challenges for timely delivery of pharmacometric analyses are the data assembly process and tracking and documenting the modeling process and results. Since clinical efficacy and safety data typically reside in the biostatistics computing area, an integrated computing platform for pharmacometric and biostatistical analyses would be ideal. A case study is presented integrating a pharmacometric modeling platform into an existing statistical computing environment (SCE). The feasibility and specific configurations of running common PK/PD programs such as NONMEM and R inside of the SCE are provided. The case study provides an example of an integrated repository that facilitates efficient data assembly for pharmacometrics analyses. The proposed platform encourages a good pharmacometrics working practice to maintain transparency, traceability, and reproducibility of PK/PD models and associated data in supporting drug development and regulatory decisions.

A cyclic fluctuation model for 24-h ambulatory blood pressure monitoring in Chinese patients with mild to moderate hypertension

AIM

The conventional method for analyzing 24-h ambulatory blood pressure monitoring (24-h ABPM) is insufficient to deal with the large amount of data collected. The aim of this study was to develop a novel cyclic fluctuation model for 24-h ABPM in Chinese patients with mild to moderate hypertension.

METHODS

The data were obtained from 4 independent antihypertensive drug clinical trials in Chinese patients with mild to moderate hypertension. The measurements of 24-h ABPM at the end of the placebo run-in period in study 1 were used to develop the cyclic fluctuation model. After evaluated, the structural model was used to analyze the measurements in the other 3 studies. Models were fitted using NONMEM software.

RESULTS

The cyclic fluctuation model, which consisted of 2 cosine functions with fixed-effect parameters for rhythm-adjusted 24-h mean blood pressure, amplitude and phase shift, successfully described the blood pressure measurements of study 1. Model robustness was validated by the bootstrap method. The measurements in the other 3 studies were well described by the same structural model. Moreover, the parameters from all the 4 studies were very similar. Visual predictive checks demonstrated that the cyclic fluctuation model could predict the blood pressure fluctuations in the 4 studies.

CONCLUSION

The cyclic fluctuation model for 24-h ABPM deepens our understanding of blood pressure variability, which will be beneficial for drug development and individual therapy in hypertensive patients.

Clinical Trial Simulation to Inform Phase 2: Comparison of Concentrated vs. Distributed First-in-Patient Study Designs in Psoriasis

Clinical trial simulation (CTS) and model-based meta-analysis (MBMA) can increase our understanding of small, first-in-patient (FIP) trial design performance to inform Phase 2 decision making. In this work, we compared dose-ranging designs vs. designs testing only placebo and the maximum dose for early decision making in psoriasis. Based on MBMA of monoclonal antibodies in the psoriasis space, a threshold of greater than a 50 percentage point improvement over placebo effect at the highest feasible drug dose was required for the advancement in psoriasis. Studies testing only placebo and the maximum dose made the correct advancement decision marginally more often than dose-ranging designs in the majority of the cases. However, dose-ranging studies in FIP trials offer important design advantages in the form of dose–response (D–R) information to inform Phase 2 dose selection. CTS can increase the efficiency and quality of drug development decision making by studying the limitations and benefits of study designs prospectively.

Model based design and analysis of phase II HIV-1 trials

This work explores the advantages of a model based drug development (MBDD) approach for the design and analysis of antiretroviral phase II trials. Two different study settings were investigated: (1) a 5-arm placebo-controlled parallel group dose-finding/proof of concept (POC) study and (2) a comparison of investigational drug and competitor. Studies were simulated using a HIV-1 dynamics model in NONMEM. The Monte-Carlo Mapped Power method determined the sample size required for detecting a dose-response relationship and a significant difference in effect compared to the competitor using a MBDD approach. Stochastic simulation and re-estimation were used for evaluation of model parameter precision and bias given different sample sizes. Results were compared to those from an unpaired, two-sided t test and ANOVA (p ≤ 0.05). In all scenarios, the MBDD approach resulted in smaller study sizes and more precisely estimated treatment effect than conventional statistical analysis. Using a MBDD approach, a sample size of 15 patients could be used to show POC and estimate ED50 with a good precision (relative standard error, 25.7 %). A sample size of 10 patients per arm was needed using the MBDD approach for detecting a difference in treatment effect of ≥20 % at 80 % power, a 3.4-fold reduction in sample size compared to a t test. The MBDD approach can be used to achieve more precise dose-response characterization facilitating decision making and dose selection. If necessitated, the sample size needed to reach a desired power can potentially be reduced compared to traditional statistical analyses. This may allow for comparison against competitors already in early clinical studies.

Establishing best practices and guidance in population modeling: an experience with an internal population pharmacokinetic analysis guidance

This tutorial describes the development of a population pharmacokinetic (Pop PK) analysis guidance within Pfizer, which strives for improved consistency and efficiency, and a more systematic approach to model building. General recommendations from the Pfizer internal guidance and a suggested workflow for Pop PK model building are discussed. A description is also provided for mechanisms by which conflicting opinions were captured and resolved across the organization to arrive at the final guidance.

CPT: Pharmacometrics & Systems Pharmacology (2013) 2, e51; doi:10.1038/psp.2013.26; advance online publication 3 July 2013

Optimization of anti-pseudomonal antibiotics for cystic fibrosis pulmonary exacerbations: VI. Executive summary

Acute pulmonary exacerbations (APE) are well-described complications of cystic fibrosis (CF) and are associated with progressive morbidity and mortality. Despite aggressive management with two or more intravenous anti-pseudomonal agents, approximately 25% of exacerbations will result in a loss of lung function. The aim of this review is to provide an overview of the classes of intravenous anti-pseudomonal antibiotics, the findings of anti-pseudomonal antibiotic utilization surveys, the current antibiotic dosing recommendations from the U.S. and Europe, and the pharmacokinetic (PK) and pharmacodynamic (PD) differences between CF and non-CF individuals. Anti-pseudomonal antibiotic classes include beta-lactams, aminoglycosides, fluoroquinolones, and colistimethate sodium. Recent surveys of antibiotic utilization in CF Foundation-accredited care centers have shown that a large number of centers are not following recommended dosing strategies despite published recommendations in the U.S. and Europe. The recommended doses for anti-pseudomonal antibiotics may be higher than FDA-approved doses due to PK and PD differences. As a large portion of CF patients will not regain their lung function following an APE, it seems possible that currently available anti-pseudomonal agents are being used sub-optimally. As new anti-pseudomonal agents are not currently available, we suggest the need to optimize antibiotic dosing and dosing regimens used to treat pulmonary exacerbations in an effort to improve outcomes for CF patients infected with Pseudomonas aeruginosa.

The utility of modeling and simulation in drug development and regulatory review

US Food and Drug Administration (FDA) has identified innovation in clinical evaluations as a major scientific priority area. This paper provides case studies and updates to describe the efforts by the FDA's Office of Clinical Pharmacology in its development and application of regulatory science, focusing on modeling and simulation. Key issues and challenges are identified that need to be addressed to promote the uptake of modeling and simulation approaches in drug regulation. Published 2013. This article is a U.S. Government work and is in the public domain in the USA. 102:2912-2923, 2013.

Moving from basic toward systems pharmacodynamic models

Building upon many classical foundations of pharmacology, a diverse array of mechanistic pharmacokinetic-pharmacodynamic (PK/PD) models have emerged based on mechanisms of drug action and primary rate-limiting or turnover processes in physiology. An array of basic models can be extended to handle various complexities including tolerance and can readily be employed as building blocks in assembling enhanced PK/PD or small systems models. Our corticosteroid models demonstrate these concepts as well as elements of horizontal and vertical integration of molecular to whole-body processes. The potential advantages and challenges in moving PK/PD toward systems models are described.

Advancing clinical trial design in pulmonary hypertension

In pulmonary hypertension, as in many other diseases, there is a need for a smarter approach to evaluating new treatments. The traditional randomized controlled trial has served medical science well, but constrains the development of treatments for rare diseases. A workshop was established to consider alternative clinical trial designs in pulmonary hypertension and here discusses their merits, limitations and challenges to implementation of novel approaches.