Quantitative disease, drug, and trial models

Modeling of Parkinson's Disease Progression and Implications for Detection of Disease Modification in Treatment Trials

Background

Objectively measuring Parkinson's disease (PD) signs and symptoms over time is critical for the successful development of treatments aimed at halting the disease progression of people with PD.

Objective

To create a clinical trial simulation tool that characterizes the natural history of PD progression and enables a data-driven design of randomized controlled studies testing potential disease-modifying treatments (DMT) in early-stage PD.

Methods

Data from the Parkinson's Progression Markers Initiative (PPMI) were analyzed with nonlinear mixed-effect modeling techniques to characterize the progression of MDS-UPDRS part I (non-motor aspects of experiences of daily living), part II (motor aspects of experiences of daily living), and part III (motor signs). A clinical trial simulation tool was built from these disease models and used to predict probability of success as a function of trial design.

Results

MDS-UPDRS part III progresses approximately 3 times faster than MDS-UPDRS part II and I, with an increase of 3 versus 1 points/year. Higher amounts of symptomatic therapy is associated with slower progression of MDS-UPDRS part II and III. The modeling framework predicts that a DMT effect on MDS-UPDRS part III could precede effect on part II by approximately 2 to 3 years.

Conclusions

Our clinical trial simulation tool predicted that in a two-year randomized controlled trial, MDS-UPDRS part III could be used to evaluate a potential novel DMT, while part II would require longer trials of a minimum duration of 3 to 5 years underscoring the need for innovative trial design approaches including novel patient-centric measures.

Disease progression modeling with temporal realignment: An emerging approach to deepen knowledge on chronic diseases

The recent development of the first disease-modifying drug for Alzheimer's disease represents a major advancement in dementia treatment. Behind this breakthrough is a quarter century of research efforts to understand the disease not by a particular symptom at a given moment, but by long-term sequential changes in multiple biomarkers. Disease progression modeling with temporal realignment (DPM-TR) is an emerging computational approach proposed with this biomarker-based disease concept. By integrating short-term clinical observations of multiple disease biomarkers in a data-driven manner, DPM-TR provides a way to understand the progression of chronic diseases over decades and predict individual disease stages more accurately. DPM-TR has been developed primarily in the area of neurodegenerative diseases but has recently been extended to non-neurodegenerative diseases, including chronic obstructive pulmonary, autoimmune, and ophthalmologic diseases. This review focuses on opportunities for DPM-TR in clinical practice and drug development and discusses its current status and challenges.

Advancing cancer drug development with mechanistic mathematical modeling: bridging the gap between theory and practice

Quantitative predictive modeling of cancer growth, progression, and individual response to therapy is a rapidly growing field. Researchers from mathematical modeling, systems biology, pharmaceutical industry, and regulatory bodies, are collaboratively working on predictive models that could be applied for drug development and, ultimately, the clinical management of cancer patients. A plethora of modeling paradigms and approaches have emerged, making it challenging to compile a comprehensive review across all subdisciplines. It is therefore critical to gauge fundamental design aspects against requirements, and weigh opportunities and limitations of the different model types. In this review, we discuss three fundamental types of cancer models: space-structured models, ecological models, and immune system focused models. For each type, it is our goal to illustrate which mechanisms contribute to variability and heterogeneity in cancer growth and response, so that the appropriate architecture and complexity of a new model becomes clearer. We present the main features addressed by each of the three exemplary modeling types through a subjective collection of literature and illustrative exercises to facilitate inspiration and exchange, with a focus on providing a didactic rather than exhaustive overview. We close by imagining a future multi-scale model design to impact critical decisions in oncology drug development.

A quantitative systems pharmacology workflow toward optimal design and biomarker stratification of atopic dermatitis clinical trials

BACKGROUND

The development of atopic dermatitis (AD) drugs is challenged by many disease phenotypes and trial design options, which are hard to explore experimentally.

OBJECTIVE

We aimed to optimize AD trial design using simulations.

METHODS

We constructed a quantitative systems pharmacology model of AD and standard of care (SoC) treatments and generated a phenotypically diverse virtual population whose parameter distribution was derived from known relationships between AD biomarkers and disease severity and calibrated using disease severity evolution under SoC regimens.

RESULTS

We applied this workflow to the immunomodulator OM-85, currently being investigated for its potential use in AD, and calibrated the investigational treatment model with the efficacy profile of an existing trial (thereby enriching it with plausible marker levels and dynamics). We assessed the sensitivity of trial outcomes to trial protocol and found that for this particular example the choice of end point is more important than the choice of dosing regimen and patient selection by model-based responder enrichment could increase the expected effect size. A global sensitivity analysis revealed that only a limited subset of baseline biomarkers is needed to predict the drug response of the full virtual population.

CONCLUSIONS

This AD quantitative systems pharmacology workflow built around knowledge of marker-severity relationships as well as SoC efficacy can be tailored to specific development cases to optimize several trial protocol parameters and biomarker stratification and therefore has promise to become a powerful model-informed AD drug development and personalized medicine tool.

In Silico Clinical Trials: Is It Possible?

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

Combining pharmacometric models with predictive and prognostic biomarkers for precision therapy in Crohn's disease: A case study of brazikumab

Pharmacometric models were used to investigate the utility of biomarkers in predicting the efficacy (Crohn's Disease Activity Index [CDAI]) of brazikumab and provide a data-driven framework for precision therapy for Crohn's disease (CD). In a phase IIa trial in patients with moderate to severe CD, treatment with brazikumab, an anti-interleukin 23 monoclonal antibody, was associated with clinical improvement. Brazikumab treatment effect was determined to be dependent on the baseline IL-22 (BIL22) or baseline C-reactive protein (BCRP; predictive biomarkers), and placebo effect was found to be correlated with the baseline CDAI (a prognostic biomarker). A maximal total inhibition on CDAI input function of 50.6% and 42.4% was predicted for patients with extremely high BIL22 or BCRP, compared to a maximal total inhibition of 20.9% and 17.8% for patients with extremely low BIL22 or BCRP, respectively, which were mainly due to the placebo effect. We demonstrated that model-derived baseline biomarker levels that achieve 50% of maximum unbound systemic concentration of 22.8 pg/mL and 8.03 mg/L for BIL22 and BCRP as the cutoffs to select subpopulations can effectively identify high-response subgroup patients with improved separation of responders when compared to using the median values as the cutoff. This work exemplifies the utility of pharmacometrics to quantify biomarker-driven responses in biologic therapies and distinguish between predictive and prognostic biomarkers, complementing clinical efforts of identifying subpopulations with higher likelihood of response to brazikumab.

Empirical methods for the validation of time-to-event mathematical models taking into account uncertainty and variability: application to EGFR + lung adenocarcinoma

BACKGROUND

Over the past several decades, metrics have been defined to assess the quality of various types of models and to compare their performance depending on their capacity to explain the variance found in real-life data. However, available validation methods are mostly designed for statistical regressions rather than for mechanistic models. To our knowledge, in the latter case, there are no consensus standards, for instance for the validation of predictions against real-world data given the variability and uncertainty of the data. In this work, we focus on the prediction of time-to-event curves using as an application example a mechanistic model of non-small cell lung cancer. We designed four empirical methods to assess both model performance and reliability of predictions: two methods based on bootstrapped versions of parametric statistical tests: log-rank and combined weighted log-ranks (MaxCombo); and two methods based on bootstrapped prediction intervals, referred to here as raw coverage and the juncture metric. We also introduced the notion of observation time uncertainty to take into consideration the real life delay between the moment when an event happens, and the moment when it is observed and reported.

RESULTS

We highlight the advantages and disadvantages of these methods according to their application context. We have shown that the context of use of the model has an impact on the model validation process. Thanks to the use of several validation metrics we have highlighted the limit of the model to predict the evolution of the disease in the whole population of mutations at the same time, and that it was more efficient with specific predictions in the target mutation populations. The choice and use of a single metric could have led to an erroneous validation of the model and its context of use.

CONCLUSIONS

With this work, we stress the importance of making judicious choices for a metric, and how using a combination of metrics could be more relevant, with the objective of validating a given model and its predictions within a specific context of use. We also show how the reliability of the results depends both on the metric and on the statistical comparisons, and that the conditions of application and the type of available information need to be taken into account to choose the best validation strategy.

A model-based approach for historical borrowing, with an application to neovascular age-related macular degeneration

Bayesian historical borrowing has recently attracted growing interest due to the increasing availability of historical control data, as well as improved computational methodology and software. In this article, we argue that the statistical models used for borrowing may be suboptimal when they do not adjust for differing factors across historical studies such as covariates, dosing regimen, etc. We propose an alternative approach to address these shortcomings. We start by constructing a historical model based on subject-level historical data to accurately characterize the control treatment by adjusting for known between trials differences. This model is subsequently used to predict the control arm response in the current trial, enabling the derivation of a model-informed prior for the treatment effect parameter of another (potentially simpler) model used to analyze the trial efficacy (i.e. the trial model). Our approach is applied to neovascular age-related macular degeneration trials, employing a cross-sectional regression trial model, and a longitudinal non-linear mixed-effects drug-disease-trial historical model. The latter model characterizes the relationship between clinical response, drug exposure and baseline covariates so that the derived model-informed prior seamlessly adapts to the trial population and can be extrapolated to a different dosing regimen. This approach can yield a more accurate prior for borrowing, thus optimizing gains in efficiency (e.g. increasing power or reducing the sample size) in future trials.

Opportunities and Challenges of Disease Progression Modeling in Drug Development - An IQ Perspective

Disease progression modeling (DPM) represents an important model-informed drug development framework. The scientific communities support the use of DPM to accelerate and increase efficiency in drug development. This article summarizes International Consortium for Innovation & Quality (IQ) in Pharmaceutical Development mediated survey conducted across multiple biopharmaceutical companies on challenges and opportunities for DPM. Additionally, this summary highlights the viewpoints of IQ from the 2021 workshop hosted by the US Food and Drug Administration (FDA). Sixteen pharmaceutical companies participated in the IQ survey with 36 main questions. The types of questions included single/multiple choice, dichotomous, rank questions, and open-ended or free text. The key results show that DPM has different representation, it encompasses natural disease history, placebo response, standard of care as background therapy, and can even be interpreted as pharmacokinetic/pharmacodynamic modeling. The most common reasons for not implementing DPM as frequently seem to be difficulties in internal cross-functional alignment, lack of knowledge of disease/data, and time constraints. If successfully implemented, DPM can have an impact on dose selection, reduction of sample size, trial read-out support, patient selection/stratification, and supportive evidence for regulatory interactions. The key success factors and key challenges of disease progression models were highlighted in the survey and about 24 case studies across different therapeutic areas were submitted from various survey sponsors. Although DPM is still evolving, its current impact is limited but promising. The success of such models in the future will depend on collaboration, advanced analytics, availability of and access to relevant and adequate-quality data, collaborative regulatory guidance, and published examples of impact.

Pharmacometrics: The Already-Present Future of Precision Pharmacology

The use of mathematical modeling to represent, analyze, make predictions or providing information on data obtained in drug research and development has made pharmacometrics an area of great prominence and importance. The main purpose of pharmacometrics is to provide information relevant to the search for efficacy and safety improvements in pharmacotherapy. Regulatory agencies have adopted pharmacometrics analysis to justify their regulatory decisions, making those decisions more efficient. Demand for specialists trained in the field is therefore growing. In this review, we describe the meaning, history, and development of pharmacometrics, analyzing the challenges faced in the training of professionals. Examples of applications in current use, perspectives for the future, and the importance of pharmacometrics for the development and growth of precision pharmacology are also presented.

Role of Disease Progression Models in Drug Development

The use of Disease progression models (DPMs) in Drug Development has been widely adopted across therapeutic areas as a method for integrating previously obtained disease knowledge to elucidate the impact of novel therapeutics or vaccines on disease course, thus quantifying the potential clinical benefit at different stages of drug development programs. This paper provides a brief overview of DPMs and the evolution in data types, analytic methods, and applications that have occurred in their use by Quantitive Clinical Pharmacologists. It also provides examples of how these models have informed decisions and clinical trial design across several therapeutic areas and at various stages of development. It briefly describes potential new applications of DPMs utilizing emerging data sources, and utilizing new analytic techniques, and discuss new challenges faced such as requiring description of multiple endpoints, rapid model development, application of machine learning-based analytics, and use of high dimensional and real-world data. Considerations for the continued evolution future of DPMs to serve as community-maintained expert systems are also provided.

Conditional distribution modeling as an alternative method for covariates simulation: Comparison with joint multivariate normal and bootstrap techniques

Clinical trial simulation (CTS) is a valuable tool in drug development. To obtain realistic scenarios, the subjects included in the CTS must be representative of the target population. Common ways of generating virtual subjects are based upon bootstrap (BS) procedures or multivariate normal distributions (MVNDs). Here, we investigated the performance of an alternative method based on conditional distributions (CDs). Covariate data from a hypertension drug development program were used. The methods were evaluated based on the original data set (internal evaluation) and on their ability to reproduce an older, unobserved population (extrapolation). Similar results were obtained in the internal evaluation for summary statistics, yet BS was able to preserve the correlation structure of the empirical distribution, which was not adequately reproduced by MVND; CD was in between BS and MVND. BS does not allow to extrapolate to an unobserved population. When the data set used to inform the extrapolation was well approximated by an MVND, the results from CD and MVND were comparable. However, improved extrapolation performance was observed for CD when deviations from normality assumptions occurred. If CTS is used to simulate within the observed distribution, BS is the preferred method. When extrapolating to new populations, a parametric method like CD/MVND is needed. In case the empirical multivariate distribution is characterized by linearly related covariates and unimodal marginal distributions, MVND can be used because of the simpler statistical framework and well‐established use; however, if uncertainty about the MVND assumptions exists, CD will increase the confidence in the simulations compared to MVND.

Mathematical Multidimensional Modelling and Structural Artificial Intelligence Pipelines Provide Insights for the Designing of Highly Specific AntiSARS-CoV2 Agents

COVID19 is the most impactful pandemic of recent times worldwide. It is a highly infectious disease that is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 virus), To date there is specific drug nor vaccination against COVID19. Therefor the need for novel and pioneering anti-COVID19 is of paramount importance. In this direction, computer-aided drug design constitutes a very promising antiviral approach for the discovery and analysis of drugs and molecules with biological activity against SARS-CoV2. In silico modelling takes advantage of the massive amounts of biological and chemical data available on the nature of the interactions between the targeted systems and molecules, as well as the rapid progress of computational tools and software. Herein, we describe the potential of the merging of mathematical modelling, artificial intelligence and learning techniques into seamless computational pipelines for the rapid and efficient discovery and design of potent anti- SARS-CoV-2 modulators.

Concept and utility of population pharmacokinetic and pharmacokinetic/pharmacodynamic models in drug development and clinical practice

Due to frequent clinical trial failures and consequently fewer new drug approvals, the need for improvement in drug development has, to a certain extent, been met using model-based drug development. Pharmacometrics is a part of pharmacology that quantifies drug behaviour, treatment response and disease progression based on different models (pharmacokinetic - PK, pharmacodynamic - PD, PK/PD models, etc.) and simulations. Regulatory bodies (European Medicines Agency, Food and Drug Administration) encourage the use of modelling and simulations to facilitate decision-making throughout all drug development phases. Moreover, the identification of factors that contribute to variability provides a basis for dose individualisation in routine clinical practice. This review summarises current knowledge regarding the application of pharmacometrics in drug development and clinical practice with emphasis on the population modelling approach.

Incorporating Pharmacometrics into Pharmacoeconomic Models: Applications from Drug Development

Pharmacometrics is the science of quantifying the relationship between the pharmacokinetics and pharmacodynamics of drugs in combination with disease models and trial information to aid in drug development and dosing optimization for clinical practice. Considering the variability in the dose-concentration-effect relationship of drugs, an opportunity exists in linking pharmacokinetic and pharmacodynamic model-based estimates with pharmacoeconomic models. This link may provide early estimates of the cost effectiveness of drug therapies, thus informing late-stage drug development, pricing, and reimbursement decisions. Published case studies have demonstrated how integrated pharmacokinetic-pharmacodynamic-pharmacoeconomic models can complement traditional pharmacoeconomic analyses by identifying the impact of specific patient sub-groups, dose, dosing schedules, and adherence on the cost effectiveness of drugs, thus providing a mechanistic basis to predict the economic value of new drugs. Greater collaboration between the pharmacoeconomics and pharmacometrics community can enable methodological improvements in pharmacokinetic-pharmacodynamic-pharmacoeconomic models to support drug development.

A disease progression model of longitudinal lung function decline in idiopathic pulmonary fibrosis patients

Pirfenidone and nintedanib are the first two FDA-approved therapies for treatment of idiopathic pulmonary fibrosis (IPF). The clinical programs for pirfenidone and nintedanib included 1132 patients in the placebo arms and 1691 patients in the treatment arms across 6 trials. We developed a disease progression model to characterize the observed variability in lung function decline, measured as percent predicted forced vital capacity (%p-FVC), and its decrease in decline after treatment. The non-linear longitudinal change in %p-FVC was best described by a Weibull function. The median decreased decline in %p-FVC after treatment was estimated to be 1.50% (95% CI [1.12, 1.79]) and 1.96% (95% CI [1.47, 2.36]) at week 26 and week 52, respectively. Smoking status, weight, %p-FVC, %p-DLco and oxygen use at baseline were identified as significant covariates affecting decline in %p-FVC. The decreased decline in %p-FVC were observed among all subgroups of interest, of which the effects were larger at 1 year compared to 6 months. Based on the disease progression model smoking status and oxygen use at baseline may affect the treatment effect size. At week 52, the decreased decline in %p-FVC for current smokers and patients with oxygen use at baseline were 1.56 (90% CI [1.02, 1.99]) and 2.32 (90% CI [1.74, 2.86]), respectively. These prognostic factors may be used to enrich studies with patients who are more likely to respond to treatment, by demonstrating a lesser decline in lung function, and therefore provide the potential to allow for IPF studies with smaller study populations or shorter durations.

An Innovative Disease-Drug-Trial Framework to Guide Binge Eating Disorder Drug Development: A Case Study for Topiramate

As with other psychiatric disorders, development of drugs to treat binge-eating disorder (BED) has been hampered by high placebo response and dropout rates in randomized controlled trials (RCTs). Although not approved for use in BED, several RCTs have suggested that topiramate is efficacious for BED in obese individuals. Using data from a positive investigator-initiated RCT of topiramate in 61 obese individuals with BED, the objective of the present study is (i) to develop a quantitative disease-drug-trial framework to inform future BED clinical trial designs, and (ii) to determine the optimal topiramate dose to achieve therapeutic efficacy. Disease-drug-trial models were developed separately for the two efficacy measures, namely, longitudinal normalized weekly binge-eating episode frequency (BEF) and binge day frequency (BDF). Model building consisted of (i) developing a placebo effect model that describes response from the placebo group, (ii) adding a drug effect to the placebo model to describe dose-response relationships, and (iii) developing a parametric time to event model to characterize patient dropout patterns. The placebo effect on normalized BEF and BDF over time demonstrated a maximum decrease of ~ 57% by 5 weeks. Participants had a higher dropout probability if no weight loss occurred during the trial period. The identified dose-response relationship demonstrated a daily dose of 125 mg was needed to exhibit a marked reduction in weekly BEF. The developed comprehensive disease-drug-trial model will be utilized to simulate different clinical trial designs to increase the success for future BED drug development programs.

Cancer Clinical Investigators Should Converge with Pharmacometricians

The applied quantitative science pharmacometrics has significantly enhanced cancer therapeutics development. Pharmacometrics is now improving our understanding of complex diagnostics. Through the concept of convergence and methods of quantitative and systems pharmacology, pharmacometrics is poised to interconnect mathematical models of disease and therapy to advance cancer care.See related article by Colomban et al., p. 5342.

Emerging Role of Organ-on-a-Chip Technologies in Quantitative Clinical Pharmacology Evaluation

The recently enacted Prescription Drug User Fee Act (PDUFA) VI includes in its performance goals "enhancing regulatory science and expediting drug development." The key elements in "enhancing regulatory decision tools to support drug development and review" include "advancing model-informed drug development (MIDD)." This paper describes (i) the US Food and Drug Administration (FDA) Office of Clinical Pharmacology's continuing efforts in developing quantitative clinical pharmacology models (disease, drug, and clinical trial models) to advance MIDD, (ii) how emerging novel tools, such as organ-on-a-chip technologies or microphysiological systems, can provide new insights into physiology and disease mechanisms, biomarker identification and evaluation, and elucidation of mechanisms of adverse drug reactions, and (iii) how the single organ or linked organ microphysiological systems can provide critical system parameters for improved physiologically-based pharmacokinetic and pharmacodynamic evaluations. Continuous public-private partnerships are critical to advance this field and in the application of these new technologies in drug development and regulatory review.

Modelling and simulation of edoxaban exposure and response relationships in patients with atrial fibrillation

Edoxaban is a novel, orally available, highly specific direct inhibitor of factor Xa and is currently being developed for the treatment and prevention of venous thromboembolism and prevention of stroke and systemic embolism in patients with non-valvular atrial fibrillation (NVAF). The objectives of the present analyses were to characterise edoxaban population pharmacokinetics (PPK) and identify potential intrinsic and extrinsic factors affecting variability in edoxaban exposure, determine if there are relationships between edoxaban pharmacokinetics or biomarkers and the risk of bleeding in patients with NVAF using an exposure-response model, and to use the PPK and exposure-response model to support dose selection for a phase III trial of edoxaban in patients with NVAF. PPK analysis of data from 1,281 edoxaban-dosed subjects with intrinsic factors such as renal impairment or NVAF and extrinsic factors such as concomitant medications revealed significant effects of renal impairment and concomitant strong P-glycoprotein (P-gp) inhibitors on the pharmacokinetics of edoxaban. Exposure-response analysis found that in patients with NVAF, the incidence of bleeding events increased significantly with increasing edoxaban exposure, with steady-state minimum concentration (Cmin,ss) showing the strongest association. Clinical trial simulations of bleeding incidence were used to select 30 mg and 60 mg once-daily edoxaban with 50% dose reductions for patients with moderate renal impairment or receiving concomitant strong P-gp inhibitors as the treatment regimens in the ENGAGE AF-TIMI 48 (NCT00781391) trial.

The results of this study were previously presented at the 2009 International Society on Thrombosis and Haemostasis, July 2009, Boston, Massachusetts, USA.

Exposure-response analysis after subcutaneous administration of RBP-7000, a once-a-month long-acting Atrigel formulation of risperidone

AIMS

A new, long-acting, subcutaneous (SC) formulation of risperidone (RBP-7000) has been developed for the treatment of schizophrenia to address issues of non-adherence associated with oral risperidone treatment. The objective of this work was to establish an exposure-response relationship between total active moiety (AM) plasma exposure (risperidone + 9-hydroxy-risperidone) and Positive and Negative Syndrome Scale (PANSS) or Clinical Global Impression severity (CGI-S) scores using data from a registration trial.

METHODS

This was a Phase 3 randomized, double-blind, placebo-controlled, multicenter study in 354 patients to evaluate the efficacy, safety and tolerability of RBP-7000 (90 mg and 120 mg). Non-linear mixed effects modelling was used to develop an integrated population pharmacokinetic/pharmacodynamic (PK/PD) model that included a joint PK model for risperidone and 9-hydroxy-risperidone with placebo and drug-effect models to establish the relation between total AM exposure and PANSS or CGI-S scores.

RESULTS

CYP2D6 poor and intermediate metabolizers had lower formation rates of 9-hydroxy-risperidone (94% and 76% lower, respectively) compared to the extensive CYP2D6 metabolizers. The maximum placebo-corrected relative decrease in PANSS score from baseline following RBP-7000 treatment was 5.4%, half of which could be achieved at plasma concentrations of 4.6 ng ml^-1 of the total AM. A proportional odds model for the CGI-S score related the total AM plasma concentration to the probability of improving/worsening scores over time.

CONCLUSIONS

Exposure-response analysis was established between total AM concentrations and PANSS and CGI-S scores, with good precision in parameter estimates. CYP2D6 phenotype on risperidone metabolism was the only identified covariate.

Impact of disease, drug and patient adherence on the effectiveness of antiviral therapy in pediatric HIV

INTRODUCTION

Maintaining effective antiretroviral treatment for life is a major problem in both resource-limited and resource-rich countries. Despite the progress observed in paediatric antiretroviral therapy, approximately 12% of children still experience treatment failure due to drug resistance, inadequate dosing and poor adherence. We explore the current status of antiretroviral therapy in children with focus on the interaction between disease, drug pharmacokinetics and patient behavior, all of which are strongly interconnected and determine treatment outcome. Areas covered: An overview is provided of the viral characteristics and available drug combinations aimed at the prevention of resistance. In this context, the role of patient adherence is scrutinized. A detailed assessment of factors affecting adherence is presented together with the main strategies to enhance treatment response in children. Expert opinion: Using modeling and simulation, a framework for characterizing the forgiveness of non-adherence for specific antiretroviral drugs in children is proposed in which information on pharmacokinetics, pharmacokinetic-pharmacodynamic relationships and viral dynamics is integrated. This approach represents an opportunity for the simplification of dosing regimens taking into account the interaction between these factors. Based on clinical trial simulation scenarios, we envisage the possibility of assessing the impact of variable adherence to antiretroviral drug combinations in HIV-infected children.

Perspectives in regulatory science: translational and clinical pharmacology

This paper focuses on the role of clinical and translational pharmacology in the drug development and the regulatory process. Contemporary regulatory issues faced by FDA's Office of Clinical Pharmacology (OCP) in fulfilling its mission to advance the science of drug response and translate patient diversity into optimal drug therapy are discussed. Specifically current focus of the following key aspects of the drug development and regulatory science processes are discussed: the OCP vision and mission, two key OCP initiatives (i.e. guidance modernization, labeling and health communications), and translational and clinical pharmacology related regulatory science issues in (i.e. uncertainty, breakthrough therapies, individualization).

Disease Progression Modeling: Key Concepts and Recent Developments

Disease modeling involves the use of mathematical functions to describe quantitatively the time course of disease progression. In order to characterize the natural progression of disease, these models generally incorporate longitudinal data for some biomarker(s) of disease severity or can incorporate more direct measures of disease severity. Disease models are also often linked to pharmacokinetic-pharmacodynamic models so that the influence of drug treatment on disease progression can be quantified and evaluated. Regulatory agencies have embraced disease progression models as powerful tools that can be used to improve drug development productivity. This article provides a brief overview of key concepts in disease progression modeling followed by illustrative examples from models for Alzheimer's disease. Finally, recent novel applications in which disease progression models have been linked to cost-effectiveness analysis and genomic analysis are described.

Response Surface Analysis and Nonlinear Optimization Algorithm for Maximization of Clinical Drug Performance: Application to Extended-Release and Long-Acting Injectable Paliperidone

Model-based approach is recognized as a tool to make drug development more productive and to better support regulatory and therapeutic decisions. The objective of this study was to develop a novel model-based methodology based on the response surface analysis and a nonlinear optimizer algorithm to maximize the clinical performances of drug treatments. The treatment response was described using a drug-disease model accounting for multiple components such as the dosage regimen, the pharmacokinetic characteristics of a drug (including the mechanism and the rate of drug delivery), and the exposure-response relationship. Then, the clinical benefit of a treatment was defined as a function of the diseases and the clinical endpoints and was estimated as a function of the target pharmacodynamic endpoints used to evaluate the treatment effect. A case study is presented to illustrate how the treatment performances of paliperidone extended release (ER) and paliperidone long-acting injectable (LAI) can be improved. A convolution-based approach was used to characterize the pharmacokinetics of ER and LAI paliperidone. The drug delivery properties and the dosage regimen maximizing the clinical benefit (defined as the target level of D2 receptor occupancy) were estimated using a nonlinear optimizer. The results of the analysis indicated that a substantial improvement in clinical benefit (from 15% to 27% for the optimization of the in vivo release and from ∼30% to ∼70% for the optimization of dosage regimen) was obtained when optimal strategies were deployed either for optimizing the in vivo drug delivery properties of ER formulations or for optimizing the dosage regimen of LAI formulations.

Pharmacology-based toxicity assessment: towards quantitative risk prediction in humans

Despite ongoing efforts to better understand the mechanisms underlying safety and toxicity, ~30% of the attrition in drug discovery and development is still due to safety concerns. Changes in current practice regarding the assessment of safety and toxicity are required to reduce late stage attrition and enable effective development of novel medicines. This review focuses on the implications of empirical evidence generation for the evaluation of safety and toxicity during drug development. A shift in paradigm is needed to (i) ensure that pharmacological concepts are incorporated into the evaluation of safety and toxicity; (ii) facilitate the integration of historical evidence and thereby the translation of findings across species as well as between in vitro and in vivo experiments and (iii) promote the use of experimental protocols tailored to address specific safety and toxicity questions. Based on historical examples, we highlight the challenges for the early characterisation of the safety profile of a new molecule and discuss how model-based methodologies can be applied for the design and analysis of experimental protocols. Issues relative to the scientific rationale are categorised and presented as a hierarchical tree describing the decision-making process. Focus is given to four different areas, namely, optimisation, translation, analytical construct and decision criteria. From a methodological perspective, the relevance of quantitative methods for estimation and extrapolation of risk from toxicology and safety pharmacology experimental protocols, such as points of departure and potency, is discussed in light of advancements in population and Bayesian modelling techniques (e.g. non-linear mixed effects modelling). Their use in the evaluation of pharmacokinetics (PK) and pharmacokinetic-pharmacodynamic relationships (PKPD) has enabled great insight into the dose rationale for medicines in humans, both in terms of efficacy and adverse events. Comparable benefits can be anticipated for the assessment of safety and toxicity profile of novel molecules.

Recent developments in using mechanistic cardiac modelling for drug safety evaluation

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Modelling and simulation can streamline decision making in drug safety testing.

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Computational cardiac electrophysiology is a mature technology with a long heritage.

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There are many challenges and opportunities in using in silico techniques in future.

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We discuss how models can be used at different stages of drug discovery.

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CiPA will combine screening platforms, human cell assays and in silico predictions.

On the tenth anniversary of two key International Conference on Harmonisation (ICH) guidelines relating to cardiac proarrhythmic safety, an initiative aims to consider the implementation of a new paradigm that combines in vitro and in silico technologies to improve risk assessment. The Comprehensive In Vitro Proarrhythmia Assay (CiPA) initiative (co-sponsored by the Cardiac Safety Research Consortium, Health and Environmental Sciences Institute, Safety Pharmacology Society and FDA) is a bold and welcome step in using computational tools for regulatory decision making. This review compares and contrasts the state-of-the-art tools from empirical to mechanistic models of cardiac electrophysiology, and how they can and should be used in combination with experimental tests for compound decision making.

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

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

Assessment of hemoglobin responsiveness to epoetin alfa in patients on hemodialysis using a population pharmacokinetic pharmacodynamic model

A population pharmacokinetic pharmacodynamic (PK/PD) model describing the effect of epoetin alfa on hemoglobin (Hb) response in hemodialysis patients was developed. Epoetin alfa pharmacokinetics was described using a linear 2-compartment model. PK parameter estimates were similar to previously reported values. A maturation-structured cytokinetic model consisting of 5 compartments linked in a catenary fashion by first-order cell transfer rates following a zero-order input process described the Hb time course. The PD model described 2 subpopulations, one whose Hb response reflected epoetin alfa dosing and a second whose response was unrelated to epoetin alfa dosing. Parameter estimates from the PK/PD model were physiologically reasonable and consistent with published reports. Numerical and visual predictive checks using data from 2 studies were performed. The PK and PD of epoetin alfa were well described by the model.

Molecular mechanism matters: Benefits of mechanistic computational models for drug development

Making drug development a more efficient and cost-effective process will have a transformative effect on human health. A key, yet underutilized, tool to aid in this transformation is mechanistic computational modeling. By incorporating decades of hard-won prior knowledge of molecular interactions, cellular signaling, and cellular behavior, mechanistic models can achieve a level of predictiveness that is not feasible using solely empirical characterization of drug pharmacodynamics. These models can integrate diverse types of data from cell culture and animal experiments, including high-throughput systems biology experiments, and translate the results into the context of human disease. This provides a framework for identification of new drug targets, measurable biomarkers for drug action in target tissues, and patient populations for which a drug is likely to be effective or ineffective. Additionally, mechanistic models are valuable in virtual screening of new therapeutic strategies, such as gene or cell therapy and tissue regeneration, identifying the key requirements for these approaches to succeed in a heterogeneous patient population. These capabilities, which are distinct from and complementary to those of existing drug development strategies, demonstrate the opportunity to improve success rates in the drug development pipeline through the use of mechanistic computational models.

Generating Virtual Patients by Multivariate and Discrete Re-Sampling Techniques

Purpose

Clinical Trial Simulations (CTS) are a valuable tool for decision-making during drug development. However, to obtain realistic simulation scenarios, the patients included in the CTS must be representative of the target population. This is particularly important when covariate effects exist that may affect the outcome of a trial. The objective of our investigation was to evaluate and compare CTS results using re-sampling from a population pool and multivariate distributions to simulate patient covariates.

Methods

COPD was selected as paradigm disease for the purposes of our analysis, FEV1 was used as response measure and the effects of a hypothetical intervention were evaluated in different populations in order to assess the predictive performance of the two methods.

Results

Our results show that the multivariate distribution method produces realistic covariate correlations, comparable to the real population. Moreover, it allows simulation of patient characteristics beyond the limits of inclusion and exclusion criteria in historical protocols.

Conclusion

Both methods, discrete resampling and multivariate distribution generate realistic pools of virtual patients. However the use of a multivariate distribution enable more flexible simulation scenarios since it is not necessarily bound to the existing covariate combinations in the available clinical data sets.

Electronic supplementary material

The online version of this article (doi:10.1007/s11095-015-1699-x) contains supplementary material, which is available to authorized users.

Development of a unified clinical trial database for Alzheimer's disease

INTRODUCTION

Data obtained in completed Alzheimer's disease (AD) clinical trials can inform decision making for future trials. Recognizing the importance of sharing these data, the Coalition Against Major Diseases created an Online Data Repository for AD (CODR-AD) with the aim of supporting accelerated drug development. The aim of this study was to build an open access, standardized database from control arm data collected across many clinical trials.

METHODS

Comprehensive AD-specific data standards were developed to enable the pooling of data from different sources. Nine member organizations contributed patient-level data from 24 clinical trials of AD treatments.

RESULTS

CODR-AD consists of control arm pooled and standardized data from 24 trials currently numbered at 6500 subjects; Alzheimer's Disease Assessment Scale-cognitive subscale 11 is the main outcome and specific covariates are also included.

DISCUSSION

CODR-AD represents a unique integrated standardized clinical trials database available to qualified researchers. The pooling of data across studies facilitates a more comprehensive understanding of disease heterogeneity.

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.

Characterization of an in vivo concentration-effect relationship for piperaquine in malaria chemoprevention

A randomized, placebo-controlled trial conducted on the northwest border of Thailand compared malaria chemoprevention with monthly or bimonthly standard 3-day treatment regimens of dihydroartemisinin-piperaquine. Healthy adult male subjects (N = 1000) were followed weekly during 9 months of treatment. Using nonlinear mixed-effects modeling, the concentration-effect relationship for the malaria-preventive effect of piperaquine was best characterized with a sigmoidal Emax relationship, where plasma concentrations of 6.7 ng/ml [relative standard error (RSE), 23%] and 20 ng/ml were found to reduce the hazard of acquiring a malaria infection by 50% [that is, median inhibitory concentration (IC50)] and 95% (IC95), respectively. Simulations of monthly dosing, based on the final model and published pharmacokinetic data, suggested that the incidence of malaria infections over 1 year could be reduced by 70% with a recently suggested dosing regimen compared to the current manufacturer's recommendations for small children (8 to 12 kg). This model provides a rational framework for piperaquine dose optimization in different patient groups.

An updated Alzheimer's disease progression model: incorporating non-linearity, beta regression, and a third-level random effect in NONMEM

Our objective was to expand our understanding of the predictors of Alzheimer's disease (AD) progression to help design a clinical trial on a novel AD medication. We utilized the Coalition Against Major Diseases AD dataset consisting of control-arm data (both placebo and stable background AD medication) from 15 randomized double-blind clinical trials in mild-to-moderate AD patients (4,495 patients; July 2013). Our ADAS-cog longitudinal model incorporates a beta-regression with between-study, -subject, and -residual variability in NONMEM; it suggests that faster AD progression is associated with younger age and higher number of apolipoprotein E type 4 alleles (APOE*4), after accounting for baseline disease severity. APOE*4, in particular, seems to be implicated in the AD pathogenesis. In addition, patients who are already on stable background AD medications appear to have a faster progression relative to those who are not receiving AD medication. The current knowledge does not support a causality relationship between use of background AD medications and higher rate of disease progression, and the correlation is potentially due to confounding covariates. Although causality has not necessarily been demonstrated, this model can inform inclusion criteria and stratification, sample size, and trial duration.

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.

Challenges in conducting clinical trials in children: approaches for improving performance

Recent legislative changes in both Europe and the USA have increased the responsibility of drug developers to purposefully study the agents they market in children so that specific dosing recommendations can be made to assist clinicians in their use. Typically, clinicians use empiricalor experiential-based rationales for selecting the dose to use in children, generally in a manner that attempts to achieve the same dose-exposure or pharmacokinetic profile in children as in adults. However, whether this approach achieves the necessary dose exposure or exposure effect needed may not be systematically explored during off-label use. This creates the opportunity for under- or over-exposure in children, particularly in very young children (i.e., less than 2 years old) where a combination of factors during development can effect both pharmacokinetics and pharmacodynamics. The ethical, physiological and statistical differences of studying new therapeutic agents in children present economic challenges that may create unintended incentives - both positive and negative - for any individual developer who tries to meet the requirements of new legislation to study pharmaceutical agents in children. There should be a continued emphasis in academic clinical pharmacology programs towards creative methods and approaches to better understand these differences in children compared with adults. The ability to use information from knowledge obtained from adult studies, from preclinical studies, from studies of compounds with similar chemistry or pharmacology, or from known physiological differences between children and adults is essential to choosing a suitable dose for children and achieving these regulatory aims.