Model-based Drug Development Research Articles

Model-Based Drug Development: A Rational Approach to Efficiently Accelerate Drug Development

The pharmaceutical industry continues to face significant challenges. Very few compounds that enter development reach the marketplace, and the investment required for each success can surpass $1.8 billion. Despite attempts to improve efficiency and increase productivity, total investment continues to rise whereas the output of new medicines declines. With costs increasing exponentially through each development phase, it is failure in phase II and phase III that is most wasteful. In today's development paradigm, late-stage failure is principally a result of insufficient efficacy. This is manifested as either a failure to differentiate sufficiently from placebo (shown for both novel and precedented mechanisms) or a failure to demonstrate sufficient differentiation from existing compounds. Set in this context, this article will discuss the role model-based drug development (MBDD) approaches can and do play in accelerating and optimizing compound development strategies through a series of illustrative examples.

Drug and Disease Model Resources: A Consortium to Create Standards and Tools to Enhance Model‐Based Drug Development

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

Model-Based Drug Development in Oncology: What’s Next?

Model-based estimates of tumor growth inhibition (TGI) metrics have the potential to enhance learning in early (phase II) clinical studies. They can be used as end points and biomarkers to predict treatment effect on clinical outcome measures-e.g., overall survival (OS)-and support phase II study design, end-of-phase II decisions, and phase III planning and execution. Efforts should be made to assess models in simulating independent studies with treatments of varying mechanisms of action.

Pharmacometrics: a quantitative tool of pharmacological research

Pharmacometrics is an interdisciplinary science with tremendous potential to influence decision making through the construction of mathematical and statistical models combined with graphical methods that define, challenge, and resolve queries surrounding the biological processes of a drug. In 2004, FDA published the white paper the Challenge and Opportunity on the Critical Path to New Products, which advocates the model-based drug development (MBDD). Since then, pharmacometric analysis has become an increasingly important component of New Drug Application (NDA) and Biological License Application (BLA) submitted to FDA for drug approval, labeling and trial design decisions1,2. The academic clinical researchers are likewise confronted with the need to use data from various sources (experimental biology, preclinical models of disease, etc) in order to organize translational clinical studies in a therapeutic area. The administrators are faced with bringing together a variety of data (preclinical and clinical data, literatures, and other regulatory submissions of similar therapies) in order to make regulatory decisions. A great deal has been published in the literatures on quantitative research, especially in the field of clinical pharmacology. Therefore, wherever in industry, academia, or regulatory agencies, pharmacometrics is at the center of the translational medicine paradigm3,4. It is difficult to imagine a more efficient, powerful, and informative drug development and evaluation process without comprehensive pharmacometric studies. China is growing to be not only one of the biggest pharmaceutical markets in the world, but potentially one of most productive countries in R&D of new drugs as well. since 1979, the Professional Committee of Mathematical Pharmacology (PCMP) led by the Chinese Society of Pharmacology has been devoting to promote research, education and training of Chinese scientists in academia, industry and regulatory agencies with regard to the use of PK/PD modeling to help drug development and to optimize drug application in clinical therapies5,6. The papers organized by PCMP in this Special Issue of Acta Pharmacologica Sinica are focused on phase I trial design7, phase I PK study8,9, physiologically based PK modelling (PBPK)10, PK/PD linking11,12,13,14, population approach15,16,17, dose finding18, and stochastic simulation19. We hope that these papers serve as an introduction for the readers in academia, regulatory authorities and pharmaceutical industry to understand the role of pharmacometrics in drug development and approval processes, which will improve the success rate in drug development, and the beneficiaries will ultimately be the patients in need.

Limitations of model based dose selection for indacaterol in patients with chronic obstructive pulmonary disease

Indacaterol is a long-acting β-agonist (LABA) approved by FDA in 2011 at a dose of 75 μg once daily for the treatment of chronic obstructive pulmonary disease (COPD). During the review process for indacaterol approval, data were reanalyzed by FDA to evaluate the validity of the model based conclusions regarding dose selection. The same dose response model applied by the sponsor was used to analyze a subset of the original data. Model predictions were compared with observed data to evaluate the model. Subgroups were created to visualize the relationship between key model parameters and covariates. The Emax model structure was evaluated for a meta-analysis. Patient-level analyses showed that the model based claim of additional benefit of 150 μg over 75 μg for more severe patients is not supported by the data. Mis-specified covariate model structures for key parameters contributed to this inconsistency. The assumed Emax model structure is not supported by the study-level data and the study-level analysis overestimates the incremental difference between two adjacent doses. Even though model based drug development is highly desirable, thorough model evaluation and justification is necessary to ensure the validity of related decisions.

In Vivo-In Vitro-In Silico Pharmacokinetic Modelling in Drug Development

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

A Semiphysiological Population Model for Prediction of the Pharmacokinetics of Drugs under Liver and Renal Disease Conditions

The application of model-based drug development in special populations becomes increasingly important for clinical trial optimization, mostly by providing a rationale for dose selection and thereby aiding risk-benefit assessment. In this article, a semiphysiological approach is presented, enabling the extrapolation of the pharmacokinetics from healthy subjects to patients with different disease conditions. This semiphysiological approach was applied to solifenacin, using clinical data on total and free plasma and urine concentrations in healthy subjects. The analysis was performed using nonlinear mixed-effects modeling and relied on the use of a general partitioning framework to account for binding to plasma proteins and to nonplasma tissues together with principles from physiology that apply to the main pharmacokinetic process, i.e., bioavailability, distribution, and elimination. Application of these physiology principles allowed quantification of the impact of key physiological parameters (i.e., body composition, glomerular function, liver enzyme capacity, and liver blood flow) on the pharmacokinetics of solifenacin. The prediction of the time course of the drug concentration in liver- and renal-impaired patients only required adjustment of the physiological parameters that are known to change upon liver and renal dysfunction without modifying the pharmacokinetic model structure and/or its respective parameter estimates. Visual predictive checks showed that the approach applied was able to adequately predict the pharmacokinetics of solifenacin in liver- and renal-impaired patients. In addition, better insight into the pharmacokinetic properties of solifenacin was obtained. In conclusion, the proposed semiphysiological approach is attractive for prediction of altered pharmacokinetics of compounds influenced by liver and renal disease conditions.

Pharmacodynamic Biomarkers in Model-Based Drug Development in Oncology

Model-based drug development (MBDD) is recognized as an initiative able to improve success rates in the development of new anti-cancer agents. The use of pharmacodynamic (PD) biomarkers may be valuable in this context. The implementation of biomarkers in MBDD in oncology is the subject of this review. Literature was searched for articles and relevant conference abstract concerning application of biomarkers in MBDD in oncology. First, papers are discussed concerning the use of biomarkers in modeling and simulation analyses in preclinical and early clinical phases of drug development. Subsequently, articles concerning late-stage clinical drug development are discussed. Only a limited set of articles and conference presentations were identified. As expected, the majority of publications are concerned with targeted anti-cancer drugs. In the early development of novel anti-cancer agents, most publications are concerned to the evaluation of dosing regimens for further clinical evaluation, or the identification of the required levels of target modulation. In general, combined analysis of clinical and preclinical data provide the most informative analyses. The use of biomarkers in late-stage drug development has mainly been confined to the prediction of phase III outcome on the basis of tumor growth data obtained from phase II trials, with tumor growth as biomarker for outcome. The use of suitable biomarkers in MBDD, has shown its merits in oncology, especially in early clinical development. Considering the low number of reports in literature, we would propose a more active use of presented techniques during all developmental phases of new anticancer agents.

日本における MBDD の現状の問題点と今後の課題

日本における MBDD の現状の問題点と今後の課題

Quantitative clinical pharmacology is transforming drug regulation

Prior to 1970s, development and regulation of new drugs was devoid of a fully quantitative, pathophysiological conceptual foundation. Malcolm Rowland pioneered, in collaboration with colleagues and friends, our modern understanding of drug clearance concepts, and equipped drug development and regulatory scientists with key investigative tools such as physiologically-based pharmacokinetic (PBPK) modeling, standardized approaches to characterizing drug metabolism, and microdosing. From the 1970s to the present, Malcolm Rowland has contributed to key advances in pharmacokinetics that have had transformational impacts on drug regulatory science. These advances include concepts that have led to the fundamental understanding that mechanistically derived, quantitative variations in drug concentrations, rather than assigned dosage alone, drive pharmacodynamic effects (PKPD)-including disease biomarkers and clinical outcomes. This body of knowledge has transformed drug development and regulatory science theory and practice from naïve empiricism to a mechanism/model-based, quantitative scientific discipline. As a result, it is now possible to incorporate pre-clinical in vitro data on drug physico-chemical properties, metabolizing enzymes, transporters and permeability properties into PBPK-based simulations of expected PK distributions and drug-drug interactions in human populations. The most comprehensive application of PK-PD is in the modeling and simulation of clinical trials in the context of model-based drug development and regulation, imbedded in the "learn-confirm paradigm". Regulatory agencies have embraced these advances and incorporated them into regulatory requirements, approval acceleration pathways and regulatory decisions. These developments are reviewed here, with emphasis on key contributions of Malcolm Rowland that facilitated this transformation.

Probabilistic Risk Analysis: Improving Early Drug Development Decision Making

Model-based drug development (MBDD) is advocated by industry, academia, and regulatory agencies as a powerful tool to improve the efficiency of drug development. Clinical pharmacologists play key roles in implementing MBDD because of their ability to integrate mechanistic, preclinical, and clinical information, using quantitative approaches to address the complex questions arising during drug development. Use of probabilistic risk analysis (PRA) approaches (e.g., value of information, combining uncertain information, and modeling expert opinion) can improve the implementation of MBDD.

Fostering Culture and Optimizing Organizational Structure for Implementing Model‐Based Drug Development

Model-based drug development (MBDD) is a promising approach to improve decision making in drug development. The pharmaceutical industry has made substantial progress from engaging in empirical decision making to increasingly using pharmacometrics (ie, modeling and simulation [M&S]) as a quantitative decision-making tool. Focusing on culture and an organizational structure perspective, this commentary summarizes experiences and vision from industry M&S leaders on implementing MBDD. A culture for MBDD needs to have wide acceptance of MBDD, enhanced decision making with probability-based evidence and transparent rationale, quantitative impact metrics, and a brand that emphasizes cross-disciplinary collaboration and ownership. An organizational structure for MBDD needs to have a dedicated pharmacometrics function, fine balance between quick wins and impact on long-term R&D goals, and collaborative MBDD teams among clinical pharmacologists, statisticians, pharmacometricians, and clinicians. Pharmaceutical companies with these characteristics are prepared to fully embrace and implement MBDD.

Model‐Based Drug Development: Strengths, Weaknesses, Opportunities, and Threats for Broad Application of Pharmacometrics in Drug Development

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

Model‐Based Drug Development Survey Finds Pharmacometrics Impacting Decision Making in the Pharmaceutical Industry

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

Improving Productivity With Model-Based Drug Development: An Enterprise Perspective

The productivity of the pharma enterprise has fallen, as evidenced by a decline in regulatory submissions and less commercial success. Two major impediments to better productivity are an empirical decision-making process and barriers between functional areas. Better integration of the functional areas is a critical missing element. We contend that this integration requires a shift to model-based drug development (MBDD), effective integrated project teams (IPTs), and a structured, disciplined, and continual evaluation of the probability that a new drug will achieve clinical and commercial success. Clinical Pharmacology & Therapeutics (2010) 88 2, 263–268. doi: 10.1038/clpt.2010.117

Next-Generation Model-Based Drug Discovery and Development: Quantitative and Systems Pharmacology

Pharmaceutical researchers have undertaken many initiatives and technologies to stem the rising costs of drug discovery and development. Biomarkers, adaptive trial designs, modeling, trial simulations, predictive metabolism, data mining, and disease models have reshaped the way in which researchers approach discovery and development. Quantitative pharmacology (QP), which leverages model-based approaches, operates at both cultural and technical levels to integrate data and scientific disciplines so as to utilize existing knowledge while concomitantly enhancing the ability to make predictions about future experiments and results.

Non-compartment model to compartment model pharmacokinetics transformation meta-analysis – a multivariate nonlinear mixed model

BackgroundTo fulfill the model based drug development, the very first step is usually a model establishment from published literatures. Pharmacokinetics model is the central piece of model based drug development. This paper proposed an important approach to transform published non-compartment model pharmacokinetics (PK) parameters into compartment model PK parameters. This meta-analysis was performed with a multivariate nonlinear mixed model. A conditional first-order linearization approach was developed for statistical estimation and inference.ResultsUsing MDZ as an example, we showed that this approach successfully transformed 6 non-compartment model PK parameters from 10 publications into 5 compartment model PK parameters. In simulation studies, we showed that this multivariate nonlinear mixed model had little relative bias (<1%) in estimating compartment model PK parameters if all non-compartment PK parameters were reported in every study. If there missing non-compartment PK parameters existed in some published literatures, the relative bias of compartment model PK parameter was still small (<3%). The 95% coverage probabilities of these PK parameter estimates were above 85%.ConclusionsThis non-compartment model PK parameter transformation into compartment model meta-analysis approach possesses valid statistical inference. It can be routinely used for model based drug development.

The current role of model-based drug development

Importance of the field: Current drug discovery and development programs are under growing scrutiny for low productivity and escalating costs. Model-based drug development (MBDD) has been recognized as a promising tool to address some of the related challenges.Areas covered in this review: This review introduces the concept of MBDD and the associated quantitative pharmacology-based iterative ‘learn and confirm’ paradigm in the drug discovery and development process to provide concise information for rational decision making. It summarizes the evolving role of MBDD in drug development programs and outlines the full armamentarium of modeling and simulation approaches utilized to facilitate its application.What the reader will gain: Different aspects and applications of MBDD are introduced to the reader and illustrated in prime examples. The reader is provided with an understanding of potential applications of MBDD in drug development as well as the associated limitations and challenges in its implementation.Take home message: MBDD is a tool that is increasingly used throughout the drug discovery and development continuum to support fast and rationale decision making and has thereby the potential to accelerate and increase the cost-effectiveness of the drug development process.

Quantitative Decision Making Using Modeling and Simulation

An efficient drug development depends on continuous cycles of learning and confirming throughout its entire process. Realization of each of these cycles can be characterized in terms of six components of the model-based drug development (MBDD) as described by Kowalski, et al. (2007) and Lalonde, et al. (2007). The six components can be grouped into three categories: constructing models based on available information, clinical trial simulation, and quantitative decision making (QDM). In this paper, the basic concept of QDM as given in the former two papers is explained in a simplified form, and its application to the COX-2 inhibitor development as reported in Kowalski, et al. (2008) is briefly summarized.

Advances in clinical trials technologies

T reported failure rates in phase 2A and 2B for drugs completing phase 1 and proof of concept (POC) and those in phase 3 for drugs completing phase 2B are as high as 40% and 50%, respectively. These attrition rates are often due to poor design and analysis of data, such as insufficient size and duration for safety, insufficient dose range, neglected time course of drug response, poor analysis of categorical data, and neglected subgroups. Collaborations among industry, academia, and government are becoming more important in developing new clinical trial technology and bridging the gap between the discoveries in basic science and product development for public health. The benefit of using new science are emphasized in a white paper “Critical Path Initiatives” and following reports by United States Food and Drug Administration (US FDA) and consortium activities such as the Critical Path Institute of the US. When properly analyzed, biomarkers of pharmacodynamics (PD), disease progress, or toxicity can reduce failure rates in phase 2 or 3 by providing tools for an early decision of GO or NO-GO, optimal dose range, etc. Eventually this can lead to monitoring tools for outcomes of disease, better and safer therapy, and tailored medicine. In the past, traditional biomarkers were single measures of physiological or biochemical processes such as HIV burden, cancer markers, blood glucose, blood pressure, etc. Now multiple or clusters of omics measures and in vivo imaging are being added to the list. These biomarkers are best utilized when more quantitative model based drug development tools such as modeling and simulation of pharmacokinetics/PD and disease model are implemented in the overall drug development processes. New clinical trial designs such as microdosing and adaptive designs are also useful for the application of biomarkers for efficient clinical trials. Bioinformatics can also facilitate clinical trials with electronic data capturing, data transfer, and so on.