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Pharmacometrics as a Discipline Is Entering the ''Industrialization'' Phase: Standards, Automation, Knowledge Sharing, and Training Are Critical for Future Success Klaus Romero, Brian Corrigan, Christoffer W. Tornoe, Jogarao V. Gobburu, Meindert Danhof, William R. Gillespie, Marc R. Gastonguay, Bernd Meibohm and Hartmut Derendorf J Clin Pharmacol 2010 50: 9S DOI: 10.1177/0091270010377788

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Downloaded from jcp.sagepub.com at ACCP Member on September 16, 2010 Pharmacometrics as a Discipline Is Entering the “Industrialization” Phase: Standards, Automation, Knowledge Sharing, and Training Are Critical for Future Success

Klaus Romero, MD, Brian Corrigan, PhD, Christoffer W. Tornoe, PhD, Jogarao V. Gobburu, PhD, Meindert Danhof, PhD, William R. Gillespie, PhD, Marc R. Gastonguay, PhD, Bernd Meibohm, PhD, and Hartmut Derendorf, PhD

he development of drug models that incorporate address the shared problem of lengthy and expen- Tcharacteristics such as pharmacokinetics, phar- sive medical-product development programs with macodynamics, pathophysiology, and genetics has high attrition rates.4 This requires not only well- gained importance in drug discovery, development, structured translational sciences and precompetitive and innovation in recent years. Integration of drug research but also a workforce with the necessary models with disease progression models has been training background, for which training and skill proposed but has been limited by the lack of con- building are essential. temporary and robust data.1 This article presents the advances being made In 2004, the US Food and Drug Administration with respect to developing standards and automa- (FDA) Critical Path Initiative identified neuropsy- tion at the FDA, 3 data-sharing initiatives that focus chiatric diseases and disease models as priority on developing modeling and simulation as a useful areas of active research opportunities.2 The World tool for drug development, and a discussion about Health Organization reached similar conclusions the training of future pharmacometricians. that same year.3 In such a context, precompetitive research, defined as collaborative scientific efforts by entities that Development of Standards, Automation, ordinarily are commercial competitors, plays a central and Knowledge-Sharing at the FDA role.4 To that end, data-sharing initiatives and the training of professionals with the abilities to tackle Pharmacometrics has become an integral part of such tasks become key needs. regulatory decision making. The Division of In the face of these challenges, industry, regula- Pharmacometrics has reviewed more than 200 tory bodies, and academia need to collaboratively new drug applications (NDAs) and biologics license applications (BLAs) in the past 10 years. The early phase of pharmacometrics’ application From Coalition Against Major Diseases, Critical Path Institute, Tucson, AZ to key decisions has come to fruition. During this (Dr Romero); Pfizer, New London, CT (Dr Corrigan); Division of Pharmacometrics, US Food and Drug Administration, Silver Spring, period, analyses have been predominantly tai- 5,6 Maryland (Dr Tornoe, Dr Gobburu); Leiden-Amsterdam Center for Drug lored for each problem. The discipline is now Research, Leiden University and Top Institute Pharma (TI Pharma), Leiden, entering the “industrialization” phase, during the Netherlands (Dr Danhof); Metrum Institute, Tariffville, CT (Dr Gillespie, which similar analyses will be performed over and Dr Gastonguay); University of Tennessee Health Science Center, College of over. Quality, consistency, and speed along with Pharmacy, Memphis, Tennessee (Dr Meibohm); and University of Florida, the ability to access prior knowledge are needed, Gainesville, FL (Dr Derendorf). Submitted for publication April 23, 2010; revised version accepted July 14, 2010. Address for correspondence: Klaus as is knowledge sharing, to accelerate this phase. Romero, MD, Coalition Against Major Diseases, Critical Path Institute, This section describes the advances being made 1730 East River Road #200, Tucson, AZ 85718; [email protected]. with respect to developing standards and automa- DOI: 10.1177/0091270010377788 tion and sharing knowledge at the FDA.

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Sharing Knowledge

Data are information and by themselves are of mini- mal utility. Quantitative analysis transforms information from data into knowledge, which is more generalizable and shareable. Figure 1 describes the “knowledge pyramid.” Initiatives to share clinical data have made little progress in the past and might be overly ambitious, given that data are perceived to be a commercial advantage. At the other end of the spectrum is the sharing of model parameters, which routinely is done through publications and scientific meetings. However, it is hard to use this type of information because the level of detail often is insuf- ficient to allow investigators to reproduce the results Figure 1. Knowledge pyramid. or update them with new data. A more pragmatic deliverable could be sharing standards in addition to models. The authors pro- Developing Standards and Automation pose that the pharmacometrics community align and share knowledge through a system that enables The workload for a pharmacometrician is ever- acquiring, storing, analyzing, and reporting informa- increasing, the number of experts trained per year is tion through the development of data standards, constant at best, and the collective experience to tools and script libraries, and report templates to solve problems is increasing. Tailored modeling maximize the use of knowledge. work leads to reinventing the wheel and diversity in The following proposal lists 5 elements that will analysis and reporting. A leading factor for the suc- increase productivity and quality of quantitative cessful integration of disciplines such as statistics analyses: and engineering into the critical path of decision making is standardization. The result of a statistical 1. Data standards: Data standards and templates with analysis is always shown as mean or median with definitions and controlled terms for different dis- confidence interval for continuous data and a eases and types of analyses. Kaplan-Meier plot for time-to-event data. Inter- 2. Input database: A relational database of raw data disciplinary scientists have gradually developed a along with scripts to create analyses-ready data sets. comfort level in basing decisions on such summa- This enables adherence to defined data standards ries. Pharmacometricians should develop similar for each drug and study in the database as well as standards within their area. version control. The format and nomenclature of input and output 3. Tool library: Library of tools and scripts for routine data are seldom consistent across trials even within analyses: for example, population pharmacokinetic, an organization. Analysts spend disproportionate exposure–response, and concentration–QTc analyses. efforts locating the data and creating analysis data 4. Output database: Automatic archiving of analyses sets, leaving relatively little time for interpretation results to leverage existing knowledge through and interactions with drug development teams. pooled data analyses. Speed is especially important if a company has 3 5. Report standards: Reporting templates for consist- months to file an NDA! The first few exposure– ency and easier communication across disciplines. response analyses that a typical pharmacometrician conducts for a given clinical end point are reasona- Among the 5 elements, data standards, tool bly constant across trials and drugs. Additional tai- library, and report standards can be developed and lored analysis based on the initial results from these shared across organizations. The following have default analyses might be necessary. From the FDA been developed and shared by FDA: experience, trial data for a given disease are submit- • Data and report standards: The QT knowledge man- ted in varying formats and initial modeling is rea- agement system for automatic data analyses, stor- sonably constant for a given disease.7,8 age, and reporting of QT interval (measure of the

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time between the start of the Q wave and the end of institutions in the Netherlands and 7 international the T wave in an electrocardiogram) is implemented leading pharmaceutical companies, under the aus- as an R package called “QT” (http://cran.r-project. pices of the Dutch Top Institute Pharma (TI Pharma) org).9 The tool enables automatic creation of analysis- established in 2006 (www.tipharma.com). The ready data sets. The QT–RR, QTc–time, and concen- objective of the platform is to develop a mechanism- tration–QTc analyses results are stored in a searchable based PKPD model library plus a database of bio- repository and used for automatic reporting. The QT logical system–specific information to enhance drug knowledge management system has improved the discovery, development, and innovation research. productivity, quality, and communication of regula- To this end, the partners have agreed to share data, tory review of thorough QT studies. The source code models, and biological system–specific informa- is furthermore made available as a Google code tion. The education of graduate students and post- (http://qttool.googlecode.com), which is a collaborative space for community-developed tools. doctoral fellows is an integral part of the activities of the platform. • Tool Library i. PopPK: The population pharmacokinetics (PK) The Concepts reporting tool (“popPK” R package, http:// cran.r-project.org and http://poppk.googlecode. The scientific basis of the TI Pharma Platform rests com) was developed to summarize population on the theoretical concepts of mechanism-based 12,13 PK analyses and standardize reporting. The idea PKPD modeling. Mechanism-based PKPD mod- is to create a pharmacometric wiki page (http:// eling considers the cascade of processes on the pmetricopedia.org) and use it as a platform for causal path between drug administration and the pharmacometric community to collaborate response. In this context, mechanism-based PKPD and improve on how population pharmacoki- models contain expressions to characterize: (1) the netic analyses are presented and used to influ- kinetics of target site distribution, (2) the target ence drug development decisions. binding/activation process, and (3) the transduction ii. Disease Models: The non–small cell lung cancer mechanisms that govern the time course of the drug (NSCLC) quantitative drug–disease trial (QD2T) effect, using concepts from physiologically based model is an exploratory tool to improve oncol- pharmacokinetic modeling, receptor theory, and ogy drug development. The NSCLC model is in- dynamic systems analysis. A pertinent feature of tended to aid in go/no-go decisions early based mechanism-based PKPD modeling is the strict dis- on effects on tumor size and design of late-phase clinical trials including dose selection. This tinction between “drug-specific” and “biological work has been shared at an FDA advisory com- system–specific” properties that govern the response. mittee10 and in a publication.11 Work is in prog- In this context, drug-specific properties character- ress to create a collaborative space for interested ize the interaction of the drug molecule with the parties to share their knowledge and experience. biological system. Drug-specific properties often can be predicted from in vitro bio-assays. In con- Development of standards can be undertaken collec- trast, biological system–specific parameters can tively via consortia and initiatives that make model and only be estimated from in vivo biological systems data sharing a key priority. Strategically, it is best to start analysis. The values of these parameters can differ by developing a few standards and tools for demonstra- between biological systems (ie, species, subjects) tive purposes, which then allow appreciation of their and within biological systems (ie, nonstationarity, value. Individual organizations should come forward environmental factors; Figure 2). Pertinent infor- and share their standards to build a momentum. In mation on the structure and functioning of the bio- summary, the development of tools and standards for logical system in conjunction with the values of quantitative analyses will allow the pharmacometric biological system–specific parameters constitutes community to become more efficient, increase the qual- the scientific basis for the prediction of (variation ity of data, and ease communication across disci- in) drug effects. plines—to ultimately become more influential. The most recent developments in mechanism- based PKPD modeling have been the introduction of TI Pharma Mechanism-Based PKPD novel concepts for predicting drug effects in nonsta- 14 Modeling Platform tionary systems, in relation to aging and disease progression.15 In this respect, time-variant changes The TI Pharma Mechanism-Based PKPD Modeling in the functioning of a system are treated as system- Platform is a public–private partnership of 4 academic specific properties.

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Figure 2. Mechanism-based PKPD modeling: modeling of the functioning of the biological system.

The Research Themes Figure 3. Work group workflow.

The TI Pharma Mechanism-Based PKPD platform focuses on key aspects of drug discovery, develop- part of a systems approach, researchers attempt to ment, and innovation research: (1) translational model drug effects on relevant biomarkers in con- pharmacology, (2) developmental pharmacology, junction with effects on clinical end points. and (3) disease systems analysis. Research in the field of translational pharmacol- The Operation ogy focuses on the interspecies extrapolation of pharmacodynamics with the aim of predicting the The platform aims to develop mechanism-based cardiovascular safety of novel drugs and the thera- PKPD models on the basis of existing data that peutic effects of drugs for the treatment of schizo- become available through the partners. It is impor- phrenia and chronic (neuropathic) pain. tant that data are provided by both the public and The research in developmental pharmacology the private partners in the platform. Data are also aims at predicting age-related changes in pharma- provided by external parties. cokinetics and pharmacodynamics. With regard to For the purpose of data management and analysis, pharmacokinetics, the emphasis is on modeling a dedicated, centralized computing network facility developmental changes in key pharmacokinetic has been established. Within this environment, processes such as plasma protein binding, renal access to the pertinent, deidentified, merged data function (glomerular filtration, active tubular secre- sets is restricted to those scientists who are directly tion), (hepatic) blood flow, and the function of a involved in specific projects. However, as part of diversity of drug-metabolizing enzymes (eg, uridine their membership, all partners in the platform have diphosphoglucuronate–glucuronosyltransferases, access to the resulting models and the correspond- cytochrome P450-3A4). With regard to pharmacody- ing biological system–specific information for use in namics, the emphasis is on analgesic and sedative proprietary research programs. effects of drugs used perioperatively, anti-infective As models are being developed, the need for addi- drugs, and drugs used in stem cell transplantation. tional data has arisen: complete data to develop The ultimate goal is to develop a systems model that meaningful models and data required for the (exter- can be used both for the initial dose selection of nal) validation of mechanistic PK and PKPD models. novel drugs in pediatric clinical trials and for indi- In a number of cases, the partners have agreed to vidualized treatment with novel and existing drugs generate these crucial data. An example is the gen- in pediatric clinical practice. eration of additional animal data in a number of Finally, research in disease systems analysis focuses translational projects. As well, prospective clinical on the modeling of disease progression in osteoporo- trials have been started to validate novel dosing algo- sis and chronic obstructive pulmonary disease. As rithms for individualized treatment in pediatrics.

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Communication, Education, and Training data elements, and the use of quantitative disease models for more efficient trial design. Within the platform, an effective network for com- The work of the coalition focuses on sharing munication between the partners has been estab- precompetitive data that improve knowledge across lished. Within the multidisciplinary project teams, disciplines, an important condition for developing frequent meetings and teleconferences are held with treatments for PD and AD. Improved management of all partners to ensure maximum transparency and existing knowledge is aimed at qualifying novel imag- progress in all projects. Biannual plenary platform ing or biochemical markers (in this document both are meetings are held to discuss strategy and progress of referred to as biomarkers) and quantitative disease the platform as a whole. progression models to inform trial design. This in turn Because education and training of PhD students is expected to increase efficiency in decision making and postdoctoral fellows are an integral part of the during the drug development process and decrease activities of the platform, an education and training development failures during late-phase testing. program has been established. Within this program, Longitudinal models that quantify changes in advanced courses on PKPD modeling concepts, neurodegenerative diseases models allow for vari- PKPD data analysis, epidemiology, and statistics as ous trial designs to be tested and optimized for dura- well as workshops on specialized topics are regu- tion, visit times, patient inclusion criteria, and larly organized. Participants in these courses are the mechanism of action of the proposed interventions. PhD students and postdoctoral fellows in the plat- These models can improve decision making in drug form as well as scientists from the partnering phar- development.18,19 maceutical industries. Based on the successful model from C-Path’s All models resulting from the work in the plat- other collaborative efforts (PSTC, PRO, AzCERT),16 form are published in the international scientific CAMD is establishing a process and executing a plan literature. for compiling and evaluating the scientific merit of potentially useful candidate biomarkers for drug or Partners diagnostic test development for AD and PD. The academic partners in the TI Pharma Mechanism- Based PKPD Platform are University of Leiden, State Data, Models, and Biomarkers University Groningen, Erasmus Medical Center Rotterdam, and University Medical Center Utrecht, In the specific cases of PD and AD—given the com- all in the Netherlands. The private partners, all of plex nature of the underlying disease, uncertainty in which are international pharmaceutical companies, differential diagnosis, and the need to understand are Astellas, GlaxoSmithKline, Johnson & Johnson, the clinical outcomes that are typically used for Lilly, Merck, Nycomed, and Pfizer. registration—a top-down approach is being used that initially focuses on the registration end points. Four Coalition Against Major Diseases (CAMD) working groups were established, and the workflow is shown in Figure 3. The framework and considera- Critical Path Institute, in collaboration with the tions for the CAMD modeling and simulation plan Engelberg Center for Health Care Reform at the are outlined in Table I. Brookings Institution, formed CAMD in September Leveraging work from member companies and 2008.16 This coalition includes the FDA, the European other collaborators, a longitudinal model has been Medicines Agency (EMA), 2 branches of the US developed based on clinical observations for the National Institutes of Health (NIH), academic scien- Alzheimer’s Disease Assessment Scale–cognitive tists, patient groups, and leading global companies subscale (ADAS-cog) for mild to moderate AD, mild representing the medical product industry.17 cognitive impairment, and normal controls, using data from the Alzheimer’s Disease Neuroimaging 20 Goal Initiative (ADNI) database. This model is being refined and enriched with the The coalition’s goal is to accelerate drug development incorporation of deidentified control-arm data from in neurodegenerative diseases, such as Alzheimer clinical trials conducted by CAMD member compa- disease (AD) and Parkinson disease (PD), through nies. To pool these disparate data sets, CAMD asked identification of biomarkers, standardization of common members to map their data to Clinical Data Interchange

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Table I Framework and Considerations for the CAMD Modeling and Simulation Plan for Parkinson Disease and Alzheimer Disease

Framework for the Modeling and Simulation Plan Considerations and Scenarios Simulate clinical trial data sets based on: Trial design considerations:

• Proposed model • Magnitude and frequency of doses that are most informative • Hypothesized drug effects • Sample size • Candidate trial design options • Trial duration • Sensitivity for effects • Impact of patient inclusion criteria • Effect of attrition on analyses • Comparison of designs’ efficiency (development stages) • Data analytic techniques Compare operating characteristics based Design scenarios to test: on the variation in results across simulated trial data sets • Drug effects: symptomatic, protective, both • Patient considerations: baseline severity • Design considerations: parallel, crossover, staggered start, 2-stage with adaptive dose selection • Data considerations: MAR, MNAR (efficacy or tolerability) • Data analytics dependent on design being simulated: MMRM, Hochberg, DM method, NHSS

CAMD, Coalition Against Major Diseases; DM, data mining; MAR, missing at random; MMRM, mixed modeling for repeated measures; MNAR, missing not at random, NHSS, natural history staggered start.

Standards Consortium (CDISC) Study Data Tabulation Moving Ahead Model (SDTM) standards, including new therapeutic- area standards developed by CAMD to accommodate Considering all of the above, CAMD will address the these data types. need for tools and methods that can result in a more As of April 2010, a proposed approach to refine reliable and predictable process of drug develop- the existing model and incorporate placebo effects ment for PD and AD. The combination of qualified descriptions is being reviewed by FDA and EMA. biomarkers and quantitative disease progression Similarly, a previous PD model based on the Unified models will provide tools for the pharmaceutical Parkinson’s Disease Rating Scale (UPDRS), devel- industry to accelerate drug development, learn more oped by FDA, is being used as the foundation to about how to terminate low-probability approaches carry on the work in PD.21 sooner, and ensure that when a new therapy is Demographics and genetics, as well as the impact found, it has a much higher probability to move of biomarkers, are being tested in the models. Various through the development and regulatory system suc- trial design and data analytic methods for proof of cessfully and more rapidly. concept, dose-ranging, and pivotal trials are also OpenDiseaseModels.Org being tested and being optimized. Biomarkers that are being studied as potential In February 2009, Metrum Institute launched Open tools to be qualified for use in drug development DiseaseModels.org (http://OpenDiseaseModels.org), include, among others, cognitive tests, cerebrospinal a Web site and supporting organization that provide fluid (CSF) Aβ1-42, P-tau181, t-tau, and volumetric an open forum for collaborative model building and magnetic resonance imaging for AD as well as clini- evaluation, driven by the following factors: cal scores, DaT-Scan (Ioflupane123I), and β CIT SPECT–based imaging of the dopamine transporter • Development of disease/systems models is an system for PD. extremely resource-intensive effort.

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• Precompetitive insights and resources shared across • Models are developed using publicly available data, companies/institutions will lead to better systems and those data sets will also be openly shared as models than could be developed by a single institution. part of the project. • Open models, which are transparently developed • Documentation of modeling efforts, features, and publicly vetted, will be more widely accepted improvements, and bug fixes is transparently avail- and will be better positioned to affect the entire sci- able within each project. entific/biomedical/health community. • All model code, data sets, and documentation are version controlled using a modern software devel- OpenDiseaseModels.org serves as a home for mul- opment versioning system. tiple disease/systems modeling projects. The • Publication of modeling results in peer-reviewed approach is analogous to both open-source software literature must be allowed and is encouraged for all development and wiki-based information sharing. development projects. Each individual project consists of 3 participant • Public review, discussion, and extension of models groups: a core model development team, an advisory are facilitated via a Web-based discussion board. panel, and the general public. The core model devel- • All model code is distributed under the GNU opment team is made up of expert modeling and General Public License. simulation scientists with experience in disease progression or systems biology modeling for the partic- To date, 3 open-model development projects have ular biomedical domain of interest. They serve as been initiated at OpenDiseaseModels.org. the primary developers and maintainers of the model source code and review input from the advisory • A systems biology model for calcium homeostasis panel and the general public regarding model revi- and bone resorption sions and improvements. The advisory panel con- • An AD progression model based on the ADAS-cog sists of scientists, clinicians, policy makers, and end point A schizophrenia disease progression model based on patient advocates with demonstrated expertise or • the Positive and Negative Syndrome Scale (PANSS) interest in the diseases of interest. This panel pro- total end point vides guidance to the core model development team regarding clinical and therapeutic utility, biologic plausibility, and, potentially, external research and Substantial content has been posted for the first 2 funding opportunities. The general public includes projects. OpenDiseaseModels.org projects are com- other domain-relevant scientists, clinicians, policy munity driven. Suggestions for new modeling efforts makers, patient advocates, and anyone else with an are encouraged and collaborators are welcome. interest in the modeling project. This group affects The calcium homeostasis and bone resorption model development by exploring, challenging, and model is a systems biology model that describes motivating through contributed examples and open rates of bone turnover due to natural disease pro- discussion. All participants are allowed to down- gression and therapeutic intervention and describes load and review model documentation, data, and time courses of the specific bone remodeling markers urine N-telopeptide, serum C-telopeptide, and source codes; participate in open discussion groups; 22 and contribute new models or data via discussion bone-specific alkaline phosphatase. The model is group uploads. implemented in Berkeley Madonna 8.0. This phys- Some important characteristics of OpenDisease iologically based model allows evaluation of Models.org development projects are these: potential causal mechanisms for observed effects and prediction of the potential effects of proposed interventions. The model has been applied to the • Models are developed with readily accessible (preferably open-source) modeling tools with model following disease states and therapeutic interventions: code openly available. Ultimately these models may hyperparathyroidism (primary), secondary hyperpar- be translated to a common model language (eg, an athyroidism caused by progressive renal insufficiency, SBML-like language). hypoparathyroidism, once-daily parathyroid hormone • Fully Bayesian modeling methods are encouraged therapy, RANK-L inhibition, and estrogen replace- in order to formally include prior information ment therapy (initiation and withdrawal). The core sources in the model development process and model development team is Matthew M. Riggs facilitate exploration of sensitivity of model-based (Metrum Institute), Mark C. Peterson (Biogen Idec), inferences to parameter (and model) uncertainty. and Marc R. Gastonguay (Metrum Institute).

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The schizophrenia disease progression model is concepts that include advanced statistical tech- under development by scientists at Metrum Research niques and numerical simulations and with the Group and Pfizer. The model will describe the time biological mechanisms of physiology, pathophysiol- course of the total PANSS score as a function of time ogy, and action of drugs and biologics, which must and selected covariates during placebo treatment. be summarized in relatively simple mathematical– The model will be posted to OpenDiseaseModels statistical expressions amenable to computer solu- .org once completed and vetted by the participating tion. Pharmacometricians interact with a variety of companies. different disciplines in the drug development team OpenDiseaseModels.org demonstrates the feasi- approach and thus need to have excellent communi- bility of an open, community-based, disease mode- cation skills to successfully interface with clinicians, ling collaboration. This paradigm also provides an statisticians, and laboratory-based scientists.27,28 opportunity for integration with other model-shar- The increasing demand for pharmacometricians ing initiatives, for example, sharing of nonproprie- in the pharmaceutical industry as a consequence of tary components of models, data, and modeling the widespread integration of pharmacometrics in tools from CAMD (http://www.c-path.org/camd. the drug development process currently far exceeds the cfm), the FDA Disease Specific Model Library (http:// available supply, and the gap will only widen in the www.fda.gov/AboutFDA/CentersOffices/CDER/ coming years. The major bottleneck responsible for ucm180485.htm), and TI Pharma (http://www.tip- this shortage is a stagnant or even decreasing number harma.com/home.html). Future goals include devel- of academic sites for education and training of these opment of self-sustaining funding to support highly specialized scientists. A major reason for this OpenDiseaseModels.org projects, construction of a situation is the reorientation of many academic single infrastructure for the shared model database departments across universities during the last dec- (which currently relies on individual Google Code ades to a strong emphasis on molecular biological sites), implementation of advisory panels for exist- sciences secondary to the focus of federal funding ing modeling projects, and identification of new opportunities in this area and the consequent fading disease model projects and collaborators. of the academic disciplines of pharmacokinetics and clinical pharmacology as traditional feeder programs Education and Training of for pharmacometricians. In addition, a very limited Pharmacometricians number of graduate and postgraduate training programs in the United States and abroad have embraced The discipline of pharmacometrics has evolved over the multidisciplinary scope of educating and/or decades from an often ad hoc collection of straightfor- training pharmacometricians and developed corre- ward analytical methods to a sophisticated, rigorous, sponding curricula. Tables II and III provide noncom- and multipronged scientific discipline that uses math- prehensive lists of such programs. ematical models based on biology, pharmacology, Although the disparity between supply and physiology, and disease to quantify the interactions demand for pharmacometricians may be viewed by between drugs and patients.23 In recent years, the some as positive in the short term because it pro- impact of pharmacometrics has increased in all facets vides job security and increased compensation, it of drug discovery, development, and innovation, will be essential to fill this gap in the near future to because the cluster of skills that define pharmacomet- maintain the momentum for the implementation of rics provides powerful tools to maximize the informa- quantitative, model-based approaches in the drug tion flow between different development stages and development process. delivers crucial information for rational, scientifically Increasing the number of individuals who are edu- driven decision making throughout the drug develop- cated and trained in pharmacometrics will require a ment process.24 Today, a rigorous application of phar- concerted effort from industry and academia. Most macometrics in drug development is widely advocated important, pharmacometrics needs to be accepted as by industry, academia, and regulatory agencies.19,25,26 a self-standing discipline with a defined and well- A successful contribution of pharmacometrics to accepted core curriculum. Professional organizations increase the speed and efficiency of the drug develop- and groups such as the American Conference of ment process, however, requires highly trained indi- Pharmacometrics will be crucial to define the core viduals—pharmacometricians. Pharmacometricians competencies of pharmacometricians. need to have considerable technical expertise, both Pathways to become proficient in pharmacomet- with highly developed computational and algorithmic rics have traditionally been graduate programs in

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Table II Examples of Degree and Postgraduate Training Programs in Pharmacometrics in the United States

Institution Director Degree Program Postgraduate Training Children’s Hospital of Philadelphia J. Barrett MS Fellowship Cincinnati Children’s Hospital A. Vinks Fellowship Indiana University R. Bies PhD Postdoctoral SUNY Buffalo W. Jusko, D. Mager, J. Balthasar PhD/MS Postdoctoral University of California–San Diego E. Capparelli Fellowship University of California–San Francisco H. Lee, N. Sambol, C. Peck Fellowship University of Florida H. Derendorf, G. Hochhaus PhD Postdoctoral University of Minnesota R. Brundage PhD/MS Postdoctoral University of Southern California R. Jelliffe, D. D’Argenio PhD Postdoctoral University of Tennessee B. Meibohm PhD Postdoctoral University of Washington D. Chen Postdoctoral Virginia Commonwealth University J. Venitz PhD Postdoctoral

Modified from Hofman et al.30

Table III Examples of Degree and Postgraduate Training Programs in Pharmacometrics Outside the United States

Postgraduate Region Institution Director Degree Program Training Europe University Paris Diderot F. Mentré Leiden University M. Danhof PhD/MS Postdoctoral Martin-Luther-University Halle-Wittenberg C. Kloft PhD University of Gothenburg J. Gabrielsson, G. Tobin MS University of Manchester L. Aarons, A. Rostami PhD/MS Postdoctoral University of Navarra, Pamplona I. Troconiz PhD/MS University of Paris-Sud M. Lavielle Uppsala University M. Karlsson PhD/MS Postdoctoral Asia/Pacific Rim Catholic University of Seoul R. Y. Sun PMECK/Yonsei University K. Park PhD Postdoctoral Peking University, Beijing W. Lu PhD Postdoctoral Auckland University N. Holford PhD, MS University of Otago S. Dufful PhD, MS Postdoctoral University of Queensland B. Green, C. Kirkpatrick PhD Fellowship

Modified from Hofman et al.30

(bio)pharmaceutical sciences or biomedical engi- support of these programs. Beyond bilateral agree- neering with focus on pharmacometrics or post- ments between a specific academic program and a graduate pharmacometric training programs. To company, consortia-type pharmacometrics programs foster these pathways, academic–industry partner- between multiple industrial and academic partners ships can provide financial support for academic have been suggested. This suggestion is especially programs either directly through unrestricted educa- intriguing in an era of Web-based instructional deliv- tional support or indirectly via industry-supported ery because it would allow programs to pool instruc- research projects. Industry will need to create addi- tors from participating institutions and to accept tional postgraduate training opportunities, either students from different geographic locations. through joint programs with academia or as their Besides forming partnerships and supporting own, independent program. Professional organiza- collaborative approaches, professional and other tions as well as regulatory agencies should contribute nonprofit organization can supplement training to this process by facilitating the establishment and opportunities, especially for individuals already in

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Downloaded from jcp.sagepub.com at ACCP Member on September 16, 2010 ROMERO ET AL the workplace who wish to refocus their work or 8000 elderly subjects with repeated electrocardio- add pharmacometrics to their portfolio of skills. A graphic measures and pertinent information on sud- recently announced pharmacometrics certificate den cardiac death.31 The use of public databases also program based on live and/or Web-based courses extends to TI Pharma’s pediatric research. In con- offered by Metrum Institute is a prime example for trast, for the disease projects TI Pharma relies more such an approach.29 on proprietary databases. As happens in any applied science, however, Similarly, CAMD pools data from publicly avail- learning on the job may always be a mainstay of the able sources like ADNI and precompetitive proprie- learning path for pharmacometricians.27 Thus, cor- tary data from member companies (patient-level porate internal training and exchange of ideas will data from control arms and active treatment groups likely remain a substantial element in training phar- from failed trials), which are being remapped to macometricians, and companies should strive to existing and newly developed CDISC standards.16,17 establish formal venues for interaction and mentor- The deliverables from the work will be made pub- ing programs to grow an in-house talent base.26 licly available by CAMD itself and also through col- In summary, major efforts and intense academic– laborations like OpenDiseaseModels.org and through industry collaborations are necessary to resolve the peer-reviewed publications. pharmacometrician shortage and ensure a supply of OpenDiseaseModels.org focuses preferably on pub- well-trained, versatile, and competent scientists licly available databases but does not preclude the use who can support the discipline of pharmacometrics of proprietary data sets, as long as the deliverables can and move it to the next level. be made publicly available through this Web site. Considering all of the above, standardization of Discussion legacy data is certainly important but should be accompanied by the development and adoption of The development of integrated quantitative models standards for collection of prospective data, which that add value to the drug discovery, development, in turn facilitate future collaborative efforts. One and innovation processes requires reliable databases clear advantage is that collaborative environments that contain sufficient data. More often than not, allow multiple users to develop models and codes. such databases are best built with data from different Such collaborative environments provide a unique sources, hence the importance of data-sharing initia- opportunity for seasoned pharmacometricians to tives. To be successful, such initiatives require the increase their experience and for future ones to implementation of data standards and searchable develop critical skills in a “work force” environment. databases that allow for easy and efficient access to The combination of data sharing with education and information. training provides a unique opportunity not necessar- Data standards are mandatory for the success of ily available in standard academic environments. precompetitive collaborative efforts. The need to Standards, automation, knowledge sharing, and remap legacy data to common standards in order to training with an applied focus are critical for the success create an operational database is labor and resource of pharmacometrics as a discipline. The interplay of intensive, but at the same time, precompetitive these factors will make possible the application of an research provides the framework to develop univer- integrated drug development process that incorpo- sally accepted standards for data collection and stor- rates drug models, quantitative disease progression, age, which are useful to not only remap legacy data and trial models as well as biomarkers that provide but to increase the data-sharing efficiency to incor- useful and significant insights into the nature of porate prospective information.4 In this context, col- neurodegenerative diseases and their response to laboration with organizations such as CDISC is pharmacological interventions. essential, not to mention buy-in from industry and regulatory authorities. The authors thank the following individuals for their contribu- For these types of efforts, the distinction between tions and collaboration in the development of this manuscript. public versus private and pre- versus pro-competi- From CAMD: Raymond L. Woosley, MD, PhD; Dennie Frank, PhD; tive databases becomes a crucial issue. In the case of Jon Neville, PSM; Richard T. Myers; Marc Cantillon, MD; and Frank Casty, MD. From OpenDiseaseModels.org: James A Rogers, the TI platform, both public and private databases PhD; Matthew A Riggs, PhD; and Joseph Hebert, PhD. are used. For example, this initiative has access to Financial disclosure: The articles in this supplement are the Rotterdam Study, a prospective cohort study in sponsored by the American Conference on Pharmacometrics.

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References 15. Romero K, de Mars M, Frank D, et al. The Coalition Against Major Diseases: developing tools for an integrated drug develop- 1. Gobburu JV, Lesko LJ. Quantitative disease, drug, and trial ment process for Alzheimer’s and Parkinson’s diseases. Clin models. Annu Rev Pharmacol Toxicol. 2009;49:291-301. Pharmacol Ther. 2009;86:365-367. 2. US Food and Drug Administration. Challenge and opportunity on 16. Bies RR, Gastonguay MR, Schwartz SL. Mathematics for the critical path to new medical products. http://www.fda.gov/ understanding disease. Clin Pharmacol Ther. 2008;83:904-908. ScienceResearch/SpecialTopics/CriticalPathInitiative/ucm204289. 17. Lalonde RL, Kowalski KG, Hutmacher MM, et al. Model-based htm. Accessed April 9, 2010. drug development. Clin Pharmacol Ther. 2007;82:21-32. 3. World Health Organization. Department of Essential Drugs and 18. The Alzheimer’s Disease Neuroimaging Initiative (ADNI). Medicines Policy. Priority medicines for Europe and the world. http://www.loni.ucla.edu/ADNI/. Accessed April 9, 2010. http://apps.who.int/medicinedocs/index/assoc/s14208e/s14208e. pdf. Accessed April 13, 2010. 19. Bhattaram VA, Siddiqui O, Kapcala LP, Gobburu JV. Endpoints and analyses to discern disease-modifying drug effects in early 4. Woodcock J. Precompetitive research: a new prescription for Parkinson’s disease. AAPS J. 2009;11:456-464. drug development? Clin Pharmacol Ther. 2010;87:521-523. 20. Peterson MC, Riggs MM. A physiologically based mathemati- 5. Bhattaram VA, Booth BP, Ramchandani RP, et al. Impact of cal model of integrated calcium homeostasis and bone remode- pharmacometrics on drug approval and labeling decisions: a sur- ling. Bone. 2010;46:49-63. vey of 42 new drug applications. AAPS J. 2005;7:E503-E512. 6. Bhattaram VA, Bonapace C, Chilukuri DM, et al. Impact of 21. Gillespie WR, Rogers JA, Ito K, Gastonguay MR. Population pharmacometric reviews on new drug approval and labeling dose-response model for ADAS-Cog scores in patients with decisions—a survey of 31 new drug applications submitted Alzheimer’s disease by meta-analysis of a mixture of summary between 2005 and 2006. Clin Pharmacol Ther. 2007;81(2):213-221. and individual data. Clin Pharmacol Ther. 2009;85(suppl 1):S62. 7. Tornoe CW, Garnett CE, Wang Y, et al. Creation of an automated 22. Zhang L, Pfister M, Meibohm B. Concepts and challenges in thorough QT data analyses, repository, and reporting tool for quantitative pharmacology and model-based drug development. regulatory review. J Clin Pharmacol. in press. AAPS J. 2008;10:552-559. 8. Wang Y. FDA Advisory Committee for Pharmaceutical Science 23. Suryawanshi S, Zhang L, Pfister M, Meibohm B. The current and Clinical Pharmacology meeting, March 18 and 19, 2008. role of model-based drug development. Expert Opin Drug Discov. http://www.fda.gov/ohrms/dockets/ac/08/briefing/2008-4351b1- 2010;5:311-321. 00-index.htm. Accessed March 30, 2010. 24. Lesko LJ. Paving the critical path: how can clinical pharma- 9. Wang Y, Sung C, Dartois C, et al. Elucidation of relationship cology help achieve the vision? Clin Pharmacol Ther. 2007;81: between tumor size and survival in non-small-cell lung cancer 170-177. patients can aid early decision making in clinical drug develop- 25. Zhang L, Sinha V, Forgue ST, et al. Model-based drug development. Clin Pharmacol Ther. 2009;86:167-174. ment: the road to quantitative pharmacology. J Pharmacokinet 10. Danhof M, de Jongh J, De Lange EC, et al. Mechanism-based Pharmacodyn. 2006;33:369-393. pharmacokinetic-pharmacodynamic modeling: biophase distribu- 26. Barrett JS, Fossler MJ, Cadieu KD, Gastonguay MR. tion, receptor theory, and dynamical systems analysis. Annu Rev Pharmacometrics: a multidisciplinary field to facilitate critical Pharmacol Toxicol.2007;47:357-400. thinking in drug development and translational research settings. 11. Danhof M, de Lange EC, Della Pasqua OE, Ploeger BA, J Clin Pharmacol. 2008;48:632-649. Voskuyl RA. Mechanism-based pharmacokinetic-pharmacodynamic (PK-PD) modeling in translational drug research. Trends 27. Holford N, Karlsson MO. Time for quantitative clinical phar- Pharmacol Sci. 2008;29:186-191. macology: a proposal for a pharmacometrics curriculum. Clin Pharmacol Ther. 2007;82:103-105. 12. Ince I, de Wildt SN, Tibboel D, Danhof M, Knibbe CA. Tailor- made drug treatment for children: creation of an infrastructure for 28. Metrum Institute. Metrum Institute Pharmacometrics data sharing and population PK-PD modeling. Drug Discov Today. Certificate Program. www.metruminstitute.org. Accessed April 1, 2009;14:316-320. 2010. 13. Post TM, Freijer JI, de Jongh J, Danhof M. Disease system 29. Brouwer KL. Pharmacometrics Program Chart. Quantitative analysis: basic disease progression models in degenerative dis- and Systems Pharmacology Workshop. Bethesda, Md: NIGMS/ ease. Pharm. Res. 2005;22:1038-1049. National Institutes of Health; 2008. 14. Woosley RL, Myers RT, Goodsaid F. The Critical Path Institute’s 30. Hofman A, Breteler MM, van Duijn CM, et al. The Rotterdam approach to precompetitive sharing and advancing regulatory sci- Study: 2010 objectives and design update. Eur J Epidemiol. ence. Clin Pharmacol Ther. 2010;87:530-533. 2009;24:553-572.

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