151S–157S

8/13/2019 151S157S

1/7

J Clin Pharmacol xxxx;xx:x-x 151J Clin Pharmacol 2010;50:151S-157S 151S

Pharmacometrics is the science of quantifying dis-ease, drug, and trial characteristics with the goalof influencing drug development and regulatory andtherapeutic decisions. Figure 1 depicts a macro-scopic view of the evolution of pharmacometrics.The field started out (prior to 1960) with quantifyingtime courses of drug concentrations in biofluids(pharmacokinetics, or PK) from laboratory experi-

ments. 1 Scientists then developed methods to linkdrug exposures to steady-state pharmacologic res-ponses (pharmacodynamics, or PD). 2-4 The fusion ofPK and clinical pharmacology principles was essen-tial to develop innovative methods and tools toanalyze data from clinical trials. 5 In 1979, pharmaco-metric scientists adopted econometric and biometricmethods (mixed-effects modeling) to quantify pat-terns in observational data, opening opportunities

beyond the small homogeneous studies. 6-8 In 1989,guidelines from the US Food and Drug Administration(FDA) (found on www.fda.gov) for the study ofdrugs likely to be used in elderly people called for

pharmacokinetic screening, where sparse PK infor-mation is to be collected from registration trials tounderstand the sources of variability. Later, the FDAissued guidances for industry on population analysesin 1999 and on exposureres ponse relationships in2003. By this time, labeling statements pertaining tointrinsic and extrinsic factors were being supported

by pharmacometric analyses. More recent research

efforts are focused on building quantitative diseaseand drug trial models. 9-14 The importance and numberof pharmacometricians have increased to an extentthat a focused conference on pharmacometrics (calledthe American Conference of Pharmacometrics, ACoP)was created in 2008.

ENTREPRENEURIAL SUCCESS

During the late 1990s and early 2000s, 2 importantdevelopments defined the value of pharmacomet-rics. First, pharmacometric analyses started to influ-ence drug approvals and drug developmentdecisions. Second, the introduction of clinical trialsimulations began to shift toward designing trials.Figure 2 shows a few selected case studies forwhich pharmacometric analyses played a criticalrole in decision making. 15-22 Analyses for most of thecase studies in Figure 2 were conducted by industryscientists, although some might have been prompted

by the FDA. Pharmacometrics analyses began to be recognized as more efficient, compared with

This special issue of the Journal of Clinical Pharmacology is dedicated to pharmacometrics, covering topics relatedto methodological research, application to decisions,standardization, PhRMA survey, and growth strategy.Inno vative methodological and technological advancesin analyzing disease, drug, and trial data have equipped

pharmacometricians with the know-how to influencehigh-level decisions, which in turn creates more pharma-cometric opportunities. Pharmacometrics is revolution-izing drug development and regulatory decision making.To sustain the success and growth of this field, we needto up the ante. Strategic goals for pharmacometric groups

in industry, regulatory agencies, and academia are proposed in this report. These goals should be of signifi-cance to all stakeholders who have a vested interest indrug development and therapeutics. The future of phar-macometrics depends on how well we all can deliver onthe strategic goals.

Keywords: Clinical pharmacology; clinical trials; pharma-ceutical research and development; regulatory/ scientific affairs; pharmacodynamics.

Journal of Clinical Pharmacology, 2010;50:151S-157S 2010 The Author(s)

From the Division of Pharmacometrics, Office of Clinical Pharmacology,Center for Drug Evaluation and Research, Food and Drug Administration,Silver Spring, Maryland. Submitted for publication May 24, 2010;revised version accepted June 4, 2010. Address for correspondence:Jogarao V. S. Gobburu, 10903 New Hampshire Avenue, Building 51,Rm 3186, Silver Spring, MD 20993-0002; e-mail: [email protected]:10.1177/0091270010376977

Pharmacometrics 2020

Jogarao V. S. Gobburu, PhD

8/13/2019 151S157S

2/7

152S J Clin Pharmacol 2010;50:151S-157S

GOBBURU

conventional hypothesis testing, in that they ena- bled investigators to quantify the totality of infor-mation. Building on the initial success, about 250new drug applications (NDAs) or biologic licenseapplications (BLAs) with pharmacometric analyseshave been submitted by the industry and reviewed

by the FDA since 2000. Of these, 140 were submit-ted between 2005 and 2009, signifying an exponen-tial increase in this type of work. More than 70% ofthe reviews influenced approval, and almost all ofthem affected labelingspecifically dose selectionto balance benefit and risk (JY Lee, et al. Unpublisheddata, 2010) Pharmacometrics also influenced theapproval of drugs against bioterrorism and for emer-gency preparedness. Approval of Levaquin (levo-floxacin) dosing in pediatrics to treat anthraxexposure was primarily based on mechanistic mod-eling and simulation. 23 Approval decisions previ-ously were driven primarily by medical andstatistical reviewers. Allowing model-based infer-ences (rooted in clinical pharmacology theory) todrive an approval decision is a game-changingachievement. The use of modeling and simulationfor internal decisions by industry is not reflected by

these statistics. The PhRMA survey 24 reports that 10companies performed 115 modeling and simula-tion projects in 2008 alone. The survey also statesthat all companies are anticipating an increase inpharmacometric activity in the future.

The introduction of clinical trial simulations to des-ign trials (prospectively) created more opportunitiesfor pharmacometricians. Clinical trial simulation ofCellcept (mycophenolate mofetil) is considered anearly example of model-based trial design. 25 Exp-erience with NDA/BLA review identified weaknessesin drug development efficiency. In 2004, the FDAintroduced a pilot end-of-phase 2A (EOP2A) meetingto provide pharmaceutical companies an opportu-nity for an early dialogue with the FDA on designinglate-phase clinical trials. 26 The first NDA with anEOP2A meeting during development to be submittedto the FDA is Firmagon (degarelix). The extensivemodeling and simulation performed by the compa-nys scientists aided in the dose and regimen selec-tion for the registration trial. The studied dosing metthe primary end point, and Firmagon is currentlyapproved for treatment of patients with advancedprostate cancer. 27 Several pharmacometrics groups in

Figure 1. Brief history of pharmacometrics. The key milestones in the growth of this discipline are depicted.

8/13/2019 151S157S

3/7

PHARMACOMETRICS 2020

IMPACT 153S

the industry are now engaged early in developmentto guide trial design and go/no-go decisions.

STRATEGIC 2020 GOALS

Despite the success of pharmacometrics, there aresignificant challenges to overcome. The strategicgoals can be categorized into 2 broad areas: (1)enhanced efficiency of the current scope and (2)innovation to expand the future scope of pharma-cometrics. Scope is defined here as the quantityand type of pharmacometric projects. These goalsare intended for ind ustry, academia, and regula-tory agencies. The spe cific strategic goals of the

FDAs Division of Pharma cometrics are presentedelsewhere. 28

Enhancing Efficiency of Current Scope

The SWOT (strengths, weaknesses, opportunities,threats) analysis 29 suggests that lack of enoughtrained scientists, lack of motivation to share suc-cess stories, and lack of efficient tools are some ofthe important weaknesses. In Table I, a set of strate-gic goals are presented for consideration. Ardentcommitment by each of us will dictate the level ofaccomplishment.

Figure 2. Selected case studies that highlight the value of pharmacometric analyses. These case studies aided in demonstrating thevalue of exposureresponse modeling and simulation in trial design and in drug approval and labeling. The symbols shown in theDrug column signify time and cost savings and improved product profile (better dose). The list is not meant to be exclusive.

8/13/2019 151S157S

4/7


GOBBURU

Train 500 Pharmacometricians

There are several challenges to obtain training in thefield of pharmacometrics. US educational institu-tions produce about 75 000 statisticians and an equal

number of medical doctors per year. Of course, onlya portion of these graduates elect to work in thepharmaceutical sector. The number of pharmacom-etricians trained in the United States is probablyabout 5 per year, primarily because of 2 reasons:(1) the number of training institutions is limited,and (2) pharmacometric research is not well sup-ported by federal research grants. Even if these chal-lenges are addressed, pharmacometric training willneed to differ from a typical graduate curriculum.Training can be considered with 3 different specialtytracks, as described below. All 3 tracks will need thesame core pharmacometric expertise.

Core expertise . The goal of gaining core expertise isto allow scientists to perform pharmacometric analysisindependently. Pharmacometricians will have tomaster several fundamental PK, clinical pharmacol-ogy, and statistical concepts and develop the tools toimplement these concepts. Most universities areself-sufficient in providing this type of training.Such modelers will need to have reasonable under-standing of statistical concepts ranging from mixed-effects modeling to clinical trial design.

Technical track . Scientists can elect to develop in-depth skills and knowledge of mechanistic, semi-mechanistic, or empirical modeling. Advancedtechnical training might involve more statisticalcourses and project work pertaining to, for example,analysis of discrete data and Bayesian theory. Mostuniversities can provide this type of training.Graduates of the technical track will play a key rolein developing standards for analysis.

Disease track . In addition to acquiring core expertise,pharmacometricians must have advanced know ledge

of a particular disease area. As the influence of phar-macometrics expands, the need for specialization indifferent diseases both during drug development andin the clinical setting increases. For example, design-

ing the next depression trial will require not onlymodeling (technical) skills but also understandingthe clinical setting. Disease track training requiresthat the scientist undertakes medical or pharmacol-ogy courses. A PharmD program with clinical spe-cialization along with the core course work inpharmacometrics would comprise such training.Graduates of the disease track will play a key role intherapeutic (eg, dose) decisions.

Drug development track. In addition to gaining coreexpertise, pharmacometricians must have adv ancedknowledge of drug developmentspecifically howdecisions are made. Such training is only possible ifacademic institutions collaborate with industry andregulatory agencies. Reviewing literature and attend-ing lectures on drug development will not suffice.Scientists will need to apply pharmacometricapproaches to influence real-world decisions. Forexample, using exposureresponse analysis as evi-dence of effectiveness requires skills beyond techni-cal skills. It requires (1) understanding of whatcomprises evidence of effectiveness, (2) knowledgeabout the organization responsible for making thefinal decisions, and (3) an effective strategy (consist-ing of judgment and skills in communication andnegotiation) to influence the decision. Graduates ofthe drug development track will play a key role in

bringing more business to the group. There is anurgent need for scientists with this training.

To increase training opportunities, we needfinancing and expertise. Industry should be themajor source of sustained financial support. Unlessmore internal work is generated by industry, ouracademic research cannot be fully supported for train-ing future scientists and methodological innovation.Industry and academia could benefit from investingin development of detailed disease and drug trial

models using data from early experiments or trials.Data from unsuccessful programs are seldom rigor-ously analyzed to learn and apply the lessons tofuture programs. Academic researchers should begiven access to such data for building disease anddrug trial models, which in turn can be used toguide industry decisions. There may be merit informing an academic consortium, for example, todevelop tools for a specific set of diseases that areactive in terms of development. Professional socie-ties and regulatory agencies can contribute finan-cially, but only nominally. Both regulatory agencies

Table I Proposed Strategic 2020 Goals for theField of Pharmacometrics

Strategic 2020 Goals

Train 500 pharmacometriciansDesign 100% trials using advanced simulationsDevelop data and analysis standards for 15 indicationsShare 250 case studies a

a. Of the 250 case studies, 25 should be presented at conferences whereclinicians and investigators attend.

8/13/2019 151S157S

5/7


IMPACT 155S

and industry can play a major role in training drugdevelopment track scientists.

Design 100% Trials Using Advanced Simulations

One of the challenges is that pharmacometric work isnot considered critical path in terms of managingdrug development. That is, pharmacometricians arenot routinely considered part of the decision-makingteam with respect to trial design and analysis. Alltrials are designed using sample size simulations.Published reports have shown that pharmacometric-

based simulations allow more informed decisions.The conventional simulations help determine thesample size given a projected drug effect and varia-

bility. In this case, the goal is not optimization.However, pharmacometric-based simulations pro-vide the basis for the projected drug effect and, moreimportant, strategies on how to maximize the suc-cess rate. The low productivity of pharmaceuticalresearch and development (50%) in the late phasesattests to the inefficiency of the conventional app-roach to designing trials. On the contrary, from thesuccess stories published by the industry and theFDA, investment in pharmacometrics has highreturns and low risks. We should strive to design allclinical trials using pharmacometric approaches by2020. The PhRMA survey of 10 companies withpharmacometrics groups indicates that senior leader-

ship recognizes modeling and simulation to increasethe chance of trial and regulatory filing success.

Develop Data and AnalysisStandards for 15 Indications

More projects will entail more routine work, whichcalls for standardization and automation to improveproductivity (ie, more quality reports per unit oftime). Anecdotal evidence indicates that time con-straints often limit the extent of pharmacometricinvolvement in the industry. Most often, the initialanalysis of late-phase clinical trials for a givenindication will be identical. The subsequent analy-sis could be tailored for each drug. Having consen-sual standards for data analysis and reportingautomates most parts of the pharmacometric analy-sis, leaving more think time, and decreases uncer-tainty regarding decision making. Standardizationalso improves consistency within and across organi-zations. Scientists from other disciplines will gainmore comfort reviewing results presented in thesame format for a given type of problem. For exam-ple, Tornoe et al 30 describe the data and analysis

standards for a thorough QT study. 31 Such standardsrender regulatory review more predictable.Additionally, the analysis time is reduced with theautomated QT knowledge management system. The

Coalition Against Major Diseases (CAMD) initiativeaims to bring several companies together not only toshare data but also to develop standards for data anddisease models. The FDAs Antiviral InformationManagement System (AIMS) uses data and analysisstandards for automating modeling and simulation ofanti-HCV trials. 32

A related and important requirement for improvedefficiency is the availability of efficient tools to cre-ate and manage knowledge. Currently, analysis datasets and results are saved as flat files as opposed toa database that allows cross-trial analysis. The soft-ware programs used to format, model, and simulatedata and to process and archive results are com-pletely different. Development of more integratedsoftware is critical to industrialize pharmacometricprojects.

Share 250 Case Studies

Despite the increasing number of projects, organiza-tions have not been sharing the success (or failure)stories of pharmacometrics. The number of publica-tions is not commensurate with the number of pharma-cometric projects reported in the PhRMA survey.

There could be several reasons for this discrepancy, themost important being the organizations policy withrespect to publications. Most scientific journals do notpublish articles with blinded drug names. Yet sharingthe success stories with respect to decision making isone of the most critical steps required for expandingour field. Knowing the name and the specifics of thedrug is not necessary to appreciate the drug develop-ment problem and the pharmacometric-based solu-tion. Organizations such as the American Conferenceon Pharmacometrics (ACoP), American Society forClinical Pharmacology and Therapeutics (ASCPT),American Association for Pharmaceutical Scientists(AAPS), and American College of Clinical Pharmacology(ACCP) should join forces in creating a free-for-all web-

based resource to share success stories with fewerconstraints. An obvious advantage of such a central-ized resource is streamlining the impact metrics.

Exemplary case studies should be presentedat clinical conferences such as the AmericanHeart Association and American Society forClinical Oncology. Reaching out to nonpharma-cometricians will catalyze the growth of ouropportunities.

8/13/2019 151S157S

6/7


GOBBURU

Innovation to Expand Future Scope

A myriad of external factors will dictate the scope ofour business. Figure 3 presents some of the key driv-ers. An increase in consumers and patients expecta-tions will emerge as a major source of opportunities.An important opportunity would be in health tech-

nology assessment or comparative effectiveness. TheUnited Kingdom already has an established infra-structure, NICE, 33 to assess good value for money byweighing up the cost and benefits of treatments.

Other opportunities, according to Figure 3,include increasing the use of systems biology mod-els in discovery to identify new targets, tailoringindications for targeted populations, integrating evi-dence from global clinical trials, and conductingmore efficient safety assessments. These opportuni-ties call for collaboration across multiple disciplinesto develop rational methods.

CONCLUDING REMARKS

The field of pharmacometrics has come a long waywith respect to its size and achievements. The hardwork of numerous dedicated scientists in variousorganizations has come to fruition. Demand isincreasing in quantity and complexity. However, the

supply is trailing this high demand, in both quantityand efficiency. The current report proposes the topstrategic goals that we can aspire to achieve by year2020. The success depends on our commitment toaccomplishing these goals.

The author wishes to thank Drs. Carl Peck, Steven Shafer,Donald Stanski, Robert Powell, Marc Pfister, Raj Madabushi,Pravin Jadhav, Hao Zhu, Christine Garnett for helpful commentson the manuscript.

Financial disclosure: The articles in this supplement are spon-sored by the American Conference on Pharmacometrics.

Figure 3. The external drivers and the potential opportunities for pharmacometrics and related fields (eg, informatics, statistics) in the future.

8/13/2019 151S157S

7/7


IMPACT 157S

REFERENCES

1. Wagner JG. History of pharmacokinetics. Pharmacol Ther .1981;12:537-562.

2. Nagashima R, OReilly RA, Levy G. Kinetics of pharmacologiceffects in man: the anticoagulant action of warfarin. Clin PharmacolTher . 1969;10:22-35.3. Sheiner LB, Stanski DR, Vozeh S, Miller RD, Ham J. Simultaneousmodeling of pharmacokinetics and pharmacodynamics: applica-tion to d-tubocurarine. Clin Pharmacol Ther . 1979;25:358-371.4. Holford NH, Sheiner LB. Kinetics of pharmacologic response.Pharmacol Ther . 1982;16:143-166.5. Peck CC, Barr WH, Benet LZ, et al. Opportunities for integrationof pharmacokinetics, pharmacodynamics, and toxicokinetics inrational drug development. Clin Pharmacol Ther . 1992;51:465-473.6. Sheiner LB, Beal SL, Rosenberg B, Marathe VV. Forecastingindividual pharmacokinetics. Clin Pharmacol Ther . 1979;26:294-305.7. Peck CC, Barrett BB. Nonlinear least-squares regression programsfor microcomputers. J Pharmacokinet Biopharm . 1979;7:537-541.8. Weiner DL. NONLIN84/PCNONLIN: software for the statisticalanalysis of nonlinear models. Methods Find Exp Clin Pharmacol .1986;8:625-628.9. Chan PLS, Holford NHG. Drug treatment effects on diseaseprogression. Annu Rev Pharmacol Toxicol . 2001;41:625-659.10. Gobburu JV, Lesko LJ. Quantitative disease, drug, and trialmodels. Ann Rev Pharmacol Toxicol . 2008;49:291-301.11. Eddy DM, Schlessinger L. Archimedes: a trial-validated modelof diabetes. Diabetes Care . 2003;26:3093-3101.12. Bangs A. Predictive biosimulation and virtual patients in phar-maceutical R and D. Stud Health Technol Inform . 2005;111:37-42.13. Landersdorfer CB, Jusko WJ. Pharmacokinetic/pharmacodynamicmodelling in diabetes mellitus. Clin Pharmacokinet . 2008;47:417-448.14. Danhof M, de Jongh J, De Lange EC, et al. Mechanism-basedpharmacokinetic-pharmacodynamic modeling: biophase distribu-tion, receptor theory, and dynamical systems analysis. Annu RevPharmacol Toxicol . 2007;47:357-400.15. Dobin RE. Regulation of the medical use of psychedelics andmarijuana. Doctoral dissertation, Harvard University. http://www.maps.org/dissertation/dissertation.pdf. 2000:168-169. Accessed

June 1, 2010.16. Egan TD, Muir KT, Hermann DJ, Stanski DR, Shafer SL. Theelectroencephalogram (EEG) and clinical measures of opioid potency:defining the EEG-clinical potency relationship (fingerprint) withapplication to remifentanil. Int J Pharm Med . 2001;15:11-19.

17. Reigner BG, Williams PE, Patel IH, Steimer JL, Peck C, van BP.An evaluation of the integration of pharmacokinetic and pharma-codynamic principles in clinical drug development: experiencewithin Hoffmann La Roche. Clin Pharmacokinet . 1997;33:142-152.18. Olson SC, Bockbrader H, Boyd RA, et al. Impact of popula-tion pharmacokinetic-pharmacodynamic analyses on the drug

development process: experience at Parke-Davis. Clin Pharmacokinet .2000;38:449-459.19. Zhang L, Sinha V, Forgue ST, et al. Model-based drug devel-opment: the road to quantitative pharmacology. J PharmacokinetPharmacodyn . 2006;33: 369-393.20. Lalonde RL, Kowalski KG, Hutmacher MM, et al. Model-baseddrug development. Clin Pharmacol Ther . 2007;82:21-32.21. Lee H, Yim DS, Zhou H, Peck CC. Evidence of effectiveness: howmuch can we extrapolate from existing studies? AAPS J . 2005;7:E467-E474.22. Bhattaram VA, Booth BP, Ramchandani RP, et al. Impact ofpharmacometrics on drug approval and labeling decisions: asurvey of 42 new drug applications. AAPS J . 2005;7:E503-E512.23. Li F, Nandy P, Chien S, Noel GJ, Tornoe CW. Pharmacometrics-

based dose selection of levofloxacin as a treatment for postexpo-sure inhalational anthrax in children. Antimicrob AgentsChemother . 2010;54:375-379.24. Stone J, Banefield C, Pfister M, et al. Model-based drug devel-opment survey finds pharmacometrics impacting decision mak-ing in the pharmaceutical industry. J Clin Pharmacol . 2010;50(suppl 1):20S-30S.25. Hale MD, Nicholls AJ, Bullingham RE, et al. The pharmacok-inetic-pharmacodynamic relationship for mycophenoalte mofetilin renal transplant. Clin Pharmacol Ther . 1998;64:672-683.26. Wang Y, Bhattaram AV, Jadhav PR, et al. Leveraging priorquantitative knowledge to guide drug development decisions andregulatory science recommendations: impact of FDA pharmaco-metrics during 2004-2006. J Clin Pharmacol . 2008;48:146-156.27. Firmagons approval history. www.accessdata.fda.gov/scripts/cder /drugsa t fda / index .c fm?fuseac t ion=search .Labe l_ApprovalHistory. Accessed May 21, 2010.28. Division of Pharmacometrics, Office of Clinical Pharmacology,

Office of Translational Sciences, Center for Drug Evaluation andResearch, Food and Drug Administration. http://www.fda.gov/AboutFDA/CentersOffices/CDER/ucm167032.htm. Accessed June1, 2010.29. Wetherington J, Pfister M, Banefield C, et al. Model-baseddrug development: strengths, weaknesses, opportunities andthreats for broad implementation in drug development. J ClinPharmacol . 2010;50(suppl 1):31S-46S.30. Tornoe CW, Garnett CE, Wang Y, Florian J, Li M, Gobburu JVS.Creation of a knowledge management system for QT analyses. J ClinPharmacol . (in press).31. Romero K, Corrigan B, Tornoe C, et al. Pharmacometrics as adiscipline is entering the industrialization phase: standards,automation, knowledge sharing and training are critical for futuresuccess. J Clin Pharmacol . 2010;50(suppl 1):20S-31S.32. Jadhav P, Neal L, Florian J, et al. Antiviral InformationManagement System (AIMS): a prototype for operational innovationin drug development. J Clin Pharmacol . 2010;50(suppl 1):50S-55S.33 . http://www.nice.org.uk/aboutnice/scientificadvice/AboutScientificAdvice.jsp. Accessed June 1, 2010.

For reprints and permission queries, please visit SAGEs Web site at http://www.sagepub.com/journalsPermissions.nav.

151S–157S

Documents

Transcript of 151S–157S