PHARMACEUTICAL STATISTICS

Pharmaceut. Statist. 2009; 8: 173–175

Published online in Wiley InterScience

(www.interscience.wiley.com) DOI: 10.1002/pst.401

Editorial

Exposure Response – Getting the Dose Right

Dose regimen selection for confirmatory trialsand characterization of the dose–response (DR)relationship, for both effectiveness and safety, isarguably among the most important and difficulttasks in clinical drug development. Improper selec-tion of the dose regimen at the end of the learningphase of development is widely recognized as one ofthe key drivers of the high Phase 3 attrition ratecurrently plaguing the pharmaceutical industry [1].There is also evidence that, even after registration,dose-adjustments in the label continue to berequired with some frequency [2]. Reasons for thisare clear-cut: selecting too low a dose may preventadequate efficacy to be attained for the majority ofpatients, while doses higher than necessary typicallywill lead to safety issues. In recognition of theimportance of the problem, several recent initiativesaimed at improving the efficiency of drug develop-ment, such as the Food and Drug Administration(FDA’s) Critical Path Initiative [3,4] and PhRMA’sPharmaceutical Innovation working groups [1],have identified better clinical trial designs andanalysis methods for dose selection and character-ization of DR as key components of their proposedimprovement strategies.

By and large, the pharmaceutical industry stillrelies on a dose selection paradigm which has beenaround for over 40 years: collecting informationvia a sequence of trials aimed at determining,initially, a maximum tolerated dose (MTD), then amaximum ‘no effect’ dose, and eventually the bestguess of a dose (or doses) for the final confirmatory

trials. The last step generally uses statisticalhypothesis testing methods, as opposed to model-ing and estimation. One of the key pitfalls of thistraditional dose selection paradigm is that it does apoor job at integrating information across differenttrials and from other sources (e.g. similar drugs,studies with the same drug in other populations,etc.).

Different strategies have been proposed forimproving dose selection and DR characterizationin drug development, such as adaptive dose-ranging designs [1], optimal dose allocation [5],and model-based methods [6,7]. Modeling andsimulation (M&S) techniques, which have experi-enced a substantial growth and acceptance overthe past decade, play a prominent role in this folioof improvement strategies [8]. However, with a fewnotable and commendable exceptions, the use ofM&S for dose selection in the pharmaceuticalindustry has been mostly in a supportive role,often associated with exploratory analyses tofurther justify a selected dose, or to better planthe Phase 3 program.

Within the M&S umbrella, exposure–response(ER) modeling is a particularly promising area ofinvestigation, with great potential to improveunderstanding and characterization of DR and,by extension, dose selection. The motivationbehind the use of exposure information to improveDR characterization is two fold: (1) that theobserved variability in the dose–response relation-ship (for effectiveness and/or safety) is partlyexplained by inter-individual pharmacokinetic(PK) differences (e.g. variability in the elimination

Copyright r 2009 John Wiley & Sons, Ltd.

of a drug, etc.) and (2) exposure metrics are basedon time (e.g. concentration at a specified time)which then naturally incorporates the change inconcentration and therefore response over time(i.e. the time course of onset and duration can beunderstood more fully when the dependence ofeffect on concentration is considered). When PKand pharmacodynamics (PD) are coupled toprovide a complete description of the dose–exposure–response relationship we use the abbre-viation PKPD. Therefore, knowledge of theindividual PK response (i.e. exposure) can be usedto improve the accuracy and precision of DRcharacterization. ER modeling is quite feasible inpractice because PK information is routinelycollected in clinical studies throughout the devel-opment process. In particular, several healthauthorities recommend that drug concentrationdata be collected in Phase 2 dose-ranging studiesto enable characterization of concentration-effect(or ER) relationships in the population of interest[9,10] as a means of assessing the adequacy of thedose-regimen selected for subsequent confirmatoryclinical trials.

This special issue of Pharmaceutical Statisticsfeatures articles focusing on different aspects ofER modeling, with the goal of creating greaterawareness about designs and methods which canbe used to improve DR characterization and doseselection. A total of six articles are included in thisissue, some dealing more directly with the use ofER modeling for dose selection, while others do soin a more indirect way.

The opening article by Al-Sallami, Kumar,Landersdorfer, Bulitta and Duffull provides anintroductory overview of PK and PD (PKPD)modeling, aimed at scientists typically less familiarwith those techniques, such as statisticians andclinicians. Since the path toward improving drugdevelopment efficiency is certainly multidisciplin-ary, this article is aimed at bridging the knowledgegap among the various stakeholders involved byproviding a very brief overview of the models usedto describe the processes ranging from PK throughdisease progression.

Johnson, Kerbush, Jones, Tucker, Rostami-Hodjegan, and Milligan tackle a more specific,

but highly relevant topic: the benefit of mixed-effects modeling for quantifying drug–drug inter-actions (DDI), through the use of in vitromeasurements. DDI studies play a key role indetermining safe doses and identifying compoundswhose concurrent use is best avoided. The extra-polation of in vitro information coupled with theuse of adequately sophisticated mixed-effectsmodels, outlined in the paper, can providesubstantial savings in development costs and time.Their methods highlight the importance of in vitroinformation for supplementing clinical decision-making in drug development.

The article by Hsu addresses more directly theuse of ER modeling for DR characterization anddose selection. Through a simplified, yet realistic,modeling framework, it establishes a direct linkbetween exposure- and DR models. This isestablished in a comprehensive simulation studyto identify conditions where ER modeling isbeneficial and quantifying the magnitude of thebenefit, when present.

Jadhav, Zhang, and Gobburu discuss a casestudy in pediatric drug development in which priorinformation from similar compounds for the sameindication, and on the same compound in the adultpopulation, are used with M&S techniques toestablish a more efficient study design for detectingclinically meaningful effectiveness and whereappropriate adequate dose selection. In additionto dealing with the important, and not yetsufficiently studied problem of dose finding forpediatric populations, the article touches on thehighly relevant topic of integration of informationacross populations, phases of development, andcompounds.

The important, and difficult, problem of identify-ing disease-modifying effects in slowly progressingdiseases is discussed in the paper by Ploeger andHolford. Disease-modifying drug-effects differ fromsymptomatic drug-effects in that the benefits of thedrug [permanently] exceed the duration of treat-ment. Accurately characterizing this process istherefore of considerable significance, albeit fraughtwith difficulties, to clinical drug development. Theauthors address this important issue using a mixed-effects modeling framework for representation of

Copyright r 2009 John Wiley & Sons, Ltd. Pharmaceut. Statist. 2009; 8: 173–175

174 Editorial

both symptomatic and disease modifying drug-effects in the context of Parkinson’s disease. Theirmodel can be used to develop formal tests to assessthe assumption of disease modification for a givendrug. The approach can be easily extended toexplore DR in disease modification.

The final article in the special issue, byOgungbenro, Dokoumetzidis, and Aarons intro-duces the important topic of the design of PK andPKPD studies. Their discussions are based on thewell-defined area of optimal design of experiments.Although this topic is not new in the statisticiansarmamentarium, its application to nonlinearmixed-effects modeling is comparatively new. Inparticular, they review the main approaches thathave been proposed over the past decade foroptimizing the design of PK and PKPD studies, inthe context of parameter estimation. Optimizingthe design of PK and PKPD studies has beenshown, in limited examples, to improve theefficiency of the investigation to characterize theER relationship. Readers may find the compre-hensive list of references particularly useful forthose interested in learning more about thisimportant area.

When considered in combination, the articlesfeatured in this special issue cover a broad range ofimportant topics related to improving our under-standing of the ER relationship and, by extension,DR. The overall emphasis on the models describedin this issue has been based on those that have amechanistic flavor and therefore can be used toextrapolate findings to new circumstances. Whilethe approaches the articles depict are certain tohave a significant impact in advancing the cause ofimproving the efficiency of drug development, asustainable improvement in drug developmentcannot be fueled by methodological improvementsin design and analysis alone. The need foradequate allocation of resources in the learningphase of development is also required. In addition,a better balance in resource allocation across theentire development program, to optimize theoverall probability of success and expected netpresent value deserves special attention.

REFERENCES

1. Bornkamp B, Bretz F, Dmitrienko A, Enas G,Gaydos B, Hsu CH, Koenig F, Krams M, Liu Q,Neuenschwander B, Parke T, Pinheiro J, Roy A,Sax R, Shen F. Innovative approaches for designingand analyzing adaptive dose-ranging trials (withdiscussion). Journal of Biopharmaceutical Statistics2007; 17:965–995.

2. Stanski D, Rowland M, Sheiner L. Getting the doseright: report form the tenth European Federation ofPharmaceutical Sciences (EUFEPS) conference onoptimizing drug development. Journal of Pharma-cokinetics and Pharmacodynamics 2005; 32:199–211.

3. USA Food and Drug Administration. Challengeand opportunity on the critical path to new medicalproducts. USA Food and Drug Administration,2004.

4. USA Food and Drug Administration. Critical pathopportunities report. USA Food and Drug Admini-stration, 2006.

5. Dette H, Bretz F, Pepelyshev A, Pinheiro J.Optimal designs for dose finding studies. Journalof the American Statistical Association 2008; 103:1225–1237

6. Bretz F, Pinheiro J, Branson M. Combining multi-ple comparisons and modeling techniques in dose-response studies. Biometrics 2005; 61:738–748.

7. Krams M, Lees KR, Hacke W, Grieve AP,Orgogozo JM. Ford GA for the ASTIN studyinvestigators. ASTIN: an adaptive dose-responsestudy of UK-279, 276 in acute ischemic stroke.Stroke 2003; 34, 2543–2548.

8. Miller R, Ewy W, Corrigan BW, Ouellet D,Hermann D, Kowalski KG, Lockwood P, KoupJR, Donevan S, El-Kattan A, Li CSW, Werth JL,Feltner DE, Lalonde RL. How modeling andsimulation have enhanced decision making in newdrug development. Journal of Pharmacokinetics andPharmacodynamics 2005; 32:185–197.

9. International Conference on Harmonization. ICHTopic E4: dose response information to supportdrug registration. International Conference onHarmonization. 1994.

10. United States Food and Drug Administration.Guidance for industry: exposure–response relation-ships – study design, data analysis, and regulatoryapplications. United States Food and Drug Admin-istration. 2003.

Jose PinheiroStephen Duffull

Editorial 175

Copyright r 2009 John Wiley & Sons, Ltd. Pharmaceut. Statist. 2009; 8: 173–175