Methods and strategies for assessing uncontrolled drug–drug interactions in population pharmacokinetic analyses: results from the International Society of Pharmacometrics (ISOP) Working Group

  • Peter L. Bonate
  • Malidi Ahamadi
  • Nageshwar Budha
  • Amparo de la Peña
  • Justin C. Earp
  • Ying Hong
  • Mats O. Karlsson
  • Patanjali Ravva
  • Ana Ruiz-Garcia
  • Herbert Struemper
  • Janet R. Wade
Commentary

DOI: 10.1007/s10928-016-9464-2

Cite this article as:
Bonate, P.L., Ahamadi, M., Budha, N. et al. J Pharmacokinet Pharmacodyn (2016) 43: 123. doi:10.1007/s10928-016-9464-2

Abstract

The purpose of this work was to present a consolidated set of guidelines for the analysis of uncontrolled concomitant medications (ConMed) as a covariate and potential perpetrator in population pharmacokinetic (PopPK) analyses. This white paper is the result of an industry-academia-regulatory collaboration. It is the recommendation of the working group that greater focus be given to the analysis of uncontrolled ConMeds as part of a PopPK analysis of Phase 2/3 data to ensure that the resulting outcome in the PopPK analysis can be viewed as reliable. Other recommendations include: (1) collection of start and stop date and clock time, as well as dose and frequency, in Case Report Forms regarding ConMed administration schedule; (2) prespecification of goals and the methods of analysis, (3) consideration of alternate models, other than the binary covariate model, that might more fully characterize the interaction between perpetrator and victim drug, (4) analysts should consider whether the sample size, not the percent of subjects taking a ConMed, is sufficient to detect a ConMed effect if one is present and to consider the correlation with other covariates when the analysis is conducted, (5) grouping of ConMeds should be based on mechanism (e.g., PGP-inhibitor) and not drug class (e.g., beta-blocker), and (6) when reporting the results in a publication, all details related to the ConMed analysis should be presented allowing the reader to understand the methods and be able to appropriately interpret the results.

Keywords

Population pharmacokinetics Covariate modeling Drug interactions Concomitant Medications 

Supplementary material

10928_2016_9464_MOESM1_ESM.docx (425 kb)
Supplementary material 1 (DOCX 425 kb)

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Peter L. Bonate
    • 1
  • Malidi Ahamadi
    • 2
  • Nageshwar Budha
    • 3
  • Amparo de la Peña
    • 4
  • Justin C. Earp
    • 5
  • Ying Hong
    • 6
  • Mats O. Karlsson
    • 7
  • Patanjali Ravva
    • 8
  • Ana Ruiz-Garcia
    • 9
  • Herbert Struemper
    • 10
  • Janet R. Wade
    • 11
  1. 1.AstellasNorthbrookUSA
  2. 2.Merck and Co. Inc.North WalesUSA
  3. 3.Genentech Inc.South San FranciscoUSA
  4. 4.Eli Lilly and Company|ChorusIndianapolisUSA
  5. 5.U.S. Food and Drug AdministrationSilver SpringUSA
  6. 6.Novartis Pharmaceuticals CorporationEast HanoverUSA
  7. 7.Uppsala UniversityUppsalaSweden
  8. 8.Boehringer Ingelheim Pharmaceutical Inc.RidgefieldUSA
  9. 9.PfizerSan DiegoUSA
  10. 10.Parexel International, Inc.DurhamUSA
  11. 11.Occams Coöperatie U.A.AmstelveenThe Netherlands