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

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)

References

  1. 1.
    Sheiner LB, Beal SL (1981) Evaluation of methods for estimating population pharmacokinetic parameters. II. Biexponential model and experimental pharmacokinetic data. J Pharmacokinet Biopharm 9:635–651CrossRefPubMedGoogle Scholar
  2. 2.
    Sheiner LB, Beal SL (1983) Evaluation of methods for estimating population pharmacokinetic parameters. III. Monoexponential model: routine clinical pharmacokinetic data. J Pharmacokinet Biopharm 11(3):303–319CrossRefPubMedGoogle Scholar
  3. 3.
    Sheiner LB, Beal SL (1980) Evaluation of methods for estimating population pharmacokinetics parameters. I. Michaelis-Menten model: routine clinical pharmacokinetic data. J Pharmacokinet Biopharm 8:553–571CrossRefPubMedGoogle Scholar
  4. 4.
    Bonate PL (2011) Pharmacokinetic-pharmacodynamic modeling and simulation, 2nd edn. Springer, New YorkCrossRefGoogle Scholar
  5. 5.
    Li A (ed) (2007) Drug-drug interactions in pharmaceutical development. Hoboken, WileyGoogle Scholar
  6. 6.
    Ludden TM (1997) Evaluation of potential drug-drug interactions using the population approach. COST B1 medicine: the population approach: measuring and managing variability in reponse, concentration, and dose. European Commission, Geneva, pp 39–46Google Scholar
  7. 7.
    U.S. Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research (2012) Guidance for industry: drug interaction studies—study design, data analysis, implications for dosing, and labeling recommendations (Draft)Google Scholar
  8. 8.
    European Medicines Agency (EMA) (2013) Guideline on the investigation of drug interactionsGoogle Scholar
  9. 9.
    Chow AT, Earp JC, Gupta M, Hanley W, Hu C, Wang DD et al (2013) Utility of population pharmacokinetic modeling in the assessment of therapeutic protein-drug interactions. J Clin Pharmacol 54:593–601CrossRefPubMedGoogle Scholar
  10. 10.
    Janssen Biotech I (2012) STELARA (ustekinumab) Package InsertGoogle Scholar
  11. 11.
    GlaxoSmithKline (2015) PROMACTA (eltrombopag) Package InsertGoogle Scholar
  12. 12.
    International conference on harmonisation of technical requirements for registration of pharmaceuticals for human use. Statistical Principles for Clinical Trials (E9), 1998Google Scholar
  13. 13.
    United States Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research, Center for Biologics Evaluation and Research (1999) Guidance for industry: population pharmacokineticsGoogle Scholar
  14. 14.
    Byon W, Smith MK, Chan P, Tortorici MA, Riley S, Dai H et al (2013) Establishing best practices and guidance in population modeling: an experience with an internal population pharmacokinetic analysis guidance. CPT. 2:1–8Google Scholar
  15. 15.
    de Montjoie AJ (2009) Introducing the CDISC standards: new efficiencies for medical research. CDISC, AustinGoogle Scholar
  16. 16.
    CDISC Submission Data Standards Team (2004) Study data tabulation model implementation guide: human clinical trials. CDISC, AustinGoogle Scholar
  17. 17.
    The Uppsala Monitoring Centre (2015) World Health Organization (WHO) Drug dictionary enhancedGoogle Scholar
  18. 18.
    Mu S, Ludden TM (2003) Estimation of population pharmacokinetic parameters in the presence of non-compliance. J Pharmacokinet Pharmacodyn 30:53–81CrossRefPubMedGoogle Scholar
  19. 19.
    Vrijens B, Goetghebeur E (1999) The impact of compliance in pharmacokinetic studies. Stat Methods Med Res 8:247–262CrossRefPubMedGoogle Scholar
  20. 20.
    CDISC Submission Data Standards Team (2009) Analysis data model (ADaM) implementation guide. CDISC, AustinGoogle Scholar
  21. 21.
    Chowdury BA, Staab A, Liesenfeld K-H, Burger C, Wiersema M (2005) Producing NONMEM dataset using a standard SAS program.In: Presented at the Population Analysis Group Europe (PAGE), Pamploma, SpainGoogle Scholar
  22. 22.
    Varma MV, Pang KS, Isoherranen N, Zhao P (2015) Dealing with the complex drug–drug interactions: towards mechanistic models. Biopharm Drug Dispos 36:71–92CrossRefPubMedGoogle Scholar
  23. 23.
    Hachad H, Raqueneau-Majilessie I, Levy RH (2010) A useful tool for drug interaction evaluation: the University of Washington Metabolism and Transporter Drug Interaction Database. Hum Genom 5:61–72CrossRefGoogle Scholar
  24. 24.
    Pitsiu M, Hussein Z, Majid O, Aarons L, de Longueville M, Stockis A (2004) Retrospective population pharmacokinetic analysis of cetirizine in children aged 6 months to 12 years. Br J Clin Pharmacol 57:402–411CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Elsherbiny D, Cohen K, Jansson B, Smith P, McIlleron H, Simonsson U (2009) Population pharmacokinetics of nevirapine in combination with rifampicin-based short course chemotherapy in HIV- and tuberculosis-infected South African patients. Eur J Clin Pharmacol 65:71–80CrossRefPubMedGoogle Scholar
  26. 26.
    Vermeulen A, Piotrovski VK, Ludwig EA (2007) Population pharmacokinetics of risperidone and 9-hydroxyrisperidone in patients with acute episodes associated with bipolar I disorder. J Pharmacokinet Pharmacodyn 34:183–206CrossRefPubMedGoogle Scholar
  27. 27.
    Zingmark P-H, Ekblom M, Odergren T, Ashwood T, Lyden P, Karlsson MO (2003) Population pharmacokinetics of clomethiazole and its effect on the natural course of sedation in acute stroke patients. Br J Clin Pharmacol 56:173–183CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Jorga K, Banken L, Fotteler B, Snell P, Steimer J-L (2000) Population pharmacokinetics of levodopa in patients with Parkinson’s Disease treated with tolcapone. Clin Pharmacol Ther 67:610–620CrossRefPubMedGoogle Scholar
  29. 29.
    Zhou H, Choi L, Lau H, Bruntsch U, de Vries EEGE, Eckhardt G et al (2000) Population pharmacokinetics/toxicodynamics (PK/TD) relationship of SAM486A in phase I studies in patients with advanced cancers. J Clin Pharmacol 40:275–283CrossRefPubMedGoogle Scholar
  30. 30.
    Jorga KM, Fotteler B, Banken L, Snell P, Steimer JL (2000) Population pharmacokinetics of tolcapone in parkinsonian patients in dose finding studies. Br J Clin Pharmacol 49:39–48CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Janssen Pharmaceuticals I (2014) Sporanox (itraconazole) Package InsertGoogle Scholar
  32. 32.
    Lehr T, Staab A, Trommeshauser D, Schaefer HG, Kloft C (2010) Semimechanistic population pharmacokinetic drug-drug interaction modeling of a long half-life substrate and itraconazole. Clin Pharmacokinet 49:53–66CrossRefPubMedGoogle Scholar
  33. 33.
    Frechen S, Junge L, Saari TI, Abbas Suleiman A, Rokitta D, Neuvonen PJ et al (2013) A semiphysiological population pharmacokinetic model for dynamic inhibition of liver and gut wall cytochrome P450 3A by voriconazole. Clin Pharmacokinet 52:763–781CrossRefPubMedGoogle Scholar
  34. 34.
    Lu JF, Blaschke T, Flexner C, Rosenkranz SL, Sheiner LB, AIDS Clinical Trial Protocol 378 Investigators (2002) Model-based analysis of the pharmacokinetic interations between ritonavir, nelfinavir, and saquinavir after simultanous and staggered oral administration. Drug Metab Dispos 30:1455–1461CrossRefPubMedGoogle Scholar
  35. 35.
    Grasela TH Jr, Antal EJ, Ereshefsky L, Wells BG, Evans RL, Smith RB (1987) An evaluation of population pharmacokinetics in therapeutic trials. Part II. Detection of a drug-drug interaction. Clin Pharmacol Ther 42:433–441CrossRefPubMedGoogle Scholar
  36. 36.
    Renard D, Bouillon T, Zhou P, Flesch G, Quinn D (2015) Pharmacokinetic interactions among imatinib, bosentan and sildenafil, and their clinical implications in severe pulmonary arterial hypertension. Br J Clin Pharmacol 80:75–80CrossRefPubMedGoogle Scholar
  37. 37.
    Schmidli H, Peng B, Riviere GJ, Capdeville R, Hensley M, Gathmann I et al (2005) Population pharmacokinetics of imatinib mesylate in patients with chronic-phase chronic myeloid leukaemia: results of a phase III study. Br J Clin Pharmacol 60:35–44CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Hutmacher MM, Kowalski KG (2015) Covariate selection in pharmacometric analyses: a review of methods. Br J Clin Pharmacol 79:132–147CrossRefPubMedPubMedCentralGoogle Scholar
  39. 39.
    Bruno R, Washington CB, Lu J-F, Lieberman G, Banken L, Klein P (2005) Population pharmacokinetics of trastuzumab in patients with HER2 + metastatic breast cancer. Cancer Chemother Pharmacol 56:361–369CrossRefPubMedGoogle Scholar
  40. 40.
    Johnson TN, Kerbusch T, Jones B, Tucker GT, Rostami-Hodjegan A, Milligan PA (2006) Assessing the efficiency of mixed effects modeling in quantifying metabolism based drug-drug interactions: using in vitro data as an aid to assess study power. Pharm Stat 8:186–202CrossRefGoogle Scholar
  41. 41.
    Zhou H (2006) Population-based assessments of clinical drug-drug interactions: qualitative indices or quantitative measures? J Clin Pharmacol 46:1268–1289CrossRefPubMedGoogle Scholar
  42. 42.
    Lee PID (2001) Design and power of a population pharmacokinetic study. Pharm Res 18:75–82CrossRefPubMedGoogle Scholar
  43. 43.
    Yang S, Beerahee M (2011) Power estimation using a population pharmacokinetics model with optimal design by clinical trial simulations: application in pharmacokinetic drug-drug interaction studies. Eur J Clin Pharmacol 67:225–233CrossRefPubMedGoogle Scholar
  44. 44.
    Wang DD, Zhu M, Kassir N, William H, Earp JC, Chow A et al. (2013) The utility of a population approach in DDI assessments: an evaluation of using simulation approaches. In: Presented at the annual meeting of the american conference on pharmacometrics, Fort LauderdaleGoogle Scholar
  45. 45.
    Kowalski KG, Hutmacher M (2001) Design evaluation for a population pharmacokinetic study using clinical trial simulations: a case study. Stat Med 20:75–91CrossRefPubMedGoogle Scholar
  46. 46.
    Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research (2004) Clinical pharmacology and biopharmaceutics report templateGoogle Scholar
  47. 47.
    Earp JC, Fang L, Ma L, Wang Y-M, Rosario M, Dirks NL et al. (2013) Assessing labeling claims for drug interactions using a population PK approach: vedolizumab. In: Presented at the annual meeting of the American conference on pharmacometrics, Fort LauderdaleGoogle Scholar
  48. 48.
    Takeda Pharmaceuticals America I (2014). Entyvio (vedolizumab) Package InsertGoogle Scholar

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

Personalised recommendations