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Research ArticleArticle

Utilizing Drug-Drug Interaction Prediction Tools during Drug Development: Enhanced Decision Making Based on Clinical Risk

Carole E. Shardlow, Grant T. Generaux, Christopher C. MacLauchlin, Nicoletta Pons, Konstantine W. Skordos and Jackie C. Bloomer
Drug Metabolism and Disposition November 2011, 39 (11) 2076-2084; DOI: https://doi.org/10.1124/dmd.111.039214
Carole E. Shardlow
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Grant T. Generaux
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Christopher C. MacLauchlin
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Nicoletta Pons
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Konstantine W. Skordos
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Jackie C. Bloomer
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Abstract

Several reports in the literature present the utility and value of in vitro drug-metabolizing enzyme inhibition data to predict in vivo drug-drug interactions in humans. A retrospective analysis has been conducted for 26 GlaxoSmithKline (GSK) drugs and drug candidates for which in vitro inhibition parameters have been determined, and clinical drug interaction information, from a total of 46 studies, is available. The dataset, for drugs with a diverse range of physiochemical properties, included both reversible and potentially irreversible cytochrome P450 inhibitors for which in vitro inhibition parameters (IC50 or KI/kinact as appropriate) were determined using standardized methodologies. Mechanistic static models that differentiated reversible and metabolism-dependent inhibition, and also considered the contribution of intestinal metabolism for CYP3A4 substrates, were applied to estimate the magnitude of the interactions. Several pharmacokinetic parameters, including total Cmax, unbound Cmax, as well as estimates of hepatic inlet and liver concentration, were used as surrogates for the inhibitor concentration at the enzyme active site. The results suggest that estimated unbound liver concentration or unbound hepatic inlet concentration, with consideration of intestinal contribution, offered the most accurate predictions of drug-drug interactions (occurrence and magnitude) for the drugs in this dataset. When used with epidemiological information on comedication profiles for a given therapeutic area, these analyses offer a quantitative risk assessment strategy to inform the necessity of excluding specific comedications in early clinical studies and the ultimate requirement for clinical drug-drug interaction studies. This strategy has significantly reduced the number of clinical drug interaction studies performed at GSK.

Footnotes

  • Article, publication date, and citation information can be found at http://dmd.aspetjournals.org.

    doi:10.1124/dmd.111.039214.

  • ABBREVIATIONS:

    DDI
    drug-drug interaction
    P450
    cytochrome P450
    fm
    fraction substrate eliminated by a single (P450) pathway
    Fg
    gut availability (fraction of absorbed substrate escaping gut metabolism)
    PK
    pharmacokinetics
    GSK
    GlaxoSmithKline
    BCS
    Biopharmaceutics Classification System
    AUC
    area under the plasma or blood concentration vs time curve
    HLM
    human liver microsomes
    KI
    concentration of inhibitor required to achieve half-maximal inactivation
    Ki
    inhibition constant
    kinact
    maximal rate constant of enzyme inactivation
    QWBA
    quantitative whole-body autoradiography
    ka
    absorption rate constant
    Fa
    fraction of dose absorbed
    D
    dose
    Qh
    liver blood flow
    [I]
    concentration of inhibitor available to enzyme
    Qg
    intestinal blood flow
    kdeg
    rate constant for enzyme degradation
    AUCi/AUC
    ratio of AUC in presence and absence of inhibitor
    TP
    true positive
    TN
    true negative
    FP
    false positive
    FN
    false negative
    AD
    average deviation
    RMSE
    root-mean-square error
    NCE
    new chemical entity
    PoC
    proof of concept.

  • Received March 28, 2011.
  • Accepted August 10, 2011.
  • Copyright © 2011 by The American Society for Pharmacology and Experimental Therapeutics
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Drug Metabolism and Disposition: 39 (11)
Drug Metabolism and Disposition
Vol. 39, Issue 11
1 Nov 2011
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Research ArticleArticle

PREDICTING DDIs AND APPLYING CONTEXT OF CLINICAL RISK

Carole E. Shardlow, Grant T. Generaux, Christopher C. MacLauchlin, Nicoletta Pons, Konstantine W. Skordos and Jackie C. Bloomer
Drug Metabolism and Disposition November 1, 2011, 39 (11) 2076-2084; DOI: https://doi.org/10.1124/dmd.111.039214

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Research ArticleArticle

PREDICTING DDIs AND APPLYING CONTEXT OF CLINICAL RISK

Carole E. Shardlow, Grant T. Generaux, Christopher C. MacLauchlin, Nicoletta Pons, Konstantine W. Skordos and Jackie C. Bloomer
Drug Metabolism and Disposition November 1, 2011, 39 (11) 2076-2084; DOI: https://doi.org/10.1124/dmd.111.039214
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