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Research ArticleArticle

Static and Dynamic Projections of Drug-Drug Interactions Caused by Cytochrome P450 3A Time-Dependent Inhibitors Measured in Human Liver Microsomes and Hepatocytes

Elaine Tseng, Heather Eng, Jian Lin, Matthew A. Cerny, David A. Tess, Theunis C. Goosen and R. Scott Obach
Drug Metabolism and Disposition October 2021, 49 (10) 947-960; DOI: https://doi.org/10.1124/dmd.121.000497
Elaine Tseng
Medicine Design, Worldwide Research and Development, Pfizer Inc., Groton, Connecticut
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Heather Eng
Medicine Design, Worldwide Research and Development, Pfizer Inc., Groton, Connecticut
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Jian Lin
Medicine Design, Worldwide Research and Development, Pfizer Inc., Groton, Connecticut
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Matthew A. Cerny
Medicine Design, Worldwide Research and Development, Pfizer Inc., Groton, Connecticut
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David A. Tess
Medicine Design, Worldwide Research and Development, Pfizer Inc., Groton, Connecticut
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Theunis C. Goosen
Medicine Design, Worldwide Research and Development, Pfizer Inc., Groton, Connecticut
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R. Scott Obach
Medicine Design, Worldwide Research and Development, Pfizer Inc., Groton, Connecticut
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    Fig. 1.

    Comparison of TDI parameters obtained in HLM versus HHEP). (A) KI,u; (B) kinact; and (C) kinact/KI,u. Solid black lines represent unity, dotted and dashed lines represent 2-fold and 3-fold deviation from unity, and solid red line represents bias. Azithromycin, nelfinavir, terfenadine, paroxetine, and eplerenone are not shown in (A) and (B) because individual KI and kinact were not able to be determined.

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    Fig. 2.

    Predicted versus observed AUC ratios from mechanistic static Model 4 (A and B) and Simcyp modeling (C and D). (A and C) are results using human liver microsome–generated inactivation parameters, and (B and D) are results human hepatocyte–generated inactivation parameters. Solid black lines represent unity, dotted lines represent 2-fold and 3-fold deviation from unity, and red solid lines represent the bias.

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    Fig. 3.

    Model performance versus observed AUCR from mechanistic static Model 4 (A and B) and Simcyp modeling (C and D). (A and C) are results using human liver microsome–generated inactivation parameters, and (B and D) are results using human hepatocyte–generated inactivation parameters. Shaded area represents 0.5- to 2-fold criteria. Azi, azithromycin; Boc, boceprevir; Car, carfilzomib; Cla, clarithromycin; Con, conivaptan; Dil, diltiazem; Dis, disulfiram; Epl, eplerenone; Ery, erythromycin; Ima, imatinib; Mid, midostaurin; Nel IV, nelfinavir (IV midazolam); Nit, nitrendipine; Pan, panobinostat; Par, paroxetine; Ppv, propiverine; Pro, propranolol; Sim, simvastatin; Tab, tabimorelin; Tad, tadalafil; Tel, telaprevir; Tel IV, telaprevir (IV midazolam); Ter, terfenadine; Ver, verapamil.

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    Fig. 4.

    Classification of predicted (Pred) AUCR versus observed (Obs) AUCR using 1.25- and 2-fold cutoff criteria in mechanistic static models 1–4. Values in each section of the bar graphs represent the number of drugs that were predicted to be TP, TN, FP, or FN using liver microsome (A) or hepatocyte- (B) generated parameters.

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    Fig. 5.

    Classification of predicted (Pred) AUCR versus observed (Obs) AUCR using 1.25- and 2-fold cutoff criteria using Simcyp. Values in each section of the bar graphs represent the number of drugs that were predicted to be TP, TN, FP, or FN using HLM or HHEP generated parameters.

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    Fig. 6.

    Correlation of predicted AUC ratios (AUCR) from mechanistic static Model 4 and Simcyp modeling. (A) represents predictions using human liver microsome–generated inactivation parameters and (B) represents predictions using human hepatocyte–generated inactivation parameters. The solid black line represents unity.

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    TABLE 1

    Summary of observed clinical drug-drug interactions for CYP3A cleared drugs

    Drug NameInhibitor DoseSubstrate DosebClinical Interaction (AUCR)Clinical Interaction Reference
    Azithromycin500 mg once a day; 3 d15 mg oral midazolam1.23a(Yeates et al., 1996; Zimmermann et al., 1996)
    Boceprevir800 mg three times a day; 6 d4 mg oral midazolam5.05(FDA, 2011)
    Carfilzomib27 mg/m2 i.v.; various2 mg oral midazolam1.10(Wang et al., 2013)
    Clarithromycin500 mg twice a day; 7 d4 mg oral midazolam6.69a(Gorski et al., 1998; Gurley et al., 2006; Gurley et al., 2008; Quinney et al., 2008; Prueksaritanont et al., 2017)
    Conivaptan40 mg twice a day; 5 d2 mg oral midazolamc5.76(FDA, 2005)
    Diltiazem60 mg three times a day; 2 d2 mg oral midazolam3.93a(Backman et al., 1994; Friedman et al., 2011)
    Disulfiram500 mg single dose1 mg i.v. midazolam1.05(Kharasch et al., 1999)
    Eplerenone100 mg once a day; 6 d10 mg oral midazolam0.96(Cook et al., 2004)
    Erythromycin500 mg three times a day; 7 d4 mg oral midazolam4.12a(Olkkola et al., 1993; Zimmermann et al., 1996)
    Imatinib400 mg once a day; 7 d40 mg oral simvastatin2.92(O'Brien et al., 2003)
    Midostaurin100 mg single dose4 mg oral midazolam1.00(Dutreix et al., 2013)
    Nelfinavir1250 mg twice a day; 14 d2 mg oral midazolam
    1 mg i.v. midazolam
    4.29a
    1.83
    (Kirby et al., 2011)
    Nitrendipine20 mg single dose0.07 mg/kg i.v. plus infusion midazolam0.93 change in CL(Handel et al., 1988)
    Panobinostat20 mg every other day; 15 d5 mg oral midazolam1.04(Einolf et al., 2017)
    Paroxetine20 mg once a day; 15 d60 mg oral terfenadined0.97(Martin et al., 1997)
    Propiverine15 mg twice a day; 7 d2 mg oral midazolam1.46(Tomalik-Scharte et al., 2005)
    Propranolol40 mg four times a day; 2 d0.5 mg oral triazolam0.89(Friedman et al., 1988)
    Simvastatin10 mg once a day; 14 d15 ug/kg oral midazolam1.24(Kokudai et al., 2009)
    Tabimorelin3 mg/kg once a day; 7 d7.5 mg oral midazolam1.93(Zdravkovic et al., 2003)
    Tadalafil10 mg once a day; 14 d15 mg oral midazolam0.90(Ring et al., 2005)
    Telaprevir750 mg three times a day; 16 d2 mg oral midazolam
    0.5 mg i.v. midazolam
    13.5
    4.92
    (Garg et al., 2012)
    Terfenadine120 mg once a day; 3 d10 mg oral buspirone1.19(Lamberg et al., 1999)
    Verapamil80 mg three times a day; 2 d15 mg oral midazolam2.92(Backman et al., 1994)
    • ↵aAUCR was calculated as a weighted average AUCR based on number of subjects in each study at the same dose per route (eq. 1).

    • ↵bSubstate (single dose) was given at the final day of inhibitor dose.

    • ↵cSubstrate (once a day), 5 days.

    • ↵dSubstrate (twice a day), days 8 to 15, 8 days.

    • View popup
    TABLE 2

    Total and free inhibition parameters determined in human liver microsomes

    Reversible InhibitionTime-Dependent Inhibition
    Drug NameKiafu,micKi,uKI (S.E.)fu,mic (%CV)KI,ukinact (S.E.)kinact/KI,u
    µMat 0.01 mg/mldµMµMat 0.3 mg/mlµMmin−1ml·min−1·µmol−1
    Azithromycin>50.00.982>49.1NR0.650 (9)NRNR0.025
    Boceprevir11.90.99011.813.8 (1.7)0.766 (12)10.60.304 (0.010)28.8
    Carfilzomib2.190.9642.111.18 (0.22)0.473b0.5580.107 (0.007)192
    Clarithromycin43.90.98243.155.9 (13.4)0.647 (16)36.20.0812 (0.0086)2.25
    Conivaptan4.140.9293.841.04 (0.16)0.303 (4)0.3150.329 (0.013)1040
    Diltiazem20.30.98620.01.90 (0.28)0.696 (9)1.320.0109 (0.0004)8.24
    N-desmethyl DiltiazemcNDNDND0.961 (0.115)ND0.9610.00954 (0.00027)9.94
    Disulfiramc1.18ND1.1816.4 (5.2)ND16.40.129 (0.012)7.87
    Eplerenone>50.00.991>49.5202 (58)0.779 (10)1570.0223 (0.0029)0.142
    Erythromycin32.00.97931.323.3 (3.3)0.613 (38)14.30.0557 (0.0025)3.90
    Imatinib28.90.97428.216.4 (4.9)0.560 (15)9.180.0348 (0.0029)3.79
    Midostaurin2.790.2670.7400.360 (0.120)0.0118 (29)0.004250.0207 (0.0015)4870
    Nelfinavir1.460.5610.8161.44 (0.45)0.0408 (21)0.05880.510 (0.057)8680
    Nitrendipine1.370.9601.3210.4 (3.1)0.444 (22)4.620.0266 (0.0020)5.76
    Panobinostat4.990.9714.8430.3 (6.8)0.530 (9)16.10.0436 (0.0027)2.71
    Paroxetine13.40.83311.249.4 (22.0)0.143 (17)7.060.0277 (0.0084)3.92
    Propiverine8.050.9617.741.71 (0.23)0.451 (25)0.7710.0298 (0.0012)38.7
    Propranolol>50.00.978>48.9No TDI0.594 (3)No TDINo TDINo TDI
    Simvastatin0.1460.6510.095NR0.0585 (12)NRNR0.195
    Tabimorelin8.300.9738.081.98 (0.31)0.547 (12)1.080.0652 (0.0023)60.2
    Tadalafil8.550.9908.4613.0 (1.7)0.776 (4)10.10.143 (0.004)14.2
    Telaprevir11.60.99211.50.644 (0.109)0.806 (9)0.5190.108 (0.004)208
    Terfenadine0.2180.5590.1229.32 (5.85)0.0405 (3)0.3770.0276 (0.0111)73.2
    Verapamil12.90.97912.62.80 (0.54)0.610 (23)1.710.0487 (0.0023)28.5
    • %CV, percent coefficient of variation; ND, not determined (assume 1); NR, not reported (see data analysis section for the estimation of kinact/KI,u).

    • ↵aCalculated as measured IC50/2.

    • bBased on in silico modeling.

    • cTotal values were reported since unbound fractions were not determined.

    • dfu,mic was calculated from fu,mic measured at 0.3 mg/ml (n = 3 to 4) using equation from (Austin et al., 2002).

    • View popup
    TABLE 3

    Total and free inhibition parameters determined in human hepatocytes

    Reversible InhibitionTime-Dependent Inhibition
    Drug NameKiaKp,uubKi,uKI (S.E.)KI,ukinact (S.E.)kinact/KI,u
    µM%CVµMµMµMmin−1ml·min−1·µmol−1
    Azithromycin>25.05.70 (5)>14351.2 (17.3)2920.0327 (0.0032)0.112
    Boceprevir10.80.190 (0.5)2.0425.9 (10.7)4.920.0978 (0.0161)19.9
    Carfilzomib1.710.0160 (33)0.03007.76 (2.26)0.1260.0289 (0.0020)229
    Clarithromycin13.80.600 (7)8.257.45 (2.06)4.470.0112 (0.0007)2.51
    Conivaptan1.271.70 (8)2.160.634 (0.124)1.080.0182 (0.0010)16.9
    Diltiazem13.00.253 (10)3.2835.4 (6.6)8.960.0217 (0.0010)2.42
    N-desmethyl DiltiazemcNDNDND2.96 (1.12)2.960.0127 (0.0011)4.29
    DisulfiramNDNDNDNDNDNDND
    Eplerenone>25.00.141 (13)>3.53NRNRNR0.0166
    Erythromycin16.90.328 (9)5.0627.1 (19.7)8.130.0141 (0.0044)1.73
    Imatinib22.51.00 (16)22.529.4 (8.1)29.40.0202 (0.0014)0.687
    Midostaurin>25.00.005 (43)>0.1200.574 (0.165)0.002760.00566 (0.00041)2050
    Nelfinavir0.4961.70 (12)0.842NRNRNR6.24
    NitrendipineNDNDNDNDNDNDND
    Panobinostat>25.00.600 (11)>15.026.0 (7.2)15.60.00446 (0.00038)0.286
    Paroxetine14.30.600 (14)8.55NRNRNR0.113
    Propiverine7.500.500 (15)3.751.38 (0.62)0.6900.0196 (0.0040)28.4
    PropranololNDNDNDNDNDNDND
    SimvastatinNDNDNDNDNDNDND
    Tabimorelin2.850.200 (18)0.5697.57 (2.62)1.510.0148 (0.0013)9.78
    Tadalafil12.90.600 (5)7.714.26 (1.44)2.560.028 (0.002)11.0
    Telaprevir0.2730.450 (17)0.1232.24 (0.95)1.010.0112 (0.0011)11.1
    Terfenadine2.171.403.04NRNRNR0.426
    Verapamil13.40.310 (20)4.140.661 (0.143)0.2050.0172 (0.0010)83.9
    • %CV, percent coefficient of variation; ND, not determined since no TDI was detected in a single concentration screen at 30 µM; NR, not reported see data analysis section for the estimation of kinact/KI,u.

    • ↵aCalculated as measured IC50/2.

    • ↵bCalculated from Kp (n=3) and fu,liver reported in supplemental tables.

    • ↵cTotal value was reported since Kp,uu was not determined.

    • View popup
    TABLE 4

    Numerical accuracy of DDI predictions determined from human liver microsomes and human hepatocytes using mechanistic static models

    Model 1Model 2Model 3Model 4
    Relevant [I]gentranceentranceexitexit
    Relevant [I]hentranceentranceexitexit
    Fixed Input Parameters
    Fa1
    CYP3A kdeg,g0.00050 min−1
    CYP3A kdeg,h0.00032 min−1
    Qg300 ml/min300 ml/min1213 ml/min1213 ml/min
    Qh1617 ml/min1617 ml/minNANA
    Varied Input Parameters
    ka0.1 min−1customcustomcustom
    [I]gTotal EnterocyteaFree EnterocytebCmax,portal,ucCavg,portal,ud
    [I]hCmax,hepatic inlet,ueCmax,hepatic inlet,ueCmax,systemic,uCavg,systemic,u
    fu,gut1fu,plasmafu,plasmafu,plasma
    PerformanceHuman Liver Microsomes
    Bias (CI90%)6.3 (4.8–8.1)5.0 (3.8–6.5)2.7 (2.2–3.4)1.8 (1.5–2.3)
    GMFE (CI90%)6.3 (4.8–8.1)5.1 (3.9–6.6)2.8 (2.3–3.5)2.0 (1.6–2.4)
    RMSFE7.386.093.372.42
    % Within 2-fold12123656
    % Within 3-fold16284880
    % Outside 10-fold241640
    PerformanceHuman Hepatocytes
    Bias (CI90%)3.8 (2.9–4.9)2.7 (2.0–3.4)1.6 (1.2–2.1)1.1 (0.88–1.4)
    GMFE (CI90%)3.8 (3.0–4.9)2.7 (2.1–3.5)2.0 (1.6–2.5)1.7 (1.4–2.0)
    RMSFE4.703.492.522.04
    % Within 2-fold24445668
    % Within 3-fold36607684
    % Outside 10-fold12400
    • CI90%, 90% confidence interval; Cmax,systemic,u, unbound maximum systemic concentration; fu,gut, intestinal free fraction; kdeg,g, intestinal degradation rate; kdeg,h, hepatic degradation rate; NA, not applicable; Qg, intestinal blood flow; Qh, liver blood flow.

    • aAs calculated per eq. 7a.

    • bAs calculated per eq. 7a corrected for free fraction in plasma.

    • cAs calculated per eq. 7b.

    • dAs calculated per eq. 7c.

    • eAs calculated per eq. 6.

    • View popup
    TABLE 5

    Categorical accuracy of DDI predictions using mechanistic static models

    DDI CutoffMatrixModelNSensitivitySpecificityPPVNPVPPENPE
    %%%%%%
    Clinical ≥ 1.25-fold and prediction ≥ 1.25-foldHuman liver microsomes125100854100460
    2251001757100430
    3251002559100410
    4251006776100240
    Human hepatocytes1251003362100380
    2251003362100380
    3251005872100280
    425926775892511
    Clinical ≥ 2-fold and prediction ≥ 2-foldHuman liver microsomes1251001343100570
    2251001343100570
    3251003350100500
    4251006767100330
    Human hepatocytes1251002748100520
    2251004053100470
    32590676491369
    425708070803020
    • View popup
    TABLE 6

    Numerical accuracy of DDI predictions using Simcyp

    PerformanceHuman Liver MicrosomesHuman Hepatocytes
    Bias (CI90%)1.6 (1.3–1.9)1.1 (0.85–1.3)
    GMFE (CI90%)1.7 (1.5–2.0)1.6 (1.4–1.8)
    RMSFE2.021.88
    % Within 2-fold6468
    % Within 3-fold8888
    % Outside 10-fold00
    • View popup
    TABLE 7

    Categorical accuracy of DDI predictions using Simcyp

    DDI CutoffMatrixNSensitivitySpecificityPPVNPVPPENPE
    %%%%%%
    Clinical ≥ 1.25-fold and prediction ≥ 1.25-foldHuman liver microsomes251006776100240
    Human hepatocytes25858385831517
    Clinical ≥ 2-fold and prediction ≥ 2-foldHuman liver microsomes251008077100230
    Human hepatocytes25708070803020

Additional Files

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    • Supplemental Tables -

      Supplemental Tables 1-38.

      Supplemental references. 

    • Supplemental Figures -

      Supplemental Figures 1-23.

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Research ArticleArticle

Drug Interactions for CYP3A Time-Dependent Inhibitors

Elaine Tseng, Heather Eng, Jian Lin, Matthew A. Cerny, David A. Tess, Theunis C. Goosen and R. Scott Obach
Drug Metabolism and Disposition October 1, 2021, 49 (10) 947-960; DOI: https://doi.org/10.1124/dmd.121.000497

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Research ArticleArticle

Drug Interactions for CYP3A Time-Dependent Inhibitors

Elaine Tseng, Heather Eng, Jian Lin, Matthew A. Cerny, David A. Tess, Theunis C. Goosen and R. Scott Obach
Drug Metabolism and Disposition October 1, 2021, 49 (10) 947-960; DOI: https://doi.org/10.1124/dmd.121.000497
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