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Research ArticleMetabolism, Transport, and Pharmacogenetics

Refined Prediction of Pharmacokinetic Kratom-Drug Interactions: Time-Dependent Inhibition Considerations

Rakshit S. Tanna, Dan-Dan Tian, Nadja B. Cech, Nicholas H. Oberlies, Allan E. Rettie, Kenneth E. Thummel and Mary F. Paine
Journal of Pharmacology and Experimental Therapeutics January 2021, 376 (1) 64-73; DOI: https://doi.org/10.1124/jpet.120.000270
Rakshit S. Tanna
Department of Pharmaceutical Sciences, College of Pharmacy and Pharmaceutical Sciences, Washington State University, Spokane, Washington (R.S.T., D.-D.T., M.F.P.); Department of Chemistry and Biochemistry, University of North Carolina at Greensboro, Greensboro, North Carolina (N.B.C., N.H.O.); Departments of Medicinal Chemistry (A.E.R.) and Pharmaceutics (K.E.T.), School of Pharmacy, University of Washington, Seattle, Washington; and Center of Excellence for Natural Product Drug Interaction Research, Spokane, Washington (N.B.C., N.H.O., A.E.R., K.E.T., M.F.P.)
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Dan-Dan Tian
Department of Pharmaceutical Sciences, College of Pharmacy and Pharmaceutical Sciences, Washington State University, Spokane, Washington (R.S.T., D.-D.T., M.F.P.); Department of Chemistry and Biochemistry, University of North Carolina at Greensboro, Greensboro, North Carolina (N.B.C., N.H.O.); Departments of Medicinal Chemistry (A.E.R.) and Pharmaceutics (K.E.T.), School of Pharmacy, University of Washington, Seattle, Washington; and Center of Excellence for Natural Product Drug Interaction Research, Spokane, Washington (N.B.C., N.H.O., A.E.R., K.E.T., M.F.P.)
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Nadja B. Cech
Department of Pharmaceutical Sciences, College of Pharmacy and Pharmaceutical Sciences, Washington State University, Spokane, Washington (R.S.T., D.-D.T., M.F.P.); Department of Chemistry and Biochemistry, University of North Carolina at Greensboro, Greensboro, North Carolina (N.B.C., N.H.O.); Departments of Medicinal Chemistry (A.E.R.) and Pharmaceutics (K.E.T.), School of Pharmacy, University of Washington, Seattle, Washington; and Center of Excellence for Natural Product Drug Interaction Research, Spokane, Washington (N.B.C., N.H.O., A.E.R., K.E.T., M.F.P.)
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Nicholas H. Oberlies
Department of Pharmaceutical Sciences, College of Pharmacy and Pharmaceutical Sciences, Washington State University, Spokane, Washington (R.S.T., D.-D.T., M.F.P.); Department of Chemistry and Biochemistry, University of North Carolina at Greensboro, Greensboro, North Carolina (N.B.C., N.H.O.); Departments of Medicinal Chemistry (A.E.R.) and Pharmaceutics (K.E.T.), School of Pharmacy, University of Washington, Seattle, Washington; and Center of Excellence for Natural Product Drug Interaction Research, Spokane, Washington (N.B.C., N.H.O., A.E.R., K.E.T., M.F.P.)
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Allan E. Rettie
Department of Pharmaceutical Sciences, College of Pharmacy and Pharmaceutical Sciences, Washington State University, Spokane, Washington (R.S.T., D.-D.T., M.F.P.); Department of Chemistry and Biochemistry, University of North Carolina at Greensboro, Greensboro, North Carolina (N.B.C., N.H.O.); Departments of Medicinal Chemistry (A.E.R.) and Pharmaceutics (K.E.T.), School of Pharmacy, University of Washington, Seattle, Washington; and Center of Excellence for Natural Product Drug Interaction Research, Spokane, Washington (N.B.C., N.H.O., A.E.R., K.E.T., M.F.P.)
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Kenneth E. Thummel
Department of Pharmaceutical Sciences, College of Pharmacy and Pharmaceutical Sciences, Washington State University, Spokane, Washington (R.S.T., D.-D.T., M.F.P.); Department of Chemistry and Biochemistry, University of North Carolina at Greensboro, Greensboro, North Carolina (N.B.C., N.H.O.); Departments of Medicinal Chemistry (A.E.R.) and Pharmaceutics (K.E.T.), School of Pharmacy, University of Washington, Seattle, Washington; and Center of Excellence for Natural Product Drug Interaction Research, Spokane, Washington (N.B.C., N.H.O., A.E.R., K.E.T., M.F.P.)
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Mary F. Paine
Department of Pharmaceutical Sciences, College of Pharmacy and Pharmaceutical Sciences, Washington State University, Spokane, Washington (R.S.T., D.-D.T., M.F.P.); Department of Chemistry and Biochemistry, University of North Carolina at Greensboro, Greensboro, North Carolina (N.B.C., N.H.O.); Departments of Medicinal Chemistry (A.E.R.) and Pharmaceutics (K.E.T.), School of Pharmacy, University of Washington, Seattle, Washington; and Center of Excellence for Natural Product Drug Interaction Research, Spokane, Washington (N.B.C., N.H.O., A.E.R., K.E.T., M.F.P.)
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  • Fig. 1.
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    Fig. 1.

    Workflow for reevaluating CYP–mediated drug interaction risk associated with kratom use using mitragynine as the marker constituent. Inhibition of a CYP in a concentration-dependent manner and by at least 50% at the highest tested concentration indicates mitragynine-mediated inhibition. A leftward shift of ≥1.5-fold in IC50 indicates potential TDI (Grimm et al., 2009). An IC50 value <20 µM indicates potential clinical relevance of CYP inhibition, determined relative to the highest mitragynine concentration quantified from autopsy blood samples.

  • Fig. 2.
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    Fig. 2.

    IC50 curves for mitragynine after preincubation with HLMs using the index reactions diclofenac 4′-hydroxylation (CYP2C9) (A), dextromethorphan O-demethylation (CYP2D6) (B), and midazolam 1′-hydroxylation (CYP3A) (C) and with HIMs using midazolam 1′-hydroxylation (D) in the presence and absence of NADPH. Mitragynine was tested from 0.015 to 100 µM. Symbols and error bars denote means and S.D., respectively, of triplicate incubations. Curves denote nonlinear least-squares regression of the data.

  • Fig. 3.
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    Fig. 3.

    Concentration-dependent inhibition of CYP2D6-mediated dextromethorphan O-demethylation by mitragynine (0.37–10 µM) in HLMs. (A) Dixon and (B) Michaelis-Menten plots of the data. Symbols denote individual data points of duplicate incubations. Curves denote nonlinear least-squares regression of the data using the competitive inhibition model.

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    Fig. 4.

    Concentration- and time-dependent inhibition of CYP3A-mediated midazolam 1′-hydroxylation by mitragynine using a nondilution (1.67–30 µM) method with HLMs (A) and HIMs (B) and a dilution method (3–60 µM) with HLMs (C). Upper panels show log-linear decline in CYP3A activity with time. Symbols denote individual data points of duplicate incubations. Lines denote linear regression of the initial monoexponential decline. Lower panels show replots of the rate constants against inhibitor concentration. Curves denote nonlinear least-squares regression of the data. Insets depict the Kitz-Wilson plot of the time-dependent inhibition observed for mitragynine.

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    Fig. 5.

    Speculated bioactivation mechanisms for mitragynine causing time-dependent inhibition of CYP3A activity (midazolam 1′-hydroxylation).

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    Fig. 6.

    Drug interaction risks predicted via mechanistic static models associated with kratom use for stimulant effects/lower dose range (1–5 g), intermediate dose range (5–8 g), and predominantly opioid-like effects/higher dose range (>8 g). An interaction risk (AUCR >1.25, dashed red line) via reversible inhibition of CYP2D6 (dextromethorphan O-demethylation activity) (A) and reversible (from previous reports, dashed black line) and time-dependent (from current work, solid black line) inhibition of CYP3A (midazolam 1′-hydroxylation activity) (B).

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    TABLE 1

    Major enzymes involved in the metabolism of opioids and/or other drugs detected in postmortem blood samples obtained from kratom-related death cases in Colorado (Gershman et al., 2019)

    Case no.Blood Mitragynine Concentration (μM)Other DrugsMajor Enzyme(s)References
    1PositiveaButyryl-fentanyl, oxycodone, etizolam, diphenhydramine, THCCYP3A, CYP2D6Lalovic et al. (2004); Niwa et al. (2005); Kanamori et al. (2019)
    2PositiveOxycodone, fluoxetine, pseudoephedrineCYP3A, CYP2D6Lalovic et al. (2004); LLerena et al. (2004)
    30.04Etizolam, 5-MeO-AMTCYP3ANiwa et al. (2005)
    4PositiveMorphine, codeineUGT2B7, CYP3A, CYP2D6Crews et al. (2014)
    50.35Oxycodone, tramadol, topiramate, diphenhydramine, zolpidemCYP3A, CYP2D6Pichard et al. (1995); Miotto et al. (2017); Yamamoto et al. (2017)
    65.3CitalopramCYP3A, CYP2C19, CYP2D6von Moltke et al. (2001)
    73.5Furanyl-fentanylNANA
    82.5Temazepam, olanzapine, sertraline, clonazepamCYP3A, CYP2C9, CYP2C19, CYP2D6, UGT2B7Kobayashi et al. (1999); Urichuk et al. (2008); Tóth et al. (2016)
    90.423,4-Dimethoxy-N-methylamphetamineNANA
    106.7Etizolam, nordiazepam, mirtazapineCYP3A, CYP2D6, CYP1A2Ono et al. (1996); Störmer et al. (2000); Niwa et al. (2005)
    11122,4,5-TrimethoxyamphetamineCYP2D6Ewald and Maurer (2008)
    120.62OxycodoneCYP3A, CYP2D6Lalovic et al. (2004)
    131.9Oxycodone, fentanyl, cocaineCYP3A, CYP2D6, esterasesKamendulis et al. (1996); Guitton et al. (1997); Kanamori et al. (2019)
    14Positive—b——
    15PositiveU-47700NANA
    • 5-MeO-AMT, 5-methoxy-α-methyltryptamine; NA, information not available; THC, tetrahydrocannabinol; U-47700, synthetic opioid; UGT, uridine diphosphate-glucuronyltransferase.

    • ↵a Qualitative detection.

    • ↵b No other drugs reported.

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    TABLE 2

    IC50 for mitragynine against CYP2C9, CYP2D6, and CYP3A activity with and without NADPH, post–30-min preincubation

    Values represent means ± S.E. of the estimates obtained via nonlinear least-squares regression.

    EnzymeIC50 (µM)Fold Shift
    (−)-NADPH(+)-NADPH
    CYP2C939.7 ± 4.740.0 ± 3.21.0
    CYP2D60.67 ± 0.050.76 ± 0.110.9
    CYP3A (HLMs)18.9 ± 1.82.6 ± 0.37.3
    CYP3A (HIMs)21.9 ± 2.73.2 ± 0.36.8
    • View popup
    TABLE 3

    Prediction of CYP3A-mediated kratom-drug interactions via a mechanistic static model

    The object drugs were selected to encompass a wide range of Fg and fm values with minimal influence of transporters.

    Victim DrugTherapeutic Drug ClassFg (Galetin et al., 2008)fm (Shou et al., 2008)AUCR
    AlfentanilOpioid, analgesic0.60.874.26
    AlprazolamAnxiolytic0.940.82.43
    BuspironeAnxiolytic0.210.9514.04
    CyclosporineImmunosuppressant0.440.714.5
    FelodipineAntianginal0.450.81a5.12
    NifedipineAntianginal0.780.742.66
    QuetiapineAntipsychotic0.99b0.842.46
    QuinidineAntiarrhythmic0.90.762.38
    TrazodoneAntidepressant0.830.35c1.60
    TriazolamHypnotic0.750.923.76
    ZolpidemHypnotic0.790.261.55
    • a Yadav et al. (2018).

    • b Mano et al. (2015).

    • c Mao et al. (2011).

Additional Files

  • Figures
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  • Data Supplement

    • Supplemental Data -

      Supplementary Figure 1 -   Inhibitory effects of kratom extracts (coded K-50, K-51, and K-52) and mitragynine on
      cytochrome P450 (CYP) 2C9 (diclofenac 4′-hydroxylation) (A), CYP2D6 (dextromethorphan Odemethylation) (B), and CYP3A (midazolam 1′-hydroxylation) (C) activities in HLMs and CYP3A (D) activity in HIMs. Extracts were tested at 2, 10, and 20 µg/mL; mitragynine was tested at 1, 10, and 100 µM. Positive control inhibitors included sulfaphenazole (1 µM), quinidine (2 µM), and ketoconazole (0.1 µM) for CYP2C9, CYP2D6, and CYP3A, respectively.

      Supplementary Figure 2 - Representative Lineweaver–Burk plot showing inhibition of CYP2D6-mediated
      dextromethorphan O-demethylation by mitragynine (0.37-10 µM) in HLMs.

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Journal of Pharmacology and Experimental Therapeutics: 376 (1)
Journal of Pharmacology and Experimental Therapeutics
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1 Jan 2021
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Research ArticleMetabolism, Transport, and Pharmacogenetics

Refined Prediction of Potential Kratom-Drug Interactions

Rakshit S. Tanna, Dan-Dan Tian, Nadja B. Cech, Nicholas H. Oberlies, Allan E. Rettie, Kenneth E. Thummel and Mary F. Paine
Journal of Pharmacology and Experimental Therapeutics January 1, 2021, 376 (1) 64-73; DOI: https://doi.org/10.1124/jpet.120.000270

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Research ArticleMetabolism, Transport, and Pharmacogenetics

Refined Prediction of Potential Kratom-Drug Interactions

Rakshit S. Tanna, Dan-Dan Tian, Nadja B. Cech, Nicholas H. Oberlies, Allan E. Rettie, Kenneth E. Thummel and Mary F. Paine
Journal of Pharmacology and Experimental Therapeutics January 1, 2021, 376 (1) 64-73; DOI: https://doi.org/10.1124/jpet.120.000270
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