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Review ArticleReview Article

Modeling Pharmacokinetic Natural Product–Drug Interactions for Decision-Making: A NaPDI Center Recommended Approach

Emily J. Cox, Dan-Dan Tian, John D. Clarke, Allan E. Rettie, Jashvant D. Unadkat, Kenneth E. Thummel, Jeannine S. McCune and Mary F. Paine
Hyunyoung Jeong, ASSOCIATE EDITOR
Pharmacological Reviews April 2021, 73 (2) 847-859; DOI: https://doi.org/10.1124/pharmrev.120.000106
Emily J. Cox
Center of Excellence for Natural Product Drug Interaction Research, Spokane, Washington (J.D.C., A.E.R., J.D.U., K.E.T., J.S.M., M.F.P.); Department of Pharmaceutical Sciences, Washington State University, Spokane, Washington (E.J.C., D.-D.T., J.D.C., M.F.P.); Departments of Medicinal Chemistry (A.E.R.) and Pharmaceutics (J.D.U., K.E.T.), University of Washington, Seattle, Washington; and Department of Population Sciences, City of Hope, Duarte, California (J.S.M.)
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Dan-Dan Tian
Center of Excellence for Natural Product Drug Interaction Research, Spokane, Washington (J.D.C., A.E.R., J.D.U., K.E.T., J.S.M., M.F.P.); Department of Pharmaceutical Sciences, Washington State University, Spokane, Washington (E.J.C., D.-D.T., J.D.C., M.F.P.); Departments of Medicinal Chemistry (A.E.R.) and Pharmaceutics (J.D.U., K.E.T.), University of Washington, Seattle, Washington; and Department of Population Sciences, City of Hope, Duarte, California (J.S.M.)
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John D. Clarke
Center of Excellence for Natural Product Drug Interaction Research, Spokane, Washington (J.D.C., A.E.R., J.D.U., K.E.T., J.S.M., M.F.P.); Department of Pharmaceutical Sciences, Washington State University, Spokane, Washington (E.J.C., D.-D.T., J.D.C., M.F.P.); Departments of Medicinal Chemistry (A.E.R.) and Pharmaceutics (J.D.U., K.E.T.), University of Washington, Seattle, Washington; and Department of Population Sciences, City of Hope, Duarte, California (J.S.M.)
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Allan E. Rettie
Center of Excellence for Natural Product Drug Interaction Research, Spokane, Washington (J.D.C., A.E.R., J.D.U., K.E.T., J.S.M., M.F.P.); Department of Pharmaceutical Sciences, Washington State University, Spokane, Washington (E.J.C., D.-D.T., J.D.C., M.F.P.); Departments of Medicinal Chemistry (A.E.R.) and Pharmaceutics (J.D.U., K.E.T.), University of Washington, Seattle, Washington; and Department of Population Sciences, City of Hope, Duarte, California (J.S.M.)
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Jashvant D. Unadkat
Center of Excellence for Natural Product Drug Interaction Research, Spokane, Washington (J.D.C., A.E.R., J.D.U., K.E.T., J.S.M., M.F.P.); Department of Pharmaceutical Sciences, Washington State University, Spokane, Washington (E.J.C., D.-D.T., J.D.C., M.F.P.); Departments of Medicinal Chemistry (A.E.R.) and Pharmaceutics (J.D.U., K.E.T.), University of Washington, Seattle, Washington; and Department of Population Sciences, City of Hope, Duarte, California (J.S.M.)
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Kenneth E. Thummel
Center of Excellence for Natural Product Drug Interaction Research, Spokane, Washington (J.D.C., A.E.R., J.D.U., K.E.T., J.S.M., M.F.P.); Department of Pharmaceutical Sciences, Washington State University, Spokane, Washington (E.J.C., D.-D.T., J.D.C., M.F.P.); Departments of Medicinal Chemistry (A.E.R.) and Pharmaceutics (J.D.U., K.E.T.), University of Washington, Seattle, Washington; and Department of Population Sciences, City of Hope, Duarte, California (J.S.M.)
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Jeannine S. McCune
Center of Excellence for Natural Product Drug Interaction Research, Spokane, Washington (J.D.C., A.E.R., J.D.U., K.E.T., J.S.M., M.F.P.); Department of Pharmaceutical Sciences, Washington State University, Spokane, Washington (E.J.C., D.-D.T., J.D.C., M.F.P.); Departments of Medicinal Chemistry (A.E.R.) and Pharmaceutics (J.D.U., K.E.T.), University of Washington, Seattle, Washington; and Department of Population Sciences, City of Hope, Duarte, California (J.S.M.)
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Mary F. Paine
Center of Excellence for Natural Product Drug Interaction Research, Spokane, Washington (J.D.C., A.E.R., J.D.U., K.E.T., J.S.M., M.F.P.); Department of Pharmaceutical Sciences, Washington State University, Spokane, Washington (E.J.C., D.-D.T., J.D.C., M.F.P.); Departments of Medicinal Chemistry (A.E.R.) and Pharmaceutics (J.D.U., K.E.T.), University of Washington, Seattle, Washington; and Department of Population Sciences, City of Hope, Duarte, California (J.S.M.)
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Hyunyoung Jeong
Roles: ASSOCIATE EDITOR
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    Fig. 1.

    Variability in the geometric mean of in-silico-to-observed fu ratios for high binding (low fu, denoted by experimental fu ≤ 20%), moderate binding (moderate fu, denoted by 20% < experimental fu < 80%), and low binding (high fu, denoted by experimental fu ≥ 80%) natural product constituents in human liver microsomes and plasma. Error bars denote 90% confidence intervals. Closed diamonds denote values generated by GastroPlus, whereas open diamonds denote values generated by Simcyp. Natural product constituents evaluated are 4-methylumbelliferone, 7-hydroxymitragynine, berberine, bergamottin, hydrastine, hydrastinine, isosilybin A, isosilybin B, isosilychristin, mitraciliatine, mitragynine, paynantheine, silybin A, silybin B, silychristin, silydianin, and speciogynine.

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    Fig. 2.

    General structure of a physiologically-based pharmacokinetic model designed to evaluate a natural product–drug interaction. Intravenous administration is rarely, if ever, used for natural products; rather, common routes include oral consumption and inhalation. The number of tissue compartments is variable, but N compartments can be included in a full physiologically-based pharmacokinetic model. Input and output blood flow rates (Q) describe constituent passage between the arterial and venous circulation.

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    Fig. 3.

    Decision tree for the development of PBPK models of natural product–drug interactions. Selection of a modeling strategy depends on the available data. If data about the induction and inhibition behavior of the natural product constituent(s) are not available in the literature, these data can be gathered from in vitro experiments. If the predicted concentrations of the constituent(s) in either the gut or the plasma exceed the cutoffs [Table 4 and FDA and European Medicines Agency (EMA) guidance], different types of modeling are warranted. Cmax,u, maximum unbound concentration; Emax, maximum inductive effect; kdeg, degradation rate constant; KI, inhibitor concentration at one-half maximum inactivation rate; kobs, inactivation rate constant (observed).

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    Fig. 4.

    Illustration of intestinal cell polarization and the relative orientations of uptake and efflux transporters.

Tables

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    TABLE 1

    Structural alerts for constituents in select natural products

    Reprinted with permission from the American Society for Pharmacology and Experimental Therapeutics from Johnson et al. (2018).

    Constituent(s)/Natural ProductStructural AlertAlert Substructure
    Flavonoids, phenylpropanoids/Echinacea glycyrrhizin, glycyrrhizinic acid/licoriceCatecholsEmbedded Image
    Isoquinoline alkaloids/goldenseal terpenoids/cinnamon curcuminoids/turmericMasked catecholEmbedded Image, Embedded Image
    Isoquinoline alkaloids/goldenseal shizandrins/Schisandra spp. Gomisins/Schisandra spp.MethylenedioxyphenylEmbedded Image
    Cycloartenol/black cohoshSubterminal olefinEmbedded Image
    Polyacetylenes/EchinaceaTerminal and subterminal acetylenesEmbedded Image, Embedded Image
    Terpenoids/cinnamon diallyl disulfides and trisulfides/garlicTerminal olefinEmbedded Image
    Cinnamaldehyde/cinnamonα,β-Unsaturated aldehydeEmbedded Image
    Curcuminoids/turmericα,β-Unsaturated ketoneEmbedded Image
    • View popup
    TABLE 2

    Recommended enzymes, transporters, and experimental systems for screening natural products for inhibition and/or induction

    Adapted with permission from the American Society for Pharmacology and Experimental Therapeutics from Johnson et al. (2018).

    Cytochrome P450 enzymes
     Essential: CYP1A2, CYP2B6, CYP2C19, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP3A
    Experimental SystemInhibitionInduction
    Recombinant enzymesAs neededNA
    Human liver microsomesaNA
    Human hepatocytesAs neededa
    Human intestinal microsomesAs neededNA
    Human intestinal cellsAs neededAs neededb
    Human kidney microsomesAs neededNA
    Human kidney cellsAs neededAs neededb
    Other cell linesAs neededNA
    UGTs
    Essential: UGT1A1, UGT1A3, UGT1A4, UGT1A6, UGT1A8, UGT1A9, UGT1A10, UGT2B7, UGT2B10, UGT2B15
    Experimental SystemInhibitionInduction
    Recombinant enzymesAs neededNA
    Human liver microsomesaNA
    Human hepatocytesaa
    Human intestinal microsomesAs neededNA
    Human intestinal cellsAs neededAs neededb
    Human kidney microsomesAs neededNA
    Human kidney cellsAs neededAs neededb
    Other cell linesAs neededNA
    Other Enzymes that May Be Considered
    hCE1, hCE2, SULT1A1, SULT1A3, SULT1B1, SULT1E1, SULT2A1
    Experimental SystemInhibitionInduction
    Recombinant enzymesaNA
    Human liver microsomesaNA
    Human hepatocytesAs neededa
    Human intestinal microsomesAs neededNA
    Human intestinal cellsAs neededbAs neededb
    Human kidney microsomesAs neededbNA
    Human kidney cellsAs neededbAs neededb
    Transporters
    Essential: BCRP, BSEP, MATE1, MATE2-K, MRP2, MRP3, NTCP, OATP1B1, OATP1B3, OATP2B1, OAT, OCT, P-gp
    Experimental SystemInhibitionInduction
    Transfected cell lines (single, double)aNA
    Human intestinal cellsAs neededbAs neededb
    Human kidney cellsAs neededbAs neededb
    Human hepatocytesaa
    Membrane vesiclesaNA
    • BCRP, breast cancer resistance protein; BSEP, bile salt export pump; hCE, human carboxylesterase; MATE, multidrug and toxin extrusion protein; MRP, multidrug resistance–associated protein; NA, not applicable; NTCP, sodium taurocholate–cotransporting polypeptide; OAT, organic anion transporter; OATP, organic anion–transporting polypeptide; OCT, organic cation transporter; P-gp, P-glycoprotein; SULT, sulfotransferase.

    • ↵a Essential system. Inhibition studies in hepatocytes may involve multiple transporters.

    • ↵b These models represent an emerging field and will be refined with time. Expression levels of enzymes and transporters in these models are lower than those in vivo (Speer et al., 2019; Chapron et al., 2020; Kasendra et al., 2020).

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    TABLE 3

    Examples of natural product–drug interactions predicted using static and PBPK models

    Natural ProductObject Drug(s)Biochemical Target(s)Model TypeChange in Object-Drug AUC or R2Reference(s)
    Common NameLatin NamePrecipitant Constituent(s)PredictedObserved
    Cannabis, marijuanaCannabis sativa L.CBD, THCPhenacetin, diclofenac, omeprazole, dextromethorphan, testosteroneCYP1A2, CYP2C9, CYP2C19, CYP2D6, CYP3AStaticCBD: >5-fold ↑ for CYP2C19 and CYP3A substrates; THC: >5-fold ↑ for CYP2C9 substratesNRBansal et al. (2020)
    CinnamonCinnamomum spp.trans-Cinnamic aldehydeLetrozole, nicotineCYP2A6Static1.1- to 3.6-fold ↑NRChan et al. (2016)
    trans-Cinnamic aldehyde, 2-methoxycinnamaldehyde∼4- to 5-fold ↑Espiritu et al. (2020)
    Curcumin (as a solid lipid nanoparticle)Curcuma longa L.Curcumin, curcumin glucuronideImatinib, bosutinib, paclitaxelCYP2C8, CYP3A4PBPK≤1.10-Fold ↑ for imatinib and bosutinibNRAdiwidjaja et al. (2020a)
    GoldensealHydrastis canadensis L.Berberine, (-)-β-hydrastine, hydrastinineDextromethorphan, midazolam, diclofenacCYP2C9, CYP2D6, CYP3AStaticR2 = 1.00, 7.90, and 1.26 for berberine and CYP2C9, CYP2D6, and CYP3A4, respectively; R2 = 8.94, ∼4, and 17.8 for (-)-β-hydrastine and CYP2C9, CYP2D6, and CYP3A4, respectivelyNR for dextromethorphana 1.62-fold ↑ for midazolam after 2 wk of goldenseal administration (1323 mg three times daily) NR for diclofenacGurley et al., (2008); Guo et al. (2012); McDonald et al. (2020)
    Grapefruit juiceCitrus × paradisi Macfad.6′,7′-Dihydroxy-bergamottinLoperamideCYP3AStatic1.6-Fold ↑1.7-fold ↑Ainslie et al. (2014)
    Green teaCamellia sinensis (L.) KuntzeECG, EGCGRaloxifeneUGTsStatic6.1- and 1.3-fold ↑, respectively, based on estimated concentrations in intestinal lumen and enterocyte30% ↓Tian et al. (2018b); Judson et al. (2020)
    KratomMitragyna speciosa (Korth.) Havil.MitragynineDiclofenac, dextromethorphan, midazolamCYP2C9, CYP2D6, CYP3AStatic1.1- and 5.7-fold ↑ for dextromethorphan and midazolam, respectivelyNRTanna et al. (2020)
    St. John’s wortHypericum perforatum L.HyperforinbAlprazolam, carbamazepine, docetaxel, ethinyl estradiol, imatinib, midazolam, tacrolimus, verapamil, zolpidem, ibuprofen, tolbutamide, S-warfarin, clopidogrel, omeprazoleCYP2C19, CYP2C9, CYP3APBPK2%–79% ↓ for all substrates except clopidogrel (1.31-fold ↑) and S-warfarin (1.06-fold ↑).Close agreement with observed changes (18%–23% difference)Adiwidjaja et al. (2019)
    SilibininbSilybum marianum (L.) gaertn.Silybin A, silybin BMidazolam, warfarinCYP3A, CYP2C9PBPK1.05- and 1.04-fold ↑ for midazolam and S-warfarin, respectively1.09 and 1.13-fold ↑ for midazolam and S-warfarin, respectivelyBrantley et al. (2014b)
    SilymarinSilybum marianum (L.) gaertn.Silybin A, silybin B, isosilybin A, isosilybin B, silychristinMidazolamCYP3AStatic1.75-fold ↑NRBrantley et al. (2013)
    SilibininSilybum marianum (L.) gaertn.Silybin A, silybin BRaloxifeneUGTsPBPKUp to 1.3-fold ↑1.09-fold ↑Gufford et al. (2015a)
    Silibinin, silymarinNA, Silybum marianum (L.) gaertn.Silybin A, silybin BRaloxifeneUGT1A1, UGT1A8, UGT1A10Static4- to 5-fold ↑ by silibinin and silymarin, respectively1.09-fold ↑Gufford et al. (2015a,b)
    • CBD, cannabidiol; CYP, cytochrome P450; EGCG, epigallocatechin gallate; NA, not applicable; NR, not reported; R2, intrinsic clearance ratio (without:with a time-dependent inhibitor); THC, tetrahydrocannabinol; UGT, UDP-glucuronosyltransferase.

    • ↵a 9-Fold ↑ in urinary dextromethorphan:dextrorphan after 2 wk of goldenseal administration (300 mg three times daily).

    • ↵b Semipurified extract of milk thistle containing silybin A and silybin B in approximately an equimolar ratio.

    • View popup
    TABLE 4

    Gut-specific cutoffs or criteria for natural product–drug interactions

    Cutoffs or criteria used in decision tree for physiologically-based pharmacokinetic modeling of natural product–drug interactions depicted in Fig. 3. For additional information or for cutoff values related to other organ systems, refer to (http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2012/07/WC500129606.pdf; FDA, 2020).

    Transporter Inhibition
    P-gp and BCRP(Dose/250 ml)/Ki,u (or IC50,u) ≥ 10
    Enzyme Inhibition
    Reversible InhibitionTime-Dependent Inhibition
    CYP3A(Dose/250 ml)/Ki,u ≥ 10kobs/kdeg ≥ 10, wherein kobs = (kinact ⋅ Dose/250 ml)/(KI,u + Dose/250 ml)
    CYP Inductiona
    1. Concentration-dependent increase in mRNA expression of a CYP
    2. ≥ 2-Fold increase of CYP mRNA expression relative to vehicle control at expected gut drug concentrations
    3. Increase ≥20% of the positive control response
    • BCRP, breast cancer resistance protein; CYP, cytochrome P450; IC50,u, unbound IC50; kdeg, degradation rate constant; Ki,u, unbound reversible inhibition constant; kobs, inactivation rate constant (observed); P-gp, P-glycoprotein.

    • ↵a Must satisfy all three criteria to qualify as a CYP inducer. Criteria are based on those recommended for hepatic CYP induction (http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2012/07/WC500129606.pdf; FDA, 2020).

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Pharmacological Reviews: 73 (2)
Pharmacological Reviews
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Review ArticleReview Article

Modeling Pharmacokinetic Natural Product–Drug Interactions

Emily J. Cox, Dan-Dan Tian, John D. Clarke, Allan E. Rettie, Jashvant D. Unadkat, Kenneth E. Thummel, Jeannine S. McCune and Mary F. Paine
Pharmacological Reviews April 1, 2021, 73 (2) 847-859; DOI: https://doi.org/10.1124/pharmrev.120.000106

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Review ArticleReview Article

Modeling Pharmacokinetic Natural Product–Drug Interactions

Emily J. Cox, Dan-Dan Tian, John D. Clarke, Allan E. Rettie, Jashvant D. Unadkat, Kenneth E. Thummel, Jeannine S. McCune and Mary F. Paine
Pharmacological Reviews April 1, 2021, 73 (2) 847-859; DOI: https://doi.org/10.1124/pharmrev.120.000106
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  • Article
    • Abstract
    • I. Introduction: Application of Static and Dynamic Models to Natural Products
    • II. Generating and Selecting Data for Static and Physiologically Based Pharmacokinetic Models
    • III. Applying or Developing Static and Physiologically Based Pharmacokinetic Models
    • IV. Building Physiologically Based Pharmacokinetic Models De Novo for NPDIs
    • V. Using Static and Physiologically Based Pharmacokinetic Models to Prioritize Natural Product–Drug Interaction Risk
    • VI. Future Research
    • VII. Conclusions
    • Acknowledgments
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