Article Figures & Data
Figures
- 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.
- 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.
- 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).
- Fig. 4.
Illustration of intestinal cell polarization and the relative orientations of uptake and efflux transporters.
Tables
- 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 Product Structural Alert Alert Substructure Flavonoids, phenylpropanoids/Echinacea glycyrrhizin, glycyrrhizinic acid/licorice Catechols 
Isoquinoline alkaloids/goldenseal terpenoids/cinnamon curcuminoids/turmeric Masked catechol , Isoquinoline alkaloids/goldenseal shizandrins/Schisandra spp. Gomisins/Schisandra spp. Methylenedioxyphenyl Cycloartenol/black cohosh Subterminal olefin Polyacetylenes/Echinacea Terminal and subterminal acetylenes , Terpenoids/cinnamon diallyl disulfides and trisulfides/garlic Terminal olefin Cinnamaldehyde/cinnamon α,β-Unsaturated aldehyde Curcuminoids/turmeric α,β-Unsaturated ketone - 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 System Inhibition Induction Recombinant enzymes As needed NA Human liver microsomes a NA Human hepatocytes As needed a Human intestinal microsomes As needed NA Human intestinal cells As needed As neededb Human kidney microsomes As needed NA Human kidney cells As needed As neededb Other cell lines As needed NA UGTs Essential: UGT1A1, UGT1A3, UGT1A4, UGT1A6, UGT1A8, UGT1A9, UGT1A10, UGT2B7, UGT2B10, UGT2B15 Experimental System Inhibition Induction Recombinant enzymes As needed NA Human liver microsomes a NA Human hepatocytes a a Human intestinal microsomes As needed NA Human intestinal cells As needed As neededb Human kidney microsomes As needed NA Human kidney cells As needed As neededb Other cell lines As needed NA Other Enzymes that May Be Considered hCE1, hCE2, SULT1A1, SULT1A3, SULT1B1, SULT1E1, SULT2A1 Experimental System Inhibition Induction Recombinant enzymes a NA Human liver microsomes a NA Human hepatocytes As needed a Human intestinal microsomes As needed NA Human intestinal cells As neededb As neededb Human kidney microsomes As neededb NA Human kidney cells As neededb As neededb Transporters Essential: BCRP, BSEP, MATE1, MATE2-K, MRP2, MRP3, NTCP, OATP1B1, OATP1B3, OATP2B1, OAT, OCT, P-gp Experimental System Inhibition Induction Transfected cell lines (single, double) a NA Human intestinal cells As neededb As neededb Human kidney cells As neededb As neededb Human hepatocytes a a Membrane vesicles a NA 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).
- TABLE 3
Examples of natural product–drug interactions predicted using static and PBPK models
Natural Product Object Drug(s) Biochemical Target(s) Model Type Change in Object-Drug AUC or R2 Reference(s) Common Name Latin Name Precipitant Constituent(s) Predicted Observed Cannabis, marijuana Cannabis sativa L. CBD, THC Phenacetin, diclofenac, omeprazole, dextromethorphan, testosterone CYP1A2, CYP2C9, CYP2C19, CYP2D6, CYP3A Static CBD: >5-fold ↑ for CYP2C19 and CYP3A substrates; THC: >5-fold ↑ for CYP2C9 substrates NR Bansal et al. (2020) Cinnamon Cinnamomum spp. trans-Cinnamic aldehyde Letrozole, nicotine CYP2A6 Static 1.1- to 3.6-fold ↑ NR Chan 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 glucuronide Imatinib, bosutinib, paclitaxel CYP2C8, CYP3A4 PBPK ≤1.10-Fold ↑ for imatinib and bosutinib NR Adiwidjaja et al. (2020a) Goldenseal Hydrastis canadensis L. Berberine, (-)-β-hydrastine, hydrastinine Dextromethorphan, midazolam, diclofenac CYP2C9, CYP2D6, CYP3A Static R2 = 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, respectively NR for dextromethorphana 1.62-fold ↑ for midazolam after 2 wk of goldenseal administration (1323 mg three times daily) NR for diclofenac Gurley et al., (2008); Guo et al. (2012); McDonald et al. (2020) Grapefruit juice Citrus × paradisi Macfad. 6′,7′-Dihydroxy-bergamottin Loperamide CYP3A Static 1.6-Fold ↑ 1.7-fold ↑ Ainslie et al. (2014) Green tea Camellia sinensis (L.) Kuntze ECG, EGCG Raloxifene UGTs Static 6.1- and 1.3-fold ↑, respectively, based on estimated concentrations in intestinal lumen and enterocyte 30% ↓ Tian et al. (2018b); Judson et al. (2020) Kratom Mitragyna speciosa (Korth.) Havil. Mitragynine Diclofenac, dextromethorphan, midazolam CYP2C9, CYP2D6, CYP3A Static 1.1- and 5.7-fold ↑ for dextromethorphan and midazolam, respectively NR Tanna et al. (2020) St. John’s wort Hypericum perforatum L. Hyperforinb Alprazolam, carbamazepine, docetaxel, ethinyl estradiol, imatinib, midazolam, tacrolimus, verapamil, zolpidem, ibuprofen, tolbutamide, S-warfarin, clopidogrel, omeprazole CYP2C19, CYP2C9, CYP3A PBPK 2%–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) Silibininb Silybum marianum (L.) gaertn. Silybin A, silybin B Midazolam, warfarin CYP3A, CYP2C9 PBPK 1.05- and 1.04-fold ↑ for midazolam and S-warfarin, respectively 1.09 and 1.13-fold ↑ for midazolam and S-warfarin, respectively Brantley et al. (2014b) Silymarin Silybum marianum (L.) gaertn. Silybin A, silybin B, isosilybin A, isosilybin B, silychristin Midazolam CYP3A Static 1.75-fold ↑ NR Brantley et al. (2013) Silibinin Silybum marianum (L.) gaertn. Silybin A, silybin B Raloxifene UGTs PBPK Up to 1.3-fold ↑ 1.09-fold ↑ Gufford et al. (2015a) Silibinin, silymarin NA, Silybum marianum (L.) gaertn. Silybin A, silybin B Raloxifene UGT1A1, UGT1A8, UGT1A10 Static 4- to 5-fold ↑ by silibinin and silymarin, respectively 1.09-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.
- 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 Inhibition Time-Dependent Inhibition CYP3A (Dose/250 ml)/Ki,u ≥ 10 kobs/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).
In this issue
Modeling Pharmacokinetic Natural Product–Drug Interactions
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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
- Authorship Contributions
- Footnotes
- Abbreviations
- References
- Figures & Data
- Info & Metrics
- eLetters