Enterohepatic recirculation/recycling (i.e. EHR) refers to the recirculation/recycling of endogenous (produced within the body, like biliary acids and bilirubin) and exogenous (e.g. drugs or other substances) compounds. EHR involves the excretion of substances from the liver to the small intestine via bile, followed by absorption into enterocytes, which then enters the liver again via portal vein (Lennartsson et al., 2012). EHR is important in the disposition of both endogenous and exogenous compounds. For endogenous compounds (e.g. bile acids), the recirculation through EHR keeps them from being eliminated into the feces and reduces the requirement of daily biosynthesis. For exogenous compounds, EHR prolonged the in vivo exposure and increased their apparent half-life. In other words, EHR could greatly affect the efficacy of drugs and the toxicity of xenobiotics (or exogenous compounds).

EHR was first conceptualized in the 1950s for bile acid recirculation (Norman and Sjovall, 1958). The recirculation of bile acids requires only the re-absorption of the intact bile acids in the intestine without any change. In the 1960s, this concept was expanded into the disposition of drugs and other xenobiotics that undergo phase II metabolism via enzymes such as uridine 5'-diphospho-glucuronosyltransferase (UGTs) and sulfotransferases (SULTs) primarily expressed in the small intestine and liver. Currently, a variety of drugs are known to undergo EHR, including anticancer drugs (e.g. sorafenib, SN-38), anti-cholesterol drugs (e.g. ezetimibe), anti-osteoporosis drugs (e.g. raloxifene), analgesics (e.g. morphine) Gårdmark et al., 1993, and others (e.g. mycophenolic acid) (Trdan Lušin et al., 2012; Oswald et al., 2008; Ando and Hasegawa, 2005; Vasilyeva et al., 2015; Hasselström and Säwe, 1993). In the case of exogenous compounds, the R in EHR was ‘recycling’ not ‘recirculation,’ as the enzymes of the microflora present in the intestine are required to hydrolyze the phase II metabolites and regenerate the aglycones (the parent form of the phase II metabolites) for reabsorption (Williams et al., 1965). The current concept of EHR does not define or specify the sources of phase II metabolites (i.e. where they are formed) that excreted in bile. Previously, it is postulated that the phase II metabolites (especially glucuronide metabolites) found in bile are mainly produced by the liver because bile acids are biosynthesized in the liver.

EHR of bile acids led most investigators, ourselves included, to believe that liver is the major organ contributing to the formation of conjugated metabolites. Faster in vitro microsomal conjugation rates observed in the liver as compared to intestinal microsomes for many compounds also supported this assumption (Hu et al., 2014; Zhou et al., 2010). However, we recently found that certain phenolic glucuronides formed in the intestine were efficiently taken up by hepatocytes and then rapidly excreted into the bile (Zeng et al., 2016). These phenomena suggest that glucuronides formed extrahepatically (such as in the intestine), available for hepatic uptake transporters (e.g. Organic Anion Transporting Polypeptides) and subsequently excreted by efflux transporters (e.g. Multidrug Resistance-associated Protein 2), could be recycled by the liver. This led us to hypothesize a new recycling phenomenon, 'Hepatoenteric Recycling (HER)'.

EHR differs from HER in the predominant organs for formation and transport of phase II metabolites. We believe that it is important to delineate HER from EHR since the disposition of phase II metabolites in different metabolic organs (such as liver and intestine) could greatly change with age, gender, and disease conditions (Bolling et al., 2011; Bhatt et al., 2019; Liu et al., 2013; Zhou et al., 2013; Pimentel et al., 2019; Langmann et al., 2004). These conditions often dictate the organ-level expression and activities of phase II enzymes and transporters (both efflux and uptake) that could significantly impact the EHR vs HER-mediated disposition of glucuronides. The changes in HER-mediated disposition can lead to changes in systemic and intestinal exposure of drugs and their glucuronides by altering the hepatic uptake and biliary excretion of glucuronides, which could significantly affect the drug efficacy and toxicity, both locally (intestinal lumen) and systemically.

However, EHR redefinition, which could explain the disposition of extrahepatically derived phase II metabolites, requires a systemic study to provide initial evidence that is based on the sources of biliary phenolic glucuronides. The current study provides answers to the three key questions: (1) how efficiently extrahepatically derived phenolic glucuronides are recycled by liver; (2) how these glucuronides are taken up by the liver cells; and (3) whether the aglycone or glucuronides entering the liver is better source of biliary excreted glucuronides. By answering these questions, we aim to elucidate the mechanism of HER, and the significance of distinguishing it from traditional EHR.

Through the analysis of our study results, a new disposition mechanism called ‘Hepatoenteric Recycling (HER)’ mainly applied to the disposition of drugs and their glucuronides, with significant intestinal metabolism, is proposed. The new mechanism distinguish from the enterohepatic recirculation/recycling (Figure 1), which is mainly applied to compounds which undergo recirculation in intact form or are metabolized by the liver. In HER, intestine is the organ for glucuronide formation and liver is the organ for its recycling (Figure 1A) as opposed to in EHR, where liver is the organ for glucuronide formation and intestine is the organ for its recycling (Figure 1B). The newly proposed HER concept delineates more clearly the disposition mechanism of phenolic drugs and phytochemicals such as flavonoids and polyphenols, which are significantly metabolized in the human intestine (up to ~70% or more) before entering the liver (Patrick et al., 2002; Yang et al., 2012; Strassburg et al., 1998; Kokawa et al., 2013; Sun et al., 2013; Kemp et al., 2002; Kosoglou et al., 2005; Teeter and Meyerhoff, 2002).

Figure 1

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We have defined a new disposition process as ‘Hepatoenteric Recycling (HER)’ for phenolic drugs and phytochemicals (e.g. flavonoids) as well as their glucuronides (Figure 1). HER (Figure 1A) differs significantly from EHR (Figure 1B) associated with bile acids and certain drugs, in that intestine is the main metabolite forming organ and liver is the recycling organ. Previously, EHR of bile acids and various drugs (Nielsen et al., 2009) is associated with metabolites that are generated in liver and recycled in intestine. This new definition for the first-pass metabolism of phenolic drugs and phytochemicals pinpoints where the metabolites are formed and subsequently recycled. In other words, the roles of liver and intestine are reversed in the new HER vs the classical EHR. The word ‘Recycling’ refers to the fact that many extrahepatically generated phenolic glucuronides were able to achieve hepatic recycling in HER. The word ‘Recycling’ is also important in HER because biliary excreted glucuronides are not well absorbed in the intestine. They must be reactivated to aglycones, by the microflora β-glucuronidases, before reabsorption can occur (Figure 1A).

The new definition is needed because: (1) unlike EHR of bile acids, intestine, not liver, is the source of the recycled metabolites (i.e. phenolic glucuronides); (2) unlike EHR of bile acids, liver, not the intestine, is the recycling organ for glucuronides; and (3) unlike EHR of bile acids, hepatic (e.g. OATPs) but not intestinal uptake transporters (e.g. ASBT) appears to be the rate-limiting step in determining the LRE% in HER. Due to the presence of HER, hepatic transporters (e.g. OATPs) will often determine the plasma concentrations of glucuronides, and play a much more important role than the relevant hepatic enzymes (e.g. UGTs) in determining how much glucuronides are excreted into the bile and participate in recycling. For orally administered dietary phenolics such as wogonin and drugs such as ezetimibe with high LRE%, double peaks caused by HER were observed in their plasma profiles (Figure 4—figure supplement 3A to 3D) even though biliary metabolites are not generated by the liver. For phenolics with more moderate LRE%, the role of metabolite generating organ or recycling organ is not mutually exclusive in HER vs EHR, in that biliary glucuronides may come from metabolites generated in the liver as well as the intestine, with variable contributions depending on the structure of aglycones.

How significant is this new disposition process called HER in determining how phenolics and their glucuronides are handled in vivo? We believe that HER is critically important in determining an orally administered phenolic’s pharmacokinetic profile with extensive intestinal metabolism. A high LRE% means that a phenolic could have double peaks in their plasma profile and a longer terminal half-life. This could mean high plasma levels of aglycone if glucuronides are readily hydrolyzed back into aglycones (Zhang et al., 2010). In other words, for extrahepatically generated glucuronides, HER is critically important because almost every glucuronides we tested were more likely to be excreted into bile if they are given as glucuronides vs corresponding aglycones into the portal vein (Figure 2B1–B7). If HER is critical, how important is EHR in the recycling process? We believe that EHR is not that important here for compounds with high LRE%, since these compounds were nearly fully (>80%) metabolized after the aglycones were absorbed in small intestine (Figure 5). Therefore, we believe that HER is much more important than EHR for orally administered phenolics whose major metabolic pathway is rapid intestinal glucuronidation. The importance of HER can be further manifested in disease conditions. For example, in chemically (e.g. trinitrobenzene sulfonic acid) induced colitis model, small intestinal UGTs were significantly downregulated but hepatic UGTs were not (Zhou et al., 2013). These differences in UGTs regulation indicated that HER would likely to be impacted, whereas EHR might not, since changes in metabolism only occurs in the intestine. On the other hand, in hepatitis C, where hepatic CYPs, OATPs, OCTs (Nakai et al., 2008) and UGTs (Smolders et al., 2016) were all downregulated, EHR might be affected more than HER. However, for compounds that were metabolized both in the intestine and liver, the situation might be more convoluted.

We have proposed the new HER mechanism to delineate more clearly the significance and the need to describe the recycling of phenolics using a terminology different from EHR. How would HER impact the beneficial effects of dietary phenolics, therapeutic efficacy of phenolic drugs, and toxicities of phenolic toxins that are taken orally, produced by microbiota or formed as the result of phase II metabolism in the liver? The possible answers are made up of two parts, and subsequently combined to argue for the importance of HER in determining the disposition of orally administered phenolics with mainly extrahepatic glucuronidation.

Firstly, HER will shuttle more phenolic compounds and their metabolites, both beneficial and toxic to the colon. This is because there is no known mechanism that can rapidly take up glucuronides in the small intestine. For compounds with beneficial effects, including therapeutic efficacy, this new disposition mechanism can be useful in preventing or treating diseases in colon. The prevention or treatment effects can be achieved via a direct effect (e.g. suppress inflammation) or indirect effect (e.g. production of beneficial microbial metabolites that suppress inflammation). In contrary, some of the toxic phenolic metabolites can be further biotransformed by the microbial enzymes to more potent toxins (Koppel et al., 2017). Taken together, the presence of HER for an oral phenolic could substantially enhance its colonic impact and maintenance of a healthy colon microbiota (i.e. a desirable colon microbiome homeostasis) that is essential for our health. Therefore, the use of phenolics to maintain and improve the health of microbiota is an important future research goal. On the other hand, HER could promote the recycling of toxic phenolic metabolites, enhancing its exposure and toxicities, and this should be limited to improve human health.

Second, it may provide the theoretical foundation to understand a myriad phenomenon when there are drug-drug interactions involving glucuronidation. For example, if we only recognize the importance of EHR, if would be difficult to understand why inhibition of hepatic UGTs does not always increase aglycone concentration and decrease glucuronide concentration in the blood. If we recognize that a phenolic is mainly processed by HER, an inhibitor that only inhibit liver UGTs is not expected to affect plasma concentrations of orally administered aglycone and its glucuronides as long as intestine is the metabolite forming organ. In contrast, the emerging role of OATPs means that the plasma concentrations of both an aglycone and its glucuronide (if both are substrates of OATPs) or just glucuronide (if only glucuronide is substrate of OATPs), could increase by a drug that did not affect intestinal or liver UGT enzymes. Furthermore, one can propose a variety of drug-drug interaction schemes that can be explained by both HER and EHR. Therefore, the proposed HER mechanism should help us determine the dominating mechanism by which drug-drug interactions occur to influence the plasma concentrations of a phenolic aglycone and its glucuronides that are formed in the intestine. This influence is more significant when the glucuronides are pharmacologically active (i.e. ezetimibe and dabigatran [Jia and Zhao, 2011; Jiang and Li, 2010]) or can inhibit CYP enzymes (i.e. gemfibrozil and clopidogrel [Sivapathasekaran et al., 2010; Mutwil et al., 2010]).

When combined together, the new HER definition could help predict the potential drug-drug interactions under pathological conditions. Since small intestinal UGTs play a significant role in the HER of the compounds, factors that alter the UGT expression in small intestine will significantly influence the recycling of the compounds and their in vivo exposure. Searching in Google Scholar by using key words combination ‘UGT + intestine + disease’, yielded a total of 14,500 hits. Among the top 50 results ranked by relevance, 15 of them reported different causes leading to change in intestinal UGTs including diseases (colitis, Gilbert’s disease etc.), genetic polymorphism and drug-drug interactions. The results indicated that pathological conditions will greatly influence the intestinal UGTs expression level and thus change the in vivo exposure of a phenolic whose disposition is controlled by a HER process. Similar conclusion could be made on hepatic OATPs, since OATPs mediate the hepatic uptake step of glucuronides generated from intestinal metabolism. Using ‘OATP +liver + disease’ as key words combination, a total of 14,000 hits were found and 21 out of top 50 results ranked by relevance reported the hepatic OATPs expression changes due to disease like cholestasis, liver hepatitis, and carcinoma.

We have obtained several lines of evidence in support of new HER definition, especially for oral phenolics such as wogonin and ezetimibe. First, our data showed that when infused directly into the portal vein, the vast majority of their glucuronides reached bile, displaying a highly efficient recycling (LRE%) (Appendix 2—table 5). For those compounds, portal vein concentrations of glucuronides were much higher than the corresponding aglycones in the rat intestinal perfusion studies (Figure 5). Second, direct portal vein infusion of aglycone was substantially less effective in producing biliary excretion of glucuronides than their corresponding glucuronides (Figure 2B1–B7). Third, hepatic OATPs took up these glucuronides in a saturable process that can be inhibited by OATP inhibitors (Figure 3B, Figure 3C1–C3). Fourth, the structural differences between phenolic-glucuronides had a substantial impact on the LRE%. Taken together, these results showed clearly that intestine is the glucuronide forming organ, and liver is the glucuronide recycling organ for these two compounds. Hence, the term HER properly describes the determining disposition processes of these two compounds, a naturally occurring polyphenol flavonoid and a prescription drug, which share minimal structural similarity. The latter suggest that HER could be broadly applicable to other phenolic phytochemicals and drugs.

The new HER disposition process could explain the pharmacokinetic behaviors of many phenolic drugs, including ezetimibe, raloxifene and diclofenac. All three drugs were rapidly glucuronidated by intestinal microsomes and had a double peak PK profiles (Watanabe et al., 1983; Herz and Bläsig, 1974). For diclofenac, extensive intestinal glucuronidation by microsomes (Bläsig et al., 1973) with >95% glucuronides formation (Herz and Bläsig, 1974), are consistent with it undergoing through HER instead of EHR. In addition, the recycle properties of ezetimibe reported in human (Christoffersen et al., 2015) was consistent with the proposed HER hypothesis. As over 80% of ezetimibe was converted into glucuronides in intestine (Christoffersen et al., 2015) with a second peak in its PK profile attributable to hepatic recycling. Therefore, for compounds with extensive intestinal glucuronidation and hepatic recycling, HER will fully explain their pharmacokinetic behaviors.

Although many glucuronides with rapid uptake by OATPs are correlated with higher LRE% (Figure 4), two glucuronides (i.e. Lut-G and Ica-G) (Wu et al., 2016; Wu et al., 2015) did not. We found that these two glucuronides were further metabolized in liver, forming di-glucuronides of Lut and di-glucuronides of Ica, respectively, rendering the original glucuronides unavailable for biliary excretion. Whereas, we have obtained large amount of evidence in support of roles of OATP uptake transporters in enabling HER (Figure 4), we could not rule out the contribution of other transporters. This is because we could only achieve a maximum of 50% inhibition of Won-7-G and Lut-3’-G LRE% in portal vein infusion experiment (Figure 6). It is likely that transporters belong to the OAT3 subfamily contributed to their liver uptake, since several flavonoid glucuronides were shown to be substrates of OAT3 (Rees et al., 2012), which is also expressed in the liver (Nema et al., 2011).

Lastly, we have significant but not overwhelming structural diversity in our phenolic glucuronides, and as such, we could not predict which structure will have high LRE%. The empirical evidence supports the hypothesis that good uptake of glucuronides by the OATPs will correlate with high LRE%, as long as the glucuronides taken up by the hepatocytes are not further metabolized. Because the direct portal vein infusion method is convenient to use with cassette dosing (Figure 2—figure supplement 1), HER potential can be readily estimated using OATP overexpressing cells and confirmed using the portal vein perfusion model. The latter should allow medicinal chemists to design new compounds with primarily intestinal glucuronidation that are tailored to treat intestinal, especially colonic diseases. In addition to the involvement of multiple uptake transporters, the role of efflux transporters may further complicate the disposition behaviors of phenolics and their glucuronides, but that is beyond the scope of this paper.

In conclusion, a new disposition terminology HER has been proposed to describe and delineate more clearly the disposition of dietary phenolics and phenolic drugs that are taken orally with intestine as their major metabolism (i.e. glucuronidation) organ and liver as their major recirculating organ. The new terminology more accurately captures the recycling of relevant phenolics, similar to the use of ‘EHR’ to capture the recycling of bile acids, where the liver is the metabolism organ and intestine as the recycling organ. This new HER more accurately captures the disposition of oral phenolics and can be used to better understand why this class of compounds may have larger than expected effects in the colon for human health and diseases. It may also help to delineate drug-drug interaction mechanisms involving intestinal UGT enzymes and hepatic transporters of glucuronides, a major challenge that we are still facing today because of all the complexity involved.

All datasets associated with the published data will be made freely and widely available in the most useful formats, and according to the relevant reporting standards. All data generated or analysed during this study are included in the manuscript and supporting files and has been uploaded into Dryad Digital Repository. The files included the original version of the figures and tables included in the manuscript, appendix and supplement documents.

1. 1. Tu Y
   2. Wang L
   3. Rong Y
   4. Tam V
   5. Yin T
   6. Gao S
   7. Singh R
   8. Hu M

   (2021) Dryad Digital Repository

   Dataset for manuscript EHR.

   https://doi.org/10.5061/dryad.3ffbg79hn

1. 1. Ando Y
   2. Hasegawa Y

   (2005) Clinical Pharmacogenetics of Irinotecan (CPT-11)

   Drug Metabolism Reviews 37:565–574.

   https://doi.org/10.1080/03602530500316254

   • Google Scholar
2. 1. Bhatt DK
   2. Mehrotra A
   3. Gaedigk A
   4. Chapa R
   5. Basit A
   6. Zhang H
   7. Choudhari P
   8. Boberg M
   9. Pearce RE
   10. Gaedigk R
   11. Broeckel U
   12. Leeder JS
   13. Prasad B

   (2019) Age‐ and Genotype‐Dependent Variability in the Protein Abundance and Activity of Six Major Uridine Diphosphate‐Glucuronosyltransferases in Human Liver

   Clinical Pharmacology & Therapeutics 105:131–141.

   https://doi.org/10.1002/cpt.1109

   • Google Scholar
3. 1. Bläsig J
   2. Herz A
   3. Reinhold K
   4. Zieglgänsberger S

   (1973) Development of physical dependence on morphine in respect to time and dosage and quantification of the precipitated withdrawal syndrome in rats

   Psychopharmacologia 33:19–38.

   https://doi.org/10.1007/BF00428791

   • PubMed
   • Google Scholar
4. 1. Blevins L
   2. McDonald CJ

   (1985)

   Fisher's Exact Test: an easy-to-use statistical test for comparing outcomes

   M.D. Computing : Computers in Medical Practice 9:68.

   • Google Scholar
5. 1. Bolling BW
   2. Court MH
   3. Blumberg JB
   4. Chen C-YO

   (2011) Microsomal Quercetin Glucuronidation in Rat Small Intestine Depends on Age and Segment

   Drug Metabolism and Disposition 39:1406–1414.

   https://doi.org/10.1124/dmd.111.038406

   • Google Scholar
6. 1. Christoffersen DJ
   2. Brasch-Andersen C
   3. Thomsen JL
   4. Worm-Leonhard M
   5. Damkier P
   6. Brøsen K

   (2015) Quantification of morphine, morphine 6-glucuronide, buprenorphine, and the enantiomers of methadone by enantioselective mass spectrometric chromatography in whole blood

   Forensic Science, Medicine, and Pathology 11:193–201.

   https://doi.org/10.1007/s12024-015-9673-9

   • Google Scholar
7. 1. Davies B
   2. Morris T

   (1993) Physiological parameters in laboratory animals and humans

   Pharmaceutical Research 10:1093–1095.

   https://doi.org/10.1023/A:1018943613122

   • Google Scholar
8. 1. Dougherty PM
   2. Dong WQ
   3. Faillace LA
   4. Dafny N

   (1990) Trans-cranial electrical stimulation attenuates abrupt morphine withdrawal in rats assayed by remote computerized quantification of multiple motor behavior indices

   European Journal of Pharmacology 175:187–195.

   https://doi.org/10.1016/0014-2999(90)90229-Y

   • PubMed
   • Google Scholar
9. 1. Fahrmayr C
   2. Fromm MF
   3. König J

   (2010) Hepatic OATP and OCT uptake transporters: their role for drug-drug interactions and pharmacogenetic aspects

   Drug Metabolism Reviews 42:380–401.

   https://doi.org/10.3109/03602530903491683

   • Google Scholar
10. 1. Gårdmark M
    2. Ekblom M
    3. Bouw R
    4. Hammarlund-Udenaes M

    (1993)

    Quantification of effect delay and acute tolerance development to morphine in the rat

    The Journal of Pharmacology and Experimental Therapeutics 267:1061–1067.

    • PubMed
    • Google Scholar
11. 1. Gjerde H
    2. Fongen U
    3. Gundersen H
    4. Christophersen AS

    (1991) Evaluation of a method for simultaneous quantification of codeine, ethylmorphine and morphine in blood

    Forensic Science International 51:105–110.

    https://doi.org/10.1016/0379-0738(91)90210-A

    • Google Scholar
12. 1. Hasselström J
    2. Säwe J

    (1993) Morphine pharmacokinetics and metabolism in humans. enterohepatic cycling and relative contribution of metabolites to active opioid concentrations

    Clinical Pharmacokinetics 24:344–354.

    https://doi.org/10.2165/00003088-199324040-00007

    • PubMed
    • Google Scholar
13. 1. Herz A
    2. Bläsig J

    (1974)

    Problems in quantification of provoked morphine withdrawal in rats

    Arzneimittel-Forschung 24:1012–1014.

    • PubMed
    • Google Scholar
14. 1. Hou W-Y
    2. Xu S-F
    3. Zhu Q-N
    4. Lu Y-F
    5. Cheng X-G
    6. Liu J

    (2014) Age- and sex-related differences of organic anion-transporting polypeptide gene expression in livers of rats

    Toxicology and Applied Pharmacology 280:370–377.

    https://doi.org/10.1016/j.taap.2014.08.020

    • Google Scholar
15. 1. Hu N
    2. Mei M
    3. Ruan J
    4. Wu W
    5. Wang Y
    6. Yan R

    (2014) Regioselective Glucuronidation of Oxyresveratrol, a Natural Hydroxystilbene, by Human Liver and Intestinal Microsomes and Recombinant UGTs

    Drug Metabolism and Pharmacokinetics 29:229–236.

    https://doi.org/10.2133/dmpk.DMPK-13-RG-102

    • Google Scholar
16. 1. Jia X
    2. Chen J
    3. Lin H
    4. Hu M

    (2004) Disposition of Flavonoids via Enteric Recycling: Enzyme-Transporter Coupling Affects Metabolism of Biochanin A and Formononetin and Excretion of Their Phase II Conjugates

    Journal of Pharmacology and Experimental Therapeutics 310:1103–1113.

    https://doi.org/10.1124/jpet.104.068403

    • Google Scholar
17. Conference

    1. Jia P
    2. Zhao Z

    (2011) Network-assisted causal gene detection in Genome-wide association studies: an improved module search algorithm

    IEEE International Workshop on Genomic Signal Processing and Statistics: [Proceedings]. pp. 131–134.

    https://doi.org/10.1109/GENSiPS.2011.6169462

    • Google Scholar
18. 1. Jiang P
    2. Li S

    (2010) A Sensor Network Data Compression Algorithm Based on Suboptimal Clustering and Virtual Landmark Routing Within Clusters

    Sensors 10:9084–9101.

    https://doi.org/10.3390/s101009084

    • Google Scholar
19. 1. Kashani Z
    2. Ahrabian H
    3. Elahi E
    4. Nowzari-Dalini A
    5. Ansari E
    6. Asadi S
    7. Mohammadi S
    8. Schreiber F
    9. Masoudi-Nejad A

    (2009) Kavosh: a new algorithm for finding network motifs

    BMC Bioinformatics 10:318.

    https://doi.org/10.1186/1471-2105-10-318

    • Google Scholar
20. 1. Kemp DC
    2. Fan PW
    3. Stevens JC

    (2002) Characterization of Raloxifene Glucuronidation in Vitro: Contribution of Intestinal Metabolism to Presystemic Clearance

    Drug Metabolism and Disposition 30:694–700.

    https://doi.org/10.1124/dmd.30.6.694

    • Google Scholar
21. 1. Kokawa Y
    2. Kishi N
    3. Jinno H
    4. Tanaka-Kagawa T
    5. Narimatsu S
    6. Hanioka N

    (2013) Effect of UDP-glucuronosyltransferase 1A8 polymorphism on raloxifene glucuronidation

    European Journal of Pharmaceutical Sciences 49:199–205.

    https://doi.org/10.1016/j.ejps.2013.03.001

    • Google Scholar
22. 1. Koppel N
    2. Maini Rekdal V
    3. Balskus EP

    (2017) Chemical transformation of xenobiotics by the human gut microbiota

    Science 356:eaag2770.

    https://doi.org/10.1126/science.aag2770

    • Google Scholar
23. 1. Kosoglou T
    2. Statkevich P
    3. Johnson-Levonas AO
    4. Paolini JF
    5. Bergman AJ
    6. Alton KB

    (2005) Ezetimibe: a review of its metabolism, pharmacokinetics and drug interactions

    Clinical Pharmacokinetics 44:467–494.

    https://doi.org/10.2165/00003088-200544050-00002

    • PubMed
    • Google Scholar
24. 1. Langmann T
    2. Moehle C
    3. Mauerer R
    4. Scharl M
    5. Liebisch G
    6. Zahn A
    7. Stremmel W
    8. Schmitz G

    (2004) Loss of detoxification in inflammatory bowel disease: dysregulation of pregnane X receptor target genes

    Gastroenterology 127:26–40.

    https://doi.org/10.1053/j.gastro.2004.04.019

    • Google Scholar
25. 1. Lennartsson J
    2. Håkansson N
    3. Wennergren U
    4. Jonsson A

    (2012) SpecNet: A Spatial Network Algorithm that Generates a Wide Range of Specific Structures

    PLOS ONE 7:e42679.

    https://doi.org/10.1371/journal.pone.0042679

    • Google Scholar
26. 1. Lin CW
    2. Yang YT
    3. Wang JS
    4. Yang YC

    (2012) A wearable sensor module with a neural-network-based activity classification algorithm for daily energy expenditure estimation

    IEEE Transactions on Information Technology in Biomedicine : A Publication of the IEEE Engineering in Medicine and Biology Society 16:991–998.

    https://doi.org/10.1109/TITB.2012.2206602

    • PubMed
    • Google Scholar
27. 1. Liu W
    2. Kulkarni K
    3. Hu M

    (2013) Gender-dependent differences in uridine 5'-diphospho-glucuronosyltransferase have implications in metabolism and clearance of xenobiotics

    Expert Opinion on Drug Metabolism & Toxicology 9:1555–1569.

    https://doi.org/10.1517/17425255.2013.829040

    • Google Scholar
28. 1. Mo S
    2. Pan T
    3. Wu F
    4. Zeng M
    5. Huang D
    6. Zhang L
    7. Jia L
    8. Chen Y
    9. Cheng Z

    (2019) Facile one-step microwave-assisted modification of kaolinite and performance evaluation of pickering emulsion stabilization for oil recovery application

    Journal of Environmental Management 238:257–262.

    https://doi.org/10.1016/j.jenvman.2019.03.003

    • Google Scholar
29. 1. Musshoff F
    2. Daldrup T

    (1993) Evaluation of a method for simultaneous quantification of codeine, dihydrocodeine, morphine, and 6-monoacetylmorphine in serum, blood, and postmortem blood

    International Journal of Legal Medicine 106:107–109.

    https://doi.org/10.1007/BF01225050

    • PubMed
    • Google Scholar
30. 1. Mutwil M
    2. Usadel B
    3. Schütte M
    4. Loraine A
    5. Ebenhöh O
    6. Persson S

    (2010) Assembly of an interactive correlation network for the Arabidopsis genome using a novel heuristic clustering algorithm

    Plant Physiology 152:29–43.

    https://doi.org/10.1104/pp.109.145318

    • PubMed
    • Google Scholar
31. 1. Nakai K
    2. Tanaka H
    3. Hanada K
    4. Ogata H
    5. Suzuki F
    6. Kumada H
    7. Miyajima A
    8. Ishida S
    9. Sunouchi M
    10. Habano W
    11. Kamikawa Y
    12. Kubota K
    13. Kita J
    14. Ozawa S
    15. Ohno Y

    (2008) Decreased Expression of Cytochromes P450 1A2, 2E1, and 3A4 and Drug Transporters Na ⁺ -Taurocholate-Cotransporting Polypeptide, Organic Cation Transporter 1, and Organic Anion-Transporting Peptide-C Correlates with the Progression of Liver Fibrosis in Chronic Hepatitis C Patients

    Drug Metabolism and Disposition 36:1786–1793.

    https://doi.org/10.1124/dmd.107.020073

    • Google Scholar
32. 1. Nema T
    2. Chan ECY
    3. Ho PC

    (2011) Efficiency of a miniaturized silica monolithic cartridge in reducing matrix ions as demonstrated in the simultaneous extraction of morphine and codeine from urine samples for quantification with liquid chromatography-tandem mass spectrometry (LC-MS/MS)

    Journal of Mass Spectrometry 46:891–900.

    https://doi.org/10.1002/jms.1966

    • Google Scholar
33. 1. Nielsen M
    2. Lund O
    3. Lund, NN-align O

    (2009) NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction

    BMC Bioinformatics 10:296.

    https://doi.org/10.1186/1471-2105-10-296

    • Google Scholar
34. 1. Norman A
    2. Sjovall J

    (1958)

    On the transformation and enterohepatic circulation of cholic acid in the rat: bile acids and steroids 68

    The Journal of Biological Chemistry 233:872–885.

    • PubMed
    • Google Scholar
35. 1. Oswald S
    2. König J
    3. Lütjohann D
    4. Giessmann T
    5. Kroemer HK
    6. Rimmbach C
    7. Rosskopf D
    8. Fromm MF
    9. Siegmund W

    (2008) Disposition of ezetimibe is influenced by polymorphisms of the hepatic uptake carrier OATP1B1

    Pharmacogenetics and Genomics 18:559–568.

    https://doi.org/10.1097/FPC.0b013e3282fe9a2c

    • Google Scholar
36. 1. Patrick JE
    2. Kosoglou T
    3. Stauber KL
    4. Alton KB
    5. Maxwell SE
    6. Zhu Y
    7. Statkevich P
    8. Iannucci R
    9. Chowdhury S
    10. Affrime M
    11. Cayen MN

    (2002) Disposition of the Selective Cholesterol Absorption Inhibitor Ezetimibe in Healthy Male Subjects

    Drug Metabolism and Disposition 30:430–437.

    https://doi.org/10.1124/dmd.30.4.430

    • Google Scholar
37. 1. Pérez-Gandía C
    2. Facchinetti A
    3. Sparacino G
    4. Cobelli C
    5. Gómez EJ
    6. Rigla M
    7. de Leiva A
    8. Hernando ME

    (2010) Artificial neural network algorithm for online glucose prediction from continuous glucose monitoring

    Diabetes Technology & Therapeutics 12:81–88.

    https://doi.org/10.1089/dia.2009.0076

    • PubMed
    • Google Scholar
38. 1. Pimentel AM
    2. Rocha R
    3. Santana GO

    (2019) Crohn’s disease of esophagus, stomach and duodenum

    World Journal of Gastrointestinal Pharmacology and Therapeutics 10:35–49.

    https://doi.org/10.4292/wjgpt.v10.i2.35

    • Google Scholar
39. 1. Rees KA
    2. McLaughlin PA
    3. Osselton MD

    (2012) Validation of a Gas Chromatography–Ion Trap-Tandem Mass Spectrometry Assay for the Simultaneous Quantification of Cocaine, Benzoylecgonine, Cocaethylene, Morphine, Codeine, and 6-Acetylmorphine in Aqueous Solution, Blood, and Skeletal Muscle Tissue

    Journal of Analytical Toxicology 36:1–11.

    https://doi.org/10.1093/jat/bkr006

    • Google Scholar
40. 1. Riveros T
    2. Hanrahan G
    3. Muliadi S
    4. Arceo J
    5. Gomez FA

    (2009) On-capillary derivatization using a hybrid artificial neural network-genetic algorithm approach

    The Analyst 134:2067–2070.

    https://doi.org/10.1039/b909143b

    • Google Scholar
41. 1. Sivapathasekaran C
    2. Mukherjee S
    3. Ray A
    4. Gupta A
    5. Sen R

    (2010) Artificial neural network modeling and genetic algorithm based medium optimization for the improved production of marine biosurfactant

    Bioresource Technology 101:2884–2887.

    https://doi.org/10.1016/j.biortech.2009.09.093

    • Google Scholar
42. 1. Smolders EJ
    2. de Kanter CTMM
    3. van Hoek B
    4. Arends JE
    5. Drenth JPH
    6. Burger DM

    (2016) Pharmacokinetics, Efficacy, and Safety of Hepatitis C Virus Drugs in Patients with Liver and/or Renal Impairment

    Drug Safety 39:589–611.

    https://doi.org/10.1007/s40264-016-0420-2

    • Google Scholar
43. 1. Strassburg CP
    2. Manns MP
    3. Tukey RH

    (1998) Expression of the UDP-glucuronosyltransferase 1A locus in human Colon. identification and characterization of the novel extrahepatic UGT1A8

    The Journal of Biological Chemistry 273:8719–8726.

    https://doi.org/10.1074/jbc.273.15.8719

    • PubMed
    • Google Scholar
44. 1. Sun D
    2. Jones NR
    3. Manni A
    4. Lazarus P

    (2013) Characterization of Raloxifene Glucuronidation: Potential Role of UGT1A8 Genotype on Raloxifene Metabolism In Vivo

    Cancer Prevention Research 6:719–730.

    https://doi.org/10.1158/1940-6207.CAPR-12-0448

    • Google Scholar
45. 1. Talbi A
    2. Zhao D
    3. Liu Q
    4. Li J
    5. Fan A
    6. Yang W
    7. Han X
    8. Chen X

    (2014) Pharmacokinetics, Tissue Distribution, Excretion and Plasma Protein Binding Studies of Wogonin in Rats

    Molecules 19:5538–5549.

    https://doi.org/10.3390/molecules19055538

    • Google Scholar
46. 1. Teeter JS
    2. Meyerhoff RD

    (2002) Environmental fate and chemistry of raloxifene hydrochloride

    Environmental Toxicology and Chemistry 21:729–736.

    https://doi.org/10.1002/etc.5620210407

    • Google Scholar
47. 1. Trdan Lušin T
    2. Stieger B
    3. Marc J
    4. Mrhar A
    5. Trontelj J
    6. Zavratnik A
    7. Ostanek B

    (2012) Organic anion transporting polypeptides OATP1B1 and OATP1B3 and their genetic variants influence the pharmacokinetics and pharmacodynamics of raloxifene

    Journal of Translational Medicine 10:76.

    https://doi.org/10.1186/1479-5876-10-76

    • Google Scholar
48. 1. Vasilyeva A
    2. Durmus S
    3. Li L
    4. Wagenaar E
    5. Hu S
    6. Gibson AA
    7. Panetta JC
    8. Mani S
    9. Sparreboom A
    10. Baker SD
    11. Schinkel AH

    (2015) Hepatocellular Shuttling and Recirculation of Sorafenib-Glucuronide Is Dependent on Abcc2, Abcc3, and Oatp1a/1b

    Cancer Research 75:2729–2736.

    https://doi.org/10.1158/0008-5472.CAN-15-0280

    • Google Scholar
49. 1. Wang HQ
    2. Jin KP
    3. Zeng MS
    4. Chen CZ
    5. Rao SX
    6. Ji Y
    7. Fu CX
    8. Sheng RF

    (2019) Assessing liver fibrosis in chronic hepatitis B using MR extracellular volume measurements: comparison with serum fibrosis indices

    Magnetic Resonance Imaging 59:39–45.

    https://doi.org/10.1016/j.mri.2019.03.002

    • PubMed
    • Google Scholar
50. 1. Watanabe H
    2. Watanabe K
    3. Shirasaki K
    4. Fujita H

    (1983)

    [Electromyographic quantification of muscle rigidity in rats caused by morphine and drug actions on the symptoms]

    Yakugaku Zasshi: Journal of the Pharmaceutical Society of Japan 103:790–794.

    • PubMed
    • Google Scholar
51. 1. Williams RT
    2. Millburn P
    3. Smith RL

    (1965) The influence of enterohepatic circulation on toxicity of drugs

    Annals of the New York Academy of Sciences 123:110–124.

    https://doi.org/10.1111/j.1749-6632.1965.tb12249.x

    • PubMed
    • Google Scholar
52. 1. Wu L
    2. Liu J
    3. Han W
    4. Zhou X
    5. Yu X
    6. Wei Q
    7. Liu S
    8. Tang L

    (2015) Time-Dependent Metabolism of Luteolin by Human UDP-Glucuronosyltransferases and Its Intestinal First-Pass Glucuronidation in Mice

    Journal of Agricultural and Food Chemistry 63:8722–8733.

    https://doi.org/10.1021/acs.jafc.5b02827

    • Google Scholar
53. 1. Wu H
    2. Kim M
    3. Han J

    (2016) Icariin Metabolism by Human Intestinal Microflora

    Molecules 21:1158.

    https://doi.org/10.3390/molecules21091158

    • Google Scholar
54. 1. Yang Z
    2. Kulkarni K
    3. Zhu W
    4. Hu M

    (2012) Bioavailability and pharmacokinetics of genistein: mechanistic studies on its ADME

    Anti-Cancer Agents in Medicinal Chemistry 12:1264–1280.

    https://doi.org/10.2174/187152012803833107

    • Google Scholar
55. 1. Zeng M
    2. Sun R
    3. Basu S
    4. Ma Y
    5. Ge S
    6. Yin T
    7. Gao S
    8. Zhang J
    9. Hu M

    (2016) Disposition of flavonoids via recycling: Direct biliary excretion of enterically or extrahepatically derived flavonoid glucuronides

    Molecular Nutrition & Food Research 60:1006–1019.

    https://doi.org/10.1002/mnfr.201500692

    • Google Scholar
56. 1. Zhang Y
    2. Xu J
    3. Yuan Z
    4. Xu H
    5. Yu Q

    (2010) Artificial neural network-genetic algorithm based optimization for the immobilization of cellulase on the smart polymer Eudragit L-100

    Bioresource Technology 101:3153–3158.

    https://doi.org/10.1016/j.biortech.2009.12.080

    • Google Scholar
57. 1. Zhou Q
    2. Zheng Z
    3. Xia B
    4. Tang L
    5. Lv C
    6. Liu W
    7. Liu Z
    8. Hu M

    (2010) Use of Isoform-Specific UGT Metabolism to Determine and Describe Rates and Profiles of Glucuronidation of Wogonin and Oroxylin A by Human Liver and Intestinal Microsomes

    Pharmaceutical Research 27:1568–1583.

    https://doi.org/10.1007/s11095-010-0148-0

    • Google Scholar
58. 1. Zhou X
    2. Xie Y
    3. Qi Q
    4. Cheng X
    5. Liu F
    6. Liao K
    7. Wang G
    8. Hao H

    (2013) Disturbance of Hepatic and Intestinal UDP-glucuronosyltransferase in Rats with Trinitrobenzene Sulfonic Acid-induced Colitis

    Drug Metabolism and Pharmacokinetics 28:305–313.

    https://doi.org/10.2133/dmpk.DMPK-12-RG-097

    • Google Scholar

Author details

1. Yifan Tu

   Department of Pharmacological and Pharmaceutical Sciences, College of Pharmacy, University of Houston, Houston, United States

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2728-6753

2. Lu Wang

   Department of Pharmacological and Pharmaceutical Sciences, College of Pharmacy, University of Houston, Houston, United States

   Contribution

   Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8643-6790

3. Yi Rong

   Department of Pharmacological and Pharmaceutical Sciences, College of Pharmacy, University of Houston, Houston, United States

   Contribution

   Resources, Data curation, Investigation, Methodology

   Competing interests

   No competing interests declared

4. Vincent Tam

   Department of Pharmacological and Pharmaceutical Sciences, College of Pharmacy, University of Houston, Houston, United States

   Contribution

   Data curation, Formal analysis, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

5. Taijun Yin

   Department of Pharmacological and Pharmaceutical Sciences, College of Pharmacy, University of Houston, Houston, United States

   Contribution

   Resources, Supervision, Investigation, Methodology

   Competing interests

   No competing interests declared

6. Song Gao

   Department of Pharmaceutical Sciences, College of Pharmacy, Texas Southern University, Houston, United States

   Contribution

   Supervision, Validation, Investigation, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

7. Rashim Singh

   Department of Pharmacological and Pharmaceutical Sciences, College of Pharmacy, University of Houston, Houston, United States

   Contribution

   Resources, Formal analysis, Writing - review and editing

   Competing interests

   No competing interests declared

8. Ming Hu

   Department of Pharmacological and Pharmaceutical Sciences, College of Pharmacy, University of Houston, Houston, United States

   Contribution

   Conceptualization, Formal analysis, Supervision, Funding acquisition, Methodology, Writing - original draft, Project administration, Writing - review and editing

   For correspondence

   mhu@uh.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2744-6294

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