Enantioselective in vitro metabolism of the herbicide diclofop-methyl: Prediction of toxicokinetic parameters and reaction phenotyping

Human exposure to contaminants of emerging concern, like pesticides, has increased in the past decades. Diclofop-methyl (DFM) is a chiral herbicide that is employed as a racemic mixture (rac-DFM) in soybean and other crops against wild oats. Studies have shown that DFM has enantioselective action (higher for R-DFM), degradation (faster for S-DFM), and metabolism, producing diclofop (DF) which is also a pesticide. Although toxic effects have been reported for DFM, information regarding how DFM affects humans is lacking, especially when its chirality is concerned. In this study, the in vitro metabolism of rac-DFM and its isolated enantiomers was assessed by using a human model based on human liver microsomes. The kinetic model and parameters were obtained, and the hepatic clearance (CLH) and hepatic extraction ratio (EH) were estimated. Enzyme phenotyping was carried out by employing carboxylesterase isoforms (CES 1 and CES 2). DFM was metabolized through positive homotropic cooperativity with slight preference for (-)-DFM metabolism to (-)-DF. CLH and EH were above 19.60 mL min-1 kg-1 and 98 % for all the monitored reactions, respectively, and CES 1 was the main enzyme underlying the metabolism. These findings point out that liver contributes to DFM metabolism, which is fast, resulting in nearly complete conversion to DF after exposition to DFM.

Models Used to Predict Chemical Bioaccumulation in Fish from in Vitro Biotransformation Rates Require Accurate Estimates of Blood-Water Partitioning and Chemical Volume of Distribution

Methods for extrapolating measured in vitro intrinsic clearance to a whole-body biotransformation rate constant (kB ) have been developed to support modeled bioaccumulation assessments for fish. The inclusion of extrapolated kB values into existing bioaccumulation models improves the prediction of chemical bioconcentration factors (BCFs), but there remains a tendency for these methods to overestimate BCFs relative to measured values. Therefore, a need exists to evaluate the extrapolation procedure to assess potential sources of error in predicted kB values. We examined how three different approaches (empirically based, composition based, and polyparameter linear free energy relationships [ppLFERs]) used to predict chemical partitioning in vitro (liver S9 system; KS9W ), in blood (KBW ), and in whole fish tissues (KFW ) impact the prediction of a chemical's hepatic clearance binding term (fU ) and apparent volume of distribution (VD ), both of which factor into the calculation of kB and the BCF. Each approach yielded different KS9W , KBW , and KFW values, but resulted in fU values that were of similar magnitude and remained relatively constant at log octanol-water partition ratios (KOW ) greater than 4. This is because KBW and KS9W values predicted by any given approach exhibit a similar dependence on log KOW (i.e., regression slope), which results in a cancelation of "errors" when fU is calculated. In contrast, differences in KBW values predicted by the three approaches translate to differences in VD , and by extension kB and the BCF, which become most apparent at log KOW greater than 6. There is a need to collect KBW and VD data for hydrophobic chemicals in fish that can be used to evaluate and improve existing partitioning prediction approaches in extrapolation models for fish. Environ Toxicol Chem 2023;42:33-45. © 2022 SETAC.

Nucleoside analog GS-441524: pharmacokinetics in different species, safety, and potential effectiveness against Covid-19

GS-441524, the parent nucleoside of remdesivir, has been proposed to be effective against Covid-19 based on in vitro studies and studies in animals. However, randomized clinical trials of the agent to treat Covid-19 have not been conducted. Here, we evaluated GS-441524 for Covid-19 treatment based on studies reporting pharmacokinetic parameters of the agent in mice, rats, cats, dogs, monkeys, and the single individual in the first-in-human trial supplemented with information about its activity against severe acute respiratory syndrome coronavirus 2 and safety. A dosing interval of 8 h was considered clinically relevant and used to calculate steady-state plasma concentrations of GS-441524. These ranged from 0.27 to 234.41 μM, reflecting differences in species, doses, and administration routes. Fifty percent maximal inhibitory concentrations of GS-441524 against severe acute respiratory syndrome coronavirus 2 ranged from 0.08 μM to above 10 μM with a median of 0.87 μM whereas concentrations required to produce 90% of the maximal inhibition of the virus varied from 0.18 µM to more than 20 µM with a median of 1.42 µM in the collected data. Most of these concentrations were substantially lower than the calculated steady-state plasma concentrations of the agent. Plasma exposures to orally administered GS-441524, calculated after normalization of doses, were larger for dogs, mice, and rats than cynomolgus monkeys and humans, probably reflecting interspecies differences in oral uptake with reported oral bioavailabilities below 8.0% in cynomolgus monkeys and values as high as 92% in dogs. Reported oral bioavailabilities in rodents ranged from 12% to 57%. Using different presumptions, we estimated human oral bioavailability of GS-441524 at 13% and 20%. Importantly, doses of GS-441524 lower than the 13 mg/kg dose used in the first-in-human trial may be effective against Covid-19. Also, GS-441524 appears to be well-tolerated. In conclusion, GS-441524 has potential for oral treatment of Covid-19.

Inhibition of human UDP-glucuronosyltransferase enzyme by Dabrafenib: Implications for drug-drug interactions

Dabrafenib is a novel small molecule tyrosine kinase inhibitor (TKI) which is used to treat metastatic melanoma. The aim of this research was to survey the effects of dabrafenib on human UDP-glucuronosyltransferases (UGTs) and to evaluate the risk of drug-drug interactions (DDIs). The formation rates for 4-methylumbelliferone (4-MU) glucuronide and trifluoperazine-glucuronide in 12 recombinant human UGT isoforms with or without dabrafenib were measured and HPLC was used to investigate the inhibitory effects of dabrafenib on UGTs. Inhibition kinetic studies were also conducted. In vitro-in vivo extrapolation approaches were further used to predict the risk of DDI potentials of dabrafenib via inhibition of UGTs. Our data indicated that dabrafenib had a broad inhibitory effect on 4-MU glucuronidation by inhibiting the activities of UGTs, especially on UGT1A1, UGT1A7, UGT1A8, and UGT1A9, and dabrafenib could increase the area under the curve of co-administered drugs. Dabrafenib is a strong inhibitor of several UGTs and the co-administration of dabrafenib with drugs primarily metabolized by UGT1A1, 1A7, 1A8 or 1A9 may induce potential DDIs.

Prediction of human pharmacokinetics for low-clearance compounds using pharmacokinetic data from chimeric mice with humanized livers

Development of low-clearance (CL) compounds that can be slowly metabolized is a major goal in the pharmaceutical industry. However, the pursuit of low intrinsic CL (CL_int ) often leads to significant challenges in evaluating the pharmacokinetics of such compounds. Although in vitro-in vivo extrapolation is widely used to predict human CL, its application has been limited for low-CL_int compounds because of the low turnover of parent compounds in metabolic stability assays. To address this issue, we focused on chimeric mice with humanized livers (PXB-mice), which have been increasingly reported to accurately predict human CL in recent years. The predictive accuracy for nine low-CL_int compounds with no significant turnover in a human hepatocyte assay was investigated using PXB-mouse methods such as single-species allometric scaling (PXB-SSS) approach and a novel physiologically based scaling (PXB-PBS) approach that assumes that the CL_int per hepatocyte is equal between humans and PXB-mice. The percentages of compounds with predicted CL within 2- and 3-fold ranges of the observed CL for low-CL_int compounds were 89% and 100%, respectively, for both PXB-SSS and PXB-PBS approaches. Moreover, the predicted CL was mostly consistent among the methods. Conversely, percentages of compounds with predicted CL within 2- and 3-fold ranges of the observed CL for low-CL_int compounds were 50% and 63%, respectively for multispecies allometric scaling (MA). Overall, these PXB-mouse methods were much more accurate than conventional MA approaches, suggesting that PXB-mice are useful tool for predicting the human CL of low-CL_int compounds that are slowly metabolized.

Relevance of desorption kinetics and permeability for in vitro-based predictions of hepatic clearance in fish

The impact of desorption kinetics and permeation kinetics on in vitro-based predictions of in vivo hepatic blood clearances is investigated in the present study. Most commonly, possible limitations due to slow desorption of chemicals from albumin or slow permeation of chemicals through cellular membranes are not considered when in vivo clearances are predicted from in vitro biotransformation rate constants. To evaluate whether the most commonly used extrapolation models might thus overlook important kinetic limitations, we compare predictions of in vivo clearance that explicitly consider desorption and permeation kinetics with predictions of in vivo clearance that neglect these aspects. Our results show that strong limitations due to slow permeation kinetics are possible depending on the assumed permeability value. While permeability values estimated with a mechanistic approach are fast enough to avoid significant limitations, other experimentally derived permeability values lead to dramatically decreased in vivo clearance predictions. These latter values lead to unrealistically low in vivo biotransformation estimates. Furthermore, we also evaluated the implications of desorption kinetics using experimentally determined desorption rate constants. These evaluations show that slow desorption kinetics are unlikely to limit in vivo clearance.

Comparable Intestinal and Hepatic First-Pass Effect of YL-IPA08 on the Bioavailability and Effective Brain Exposure, a Rapid Anti-PTSD and Anti-Depression Compound

YL-IPA08, exerting rapid antidepressant-like and anxiolytic-like effects on behaviors by translocator protein (TSPO) mediation, is a novel compound that has been discovered and developed at our institute. Fit-for-purpose pharmacokinetic properties is urgently needed to be discovered as early as possible for a new compound. YL-IPA08 exhibited low bioavailability (∼6%) during the preliminary pharmacokinetics study in rats after oral administration. Our aim was to determine how metabolic disposition by microsomal P450 enzymes in liver and intestine limited YL-IPA08’s bioavailability and further affected brain penetration to the target. Studies of in vitro metabolic stability and permeability combined with in vivo oral bioavailability, panel CYP inhibitor co-administration via different routes, and double cannulation rats were conducted to elucidate the intestinal and hepatic first-pass effect of YL-IPA08 on bioavailability. Unbound brain-to-plasma ratio (K_p,uu) in rats was determined at steady state. Results indicated that P450-mediated elimination appeared to be important for its extensive first-pass effect with comparative contribution of gut (35%) and liver (17%), and no significant species difference was observed. The unbound concentration of YL-IPA08 in rat brain (6.5 pg/ml) was estimated based on K_p,uu (0.18) and was slightly higher than in vitro TSPO-binding activity (4.9 pg/ml). Based on the onset efficacy of YL-IPA08 toward TPSO in brain and K_p,uu, therapeutic human plasma concentration was predicted to be ∼27.2 ng/ml would easily be reached even with unfavorable bioavailability.

Defining in vivo dose-response curves for kidney DNA adduct formation of aristolochic acid I in rat, mouse and human by an in vitro and physiologically based kinetic modeling approach

Aristolochic acid I (AAI) is a well-known genotoxic kidney carcinogen. Metabolic conversion of AAI into the DNA-reactive aristolactam-nitrenium ion is involved in the mode of action of tumor formation. This study aims to predict in vivo AAI-DNA adduct formation in the kidney of rat, mouse and human by translating the in vitro concentration-response curves for AAI-DNA adduct formation to the in vivo situation using physiologically based kinetic (PBK) modeling-based reverse dosimetry. DNA adduct formation in kidney proximal tubular LLC-PK1 cells exposed to AAI was quantified by liquid chromatography-electrospray ionization-tandem mass spectrometry. Subsequently, the in vitro concentration-response curves were converted to predicted in vivo dose-response curves in rat, mouse and human kidney using PBK models. Results obtained revealed a dose-dependent increase in AAI-DNA adduct formation in the rat, mouse and human kidney and the predicted DNA adduct levels were generally within an order of magnitude compared with values reported in the literature. It is concluded that the combined in vitro PBK modeling approach provides a novel way to define in vivo dose-response curves for kidney DNA adduct formation in rat, mouse and human and contributes to the reduction, refinement and replacement of animal testing.

Direct Comparison of Total Clearance Prediction: Computational Machine Learning Model versus Bottom-Up Approach Using In Vitro Assay

The in vitro-in vivo extrapolation (IVIVE) approach for predicting total plasma clearance (CL_tot) has been widely used to rank order compounds early in discovery. More recently, a computational machine learning approach utilizing physicochemical descriptors and fingerprints calculated from chemical structure information has emerged, enabling virtual predictions even earlier in discovery. Previously, this approach focused more on in vitro intrinsic clearance (CL_int) prediction. Herein, we directly compare these two approaches for predicting CL_tot in rats. A structurally diverse set of 1114 compounds with known in vivo CL_tot, in vitro CL_int, and plasma protein binding was used as the basis for this evaluation. The machine learning models were assessed by validation approaches using the time- and cluster-split training and test sets, and five-fold cross validation. Assessed by five-fold validation, the random forest regression (RF) and radial basis function (RBF) models demonstrated better prediction performance in eight attempted machine learning models. The CL_tot values predicted by the RF and RBF models were within two-fold of the observed values for 67.7 and 71.9% of cluster-split test set compounds, respectively, while the predictivity was worse in the time-split dataset. The predictivity of both models tended to be improved by incorporating in vitro parameters, unbound fraction in plasma (f_u,p), and CL_int. CL_tot prediction utilizing in vitro CL_int and the well-stirred model, correcting for the fraction unbound in blood, was substantially worse compared to machine learning approaches for the same cluster-split test set. The reason that CL_tot is underestimated by IVIVE is not fully explained by considering the calculated microsomal unbound fraction (cf_u,mic), extended clearance classification system (ECCS), and omitting high clearance compounds in excess of hepatic blood flow. The analysis suggests that in silico machine learning models may have the power to reduce reliance on or replace in vitro and in vivo studies for chemical structure optimization in early drug discovery.

Interlaboratory Variability in Human Hepatocyte Intrinsic Clearance Values and Trends with Physicochemical Properties

PURPOSE

To examine the interlaboratory variability in CL_int values generated with human hepatocytes and determine trends in variability and clearance prediction accuracy using physicochemical and pharmacokinetic parameters.

METHODS

Data for 50 compounds from 14 papers were compiled with physicochemical and pharmacokinetic parameter values taken from various sources.

RESULTS

Coefficients of variation were as high as 99.8% for individual compounds and variation was not dependent on the number of prediction values included in the analysis. When examining median values, it appeared that compounds with a lower number of rotatable bonds had more variability. When examining prediction uniformity, those compounds with uniform in vivo underpredictions had higher CL_{int, in vivo} values, while those with non-uniform predictions typically had lower CL_{int, in vivo} values. Of the compounds with uniform predictions, only a small number were uniformly predicted accurately. Based on this limited dataset, less lipophilic, lower intrinsic clearance, and lower protein binding compounds yield more accurate clearance predictions.

CONCLUSIONS

Caution should be taken when compiling in vitro CL_int values from different laboratories as variations in experimental procedures (such as extent of shaking during incubation) may yield different predictions for the same compound. The majority of compounds with uniform in vitro values had predictions that were inaccurate, emphasizing the need for a better mechanistic understanding of IVIVE. The non-uniform predictions, often with low turnover compounds, reaffirmed the experimental challenges for drugs in this clearance range. Separating new chemical entities by lipophilicity, intrinsic clearance, and protein binding may help instill more confidence in IVIVE predictions.

Relevance of In Vitro Metabolism Models to PET Radiotracer Development: Prediction of In Vivo Clearance in Rats from Microsomal Stability Data

The prediction of in vivo clearance from in vitro metabolism models such as liver microsomes is an established procedure in drug discovery. The potentials and limitations of this approach have been extensively evaluated in the pharmaceutical sector; however, this is not the case for the field of positron emission tomography (PET) radiotracer development. The application of PET radiotracers and classical drugs differs greatly with regard to the amount of substance administered. In typical PET imaging sessions, subnanomolar quantities of the radiotracer are injected, resulting in body concentrations that cannot be readily simulated in analytical assays. This raises concerns regarding the predictability of radiotracer clearance from in vitro data. We assessed the accuracy of clearance prediction for three prototypical PET radiotracers developed for imaging the A₁ adenosine receptor (A₁AR). Using the half-life (t_1/2) approach and physiologically based scaling, in vivo clearance in the rat model was predicted from microsomal stability data. Actual clearance could be accurately predicted with an average fold error (AFE) of 0.78 and a root mean square error (RMSE) of 1.6. The observed slight underprediction (1.3-fold) is in accordance with the prediction accuracy reported for classical drugs. This result indicates that the prediction of radiotracer clearance is possible despite concentration differences of more than three orders of magnitude between in vitro and in vivo conditions. Consequently, in vitro metabolism models represent a valuable tool for PET radiotracer development.

Interpretation of Non-Clinical Data for Prediction of Human Pharmacokinetic Parameters: In Vitro-In Vivo Extrapolation and Allometric Scaling

Extrapolation of pharmacokinetic (PK) parameters from in vitro or in vivo animal to human is one of the main tasks in the drug development process. Translational approaches provide evidence for go or no-go decision-making during drug discovery and the development process, and the prediction of human PKs prior to the first-in-human clinical trials. In vitro-in vivo extrapolation and allometric scaling are the choice of method for projection to human situations. Although these methods are useful tools for the estimation of PK parameters, it is a challenge to apply these methods since underlying biochemical, mathematical, physiological, and background knowledge of PKs are required. In addition, it is difficult to select an appropriate methodology depending on the data available. Therefore, this review covers the principles of PK parameters pertaining to the clearance, volume of distribution, elimination half-life, absorption rate constant, and prediction method from the original idea to recently developed models in order to introduce optimal models for the prediction of PK parameters.

Quantitative Prediction of Human Hepatic Clearance for P450 and Non-P450 Substrates from In Vivo Monkey Pharmacokinetics Study and In Vitro Metabolic Stability Tests Using Hepatocytes

Accurate prediction of human pharmacokinetics for drugs remains challenging, especially for non-cytochrome P450 (P450) substrates. Hepatocytes might be suitable for predicting hepatic intrinsic clearance (CL_int) of new chemical entities, because they can be applied to various compounds regardless of the metabolic enzymes. However, it was reported that hepatic CL_int is underestimated in hepatocytes. The purpose of the present study was to confirm the predictability of human hepatic clearance for P450 and non-P450 substrates in hepatocytes and the utility of animal scaling factors for the prediction using hepatocytes. CL_int values for 30 substrates of P450, UDP-glucuronosyltransferase, flavin-containing monooxygenase, esterases, reductases, and aldehyde oxidase in human microsomes, human S9 and human, rat, and monkey hepatocytes were estimated. Hepatocytes were incubated in serum of each species. Furthermore, CL_int values in human hepatocytes were corrected with empirical, monkey, and rat scaling factors. CL_int values in hepatocytes for most compounds were underestimated compared to observed values regardless of the metabolic enzyme, and the predictability was improved by using the scaling factors. The prediction using human hepatocytes corrected with monkey scaling factor showed the highest predictability for both P450 and non-P450 substrates among the predictions using liver microsomes, liver S9, and hepatocytes with or without scaling factors. CL_int values by this method for 80% and 90% of all compounds were within 2- and 3-fold of observed values, respectively. This method is accurate and useful for estimating new chemical entities, with no need to care about cofactors, localization of metabolic enzymes, or protein binding in plasma and incubation mixture.

Measurement of kinetic parameters for biotransformation of polycyclic aromatic hydrocarbons by trout liver S9 fractions: Implications for bioaccumulation assessment

In vitro substrate depletion methods developed by the pharmaceutical industry are being used with increasing frequency to support chemical bioaccumulation assessments for fish. However, the application of these methods to high log K _ow chemicals poses special challenges. Biotransformation of three polycyclic aromatic hydrocarbons (PAHs) was measured using trout liver S9 fractions. Measured activity declined with incubation time and was reduced by acetone (used as a spiking solvent) at concentrations greater than 0.5%. Addition of alamethicin, a pore-forming peptide used to support UDP-glucuronosyltransferase activity, also reduced activity in a concentration-dependent manner. The substrate concentration dependence of activity was evaluated to estimate K _M and V _max values for each compound. Derived kinetic constants suggested that all three PAHs are transformed by the same reaction pathway and indicated an inverse correlation between K _M and chemical log K _ow. Binding effects on activity were evaluated by measuring unbound chemical concentrations across a range of S9 protein levels. Reaction rates were proportional to the unbound concentration except when these concentrations approached saturating levels, providing a direct demonstration of the free chemical hypothesis. These findings suggest that previous in vitro work with high log K _ow compounds was conducted at inappropriately high substrate concentrations resulting in underestimation of true in vivo activity. Preliminary calculations also indicate that PAH metabolism in fish may approach saturation during standardized in vivo testing efforts, potentially resulting in concentration-dependent accumulation and/or steady-state levels of accumulation greater than those which occur in a natural setting.

Optimization of intestinal microsomal preparation in the rat: A systematic approach to assess the influence of various methodologies on metabolic activity and scaling factors

The metabolic capacity of the intestine and its importance as the initial barrier to systemic exposure can lead to underestimation of first‐pass, and thus overestimation of oral bioavailability. However, the in vitro tools informing estimates of in vivo intestinal metabolism are limited by the complexity of the in vitro matrix preparation and uncertainty with the scaling factors for in vitro to in vivo extrapolation. A number of methods currently exist in the literature for the preparation of intestinal microsomes; however, the impact of key steps in the preparation procedure has not been critically assessed. In the current study, changes in enterocyte isolation, the impact of buffer constituents heparin and glycerol, as well as sonication as a direct method of homogenization were assessed systematically. Furthermore, fresh vs. frozen tissue samples and the impact of microsome freeze thawing was assessed. The rat intestinal microsomes were characterized for CYP content as well as metabolic activity using testosterone and 4‐nitropheonol as probes for CYP and UGT activity, respectively. Comparisons in metabolic activity and scaled unbound intestinal intrinsic clearance (CL_intu,gut) were made to commercially available microsomes using 25 drugs with a diverse range of metabolic pathways and intestinal metabolic stabilities. An optimal, robust and reproducible microsomal preparation method for investigation of intestinal metabolism is proposed. The importance of characterization of the in vitro matrix and the potential impact of intestinal scaling factors on the in vitro–in vivo extrapolation of F_G needs to be investigated further. © 2017 The Authors Biopharmaceutics & Drug Disposition Published by John Wiley & Sons Ltd.

The ontogeny and population variability of human hepatic dihydronicotinamide riboside:quinone oxidoreductase (NQO2)

Dihydronicotinamide riboside:quinone oxidoreductase (NQO2) is an enzyme that performs reduction reactions involved in antioxidant defense. We hypothesized that NQO2 hepatic drug clearance would develop in children over time, similar to NQO1. Using human liver cytosol (n = 117), the effects of age, sex, ethnicity, and weight on NQO2 expression and activity were probed. No significant correlations were observed. Biochemical activity of NQO2 was as high at birth as in adults (0.23 ± 0.04 nmol/min/mg protein, mean ± SEM, range 0-1.83). In contrast, modeled hepatic clearance through the NQO2 pathway was up to 10% of adult levels at birth, reaching predicted adult levels (0.3 ± 0.03 L/h) at 14 years of age. Comparisons between NQO1 and NQO2 in the same livers showed that neither protein (P = 0.32) nor activity (P = 0.23) correlated, confirming both orthologs are independently regulated. Because hepatic clearance through NQO2 does not mature until teenage years, compounds detoxified by this enzyme may be more deleterious in children.

In Vitro Inhibition of Human UDP-Glucuronosyl-Transferase (UGT) Isoforms by Astaxanthin, β-Cryptoxanthin, Canthaxanthin, Lutein, and Zeaxanthin: Prediction of in Vivo Dietary Supplement-Drug Interactions

Despite the widespread use of the five major xanthophylls astaxanthin, β-cryptoxanthin, canthaxanthin, lutein, and zeaxanthin as dietary supplements, there have been no studies regarding their inhibitory effects on hepatic UDP-glucuronosyltransferases (UGTs). Here, we evaluated the inhibitory potential of these xanthophylls on the seven major human hepatic UGTs (UGT1A1, UGT1A3, UGT1A4, UGT1A6, UGT1A9, UGT2B7 and UGT2B15) in vitro by LC-MS/MS using specific marker reactions in human liver microsomes (except UGT2B15) or recombinant supersomes (UGT2B15). We also predicted potential dietary supplement-drug interactions for β-cryptoxanthin via UGT1A1 inhibition. We demonstrated that astaxanthin and zeaxanthin showed no apparent inhibition, while the remaining xanthophylls showed only weak inhibitory effects on the seven UGTs. β-Cryptoxanthin mildly inhibited UGT1A1, UGT1A3, and UGT1A4, with IC50 values of 18.8 ± 2.07, 28.3 ± 4.40 and 34.9 ± 5.98 μM, respectively. Canthaxanthin weakly inhibited UGT1A1 and UGT1A3, with IC50 values of 38.5 ± 4.65 and 41.2 ± 3.14 μM, respectively; and lutein inhibited UGT1A1 and UGT1A4, with IC50 values of 45.5 ± 4.01 and 28.7 ± 3.79 μM, respectively. Among the tested xanthophyll-UGT pairs, β-cryptoxanthin showed the strongest competitive inhibition of UGT1A1 (Ki, 12.2 ± 0.985 μM). In addition, we predicted the risk of UGT1A1 inhibition in vivo using the reported maximum plasma concentration after oral administration of β-cryptoxanthin in humans. Our data suggests that these xanthophylls are unlikely to cause dietary supplement-drug interactions mediated by inhibition of the hepatic UGTs. These findings provide useful information for the safe clinical use of the tested xanthophylls.