Prediction of cytochrome P450-mediated bioactivation using machine learning models and in vitro validation.

Cytochrome P450 (P450)-mediated bioactivation, which can lead to the hepatotoxicity through the formation of reactive metabolites (RMs), has been regarded as the major problem of drug failures. Herein, we purposed to establish machine learning models to predict the bioactivation of P450. On the basis of the literature-derived bioactivation dataset, models for Benzene ring, Nitrogen heterocycle and Sulfur heterocycle were developed with machine learning methods, i.e., Random Forest, Random Subspace, SVM and Naïve Bayes. The models were assessed by metrics like "Precision", "Recall", "F-Measure", "AUC" (Area Under the Curve), etc. Random Forest algorithms illustrated the best predictability, with nice AUC values of 0.949, 0.973 and 0.958 for the test sets of Benzene ring, Nitrogen heterocycle and Sulfur heterocycle models, respectively. 2D descriptors like topological indices, 2D autocorrelations and Burden eigenvalues, etc. contributed most to the models. Furthermore, the models were applied to predict the occurrence of bioactivation of an external verification set. Drugs like selpercatinib, glafenine, encorafenib, etc. were predicted to undergo bioactivation into toxic RMs. In vitro, IC50 shift experiment was performed to assess the potential of bioactivation to validate the prediction. Encorafenib and tirbanibulin were observed of bioactivation potential with shifts of 3-6 folds or so. Overall, this study provided a reliable and robust strategy to predict the P450-mediated bioactivation, which will be helpful to the assessment of adverse drug reactions (ADRs) in clinic and the design of new candidates with lower toxicities.

Association between ABCB1 C3435T polymorphism and antiepileptic drug resistance in epilepsy: An updated meta-analysis based on 62 studies.

OBJECTIVE: Previous clinical studies and meta-analyses have shown controversial results on the association between C3435T polymorphism of the ABCB1 gene and anti-epileptic drug (AED) resistance. Based on the fact that sample size and confounding factors could contribute to the inconsistency, we performed an updated meta-analysis by including the most recent studies, and subgroup analysis was conducted to evaluate the effect of confounding factors on the association. MATERIALS AND METHODS: We searched articles in 6 electronic databases including PubMed, Medline, Embase, Web of science, Cochrane Library, CNKI (China National Knowledge Infrastructure) for relevant articles up to June 2020. RESULTS: The current analysis showed that the C allele of C3435T variant was a risk factor for drug resistance in the overall populations (C allele vs. T allele, OR: 1.13; 95% CI: 1.02 - 1.25; p = 0.02) and in the Caucasians (C allele vs. T allele, OR: 1.09; 95% CI: 1.09 - 1.43; p = 0.002), while no association was observed in Asians and Indians. Particularly, our study reported for the first time that the 3435T allele was more common in epilepsy patients with drug resistance in the Tunisian population (C allele vs. T allele, OR: 0.31; 95% CI: 0.15 - 0.65; p = 0.002). In addition, our present analysis suggested an association between C3435T and AED resistance in cryptogenic, symptomatic, but not in idiopathic patients. Subgroup studies based on age and gender showed no association. CONCLUSION: AED resistance in Caucasian and Tunisian populations may benefit from ABCB1 C3435T genotyping. We recommend that more details, such as gender and etiology of epilepsy, should be taken into account to draw a reliable conclusion in future studies.

Conformational turn triggers regio-selectivity in the bioactivation of thiophene-contained compounds mediated by cytochrome P450.

In the present work, we performed Density Functional Theory calculations to explore the bioactivation mechanism of thiophene-containing molecules mediated by P450s. For this purpose, relatively large size compounds, 2,5-diaminothiophene derivatives were selected particularly for this investigation. Here we found the successive regio-selectivity triggered by conformational turn played a significant role in the occurrence of bioactivation. 2,5-Diaminothiophene was oxidized to a 2,5-diimine thiophene-reactive intermediate by Compound I (Cpd I) through successive activations of two N-H bonds (H3-N11 and H1-N6). This reaction exhibited three special characteristics: (1) self-controlled regio-selectivity during the oxidation process. There was a large scale of conformational turn in the abstraction of the first H atom which triggers the selection of the second H for abstraction. (2) Proton-shuttle mechanism. In high spin (HS) state, proton-shuttle mechanism was observed for the abstraction of the second H atom. (3) Spin-selective manner. In protein environment, the energy barrier in HS state was much lower than that in low spin state. The novel proposed bioactivation mechanism of 2,5-diaminothiophene compounds can help us in rational design of thiophene-contained drugs avoiding the occurrence of bioactivation.

Cytotoxic Trichothecene Macrolides Produced by the Endophytic Myrothecium roridum.

Six new macrolides named myrothecines D-G (1-4), 16-hydroxymytoxin B (5), and 14'-dehydrovertisporin (6), including four 10,13-cyclotrichothecane derivatives, in addition to 12 known compounds (7-18), were isolated from three endophytic Myrothecium roridum, IFB-E008, IFB-E009, and IFB-E012. The isolated compounds were characterized by MS, NMR, CD, and single-crystal X-ray crystallography. The isolated macrolides exhibited an antiproliferation effect against chronic myeloid leukemia K562 and colorectal carcinoma SW1116 cell lines. Compounds 1-6 were cytotoxic, with IC50 values ranging between 56 nM and 16 µM. Since slight structural changes led to obvious activity differences, the CoMFA (comparative molecular field analysis) and CoMSIA (comparative molecular similarity indices analysis) methods were then used to explore the 3D QSAR (three-dimensional quantitative structure-activity relationship) of these macrolides. The result showed that the steric, electrostatic, hydrophobic, and H-bond acceptor factors were involved in their cytotoxicity and provided an in-depth understanding of the structure-activity relationships of these metabolites.

Correction to: CBFA2T2 is associated with a cancer stem cell state in renal cell carcinoma.

[This corrects the article DOI: 10.1186/s12935-017-0473-z.].

The association between CYP2C9/2C19 polymorphisms and phenytoin maintenance doses in Asian epileptic patients: A systematic review and meta-analysis
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OBJECTIVE: Therapeutic response to phenytoin (PHT), a first-line antiepileptic drug (AED), is highly variable, in part likely due to genetic factors. Genetic polymorphisms in cytochrome P450 (CYP) 2C9 and CYP2C19 are expected to affect the metabolism of PHT and consequently affect its maintenance doses. We aimed to clarify the effects of genetic polymorphisms in both enzymes on daily PHT maintenance dosage in Asian epileptic patients by meta-analysis. MATERIALS AND METHODS: A systematic literature search was conducted in PubMed and EMBASE for relevant studies published prior to April 14, 2017. RevMan 5.2.3 software was used to analyze the relationship between CYP2C9/2C19 polymorphisms and PHT maintenance doses. RESULTS: A total of 6 studies with 993 patients fulfilling the inclusion criteria were included in our meta-analysis. The homozygous and heterozygous CYP2C19 mutation group (i.e., CYP2C19*2/*2, CYP2C19*3/*3, or CYP2C19*2/*3 group) required significant decrease of PHT maintenance dose. The starting maintenance dose suggested in this group is 4.38 mg/kg/day. Patients with heterozygous CYP2C9 or both heterozygous CYP2C9 and CYP2C19 showed a trend but not a statistically-significant decrease of PHT dose, but dosage adjustment was recommended. CONCLUSION: The meta-analysis indicates that CYP2C9 and CYP2C19 polymorphisms are associated with lower PHT maintenance dosage in Asian epileptic patients. Ethnic differences can influence PHT maintenance dose.
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CBFA2T2 is associated with a cancer stem cell state in renal cell carcinoma.

BACKGROUND: Renal cell carcinoma (RCC) is the most common kidney cancer, accounting for approximately 80-90% of all primary kidney cancer. Treatment for patients with advanced RCC remains unsatisfactory. Rare cancer stem cells (CSCs) are proposed to be responsible for failure of current treatment. METHODS: OncoLnc was used as a tool for interactively exploring survival correlations. Gene manipulation and expression analysis were carried out using siRNA, RT-PCR and Western blotting. Wound healing and invasion assays were used for phenotypical characterization. Aldefluor assay and FACS sorting Sphere culture were used to determine the "stemness" of CSCs. Co-Immunoprecipitation (Co-IP) was used to examine the interaction between OCT4 and CBFA2T2. Student's t-test and Chi square test was used to analyze statistical significance. RESULTS: CBFA2T2 expression can significantly predict the survival of RCC patients. Knocking-down of CBFA2T2 can inhibit cell migration and invasion in RCC cells in vitro, and reduce ALDHhigh CSCs populations. CBFA2T2 expression is necessary for sphere-forming ability and cancer stem cells marker expression in RCC cell lines. CONCLUSIONS: Our data suggest that CBFA2T2 expression correlates with aggressive characteristics of RCC and CBFA2T2 is required for maintenance of "stemness" through regulation of stem cells factors, thereby highlighting CBFA2T2 as a potential therapeutic target for RCC treatment.

Regulation profile of phosphatidylcholines (PCs) and lysophosphatidylcholines (LPCs) components towards UDP-glucuronosyltransferases (UGTs) isoforms.

1.Endogenous compounds have been reported to be the regulators of UDP-glucuronosyltransferases (UGTs) isoforms. This study aims to investigate the regulatory effects of the activity of UGT isoforms by two important lipid components phosphatidylcholine (PC) and lysophosphatidylcholines (LPC) using in vitro incubation system. 2.UGTs supersomes-catalyzed 4-methylumbelliferone (4-MU) glucuronidation was used as the probe reaction to evaluate the inhibition of compounds towards UGT isoforms except UGT1A4, and UGT1A4-catalyzed trifluoperazine (TFP) glucuronidation reaction was utilized to phenotype the activity of UGT1A4. 3.About 50 µM of LPC15:0, LPC16:0, LPC17:0, LPC18:0, LPC18:1 and PC16:0, 2:0 exhibited inhibition towards more than 90% activity of UGT isoforms, and other LPC and PC components showed negligible inhibitory potential towards all the UGT isoforms. UGT1A6 and UGT1A8 were identified to be the most sensitive UGT isoforms susceptible for the inhibition by LPC15:0, LPC16:0, LPC17:0, LPC18:0, LPC18:1 and PC16:0, 2:0, indicating the strong influence of these LPC and PC components towards UGT1A6 and UGT1A8-catalyzed metabolic reaction when the concentrations of these components increased.

A Mechanism-Based Model for the Prediction of the Metabolic Sites of Steroids Mediated by Cytochrome P450 3A4.

Early prediction of xenobiotic metabolism is essential for drug discovery and development. As the most important human drug-metabolizing enzyme, cytochrome P450 3A4 has a large active cavity and metabolizes a broad spectrum of substrates. The poor substrate specificity of CYP3A4 makes it a huge challenge to predict the metabolic site(s) on its substrates. This study aimed to develop a mechanism-based prediction model based on two key parameters, including the binding conformation and the reaction activity of ligands, which could reveal the process of real metabolic reaction(s) and the site(s) of modification. The newly established model was applied to predict the metabolic site(s) of steroids; a class of CYP3A4-preferred substrates. 38 steroids and 12 non-steroids were randomly divided into training and test sets. Two major metabolic reactions, including aliphatic hydroxylation and N-dealkylation, were involved in this study. At least one of the top three predicted metabolic sites was validated by the experimental data. The overall accuracy for the training and test were 82.14% and 86.36%, respectively. In summary, a mechanism-based prediction model was established for the first time, which could be used to predict the metabolic site(s) of CYP3A4 on steroids with high predictive accuracy.

Herb-drug interaction prediction based on the high specific inhibition of andrographolide derivatives towards UDP-glucuronosyltransferase (UGT) 2B7.

Herb-drug interaction strongly limits the clinical application of herbs and drugs, and the inhibition of herbal components towards important drug-metabolizing enzymes (DMEs) has been regarded as one of the most important reasons. The present study aims to investigate the inhibition potential of andrographolide derivatives towards one of the most important phase II DMEs UDP-glucuronosyltransferases (UGTs). Recombinant UGT isoforms (except UGT1A4)-catalyzed 4-methylumbelliferone (4-MU) glucuronidation reaction and UGT1A4-catalyzed trifluoperazine (TFP) glucuronidation were employed to firstly screen the andrographolide derivatives' inhibition potential. High specific inhibition of andrographolide derivatives towards UGT2B7 was observed. The inhibition type and parameters (Ki) were determined for the compounds exhibiting strong inhibition capability towards UGT2B7, and human liver microsome (HLMs)-catalyzed zidovudine (AZT) glucuronidation probe reaction was used to furtherly confirm the inhibition behavior. In combination of inhibition parameters (Ki) and in vivo concentration of andrographolide and dehydroandrographolide, the potential in vivo inhibition magnitude was predicted. Additionally, both the in vitro inhibition data and computational modeling results provide important information for the modification of andrographolide derivatives as selective inhibitors of UGT2B7. Taken together, data obtained from the present study indicated the potential herb-drug interaction between Andrographis paniculata and the drugs mainly undergoing UGT2B7-catalyzed metabolic elimination, and the andrographolide derivatives as potential candidates for the selective inhibitors of UGT2B7.

A model of in vitro UDP-glucuronosyltransferase inhibition by bile acids predicts possible metabolic disorders.

Increased levels of bile acids (BAs) due to the various hepatic diseases could interfere with the metabolism of xenobiotics, such as drugs, and endobiotics including steroid hormones. UDP-glucuronosyltransferases (UGTs) are involved in the conjugation and elimination of many xenobiotics and endogenous compounds. The present study sought to investigate the potential for inhibition of UGT enzymes by BAs. The results showed that taurolithocholic acid (TLCA) exhibited the strongest inhibition toward UGTs, followed by lithocholic acid. Structure-UGT inhibition relationships of BAs were examined and in vitro-in vivo extrapolation performed by using in vitro inhibition kinetic parameters (Ki) in combination with calculated in vivo levels of TLCA. Substitution of a hydrogen with a hydroxyl group in the R1, R3, R4, R5 sites of BAs significantly weakens their inhibition ability toward most UGTs. The in vivo inhibition by TLCA toward UGT forms was determined with following orders of potency: UGT1A4 > UGT2B7 > UGT1A3 > UGT1A1 ∼ UGT1A7 ∼ UGT1A10 ∼ UGT2B15. In conclusion, these studies suggest that disrupted homeostasis of BAs, notably taurolithocholic acid, found in various diseases such as cholestasis, could lead to altered metabolism of xenobiotics and endobiotics through inhibition of UGT enzymes.

Computational prediction for the metabolism of human UDP-glucuronosyltransferase 1A1 substrates.

UDP-glucuronosyltransferase (UGT) 1A1, one of the most important isoforms in UGTs superfamily, has attracted increasing concerns for its special role in the clearance and detoxification of endogenous and exogenous substances. To avoid the clinical drug-drug interactions, it is of great importance to have the knowledge of the metabolic profile of UGT1A1 substrates early. Herein, we purposed to establish machine learning models to predict the metabolic propeties of UGT1A1 substrates. On the basis of the literature-derived substrates database of UGT1A1, automatic metabolism prediction models for the aromatic hydroxyl (ArOH) and carboxyl (COOH) groups were developed with eight machine learning methods, among which, three methods, i.e. Random Forest, Random Subspace and J48, illustrated the best performance either for the aromatic hydroxyl and the carboxyl model. The models illustrated good robustness when they were evaluated with functions like "Precision", "Recall", "F-Measure", "AUC", "MCC", etc. Nice accuracy was observed for the aromatic hydroxyl and carboxyl model of these methods, whose AUCs ranged from 0.901 to 0.997. Additionally, the ArOH model was applied to predict the UGT1A1-mediated metabolism of an external set. Two new unknown substrates, cytochrome P450 (CYPs)-mediated metabolites of gefitinib, were predicted and identified, which were validated by in vitro assays. In summary, this study provides a reliable and robust strategy to predict UGT1A1 metabolites, which will be helpful either in rational-optimization of drug metabolism or in avoiding drug-drug interactions in clinic.

Discovery and Characterization of the Key Constituents in Ginkgo biloba Leaf Extract With Potent Inhibitory Effects on Human UDP-Glucuronosyltransferase 1A1.

Human UDP-glucuronosyltransferase 1A1 (hUGT1A1) is one of the most essential phase II enzymes in humans. Dysfunction or strong inhibition of hUGT1A1 may result in hyperbilirubinaemia and clinically relevant drug/herb-drug interactions (DDIs/HDIs). Recently, a high-throughput fluorescence-based assay was constructed by us to find the compounds/herbal extracts with strong inhibition against intracellular hUGT1A1. Following screening of over one hundred of herbal products, the extract of Ginkgo biloba leaves (GBL) displayed the most potent hUGT1A1 inhibition in HeLa-UGT1A1 cells (Hela cells overexpressed hUGT1A1). Further investigations demonstrated that four biflavones including bilobetin, isoginkgetin, sciadopitysin and ginkgetin, are key constituents responsible for hUGT1A1 inhibition in living cells. These biflavones potently inhibit hUGT1A1 in both human liver microsomes (HLM) and living cells, with the IC50 values ranging from 0.075 to 0.41 µM in living cells. Inhibition kinetic analyses and docking simulations suggested that four tested biflavones potently inhibit hUGT1A1-catalyzed NHPN-O-glucuronidation in HLM via a mixed inhibition manner, showing the K i values ranging from 0.07 to 0.74 µM. Collectively, our findings uncover the key constituents in GBL responsible for hUGT1A1 inhibition and decipher their inhibitory mechanisms against hUGT1A1, which will be very helpful for guiding the rational use of GBL-related herbal products in clinical settings.

Investigation on quantitative structure activity relationships and pharmacophore modeling of a series of mGluR2 antagonists.

MGluR2 is G protein-coupled receptor that is targeted for diseases like anxiety, depression, Parkinson's disease and schizophrenia. Herein, we report the three-dimensional quantitative structure-activity relationship (3D-QSAR) studies of a series of 1,3-dihydrobenzo[ b][1,4]diazepin-2-one derivatives as mGluR2 antagonists. Two series of models using two different activities of the antagonists against rat mGluR2, which has been shown to be very similar to the human mGluR2, (activity I: inhibition of [(3)H]-LY354740; activity II: mGluR2 (1S,3R)-ACPD inhibition of forskolin stimulated cAMP.) were derived from datasets composed of 137 and 69 molecules respectively. For activity I study, the best predictive model obtained from CoMFA analysis yielded a Q(2) of 0.513, R(2) (ncv) of 0.868, R(2) (pred) = 0.876, while the CoMSIA model yielded a Q(2) of 0.450, R(2) (ncv) = 0.899, R(2) (pred) = 0.735. For activity II study, CoMFA model yielded statistics of Q(2) = 0.5, R(2) (ncv) = 0.715, R(2) (pred) = 0.723. These results prove the high predictability of the models. Furthermore, a combined analysis between the CoMFA, CoMSIA contour maps shows that: (1) Bulky substituents in R(7), R(3) and position A benefit activity I of the antagonists, but decrease it when projected in R(8) and position B; (2) Hydrophilic groups at position A and B increase both antagonistic activity I and II; (3) Electrostatic field plays an essential rule in the variance of activity II. In search for more potent mGluR2 antagonists, two pharmacophore models were developed separately for the two activities. The first model reveals six pharmacophoric features, namely an aromatic center, two hydrophobic centers, an H-donor atom, an H-acceptor atom and an H-donor site. The second model shares all features of the first one and has an additional acceptor site, a positive N and an aromatic center. These models can be used as guidance for the development of new mGluR2 antagonists of high activity and selectivity. This work is the first report on 3D-QSAR modeling of these mGluR2 antagonists. All the conclusions may lead to a better understanding of the mechanism of antagonism and be helpful in the design of new potent mGluR2 antagonists.

Methylophiopogonanone A is a naturally occurring broad-spectrum inhibitor against human UDP-glucuronosyltransferases: Inhibition behaviours and implication in herb-drug interactions.

Methylophiopogonanone A (MOA) is an abundant homoisoflavonoid in the Chinese herb Ophiopogonis Radix. Recent investigations revealed that MOA inhibited several human cytochrome P450 enzymes (CYPs) and stimulated OATP1B1. However, the inhibitory effects of MOA on phase II drug-metabolizing enzymes, such as human UDP-glucuronosyltransferases (hUGTs), have not been well investigated. Herein, the inhibition potentials of MOA on hUGTs were assessed. The results clearly demonstrated that MOA dose-dependently inhibited all tested hUGTs including UGT1A1 (IC50 = 1.23 µM), one of the most important detoxification enzymes in humans. Further investigations showed that MOA strongly inhibited UGT1A1-catalysed NHPH-O-glucuronidation in a range of biological settings including hUGT1A1, human liver microsomes (HLM) and HeLa cells overexpressing UGT1A1. Inhibition kinetic analyses demonstrated that MOA competitively inhibited UGT1A1-catalysed NHPH-O-glucuronidation in both hUGT1A1 and HLM, with Ki values of 0.52 and 1.22 µM, respectively. Collectively, our findings expanded knowledge of the interactions between MOA and human drug-metabolizing enzymes, which would be very helpful for guiding the use of MOA-related herbal products in clinical settings.

Anti-androgen-independent prostate cancer effects of ginsenoside metabolites in vitro: mechanism and possible structure-activity relationship investigation.

Treatment of androgen-independent prostate cancer (AIPC) remains unsatisfactory. In our present experiment, natural occurring ginsenosides (NOGs) and intestinal bacterial metabolites (IBMs) were employed to investigate their anti-AIPC cell growth activity using PC-3 cells. Our results showed that the IBMs exerted more portent anti-AIPC activity than NOGs, by decreasing survival rate, inhibiting proliferation, inducing apoptosis, and leading to cell cycle arrest in AIPC PC-3 cells. The increase of LogP and decrease of C-6 steric hindrance, which were caused by deglycosylation by intestinal bacteria, may be the reason for the higher anti-AIPC activity of IBMs.

Inhibition of UGT1A1 by natural and synthetic flavonoids.

Flavonoids are widely distributed phytochemicals in vegetables, fruits and medicinal plants. Recent studies demonstrate that some natural flavonoids are potent inhibitors of the human UDP-glucuronosyltransferase 1A1 (UGT1A1), a key enzyme in detoxification of endogenous harmful compounds such as bilirubin. In this study, the inhibitory effects of 56 natural and synthetic flavonoids on UGT1A1 were assayed, while the structure-inhibition relationships of flavonoids as UGT1A1 inhibitors were investigated. The results demonstrated that the C-3 and C-7 hydroxyl groups on the flavone skeleton would enhance UGT1A1 inhibition, while flavonoid glycosides displayed weaker inhibitory effects than their corresponding aglycones. Further investigation on inhibition kinetics of two strong flavonoid-type UGT1A1 inhibitors, acacetin and kaempferol, yielded interesting results. Both flavonoids were competitive inhibitors against UGT1A1-mediated NHPN-O-glucuronidation, but were mixed and competitive inhibitors toward UGT1A1-mediated NCHN-O-glucuronidation, respectively. Furthermore, docking simulations showed that the binding areas of NHPN, kaempferol and acacetin on UGT1A1 were highly overlapping, and convergence with the binding area of bilirubin within UGT1A1. In summary, detailed structure-inhibition relationships of flavonoids as UGT1A1 inhibitors were investigated carefully and the findings shed new light on the interactions between flavonoids and UGT1A1, and will contribute considerably to the development of flavonoid-type drugs without strong UGT1A1 inhibition.

Modeling resistance index of taxoids to MCF-7 cell lines using ANN together with electrotopological state descriptors.

AIM: To develop an artificial neural network model for predicting the resistance index (RI) of taxoids. METHODS: A dataset of 63 experimental data points were compiled from published studies and randomly subdivided into training and external test sets. Electrotopological state (E-state) indices were calculated to characterize molecular structure together with a principle component analysis to reduce the variable space and analyze the relative importance of E-state indices. Back propagation neural network technique was used to build the models. Five-fold cross-validation was performed and 5 models with different compound composition in training and validation sets were built. The independent external test set was used to evaluate the predictive ability of models. RESULTS: The final model proved to be good with the cross-validation Q2cv0.62, external testing R2 0.84, and the slope of the regression line through the origin for the testing set at 0.9933. CONCLUSION: The quantitative structure-activity relationship model can predict the RI to a relative nicety, which will aid in the development of new anti-multidrug resistance taxoids.

Correction: Long non-coding RNA HoxA-AS3 interacts with EZH2 to regulate lineage commitment of mesenchymal stem cells.

[This corrects the article DOI: 10.18632/oncotarget.11538.].

A Practical and High-Affinity Fluorescent Probe for Uridine Diphosphate Glucuronosyltransferase 1A1: A Good Surrogate for Bilirubin.

This study aimed to develop a practical and high-affinity fluorescent probe for uridine diphosphate glucuronosyltransferase 1A1 (UGT1A1), a key conjugative enzyme responsible for the elimination and detoxification of many potentially harmful compounds. Several substrates derived from N-butyl-4-phenyl-1,8-naphthalimide were designed and synthesized on the basis of the substrate preference of UGT1A1 and the principle of photoinduced electron transfer (PET). Following the preliminary screening, substrate 2 was found with a high specificity and high affinity toward UGT1A1, while such biotransformation brought remarkable changes in fluorescence emission. Both inhibition kinetic analyses and molecular docking simulations demonstrated that 2 could bind on UGT1A1 at the same ligand-binding site as bilirubin. Furthermore, this newly developed probe was successfully used for sensing UGT1A1 activities and the high-throughput screening of UGT1A1 modulators in complex biological samples. In conclusion, a practical and high-affinity fluorescent probe for UGT1A1 was designed and well-characterized, which could serve as a good surrogate for bilirubin to investigate UGT1A1-ligand interactions.