Drug Metabolism and Disposition最新文献

Sterol 12α-hydroxylase (CYP8B1) is a key regulator of bile acid (BA) homeostasis and an emerging therapeutic target for metabolic disorders. To address the challenge of cellular CYP8B1 inhibition characterization, this work developed a pharmacologically optimized HepG2 cells model using triiodothyronine-dexamethasone-bezafibrate (TDB) induction, which significantly enhances the 12α-hydroxylation activity along the acidic pathway of BA biosynthesis in HepG2 cells. Employing stable isotope tracing with apolipoprotein A1-solubilized 2,3,4-¹³C₃-cholesterol, we established a liquid chromatography-mass spectrometry-based flux analysis platform to track de novo BA synthesis. Combined with a recombinant CYP8B1 assay, flux analysis revealed that CYP8B1 participates in cholic acid synthesis in HepG2 cells, typically via 12α-hydroxylation of 7α-hydroxy-3-oxo-4-cholestenoic acid and dihydroxycholestanoic acid. In TDB-HepG2 cells, azole antifungals exhibited differentiated inhibition of 12α-hydroxylation activity, generally mirroring the enzymatic data. Econazole acted as a relatively selective CYP8B1 inhibitor with a cellular half-maximal inhibitory concentration of 0.31-0.45 μM, tioconazole and posaconazole dually inhibited CYP8B1 and sterol 27-hydroxylase (CYP27A1), itraconazole and voriconazole primarily inhibited CYP27A1, and fluconazole showed no activity toward either enzyme. This study provides the first direct evidence that CYP8B1 participates in cholic acid synthesis via the acidic pathway and establishes a high-throughput cellular platform for screening CYP8B1 inhibitors, revealing azoles as effective modulators of this pathway. SIGNIFICANCE STATEMENT: Optimized HepG2 model using a ¹³C₃-cholesterol flux assay provides direct evidence that CYP8B1 participates in cholic acid biosynthesis via the acidic pathway and establishes a high-throughput cellular platform for screening CYP8B1 inhibitors, revealing azoles as effective modulators of this pathway.

The cytochrome P450 (P450) 4F family (CYP4F) are fatty acid ⍵-hydroxylases that catalyze the insertion of a hydroxyl group at the terminal carbon. The enzymes CYP4F3A and CYP4F3B are special cases among all other human P450 enzymes because they are derived from the same gene. The CYP4F3 gene undergoes alternative splicing, resulting in the 2 distinct enzymes. CYP4F3A is exclusively expressed in monocytes and deactivates leukotriene B4 as part of the anti-inflammatory response. Conversely, CYP4F3B is expressed in the liver and kidney where its major function is the production of the potent lipid mediator 20-hydroxyeicosatetraenoic acid from arachidonic acid. Despite these differences, they share a 93% amino acid sequence identity because of their shared gene locus. Both CYP4F3A and CYP4F3B are potential therapeutic targets for autoimmune disorders, cardiovascular diseases, and cancer. Because there is a significant gap in understanding enzyme function, their use as therapeutic targets has not been realized yet. To our knowledge, we present the first protocol for the generation of functional recombinant CYP4F3A and CYP4F3B to high purity. Catalytic assays with arachidonic acid and leukotriene B4 reveal a distinct substrate preference of both enzymes, which confirm their distinct body functions. Spectral analysis confirmed a different binding mode of arachidonic acid to the splice variants with a differential interaction with the respective active site. In addition, we tested the inhibitory effect of the CYP4 pan inhibitor HET0016 on both variants. In conclusion, we successfully implemented a robust protocol for the production of recombinant CYP4F3A and CYP4F3B, which paves the way for more in-depth mechanistic and structural studies and future directed drug design. SIGNIFICANCE STATEMENT: The splice variants CYP4F3A and CYP4F3B originate from the same gene but assume different functions in the human body. However, in-depth structural and functional studies are missing owing to the lack of robust protein expression protocols. In this study, we achieved the first generation of recombinant enzyme and conducted functional studies with fatty acid substrates and drugs, paving a way to a deeper understanding of these fascinating enzymes.

CYP2B6 is an important enzyme in the phase 1 metabolism of key pharmaceuticals, and inhibition of this enzyme can lead to adverse drug events. Machine learning models can potentially predict interactions with CYP2B6; however, there is limited data with which to train these models in the public domain. We proposed enhancing the applicability domain and improving the predictive capability of our CYP2B6 inhibition model by selecting a small, diverse set of compounds to test in vitro and adding the results to our model training set. We used a distance-based approach to define the applicability domain of the model and then measured the chemical diversity by creating t-distributed stochastic neighbor embedding plots to represent the chemical space of our model. After comparing this chemical space with a 49-plate drug-repurposing library, we were able to identify a plate with the highest average minimum Euclidean distance from the model training set. We then performed in vitro testing of this plate for CYP2B6 inhibition activity at 10 μM and added this new data to our machine learning model. A one-class classification approach was used to evaluate the efficacy of our applicability domain-expansion technique. The results showed that this method did not appreciably increase the performance of the model or the applicability domain, but we did increase the diversity of the training set. Additionally, the in vitro experiments identified vilanterol and allylestrenol as inhibitors of CYP2B6 with IC₅₀ values in the sub to low micromolar range. SIGNIFICANCE STATEMENT: CYP2B6 inhibition can affect the metabolism of important drugs, like methadone and propofol, and result in variability that can lead to adverse events. Machine learning models can help uncover new molecules with inhibitory potential against CYP2B6, but only if predictions of these models are reliable. This study illustrates how the intentional expansion of a machine learning model's applicability domain is neither a simple nor straightforward task, but even a conservative effort can reveal new molecules with CYP2B6 inhibition activity.

Bosutinib monohydrate, a second-generation tyrosine kinase inhibitor, is primarily used to treat Philadelphia chromosome-positive chronic myelogenous leukemia. Pharmacokinetic studies in humans identified 3 metabolites of bosutinib: oxidative dechlorinated bosutinib, N-desmethylated bosutinib, and bosutinib N-oxide. Although a few metabolites have been reported clinically, a comprehensive understanding of bosutinib's metabolic fate is essential for optimizing its therapeutic use and minimizing risks. Therefore, the present study aimed to investigate the detailed metabolism of bosutinib using a combination of in vitro, in vivo, and in silico methods. In vitro experiments were conducted using liver microsomes and S9 fractions in the presence of suitable cofactors, whereas in vivo studies employed Sprague-Dawley rats in which bosutinib was administered as an oral suspension, followed by the collection of blood, urine, and feces at respective time points. The biological samples were analyzed using liquid chromatography-quadrupole-Orbitrap mass spectrometer. A total of 10 metabolites were identified, including 8 novel ones. The diverse metabolic reactions included oxidative O-dealkylation (B-M1, B-M2, B-M4, and B-M7), N-oxidation (B-M5), oxidative dechlorination (B-M2 and B-M3), N-dealkylation (B-M8 and B-M9), hydroxylation (B-M8), and glycine conjugation (B-M10). Interestingly, no metabolites were detected in the plasma, and the major metabolites, B-M3 (13.91%) and B-M9 (10.58%), were found predominantly in the feces. In silico predictions using Meteor Nexus matched with 6 of the experimentally observed metabolites. Toxicity and mutagenicity were further assessed using Deductive Estimation of Risk from Existing Knowledge Nexus and Structure Activity Relationship Analysis using Hypotheses Nexus, which indicated a potential mutagenic concern for B-M7. The integration of experimental and computational approaches in this work contributes significantly to understanding bosutinib's metabolic profile and can guide future strategies for its safe and effective clinical application. SIGNIFICANCE STATEMENT: This study provides an in-depth exploration of bosutinib's metabolic pathways using in vitro models and in vivo analysis of plasma, urine, and fecal samples. Prominently, in silico toxicity assessments indicated that B-M7 may pose mutagenic risks, emphasizing the need for further investigation.

Conventional preclinical in vitro approaches inaccurately predicted clinical trial outcomes of drug-drug interactions involving the peptide NN1177, a glucagon and glucagon-like peptide 1 receptor coagonist. To further study the mechanisms behind this discrepancy, we have exploited a mouse model (8HUM) humanized for the major cytochrome P450 (P450) enzymes involved in drug disposition in humans. We show that NN1177 administration to 8HUM mice suppressed hepatic in vivo expression of CYP3A4 (82% compared to vehicle) and CYP1A2 (58% compared to vehicle). This was consistent with in vitro sandwich culture hepatocyte data reported previously. However, reduction in CYP3A4 and CYP1A2 levels in vivo appeared to resolve over time, despite daily NN1177 administration. These findings suggest an adaptive response to the metabolic effects of NN1177. In vivo pharmacokinetic studies in 8HUM closely matched the findings observed in the clinical trial, because there was no relevant increase in the exposure of the CYP3A4 and CYP1A2 probe drugs. Furthermore, no suppression effects were observed when the mice had been pretreated with the inducing agents, St. John's wort or phenobarbital, respectively, suggesting that the mechanism of P450 reduction does not involve the transcription factors constitutive androgen receptor or pregnane X receptor. These data highlight the complexities associated with therapeutic peptide drug-drug interactions and the remaining challenges for accurate predictions of P450 suppression and potential clinical implications. The humanized 8HUM model provides a promising and informative preclinical tool that can add high value during drug development by providing further insights into the effects on P450 expression, together with the subsequent impact of coadministered probe drugs in an in vivo model. SIGNIFICANCE STATEMENT: The current work describes the application of a humanized cytochrome P450 mouse model that provides further insight into the potential mechanisms and outperforms conventional in vitro approaches for preclinical predictions of peptide drug-drug interaction risk. The results showed no significant effects on the Cooperstown 5 + 1 cocktail, in line with clinical findings, and thereby represent an exciting model to further explore future therapeutic peptide projects during drug development.

Drug-drug interactions (DDIs) pose significant challenges in pharmacotherapy, affecting drug efficacy and safety. Traditional in vitro and in vivo models often fail to accurately predict clinically relevant DDIs, necessitating the development of advanced testing platforms. This review explores cutting-edge in vitro and in vivo systems, including chimeric mice with humanized livers, clustered regularly interspaced short palindromic repeats-CRISPR-associated animal models, liver microphysiological systems, and 3-dimensional spheroids and organoids that enhance the assessment of DDIs. These models enable the precise evaluation of drug metabolism, enzyme induction/inhibition, and transporter-mediated interactions under physiologically relevant conditions. In addition, we discuss the latest advancements in predictive modeling techniques for DDIs, focusing on physiologically based pharmacokinetic models and machine learning approaches. Physiologically based pharmacokinetic models integrate drug-specific and system-specific parameters to simulate DDIs dynamically, bridging the gap between preclinical and clinical findings. Machine learning-based predictive tools use vast datasets to identify complex interaction patterns, improving DDI risk assessment in early drug development. By integrating these novel experimental and computational approaches, researchers can achieve more accurate, quantitative DDI predictions, facilitating safer drug design and regulatory decision-making. The review highlights these emerging methodologies, emphasizing the need for continued refinement to enhance their predictive power and translational relevance. Future research should focus on optimizing hybrid strategies that combine mechanistic and data-driven models to achieve robust, clinically meaningful DDI assessments. SIGNIFICANCE STATEMENT: This review showcases advanced experimental and computational tools to improve drug-drug interaction prediction. These innovations enhance drug-drug interaction accuracy, support safer drug development, aid regulatory and clinical decisions, reduce adverse reactions, and optimize patient care.

Glucuronides are phase 2 metabolites formed through the conjugation of glucuronic acid to the N or oxygen (O) atoms of drug molecules. The chemical and enzymatic stability of glucuronide metabolites under various conditions can aid in the processing and storage of biological samples, as well as in the interpretation of mass balance data in human absorption, distribution, metabolism, and excretion studies. Glucuronides can be hydrolyzed to parent drugs at various pH conditions or by hydrolases such as β-glucuronidases. In this study, the stability of 5 representative O-, N-, and N⁺-glucuronides (ifenprodil O-glucuronide, valsartan N-glucuronide, candesartan N-glucuronide, camizestrant N-glucuronide, and clomipramine N⁺-glucuronide) was evaluated in pooled human feces in the presence or absence of β-glucuronidase inhibitors and in aqueous solutions at various pH levels. Both clomipramine N⁺-glucuronide and ifenprodil O-glucuronide were rapidly hydrolyzed in human feces, with half-lives of 0.2-0.3 and 0.5-0.6 hours, respectively. Valsartan N-glucuronide, camizestrant N-glucuronide, and candesartan N-glucuronide exhibited half-lives of 1.5-3.9, 3.6-3.9, and 5.2-11.3 hours, respectively. All 5 glucuronides were stable at pH 1.5-12.0, except for candesartan N-glucuronide, which was hydrolyzed at pH 1.5 and 2.5 with half-lives of 61.8 and 93.5 hours, respectively. It appears that multiple hydrolases such as β-glucuronidases contributed to the hydrolysis of these O-, N-, and N⁺-glucuronides in human feces, potentially complicating the assessment of the absorbed human dose. When a drug is well absorbed and its glucuronides are the major human metabolites, the parent drug detected in human feces in absorption, distribution, metabolism, and excretion studies may originate from glucuronide deconjugation. Therefore, it is recommended that metabolite profiling of both early (0-24 hours) and late (after 24 hours) fecal pools in human absorption, distribution, metabolism, and excretion studies is conducted to determine whether the origin of parent drugs in feces is from the unabsorbed dose or glucuronide deconjugation. SIGNIFICANCE STATEMENT: This study demonstrates the rapid hydrolysis of 5 glucuronides to their parent drugs in human feces. The findings highlight the importance of analyzing early and late fecal pools in human absorption, distribution, metabolism, and excretion for drugs with prominent glucuronides in order to determine the origin of the parent drugs in feces.

The inhibition of renally expressed transport proteins such as organic cation transporter (OCT) 2 or multidrug and toxin extrusion protein (MATE) 1 can cause clinically relevant drug-drug interactions (DDIs). Endogenous biomarkers have been proposed as tools to characterize the DDI risk of new molecules in drug development. Many previously proposed biomarkers for OCT2/MATE1-mediated DDIs lack specificity and/or sensitivity indicating a need for additional, well characterized biomarkers. Recently, we demonstrated that treatment with cimetidine, a classical OCT/MATE-inhibitor, decreased the renal excretion of serotonin, tyramine, 1-methylhistamine, 5-amino valeric acid betaine, and 4-guanidinobutanoic acid in humans. So far, these compounds are incompletely characterized as substrates of OCT2, MATE1, and other drug transporters. We therefore used established cell models overexpressing OCT2 and/or MATE1, and cell models for other clinically important drug transporters (organic anion transporters 1 and 3, organic anion transporting polypeptides 1B1 and 1B3, and P-glycoprotein) to investigate the cellular uptake and/or vectorial transport of these 5 putative biomarkers. The in vitro results show that serotonin, tyramine, 1-methylhistamine, 5-amino valeric acid betaine, and 4-guanidinobutanoic acid are substrates of OCT2 and/or MATE1, supporting that the in vivo effect of cimetidine is due to inhibition of these transporters. Based on their transport by OCT2 and/or MATE1 compared to the minimal transport by other drug transporters and the in vivo effects of classical transport protein inhibitors in healthy volunteers, serotonin and 1-methylhistamine appear to be the most promising candidates for further validation as endogenous biomarkers for the early detection of clinically relevant OCT2- and MATE1-mediated renal DDIs. SIGNIFICANCE STATEMENT: This study characterizes 5 endogenous metabolites as substrates of the renally expressed transport proteins organic cation transporter (OCT) 2, multidrug and toxin extrusion protein (MATE) 1, and other important drug transporters. These transport proteins are known as important contributors to clinically observed drug-drug interactions. Of the respective 5 compounds, serotonin and 1-methylhistamine are the most promising candidates to be further investigated as biomarkers for interactions via OCT2/MATE1, which could improve drug-drug interaction assessment during clinical drug development.

Voriconazole, a broad-spectrum antifungal, exhibits significant interpatient variability in efficacy and safety. This study assessed the influence of genetic and nongenetic factors on its steady-state pharmacokinetics and adverse effects. In vitro studies characterized voriconazole metabolism. An ancillary analysis was conducted on data from a completed trial involving 61 healthy participants who received a loading dose of 400 mg twice daily on first day, followed by 200 mg twice daily for 8 days. On the third day of voriconazole treatment, plasma and urine samples were collected over a 12-hour period after dose administration. Multiple trough concentrations, adverse events, and laboratory values were recorded throughout the study. Voriconazole and its metabolites were quantified using liquid chromatography-tandem mass spectrometry methods. Genotyping for CYP2C9, CYP2C19, CYP3A4, and CYP3A5 variants was performed using TaqMan assays. In vitro, CYP2C19 predominantly catalyzed the formation of voriconazole N-oxide and methyl hydroxy voriconazole, whereas CYP3A4/5 catalyzed fluoropyrimidine ring hydroxylation. Steady-state voriconazole area under the concentration-time curve (AUC_0-ԏ) was significantly associated with CYP2C19 genotypes (P < .01); over 9-fold reduction in AUC_0-ԏ was noted in CYP2C19 ∗17/∗17 genotype compared with CYP2C19 ∗2/∗2 carriers. We identified voriconazole N-glucuronide in plasma for the first time. Noncompliant subjects exhibited lower voriconazole exposure (P = .0002). Visual and neurologic/psychiatric adverse effects occurred in 79.7% and 72.9% of subjects, respectively, predominantly during the loading dose phase, but showed no association with CYP2C19 genotypes. No liver abnormalities were observed. CYP2C19 polymorphisms and medication adherence significantly influence voriconazole pharmacokinetics but not safety outcomes. These findings support the consideration of CYP2C19 genotyping and adherence monitoring to optimize voriconazole therapy. SIGNIFICANCE STATEMENT: This study elucidated genetic and nongenetic factors contributing to interindividual variability in voriconazole pharmacokinetics and adverse effects. In vitro analyses identified CYP2C19 as the primary enzyme mediating voriconazole metabolism, with CYP3A4/5 playing a secondary role. In vivo, CYP2C19 polymorphisms and noncompliance significantly influenced voriconazole exposure. Mild visual and neurological/psychiatric symptoms were common during the loading phase. These findings support incorporating CYP2C19 genotyping and adherence monitoring into voriconazole dosing strategies to optimize therapeutic outcomes.

Coproporphyrin (CP)-I has emerged as a promising endogenous biomarker for investigating organic anion transporting polypeptide (OATP) 1B1/1B3-mediated drug-drug interactions. The precursor eliminated analytical (PEA) method selectively quantifies the endogenous CP-I that should be monitored, whereas the precursor oxidized analytical method quantifies the total CP-I concentration by converting all precursors into CP-I during the sample preparation process. Plasma concentrations of CP-I exhibited a greater magnitude of change in response to rifampicin treatment compared with its precursor in a previous monkey study. This study analyzed CP-I and its precursor to assess the contribution of OATP1Bs to their systemic elimination and determine the apparent inhibition constant of rifampicin using our previously published data. Pharmacokinetic model analysis revealed differences in key parameters between CP-I and its precursor. The apparent inhibition constant of rifampicin was 0.215-0.319 μg/mL for CP-I and 0.686-1.19 μg/mL for its precursor. The estimated contribution of OATP1Bs to systemic elimination was approximately 1 for CP-I and 0.902-0.923 for its precursor. To investigate relevance in humans, the PEA and precursor oxidized analytical methods were applied to freshly prepared human plasma from 10 healthy volunteers. The concentration of CP-I (0.0440 ng/mL) was less than one-tenth of that of its precursor (0.542 ng/mL) in human plasma. These results suggest that measuring CP-I using the PEA method should be considered to ensure accuracy and reliability in assessing the risk of OATP1B-mediated drug-drug interactions caused by new chemical entities, as well as in predicting their clinical impact based on the observed biomarker changes. SIGNIFICANT STATEMENT: Pharmacokinetic modeling revealed substrate-dependent variability in the apparent inhibition constant of rifampicin and differences in the contribution of organic anion transporting polypeptide 1B to systemic elimination between coproporphyrin I and its precursor in monkeys. This highlights the importance of refining biomarker strategies to improve clinical drug-drug interaction predictions.