Glucocorticoids promote breast cancer metastasis.

Diversity within or between tumours and metastases (known as intra-patient tumour heterogeneity) that develops during disease progression is a serious hurdle for therapy1-3. Metastasis is the fatal hallmark of cancer and the mechanisms of colonization, the most complex step in the metastatic cascade4, remain poorly defined. A clearer understanding of the cellular and molecular processes that underlie both intra-patient tumour heterogeneity and metastasis is crucial for the success of personalized cancer therapy. Here, using transcriptional profiling of tumours and matched metastases in patient-derived xenograft models in mice, we show cancer-site-specific phenotypes and increased glucocorticoid receptor activity in distant metastases. The glucocorticoid receptor mediates the effects of stress hormones, and of synthetic derivatives of these hormones that are used widely in the clinic as anti-inflammatory and immunosuppressive agents. We show that the increase in stress hormones during breast cancer progression results in the activation of the glucocorticoid receptor at distant metastatic sites, increased colonization and reduced survival. Our transcriptomics, proteomics and phospho-proteomics studies implicate the glucocorticoid receptor in the activation of multiple processes in metastasis and in the increased expression of kinase ROR1, both of which correlate with reduced survival. The ablation of ROR1 reduced metastatic outgrowth and prolonged survival in preclinical models. Our results indicate that the activation of the glucocorticoid receptor increases heterogeneity and metastasis, which suggests that caution is needed when using glucocorticoids to treat patients with breast cancer who have developed cancer-related complications.

Molecular characterization of chronic cutaneous wounds reveals subregion- and wound type-specific differential gene expression.

A limited understanding of the pathology underlying chronic wounds has hindered the development of effective diagnostic markers and pharmaceutical interventions. This study aimed to elucidate the molecular composition of various common chronic ulcer types to facilitate drug discovery strategies. We conducted a comprehensive analysis of leg ulcers (LUs), encompassing venous and arterial ulcers, foot ulcers (FUs), pressure ulcers (PUs), and compared them with surgical wound healing complications (WHCs). To explore the pathophysiological mechanisms and identify similarities or differences within wounds, we dissected wounds into distinct subregions, including the wound bed, border, and peri-wound areas, and compared them against intact skin. By correlating histopathology, RNA sequencing (RNA-Seq), and immunohistochemistry (IHC), we identified unique genes, pathways, and cell type abundance patterns in each wound type and subregion. These correlations aim to aid clinicians in selecting targeted treatment options and informing the design of future preclinical and clinical studies in wound healing. Notably, specific genes, such as PITX1 and UPP1, exhibited exclusive upregulation in LUs and FUs, potentially offering significant benefits to specialists in limb preservation and clinical treatment decisions. In contrast, comparisons between different wound subregions, regardless of wound type, revealed distinct expression profiles. The pleiotropic chemokine-like ligand GPR15L (C10orf99) and transmembrane serine proteases TMPRSS11A/D were significantly upregulated in wound border subregions. Interestingly, WHCs exhibited a nearly identical transcriptome to PUs, indicating clinical relevance. Histological examination revealed blood vessel occlusions with impaired angiogenesis in chronic wounds, alongside elevated expression of genes and immunoreactive markers related to blood vessel and lymphatic epithelial cells in wound bed subregions. Additionally, inflammatory and epithelial markers indicated heightened inflammatory responses in wound bed and border subregions and reduced wound bed epithelialization. In summary, chronic wounds from diverse anatomical sites share common aspects of wound pathophysiology but also exhibit distinct molecular differences. These unique molecular characteristics present promising opportunities for drug discovery and treatment, particularly for patients suffering from chronic wounds. The identified diagnostic markers hold the potential to enhance preclinical and clinical trials in the field of wound healing.

Drug Design and Success of Prospective Mouse In Vitro-In Vivo Extrapolation (IVIVE) for Predictions of Plasma Clearance (CLp) from Hepatocyte Intrinsic Clearance (CLint).

Hepatocyte intrinsic clearance (CLint) and methods of in vitro-in vivo extrapolation (IVIVE) are often used to predict plasma clearance (CLp) in drug discovery. While the prediction success of this approach is dependent on the chemotype, specific molecular properties and drug design features that govern these outcomes are poorly understood. To address this challenge, we investigated the success of prospective mouse CLp IVIVE across 2142 chemically diverse compounds. Dilution scaling, which assumes that the free fraction in hepatocyte incubations (fu,inc) is governed by binding to the 10% of serum in the incubation medium, was used as our default CLp IVIVE approach. Results show that predictions of CLp are better for smaller (molecular weight (MW) < 500 Da), less polar (total polar surface area (TPSA) < 100 Å2, hydrogen bond donor (HBD) ≤1, hydrogen bond acceptor (HBA) ≤ 6), lipophilic (log D > 3), and neutral compounds, with low HBD count playing the key role. If compounds are classified according to their chemical space, predictions were good for compounds resembling central nervous system (CNS) drugs [average absolute fold error (AAFE) of 2.05, average fold error (AFE) of 0.90], moderate for classical druglike compounds (according to Lipinski, Veber, and Ghose guidelines; AAFE of 2.55; AFE of 0.68), and poor for nonclassical "beyond the rule of 5" compounds (AAFE of 3.31; AFE of 0.41). From the perspective of measured druglike properties, predictions of CLp were better for compounds with moderate-to-high hepatocyte CLint (>10 µL/min/106 cells), high passive cellular permeability (Papp > 100 nm/s), and moderate observed CLp (5-50 mL/min/kg). Influences of plasma protein binding (fu,p) and P-glycoprotein (Pgp) apical efflux ratio (AP-ER) were less pronounced. If the extended clearance classification system (ECCS) is applied, predictions were good for class 2 (Papp > 50 nm/s; neutral or basic; AAFE of 2.35; AFE of 0.70) and acceptable for class 1A compounds (AAFE of 2.98; AFE of 0.70). Classes 1B, 3 A/B, and 4 showed poor outcomes (AAFE > 3.80; AFE < 0.60). Functional groups trending toward weaker CLp IVIVE were esters, carbamates, sulfonamides, carboxylic acids, ketones, primary and secondary amines, primary alcohols, oxetanes, and compounds liable to aldehyde oxidase metabolism, likely due to multifactorial reasons. Multivariate analysis showed that multiple properties are relevant, combining together to define the overall success of CLp IVIVE. Our results indicate that the current practice of prospective CLp IVIVE is suitable only for CNS-like compounds and well-behaved classical druglike space (e.g., high permeability or ECCS class 2) without challenging functional groups. Unfortunately, based on existing mouse data, prospective CLp IVIVE for complex and nonclassical chemotypes is poor and hardly better than random guessing. This is likely due to complexities such as extrahepatic metabolism and transporter-mediated disposition which are poorly captured by this methodology. With small-molecule drug discovery increasingly evolving toward nonclassical and complex chemotypes, existing CLp IVIVE methodology will require improvement. While empirical correction factors may bridge the gap in the near future, improved and new in vitro assays, data integration models, and machine learning (ML) methods are increasingly needed to address this challenge and reduce the number of nonclinical pharmacokinetic (PK) studies.

Computational Predictions of Nonclinical Pharmacokinetics at the Drug Design Stage.

Although computational predictions of pharmacokinetics (PK) are desirable at the drug design stage, existing approaches are often limited by prediction accuracy and human interpretability. Using a discovery data set of mouse and rat PK studies at Roche (9,685 unique compounds), we performed a proof-of-concept study to predict key PK properties from chemical structure alone, including plasma clearance (CLp), volume of distribution at steady-state (Vss), and oral bioavailability (F). Ten machine learning (ML) models were evaluated, including Single-Task, Multitask, and transfer learning approaches (i.e., pretraining with in vitro data). In addition to prediction accuracy, we emphasized human interpretability of outcomes, especially the quantification of uncertainty, applicability domains, and explanations of predictions in terms of molecular features. Results show that intravenous (IV) PK properties (CLp and Vss) can be predicted with good precision (average absolute fold error, AAFE of 1.96-2.84 depending on data split) and low bias (average fold error, AFE of 0.98-1.36), with AutoGluon, Gaussian Process Regressor (GP), and ChemProp displaying the best performance. Driven by higher complexity of oral PK studies, predictions of F were more challenging, with the best AAFE values of 2.35-2.60 and higher overprediction bias (AFE of 1.45-1.62). Multi-Task approaches and pretraining of ChemProp neural networks with in vitro data showed similar precision to Single-Task models but helped reduce the bias and increase correlations between observations and predictions. A combination of GP-computed prediction variance, molecular clustering, and dimensionality-reduction provided valuable quantitative insights into prediction uncertainty and applicability domains. SHAPley Additive exPlanations (SHAPs) highlighted molecular features contributing to prediction outcomes of Vss, providing explanations that could aid drug design. Combined results show that computational predictions of PK are feasible at the drug design stage, with several ML technologies converging to successfully leverage historical PK data sets. Further studies are needed to unlock the full potential of this approach, especially with respect to data set sizes and quality, transfer learning between in vitro and in vivo data sets, model-independent quantification of uncertainty, and explainability of predictions.

Can We Predict Clinical Pharmacokinetics of Highly Lipophilic Compounds by Integration of Machine Learning or In Vitro Data into Physiologically Based Models? A Feasibility Study Based on 12 Development Compounds.

While high lipophilicity tends to improve potency, its effects on pharmacokinetics (PK) are complex and often unfavorable. To predict clinical PK in early drug discovery, we built human physiologically based PK (PBPK) models integrating either (i) machine learning (ML)-predicted properties or (ii) discovery stage in vitro data. Our test set was composed of 12 challenging development compounds with high lipophilicity (mean calculated log P 4.2), low plasma-free fraction (50% of compounds with fu,p < 1%), and low aqueous solubility. Predictions focused on key human PK parameters, including plasma clearance (CL), volume of distribution at steady state (Vss), and oral bioavailability (%F). For predictions of CL, the ML inputs showed acceptable accuracy and slight underprediction bias [an average absolute fold error (AAFE) of 3.55; an average fold error (AFE) of 0.95]. Surprisingly, use of measured data only slightly improved accuracy but introduced an overprediction bias (AAFE = 3.35; AFE = 2.63). Predictions of Vss were more successful, with both ML (AAFE = 2.21; AFE = 0.90) and in vitro (AAFE = 2.24; AFE = 1.72) inputs showing good accuracy and moderate bias. The %F was poorly predicted using ML inputs [average absolute prediction error (AAPE) of 45%], and use of measured data for solubility and permeability improved this to 34%. Sensitivity analysis showed that predictions of CL limited the overall accuracy of human PK predictions, partly due to high nonspecific binding of lipophilic compounds, leading to uncertainty of unbound clearance. For accurate predictions of %F, solubility was the key factor. Despite current limitations, this work encourages further development of ML models and integration of their results within PBPK models to enable human PK prediction at the drug design stage, even before compounds are synthesized. Further evaluation of this approach with more diverse chemical types is warranted.

Evaluation of In Vitro Models for Assessment of Human Intestinal Metabolism in Drug Discovery.

Although intestinal metabolism plays an important role in drug disposition, early predictions of human outcomes are challenging, in part because of limitations of available in vitro models. To address this, we have evaluated three in vitro models of human intestine (microsomes, permeabilized enterocytes, and cryopreserved intestinal mucosal epithelium) as tools to assess intestinal metabolism and estimate the fraction escaping gut metabolism (f g) in drug discovery. The models were tested with a chemically diverse set of 32 compounds, including substrates for oxidoreductive, hydrolytic, and conjugative enzymes. Liquid chromatography-high-resolution mass spectrometry was used to quantify substrate disappearance [intrinsic clearance (CLint)] and qualify metabolite formation (quantitative-qualitative bioanalysis). Fraction unbound in the incubation (f u,inc) was determined by rapid equilibrium dialysis. Measured in vitro results (CLint and f u,inc) were supplemented with literature data [passive Caco-2 apical to basolateral permeability, enterocyte blood flow, and intestinal surface area (A)] and combined using a midazolam-calibrated Q gut model to predict human f g values. All three models showed reliable CYP and UDP-glucuronosyltransferase activities, but enterocytes and mucosa may offer advantages for low-clearance compounds and alternative pathways (e.g., sulfation, hydrolases, and flavin-containing monooxigenases). Early predictions of human f g values were acceptable for the high-f g compounds (arbitrarily f g > 0.7). However, predictions of low- and moderate-f g values (arbitrarily f g < 0.7) remain challenging, indicating that further evaluation is needed (e.g., saturation effects and impact of transporters) but not immediate compound avoidance. Results suggest that tested models offer an additional value in drug discovery, especially for drug design and chemotype evaluation. SIGNIFICANCE STATEMENT: We found that cellular models of the human gut (permeabilized enterocytes and cryopreserved intestinal mucosa) offer an alternative to and potential advantage over intestinal microsomes in studies of drug metabolism, particularly for low-clearance compounds and alternative pathways (e.g., sulfation, hydrolases, and flavin-containing monooxigenases). The predictivity of human fraction escaping gut metabolism for common CYP and UDP-glucuronosyltransferase substrates based on the Q gut model is still limited, however, and appropriate further evaluation is recommended.

Qualification of impurities based on metabolite data.

Regulatory Guidance documents ICH Q3A (R2) and ICH Q3B (R2) state that "impurities that are also significant metabolites present in animal and/or human studies are generally considered qualified". However, no guidance is provided regarding data requirements for qualification, nor is a definition of the term "significant metabolite" provided. An opportunity is provided to define those categories and potentially avoid separate toxicity studies to qualify impurities. This can reduce cost, animal use and time, and avoid delays in drug development progression. If the concentration or amount of a metabolite, in animals or human, is similar to that of the known, structurally identical impurity (arising from the administered test material), the qualification of the impurity on the grounds of it also being a metabolite is justified. We propose two complementary approaches to support conclusions to this effect: 1) demonstrate that the impurity is formed by metabolism in animals and/or man, based preferably on plasma exposures or, alternatively, amounts excreted in urine, and, where appropriate, 2) show that animal exposure to (or amount of) the impurity/metabolite is equal or greater in animals than in humans. An important factor of both assessments is the maximum theoretical concentration (or amount) (MTC or MTA) of the impurity/metabolite achievable from the administered dose and recommendations on the estimation of the MTC and MTA are presented.

Functional assessment of rat pulmonary flavin-containing monooxygenase activity.

The expression of flavin-containing monooxygenase (FMO) varies extensively between human and commonly used preclinical species such as rat and mouse. The aim of this study was to investigate the pulmonary FMO activity in rat using benzydamine. Furthermore, the contribution of rat lung to the clearance of benzydamine was investigated using an in vivo pulmonary extraction model. Benzydamine N-oxygenation was observed in lung microsomes and lung slices. Thermal inactivation of FMO and CYP inhibition suggested that rat pulmonary N-oxygenation is predominantly FMO mediated while any contribution from CYPs is negligible. The predicted lung clearance (CLlung) estimated from microsomes and slices was 16 ± 0.6 and 2.1 ± 0.3 mL/min/kg, respectively. The results from in vivo pulmonary extraction indicated no pulmonary extraction following intravenous and intra-arterial dosing to rats. Interestingly, the predicted CLlung using rat lung microsomes corresponded to approximately 35% of rat CLliver suggesting that the lung makes a smaller contribution to the whole body clearance of benzydamine. Although benzydamine clearance in rat appears to be predominantly mediated by hepatic metabolism, the data suggest that the lung may also make a smaller contribution to its whole body clearance.

Assessment of the pulmonary CYP1A1 metabolism of mavoglurant (AFQ056) in rat.

1. AFQ056 phenotyping results indicate that CYP1A1 is responsible for the formation of the oxidative metabolite, M3. In line with the predominant assumption that CYP1A1 is mainly expressed in extrahepatic tissues, only traces of M3 were detected in hepatic systems. The aim of this study was to investigate the pulmonary CYP1A1 mediated metabolism of AFQ056 in rat. 2. Western blot analysis confirmed that CYP1A1 is expressed in rat lung albeit at low levels. M3 formation was clearly observed in recombinant rat CYP1A1, lung microsomes and lung tissue slices and was strongly inhibited by ketoconazole in the incubations. As CYP3A4 and CYP2C9 metabolites were only observed at trace levels, we concluded that the reduced M3 formation was due to CYP1A1 inhibition. 3. AFQ056 lung clearance (CLlung) as estimated from in vitro data was predicted to be negligible (<1% pulmonary blood flow). This was confirmed by in vivo experiments where intravenous and intra-arterial dosing to rats failed to show significant pulmonary extraction. 4. While rat lung may make a contribution to the formation of M3, it is unlikely to be the only organ involved in this process and further experiments are required to investigate the potential metabolic elimination routes for AFQ056.

Discovery of an MLLT1/3 YEATS Domain Chemical Probe.

YEATS domain (YD) containing proteins are an emerging class of epigenetic targets in drug discovery. Dysregulation of these modified lysine-binding proteins has been linked to the onset and progression of cancers. We herein report the discovery and characterisation of the first small-molecule chemical probe, SGC-iMLLT, for the YD of MLLT1 (ENL/YEATS1) and MLLT3 (AF9/YEATS3). SGC-iMLLT is a potent and selective inhibitor of MLLT1/3-histone interactions. Excellent selectivity over other human YD proteins (YEATS2/4) and bromodomains was observed. Furthermore, our probe displays cellular target engagement of MLLT1 and MLLT3. The first small-molecule X-ray co-crystal structures with the MLLT1 YD are also reported. This first-in-class probe molecule can be used to understand MLLT1/3-associated biology and the therapeutic potential of small-molecule YD inhibitors.

A UGT2B10 splicing polymorphism common in african populations may greatly increase drug exposure.

RO5263397 [(S)-4-(3-fluoro-2-methyl-phenyl)-4,5-dihydro-oxazol-2-ylamine], a new compound that showed promising results in animal models of schizophrenia, is mainly metabolized in humans by N-glucuronidation. Enzyme studies, using the (then) available commercial uridine 5'-diphosphate-glucuronosyltransferases (UGTs), suggested that UGT1A4 is responsible for its conjugation. In the first clinical trial, in which RO5263397 was administered orally to healthy human volunteers, a 136-fold above-average systemic exposure to the parent compound was found in one of the participants. Further administration in this trial identified two more such poor metabolizers, all three of African origin. Additional in vitro studies with recombinant UGTs showed that the contribution of UGT2B10 to RO5263397 glucuronidation is much higher than UGT1A4 at clinically relevant concentrations. DNA sequencing in all of these poor metabolizers identified a previously uncharacterized splice site mutation that prevents assembly of full-length UGT2B10 mRNA and thus functional UGT2B10 protein expression. Further DNA database analyses revealed the UGT2B10 splice site mutation to be highly frequent in individuals of African origin (45%), moderately frequent in Asians (8%) and almost unrepresented in Caucasians (<1%). A prospective study using hepatocytes from 20 individual African donors demonstrated a >100-fold lower intrinsic clearance of RO5263397 in cells homozygous for the splice site variant allele. Our results highlight the need to include UGT2B10 when screening the human UGTs for the enzymes involved in the glucuronidation of a new compound, particularly when there is a possibility of N-glucuronidation. Moreover, this study demonstrates the importance of considering different ethnicities during drug development.

Phase II metabolism in human skin: skin explants show full coverage for glucuronidation, sulfation, N-acetylation, catechol methylation, and glutathione conjugation.

Although skin is the largest organ of the human body, cutaneous drug metabolism is often overlooked, and existing experimental models are insufficiently validated. This proof-of-concept study investigated phase II biotransformation of 11 test substrates in fresh full-thickness human skin explants, a model containing all skin cell types. Results show that skin explants have significant capacity for glucuronidation, sulfation, N-acetylation, catechol methylation, and glutathione conjugation. Novel skin metabolites were identified, including acyl glucuronides of indomethacin and diclofenac, glucuronides of 17ß-estradiol, N-acetylprocainamide, and methoxy derivatives of 4-nitrocatechol and 2,3-dihydroxynaphthalene. Measured activities for 10 µM substrate incubations spanned a 1000-fold: from the highest 4.758 pmol·mg skin(-1)·h(-1) for p-toluidine N-acetylation to the lowest 0.006 pmol·mg skin(-1)·h(-1) for 17ß-estradiol 17-glucuronidation. Interindividual variability was 1.4- to 13.0-fold, the highest being 4-methylumbelliferone and diclofenac glucuronidation. Reaction rates were generally linear up to 4 hours, although 24-hour incubations enabled detection of metabolites in trace amounts. All reactions were unaffected by the inclusion of cosubstrates, and freezing of the fresh skin led to loss of glucuronidation activity. The predicted whole-skin intrinsic metabolic clearances were significantly lower compared with corresponding whole-liver intrinsic clearances, suggesting a relatively limited contribution of the skin to the body's total systemic phase II enzyme-mediated metabolic clearance. Nevertheless, the fresh full-thickness skin explants represent a suitable model to study cutaneous phase II metabolism not only in drug elimination but also in toxicity, as formation of acyl glucuronides and sulfate conjugates could play a role in skin adverse reactions.

Aldehyde oxidase activity in fresh human skin.

Human aldehyde oxidase (AO) is a molybdoflavoenzyme that commonly oxidizes azaheterocycles in therapeutic drugs. Although high metabolic clearance by AO resulted in several drug failures, existing in vitro-in vivo correlations are often poor and the extrahepatic role of AO practically unknown. This study investigated enzymatic activity of AO in fresh human skin, the largest organ of the body, frequently exposed to therapeutic drugs and xenobiotics. Fresh, full-thickness human skin was obtained from 13 individual donors and assayed with two specific AO substrates: carbazeran and zoniporide. Human skin explants from all donors metabolized carbazeran to 4-hydroxycarbazeran and zoniporide to 2-oxo-zoniporide. Average rates of carbazeran and zoniporide hydroxylations were 1.301 and 0.164 pmol⋅mg skin(-1)⋅h(-1), resulting in 13 and 2% substrate turnover, respectively, after 24 hours of incubation with 10 µM substrate. Hydroxylation activities for the two substrates were significantly correlated (r(2) = 0.769), with interindividual variability ranging from 3-fold (zoniporide) to 6-fold (carbazeran). Inclusion of hydralazine, an irreversible inhibitor of AO, resulted in concentration-dependent decrease of hydroxylation activities, exceeding 90% inhibition of carbazeran 4-hydroxylation at 100 µM inhibitor. Reaction rates were linear up to 4 hours and well described by Michaelis-Menten enzyme kinetics. Comparison of carbazeran and zoniporide hydroxylation with rates of triclosan glucuronidation and sulfation and p-toluidine N-acetylation showed that cutaneous AO activity is comparable to tested phase II metabolic reactions, indicating a significant role of AO in cutaneous drug metabolism. To our best knowledge, this is the first report of AO enzymatic activity in human skin.

Another string to your bow: machine learning prediction of the pharmacokinetic properties of small molecules.

INTRODUCTION: Prediction of pharmacokinetic (PK) properties is crucial for drug discovery and development. Machine-learning (ML) models, which use statistical pattern recognition to learn correlations between input features (such as chemical structures) and target variables (such as PK parameters), are being increasingly used for this purpose. To embed ML models for PK prediction into workflows and to guide future development, a solid understanding of their applicability, advantages, limitations, and synergies with other approaches is necessary. AREAS COVERED: This narrative review discusses the design and application of ML models to predict PK parameters of small molecules, especially in light of established approaches including in vitro-in vivo extrapolation (IVIVE) and physiologically based pharmacokinetic (PBPK) models. The authors illustrate scenarios in which the three approaches are used and emphasize how they enhance and complement each other. In particular, they highlight achievements, the state of the art and potentials of applying machine learning for PK prediction through a comphrehensive literature review. EXPERT OPINION: ML models, when carefully crafted, regularly updated, and appropriately used, empower users to prioritize molecules with favorable PK properties. Informed practitioners can leverage these models to improve the efficiency of drug discovery and development process.

Discovery of Orally Available and Brain Penetrant AEP Inhibitors.

Alzheimer's Disease (AD) is the most widespread form of dementia, with one of the pathological hallmarks being the formation of neurofibrillary tangles (NFTs). These tangles consist of phosphorylated Tau fragments. Asparagine endopeptidase (AEP) is a key Tau cleaving enzyme that generates aggregation-prone Tau fragments. Inhibition of AEP to reduce the level of toxic Tau fragment formation could represent a promising therapeutic strategy. Here, we report the first orthosteric, selective, orally bioavailable, and brain penetrant inhibitors with an irreversible binding mode. We outline the development of the series starting from reversible molecules and demonstrate the link between inhibition of AEP and reduction of Tau N368 fragment both in vitro and in vivo.

UDP-glucuronic acid binds first and the aglycone substrate binds second to form a ternary complex in UGT1A9-catalyzed reactions, in both the presence and absence of bovine serum albumin.

The presence of bovine serum albumin (BSA) largely modulates the enzyme kinetics parameters of the human UDP-glucuronosyltransferase (UGT) 1A9, increasing both the apparent aglycone substrate affinity of the enzyme and its limiting reaction velocity (Drug Metab Dispos 39:2117-2129, 2011). For a better understanding of the BSA effects and an examination of whether its presence changes the catalytic mechanism, we have studied the enzyme kinetics of 4-methylumbelliferone glucuronidation by UGT1A9 in the presence and absence of 0.1% BSA, using bisubstrate enzyme kinetic experiments, in both the forward and reverse directions, as well as product and dead-end inhibition. The combined results strongly suggest that the reaction mechanism of UGT1A9, and presumably other human UGTs as well, involves the formation of a compulsory-order ternary-complex, with UDP-α-d-glucuronic acid (UDPGA) as the first binding substrate. Based on the enzyme kinetic parameters measured for the forward and reverse reactions, the equilibrium constant of the overall reaction was calculated (Keq = 574) and the relative magnitudes of the reaction rate constants were elucidated. The inclusion of BSA in the bisubstrate kinetic experiments quantitatively changed the apparent enzyme kinetic parameters, presumably by removing internal inhibitors that bind to the binary enzyme-UDPGA (E-UDPGA) complex, as well as to the ternary E-UDPGA-aglycone complex. Nevertheless, the underlying compulsory-order ternary-complex mechanism with UDPGA binding first is the same in both the absence and presence of BSA. The results offer a novel understanding of UGT enzyme kinetic mechanism and BSA effects.

Bovine serum albumin decreases Km values of human UDP-glucuronosyltransferases 1A9 and 2B7 and increases Vmax values of UGT1A9.

The human UDP-glucuronosyltransferase (UGT) enzymes UGT1A9 and UGT2B7 play important roles in the hepatic glucuronidation of many drugs. The presence of bovine serum albumin (BSA) during in vitro assays was recently reported to lower the K(m) values of both these UGTs for their aglycone substrates without affecting the corresponding V(max) values. Nonetheless, using the specific substrates entacapone and zidovudine (AZT) for UGT1A9 and UGT2B7, respectively, and using an improved ultrafiltration method for measuring drug binding to BSA and to biological membranes, we found that the presence of BSA during the glucuronidation reaction leads to a large increase in the V(max) value of UGT1A9, in addition to lowering its K(m) value. On the other hand, in the case of UGT2B7, our results agree with the previously described effect of BSA, namely lowering the K(m) value without a large effect on the enzyme's V(max) value. The unexpected BSA effect on UGT1A9 was independent of the expression system because it was found in a recombinant enzyme that was expressed in baculovirus-infected insect cells as well as in the native enzyme in human liver microsomes. Moreover, the effect of BSA on the kinetics of 4-methylumbelliferone glucuronidation by recombinant UGT1A9 was similar to its effect on entacapone glucuronidation. Contrary to the aglycone substrates, the effect of BSA on the apparent K(m) of UGT1A9 for the cosubstrate UDP-α-D-glucuronic acid was nonsignificant. Our findings call for further investigations of the BSA effects on different UGTs and the inhibitors that it may remove.

Glucuronidation of psilocin and 4-hydroxyindole by the human UDP-glucuronosyltransferases.

We have examined the glucuronidation of psilocin, a hallucinogenic indole alkaloid, by the 19 recombinant human UDP-glucuronosyltransferases (UGTs) of subfamilies 1A, 2A, and 2B. The glucuronidation of 4-hydroxyindole, a related indole that lacks the N,N-dimethylaminoethyl side chain, was studied as well. UGT1A10 exhibited the highest psilocin glucuronidation activity, whereas the activities of UGTs 1A9, 1A8, 1A7, and 1A6 were significantly lower. On the other hand, UGT1A6 was by far the most active enzyme mediating 4-hydroxyindole glucuronidation, whereas the activities of UGTs 1A7-1A10 toward 4-hydroxyindole resembled their respective psilocin glucuronidation rates. Psilocin glucuronidation by UGT1A10 followed Michaelis-Menten kinetics in which psilocin is a low-affinity high-turnover substrate (K(m) = 3.8 mM; V(max) = 2.5 nmol/min/mg). The kinetics of psilocin glucuronidation by UGT1A9 was more complex and may be best described by biphasic kinetics with both intermediate (K(m1) = 1.0 mM) and very low affinity components. The glucuronidation of 4-hydroxyindole by UGT1A6 exhibited higher affinity (K(m) = 178 microM) and strong substrate inhibition. Experiments with human liver and intestinal microsomes (HLM and HIM, respectively) revealed similar psilocin glucuronidation activity in both samples, but a much higher 4-hydroxyindole glucuronidation rate was found in HLM versus HIM. The expression levels of UGTs 1A6-1A10 in different tissues were studied by quantitative real-time-PCR, and the results, together with the activity assays findings, suggest that whereas psilocin may be subjected to extensive glucuronidation by UGT1A10 in the small intestine, UGT1A9 is likely the main contributor to its glucuronidation once it has been absorbed into the circulation.

Metabolism by Aldehyde Oxidase: Drug Design and Complementary Approaches to Challenges in Drug Discovery.

Aldehyde oxidase (AO) catalyzes oxidations of azaheterocycles and aldehydes, amide hydrolysis, and diverse reductions. AO substrates are rare among marketed drugs, and many candidates failed due to poor pharmacokinetics, interspecies differences, and adverse effects. As most issues arise from complex and poorly understood AO biology, an effective solution is to stop or decrease AO metabolism. This perspective focuses on rational drug design approaches to modulate AO-mediated metabolism in drug discovery. AO biological aspects are also covered, as they are complementary to chemical design and important when selecting the experimental system for risk assessment. The authors' recommendation is an early consideration of AO-mediated metabolism supported by computational and in vitro experimental methods but not an automatic avoidance of AO structural flags, many of which are versatile and valuable building blocks. Preferably, consideration of AO-mediated metabolism should be part of the multiparametric drug optimization process, with the goal to improve overall drug-like properties.

Comparison of Rat and Human Pulmonary Metabolism Using Precision-cut Lung Slices (PCLS).

BACKGROUND: Although the liver is the primary organ of drug metabolism, the lungs also contain drug-metabolizing enzymes and may, therefore, contribute to the elimination of drugs. In this investigation, the Precision-cut Lung Slice (PCLS) technique was standardized with the aims of characterizing and comparing rat and human pulmonary drug metabolizing activity. METHOD: Due to the limited availability of human lung tissue, standardization of the PCLS method was performed with rat lung tissue. Pulmonary enzymatic activity was found to vary significantly with rat age and rat strain. The Dynamic Organ Culture (DOC) system was superior to well-plates for tissue incubations, while oxygen supply appeared to have a limited impact within the 4h incubation period used here. RESULTS: The metabolism of a range of phase I and phase II probe substrates was assessed in rat and human lung preparations. Cytochrome P450 (CYP) activity was relatively low in both species, whereas phase II activity appeared to be more significant. CONCLUSION: PCLS is a promising tool for the investigation of pulmonary drug metabolism. The data indicates that pulmonary CYP activity is relatively low and that there are significant differences in enzyme activity between rat and human lung.