Design, synthesis, and pharmacological evaluation of 2-(4-sulfonylphenyl)-2-[(E)-pyrrolidin-1-ylimino]-N-thiazoleacetamides as glucokinase activators.

This paper presents the synthesis and glucokinase activity of novel hydrazone derivatives. The 2-(4-cyclopropylsulfonylphenyl)-2-[(E)-pyrrolidin-1-ylimino]-acetamide derivatives 5a-5h presented the in vitro glucokinase activities and in vivo blood glucose-lowering effects in mice. Particularly, 5h showed an oral hypoglycemic effect in rats at 1 mg/kg. These hydrazone derivatives are a potential new class of glucokinase activators for the treatment of type 2 diabetes.

Development of Simplified in Vitro P-Glycoprotein Substrate Assay and in Silico Prediction Models To Evaluate Transport Potential of P-Glycoprotein.

For efficient drug discovery and screening, it is necessary to simplify P-glycoprotein (P-gp) substrate assays and to provide in silico models that predict the transport potential of P-gp. In this study, we developed a simplified in vitro screening method to evaluate P-gp substrates by unidirectional membrane transport in P-gp-overexpressing cells. The unidirectional flux ratio positively correlated with parameters of the conventional bidirectional P-gp substrate assay ( R2 = 0.941) and in vivo Kp,brain ratio (mdr1a/1b KO/WT) in mice ( R2 = 0.800). Our in vitro P-gp substrate assay had high reproducibility and required approximately half the labor of the conventional method. We also constructed regression models to predict the value of P-gp-mediated flux and three-class classification models to predict P-gp substrate potential (low-, medium-, and high-potential) using 2397 data entries with the largest data set collected under the same experimental conditions. Most compounds in the test set fell within two- and three-fold errors in the random forest regression model (71.3 and 88.5%, respectively). Furthermore, the random forest three-class classification model showed a high balanced accuracy of 0.821 and precision of 0.761 for the low-potential classes in the test set. We concluded that the simplified in vitro P-gp substrate assay was suitable for compound screening in the early stages of drug discovery and that the in silico regression model and three-class classification model using only chemical structure information could identify the transport potential of compounds including P-gp-mediated flux ratios. Our proposed method is expected to be a practical tool to optimize effective central nervous system (CNS) drugs, to avoid CNS side effects, and to improve intestinal absorption.

Computational Model To Predict the Fraction of Unbound Drug in the Brain.

Knowing the value of the unbound drug fraction in the brain (fu,brain) is essential in estimating its effects and toxicity on the central nervous system (CNS); however, no model to predict fu,brain without experimental procedures is publicly available. In this study, we collected 253 measurements from the literature and an open database and built in silico models to predict fu,brain using only freely available software. By selecting appropriate descriptors, training, and evaluation, our model showed an acceptable performance on a test data set (R2 = 0.630, percentage of compounds predicted within a 3-fold error: 69.4%) using chemical structure alone. Our model is available at https://drumap.nibiohn.go.jp/fubrain/ , and all of our data sets can be obtained from the Supporting Information.

Predicting Fraction Unbound in Human Plasma from Chemical Structure: Improved Accuracy in the Low Value Ranges.

Predicting the fraction unbound in plasma provides a good understanding of the pharmacokinetic properties of a drug to assist candidate selection in the early stages of drug discovery. It is also an effective tool to mitigate the risk of late-stage attrition and to optimize further screening. In this study, we built in silico prediction models of fraction unbound in human plasma with freely available software, aiming specifically to improve the accuracy in the low value ranges. We employed several machine learning techniques and built prediction models trained on the largest ever data set of 2738 experimental values. The classification model showed a high true positive rate of 0.826 for the low fraction unbound class on the test set. The strongly biased distribution of the fraction unbound in plasma was mitigated by a logarithmic transformation in the regression model, leading to improved accuracy at lower values. Overall, our models showed better performance than those of previously published methods, including commercial software. Our prediction tool can be used on its own or integrated into other pharmacokinetic modeling systems.

Discovery of evocalcet, a next-generation calcium-sensing receptor agonist for the treatment of hyperparathyroidism.

The calcium-sensing receptor (CaSR) plays an important role in sensing extracellular calcium ions and regulating parathyroid hormone secretion by parathyroid gland cells, and the receptor is a suitable target for the treatment of hyperparathyroidism. Cinacalcet hydrochloride is a representative CaSR agonist which widely used for the hyperparathyroidism. However, it has several issues to clinical use, such as nausea/vomiting and strong inhibition of CYP2D6. We tried to improve these issues of cinacalcet for a new pharmaceutical agent as a preferable CaSR agonist. Optimization from cinacalcet resulted in the identification of pyrrolidine compounds and successfully led to the discovery of evocalcet as an oral allosteric CaSR agonist. Evocalcet, which exhibited highly favorable profiles such as CaSR agonistic activity and good DMPK profiles, will provide a novel therapeutic option for secondary hyperparathyroidism.

Utility of bilirubins and bile acids as endogenous biomarkers for the inhibition of hepatic transporters.

It is useful to identify endogenous substrates for the evaluation of drug-drug interactions via transporters. In this study, we investigated the utility of bilirubins, substrates of OATPs and MRP2, and bile acids and substrates of NTCP and BSEP, as biomarkers for the inhibition of transporters. In rats administered 20 and 80 mg/kg rifampicin, the plasma levels of bilirubin glucuronides were elevated, gradually decreased, and almost returned to the baseline level at 24 hours after administration without an elevation of alanine aminotransferase (ALT) and aspartate aminotransferase (AST). This result indicates the transient inhibition of rOatps and/or rMrp2. Although the correlation between free plasma concentrations and IC50 values of rOatps depended on the substrates used in the in vitro studies, the inhibition of rOatps by rifampicin was confirmed in the in vivo study using valsartan as a substrate of rOatps. In rats administered 10 and 30 mg/kg cyclosporin A, the plasma levels of bile acids were elevated and persisted for up to 24 hours after administration without an elevation of ALT and AST. This result indicates the continuous inhibition of rNtcp and/or rBsep, although there were differences between the free plasma or liver concentrations and IC50 values of rNtcp or rBsep, respectively. This study suggests that the monitoring of bilirubins and bile acids in plasma is useful in evaluating the inhibitory potential of their corresponding transporters.

Effects of the inhibition of intestinal P-glycoprotein on aliskiren pharmacokinetics in cynomolgus monkeys.

Aliskiren is a substrate for P-glycoprotein (P-gp) and is metabolized via cytochrome P450 3A4 (CYP3A4). The aim of the present study was to assess whether P-gp influenced the pharmacokinetics of aliskiren and also if drug-drug interactions (DDIs) mediated through P-gp could be reproduced in cynomolgus monkeys. The study investigated the pharmacokinetics of aliskiren in mdr1a/1b gene-deficient (P-gp KO) and wild-type (WT) mice. The area under the plasma concentration-time curve (AUC) following the oral administration of aliskiren was 6.9-fold higher in P-gp KO mice than in WT mice, while no significant differences were observed in the AUC or total plasma clearance following the intravenous administration of aliskiren to P-gp KO mice. Then the pharmacokinetics of aliskiren were evaluated and DDIs between aliskiren and P-gp inhibitors, such as cyclosporin A (CsA) and zosuquidar, examined in cynomolgus monkeys. The AUC for aliskiren were 8.3- and 42.1-fold higher after the oral administration of aliskiren with the concomitant oral administration of zosuquidar and CsA at doses of 10 and 30 mg/kg, respectively. In contrast, the AUC after the intravenous and oral administration of aliskiren was not significantly affected by the oral administration of zosuquidar or intravenous administration of CsA, respectively. These results indicated that P-gp strictly limited the intestinal absorption of aliskiren in mice and monkeys, and also that the effects of intestinal P-gp inhibition by CsA or zosuquidar on the pharmacokinetics of aliskiren were sensitively reproduced in monkeys. In conclusion, aliskiren can be used as a sensitive substrate to evaluate intestinal P-gp inhibition in monkeys.

DruMAP: A Novel Drug Metabolism and Pharmacokinetics Analysis Platform.

We developed a novel drug metabolism and pharmacokinetics (DMPK) analysis platform named DruMAP. This platform consists of a database for DMPK parameters and programs that can predict many DMPK parameters based on the chemical structure of a compound. The DruMAP database includes curated DMPK parameters from public sources and in-house experimental data obtained under standardized conditions; it also stores predicted DMPK parameters produced by our prediction programs. Users can predict several DMPK parameters simultaneously for novel compounds not found in the database. Furthermore, the highly flexible search system enables users to search for compounds as they desire. The current version of DruMAP comprises more than 30,000 chemical compounds, about 40,000 activity values (collected from public databases and in-house data), and about 600,000 predicted values. Our platform provides a simple tool for searching and predicting DMPK parameters and is expected to contribute to the acceleration of new drug development. DruMAP can be freely accessed at: https://drumap.nibiohn.go.jp/.

Utilizing public and private sector data to build better machine learning models for the prediction of pharmacokinetic parameters.

One solution to compensate for the shortage of publicly available data is to collect more quality-controlled data from the private sector through public-private partnerships. However, several issues must be resolved before implementing such a system. Here, we review the technical aspects of public-private partnerships using our initiative in Japan as an example. In particular, we focus on the procedure for collecting data from multiple private sector companies and building prediction models and discuss how merging public and private sector datasets will help to improve the chemical space coverage and prediction performance. Teaser: Japan's first public-private consortium in pharmacokinetics has incorporated data from multiple pharmaceutical companies to create useful predictive models.

A public-private partnership to enrich the development of in silico predictive models for pharmacokinetic and cardiotoxic properties.

A novel framework for a public-private (PP) partnership was established by a national initiative of the Development of a Drug Discovery Informatics System, supported by the Japan Agency for Medical Research and Development (AMED). This informatics PP partnership consortium comprised private and public sectors. A database of pharmacokinetic (PK) and cardiotoxic properties was developed, with considerable expansion after integrating proprietary data from private-sector members. This database led to robust in silico prediction models with higher performance than those from the original database. This partnership is a unique example worldwide and could substantially strengthen drug discovery capabilities in both sectors.

Development of an In Silico Prediction Model for P-glycoprotein Efflux Potential in Brain Capillary Endothelial Cells toward the Prediction of Brain Penetration.

Developing in silico models to predict the brain penetration of drugs remains a challenge owing to the intricate involvement of multiple transport systems in the blood brain barrier, and the necessity to consider a combination of multiple pharmacokinetic parameters. P-glycoprotein (P-gp) is one of the most important transporters affecting the brain penetration of drugs. Here, we developed an in silico prediction model for P-gp efflux potential in brain capillary endothelial cells (BCEC). Using the representative values of P-gp net efflux ratio in BCEC, we proposed a novel prediction system for brain-to-plasma concentration ratio (Kp,brain) and unbound brain-to-plasma concentration ratio (Kp,uu,brain) of P-gp substrates. We validated the proposed prediction system using newly acquired experimental brain penetration data of 28 P-gp substrates. Our system improved the predictive accuracy of brain penetration of drugs using only chemical structure information compared with that of previous studies.

Estimation of transporters involved in the hepatobiliary transport of TA-0201CA using sandwich-cultured rat hepatocytes from normal and multidrug resistance-associated protein 2-deficient rats.

N-[6-[2-[(5-Bromo-2-pyrimidinyl)oxy]ethoxy]-5-(4-methylphenyl)-4-pyrimidinyl]-4-(2-hydroxy-1,1-dimethylethyl) benzenesulfonamide sodium salt (TA-0201) carboxylic acid form (TA-0201CA) is the primary and pharmacologically active metabolite of TA-0201, which is an orally active nonpeptide antagonist for endothelin receptors. A major elimination route of TA-0201CA in rats was biliary excretion. The aim of this study was to clarify the transporters responsible for the hepatobiliary transport of TA-0201CA by in vivo pharmacokinetic study and in vitro study using sandwich-cultured rat hepatocytes (SCRH) from normal rats [Sprague-Dawley rats (SDR)] and Eisai hyperbilirubinemic rats (EHBR). After intravenous administration, TA-0201CA was extensively excreted into bile with a high biliary clearance in SDR. In contrast, the biliary clearance in EHBR was lower than that in SDR. These results indicated that multidrug resistance-associated protein 2 (Mrp2) was partly involved in the biliary excretion of TA-0201CA. In SCRH, the hepatic uptake of TA-0201CA was significantly decreased by the presence of organic anion-transporting polypeptide (Oatp) substrates/inhibitors and a Na(+)-free condition, which is a driving force of the Na(+)-taurocholate cotransporting polypeptide (Ntcp). The canalicular secretion of TA-0201CA was inhibited by the bile salt export pump (Bsep) inhibitor glibenclamide and by the Mrp2 inhibitor 3-[[3-[2-(7-chloroquinolin-2-yl)vinyl]phenyl]-(2-dimethylcarbamoylethylsulfanyl)methylsulfanyl] propionic acid (MK-571) in SCRH from SDR and EHBR. These results suggested that TA-0201CA was transported into hepatocytes via Oatps and Ntcp and excreted into bile via Mrp2 and Bsep in rats.

Pyrazolo[4,3-d]pyrimidine Derivatives as a Novel Hypoxia-Inducible Factor Prolyl Hydroxylase Domain Inhibitor for the Treatment of Anemia.

Inhibition of hypoxia-inducible factor prolyl hydroxylase domain (HIF-PHD) promotes erythropoietin (EPO) production by stabilizing the HIFα subunit. Thieno[2,3-d]pyrimidine 8 identified based on X-ray crystal structure analysis was optimized to lead to the discovery of pyrazolo[4,3-d]pyrimidine 13 as the lead compound of orally bioavailable HIF-PHD inhibitors. Conversion of the benzyl moiety in 13 gave pyrazolopyrimidine 19 with high solubility and bioavailability, which increased hemoglobin levels in anemic model rats after repeated oral administration. It was shown that pyrazolo[4,3-d]pyrimidine derivatives are promising therapeutic agents for renal anemia through the inhibition of HIF-PHD.

Data Curation can Improve the Prediction Accuracy of Metabolic Intrinsic Clearance.

A key consideration at the screening stages of drug discovery is in vitro metabolic stability, often measured in human liver microsomes. Computational prediction models can be built using a large quantity of experimental data available from public databases, but these databases typically contain data measured using various protocols in different laboratories, raising the issue of data quality. In this study, we retrieved the intrinsic clearance (CLint ) measurements from an open database and performed extensive manual curation. Then, chemical descriptors were calculated using freely available software, and prediction models were built using machine learning algorithms. The models trained on the curated data showed better performance than those trained on the non-curated data and achieved performance comparable to previously published models, showing the importance of manual curation in data preparation. The curated data were made available, to make our models fully reproducible.

Constructing an In Silico Three-Class Predictor of Human Intestinal Absorption With Caco-2 Permeability and Dried-DMSO Solubility.

Absorption of drugs is the first step after dosing, and it largely affects drug bioavailability. Hence, estimating the fraction of absorption (Fa) in humans is important in the early stages of drug discovery. To achieve correct exclusion of low Fa compounds and retention of potential compounds, we developed a freely available model to classify compounds into 3 levels of Fa capacity using only the chemical structure. To improve Fa prediction, we added predicted binary classification results of membrane permeability measured using Caco-2 cell line (Papp) and dried-dimethyl sulfoxide solubility (accuracy, 0.836; kappa, 0.560). The constructed models can be accessed via a web application.

Development of an in silico prediction system of human renal excretion and clearance from chemical structure information incorporating fraction unbound in plasma as a descriptor.

Prediction of pharmacokinetic profiles of new chemical entities is essential in drug development to minimize the risks of potential withdrawals. The excretion of unchanged compounds by the kidney constitutes a major route in drug elimination and plays an important role in pharmacokinetics. Herein, we created in silico prediction models of the fraction of drug excreted unchanged in the urine (fe) and renal clearance (CLr), with datasets of 411 and 401 compounds using freely available software; notably, all models require chemical structure information alone. The binary classification model for fe demonstrated a balanced accuracy of 0.74. The two-step prediction system for CLr was generated using a combination of the classification model to predict excretion-type compounds and regression models to predict the CLr value for each excretion type. The accuracies of the regression models increased upon adding a descriptor, which was the observed and predicted fraction unbound in plasma (fu,p); 78.6% of the samples in the higher range of renal clearance fell within 2-fold error with predicted fu,p value. Our prediction system for renal excretion is freely available to the public and can be used as a practical tool for prioritization and optimization of compound synthesis in the early stage of drug discovery.

Effect of plasma protein binding on in vitro-in vivo correlation of biliary excretion of drugs evaluated by sandwich-cultured rat hepatocytes.

In the present study, we examined in vitro biliary clearance of several compounds in sandwich-cultured rat hepatocytes (SCRH) and compared it with that observed in vivo in rats; the effect of plasma protein binding on in vitro-in vivo correlation of biliary excretion was also assessed. The in vitro biliary excretion was determined by differential cumulative uptake of compounds in SCRH preincubated in the presence and absence of Ca(2+)/Mg(2+). The cumulative uptake study of radiolabeled substrates revealed that the function of canalicular efflux transporters such as bile salt export pump, multidrug resistance-associated protein 2, breast cancer resistance protein, and multidrug resistance 1 was adequately maintained in SCRH. Unlabeled test compounds, pravastatin, rosuvastatin, valsartan, cefmetazole, and cefoperazone exhibited varying degrees of in vitro biliary excretion in the cumulative uptake study using SCRH. In vivo biliary excretions of these compounds were measured in common bile duct-cannulated rats. Whereas their biliary excretion ratios were all more than 60% of the dose, the in vivo intrinsic biliary clearances varied from 10.5 to 1787.2 ml/min/kg. The in vitro intrinsic biliary clearances of test compounds were well correlated with their corresponding in vivo intrinsic clearances calculated on the basis of the plasma unbound concentration (r(2) = 0.984), whereas less correlation was observed when they were calculated on the basis of plasma total concentration (r(2) = 0.217). These results indicate that SCRH is a useful in vitro model for predicting in vivo intrinsic biliary clearance in rats. In addition, for an accurate prediction, it is necessary to evaluate the in vivo intrinsic biliary clearance based on plasma unbound concentration but not total concentration.

Cloning of the dog bile salt export pump (BSEP; ABCB11) and functional comparison with the human and rat proteins.

The dog bile salt export pump (BSEP; ABCB11) was cloned and expressed in a Sf9 insect cell system. The deduced amino acid sequence encodes a 1325-amino-acid protein, which shows 89.4% and 80.2% homology with human BSEP and rat Bsep, respectively. The transcript of the dog Bsep gene was detected at a high level in liver, but not other tissues, by quantitative RT-PCR. The BSEP-expressing membrane vesicles isolated from Sf9 cells exhibited saturable uptake of [(3)H]taurocholic acid with Michaelis constants (K(m)) of 33.7, 22.2 and 19.9 microM for the dog, rat and human transporters, respectively. The uptake of [(3)H]taurocholic acid by all three transporters was significantly inhibited by troglitazone, glibenclamide, and other several inhibitors, while pravastatin inhibited dog Bsep and human BSEP, but not rat Bsep at 100 microM. The IC(50) of troglitazone for dog Bsep, human BSEP, and rat Bsep were 32, 20, and 60 microM, and those of pravastatin were 441, 240 and >1,000 microM, respectively. In conclusion, while dog Bsep shows similar ATP-dependent bile acid transport characteristics to human BSEP and rat Bsep, there is a species difference in affinity for drugs such as pravastatin and troglitazone.

Cytochrome P450 1A2 Messenger RNA is a More Reliable Marker than Cytochrome P450 1A2 Activity, Phenacetin O-Deethylation, for Assessment of Induction Potential of Drug-Metabolizing Enzymes Using HepaRG Cells.

BACKGROUND: The HepaRG cells have key drug metabolism functionalities comparable to those of primary human hepatocytes. Many studies have reported that this cell line can be used as a reliable in vitro model for human drug metabolism studies, including the assessment of cytochrome P450 (CYP) induction. OBJECTIVES: The objective of this study is to determine whether CYP mRNA level measurement is superior to the CYP enzyme activity measurement as a convenient high-throughput method for evaluating CYP induction potential using HepaRG cells. METHODS: QuantiGene Plex 2.0 Assay and LC/MS/MS. mRNA expression levels and enzyme activities of CYP1A2, CYP2B6, and CYP3A in HepaRG cells treated with prototypical inducers of each CYP isoform [omeprazole (OME) for CYP1A2, phenobarbital (PB) for CYP2B6, and rifampicin (RIF) for CYP3A] were evaluated. RESULTS: Although the activities of CYP2B6 and CYP3A were induced by treatment with PB and RIF, we found that the activity of phenacetin O-deethylase (PHOD), which is known as a marker of the activity of CYP1A2, was also enhanced by treatment with these non-CYP1A2 inducers in HepaRG cells. Based on previously published reports, we hypothesized that the expression ratio of CYP3A to CYP1A2 is much higher in HepaRG cells than in human hepatocytes; this may result in a nonnegligible contribution of CYP3A to the PHOD reaction in HepaRG cells. Studies using CYP3A inhibitor and pregnane X receptor-knockout HepaRG cells supported this hypothesis. CONCLUSION: The measurement of mRNA serves as a higher reliable indicator for the evaluation of CYP induction potential when using HepaRG cells.

In Silico Prediction of hPXR Activators Using Structure-Based Pharmacophore Modeling.

The activation of pregnane X receptor (PXR), a member of the nuclear receptor superfamily, can mediate potential drug-drug interactions by regulating the expression of several drug-mediated enzymes and transporters, resulting in reduced therapeutic efficacy or increased toxicity by producing reactive metabolites. Therefore, in the early stage of drug development, it is important to predict these risks using an in silico approach. We constructed a human PXR (hPXR) pharmacophore model based on known structural information of compounds that activate PXR. We evaluated the prediction accuracy of the model using data sets generated on 68 original synthetic compounds from the Mitsubishi Tanabe Pharma Corporation and over 2500 drugs from the National Institutes of Health Chemical Genomics Center Pharmaceutical Collection for their ability to activate hPXR. The prediction accuracies of the PXR pharmacophore model were 0.78 and 0.86 for the Mitsubishi Tanabe Pharma Corporation and National Institutes of Health Chemical Genomics Center Pharmaceutical Collection, respectively. The compounds resulting in the smallest root-mean square deviation hit by pharmacophore search were the well-known PXR inducers such as Bosentan. These results suggest that using the in silico approach developed in this study is useful to identify potential hPXR activators and modify the drug design during the early stage of drug development.