A new workflow for the effective curation of membrane permeability data from open ADME information.

Membrane permeability is an in vitro parameter that represents the apparent permeability (Papp) of a compound, and is a key absorption, distribution, metabolism, and excretion parameter in drug development. Although the Caco-2 cell lines are the most used cell lines to measure Papp, other cell lines, such as the Madin-Darby Canine Kidney (MDCK), LLC-Pig Kidney 1 (LLC-PK1), and Ralph Russ Canine Kidney (RRCK) cell lines, can also be used to estimate Papp. Therefore, constructing in silico models for Papp estimation using the MDCK, LLC-PK1, and RRCK cell lines requires collecting extensive amounts of in vitro Papp data. An open database offers extensive measurements of various compounds covering a vast chemical space; however, concerns were reported on the use of data published in open databases without the appropriate accuracy and quality checks. Ensuring the quality of datasets for training in silico models is critical because artificial intelligence (AI, including deep learning) was used to develop models to predict various pharmacokinetic properties, and data quality affects the performance of these models. Hence, careful curation of the collected data is imperative. Herein, we developed a new workflow that supports automatic curation of Papp data measured in the MDCK, LLC-PK1, and RRCK cell lines collected from ChEMBL using KNIME. The workflow consisted of four main phases. Data were extracted from ChEMBL and filtered to identify the target protocols. A total of 1661 high-quality entries were retained after checking 436 articles. The workflow is freely available, can be updated, and has high reusability. Our study provides a novel approach for data quality analysis and accelerates the development of helpful in silico models for effective drug discovery. Scientific Contribution: The cost of building highly accurate predictive models can be significantly reduced by automating the collection of reliable measurement data. Our tool reduces the time and effort required for data collection and will enable researchers to focus on constructing high-performance in silico models for other types of analysis. To the best of our knowledge, no such tool is available in the literature.

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/.

A large-scale targeted proteomics of plasma extracellular vesicles shows utility for prognosis prediction subtyping in colorectal cancer.

PURPOSE: The pathogenesis of cancers depends on the molecular background of each individual patient. Therefore, verifying as many biomarkers as possible and clarifying their relationships with each disease status would be very valuable. We performed a large-scale targeted proteomics analysis of plasma extracellular vesicles (EVs) that may affect tumor progression and/or therapeutic resistance. EXPERIMENTAL DESIGN: Plasma EVs from 59 were collected patients with colorectal cancer (CRC) and 59 healthy controls (HC) in cohort 1, and 150 patients with CRC in cohort 2 for the large-scale targeted proteomics analysis of 457 proteins as candidate CRC markers. The Mann-Whitney-Wilcoxon test and random forest model were applied in cohort 1 to select promising markers. Consensus clustering was applied to classify patients with CRC in cohort 2. The Kaplan-Meier method and Cox regression analysis were performed to identify potential molecular factors contributing to the overall survival (OS) of patients. RESULTS: In the analysis of cohort 1, 99 proteins were associated with CRC. The analysis of cohort 2 revealed two clusters showing significant differences in OS (p = 0.017). Twelve proteins, including alpha-1-acid glycoprotein 1 (ORM1), were suggested to be associated with the identified CRC subtypes, and ORM1 was shown to significantly contribute to OS, suggesting that ORM1 might be one of the factors closely related to the OS. CONCLUSIONS: The study identified two novel subtypes of CRC, which exhibit differences in OS, as well as important biomarker proteins that are closely related to the identified subtypes. Liquid biopsy assessment with targeted proteomics analysis was proposed to be crucial for predicting the CRC prognosis.

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.

Tracking metabolites at single-cell resolution reveals metabolic dynamics during plant mitosis.

Analyzing only one cell allows the changes and characteristics of intracellular metabolites during the chromosome segregation process to be precisely captured and mitotic sub-phases to be dissected at the metabolite level.

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.

Novel anti-flavivirus drugs targeting the nucleolar distribution of core protein.

The risk of infectious diseases caused by Flavivirus is increasing globally. Here, we developed a novel high-throughput screening (HTS) system to evaluate the inhibitory effects of compounds targeting the nuclear localization of the flavivirus core protein. We screened 4000 compounds based on their ability to inhibit the nuclear localization of the core protein, and identified over 20 compounds including inhibitors for cyclin dependent kinase and glycogen synthase kinase. The efficacy of the identified compounds to suppress viral growth was validated in a cell-based infection system. Remarkably, the nucleolus morphology was affected by the treatment with the compounds, suggesting that the nucleolus function is critical for viral propagation. The present HTS system provides a useful strategy for the identification of antivirals against flavivirus by targeting the nucleolar localization of the core protein.

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.

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.

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 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.

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.

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.

Dual Roles of Glutathione in Ecdysone Biosynthesis and Antioxidant Function During Larval Development in Drosophila.

Ecdysteroids, including the biologically active hormone 20-hydroxyecdysone (20E), play essential roles in controlling many developmental and physiological events in insects. Ecdysteroid biosynthesis is achieved by a series of specialized enzymes encoded by the Halloween genes. Recently, a new class of Halloween gene, noppera-bo (nobo), encoding a glutathione S-transferase (GST) in dipteran and lepidopteran species, has been identified and characterized. GSTs are well known to conjugate substrates with the reduced form of glutathione (GSH), a bioactive tripeptide composed of glutamate, cysteine, and glycine. We hypothesized that GSH itself is required for ecdysteroid biosynthesis. However, the role of GSH in steroid hormone biosynthesis has not been examined in any organisms. Here, we report phenotypic analysis of a complete loss-of-function mutant in the γ-glutamylcysteine synthetase catalytic subunit (Gclc) gene in the fruit fly Drosophila melanogasterGclc encodes the evolutionarily conserved catalytic component of the enzyme that conjugates glutamate and cysteine in the GSH biosynthesis pathway. Complete Gclc loss-of-function leads to drastic GSH deficiency in the larval body fluid. Gclc mutant animals show a larval-arrest phenotype. Ecdysteroid titer in Gclc mutant larvae decreases, and the larval-arrest phenotype is rescued by oral administration of 20E or cholesterol. Moreover, Gclc mutant animals exhibit abnormal lipid deposition in the prothoracic gland, a steroidogenic organ during larval development. All of these phenotypes are reminiscent to nobo loss-of-function animals. On the other hand, Gclc mutant larvae also exhibit a significant reduction in antioxidant capacity. Consistent with this phenotype, Gclc mutant larvae are more sensitive to oxidative stress response as compared to wild-type. Nevertheless, the ecdysteroid biosynthesis defect in Gclc mutant animals is not associated with loss of antioxidant function. Our data raise the unexpected hypothesis that a primary role of GSH in early D. melanogaster larval development is ecdysteroid biosynthesis, independent from the antioxidant role of GSH.

Fluorescence Probing Live Single-cell Mass Spectrometry for Direct Analysis of Organelle Metabolism.

Mitochondria in a live HepG2 cell were visualized with a fluorescent probe to specify their location and state in a living cell. Then, mitochondria were selectively captured with a nanospray tip under fluorescence microscope, and thousands of small molecular peaks were revealed and unique steroids specific to mitochondria were also found. This fluorescence imaging combined with live single-cell mass spectrometry opens the door to the analysis of site- and state-specific molecular detection to elucidate precise molecular mechanisms at the single-cell and organelle level.

Direct metabolomics for plant cells by live single-cell mass spectrometry.

Live single-cell mass spectrometry (live MS) provides a mass spectrum that shows thousands of metabolite peaks from a single live plant cell within minutes. By using an optical microscope, a cell is chosen for analysis and a metal-coated nanospray microcapillary tip is used to remove the cell's contents. After adding a microliter of ionization solvent to the opposite end of the tip, the trapped contents are directly fed into the mass spectrometer by applying a high voltage between the tip and the inlet port of the spectrometer to induce nanospray ionization. Proteins are not detected because of insufficient sensitivity. Metabolite peaks are identified by exact mass or tandem mass spectrometry (MS/MS) analysis, and isomers can be separated by combining live MS with ion-mobility separation. By using this approach, spectra can be acquired in 10 min. In combination with metabolic maps and/or molecular databases, the data can be annotated into metabolic pathways; the data analysis takes 30 min to 4 h, depending on the MS/MS data availability from databases. This method enables the analysis of a number of metabolites from a single cell with rapid sampling at sub-attomolar-level sensitivity.

Live Single-Cell Plant Hormone Analysis by Video-Mass Spectrometry.

Studies have indicated that endogenous concentrations of plant hormones are regulated very locally within plants. To understand the mechanisms underlying hormone-mediated physiological processes, it is indispensable to know the exact hormone concentrations at cellular levels. In the present study, we established a system to determine levels of ABA and jasmonoyl-isoleucine (JA-Ile) from single cells. Samples taken from a cell of Vicia faba leaves using nano-electrospray ionization (ESI) tips under a microscope were directly introduced into mass spectrometers by infusion and subjected to tandem mass spectrometry (MS/MS) analysis. Stable isotope-labeled [D(6)]ABA or [(13)C(6)]JA-Ile was used as an internal standard to compensate ionization efficiencies, which determine the amount of ions introduced into mass spectrometers. We detected ABA and JA-Ile from single cells of water- and wound-stressed leaves, whereas they were almost undetectable in non-stressed single cells. The levels of ABA and JA-Ile found in the single-cell analysis were compared with levels found by analysis of purified extracts with liquid chromatography-tandem mass spectrometry (LC-MS/MS). These results demonstrated that stress-induced accumulation of ABA and JA-Ile could be monitored from living single cells.