Browse
Submit Data
Databases
API
Help

Dataset Information

8 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Identification of average molecular weight (AMW) as a useful chemical descriptor to discriminate liver injury-inducing drugs.

ABSTRACT: Drug-induced liver injury (DILI) is one of major causes of discontinuing drug development and withdrawing drugs from the market. In this study, we investigated chemical properties associated with DILI using in silico methods, to identify a physicochemical property useful for DILI screening at the early stages of drug development. Total of 652 drugs, including 432 DILI-positive drugs (DILI drugs) and 220 DILI-negative drugs (no-DILI drugs) were selected from Liver Toxicity Knowledge Base of US Food and Drug Administration. Decision tree models were constructed using 2,473 descriptors as explanatory variables. In the final model, the descriptor AMW, representing average molecular weight, was found to be at the first node and showed the highest importance value. With AMW alone, 276 DILI drugs (64%) and 156 no-DILI drugs (71%) were correctly classified. Discrimination with AMW was then performed using therapeutic category information. The performance of discrimination depended on the category and significantly high performance (>0.8 balanced accuracy) was obtained in some categories. Taken together, the present results suggest AMW as a novel descriptor useful for detecting drugs with DILI risk. The information presented may be valuable for the safety assessment of drug candidates at the early stage of drug development.

SUBMITTER: Shimizu Y

PROVIDER: S-EPMC8232420 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

A local average distance descriptor for flexible protein structure comparison.

Project description:BACKGROUND: Protein structures are flexible and often show conformational changes upon binding to other molecules to exert biological functions. As protein structures correlate with characteristic functions, structure comparison allows classification and prediction of proteins of undefined functions. However, most comparison methods treat proteins as rigid bodies and cannot retrieve similarities of proteins with large conformational changes effectively. RESULTS: In this paper, we propose a novel descriptor, local average distance (LAD), based on either the geodesic distances (GDs) or Euclidean distances (EDs) for pairwise flexible protein structure comparison. The proposed method was compared with 7 structural alignment methods and 7 shape descriptors on two datasets comprising hinge bending motions from the MolMovDB, and the results have shown that our method outperformed all other methods regarding retrieving similar structures in terms of precision-recall curve, retrieval success rate, R-precision, mean average precision and F1-measure. CONCLUSIONS: Both ED- and GD-based LAD descriptors are effective to search deformed structures and overcome the problems of self-connection caused by a large bending motion. We have also demonstrated that the ED-based LAD is more robust than the GD-based descriptor. The proposed algorithm provides an alternative approach for blasting structure database, discovering previously unknown conformational relationships, and reorganizing protein structure classification.

| S-EPMC3992163 | biostudies-literature

Metabolomic analysis to discriminate drug-induced liver injury (DILI) phenotypes.

Project description:Drug-induced liver injury (DILI) is an adverse toxic hepatic clinical reaction associated to the administration of a drug that can occur both at early clinical stages of drug development, as well after normal clinical usage of approved drugs. Because of its unpredictability and clinical relevance, it is of medical concern. Three DILI phenotypes (hepatocellular, cholestatic, and mixed) are currently recognized, based on serum alanine aminotransferase (ALT) and alkaline phosphatase (ALP) values. However, this classification lacks accuracy to distinguish among the many intermediate mixed types, or even to estimate the magnitude and progression of the injury. It was found desirable to have additional elements for better evaluation criteria of DILI. With this aim, we have examined the serum metabolomic changes occurring in 79 DILI patients recruited and monitored using established clinical criteria, along the course of the disease and until recovery. Results revealed that free and conjugated bile acids, and glycerophospholipids were among the most relevant metabolite classes for DILI phenotype characterization. Using an ensemble of PLS-DA models, metabolomic information was integrated into a ternary diagram to display the disease phenotype, the severity of the liver damage, and its progression. The modeling implemented and the use of such compiled information in an easily understandable and visual manner facilitates a straightforward DILI phenotyping and allow to monitor its progression and recovery prediction, usefully complementing the concise information drawn out by the ALT and ALP classification.

| S-EPMC8380240 | biostudies-literature

Topological polar surface area: a useful descriptor in 2D-QSAR.

Project description:Topological polar surface area (TPSA), which makes use of functional group contributions based on a large database of structures, is a convenient measure of the polar surface area that avoids the need to calculate ligand 3D structure or to decide which is the relevant biological conformation or conformations. We demonstrate the utility of TPSA in 2D-QSAR for 14 sets of diverse pharmacological activity data. Even though a large pool of reports showing the importance of the classic 2D descriptors such as calculated logP (ClogP) and calculated molar refractivity (CMR) exists in the 2D-QSAR literature, this is the first report to demonstrate the value of TPSA as a relevant descriptor applicable to a large, structurally and pharmacologically diverse set of classes of compounds. We also address the limitations of applicability of this descriptor for 2D-QSAR analysis. We observed a negative correlation of TPSA with activity data for anticancer alkaloids, MT1 and MT2 agonists, MAO-B and tumor necrosis factor-alpha inhibitors and a positive correlation with inhibitory activity data for telomerase, PDE-5, GSK-3, DNA-PK, aromatase, malaria, trypanosomatids and CB2 agonists.

| S-EPMC7549127 | biostudies-literature

Modular Chemical Descriptor Language (MCDL): Stereochemical modules.

Project description:BackgroundIn our previous papers we introduced the Modular Chemical Descriptor Language (MCDL) for providing a linear representation of chemical information. A subsequent development was the MCDL Java Chemical Structure Editor which is capable of drawing chemical structures from linear representations and generating MCDL descriptors from structures.ResultsIn this paper we present MCDL modules and accompanying software that incorporate unique representation of molecular stereochemistry based on Cahn-Ingold-Prelog and Fischer ideas in constructing stereoisomer descriptors. The paper also contains additional discussions regarding canonical representation of stereochemical isomers, and brief algorithm descriptions of the open source LINDES, Java applet, and Open Babel MCDL processing module software packages.ConclusionsTesting of the upgraded MCDL Java Chemical Structure Editor on compounds taken from several large and diverse chemical databases demonstrated satisfactory performance for storage and processing of stereochemical information in MCDL format.

| S-EPMC3042968 | biostudies-literature

Integration of human cell lines gene expression and chemical properties of drugs for Drug Induced Liver Injury prediction.

Project description:MotivationDrug-induced liver injury (DILI) is one of the primary problems in drug development. Early prediction of DILI can bring a significant reduction in the cost of clinical trials. In this work we examined whether occurrence of DILI can be predicted using gene expression profile in cancer cell lines and chemical properties of drugs.MethodsWe used gene expression profiles from 13 human cell lines, as well as molecular properties of drugs to build Machine Learning models of DILI. To this end, we have used a robust cross-validated protocol based on feature selection and Random Forest algorithm. In this protocol we first identify the most informative variables and then use them to build predictive models. The models are first built using data from single cell lines, and chemical properties. Then they are integrated using Super Learner method with several underlying methods for integration. The entire modelling process is performed using nested cross-validation.ResultsWe have obtained weakly predictive ML models when using either molecular descriptors, or some individual cell lines (AUC ?(0.55-0.61)). Models obtained with the Super Learner approach have a significantly improved accuracy (AUC=0.73), which allows to divide substances in two categories: low-risk and high-risk.

| S-EPMC7796564 | biostudies-literature

Pharmacophore-Based Design of New Chemical Scaffolds as Translational Readthrough-Inducing Drugs (TRIDs).

Project description:Translational readthrough-inducing drugs (TRIDs) rescue the functional full-length protein expression in genetic diseases, such as cystic fibrosis, caused by premature termination codons (PTCs). Small molecules have been developed as TRIDs to trick the ribosomal machinery during recognition of the PTC. Herein we report a computational study to identify new TRID scaffolds. A pharmacophore approach was carried out on compounds that showed readthrough activity. The pharmacophore model applied to screen different libraries containing more than 87000 compounds identified four hit-compounds presenting scaffolds with diversity from the oxadiazole lead. These compounds have been synthesized and tested using the Fluc reporter harboring the UGA PTC. Moreover, the cytotoxic effect and the expression of the CFTR protein were evaluated. These compounds, a benzimidazole derivative (NV2899), a benzoxazole derivative (NV2913), a thiazole derivative (NV2909), and a benzene-1,3-disulfonate derivative (NV2907), were shown to be potential new lead compounds as TRIDs, boosting further efforts to address the optimization of the chemical scaffolds.

| S-EPMC7236267 | biostudies-literature

Hepatic neddylation deficiency triggers fatal liver injury via inducing NF-κB-inducing kinase in mice.

Project description:The conjugation of neural precursor cell expressed, developmentally downregulated 8 (NEDD8) to target proteins, termed neddylation, participates in many cellular processes and is aberrant in various pathological diseases. Its relevance to liver function and failure remains poorly understood. Herein, we show dysregulated expression of NAE1, a regulatory subunit of the only NEDD8 E1 enzyme, in human acute liver failure. Embryonic- and adult-onset deletion of NAE1 in hepatocytes causes hepatocyte death, inflammation, and fibrosis, culminating in fatal liver injury in mice. Hepatic neddylation deficiency triggers oxidative stress, mitochondrial dysfunction, and hepatocyte reprogramming, potentiating liver injury. Importantly, NF-κB-inducing kinase (NIK), a serine/Thr kinase, is a neddylation substrate. Neddylation of NIK promotes its ubiquitination and degradation. Inhibition of neddylation conversely causes aberrant NIK activation, accentuating hepatocyte damage and inflammation. Administration of N-acetylcysteine, a glutathione surrogate and antioxidant, mitigates liver failure caused by hepatic NAE1 deletion in adult male mice. Therefore, hepatic neddylation is important in maintaining postnatal and adult liver homeostasis, and the identified neddylation targets/pathways provide insights into therapeutically intervening acute liver failure.

| S-EPMC9758150 | biostudies-literature

Descriptor-augmented machine learning for enzyme-chemical interaction predictions

Project description: Not available

| S-EPMC10915406 | biostudies-literature

An impending inhibitor useful for the oil and gas production industry: Weight loss, electrochemical, surface and quantum chemical calculation.

Project description:The influence of a Schiff base namely N,N'-(pyridine-2,6-diyl)bis(1-(4-methoxyphenyl) methanimine) (PM) on the corrosion of J55 and N80 steel in 3.5 wt.% NaCl solution saturated with CO2 was evaluated using weight loss, potentiodynamic polarization, electrochemical impedance spectroscopy (EIS), X-ray diffraction (XRD), contact angle, scanning electron microscopy (SEM), atomic force microscopy (AFM) and scanning electrochemical microscopy (SECM). Potentiodynamic polarization results suggested that the inhibitor acted as a mixed type inhibitor by reducing both anodic and cathodic reactions. The adsorption of PM on the J55 and N80 steel surface obeyed the Langmuir adsorption isotherm. XRD, contact angle, SEM, AFM and SECM studies revealed that the surface of the metal was quite unaffected after the addition of inhibitor. Quantum chemical calculations and molecular dynamic simulation support the experimental results well.

| S-EPMC5668278 | biostudies-literature

Liver injury from nonsteroidal anti-inflammatory drugs in the United States.

Project description:Nonsteroidal anti-inflammatory drugs (NSAIDs) are commonly used and have been associated with hepatotoxicity. Studies of liver injury from NSAIDs have been retrospective and prospective data are lacking. The aim was to report the features and outcomes of the subjects with severe drug-induced liver injury from NSAIDS.The U.S. Drug Induced Liver Injury Network is a prospective registry of idiosyncratic drug hepatotoxicity. All patients are evaluated in a standard fashion and followed up for at least 6 months.Of 1221 Drug Induced Liver Injury Network cases that were adjudicated, 30 cases were attributed to eight different NSAIDs. The mean age was 52 years old, 24 (80%) were women, and 21 (70%) were Caucasian. The mean latency was 67 days. Common signs and symptoms at presentation were nausea (73%), jaundice (67%) and dark urine (67%). Mean peak serum aspartate aminotransferase, alanine aminotransferase, total bilirubin and alkaline phosphatase were 898 U/L, 1060 U/L, 12.2 mg/dl and 326 U/L. The most common pattern of injury was hepatocellular (70%) and autoantibodies were detected in 33% of cases. Diclofenac, was the most frequently implicated NSAID (16/30 cases), and characterized by hepatocellular injury. Seventeen cases resulted in hospitalization or prolongation of hospitalization and one patient died from complications of Stevens-Johnson syndrome because of diclofenac.Hepatocellular injury is the most common pattern seen with NSAID hepatotoxicity, and diclofenac is the most frequently implicated agent. Given the number of NSAID alternatives, diclofenac should be reserved for patients who fail other NSAIDs and a high level of suspicion for hepatotoxicity should be maintained.

| S-EPMC5035108 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data