IBUTILIDE


Synonyms	Ibutilide
Status
Molecule Category	UNKNOWN
ATC	C01BD05
UNII	2436VX1U9B


Active Pharmaceutical Ingredients Bioactive chemicals	Exact Similarity SubStructure	Threshold (%) >= 70

Structure


InChI Key	ALOBUEHUHMBRLE-UHFFFAOYSA-N
Smiles	CCCCCCCN(CC)CCCC(O)c1ccc(NS(C)(=O)=O)cc1
InChI	InChI=1S/C20H36N2O3S/c1-4-6-7-8-9-16-22(5-2)17-10-11-20(23)18-12-14-19(15-13-18)21-26(3,24)25/h12-15,20-21,23H,4-11,16-17H2,1-3H3

Physicochemical Descriptors

Property Name	Value
Molecular Formula	C20H36N2O3S
Molecular Weight	384.59
AlogP	4.16
Hydrogen Bond Acceptor	4.0
Hydrogen Bond Donor	2.0
Number of Rotational Bond	14.0
Polar Surface Area	69.64
Molecular species	BASE
Aromatic Rings	1.0
Heavy Atoms	26.0

Reference

Journal : Bioorg. Med. Chem. Lett.

Title : Prediction of hERG potassium channel affinity by traditional and hologram qSAR methods.

PubMed ID : 12873512

DOI : 10.1016/s0960-894x(03)00492-x

Year : 2003

Volume : 13

Issue : 16

First Page : 2773

Last Page : 2775

Authors : Keserü GM.

Abstract : Traditional and hologram QSAR (HQSAR) models were developed for the prediction of hERG potassium channel affinities. The models were validated on three different test sets including compounds with published patch-clamp IC(50) data and two subsets from the World Drug Index (compounds indicated to have ECG modifying adverse effect and drugs marked to be approved, respectively). Discriminant analysis performed on the full set of hERG data resulted in a traditional QSAR model that classified 83% of actives and 87% of inactives correctly. Analysis of our HQSAR model revealed it to be predictive in both IC(50) and discrimination studies.

Reference

Journal : Bioorg. Med. Chem. Lett.

Title : The pharmacophore hypotheses of I(Kr) potassium channel blockers: novel class III antiarrhythmic agents.

PubMed ID : 15324906

DOI : 10.1016/j.bmcl.2004.06.070

Year : 2004

Volume : 14

Issue : 18

First Page : 4771

Last Page : 4777

Authors : Du LP, Tsai KC, Li MY, You QD, Xia L.

Abstract : Predictive pharmacophore models were developed for a large series of I(Kr) potassium channel blockers as class III antiarrhythmic agents using HypoGen in Catalyst software. The pharmacophore hypotheses were generated using a training set consisting of 34 compounds carefully selected from documents. Their biological data, expressed as IC(50), spanned from 1.5 nM to 2.8 mM with 7 orders difference. The most predictive hypothesis (Hypo1), consisting of four features (one positive ionizable feature, two aromatic rings and one hydrophobic group), had a best correlation coefficient of 0.825, a lowest rms deviation of 1.612, and a highest cost difference (null cost-total cost) of 77.552, which represents a true correlation and a good predictivity. The hypothesis Hypo1 was then validated by a test set consisting of 21 compounds and by a cross-validation of 95% confidence level with randomizing the data using CatScramble program. Accordingly, our model has strong predictivity to identify structural diverse I(Kr) potassium channel blockers with desired biological activity by virtual screening

Reference

Journal : Bioorg. Med. Chem. Lett.

Title : A discriminant model constructed by the support vector machine method for HERG potassium channel inhibitors.

PubMed ID : 15911273

DOI : 10.1016/j.bmcl.2005.03.080

Year : 2005

Volume : 15

Issue : 11

First Page : 2886

Last Page : 2890

Authors : Tobita M, Nishikawa T, Nagashima R.

Abstract : HERG attracts attention as a risk factor for arrhythmia, which might trigger torsade de pointes. A highly accurate classifier of chemical compounds for inhibition of the HERG potassium channel is constructed using support vector machine. For two test sets, our discriminant models achieved 90% and 95% accuracy, respectively. The classifier is even applied for the prediction of cardio vascular adverse effects to achieve about 70% accuracy. While modest inhibitors are partly characterized by properties linked to global structure of a molecule including hydrophobicity and diameter, strong inhibitors are exclusively characterized by properties linked to substructures of a molecule.

Reference

Journal : Bioorg. Med. Chem.

Title : Support vector machines classification of hERG liabilities based on atom types.

PubMed ID : 18448342

DOI : 10.1016/j.bmc.2008.04.028

Year : 2008

Volume : 16

Issue : 11

First Page : 6252

Last Page : 6260

Authors : Jia L, Sun H.

Abstract : Drug-induced long QT syndrome (LQTS) can cause critical cardiovascular side effects and has accounted for the withdrawal of several drugs from the market. Blockade of the potassium ion channel encoded by the human ether-a-go-go-related gene (hERG) has been identified as a major contributor to drug-induced LQTS. Experimental measurement of hERG activity for each compound in development is costly and time-consuming, thus it is beneficial to develop a predictive hERG model. Here, we present a hERG classification model formulated using support vector machines (SVM) as machine learning method and using atom types as molecular descriptors. The training set used in this study was composed of 977 corporate compounds with hERG activities measured under the same conditions. The impact of soft margin and kernel function on the performance of the SVM models was examined. The robustness of SVM was evaluated by comparing the predictive power of the models built with 90%, 50%, and 10% of the training set data. The final SVM model was able to correctly classify 94% of an external testing set containing 66 drug molecules. The most important atom types with respect to discriminative power were extracted and analyzed.

Reference

Journal : Eur. J. Med. Chem.

Title : Predicting hERG activities of compounds from their 3D structures: development and evaluation of a global descriptors based QSAR model.

PubMed ID : 21185626

DOI : 10.1016/j.ejmech.2010.11.042

Year : 2011

Volume : 46

Issue : 2

First Page : 618

Last Page : 630

Authors : Sinha N, Sen S.

Abstract : A QSAR based predictive model of hERG activity in terms of 'global descriptors' has been developed and evaluated. The QSAR was developed by training 77 compounds covering a wide range of activities and was validated based on an external 'test set' of 80 compounds using neural network method. Statistical parameters and examination of enrichment factor indicated the effectiveness of the present model. Randomization test demonstrated the robustness of the model and cross-validation test further validated the QSAR. Domain of applicability test indicated to the high degree of reliability of the predicted results. Satisfactory performance in classifying compounds into 'active' and 'inactive' groups was also obtained. The cases where the QSAR failed, the possible sources of errors have been discussed.

Assay Description	Organism	Bioactivity
Inhibition of human Potassium channel HERG expressed in mammalian cells	Homo sapiens	10.0 nM
Inhibitory concentration against IKr potassium channel	None	20.0 nM
Inhibitory concentration against potassium channel HERG	None	10.0 nM
Inhibition of human ERG expressed in CHO cells by whole cell patch clamp technique	Homo sapiens	10.0 nM
Inhibition of human ERG	Homo sapiens	15.14 nM

Cross References

Resources	Reference
ChEBI	5856
ChEMBL	CHEMBL533
DrugBank	DB00308
DrugCentral	1412
FDA SRS	2436VX1U9B
Human Metabolome Database	HMDB0014453
Guide to Pharmacology	7200
KEGG	C07753
PharmGKB	PA449958
PubChem	60753
SureChEMBL	SCHEMBL3719856

CONTENTS

Structure
Chemical and Physical Properties