General Information of Drug Metabolite (DM) (ID: DM000871)
DM Name
2-hydroxyestrone
Synonyms
2-HYDROXYESTRONE; 362-06-1; Catecholestrone; 2-Hydroxy Estrone; 2,3-Dihydroxyestra-1,3,5(10)-trien-17-one; UQS3A06ILY; CHEBI:1156; (8R,9S,13S,14S)-2,3-dihydroxy-13-methyl-7,8,9,11,12,14,15,16-octahydro-6H-cyclopenta[a]phenanthren-17-one; 2,3-dihydroxy-estra-1,3,5(10)-trien-17-one; Estra-1,3,5(1)-trien-17-one, 2,3-dihydroxy-; UNII-UQS3A06ILY; CCRIS 9276; Estra-1,3,5(10)-trien-17-one, 2,3-dihydroxy-; SCHEMBL222517; 2-OHE1; CHEMBL1627343; DTXSID80904315; ZINC4096681; LMST02010032; AKOS030254485; HY-113251; CS-0059416; C05298; 2-Hydroxyestrone 100 microg/mL in Acetonitrile; Q27105413; (3aS,3bR,9bS,11aS)-7,8-dihydroxy-11a-methyl-1H,2H,3H,3aH,3bH,4H,5H,9bH,10H,11H,11aH-cyclopenta[a]phenanthren-1-one; (8R,9S,13S,14S)-2,3-dihydroxy-13-methyl-7,8,9,11,12,13,15,16-octahydro-6H-cyclopenta[a]phenanthren-17(14H)-one
Structure
3D MOL 2D MOL
Pharmaceutical Properties Molecular Weight 286.4 Topological Polar Surface Area 57.5
Heavy Atom Count 21 Rotatable Bond Count 0
Hydrogen Bond Donor Count 2 Hydrogen Bond Acceptor Count 3
PubChem CID
440623
Complexity
448
Formula
C18H22O3
Canonical SMILES
CC12CCC3C(C1CCC2=O)CCC4=CC(=C(C=C34)O)O
InChI
InChI=1S/C18H22O3/c1-18-7-6-11-12(14(18)4-5-17(18)21)3-2-10-8-15(19)16(20)9-13(10)11/h8-9,11-12,14,19-20H,2-7H2,1H3/t11-,12+,14-,18-/m0/s1
InChIKey
SWINWPBPEKHUOD-JPVZDGGYSA-N
IUPAC name
(8R,9S,13S,14S)-2,3-dihydroxy-13-methyl-7,8,9,11,12,14,15,16-octahydro-6H-cyclopenta[a]phenanthren-17-one
Toxicity Properties of This DM
Documented Toxicity Properties
Toxicity Class Unreported
Predicted Toxicity Properties
Physical and chemical properties LogP

The log of the n-octanol/water distribution coefficient.

LogP possess a leading position with considerable impact on both membrane permeability and hydrophobic binding to macromolecules. Therefore, LogP is widely used in drug discovery and development as an indicator of potential utility of a solute as a drug.

The predicted logP of a compound in the range from 0 to 3 log mol/L will be considered proper.

3.346 TPSA

Topological polar surface area

In TPSA, PSA is estimated only from the syntype (topology) of atoms in the molecule, without considering the three-dimensional structure of the molecule, which is the origin of the name topological polar surface area.

The TPSA of a compound in the range from 0 to 140 will be considered proper, based on Veber rule.

57.53
Pfizer Rule: Rejected

Molecules with a high log P (>3) and low TPSA (<75) are likely to be toxic.

Pfizer infered the relationship between the physicochemical properties and toxicity of the drug from an animal tolerability (IVT) study dataset of 245 preclinical Pfizer compounds.Compounds with a high log P (>3) and low TPSA ( <75) are likely to be toxic.

(Bioorg Med Chem Lett. 18(17):4872-5. 2008)

Structural Characteristics ALARM NMR Rule

Molecules containing the reactivity-related thiol substructures are likely to be toxic.

The high-throughput screening (HTS) hit rate of reactive compounds was evaluated by NMR screening, X-ray crystallography and other biochemical and biophysical experiments, and then 75 thiol substructures for predicting reactivity were obtained by computational means for 2348 screening hit reactive compounds and 1156 reactive compounds obtained by La protein experiments.The molecule was matched to 75 reactivity-related substructures to obtain the information how many alarm groups the molecule contained and determine whether it was a thiol-reactive compound. Molecules with the thiol substructures are likely to be toxic.

(J Am Chem Soc. 127(1):217-24. 2005)

1 PAINS

Molecules containing the reactive substructures are likely to be toxic.

Pan Assay Interference Compounds (PAINS) is one of the most famous frequent hitters filters, which comprises 480 substructures derived from the analysis of FHs determined by six target-based HTS assay. By application of these filters, it is easier to screen false positive hits and to flag suspicious compounds in screening databases. One of the most authoritative medicine magazines Journal of Medicinal Chemistry even requires authors to provide the screening results with the PAINS alerts of active compounds when submitting manuscripts.

(J Med Chem. 45(1):137-42. 2002)

1
BMS Rule

Molecules containing the reactivity-related substructures are likely to be toxic.

BMS's primary HTS data over the past 12 years was evaluated and analyzed to determine the correlation of a group of compound functional groups with Promiscuity, defined as a drug that acts with multiple molecular targets and exhibits different pharmacological effects.

(J Chem Inf Model. 46(3):1060-8. 2006)

0 Chelator Rule

Molecules containing the substructures associated with metalloprotease targeting are likely to be toxic.

The chelate substructure fragment library (eCFL) for targeting metalloproteinases was prepared and its effectiveness in screening metalloproteinase inhibitors was verified by analysis and fluorescence-based assay experiments, and 55 substructures associated with metalloprotease targeting were finally determined as alert structures.

(ChemMedChem. 5(2):195-9. 2010)

1
Genotoxic Carcinogenicity Rule

Molecules containing the Genotoxic substructures are likely to be carcinogenic.

By constructing a molecular structure dataset containing the corresponding Ames test data (mutagens and non-mutagens). The substructure of the dataset is searched, and then the toxic substructure obtained by using chemical and mechanical knowledge and statistical criteria is derived, and the new toxic substructure is obtained and approved, and finally the reliability of the verification set is verified. Molecules containing these substructures may cause carcinogenicity or mutagenicity through genotoxic mechanisms.There are 117 substructures in this endpoint.

(J Med Chem. 48(1):312-20. 2005)

0 Non-genotoxic Carcinogenicity Rule

Molecules containing the NonGenotoxic substructures are likely to be carcinogenic.

Through the analysis and verification of the existing molecular library or the molecular library mined by data, the list of non-gene carcinogenic substructures (SA) is obtained according to the computerized data mining analysis, and finally the reliability of the substructure is verified. Molecules containing these substructures may cause carcinogenicity through nongenotoxic mechanisms. There are 23 substructures in this endpoint.

(Mutat Res. 659(3):248-61. 2008)

0
Toxicity Model Prediction hERG Blockers

The possibility of causing cardiotoxicity.

The human ether-a-go-go related gene. The During cardiac depolarization and repolarization, a voltage-gated potassium channel encoded by hERG plays a major role in the regulation of the exchange of cardiac action potential and resting potential. The hERG blockade may cause long QT syndrome (LQTS), arrhythmia, and Torsade de Pointes (TdP), which lead to palpitations, fainting, or even sudden death.So build a model by collecting a dataset to predict whether a compound is a hERG Blocker.

The output value is the probability of being toxic, within the range of 0 to 1. 0-0.3: excellent; 0.3-0.7: medium; 0.7-1.0: poor.

(Brief Bioinform. 22(3):bbaa194. 2021)

0.088 (---) H-HT

The possibility of causing .hepatotoxicity.

The human hepatotoxicity. Drug induced liver injury is of great concern for patient safety and a major cause for drug withdrawal from the market. Adverse hepatic effects in clinical trials often lead to a late and costly termination of drug development programs.So build a model by collecting datasets to predict whether compounds will cause hepatotoxicity.

The output value is the probability of being toxic, within the range of 0 to 1. 0-0.3: excellent; 0.3-0.7: medium; 0.7-1.0: poor.

(Brief Bioinform. 22(3):bbaa194. 2021)

0.297 (--)
DILI

The possibility of causing liver injury.

Drug-induced liver injury (DILI) has become the most common safety problem of drug withdrawal from the market over the past 50 years.So build a model by collecting datasets to predict whether compounds will cause liver injury.

The output value is the probability of being toxic, within the range of 0 to 1. 0-0.3: excellent; 0.3-0.7: medium; 0.7-1.0: poor.

(Brief Bioinform. 22(3):bbaa194. 2021)

0.316 (-) CAMES Toxicity

The possibility of causing mutagenicity.

The Ames test for mutagenicity. The mutagenic effect has a close relationship with the carcinogenicity, and it is the most widely used assay for testing the mutagenicity of compounds.So build a model by collecting datasets to predict whether compounds will cause mutagenicity.

The output value is the probability of being toxic, within the range of 0 to 1. 0-0.3: excellent; 0.3-0.7: medium; 0.7-1.0: poor.

(Brief Bioinform. 22(3):bbaa194. 2021)

0.24 (--)
Carcinogencity

The possibility of causing Carcinogencity.

Among various toxicological endpoints of chemical substances, carcinogenicity is of great concern because of its serious effects on human health. The carcinogenic mechanism of chemicals may be due to their ability to damage the genome or disrupt cellular metabolic processes. Many approved drugs have been identified as carcinogens in humans or animals and have been withdrawn from the market.So build a model by collecting datasets to predict whether compounds will cause Carcinogencity.

The output value is the probability of being toxic, within the range of 0 to 1. 0-0.3: excellent; 0.3-0.7: medium; 0.7-1.0: poor.

(Brief Bioinform. 22(3):bbaa194. 2021)

0.727 (++) Respiratory Toxicity

The possibility of causing Respiratory Toxicity.

Among these safety issues, respiratory toxicity has become the main cause of drug withdrawal. Drug-induced respiratory toxicity is usually underdiagnosed because it may not have distinct early signs or symptoms in common medications and can occur with significant morbidity and mortality.Therefore, careful surveillance and treatment of respiratory toxicity is of great importance.So build a model by collecting datasets to predict whether compounds will cause Respiratory Toxicity.

The output value is the probability of being toxic, within the range of 0 to 1. 0-0.3: excellent; 0.3-0.7: medium; 0.7-1.0: poor.

(Brief Bioinform. 22(3):bbaa194. 2021)

0.897 (++)
Full List of Drug-Metabolizing Enzyme (DME) Related to This DM
DME(s) Producing This DM through Metabolism
DME Name DME ID Reactant Reaction Related Drug REF
Cytochrome P450 1A1 (CYP1A1) DME0006 Oxidation - 2-Hydroxylation Estradiol acetate [1] , [2]
Cytochrome P450 1A1 (CYP1A1) DME0006 Oxidation - Hydroxylation Estradiol acetate [2]
Cytochrome P450 1A1 (CYP1A1) DME0006 Oxidation - Hydroxylation Estradiol cypionate [1] , [2]
Cytochrome P450 1A1 (CYP1A1) DME0006 Oxidation - 2-Hydroxylation Estradiol cypionate [2]
Cytochrome P450 1A1 (CYP1A1) DME0006 Oxidation - Hydroxylation Estradiol valerate [1] , [2]
Cytochrome P450 1A1 (CYP1A1) DME0006 Oxidation - 2-Hydroxylation Estradiol valerate [2]
Cytochrome P450 1A1 (CYP1A1) DME0006 Oxidation - 2-hydroxylation E-3A [3]
Cytochrome P450 1A1 (CYP1A1) DME0006 Oxidation - Aromatic hydroxylation E-3A [4]
Cytochrome P450 1A2 (CYP1A2) DME0003 Oxidation - Hydroxylation Estrone [5]
Cytochrome P450 1A2 (CYP1A2) DME0003 Oxidation - 2-Hydroxylation Estradiol acetate [1] , [2]
Cytochrome P450 1A2 (CYP1A2) DME0003 Oxidation - Hydroxylation Estradiol acetate [2]
Cytochrome P450 1A2 (CYP1A2) DME0003 Oxidation - Hydroxylation Estradiol cypionate [1] , [2]
Cytochrome P450 1A2 (CYP1A2) DME0003 Oxidation - 2-Hydroxylation Estradiol cypionate [2]
Cytochrome P450 1A2 (CYP1A2) DME0003 Oxidation - Hydroxylation Estradiol valerate [1] , [2]
Cytochrome P450 1A2 (CYP1A2) DME0003 Oxidation - 2-Hydroxylation Estradiol valerate [2]
Cytochrome P450 1A2 (CYP1A2) DME0003 Oxidation - 2-hydroxylation E-3A [3]
Cytochrome P450 1A2 (CYP1A2) DME0003 Oxidation - Aromatic hydroxylation E-3A [4]
Cytochrome P450 1A2 (CYP1A2) DME0003 Oxidation - 2-Hydroxylation Estrone [5]
Cytochrome P450 2B6 (CYP2B6) DME0020 Oxidation - Hydroxylation Estrone [5]
Cytochrome P450 2C8 (CYP2C8) DME0018 Oxidation - Hydroxylation Estrone [5]
Cytochrome P450 2C9 (CYP2C9) DME0019 Oxidation - Hydroxylation Estrone [5]
Cytochrome P450 2D6 (CYP2D6) DME0009 Oxidation - Hydroxylation Estrone [5]
Cytochrome P450 3A4 (CYP3A4) DME0001 Oxidation - Hydroxylation Estrone [5]
Cytochrome P450 3A4 (CYP3A4) DME0001 Oxidation - 2-Hydroxylation Estradiol acetate [1] , [2]
Cytochrome P450 3A4 (CYP3A4) DME0001 Oxidation - Hydroxylation Estradiol acetate [2]
Cytochrome P450 3A4 (CYP3A4) DME0001 Oxidation - Hydroxylation Estradiol cypionate [1] , [2]
Cytochrome P450 3A4 (CYP3A4) DME0001 Oxidation - 2-Hydroxylation Estradiol cypionate [2]
Cytochrome P450 3A4 (CYP3A4) DME0001 Oxidation - Hydroxylation Estradiol valerate [1] , [2]
Cytochrome P450 3A4 (CYP3A4) DME0001 Oxidation - 2-Hydroxylation Estradiol valerate [2]
Cytochrome P450 3A4 (CYP3A4) DME0001 Oxidation - 2-hydroxylation E-3A [3]
Cytochrome P450 3A4 (CYP3A4) DME0001 Oxidation - Aromatic hydroxylation E-3A [4]
Cytochrome P450 3A4 (CYP3A4) DME0001 Oxidation - 2-Hydroxylation Estrone [5]
Cytochrome P450 3A5 (CYP3A5) DME0012 Oxidation - Hydroxylation Estrone [5]
Cytochrome P450 3A7 (CYP3A7) DME0015 Oxidation - Hydroxylation Estrone [5]
Mephenytoin 4-hydroxylase (CYP2C19) DME0021 Oxidation - Hydroxylation Estrone [5]
⏷ Show the Full List of DM
DME(s) Metabolizing This DM
DME Name DME ID Product Reaction Related Drug REF
Catechol O-methyltransferase (COMT) DME0043 Unclear - Unclear Estradiol acetate [6]
Catechol O-methyltransferase (COMT) DME0043 Unclear - Unclear Estradiol cypionate [6]
Catechol O-methyltransferase (COMT) DME0043 Unclear - Unclear Estradiol valerate [6]
Catechol O-methyltransferase (COMT) DME0043 Unclear - Unclear Estrone [7]
Catechol O-methyltransferase (COMT) DME0043 Unclear - Unclear Estrone [7]
Catechol O-methyltransferase (COMT) DME0043 Unclear - Unclear E-3A [8]
Full List of Drug(s) That Produce This DM By Metabolism
Estradiol acetate DR0636 Approved Acquired prion disease
Estradiol cypionate DR0638 Approved Acquired prion disease
Estradiol valerate DR0644 Approved Acquired prion disease
Estrone DR0648 Approved Menopausal disorder
E-3A DR0007 Phase 3 Turner syndrome
References
1 The metabolism of estradiol; oral compared to intravenous administration J Steroid Biochem. 1985 Dec;23(6A):1065-70. doi: 10.1016/0022-4731(85)90068-8.
2 Cytochromes P450: a structure-based summary of biotransformations using representative substrates Drug Metab Rev. 2008;40(1):1-100. doi: 10.1080/03602530802309742.
3 DrugBank(Pharmacology-Metabolism)Estradiol acetate
4 Cytochrome P450-mediated metabolism of estrogens and its regulation in human. Cancer Lett. 2005 Sep 28;227(2):115-24.
5 Characterization of the oxidative metabolites of 17beta-estradiol and estrone formed by 15 selectively expressed human cytochrome p450 isoforms. Endocrinology. 2003 Aug;144(8):3382-98.
6 Cytochrome P450-mediated metabolism of estrogens and its regulation in human Cancer Lett. 2005 Sep 28;227(2):115-24. doi: 10.1016/j.canlet.2004.10.007.
7 Evaluation of electrospray ionization and atmospheric pressure chemical ionization for simultaneous detection of estrone and its metabolites using high-performance liquid chromatography/tandem mass spectrometry J Chromatogr B Analyt Technol Biomed Life Sci. 2007 Dec 1;860(1):49-56. doi: 10.1016/j.jchromb.2007.10.014.
8 DrugBank(Pharmacology-Metabolism):E-3A

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