Impurity Profiling - Revision history

Kirilas: Updated for 2023 release

2023-07-26T10:53:01Z

Updated for 2023 release

← Older revision		Revision as of 10:53, 26 July 2023
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	* Other mitigating factors		* Other mitigating factors

	'''''Note:''''' Currently, ~~ACD/~~Percepta only ~~considers~~ the compounds one by one without account for potential parent-derivative relationships~~, and therefore, does not perform Class 4 assignment~~. Also, when a definitive classification cannot be made, ICH M7 Class is reported as Inconclusive (rendered as 0 in Spreadsheet workspace) – this should be treated similarly to Class 3 compounds, as requiring further attention.		'''''Note:''''' Currently, Percepta can only assign Class 4 when ICH M7 Classification is calculated in Spreadsheet workspace with indicated ID of the parent compound in the active project. Calculations in the Expert module UI (and in Spreadsheet with provided ID = 0) consider the compounds one by one without account for potential parent-derivative relationships. Also, when a definitive classification cannot be made, ICH M7 Class is reported as Inconclusive (rendered as 0 in Spreadsheet workspace) – this should be treated similarly to Class 3 compounds, as requiring further attention.

	=== Experimental Data ===		=== Experimental Data ===

Kirilas: Reformatted references to active links

2022-10-20T09:19:01Z

Reformatted references to active links

← Older revision		Revision as of 09:19, 20 October 2022
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	A complete list of modeled endpoints is provided in the Table, while the data sources are briefly described below.<br />		A complete list of modeled endpoints is provided in the Table, while the data sources are briefly described below.<br />

	'''Genetic toxicity''': data sets for standard assays reflecting different mechanisms of genetic damage were obtained from the FDA. Gene mutation tests and techniques detecting clastogenic/aneugenic effects are included. Data was collected from EPA GENE-TOX database and scientific literature ~~[1]~~.		'''Genetic toxicity''': data sets for standard assays reflecting different mechanisms of genetic damage were obtained from the FDA. Gene mutation tests and techniques detecting clastogenic/aneugenic effects are included. Data was collected from EPA GENE-TOX database [https://www.nlm.nih.gov/databases/download/genetox.html] and scientific literature.

	'''Carcinogenicity''': results of chronic (two-year term) carcinogenicity studies in rats and mice were received from FDA. This data was based on NTP technical reports, IARC monographs, Carcinogenic Potency DataBase [2] and other publicly available sources. Raw data was converted to binary classification using a weight of evidence (WOE) approach [1]. Classification using the WOE threshold corresponding to “potent carcinogens” was used to build the models in the current study.		'''Carcinogenicity''': results of chronic (two-year term) carcinogenicity studies in rats and mice were received from FDA. This data was based on NTP technical reports, IARC monographs, Carcinogenic Potency DataBase [https://pubmed.ncbi.nlm.nih.gov/15800034] and other publicly available sources. Raw data was converted to binary classification using a weight of evidence (WOE) approach [https://pubmed.ncbi.nlm.nih.gov/17207562]. Classification using the WOE threshold corresponding to “potent carcinogens” was used to build the models in the current study.

	'''Reproductive toxicity''': experimental data characterizing the potential for endocrine system disruption due to Estrogen receptor α binding were acquired from ChEMBL database [3]. Compounds were classified as binders/non-binders on the basis of their relative binding affinities (RBA) compared to reference ligand estradiol. Two cut-offs were used: LogRBA > -3 (“general binding”), and LogRBA > 0 (“strong binding”)<br />		'''Reproductive toxicity''': experimental data characterizing the potential for endocrine system disruption due to Estrogen receptor α binding were acquired from ChEMBL database [https://www.ebi.ac.uk/chembl] (Target ID 206). Compounds were classified as binders/non-binders on the basis of their relative binding affinities (RBA) compared to reference ligand estradiol. Two cut-offs were used: LogRBA > -3 (“general binding”), and LogRBA > 0 (“strong binding”)<br />
	<br />		<br />

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	=== Methods ===		=== Methods ===

	Probabilistic predictive models for all considered endpoints were developed using GALAS modeling methodology [4]. Each GALAS model consists of two parts:		Probabilistic predictive models for all considered endpoints were developed using GALAS modeling methodology [https://pubmed.ncbi.nlm.nih.gov/20814717]. Each GALAS model consists of two parts:
	# Global (baseline) model that reflects general trends in the property of interest. Baseline models were built using binomial PLS method based on fragmental descriptors.		# Global (baseline) model that reflects general trends in the property of interest. Baseline models were built using binomial PLS method based on fragmental descriptors.
	# Local corrections were applied to baseline predictions using a special similarity-based routine, after performing an analysis for the most similar compounds used in the training set. The local part of the model provides the basis for the calculation of the Reliability index (RI), a value ranging from 0 to 1 that provides a quantitative estimate of prediction accuracy.		# Local corrections were applied to baseline predictions using a special similarity-based routine, after performing an analysis for the most similar compounds used in the training set. The local part of the model provides the basis for the calculation of the Reliability index (RI), a value ranging from 0 to 1 that provides a quantitative estimate of prediction accuracy.
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	=== Genotoxicity/Carcinogenicity Hazards ===		=== Genotoxicity/Carcinogenicity Hazards ===

	The knowledge-based expert system that identifies structural fragments potentially responsible for genotoxic effect is an extension of the previously described Ames mutagenicity hazards system [5]. The list of alerting groups was augmented with structural moieties that are frequently present in compounds tested positive in chromosomal damage assays, eucaryote gene mutation tests, as well as in carcinogens acting by non-genotoxic (epigenetic) mechanisms. The final list included 67 structural alerts, 14 of which represent epigenetic carcinogens (androgens, peroxisome proliferators, etc.).<br />		The knowledge-based expert system that identifies structural fragments potentially responsible for genotoxic effect is an extension of the previously described Ames mutagenicity hazards system [https://www.sciencedirect.com/science/article/abs/pii/S0378427408007054]. The list of alerting groups was augmented with structural moieties that are frequently present in compounds tested positive in chromosomal damage assays, eucaryote gene mutation tests, as well as in carcinogens acting by non-genotoxic (epigenetic) mechanisms. The final list included 67 structural alerts, 14 of which represent epigenetic carcinogens (androgens, peroxisome proliferators, etc.) [https://www.sciencedirect.com/science/article/abs/pii/S0378427411005492].<br />
	Overall, the expert system was able to detect 94% of mutagens in the Ames test DB and 90% of compounds labeled as potent carcinogens by FDA.<br />		Overall, the expert system was able to detect 94% of mutagens in the Ames test DB and 90% of compounds labeled as potent carcinogens by FDA.<br />

	The alert list is not limited to directly acting substructures, such as planar polycyclic arenes, aromatic amines, quinones, N-nitro and N-nitroso groups, but also includes various fragments that may undergo biotransformation to reactive intermediates. As an example, troglizatone, a thiazolidinedione class antidiabetic drug, was classified by the FDA as a potent carcinogen and has since been withdrawn from the USA market. The carcinogenic effect of this drug is mediated by several reactive metabolites. In human liver microsomes, the chromane ring of troglitazone is metabolized by CYP3A4 to form quinone and quinone-methide products. Furthermore, oxidative cleavage of thiazolidinedione ring results in a reactive sulfenic acid metabolite that also contains an isocyanate moiety [6].<br />		The alert list is not limited to directly acting substructures, such as planar polycyclic arenes, aromatic amines, quinones, N-nitro and N-nitroso groups, but also includes various fragments that may undergo biotransformation to reactive intermediates. As an example, troglizatone, a thiazolidinedione class antidiabetic drug, was classified by the FDA as a potent carcinogen and has since been withdrawn from the USA market. The carcinogenic effect of this drug is mediated by several reactive metabolites. In human liver microsomes, the chromane ring of troglitazone is metabolized by CYP3A4 to form quinone and quinone-methide products. Furthermore, oxidative cleavage of thiazolidinedione ring results in a reactive sulfenic acid metabolite that also contains an isocyanate moiety [https://pubmed.ncbi.nlm.nih.gov/20869346].<br />
	<br />		<br />

	=== References ===		<!--=== References ===
	[1] Matthews EJ et al. Regul Toxicol Pharmacol. 2007, 47, 115.<br>		[1] Matthews EJ et al. Regul Toxicol Pharmacol. 2007, 47, 115.<br>
	[2] Gold LS et al. Toxicol Sci. 2005, 85, 747.<br>		[2] Gold LS et al. Toxicol Sci. 2005, 85, 747.<br>
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	[5] Didziapetris R et al. Toxicol Lett. 2008, 180, S152.<br>		[5] Didziapetris R et al. Toxicol Lett. 2008, 180, S152.<br>
	[6] Mansuy D & Dansette PM. Arch Biochem Biophys. 2011, 507, 1745.<br>		[6] Mansuy D & Dansette PM. Arch Biochem Biophys. 2011, 507, 1745.<br>
			-->
	</div>		</div>
	</div>		</div>

Kirilas: Fix copy/paste error from other module

2021-12-09T13:54:59Z

Fix copy/paste error from other module

← Older revision		Revision as of 13:54, 9 December 2021
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	<br />		<br />

	~~===Calculated quantitative parameters===~~
	For modeling purposes experimental classification results of standard Draize test (not irritating, slightly irritating, irritating, etc.) have been transformed into a binary variable according to the following scheme: compounds producing at least moderate eye or skin irritation were considered positive and all the others - negative. The resulting probabilistic predictor estimates whether the analyzed compound is likely to act as as moderate or severe eye or skin irritant.
	{\| border="1" class="wikitable" style="margin: 1em auto 1em auto; text-align: left"		{\| border="1" class="wikitable" style="margin: 1em auto 1em auto; text-align: left"
	\|+ Table. A list of bioassays and dataset sizes included in ACD/Impurity Profiler.		\|+ Table. A list of bioassays and dataset sizes included in ACD/Impurity Profiler.

Kirilas: Updated for 2021.1 release

2021-07-28T08:58:58Z

Updated for 2021.1 release

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 * Endocrine disruption mechanisms (estrogen receptor binding)
 The impurities package offers probabilistic predictive models for 21 different endpoints that cover various mechanisms of hazardous activity presented above. These predictors are supplemented with a knowledge-based expert system that identifies potentially hazardous structural fragments that could be responsible for genotoxic and/or carcinogenic activity of the compound of interest.
 <br />
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 * Gain insight into the possible mechanisms of toxic effects
 * See a display of up to 5 similar structures with experimental results in relevant bioassays
 <br />
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 [[Image:Genotoxicity_impurity_profiling.png|center]]
 <br />
 # Hover over the name of an alert to highlight the alerting group on the structure of the molecule
 # The list of all alerting groups found in the molecule. Each alert is supplied with statistical data regarding distrubution of positive and negative compounds possessing this hazardous fragment in all considered databases, along with the respective z-scores. Z-scores show whether the presence of the fragment leads to a statistically significant increase in proportion of compounds with a positive test result for a particular assay. This information provides further evidence regarding the possible mechanisms of action.
@@ Line 47: / Line 52: @@
 The expert system contains a list of 67 alerting groups of toxicophores, 53 of which account for point mutational and/or clastogenic mechanisms of DNA damage, while the remaining 14 substructures detect carcinogens acting by non-genotoxic mechanisms. The expert system was able to recognize >94% of mutagens in ACD/Ames test database, and >90% of compounds marked as potent carcinogens in the FDA's OFAS Food-Additive Knowledgebase.
 <br />
 === Experimental Data ===

Kirilas at 10:01, 15 June 2017

2017-06-15T10:01:02Z

← Older revision		Revision as of 10:01, 15 June 2017
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	<div class="mw-collapsible ~~mw-collapsed~~">		<div class="mw-collapsible">

	==Technical information==		==Technical information==

Kirilas: /* Overview */

2014-06-06T12:40:19Z

Overview

← Older revision		Revision as of 12:40, 6 June 2014
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	Impurity profiling module is a result of the collaboration between ACD/Labs and FDA Center for Food Safety and Nutrition (CFSAN). Evaluation of genotoxic and/or carcinogenic potential is based on a battery of probabilistic models for bioassays reflecting different mechanisms of hazardous activity. A knowledge-based expert system identifies potentially hazardous structural fragments that could be responsible for carcinogenic activity of the test molecule.		Impurity profiling module is a result of the collaboration between ACD/Labs and FDA Center for Food Safety and Nutrition (CFSAN). Evaluation of genotoxic and/or carcinogenic potential is based on a battery of probabilistic models for bioassays reflecting different mechanisms of hazardous activity. A knowledge-based expert system identifies potentially hazardous structural fragments that could be responsible for carcinogenic activity of the test molecule.

	The toxicity predictions in the ~~Impurities Package~~ offer greater insight into the safety of impurities, providing detailed information on toxic endpoints, reflecting various mechanisms of hazardous activity including:		The toxicity predictions in the impurity profiling package offer greater insight into the safety of impurities, providing detailed information on toxic endpoints, reflecting various mechanisms of hazardous activity including:
	* Mutagenicity (Ames test, Mouse Lymphoma Assay, and other standard assays)		* Mutagenicity (Ames test, Mouse Lymphoma Assay, and other standard assays)
	* Clastogenicity (Micronucleus test, Chromosomal Aberrations)		* Clastogenicity (Micronucleus test, Chromosomal Aberrations)

Kirilas: /* Experimental Data */

2013-02-15T11:21:58Z

Experimental Data

Show changes

Kristina at 13:19, 29 May 2012

2012-05-29T13:19:02Z

← Older revision		Revision as of 13:19, 29 May 2012
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	<br />		<br />

	[[Image:~~genotoxicity_impurity_profiling~~.png\|center]]		[[Image:Genotoxicity_impurity_profiling.png\|center]]
	<br />		<br />
	# Hover over the name of an alert to highlight the alerting group on the structure of the molecule		# Hover over the name of an alert to highlight the alerting group on the structure of the molecule
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	'''Reproductive toxicity''': experimental data characterizing the potential for endocrine system disruption due to Estrogen receptor α binding were acquired from ChEMBL database [3]. Compounds were classified as binders/non-binders on the basis of their relative binding affinities (RBA) compared to reference ligand estradiol. Two cut-offs were used: LogRBA > -3 (“general binding”), and LogRBA > 0 (“strong binding”)<br />		'''Reproductive toxicity''': experimental data characterizing the potential for endocrine system disruption due to Estrogen receptor α binding were acquired from ChEMBL database [3]. Compounds were classified as binders/non-binders on the basis of their relative binding affinities (RBA) compared to reference ligand estradiol. Two cut-offs were used: LogRBA > -3 (“general binding”), and LogRBA > 0 (“strong binding”)<br />
	<br />		<br />
	[[File:~~genotoxicity_impurity_table~~.png\|center]]		[[File:Genotoxicity_impurity_table.png\|center]]
	<br />		<br />

Kristina at 08:35, 25 May 2012

2012-05-25T08:35:53Z

← Older revision		Revision as of 08:35, 25 May 2012
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	'''Reproductive toxicity''': experimental data characterizing the potential for endocrine system disruption due to Estrogen receptor α binding were acquired from ChEMBL database [3]. Compounds were classified as binders/non-binders on the basis of their relative binding affinities (RBA) compared to reference ligand estradiol. Two cut-offs were used: LogRBA > -3 (“general binding”), and LogRBA > 0 (“strong binding”)<br />		'''Reproductive toxicity''': experimental data characterizing the potential for endocrine system disruption due to Estrogen receptor α binding were acquired from ChEMBL database [3]. Compounds were classified as binders/non-binders on the basis of their relative binding affinities (RBA) compared to reference ligand estradiol. Two cut-offs were used: LogRBA > -3 (“general binding”), and LogRBA > 0 (“strong binding”)<br />
	<br />		<br />
	[[File:genotoxicity_impurity_table.png]]		[[File:genotoxicity_impurity_table.png\|center]]
	<br />		<br />

Kristina: /* Technical information */

2012-05-25T08:34:27Z

Technical information

← Older revision		Revision as of 08:34, 25 May 2012
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	<div class="mw-collapsible-content">		<div class="mw-collapsible-content">
	ACD/Labs Package for Toxicity Screening of Impurities provides a battery of in silico tests to accurately assess the genotoxic and carcinogenic potential of impurities and degradants, found to be below the threshold of toxicological concern in drug products, helping companies remain compliant with regulatory submission requirements.		ACD/Labs Package for Toxicity Screening of Impurities provides a battery of ''in silico'' tests to accurately assess the genotoxic and carcinogenic potential of impurities and degradants, found to be below the threshold of toxicological concern in drug products, helping companies remain compliant with regulatory submission requirements.<br>
	Profile impurities using predictions for genotoxic and carcinogenic endpoints, quickly determine if an impurity is likely to pose a safety risk, and identify potentially hazardous structural fragments responsible for toxic activity.		Profile impurities using predictions for genotoxic and carcinogenic endpoints, quickly determine if an impurity is likely to pose a safety risk, and identify potentially hazardous structural fragments responsible for toxic activity.
			<br />

	The expert system contains a list of 67 alerting groups of toxicophores, 53 of which account for point mutational and/or clastogenic mechanisms of DNA damage, while the remaining 14 substructures detect carcinogens acting by non-genotoxic mechanisms. The expert system was able to recognize >94% of mutagens in ACD/Ames test database, and >90% of compounds marked as potent carcinogens in the FDA's OFAS Food-Additive Knowledgebase.		The expert system contains a list of 67 alerting groups of toxicophores, 53 of which account for point mutational and/or clastogenic mechanisms of DNA damage, while the remaining 14 substructures detect carcinogens acting by non-genotoxic mechanisms. The expert system was able to recognize >94% of mutagens in ACD/Ames test database, and >90% of compounds marked as potent carcinogens in the FDA's OFAS Food-Additive Knowledgebase.
			<br />

	~~Methods~~		=== Experimental Data ===

	~~Probabilistic predictive models for all considered~~ endpoints ~~were developed using GALAS modeling methodology [4]. Each GALAS model consists of two parts:~~		A complete list of modeled endpoints is provided in Table 1, while the data sources are briefly described below.<br />
	~~# Global (baseline) model that reflects general trends~~ in the ~~property of interest. Baseline models were built using binomial PLS method based on fragmental descriptors~~.
	# Local corrections were applied to baseline predictions using a special similarity-based routine, after performing an analysis for the most similar compounds used in the training set. The local part of the model provides the basis for the calculation of the Reliability index (RI), a value ranging from 0 to 1 that provides a quantitative estimate of prediction accuracy.		'''Genetic toxicity''': data sets for standard assays reflecting different mechanisms of genetic damage were obtained from the FDA. Gene mutation tests and techniques detecting clastogenic/aneugenic effects are included. Data was collected from EPA GENE-TOX database and scientific literature [1].
	~~A single baseline model was derived~~ for ~~each group~~ of ~~endpoints representing~~ the ~~same mechanism of hazardous action~~. ~~Such model reflects a “cumulative” toxicity potential of chemicals in these assays~~. ~~Experimental values specific for a particular assay were used during the local part of the modeling to yield final GALAS model for that endpoint~~.

	~~Experimental Data~~		'''Carcinogenicity''': results of chronic (two-year term) carcinogenicity studies in rats and mice were received from FDA. This data was based on NTP technical reports, IARC monographs, Carcinogenic Potency DataBase [2] and other publicly available sources. Raw data was converted to binary classification using a weight of evidence (WOE) approach [1]. Classification using the WOE threshold corresponding to “potent carcinogens” was used to build the models in the current study.

	~~A complete list of modeled endpoints is provided in Table 1, while the data sources are briefly described below.~~		'''Reproductive toxicity''': experimental data characterizing the potential for endocrine system disruption due to Estrogen receptor α binding were acquired from ChEMBL database [3]. Compounds were classified as binders/non-binders on the basis of their relative binding affinities (RBA) compared to reference ligand estradiol. Two cut-offs were used: LogRBA > -3 (“general binding”), and LogRBA > 0 (“strong binding”)<br />
	~~Genetic~~ toxicity: data ~~sets~~ for ~~standard assays reflecting different mechanisms of genetic damage~~ were ~~obtained~~ from ~~the FDA~~. ~~Gene mutation tests and techniques detecting clastogenic~~/~~aneugenic effects are included~~. ~~Data was collected from EPA GENE~~-~~TOX database~~ and ~~scientific literature~~ [1].		<br />
			[[File:genotoxicity_impurity_table.png]]
			<br />

	Carcinogenicity: results of chronic (two-year term) carcinogenicity studies in rats and mice were received from FDA. This data was based on NTP technical reports, IARC monographs, Carcinogenic Potency DataBase [2] and other publicly available sources. Raw data was converted to binary classification using a weight of evidence (WOE) approach [1]. Classification using the WOE threshold corresponding to “potent carcinogens” was used to build the models in the current study.

	Reproductive toxicity: experimental data characterizing the potential for endocrine system disruption due to Estrogen receptor α binding were acquired from ChEMBL database [3]. Compounds were classified as binders/non-binders on the basis of their relative binding affinities (RBA) compared to reference ligand estradiol. Two cut-offs were used: LogRBA > -3 (“general binding”), and LogRBA > 0 (“strong binding”)		=== Methods ===

	~~Genotoxicity?carcinogenicity Hazards~~		Probabilistic predictive models for all considered endpoints were developed using GALAS modeling methodology [4]. Each GALAS model consists of two parts:
			# Global (baseline) model that reflects general trends in the property of interest. Baseline models were built using binomial PLS method based on fragmental descriptors.
			# Local corrections were applied to baseline predictions using a special similarity-based routine, after performing an analysis for the most similar compounds used in the training set. The local part of the model provides the basis for the calculation of the Reliability index (RI), a value ranging from 0 to 1 that provides a quantitative estimate of prediction accuracy.
			A single baseline model was derived for each group of endpoints representing the same mechanism of hazardous action. Such model reflects a “cumulative” toxicity potential of chemicals in these assays. Experimental values specific for a particular assay were used during the local part of the modeling to yield final GALAS model for that endpoint. <br />

	~~The knowledge-based expert system that identifies structural fragments potentially responsible for~~
	~~genotoxic effect is an extension of the previously described Ames mutagenicity hazards system [5]. The list~~
	~~of alerting groups was augmented with structural moieties that are frequently present in compounds tested~~
	~~positive in chromosomal damage assays, eucaryote gene mutation tests, as well as in carcinogens acting~~
	~~by non-genotoxic (epigenetic) mechanisms. The final list included 67 structural alerts, 14 of which~~
	~~represent epigenetic carcinogens (androgens, peroxisome proliferators, etc.).~~

	~~Overall, the expert system was able to detect 94% of mutagens in the Ames test DB and 90% of~~		=== Genotoxicity/Carcinogenicity Hazards ===
	~~compounds labeled as potent carcinogens by FDA.~~

	The ~~alert~~ list ~~is not limited to directly acting substructures~~, ~~such~~ as ~~planar polycyclic arenes, aromatic~~		The knowledge-based expert system that identifies structural fragments potentially responsible for genotoxic effect is an extension of the previously described Ames mutagenicity hazards system [5]. The list of alerting groups was augmented with structural moieties that are frequently present in compounds tested positive in chromosomal damage assays, eucaryote gene mutation tests, as well as in carcinogens acting by non-genotoxic (epigenetic) mechanisms. The final list included 67 structural alerts, 14 of which represent epigenetic carcinogens (androgens, peroxisome proliferators, etc.).<br />
	~~amines, quinones, N~~-~~nitro and N-nitroso groups, but also includes various fragments that may undergo~~		Overall, the expert system was able to detect 94% of mutagens in the Ames test DB and 90% of compounds labeled as potent carcinogens by FDA.<br />
	~~biotransformation to reactive intermediates~~. ~~As an example~~, ~~troglizatone~~, ~~a thiazolidinedione class~~
	~~antidiabetic drug~~, ~~was classified by the FDA as a potent carcinogen and has since been withdrawn from the~~
	~~USA market~~. ~~The carcinogenic effect of this drug is mediated by several reactive metabolites~~. ~~In human~~
	~~liver microsomes~~, the ~~chromane ring~~ of ~~troglitazone is metabolized by CYP3A4 to form quinone~~ and
	~~quinone-methide products. Furthermore, oxidative cleavage~~ of ~~thiazolidinedione ring results in a reactive~~
	~~sulfenic acid metabolite that also contains an isocyanate moiety [6]~~.

	~~REFERENCES~~		The alert list is not limited to directly acting substructures, such as planar polycyclic arenes, aromatic amines, quinones, N-nitro and N-nitroso groups, but also includes various fragments that may undergo biotransformation to reactive intermediates. As an example, troglizatone, a thiazolidinedione class antidiabetic drug, was classified by the FDA as a potent carcinogen and has since been withdrawn from the USA market. The carcinogenic effect of this drug is mediated by several reactive metabolites. In human liver microsomes, the chromane ring of troglitazone is metabolized by CYP3A4 to form quinone and quinone-methide products. Furthermore, oxidative cleavage of thiazolidinedione ring results in a reactive sulfenic acid metabolite that also contains an isocyanate moiety [6].<br />
			<br />

	[1] Matthews EJ et al. Regul Toxicol Pharmacol. 2007, 47, 115.		=== References ===
	[2] Gold LS et al. Toxicol Sci. 2005, 85, 747.		[1] Matthews EJ et al. Regul Toxicol Pharmacol. 2007, 47, 115.<br>
	[3] ChEMBL DB (https://www.ebi.ac.uk/chembldb). Target ID 206.		[2] Gold LS et al. Toxicol Sci. 2005, 85, 747.<br>
	[4] Didziapetris R et al. J Comput Aided Mol Des. 2010, 24, 891.		[3] ChEMBL DB (https://www.ebi.ac.uk/chembldb). Target ID 206.<br>
	[5] Didziapetris R et al. Toxicol Lett. 2008, 180, S152.		[4] Didziapetris R et al. J Comput Aided Mol Des. 2010, 24, 891.<br>
	[6] Mansuy D & Dansette PM. Arch Biochem Biophys. 2011, 507, 1745.		[5] Didziapetris R et al. Toxicol Lett. 2008, 180, S152.<br>
			[6] Mansuy D & Dansette PM. Arch Biochem Biophys. 2011, 507, 1745.<br>
	</div>		</div>
	</div>		</div>