P-gp Inhibitors - Revision history

Kirilas at 10:06, 15 June 2017

2017-06-15T10:06:36Z

← Older revision		Revision as of 10:06, 15 June 2017
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	<div class="mw-collapsible ~~mw-collapsed~~">		<div class="mw-collapsible">

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

Kirilas: /* Reference database */

2013-02-15T09:37:39Z

Reference database

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	===Reference database===		===Reference database===
	~~P-gp Specificity\~~P-gp DB ~~module contains a browsable database~~ of 2,290 compounds with experimental data related to their interactions with P-gp. Each compound in the DB is classified as a P-gp substrate or non-substrate (inconclusive or contradictive data are marked as Yes/No or No/Yes in the ''''Substrate''' field) and efficiency of P-gp mediated transport is provided for substrates. '''High efficiency''' describes compounds that are transported with the rate similar to the best substrates (vinblastine, daunorubicin, paclitaxel). Similarly, drugs comprising the database are classified according to their '''Inhibitor''' liability. '''Potency''' field denotes effectivity of P-gp inhibition, '''High potency''' representing compounds that inhibit P-gp as good as standard inhibitor verapamil or even better.		ACD/Percepta includes a browsable P-gp DB comprised of 2,290 compounds with experimental data related to their interactions with P-gp. Each compound in the DB is classified as a P-gp substrate or non-substrate (inconclusive or contradictive data are marked as Yes/No or No/Yes in the ''''Substrate''' field) and efficiency of P-gp mediated transport is provided for substrates. '''High efficiency''' describes compounds that are transported with the rate similar to the best substrates (vinblastine, daunorubicin, paclitaxel). Similarly, drugs comprising the database are classified according to their '''Inhibitor''' liability. '''Potency''' field denotes effectivity of P-gp inhibition, '''High potency''' representing compounds that inhibit P-gp as good as standard inhibitor verapamil or even better.

	In the '''Assays''' section, the methods that were used in the analysis of P-gp substrate/inhibitor specificity are listed:		In the '''Assays''' section, the methods that were used in the analysis of P-gp substrate/inhibitor specificity are listed:
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	* '''MDR reversion''' – sensitization of P-gp expressing cells to “MDR profile” drugs (taxanes, anthracyclines, vinca alkaloids)		* '''MDR reversion''' – sensitization of P-gp expressing cells to “MDR profile” drugs (taxanes, anthracyclines, vinca alkaloids)
	* '''P-gp ATPase modulation''' – activation or inhibition of P-gp ATPase. This assay does not differentiate P-gp substrates and inhibitors.		* '''P-gp ATPase modulation''' – activation or inhibition of P-gp ATPase. This assay does not differentiate P-gp substrates and inhibitors.


	===Model features & prediction accuracy===		===Model features & prediction accuracy===

Kirilas: /* Model features & prediction accuracy */

2013-02-15T09:34:55Z

Model features & prediction accuracy

← Older revision		Revision as of 09:34, 15 February 2013
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	===Model features & prediction accuracy===		===Model features & prediction accuracy===
	The predictive models of P-gp ~~inhibtion~~ were derived using GALAS (Global, Adjusted Locally According to Similarity) modeling methodology (please refer to [http://www.ncbi.nlm.nih.gov/pubmed/20373217] for more details).		The predictive models of P-gp inhibition were derived using GALAS (Global, Adjusted Locally According to Similarity) modeling methodology (please refer to [http://www.ncbi.nlm.nih.gov/pubmed/20373217] for more details).

	Each GALAS model consists of two parts:		Each GALAS model consists of two parts:

Kirilas: /* Model features & prediction accuracy */

2013-02-15T09:33:57Z

Model features & prediction accuracy

← Older revision		Revision as of 09:33, 15 February 2013
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	Reliability Index ranges from 0 to 1 (0 corresponds to a completely unreliable, and 1 - a highly reliable prediction) and serves as an indication whether a submitted compound falls within the Model Applicability Domain. Compounds obtaining predictions ''RI'' < 0.3 are considered outside of the Applicability Domain of the model.		Reliability Index ranges from 0 to 1 (0 corresponds to a completely unreliable, and 1 - a highly reliable prediction) and serves as an indication whether a submitted compound falls within the Model Applicability Domain. Compounds obtaining predictions ''RI'' < 0.3 are considered outside of the Applicability Domain of the model.
			<br><br>
	The presented method also forms the basis of model Trainability. 'Trainable model' methodology addresses the issue of the chemical space of ‘in-house’ libraries being considerably wider than that of publicly available data which results in limited applicability of most third-party QSARs for analysis of ‘in-house’ data. The ‘Training engine‘ makes appropriate corrections for systematic deviations produced by the baseline QSAR model based on analysis of similar compounds from the experimental data library. Expansion of this Self-training Library with user-defined experimental data for new compounds leads to instant improvement of prediction accuracy for the respective compound classes. Moreover, addition of 'in-house' data allows adapting the existing model to the particular experimental protocol used in your company and avoiding potential issues related to discrepancies between different experimental methods used for determination of drug interactions with P-gp.		The presented method also forms the basis of model Trainability. 'Trainable model' methodology addresses the issue of the chemical space of ‘in-house’ libraries being considerably wider than that of publicly available data which results in limited applicability of most third-party QSARs for analysis of ‘in-house’ data. The ‘Training engine‘ makes appropriate corrections for systematic deviations produced by the baseline QSAR model based on analysis of similar compounds from the experimental data library. Expansion of this Self-training Library with user-defined experimental data for new compounds leads to instant improvement of prediction accuracy for the respective compound classes. Moreover, addition of 'in-house' data allows adapting the existing model to the particular experimental protocol used in your company and avoiding potential issues related to discrepancies between different experimental methods used for determination of drug interactions with P-gp.

Kirilas: /* Model features & prediction accuracy */

2013-02-15T09:33:32Z

Model features & prediction accuracy

← Older revision		Revision as of 09:33, 15 February 2013
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	===Model features & prediction accuracy===		===Model features & prediction accuracy===
	The ~~model was developed with Algorithm Builder~~ using ~~a novel~~ methodology ~~consisting~~ of two parts:		The predictive models of P-gp inhibtion were derived using GALAS (Global, Adjusted Locally According to Similarity) modeling methodology (please refer to [http://www.ncbi.nlm.nih.gov/pubmed/20373217] for more details).
	* Global baseline statistical model employing binomial PLS with multiple bootstrapping using a predefined set of fragmental descriptors.
	* ~~Local~~ correction to baseline ~~prediction based on analysis of~~ experimental ~~data~~ for similar compounds.		Each GALAS model consists of two parts:
	~~The underlying~~ methodology ~~enables obtaining an intrinsic evaluation~~ of ~~prediction confidence~~ by the means of Reliability Index (RI) values calculated for each prediction. RI ranging from 0 to 1 serves as an indication whether a submitted compound falls within the Model Applicability Domain. Two criteria influence the calculation of Reliability Index of a prediction:		* Global baseline statistical model employing binomial PLS with multiple bootstrapping using a predefined set of fragmental descriptors, that reflects general trends in P-gp inhibitor specificity.
	* Similarity of ~~the analyzed molecule~~ to ~~compounds in~~ the ~~Self-~~training ~~Library~~ (prediction is unreliable if no similar compounds have been found ~~in the Library~~).		* Similarity-based routine that performs local correction of baseline predictions taking into account the differences between baseline and experimental values for the most similar training set compounds.
	* ~~Consistency~~ of experimental ~~data~~ for similar compounds (discrepant data for similar molecules lead to lower RI values).		<br>
			GALAS methodology also provides the basis for estimating reliability of predictions by the means of calculated Reliability Index (''RI'') value that takes into account:
			* Similarity of tested compound to the training set molecules (prediction is unreliable if no similar compounds have been found).
			* Consistence of experimental values and baseline model prediction for the most similar similar compounds from the training set (discrepant data for similar molecules, i.e. alternating P-gp inhibitors and non-inhibitors lead to lower ''RI'' values).

			Reliability Index ranges from 0 to 1 (0 corresponds to a completely unreliable, and 1 - a highly reliable prediction) and serves as an indication whether a submitted compound falls within the Model Applicability Domain. Compounds obtaining predictions ''RI'' < 0.3 are considered outside of the Applicability Domain of the model.

	The presented method also forms the basis of model Trainability. 'Trainable model' methodology addresses the issue of the chemical space of ‘in-house’ libraries being considerably wider than that of publicly available data which results in limited applicability of most third-party QSARs for analysis of ‘in-house’ data. The ‘Training engine‘ makes appropriate corrections for systematic deviations produced by the baseline QSAR model based on analysis of similar compounds from the experimental data library. Expansion of this Self-training Library with user-defined experimental data for new compounds leads to instant improvement of prediction accuracy for the respective compound classes. Moreover, addition of 'in-house' data allows adapting the existing model to the particular experimental protocol used in your company and avoiding potential issues related to discrepancies between different experimental methods used for determination of drug interactions with P-gp.		The presented method also forms the basis of model Trainability. 'Trainable model' methodology addresses the issue of the chemical space of ‘in-house’ libraries being considerably wider than that of publicly available data which results in limited applicability of most third-party QSARs for analysis of ‘in-house’ data. The ‘Training engine‘ makes appropriate corrections for systematic deviations produced by the baseline QSAR model based on analysis of similar compounds from the experimental data library. Expansion of this Self-training Library with user-defined experimental data for new compounds leads to instant improvement of prediction accuracy for the respective compound classes. Moreover, addition of 'in-house' data allows adapting the existing model to the particular experimental protocol used in your company and avoiding potential issues related to discrepancies between different experimental methods used for determination of drug interactions with P-gp.

Kristina: /* Features */

2012-05-29T13:25:05Z

Features

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	* The P-gp Inhibition prediction module uses two models to estimate the probability a compound inhibits P-gp and if so, is it a potent inhibitor?		* The P-gp Inhibition prediction module uses two models to estimate the probability a compound inhibits P-gp and if so, is it a potent inhibitor?
	* Additional knowledge-based model classifies compounds as P-gp inhibitors/non-inhibitors on the basis of relevant structural features and basic physicochemical parameters.		* Additional knowledge-based model classifies compounds as P-gp inhibitors/non-inhibitors on the basis of relevant structural features and basic physicochemical parameters.
	* Statistical algorithms calculate estimate the reliability of every prediction by means of Reliability Index calculation ~~(see more information about [[Trainable_Models#Overview\|Trainable Models]])~~		* Statistical algorithms calculate estimate the reliability of every prediction by means of Reliability Index calculation.
	* Displays experimental data for the 5 most similar compounds from '''P-gp DB'''		* Displays experimental data for the 5 most similar compounds from '''P-gp DB'''.
	* Training set consisted of >1,500 compounds. Reference data were compiled from more than 800 original publications.		* Training set consisted of >1,500 compounds. Reference data were compiled from more than 800 original publications.
	<br />		<br />

Kirilas: /* Features */

2012-05-25T09:01:54Z

Features

← Older revision		Revision as of 09:01, 25 May 2012
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	===Features===		===Features===
	* The P-gp Inhibition prediction module uses two models to estimate the probability a compound inhibits P-gp and if so, is it a potent inhibitor?		* The P-gp Inhibition prediction module uses two models to estimate the probability a compound inhibits P-gp and if so, is it a potent inhibitor?
	* Additional knowledge-based ~~models classify~~ compounds as P-gp inhibitors/non-inhibitors ~~using rules based~~ on ~~ionization, molecular size~~ and ~~biological class of compounds (analogs of peptides, alkaloids, anthracyclines, etc~~.)		* Additional knowledge-based model classifies compounds as P-gp inhibitors/non-inhibitors on the basis of relevant structural features and basic physicochemical parameters.
	* Statistical algorithms calculate estimate the reliability of every prediction by means of Reliability Index calculation (see more information about [[Trainable_Models#Overview\|Trainable Models]])		* Statistical algorithms calculate estimate the reliability of every prediction by means of Reliability Index calculation (see more information about [[Trainable_Models#Overview\|Trainable Models]])
	* Displays experimental data for the 5 most similar compounds from '''P-gp DB'''		* Displays experimental data for the 5 most similar compounds from '''P-gp DB'''

Kirilas: /* Interface */

2012-05-25T08:59:29Z

Interface

← Older revision		Revision as of 08:59, 25 May 2012
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	# Probability of the compound being a P-gp inhibitor		# Probability of the compound being a P-gp inhibitor
	# Indication of the prediction reliability (Reliability Index value)		# Indication of the prediction reliability (Reliability Index value)
			# "Configure" and "Train" buttons provide the means to select the training library for use in calculations and to add new data to that library. The name of the currently selected library is indicated with italic font.
	# Probability of the compound being a potent P-gp inhibitor with K<sub>i</sub> < 1 uM		# Probability of the compound being a potent P-gp inhibitor with K<sub>i</sub> < 1 uM
	# Switch to the knowledge based classification model		# Switch to the knowledge based classification model

Kirilas: /* Features */

2012-05-25T08:57:50Z

Features

← Older revision		Revision as of 08:57, 25 May 2012
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	===Features===		===Features===
	* The P-gp ~~substrate~~ prediction module uses two models to ~~calculate the probability a compound is a P-gp substrate and if so, is it a high-affinity substrate?~~		* The P-gp Inhibition prediction module uses two models to estimate the probability a compound inhibits P-gp and if so, is it a potent inhibitor?
	* The P-gp Inhibition prediction module determines the probability a compound inhibits P-gp and if so, is it a potent inhibitor?		* Additional knowledge-based models classify compounds as P-gp inhibitors/non-inhibitors using rules based on ionization, molecular size and biological class of compounds (analogs of peptides, alkaloids, anthracyclines, etc.)
	* Additional knowledge-based models classify compounds as ~~P-gp substrates/non-substrates, or~~ P-gp inhibitors/non-inhibitors using rules based on ionization, molecular size and biological class of compounds (analogs of peptides, alkaloids, anthracyclines, etc.)		* Statistical algorithms calculate estimate the reliability of every prediction by means of Reliability Index calculation (see more information about [[Trainable_Models#Overview\|Trainable Models]])
	* Statistical algorithms calculate ~~the probabilities of compound being P-gp substrate or inhibitor and~~ estimate the reliability of every prediction by means of Reliability Index calculation (see more information about [[Trainable_Models#Overview\|Trainable Models]])
	* Displays experimental data for the 5 most similar compounds from '''P-gp DB'''		* Displays experimental data for the 5 most similar compounds from '''P-gp DB'''
	* Training set consisted ~~of >1,000 compounds for P-gp substrate algorithm, and~~ of >1,500 compounds ~~for P-gp inhibitor algorithm~~. Reference data were compiled from more than 800 original publications.		* Training set consisted of >1,500 compounds. Reference data were compiled from more than 800 original publications.
	<br />		<br />

Kirilas: /* Interface */

2012-05-25T08:56:09Z

Interface

← Older revision		Revision as of 08:56, 25 May 2012
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	# Reliability of prediction (low, medium, high)		# Reliability of prediction (low, medium, high)
	# Switch to the probabilistic model		# Switch to the probabilistic model
	# ~~Up to~~ 5 most similar structures in the P-gp DB and references		# View 5 most similar structures in the '''P-gp DB''' and references
	<br />		<br />

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	<br />		<br />

	# Probability~~, ranging from 0 (definite non~~-inhibitor~~) to 1 (definite inhibitor)~~		# Probability of the compound being a P-gp inhibitor
	# Indication of the ~~P-gp Inhibitor probability~~ prediction reliability ~~along with the~~ Reliability Index value		# Indication of the prediction reliability (Reliability Index value)
	# Probability ~~between 0 (definitely not a high-affinity inhibitor) and 1 (definitely a high-affinity inhibitor)~~		# Probability of the compound being a potent P-gp inhibitor with K<sub>i</sub> < 1 uM
	~~# Indication~~ of the potent P-gp ~~inhibition probability prediction reliability along~~ with ~~the Reliability Index value~~		# Switch to the knowledge based classification model
	# Switch to the knowledge based ~~C-SAR~~ model
	# View 5 most similar structures in the '''P-gp DB''' and references		# View 5 most similar structures in the '''P-gp DB''' and references
	<br />		<br />