成药性评价软件ADMET Predictor 6.5

Simulations Plus,Inc.

? NEW! CYP-P450-mediated kinetic parameter models (K m , V max , and CL int ) - per atom! ? NEW! Confidence estimates for classification model predictions ? NEW! Improved Miner 3D graphing capabilities

? NEW! Human skin permeability and air-water partition coefficient (Henry’s Law Constant) models ? NEW! License management with FlexNet Manager? from Flexera Software ? Updated Ames mutagenicity models and associated TOX_MUT_Risk rules

? Rapid, accurate prediction of ADMET properties – over 140 physicochemical, metabolic, and toxicity properties

? Predict pKa, lipophilicity, permeability, solubility, blood-brain barrier penetration, and more ? Cytochrome P450 metabolic site predictions for nine isoforms ? UGT substrate classification for nine isoforms

? Prediction of toxicity endpoints - hERG inhibition, estrogen and androgen receptor binding, phospholipidosis, carcinogenicity, and more!

? ADMET Risk? - potential obstacles for a compound to be successfully developed as a drug ? ADMET Modeler? - quickly and easily create your own models using over 330 atomic and molecular descriptors

A D M E T P r e d i c t o

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Predict with confidence...

What is ADMET Predictor?

NEW!CYP Kinetic Parameter Models

ADMET Predictor is an advanced computer program that enables researchers to rapidly predict a large number of ADMET properties from molecular structure. Its predictive models are grouped into Metabolism, Physicochemical and

Biopharmaceutical, Toxicity, and Simulation modules. A compound’s liabilities can be quickly assessed using our ADMET Risk scoring feature that combines 24 predicted properties

associated with absorption, metabolism, and toxicity. ADMET Predictor models have been consistently ranked as the most accurate in independent published comparisons.1-4The ADMET Modeler module enables you to build high-quality QSPR models using state-of-the-art machine learning algorithms and unique atomic and molecular descriptors.

Atomic site models for K m , V max , and CL int were created for 5 recombinant CYPs (1A2, 2C9, 2C19, 2D6, and 3A4) and 3A4 human liver microsome

? 18 total models

?K m , V max , and CL int are calculated per metabolized atom

1. Dearden JC. “In silico prediction of aqueous solubility”. Expert Opin Drug Discovery 2006; 1 (1): 31-5

2.

2. Tetko IV and Poda GI. in “Molecular Drug Properties: Measurement and Prediction.” ed. Mannhold R., Weinheim, Germany, Wiley-VCH: 381-406. (2007).

3. Mannhold R, Poda GI. Ostermann C, Tetko IV. “Calculation of Molecular Lipophilicity: State of the Art and Comparison Methods.” J Pharm Sci. 2008; 98 (3): 8

4.4. Oyarzabal J, Pastor J, Howe TJ. “Optimizing the Performance of In Silico ADMET General Models According to Local Requirements”; J. Chem. Model 2009; 49 (11): 2572.

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NEW!Confidence Estimates

ADMET Predictor now shows confidence estimate values (maximum value of 99% and a minimum of 50%) for selected classification models. We have created a unique and practical approach to evaluate the uncertainty in predictive classification that is based on the degree of concordance among the individual QSAR networks in an ensemble model. All of the networks in a well-trained ensemble will usually agree when the ensemble classification is correct. They are more likely to disagree in cases where the ensemble classification is incorrect, so the frequency of misclassification tends to be higher when there is disagreement among networks. For our initial release of this exciting feature, the confidence estimate is offset in parentheses for these models and users can sort on those predictions. We plan to include the ability to generate confidence values for new models into ADMET Modeler in future releases and to incorporate confidence values into the graphical output.

NEW!Miner 3D Enhancements

Multiple charts in one window

Figure caption: The green bars are the total number of predictions and the red bars are the incorrect predictions.The confidence estimate is lowest when the percent of

incorrect predictions is largest.

% Confidence

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NEW!Miner 3D Enhancements continued

NEW!Model of Human Skin Permeability

3D Surface Charts

2D Charts are also available

The skin permeability rate of a compound is important in the:

? Pharmaceutical industry for transdermal drug delivery ? Cosmetic industry for application of lotions

? Environmental and occupational toxicological risk assessment of materials

We have developed an artificial neural network ensemble model to predict Kp from a 2D molecular structure.

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O b s e r v e d l o g (P s k i n ) [c m /s e c x 10 7]

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NEW!Model of Air-Water Partition

Henry’s Law Constant (HLC) is a key physical property that represents the air-water equilibrium partition coefficient and reflects the relative volatility of the compound. Our S+logHLC model predicts HLC in units of atm*m3/mol at 25°C.

NEW!License management with FlexNet Manager from Flexera Software

ADMET Predictor 6.5 uses the FlexNet Manager software for license management. This offers several advantages over our previous method:

? Program files are not split between server and client machines

? Improved license administration and handling between applications

- Centralizes license structure: multiple vendor license files can be hosted by the same server

- Monitor license status and usage details

- Control access to the Flexera license server by configuring the communications port

- Efficiently perform license server administration (start, stop, restart license servers, etc.) in real time ? Ease of implementation for companies already using Flexera-based applications

? Ease of obtaining a license

- No need to call to exchange licensing codes

- Licenses will be made available through download

? Reduces license denials by allowing server redundancies

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Updated Models of Ames Mutagenicity

User Interface

We recurated our Ames mutagenicity data sets and rebuilt all ten models.

? Model values and descriptors are calculated on demand

? Property and descriptor columns are associated with their appropriate module and display is controlled via “tabs” at bottom of page

?

Custom buttons that display user-specified columns can be added

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User Interface continued

Physicochemical and Biopharmaceutical Module

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The Physicochemical and Biopharmaceutical Module in ADMET Predictor allows researchers to access several industry-leading predictive models:

? Enhanced pKa (ionization constants; a highly sophisticated multiprotic model)? logP (two models: artificial neural network ensemble and Moriguchi)

? logD (estimation of octanol-water distribution coefficient at user-defined pH)? Water solubility at user-specified pH

? Native solubility (solubility in pure water @ 25°C)? Native pH at saturation in pure water @ 25°C ? Intrinsic solubility in pure water @ 25°C ? Salt solubility factor @ 25°C

? Biorelevant solubility in FaSSIF , FeSSIF and FaSSGF ? Supersaturation ratio

? Two blood-brain barrier (BBB) models (a qualitative permeability model and a blood-brain partition coefficient model)? Human effective permeability in jejunum (Peff)? MDCK apparent permeability (Papp)? Permeability through rabbit cornea ? NEW!Human skin permeability

? Diffusion coefficient in water (Hayduk-Laudie formula)? Molal volume (Schroeder formula)? Human volume of distribution

? Human plasma protein binding as percent unbound ? Blood-to-plasma concentration ratio

? Inhibition of HIV integrase mediated via strand transfer and 3' processing ? NEW!Air-water partition coefficient (Henry’s Law Constant)

Metabolism Module

ADMET Predictor’s Metabolism Module contains:

? Models to classify compounds as substrates/nonsubstrate for 9 CYP isoforms

? Models to classify compounds as general inhibitors of 5 CYP isoforms

? Qualitative models to classify compounds as CYP3A4 inhibitors with either midazolam or testosterone as the substrate ? Quantitative Ki models for CYP3A4 with either midazolam or testosterone as the substrate

? Models to classify compounds as human uridine 5’-diphosphate-glucuronosyltransferase (UGT) substrates for 9 UGT isoforms ? Sites of metabolism models – if a compound is predicted to be a substrate of a particular CYP, then the sites of metabolism will predicted

? Metabolites can visualized in MedChem Designer

- Structures can be modified in MedChem Designer in order to quickly determine how changes

affect metabolic sites and kinetic parameters

predicted to be a site of metabolism

? NEW!Kinetic parameter models – K m, V max, and CL int are predicted for each atom

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Metabolism Module

continued

for 2C19

Predicted sites of metabolism are highlighted with red mesh circles. The

methyl group of the methylamine has the highest score (992) but CH of the

tetrahydronaphthalene also scores high (703) and is predicted to be a site

of metabolism. Predicted kinetic parameters (K m, V max, and CL int) are shown

for each predicted site of metabolism.

Both sites of sertraline are predicted to be hydroxylated. The hemiaminals

(Sertraline - M3 and Sertraline – M1) are predicted to decompose to the

primary amine (Sertraline - M4) and the ketone (Sertraline – M2).

992

703

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Examples of Metabolic Site Predictions — from the Metabolism Module

Our metabolic site prediction models were trained on

our own extensively curated and updated version of

the Accelrys Metabolite database along with published datasets of sites of metabolism and general review articles. The data underwent substantial curation based on information contained in original literature citations as well as additional references found in the course of this work. These curation updates included corrections to reactant and product structures, CYP assignments, and often additional sites identified in more recent publications. Some site assignments were removed when further literature investigations revealed the particular reaction

in question was mediated by a different CYP or even a non-CYP enzyme. The scientific literature contains further discussion on the issue of false positives in metabolic site models (Hennemann; 2009). A false positive corresponds to a model prediction of a metabolite which has not been observed experimentally. This may not mean the metabolite is not produced; only that it has not been observed or reported. These two statements are not the same! In many cases, unknown metabolites are observed, but their chemical structures are not determined. So, a false positive may correspond to such an unidentified metabolite. In other cases, experimenters are interested in a specific metabolite due to its pharmacologic or toxic properties. They may determine which CYP P450 isoforms are responsible for its formation, but are not interested in other metabolites that may be produced by those CYPs. Of course, if no metabolite is reported at a particular site, it is common to report and consequentially accept as fact that the particular site is not metabolized. Our models have uncovered numerous such EXPERIMENTAL false positives. The validation of our regioselectivity models on a few concrete examples and the illustration of several factors influencing their predictivity are shown below and on

the next few pages. In the examples shown we have deliberately chosen compounds predominantly present

in the external test sets of our models.

The first example is terbuthylazine. Experimentally, Lang observed that terbuthylazine was primarily N-dealkylated by 5 CYPs. (Lang; 1997) Only 1A2 attacked the t-butyl group. The selectivity of our predictions confirmed the observed trend, although we predict that terbuthylazine is not a substrate for 2D6.

terbuthylazine

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Why we think our models are robust

In October 2010, early in our model development, we mined available electronic databases of observed metabolic reactions "as is". In some cases, our preliminary models disagreed with electronic databases. Later, after consulting with the original or more recent literature, our predictions turned out to be correct. Thus, our models showed inaccuracies and omissions in the databases and often lead experiments. Two cases are discussed below. The example molecules were in the training sets of our preliminary models. In each case, in spite of the incorrect training data submitted to ADMET Modeler, the ANNE (Artificial Neural Network Ensemble) training algorithm refused to accept the wrong sites. This innate resistance to overtraining is the reason we believe in the robustness of our models.

Metoprolol was initially reported to have two atoms (O-methyl and benzyl-C) metabolized by 2D6 (Mautz;1995). A third site, N-isopropyl, was submitted to our preliminary CYP2D6 model as negative. Regardless, the model was stubbornly marking this atom as a center of 2D6 attack giving it the highest score! Puzzled by this egregious case of a "false positive", we conducted a literature search and found newer results (Hayhurst;

2001, Colbourne; 1998). These results not only confirmed N-isopropyl as true positive, but that the rate of N-deiso-propylation competes with the rate of O-demethylation!The 2D6 model was later retrained with the correct assignments for metoprolol.

Original false positive was later confirmed as a true positive

Original experimental sites

An example at the opposite end, that of a "false negative" that turned out to be a true negative, is

dextromethorphan. The setting is the same: October 2010,preliminary 2D6 model, sites of dextromethorphan taken "as is" from the commercial database. The three reported sites of 2D6 metabolism included O-methyl, N-methyl, and one of the ring carbons (Matsunaga; 2009). Here, our model refused to accept the marked ring carbon as a site of 2D6 attack - giving it a very low score, even though this atom was submitted in training as positive. A closer inspection of the Matsunaga paper revealed that the indicated carbon is not metabolized by the native CYP2D6, but by its unusual mutant,

CYP2D6.49, occurring in some members of the Japanese population. Quoting the authors: "CYP2D6.49 formed

a 7-hydroxydextromethorphan, with a roughly similar V(max)/K(m) value to that of O-demethylation." This crucial fact was omitted from the commercial database.

Experimental sites

False Negative?

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Why we think our models are robust continued

The last example illustrates the issue of false positives predicted not only by ours, but all in silico models of regioselectivity. A good example is found in the case of chlorpromazine. Our CYP1A2-mediated predicted sites of metabolism and those reported (Yoshii; 2000) are shown below:

As of 2009, the only reported metabolite (of which we were aware) was hydroxylation of C-7, identified in the above diagram, mediated by 1A2 and 2D6 (Yoshii; 2000)However, the 1A2 dataset has two

similar structures, perazine and promazine, shown below with arrows showing their reported CYP1A2-mediated sites of metabolism (Wójcikowski; 2004, Wójcikowski; 2003) -illustrated above right.

Based on these results, it is not unreasonable for the 1A2model to predict sites of metabolism for the sulfur and N-methyl carbons of chlorpromazine. Such assignments, as of 2009, would be considered false positives! However,in late 2010, both N-demethylation and sulfoxidation of chlorpromazine were reported as metabolites formed by CYP1A2 (Wójcikowski; 2010). Hence, false positives in the model in 2009 became true positives in 2010! Thus, only one site out of four remains as a “false positive”

prediction, perhaps pending future verification.

Predicted and observed (in 2009) sites of chlorpromazine

metabolism by CYP1A2.

Predicted and observed (in 2010) sites of chlorpromazine metabolism by CYP1A2

References

Obach RS, Cox LM, Tremaine LM. Drug Metab Dispos. 2005; 33:https://www.360docs.net/doc/f79081785.html,ng DH, Rettie AE, Boecker RH. Chem Res Tox. 1997;10:1037-1044.

Usmani KA, Karoly ED, Hodgson E, Rose RL. Drug Metab Dispos. 2004; 32:333-339.Mautz DS, Nelson WL, Shen DD. Drug Metab Dispos. 1995; 23:513-517.

Hayhurst GP , Harlow J, Chowdry J, Gross E, Hilton E, Lennard MS, Tucker GT, Ellis SW. Biochem J. 2001; 355:373-379.Colbourne PD, Baker GB, Coutts RT. ISSX Proceedings, 5th International ISSX Meeting, Cairns, Australia, Oct 27-29, 1998; International Society for the Study of Xenobiotics: Cabin John, MD, 1998; Vol. 13, 58.

Matsunaga M, Yamazaki H, Kiyotani K, Iwano S, Saruwatari J, Nakagawa K, Soyama A, Ozawa S, Sawada J, Kashiyama E, Kinoshita M, Kamataki T. Drug Metab Dispos. 2009; 37:699-701.

Yoshii K, Kobayashi K, Tsumuji M, Tani M, Shimada N, and Chiba K. Life Sciences 2000; 67:175-184.Wójcikowski J, Pichard-Garcia L, Maurel P , Daniel WA. Br J Pharmacol. 2003; 138:1465-1474.Wójcikowski J, Pichard-Garcia L, Maurel P , Daniel WA. Eur Neuropsychopharmacol. 2004; 14:199-208.Wójcikowski J, Boksa J, Daniel WA. Biochem Pharmacol. 2010; 80:1252-1259.

Hennemann M, Friedl A, Lobell M, Keldenich J, Hillisch A, Clark T, and G?ller AH. Chem Med Chem. 2009; 4:657-669

Simulation Module

The Simulation Module uses selected ADMET estimations and doses in the GastroPlus? ACAT? model in humans to simu-late passive absorption. A deterministic, region-dependent sys-tem of differential equations is solved assuming that the drug undergoes instant dissolution to maintain saturation if sufficient dose is available to saturate the compartment:

? Human fraction absorbed (by simulation at 1 mg,

10 mg, 100 mg, and 1000 mg dose levels)

? SimDOSE - assess the likely dose needed to achieve

a therapeutic concentration at steady state

Customizeable ADMET Risk Filters

A drug molecule’s potential liability can be quickly assessed using our ADMET Risk scoring function. Property predictions were calculated for a subset of greater

than 2,000 compounds from the World Drug Index (WDI). Cutoff values for each property were determined by considering the overall distribution of the property and setting the threshold so that 10% of the WDI compounds would be removed. The score is increased by one for each prediction that falls outside of the acceptable boundaries:

? Absorption Risk - Risk of low absorption from an

oral dose

- Incorporates size, charge, number of rotatable

bonds, H-bond acceptors and donors,

lipophilicity, solubility and permeability

? CYP Risk - Risk of high clearance or possible

CYP inhibition

? TOX Risk - Risk of overall toxicity

- hERG inhibition, acute rat or mouse toxicity,

hepatotoxicity, and mutagenicity ? The global ADMET Risk score summarizes all of the above risks into one consensus score

? Scoring functions can be customized to incorporate your preferred cutoffs or properties

Toxicity Module

ADMET Predictor’s Toxicity Module contains models that predict toxicity relative to food products, pharmaceuticals, and environmental chemicals such as pesticides:

? hERG-encoded K+channel affinity as pIC50

? Estrogen receptor toxicity

? Androgen receptor toxicity

? Maximum Recommended Therapeutic Dose (MRTD)? Chromosomal aberrations

? Phospholipidosis

? Qualitative filter of mutagenicity in 5 strains of

Salmonella bacteria with and without microsomal

activation (10 models)? Carcinogenicity in rats as TD50

? Carcinogenicity in mice as TD50

? Human liver adverse effects

? Fathead minnow lethality as LC50

? Acute toxicity in Daphnia magna (water fleas) as pLC50? Acute lethal toxicity in rat as LD50

? Allergenic skin sensitization

? Environmental bioconcentration factor

? Allergenic respiratory sensitization

? Reproductive/developmental toxicity

? Acute toxicity in Tetrahymena pyriformis as IGC50

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Other Features

Descriptor Sensitivity Analysis:

? Artificial neural network models are no longer black boxes. In fact, greater insight is provided by descriptor sensitivities that are unique to each molecule

? Interpretation of model predictions in structural terms for guided design of molecule’s derivatives with desired ADMET properties

Run Modes:

? Interactive ? Batch

? Command line

? Pipeline Pilot? component Accepted Input Formats:? SMILES ? SDF and RDF ? MOL

Associative modeling:

? Extending the scope of ADMET predictions using your own data, without the need to retrain the

underlying models

Descriptor Sensitivity Analysis

ADMET Modeler Module: for automated and seamless QSPR model building

ADMET Modeler automates the difficult and tedious process of making high-quality predictive structure-property models from sets of experimental data. It works seamlessly with ADMET Predictor structural descriptors as its inputs, and appends the selected final model back to ADMET Predictor as an additional predicted property.

The following modeling methods are offered by ADMET Modeler:

? Kohonen Self-Organizing Maps

? Artificial Neural Network Ensembles for regression and binary classification

? Support Vector Machine Ensembles for regression and classification

? Kernel Partial Least Squares and Ordinary PLS for regression

? Multiple Linear Regression ? SALI analysis

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An illustrated case example - ADMET Modeler

Model Building

The following case study is typical of situations encountered in the modeling of real data, in which one is trying to build ANNE’s that predict a property of interest using descriptors generated from molecular structures. ADMET Modeler can build models from descriptors generated by ADMET Predictor or by any other source, so long as the data is available in tab-delimited, ASCII format.

The screenshot above shows the Molecular Data spreadsheet after opening a SMILES file containing 371 structures plus experimentally determined pharmacokinetic Volumes of Distribution in human (Vdss (L/kg)). Such a complex

biological property is quite difficult to model. Nevertheless,a useful predictive model for studying variability trends can be built with ADMET Modeler. After the data is loaded,one calculates predictive models and descriptors by

selecting “Calculate ADMET Properties” from the “Calculate”pull-down menu.

The “Basic Modeler Settings” dialog box (above right), is one of two dialog boxes used to create models. First, one chooses VDss( L/kg)as the dependent variable in the “Basic Modeler Settings” spreadsheet. The program already preselects all ADMET Predictor descriptors in the right panel. The Logify checkbox asks the program to calculate the decimal logarithm of each raw measurement prior to modeling. Each button in the left panel initiates a separate modeling method: Kohonen Mapping (for compound clustering in descriptor space only), regression by Artificial Neural Network Ensembles (ANNE), Support Vector Machine Ensembles (SVME), Kernel Partial Least Squares (KPLS, includes ordinary PLS), and Multiple Linear Regression (MLR). Specific parameters for each available modeling method are accessible through the “Adv.

Modeler Settings”tab. In this run, Input Gradient is chosen as the descriptor selection method and the maximum number of descriptors is limited to 131.

The ANNE button is clicked to initiate the model building procedure. After internal filters remove invariant and highly correlated descriptors, 165 are left. This descriptor space becomes the playing field for automatic data division into the training plus verification (297 compounds), and external test (74 compounds) subsets by Kohonen map clustering, which is the default. ANN ensembles are then trained for different network architectures. With default settings the modeling run involves training 165 neural networks and subsequent automatic selection of the 33best networks for each of the 30 architectures. The run takes about 5 minutes on a laptop PC and

produces a grid of results where one cell represents one ensemble. A variety of statistics can be used to assess each data set (training (black), verification (blue), and test (red)) of each ensemble. The program also offers automatic selection of the best ensemble signified by the green cell background. Blank entries in the table correspond to ANNE architectures that were skipped

because their complexity put them beneath a user-defined acceptable data-parameter ratio. The ensemble model containing 20 descriptors and 4 neurons appears to have

the best all-around statistics.

Basic Modeler Settings dialog box

42505 10th Street West ? Lancaster, CA 93534 ? U.S.A. ? phone: 661-723-7723 ? fax: 661-723-5524e-mail: info@https://www.360docs.net/doc/f79081785.html, ? web site: https://www.360docs.net/doc/f79081785.html, ? NASDAQ:SLP

rev. 5/22/2013

The performance of the 20 descriptors/4 neurons model is illustrated in the plot below. The blue circles denote compounds belonging to the training/verification pool, and the red circles indicate compounds belonging

to the external test set. Clicking on a circle displays the molecule name, structure, and experimental and predicted values. This feature is useful for the rapid identification of outliers.

By clicking the Export Current Ensemble as New Model, the chosen ANNE model is then easily appended to the array of ADMET Predictor models. The whole procedure of model

creation from start to finish takes only 10 minutes.

Simulations Plus,Inc.

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