Prediction of Drug Metabolism: In Silico and Laboratory ...including all those described in “Testa...

Prediction of Drug Metabolism: In Silico and Laboratory Perspectives from Two Different Angles

F. Lombardo and M. K. Dennehy, NIBR, Metabolism and Pharmacokinetics, Cambridge

NEDMDG meeting, June 10 2009

In Silico Prediction of Metabolism/ NEDMDG/ June 10 2009

Introduction: a field alive and..kicking!

Introduction

Why predicting?

What?

How?

Meteor

Examples

MetaSite

Examples

Conclusions

• An overview that highlights the advantages and the feasibility of metabolism prediction.

• A classification of the goals in predicting metabolism.

• A classification of available methods and appropriate references.

• A brief description of two well-known methods: Meteor and MetaSite.• Examples of both methods.

• Some personal experience and literature reports.

• Some stimulus for discussion.


Why predicting metabolism?

Why asking? You might say…isn’t it obvious as for any predictive attempt?

Consider:1. Cost of drugs, reagents, instrumentation.

2. Time.

3. Labor intensive work.

4. Possibility of prioritizing compounds even before synthesis.

5. A complement to “in cerebro” work.

Introduction

Why predicting?

What?

How?

Meteor

Examples

MetaSite

Examples

Conclusions


What are the goals of these predictions?(Testa and Soine, 2003)

Introduction

Why predicting?

What?

How?

Meteor

Examples

MetaSite

Examples

Conclusions

A list of all reasonable metabolites of a given compound.

Same as above, organized in a metabolic tree.

Same as above, plus a warning for reactive/adduct-forming metabolites.

Same as above, plus a probability of formation based on molecular factors, acting as a filter against improbable metabolites

Same as above, plus a probability of formation under different biological conditions.

Com

plex

ity


And how?

Introduction

Why predicting?

What?

How?

Meteor

Examples

MetaSite

Examples

Conclusions

A classification of approaches: (Zhou et al. 2006; see also Testa and Soine 2003)

1. Molecular orbital calculations. Based on energy necessary to abstract a H-atom. Does not consider specific ligand-enzyme interactions and therefore specificity (Jones et al. 2002; Korzekwa et al. 1990; 1996; Singh, et al. 2003)

2. Molecular docking/scoring functions. Optimizes ligand-enzyme to estimate the binding mode. Predictions rely heavily on a well defined pharmacophore of the active site. (Kirton et al. 2002, Lewis et al. 1996, 2004)

3. Data mining, a probabilistic scoring method based on analysis of structure corresponding to the fragments stored in databases. (Arnby, et al. 2005; Boyer, et al. 2007; Meteor-Lhasa; MetabolExpert-CompuDrug; MetaPC-MultiCase; Metadrug-GeneGo).


And how?

Introduction

Why predicting?

What?

How?

Meteor

Examples

MetaSite

Examples

Conclusion

A classification of approaches: (Zhou et al. 2006; see also Testa and Soine 2003)

4. Reactivity-docking models. Based on binding, activation energy and spatial arrangement. (De Groot et al. 1999, 2002; Park and Harris 2003; Jung et al 2008; Oh et al 2008).

5. GRID-molecular interaction fields (MIFs). Similarity to group 4. Implemented in Metasite (Zamora et al. 2003 (2C9); Cruciani et al. 2005; Zhou et al. 2006 (3A4); Afzelius et al. 2007; Ahlstroem 2007a and 2007b).

6. QSAR/QSPR empirical models. Based on training set (Shen et al.2003; Sheridan et al. 2007)


Meteor: how does it work?

Introduction

Why predicting?

What?

How?

Meteor

Examples

MetaSite

Examples

Conclusions

A Biotransformation Dictionary:• Version 6.0 contained >200 mammalians reactions

including all those described in “Testa & Jenner, Drug Metabolism: Chemical and Biochemical Aspects, 1976".

• Version 11.0 contains 392 reactions.

• Version 11.0 is available and it should incorporate many of the hydrolytic reactions reviewed in "Testa and Mayer, Hydrolysis in Drug and Prodrug Metabolism: Chemistry, Biochemistry and Enzymology, Wiley-VHCA, 2003".



Introduction

Why predicting?

What?

How?

Meteor

Examples

MetaSite

Examples

Conclusions

Rules : 1008 rules of absolute and relative reasoning.

Rules of absolute reasoning evaluate the likelihood of a biotransformation taking place; levels of likelihood are

ProbableProbable, PlausiblePlausible, EquivocalEquivocal, DoubtedDoubted and ImprobableImprobable.

Rules of relative reasoning assign priorities to potentially competing reactions (e.g., primary alcohols are oxidized in preference to secondary alcohols). Equal priority is assigned when no preference is known.

A Reasoning Engine which uses the Logic of Argumentation

to construct arguments for and against a hypothesis.



Predictions are made and supported on the basis of:

• Mechanistic rationale.

• Literature references.

• Information about factors that make each biotransformation more or less likely.

• Comments from Lhasa’s scientists/advisors.


Some false positives possibly resulting from incomplete molecular restrictions:

N-Dealkylation

N

NN

O NH

OHNH

O

OH

indinavir

OOH

O

S

H

N

S

O

N

H

Homapatrilat

probableplausible

equivocal

Slide courtesy of Prof. Testa


Predicted by METEOR Seen experimentallyA1 Plausible: Amide hydrolysis YESA2 Equivocal: Thiol methylation YES

Predicted by METEORmA3 Probable: Benzylic hydroxylation NOmA4 Probable: N-Dealkylation NOmA5 Probable: N-Dealkylation NOmA6 Plausible : p-Phenol formation NOmA7 Equivocal; o-Phenol formation NO

mA7

mA6

mA5mA4

mA3

A2O

OH

O

S

H

N

S

O

N

H

H A1

OOH

O

SN

S

O

N

H

H

CH3

OOH

O

S

H

N

S

O

N

H

H

OH

SHNH2

O S

N

H

O

O

OHO

+

O

S

H

OH

OOH

N

S

O

N

H

H

H

+

OOH

O

S

H

N

S

O

N

H

H

OH

OOH

O

S

H

N

S

O

N

H

H

OH

O

S

H

N

S

O

NH

H

H O

O

OH

Slide courtesy of Prof. Testa


eA14

mA13 mA12 mA11 mA10

mA9

mA8

OOH

O

S

H

N

S

O

N

H

H

Predicted by METEOR Seen experimentallymA8 Plausible : Lactam hydrolysis NOmA9 Equivocal: Decarboxylation NOmA10 Probable : Acyl glucuronidation YES (as the S-methyl,

acyl glucuronide double conjugate)mA11 Equivocal: N-glucuronidation NOmA12 Equivocal: S-glucuronidation NOmA13 Equivocal: Taurine conjugation NO

Not predicted by METEOReA14 S-S-Cysteine conjugation YES

SH O

N

H

S

OH

O NH

H

OHO

O

S

H

N

S

O

N

H

H

OO-Gluc

O

S

H

N

S

O

N

H

H

OOH

O

S

H

N

S

O

N

H

GlucO

OH

O

S

Gluc

N

S

O

N

H

H

O

O

S

H

N

S

O

N

H

H

taurine

OOH

O

SN

S

O

N

H

H

SNHH

HOOC

Slide courtesy of Prof.Testa


MetaSite: how does it work?

First: it is assumed that the compound is a substrate(Work is in progress to predict whether it is or not)

The first step may be conceived as a “photographic negative”or a “complementary image” of the cavity of the enzyme.

It is created once for each enzyme, through GRID.

A similar calculation is performed for each compound and a correlogram is generated (“similarity”).

The reactivity is then calculated from stored BDE energy of fragments “modulated” or “corrected” to reflect the reactivity of the specific isoform.

Introduction

Why predicting?

What?

How?

Meteor

Examples

MetaSite

Examples

Conclusions


Working hypothesis in MetaSite

GRID depiction of 2C9 cavity (shape with H probe) with DRY probe and NH probe

Slide courtesy of Prof. Cruciani



OFeCYP

CYPPhe

CYPNH3

labile hydrogen

O

H

H

H

OH

HH

protein (pharmacophore distance) ≈ ligand (pharmacophore distance)




NH

O

O

NHOH

NH

O

O

NHOH

Medium reactivityHigh exposure

Low reactivityHigh exposure

Medium reactivityMedium exposure

MO calculation Recognition Global

NH

O

O

NHOH

Gaussian MetaSite algorithm PSM= Pr*Rs*RcypB3LYP 6-31G** AM1



MetaSite: a test at Novartis (B. Faller/A. Zimmerlin, NIBR Basel…more to come)

• In a test set of 45 compounds, 80% of the primary sites of metabolism were in the top 3 predictions.

• 17/45 compounds were known to undergo N-demethylation and, in about 65% of the cases MetaSitepredicted that tranformation. Likely to be the true main pathway when MetaSite clearly points at this mechanism.

• For the hydroxylation cases (28/45 compounds) the experimental site of metabolism was not within the 3 most likely predicted metabolites in about 30% of compounds.

• Deemed very useful in prioritizing metabolites in conjunction with LC-MS data that may not point to the precise location of hydroxylation.

Introduction

Why predicting?

What?

How?

Meteor

Examples

MetaSite

Examples

Conclusions


MetaSite:….another test at Novartis (Trunzer et al, JMC 2009)

Bepridil (m/z = 367)Introduction

Why predicting?

What?

How?

Meteor

Examples

MetaSite

Examples

Conclusions

PM – Predicted MetaboliteSRM – Selected Reaction MonitoringSRM1-1

Type of modification

With (1) or w/o (2) modification

Principal SoM % of timein top 3 ~80 in top 6 ~100at top correct ~50

http://pubs.acs.org/action/showImage?doi=10.1021%2Fjm8008663&iName=master.img-003.jpg&type=master


A look at the method of Jung et al (2008)

1. The binding-activation-space arrangement method of Jung et al has a similar predictivity (1A2 only) to MetaSite but it seems quite computationally involved and it may not offer advantages over MetaSite.

2. The approach seems similar to MetaSite but it seems that more steps and modeler intervention are required. “Automatable”?

3. The possibility of including or excluding atoms that have a solvent-accessible polar surface area ≥ 8 Å seems like a nice feature.

4. Examples reported on 12 rigid substrates (some shown for MetaSite in the preceding 2 slides) and has a high concordance with experiment (70% or 100% depending on whether the 1st or 2nd most important predicted metabolite is considered “correct”)


Some conclusions-Part 1

1. Meteor seems to be yield many false positives, while attempting to generate a more “global” picture of metabolic pathways.

2. Comments from several DMPK scientists and anecdotal reports seem in agreement in considering Meteor more as a library of “existing knowledge” than a “true predictor”, i.e. similar to the scientist’s intuition and knowledge. Some literature seems to support this notion and perhaps work needs to be done in “restricting” potential pathways.

3. Is there a comparison between Meteor and other predictive approaches? (Not aware of published reports) There are comparisons among reactivity-docking models.

Introduction

Why predicting?

What?

How?

Meteor

Examples

MetaSite

Examples

Conclusions


Some conclusions-Part 2

4. Metasite seems to predict quite well the oxidative metabolism transformation mediated by CYP450 (5 isoforms: 3A4, 2C9, 2C19, 2D6, 1A2). Widely used in industry.

5. Several reports (including presentations at the 2006 MDO conference on Oxidative Metabolism in Budapest) have shown MetaSite to have a high predictivity ( > 80% based on metabolite observed vs. predicted. Afzelius et al 2007)

6. Is the approach taken by MetaSite novel enough to explore “unusual” metabolic pathways and isoforms that may become available? (Your guess?)

7. A consortium has been formed among several pharmaceutical companies to improve its performance and expand its scope. NIBR is among the participants.

Introduction

Why predicting?

What?

How?

Meteor

Examples

MetaSite

Examples

Conclusions


Now….the present and the future(Making predictions is not difficult except when dealing with the future!)

Introduction

Why predicting?

What?

How?

Meteor

Examples

MetaSite

Examples

Conclusions

What is the future of modeling in ADME and specifically in the area of metabolic stability prediction? QSAR, recognition/reactivity, recognition/activation energy? QSAR/QSPR/SMR rely on large dataset of highly curated data and “neighbors”. Are recognition/reactivity plus “in cerebro” approaches the way to go? Plus database expert systems?Are electronic methods (DF, ab initio) here to stay in ADME? Perhaps but they do not seem to have a wide audience even though approaches and..CPUs have greatly improved. “Combinations” may be the way.What are the challenges facing us? Transporters and metabolism…passive diffusion phenomena (e.g. VDss ) are more “under control”.


Metabolite PredictionOverview of Examples

1. Meteor2. MetaSite3. MetCalc


MeteorOverview

Meteor generates metabolites using reasoning rules within a knowledge base containing biotransformations, intermediates, and reasoning rules.

Predictions take into account:

Lipophilicity

Prevalence of the biotransformation in the literature

Species

Relative likelihood of competing biotransformations

Sample Biotransformation


MeteorSelection of reasoning

MeteorOutput of Metabolites


Meteor Output.txt file used in MetCalc


MetaSiteOverview

MetaSite predicts metabolic soft-spots and generates metabolites purely using in silico computationsBased on orientation of substrate in enzyme active site and likelihood of reaction taking placeP450-mediated oxidative metabolism onlyNo knowledge base or training set


MetaSiteCompound & CYP selection

3A42D6 2C9


MetaSite OutputSubstrate Reactivity Correction


MetaSite OutputSubstrate & Cyp Reactivity Correction


MetaSite OutputCYP Results


MetaSiteSelection of biotransformations


MetaSite OutputPredicted Metabolites


MetaSite OutputPredicted Metabolites - Summary

MetaSite

MetaSite Methodology

Input Mass - Q-Toff

- Ion-TrapRaw data

MetaSitePeak Matching

And Met ID Ion-Trap

MetaSiteFragments for

metabolites


MetCalcInternal Excel Macro that generates masses of potential metabolites


MetCalcSample Biotransformations

in-vivoBiotransformati

on Number Biotransformation Name ΔMPhII_1 O-Methylation 14.0157PhII_2 N-Formyl 27.9949PhII_3 Acetylation 42.0106PhII_4 Conjugation with Glycine 57.0215PhII_5 Acetylation/hydroxylation 58.0055PhII_6 Conjugation of Hydrazines with Pyruvic Acid 70.0055PhII_7 Glycine conjugation/hydroxylation 73.0164PhII_8 Sulfation 79.9568PhII_9 Phosphorylation 79.9663PhII_10 Glycine conjugation/acid formation 86.9956PhII_11 Hydroxylation/Sulfation 95.9517PhII_12 Sulfation/Hydration 97.9674PhII_13 Conjugation of Carboxylic Acids with Taurine 107.0041PhII_16 Conjugation of Hydrazines with alpha-Ketoglutaric Acid 128.0110PhII_17 Conjugation of Carboxylic Acids with Glutamine 128.0586PhII_21 Addition of glutathione, cleavage from glutamine 176.0256PhII_22 Glucuronidation 176.0321PhII_23 Addition of glutathione + H2, cleavage from glutamine 178.0412PhII_25 Oxidation/Glucuronidation 192.0270PhII_26 Addition of glutation and water, cleavage from glutamine 194.0362PhII_27 Hydration/Glucuronidation 194.0427PhII_28 Carboxylation/Glucuruonidation 206.0062PhII_29 Carbamoyl Glucuronides from Amines 220.0219PhII_30 Addition of glutathione, cleavage from glycine 248.0467PhII_31 Addition of glutathione + H2, cleavage from glycine 250.0624PhII_32 Glucuronidation/Sulfation 255.9889PhII_33 Addition of glutathione and water, cleavage from glycine 266.0573PhII_34 Glutathione(GSH) conjugation 305.0682PhII_35 Glutathione-Addition 307.0838PhII_36 GSH/Oxidation 321.0631PhII_37 Addition of glutathione and water 323.0788PhII_38 Glutathione(GSH) conjugation/acetylation 347.0788

in-vitro/in-vivoBiotransformation

Number Biotransformation Name ΔM

PhI_1 Dehydration-

18.0106

PhI_2 Loss of O (N-oxide)-

15.9949PhI_3 Desaturation/Aromatization -6.0470PhI_4 Desaturation/Aromatization -4.0313PhI_5 Dehydrogenation -2.0157PhI_6 Hydroxylation/dehydration -2.0157PhI_7 Oxidative deamination -1.0316PhI_8 Reduction 2.0157PhI_9 Reduction (2x) 4.0313PhI_10 Ketone 13.9793PhI_11 Dioxygenation/hydroxylation 13.9793PhI_12 Hydroxylation 15.9949PhI_13 Hydrolysis 18.0106PhI_14 CH3 to COOH 29.9742PhI_15 Dioxidation/2 electron oxidation 29.9742PhI_16 bis-oxidation 31.9898PhI_17 Di-hydro-diol 34.0055PhI_18 3x oxidation 47.9847

Phase I

Phase II


MetCalcSample Output

Structure ID Chemical formula Molecular Mass [M+H]+ ΔMNVP-123456 C22H15O2N 325.1234 326.1306 0.0000

Phase I metab-invitro Dehydration 307.1128 308.1200 -18.0106Phase I metab-invitro Loss of O (N-oxide) 309.1285 310.1357 -15.9949Phase I metab-invitro Desaturation/Aromatization 319.0765 320.0837 -6.0470Phase I metab-invitro Desaturation/Aromatization 321.0921 322.0993 -4.0313Phase I metab-invitro Dehydrogenation 323.1078 324.1150 -2.0156Phase I metab-invitro Hydroxylation/dehydration 323.1078 324.1150 -2.0156Phase I metab-invitro Oxidative deamination 324.0918 325.0990 -1.0316Phase I metab-invitro Reduction 327.1390 328.1462 2.0156Phase I metab-invitro Reduction (2x) 329.1547 330.1619 4.0313Phase I metab-invitro Ketone 339.1027 340.1099 13.9793Phase I metab-invitro Dioxygenation/hydroxylation 339.1027 340.1099 13.9793Phase I metab-invitro Hydroxylation 341.1183 342.1255 15.9949Phase I metab-invitro Hydrolysis 343.1340 344.1412 18.0106Phase I metab-invitro CH3 to COOH 355.0976 356.1048 29.9742Phase I metab-invitro Dioxidation/2 electron oxidation 355.0976 356.1048 29.9742Phase I metab-invitro bis-oxidation 357.1132 358.1204 31.9898Phase I metab-invitro Di-hydro-diol 359.1289 360.1361 34.0055Phase I metab-invitro 3x oxidation 373.1081 374.1153 47.9847Phase II metab-invitro Glucuronidation 501.1555 502.1627 176.0321Phase II metab-invitro Oxidation/Glucuronidation 517.1504 518.1576 192.0270Phase II metab-invitro Hydration/Glucuronidation 519.1661 520.1733 194.0426Phase II metab-invitro Carboxylation/Glucuronidation 531.1296 532.1368 206.0062Phase II metab-invitro Carbamoyl Glucuronides from Amines 545.1453 546.1525 220.0219Phase II metab-invitro Glutathione(GSH) conjugation 630.1916 631.1988 305.0682Phase II metab-invitro Glutathione-Addition 632.2072 633.2144 307.0838Phase II metab-invitro GSH/Oxidation 646.1865 647.1937 321.0631Phase II metab-invitro Addition of glutathione and water 648.2022 649.2094 323.0788

Demethyl Demethylation (2x) 297.0922 298.0994 -28.0312Demethyl Demethylation 311.1078 312.1150 -14.0156


MetCalcSample Output

Masses to export for use in Xcalibur

[M+H]+ 298.0994 308.1200 310.1357 312.1150 320.0837 322.0993 324.1150 325.0990 326.1306 328.1462 330.1619 340.1099 342.1255

Parameter for Mass Defect Filtering:Mass Defect

Lowest Mass [amu]: 298 99Highest Mass [amu]: 649 209

Masses ΔMfor -28.0312

Metworks -18.0106-15.9949-14.0156-6.0470-4.0313-2.0156-1.03160.00002.01564.0313

13.979315.994918.010629.974231.989834.005547.9847

176.0321192.0270194.0426206.0062220.0219305.0682307.0838321.0631323.0788

Masses [M+H]+

for 298.0994DDS 308.1200

310.1357312.1150320.0837322.0993324.1150325.0990326.1306328.1462330.1619340.1099342.1255344.1412356.1048358.1204360.1361374.1153502.1627518.1576520.1733532.1368546.1525631.1988633.2144647.1937649.2094

298.0994,308.12,310.1357,312.115,320.0837,322.0993,324.115,325.099,326.1306,328.1462,330.1619,340.1099,342.1255,344.1412...

Output to Notepad


XcaliburMaking use of MetCalc string


Summary

Meteor and MetaSite complement each other and both are good predictors of metabolites

Neither Meteor or MetaSite are perfect• Meteor has a tendency to over-predict• MetaSite looks at P450-mediated oxidative

metabolism only• Both will predict metabolites that are not formed

and/or not predict metabolites that are formed

MetCalc is another tool to help expedite the metabolite identification process


AcknowledgmentsProfessor Gabriele Cruciani(University of Perugia. Several slides on Metasite)

Professor Bernard Testa(University of Lausanne, ret. Several slides on Meteor)

Dr. Alfred Zimmerlin (ADME Profiling, NIBR, Basel. In-house test)

Dr. Bernard Faller (ADME Profiling, NIBR, Basel. In-house test)

Dr. M. Kiffe (MAP Group, NIBR, Basel. MetCalc and Meteor assistance)


References-IAfzelius et al. Drug Met. Rev. 2007 39, 61. (Comparative analysis and mechanistic insight).

Ahlstroem, Riddestroem, Zamora et al. J. Med. Chem. 2007a, 50, 4444. (CYP2C9 Substrate Metabolism Relationships SMRs, COX-2 inhibitors metabolic stability, use of MetaSite).

Ahlstroem, Riddestroem and Zamora. J. Med. Chem. 2007b, 50, 5382. (CYP2C9 SMRs: substrates, inhibitors and metabolites.)

Arnby, Smith, Glen et al. The 7th International Conference on Chemical Structures, 5-9 June 2005, Abstract C-2, Noorwjkerhout, The Netherlands.

Boyer et al. J. Chem. Inf. Model. 2007, 47, 583. (Fingerprint based method)

Caron, Ermondi and Testa. Pharm. Res. 2007, 24, 480. (Prediction of the oxidative metabolism of statins via MetaSite)

Cruciani, Carosati, De Boeck et al. J. Med. Chem. 2005, 48, 6970. (MetaSite)

De Groot, Ackland and Horne. J. Med. Chem. 1999, 42, 4062. (Combined protein and pharmacophore model. 2D6 catalyzed N-dealkylation)


References-IIEgan, W.J. Computational Models for ADME. Ann. Rep. Med. Chem. 2007, 42, 449. (Review article)

Jung, Kim et al. J. Chem. Inf. Model. 2008, 48, 1074-1080.

Korzekwa, Jones and Gillette. J. Am. Chem. Soc. 1990, 112, 7042. (Studies on CYP450 mediated hydroxylation/predictive model for H. abstraction).

Korzekwa, Grogan et al. Adv. Exp. Med. Biol. 1996, 387, 361. (Electronic model for CYP450 oxidations.)

Jones, Mysinger and Korzekwa. Drug Metab. Disp. 2002, 30, 7. (Predictive electronic model for aromatic oxidation/ H. abstraction)Kirton, Kemp, et al. Protein Struct. Funct. Genet. 2002, 49, 216.

Lewis, D. F. et al. Xenobiot. 1996, 26, 723.

Lewis, D. F. et al. Xenobiot. 2004, 34, 549.

Obach, et al. Drug. Met. Disp. 2006, 34, 121.


References-IIIOh, Kim, Jung et al. J. Chem. Inf. Model. 2008, 48, 591. (Combined method for CYP450 3A4 metabolism prediction. MLite)

Park and Harris, J. Med. Chem. 2003, 46, 1645.

Shen, Xiao et al. J. Med. Chem. 2003, 46, 3013. (k-nearest neighbor QSPR model of metabolic stability).

Sheridan, Korzekwa et al. J. Med. Chem. 2007, 50, 3173. (Empirical,QSAR-based, regioselectivity models)

Singh, Shen, Walker and Sheridan. J. Med. Chem. 2003, 46, 1330.

Sorich, Smith, Miners et al. Curr. Drug. Metab. 2008, 9, 60. (Advances in the in silico modeling of UGT substrates. Conjugative metabolism).

Testa and Soine in “Burger’s Medicinal Chemistry and Drug Discovery, 6th ed. 2003 Abraham D.J. ed. v.2, ch. 13, 431.

Testa, Balmat and Long. Pure Appl. Chem. 2004, 5, 907. (Meteor)Testa, Balmat, Long and Judson. Chem. Biodiv. 2005, 2, 872. (Meteor)


References-IVTorres, Korzekwa et al. J. Med. Chem. 2007, 50, 4642. (Density functional

calculations predicting regioselectivity of metabolism by aldehydeoxidase).

Trunzer, Zimmerlin and Faller J. Med. Chem. 2009, 52, 329-355.

Zamora, Afzelius and Cruciani. J. Med. Chem. 2003, 46, 2313.(MetaSite)

Zhou, Afzelius, Grimm et al. Drug Metab. Disp. 2006, 34, 976. (MetaSite)

URLsMETEOR: http://www.lhasalimited.org/index.php

METADRUG: http://www.genego.com/

METASITE: http://www.moldiscovery.com/

METAPC: http://www.multicase.com/products/prod05.htm

MetabolExpert: http://www.compudrug.com/

http://www.lhasalimited.org/index.php

http://www.genego.com/

http://www.moldiscovery.com/


Back-Up Slides

MetaSite

MetaSite Methodology

Input Mass - Q-Toff

- Ion-TrapRaw data

MetaSitePeak Matching

And Met ID Ion-Trap

MetaSiteFragments for

metabolites


Specifying the Likelihood Included by Absolute Reasoning

Definitions:Probable There is at least one strong argument that the proposition is true

and there are no arguments against it.

Plausible The weight of evidence supports the proposition.

Equivocal There is an equal weight of evidence for and against the proposition.

Doubted The weight of evidence opposes the proposition.

Improbable There is at least one strong argument that the proposition is false and there are no arguments that it is true.

The METEOR factory default is to include Probable and Plausible biotransformations.

Absolute – relative reasoning


Absolute Reasoning

(1) The program checks the query structure to see if it can match against any of the biotransformations from the knowledge base.

(2) The absolute reasoning is applied in order to assess the level of likelihood for the matching biotransformations. METEOR displays the biotransformations that are equal to or higher than the level selected in the Reasoning tab of the Processing Constraints Window.

(3) The relative reasoning is applied to rank the remaining biotransformations.

Using three types of information or evidence:

(1) The physico-chemical properties calculated for the query structure or metabolite (lipophilicityevaluated by Log P and molecular weight).

(2) The species selected by the user within the processing constraints window.

(3) Whether the compound under consideration is administered (i.e. is the query compound submitted by the user) or is generated by a biotransformation identified by METEOR.



Relative ReasoningThe relative reasoning engine in METEOR assesses, as far as possible, which biotransformation will take precedence relative to other biotransformations. In order to understand how METEOR makes this assessment, the relationship between biotransformations can be thought of as a series of towers in which biotransformations are ranked in levels with the most likely biotransformation displayed at the top (level 1) and the least likely displayed at the bottom. Biotransformations of equal likelihood are displayed beside each other in the tower.



Relative ReasoningExample: Biotransformations 23, 24, 26, 60, 61 and 241 are identified for a query structure. The relationships between these biotransformations are as follows:

Ø Biotransformation 60 (Oxidation of Secondary (Acyclic) Alcohols) is more likely than biotransformation 61 (Oxidation of Secondary (Alicyclic) Alcohols).

Ø Biotransformation 241 (Oxidation of Primary Alcohols) is more likely than biotransformations60 and 61.

Ø Biotransformations 23 (Glucuronidation of Primary Aliphatic Alcohols), 24 (Glucuronidation of Secondary Aliphatic Alcohols) and 26 (Glucuronidation of Alicyclic Alcohols) are equally likely.The relationship between those biotransformations can be represented as follows:

Tower A Tower B241 23, 24, 266061

There are no implied relationships between towers. So, for example, METEOR does not know anything about the relative likelihood of biotransformations 241 and 23.

If a higher number of first levels is chosen from the Processing Constraints Window, a higher number of biotransformations will be retained.


Prediction of Drug Metabolism: In Silico and Laboratory ...including all those described in “Testa...

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