Meteor Nexus: The implementation of the site of metabolism prediction methodology

43rd ICGM, March 2016

New Orleans Chris Barber

chris.barber@lhasalimited.org

Meteor Nexus – the science behind the latest release

• AGENDA

• Meteor Nexus

• What it does and how it is used…

• Using machine-learning to improve the ranking of metabolites

• Impact on performance

• Development plans for Meteor during 2016

Meteor Nexus - What it does and how it is used

• Predicts the structure of metabolites • Covers phase 1 and phase 2 along with multiple generations

What could this peak be in

my analytical sample?

Which major metabolites should I expect to see?

What are all the possible metabolites?

Which parts of my molecule can be metabolised?

Are any toxic metabolites expected?

How could this metabolite be formed?

Meteor Nexus

Dictionary of over 500 biotransformations

Meteor Nexus Performance

• T’jollyn ... Drug Metab. Dispos. 2011, 39, 2066

• Meteor has higher sensitivity but lower precision

• High sensitivity is good for metabolite identification but high precision is of more value in a discovery setting

• Madden ... J. Chem. Inf. Model. 2013, 53, 1282 • The results show that … Meteor ... performed well in predicting

metabolites for both homogeneous and heterogeneous data • One problem ... is its tendency to over-predict metabolites

• Objective

• Develop methodology to better rank-order metabolite likelihoods …to improve specificity

How likely that each reaction will occur?

How Meteor Nexus Worked (pre-2016)

What reactions could occur?

Processing constraint

Rule base

Knowledge base

Dictionary of biotransformations

Rule Base

• Biotransformation ranking is determined by a reasoning-based interpretation of two types of rules describing

WG Button et al, J Chem Inf Comput Sci 43 371–1377 (2003)

Improbable

Probable

Plausible

Equivocal

Doubted Probable

Probable

Absolute likelihood of a single biotransformation

Relative likelihood of a pair of biotransformations

Meteor 2016 - Occurrence Ratio Method

Large metabolism database

How often could a reaction occur?

How often does a reaction actually occur?

Occurrence Ratio

Occurrence Ratio Method: Biotransformation 243

How often could a reaction occur? 1946

How often does a reaction actually occur?

636

Occurrence Ratio 32.7%

Occurrence Ratio Method – Making a Prediction

Rank using Occurrence Ratios

N

O O

Biotransformation Number

Name Score*

27 Glucuronidation of Aromatic Alcohols 449 243 Oxidative N-Dealkylation 314 253 Oxidative O-Dealkylation 237 20 O-Sulphonation of Aromatic Alcohols 232 78 Para Hydroxylation of Monosubstituted Benzene Compounds 222 66 Hydroxylation of Alicyclic Methylene Adjacent to an Aromatic Ring 221 69 Hydroxylation of Unfunctionalised Alicyclic Methylene 104 69 Hydroxylation of Unfunctionalised Alicyclic Methylene 104 67 Lactams from Aza-Alicyclic Compounds 104 245 Oxidative N-Dealkylation 93 235 5-Hydroxylation of 1,2,4-Trisubstituted Benzenes 72 100 Amine Oxides from Tertiary Alicyclic Amines 68

100 245

235 67 78

66

27

243 69 20

253

*Score = Occurrence Ratio x 1000

0

20

40

60

80

100

0 20 40 60 80 100

Sens

itivi

ty %

Positive predictivity %

Occurrence Ratio MethodMeteor Nexus

Occurrence Ratio Method Versus Meteor Nexus

At any given sensitivity, the Occurrence Ratio method gives higher precision than Meteor Nexus Test set: 100 compounds

Query-specific Occurrence Ratios

Hanser et al, J Cheminform 2014, 6, 21

• Metabolite formation is sensitive to the area around the site of metabolism • …weight contributions from nearest training examples on

their similarity to the query compound

Atom 3: O; O-C; O-C; O-C=C. Atom

Feature Extractor

Calculating the score…

• Default settings also restricts examples to be similar in mw

013 N-acetylation

N N

O

1000⋅+

=∑∑

∑∈∈

∈

NotObsj jObsi i

Obsi i

SSS

Score

5001000.35171000

657588886588

==⋅+++

+=score

0

20

40

60

80

100

0 20 40 60 80 100

Sens

itivi

ty %

Positive predictivity %

Occurrence Ratio Method

Site Of Metabolism Method

Site Of Metabolism Versus Occurrence Ratio Method

At any given sensitivity, the Site Of Metabolism method gives higher precision than the Occurrence Ratio method Test set: 1938 compounds Cut-offs defined by

Predicted metabolites for AZD1656 O

O

O

N

N

N

N

N

O

N

O

Database extraction of metabolite information of drug candidates… Drug Met. Disp. Published on February 11, 2016 DOI: 10.1124/dmd.115.067850

Probable 2 Plausible 102 Equivocal 160

‘old Meteor’ prediction, depth 2

Predicted metabolites for AZD1656

Database extraction of metabolite information of drug candidates… Drug Met. Disp. Published on February 11, 2016 as DOI: 10.1124/dmd.115.067850

O

O

O

N

N

N

N

N

O

N

O

Primary metabolites predicted for AZD1656

O

O

O

N

N

N

N

N

O

N

OO

ON

N

O

N

O

N

N

NO

O

N

N

N

O

O

O

O

O

N

N

N

N

N

O

N

O

Full metabolic profile for AZD1656

HO

O

N

OO

O

H

OO

Glu

OO

O

H

N

O

NN

OH

O

O

O

N

N

N

N

N

O

N

O

O

O

H

O

O

O

N

N

N

N

N

O

OH

OHO

O

O

N

N

N

N

N

O

N

OH

O

O

H

O

O

O

N

N

N

N

N

O

OH

O

OO

H

O

O

OO

O

SHO

O

observed

Not observed

Predicted metabolites for AZD1656

• Machine-learnt ranking approach • Significantly improves metabolite ranking

• 218 → 36 • Transparent approach • Provides supporting examples • Retains the transformation expert analysis

O

O

O

N

N

N

N

N

O

N

O

Remove • duplicates • mw<200 • Score <300

680

675 362 448

675

425 381 408

()

448 409

Metabolism Database – data collection

• Metabolic data collected from the following journals • Drug Metabolism and Disposition • Xenobiotica • Biochemical Pharmacology • Journal of Pharmacology and Experimental Therapeutics • Chemical Research in Toxicology • Journal of Medicinal Chemistry • Journal of Agriculture and Food Chemistry

• Currently 2,400 parents; 18,000 transformations

Metabolism Database – Data Diversity

Carboaliphatic Hydroxylation

22%

O-Glucuronidation of Alcohol

16%

Carboaromatic Hydroxylation

12%

O-Sulphonation 5%

N-Dealkylation 5%

Dehydrogenation 5%

O-Demethylation 4%

O-Glucuronidation of Carboxylic Acid or

Derivative 4%

Amide Hydrolysis 3%

Miscellaneous Oxidative Ring Opening

3%

N-Demethylation 3%

N-Glucuronidation of Amine 3%

Other Redox Reactions 3%

N-Acetylation of Amines 2%

Oxidation at Aromatic Nitrogen

2%

Carbonyl Reduction 2%

O-Dealkylation 2%

Conjugation with Amino Acids 2% O-Methylation

2%

Scientific plans for 2016 – metabolite predictions

• Currently starting an external evaluation of performance • Relatively small sets of compounds • Looking for partners for this for a co-publication

• Continuing data collection

• UI enhancements

• Research

• Can we machine-learn new transformations from the training data?

Questions

• Acknowledgements

• Research

• Carol Marchant • Ed Rosser • Jonathan Vessey

• Implementation

• Tony Long • Ernest Murray

• Metabolism database

• Emma Hill • Rob Davies • Liam Corley • + previous interns…

• Software

• Tim Furze • Jing Ma

O

N S

Rotigotine – Meteor Nexus “Traditional Reasoning”

W Cawello et al, Drug Metab. Dispos. 37 2055–2060 (2009)

Rotigotine – Meteor Nexus “Traditional Reasoning”

O

N S

First generation metabolites displayed.

Rotigotine – Experimentally Observed SOMs

O

N S

W Cawello et al, Drug Metab. Dispos. 37 2055–2060 (2009)

O-Glucuronidation O-Sulphonation N-Dealkylation (thienylethyl) N-Dealkylation (propyl)

Rotigotine – Meteor Nexus “SOM Reasoning”

O

N S

Rotigotine – Meteor Nexus “SOM Reasoning”

O

N S

O

O

O

N

N

N

N

N

O

N

O

Sites of metabolism predicted for AZD1656

N

N O

N

HO

O

N

O

OH

O

OO

H

O

O

N

OO

O

H

448

680

409

675

225

Database extraction of metabolite information of drug candidates… Drug Met. Disp. Published on February 11, 2016 as DOI: 10.1124/dmd.115.067850

observed

Not observed

Unobserved intermediate

O

HO

N

N