Meteor Nexus: The implementation of the site of metabolism prediction methodology
43rd ICGM, March 2016
New Orleans Chris Barber
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 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
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 – 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)
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
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