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Predicting Metabolites
Enhancing An Expert System With Machine Learning
Boston, May 2015
Chris BarberDirector Of Science
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Lhasa Limited: What We Do
Derek NexusAn expert system for the assessment of toxicity
Meteor NexusAn expert system for the assessment of xenobiotic metabolism
Sarah NexusA (Q)SAR tool for the assessment of mutagenicity
Vitic NexusA structure-searchable toxicity database
ZenethAn expert system for the assessment of chemical degradation
Lhasa Limited is a not-for-profit organisation and educational charity that promotes knowledge and data sharing in chemistry and the life sciences. It is
controlled by its members (more than 200 organisations worldwide)
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Why Predict The Structure Of Metabolites?• Support identification of…
• Metabolites formed in analytical studies
• Sites of metabolism driving high metabolic clearance
• Potentially toxic metabolites• Some in silico toxicity models implicitly include metabolism• …but they may miss unusual metabolic precursors• …specific off-target pharmacology, or modelling through AOP’s
• Metabolites that may not translate between assays• Some in vitro / in vivo assays may not translate
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Meteor Nexus
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Meteor Nexus
Tabular summary of metabolic tree
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Meteor Nexus
Metabolic path for selected metabolite including identification of potentially
adduct-forming and other intermediates
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Meteor Nexus
Options to filter by mass, molecular formula etc
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Meteor Nexus
Biotransformation scope
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Meteor Nexus
Biotransformation supporting information
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Dictionary Of Biotransformations
• Dictionary of 500 biotransformations• Covering both phase I and phase II reactions
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How likely that each reaction will occur?
How Meteor Nexus Works
What reactions could occur?
Processing constraint
Rule base
Dictionary of biotransformations
Knowledge base
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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
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Meteor Nexus Performance
• T’jollyn et al, Drug Metab Dispos 39, 2066-2075 (2011)
• Comparative study of Meteor, MetaSite and StarDrop
• 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
• Research objective• Develop methodology to better rank-order metabolite likelihoods
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Lasofoxifene: Meteor Nexus Prediction
• Man|rat|monkey, first generation metabolites
OSO3HO
N
OHO
N
OO
N
O
N
OHO
N OHO
N
GluO
OHO
HN
HO O
HO
HO
OH
Prakash et al, Drug Metab Dispos 36 1218-1226, 1753-1769 (2008)
67
245(233)234235
76(77)78
2720
243662222533469100445540
Improbable
ProbablePlausibleEquivocalDoubted
Not reported
Metabolite
Observed Not Observed
Predicted 8 9
Not Predicted 2
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How likely that each reaction will occur?
How Meteor Nexus Could Work
What reactions could occur?
Processing constraint
Experimental reactions
Dictionary of biotransformations
Knowledge base
Database
Expertsystem
Machinelearning
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Other statistical approaches to metabolite ranking
• SyGMa• L Ridder & M Wagener, ChemMedChem 3 821-832 (2008)
• MetaPrint2D-React• SE Adams, Molecular Similarity and Xenobiotic Metabolism,
PhD Thesis, University of Cambridge (2010)
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Occurrence Ratio Method
Largemetabolism database
How often could a reaction occur?
How often does a reaction actually occur?
Occurrence Ratio
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Occurrence Ratio Method: Biotransformation 243
How often could a reaction occur?1946
How often does a reaction actually occur?
636
Occurrence Ratio32.7%
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Occurrence Ratio Method
Green bar: the biotransformation has been experimentally
observed for this substrate
Red bar: the biotransformation has
NOT been experimentally observed
for this substrate
Selected supporting examples containing the biophore for the
biotransformation
Screenshot from research prototype
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0
20
40
60
80
100
120
0 20 40 60 80
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 compoundsBiotransformation countsRelative threshold
Improbable
ProbablePlausibleEquivocalDoubted
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Lasofoxifene: Occurrence Ratio Prediction
• Man|rat|monkey, first generation metabolites
OSO3HO
N
OHO
N
OO
N
O
N
OHO
N OHO
N
GluO
OHO
HN
HO O
HO
HO
OH
Prakash et al, Drug Metab Dispos 36 1218-1226, 1753-1769 (2008)
(67)
(245)(233)(234)(235)
(76)(77)78
2720
24366253
Not reported
Metabolite
Observed Not Observed
Predicted 8 3 9 3
Not Predicted 2 7
OR
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Ways to Calculate the Occurrence Ratios
• How often is a predicted transformation observed?• Ratio of observed / predicted across all data
• If 2 transformations could occur, which will win?• Relative ranking of each pair of transformations
• How often is a predicted transformation observed……. for compounds like mine?
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Similarity-based Occurrence Ratios
• Meteor biotransformation structural key
• Ceres fingerprint (whole structure)
• Ceres fingerprint (site of metabolism)
Biotransformation:
1 0 0 1 0
1 2 3 4 563
Atom 3: O; O-C; O-C; O-C=C.Atom
feature extractor
Hashing algorithm
1 0 0 1 0
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Site of Metabolism-driven Occurrence Ratios
• Occurrence ratio for a biotransformation determined by• signal of nearest neighbours• weighted by similarity around the site of metabolism
Screenshot from research prototype
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0
20
40
60
80
100
120
0 20 40 60
Sens
itivi
ty %
Positive predictivity %
Occurrence Ratio Method
Site Of Metabolism Method
Site Of Metabolism vs. Occurrence Ratio Method
At any given sensitivity, the Site Of Metabolism method gives higher precision than the Occurrence Ratio method
Test set: 1938 compoundsSite of metabolism countsRelative threshold
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Lasofoxifene: Site Of Metabolism Prediction
• Man|rat|monkey, first generation metabolites
OSO3HO
N
OHO
N
OO
N
O
N
OHO
N OHO
N
GluO
OHO
HN
HO O
HO
HO
OH
Prakash et al, Drug Metab Dispos 36 1218-1226, 1753-1769 (2008)
67
(245)233234235
(76)(77)78
2720
-Not reported
Metabolite
Observed Not Observed
Predicted 8 3 7 9 3 0
Not Predicted 2 7 3
SOMOR
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Extending Predictions To Multiple Generations
• Propagate occurrence ratios down branches of metabolic tree
• Apply threshold constraint to the overall metabolic tree
First generation: 49.4% x Second generation: 7.5% = 18.5%30% Relative threshold 49.4% x 0.3 = 14.8%
Screenshot from research prototype
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Summary
• Developed transparent statistical approach to rank expert system-generated metabolites• More granularity over previous rule-based approach• Leads to increased positive predictivity• Allows Meteor Nexus to support a wider range of use cases
Screenshot from research prototype
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Summary
• Developed transparent statistical approach to rank expert system-generated metabolites• More granularity over previous rule-based approach• Leads to increased positive predictivity• Allows Meteor Nexus to support a wider range of use cases
• Future Plans• Continue collection of metabolism reactions• Have been collecting data for a year (4 student interns)• Currently ~1,370 parent compounds (>10K reactions)
• Test performance against member proprietary data• Implement into Meteor Nexus
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Questions
Acknowledgements• Carol Marchant
• Ed Rosser
• Jonathan Vessey
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Distribution Of Training Set Occurrence Ratios
0
20
40
60
80
100
120
140
160
0 10 20 30 40 50 60 70 80 90 100
Num
ber o
f bio
tran
sfor
mat
ions
Occurrence ratio %
Omits 145 biotransformations with rare biophores
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Meteor Nexus Data And Knowledge Sharing
Dictionary of biotransformations
Knowledge base
Experimental reactions
Database Public domain data
Member data
Consortium-shared data
Knowledge from public domain data
Knowledge from member data
Member knowledge
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Site Of Metabolism View
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Metabolite Toxicity View
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Threshold Definitions
• Top N threshold• Only display biotransformations with the top N scores
• Absolute threshold• Only display biotransformations with scores at or above some
absolute value• Relative threshold• Only display biotransformations with scores at or above some
percentage of the maximum score (eg 60% of 49.4% = 29.6%)
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Query-specific Occurrence Ratios
k-Nearest neighbour methodology (k = 8)
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Query-specific Occurrence Ratios
Similarity between query and example substrates determined
by Tanimoto index
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Query-specific Occurrence Ratios
Scores are weighted according to the(non-)observation of the biotransformation
according to √similarity
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Summary
• Developed machine-learnt approach to rank expert system-generated metabolites• More granularity over previous rule-based approach• Leads to increased positive predictivity• Allows Meteor Nexus to support a wider range of use cases
• Dependent upon database of metabolic reactions• Have been collecting data for a year (4 student interns)• Currently ~1,370 parent compounds (>10K reactions)
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Performance With Training Data Set Size
0
10
20
30
40
50
60
70
80
90
100
0 5000 10000
%
Number of substrates
Positive predictivitySensitivity
Vertical line shows size of data gathering efforts using equivalent data preparation to the original training set
Test set: 1938 compoundsSite of metabolism countsRelative threshold
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Work in progress disclaimerThis document is intended to outline our general product direction and is for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon. The development, release, and timing of any features or functionality described for Lhasa Limited’s products remains at the sole discretion of Lhasa Limited.