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Title
Martin A. Ott Lhasa Limited www.lhasalimited.org
In silico Prediction of Forced Degradation
Building an Expert Computer System
to Predict Degradation Pathways
Forced Degradation Studies, 27-28 January 2010 – Renaissance Hotel, Brussels
• Introduction
• Degradation prediction software
• Chemical knowledge base of transformations
• Scope and limitations
• New developments
• Information sharing and confidentiality
• Conclusion
Contents
Lhasa Limited is a not-for profit organisation that promotes knowledge and data sharing in chemistry and the life sciences
What is Lhasa Limited?
• Drugs (formulated or not) are exposed to harsh conditions to study their degradation behaviour
• Structural identification of degradation products
• Elucidation of degradation pathways
• Educated guesses on degradation are welcome
• Plenty of information available but very dispersed
Need for a predictive (expert) system
Forced Degradation
Drug Degradation Database (D3) * No prediction * Limited size http://d3.cambridgesoft.com/
CAMEO (reaction prediction) * No longer available * Not adaptable to specific needs Pure Appl. Chem. 62, 1921-1932 (1990) J. Org. Chem. 60, 490-498 (1995)
Delphi (degradation prediction) * In-house Pfizer project Mol. Pharm. 4, 539-549 (2007); DOI 10.1021/mp060103+
Degradation Software
A computer program that predicts chemical reactions needs to:
Predicting Reactions
• Understand chemical structures – chemistry engine
• Know chemistry – knowledge base
• Assess reaction likelihoods under different conditions
• Assess competition between reactions
• Hydrolytic
• Oxidative
• Photochemical
H2OO
R RR
OH
OH
R
RN
R
R
RN
+
R
ORROOH
R O
OR
R OH
O
OHR
H2O+
R
RO
R
RO
R
R
R
R 1O2+
RBr
RHhν R
RR
Rhν
Degradation Chemistry
• In Zeneth’s knowledge base, chemical reactions are represented through patterns, e.g.:
• The pattern defines both the transformation and the scope
N
OO
R1
R2
R3
N
OO
R1 R2
R3
O21
NR1
R2
R3
*
R1-R3 = aliphatic carbon (not multiply bonded to a heteroatom) or aromatic carbon or hydrogenThe bond marked * must be fused to another aromatic ring
Chemical Patterns
• Heat (temperature) #
• Acid & base catalysis (pH) #
• Hydrolysis (H2O)
• Molecular oxygen (O2)
• Peroxides
• Radical initiator
• Metal (Fe[III] or Cu[II])
• Photochemical (light)
Reaction Conditions
# = numerical; others indicate presence/absence
Reasoning
Seven likelihood levels are used:
Absolute reasoning: Determine the likelihood of transformations
Relative reasoning: Assess competing transformations
• (Certain) • Very likely • Likely • Equivocal • Unlikely • Very unlikely • (Impossible)
Conditions / Reasoning
Oxidative and photochemical reactions:
• Presence of a specific oxidant (or light) is a prerequisite for setting the likelihood level
• Any combination of conditions can be used
• Examples:
“S-Oxidation of thioethers is very likely when
either O2 or peroxides are present”
“Oxidation at benzylic positions is likely when
O2 and a radical initiator are both present”
Conditions / Reasoning
Hydrolysis reactions:
• Water is a prerequisite
• Likelihood of many reactions is dependent on pH
• Reactions that are both acid- and base-catalysed display a minimum in the pH-dependency
• Example of a pH profile: pH < 6 VERY LIKELY pH = 6-8 LIKELY pH = 8-10 EQUIVOCAL pH = 10-12 LIKELY pH > 12 VERY LIKELY
Conditions / Reasoning
Various pH profiles:
pH profile from preceding slide
Knowledge Sources
General Pharmacological and Pharmaceutical Journals Eur. J. Pharm. Biopharm. Int. J. Pharm. J. Pharm. Biomed. Anal. J. Pharm. Sci. J. Pharm. Pharmacol. Pharm. Res.
Editors: Dinos Santafianos (Pfizer) Steve Baertschi, Pat Jansen (Eli Lilly)
Knowledge Base Editor
Name Description Comments
Knowledge Base Editor
Transformation Attributes
R-group definition
Hydrolyses
Oxidations
Condensations/additions
Eliminations
Isomerisations/rearrangements
Photochemical reactions
Total
Knowledge Base Status
30
33
16
9
12
9
109
Sample Degradation
Hydrolysis
OO
OO
O ON
NN
O
N
OO
O
O
O
Oxidation
Hydrolysis
Oxidation
Degradation sites of rifampicin
Sample Degradation
Hydrolysis
OO
OO
O ON
NN
O
N
OO
O
O
O
Oxidation
Hydrolysis
Oxidation
Zeneth predictions (pH 7, water, oxygen, peroxide, one step):
Likely
Likely
Very likely Likely One more reaction
at the equivocal level
Degradation prediction of nordazepam
Sample Degradation
Degradation prediction of nordazepam
Sample Degradation
Scope and Limitations
Prediction of degradants as a result of:
• Shelf life time or stability studies
• Accelerated degradation studies (e.g. 2 months at 75% humidity)
• Forced degradation studies (e.g. O2/AIBN, 1 hour at pH 1)
Quantities, reaction rates
Likelihood of degradant formation
No
No
Yes
No
Yes
New Developments
New developments in 2009:
• Prediction of intermolecular (bimolecular) reactions
• Handling of chemical structures with radicals
• Support for more structure editors
• Continuous growth of the knowledge base (50 109)
Bimolecular Reactions
• One query compound is considered to be the “primary query compound” = Q (typically the API)
• Additional compounds entered are considered to be the “secondary query compounds” = A, B, … (typically excipients, counterions etc. but can also be another API)
• Intermolecular reactions are predicted between Q and A, Q and B, etc. but not between A and B, etc.
• Dimerisations (and polymerisations) are predicted when A is the same compound as Q.
Bimolecular Reactions
The knowledge base currently contains four intermolecular transformations
O
OH
O
N
OH
O
Ph
NH2Ph
O
NH
O
Ph
O
OH
ONH
Ph
O
OH
O
OH
O
O
OOH−
[ O ]
+
Q
Q
A
Reactants
Currently three classes of “secondary query compounds” have been identified:
• Excipients e.g. fructose, triacetin, aspartame
• Counterions e.g. succinate, citrate, maleate
• Contaminants (impurities from excipients including degradants) e.g. formaldehyde, glyoxal
Reactions Involving Radicals
Full support for radical structures has been added.
Radical compounds mainly occur as intermediates: Radicals in query compounds and product structures are supported as well.
C
OO
O2R RH
O
OOH
OH
RH
Alternative Structure Editors
In addition to ISIS/Draw, two more structure editors are now supported: Symyx Draw and ChemDraw.
• More chemistry − 160 transformations by the end of 2010
• Fine-tune likelihoods − through feedback from users
• Experimental data for examples
• More literature references
Work in Progress
Use of physicochemical properties to enhance predictions:
• pKa to assess protonation state
and deprotonation reactions
• bond dissociation energies to assess H abstraction reactions
• HOMO and LUMO energies
Plug-in calculators will be used that interface with the knowledge base
Work for the Future
Data Sharing
• A collaborative group has been set up
• Currently four members: Amgen Eli Lilly GlaxoSmithKline Johnson & Johnson
• Members co-direct development
• Handling of confidential data Transforming confidential data into non-confidential knowledge
Data Sharing
• Contributions from members Compound/degradation profiles New transformations / literature references
• Partial structures are often sufficient to describe the chemistry Unless the transformation is specific to a confidential scaffold
• Confidentiality status is covered by project agreement
• Data can be shared at different levels Fully public Anonymous
Benefits of Participation
Impact:
• Improve assessment of drug candidate stability through faster identification of degradation pathways
• Minimise studies for related compounds • Education and training of individuals • Potential to build and maintain institutional
knowledge
Benefits of Participation
• A strong team contributing chemical knowledge • Careful testing against actual pharmaceutical
models • Suggestions for functionality to meet industry
needs
• Improving the system over time • Maintaining the software over time and across
platforms
Development - going beyond basic functionality requires: Sustainability - the consortium model provides stability and a mechanism for:
Conclusion
• Development on Zeneth is going strongly – expansion of functionality – sustained growth of the knowledge base
• Collaborative group of members – co-direction of development – contributions – handling of confidential data
• Benefits of participation – faster identification of degradation pathways – preserving knowledge & training/teaching – sustained development and support
William Button
Alex Cayley
Tony Long
Nicole McSweeney
Ernest Murray
Rob Toy
Thanks to …
Steve Baertschi Eli Lilly
Rhonda Jackson
J&J
Mark Kleinman GSK
Darren Reid
Amgen