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Peter T.A. Galek • Cambridge Crystallographic Data Centre

24th March 2009 – ACS Spring Meeting, Salt Lake City, USA

Hydrogen Bond Propensities:Knowledge-based Predictions to Aid Pharmaceutical Solid Form Selection

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Outline

► Present H-bond propensity model screens for 2 major APIs

► How the theory is applied

The role of propensity screens in drug development

► What is an H-bond propensity model?

► Future Directions

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Structural Stability

• What structural features are conserved in comparing two modifications?– Atom/group interactions

– Conformational changes?

– Hydrogen bonding?

– Other comparisons??

P.T.A. Galek, L. Fabian & F. H. Allen, 2009. Acta Cryst B65, 68-85

SpiperoneFBPAZD : FBPAZD01

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Predicting H-Bonds?• Assumption:

unusual/metastable forms can display weaker (≡less likely) hydrogen bonding

• Often subtle contrasts (e.g. strong synthon always observed, change in a third interaction upon polymorphism)

Feb ’09: #472,200

• Use all H-bonds as polymorph screen: create flexible measure of crystal stability

• Apply CSD as H-bond knowledge base.

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Predicting H-bonds?

Peter T. A. Galek, László Fábián, W. D. Samuel Motherwell,Frank H. Allen and Neil Feeder, Acta Cryst B63, 768-782,2007

“Predict which donors and acceptors 

form hydrogen bonds in a crystal structure…”

“…Identify both likely and unusual

hydrogen bonding.” 

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Predicting H-bonds?

• Model H-bonds as binary (true/false) distribution

– Pairs that do and do not interact

– Logit model

– Built on set of descriptors- try to capture the ”influential chemistry”

– Regression procedure optimises their contribution

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Creating the Tool

Process Data

True/False?

Molecular descriptors?

Start: Structure Analysis

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Model Descriptors:

• For the best predictivity, influences on H-bond formation need to be well described

• Descriptors are key to the method– H-Bond competition– Atom accessibility/

Steric effects– D & A chemical type– π- stacking/ donor- π interactions– …

• Influences change per system– Descriptors redundant– Highly Correlated– Needs for flexibility

• Aim for truly predictive approach

Capturing Influences

Caffeine Theobromine

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Creating the Tool

Process Data

True/False?

Molecular descriptors?

f (π)

Logistic

Regression

Model

Start: Structure Analysis

ProcessObservations

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• Model equation approximates log odds of prediction.– Assumes linear function

Linking Observations to Predictions

⎟⎟⎠

⎞⎜⎜⎝

⎛

−= i

kc

ikci

kc,

,, 1

log)logit(π

ππ ∑+=

kk

ikx βα

• π predictions obtained from inversion of logit. Gives function:

)exp(1

)exp(

∑∑++

+=

kkk

kkk

x

x

βα

βαπ

Peter T. A. Galek, László Fábián, W. D. Samuel Motherwell,Frank H. Allen and Neil Feeder, Acta Cryst B63, 768-782,2007

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Critical Assessment

• Model statistics

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Creating the Tool

Process Data

True/False?

Molecular descriptors?

f (π)

Logistic

Regression

A vs. B?

Screening

Model

Start: Structure Analysis

Target Prediction

ProcessObservations

In Silico & In Vitro

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Application I: Indomethacin

• 2 known polymorphic forms characterised in literature.

– Archived in CSD

– INDMET02, INDMET03

• Predict interactions with the methodology using existing crystal structures…

Identify donors and acceptors: carboxyl, tertiary amide, methoxy, chlorine

Generate related crystal set: 1333 CSD structures.

Extract model data: 1616 H-bond observations (35% true; 65% false). Convergence after 9 iterations. AUC = 84.2%. 3% predictivity loss under 30% random hold-out validation

Perform predictions on target…

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Indomethacin: Predictions

• Carboxyl interaction clearly most likely (suggests 2-fold ring-motif )

• There is a 50:50 chance that the amide will accept

• The remaining acceptors have a low likelihood

• Individual parameters show that the accepting ability of carboxyl and amide is ~same – coeffs.= 1.273,1.217

• Sterics around acceptors differentiate in this target: – 1.9, 3.167.

• Expect γ form most stable…

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Form γ P21

Form α

P-1

• α form converts to γform at 152-154C

• TGA and DSC analysis

• Melting point γform (160-161C)

Lin S.-Y. (2006). J. Pharm. Sci.6, 572-576.Chen, X. et al (2002). J. Am. Chem. Soc., 124, 15012-15019.

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Indomethacin: Form I vs. II

• Five potential hydrogen bonds; 1 acceptor dominates, 2 acceptors very weak

• Stable structure likely to have strongest carboxyl donor and acceptor paired.

• Next most likely pairing (carboxyl-amide) denies strongest acceptor.– High Z’ structures can allow combinations

• Seem rather suboptimal– Other Z’ =1 structures very unlikely

• γ Form with most probable H-bonding is revealed as most stable form

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Which form is likely to be more stable?

Application II: Ritonavir

• Pair-wise predictions:

Generate related crystal set: 836 CSD structures.

Extract model data: 8731 H-bond observations (36% true; 64% false). Convergence after 9 iterations. AUC = 83.2%. 1% predictivity loss under 40% random hold-out validation

Perform predictions on target…

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Form I vs. Form II…

Galek et al. Angew. Chem. Int. Ed. 2009 submitted

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Form I vs. Form II H-bond Geometries

Galek et al. Angew. Chem. Int. Ed. 2009 submitted

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Ritonavir: Form I vs. Form II

• Many likely pairings. Unlikely pairings are indicators in this system.• 2 low propensity H-bonds in Form I suggest alternative arrangement

possible.• Given form II structure discovered displaying probable and stable H-bonds,

likely more stable• Form I is now known as kinetic form, thermodynamics governs

disappearance• Form II with most probable H-bonding is revealed as most stable form

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Summary• Relative polymorphic stability of 2 APIs correctly predicted by

comparing H-bond propensities

• Chemically unrelated examples- flexible method has diverse potential application– E.g. cocrystals; salts; large, flexible targets

• Inherent uncertainty estimation – Important for risk analysis

• Scope for further development and application– Intramolecular H-bonding, simplifying data extraction, linking

existing CCDC product base

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Thank you

Collaborators: László Fábián CCDC, Pfizer Institute & CCDC

Frank Allen, Sam Motherwell CCDC

Neil Feeder Pfizer Global R&D

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Model Descriptors

• Functional group labels

a: amino

b,c: amido

d: carboxyl

Peter T. A. Galek, László Fábián, W. D. Samuel Motherwell, Frank H. Allen and Neil Feeder, Acta Cryst B63, 768-782,2007

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Model Descriptors

• Functional group labels

• Competition

e.g. primary amide-carboxyl(=O)

κc(N3, O2) = (5+7) = 3(2+2)

ai

c c ccc AD

ADai

+

+= ∑ ∑),(κ

1

1

2

2

3

4

3

Peter T. A. Galek, László Fábián, W. D. Samuel Motherwell, Frank H. Allen and Neil Feeder, Acta Cryst B63, 768-782,2007

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Model Descriptors

• Functional group labels

• Competition

• Donor- and Acceptorsteric density

e.g. indole

ρA(N1)= 10 / 7 = 1.43

ai

c c ccc AD

ADai

+

+= ∑ ∑),(κ

jc

jcc r

i∉

∉Σ=)(ρ

1

Peter T. A. Galek, László Fábián, W. D. Samuel Motherwell, Frank H. Allen and Neil Feeder, Acta Cryst B63, 768-782,2007

Peter T. A. Galek, László Fábián, W. D. Samuel Motherwell, Frank H. Allen and Neil Feeder, Acta Cryst B63, 768-782,2007

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Model Descriptors

• Functional group labels

• Competition

• Donor- and Acceptorsteric density

• Aromaticity

a= 10 / 22 = 0.4545ai

c c ccc AD

ADai

+

+= ∑ ∑),(κ

jc

jcc r

i∉

∉Σ=)(ρ

.

.)(pot

aromc bonds

bondsiaΣΣ

=

Peter T. A. Galek, László Fábián, W. D. Samuel Motherwell, Frank H. Allen and Neil Feeder, Acta Cryst B63, 768-782,2007

Peter T. A. Galek, László Fábián, W. D. Samuel Motherwell, Frank H. Allen and

Neil Feeder, Acta Cryst B63, 768-782,2007