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Structure-based maximal affinity model predicts

small-molecule druggability

Alan Chengalan.cheng@amgen.com

IMA Workshop (Jan 17, 2008)

Druggability prediction• Introduction• Affinity model• Some results

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Why estimate ‘druggability’?

60% of programs fail in HTS and Hit-to-lead

Brown & Superti-Furga Drug Discovery Today (2003)

Traditional way: Sequence homology

Certain gene families tend to be druggable• e.g., Kinases and GPCRs• Used to estimate “druggable genome”

Hopkins & Groom Nature Rev. Drug Disc. (2002)

Unprecedented targets and gene families

Not all members of a gene family are equally druggable

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HTS way: Screening a diverse library

• NMR screening hit-rate* • Diverse compound collection screening hit-rate

• Reagent, screening investment

* Hajduk et al. J Med Chem. 2005

Biophysically-inspired way: Structure-based

“Druggable” “Undruggable”

Qualitative, intuitive: Can we make this quantitative?

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• Maximal affinity of ligands ~1.5 kcal/mol/atom Kuntz et al. PNAS (1999)

• Extend to binding sites?

• Restrict to “drug-like” ligands

Concept of maximal affinity

Oral drugs tend to have drug-like properties

0%

5%

10%

15%

20%

0 250 500 750 1000

weightMolecular Weight (Da)

0%5%

10%15%20%25%30%

0 50 100 150 200 250

tpsaPolar surface area (tPSA, A2)

Similar to Lipinski et al. 2001, Palm et al. 1999Marketed oral tablets in MDDR v.2001

>90% of oral drugs fall within physiochemical ranges

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Translating to the protein binding pocket

550 MW ~ 300A2

Maximal affinity predicted, ΔGMAP-POD

ΔGMAP-POD ~ – γ(r)

Non-polar surface areafor binding site

300A total

300A2 surface area ~ 550 MW

A NP

0

0.05

0.1

0.15

0.2

0.25

-40 -30 -20 -10 0 10 20 30 40

γ(r) (kcal/mol)

Curvature r (Å)

= 45 kcal/mol/A2

p = 1.4ASharp et al. Science (1991)Dill et al. J Phys Chem B (2003)De Young & Dill, J Phys Chem (1990)

Curvature-dependent HPO desolvation term

One fitted parameter

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Implementation

Algorithms

Generate tetrahedrarepresentation

Define pockettetrahedra

Calculate protein core

Curvature:New sphere fitting approach using geometric inversion

Precisely defining pocket for surface area calculation

Liang, et al. (1998) Protein Sci.

Koehl, POCKET

Brannan , Esplen, Gray (1999) Geometry

Coleman, Burr, Souvaine, Cheng (2005) Proteins

Appolonius (200BC)

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All models are wrong, some are useful.– George Box

Validation on 27 targets

Druggable Undruggable

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Scholarship finds outliers are prodrugs

Druggable Undruggable

Prediction of druggable and difficult targets

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Prediction and validation with two novel targets

Predicted Druggability

Raw hits20 uM, 60% cutoff

Confirmed hits IC50<5uMIC50<1uM

• Unprecedented targets/ unprecedented gene families• Predictions made before targets entered portfolio • Screened 11k “chemical space” diverse compound set

H-PGDS30

200 raw hits

33 confirmed hits11 confirmed hits

Fungal HSD240

16 raw hits

2 confirmed hits 0 confirmed hits

Cheng et al. (2007) Nature Biotechnology

Do experimental maximal affinities correlate?

• Experimental affinities for orally bioavailablecompounds.

• Literature mining; values are approximate (combination of Kd’s, Ki’s, IC50’s)

Correlation is very encouraging

Cheng et al. (2007) Nature Biotechnology

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Druggability in practice: Caveats

• Large conformational changes(especially loops)

• Unspecified binding sites

• Metal chelation• Covalent adducts• Active transport• Prodrug strategy• Alternate delivery/approaches

Predictions are for oral, passively absorbed, non-covalent drugs

Take a measured risk for compelling biology• These are predictive risk assessment tools• Significant conformational change

Binding site structures are treated explicitly

Druggability prediction• Model based on nonpolar desolvation• Correlation with HTS and Phase II

outcomes

Target space

Druggable Disease modifying

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Expanding druggable space• Structure-based drug design• Allosteric sites

Shape• VDW, hydrophobic• Can be optimized “by eye” with reasonable

success.

ΔGelectrostatic = ΔGinteraction + ΔGdesolvation + ΔGdesolvationligand proteinprot-ligand

Charge• Hydrogen bonds, Ionic pairs• More difficult to optimize b/c affinity is not as

intuitive (not just interaction, also desolvation)

Structure-based drug design

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Tidor Lab charge optimization

Ligand charge

Free

Ener

gy

Ligand desolvation (Q2)

P-L interaction—Coulombic (Q)

Tidor et al. Protein Sci. (1998)

Protein desolvation

Net Electrostatic Energy

Charge optimization in lead progression

• Applied to available series of six co-crystal structures for neuraminidase (antiviral target)

• Goal, retrospectively study utility in lead progression

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Neuraminidase case study

• Focus on “R-groups”

• Increasingly potent compounds--generally R-groups closer to optimal charge distribution

• Lead optimization results in charge optimization

Armstrong, Tidor, Cheng. J Med Chem (2006)

Crystallographic water for Oseltamivir binding

Optimal charge distribution provides an explanation for crystallographic water

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Towards Identifying Druggable Allosteric Sites

Protein surface

Druggable Functionallyrelevant

Computational bioinformatics approach

1. Large sequence alignment

2. Identify coupled residues3. Map to structure

Lockless & Ranganathan, Science (1999)

Statistical coupling analysis

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“Local” version of druggability equation

Potential allosteric sites in p38a/kinases

• Top site identical to small molecule allosteric inhibitor site recently identified in cAbl (Nature Chem. Biol. 2006)

• Other predicted site: Inhibitor recently found for Jnk1 (Abbott Pharmaceuticals, Oct 2007, Manuscript in preparation)

Coleman, Salzberg, Cheng, J Chem Inf Model (2006)

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Summary and References

Druggability

Expanding druggablespace Finding allosteric sites by

combining functional residue prediction and druggability predictions.

Charge optimization is helpful for SBDD in polar binding sites

Nonpolar desolvationdrives maximal drug-like affinity. This is quantitatively useful.

J Chem Inf Model (2006) 46, 2631–2637

Proteins (2005) 61, 1068–1074

Nature Biotechnology(2007) 25, 71–75

J Med Chem (2006) 49, 2470–2477

Acknowledgements

Computational geometryRyan Coleman (Pfizer, Tufts Univ.)

Diane Souvaine (Tufts Univ.)

Structure-based druggabilityKate Smyth and Patricia Soulard (Pfizer Biology)

Qing Cao, Daniel Caffrey, Anna Salzberg, Enoch Huang, RTC MI colleagues

Advice from Eric Fauman, Ken Dill (UCSF),Pfizer Cambridge and Pfizer Global R&D colleagues

Charge optimizationKathryn Armstrong (MIT, Pfizer)

Bruce Tidor (MIT)

Allosteric sitesAnna Salzberg (Brandeis, Pfizer)

alan.cheng@amgen.com