Design and application of fragment libraries for protein crystallography

John Badger

March 23, 2009

Library Design, Search Methods and Applications of Fragment-based Drug Design

237th ACS National Meeting, Salt Lake City

Why fragment screening ?

More sophisticated view of potency emphasizes ligand efficiency over IC50 (I.D. Kuntz, K. Chen, K. A. Sharp, and P. A. Kollman, PNAS,1999 96, 9997) with LE > 0.3kcal/atom for the clinic

Feature analysis shows exponentially falling probability of binding with increasing compound complexity (M.M.Hann, A.R.Leach,G.HarperJ.Chem.Inf.Comput.Sci., 2001 41, 856)

Simpler to control compound properties by growing small compounds than by modifying large compounds

Practical methodology for academic and small biotech environments

An operational approach to assess the drugability of a target site

Some disease classes ill-suited to HTS discovery (CNS diseases)

A decade of positive results from early leads to clinic (P.J. Hajduk, J.Greer Nature Reviews Drug Discovery, 2007 6, 211) with 47 compounds at significant development stages, 4 compounds in clinical trials including 2 compounds developed in < 2 years

Some preconceptions

A poster child for modern drug discovery ?

Some preconceptions

But small compounds may have a big physiological impact

Some preconceptions

But small compounds may have a big physiological impact

caffeine

tylenol (acetaminophen)

tegretol(carbamazepine)

Library Design

Special aspects of library design for crystallographic screening

Resource and technology intensive

- small, efficient libraries (typically 300-2000 compounds, assayed in shape-diverse mixtures of 4-10 compounds)

Allows detection of small and very weakly binding compounds- binding constants < 10 mM

Demands high compound solubility- ~200mM DMSO allows mixture experiments

Structure data enables rational, efficient and creative exploitation of small fragment hits- grow compounds by addition of functional groups into accessible space

Fragments for drug discovery

Use small and relatively rigid cores- tabulation of drug-like ring systems and preferred side chains by Hartshorn et al, J.Med.Chem., 48, 403-413, 2005

Combine each selected ring system with preferred side chain(s)Obtain ‘classic’ (small) fragments

Examples of simple ring systems

Examples of preferred side chains

Compounds resemble fragments of drugs

Compound properties

Congreve et al (Drug Discovery Today, 8, 876-877, 2003) suggested ‘rule of 3’ as optimal properties for screening fragments- molecular weight <300Da

- no. H-bond donors/acceptors ≤3

- clogP < 3

- no. rotatable bonds ≤3

- polar surface area <60Å2

Example commercial screening collection containing ~500,000 compounds with ‘rule of 3’ properties

MW < 300Da-> 4,700 compounds

MW < 250Da-> 2,400 compounds

MW < 200Da-> 120 compounds

Commercial screening collections are biased towards larger compoundsand may not meet the design needs of asmall fragment library

Exceptions to the rule

Very low MW compounds might be useful

High counts of H-bond acceptors and high PSA for some unique nitrogen-containing ring systems

Theoretical predictions for logP might eliminate some interesting and usable compounds

Data from www.chemexper.com/tools/propertyExplorer/cLogP.html

MW 110.1

Many hsp90 lead compounds areresorcinol and adenine derivatives !

Design strategy

1. Search available compounds for drug-like core substructures and retain those containing appropriate side chains

2. Check molecular properties of the compounds containing appropriate cores/side chains- filtering pass using the ‘rule of three’ provides

good working set

- filtering pass using relaxed rules may provide useful additional compound types

3. Finalize with visual check - weed out overt toxicities, compounds related to controlled substances, unstable compounds..

4. Experimental determination of solubility

SDsearch:Library design by cherry-picking from compound collections

Molecular weightNumber of H-bond acceptorsNumber of H-bond donorsSolubility (clogP)Polar surface areaNumber of rotatable bonds

Number of non-hydrogen atomsNumber of stereo centersPresence of phosphate atomsBlood-brain barrier permeabilitySpecific structure rejection (vendor ID or similarity check)

SMILES substructuresThree-dimensional pharmacophoresProtein interaction motifs

‘Rule of three’ and associated filters

Useful additional filters

Structure filters

-Supports Maybridgeand Chembridge property tags-Property calculations employOpenBabel 2 (OpenSource)

Zenobia small fragment Library

352 decorated ring compoundsSoluble in 200mM DMSOMean properties- MW 154 Da

- No. H-bond acceptors 2.6

- No. H-bond donors 1.4

- clogP 1.6

- tPSA 52 Å2

Four 96 well plates-100µL/compound

- shape diverse mixtures of 8 compounds organized as plate columns

Chemical images and PDBfiles sorted into mixture groups

SD and Excel files of library

Application to CNS targets

CNS disease targets

Compounds emerging from HTS are often too large for passive diffusion across the blood-brain barrier

- FBDD allows growing by 1-2 functional groups while maintaining desirable compounds properties

Properties that characterize fragments resemble the properties required for leads for effective CNS drugs

Fragments

MW 110-250 Da

HBa, HBd ≤ 3clogP < 3PSA <60Å2

CNS drugs

MW < 450 Da (mean 350 Da)

Hba+Hbd < 8

Ideal logP ~2

PSA < 60Å2

Targets at Zenobia Therapeutics include:- Leucine Rich Repeat Kinase 2 (LRRK2)

- Glycogen Synthase Kinase 3 (GSK3)

Evolution of fragment based drug discovery

Use larger libraries of larger compounds (scaffolds) for initial screen - allows multiple assay methods because larger compounds may have higher binding affinity

- development pathways closer to traditional HTS

Use small libraries of small compounds in initial screen and follow by opportunistic addition of functional groups- x-ray crystallography optimal technology for detecting very weak binder

- computational analog is analyzing PDB data to identify key binding interaction motifs (2-4 atoms) and substructures from known ligands

Early lead discovery pathway

Identify key binding interactions- Experimental small fragment screening (x-ray, SPR)- Computational motif identification by mining protein:ligand structures (PDB), binding database,…

First scaffold screen with focused libraries- Select 80 commercially available scaffold compounds- Perform activity inhibition assays- Perform IC50 measurement on top hits

Second round libraries, expansion around best hits and ‘interesting’ structures- Select 50-100 commercially available small lead compounds per chemotype- Perform activity inhibition assays- Perform IC50 measurement on top hits- Perform cell/PK/BBB analysis on top hit

MW: 253 DaHba: 2.0HBd:1.4clogP:1.9PSA:46.4 Å2

MW: 325 DaHba: 3.0HBd:1.3clogP:2.3PSA:54.6 Å2

Computational pilot study to identify G2019S LRRK2 and GSK3β scaffolds

155

303

7855

450,000 Total

Predictive BBB penetration

Predictive kinasebinding motifs

Steric fit to kinaseactive site

80 compounds chosen for assay to test computational model

LeadModel3D:Viable compound expansion from small fragments or motifs

Input the 3D protein:fragmentstructure and a library of candidate compounds that contain the fragment as a substructureDocks each candidate compound onto the fragment and explodes each compound into conformersPoses that severely clash with protein are not viable; score remaining poses with contact potentialCapture list of viable candidate compounds for synthesis or purchase

Predicted best binding mode (yellow bonds) fits the protein cavity and is close to the experimentally determined structure (green bonds)

Candidate compoundcontaining the fragmentFragment hit

Explode each candidate intoconformers and match fragment atoms

Hits were identified for GSK3β

-60

-40

-20

0

20

40

60

80

100

120

0 20 40 60 80 100

% Inhibition at 200μM

30 hits >80%

4 unique scaffolds chosen for further analysis

IC50, LE, PSA for GSK3β inhibitor scaffolds: Promising Leads

Scaffold 1 Scaffold 2 Scaffold 3 Scaffold 4

%Inh at 200μM 100 100 91 82

IC50(μM) < 2.5 6 25 70

MW 222 237 238 291

HA 14 15 15 14

LE 0.55 0.48 0.42 0.41

PSA (Å2) 42 49 41 41

LRRK2 screen produced hits

-20

0

20

40

60

80

100

120

0 20 40 60 80 100

% Inhibition at 200μM5 unique scaffolds identified and chosen for further analysis

8 hits >80%

IC50 and LE for LRRK2 inhibitor scaffolds: Promising Leads

Scaffold 1 Scaffold 2 Scaffold 3 Scaffold 4 Scaffold 5

%Inh at 200μM

82 76 94 92 87

IC50(μM) 15 66 5 30 70

MW 264 216 237 241 291

HA 18 16 15 16 14

LE 0.36 0.35 0.47 0.38 0.40

PSA (Å2) 59 58 49 58 41

Results from follow-on LRRK2 screen

25

-20

0

20

40

60

80

100

120

0 10 20 30 40 50 60 70 80

% Inhibition at 20uM

IC50’s for follow-on compounds: SARbyCatalogue

Scaffold 3 Cmpnd1 Cmpnd2 Cmpnd3 Cmpnd4 Cmpnd5

IC50 (uM) 5 10 0.8 0.15 3 1

MW 237 323 302 287 302 440

HA 15 21 20 19 18 25

LE 0.49 0.33 0.42 0.49 0.42 0.33

PSA 49 65 62 58 59 71

Chosen to test di-substitution

Acknowledgements

Zenobia Therapeutics

Vicki Nienaber (CEO/CSO)

Ruo Steensma

Barbara Chie-Leon

Vandana Sridhar

Cheyenne Logan

Leslie Hernandez

Kristina Bull

Johns Hopkins

Christopher Ross

Shanshan Zhu