Important Aspects of Fragment Screening Collection Design

Transcription

1 Important Aspects of Fragment Screening Collection Design Phil Cox, Ph. D., Discovery Chemistry and Technology, AbbVie, USA Cresset User Group Meeting, Cambridge UK. Thursday, June 29 th 2017 Disclosure- Phil Cox is an Employee at AbbVie. The design, study conduct, and financial support for this research was provided by AbbVie. AbbVie participated in the interpretation of data, review, and approval of this presentation.

2 AbbVie Fragment Screening Paradigm Fragment Collections Fragment Screening Fragment Optimization Ro3 (4K) and Ro3.5 (9K) Collections For both high concentration HTS and BioPhysical Screens (BPS) HTS Biochemical assays Triage XRC SBDD Design (XRC and Props) BP Collection (1K) BPS N O N N Screening Synthesis Biophysical (BP) collection for primary and secondary site BP screens only NMR SPR Screening and confirmation Purification and sample logistics Lead 2

3 Property Space of Fragment Decks (Ro3, 3.5, and BP) CLogP MWt TPSA HBD HBA NRTB NHA Ro <20 Ro3.5 <3.5 <300 < BP <2 < <17 Rule of three represents the generally accepted ideal physicochemical property space of fragment hits. For Ro3.5 deck a number of parameters were relaxed beyond Ro3. BP (Biophysical Library for BP screening only) 3

4 Design of the Ro3 Deck- Saturation? Analysis of AbbVie legacy and commercial fragment decks Fsp3 distribution of AbbVie legacy fragment deck Saturation of commercial decks varies greatly (Swain) Key Organics Infarmatik Less saturated More saturated No real consensus on how saturated fragments should be Analyzed internal data to look for trends 4

5 Effect of Saturation (Fsp3) on Potency? pic50 data of 78K fragment-like compounds (maximum Ro4) vs Binned Fsp3 < >0.9 Binned Fsp3 Fsp3 ( ) bin contains fragments with the highest odds of pic50 >7. These fragments contain approximately equal numbers of saturated and unsaturated carbon atoms 5

6 Effect of Saturation(Fsp3 and NAR) on Potency? Mapping Fsp3, Num aromatic rings (NAR), and pic50 NAR Binned Fsp3 NAR = 1 and Fsp3 ( ) highest odds of pic50 > 6 and 7 criteria to purchase new fragments 6

7 Ro3 Fragment Collection 10 K Legacy library Aroom compounds >50mg Monomer Collection Vendors Ro3 Filter 3.5 K Fragments 3 K Fragments Ro3 Filter Deprotect Ro3 NAR = 1 Fsp3 = Design Synthesis 2.5 K Fragments 1 K Fragments 500 Fragments 1837 Fragments 4011 Ro3 Fragments Structural confirmation and solubility data 7

8 Ro3 Deck- Structures Confirmed and Solubility Measured Solubility (CAC, µm) NHA Ro3 fragments have high solubility (Average CAC=2330 µm) and purity (>95% LC/MS). High quality collection. 8

9 Case Study-Ro3 Fragment to Lead in 6 Months A B C Fragment Hit Ki=15 µm, LipE=5.2, BEI=18, LigE= 0.33 Biacore KD =16 µm, NMR Q score=5 From Ro3 fragment library XRC of Fragment Hit First Iteration Ki=0.02µM, LipE=5.2, BEI=21, LigE=0.39 Second Iteration TR-Fret Ki=0.003µM, LipE=5.3, BEI=20, LigE=0.40 Properties Fragment Hit ClogP 0.4 MWt 272 NAR 1 Fsp3 0.5 Tier 1 HLM Cl int,u 2.6 PAMPA 4.5 elogd 2.1 PFI 3.1 First Iteration Second Iteration

10 LigE vs LipE plot Lead Compound LigE Many compounds in very good property and efficiency space Ro3 fragment rapidly optimized to high quality drug-like chemical matter LipE 10

11 Advantages of a BP Library Probability of engaging a target and extent of chemical space NHA Probability of hitting a target is inversely proportional to the extent of chemical space with increasing number of heavy atoms 11

12 Advantages of a BP Library Probability of engaging a target and extent of chemical space M. Hann, Nature Reviews Drug Discovery, 2012, 11, 359 NHA Probability of hitting a target is inversely proportional to the extent of chemical space with increasing number of heavy atoms 12

13 Extent of chemical space BP chemical space much smaller than R03/3.5 % Compounds Per Heavy Atom BP library- Extent of chemical space = 3 x Ro3-3.5 librariesextent of chemical space = 5 x NHA Extent of chemical space is much smaller for BP than Ro3 and Ro3.5 chemical space and can be sampled by a smaller set of compounds. 13

14 Extent of chemical space Average MWt mapped onto NHA % Compounds Per Heavy Atom, Mean MWt BP library- Extent of chemical space = 3 x Ro3-3.5 librariesextent of chemical space = 5 x NHA Extent of chemical space is much smaller for BP than Ro3 and Ro3.5 chemical space and may be sampled by a smaller number of compounds. 14

15 Biophysical Deck (BP)- initial concept Ro3 library Ro 3.5 library Aroom Monomer Initiative Vendors MWt <200 LogD <=1 NAR <=2 Mean NHA = 11 Highly Soluble Fragments Measure Solubility NMR Aldrich MS BP fragments overlap with 75% of all other BP fragments. AMS used as sole source of BP fragments BP Library 15

16 Analysis of Vendor Overlap in BP Fragment Space* Libraries Total # # BP Fragments % Coverage by Aldrich Aldrich MS Abbvie Ro Abbvie Ro Aroom (ROOT, >50mg) ChemBridge ChemDiv Chem-X-Infinity DuPont Enamine Informatik Innovapharm InterBioscreen Key Organics LC Maybridge OTAVA Prestwick Vitas Wuxi Aldrich MS BP fragments overlap with 75% of all other BP fragments. AMS used as sole source of BP fragments BP Fragment Space Thanks to AbbVie s Cheminformatics group* 16

17 Aldrich MS BP Fragments- Selection Process 37,272 BP Fragments- 750 clusters Remove NAR=0 and NR=0 NAR>0, pick 20 per cluster* NAR= 0 and NR>0, pick 5 per cluster* 4.9K BP Fragments 7 scientists 1K BP Fragments 37K clustered (t-sne) 4.9K most diverse per cluster * most diverse per cluster 17

18 Size Distributions of Screening Decks % Compounds Per Heavy Atom BP Fragment Deck Ro3 Fragment Deck Ro3.5 Fragment Deck HTS Deck Number of Heavy Atoms Well positioned to cover fragment space with new fragment libraries 18

19 Size Distributions of Screening Decks and Hits Known fragment hits from literature (Swain analysis) % Compounds Per Heavy Atom BP Fragment Deck Ro3 Fragment Deck Ro3.5 Fragment Deck HTS Deck Number of Heavy Atoms Average NHA for hits from literature = 14.3, MWt =

20 Size Distributions of Screening Decks and Hits Known fragment hits from literature (Swain analysis) AZ comparison Fuller et al, DDT, 2016, % Compounds Per Heavy Atom BP Fragment Deck Ro3 Fragment Deck Ro3.5 Fragment Deck HTS Deck NHA Number of Heavy Atoms AZ fragment hits- NHAs trend higher 20

21 Size distributions of screening decks and hits AZ comparison Fuller et al, DDT, 2016, % Compounds Per Heavy Atom BP Fragment Deck Ro3 Fragment Deck Ro3.5 Fragment Deck HTS Deck NHA Number of Heavy Atoms AZ fragment hits- NHAs trend higher 21

22 Size distributions of screening decks and hits % Compounds Per Heavy Atom BP Fragment Deck Ro3 Fragment Deck Ro3.5 Fragment Deck HTS Deck AbbVie Fragment Hits Similar size to AZ hits Number of Heavy Atoms Internal fragment hits- average NHA = 16, MWt =

23 Lipophilicity distributions of fragment decks CLogP BP library least lipophilic with an average CLogP <=2 23

24 Saturation distributions of fragment decks Ar-sp3 = Aromatic atoms sp3 carbons Unsaturated Saturated Ar-sp3 Unsaturated Ro3 library contains the most saturated fragments 24

25 Property distribution maps Parallel coordinate plot of property space for fragment libraries Each line is a unique fragment BP library map most dissimilar Ro3 and Ro3.5 similar except for saturation space 25

26 Shape- 3D character of fragment decks PMI: Sauer and Schwartz J. Chem. Inf. Comput. Sci 2003, 43,

27 Using CRUK 3D Criteria* Cancer Research UK*: Firth et al J. Chem. Inf. Model. 2012, 52, D fragment hits from literature. Relatively flat Data courtesy of Chris Swain. 27

28 PBF (Plain of Best Fit) vs saturation (NAR and Fsp3) PBF: Firth, Brown, and Blagg J. Chem. Inf. Model. 2012, 52,

29 Charge state distributions of fragment decks Charge state of fragment libraries Charge state of fragment hits (Swain) Distribution similar to known fragment hits (Swain) 29

30 Potential for synthesis follow up Not trivial to determine the potential for synthetic follow up of fragments We have determined distribution of synthetic handles Overall Distribution However if the handles are required for binding or are embedded within a protein they are no longer useful for elaboration of the fragment Concept: C-H activation chemistry provides a Latent-Active approach to fragment optimization C-H bond the most ubiquitous bond in fragments! 30

31 C-H Activation for fragment follow-up We determined which substructures have potential for C-H activation from the current literature Analyzed the potential for C-H activation based on whether the substructure was present in the fragment Overall Distribution Have a number of collaborations focused on employing C-H activation chemistry to produce novel fragments Using this chemistry for both potential fragment follow-up and to provide bespoke fragments for augmentation. 31

32 Examples of enabling C-H activation chemistry S N Me C H Borylation Hartwig Bpin S N Me OMe Me N C H Silylation Stoltz & Grubbs OMe Me N Sin-Bu O N O NH Me C H Borylation Hartwig Bpin O N O NH Me Me N NH NH 2 2 N N N C H and hv MacMillan Me O O N N N C H Borylation Hartwig Bpin O N N N The burgeoning field of CH activation chemistry brings more potential flexibility to fragment follow-up as well as providing methodology for producing novel fragments for library enrichment. 32

33 Fragment chemistry collaborations Collaboration with Professor Brian Cox at the University of Sussex/Photodiversity to provide 3-D fragment scaffolds Most fragments within Ro3, with good balance of saturation (Average Fsp3 = 0.53). Collaboration with Professor Phil Garner (WSU) to supply uniquely substituted pyrrolidines. Stereo and enantiomerically enriched pyrrolidines accessible via Garner s 1,3- dipolar cycloaddition chemistry. 33

34 Summary A significant amount of time, effort, and investment has been dedicated to revamping and augmenting the fragment screening collection. Ro3 and Ro3.5 and BP fragment libraries cover all important regions of fragment space. Consistent with known fragment hits. The team has demonstrated the successful utility of FBDD in a number of programs. 34

35 Acknowledgements FBDD Chemistry HtL and HTC Chemistry FBDD Biophys and GPS Structural Biology Cheminformatics TEST Leadership 35