Progressing fragments for challenging targets

Transcription

1 For slides Progressing fragments for challenging targets Roderick E Hubbard Vernalis (R&D) Ltd, Cambridge YSBL & HYMS, Univ of York, UK Fragments

2 Trends for new technologies In the beginning lots of excitement Which can lead to hype and over selling Expectation 2 Technology Trigger Analysis / terms initially proposed by Gartner group (Murcko) Time

3 Trends for new technologies In the beginning lots of excitement Which can lead to hype and over selling Expectation Inflated expectations 3 Technology Trigger Analysis / terms initially proposed by Gartner group Time

4 Trends for new technologies Too rapid (often inexpert) deployment It doesn t work disillusionment Expectation Inflated expectations 4 Technology Trigger Trough of Disillusionment Analysis / terms initially proposed by Gartner group Time

5 Trends for new technologies Too rapid (often inexpert) deployment It doesn t work disillusionment Expectation Inflated expectations 5 Technology Trigger Trough of Disillusionment Analysis / terms initially proposed by Gartner group Time

6 Trends for new technologies Eventually, expertise grows Begin to understand how and where to apply methods Expectation Inflated expectations Slope of Enlightenment 6 Technology Trigger Trough of Disillusionment Analysis / terms initially proposed by Gartner group Time

7 Trends for new technologies Learn how to integrate the methods into the process Add to productivity Expectation Inflated expectations Slope of Enlightenment Plateau of productivity 7 Technology Trigger Trough of Disillusionment Analysis / terms initially proposed by Gartner group Time

8 Fragments The ideas established in the molecular modelling / structural biology community during the 1980s and early 1990s First reduced to practice by Abbott in SAR by MR approach in mid 1990s ther pharmaceutical companies unable to replicate success Approaches developed in small pharma companies in late 1990s / early 2000s Astex, Vernalis, Plexxikon, SGX.. and underground in large pharma Underpinning concepts developed in the 2000s complexity, ligand efficiency Success has led to increased use Different aspects of FBLD are on different parts of this curve And in different organisatiions Expectation Inflated expectations Slope of Enlightenment Plateau of productivity 8 Technology Trigger Trough of Disillusionment Time

9 Fragments 2013 talk Summary from 2009 Where we were 4 years ago ew approaches / ideas for conventional targets Screening methods ff rate screening for fragment to hit optimisation How to approach non conventional targets What is a non conventional target? Methods for determining binding mode Issues assays, plasticity, compound properties, 3D Final remarks 9

10 Fragments 2013 talk Summary from 2009 Where we were 4 years ago ew approaches / ideas for conventional targets Screening methods ff rate screening for fragment to hit optimisation How to approach non conventional targets What is a non conventional target? Methods for determining binding mode Issues assays, plasticity, compound properties, 3D Final remarks 10

11 Fragments 2009 talk SeeDs process 2008 Structural Exploitation of Experimental Drug Startpoints* Target a b c Validation Biacore X-ray Structures MR Competitive Binding Experiment HSQC H 2 H H H H H H Wet assay / Biacore H H H H H H H H H H Fragment Library 11 *Hubbard et al (2007), Curr Topics Med Chem, 7, 1568 H Evolution

12 Fragments 2009 talk Finding fragments Chen & Hubbard (2009), JCAMD, 23, 603 Finding fragments that bind is not difficult A good way of assessing target ligandability 8% 7% Low hit rates (< 2%) High hit rates (> 2%) Class Validated 1 hits rates 6% 5% 4% 3% 2% 1% protein-protein interaction (0.4-3% hit rate) Poor targets Kinases (3-5% hit rate) 12 0% LigandabilityCalculate calculated Dscore druggability from structure (DScore) 1

13 Fragments 2009 talk Finding fragments Chen & Hubbard (2009), JCAMD, 23, 603 Finding fragments that bind is not difficult A good way of assessing target ligandability Low hit rate can indicate difficult to progress See also Hajduk (2005) J Med Chem, 48, % 7% Low hit rates (< 2%) High hit rates (> 2%) Class Validated 1 hits rates 6% 5% 4% 3% 2% 1% protein-protein interaction (0.4-3% hit rate) Poor targets Kinases (3-5% hit rate) 13 0% LigandabilityCalculate calculated Dscore druggability from structure (DScore) 1

14 Fragments 2009 talk Using fragments Growing fragments 14

15 Fragments 2009 talk Using fragments Growing fragments CHK1 example Chk-1 IC 50 >100µM LE ~

16 Fragments 2009 talk Using fragments Growing fragments CHK1 example Chk-1 IC 50 = 5µM LE = 0.39 GI 50 HCT116 >80µM ph2ax (MEC) inactive 16

17 Fragments 2009 talk Using fragments Growing fragments CHK1 example Chk-1 IC 50 = 0.2µM LE = 0.33 GI 50 HCT116 = 4µM ph2ax (MEC) = 7µM 17

18 Fragments 2009 talk Using fragments Growing fragments CHK1 example Chk-1 IC 50 = 0.013µM LE = 0.39 GI 50 HCT116 = 1.8µM ph2ax (MEC) =0.2µM 18 Series members further optimised to identify Candidate V158411

19 Fragments 2009 talk Using fragments Merging HSP90 example Screen Known Ligands Virtual Screening hits Detailed Design 19

20 Fragments 2009 talk HSP90 BEP800 story Brough et al (2009) J Med Chem 52, Roughley et al (2012) Top Curr Chem 317, 61 Fragment Evolved fragment H 2 VER FP IC 50 >5mM Me H 2 S VER FP IC 50 =535 M H VER VP-BEP800 D93 G97 K58 FP IC 50 =0.058 M K D = 0.9nM (SPR) HCT116 GI 50 =0.161 M BT474 GI 50 =0.057 M H 2 Cl Cl F138 S H L107 Virtual Screening Hit H 2 S H 2 H 2 H 2 Cl Cl H H Et VER FP IC 50 =0.9 M VER FP IC 50 =1.56 M 20 Virtual Screening Hit H VP-AUY922 Vernalis Phase II candidate (FBLD / SBDD derived)

21 Fragments 2009 talk Pin1 Story Proline isomerase persuasive biological rationale that key oncology target Structure available + D peptide tool compound 21

22 Fragments 2009 talk Pin1 Story H H H 3 C Proline isomerase persuasive biological rationale that key oncology target Fragments identified: fragment to hit evolution o correlation between biophysical and enzyme assays 22

23 Fragments 2009 talk Pin1 Story H H H 3 C Proline isomerase persuasive biological rationale that key oncology target Fragments identified: fragment to hit evolution Issue with over binding multiple copies of fragment binding to the protein SPR and Xray 23

24 Fragments 2009 talk Pin1 Story H H H H H CH 3 Potter AJ et al, Bioorg Med Chem Lett. 2010; 20: H 3 C Proline isomerase persuasive biological rationale that key oncology target Fragments identified: fragment to hit evolution Issue with over binding multiple copies of fragment binding to the protein SPR and Xray Designed 3D fragment progressed multiple series < 100nM on target showing cellular activity 24

25 Fragments 2013 talk Summary from 2009 Where we were 4 years ago ew approaches / ideas for conventional targets Screening methods ff rate screening for fragment to hit optimisation How to approach non conventional targets What is a non conventional target? Methods for determining binding mode Issues assays, plasticity, compound properties, 3D Final remarks 25

26 Hubbard et al (2007), Curr Topics Med Chem, 7, 1568 Hubbard and Murray (2011), Methods Enzym, 493, 509 The Vernalis process Target Screen by SPR, DSF, WAC, biochem or binding assay, Xray Virtual screen; literature; library screen Fragment Library ~ 1200 compounds Ave MW 190 Competitive MR screen H 2 H 2 Me S H 2 Hits H 2 ptimise fragment Characterisation 26 Drug? H 2 S H 2 Cl H Cl H 2 Cl Design, Build & Test S H Cl H H H Et Response RU Fragment to hit : SAR by catalog off rate screening Time s Response RU Time s X ray or MR guided model

27 Some comments on fragment screening Hubbard & Murray (2011), Meth Enzymology, 493, 509 For good target sites (many enzymes): If assays configured correctly Same hits identified by ligand observed MR and SPR validated hits tend to give crystal structures Careful QC of fragment library attention to assay conditions There can be lots of false negatives from screening by X ray Requires suitable crystal system Wet assays can work sometimes But high concentrations can confound the assay Thermal melt methods unreliable For non conventional targets (such as protein protein): Many issues verbinding, problems due to properties of compounds and target Cross validate binding by different techniques 27

28 Leads generated for many Targets Disclosed targets include: Kinases: CDK2, Chk1, PDPK1, PDHK1, Pim1, STK33, Pak4 ATPases: DA gyrase, Grp78, HSP70 and HSP90 protein protein interaction targets: Pin1 and Bcl 2 FAAH and tankyrase Undisclosed targets include: kinases with unusual binding sites protein protein interaction targets novel classes of enzymes in large multi domain complexes 28

29 Summary fragments and conventional targets Fragment screen to assess targets Fragments alongside HTS and knowledge based You always find something new Fragments to hits for conventional targets is relatively straightforward (when crystal structures / good models available) The main issues are organisational and cultural Discuss!! Expectation Inflated expectations Slope of Enlightenment Plateau of productivity 29 Technology Trigger Trough of Disillusionment Time

30 Fragments 2013 talk Summary from 2009 Where we were 4 years ago ew approaches / ideas for conventional targets Screening methods ff rate screening for fragment to hit optimisation How to approach non conventional targets What is a non conventional target? Methods for determining binding mode Issues assays, plasticity, compound properties, 3D Final remarks 30

31 James Murray, Steve Roughley, atalia Matassova Exploiting the dissociation rate constant K D = k off (s -1 ) k on (M -1 s -1 ) PL P + L k off k on 31

32 James Murray, Steve Roughley, atalia Matassova Exploiting the dissociation rate constant K D = k off (s -1 ) k on (M -1 s -1 ) Cannot improve association rate constant above ~10 8 M 1 s 1 o point in drug discovery, too many membranes in the way! Dissociation rate constant can be infinite (ie covalent!) We have seen that it is the key driver of potency As have others (review Copeland, Future Med. Chem. 3(12), 2011) 32

33 James Murray, Steve Roughley, atalia Matassova Exploiting the dissociation rate constant K D = k off (s -1 ) k on (M -1 s -1 ) Cannot improve association rate constant above ~10 8 M 1 s 1 o point in drug discovery, too many membranes in the way! Dissociation rate constant can be infinite (ie covalent!) We have seen that it is the key driver of potency As have others (review Copeland, Future Med. Chem. 3(12), 2011) 33 Resonance Signal (RU) Association - k on Kinetics Concentration Dissociation - k off Time (s) Surface plasmon resonance a way to measure kinetics FCUS THE ff RATE k off

34 James Murray, Steve Roughley, atalia Matassova Exploiting the dissociation rate constant K D = k off (s -1 ) k on (M -1 s -1 ) Cannot improve association rate constant above ~10 8 M 1 s 1 o point in drug discovery, too many membranes in the way Dissociation rate constant can be infinite (ie covalent) We have seen that it is the key driver of potency As have others (review Copeland, Future Med. Chem. 3(12), 2011) Independent of concentration Exploit this to assess unpurified reactions, off rate screening (RS) 34

35 James Murray, Steve Roughley, atalia Matassova ff rate screening (RS): Example Historical Hsp90: thienopyrimidines 200 nm 5 MIC 50 by FP assay Re prepared compounds by Suzuki reaction H 2 Cl S R DMF / H 2 ahc 3 B(H) 2 Pd(Ph 3 P) 2 Cl 2 H 2 R S 100 ºC Microwave 10 min Minimal work up Evaporate, partition Purity % (LCMS) Screened by RS 35

36 James Murray, Steve Roughley, atalia Matassova ff rate screening (RS): Example Historical Hsp90: thienopyrimidines 200 nm 5 MIC 50 by FP assay Re prepared compounds by Suzuki reaction H 2 Cl Minimal work up Evaporate, partition Purity % (LCMS) Screened by RS S R DMF / H 2 ahc 3 B(H) 2 Pd(Ph 3 P) 2 Cl ºC Microwave 10 min H 2 R S A: Pure starting material B: Faux reaction 36

37 James Murray, Steve Roughley, atalia Matassova ff rate screening (RS): Example Historical Hsp90: thienopyrimidines 200 nm 5 MIC 50 by FP assay Re prepared compounds by Suzuki reaction H 2 Cl Minimal work up Evaporate, partition Purity % (LCMS) Screened by RS S R DMF / H 2 ahc 3 B(H) 2 Pd(Ph 3 P) 2 Cl ºC Microwave 10 min H 2 R S A: Pure starting material B: Faux reaction C: Purified product D: Crude reaction 37

38 Tankyrase Introduction Axin is targeted for turnover through poly D ribose labelling by Tankyrase. This removes axin, which has a role in stabilising catenin Inhibition of Tankyrase has been proposed to enhance the degradation of catenin, an indirect way of affecting the WT pathway Crystal structure of tankyrase available in RF5 Structural Genomics Consortium (2007) 3UH4 ovartis (2012) 38

39 Tankyrase Introduction Alba Macias, Chris Graham and team Axin is targeted for turnover through poly D ribose labelling by Tankyrase. This removes axin, which has a role in stabilising catenin Inhibition of Tankyrase is therefore proposed to enhance the degradation of catenin, an indirect way of affecting the WT pathway Crystal structure of tankyrase available in 2007 At Vernalis: Initially low levels of protein production insufficient material for fragment screen by MR Able to produce large numbers of apo crystals that preliminary trials showed were suitable for ligand soaking 39

40 Tankyrase Hit identification Alba Macias, Chris Graham and team Crystals Soaked fragments in pools of 8 Data collection (up to 1.6Å resolution) Streamlined structure solution 62 hits from 1563 fragments Characterised by SPR and TSA 40

41 41 Tankyrase Fragment to hit evolution The most attractive fragments were not suitable for rapid chemistry Designed a modified fragment ff rate screening libraries identified vectors and substituents Crystals soaked directly with reaction mixtures Lead Series driven using combinations of tools Computational chemistry X ray crystallography SPR (RS), DSF Medicinal Chemistry Properties 5 nm vs TKS2, high ligand efficiency (0.60) Affects PD markers in cells (stabilises Axin2, inhibits WT pathway) Tools to probe the biology Alba Macias, Chris Graham and team

42 PDHK an unusal kinase GHKL family protein (like HSP90) ther companies have targetted for diabetes AZ and ovartis in the 1990s various allosteric sites Vernalis investigated as a cancer metabolism target ff rate screening to selectively evolve a fragment DCA 2q8h Pfizer 2bu7 E2 / L2 site AZD q8g ovartis 2bu5 42 ATP site 2q8i

43 PDHK project Initial fragment hit for ATP site also binds to HSP90 Structure shows which vector(s) to explore Parallel libraries synthesised and screened by SPR Using the off rate screening method changes in k off ne SPR channel with HSP90, one with PDHK Can identify PDHK selective compounds And HSP90 selective compounds Fragment bound to PDHK1 VER PDHK1 HSP90 VER PDHK1 HSP90 43

44 Fragments 2013 talk Summary from 2009 Where we were 4 years ago ew approaches / ideas for conventional targets Screening methods ff rate screening for fragment to hit optimisation How to approach non conventional targets What is a non conventional target? Methods for determining binding mode Issues assays, plasticity, compound properties, 3D Final remarks 44

45 Fragments 2013 talk Summary from 2009 Where we were 4 years ago ew approaches / ideas for conventional targets Screening methods ff rate screening for fragment to hit optimisation How to approach non conventional targets What is a non conventional target? Methods for determining binding mode Issues assays, plasticity, compound properties, 3D Final remarks 45

46 46 on conventional targets

47 on conventional targets Can find fragments that bind rthogonal biophysical methods can validate and characterise fragment binding 47

48 on conventional targets Can find fragments that bind Evolution requires robust model of fragment binding Best model is from X ray structure But sometimes high affinity ligand required for structure 48

49 on conventional targets Can find fragments that bind Evolution requires robust model of fragment binding Best model is from X ray structure MR methods can provide sufficient quality of model Experiments can be filtered to reveal just the interactions between protein and ligand Unfiltered ESY - All Es 49

50 on conventional targets Can find fragments that bind Evolution requires robust model of fragment binding Best model is from X ray structure MR methods can provide sufficient quality of model Experiments can be filtered to reveal just the interactions between protein and ligand Protein/ligand Filtered ESY - Intermolecular Es Unfiltered ESY - All Es 50

51 on conventional targets Can find fragments that bind Evolution requires robust model of fragment binding Best model is from X ray structure MR methods can provide sufficient quality of model Experiments can be filtered to reveal just the interactions between protein and ligand Have developed leads from fragments using MR models High affinity ligands give X ray structures that confirm model 51

52 52 Video of Bcl 2 plasticity

53 Bcl 2 ligandability Class Validated 1 hits rate rates 8% 7% 6% 5% 4% 3% 2% 1% Low hit rates (< 2%) High hit rates (> 2%) protein-protein interaction (0.4-3% hit rate) 0% Calculate druggability Dscore Ligandability calculated from structure (DScore) changes dramatically as ligands explore available pockets / flexibility 53

54 Geneste, Murray, Davidson, lediguarher et al Selective Bcl 2 inhibitor program Structure guided optimisation has generated selective Bcl 2 inhibitors MW < 780; > 100 fold selective over other BH 3 domains Sub 10 nm efficacy in cell models of AML In vivo, rapid and strong apoptotic response in RS4;11 xenograft models, both iv and oral Platelet sparing (cf ABT 263) Key was biophysical assays to assess cell penetration and compound aggregation Tumour volumes (mm 3 ) Median +/- InterQuartile Range Compound 3 50 mg/kg Compound mg/kg Compound 4 50 mg/kg Compound mg/kg ABT mg/kg ABT mg/kg Control (untreated) Time after tumour inoculation (days) Treatment schedule (per os) (Twice a week)

55 Summary non conventional targets For non conventional targets And where structures are hard to obtain It takes time and not yet clear how often fragments can be successful Integration with biophysical methods can be key Also a commitment to the long haul Expectation Inflated expectations Slope of Enlightenment Plateau of productivity 55 Technology Trigger Trough of Disillusionment Time

56 Fragments 2013 talk Summary from 2009 Where we were 4 years ago ew approaches / ideas for conventional targets Screening methods ff rate screening for fragment to hit optimisation How to approach non conventional targets What is a non conventional target? Methods for determining binding mode Issues assays, plasticity, compound properties, 3D Final remarks 56

57 Finding fragments issues Updated from Chen & Hubbard (2009), JCAMD, 23, 603 For some targets, it can take time to configure a robust assay For fragment screening but also hit progression Protein construct, assay conditions, etc, etc 8% 7% Low hit rates (< 2%) High hit rates (> 2%) Class Validated 1 hits rates 6% 5% 4% 3% 2% 1% protein-protein interaction targets varying hit rates Poor targets Kinases high hit rate 57 0% LigandabilityCalculate calculated Dscore druggability from structure (DScore) 1

58 3D fragments Most of the compounds in fragment libraries are commercially available small molecules Medicinal chemist emphasis on chemical tractability Some use privileged fragments from existing drugs Most of the fragments are flat heterocycles This is fine for some targets (kinases, ATPases) Perhaps limiting for other (new) target classes Some initiatives underway to introduce more 3D fragments The challenge will be synthetic tractability A York initiative 58

59 York 3D fragments Peter Brien has developed chemistry for adding lithiated Boc heterocycles 2 (formed from 1) to heterocyclic, symmetrical ketones The resulting fragments have distinctive 3D shapes, presenting useful looking pharmacophores Shape measured by principle moments rod Vernalis disc sphere rod York fragments disc sphere

60 York 3D fragments Peter Brien has developed chemistry for adding lithiated Boc heterocycles 2 (formed from 1) to heterocyclic, symmetrical ketones The resulting fragments and lead like compounds have distinctive 3D shapes, presenting useful looking pharmacophores Project underway to explore the chemistry Generate 500 member library See how it performs against various targets 60

61 Concluding remarks 61 Straight forward to find fragments for most sites on most proteins pportunities for new 3D fragments? The challenge is knowing what to do with the fragments ff rate screening allows exploration of vectors Evolving fragments in absence of structure? For conventional targets Lots of starting points; opportunity for good medicinal chemistry Issue in some organisations is integration with medicinal chemistry For non conventional targets Provides starting points when other techniques fail Close integration with biophysics is crucial; takes time and commitment ot necessarily faster patience required But hopefully better

62 End References in the slides acknowledge who did the work FBLD conference 2008 San Diego 2009 York 2010 Philadelphia 2012 San Francisco 2014 Basle 62

63 63 Vernalis Research verview Approximately 60 staff in research Based in Cambridge, UK (Granta Park) Recognised for innovation and delivery in structure and fragment based drug discovery Structure based drug discovery since 1997 Distinctive expertise combining X ray, MR, ITC and SPR to enable drug discovery against established and novel, challenging targets Portfolio of discovery projects Six development candidates generated in the past six years Protein structure, fragments and modelling integrated with medicinal chemistry Internal Vernalis projects in oncology Collaborations across all therapeutic areas e.g. oncology, neurodegeneration, anti infectives Aim to establish additional collaborations during 2013 / 14