Selecting compounds to find new leads for kinetoplastids and other NTDs

Transcription

1 Selecting compounds to find new leads for kinetoplastids and other TDs Mark Gardner 4 th December 2013 salvensis.org

2 two topics Importance of finding good leads selecting compounds for TD HTS what factors affect hit rate in HTS? are phenotypic screens for TD different to biochemical screens? using information from HTS to find more hits models & blinding 2

3 Candidates are similar to hits : Data points are lead & drug pairs Data set from E Perola J. Med. Chem. 2010, 53, ECFP4 similarity MDL public keys similarity H H O O S H O O O O H O F F H O O H O O O O H H

4 Lots of analyses which broadly point in the same direction TD once a day or single dose cures, MDA simple formulations Oral drug properties The influence of drug-like concepts on decision-making in medicinal chemistry, Paul D. Leeson and Brian Springthorpe, ature Reviews: Drug Discovery, 2007, 6, 881 4

5 File Enrichment strategy rationale If the target is druggable then the industry will succeed, but any individual company may not Increase Pfizer s file to match that of the industry George M Milne Jr, Annual Reports in Medicinal Chemistry 2003, 38, 383

6 Analysis of compound sets All_FE predominantly 4 collaborations ArQule, ChemRx, ChemBridge, Tripos nonfe compounds in the file from Medchem & animal health singleton synthesis over the years Compound acquisitions mostly of singletons includes Warner Lambert/Parke Davis

7 FE compounds fit in Ro5 C.A. Lipinski; F. Lombardo; B.W. Dominy and P.J. Feeney (2001) Adv Drug Del Rev 46: 3 26

8 Pfizer candidates from FE hits (analysis 3 years old) 1. Hit optimised to candidate (these examples) Hit Lead Candidate 2. Parallel synthesis protocols used in hit-> candidate process (other examples)

9 Analysis of performance in High Throughput Screening: An example kinase compressed HTS 1. 40% inhibition cutoff (3xSD + mean) 2. Singleton confirmation (31% > 40% inhibition) count count count % inhibition bin % inhibition bin % inhibition bin > 3. 68% of these have a dose-response value count = = = = = < pki bin

10 Performance in >90 High Throughput Screens (HTS) Primary hit rate Confirmation rate dose-response rate overall hit rate File Enrichment 1.3 % 9.7 % 66 % 0.08 % on FE (legacy) 2.0 % 19.2 % 75 % 0.29 % File enrichment compounds are 3-4 fold less likely to have a confirmed dose-response activity than non FE (legacy) compounds Confirmation rate shows the biggest difference between FE & non FE.

11 Properties & confirmation-rate Confirmaton rate * Bars only shown if at least 100 compounds populate that bin Positive association between MWt & confirmation rate

12 lipophilicity - clogp

13 Charge state nonfe FE Sized by population FE compounds have proportionately more bases (and fewer acids) than nonfe

14 Confirmation rate aromaticity/sp2 character Proportion of all bonds which are aromatic Proportion of atoms which are sp2 At high values of aromatic character, confirmation rates become similar

15 But undesirable characteristics may increase too... Proportion of compounds clean in a THLE in vitro toxicity assay (n=47k) Proportion of all bonds which are aromatic Clean Dose-response Curve in tox assay Timothy J. Ritchie and Simon J.F. Macdonald, Drug Discovery Today Volume 14, umbers 21/22 ovember 2009, p1011

16 What factors most influence confirmation rate? regression equation to attempt to predict confirmation rate based on properties IGORE FE vs non FE difference at this point All values have been scaled (mean centred and scaled to unit variance) Predicted confirmation rate = * P_arombonds + 4 * P_HBD * P_onAromRingBonds +... Thanks to Phil Brain for statistical advice

17 Do physchem property differences explain the FE/nonFE hit-rate difference? Predicted file set confirmation rate mean (calc properties) Predicted conf rate (calc props plus concn) Confirmation rate mean non_fe 17.5 ~ all_fe 12.5 ~ Fold difference 1.4 ~ Conclusion Approximately half of the difference in confirmation rate between FE & nonfe sets IS explainable by property differences

18 High confirmation-rate ring systems (n>100) 93% 83% 82% 77% 71% 69% 69% 67% 67% 63% 63% 64% 62% 59% 59% 56% 55% 54% 52% 50%

19 High confirmation-rate ring systems (n>100) 93% 83% 82% 77% 71% Several undesirable cores in here 69% 69% 67% 67% 63% Lots of aromaticity 63% 64% 62% 59% 59% 56% 55% 54% 52% 50%

20 Low confirmation-rate ring systems (n>100) 1% 1% 1% 1% 2% 2% 2% 2% 3% 3% 3% 3% 3% 3% 3% 3% 4% 4% 4% 4%

21 Low confirmation-rate ring systems (n>100) 1% 1% 1% 1% 2% Do these look like they d never be active? 2% 2% 2% 3% Low aromaticity, but some nice 3D character 3% Just waiting for the right target? 3% 3% 3% 3% 3% 3% 4% 4% 4% 4%

22 Confirmation rate differences (FE vs nonfe) Properties explain approx half of the differences between FE & nonfe aromaticity seems particularly important Hypothesis for remainder of difference: non-fe compounds made in clusters when active (against something, usually a single target) When tested against related targets, good chance of activity FE compounds made in clusters without certainty of activity Lower hit rate

23 Compound diversity & re-sculpting the Pfizer File A ew Hit Identification and Screening File Strategy for Pfizer Jeremy Everett, Special Operations, Worldwide Research Centres of Emphasis, Pfizer PharmaTherapeutics R&D David Baines (Finance) Greg Bakken (Comp. Sci. CoE) Andy Bell (Med. Chem.) Markus Boehm (Comp Chem) Mark Gardner (Med. Chem.) Rose Gonzales (Material Management) Dave Hepworth (Med. Chem.) Caroline Hiseman (Finance) Branden Humphrey (IT) Jackie Klug-McLeod (Comp. Sci. CoE) Jens Loesel (Comp. Sciences CoE) Jerry Lanfear (Data Support) Tom Maloney (IT) John Mathias (HTS CoE) Gonghua Pan (External Research Solutions) Bill Skinner (Data Support) Steve Street (WW RCoEs) Lisa Thomasco (External Research Solutions) Terry Wood (Material Management) Tony Wood (WW Med. Chem.) 23

24 purpose of Pfizer screening file primary purpose of Pfizer screening file is to find representatives of most or all of chemical hit series in the screening file in an efficient manner purpose is OT to mine out SAR during the Hit ID phase intention is to have top quality file with all hits potentially attractive to medicinal and computational chemists on Therapeutic Area project teams hit lead candidate drug

25 Similarity Significant component of slimming the screening file for effective Hit ID, a cluster size limit of 40 at a Tanimoto = 0.6 (ECFP6) was selected redundancy is calculated on a per compound basis and is defined as 40 redundancy =1 # eighbours remove compounds from densely populated clusters

26 what happens to HTS output? in silico analysis of impact of redundancy reduction on series retrieval using IC50 level data impact is minimal given the scale of compound elimination HTS target Series with IC50 actives Active series removed T T T3 6 0 T T T6 8 0 T T T T T T T # of hit series Active series removed Series with IC50 actives 0 T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 T11 T12 T13 target

27 Analysis of phenotypic parasite HTS Hit rate vs MWt 96 non-td HTS 4 TD HTS (or 150K) P. falciparum leishmania T. cruzi T. brucei Mostly macrolides 27

28 lipophilicity 96 non-td HTS 4 TD HTS clogp clogp / MWt 28

29 Proportion of aromatic bonds 96 non-td HTS 4 TD HTS Mostly macrolides Conclusions: TD vs non-htd HTS Similar property trends between phenotypic TD HTS and biochemical HTS. The same fundamental recognition factors are at work. Large/lipophilic/aromatic molecules tend to bind to protein targets 29

30 Acknowledgements: part 1 100s of people who devoted their careers to this effort for several years George Milne & ick Saccomano persuasive leaders who got things going Alex Polinsky leader of the File Enrichment steering group Eric Roskamp, Larry Truesdale, Bob Kennedy, Spiros Liras Pfizer collaboration chairs Atsuo Kuki drove the IT requirements Mike Clark & Peter Luke got the deals done Rod MacKenzie, Tony Wood, Peter Luke, Andy Bell, Graham Smith colleagues who were extremely influential

31 Finding leads for Leishmaniasis, Chagas & HAT Diseases Identifying new leads for Leishmaniasis, Chagas & HAT is difficult Limited screening capacity and throughput Low hit rates eed to make the most of few, precious hits Build predictive models of activity in leishmania, T. cruzi & T. brucei phenotypic assays based on data held by DDi Point these models at commercial collections, purchase & test virtual hits Experiment in progress Request permission from DDi partners to share the models with Pharma partners to point the models at their collections 31

32 Information about structure without revealing structure virtual screening using activity models trained on proprietary compounds large compound database active structures* some memory or image of the active compounds new set of structures * * not real data 32

33 Objectives for (ligand based) virtual screening identify analogues of existing series that might not get synthesised longer shot than most synthetic targets mix and match structural features from different active series compounds displaying 3D pharmacophore on different scaffolds Bayesian statistical model using KIME* & feature based Morgan fingerprints^ without revealing the structures of the training set * ^ 33

34 QSAR models with & without pharma data with pharma data T. brucei Plots of test compounds (20%) scored by model trained on 80% of data QSAR on real data without pharma data T. brucei QSAR on Y-scrambled data without pharma data, Y-scrambled model as good as the real model (model is overtrained & therefore useless) 34

35 Feature based Morgan fingerprints Find: donor, acceptor, aromatic, haolgen, basic and acidic centres in the molecule (Docs at Encode each atom with connected features at 0, 1, 2 and 3 bonds away O Identifiers O H 2 O H 3 C H 2 H 2 C O H 2 CH 3 O CH 2 CH 2 H 3 C CH 2 CH H 2 CH 3 O H 2 O H 2 C CH 2 CH H 2 O 3 CH 2 CH CH 2 CH 2 2 CH 2 CH Identifiers Hash function

36 Example features Positive score because it s found in 36 actives and 9 inactives in the training set (activity is less likely than inactivity) egative score because it s found in 0 actives and 916 inactives

37 KIME Bayesian model. list of features & associated scores from training set 37

38 What can you tell about training set from QSAR models? The model contains list of features present in molecules in the training set, but there is degeneracy (eg halogens are all treated as equal) What can be deduced about the training set compounds by applying the model to a database of compounds? Using T. cruzi model (only reasonable model with non-proprietary data) T. cruzi 38

39 Compounds from same series score similarly whether or not they were in the training set Test Series 1 Scaffold modifications Training Chagas model - score Training set and test set compounds within the same chemical series score in the same region Series 2 Series 3 ID 39

40 Ref 4 Test Series 1 Scaffold modifications 9 Training Chagas model - score Scaffold modification of a reference training compound score in the same region Series Series 3 10 ID 40

41 Conclusions For Bayesian activity models of leishmania, T. cruzi & T. brucei, including proprietary data in the training set is crucial Models are blinded to the training set training set compounds not present in the model training set compounds score the same as similar non-training set compounds The model aims to find similar compounds to those in the training set Screening data is eagerly awaited! 41

42 Acknowledgements: Part II AMG Alexander Alex DDi Charlie Mowbray, Jean-Robert Ioset, Leela Pavan Tadoori Evotec Mike Mazanetz, Dan Warner, Mirco Meniconi ovartis (GF), Institut Pasteur Korea, Abbvie, GSK and Dundee University (DDU) 42

43 Selecting compounds for TD HTS screening: conclusions there are no magic property combinations in chemical space for TD activity vs ADMET battles are approx. the same as in other human drug targets for oral administration the clinical candidate will usually resemble the screening hit Do screen: in assays that translate to the patient chemical space that you know is readily compatible with your product profile until/unless you ve shown it is inactive for your disease compounds which incorporate learning from what has worked before novelty, provided the path through development is not certain failure access to pharma companies compounds & data can be enormously enabling for TD drug discovery 43