Supporting Compound Optimisation in Not-for- Profit and Academic Research

Transcription

1 Supporting Compound Optimisation in Not-for- Profit and Academic Research ACS Spring National Meeting. CINF, April 5 th 2017 Matthew Segall, Tamsin Mansley, Peter Hunt, Kelly Chibale*, Tanya Paquet*, James Duffy *University of Cape Town, Medicines for Malaria Ventures matt.segall@optibrium.com Optibrium, StarDrop, Auto-Modeller, Card View and Glowing Molecule are trademarks of Optibrium Ltd.

2 Overview Drug discovery challenges in not-for-profit (NFP) and academic sectors Guiding decisions in compound optimisation Targeting high quality compounds, fast Example application to optimisation of anti-malarial therapeutics Supporting not-for-profit and academic research Conclusions 2

3 Challenges in NFP and Academic Drug Discovery

4 Challenges in NFP and Academic Drug Discovery Unmet clinical needs for challenging diseases Often diseases primarily affecting the developing world Resistance to current therapies Challenges with distribution and compliance target product profile Limited resources Can t afford to throw the kitchen sink at the problem Need to carefully choose compounds for synthesis and assays to perform Collaborative projects Need to manage data between multiple academic labs and CROs Making good decisions is critical 4

5 Guiding Decisions in Compound Optimisation

6 Guiding Decisions in Compound Optimisation Data visualisation is important but not enough How can you make a confident decision by looking at these? *Segall and Champness (2010) GEN, 30 (Sep 1) 6

7 Guiding Decisions in Compound Optimisation Understanding structure-activity relationships For example: Matched Molecular Pair Analysis logs H Matched Pair Two compounds that differ only by a small replacement at a single position 7

8 Guiding Decisions in Compound Optimisation Understanding structure-activity relationships M.D. Segall et al. (2015) Drug Discov. Today 20(9) pp

9 Guiding Decisions in Compound Optimisation Interactive design with predictive modelling. Design compounds with improved balance of properties Predict properties of new compounds before synthesis and testing Interactively explore new ideas Instant feedback on changes in properties Glowing Molecule Link compound s structure with properties Highlight regions with biggest influence on properties M.D. Segall et al. (2009) Chem. & Biodiv. 6(11) p

10 Property 2 Property 2 Guiding Decisions in Compound Optimisation Multi-parameter optimisation Identify chemistries with an optimal balance of properties Quickly identify situations when such a balance is not possible Fail fast, fail cheap Only when confident Avoid missed opportunities Solubility Potency Property 1 Metabolic stability Safety Absorption 10 Hit Solubility No good drug X Potency Property 1 M.D. Segall (2012) Curr. Pharm. Des. 18(9) pp X Drug Safety Metabolic stability Absorption

11 Guiding Decisions in Compound Optimisation Multi-parameter optimisation Probabilistic Scoring Integrated assessment of data against project criteria Uniquely accounts for the uncertainties in all compoundrelated data (experimental or calculated) User-defined scoring profile Compounds ranked by likelihood of success Histograms for quick visual guide to compound properties M.D. Segall (2012) Curr. Pharm. Des. 18(9) pp

12 Example Application to Anti-malarial Drug Discovery

13 Introduction to Malaria Infectious disease caused by Plasmodium parasites, mainly P. falciparum and P. vivax Transmitted through Anopheles mosquito ~212 million cases of malaria occurred worldwide in 2015, mortalities * Global target: 90% reduction in incidence and mortality by 2030 compared to 2015 Drug treatment threatened by emergence of resistance to artemisinin-based combination therapy in South-East Asia Novel drugs needed for treatment and transmission-blocking * World Health Organisation 2016 World Malaria Report 13

14 Project Background Initial screen of large commercial library (~36,000 compounds) Several series had previously progressed to lead optimisation, e.g. Diego González Cabrera et al. J. Med. Chem. 2011, 54, 7713 Diego González Cabrera et al. J. Med. Chem. 2012, 55, Yassir Younis et al. J. Med. Chem. 2012, 55, 3479 Yassir Younis et al. J. Med. Chem. 2013, 56, 8860 Peter Mubanga Cheuka et al. Bioorg. Med. Chem. Lett. 2014, 24, 5207 Diego González Cabrera et al. J. Med. Chem. 2014, 57, 1014 Diego González Cabrera et al. J. Med. Chem. 2015, 58, 7572 Claire Le Manach et al. J. Med. Chem. 2016, 59, 9890 Claire Le Manach et al. J. Med. Chem. 2015, 58, 8713 Claire Le Manach et al. J. Med. Chem. 2014, 57, 8839 Claire Le Manach et al. J. Med. Chem. 2014, 57,

15 Project Overview Property criteria Chemical Series R2 Core R1 15

16 parasite survival (%) parasite survival (%) Optimisation Challenges Potency Achieve sufficient potency against drug sensitive (NF54) and multi-drug resistant (K1) strains Dose-response Solubility Early compounds were not sufficiently soluble Bi-phasic response against K1 strain Undesirable dose-response, possibly indicative of polypharmacology [compound] (µm) Bi-phasic -response We will illustrate approaches to tackle these challenges and progress over the most recent 3 months [compound] (µm) 16

17 Optimising Potency Learning from initial screen Matched pair analysis of ~3000 compounds (% 1.3 μm) 17

18 Optimising Potency Learning from initial screen Alignment between series from screen and current series suggests that SAR will be transferable Piperazine or morpholino substitution at R2 may be beneficial for activity Cores Alignment performed in torch3d module, developed by Cresset, in StarDrop torch3d is a trademark of Cresset BioMolecular Discovery Ltd. 18

19 Optimising Solubility QSAR Model Assay data not directly comparable with QSAR model PBS ph 6.5 versus intrinsic aqueous solubility (logs) However high predicted intrinsic solubility indicates high chance of achieving required solubility 19

20 Optimising Solubility QSAR Model In the subsequent round of synthesis, the majority of compounds achieved the experimental solubility criterion Substituted piperazines at R2 exhibit good solubility Latest round of synthesis Core 20

21 Overcoming Bi-phasic Response Relationship observed between topological polar surface area (TPSA) and bi-phasic response in K1 strain Peak at 66 Å 2 corresponds to TPSA of core i.e. absence of polar substituents increases risk of bi-phasic response Core Bi-phasic dose-response Normal dose-response Core Core 21

22 Overcoming Bi-phasic Response Relationship observed between topological polar surface area (TPSA) and bi-phasic response in K1 strain Outliers all contain nitrile substituents Bi-phasic dose-response Normal dose-response Core Core Core Core 22

23 Overcoming Bi-phasic Response Subsequent round of testing supported this hypothesis New compounds synthesised with normal dose-response 23

24 Bringing it All Together No compounds have yet progressed through all assays Consider a subset of most important properties New version of K1 assay shows systematically lower pec 50 Relaxed K1 pec 50 criterion Interesting alternative compounds 24

25 Bringing it All Together Core Core 25

26 Bringing it All Together Current front runner Core 26

27 Supporting Not-for-profit and Academic Research 27

28 Conclusions Not-for-profit and academic drug discovery is difficult! Challenging diseases Limited resources Large collaborations Making good decisions is essential to achieving project goals We have illustrated some approaches to: Understanding SAR to identify optimisation strategies Predictive modelling to guide design Multi-parameter optimisation to identify high-quality compounds Publications can be downloaded from: For more information, go to or contact 28

29 Acknowledgements Optibrium Ed Champness Chris Leeding James Chisholm Nick Foster Alex Elliott Fayzan Ahmed Rasmus Leth Coran Hoskin Mario Öeren Aishling Cooke University of Cape Town Kelly Chibale Leslie Street Claire Le Manach Aloysius Nchinda Kathryn Wicht Tanya Paquet Dale Taylor Christel Brunschwig Nina Lawrence Joe Eyermann MMV James Duffy Fanny Escudie Benjamin Blasco Swiss TPH Sergio Wittlin 29