Hit Finding and Optimization Using BLAZE & FORGE

Transcription

1 Hit Finding and Optimization Using BLAZE & FORGE Kevin Cusack,* Maria Argiriadi, Eric Breinlinger, Jeremy Edmunds, Michael Hoemann, Michael Friedman, Sami Osman, Raymond Huntley, Thomas Vargo AbbVie, Immunology October 18 th, 2018 Cambridge, MA

2 Iterative design cycle is the backbone of new molecules Synthesizers Designers Structural Bio/CADD Cresset UGM, Cambridge, MA

3 Drug design is reflective of medicinal chemistry in the 21 st century Available Info. Type of Screen PIPER/ATLAS IsoStar - CSD Cresset UGM, Cambridge, MA

4 Starting chemical series can be obtained from literature or derived from complementary screening approaches Natural ligand in target of interest Off target screening hit from unrelated project Literature compound Competitive Intelligence Model or structure if validated/available Virtual ligand screening (VLS) VLS is highly successful across target classes due to complementarity of approaches used High throughput screen (HTS) Results of multiple complimentary virtual screens against deiminase target Each color represents a different VLS software platform Note complementarity of results Cresset UGM, Cambridge, MA

5 Identification of internal chemical matter for kinase project VLS screen was able to be completed in weeks, well ahead of timeframe for HTS screen HTS hits were complementary to VLS screen and identified additional chemical matter for program Selections not intended to be exhaustive as this was one of multiple approaches to identify viable starting point(s) for program Only requires testing of a couple hundred compounds! Kinase TR-FRET IC 50 = um clogp/tpsa 4.6; 60 h Cl,unbound 68 L/hr/kg m Cl,unbound 2004 L/hr/kg Viable and novel starting point identified >1,000,000 ROCS overlay 10,000 1,000 MAESTRO docking and SIFT analysis Cresset UGM, Cambridge, MA

6 Serial application of different software is successful across multiple target classes Deiminase (structure based) Hits identified, target dropped GPCR (homology model) Identified a novel series for advancement from VLS Related chemotype showed up in HTS Kinase (structure based) Lead series identified from VLS screen, still advancing Also identified in HTS GPCR (homology model) 5 antagonist scaffolds identified from original model Refined model identified to help flip functional response Awaiting full HTS results Bottom line: Identified and advanced chemical matter up to six months ahead of the wet HTS results and series were identical to or complementary to HTS picks Advanced chemical matter from VLS hits enables earlier validation of screening funnel and provides multiple tool compounds ahead of HTS Cresset UGM, Cambridge, MA

7 Generation of next generation concept molecules Multiple ways to generate new chemical matter from an existing starting point, see below for example Real or virtual libraries Core hop, fragment replace, join Ligand Example of virtual library Literature based or de novo transformation, hybridization ATLAS Cresset UGM, Cambridge, MA

8 Ranking concepts: Multiple ways to rank order ideas for synthesis Target engagement in SBDD programs is multifactorial Physicochemical eadme Properties Target Engagement Calculated properties Similarity shape, electrostatics, conformation, field QSAR rank ordering Safety/Tox Risk Synthesis Sustainable Practices in Medicinal Chemistry Part 2: Green by Design What should be made versus what could be made? J. Med. Chem., Aliagas et al. 2017, 60, Cresset UGM, Cambridge, MA

9 AIDEAS tool allows manipulation of virtual compound space Cresset UGM, Cambridge, MA

10 Cresset 3D-QSAR as a tool to rank hits from a large data set based on predicted potency Pros Provides a quantitative value of results Provides interpretable pictures which help user understand what the protein is looking for in proposed ligands Cons Requires a large enough set of compounds to start with and three logs of evenly distributed potency Alignment and conformational noise can make it hard to achieve a good model Cresset UGM, Cambridge, MA

11 Setup: Align in 3D versus a reference pose as template Cresset UGM, Cambridge, MA

12 Results vs Activity (Binding) - Poor predictability for entire set r 2 = r 2 = NOTE: Too much scatter, not a model! Cresset UGM, Cambridge, MA

13 Q2 and RMSE Panels Poor RMSE Prefer 3-5 components Cresset UGM, Cambridge, MA

14 Repeat using individual subsets: Series A (Kinase) r 2 = r 2 = NOTE: Starting to see a model that is useful Cresset UGM, Cambridge, MA

15 3D-QSAR Models to Evaluate Binding Poses for GPCR Agonists Problem for GPCR Program No crystal structure. Build a homology model and docked agonist series. Two binding poses came out that we could not distinguish through scoring functions. Can we use the Cresset 3D-QSAR models to identify preferred binding mode? 146 compounds with EC 50 data spanning 5 logs of activity. Randomly assigned 10% of set as the test set. Binding Pose A Binding Pose B R 2 = R 2 = D-QSAR correlations from different binding poses are quite similar Cresset UGM, Cambridge, MA

16 Conclusions BLAZE is a useful tool for identification of chemical matter in virtual screening campaigns Prefer qualitative consensus view across multiple tools versus numerical scoring This is particularly relevant in VLS, library analysis 3D QSAR in FORGE can be useful to bucket compounds for synthesis and avoid making inactive analogs Both tools can be leveraged to improve efficiency of design cycle Cresset UGM, Cambridge, MA

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