Design Drugs Collaboratively Using Spotfire Visualization and Analysis

Transcription

1 Design Drugs Collaboratively Using Spotfire Visualization and Analysis Anthony Donofrio PKI Data Analysis & R&D Informatics East Coast User Conference GSK Upper Merion, PA 09/15/2016

2 Merck Research Laboratories Rahway, NJ Boston, MA San Francisco San Francisco, CA CAMBRIDGE Cambridge, MA Kenilworth, NJ 2 West Point, PA

3 Background for Data-driven Drug Design Multi-parameter optimization (MPO) is essential to drug discovery Intrinsic to MPO is complex data that cannot be easily visualized MPO scores have been developed to simplify complexity and are especially useful for prioritization in early discovery however a single score may diminish a medicinal chemist s creativity through less insight into a compounds strengths and deficiencies Multi-parameter visualization (MPV) seeks to reduce complexity in MPO through data visualization MPV utilizes data-rich views to provide depth through trends and parameter relationships The depth of MPV enables a chemist to focus creativity on a compound liability and provides synergy when design is discussed as a team This presentation will show how Spotfire MPV was used to review weekly experimental data in the context of virtual libraries, physicochemical properties, and predictive models to focus the team on new chemical target design 3

4 Identifying Favorable Physical Property Space With a goal of improving metabolic stability, a traditional hypothesis of reducing lipophilicity was identified. Data analysis enabled a strategy of designing targets with an ALogP98 < 4 that increased the probability of improving metabolic stability and off-targets. 4

5 Maximizing Utility of MERCK Predictive Models Increase in polarity is beneficial for improving Cl int and PXR but can be detrimental to Pgp. Can we synthesize compounds with a PSA > 80 and derisk a potential Pgp liability? 5

6 Automated Weekly Data Browser Assay data is generally reported weekly, thereby producing a weekly data cycle. A weekly data browser was designed to visualize new data in the context of multiple parameters. This browser helped facilitate weekly compound nominations for additional assays and was used to focus a weekly data discussion with 20 medicinal chemists. 6

7 Automated Weekly Data Browser Free Wilson affinity analysis confirmed additivity and an automated virtual matrix was designed containing all relevant calculated properties and predictive models. The virtual matrix prospective analysis enabled a data-driven discussion and decision on new compounds in real time during the weekly data meeting. 7

8 Three Drug Design Scenarios using Spotfire 1. Classic library design a) Evaluate a hypothesis utilizing parallel synthetic chemistry b) Obtain monomers, enumerate, and gather desired data to select library members 2. Virtual Matrix Library a) Identify an interesting set of existing compounds b) Can different vectors be hybridized to improve overall profile? c) Deconstruct these compounds and enumerate forming a full matrix to evaluate this hypothesis 3. Parallel Target Design a) Design specific compounds based on a hypothesis b) Retrosynthetic deconstruction of compounds into monomers c) Obtain monomer commercial availability along with calculated properties of products within one scheme 8

9 1. Library Design Cycle Using Informatics Tools Generate hypothesis Analyze data, assess hypothesis & record outcome Data visualization and decision making Enumerate virtually and select library Test library and collect data Set up and execute synthesis Purify and register compounds 9

10 Our Service-oriented Architecture A Transparent Transition from Thick Clients 1 10

11 Merck Advanced Reagent Search (MARS) & FGC Libraries are typically enumerated with monomers based on: Substructure(s) that contain a desired functional group for the purpose of synthesis Substructure(s) that refine a monomer set based on relevant SAR Functional Group Count (FGC) filtering enables further refinement of a monomer set 11

12 Enumerate Easily enumerate reactants to products Pre-defined named reactions are available in a drop down menu Reaction schemes can be sketched and edited Reaction exclusions are added to prevent undesired products 12

13 Calculate properties with ADMET WorkBench Calculate relevant properties and predictive models through an extension 13

14 Spotfire: Library Member Selection Selection of library members is crucial to hypothesis evaluation ALDaS virtual libraries (VL) can be analyzed and combined with other sets of compounds Library member selections in the context of molecular match-pair analysis between the VL and project compounds (Library at R8, while keeping R6 constant) Virtual Library 14

15 2. Virtual Matrix Library Scenario: Two libraries were designed and synthesized to explore SAR of R1 (keep R2 constant) and R2 (keep R1 constant). Is potency additive? Are there hybrids of the R1 and R2 libraries worth evaluating? 1. Select compounds of interest from both R1 and R2 2. Deconstruct the molecules into three parts Core (P1) R1 (P2) R2 (P3) 3. Enumerate to form a virtual matrix 4. Calculate properties, predictive models and perform a Free Wilson analysis 5. Select library members 6. Synthesize library 7. Analyze data 8. Determine if hypothesis is true 15

16 Virtual Matrix Library 1. Select key compounds based on predicted affinity and calculated properties 2. Synthesize and collect data on compounds 3. Visualize and evaluate hypothesis 16

17 3. Target Design Process Typical design process for a traditional medicinal chemist: 1. Review project data and generate a hypothesis 2. Design targets regardless of building block availability to test a hypothesis and evaluate calculated properties/ predictive models 3. Design synthesis of the targets 4. Search for specific monomers and precursors 5. Prioritize targets based importance and timeline to deliver compound 6. Begin Synthesis Typical design process for a parallel medicinal chemist: 1. Review project data and generate a hypothesis 2. Identify chemistry amenable to parallel synthesis 3. Search for monomers with availability, enumerate, and calculate properties/ predictive models 4. Select targets from a virtual library to evaluate the hypothesis 5. Begin Synthesis Can data visualization improve this process? 17

18 Parallel Target Design using ALDaS (Suzuki) Reactants (1) came from SAR brainstorming meeting based on data, not from a structure search for commercial availability. Paste structures from ChemDraw into reactant node 1 from SAR meeting Enumerate to obtain different FG handles Enumerate to obtain final products Calculate properties on final products Get inventory on compiled monomer set using an extension 18 This approach can also be used for finding precursors from a retrosynthetic analysis

19 Using Spotfire to Select Monomers Chemists can choose a target based on calculated properties, predictive models, and availability of monomer with a choice of synthetic FG handle 19

20 Acknowledgements Computational Chemistry / Cheminformatics Michael Altman Frank Brown Xevi Fradera Scott Harrison Scott Johnson Brian Lahue Medicinal Chemistry Mike Reutershan Dave Sloman Ryan Otte Christian Fischer Chunhui Huang Stephane Bogen Tesfaye Biftu Xianhai Huang Qingmei Hong Min Park Yang Yu Zhicai Wu Lei Chen Biju Purakkattle Catherine White Tony Siu David Witter Craig Gibeau Michelle Machacek Mike Ellis Clare London Dann Parker Nunzio Sciammetta Graham Smith Minja Maletic Petr Vachal 20