Design Drugs Collaboratively Using Spotfire® Visualization and Analysis

Anthony Donofrio High Throughput Experimentation and Lead Discovery Chemistry, MRL PKI Data Analysis & R&D Informatics West Coast User Conference Gilead Foster City, CA 10/04/2016 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

2 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.

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3 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.

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.

5 Maximizing Utility of MERCK Predictive Models

• Increase in polarity is beneficial for improving Clint and PXR but can be detrimental to Pgp. Can we synthesize compounds with a PSA > 80 and de- risk a potential Pgp liability?

6 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

7 1. Integrated “Library” Design Cycle

GenerateGenerateGenerateGenerate hypothesishypothesishypothesishypothesis

Analyze data, EnumerateEnumerate assess AnalyzeAnalyze virtually and Analyzehypothesis data & virtuallyvirtually and data select library recorddata outcome selectselect library Data visualization Spotfireand decision making

Test library SetSet upup andand Test library and execute andcollect collect data execute data synthesissynthesis

PurifyPurifyPurify andandand registerregisterregister compoundscompoundscompounds

8 Our Service-oriented Architecture A Transparent Transition from Thick Clients

99 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

10 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

11 Calculate properties with ADMET WorkBench

Calculate relevant properties and predictive models through an extension

12 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

13 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. Evaluate hypothesis

14 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

15 3. Target Design Process

• Typical design process for • Typical design process a traditional medicinal for a parallel medicinal chemist: chemist:

1. Review project data and generate a 1. Review project data and generate a hypothesis hypothesis

2. Design targets regardless of building block 2. Identify chemistry amenable to parallel availability to test a hypothesis and evaluate synthesis calculated properties/ predictive models 3. Search for monomers with availability, 3. Design synthesis of the targets enumerate, and calculate properties/ 4. Search for specific monomers and predictive models precursors 4. Select targets from a virtual library to 5. Prioritize targets based importance and evaluate the hypothesis timeline to deliver compound 5. Begin Synthesis 6. Begin Synthesis

Can data visualization improve this process?

16 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

17 This approach can also be used for finding precursors from a retrosynthetic analysis 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

18 Acknowledgements

Modelling & Medicinal Chemistry Informatics Mike Reutershan Dave Sloman Michael Altman Ryan Otte Christian Fischer Frank Brown Chunhui Huang Stephane Bogen Xevi Fradera Tesfaye Biftu Xianhai Huang Scott Harrison Qingmei Hong Min Park Scott Johnson Yang Yu Zhicai Wu Brian Lahue 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

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