A novel interactive tool for multidimensional biological data analysis Zhaowen Luo, Xuliang Jiang Serono Research Institute, Inc.

2 1. Introduction 2. Methods 3. Applications and Examples  H2L decision making  Multiple kinases inhibitors analysis Outline

3 1. Introduction

4 Data representation in drug discover 1. Two dimensional view: Chemistry vs. Biology  Chemistry – different compound structures  Biology - different assay data (potency, selectivity profiles, ADEM/PK and toxicity). 2. A heat map is a graphical representation of data where the values taken by a variable in a two- dimensional map are represented as colors. (WikiPedia). Heat map meets the need of data representation in drug discovery and could be a good decision support tool.

5 My first heat map Nice picture Some interesting patterns But what is that??? A 200 X 200 Map

6 Heat map is not enough 1. Lack of interactivity:  Difficult to retrieve information  Unable to display related information, such as structure 2. Static  Unable to manipulate data  Unable to do real-time analysis Solution: Fully interactive heat map application – only application can satisfy all need for decision support.

7 2. Methods

8 An interactive heat map application

9 Methods

10 Features Point to any point in heat map, a tooltip box will show structure as well as assay result. More details about the point shows here Double click on any points will bring user to the source of original data Draw a box in heat map will create a focus heat map for the area of interesting.

11 Example of focus map Assay Name Compound ID

12 More operations  Color spectrum can be changed.  Map Orientation can be changed.  Data analysis tools 1. Data points can be re-arranged based on analysis results. 2. Analysis results can be exported.

13 Normalize biological endpoints  Problem: Compare Orange with Apple  Solution: Use relative scale: MIN-MAX method  Define good and bad end for each endpoint.  Normalize result based both ends  For different kinds of assays, we define deferent methods to normalize result.  User can customize their own normalization methods.

14 Normalization examples GoodBad  Potency – Enzyme Assay - logIC 50  Good: -8  Bad: -5  Potency – Cell Based Assay – logIC 50  Good: -7  Bad: -4.5  Cytochrom C P450 Inhibition – logIC 50  Good: -4.5  Bad: -7  Rat T 1/2  Good: > 2 hours  Bad: < 0.25 hour

15 Normalized results Raw Data (different units) Normalized data (No unit) Distance Matrix For Compounds Distance Matrix For Assays Assays as descriptors Compounds as descriptors

16 Data analysis Distance Matrix SortingClusteringSimilarity analysis Analysis can be done for compounds and assays Based on biological assays results Results can exported to Excel file for further analysis

17 Structural analysis 1. Clustering and sorting compounds by their structural similarity.  Using fingerprint to calculate the similarity between compounds. 2. Provides structural-activity representation and analysis.

18 Business consideration 1. Hide information  Use generic name for compounds and assays  For example, compounds use prefix and sequence number.  Use generic structure, such as Benzene, to hide real structure.  Look-up table for symbol replacement 2. Offline (offsite) capability  Export and import heat map to binary file  Re-import map offline without connecting to corporate database.

19 Application development 1. JAVA™ JDK 1.5 (from Sun Microsystems) 2. ChimePro™ for JAVA from MDL 3. CDK 4. JDBC 1.4 from Oracle Features: 1. Direct extract structural and assays information from Accord Enterprise database, MDL ISIS/Host database. 2. Web deployed (Java Web Start)

20 3. Applications and Examples

21 H2L Project data analysis and decision making Heat map details: compounds from a list of Accord Enterprise assays in four assays group 1. Potency 2. CYP450 inhibition 3. In vitro ADME 4. In vivo PK

22 Heat map

23 Heat map – after sorting by biological profile

24 Focus on most active area (top area) All top compounds are in clinical or lead candidates

25 Summary for H2L data analysis  Bring together structural, as well as many biological assays for a discovery project.  Multiple dimensional data analysis  Most activity compounds is not the top compounds in overall profile score.  Heat map can pick up the drug candidates Problems:  Missing data point: lots of compounds do not have in vivo data.  Clustering analysis is not accurate in this case.

26 Kinase activity and selective analysis Heat map details: positives in multiple kinases screen Kinase assays  4 Kinase family  AGC  OTHER  STE  TK

27 Sorted by Active Profile Multiple-kinase inhibitors are ranked in top.

28 Cluster by structural similarity 1.Compounds are colored by clusters 2.Cluster 1: AGC-2, Other_3, and TK_10 inhibitors 3.Cluster 2: Other_3 inhibitors 4.Pan-inhibitors Structural similar compounds (in same structural cluster) have similar kinase inhibitory behaviors.

29 Cluster by overall kinase inhibitory profile Multiple inhibitor cluster Three singlet exhibit different inhibitory pattern AGC_1 inhibitor cluster AGC_1,AGC_2 and TK_10 inhibitors

30 Cluster assays based on overall compounds profile The clusters of assays based on compounds profile is not same as phylogeny tree. Identify kinases with possible cross-interaction.

31 Summary of kinase inhibitors heat map 1. Identify pan-inhibitors. 2. Graphics structural-activity relationship 3. Identify kinase inhibitors activity patter for selectivity analysis. 4. Clustering kinase based on compounds profile and identify possible cross-interaction group.

32 Conclusion 1. Provide a interactive graphics tool for decision making in drug discovery process.  Direct get data from corporate database  Interactive  Information-rich: structural and biological assay in one place  One-stop shop for information analysis of drug discovery 2. Statistic analysis based on result code provides powerful tool in decision making  Based on overall biological profile  Can pick winner in H2L process  Provide useful SAR analysis for compounds  Provide selectivity profiles for biological targets.

33 Acknowledge Ben Askew Steve Arkinstall Brian Healey