Download Automated Workflows for Integrated and Complex

Automated Workflows for Integrated Project Data Analysis
Using Spotfire: the SpotAPP Family
Nicolas Zorn, CADD Group, Roche Innovation Center Basel
Basel Tibco Spotfire UGM, Nov 3rd 2016
Preamble
Data optimization cycle
Drug discovery cycle
Design
Retrieval
Analysis
Presentation
Synthesis
Testing
Processing
Enrichment
Key features for success: relevance, efficiency, agility – integration & interactivity
Building-up Capabilities to Help Answer Key Projects Questions
System for Agile Development
Action
Flexibility •
Knowledge
Information
Modularity •
Decision-making!
• Project-centric data
mining and analysis
• SAR, SPR exploration
A
Data
• Predicted properties
Automation •
• Virtual Compounds
Infrastructure
Presentation Outline
• Overview and concepts of the SpotAPP workflow
• Features highlights
– Activity-efficacy data analysis
– MMPs and project SAR analyses
– ivive and PK/PD
• Workflow expansion to HTS data analysis: SpotHTS
• Conclusions, Perspectives
SpotAPP: Integrated Project Data Mining and Analysis
Spotfire Automated Project Data Processing
Roche internal databases
External data
(Chembl, patent data…)
in house
Off-target, HTS
and LTS data
PK/PD and ivive
Custom calculations
(selectivity, LE, ...)
In house molecular
property predictions
Matched Molecular Pairs
and SAR analyses
Cluster analysis and
series tagging
Structural data
Implementation Overview
• Project data flow
processing
Data sources
Tables are linked
and auto-embedded
Project data tables
(linux FS and WIN shares)
Spotfire library
project template
Spotfire server automation service: reloads
and embeds data tables after each update.
• Overall process control: CRON table drives SpotAPP project runs (frequency and options)
Full flexibility
Options passed as one-letter codes
for CRON jobs
Daily update
All projects run in ~3h every night
Project Data Structure during Processing
Non-pivoted,
non-aggregated
1 row per result
Batch aggregation
Substance aggregation
1 row per result
& per batch
1 row per result
& per subst.
Unpivoted
data table
Project custom
aggregation
then pivoting
1 row per substance
Pivoted
data table
Partially aggregated
or raw data tables
• Standard aggregation rules apply, then possibility to override/refine with project-custom rules
• Optionally, unpivoted data can be exposed at several aggregation levels
Project Data Processing: Standard & Customized
Final
formatting
Retrieve data
Input cleanup
Std aggregation
Unpivoted data
Process structures,
properties
Final
formatting
Retrieve and process off-tgt data
ancillary
data
Project aggregation,
Pivoting
Clustering
Series
Tagging
Predicted
properties
PKPD
module
…
Final
pivoted data
formatting
• Auxiliary Pipeline pilot protocol: easy to setup from template to insert specific data manipulations
• Project control file: defines input, processing options and team decisions on desired data format
Advanced Series Tagging
• Project-specific substructure definitions (Molfile)
Complex search hierarchy, multiple fields possible
• Used to label series, substructures, motifs…
• Can be combined with on-the-fly SS searches.
SpotAPP Highlight: Activity and Efficacy Data Analysis
Roche internal databases
External data
(Chembl, patent data…)
in house
Off-target, HTS
and LTS data
PK/PD and ivive
Custom calculations
(selectivity, LE, ...)
In house molecular
property predictions
Matched Molecular Pairs
and SAR analyses
Cluster analysis and
series tagging
Structural data
On-target Activity Data Browsing and Compound Profiling
• Latest processed data is always exposed in project SpotAPP package
• Browse data across all available dimensions (activities, properties…)
Marked compounds put in ‘shopping cart’
for side-by-side comparison and profiling
Pivoted data used for overview and data correlations.
IronPython script captures marked compounds as tag.
Activity–Efficacy Data Analysis and Drilldown
• Efficacy data analyzed using complex set of conditions: target subtype, species, doses, measurement mode…
Interplay of Spotfire pivoted and unpivoted tables: instantaneous data drill-down.
SpotAPP Highlight: SAR Analysis of a Complex Data Set
Roche internal databases
External data
(Chembl, patent data…)
in house
Off-target, HTS
and LTS data
PK/PD and ivive
Custom calculations
(selectivity, LE, ...)
In house molecular
property predictions
Matched Molecular Pairs
and SAR analyses
Cluster analysis and
series tagging
Structural data
SAR Analyses: Integration of Standard Methods into Project
Context
• SpotAPP exposes and connects in-house Python-based SAR tools:
MMPs,
Non-add
FreeWilson
Project
SAR
– R-group decomposition
– Matched pairs/series
– Non-additivity analysis
R-groups
• Advanced SAR analyses are pre-processed using activities/properties selected by the team
and can make use of the SpotAPP series tags.
• Analysis results from SAR tools can then be connected to any other project data.
• Customization is possible by applying the same concepts as presented for regular project data.
R-group Decomposition Analysis
1. Select core, R-groups and activity
2. Select compounds in R-group matrix
3. See SAR and trends for 1+ activity(ies)
Connect
to MMPs
data
• Setup for single/multiple activity R-group matrix visualization and analysis
• Uses series tags when R attachments are defined à browse R-group cores using project definitions
MMPs
Matched Molecular Pairs in SpotAPP
N H
2
O H
common core
X
Y
ΔPgp
X
X
Y
Y
ΔHepCl
ΔIC50
‘Entry point’ data
Other project
activity data
C H
X
Y
X
Y
F
F
3
Project MDO data
F
C H
F
3
F
F
Global MMPs
knowledge
ΔIC50
ΔKi
ΔIC50
ΔKi
Approach can be combined
with ad hoc MMPs calculations…
MMPs Workflow: Filter-down to Desired Pairs / Series
1. Filter-to/mark set of MMPs to answer question
3. Analyze D(activity/properties)
for selected pairs
Current marked compounds (R-groups…)
and/or
Aggregated MMPs trends (e.g. Avg, Geo… )
2. Browse/select individual pairs
and/or
Line plot: identify outliers
MMPs workhorse: Python platform designed for processing of large data & interactive queries
MMPs Workflow: Interactive Substructure Search
• Allows fast substructure-based filtering using Core and variable fragments; can be combined with other filterings
Discngine Panel used as UI for SS search process and result reporting into SpotAPP package
SpotAPP Highlight: Prototype ivive Calculations
Roche internal databases
External data
(Chembl, patent data…)
in house
Off-target, HTS
and LTS data
PK/PD and ivive
Custom calculations
(selectivity, LE, ...)
In house molecular
property predictions
Matched Molecular Pairs
and SAR analyses
Cluster analysis and
series tagging
Structural data
Concepts for SpotAPP ‘PK/PD’ Module Prototype
Goals:
• Provide facilitated and standardized calculations of derived PK properties to team PK reps.
Enable consistent decision making & expose key visualizations to teams.
Key principles:
• Automation of calculations using a central, validated, R script
• Implementation of different clearance scaling calculation methods
è
Comparison and selection of most appropriate method to share and use
• Customization of script behavior per project using control file and ad hoc data files (if needed)
Internal DB’s
Main SpotAPP package
Main data table
Process controlled by PK rep.
PK/PD processing
Custom input / parameters
Complete PK tables
Advanced PK and PK/PD data package; & features
Example of Advanced PK Calculations
Available to PK Rep. for Decision-making
• Example of different methods providing clearance scaling from hepatocytes
Dilution method
Estimate unspecific binding (Houston)
in vivo CLb_unbound_int_hep
Assume no binding
CLint in
[mL/min/kg]
in vitro CLint_hep
Estimation of protein binding in hepatocyte incubation medium: fu from preclinical species
• PK concentration-time curves from individual animals
and aggregated over treatment groups
SpotAPP Workflow Adaptation To HTS Analysis
Re-use of Standard Processing Modules
Flexibility •
Action
Knowledge
Information
Modularity •
HTS data package
• HTS screen results,
general compound info
• Early HIT SAR exploration
Data
• Off-target, ancillary profile
Automation •
• Hit-expansion
Infrastructure
Multi-Dimensional HTS Data Analysis from a Chemistry Perspective
SAR potential
Properties
Internal
&
External
knowledge
Chemical
tractability
availability, purity,
synthesis…
HTS potency
•
HTS counter-screens
Chemical diversity
HTS promiscuity
Ligand
efficiency
Target /
gene /
pathway data
SpotHTS Workflow Overview
Primary screen data (single points)
Roche DB
Clusterings
Tags, labels
MDO, props
HTS confirmation data (dose-responses)
DR curves
CEREP panel
Off-tgt. data
HTS results
HTS hit history
Tox/Safety data
Advanced data mining
(Phenotypic/assay profiling…)
MDO, purity
Ligand eff.
HTS Package
Kinase panel
PAINs
SEA analysis
External data
Ontology analysis
External
data
HTS Data Package: Highlights
HTS history and known in-house activities
Dashboard for multi-parametric hit analysis
• Platform for narrowing high Primary hit-rate, analyze hits
• Used for internal prioritization & sharing with external partners
Chemical space clustering and diversity analysis
Knowledge Capture: Hits Annotation within SpotHTS Package
• Team members can annotate and label compounds inside SpotHTS package.
1. Select hit(s) in analyses and provide annotation
2. Retrieve and analyze compounds based on annotations
Annotations captured in Oracle DB via information link,
then embedded in HTS package as data table.
Integrated Hit Expansion within SpotHTS Package
• Retrieval of top-100 similar compounds for all hits with dose-response data.
Done as part of data processing (2D-based similarity).
à Use as initial info to assess hit SAR potential and screening follow-up activities
1. Select hit(s) in analyses
2. See all IRCI closest neighbors
3. Find overlap and compounds not screened yet
Visualize data and stock availability, purity...
IronPython scripts in Spotfire to perform automatic list logic and markings.
Conclusions
• SpotAPP ecosystem has organically grown last couple of years at Roche as DEV platform
Ø For project teams: provides integrated project data, advanced SAR and PK analyses…
Ø For experts-developers: helps test new features, data models & custom visualizations.
• Core design features for efficient data delivery:
Flexible, automated, customizable to project critical needs
• SpotAPP standard components and logic shared by other Spot* family members
(potentially also by Roche New Assay Data Analysis Landscape tools)
Ø SpotHTS variant for HTS analysis: on the way to integrated hit delivery
HTS
PK
MDO
*
Perspectives
Retrieval
• Spotfire is efficient and versatile as vector of new data models
– especially for fast/complex data optimization cycles
– excellent for relational data tables and large data volumes
Presentation
Processing
– iPython, R, information links and data connections are powerful
– enhanced features possible via add-ons (Discngine, JS D3,…)
Enrichment
• Challenges still remain for drug discovery community
– UI and data presentation simplification for non-experts
– Increased chemical intelligence & cheminformatics-guided workflows
Acknowledgements
• Brian Jones Yi Lin Lisa Sach-Peltason Christian Blumenroehr Daniel Wenger
Olivier Roche Martin Blapp Gunther Doernen Peter Hilty (pREDi) Paula Petrone
• Jerome Hert
Christian Kramer
• Stefanie Bendels
Michael Reutlinger
Wolfgang Guba (CADD)
Martin Kapps + many other contributors (PS)
• Katrin Groebke-Zbinden
• Eric Leroux (Discngine)
John Cumming + many medicinal chemists for feedback-suggestions
Doing now what patients need next