Information Sharing and Automation of PK Calculations for Efficient Project Support
Stefanie BendelsQuantitative Systems PharmacologyPharmaceutical Sciences, Roche Innovation Center Basel
Tibco Spotfire UM, Basel, Nov 3rd, 2016
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Data storage alone is not sufficientAccess makes the difference
Efficiency – Relevance – Real-time – Integrity
High quality data Knowledge generation Informed decisions
For value generation efficient access to data is crucial
Consistency & accessibility Find patterns & relationships Right compound & target
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Workflow in Drug Discovery
Efficient access• Structured data storage• Combination of different
data sources• Automation of workflows
Visual exploration• Panel of exploratory tools• Catalogue of pre-defined analysis• Flexibility by interactive visualizationsPKPD and statistical analysis
• Interpretation of results
• Clear documentation and communication
Talk
Replacements for manual workflows to enable exploratory analysis using Spotfire and Pipeline Pilot (Discngine)
Former Workflow for pharmacokinetic calculations Compilation of in vivo and in vitro data
Manual collection from different sources:
Internal databases with different user interfaces
Excel sheets from assay owners
Data management software
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user
user
user
user
user
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Filtering, Pivoting, and Calculations
– Manual input and updates in Excel files Decrease in efficiency
– Different methods for calculations Dependent on user different results might be reported
– Potential to do mistakes or do not use different methods
New PKPD Module in SpotAPPPrototype enabling project-specific analyses and views
Visualizationswith interactivity
Input
Processing
Output
Custom parameters
PKPD calculation module
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PK profiles
Spotfire Automated Project Data Processing
Roche internal databases
User
Concepts for the PKPD Module Design
Goal:
Provide facilitated processing of PKPD data for DMPK experts and providekey visualizations for project teams
Key principles:
Automation using a central R script
Implementation of different calculation methods
Comparison and selection of most appropriate workflow
Simple customization of script behavior per project
PKPD tables
Main data table
Team file
Data processing
DEV: File used for DMPK expert work
Process controlled by DMPK expert
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SpotAPP
Intrinsic Liver Clearance from in vitro HepatocytesResults using different calculation methods
Assume no bindingHouston methodEstimate unspecific binding
Dilution methodEstimate protein binding (from fraction unbound measured for different species)
in vivo intrinsic clearance
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• Intrinsic clearance [mL/min/kg]:Measure for liver capacity to metabolize compound
• In vitro/in vivo extrapolation IVIVE:Confidence in clearance translation and dose estimate for human?
• For calculation:Parameter needed that corrects for binding effects in in vitro incubation medium
SpotAPP
in v
itro
intr
insi
c cl
eara
nce
in vivo intrinsic clearance
in v
itro
intr
insi
c cl
eara
nce
in vivo intrinsic clearance
in v
itro
intr
insi
c cl
eara
nce
Interactive Database Information Link for SDPK Data on Animal Level
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Administration route and matrix
Con
cent
ratio
n
Matrix
Dose
Animal and matrixC
once
ntra
tion
Time [h]
Dilution method
SpotAPP
Time [h]Details
in vivo intrinsic clearance
in v
itro
intr
insi
c cl
eara
nce
Retrieve raw data for observed clearance
Storage and access of in vivo PK and PKPD data
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For PKPD analysis pharmacokinetic (PK) and pharmacological data (PD) have to be combined and have to be easily accessible
DatabasePharmacokinetic (PK)
Access PK and PKPD data
Pharmacology (PD)
For DMPK experts and project teams
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Concentration Time Profiles (PK) in SpotfireOverview on available data
Filters
Cross tables for selections
Dose-normalized concentrations
Concentrations
Details on demand
Species
Doses
Administrations
Projects
Compounds
Matrices
Discngine/Pipeline Pilot
Result tables are loaded into predefined Spotfire templates
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Concentration Time Profiles (PK) in SpotfireLinear/nonlinear concentration-dose dependency
Dose-normalized concentrationConcentration
Con
cent
ratio
n
Time [h]
D n
orm
. con
cent
ratio
n
Time [h]
Discngine/Pipeline Pilot
Linear PK in the tested dose range
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PKPD Data in SpotfireOverview on available data
Cross table for selections
Rea
dout
in %
Vehi
cle
Compound might have been tested in different experiments
Cmpd 2 VehicleCmpd 2 Vehicle
Readout over Time, Mean per dose group with SD
Readout over Time, Animal level
Time [h]Time [h]
Pos. Control Cmpd 1Pos. Control Cmpd 1
Rea
dout
in %
Vehi
cle
Discngine/Pipeline Pilot
Enzyme inhibition by tested compound
PKPD Data in SpotfirePharmacological response per experiment
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Pos. Control
Vehicle
Readout over Time, Mean per dose group with SD
Readout over Time, Animal level
Rea
dout
in %
Vehi
cle
Time [h] Time [h]
Rea
dout
in %
Vehi
cle
Discngine/Pipeline Pilot
PKPD Data in SpotfirePharmacological response over plasma concentration
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Readout over Cplasma, Mean per dose group with SD
Readout over Cplasma, Animal level
Cmpd 1Cmpd 1
Cmpd 2Cmpd 2
Plasma concentration
Rea
dout
in %
Vehi
cle
Plasma concentration
Rea
dout
in %
Vehi
cle
Discngine/Pipeline Pilot
PKPD Data in SpotfirePharmacological response over plasma concentration
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Readout over Cplasma, Mean per dose group with SD
Readout over Cplasma, Animal level
Cmpd 1Cmpd 1
Cmpd 2Cmpd 2
Rea
dout
in %
Vehi
cle
Rea
dout
in %
Vehi
cle
Plasma concentrationPlasma concentration
Discngine/Pipeline Pilot
Easy switch to semi-log plots
Impact
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Quick and informative overview on generated data with quality assessment(controls over time, extreme values)
Easy evaluation of different calculation methods
Consistent results due to same methods used for calculations and processing
Time savings– Calculations and updates without manual input – Processed and formatted data for detailed data analysis
Control of which data is exposed to project teams to avoid overloading
Tools for efficient data access using flexible and customizable solutions
Pipeline Pilot protocols can be implemented in Spotfire/Discngine and in other applications like SpotAPP
Direct interaction with customers needed to ensure engagement !!
Acknowledgments
Pharmaceutical SciencesRubén Alvarez
Dragomir Draganov
Holger Fischer
Rodolfo Gasser
Michael Gertz
Martin Kapps
Nicole Kratochwil
Thierry Lavé
Andrès Olivares
Axel Pähler
Neil Parrott
pREDiChristian Blumenröhr
Valerie Chabrier
Hansjörg Graesslin
Torsten Schindler
Daniel Wenger
TMo CADDNicolas Zorn
TMo Medicinal ChemistryGiuseppe Cecere
Wolfgang Haap
DTA-NORDKarlheinz Baumann
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Doing now what patients need next
Graphical data exploration is the first step in data analysis
It enables to
– Comprehend information quickly
– Display information in a way that is meaningful
– Find relationships, patterns, and understand data
– Identify areas that need attention or improvement
– Clarify which factors influence experimental results
… without using more elaborate PKPD methods
We need …
– The combination of multiple data sources
– An automatic and up-to-date project data delivery
– The possibility for customized data processing (filtering, pivoting, calculations)
– Efficiency (spending less time preparing data)
– Evaluation of different calculation methods to identify most suitable for specific project
… to enable pharmacokinetic (PK) calculations e.g. in vitro-in vivo extrapolation (IVIVE), early dose estimates
Implementation of PK calculations ready for all projects
– Scaling of in vitro hepatocyte/microsome clearance with IVIVE (different methods for estimation of fraction unbound in experiments)
– Estimation of human dose for best case scenario (100% absorption, hepatic clearance as main pathway, IVIVE established)
– Efficacy index EI
Interactive visualization of concentration-time profiles from database(database information link)
PK Calculations with Direct Access to PK Profiles in SpotAPP
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Example 1: In vivo Concentration-Time Profiles (PK)
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Goals
Overview on generated data (compounds, conditions)
Download and processing for interactive, exploratory analysis(dose normalizations, harmonization of units)
Quality of measured data
Identification of compounds with linear/nonlinear PK properties
Inter-individual variability for concentrations measured in different animals
Formatting for further PK analysis in additional software packages
Time saving and reduction of copy/paste errors
Customers: DMPK experts, project teams
Discngine/Pipeline Pilot
Example 2: In vivo PKPD project data
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Goals
Overview on generated data (compounds, conditions, dose, time points)
Download and processing for interactive, exploratory analysis(normalizations, mean and variability within dose groups)
PD effect over time, plasma and brain concentrations
Brain/plasma distribution
Identification of animals with extreme values
Quality control for reproducibility of negative and positive controls
Formatting for further PKPD analysis in additional software packages
Time saving and reduction of copy/paste errors
Customers: Project teams, M&S scientists
Discngine/Pipeline Pilot
Doing now what patients need next
