Integration of DiscoveryQuant Software into Automated In-Vitro ADME Assay Workflows

Transcription

1 Integration of DiscoveryQuant Software into Automated In-Vitro ADME Assay Workflows John D. Laycock Ph.D. Principle Scientist, Amgen Pharmacokinetics and Drug Metabolism Applied Biosystems User Meeting May 31 st, 2009

2 Objectives Discovery ADME screening Laboratory integration Workflows, data handling & informatics Mass spectrometry automated tuning & analysis Automaton software DiscoveryQuant software Assay overview Visual walkthrough Conclusions 2

3 3 Discovery ADME Numerous in-vitro ADME assays have been designed and can be classified in 2 groups: Probe substrate assays CYP inhibition Test compound = measured compound Metabolic stability Protein binding Permeability, transcellular transport Redundancy and bottlenecks exist in method development and data processing Assay protocols vary between scientists and labs Throughput and capacity demands are increasing

4 Workflow for Automated ADME Data Warehouse Discovery ADME Workflow Compound Store LIMS Database Integration: SOAP Web Services Automated Tuning/Acqusition Software Tuning Plate Input Assay Plates File Robotic Plate Format Compatibility 4 MS results have information necessary for LIMS Calcs. MS/MS Tuning Database Software Flexibility

5 Laboratory Instrumentation and Software (PKDM In-Vitro Technologies) Mass Spectrometry API 5000 LC/MS/MS Analyst Software DiscoveryQuant 2.0 (DQ) automated tuning and analysis software Robotics Tecan EVO, EVOWare Plus, Multichannel Arm (MCA 96) liquid and plate handling robotic Laboratory Information Management Systems Galileo LIMS for in-vitro ADME studies Watson LIMS for in-vivo PK studies 5

6 6 Integration & Informatics Strategy Custom informatics to minimize bottlenecks at system interfaces Development tools Accelrys Pipeline Pilot Enterprise Server Client-server platform Web portal client.net client Visual programming environment for development of web-service applications Drag-drop components allow for very rapid development of applications Microsoft Office Excel and VBA remain important Applications Instrument & robotics integration Automated reporting Data mining & exploratory data analysis Cheminformatics

7 Pipeline Pilot Custom Web Application for Automaton 7

8 Feature Set of Automaton and DQ Compound Description File ID, molecular weight, and well position required Compound Optimization Tune mass spectrometer parameters for unknown compound Store optimal conditions in database Method Creation Construct MRM acquisition methods Batch Creation Build acqusition batch from starter template and compound input list Submits batches to the acquisition queue 8

9 Automaton Limitations Automaton Limited utilization Significant integration effort required to streamline usage Transfer of workflow across labs is difficult Lack of review and status features 9

10 Migration from Automaton to DQ Ease of use Integrated and flexible template creation Visual plate mapping Batch preview User interface for review of tune and analysis results LIMS integration features Custom columns Export customization Database enhancements Central database for upload of local results Store multiple tune conditions per compound Improved tuning Multiple mrms Advanced tuning algorithms* 10 *ASMS 2008, TP320

11 DQ Software Infrastructure DQ Includes 2 Software Components including capabilities to easily share tuning conditions globally Global db (SQL Server) 11 Upload Tool Local db DQ Optimize Compound optimization Bioanalytical Local db DQ-Analyze Bioanlysis Reuse MS/MS optimization data

12 Assay Overview: Permeability and Transcellular Transport Cell Based Models for Intestinal Permeability and transport of Drugs (Caco-2, MDCK/LLC-PK1) 12

13 Transcellular Transport Assay Cells are grown on polycarbonate membranes Dose in triplicate wells Apical Chamber A>B Basolateral Chamber B>A. Incubated 2 h at 37 C Cell Monolayer 13 Up to 32 compounds per 96 Well plate Sample both chambers for A>B and B>A (4x96=384 samples) Pool n=4, 96 injections/cell Line 1-6 Cell Lines B A>B B>A A A Papp B Papp A=Apical Chamber B=Basolateral Chamber Papp = Apparent Permeability Efflux Ratio = Papp(B>A)/Papp(A>B) Drug

14 Transporter Assay Robotics & Bioanalysis Process Challenges Low permeability compounds require low nm detection limits Increasing number of cells lines of interest More complex LIMS import file Pooled compound analysis Bioanlysis analysis steps Dilute samples from dosing chamber Add internal standard in acetonitrile, centrifuge, & transfer Inject sample by rapid gradient RP LC/MS/MS Migrate from 96WP to 384WP for sample injection 14

15 15 DQ Optimize

16 16 DQ Optimize - Setup

17 17 DQ Optimize Experiment Setup

18 18 DQ Optimize Real-Time Status

19 19 DQ Optimize Review

20 21 DQ Analyze

21 22 DQ Analyze Batch Setup

22 Analyst Custom Columns Analyst Batches support custom columns Custom information persists throughout the analysis cycle Embedded in Wiff file and appears in the Result Table Custom Column data facilitates rapid conversion of mass spectrometry data to LIMS import file DQ Supports Custom Columns and transfer of this information to Analyst 23 Note: In Analyst, use an underscore if importing batch from a file: e.g. _Group Text, _Time (min), Well Replicate Number

23 DQ Analyze Template Setup Custom Columns STDs Cell 1 Dose 24

24 25 DQ Analyze Preview Batch

25 26 DQ Analyze Batch Queue

26 DQ Analyze - Data Review STDs Dose Cell 1 Samples stitched together on a single vertical access by compound for quick review Integration algorithms from Analyst Software incorporated. 27 Simple results export to Excel file

27 28 DQ Analyze Batch Results Single Peak

28 29 DQ Analyze Calibration Review

29 30 DQ Analyze Export File Settings

30 Galileo In-Vitro Gallery after Rapid Study Entry 31

31 32 Galileo Gallery Detail

32 Conclusions & Future Developments 33 DiscoveryQuant has been tested and performed robustly while meeting key requirements relating to our laboratory workflow Custom Columns are an important part of our integration strategy DQ provides significant usability advantages over Automaton Incorporate DQ into standardized workflow for permeabilty transcellular transport assays Requested Features Incorporate Advance Tuning Algorithms Compound Name in the Analyze Review Grid Ability to deactivate tune conditions Training and Implementation within our In-Vitro labs

33 Acknowledgements 34 PKDM Gary Skiles Vimal Patel PKDM In-Vitro Technologies Group Steven Louie Jun Lammawin Kelly MacLennan Jessica Lontoh Research & Automation Technologies Justin Provchy Applied Biosystems Loren Olsen

34 Back Up Slides

35 Cell based permeability Caco-2 & MDCK What are Caco-2 and MDCK? 1) Cell Based Models for Intestinal Permeability and transport of Drugs. 2) Caco-2 is a cell line isolated from human colon adenocarcinoma. 3) MDCK is cell line derived from canine kidney tumor cells 4) Caco-2 area available through the ATCC. 5) Used as a predictor of efflux and gutwall absorption. 36

36 37 The Caco-2 Assay System

37 38 Pipeline Pilot Developer Client

38 39 Excel Cassetting Tool

39 Galileo LIMS Workflows Compound Task Manager LIMS Enter Compound in Galileo Database Assign a list of compounds to study design template Accept design: study is expanded based on number of incoming compounds Create analytical runs (specify plate/well location of samples) Generate Sciex Analyst sequence file LCMS Export rdb file to text file. Import into Galileo Perform analytical regression Enter Compound in custom Excel template Rapid Study Entry (RSE) Forward-Only Process Custom Template Approach (Excel VBA) Generate Sciex Analyst sequence file LCMS Export results file(s) to text file(s). Merge File(s) and tranform to RSE format with Pipeline Pilot LIMS Import RSE into Galileo Perform analytical regression Review In-Vitro results Automaton 40 Enter compound mass and vial location in Automaton sample description file DQ Step Optional Automated