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Daniel Weinstock DVM PhD DACVP sanofi aventis U.S., Inc. Bridgewater, N.J. USA

Digital Pathology / Image Analysis in Pharmaceutical Discovery and Development - different uses, different concerns. Daniel Weinstock DVM PhD DACVP sanofi aventis U.S., Inc. Bridgewater, N.J. USA. The Digital Image Revolution. Histopathologic assessment ( the traditional method):

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Daniel Weinstock DVM PhD DACVP sanofi aventis U.S., Inc. Bridgewater, N.J. USA

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  1. Digital Pathology / Image Analysis in Pharmaceutical Discovery and Development- different uses, different concerns Daniel Weinstock DVM PhD DACVP sanofi aventis U.S., Inc. Bridgewater, N.J. USA

  2. The Digital Image Revolution • Histopathologic assessment (the traditional method): - glass slides and an optical microscope - subjective semi-quantitative assessment by a pathologist with peer review of results • New approach: - digital image acquisition with computer based image handling and viewing - pathologist driven analysis with generation of objective quantitative, data (Why) is this such a good thing?

  3. Pathology Applications in a Pharmaceutical Company • Discovery (Research) • Target Validation • High Content Screening (HCS) • Animal models • Proof of concept / proof of mechanismstudies • Development • GLP toxicologic pathology • Biomarker development / validation • Investigational toxicologic pathology

  4. Image Analysis:What kinds of questions? • Characterization of changes in cells / tissues • what kinds of changes • severity and distribution • Frequency and distribution of a microscopic feature • normal versus diseased • treated versus untreated • Challenges • non-uniformity of samples • Variations in sample source, handling and staining • small sample numbers • large sample numbers • subtlety of change • spectrum of change

  5. Digital Imaging and Image Analysis:Applications, Concerns and Reasons for Use • Repeated measures • uniform analysis (application of algorithm) • Quantitative analysis • “hard” numbers for diverse scientists (committee decisions) • Large sample numbers • prevents “drift” • “Distance” pathology / telepathology • remote image sharing • collaboration / consultation • GLP principles – image handling, storage and archive

  6. Digital Images:Acceptance by Pathologists Ultimate goal: replace glass slide evaluation via microscope with digital image evaluation on computer screens • Quality • images • data • Speed • slide scanning • image access, handling • field of view, magnification change, etc… • Cost and benefit • Integration • Ease of use These issues must be addressed to the satisfaction of the primary users of the technology. Very good progress to date, but improvement possible.

  7. Image Analysis – Practical Aspects • Team approach needed • fusion of engineering and biological skill sets • statisticians needed for complex analytical techniques • Criteria for evaluation • modifiable algorithm until final parameters established • Reiterative evaluation and modification of algorithm required • Should be able to review results of each modification • Repeated modification should yield incremental improvements in discrimination • final application of unchangeable algorithm to total image set • End point: believable, repeatable, biologically relevant results e.g. recognition of a nucleus – many ways to do it

  8. Image Analysis – How To • Digital image files acquired and stored • “working” algorithm applied • 1st round results generated • can apply to smaller representative image set • evaluation of results and assessment of discrimination • algorithm modification, data set exclusion • Application of 2nd, 3rd, etc… modified algorithms • reiterative cycle of modification and data assessment • Final data generation and analysis • applied to total set of images • final review of analyzed images for QA is desirable

  9. Discovery versus Development • Types of questions • therapeutic effects (discovery) versus toxicologic effects (development) • disease status, model and assay development (discovery) • Types of tissues / experiments • species differences • Standard toxicology species (rat, dog, etc.) versus mice (genetically modified, knock-outs, knock-downs, etc.) and other species • group size constraints • reagent concerns • Clients (end user) • regulatory oriented (development) versus diverse scientific community (discovery) • GLP compliance • essential in Development, not relevant in Discovery • Investigational Toxicologic Pathology – hybrid between the two

  10. Image Analysis Concerns – Tissues • Liver (example tissue) • Multiple types of changes possible Variable combinations of changes – separate, intermixed, etc. Range of severity of each type of change • Necrosis • Fibrosis • Inflammation • Bile duct proliferation • others…… • Normal features difficult to differentiate • Red blood cells • Sinusoids – amount of space affected by degree of exsanguination • Kupffer cell – nuclei difficult to discern from inflammatory cells

  11. Range of Changes in a Lesion • Liver - necrosis Issues: Red cells within area of necrosis Clear spaces within necrosis vs. sinusoids Pyknotic nuclei vs. Kupffer cell nuclei

  12. Range of Changes in a Lesion • Liver - bile duct proliferation Issues Edge effect. Differentiation between bile ducts and arterioles. Relatively uncomplicated change in this field.

  13. Range of Changes in a Lesion • Liver - bile duct proliferation - fibrosis - inflammation Issues Differentiation between bile ducts and arterioles. Complicated by fibrosis and inflammation. Discrimination between nuclei of inflammatory cells and Kupffer cells

  14. Range of Changes in a Lesion • Liver - bile duct proliferation - fibrosis - inflammation Issues Complex morphology of multiple changes in one focus of interest. Severity change varies by focus.

  15. Range of Changes in a Lesion • Liver - necrosis - fibrosis - bile duct proliferation - inflammation Issues Similar issues as previous images, but now complicated by multiple contiguous types of changes per field.

  16. Range of Changes in a Lesion • Liver - necrosis - bile duct proliferation Issues Multiple non contiguous changes. Bile duct proliferation – uncomplicated. Necrosis – complex morphology in area of change.

  17. Image Analysis – “How to…” and “Multiple interactions…” What’s needed? • turn key library with many validated algorithms • can be located distant or local • useful as starting point for further modification • tool box for modification • should be local (desktop) • should be user (pathologist / scientist) friendly • easily modified with rapid, repeated application to a test data set • format for easy review of results and assessment of discriminations being made • data should be accessible for statistical analysis • final results should be biologically relevant

  18. FAQs – common concerns • What must be done to validate an image analysis algorithm? • What justifies the time and effort investment to develop an image analysis algorithm? • How predictive is a 2 dimensional slice of a tissue (histologic section) for quantification of an effect on a organ? How much sampling is required? What kind of sampling is required? Are we making appropriate comparisons? • What is necessary to power the experiment appropriately?

  19. Integration of Images and Data • GLP or non-GLP • Necessary to be able to associate images with blocks, tissues, animal identification, treatments, experiments, etc… • source information, interface with LIMS (Laboratory Information Management System) • cross reference to lab books • Necessary to be able to associate images with multiple analyses and results • cross reference in reports • interface with document generation programs • Storage and retrieval of images and data IS/IT participation essential • searchable(on how many and what criteria?) • image quality / integrity • Compression, storage space and location • storage of primary image, annotated images, etc…. • Trade off: amount of annotation vs. ease of use (data entry time) • potential for retrospective analysis

  20. Technical Needs • Rapid, automated slide scanning • Multiple formats • brightfield • fluorescence • Rapid, seamless change between magnifications • Depth perception, polarization? • Volumetric determinations? • Pathologist / scientist supervised computer self learning for image analysis

  21. Other applications • Digital Imaging - Telepathology - sharing of digital images • Image Analysis - cellular to whole animal - HCS (High Content Screen) - Transgenic mice with in vivo light emission (e.g. luciferase)

  22. control 6 hours 18 hours Large Scale High Content Screeninge.g. Anti-mitotics What is the relevant measurement? Discrimination parameters based on experimental observations (data) with appropriate controls is essential. - Parameters are often not intuitive. - Results must be biologically relevant to mechanism of action. Morphology varies with time, dose, staining and mechanism of action. Sophisticated approach with complex analysis (re-analysis) is needed.

  23. Image Analysis: HCS – special issues • Large experiments • up to 384 well plates • very large screens, very large data sets • Feature extractions – what, how, etc… • Image compression – current use and archive • resources for data storage become important with time • loss of image integrity with compression may be an issue – especially for retrospective analysis • Data normalization • inherent variations within an experiment • Data mining – multivariate analysis • need for sophisticated statistical analysis – multiple possible methods • team approach essential • final biological relevance is essential

  24. Whole animal – in vivo Bioimaging • Transgenic animal with luciferase reporter • luciferase (enzyme) is produced in response to specified gene expression • enzyme substrate given intravenously • whole mouse is imaged for in vivo light emission • tissue imaged ex vivo • image analysis used to quantify gene expression based on light emission Journal of Molecular Endocrinology (2005) 35, 293-304

  25. Evolution of the Process • Technology and applications are in infancy • New, easier, less expensive technology required for widespread acceptance and use • Current investigators will validate the technology for traditional applications • Future investigators who evolve with the technology will likely be ones to define new, unorthodox, innovative applications

  26. Summary:what a pathologist wants / needs • Digital Images • Quality images • Rapid manipulations • Integrated systems • Easy to use • Image analysis • Quality data • Pathologist / scientist driven • Reiterative process for refinement of criteria • Easy to use “You can’t always get what you want…” - Rolling Stones, Hot Rocks, 1964-1971 Consider the constraints of the individual workplace.

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