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 DACVPsanofi aventis U.S., Inc.Bridgewater, N.J. USA

2Pathology Visions, October, 2008 | D. Weinstock

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?


Pathology Applications in a Pharmaceutical Company

Discovery (Research) Target Validation

High Content Screening (HCS)

Animal models

Proof of concept / proof of mechanism studies

Development GLP toxicologic pathology

Biomarker development / validation

Investigational toxicologic pathology


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


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


Digital Images:Acceptance by Pathologists

Quality images data

Speed slide scanning image access, handling field of view, magnification change, etc…

Cost and benefit Integration Ease of use

Ultimate goal: replace glass slide evaluation via microscope with digital image evaluation on computer screens

These issues must be addressed to the satisfaction of the primary users of the technology.

Very good progress to date, but improvement possible.


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


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


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


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

NecrosisFibrosisInflammationBile duct proliferationothers……

Normal features difficult to differentiateRed blood cellsSinusoids – amount of space affected by degree of exsanguinationKupffer cell – nuclei difficult to discern from inflammatory cells


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



Liver - bile duct proliferation

Issues

Edge effect.

Differentiation between bile ducts and arterioles.

Relatively uncomplicated change in this field.



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



Liver - bile duct proliferation - fibrosis - inflammation

Issues

Complex morphology of multiple changes in one focus of interest.

Severity change varies by focus.



Liver - necrosis - fibrosis - bile duct proliferation - inflammation

Issues

Similar issues as previous images, but now complicated by multiple contiguous types of changes per field.


Liver - necrosis - bile duct proliferation

Issues

Multiple non contiguous changes.

Bile duct proliferation – uncomplicated.Necrosis – complex morphology in area of change.



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


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?


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


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


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)


Large Scale High Content Screeninge.g. Anti-mitotics

6 hours 18 hourscontrol

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.


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


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


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


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.

daniel weinstock dvm phd dacvp sanofi aventis u.s., inc. bridgewater, n.j. usa

Documents

digital image evaluation

digital image acquisition

digital image files

digital images

total image set end

pathologist driven analysis

digital imaging

pathology applications