The Role of the Statistician in the Evolving Preclinical Drug Safety

Environment

Steven Bailey Pfizer, Drug Safety R&D Statistics

Pfizer Internal Use

Preclinical Drug Safety testing • integral part of drug development for decades• Historically more emphasis on running studies

needed for regulatory approval

Recently scope of work in Drug Safety has been evolving

• Goal of identifying issues earlier

Introduction

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Drug Safety getting involved in new areas

Interesting new areas of support

Openness to statistical support

To date, our response have been underwhelming- resource limitations

‘Conclusions’

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Regulatory studies required for IND, NDA submissions

Toxicology

Reproductive Toxicology

Carcinogenicity

Genetic Toxicology

Safety Pharmacology

Juvenile Animal Toxicity

Generally standard study design

Standardized statistical methods

Challenges: generally basic from a statistical point of view

haven’t changed much over time

The past

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In 2004 – ‘New Topics’

Session at the Midwest Biopharm Statistics Workshop

Title: Statistical Analysis of New Studies Required in Preclinical Safety Testing

Talks:• New methodologies for QT interval prolongation adjustments

in preclinical safety pharmacology studies• Design and analysis of CNS/FOB studies in preclinical safety

pharmacology testing• Design and analysis of juvenile animal toxicity studies in

support of pediatric drug products• Statistical aspects of auditory startle experiments in

behavioral toxicity studies

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Currently: 5/5000 compounds progress to clinical trials

1/5000 compounds become approved drugs

Average time for development to approval is 12 years

- MedicineNet.com

Avg. cost for R&D for each drug approved is $4 billion

- Forbes, 2012

Failure rate increasing, and development cost increasing over time

More conservative, safety conscious FDA

In preclinical Drug Safety, more types of testing required

Evolving Development Landscape

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New development paradigms

Fail early

- Identify problems early so compounds killed early

Personalized medicine

Biopharmaceuticals

Creates new types of development activities,

and new opportunities for statistical support

Evolving Development Landscape

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Quantitative Pathology- Imaging- Stereology- Comparison of Methods

Efficacy Model Development

Safety Biomarker Development and QualificationmiRNA biomarkers

New Areas of Support

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(Automated) imaging data is increasingly used to assess pathology

– Less subjective

– Once processes is set up (staining, equipment learning), is less expensive.

– Process is time consuming, but equipment can be set and left to operate automatically

Quantitative Pathology - Imaging

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Objective: Quantify nephrin expression by QIHC on sections of kidney

• Dosed animals vs. controls

• IHC for nephrin on sections of kidney using Ventana Discovery XT automated immuno-staining platform

• Nanozoomer slide scanner used to capture images

• PE Vectra image analysis software used to measure area in the region of interest (glomerular tuft) that is occupied by chromogen (IHC%)

Example: Quantitative Immunohistochemistry for Nephrin

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QIHC: Nephrin Expression

%Glomerular Tuft Expressing Nephrin

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Ben Wei, Yutian Zhan and Shawn O’Neil

Region of

interest (ROI)

Example Data

Quantitative Pathology - Imaging

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sampleNephrin

IHC (µm²)Area of All Tufts (µm²)

Nephrin IHC Area %

1525 142482 306656 46.46

4027 135534 493650 27.46

4525 117349 306323 38.31

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CD25 / FoxP3

Image Analysis Quantification of CD25+/FoxP3+

CD25 / FoxP3

Pseudocoloring via Nuance Multispectral Camera

CD25+/FoxP3+

FoxP3+

CD25+/FoxP3+

CD25+

Imaging technologies can be used to measure:

– Area

– Cell counts• Cell area • Cell nuclei

– Cells exhibiting specific markers

Quantitative Pathology - Imaging

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Stereology – the study of 3 dimensional structures using two-dimensional cross sections

- volume- surface area- length- number

Techniques require a few 'representative' plane sections, then statistically extrapolate to three-dimensional object

Quantitative Pathology - Stereology

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Very old field – can be traced back to the Buffon’s needle problem, posed by Geoges-Louis Leclerc, Comte de Buffon in 1777

Suppose we have a floor made of parallel strips of wood, each the same width, and we drop a needle onto the floor. What is

the probability that the needle will lie across a line between two strips?

Quantitative Pathology - Stereology

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Image Created By: Wolfram MathWorld

In the past – heavily reliant on assumptionsModel basedAssumptions regarding structure (shape and

homogeneity) of the 3-dimensional object

‘New’ stereology – “Design-Based Stereology”- assumption and model-free- unbiased - also called ‘Assumption free stereology’

Quantitative Pathology - Stereology

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Example – Count of neurons in ganglia

Estimated Total Count = # counted x 1/SSF x 1/TSF x 1/ASF= 178 x 1/0.05 x 1/0.739 x 1/0.025= 178 x 1082= 192,596

SSF = 24/480 (0.05)1 21 41 61 81 101 121

Sections 1-20(360 µm)

18 µm thickness

8 µm Dissector height (fixed)(1 µm guard zones)

TSF = 8/10.8251 (0.739)

Avg thickness = 10.8251 µm

ASF = 0.025 (2.5%)

6 5

6 7

Total # neurons counted: 178

Nerves - Neurons within ganglia count, volume

Kidney - measurement of glomeruli in kidney volume, number - measurement of mesangial matrix within glomeruli volume, percent of tissue

Lungs - Alveoli volume, surface area

Stereology - Uses

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Sampling plan - Define reference space - Design a ‘systematic-random’ sampling plan

- removes subjective choices and options- independent of properties of the tissue- efficient, unbiased- Multiple layers of sampling - Variation in each layer- Sampling plan dependent on precision needed

Model assumptions – introduce bias - modeling of the geometry of the structures - popular in materials science-simple geometry

Replication of results impossible due to sampling plans

Stereology - Issues

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Appropriate definition of reference space Appropriate sampling strategies

Quantification of variability - method variability - biological variability

Appropriate endpoint - not always correct - stereology example: percentage mesangial matrix/glomeruli vs. total area mesangial matrix

Imaging and Stereology – Statistical Issues

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Sample size/power calculations

Reproducibility - method (staining, sampling, observer effect, etc.)

Appropriate statistical methodology nested models repeated measures parametric or nonparametric approaches unequal variance

Imaging and Stereology – Statistical Issues

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- Factor of imaging, stereology, and histopathology is cost

- Example (imaging): Comparison of Cri Vectra vs. Stereologer in measuring glomerular area

- Example (imaging vs. histopathology): histopathological scoring for severity of spinal cord demyelination and quantitative image analysis for myelin content

Quantitative Pathology – Comparison of Methods

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0 20 40 60 80 1000

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Cervical Spinal Cord

LFB/PAS QIHC - Cervical

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p<0.05

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Thoracic Spinal Cord

LFB/PAS QIHC - Thoracic

p<0.0001

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Lumbar Spinal Cord

LFB/PAS QIHC - Lumbar

p<0.0001

Quantitative Pathology – Comparison of Methodsl

*Statistical analysis: Linear regression .

There was a statistically significant inverse correlation between histopathological scoring for severity of spinal cord demyelination and quantitative image analysis for myelin content based on Luxol

Fast Blue histochemical staining at all levels of spinal cord evaluated (p<0.05).

Now:- Regression / p-value / R2

- Correlation

Better:- Bland Altman

- Lin L, Hedayat A, Wu W, (2012), Statistical Tools for Measuring Agreement, Springer

- Hshieh and Ng, Assessing agreement: a graphical approach, poster presentation at 2015 JSM

- Simulations – do the two methods arrive at the same conclusion?

Quantitative Pathology – Comparison of Methods

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Example: Mouse Collagen Induced Arthritis Model• Collagen Induced Arthritis (CIA) model used to test

efficacy of new therapeutics– Arthritis induced using monoclonal antibodies – Used to simulate rheumatoid arthritis – Compound administered, joints examined

pathologically for inflammation– Compound screening completed in a short time

• Through the use of historical data characterization and simulations, were able to determine that only lost 2% in power (98% power to 96%) despite reducing the

number of pathology slides read by 50%.

Efficacy Model Development

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• Develop Safety Biomarkers so we can:

- Screen compounds faster at less cost- Less compound needed- Fewer animals- Faster

- Possibly go into clinical trials by closely monitoring compounds that have side effects

Safety Biomarker Development

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‘Standard’ Development Paradigm

– Study with 2 groups – control and treated Treated @ dose expected to cause 100% response

– Treated group is dosed with an article that is known to cause effect (eg, kidney damage)

– Perform simple statistics (eg, t-test) on every parameter of potential interest

Safety Biomarker Development

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Issues

– Correlation does not imply causality

– Need biomarkers specific to injury under study, but may find biomarkers indicative of non-specific injury

– No ability to develop biomarkers that depend on combinations of parameters

– How to choose among set of candidates with significant p-values

– How to handle transient effects

Safety Biomarker Development

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Statistical Issues

– Should be comparing affected vs. non-affected animals, not dosage groups

– Design: prefer additional dosage groups where only a percentage of animals respond

– Sample size / lack of reproducibility

Safety Biomarker Development

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Statistical tools

- Predictive modeling

- Classification and Regression Trees (CART)

- Random Forest

- Logistic Regression/Linear Discriminant Analysis

- Allow models using multiple parameters

- Allow ranking of models

Reference: Johnson, Kjell and Kuhn, Max, (2013), Applied Predictive Modeling, Springer.

Safety Biomarker Development

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microRNAs (miRNAs)

MicroRNAs (miRNAs) are non-coding, single-stranded, short

( 22-nucleotide) regulatory RNAs ∼

Estimated 1000 miRNA genes in human; ~1% of human ∼genome

Wide range of expression: Few to 100,000 copies per cell miRNA are highly conserved and often tissue specific

Landgraf et al., Cell, 2007

miRNA research trends

The growing awareness of the

importance of miRNAs has generated intense

activity in the biomedical research

community.

Vergoulis T et al. 20111

miRNAs in human body fluids (blood, urine, saliva, feces) are non-invasive markers for disease. miRNA expression patterns are tissue specific miRNA expression can easily be detected miRNAs are conserved across multiple species miRNAs may be used to localize the site of injury miRNAs may reliably track progression of injury/recovery

NormalizationNeeded to account for sample preparation

and other technical artifacts

Missing Values

High percentage of missing values. Missing values can be due to:

1.Technical issues - missing at random2.Value greater than machine sensitivity3.Value unreliable (greater than a threshold,

eg 32, that the scientist sets) – not missing at random

MultiplicityDepends on experiment. Cards we use have

384 wells. For screening, we generally collect data on 384 or 768 miRNAs.

Issues with analysis of miRNAs

Normalization

1.Quantile normalization2.LOESS normalization3.MiR-Adaptive normalization (Zhao and Zhu, Poster at

Joint Statistical Meetings, 2015)

Missing Values

1.Many miRNAs have no analysis due to prevalence of missing values (eg, in a recent investigation, only 110 of 384 miRNAs had at least 3 observations per group.

2.Technical issues are missing at random; can be ignored3.Values greater than machine sensitivity or with

unreliable values are not missing at random. Currently are being ignored and only captured in biological interpretation through scan of missing values pattern.

MultiplicityWhat are appropriate adjustment procedures. Use of False Discovery Rate adjustment prevalent.

Statistical Techniques

Willingness to do ‘Business Development’

Interest in learning subject area(s)- Can be extremely time-consuming

Ability to think ‘outside the box’- Not just about statistics

STRONG consulting skills- ability to work with scientists without overwhelming them

Resources – i.e., time

To Get Involved - What’s Needed

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Investigative PathologyShawn O’Neil Yutian Zhan

PathologyScott SchellingTim LabrancheBruce Homer

Safety BiomarkersShashi RamaiahRounak Nassirpour

StatisticsDavid Potter

Acknowlegements

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