quantitative signal detection for the mid sized pharma - webcast

Copyright © 2014, Oracle and/or its affiliates. All rights reserved. 1

Quantitative Signal Detection for Mid-

sized Biopharmaceutical Companies

Robert Weber, Senior Product Strategy Manager

Oracle Health Sciences

Dr. Marc A. Zittartz, Chief Quality Officer

PharmaSOL


The following is intended to outline our general product direction. It is

intended for information purposes only, and may not be incorporated into

any contract. It is not a commitment to deliver any material, code, or

functionality, and should not be relied upon in making purchasing decisions.

The development, release, and timing of any features or functionality

described for Oracle’s products remains at the sole discretion of Oracle.


Signal Detection Overview

Signal Detection and Management has moved into the focus of Pharmacovigilance activities

Signals can be detected multiple ways – Single Case Review – During PSUR or DSUR creation – Literature Review – Authority inquiries – (Automated) Signal Detection

Signal can be found from multiple sources – Spontaneous Reporting Databases – Clinical Trials – Electronic Health Records


Signal Detection Approaches

Identification of new risks: New Signals – Quantitative Signal Detection – Disproportionality Statistics – Tracking of Designated Medical Events (DMEs) – Case Scoring – Temporal Pattern (Aberration) Detection

Monitoring of known risks: “Re-Signaling”

– “Keep under Review” – Targeted Medical Events (TMEs) – Increased Frequency – Increased Severity (Seriousness, Fatalities…)


History of Signal Detection Modern Pharmacovigilance started in the 1960‘s – Thalidomide being one of the main

triggers at the time Spontaneous reporting systems were established, intially nationally, from 1968 also as

international collaboration (AU, CA, DE, NL, NZ, SE, UK, US) With growing numbers of reports, regulators looked for ways to systematically identify

signals. Napke‘s Pigeon Holes“ (CA 1966) are a famous example of an early manual system Computerized signal detection using disproportionality measures began at several centers

during the 80s/90s At the end of the century, serveral methods were published: A group at the WHO (Bate) BCPNN in 1998;

GPS/EBGM (DuMouchel) using FDA data in 1999; PRR (Evans) using UK MHRA data in 2000


Disproportionality Methods Quantitative Signal Detection refers to the identification of

drug-event-combinations within a dataset that appear more often than expected Signal (Statistic) of Disproportionate Reporting

Non-Bayesian Methods

– PRR – Proportional Reporting Ratio – ROR – Reporting Odds Ratio

Bayesian Methods

– IC (BCPNN) – Information Component – EBGM (MGPS) – Empirical Bayes Geometric Mean

Logistic Regression-based Methods

– ELR – Extended Logistic Regression – RGPS – Regression-enhanced Gamma-Poisson Shrinker


Issues / Challenges “Noise” – False Positives

– Many adverse events are rare (especially if drugs are new) – low counts lead to great fluctuations of PRR or ROR

– Bayesian methods can reduce false positives

Detecting Interactions (Drug-Drug-Event Signals)

– Multi-item disproportionality analysis (MGPS -> INTSS)

– LR analysis for computing interaction scores

Signal Leakage and Masking

– Bias in the database can suppress or falsely elevate signals

– “Innocent bystanders” in polypharmacy situations

– LR can identify the contribution of individual drugs and other factors

– RGPS combines LR with Bayesian shrinkage

For a certain product 5,4% of all drug-event-combinations are related to a specific event.

However only 1,4% of all drug-event combinations are related to this event.

This drug-event combination appears 3,8 times more than would be expected.

pharmaSOL All Rights Reserved Slide 8

Event All other events

Percentage PRR

Medicinal Product 52 958 5,4%

3,8 All other medicinal products 691 50.000 1,4%

Strengths o Observation in Real-Time o Case Details

o Availability of Source Data o Full narrative

Possible Weaknesses

o Product specific volume o Total case volume (background) o Non-diverse product portfolio

o Mix of new and mature products o Different indications


FDA AERS (USA)

o since 1968, focus on US data, released quarterly

WHO Vigibase

o since 1968, data from regulatory agencies worldwide, released quarterly

PMDA (Japan)

o Recently released, focus on Japan

Eudravigilance

o EMA intends to publish data in the future

o Focus on European Economic Area (EEA)


Strengths

o Size and Diversity of public databases

o Information on generic competition

o Ability to detect Class Effects

Possible Weaknesses

o Case details

o Duplicates

o Time delay (ca. 6 months)



Big Pharma

Mid-sized Pharma

Small Pharma

Need High Medium Medium

Suitable company dataset

Yes Maybe No

Quantitative Signal Detection

In use Partly No


Company dataset

too small

Likely to be dominated

by few products or indication areas

Public Data available with a time delay

Spiking: Merge of Company Data with Public Data

o Information related to company product is removed from public

dataset

o Identifier used: compound name

Company data is injected.


Public Data

Company

Data

Company

Data within

Public Data


Risks

If product has generic

competition, information is

lost.

Benefits

Up-to-date with company

data

Case Details from

company data

Broad background from

public dataset


Customer Experience Mid-sized pharma in Germany

Employed Argus Safety and Empirica Signal separately

Developed custom ETL from Argus Safety to Empirica Signal

Spiked Dataset: Company data was merged with WHO Vigibase

Different datasets used for different products

Drug Data Interval

Up to 2 years after Launch Spiked Dataset Monthly

Risk Management Plan Spiked Dataset Monthly to Annual

Generic WHO Vigibase 12 months

quantitative signal detection for the mid sized pharma - webcast

Health & Medicine