Evaluating and Investigating Drug Safety Signals with Public Databases

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Evaluating and Investigating Drug Safety

Signals with Public Databases

Rodney L. Lemery, MPH, PhD Vice President Safety and Pharmacovigilance BioPharm Systems, Inc.

Evaluating and Investigating Drug Safety Signals with Public Databases 2

Contents

• Brief Overview of Common Language and

Pharmacoepidemiology

• Online Free and Fee-based Databases

– Overview of Online Databases Available for Pay

– Overview of Online Health Databases Available for

Free

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Part I: Common Language and PV

Overview


Common Language

ADR Adverse Drug Reaction

APR Adverse Product Reaction

CIOMS Council for International Organizations of Medical Sciences

EMA European Medicines Agency


Common Language…

Much debate on the definition (we will use the following):

Information that arises from one or multiple sources, which suggests a new potentially causal association, or a new aspect of a known association, between an intervention and an event or set of related events, either adverse or beneficial, that is judged to be of sufficient likelihood to justify verificatory actions. (CIOMS, 2010 p.14)

Signal


Common Language…

Much debate on the definition (we will use the following):

The act of looking for and/or

identifying signals using event data from any source.

(CIOMS, 2010 p.116)

Signal Detection


Common Language…

Waller (2010, p.50) defines this as an important and controversial method of ensuring only those signals worthy of internal resources are passed into the formal evaluation process

—The WHO uses a method similar to Emergency Room triage processes in hospital settings to quickly evaluate the aspects of a case that make it critical for research while placing other cases on hold until a later investigation period —The MHRA uses an analytic methodology comprised of two mathematical scores contributing to a final score that will prioritize the case —Other articles exist in the literature suggesting valid decision support methods

Signal Prioritization


Common Language

The formal process of reviewing scientific data sources to refute or confirm the existence of a signal in a company product safety profile; this confirmation will elevate the signal to a potential or identified risk CIOMS VIII (2010, p. 90) indicates that this process should be multi-faceted: 1.Collect evidence to evaluate causal link between the product and the event 2.Determine if the signal represents an identified or potential risk 3.Communicate the identified risk and to propose its further evaluation and mitigation

Signal Evaluation


Detailed Signal Management Lifecycle

Signal

Prioritization

Signal

Detection

Signal

Evaluation


Simplified Safety Signal Management Lifecycle

Signal Prioritization

Signal Evaluation

Signal Detection

CIOMS (2010, p. 9)


Pharmacoepidemiologic Studies

Study Design Advantages Disadvantages

Randomized Control Trial

Most convincing design Only design which can control for unknown confounders Only experimental design

Most expensive Artificial (nothing like the "real-world“) Logistically difficult Ethical objections can lead to non-investigation (children, very sick patients)

Cohort Studies Can study multiple outcomes Can study uncommon exposures Selection bias less likely (than case/control) Unbiased exposure data Incidence data available

Possibly biased outcome data More expensive If done prospectively, may take years to complete

Case-control Studies

Can study multiple exposures Can study uncommon diseases Logistically easier and faster Less expensive

Control selection problematic (selection bias) Possibly biased exposure data

Analyses of secular trends

Can provide rapid answers Confounding is not controlled

Case Series Easy quantitation of Incidence No control group, so cannot be used for hypothesis testing

Case Reports Cheap and easy method for generating hypotheses

Cannot be used for hypothesis testing

Ord

er

of D

ifficu

lty a

nd

Ca

usa

l E

vid

en

ce


Pharmacoepidemiologic Studies

Case/Control Exposure of Interest

(Unknown)

Disease of Interest

(Known)

Prospective

Cohort

Exposure of

Interest

(Known)

Disease of Interest

(Unknown)

Retrospective

Cohort

Exposure of Interest

(Known)

Disease of Interest

(Unknown)


Reasons for Pharmacoepidemiologic Studies…

• Regulatory – Required for approval

– Response to audit

• Marketing – Assist in market penetration by further documenting safety

• Comparator studies

– Increase Name recognition

– Repositioning of drug • New patient populations (age or gender focus)

• Different outcomes (QOL)

• Explore unintended benefits of the product

• Legal – In anticipation or response to legal action


Reasons for Pharmacoepidemiologic Studies

• Clinical

– Hypothesis generation

• Increasing our knowledge on the safety profile of new entities

in the market

– Hypothesis testing

• Look at beneficial product effects as well as harmful ones – Case/Control and cohort studies of estrogen compounds and their use in

preventing osteoporotic fractures

(Strom & Kimmel, 2006, p.59)

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Part II: Overview of Free Online

Databases


General Principles

Since pharmacoepidemiology studies can be large,

long term and ultimately expensive; the use of existing

databases could aid in the conduct of these types of

observational studies.

– There are two broad kinds of databases

available for use:

• For FREE

• For FEE


For Free Databases

A number of entities have developed simple and

complex online databases available via the web for

querying and display of epidemiologic information.

– CDC-WONDER

– EU-ADR


CDC WONDER

• Wide-ranging Online Data for Epidemiologic Research (WONDER)

– An easy-to-use internet based tool that makes the information

resources of the Centers for Disease Control and Prevention

(CDC) available to public health professionals and the public at

large

– It allows us to search for and read published documents on

public health concerns, including reports, recommendations and

guidelines, articles as well as statistical research data

published by CDC

– Query numeric data sets on CDC's mainframe and other

computers, via "fill-in-the blank" web pages.

• Public-use data sets about mortality (deaths), cancer

incidence, HIV and AIDS, TB, natality (births), census data

and many other topics are available for query, and the

requested data are readily summarized and analyzed.


CDC WONDER

The WONDER

homepage provides

a number of

queryable databases


CDC WONDER (Mortality Rates)

• Assuming that not 100% of the sub-population

afflicted with a disease state dies from the disease

state, we may be able to use mortality rates as a

confirmation or refutation of a suspect ADR

– NOTE: Cause of death records may have

information (classification) bias involved that may

not provide a realistic measure



Using [Open] allows us to drive into ICD-10 codes used in

the death classifications



Select the

region in

which you

are

attempting

to get rates



Various demographic breakdowns are also available in this

database



Year and Month

categories are also

available to segregate

the data



Autopsy sub-grouping choices



Using [Open] allows

us to dive into ICD-

10 codes used in the

death classifications



• Options for rate display and data export are also

available in WONDER

• Once all options have been entered, clicking [Send] will

execute the report generation



WONDER mortality data for “Stomach Cancers”


CDC WONDER Limitations

• The limitations of this database is that the

information provided does not have any

drug information associated to the diseases

of interest

• This makes the use of the system limited to

finding incidence or prevalence rates of

underlying diseases only

– This would limit the use to only confirmation or

refutation of the potential signal with the

appropriate assumptions made earlier


EU-ADR Web-Based System

• Also available now is an amazing online database that brings

together multiple sources into a single queryable system

• The EU-ADR project is the development of an innovative

computerized system to detect adverse drug reactions

(ADRs), supplementing spontaneous reporting systems.

– EU-ADR will exploit clinical data from electronic healthcare

records (EHRs) of over 30 million patients from several

European countries (The Netherlands, Denmark, United

Kingdom, and Italy). In this project a variety of text mining,

epidemiological and other computational techniques will be

used to analyze the EHRs in order to detect ‘signals’

(combinations of drugs and suspected adverse events that

warrant further investigation).



Once registered and logged in, the home page has 2 main tabs

– Datasets

– Workflow



Datasets allow you to create Drug and Event pairs

– The Drugs are coded to the WHO Drug ATC Level 5

– The Events are coded to event term abbreviations specific

to this system



The Workflow tab allows you to select a particular

method of substantiation and a Drug/Event pair

– The Drugs are coded to the WHO Drug ATC Level 5

– The Events are coded to event term abbreviations specific to

this system



MEDLINE ADR

This search engine

workflow looks at the

available literature in the

MEDLINE literature

database and looks for

situations where 3 or more

articles exist with the event

and product listed with

subheadings of <<Chemical

induced>> and <<Adverse

effects>>

This is one way to

substantiate your ADR



MEDLINE Co-occurrence

This search engine workflow

looks at the available literature

in the PubMed literature

database



DailyMed


looks at the available

drug/event pair in the

DailyMed database provided

by the Dutch Universitair

Medisch Centrum

Rotterdam, Netherlands



Drugbank


looks at the available

drug/event pair in the

Drugbank database

maintained by the Dutch

Universitair Medisch

Centrum Rotterdam,

Netherlands



Substantiation

This search engine workflow looks at the clinical connection between the drug and the event by considering drug metabolism and looking up the phenotypes of this interaction against a database of gene-disease associations maintained by the IMIM (Research Unit on Biomedical Informatics (GRIB) IMIM/UPF), Spain



If there are not results in the selected engine,

then the search will return no results


EU-ADR Web-Based System Limitations

• The limitations of this database is that the

EHR information used originated from EU

countries only and the results found here

may be limited to only the EU and not

generalizable to the US population

• This system does provide a wonderful

method of substantiating the biologic

plausibility of a Drug/AE pair and does allow

the advanced review of the scientific peer-

reviewed literature for Drug/AE pairs

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Part III: Overview of Fee-based Online

Databases


For Fee Databases

A number of private entities have developed

databases available that can aid in the display

of information that may help conduct

observational studies or aid in the confirmation

or refutation of identified signals

– Group Health Cooperative

– Kaiser Permanente Medical Care Program

– UK Clinical Practice Research Datalink (CPRD

formally known as the GPRD)


Group Health Cooperative


Group Health Cooperative

• Group Health Cooperative (GHC) is a large non-

profit consumer-directed HMO established in 1947

– Provides health care on a prepaid basis to

~600K people in Washington and Idaho

• GHC has a number of automated and manual

databases whose data serves multiple

epidemiologic studies

– The linking of comprehensive EHR to other

datasets of interests using the consumer

(enrollee number)

– Stable population over time


Group Health Cooperative Summary of Use

A retrospective cohort study of the GHC data looked

at perinatal outcomes, congenital malformations and

early growth and development of infants with and

without prenatal exposure to antidepressants

– Discharge data was used to find all live births from 1986-

1998

– Pharmacy data was used to identify all tricyclic and SSRI

antidepressant prescriptions 360 days prior to delivery

– Infants exposed to antidepressants were matched to those

not exposed

– Blinded (to exposure details) medical reviewers looked at

the various outcomes being studied


Group Health Cooperative Summary of Use

• Infants exposed to tricyclic or SSRIs during pregnancy were not at an increased risk for congenital malformations or developmental delay

• Exposure to SSRIs in the third trimester was associated to lower Apgar scores

• Exposure to SSRIs anytime during pregnancy was associated to premature birth and lower delivery weight

– Tricyclic exposure did not have associations to these outcomes

• (Strom & Kimmel, 2006, p.178)


Group Health Cooperative Limitations

• GHC information has been used primarily to study

drug utilization and AE risk/benefit evaluation of

medicinal products and procedures

• The size of the database does indicate that rare

drug/AE combinations are not likely to be found

• GHC does dictate the drugs available to their

members via their formulary so this may limit the

studies on newer medications on the market


Kaiser Permanente Medical Care Program

• Created in the 1930’s KP was a fee-for-service

medical care initiative originally available only to

construction, shipyard and steel mill workers

employed by Kaiser industries

• Today it is one of the US’ largest non-profit HMOs

– It services over 8.2 million individuals

– Covers eight states


Kaiser Permanente Medical Care Program

Since Kaiser is its own full coverage HMO system, the

data collected on the participants ranges from

pharmacy records, hospitalization records, outpatient

lab results and claims received by non-KP providers

plus other sources

– This empowers researchers to perform large

scale observational trials in the databases using

multiple sources


Kaiser Permanente Medical Care

Program Summary of Use

A retrospective cohort study of patients exposed to

troglitazone (Rezulin) in an attempt to understand the

relative risks associated to hepatic failure and

troglitazone exposure

– Cohort included 9600 diabetic patients with over

three years of Rezulin exposure

– Hospital discharge summaries and procedure

documentation indicative of acute hepatic injury

were identified and ~1200 individual medical

records were reviewed

– 109 of these records were sent to a blinded

panel of hepatologist for outcome adjudication



Program Summary of Use

• The blinded panel identified only 35 cases where the hepatic injury

was attributed only to the use of diabetic medications

• Risk of hepatic failure in patients using Rezulin was not any higher

compared to other diabetic patients

– However, the entire diabetic population did have an increased

risk of hepatic injury compared to the general population

• Currently the spontaneous FDA AERS database places the risk of

hepatic failure in those using Rezulin at 20-25 fold higher than any

other reported drug use

• This observational study disputes this finding and suggests the rate of

hepatic failure in Rezulin users is 1 per 10,000 person-years. To put

it in perspective, according to CDC WONDER data, the mortality rate

for hepatic failures NEC is 0.1347 per 10,000 person-years

• (Strom & Kimmel, 2006, p.182)

• (CDC WONDER Mortality Rates, 2013)



Program Limitations

• Drop-out rates are higher than in similar volunteer studies

• Some of the datasets (like the cancer and HIV/AIDS registries) collect race, SES and other demographics useful in multivariate analysis while other datasets within Kaiser are missing this data

• KP like other HMOs does restrict their prescription formularies which may bias the drug use data

• KP records the prescriptions filled data but this is not an accurate measure of drug consumption/exposure (it is only a proxy measure)


UK Clinical Practice Research Datalink (CPRD) AKA General

Practice Research Database (GPRD)

The databases built from EHR discussed so far can be

generalized into 2 broad categories

– Administrative

• Administrative records are often captured for billing

purposes and may not have accurate diagnosis data

for use in observational studies (depending on the

research questions being asked)

• These datasets may also be missing needed additional

data like family history, lifestyle practice etc.

– Patient Care

• Given that these records are used in the allopathic

care of the individual patient, their collection and

storage may not be appropriate for the research

questions asked in an observational, epidemiologic

study.


• In the UK, the CPRD is considered to be the world’s largest

medical records database in use by epidemiologists for

investigation

• Originally called the Value Added Medical Products (VAMP)

Research Databank, this system originated in 1987 and has

been adding ~3 million patients per year into the database

ever since

– ~1 million of these patients have more than 11 years worth

of data

– The participants represent ~5% of the general UK

population and are generally represented across SES and

demographic attributes

(Strom and Kimmel, 2006, p. 205)

UK Clinical Practice Research Datalink (CPRD) AKA General

Practice Research Database (GPRD)


• A retrospective cohort study looked at acne patients

from 1987-2002 who had been exposed to

antibiotics and those who had not

• Outcome measures were the occurrence of any

upper respiratory infections over a 12 month period

• Results were adjusted for age, sex, year of

diagnosis, number of prescriptions (for acne),

number of office visits, history of diabetes and

history of asthma

– All potential confounders for the outcome

measure

CPRD Summary of Use


• Acne patients exposed to chronic antibiotic treatments had and increased risk of URI (OR of 2.15, 95% CI 2.05-2.23)

• This finding did not change when adjusting for the confounders or the measure of health care seeking behavior

– The etiology of the URI was not evaluated (bacterial or viral)

– It is also not known if acne patients are more prone to URIs independent of antibiotic use

CPRD Summary of Use


• CPRD may contain incomplete information on some data from specialists and the information contained may be more biased to more serious medical diagnoses as minor issues are not always captured

• There is incomplete data present pre-2002 as consistent EHR weren’t used until that year

• The general size and complexity of this database requires researchers to have IS staff available for assistance in the query and analysis of the data

– The CPRD can be accessed via a web interface to mitigate this complexity

• (Strom and Kimmel, 2006, p. 209)

CPRD Limitations


References

• Abenhaim L, Moore N, Begaud B. (1999). The role of pharmacoepidemiology in pharmacovigilance: a conference at the 6th ESOP Meeting, Budapest, 28 September 1998. Pharmacoepidemiol Drug Saf. (8 Suppl 1) S1-7

• CDC WONDER Mortality Rates. (2013). Retrieved from http://wonder.cdc.gov/ucd-icd10.html on September 18th, 2013

• Coloma PM, Trifiro` G, Schuemie MJ et al. On behalf of the EUADR Consortium. Electronic healthcare databases for active drug safety surveillance: is there enough leverage? Pharmacoepidemiol Drug Saf. Epub 2012 Feb 8.

• Council for International Organizations of Medical Sciences (CIOMS). (2010). Practical Aspects of Signal Detection in Pharmacovigilance. Report of CIOMS Working Group VIII, Geneva .

• EMA. (2012). Guideline on good pharmacovigilance practices. Retrieved from http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2012/06/WC500129138.pdf on September 10, 2012

• FDA. (2005). Good Pharmacovigilance Practices and Pharmacoepidemiologic Assessment. Retrieved from http://www.fda.gov/downloads/regulatoryinformation/guidances/ucm126834.pdf on September 10, 2012

• Glass T. A., Goodman, S. N., Hernán, M. A., and Samet, J. M. (2013). Causal inference in public health. Annual Rev Public Health. 34, pp. 61-67

• Hauben M, Reich L. Drug-induced pancreatitis: lessons in data mining. Br J Clin Pharmacol. 2004;58(5):560–2.

• Strom, B., Kimmel, S. (2006). Textbook of Pharmacoepidemiology. John Wiley and Sons, England.

http://wonder.cdc.gov/ucd-icd10.html



http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2012/06/WC500129138.pdf on September 10

http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2012/06/WC500129138.pdf on September 10

http://www.fda.gov/downloads/regulatoryinformation/guidances/ucm126834.pdf on September 10

http://www.fda.gov/downloads/regulatoryinformation/guidances/ucm126834.pdf on September 10


References

• Tan, J. (2010). Adaptive Health Management Information Systems. Jones and Bartlett Publishers, Sudbury MA, USA

• Waller, P. (2010). An Introduction to Pharmacovigilance. Wiley-Blackwell. Oxford, UK


Contact

Rodney has over 15 years experience in clinical research

including in-hospital epidemiology, laboratory

experimentation, clinical data management, clinical trial

design, dictionary coding and safety

management/pharmacovigilance.

Rodney has worked for BioPharm Systems for eleven years

now serving in a variety of roles all related to the technical

and/or clinical implementations of software systems used in

the clinical trial process.

Prior to coming to BioPharm Systems Rodney worked at

pharmaceutical and technology companies in the Dictionary

Coding, Statistical Programming and Data Management

areas.

In addition to his current work at BioPharm Systems,

Rodney holds an Contributing faculty position at Walden

University teaching Public Health Informatics and disease

surveillance courses.

Rodney holds a Bachelor of Science in Genetic Engineering,

a Masters of Public Health in International Epidemiology and

a Ph.D. in Epidemiology focusing on Social Epidemiology

Technology

Evaluating and Investigating Drug Safety Signals with Public Databases