data mining approaches to signal detection

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Dr. Bhaswat S. Chakraborty Senior VP, R&D, Cadila Pharmaceuticals Jnauary 22, 2009 Presented at Pharmacovigilance 2010, January 21-22, 2010

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Page 1: Data mining approaches to signal detection

Dr. Bhaswat S. ChakrabortySenior VP, R&D, Cadila

PharmaceuticalsJnauary 22, 2009

Presented at Pharmacovigilance 2010, January 21-22, 2010

Page 2: Data mining approaches to signal detection

Risk Management and Pharmacovigilance Increased focus on safety and risk management is a

global issue with diminishing boundaries

Knowledge and experience of the drug and its life-cycle further develops as we understand its use and hazards

Development of global risk management plan rather than individual region or country Risk

Management plans would be an important step forward to more effectively and accurately assess the safety of pharmaceutical drug products

An early risk management planning between company, regulator and healthcare professionals is key for successful product life cycle management

Risk Communication Plan between Company, Regulator, Healthcare Professionals and Patients is getting more transparent

Page 3: Data mining approaches to signal detection

And Yet…Despite this increased importance, stakeholders

still use traditional methods for PV

Conservative methods do not capture many SAEs and USAEs that are possible to capture from huge databases without further experimentation

Data mining is one such approach

Data mining can help find unrecognized toxic signals

Page 4: Data mining approaches to signal detection

Two categories of approved drugCategory 1

Those who are unequivocally superior to existing drugs of that class in efficacy

May or may not be superior in safetyCategory 2

Those who are not superior to existing drugs of that class in efficacy

Non-inferiorSuperior to placebo but inferior to existing

standard careMay or may not be superior in safety

Page 5: Data mining approaches to signal detection

Premature Approval?Many Category 2 drugs whose complete safety

profile is still unknown were approvedIn some cases, drugs are approved despite

identification of SAEs in premarketing trialsAlosetron hydrochloride – ischemic colitisGrepafloxacin hydrochloride – QT prolongation and deathsRofecoxib – heart attack and stroke (long-term, high-

dosage use)

They were all subsequently withdrawn from the market because of these SAEs

Page 6: Data mining approaches to signal detection

Market Uptake and Sales Volume Many drugs whose complete safety profile is still

unknown actually have/had a rapid market and very high sales volume through increased Rx.

Promotion of early use of new drugs by sponsorsPhysicians' adoption of such drugsDirect-to-consumer drug advertisingPharma companies concern for patent life, a desire to

mold prescribing habits prior to the market entry of competitors

“Ramp-up" in sales encourages investors and increase stock prices.

New drug safety may be further compromised by the failure to conduct postmarketing studies

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J. Herson. In Data and Safety Monitoring Committees in Clinical Trials

Page 10: Data mining approaches to signal detection

Having an Adverse Events Database

Is not a bad ideaAll good pharma companies have AE databaseAlmost all developed country regulatory

agencies have AE databaseThe WHO Uppsala Monitoring Centre (UMC)

now receive >1,000,000 reports per year

Such databses can really help in bringing down drug induced morbidity

Page 11: Data mining approaches to signal detection

Desirable Attributes of AE Database SoftwareShould be well integrated with Clinical data

management softwareUser friendlyIndividual reports management featuresEasy for queryLine listing of the entire database or part is

possible and easyData extraction is easy, with desirable filtersMay also keep track of postmarketing Rx

utility and complaints data

Page 12: Data mining approaches to signal detection

Collection of ICSRs from CADRMP

Conversion of free text to structured information

Data cleaning and duplicate detection

Applying quantitative or statistical methods

Computing an accurate measure for SD

Gavali, Kulkarni, Kumar and Chakraborty (2009), Ind J Pharmacol, 41, 162-166

or any comprehensive database

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Targeted Event Y All other events Total

Targeted Drug X

A B A+B

All other drugs

C D C+D

Total A+C B+D A+B+C+D

Page 14: Data mining approaches to signal detection

Criteria for a Toxic Disproportional ADR

ROR =

χ2 =

)(

)(

DCC

BAA

PRR

DC

BA

Expected

ExpectedObserved 2)(

Significant disproportional Signal is detected when 2 is ≥ 4.0 and the rest ≥ 2.0

Page 15: Data mining approaches to signal detection

Casestudy Example: Propranolol-Bradycardia

PRR = 2.51

ROR = 2.58

χ2 = 3.26

Therefore, bradycardia is not a significant disproportional signal (Serious Adverse Event) associated with Propranolol

BradycardiaNot

Bradycardia

Propranolol HCL

4 82

Not Propranolol HCL

52 2749

Gavali, Kulkarni, Kumar and Chakraborty (2009), Ind J Pharmacol, 41, 162-166

Page 16: Data mining approaches to signal detection

Casestudy Examples – Significant Signals

AssociationBupropion – seizuresOlanzapine – thrombosisPergolide – increased libidoRisperidon – diabetes mellitusTerbinafine – stomatistisRosiglitazone – liver function abnormalities

Dis-associationIsotretinoine– suicide

Source: LAREB

Page 17: Data mining approaches to signal detection

Thank You Very Much

Acknowledgment:Sharwan Singhal