This document summarizes a presentation given at the Pharmacovigilance 2010 conference by Dr. Bhaswat S. Chakraborty from Cadila Pharmaceuticals on January 22, 2010. The presentation discusses increased focus on drug safety and risk management globally, the importance of developing global risk management plans, and ensuring risk communication between stakeholders. It also notes that traditional pharmacovigilance methods may miss some safety issues, and that data mining can help identify previously unrecognized toxic signals in large safety databases.

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

2. 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

3. 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

4. Two categories of approved drug
Category 1
Those who are unequivocally superior to existing drugs
of that class in efficacy
May or may not be superior in safety
Category 2
Those who are not superior to existing drugs of that
class in efficacy
Non-inferior
Superior to placebo but inferior to existing standard
care
May or may not be superior in safety

5. Premature Approval?
Many Category 2 drugs whose complete safety profile is
still unknown were approved
In some cases, drugs are approved despite identification of
SAEs in premarketing trials
 Alosetron hydrochloride – ischemic colitis
 Grepafloxacin hydrochloride – QT prolongation and deaths
 Rofecoxib – heart attack and stroke (long-term, high-dosage use)
They were all subsequently withdrawn from the market
because of these SAEs

6. 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 sponsors
Physicians' adoption of such drugs
Direct-to-consumer drug advertising
Pharma 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

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

10. Having an Adverse Events
Database
Is not a bad idea
All good pharma companies have AE database
Almost all developed country regulatory agencies
have AE database
The WHO Uppsala Monitoring Centre (UMC) now
receive >1,000,000 reports per year
Such databses can really help in bringing down drug
induced morbidity

11. Desirable Attributes of AE Database
Software
Should be well integrated with Clinical data
management software
User friendly
Individual reports management features
Easy for query
Line listing of the entire database or part is possible
and easy
Data extraction is easy, with desirable filters
May also keep track of postmarketing Rx utility and
complaints data

12. Collection of ICSRs from CADRMP
or any comprehensive database
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

13. Targeted Event Y All other events Total
Targeted
A B A+B
Drug X
All other
C D C+D
drugs
Total A+C B+D A+B+C+D

14. Criteria for a Toxic Disproportional ADR
A ( A +B )
PRR =
C (C +D )
AB Significant disproportional
ROR = Signal is detected when χ2
C D is ≥ 4.0 and the rest ≥ 2.0
χ =
2 (Observed − Expected ) 2
Expected

15. Casestudy Example: Propranolol-Bradycardia
Not
PRR = 2.51 Bradycardia
Bradycardia
ROR = 2.58 Propranolol
4 82
HCL
χ2 = 3.26
Not Propranolol
52 2749
HCL
Therefore, bradycardia is not a significant
disproportional signal (Serious Adverse Event)
associated with Propranolol
Gavali, Kulkarni, Kumar and Chakraborty (2009), Ind J Pharmacol, 41, 162-166

16. Casestudy Examples – Significant Signals
Association
Bupropion – seizures
Olanzapine – thrombosis
Pergolide – increased libido
Risperidon – diabetes mellitus
Terbinafine – stomatistis
Rosiglitazone – liver function abnormalities
Dis-association
Isotretinoine– suicide
Source: LAREB

17. Acknowledgment:
Sharwan Singhal
Thank You Very Much