This document summarizes Bayesian estimation methods for detecting toxic signals from pharmacovigilance databases. It discusses why pharmacovigilance is important given some drugs approved with incomplete safety profiles. It then describes the pharmacovigilance process and challenges in detecting adverse drug reactions. The document introduces various Bayesian statistical methods for signal detection, including the multi-item gamma poisson shrinker, Bayesian confidence propagation neural network, and information component. It provides examples of signals detected and not detected using these methods. The document concludes that statistical data mining offers a useful tool for identifying unknown or incompletely documented drug safety signals.

1. BAYESIAN ESTIMATIONS OF
STRONG TOXIC SIGNALS
FOLLOWING MINING OF
PHARMACOVIGILANCE DATABASES
Dr. Bhaswat S. Chakraborty
Sr. VP & Chair, R&D Core Committee
Cadila Pharmaceuticals Ltd., Ahmedabad

2. CONTENTS
O S
Why pharmacovigilance (PV)?
PV process
Toxic signals & signal detection (SD)
Data
Bayesian statistics in SD
Multi-item gamma poisson shrinker (MGPS)
Bayesian confidence propagation neural network
(BCPNN)
Examples
E l
Concluding remarks

3. PREMATURE APPROVAL, INCOMPLETE
SAFETY PROFILE?
Many drugs whose complete safety profile is still
unknown have been approved
k h b d
In some cases, drugs are approved despite
identification of SAEs in premarketing trials
p g
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They were all subsequently withdrawn from the
y q y
market because of these SAEs
In currently marketed drugs black box
warnings (SAEs caused by prescription drugs) is
very common

4. CHANCE TO OBSERVE SAES
Reaction Sample Pr(at least 1) Pr(at least 2)
Rate Size
1% 500 0.993 0.960
0.5% 500 0.918 0.713
1000 0.993 0.960
0.1% 1500 0.777 0.442
3000 0.950 0.801
0.01% 6000 0.451 0.122
10000 0.632 0.264
20000 0.865 0.594

5. PHARMACOVIGILANCE
Detection and quantitation
of adverse drug reactions (ADRs)
novel or partially known
previously unknown
known hazard ↑frequency or ↑severity
in their Clinical nature, Severity or Frequency

6. THE PHARMACOVIGILANCE PROCESS
Traditional Data
Detect Signals
Methods Mining
Generate Hypotheses
Insight from
Outliers
Public Health
Refute/Verify
Impact,
Impact Benefit/Risk
Type A
(Mechanism-based) Estimate Act
Incidence
Inform
Type B
(Idiosyncratic) Restrict use/
Change Label withdraw

7. DRUG TOXIC SIGNALS
WHO: “reported information on a p
p possible causal
relationship between an adverse event and a
drug, the relationship being unknown or
incompletely documented previously ”
previously.
More than a single report needed
Suggests Drug-ADR (D-R) association (doesn't
Drug ADR (D R) (doesn t
establish causality)
An alert from any available source
Pre or post-marketing data generated
Data-mining of especially post-marketing safety
databases

8. SIGNAL DETECTION
Comes originally from electronics engg.
In signal detection theory
a receiver operating characteristic
(ROC) illustrates performance of true
positives vs. false positives out of the
negatives
at various threshold settings
t i th h ld tti
Sensitivity is high with low true negative
rate
Specificity is high with a true positive
rate

9. Increasing the threshold would mean fewer false
p
positives (and more false negatives). The actual shape of the
( g ) p
curve is determined by the overlap the two distributions.

10. GOALS FOR ADR SIGNALS
Low false positive signals
Drug-ADR association should be real
Low false negative signal
Should not miss any Drug-ADR signal
Early detection of signals is desirable
False discovery rate → 0
Association
Bupropion – seizures
Olanzapine – thrombosis
Pergolide – increased libido
Risperidon – diabetes mellitus
Terbinafine – stomatistis
Rosiglitazone – liver function abnormalities
Dis-association
Isotretinoine
Isotretinoine– suicide
Source: LAREB

11. Collection of ICSRs from
SD CADRMP
PROTOCOL
Conversion of free text to
structured information
Report collection
Database Data cleaning and duplicate
cleaning detection
Quantitative
assessment
Applying quantitative or
Qualitative
statistical methods
assessment
Evaluation
E l ti
Communication Computing an accurate
measure for SD
Gavali, Kulkarni, Kumar and Chakraborty (2009),
Ind J Pharmacol, 41, 162-166

12. EXAMPLES – SIGNIFICANT SIGNALS
Association
Bupropion – seizures
Olanzapine – thrombosis
Pergolide – i
P lid increased libido
d libid
Risperidon – diabetes mellitus
Terbinafine – stomatistis
Rosiglitazone – liver function abnormalities
Dis-association
Di i ti
Isotretinoine– suicide
Source: LAREB

13. DATA DISPLAY
No. Reports Target R Other R Total
Target D
g a b nTD
Other D c d nOD
Total nTA nOA n
Basic approach: possible Signal when R = a/E(a) is “large”
Methods for Mining
Reporting Ratio (RR): E(a) = nTD × nTA/n
Proportional Reporting R i (PRR):
P i lR i Ratio (PRR) E( ) = nTD × c/nOD
E(a) TD / OD
Odds Ratio (OR): E(a) = b × c/d
Need to accommodate uncertainty, especially if a is small
13
Bayesian approaches provide a way to do this

14. BAYESIAN STATISTICS IN SD
S S CS S
Pr(R|D) P (R) Pr(R,D) P (R)*P (D)
P (R|D) / Pr(R) = P (R D) / Pr(R)*Pr(D)
where Pr(R|D) is the posterior probability of observing a
specific adverse event R given that a specific drug D is
the suspect drug.
Pr(R) and Pr(D) are prior probabilities of observing R
and D in the entire database.
Pr(R,D) is joint probability that both R and D were
observed in the same database coincidentally.

15. MULTI-ITEM GAMMA POISSON
SHRINKER (MGPS)
It ranks drug-event combinations
g
According to how ‘interestingly large’ the number
of reports of that R-D combination
compared with what would be expected if the drug
and event were statistically independent.
Unlike the Information Component ( ), MGPS
p (IC),
technique gives an overall ranking of R-D
combinations
IC gives a ki d of non-relative measure (IC) for
i kind f l ti f
each R-D combination

16. MULTI-ITEM GAMMA POISSON SHRINKER
(MGPS)
Modified Modeled
Reporting Reporting
ratio ratio
Bayesian
Stratification by shrinkage for
gender, age, yr. cell sizes
etc.) Empirical Bayes
Geometric Mean
Reporting (EBGM)
ratio
If the lower bound of 90%CI of EBGM (EB05) ≥2, R-D
combinations occur twice as often as expected; also,
For N>20 or so, N/E = EBGM = PRR

17. Hauben & Zhou. (2003) Drug Safety 26, 159-186

18. BAYESIAN CONFIDENCE PROPAGATION
NEURAL NETWORK (BCPNN)
The Uppsala Monitoring Centre (UMC) for WHO
databases uses BCPNN architecture for SD
Neural networks are highly organized & efficient
Give simple probabilistic interpretation of network
weights
Analogous to a living neuron with its multiple
dendrites and single axon
BCPNN calculates cell counts for all potential R-D
combinations in the database, not just those
appearing in at least one report
i i tl t t
Done with two fully interconnected layers
One for all drugs and one for all adverse events

19. INFORMATION COMPONENT (IC)
( )
IC = log2 [P (R D) / Pr(R)*Pr(D)
l [Pr(R,D) P (R)*P (D)
IC is used to decide whether the joint
probabilities of ADRs are different from
independent D & R
i d d R.
This makes sense because if the events are
independent
the knowledge of one of the variables contributes no
new information about the other &
does not reduce the uncertainty about Y (d t
d t d th t i t b t (due to
knowledge about X)

20. POSITIVE IC AND TIME SCANS
If Pr of co-occurrence of R & D is the same as the
p
product of the individual Pr of R & D, the
,
Bayesian likelihood estimator
Pr(R,D)/Pr(R)*Pr(D) will be equal to 1
This means equal prior and posterior
probabilities
Log2 1 = 0, therefore IC = 0
g
However, when posterior probability Pr(R|D)
exceeds the prior probability P(R), the IC
becomes more positive
An IC with a lower bound of 95% CI>0 that
increases with sequential time scans is positive
stable signal

21. CAPTOPRIL AND COUGH
The diagram shows the IC for the drug‐ADR association. Error bars: + 95% CI.

22. A well known signal: suprofen and back pain. The diagram shows the IC for the
drug‐ADR association. Error bars: + 9 % C
d i i b 95% CI.
R. Orre et al. (2000) Computational Statistics & Data Analysis 34, 473-493

23. The development from 1973 to 1990 of the IC for the drug azapropazone
vs. the photosensitivity reaction with 95% CI.
p y
R. Orre et al. (2000) Computational Statistics & Data Analysis 34, 473-493

24. CHARACTERISTICS OF IC
The preceding
p g
diagrams show how the
IC for the D-R (e.g.,
suprofen-back
suprofen back pain
association varies over
a span of time (e.g.,
1983 – 1990))
The cumulative probability function for IC being
greater than zero [Pr(IC>0)] develops over time. This
association is seen with 80% certainty after the Q1,
1984.

25. DIGOXINE & RASH: AN INTERESTING CASE
Although overall negative IC, when examined across age group,
increasing age was aasociated with positive IC.
R. Orre et al. (2000) Computational Statistics & Data Analysis 34, 473-493

26. PACLITAXEL-TACHYCARDIA
Change of IC between 1970 to 2010 for the association of tachycardia-
paclitaxel. The IC is plotted from year of 1970 to 2010 with five year
intervals with 95% CI
Sighal & Chakraborty. Unpublished data

27. DOCETAXEL- FLUSHING
7
6
5
4
E(IC)
3
2
E
1
0
-1
-2
1970-1975 1976-1980 1981-1985 1986-1990 1991-1995 1996-2000 2001-2005 2006-2010
1970 1975 1976 1980 1981 1985 1986 1990 1991 1995 1996 2000 2001 2005 2006 2010
Time (Year)
Change of IC between 1970 to 2010 for the association of Doclitaxel-
flushing.
Sighal & Chakraborty. Unpublished data

28. CONCLUDING REMARKS
Statistical data mining for drug-adverse reaction offers a
useful, non-invasive and sophisticated tool for unknown or
incompletely signals
Mainly proportional reporting ratios (PRR) and Bayesian
data mining including Empirical Bayesian Screening (EBS)
& Bayesian Confidence Propagation Neural Network
(BCPNN) are used
PRRs and EBS are comparable, only EBS has an
advantage with D-R combinations in very small numbers
g y
but it is based on relative ranking
BCPNN provides an IC (a kind of threshold) for signaling
that applies to any D-R cells irrespective of ranking
pp y p g
The signals do not establish causality, they only indicate
very strong association between D & R
With all methods of data mining (especially PRR EBS &
PRR,
BCPNN), the quality & size of the database is very
important (can amplify or dilute a signal)

29. Acknowledgement: Mr Sharwan Singhal
Mr.
THANK YOU VERY MUCH