DATA MINING IN PHARMACOVIGILANCE

Dr. Bhaswat S. ChakrabortySr. VP & Chair, R&D Core CommitteeCadila Pharmaceuticals Ltd., Ahmedabad

Presented at Indian Pharmacological Society Meeting, Ahmedabad, October 5, 2013

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CONTENTS Pharmacovigilance (PV) PV process PV databases Data mining in PV Toxic signals & signal detection (SD) Non-Bayesian SD

Disproportionality Bayesian SD

Multi-item gamma poisson shrinker (MGPS) Bayesian confidence propagation neural network

(BCPNN) Examples Concluding remarks 2

PREMATURE APPROVAL, INCOMPLETE SAFETY PROFILE? Many drugs whose complete safety profile is

still unknown have been 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high-dosage use) They were all subsequently withdrawn from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 In currently marketed drugs black box

warnings (SAEs caused by prescription drugs) is very common

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CHANCES TO OBSERVE SAES THROUGH CTS

Reaction Rate

Sample Size

Pr(at least 1)

Pr(at least 2)

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 0.5944

PHARMACOVIGILANCE (PV) Monitoring, evaluation and

implementation of drug safety Detection and quantitation

of adverse drug reactions (ADRs)novel or partially known

previously unknownknown hazard ↑frequency or ↑severity

in their Clinical nature, Severity or Frequency

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THE PHARMACOVIGILANCE PROCESS

Source: A.L. Gould, Internet PPT

PHARMACOVIGILANCE DATABASES PV is usually practiced by agencies and pharmaceutical

companies by focusing on SD in large databases These databases are of huge sizes, e.g.,

USFDA database, AERS: > 6.2 million records WHO database, VIGIBASE: >7.2 million records GSK databse, OCEANS: > 2 million records

Based on a study, the highest power for finding a true signal is achieved by combining those databases with the most drug-specific data.

Also early safety SD should involve the use of multiple large global databases

Reliance on a single database may reduce statistical power and diversity of ADRs

Hammond IW et al. (2007). Expert Opin Drug Saf. 6:713-21

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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 data8

DATA MINING Getting something useful from lots and lots and lots of

data

Although it might appear so, the methodology is not linear, as it involves building and assessing models, carrying out simultaneous as well as serial steps

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DRUG TOXIC SIGNALS WHO: “reported information on a possible

causal relationship between an adverse event and a drug, the relationship being unknown or incompletely documented previously.”

More than a single report needed Suggests Drug-ADR (D-R) association

(doesn't establish causality) An alert from any available source

Pre or post-marketing data generated Data-mining of especially post-marketing safety

databases 10

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 Sensitivity is high with low true

negative rate Specificity is high with a true positive

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

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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– suicide

Source: LAREB13

DATA MINING & SD PROTOCOL

Report collection Database

cleaning Quantitative

assessment Qualitative

assessment Evaluation Communication

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

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DATA DISPLAY & MINING METHODS IN PV

No. Reports

Target R Other R Total

Target D a b nTD

Other D c d nOD

Total nTA nOA n

Methods for Mining

Reporting Ratio (RR): E(a) = nTD nTA/nProportional Reporting Ratio (PRR): E(a) = nTD c/nODOdds Ratio (OR): E(a) = b c/d

Need to accommodate uncertainty, especially if a is smallBayesian approaches provide a way to do this

Basic approach: possible Signal when R = a/E(a) is “large”

CRITERIA FOR A TOXIC DISPROPORTIONAL ADR

ROR =

χ2 =

Expected

ExpectedObserved 2)(

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

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c

baa )( PRR

dc

ba /

CASESTUDY EXAMPLE: PROPRANOLOL-BRADYCARDIA

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

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BAYESIAN STATISTICS IN SD

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.

Pr(R|D) / Pr(R) = Pr(R,D) / Pr(R)*Pr(D)

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MULTI-ITEM GAMMA POISSON SHRINKER (MGPS) It ranks drug-event combinations 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 (IC), MGPS

technique gives an overall ranking of R-D combinations

IC gives a kind of non-relative measure (IC) for each R-D combination

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MULTI-ITEM GAMMA POISSON SHRINKER(MGPS)

Reporting ratio

Modified Reporting

ratio

Modeled Reporting

ratio

Empirical Bayes

Geometric Mean (EBGM)

Stratification by gender, age, yr. etc.)

Bayesian shrinkage

for cell sizes

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

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

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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 Done with two fully interconnected layers

One for all drugs and one for all adverse events22

INFORMATION COMPONENT (IC)

IC is used to decide whether the joint probabilities of ADRs are different from independent 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 (due to

knowledge about X)

IC = log2 [Pr(R,D) / Pr(R)*Pr(D)

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POSITIVE IC AND TIME SCANS If Pr of co-occurrence of R & D is the same as

the 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

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 24

CAPTOPRIL AND COUGH

The diagram shows the IC for the drug-ADR association. Error bars: + 95% CI.

R. Orre et al. (2000) Computational Statistics & Data Analysis 34, 473-493

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A well known signal: suprofen and back pain. The diagram shows the IC for the drug-ADR association. Error bars: + 95% CI.

R. Orre et al. (2000) Computational Statistics & Data Analysis 34, 473-493

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The development from 1973 to 1990 of the IC for the drug azapropazonevs. the photosensitivity reaction with 95% CI.

R. Orre et al. (2000) Computational Statistics & Data Analysis 34, 473-493

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CHARACTERISTICS OF IC The preceding

diagrams show how the IC for the D-R (e.g., 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. 28

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

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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

Singhal & Chakraborty. Unpublished data

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DOCETAXEL - FLUSHING

Change of IC between 1970 to 2010 for the association of Doclitaxel-flushing.

Singhal & Chakraborty. Unpublished data

-2-101234567

1970-1975 1976-1980 1981-1985 1986-1990 1991-1995 1996-2000 2001-2005 2006-2010

E(IC)

Time (Year)

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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 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

The signals do not establish causality, they only indicate very strong association between D & R

With all methods of data mining (especially PRR, EBS & BCPNN), the quality & size of the database is very important (can amplify or dilute a signal)

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THANK YOU VERY MUCH

Acknowledgement: Ms. Raji Nair

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