BAYESIAN ESTIMATIONS OFSTRONG TOXIC SIGNALSFOLLOWING MINING OFPHARMACOVIGILANCE DATABASESPHARMACOVIGILANCE DATABASES

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

CONTENTSCO SWhy pharmacovigilance (PV)?PV processPV processToxic signals & signal detection (SD)DataDataBayesian statistics in SDMulti-item gamma poisson shrinker (MGPS)Bayesian confidence propagation neural network (BCPNN)E lExamplesConcluding remarks

PREMATURE APPROVAL, INCOMPLETESAFETY PROFILE?SAFETY PROFILE?

Many drugs whose complete safety profile is still k h b dunknown have been approved

In some cases, drugs are approved despite identification of SAEs in premarketing trialsp g

Alosetron hydrochloride – ischemic colitisGrepafloxacin hydrochloride – QT prolongation and deathsRofecoxib – heart attack and stroke (long-term, high-dosage use)

They were all subsequently withdrawn from the y q ymarket because of these SAEsIn currently marketed drugs black box warnings (SAEs caused by prescription drugs) is warnings (SAEs caused by prescription drugs) is very common

CHANCE TO OBSERVE SAES

Reaction Rate

Sample Size

Pr(at least 1) Pr(at least 2)Rate Size1% 500 0.993 0.9600.5% 500 0.918 0.713

1000 0.993 0.9600.1% 1500 0.777 0.442

3000 0.950 0.8010.01% 6000 0.451 0.122

10000 0.632 0.26420000 0.865 0.594

PHARMACOVIGILANCEPHARMACOVIGILANCEDetection and quantitationDetection and quantitation

of adverse drug reactions (ADRs)novel or partially knownnovel or partially known

previously unknownknown hazard ↑frequency or ↑severityknown hazard ↑frequency or ↑severity

in their Clinical nature, Severity or Frequency

THE PHARMACOVIGILANCE PROCESS

Detect SignalsTraditionalMethods

DataMiningMethods Mining

Generate Hypotheses

Refute/Verify

Insight fromOutliers Public Health

Impact Benefit/Risk

Type A(Mechanism-based) Estimate

Impact, Benefit/Risk

Act

Type B(Idiosyncratic)

IncidenceAct

Inform

Restrict use/(Idiosyncratic)Change Label

Restrict use/withdraw

DRUG TOXIC SIGNALSDRUG TOXIC SIGNALSWHO: “reported information on a possible causal p prelationship between an adverse event and a drug, the relationship being unknown or incompletely documented previously ”incompletely documented previously.More than a single report neededSuggests Drug-ADR (D-R) association (doesn't Suggests Drug ADR (D R) association (doesn t establish causality)An alert from any available source

Pre or post-marketing data generatedData-mining of especially post-marketing safety databases

SIGNAL DETECTIONSIGNAL DETECTIONComes originally from electronics engg.In signal detection theory

a receiver operating characteristic(ROC) illustrates performance of true (ROC) illustrates performance of true positives vs. false positives out of the negatives

t i th h ld ttiat various threshold settingsSensitivity is high with low true negative rateSpecificity is high with a true positive rate

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

GOALS FOR ADR SIGNALSLow false positive signals

Drug-ADR association should be real Low false negative signalLow false negative signal

Should not miss any Drug-ADR signalEarly detection of signals is desirableFalse discovery rate → 0

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

Dis-associationIsotretinoine– suicide Isotretinoine suicide

Source: LAREB

SD P

Collection of ICSRs from CADRMP

PROTOCOLConversion of free text to structured information

Report collectionDatabase

structured information

Data cleaning and duplicate cleaningQuantitative assessment

detection

Qualitative assessmentE l ti

Applying quantitative or statistical methods

EvaluationCommunication Computing an accurate

measure for SD

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

EXAMPLES – SIGNIFICANT SIGNALSAssociation

Bupropion – seizuresOlanzapine – thrombosisP lid i d libidPergolide – increased libidoRisperidon – diabetes mellitusTerbinafine – stomatistisRosiglitazone – liver function abnormalities

Di i tiDis-associationIsotretinoine– suicide

Source: LAREB

DATA DISPLAYNo. Reports Target R Other R TotalTarget D a b nTDgOther D c d nODTotal nTA nOA n

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

Methods for Mining

Reporting Ratio (RR): E(a) = nTD × nTA/nP i l R i R i (PRR) E( ) TD / ODProportional Reporting Ratio (PRR): E(a) = nTD × c/nODOdds Ratio (OR): E(a) = b × c/d

Need to accommodate uncertainty, especially if a is small13

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

BAYESIAN STATISTICS IN SDS S S CS S

P (R|D) / P (R) P (R D) / P (R)*P (D)Pr(R|D) / Pr(R) = Pr(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.

MULTI-ITEM GAMMA POISSONSHRINKER (MGPS)

It ranks drug-event combinations gAccording 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 p ( ),technique gives an overall ranking of R-D combinationsIC i ki d f l ti (IC) f IC gives a kind of non-relative measure (IC) for each R-D combination

MULTI-ITEM GAMMA POISSON SHRINKER(MGPS)(MGPS)

Modified Modeled Reporting

ratioReporting

ratioBayesian

Empirical BayesGeometric Mean

Stratification by gender, age, yr. etc.)

shrinkage for cell sizes

Reporting ratio

Geometric Mean (EBGM)

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

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

BAYESIAN CONFIDENCE PROPAGATIONNEURAL NETWORK (BCPNN)NEURAL NETWORK (BCPNN)

The Uppsala Monitoring Centre (UMC) for WHO databases uses BCPNN architecture for SDdatabases uses BCPNN architecture for SDNeural networks are highly organized & efficientGive simple probabilistic interpretation of network Give simple probabilistic interpretation of network weightsAnalogous to a living neuron with its multiple dendrites and single axondendrites and single axonBCPNN calculates cell counts for all potential R-D combinations in the database, not just those

i i t l t tappearing in at least one reportDone with two fully interconnected layersOne for all drugs and one for all adverse eventsOne for all drugs and one for all adverse events

INFORMATION COMPONENT (IC)( )

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

IC is used to decide whether the joint probabilities of ADRs are different from i d d D & R independent D & R. This makes sense because if the events are independentindependent

the knowledge of one of the variables contributes no new information about the other &d t d th t i t b t Y (d t does not reduce the uncertainty about Y (due to knowledge about X)

POSITIVE IC AND TIME SCANSPOSITIVE IC AND TIME SCANSIf Pr of co-occurrence of R & D is the same as the product of the individual Pr of R & D, the p ,Bayesian likelihood estimator Pr(R,D)/Pr(R)*Pr(D) will be equal to 1This means equal prior and posterior This means equal prior and posterior probabilities

Log2 1 = 0, therefore IC = 0g2

However, when posterior probability Pr(R|D) exceeds the prior probability P(R), the IC becomes more positivebecomes more positiveAn IC with a lower bound of 95% CI>0 that increases with sequential time scans is positive stable signal

CAPTOPRIL AND COUGH

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

A well known signal: suprofen and back pain. The diagram shows the IC for the d i i b 9 % Cdrug‐ADR association. Error bars: + 95% CI.

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

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

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

CHARACTERISTICS OF ICCHARACTERISTICS OF ICThe preceding p gdiagrams show how the IC for the D-R (e.g., suprofen back pain suprofen-back pain association varies over a span of time (e.g.,

)1983 – 1990)

The cumulative probability function for IC being 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.

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

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

DOCETAXEL- FLUSHING

7

123456

E(I

C)

-2-101

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

E

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

CONCLUDING REMARKSStatistical data mining for drug-adverse reaction offers a useful, non-invasive and sophisticated tool for unknown or incompletely signalsMainly proportional reporting ratios (PRR) and Bayesian data mining including Empirical Bayesian Screening (EBS) & Bayesian Confidence Propagation Neural Network (BCPNN) are usedPRRs and EBS are comparable, only EBS has an advantage with D-R combinations in very small numbers g ybut 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 rankingpp y p gThe signals do not establish causality, they only indicate very strong association between D & RWith all methods of data mining (especially PRR EBS & 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)

Acknowledgement: Mr Sharwan Singhal

T V M

Acknowledgement: Mr. Sharwan Singhal

THANK YOU VERY MUCH