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- 1. DATA MINING IN PHARMACOVIGILANCE Dr. Bhaswat S. Chakraborty Sr. VP & Chair, R&D Core Committee Cadila Pharmaceuticals Ltd., Ahmedabad Presented at Indian Pharmacological Society Meeting, Ahmedabad, October 5, 2013 1
- 2. 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
- 3. PREMATURE APPROVAL, INCOMPLETE SAFETY PROFILE? Many drugs whose complete safety profile is still unknown have been approved In some cases, drugs are approveddespite identification of SAEs in premarketing trials Alosetron hydrochloride – ischemic colitis Grepafloxacinhydrochloride – QT prolongationand deaths Rofecoxib – heart attack and stroke (long-term, high- dosage use) They were all subsequently withdrawn fromthe market because of these SAEs In currently marketed drugs black box warnings (SAEs caused by prescription drugs) is very common 3
- 4. 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 0.594 4
- 5. PHARMACOVIGILANCE (PV) Monitoring, evaluation and implementation of drug safety 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 5
- 6. 6 THE PHARMACOVIGILANCE PROCESS Source: A.L. Gould, Internet PPT
- 7. 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 7
- 8. 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 8
- 9. 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 9
- 10. 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
- 11. 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 rate 11
- 12. 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. 12
- 13. 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: LAREB 13
- 14. 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 14
- 15. 15 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/n Proportional Reporting Ratio (PRR): E(a) = nTD × c/nOD Odds Ratio (OR): E(a) = b × c/d Need to accommodate uncertainty, especially if a is small Bayesian approaches provide a way to do this Basic approach: possible Signal when R = a/E(a) is “large”
- 16. CRITERIA FOR A TOXIC DISPROPORTIONAL ADR ROR = χ2 = Expected ExpectedObserved 2 )( − Significant disproportional Signal is detected when χ2 is ≥ 4.0 and the rest ≥ 2.0 16 c baa )( + =PRR dc ba /
- 17. CASESTUDY EXAMPLE: PROPRANOLOL-BRADYCARDIA Gavali, Kulkarni, Kumar and Chakraborty (2009), Ind J Pharmacol, 41, 162-166 17
- 18. 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) 18
- 19. 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 19
- 20. 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 20
- 21. Hauben & Zhou. (2003) Drug Safety 26, 159-186 21
- 22. 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 events 22
- 23. 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) 23
- 24. 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
- 25. 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 25
- 26. 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 26
- 27. The development from 1973 to 1990 of the IC for the drug azapropazone vs. the photosensitivity reaction with 95% CI. R. Orre et al. (2000) Computational Statistics & Data Analysis 34, 473-493 27
- 28. 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
- 29. 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 29
- 30. 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 30
- 31. DOCETAXEL - FLUSHING Change of IC between 1970 to 2010 for the association of Doclitaxel- flushing. Singhal & Chakraborty. Unpublished data -2 -1 0 1 2 3 4 5 6 7 1970-1975 1976-1980 1981-1985 1986-1990 1991-1995 1996-2000 2001-2005 2006-2010 E(IC) Time(Year) 31
- 32. 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) 32
- 33. THANK YOU VERY MUCH Acknowledgement: Ms. Raji Nair 33

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