Faculty Bibliography

Drug-induced immune thrombocytopenia (DITP) is often suspected in patients with acute thrombocytopenia unexplained by other causes, but documenting that a drug is the cause of thrombocytopenia can be challenging. To provide a resource for diagnosis of DITP and for drug safety surveillance, we analyzed 3 distinct methods for identifying drugs that may cause thrombocytopenia. (1) Published case reports of DITP have described 253 drugs suspected of causing thrombocytopenia; using defined clinical criteria, 87 (34%) were identified with evidence that the drug caused thrombocytopenia. (2) Serum samples from patients with suspected DITP were tested for 202 drugs; drug-dependent, platelet-reactive antibodies were identified for 67 drugs (33%). (3) The Food and Drug Administration's Adverse Event Reporting System database was searched for drugs associated with thrombocytopenia by use of data mining algorithms; 1444 drugs had at least 1 report associated with thrombocytopenia, and 573 (40%) drugs demonstrated a statistically distinctive reporting association with thrombocytopenia. Among 1468 drugs suspected of causing thrombocytopenia, 102 were evaluated by all 3 methods, and 23 of these 102 drugs had evidence for an association with thrombocytopenia by all 3 methods. Multiple methods, each with a distinct perspective, can contribute to the identification of drugs that can cause thrombocytopenia.

BACKGROUND: It has been postulated that the time to onset of adverse drug reactions is connected to the underlying pharmacological (or toxic) mechanism of adverse drug reactions whether the reaction is time dependent or not. OBJECTIVE: We have conducted a preliminary study using the parametric modelling of the time to onset of adverse reactions as an approach to signal detection on spontaneous reporting system databases. METHODS: We performed a parametric modelling of the reported time to onset of adverse drug reactions for which the underlying toxic mechanism is characterized. For the purpose of our study, we have used the reported liver injuries associated with bosentan, and the infections associated with the use of the tumour necrosis factor (TNF) inhibitors, adalimumab, etanercept and infliximab, which are used in Crohn's disease and rheumatoid arthritis, reported to EudraVigilance between December 2001 and September 2006. RESULTS: The main results reflect the fact that the reported time to onset is a surrogate of the true time to onset of the reaction and combines three hazards (occurrence, diagnosis and reporting) that cannot be disentangled. Consequently, the modelling of the time to onset of reactions reported with TNF inhibitors showed differences that could reflect different pharmacological activities, indications, monitoring of the patients or different reporting patterns. These variations could also limit the interpretation of the parametric modelling. CONCLUSIONS: Some consistency that was found between the occurrences of the infections with the TNF inhibitors suggests a causal association. There are statistical issues that are important to keep in mind when interpreting the results (the impact of the data quality on the fit of the distributions and the absence of a test of hypothesis linked to the absence of a relevant comparator). The study suggests that the modelling of the reported time to onset of adverse reactions could be a useful adjunct to other signal detection methods.

A recent literature review linked norepinephrinergic stimulation to alterations in cyclic adenosine monophosphate (cAMP)-mediated signaling in cardiac myocytes and suggested that this might contribute to the pathological mechanisms that lead to chamber enlargement and hypocontractility, which are seen in dilated cardiomyopathy. This accompanies a large body of literature linking cardiac sympathetic outflow activation in early heart failure with progressive myocyte deterioration. As the mode of action of a number of antidepressants involves the inhibition of neuronal norepinephrine reuptake to varying degrees, this study was conducted to assess whether such agents might be associated with disproportionate reporting of cardiomyopathy. Limited data exist specifically examining the association between the antidepressant use and the cardiomyopathy. Using a data mining algorithm (DMA), we quantitatively investigated the association between antidepressant agents that predominantly exert their effects through inhibiting neuronal norepinephrine reuptake (rather than serotonin) and cardiomyopathy. We retrospectively applied a Bayesian DMA, the Bayesian Confidence Propagation Neural Network, to the cumulative reports in the Food and Drug Administration Adverse Event Reporting System (through the fourth quarter of 2006) and World Health Organization Vigibase (through the second quarter of 2007) databases. A threshold of the posterior interval 95% lower limit > 0 was used to define a signal of disproportionate reporting with individual or groups of antidepressants and cardiomyopathy-related terms. The analysis suggested that there is no direct relationship between antidepressants with greater norepinephrine reuptake inhibitor activity (affinity for norepinephrine reuptake transporter or selectivity for norepinephrine versus serotonin) and reporting of cardiomyopathy. In contrast, an inverse correlation might exist with a higher number of cases identified with tricyclic antidepressants showing lower norepinephrine reuptake inhibition and the serotonin/norepinephrine reuptake inhibitors as well as with serotonin/norepinephrine/slight dopamine reuptake inhibitor

BACKGROUND: A phenomenon of 'masking' or 'cloaking' in pharmacovigilance data mining has been described, which can potentially cause signals of disproportionate reporting (SDRs) to be missed, particularly in pharmaceutical company databases. Masking has been predicted theoretically, observed anecdotally or studied to a limited extent in both pharmaceutical company and health authority databases, but no previous publication systematically assesses its occurrence in a large health authority database. OBJECTIVE: To explore the nature, extent and possible consequences of masking in the US FDA Adverse Event Reporting System (AERS) database by applying various experimental unmasking protocols to a set of drugs and events representing realistic pharmacovigilance analysis conditions. METHODS: This study employed AERS data from 2001 through 2005. For a set of 63 Medical Dictionary for Regulatory Activities (MedDRA(R)) Preferred Terms (PTs), disproportionality analysis was carried out with respect to all drugs included in the AERS database, using a previously described urn-model-based algorithm. We specifically sought masking in which drug removal induced an increase in the statistical representation of a drug-event combination (DEC) that resulted in the emergence of a new SDR. We performed a series of unmasking experiments selecting drugs for removal using rational statistical decision rules based on the requirement of a reporting ratio (RR) >1, top-ranked statistical unexpectedness (SU) and relatedness as reflected in the WHO Anatomical Therapeutic Chemical level 4 (ATC4) grouping. In order to assess the possible extent of residual masking we performed two supplemental purely empirical analyses on a limited subset of data. This entailed testing every drug and drug group to determine which was most influential in uncovering masked SDRs. We assessed the strength of external evidence for a causal association for a small number of masked SDRs involving a subset of 29 drugs for which level of evidence adjudication was available from a previous study. RESULTS: The original disproportionality analysis identified 8719 SDRs for the 63 PTs. The SU-based unmasking protocols generated variable numbers of masked SDRs ranging from 38 to 156, representing a 0.43-1.8% increase over the number of baseline SDRs. A significant number of baseline SDRs were also lost in the course of our experiments. The trend in the number of gained SDRs per report removed was inversely related to the number of lost SDRs per protocol. Both the number and nature of the reports removed influenced the number of gained SDRs observed. The purely empirical protocols unmasked up to ten times as many SDRs. None of the masked SDRs had strong external evidence supporting a causal association. Most involved associations for which there was no external supporting evidence or were in the original product label. For two masked SDRs, there was external evidence of a possible causal association. CONCLUSIONS: We documented masking in the FDA AERS database. Attempts at unmasking SDRs using practically implementable protocols produced only small changes in the output of SDRs in our analysis. This is undoubtedly related to the large size and diversity of the database, but the complex interdependencies between drugs and events in authentic spontaneous reporting system (SRS) databases, and the impact of measures of statistical variability that are typically used in real-world disproportionality analysis, may be additional factors that constrain the discovery of masked SDRs and which may also operate in pharmaceutical company databases. Empirical determination of the most influential drugs may uncover significantly more SDRs than protocols based on predetermined statistical selection rules but are impractical except possibly for evaluating specific events. Routine global exercises to elicit masking, especially in large health authority databases are not justified based on results available to date. Exercises to elicit unmasking should be driven by prior knowledge or obvious data imbalances

Data mining may improve identification of signals, but its incremental utility is in question. The objective of this study was to compare associations highlighted by data mining vs. those highlighted through the use of traditional decision rules. In the case of 29 drugs, we used US Food and Drug Administration (FDA) Adverse Event Reporting System (AERS) data to compare three data-mining algorithms (DMAs) with two traditional decision rules: (i) N >or= 3 reports for a designated medical event (DME) and (ii) any event comprising >2% of reports in relation to a drug. Data-mining methods produced 101-324 signals vs. 1,051 for the N >or= 3 rule but yielded a higher proportion of signals having publication support. For the 2% rule, the fraction of signals having publication support was similar to that associated with data mining. Data-mining signals lagged N >or= 3 signaling by 1.5-11.0 months. It may therefore be concluded that data mining identifies fewer signals than the "N >or= 3 DME" rule. The signals appear later with data mining but are more often supported by publications. In the case of the 2% rule, no such difference in publication support was observed.

The optimum timing of drug safety data mining for a new drug is uncertain. The objective of this study was to compare cumulative data mining versus mining with sliding time windows. Adverse Event Reporting System data (2001-2005) were studied for 27 drugs. A literature database was used to evaluate signals of disproportionate reporting (SDRs) from an urn model data-mining algorithm. Data mining was applied cumulatively and with sliding time windows from 1 to 4 years in width. Time from SDR generation to the appearance of a publication describing the corresponding adverse event was calculated. Cumulative data mining and 1- to 2-year sliding windows produced the most SDRs for recently approved drugs. In the first postmarketing year, data mining produced SDRs an average of 800 days in advance of publications regarding the corresponding drug-event combination. However, this timing advantage reduced to zero by year 4. The optimum window width for sliding windows should increase with time on the market. Data mining may be most useful for early signal detection during the first 3 years of a drug's postmarketing life. Beyond that, it may be most useful for supporting or weakening hypotheses.

BACKGROUND: Pharmacovigilance data-mining algorithms (DMAs) are known to generate significant numbers of false-positive signals of disproportionate reporting (SDRs), using various standards to define the terms 'true positive' and 'false positive'. OBJECTIVE: To construct a highly inclusive reference event database of reported adverse events for a limited set of drugs, and to utilize that database to evaluate three DMAs for their overall yield of scientifically supported adverse drug effects, with an emphasis on ascertaining false-positive rates as defined by matching to the database, and to assess the overlap among SDRs detected by various DMAs. METHODS: A sample of 35 drugs approved by the US FDA between 2000 and 2004 was selected, including three drugs added to cover therapeutic categories not included in the original sample. We compiled a reference event database of adverse event information for these drugs from historical and current US prescribing information, from peer-reviewed literature covering 1999 through March 2006, from regulatory actions announced by the FDA and from adverse event listings in the British National Formulary. Every adverse event mentioned in these sources was entered into the database, even those with minimal evidence for causality. To provide some selectivity regarding causality, each entry was assigned a level of evidence based on the source of the information, using rules developed by the authors. Using the FDA adverse event reporting system data for 2002 through 2005, SDRs were identified for each drug using three DMAs: an urn-model based algorithm, the Gamma Poisson Shrinker (GPS) and proportional reporting ratio (PRR), using previously published signalling thresholds. The absolute number and fraction of SDRs matching the reference event database at each level of evidence was determined for each report source and the data-mining method. Overlap of the SDR lists among the various methods and report sources was tabulated as well. RESULTS: The GPS algorithm had the lowest overall yield of SDRs (763), with the highest fraction of events matching the reference event database (89 SDRs, 11.7%), excluding events described in the prescribing information at the time of drug approval. The urn model yielded more SDRs (1562), with a non-significantly lower fraction matching (175 SDRs, 11.2%). PRR detected still more SDRs (3616), but with a lower fraction matching (296 SDRs, 8.2%). In terms of overlap of SDRs among algorithms, PRR uniquely detected the highest number of SDRs (2231, with 144, or 6.5%, matching), followed by the urn model (212, with 26, or 12.3%, matching) and then GPS (0 SDRs uniquely detected). CONCLUSIONS: The three DMAs studied offer significantly different tradeoffs between the number of SDRs detected and the degree to which those SDRs are supported by external evidence. Those differences may reflect choices of detection thresholds as well as features of the algorithms themselves. For all three algorithms, there is a substantial fraction of SDRs for which no external supporting evidence can be found, even when a highly inclusive search for such evidence is conducted

Having surveyed the etymology and previous definitions of the pharmacovigilance term 'signal', we propose a definition that embraces all the surveyed ideas, reflects real-world pharmacovigilance processes, and accommodates signals of both harmful and beneficial effects. The essential definitional features of a pharmacovigilance signal are (i) that it is based on one or more reports of an association between an intervention or interventions and an event or set of related events (e.g. a syndrome), including any type of evidence (clinical or experimental); (ii) that it represents an association that is new and important and has not been previously investigated and refuted; (iii) that it incites to action (verification and remedial action); (iv) that it does not encompass intervention-event associations that are not related to causality or risk with a specified degree of likelihood and scientific plausibility. Based on these features, we propose this definition of a signal of suspected causality: 'information that arises from one or multiple sources (including observations and experiments), which suggests a new potentially causal association, or a new aspect of a known association, between an intervention and an event or set of related events, either adverse or beneficial, which would command regulatory, societal or clinical attention, and is judged to be of sufficient likelihood to justify verificatory and, when necessary, remedial actions.' This defines an unverified signal; we have also defined terms - indeterminate, verified, and refuted signals - that qualify it in relation to verification. This definition and its accompanying flowchart should inform decision making in considering benefits and harms caused by pharmacological and nonpharmacological interventions