Rachel E. Sobel

Monitoring the long-term safety of therapies for children with juvenile idiopathic arthritis: time for a consolidated patient registry.

Introduction Juvenile idiopathic arthritis (JIA; formerly juvenile rheumatoid arthritis) is a chronic rheumatic disease characterized by persistent joint inflammation and an onset prior to age 16 years. The JIA phenotype consists of 7 categories: oligoarticular (up to 4 affected joints), polyarticular (more than 4 joints) rheumatoid factor (RF) positive, polyarticular RF negative, systemic, enthesitis-related arthritis, psoriatic, and undifferentiated. JIA is an uncommon disease with an estimated incidence of 1.3 to 22.6 new cases per 100,000 person-years (1–7). Treatment for JIA includes simple analgesics and nonsteroidal antiinflammatory drugs to reduce pain and inflammation, and intraarticular and oral corticosteroids, traditional disease-modifying antirheumatic drugs (DMARDs), and biologic agents, such as tumor necrosis factor antagonists, to prevent joint destruction. The goal of therapy for JIA is clinical remission (i.e., the absence of the signs and symptoms over time). Although many children do achieve remission while receiving medication, the majority experiences disease reoccurrence within 2–3 years of discontinuing drug therapy (8). Therefore, most children with JIA are typically treated with one or more medications over the long term. However, there are critical gaps in medical knowledge on the effectiveness and safety of existing JIA drug regimens used over an extended period of time, the long-term impact of such medications on children’s growth and development, and which drug or drug combinations are most efficacious for which particular categories of JIA. In addition, certain key questions have not been addressed, including the use of medications in the outpatient setting, optimal dosing regimens, and toxicity rates compared with background rates of adverse events in children with JIA. Longitudinal data for many patients with JIA are urgently needed to guide clinicians in the optimal selection of JIA medications and the management of their risks and toxicities to improve the overall quality of care of children with JIA. At present, the long-term safety of traditional DMARDs and biologic agents used in the treatment of JIA is monitored in two ways: through passive adverse event surveillance systems (e.g., the US Federal Adverse Event Reporting System), and via product-specific observational registry studies conducted by pharmaceutical industry sponsors to fulfill mandatory Food and Drug Administration (FDA) postmarketing commitments. Both approaches, however, have significant limitations. Passive adverse event surveillance systems are known to substantially underestimate the true incidence rate of adverse event occurrence, are subject to reporting bias, and often do not provide reliable denominator data (9). Similarly, for an uncommon condition such as JIA, single-drug registries have inherent limitations of scale, thus making it difficult to identify rarer safety signals. Existing product-based registries result in competing and duplicative efforts by multiple biopharmaceutical companies to collect similar information on a small patient population. Furthermore, current registries typically have restrictive enrollment criteria and limited sample sizes, and are usually not designed to capture data on background rates of diseaserelated adverse events and medication switching. In contrast, a consolidated registry with broad sponsorship by industry, federal agencies, research networks, and patient advocacy groups would offer a unique alternative approach to monitoring the long-term safety of pediatric rheumatology treatments, one that does not have the shortcomings of either of the two foregoing options. The topic of a consolidated registry was addressed in a public workshop sponsored by the FDA on May 12–13, 2009, in BeDr. Wallace’s work was supported by grants from Amgen, Centocor, and Pfizer. Meredith Y. Smith, PhD, MPA: Abbott Laboratories, Abbott Park, Illinois; Rachel E. Sobel, DrPH: Pfizer, New York, New York; Carol A. Wallace, MD: University of Washington and Seattle Children’s Hospital, Seattle. Dr. Smith owns stock and/or holds stock options in Abbott Laboratories. Dr. Sobel owns stock and/or holds stock options in Pfizer. Address correspondence to Carol A. Wallace, MD, University of Washington School of Medicine, Division of Rheumatology–Pediatrics, Seattle Children’s Hospital, 4800 Sand Point Way NE R-5420, Seattle, WA 98104. E-mail: cwallace@ u.washington.edu. Submitted for publication October 12, 2009; accepted in revised form February 4, 2010. Arthritis Care & Research Vol. 62, No. 6, June 2010, pp 800–804 DOI 10.1002/acr.20128

Using Registries to Identify Adverse Events in Rheumatic Diseases

The proven effectiveness of biologics and other immunomodulatory products in inflammatory rheumatic diseases has resulted in their widespread use as well as reports of potential short- and long-term complications such as infection and malignancy. These complications are especially worrisome in children who often have serial exposures to multiple immunomodulatory products. Post-marketing surveillance of immunomodulatory products in juvenile idiopathic arthritis (JIA) and pediatric systemic lupus erythematosus is currently based on product-specific registries and passive surveillance, which may not accurately reflect the safety risks for children owing to low numbers, poor long-term retention, and inadequate comparators. In collaboration with the US Food and Drug Administration (FDA), patient and family advocacy groups, biopharmaceutical industry representatives and other stakeholders, the Childhood Arthritis and Rheumatology Research Alliance (CARRA) and the Duke Clinical Research Institute (DCRI) have developed a novel pharmacosurveillance model (CARRA Consolidated Safety Registry [CoRe]) based on a multicenter longitudinal pediatric rheumatic diseases registry with over 8000 participants. The existing CARRA infrastructure provides access to much larger numbers of subjects than is feasible in single-product registries. Enrollment regardless of medication exposure allows more accurate detection and evaluation of safety signals. Flexibility built into the model allows the addition of specific data elements and safety outcomes, and designation of appropriate disease comparator groups relevant to each product, fulfilling post-marketing requirements and commitments. The proposed model can be applied to other pediatric and adult diseases, potentially transforming the paradigm of pharmacosurveillance in response to the growing public mandate for rigorous post-marketing safety monitoring.

Computer-assisted expert case definition in electronic health records

PURPOSE To describe how computer-assisted presentation of case data can lead experts to infer machine-implementable rules for case definition in electronic health records. As an illustration the technique has been applied to obtain a definition of acute liver dysfunction (ALD) in persons with inflammatory bowel disease (IBD). METHODS The technique consists of repeatedly sampling new batches of case candidates from an enriched pool of persons meeting presumed minimal inclusion criteria, classifying the candidates by a machine-implementable candidate rule and by a human expert, and then updating the rule so that it captures new distinctions introduced by the expert. Iteration continues until an update results in an acceptably small number of changes to form a final case definition. RESULTS The technique was applied to structured data and terms derived by natural language processing from text records in 29,336 adults with IBD. Over three rounds the technique led to rules with increasing predictive value, as the experts identified exceptions, and increasing sensitivity, as the experts identified missing inclusion criteria. In the final rule inclusion and exclusion terms were often keyed to an ALD onset date. When compared against clinical review in an independent test round, the derived final case definition had a sensitivity of 92% and a positive predictive value of 79%. CONCLUSION An iterative technique of machine-supported expert review can yield a case definition that accommodates available data, incorporates pre-existing medical knowledge, is transparent and is open to continuous improvement. The expert updates to rules may be informative in themselves. In this limited setting, the final case definition for ALD performed better than previous, published attempts using expert definitions.

Integrating evidence from multiple sources to evaluate post-approval safety: an example of sildenafil citrate and cardiovascular events

ABSTRACT Objective: Recent high-profile medicine withdrawals have highlighted the complex decision-making process that regulators, pharmaceutical companies, prescribers, and patients must undertake in determining whether a drug has an appropriate benefit–risk balance. Our objective was to analyze the utility of different drug safety data sources and methods, using the experience of sildenafil citrate (Viagra*) and post-approval concerns about its potential association with cardiovascular (CV) events (i.e., myocardial infarction [MI] and death) as a case study. Methods: We evaluated safety data from three sources: the standard passive surveillance system (i.e., spontaneous reports filed to Pfizer Inc), pooled clinical trial data, and a prospective observational cohort study, the International Mens Health Study (IMHS). Results: More than 28 000 spontaneous reports were received in the first 7 years after approval. Between 2001 and 2005, the proportion filed by persons other than healthcare professionals (61%) was approximately double the proportion averaged across five other drugs from the manufacturers safety database. CV events and/or deaths represented 22.0% of reports, and 23% of reported deaths were medically unconfirmed reports made by persons other than healthcare professionals. In contrast, MI and all-cause mortality rates for sildenafil from both the pooled clinical trial data and the IMHS were similar to placebo, despite differences in methods and populations. Conclusions: These results suggest that passive surveillance may generate apparent signals of risk, as was the case with sildenafil and CV events. However, to adequately assess the benefit-risk profile of a drug, these signals must be evaluated via other data sources such as clinical trial and epidemiologic studies, as the apparent signal was not supported by more rigorously collected data. Our post-marketing analysis was unable to examine all potential influences of spontaneous reports, and the study data sources (although large for erectile dysfunction studies) were not designed to exclude small CV risks.

Novel Method to Collect Medication Adverse Events in Juvenile Arthritis: Results From the Childhood Arthritis and Rheumatology Research Alliance Enhanced Drug Safety Surveillance Project

Few data are available regarding the rates of serious adverse events (SAEs) and important medical events (IMEs) outside of product‐based registries and clinical trials for juvenile idiopathic arthritis (JIA). The Enhanced Drug Safety Surveillance Project (EDSSP) was developed to pilot a novel system to collect SAEs/IMEs in children with JIA. This analysis reports the results from this 4‐year (2008–2012) EDSSP.

Do FDA label changes work? Assessment of the 2010 class label change for proton pump inhibitors using the Sentinel System's analytic tools

To pilot use of the US Food and Drug Administrations (FDAs) Sentinel System data and analytic tools by a non‐FDA stakeholder through the Innovation in Medical Evidence Development and Surveillance system of the Reagan Udall Foundation. We evaluated the US FDA 2010 proton pump inhibitor (PPI) class label change that warned of increased risk of bone fracture, to use PPIs for the lowest dose and shortest duration, and to manage bone status for those at risk for osteoporosis.

Current needs in pediatric pharmacoepidemiology.

We report on a needs assessment conducted by the International Society of Pharmacoepidemiology (ISPE) Pediatric Special Interest Group (SIG) to identify critical needs in pediatric pharmacoepidemiology and directions for future activities.

Real World Evidence: Time for a Switch?

Real World Evidence (RWE) provides critical information about the performance of medicines in routine clinical practice, as actually prescribed by physicians and taken by patients. For decades, observational designs and electronic healthcare databases have been used to characterize patient populations, describe the natural history of diseases, assess postapproval safety and conduct risk management programs as part of the drug development lifecycle. These approaches have evolved in recent years due to advances in electronic data collection, data linkage, computing power, the greater availability of analytic tools and methodologies, and established best practices. The US FDA Sentinel Initiative illustrates these advances well by rapidly generating evidence (in days, not months) in large populations (more than half of the US population in some analyses) and with increased transparency [1]. These advances have made possible the potential use of these systems for outcomes other than safety. Under the Twenty-First Century Cures Act and Prescription Drug User Fee Act (PDUFA) VI, for example, the FDA will issue draft guidance related to using RWE to augment the insights of traditional clinical trials [2]. Despite the availability of numerous data sources and analytic tools for pharmacovigilance, it can be difficult to assess the safety or effectiveness of generics in Real World Data (RWD). This inability to easily identify and distinguish between generics produced by various manufacturers hampers these assessments, including differentiating among various authorized generics (e.g. the branded generic manufactured by the innovator, an innovator-owned generics company, or through an authorized arrangement). This limitation is important to public health, particularly as nearly 90% of medicines used by patients in the US are generic [3]. In addition, potential concerns have sometimes been raised about bioequivalence and/or manufacturing quality issues between branded and generic medicines [4], especially among certain indications or drug classes [e.g. attention-deficit hyperactivity disorder (ADHD), epilepsy, angiotensin receptor blockers (ARBs)]. In light of the FDA’s recent focus on accelerating generic regulatory reviews and availability [5], the absence of a tool to easily evaluate the safety and effectiveness of generics in RWD may become even more pronounced. Ideally, pharmacovigilance researchers should be able to assess real world outcomes comparing generics across different manufacturers or authorized generics with other generics. Such analyses might generate potential signals of differential efficacy or safety if they exist, which, taking into account important confounding factors (e.g. preferential prescribing, drug availability), could act as a trigger to consider the underlying reasons. Some examples might be bioequivalence factors or the presence of contaminants, to be investigated further if warranted. In this issue, Gagne and colleagues [3] describe the development of a modular statistical tool to assess drug utilization and switching patterns between generic and branded medications within the US FDA’s Sentinel System. As noted earlier, this type of switching was difficult and cumbersome to assess previously, particularly across a distributed data network of several databases, as it required bespoke computer programming for each individual National Drug Code (NDC). The authors created the tool and conducted an analysis intended to test its functionality using two case studies, examining lamotrigine extended release (ER), indicated for seizure and mood disorders, and metoprolol ER, a β-blocker used for hypertension, angina pectoris and heart failure. Both drugs are modified-release products. The authors suggest these are typically more complex to develop and manufacture, and thus may be at higher risk for equivalence concerns and have more complex utilization and switching patterns. Metoprolol was chosen specifically because it This comment refers to the article available at https ://doi. org/10.1007/s4026 4-018-0709-4.

Novel Method to Collect Medication Adverse Events in Juvenile Arthritis: Results From the Childhood Arthritis and Rheumatology Research Alliance Enhanced Drug Safety Surveillance Project: Enhanced Drug Safety Surveillance Project

Few data are available regarding the rates of serious adverse events (SAEs) and important medical events (IMEs) outside of product‐based registries and clinical trials for juvenile idiopathic arthritis (JIA). The Enhanced Drug Safety Surveillance Project (EDSSP) was developed to pilot a novel system to collect SAEs/IMEs in children with JIA. This analysis reports the results from this 4‐year (2008–2012) EDSSP.

Novel method to collect medication adverse events in juvenile arthritis

Few data are available regarding the rates of serious adverse events (SAEs) and important medical events (IMEs) outside of product‐based registries and clinical trials for juvenile idiopathic arthritis (JIA). The Enhanced Drug Safety Surveillance Project (EDSSP) was developed to pilot a novel system to collect SAEs/IMEs in children with JIA. This analysis reports the results from this 4‐year (2008–2012) EDSSP.