Drug Safety Monitoring in Children: Performance of Signal Detection Algorithms and Impact of Age Stratification

Caveats of Covariate Adjustment in Disproportionality Analysis for Best Practices

Spontaneous reporting systems (SRS) provide valuable data for detecting unidentified adverse events not observed in clinical trials and for conducting safety assessments that accurately reflect real-world clinical practice. With the increasing number of publications using the SRS for disproportionality analysis (DA), there is an increasing demand for a comprehensive understanding of the research limitations associated with the SRS. However, there is a lack of understanding of the caveats associated with adjusting covariates in DA of the SRS. Herein, we summarized the use of covariate adjustment and its caveats in DA. The Council for International Organizations of Medical Sciences VIII suggests considering adjustments such as stratification when they can enhance the sensitivity and/or specificity of statistical analysis. However, several database-specific and statistical caveats have been identified when adjusting for covariates derived from the SRS. Disproportionality analysis may be affected not only by reporting bias at the time of enrollment but also by sparse-data bias due to variations in the number of enrollment reports. Statistical evidence is needed to determine in which cases and to what extent sensitivity and/or specificity are affected. Nevertheless, it is important for researchers to acknowledge that certain limitations discussed in this context may be inherent and cannot be rectified. Based on this understanding, they can then make an informed decision on whether to perform a covariate adjustment.

The State of Use and Utility of Negative Controls in Pharmacoepidemiologic Studies

Uses of real-world data in drug safety and effectiveness studies are often challenged by various sources of bias. We undertook a systematic search of the published literature through September 2020 to evaluate the state of use and utility of negative controls to address bias in pharmacoepidemiologic studies. Two reviewers independently evaluated study eligibility and abstracted data. Our search identified 184 eligible studies for inclusion. Cohort studies (115, 63%) and administrative data (114, 62%) were, respectively, the most common study design and data type used. Most studies used negative control outcomes (91, 50%), and for most studies the target source of bias was unmeasured confounding (93, 51%). We identified 4 utility domains of negative controls: 1) bias detection (149, 81%), 2) bias correction (16, 9%), 3) P-value calibration (8, 4%), and 4) performance assessment of different methods used in drug safety studies (31, 17%). The most popular methodologies used were the 95% confidence interval and P-value calibration. In addition, we identified 2 reference sets with structured steps to check the causality assumption of the negative control. While negative controls are powerful tools in bias detection, we found many studies lacked checking the underlying assumptions. This article is part of a Special Collection on Pharmacoepidemiology.

Important Considerations for Signal Detection and Evaluation

Safety clinicians have a wealth of resources describing how to perform signal detection. Nevertheless, there are some nuances concerning approaches taken by regulatory authorities and statistical considerations that should be appreciated. New approaches, such as the FDA Medical Queries, illustrate the value of considering medical concepts over individual adverse events. One area which would benefit from further clarity is how safety signals may be evaluated for evidence of a causal relationship to the drug of interest. Just as such safety signals can take many forms, the types of tools and methods required to interrogate these signals are equally as diverse. An understanding of the complexity of this process can aid the safety reviewer in successfully characterizing the emerging safety profile of a drug during the pre-marketing phase of development.

A database of pediatric drug effects to evaluate ontogenic mechanisms from child growth and development

BACKGROUND

Adverse drug effects (ADEs) in children are common and may result in disability and death, necessitating post-marketing monitoring of their use. Evaluating drug safety is especially challenging in children due to the processes of growth and maturation, which can alter how children respond to treatment. Current drug safety-signal-detection methods do not account for these dynamics.

METHODS

We recently developed a method called disproportionality generalized additive models (dGAMs) to better identify safety signals for drugs across child-development stages.

FINDINGS

We used dGAMs on a database of 264,453 pediatric adverse-event reports and found 19,438 ADEs signals associated with development and validated these signals against a small reference set of pediatric ADEs. Using our approach, we can hypothesize on the ontogenic dynamics of ADE signals, such as that montelukast-induced psychiatric disorders appear most significant in the second year of life. Additionally, we integrated pediatric enzyme expression data and found that pharmacogenes with dynamic childhood expression, such as CYP2C18 and CYP27B1, are associated with pediatric ADEs.

CONCLUSIONS

We curated KidSIDES, a database of pediatric drug safety signals, for the research community and developed the Pediatric Drug Safety portal (PDSportal) to facilitate evaluation of drug safety signals across childhood growth and development.

FUNDING

This study was supported by grants from the National Institutes of Health (NIH).

Efficacy and Safety of NSAIDs in Infants: A Comprehensive Review of the Literature of the Past 20 Years

Non-steroidal anti-inflammatory drugs (NSAIDs) are commonly used in infants, children, and adolescents worldwide; however, despite sufficient evidence of the beneficial effects of NSAIDs in children and adolescents, there is a lack of comprehensive data in infants. The present review summarizes the current knowledge on the safety and efficacy of various NSAIDs used in infants for which data are available, and includes ibuprofen, dexibuprofen, ketoprofen, flurbiprofen, naproxen, diclofenac, ketorolac, indomethacin, niflumic acid, meloxicam, celecoxib, parecoxib, rofecoxib, acetylsalicylic acid, and nimesulide. The efficacy of NSAIDs has been documented for a variety of conditions, such as fever and pain. NSAIDs are also the main pillars of anti-inflammatory treatment, such as in pediatric inflammatory rheumatic diseases. Limited data are available on the safety of most NSAIDs in infants. Adverse drug reactions may be renal, gastrointestinal, hematological, or immunologic. Since NSAIDs are among the most frequently used drugs in the pediatric population, safety and efficacy studies can be performed as part of normal clinical routine, even in young infants. Available data sources, such as (electronic) medical records, should be used for safety and efficacy analyses. On a larger scale, existing data sources, e.g. adverse drug reaction programs/networks, spontaneous national reporting systems, and electronic medical records should be assessed with child-specific methods in order to detect safety signals pertinent to certain pediatric age groups or disease entities. To improve the safety of NSAIDs in infants, treatment needs to be initiated with the lowest age-appropriate or weight-based dose. Duration of treatment and amount of drug used should be regularly evaluated and maximum dose limits and other recommendations by the manufacturer or expert committees should be followed. Treatment for non-chronic conditions such as fever and acute (postoperative) pain should be kept as short as possible. Patients with chronic conditions should be regularly monitored for possible adverse effects of NSAIDs.

Detection of Drug-Induced Thrombocytopenia Signals in Children Using Routine Electronic Medical Records

Background: Drug-induced thrombocytopenia (DITP) is a severe adverse reaction and a significantly under-recognized clinical problem in children. However, for post-marketing pharmacovigilance purposes, detection of DITP signals is crucial. This study aimed to develop a signal detection model for DITP using the pediatric electronic medical records (EMR) data.

Methods: This study used the electronic medical records collected at Beijing Children’s Hospital between 2009 and 2020. A two-stage modeling method was developed to detect the signal of DITP. In the first stage, we calculated the crude incidence by mining cases of thrombocytopenia to select the potential suspected drugs. In the second stage, we constructed propensity score–matched retrospective cohorts of specific screened drugs from the first stage and estimated the odds ratio (OR) and 95% confidence interval (CI) using conditional logistic regression models. The novelty of the signal was assessed by current evidence.

Results: In the study, from a total of 839 drugs, 21 drugs were initially screened as potentially inducing thrombocytopenia. In total, we identified 18 positive DITP associations. Of these, potential DITP risk of nystatin (OR: 1.75, 95% CI: 1.37–2.22) and latamoxef sodium (OR: 1.61, 95% CI: 1.38–1.88) were two new DITP signals in both children and adults. Six associations between thrombocytopenia and drugs including imipenem (OR: 1.69, 95% CI: 1.16–2.45), teicoplanin (OR: 4.75, 95% CI: 3.33–6.78), fusidic acid (OR: 2.81, 95% CI: 2.06–3.86), ceftizoxime sodium (OR: 1.83, 95% CI: 1.36–2.45), ceftazidime (OR: 2.16, 95% CI: 1.58–2.95), and cefepime (OR: 5.06, 95% CI: 3.77–6.78) were considered as new signals in children.

Conclusion: This study developed a two-stage algorithm to detect safety signals of DITP and found eighteen positive signals of DITP, including six new signals in a pediatric population. This method is a promising tool for pharmacovigilance based on EMR data.

Evaluating risk detection methods to uncover ontogenic-mediated adverse drug effect mechanisms in children

BACKGROUND

Identifying adverse drugs effects (ADEs) in children, overall and within pediatric age groups, is essential for preventing disability and death from marketed drugs. At the same time, however, detection is challenging due to dynamic biological processes during growth and maturation, called ontogeny, that alter pharmacokinetics and pharmacodynamics. As a result, methodologies in pediatric drug safety have been limited to event surveillance and have not focused on investigating adverse event mechanisms. There is an opportunity to identify drug event patterns within observational databases for evaluating ontogenic-mediated adverse event mechanisms. The first step of which is to establish statistical models that can identify temporal trends of adverse effects across childhood.

RESULTS

Using simulation, we evaluated a population stratification method (the proportional reporting ratio or PRR) and a population modeling method (the generalized additive model or GAM) to identify and quantify ADE risk at varying reporting rates and dynamics. We found that GAMs showed improved performance over the PRR in detecting dynamic drug event reporting across child development stages. Moreover, GAMs exhibited normally distributed and robust ADE risk estimation at all development stages by sharing information across child development stages.

CONCLUSIONS

Our study underscores the opportunity for using population modeling techniques, which leverage drug event reporting across development stages, as biologically-inspired detection methods for evaluating ontogenic mechanisms.

The impact of regulation changes in the spontaneous reporting system for vaccines on reporting trends and signal detection in Japan

PURPOSE

Spontaneous reporting constitutes one of the most fundamental and important systems for pharmacovigilance. In Japan, important regulation changes in the vaccine spontaneous reporting were implemented between 2009 and 2013; however, no studies had yet assessed the impact of the changes. The objective of this study was to assess the impact on the reporting trends in vaccine reports and on signal detection for vaccines.

METHODS

For assessment of the impact on the reporting trends, we performed the joinpoint trend analysis and descriptively considered number of vaccine reports grouped by the timing of the regulation change. For assessment of the impact on signal detection, we performed signal detection using dataset during the pre or postperiod of the regulation changes, and compared their agreement rates, which was calculated with a reference set for vaccines, created by the Global Research in Paediatrics project.

RESULTS

We retrieved 467 635 spontaneous reports, including 12 287 vaccine reports from April 2004 to March 2019. The average number of vaccine reports per year increased from 231 reports during the preperiod to 1227 during the postperiod. The joinpoint trend analysis found two joinpoints and differentiated three trends, significant increased trend of which was observed when regulations had changed. For signal detection, the agreement rate was improved when using data during the postperiod.

CONCLUSION

We concluded that the regulation changes increased the number of vaccine reports, and could have improved signal detection performance for vaccines by accelerating accumulation of reports, while more spontaneous reports are necessary to optimize signal detection.

Factors Influencing Regulatory Decision-Making in Signal Management: Analysis Based on the Signals Identified from the FAERS

PURPOSE

This study aimed to identify factors that influence the decision to take safety regulatory actions in routine signal management based on spontaneous reports. For this purpose, we analyzed the safety signals identified from the Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) and related information.

METHOD

From the signals that the FDA identified in the FAERS between 2008 1Q and 2014 4Q, we selected 216 signals for which regulatory action was or was not taken. Characteristics of the signals were extracted from the FAERS quarterly reports that give information about what signals were identified from the FAERS and what actions were taken for them, and the FAERS data released in the same quarter when the signal was published. Univariate and multivariable logistic regression analysis was used to assess the relationship between the characteristics of each of the signals and the decision on regulatory action.

RESULT

As a result of the univariate logistic regression analysis, we selected 5 factors (positive rechallenge, number of cases accumulated in the last one-year period before the signal indication, previous awareness, serious outcome, risk for special populations) to include in the multivariable logistic regression model (p < 0.2). The multivariate logistic regression analysis showed that the number of cases accumulated in the last one-year period before the signal indication and previous awareness were associated with the regulatory action (p < 0.05).

CONCLUSION

The present study showed that number of cases accumulated in the last one-year period before the signal indication and previous awareness potentially associated with the United States regulatory action. When assessing safety signals, we should be careful of the adverse events with a large number of cases accumulated rapidly in a short period. In addition, we should pay attention to new information on not only unknown risks but also previously identified and potential risks.

No population left behind: Improving paediatric drug safety using informatics and systems biology

Adverse drugs effects (ADEs) in children are common and may result in disability and death. The current paediatric drug safety landscape, including clinical trials, is limited as it rarely includes children and relies on extrapolation from adults. Children are not small adults but go through an evolutionarily conserved and physiologically dynamic process of growth and maturation. Novel quantitative approaches, integrating observations from clinical trials and drug safety databases with dynamic mechanisms, can be used to systematically identify ADEs unique to childhood. In this perspective, we discuss three critical research directions using systems biology methodologies and novel informatics to improve paediatric drug safety, namely child versus adult drug safety profiles, age-dependent drug toxicities and genetic susceptibility of ADEs across childhood. We argue that a data-driven framework that leverages observational data, biomedical knowledge and systems biology modelling will reveal previously unknown mechanisms of pediatric adverse drug events and lead to improved paediatric drug safety.

Exploratory Study of Signals for Asthma Drugs in Children, Using the EudraVigilance Database of Spontaneous Reports

Introduction

As asthma medications are frequently prescribed for children, knowledge of the safety of these drugs in the paediatric population is important. Although spontaneous reports cannot be used to prove causality of adverse events, they are important in the detection of safety signals.

Objective

Our objective was to provide an overview of adverse drug events associated with asthma medications in children from a spontaneous reports database and to identify new signals.

Methods

Spontaneous reports concerning asthma drugs were obtained from EudraVigilance, the European Medicine Agency’s database for suspected adverse drug reactions. For each drug–event combination, we calculated the proportional reporting ratio (PRR) in the study period 2011–2017. Signals in children (aged 0–17 years) were compared with signals in the whole population. Analyses were repeated for different age categories, by sex and by therapeutic area.

Results

In total, 372,345 reports in children resulted in 385 different signals concerning asthma therapy. The largest group consisted of psychiatric events (65 signals). Only 30 signals were new, with seven, including herpes viral infections, associated with omalizumab. Stratification by age, sex and therapeutic area provided additional new signals, such as hypertrichoses with budesonide and encephalopathies with theophylline. Of all signals in children, 60 (16%) did not appear in the whole population.

Conclusions

The majority of signals regarding asthma therapy in children were already known, but we also identified new signals. We showed that signals can be masked if age stratification is not conducted. Further exploration is needed to investigate the risk and causality of the newly found signals.

Electronic supplementary material

The online version of this article (10.1007/s40264-019-00870-x) contains supplementary material, which is available to authorized users.

Serious Adverse Drug Reactions and Safety Signals in Children: A Nationwide Database Study

Children are more exposed to inappropriate medicine use and its consequent harms. Spontaneous reporting of suspected Serious Adverse Drug Reactions (SADR) increases knowledge and prevention of pharmacotherapy risk. Disproportionality measures are useful to quantify unexpected safety issues associated with a given drug-event pair (signals of disproportionality). This cross-sectional study aimed to assess SADR reporting and safety signals for Brazilian children from 0-12 years old, notified between January 2008 and December 2013 from the Brazilian Surveillance Agency (Notivisa). Information from serious reports (gender and age of the patient, event description, suspected drug) was included. Disproportionality analysis based on Reporting Odds Ratios with a confidence interval of 95% was conducted to identify possible signals of disproportionate reporting (SDR). Almost 30% of 1,977 suspected SADR was related to babies (0-1-year-old). 69% of reports happened with intravenous dosage forms, and 35% of suspected SADR involved off label use according to age. Laronidase, miglustat, imipenem/cilastatin, and clofarabine were involved in six or more suspected deaths among 75 deaths reported. There were 107 SDRs, of which 16 events (15%) were not described in the product labels. There was a relatively higher number of SADRs in Brazilian children compared with studies from other countries. SDRs found, (especially drug-event pairs ‘imipenen/cilastatin–pneumonia’ and ‘laronidase–respiratory insufficiency’) should be investigated more. The reports of SADR with IV dosage forms and OL drug use suggest the need for drug research and the use of better dosage forms for children in Brazil.

Evaluating Safety Reporting in Paediatric Antibiotic Trials, 2000-2016: A Systematic Review and Meta-Analysis

BACKGROUND

There are very few options to treat multidrug-resistant bacterial infections in children. A major barrier is the duration and complexity of regulatory trials of new antibiotics. Extrapolation of safety data from adult trials could facilitate drug development for children.

OBJECTIVE

We performed a systematic review on the safety of antibiotic clinical trials (CTs) in children (0-18 years) to evaluate the overall quality of safety trials conducted in children and to determine if age-specific adverse events (AEs) could be identified for specific antibiotic classes.

DATA SOURCES

We searched the MEDLINE, Cochrane CENTRAL, and ClinicalTrials.gov electronic databases for trials conducted between 2000 and 2016.

STUDY SELECTION

All trials in which safety was declared a primary or secondary endpoint were included. Exclusion criteria were (1) topical or inhalational route of administration; (2) non-infectious conditions; (3) administration for prophylaxis rather than treatment; (4) selected population (i.e. cystic fibrosis, malignancies, HIV and tuberculosis); and (5) design other than randomized controlled trials. Trials reporting data on both adults and children were included only if paediatric results were reported separately.

DATA EXTRACTION AND SYNTHESIS

Two authors independently extracted the data. To assess the quality of published trials, the Extension for harms for Consolidated Standards of Reporting Trials (CONSORT) Statement 2004 was used.

MAIN OUTCOME AND MEASURE

In order to quantitatively assess the rate of developing AEs by drug class, the numbers of overall and body-system-specific AEs were collected for each study arm, and then calculated per single drug class as median and interquartile range (IQR) of the proportions across CTs. The AEs most frequently reported were compared in the meta-analysis by selecting the CTs on the most represented drug classes.

RESULTS

Eighty-three CTs were included, accounting for 27,693 children. Overall, 69.7% of CONSORT items were fully reported. The median proportion of children with any AE was 22.5%, but did not exceed 8% in any single body system. Serious drug-related AEs and drug-related discontinuations were very rare (median 0.3 and 0.9%, respectively). Limitations included the inability to stratify by age group, particularly neonates.

CONCLUSIONS AND RELEVANCE

Overall, AEs in paediatric antibiotic CTs were predictable and class-specific, and no unexpected (age-specific) side effects were identified. Smaller, open-label, dose-finding, high-quality, single-arm pharmacokinetic trials seem potentially sufficient for certain common antibiotic classes, extrapolating well-established safety profiles determined from large adult efficacy trials. This approach could reduce duration and enhance subsequent registration of urgently needed new antibiotics. This will need to be combined with enhanced methods of pharmacovigilance for monitoring of emerging AEs in routine clinical practice.

Useful pharmacodynamic endpoints in children: selection, measurement, and next steps

Pharmacodynamic (PD) endpoints are essential for establishing the benefit-to-risk ratio for therapeutic interventions in children and neonates. This article discusses the selection of an appropriate measure of response, the PD endpoint, which is a critical methodological step in designing pediatric efficacy and safety studies. We provide an overview of existing guidance on the choice of PD endpoints in pediatric clinical research. We identified several considerations relevant to the selection and measurement of PD endpoints in pediatric clinical trials, including the use of biomarkers, modeling, compliance, scoring systems, and validated measurement tools. To be useful, PD endpoints in children need to be clinically relevant, responsive to both treatment and/or disease progression, reproducible, and reliable. In most pediatric disease areas, this requires significant validation efforts. We propose a minimal set of criteria for useful PD endpoint selection and measurement. We conclude that, given the current heterogeneity of pediatric PD endpoint definitions and measurements, both across and within defined disease areas, there is an acute need for internationally agreed, validated, and condition-specific pediatric PD endpoints that consider the needs of all stakeholders, including healthcare providers, policy makers, patients, and families.