Analysis Of Drug Safety Research Articles

Six Years of FDA's Postmarket Active Risk Identification and Analysis System (ARIA) in the Sentinel Initiative: Implications for Real World Evidence Generation.

Congress mandated the creation of a postmarket active risk identification and analysis (ARIA) system containing data on 100 million individuals for monitoring risks associated with drug and biologic products using data from disparate sources to complement the US Food and Drug Administration's (FDA's) existing postmarket capabilities. We report on the first six years of ARIA utilization in the Sentinel System (2016 -2021). The FDA has used the ARIA system to evaluate 133 safety concerns; 54 of these evaluations have closed with regulatory determinations while the rest remain in progress. If the ARIA system and FDA's Adverse Event Reporting System are deemed insufficient to address a safety concern, then FDA may issue a postmarket requirement to a product's manufacturer. One-hundred-ninety-seven ARIA insufficiency determinations have been made. The most common situation for which ARIA was found to be insufficient is the evaluation of adverse pregnancy and fetal outcomes following in utero drug exposure, followed by neoplasms and death. ARIA was most likely to be sufficient for thromboembolic events, which have high positive predictive value in claims data alone and do not require supplemental clinical data. The lessons learned from this experience illustrate the continued challenges using administrative claims data, especially to define novel clinical outcomes. This analysis can help to identify where more granular clinical data are needed to fill gaps to improve the use of real-world data for drug safety analyses and provide insights into what is needed to efficiently generate high-quality real-world evidence for efficacy.

Rewiring Drug Research and Development through Human Data-Driven Discovery (HD3).

In an era of unparalleled technical advancement, the pharmaceutical industry is struggling to transform data into increased research and development efficiency, and, as a corollary, new drugs for patients. Here, we briefly review some of the commonly discussed issues around this counterintuitive innovation crisis. Looking at both industry- and science-related factors, we posit that traditional preclinical research is front-loading the development pipeline with data and drug candidates that are unlikely to succeed in patients. Applying a first principles analysis, we highlight the critical culprits and provide suggestions as to how these issues can be rectified through the pursuit of a Human Data-driven Discovery (HD3) paradigm. Consistent with other examples of disruptive innovation, we propose that new levels of success are not dependent on new inventions, but rather on the strategic integration of existing data and technology assets. In support of these suggestions, we highlight the power of HD3, through recently published proof-of-concept applications in the areas of drug safety analysis and prediction, drug repositioning, the rational design of combination therapies and the global response to the COVID-19 pandemic. We conclude that innovators must play a key role in expediting the path to a largely human-focused, systems-based approach to drug discovery and research.

Harnessing the Power of Electronic Health Records and Genomics for Drug Discovery.

A long-standing recognition that information from human genetics studies has the potential to accelerate drug discovery has led to decades of research on how to leverage genetic and phenotypic information for drug discovery. Established simple and advanced statistical methods that allow the simultaneous analysis of genotype and clinical phenotype data by genome- and phenome-wide analyses, colocalization analyses with quantitative trait loci data from transcriptomics and proteomics data sets from different tissues, and Mendelian randomization are essential tools for drug development in the postgenomic era. Numerous studies have demonstrated how genomic data provide opportunities for the identification of new drug targets, the repurposing of drugs, and drug safety analyses. With an increase in the number of biobanks that enable linking in-depth omics data with rich repositories of phenotypic traits via electronic health records, more powerful ways for the evaluation and validation of drug targets will continue to expand across different disciplines of clinical research.

Preclinical species gene expression database: Development and meta-analysis.

The evaluation of toxicity in preclinical species is important for identifying potential safety liabilities of experimental medicines. Toxicology studies provide translational insight into potential adverse clinical findings, but data interpretation may be limited due to our understanding of cross-species biological differences. With the recent technological advances in sequencing and analyzing omics data, gene expression data can be used to predict cross species biological differences and improve experimental design and toxicology data interpretation. However, interpreting the translational significance of toxicogenomics analyses can pose a challenge due to the lack of comprehensive preclinical gene expression datasets. In this work, we performed RNA-sequencing across four preclinical species/strains widely used for safety assessment (CD1 mouse, Sprague Dawley rat, Beagle dog, and Cynomolgus monkey) in ∼50 relevant tissues/organs to establish a comprehensive preclinical gene expression body atlas for both males and females. In addition, we performed a meta-analysis across the large dataset to highlight species and tissue differences that may be relevant for drug safety analyses. Further, we made these databases available to the scientific community. This multi-species, tissue-, and sex-specific transcriptomic database should serve as a valuable resource to enable informed safety decision-making not only during drug development, but also in a variety of disciplines that use these preclinical species.

Use of statins and other lipid-modifying agents across pregnancy: A nationwide drug utilization study in Norway in 2005–2018

Background and aimsStatins are becoming more widely used among women of reproductive age; however, nationwide data on statin use across pregnancy is scarce. We therefore aimed to describe the drug utilization patterns for statins and other lipid-modifying agents (LMAs) before, during, and after pregnancy, for all pregnancies in Norway from 2005 to 2018. MethodsWe linked individual-level data from four nationwide electronic health care registries in Norway and characterized the prescription fills of statins and other LMAs across pregnancy. We also examined trends in pregnancy-related LMA use, and characterized women using statins and other LMAs on parameters of health status and co-morbidity. ResultsIn total, 822,071 pregnancies for 503,723 women were included. The number of statin prescription fills decreased rapidly during the first trimester and returned to pre-pregnancy levels about one year postpartum. Pregnancy-related statin use increased from 2005 (approx. 0.11% of all pregnancies) to 2018 (approx. 0.29% of all pregnancies); however, in total, few statin prescriptions were filled within any trimester of pregnancy (n = 331, 0.04% of all pregnancies). Statin use was more common in women with higher age, higher weight, smoking, and comorbidities such as hypertension and diabetes mellitus; also, statin users often had co-medication pertinent to these conditions. ConclusionsAlthough statins and other LMAs were increasingly being used around the time of pregnancy among women in Norway, drug use was mostly discontinued during the first trimester. Our results suggest that pregnancy-related statin use should be monitored, and that drug safety analyses for maternal and offspring health outcomes are needed.

Emerging Bioinformatics Methods and Resources in Drug Toxicology.

Assessing the drug safety at an early stage of a drug discovery program is a critical issue. With the recent advances in molecular biology and genomic, massive amounts of generated and accumulated data by advanced experimental technologies such as RNA sequencing or proteomics start to be at the disposal of the scientific community. Innovative and adequate bioinformatic methods, tools, and protocols are required to analyze properly these diverse and extensive data sources with the aim to identify key features that are related to toxicity observations. Furthermore, the assessment of drug safety can be performed across multiple scales of complexity from molecular, cellular to phenotypic levels; therefore, the application of network science contributes to a better interpretation of the drug's exposure effect on human health. Here, we review databases containing toxicogenomics and chemical-phenotype information, as well as appropriated bioinformatics approaches that are currently used to analyze such data. Extension to others methods such as dose-responses, time-dependent processes, and text mining is also presented giving an overview of suitable tools available for a best practice of drug safety analysis.

Disproportionality analysis of anaphylactic reactions after vaccination with messenger RNA coronavirus disease 2019 vaccines in the United States

Disproportionality analysis of anaphylactic reactions after vaccination with messenger RNA coronavirus disease 2019 vaccines in the United States

Modest Association of Long-Term ACE Inhibitor Treatment With Lung Cancer: The Promise and Pitfalls of Epidemiological Drug-Safety Analyses.

Results of the carefully executed Evaluation of Treatment with Angiotensin Converting Enzyme Inhibitors and the Risk of Lung Cancer (ERACER) study, reported in this issue, echo those of several previous observational analyses of the association of long-term angiotensin-converting enzyme (ACE) inhibitor use with incident lung cancer. These epidemiological drug-safety analyses merit cautious interpretation. First, the number needed to harm (NNH) of 6667 reported in ERACER for ACE inhibitors compared with angiotensin-2 receptor blockers (ARBs) after approximately 12 years of follow-up should be balanced against therapeutic benefits. Previously reported meta-analyses of randomized controlled trials (RCTs) over a mean 4.3-year follow-up suggested number needed to treat (NNT) of 67 for all-cause mortality, 116 for cardiovascular mortality, and 86 for a composite of myocardial infarction (MI) and stroke for ACE inhibitors, compared with nonsignificant benefits for ARBs on the mortality outcomes and NNT of 157 for ARBs on the MI/stroke composite. Second, confounding by indication is possible because until 2013, ACE inhibitors, not ARBs, were first-line medications for heart failure, which is associated with incident lung cancer. Third, findings may be compromised by detection bias due to investigation of ACE inhibitor-induced cough, or by residual confounding due to influential factors not measurable in the available data, such as socioeconomic status (SES) or smoking history. The important questions raised by ERACER and similar drug-safety analyses should be addressed in long-term RCTs or in enhanced large-database pharmacoepidemiological analyses, measuring both NNH and NNT and controlling for SES, indication, medication, and dosage.

DIVA: Exploration and Validation of Hypothesized Drug‐Drug Interactions

AbstractAdverse reactions caused by drug‐drug interactions are a major public health concern. Currently, adverse reaction signals are detected through a tedious manual process in which drug safety analysts review a large number of reports collected through post‐marketing drug surveillance. While computational techniques in support of this signal analysis are necessary, alone they are not sufficient. In particular, when machine learning techniques are applied to extract candidate signals from reports, the resulting set is (1) too large in size, i.e., exponential to the number of unique drugs and reactions in reports, (2) disconnected from the underlying reports that serve as evidence and context, and (3) ultimately requires human intervention to be validated in the domain context as a true signal warranting action. In this work, we address these challenges though a visual analytics system, DIVA, designed to align with the drug safety analysis workflow by supporting the detection, screening, and verification of candidate drug interaction signals. DTVA's abstractions and encodings are informed by formative interviews with drug safety analysts. DIVA's coordinated visualizations realize a proposed novel augmented interaction data model (AIM) which links signals generated by machine learning techniques with domain‐specific metadata critical for signal analysis. DIVA's alignment with the drug review process allows an analyst to interactively screen for important signals, triage signals for in‐depth investigation, and validate signals by reviewing the underlying reports that serve as evidence. The evaluation of DIVA encompasses case‐studies and interviews by drug analysts at the US Food and Drug Administration ‐ both of which confirm that DIVA indeed is effective in supporting analysts in the critical task of exploring and verifying dangerous drug‐drug interactions.

Eliciting adverse effects data from participants in clinical trials.

Eliciting adverse effects data from participants in clinical trials.

Drug Safety Analysis in a Real-Life Cohort of Parkinson's Disease Patients with Polypharmacy.

Polypharmacy is common in geriatric Parkinson's disease (PD) patients in advanced disease stages with multiple comorbidities, bearing multiple risks for drug safety in theory. The aim of this study was to empirically identify the most frequent and relevant contraindications and drug interactions actually occurring and compromising drug safety in PD in real life. We conducted a prospective observational study in a multimorbid cohort of PD patients with polypharmacy admitted to a specialized hospital. Inclusion criteria were the presence of at least one comorbidity requiring pharmacotherapy and at least five different drugs in the discharge prescription. Hoehn and Yahr stage during the 'on' state, therapeutic problems related to motor and non-motor PD symptoms, comorbidities, and drug regimens on admission and discharge were analyzed for contraindications and interactions. Overall, 127 patients were included (medium Hoehn and Yahr stage=IV, range II-V). Interactions with the anti-PD medication were mainly caused by other central nervous system (CNS)-active substances, cytochrome P450-metabolized substances, and QT-time prolonging substances. Contraindications against the anti-PD medication mainly occurred from internal, haematopoietic, neurologic and psychiatric diseases, and QT-time prolonging drugs. The highest frequency of interactions and contraindications were identified with levodopa (n=119 at admission/n=126 at discharge), entacapone (n=46/42), pramipexole (n=44/24), and amantadine (n=32/30). Several medically relevant risk factors (interactions and contraindications) frequently occurred in advanced PD patients. These findings provide a basis for developing programmes for awareness, education, monitoring, and preventive interventions to avoid adverse incidents. Future studies will need to evaluate preventive efficacy of structured drug safety programmes.

The development and validation of an easy to use automatic QT-interval algorithm.

BackgroundTo evaluate QT-interval dynamics in patients and in drug safety analysis, beat-to-beat QT-interval measurements are increasingly used. However, interobserver differences, aberrant T-wave morphologies and changes in heart axis might hamper accurate QT-interval measurements.ObjectiveTo develop and validate a QT-interval algorithm robust to heart axis orientation and T-wave morphology that can be applied on a beat-to-beat basis.MethodsAdditionally to standard ECG leads, the root mean square (ECGRMS), standard deviation and vectorcardiogram were used. QRS-onset was defined from the ECGRMS. T-wave end was defined per individual lead and scalar ECG using an automated tangent method. A median of all T-wave ends was used as the general T-wave end per beat.Supine-standing tests of 73 patients with Long-QT syndrome (LQTS) and 54 controls were used because they have wide ranges of RR and QT-intervals as well as changes in T-wave morphology and heart axis orientation. For each subject, automatically estimated QT-intervals in three random complexes chosen from the low, middle and high RR range, were compared with manually measured QT-intervals by three observers.ResultsAfter visual inspection of the randomly selected complexes, 21 complexes were excluded because of evident noise, too flat T-waves or premature ventricular beats. Bland-Altman analyses of automatically and manually determined QT-intervals showed a bias of <4ms and limits of agreement of ±25ms. Intra-class coefficient indicated excellent agreement (>0.9) between the algorithm and all observers individually as well as between the algorithm and the mean QT-interval of the observers.ConclusionOur automated algorithm provides reliable beat-to-beat QT-interval assessment, robust to heart axis and T-wave morphology.

Drug safety analyses in a rheumatoid arthritis registry: application of different approaches regarding timing of exposure and confounder measurement

BackgroundPatient registry data serves an increasing role in drug safety and comparative effectiveness research, but registry databases often do not contain confounder information measured at the same time that treatments begin. This study evaluated a set of approaches for estimating confounder values at treatment initiation using actual data from a rheumatoid arthritis (RA) registry to examine the risk of infection associated with different biologic DMARDs (bDMARDs).MethodsWe examined the risk of infection associated with starting a TNF inhibitor (TNFi) versus any of the other non-TNFi bDMARDs. Different confounder assessment approaches were tested. All approaches were tested in Cox proportional hazard regression models that used a propensity score (PS). The confounder of interest was the disease activity score (DAS28-CRP). The confounder assessment approaches utilized different temporal relationships between the DAS28-CRP measurement and the start of the treatments of interest.ResultsWe included 219 subjects with RA with 269 initiations of either a TNFi or a different bDMARD or both. Among this group, 305 infections were reported and confirmed through chart review. The hazard ratio (HR) for the risk of infection associated with use of a non-TNFi bDMARD ranged from 1.17 to 3.03 using 13 different approaches; only the approach with the highest HR produced results significantly different than one, but this approach included the fewest subjects and infections.ConclusionsThe relative risk of infection for TNFi and other non-TNFi bDMARDs was similar using various approaches regarding which DAS28-CRP score should be used as the baseline measure in adjusted analyses.

Safety and feasibility of a quality of life intervention in a randomized controlled trial on patients with colorectal cancer.

e18200 Background: The analysis of drug safety is a mandatory part of pharmaceutical studies. For quality of life (QoL) interventions equal standards are missing. We performed a safety analysis for a QoL intervention in a randomized controlled trial (RCT) on colorectal cancer (CRC) patients. Methods: A clinical pathway with QoL diagnosis and therapy was developed in a complex intervention. In a 2-arm RCT N = 220 primary CRC patients have been recruited in 4 certified colorectal cancer centres. In the intervention group (IG) QoL of patients is measured at 0, 3, 6, 12, 18 months postoperative (EORTC QLQ-C30, QLQ-CR29). The coordinating practitioner (CP: physician, responsible for outpatient treatment) is informed about the results of QoL measurement (QoL profile) and receives recommendations for QoL therapy (expert report). Control group (CG) patients` QoL is also measured but CPs neither receive QoL profile nor expert report (routine follow-up care). Safety analysis was performed after data for the first 50% of patients were available. The following safety parameters were investigated: (1) exclusion of patients from RCT due to detrimental effects of study participation; (2) drop-outs of patients; (3) physician workload related to RCT; (4) physician benefit related to RCT. Descriptive statistics and chi square tests or Fisher`s exact tests were used to analyze frequencies. Results: Mean age of the 110 patients was 66.2 years (SD = 9.7) with 64% being male (n = 70). Tumor stages were UICC III (n = 40), II (n = 33), I (n = 24), and IV (n = 13). Only 1 out of 110 patients was excluded from the study by the physician due to patient`s psychological burden. Four more patients dropped out of the study due to either lack of motivation, comorbidity, or positive health situation. Workload regarding RCT was rated low by most physicians (33/35 CPs with IG-patients vs. 24/27 CPs with CG-patients; p = .65). In contrast, many physicians reported benefits connected with RCT participation (19/33 CPs with IG-patients vs. 8/27 CPs with CG-patients; p = .03). Conclusions: Data showed that the study is feasible and safe. Thus, the RCT will be continued to the end according to trial protocol.Clinical trial information: NCT02321813 Clinical trial information: NCT02321813.

Preexisting Antibodies to an F(ab')2 Antibody Therapeutic and Novel Method for Immunogenicity Assessment.

Anti-therapeutic antibodies (ATAs) may impact drug exposure and activity and induce immune complex mediated toxicity; therefore the accurate measurement of ATA is important for the analysis of drug safety and efficacy. Preexisting ATAs to the hinge region of anti-Delta like ligand 4 (anti-DLL4) F(ab′)2, a potential antitumor therapeutic, were detected in cynomolgus monkey serum, which presented a challenge in developing assays for detecting treatment induced ATA. A total ATA assay was developed using a bridging ELISA that detected both anti-CDR and anti-framework ATA including anti-hinge reactivity. A competition assay that could detect 500 ng/mL of anti-CDR ATA in the presence of preexisting ATA was also developed to determine ATA specific to the anti-DLL4 F(ab′)2 CDR using anti-DLL4 F(ab′)2 and a control F(ab′)2. We used these assay methods in a cynomolgus monkey in vivo study to successfully evaluate total and anti-CDR ATA. The preexisting anti-hinge reactivity was also observed to a lesser extent in human serum, and a similar approach could be applied for specific immunogenicity assessment in clinical trials.

A Critical Analysis of Drug Safety and Reasons for Cessation of Thiopurines in Patients With Newly Diagnosed Ulcerative Colitis: A Nationwide Population Cohort Analysis

Introduction: Immunodulators, such as thiopurines (TP), are used for the induction and maintenance of remission of ulcerative colitis (UC). There are few retrospective studies describing the indications for discontinuation of TP. Our aim was to report the reasons for discontinuation of medication and critically analyze the safety of azathioprine (AZA) and 6-Mercaptopurine (6-MP). Methods: A previously validated cohort of newly diagnosed UC patients from the Veterans Affairs Healthcare System (Khan et al. Aliment Pharmacol Ther 2014) was evaluated as the study population. All of the patients had endoscopic evaluation at the time of diagnosis. A retrospective analysis was performed to evaluate the reasons to discontinue TP, as well as compare the side effect profile of AZA to 6-MP, regardless of duration of use. Descriptive data and outcomes were analyzed. Results: There were 163 newly diagnosed UC patients over the observation period from 2001 to 2011. The average age at diagnosis was 54.6 years old, 95.7% were male, and 76% were Caucasian. 58/163 (35.6%) patients were on 6MP; 105/163 (64.4%) were on AZA. The mean duration of treatment was 2.55 years (0.03-12 years). At the conclusion of the observation period, 51/163 (33%) were still taking TP while 112/163 (67%) stopped treatment. 31/163 (19%) stopped within the first 3 months due to side effects. Of the 132 that continued TP for more than 3 months, 81/132 (61.4%) stopped TP with a mean duration of 3.12 years. The most common reasons for stopping 6MP and AZA were similar. The most reasons included: unknown reasons (21), elevated LFTs (14), cytopenia (12), nausea/vomiting (10), abdominal pain (8), surgery (8) - see Table 1. The type of TP (AZA vs 6MP) was not significant for TP cessation (p=0.111) or reason for cessation of therapy (p=0.144) which included cytopenia, elevated LFTs, or pancreatitis.Table 1: Side effects at time of thiopurine discontinuationConclusion: Of the 163 that started TP, between one-third and one-half stopped taking TP due to medication side effects rather than ineffectiveness. The side effect profile was similar between AZA and 6-MP.

Analysis of drug safety in an intensive care unit

A failure mode, effects and criticality analysis (FMECA) was performed by a multidisciplinary team consisting of a clinical pharmacist, a physician, a nurse and a patient safety officer. The steps of the ICU drug process and their possible failure modes (FM) were identified. To each of these, a criticality index (CI, 1 to 810) that takes into account the occurrence, the severity and the detectability was assigned (Table 1). For CI higher than 100, improvement measures were considered and their impact estimated by recalculating the CI.

Bridging islands of information to establish an integrated knowledge base of drugs and health outcomes of interest.

The entire drug safety enterprise has a need to search, retrieve, evaluate, and synthesize scientific evidence more efficiently. This discovery and synthesis process would be greatly accelerated through access to a common framework that brings all relevant information sources together within a standardized structure. This presents an opportunity to establish an open-source community effort to develop a global knowledge base, one that brings together and standardizes all available information for all drugs and all health outcomes of interest (HOIs) from all electronic sources pertinent to drug safety. To make this vision a reality, we have established a workgroup within the Observational Health Data Sciences and Informatics (OHDSI, http://ohdsi.org) collaborative. The workgroup’s mission is to develop an open-source standardized knowledge base for the effects of medical products and an efficient procedure for maintaining and expanding it. The knowledge base will make it simpler for practitioners to access, retrieve, and synthesize evidence so that they can reach a rigorous and accurate assessment of causal relationships between a given drug and HOI. Development of the knowledge base will proceed with the measureable goal of supporting an efficient and thorough evidence-based assessment of the effects of 1,000 active ingredients across 100 HOIs. This non-trivial task will result in a high-quality and generally applicable drug safety knowledge base. It will also yield a reference standard of drug–HOI pairs that will enable more advanced methodological research that empirically evaluates the performance of drug safety analysis methods.

Exploration of heterogeneity in distributed research network drug safety analyses.

Distributed data networks representing large diverse populations are an expanding focus of drug safety research. However, interpreting results is difficult when treatment effect estimates vary across datasets (i.e., heterogeneity). In a previous study, risk estimates were generated for selected drugs and potential adverse outcomes. Analyses were replicated across eight distributed data sources using an identical analytic structure. To evaluate heterogeneity of risk estimates across data sources, the estimates were combined with summary-level data characterizing the population of each data source. Meta-analysis, meta-regression, and plots of the influence on overall results versus contribution to heterogeneity were examined and used to illustrate an approach to heterogeneity assessment. Heterogeneity, as measured by the I-squared statistic, was high with variability across outcomes. Plots of the relationship between influence on overall results and contribution to heterogeneity suggest that certain datasets and characteristics were influential but there was variability dependent on the drug and outcome being assessed. Exploratory meta-regression identified many possible influential factors, but may be subject to ecological bias and false positive conclusions. Distributed data network drug safety analyses can produce heterogeneous risk estimates that may not be easily explained. Approaches illustrated here can be useful for research that is subject to similar problems with heterogeneity.

European Medicines Agency accused of censoring researchers over drug safety analyses

European Medicines Agency accused of censoring researchers over drug safety analyses