Adverse Effects of Cholinesterase Inhibitors in Dementia, According to the Pharmacovigilance Databases of the United-States and Canada.

This survey analyzes two national pharmacovigilance databases in order to determine the major adverse reactions observed with the use of cholinesterase inhibitors in dementia. We conducted a statistical analysis of the Food and Drug Administration Adverse Event Reporting System (FAERS) and the Canada Vigilance Adverse Reaction Database (CVARD) concerning the side effects of cholinesterase inhibitors. The statistics calculated for each adverse event were the frequency and the reporting odds ratios (ROR). A total of 9877 and 2247 reports were extracted from the FAERS and CVARD databases, respectively. A disproportionately higher frequency of reports of death as an adverse event for rivastigmine, compared to the other acetylcholinesterase inhibiting drugs, was observed in both the FAERS (ROR = 3.42; CI95% = 2.94-3.98; P<0.0001) and CVARD (ROR = 3.67; CI95% = 1.92-7.00; P = 0.001) databases. While cholinesterase inhibitors remain to be an important therapeutic tool against Alzheimer's disease, the disproportionate prevalence of fatal outcomes with rivastigmine compared with alternatives should be taken into consideration

Associating adverse drug effects with protein targets by integrating adverse event, in vitro bioactivity, and pharmacokinetic data

Adverse drug effects are unintended and undesirable effects of medicines, causing attrition of molecules in drug development and harm to patients. To anticipate potential adverse effects early, drug candidates are commonly screened for pharmacological activity against a panel of protein targets. However, there is a lack of large-scale, quantitative information on the links between routinely screened proteins and the reporting of adverse events (AEs). This work describes a systematic analysis of associations between AEs observed in humans and bioactivities of drugs while taking into account drug plasma concentrations. In the first chapter, post-marketing drug-AE associations are derived from the United States Food and Drug Administration Adverse Event Reporting System using disproportionality methods, while applying Propensity Score Matching to reduce confounding factors. The resulting drug-AE associations are compared to those from the Side Effect Resource, which are primarily derived from clinical trials. The analysis reveals that the datasets generally share less than 10% of reported AEs for the same drug and have different distributions of AEs across System Organ Classes (SOCs). Using the drugs from the two AE datasets described in the first chapter, the second chapter integrates corresponding bioactivities, i.e. measured potencies and affinities from the ChEMBL database and ligand-based target predictions obtained with the tool PIDGIN, with drug plasma concentrations compiled from literature, such as Cmax. Compared to a constant bioactivity cut-off of 1 uM, using the ratio of the unbound drug plasma concentration over the drug potency, i.e. Cmax/XC50, results in different binary activity calls for protein targets. Whether deriving activity calls in this way results in the selection of targets with greater relevance to human AEs is investigated in the third chapter, which computes relationships between targets and AEs using different measures of statistical association. Using the Cmax/XC50 ratio results in higher Likelihood Ratios and Positive Predictive Values (PPVs) for target-AE associations that were previously reported in the context of secondary pharmacology screening, at the cost of a lower recall, possibly due to the smaller size of the dataset with available plasma concentrations. Furthermore, a large-scale quantitative assessment of bioactivities as indicators of AEs reveals a trade-off between the PPV and how many AE-associated drugs can potentially be detected from in vitro screening, although using combinations of targets can improve the detection rate in ~40% of cases at limited cost to the PPV. The work highlights AEs most strongly related to bioactivities and their SOC distribution. Overall, this thesis contributes to knowledge of the relationships between in vitro bioactivities and empirical evidence of AEs in humans. The results can inform the selection of proteins for secondary pharmacology screening and the development of computational models to predict AEs.Lhasa Limite

Methods to Compare Adverse Events in Twitter to FAERS, Drug Information Databases, and Systematic Reviews: Proof of Concept with Adalimumab

Introduction: Adverse drug reactions (ADRs) are associated with significant health-related and financial burden, and multiple sources are currently utilized to actively discover them. Social media has been proposed as a potential resource for monitoring ADRs, but drug-specific analytical studies comparing social media with other sources are scarce. Objectives: Our objective was to develop methods to compare ADRs mentioned in social media with those in traditional sources: the US FDA Adverse Event Reporting System (FAERS), drug information databases (DIDs), and systematic reviews. Methods: A total of 10,188 tweets mentioning adalimumab collected between June 2014 and August 2016 were included. ADRs in the corpus were extracted semi-automatically and manually mapped to standardized concepts in the Unified Medical Language System. ADRs were grouped into 16 biologic categories for comparisons. Frequencies, relative frequencies, disproportionality analyses, and rank ordering were used as metrics. Results: There was moderate agreement between ADRs in social media and traditional sources. “Local and injection site reactions” was the top ADR in Twitter, DIDs, and systematic reviews by frequency, ranked frequency, and index ranking. The next highest ADR in Twitter—fatigue—ranked fifth and seventh in FAERS and DIDs. Conclusion: Social media posts often express mild and symptomatic ADRs, but rates are measured differently in scientific sources. ADRs in FAERS are reported as absolute numbers, in DIDs as percentages, and in systematic reviews as percentages, risk ratios, or other metrics, which makes comparisons challenging; however, overlap is substantial. Social media analysis facilitates open-ended investigation of patient perspectives and may reveal concepts (e.g. anxiety) not available in traditional sources

Under-reporting of Adverse Drug Reactions to the Food & Drug Administration

This study examined the potential significant differences in the distribution of adverse drug reactions (ADRs) by reporter (consumer versus physician) and patient outcome at case and event level. This study also contains exploratory questions to evaluate reporting of ADRs by consumers versus physician by system organ class (SOC) and reporter demographics within the United States Food & Drug Administration Adverse Event Reporting System (FAERS). The theoretical foundation applied in this quantitative study was the social amplification of risk framework. Data from the second quarter of 2016 were obtained from FAERS, and a total of 87,807 ADR reports corresponding to 143,399 ADRs were analyzed by utilizing the chi-square test, the odds ratio, and logistic regression. Cross-sectional design was employed to compare reporting of ADRs at the case and event level (case-based and event-based analyses, respectively) between reporters (consumer versus physician), specifically, for patient outcome, as well as SOC and reporter demographics. For both the case-based and event-based analyses, findings revealed that consumers reported more serious ADRs in comparison to physicians. Furthermore, findings confirmed a difference in ADR reporting between consumers and physicians depending on SOC groups. Additionally, consumers reported more nonserious ADRs in comparison to physicians. The results from this study may have implications for positive social change by augmenting pharmacovigilance systems at a national and international level to identify risks and risk factors spontaneously reported after drugs have been on the market

A Focus on Abuse/Misuse and Withdrawal Issues with Selective Serotonin Reuptake Inhibitors (SSRIs): Analysis of Both the European EMA and the US FAERS Pharmacovigilance Databases

Despite increasing reports, antidepressant (AD) misuse and dependence remain underestimated issues, possibly due to limited epidemiological and pharmacovigilance evidence. Thus, here we aimed to determine available pharmacovigilance misuse/abuse/dependence/withdrawal signals relating to the Selective Serotonin Reuptake Inhibitors (SSRI) citalopram, escitalopram, paroxetine, fluoxetine, and sertraline. Both EudraVigilance (EV) and Food and Drug Administration-FDA Adverse Events Reporting System (FAERS) datasets were analysed to identify AD misuse/abuse/dependence/withdrawal issues. A descriptive analysis was performed; moreover, pharmacovigilance measures, including the reporting odds ratio (ROR), the proportional reporting ratio (PRR), the information component (IC), and the empirical Bayesian geometric mean (EBGM) were calculated. Both datasets showed increasing trends of yearly reporting and similar signals regarding abuse and dependence. From the EV, a total of 5335 individual ADR reports were analysed, of which 30% corresponded to paroxetine (n = 1,592), 27% citalopram (n = 1,419), 22% sertraline (n = 1,149), 14% fluoxetine (n = 771), and 8% escitalopram (n = 404). From FAERS, a total of 144,395 individual ADR reports were analysed, of which 27% were related to paroxetine, 27% sertraline, 18% citalopram, 16% fluoxetine, and 13% escitalopram. Comparing SSRIs, the EV misuse/abuse-related ADRs were mostly recorded for citalopram, fluoxetine, and sertraline; conversely, dependence was mostly associated with paroxetine, and withdrawal to escitalopram. Similarly, in the FAERS dataset, dependence/withdrawal-related signals were more frequently reported for paroxetine. Although SSRIs are considered non-addictive pharmacological agents, a range of proper withdrawal symptoms can occur well after discontinuation, especially with paroxetine. Prescribers should be aware of the potential for dependence and withdrawal associated with SSRIs

Translational high-dimesional drug interaction discovery and validation using health record databases and pharmacokinetics models

Indiana University-Purdue University Indianapolis (IUPUI)Polypharmacy leads to increased risk of drug-drug interactions (DDI’s). In this dissertation, we create a database for quantifying fraction of metabolism (fm) of CYP450 isozymes for FDA approved drugs. A reproducible data collection protocol was developed to extract key information from publicly available in vitro selective CYP enzyme inhibition studies. The fm was then estimated from the curated data. Then, proposed a random control selection approach for nested case-control design for electronical health records (HER) and electronical medical records (EMR) databases. By relaxing the matching by case’s index time restriction, random control dramatically reduces the computational burden compared with traditional control selection approaches. Using the Observational Medical Outcomes Partnership gold standard and an EMR database, random control is demonstrated to have better performances as well. Finally, combining epidemiological studies and pharmacokinetic modeling with fm database, we detected and evaluated high-dimensional drug-drug interactions among thirty high frequency drugs. Multi-drug combinations that increased risk of myopathy were identified in the FAERS and EMR databases by a mixture drug-count response model (MDCM) model. Twenty-eight 3-way and 43 4-way DDI’s increased ratio of area under plasma concentration–time curve (AUCR) >2-fold and had significant myopathy risk in both databases. The predicted AUCR of omeprazole in the presence of fluconazole and clonidine was 9.35; and increased risk of myopathy was 6.41 (LFDR = 0.002) in FAERS and 18.46 (LFDR = 0.005) in EMR. We demonstrate that combining health record informatics and pharmacokinetic modeling is a powerful translational approach to detect high-dimensional DDI’s.2 year

Toxicities with Immune Checkpoint Inhibitors: Emerging Priorities From Disproportionality Analysis of the FDA Adverse Event Reporting System

Background: Immune checkpoint inhibitors (ICIs), including antibodies targeting cytotoxic T-lymphocyte associated protein 4 (CTLA4) and programmed cell death 1 or its ligand (PD1/PDL1), elicit different immune-related adverse events (irAEs), but their global safety is incompletely characterized. Objective: The aim of this study was to characterize the spectrum, frequency, and clinical features of ICI-related adverse events (AEs) reported to the FDA Adverse Event Reporting System (FAERS). Patients and methods: AEs from FAERS (up to June 2018) recording ICIs (ipilimumab, nivolumab, pembrolizumab, atezolizumab, avelumab, durvalumab) as suspect were extracted. Comprehensive disproportionality analyses were performed through the reporting odds ratio (ROR) with 95% confidence interval (95% CI), using other oncological drugs as comparison. An overview of systematic reviews (OoSRs) was also undertaken to identify irAEs with consistent positive associations. Results: ICIs were recorded in 47,266 reports, submitted mainly by consumers receiving monotherapy with anti-PD1/PDL1 drugs. Three areas of toxicity emerged from both disproportionality analysis and the OoSRs (32 studies): endocrine (N = 2863; ROR = 6.91; 95% CI 6.60–7.23), hepatobiliary (2632; 1.33; 1.28–1.39), and respiratory disorders (7240; 1.04; 1.01–1.06). Different reporting patterns emerged for anti-CTLA4 drugs (e.g., hypophysitis, adrenal insufficiency, hypopituitarism, and prescribed overdose) and anti-PD1/PDL1 agents (e.g., pneumonitis, cholangitis, vanishing bile duct syndrome, tumor pseudoprogression, and inappropriate schedule of drug administration). No increased reporting emerged when comparing combination with monotherapy regimens, but multiple hepatobiliary/endocrine/respiratory irAEs were recorded. Conclusions: This parallel approach through contemporary post-marketing analysis and OoSRs confirmed that ICIs are associated with a multitude of irAEs, with different reporting patterns between anti-CTLA4 and anti-PD1/PDL1 medications. Close clinical monitoring is warranted to early diagnose and timely manage irAEs, especially respiratory, endocrine, and hepatic toxicities, which warrant further characterization; patient- and drug-related risk factors should be assessed through analytical pharmaco-epidemiological studies and prospective multicenter registries

Translational high-dimensional drug Interaction discovery and validation using health record databases and pharmacokinetics models

Polypharmacy increases the risk of drug-drug interactions (DDI's). Combining epidemiological studies with pharmacokinetic modeling, we detected and evaluated high-dimensional DDI's among thirty frequent drugs. Multi-drug combinations that increased risk of myopathy were identified in the FDA Adverse Event Reporting System (FAERS) and electronic medical record (EMR) databases by a mixture drug-count response model. CYP450 inhibition was estimated among the 30 drugs in the presence of 1 to 4 inhibitors using in vitro in vivo extrapolation. Twenty-eight 3-way and 43 4-way DDI's had significant myopathy risk in both databases and predicted increases in the area under the concentration time curve ratio (AUCR) >2-fold. The HD-DDI of omeprazole, fluconazole and clonidine was associated with a 6.41-fold (FAERS) and 18.46-fold (EMR) increase risk of myopathy (LFDR<0.005); the AUCR of omeprazole in this combination was 9.35.The combination of health record informatics and pharmacokinetic modeling is a powerful translational approach to detect high-dimensional DDI's

Impulse Control Disorders by Dopamine Partial Agonists: A Pharmacovigilance-Pharmacodynamic Assessment Through the FDA Adverse Event Reporting System

BACKGROUND: The dopaminergic partial agonism of the so-called third-generation antipsychotics (TGAs; aripiprazole, brexpiprazole, cariprazine) is hypothesized to cause impulse control disorders (ICDs). Relevant warnings by the Food and Drug Administration (FDA) were posted on aripiprazole (2016) and brexpiprazole (2018). Our study investigated the FDA Adverse Event Reporting System and the pharmacodynamic CHEMBL database to further characterize TGA-induced ICDs. METHODS: We downloaded and pre-processed the FDA Adverse Event Reporting System up to December 2020. We adapted Bradford Hill criteria to assess each TGA's -and secondarily other antipsychotics'-causal role in inducing ICDs (pathological gambling, compulsive shopping, hyperphagia, hypersexuality), accounting for literature and disproportionality. ICD clinical features were analyzed, and their pathogenesis was investigated using receptor affinities. RESULTS: A total of 2708 reports of TGA-related ICDs were found, primarily recording aripiprazole (2545 reports, 94%) among the drugs, and gambling (2018 reports, 75%) among the events. Bradford-Hill criteria displayed evidence for a causal role of each TGA consistent across subpopulations and when correcting for biases. Significant disproportionalities also emerged for lurasidone with compulsive shopping, hyperphagia, and hypersexuality, and olanzapine and ziprasidone with hyperphagia. Time to onset varied between days and years, and positive dechallenge was observed in 20% of cases. Frequently, co-reported events were economic (50%), obsessive-compulsive (44%), and emotional conditions (34%). 5-Hydroxytryptamine receptor type 1a agonism emerged as an additional plausible pathogenetic mechanism. CONCLUSIONS: We detected an association between TGAs and ICDs and identified a new signal for lurasidone. ICD characteristics are behavior specific and may heavily impact on life. The role of 5-Hydroxytryptamine receptor type 1a agonism should be further explored

Chapter Evolving Roles of Spontaneous Reporting Systems to Assess and Monitor Drug Safety

This chapter aims to describe current and emerging roles of spontaneous reporting systems (SRSs) for assessing and monitoring drug safety. Moreover, it offers a perspective on the near future, which entails the so-called era of Big Data, keeping in mind both regulator and researcher viewpoints. After a panorama on key data sources and analyses of post-marketing data of adverse drug reactions, a critical appraisal of methodological issues and debated future applications of SRSs will be presented, including the exploitation and challenges in evidence integration (i.e., merging and combining heterogeneous sources of data into a unique indicator of risk) and patient’s reporting via social media. Finally, a call for a responsible use of these studies is offered, with a proposal on a set of minimum requirements to assess the quality of disproportionality analysis in terms of study conception, performing and reporting