Reverse translation of adverse event reports paves the way for de-risking preclinical off-targets

Long-term hepatobiliary disorder associated with trastuzumab emtansine pharmacovigilance study using the FDA Adverse Event Reporting System database

Trastuzumab emtansine (T-DM1) is widely utilized as a second-line and subsequent treatment for metastatic HER2+ breast cancer and has shown promise in early breast cancer treatment, particularly in adjuvant settings for residual disease after neoadjuvant chemotherapy. However, concerns have arisen regarding long-term hepatic adverse drug reactions (ADRs) not identified in clinical trials. We investigated potential safety signals of T-DM1 in hepatobiliary disorders and the time-to-onset of ADRs using the FDA Adverse Event Reporting System (FAERS) database. Suspected ADRs were extracted and divided into two groups: T-DM1 (N = 3387) and other drugs (N = 11,833,701). Potential signal for T-DM1 in hepatobiliary disorder were identified (reporting odds ratio [ROR] = 5.66, 95% confidence interval [CI] = 5.11-6.27; information component [IC] = 2.35, 95% Credibility Interval [Crl] = 2.18-2.51). A breast cancer indicated subgroup analysis (2519 T-DM1; 172,329 other drugs) also identified a potential safety signal (ROR = 3.28, 95% CI = 2.92-3.68; IC = 1.53, 95%CrI = 1.35-1.71). The median time-to-onset for T-DM1-associated hepatobiliary disorders was 41 days. For prolonged and chronic hepatobiliary disorders, median times were 322.5 and 301.5 days, respectively. These findings highlight the need for further research to inform clinical decisions on optimal T-DM1 treatment duration, balancing benefits with potential adverse reactions.

DREAMER: Exploring Common Mechanisms of Adverse Drug Reactions and Disease Phenotypes through Network-Based Analysis

Adverse drug reactions (ADRs) are a major concern in clinical healthcare, significantly affecting patient safety and drug development. This study introduces DREAMER, a novel network-based method for exploring the mechanisms underlying ADRs and disease phenotypes at a molecular level by leveraging a comprehensive knowledge graph obtained from various datasets. By considering drugs and diseases that cause similar phenotypes, and investigating their commonalities regarding their impact on specific modules of the protein-protein interaction network, DREAMER can robustly identify protein sets associated with the biological mechanisms underlying ADRs and unravel the causal relationships that contribute to the observed clinical outcomes. Applying DREAMER to 649 ADRs, we identified proteins associated with the mechanism of action for 67 ADRs across multiple organ systems. In particular, DREAMER highlights the importance of GABAergic signaling and proteins of the coagulation pathways for personality disorders and intracranial hemorrhage, respectively. We further demonstrate the application of DREAMER in drug repurposing and propose sotalol, ranolazine, and diltiazem as candidate drugs to be repurposed for cardiac arrest. In summary, DREAMER effectively detects molecular mechanisms underlying phenotypes.

Human Genetics and Genomics for Drug Target Identification and Prioritization: Open Targets' Perspective

Open Targets, a consortium among academic and industry partners, focuses on using human genetics and genomics to provide insights to key questions that build therapeutic hypotheses. Large-scale experiments generate foundational data, and open-source informatic platforms systematically integrate evidence for target-disease relationships and provide dynamic tooling for target prioritization. A locus-to-gene machine learning model uses evidence from genome-wide association studies (GWAS Catalog, UK BioBank, and FinnGen), functional genomic studies, epigenetic studies, and variant effect prediction to predict potential drug targets for complex diseases. These predictions are combined with genetic evidence from gene burden analyses, rare disease genetics, somatic mutations, perturbation assays, pathway analyses, scientific literature, differential expression, and mouse models to systematically build target-disease associations (https://platform.opentargets.org). Scored target attributes such as clinical precedence, tractability, and safety guide target prioritization. Here we provide our perspective on the value and impact of human genetics and genomics for generating therapeutic hypotheses.

Thyroid Hyperplasia and Neoplasm Adverse Events Associated With Glucagon-Like Peptide-1 Receptor Agonists in the Food and Drug Administration Adverse Event Reporting System: Retrospective Analysis

Background

Glucagon-like peptide-1 (GLP-1) receptor agonists (RAs) are one of the most commonly used drugs for type 2 diabetes mellitus. Clinical guidelines recommend GLP-1 RAs as an adjunct to diabetes therapy in patients with chronic kidney disease, presence or risk of atherosclerotic cardiovascular disease, and obesity. The weight loss observed in clinical trials has been explored further in healthy individuals, putting GLP-1 RAs on track to be the next weight loss treatment.

Objective

Although the adverse event profile is relatively safe, most GLP-1 RAs come with a labeled boxed warning for the risk of thyroid cancers, based on animal models and some postmarketing case reports in humans. Considering the increasing popularity of this drug class and its expansion into a new popular indication, a further review of the most recent postmarketing safety data was warranted to quantify thyroid hyperplasia and neoplasm instances.

Methods

GLP-1 RA patient reports from the US Food and Drug Administration (FDA) Adverse Event Reporting System database were analyzed using reporting odds ratios and 95% CIs.

Results

In this study, we analyzed over 18 million reports from the US FDA Adverse Event Reporting System and provided evidence of significantly increased propensity for thyroid hyperplasias and neoplasms in patients taking GLP-1 RA monotherapy when compared to patients taking sodium-glucose cotransporter-2 (SGLT-2) inhibitor monotherapy.

Conclusions

GLP-1 RAs, regardless of indication, are associated with an over 10-fold increase in thyroid neoplasm and hyperplasia adverse event reporting when compared to SGLT-2 inhibitors.

Thyroid hyperplasia and neoplasm adverse events associated with GLP-1 receptor agonists in FDA Adverse Event Reporting System

Glucagon receptor-like peptide receptor agonists, GLP-1 RAs, are one of the most commonly used drugs for type-2 diabetes mellitus. The clinical guidelines recommend GLP-1 RAs as adjunct to diabetes therapy in patients with chronic kidney disease, presence or risk of atherosclerotic cardiovascular disease, obesity, and other cardiometabolic conditions. The weight loss seen in clinical trials has been explored further in healthy individuals, putting GLP-1 RAs on track to be the next weight loss treatment. Although the adverse event profile is relatively safe, most GLP-1 RAs come with a labeled black boxed warning of the risk of thyroid cancers, based on animal models and some postmarketing case reports in humans. Considering the increasing popularity of this drug class and its expansion into a new popular indication, a further review of most recent postmarketing safety data is warranted to quantify thyroid hyperplasia and neoplasms instances. In this study we analyzed over eighteen million reports from United States Food and Drug Administration Adverse Event Reporting System and identified 17,653 relevant GLP-1 RA monotherapy reports to provide the evidence of significantly increased propensity for thyroid hyperplasias and neoplasms in patients taking GLP-1 RA as monotherapy when compared to patients taking sodium-glucose cotransporter-2 inhibitor monotherapy.

Trajectory of blood pressure after initiating anti-calcitonin gene-related peptide treatment of migraine: a target trial emulation from the veterans health administration

BACKGROUND

Calcitonin gene-related peptide (CGRP) is involved in migraine pathophysiology and blood pressure regulation. Although clinical trials have established the cardio-cerebrovascular safety profile of anti-CGRP treatment, limited high-quality real-world evidence exists on its long-term effects on blood pressure (BP). To address this gap, we examined the safety of anti-CGRP treatment on BP in patients with migraine headache in the Veterans Health Administration (VHA).

METHODS

We emulated a target trial of patients who initiated anti-CGRP treatment or topiramate for migraine prevention between May 17th, 2018 and February 28th, 2023. We calculated stabilized inverse probability weights to balance between groups and then used weighted linear mixed-effect models to estimate the systolic and diastolic BP changes over the study period. For patients without hypertension at baseline, we estimated the cumulative incidence of hypertension using Kaplan-Meier curve. We also used weight mixed-effect Poisson model to estimate the number of antihypertension medications for patients with hypertension at baseline.

RESULTS

This analysis included 69,589 patients and 554,437 blood pressure readings. of these, 18,880 patients received anti-CGRP treatment, and they were more likely to be women, have a chronic migraine diagnosis and higher healthcare utilization than those received topiramate. Among patients without hypertension at baseline, we found no significant differences in systolic BP changes over the four-year follow-up between anti-CGRP (slope [standard error, SE] = 0.48[0.06]) and topiramate treated patients (slope[SE] = 0.39[0.04]). The incidence of hypertension was similar for anti-CGRP and topiramate group (4.4 vs 4.3 per 100 person-years). Among patients with hypertension at baseline who initiated anti-CGRP treatment, we found a small but persistent effect on exacerbating hypertension during the first four years of treatment, as evidenced by a significant annual 3.7% increase in the number of antihypertensive medications prescribed (RR = 1.037, 95%CI 1.025-1.048).

CONCLUSIONS

Our findings suggest that anti-CGRP treatment is safe regarding blood pressure in patients without hypertension. However, for those with baseline hypertension, anti-CGRP treatment resulted in a small but persistent increase in the number of antihypertensives, indicating an exacerbation of hypertension. Future studies are needed to evaluate the cardio-cerebrovascular safety of anti-CGRP treatment beyond the first four years.

A preclinical secondary pharmacology resource illuminates target-adverse drug reaction associations of marketed drugs

In vitro secondary pharmacology assays are an important tool for predicting clinical adverse drug reactions (ADRs) of investigational drugs. We created the Secondary Pharmacology Database (SPD) by testing 1958 drugs using 200 assays to validate target-ADR associations. Compared to public and subscription resources, 95% of all and 36% of active (AC50 < 1 µM) results are unique to SPD, with bias towards higher activity in public resources. Annotating drugs with free maximal plasma concentrations, we find 684 physiologically relevant unpublished off-target activities. Furthermore, 64% of putative ADRs linked to target activity in key literature reviews are not statistically significant in SPD. Systematic analysis of all target-ADR pairs identifies several putative associations supported by publications. Finally, candidate mechanisms for known ADRs are proposed based on SPD off-target activities. Here we present a freely-available resource for benchmarking ADR predictions, explaining phenotypic activity and investigating clinical properties of marketed drugs.

Anti-TNF-α therapy induced psoriasis in rheumatoid arthritis patients according to FDA postmarketing surveillance data

Rheumatoid arthritis, RA, is a chronic autoimmune disease characterized by joint pain, tenderness, swelling, and stiffness. This disease affects nearly 1% of the world population. RA predominates in females and typically develops between the ages of 30 and 50 years. Common therapeutics for the treatment of RA include immune system suppressants such as tumor necrosis factor, or TNF, inhibitors. There is growing concern related to multiple clinical cases reporting an unexpected onset of psoriasis following the use of TNF inhibitors. This adverse event is counterintuitive since some tumor necrosis factor inhibitors are indicated for the treatment of plaque psoriasis. In this study, we analyzed over 880 thousand postmarketing safety reports from patients being treated for RA with a single therapeutic and provided evidence for a statistically significant association of psoriasis adverse events with TNF inhibitor use as compared to methotrexate. Additionally, we quantified the reported odds ratios and their 95% confidence intervals between four individual TNF inhibitors and found that the degree of association with psoriasis was variable among the drugs studied, with certolizumab pegol exhibiting the highest reported risk.

Evaluating the Impact of Black Box Warning Updates on the Reporting of Drug-Related Adverse Events: a Cross Sectional Study of the FAERS Database

BACKGROUND

The Food and Drug Administration (FDA)'s Adverse Event Reporting System (FAERS) is a post-marketing surveillance system which relies on spontaneous reports of adverse drug reactions (ADRs). Our objective was to evaluate how black box warning (BBW) updates impact ADR reporting rates.

RESEARCH DESIGN AND METHODS

We searched MEDWATCH for all BBW updates issued between January 2014 and December 2016 and categorized them as new, major, and minor. Rates of relevant ADR reports from the FAERS database in the 4 years preceding and following a BBW update were assessed among the different BBW categories.

RESULTS

Forty BBW updates were included (16 major, 3 new, and 21 minor). A meaningful increase in the proportion of relevant ADRs of all ADRs reported following BBW updates was documented for 53% of new or major updates and 24% of minor updates (p = 0.06). The median percentage of reported relevant ADRs increased by 5% following new and major BBW updates and decreased by 30% following minor BBW updates (p = 0.3).

CONCLUSIONS

Reporting of adverse events to the FAERS database is affected by the severity and timing of related BBW updates, highlighting the drawbacks of spontaneous reporting systems. Regulators should promote proactive pharmacovigilance strategies to cope with these limitations.

Characterization and corroboration of safety signals identified from the US Food and Drug Administration Adverse Event Reporting System, 2008-19: cross sectional study

OBJECTIVE

To characterize potential drug safety signals identified from the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS), from 2008 to 2019, to determine how often these signals resulted in regulatory action by the FDA and whether these actions were corroborated by published research findings or public assessments by the Sentinel Initiative.

DESIGN

Cross sectional study.

SETTING

USA.

POPULATION

Safety signals identified from the FAERS and publicly reported by the FDA between 2008 and 2019; and review of the relevant literature published before and after safety signals were reported in 2014-15. Literature searches were performed in November 2019, Sentinel Initiative assessments were searched in December 2021, and data analysis was finalized in December 2021.

MAIN OUTCOME MEASURES

Safety signals and resulting regulatory actions; number and characteristics of published studies, including corroboration of regulatory action as evidenced by significant associations (or no associations) between the drug related to the signal and the adverse event.

RESULTS

From 2008 to 2019, 603 potential safety signals identified from the FAERS were reported by the FDA (median 48 annually, interquartile range 41-61), of which 413 (68.5%) were resolved as of December 2021 (372 of 399 (93.2%) signals ≥3 years old were resolved). Among the resolved safety signals, 91 (22.0%) led to no regulatory action and 322 (78.0%) resulted in regulatory action, including 319 (77.2%) changes to drug labeling and 59 (14.3%) drug safety communications or other public communications from the FDA. For a subset of 82 potential safety signals reported in 2014-15, a literature search identified 1712 relevant publications; 1201 (70.2%) were case reports or case series. Among these 82 safety signals, 76 (92.7%) were resolved, of which relevant published research was identified for 57 (75.0%) signals and relevant Sentinel Initiative assessments for four (5.3%) signals. Regulatory actions by the FDA were corroborated by at least one relevant published research study for 17 of the 57 (29.8%) resolved safety signals; none of the relevant Sentinel Initiative assessments corroborated FDA regulatory action.

CONCLUSIONS

Most potential safety signals identified from the FAERS led to regulatory action by the FDA. Only a third of regulatory actions were corroborated by published research, however, and none by public assessments from the Sentinel Initiative. These findings suggest that either the FDA is taking regulatory actions based on evidence not made publicly available or more comprehensive safety evaluations might be needed when potential safety signals are identified.

Language-agnostic pharmacovigilant text mining to elicit side effects from clinical notes and hospital medication records

We sought to craft a drug safety signalling pipeline associating latent information in clinical free text with exposures to single drugs and drug pairs. Data arose from 12 secondary and tertiary public hospitals in two Danish regions, comprising approximately half the Danish population. Notes were operationalised with a fastText embedding, based on which we trained 10,720 neural-network models (one for each distinct single-drug/drug-pair exposure) predicting the risk of exposure given an embedding vector. We included 2,905,251 admissions between May 2008 and June 2016, with 13,740,564 distinct drug prescriptions; the median number of prescriptions was 5 (IQR: 3-9) and in 1,184,340 (41%) admissions patients used ≥5 drugs concomitantly. 10,788,259 clinical notes were included, with 179,441,739 tokens retained after pruning. Of 345 single-drug signals reviewed, 28 (8.1%) represented possibly undescribed relationships; 186 (54%) signals were clinically meaningful. 16 (14%) of the 115 drug-pair signals were possible interactions and 2 (1.7%) were known. In conclusion, we built a language-agnostic pipeline for mining associations between free-text information and medication exposure without manual curation, predicting not the likely outcome of a range of exposures, but the likely exposures for outcomes of interest. Our approach may help overcome limitations of text mining methods relying on curated data in English and can help leverage non-English free text for pharmacovigilance.

Postmarketing safety surveillance data reveals protective effects of botulinum toxin injections against incident anxiety

Randomized controlled trials (RCTs) have shown an antidepressant effect of glabellar botulinum toxin (BoNT) injections. In the FDA Adverse Event Reporting System (FAERS) database, BoNT injection is associated with reduced incidence rates of depression across various non-psychiatric indications, which confirms the previous findings independently of specific expectations to an antidepressant effect of BoNT. The rationale of using BoNT to treat depression is to interrupt proprioceptive body feedback that may reinforce negative emotions. Negative emotions also occur in other mental disorders, suggesting a transdiagnostic therapeutic potential of BoNT in psychiatry. Here we report an analysis of the FAERS database, in which we found that, compared to alternative treatments, BoNT injections were associated with lower incidence of anxiety symptoms and related disorders. Among seven indications/injection sites, we found this protective effect of BoNT in cosmetic use/facial muscles, migraine/facial and head muscles, spasms and spasticity/upper and lower limbs, torticollis and neck pain/neck muscles, and sialorrhea/parotid and submandibular glands (reporting odds ratios 0.79-0.27). These findings are encouraging for possible future RCTs on the use of BoNT as a treatment for anxiety and related disorders.

Myocarditis occurrence with cancer immunotherapy across indications in clinical trial and post-marketing data

Antibodies targeting the PD-1, PD-L1, and CTLA-4 immune checkpoint axis have been used in a variety of tumor types. They achieve anti-tumor activity through activating the patient’s own immune system to target immune response evading cancer cells. However, this unique mechanism of action may cause immune-related adverse events, irAEs. One of these irAEs is myocarditis which is associated with an alarming mortality rate. In this study we presented clinical cases of myocarditis from safety trial datasets submitted to the U.S. Food and Drug Administration, FDA. Additionally, we analyzed over fourteen million FDA Adverse Event Reporting System, FAERS, submissions. The statistical analysis of the FAERS data provided evidence of significantly increased reporting of myocarditis in patients administered immune checkpoint inhibitors alone, in combination with another immune checkpoint inhibitor, the kinase inhibitor axitinib, or chemotherapy, for all cancer types, when compared to patients administered chemotherapy. All combination therapies led to further increased reporting odds ratios of myocarditis. We further analyzed the occurrence of myocarditis by stratifying the reports into sub-cohorts based on specific cancer types and treatment/control groups in major cancer immunotherapy efficacy trials and confirmed the observed trend for each cohort.

Thyroid dysfunction related to vascular endothelial growth factor receptor tyrosine kinase inhibitors: A real-world study based on FAERS

WHAT IS KNOWN AND OBJECTIVE

The adverse events of thyroid dysfunction caused by the use of vascular endothelial growth factor receptor tyrosine kinase inhibitors (VEGFR-TKIs) have not been confirmed in a real-world investigation. The aim of this study was to evaluate the relationship between thyroid dysfunction and treatment using Food and Drug Administration (FDA)-approved VEGFR-TKIs.

METHODS

Four data-mining algorithms were employed to detect thyroid dysfunction signals for VEGFR-TKIs, using data in the FDA Adverse Event Reporting System (FAERS) database from 68 quarters. MySQL Workbench and R were used to conduct statistical analysis.

RESULTS AND DISCUSSION

We identified 32679 reports of thyroid dysfunction, of which 1567 listed VEGFR-TKIs as the primary suspected drugs. All four algorithms showed that the strength of the signals for hypothyroidism were greater than those for hyperthyroidism, for all the VEGFR-TKIs. In most cases, the median appearance time was within 100 days of initiation of VEGFR-TKIs therapy, except in the case of ponatinib. This indicated the need to actively identify and manage thyroid dysfunction during the early stages of VEGFR-TKIs treatment.

WHAT IS NEW AND CONCLUSIONS

This study systematically identified the pharmacovigilance signals of thyroid dysfunction associated with the use of VEGFR-TKIs, using the FAERS database.

Systemic quinolones and risk of acute liver failure I: Analysis of data from the US FDA adverse event reporting system

Background and Aim

Quinolones are a potent and globally popular group of antibiotics that are used to treat a wide range of infections. Some case reports have raised concern about their possible association with acute hepatic failure (AHF). Data from the US FDA Adverse Event Reporting System were evaluated for signals of AHF in association with systemically administered quinolone antibiotics.

Methods

AHF reports between 1969 and 2019q2, with a focus on 2010–2019q2, were analyzed. Specifically, AHF reports linked to non‐quinolone antibiotics of known hepatotoxicity were compared to reports with non‐quinolone, non‐hepatotoxic (reference) antibiotics; and AHF reports with quinolones were also compared to reports with the same group of reference antibiotics. Two disproportionality signal detection techniques (proportional reporting ratio, PRR, and empirical Bayes geometric mean, EBGM) were used to assess the AHF signal for both analyses.

Results

Only ciprofloxacin showed a marginal and significant AHF signal (PRR: 1.85 [1.21, 2.81]; EBGM: 1.54 [1.06, 1.81]); moxifloxacin, levofloxacin, and ofloxacin showed weak and nonsignificant signals.

Conclusion

Further pharmacovigilance studies are required to confirm the association between ciprofloxacin and AHF seen in the present analysis.

Do dimethyl fumarate and nicotinic acid elicit common, potentially HCA2 -mediated adverse reactions? A combined epidemiological-experimental approach

AIM

Dimethyl fumarate and nicotinic acid activate the hydroxy-carboxylic acid receptor 2 (HCA₂ ) and induce flushing. It is not known whether HCA₂ mediates other adverse drug reactions (ADRs) to these two substances. This study aims to compare ADRs associated with dimethyl fumarate and nicotinic acid, and to discuss whether they are HCA₂ -mediated.

METHODS

We identified spontaneous reports of suspected ADRs to dimethyl fumarate and nicotinic acid in the European Adverse Drug Reaction Database (EudraVigilance). These reports were analysed at different hierarchical levels of the Medical Dictionary for Regulatory Activities (MedDRA). In addition, we screened murine organs for HCA₂ expression.

RESULTS

Similarities in the ADR profile of dimethyl fumarate and nicotinic acid included "gastrointestinal signs and symptoms" (odds ratio [OR] 0.8 [0.6-1.1]), "hepatobiliary investigations" (OR 1.3 [0.7-2.5]) and "anxiety disorders and symptoms" (OR 0.9 [0.3-2.2]) in High Level Group Terms; "diarrhoea (excluding infective)" (OR 1.2 [0.7-1.8]) and "liver function analyses" (OR 1.3 [0.7-2.6]) in High Level Terms; and "diarrhoea" (OR 1.2 [0.7-2.0]) and "vomiting" (OR 0.9 [0.4-1.7]) in Preferred Terms. In analogy, HCA₂ was expressed in the gastrointestinal tract, liver and central nervous system (CNS) of murine organs. A discrepant ADR profile was seen for "lymphopenia" (n = 777) at the preferred term level (only reported for dimethyl fumarate) and "blood glucose increased" (more often reported for nicotinic acid; OR 0.1 [0.0-0.5]).

CONCLUSION

The gastrointestinal ADRs common to both substances may be mediated by HCA₂ . Other ADRs not common to both substances are compound or indication-specific reactions and likely do not involve HCA₂ .

Open Targets Platform: supporting systematic drug-target identification and prioritisation

The Open Targets Platform (https://www.targetvalidation.org/) provides users with a queryable knowledgebase and user interface to aid systematic target identification and prioritisation for drug discovery based upon underlying evidence. It is publicly available and the underlying code is open source. Since our last update two years ago, we have had 10 releases to maintain and continuously improve evidence for target-disease relationships from 20 different data sources. In addition, we have integrated new evidence from key datasets, including prioritised targets identified from genome-wide CRISPR knockout screens in 300 cancer models (Project Score), and GWAS/UK BioBank statistical genetic analysis evidence from the Open Targets Genetics Portal. We have evolved our evidence scoring framework to improve target identification. To aid the prioritisation of targets and inform on the potential impact of modulating a given target, we have added evaluation of post-marketing adverse drug reactions and new curated information on target tractability and safety. We have also developed the user interface and backend technologies to improve performance and usability. In this article, we describe the latest enhancements to the Platform, to address the fundamental challenge that developing effective and safe drugs is difficult and expensive.

Systematic Analysis of Protein Targets Associated with Adverse Events of Drugs from Clinical Trials and Postmarketing Reports

Adverse drug reactions (ADRs) are undesired effects of medicines that can harm patients and are a significant source of attrition in drug development. ADRs are anticipated by routinely screening drugs against secondary pharmacology protein panels. However, there is still a lack of quantitative information on the links between these off-target proteins and the reporting of ADRs in humans. Here, we present a systematic analysis of associations between measured and predicted in vitro bioactivities of drugs and adverse events (AEs) in humans from two sources of data: the Side Effect Resource, derived from clinical trials, and the Food and Drug Administration Adverse Event Reporting System, derived from postmarketing surveillance. The ratio of a drug's therapeutic unbound plasma concentration over the drug's in vitro potency against a given protein was used to select proteins most likely to be relevant to in vivo effects. In examining individual target bioactivities as predictors of AEs, we found a trade-off between the positive predictive value and the fraction of drugs with AEs that can be detected. However, considering sets of multiple targets for the same AE can help identify a greater fraction of AE-associated drugs. Of the 45 targets with statistically significant associations to AEs, 30 are included on existing safety target panels. The remaining 15 targets include 9 carbonic anhydrases, of which CA5B is significantly associated with cholestatic jaundice. We include the full quantitative data on associations between measured and predicted in vitro bioactivities and AEs in humans in this work, which can be used to make a more informed selection of safety profiling targets.

The activities of drug inactive ingredients on biological targets

Excipients, considered "inactive ingredients," are a major component of formulated drugs and play key roles in their pharmacokinetics. Despite their pervasiveness, whether they are active on any targets has not been systematically explored. We computed the likelihood that approved excipients would bind to molecular targets. Testing in vitro revealed 25 excipient activities, ranging from low-nanomolar to high-micromolar concentration. Another 109 activities were identified by testing against clinical safety targets. In cellular models, five excipients had fingerprints predictive of system-level toxicity. Exposures of seven excipients were investigated, and in certain populations, two of these may reach levels of in vitro target potency, including brain and gut exposure of thimerosal and its major metabolite, which had dopamine D3 receptor dissociation constant K _d values of 320 and 210 nM, respectively. Although most excipients deserve their status as inert, many approved excipients may directly modulate physiologically relevant targets.

Postmarketing safety surveillance data reveals antidepressant effects of botulinum toxin across various indications and injection sites

The World Health Organization estimates the number of people suffering from depression to be over 264 million. Current monoamine transmission modulating therapeutics, even with proper adherence and acceptable tolerability, are not effective for nearly one third of the patients, leading clinicians to explore other therapeutic options such as electroconvulsive therapy, transcranial magnetic stimulation, ketamine infusions, and, more recently, glabellar botulinum toxin, BoNT, injections. The scale and mechanism of antidepressant action of BoNT is unclear and maybe hypothetically attributed to the disruption of proprioceptive facial feedback reinforcing negative emotions. Here we verify the antidepressant effect of botulinum toxin by analysis of over 40 thousand BoNT treatment reports out of thirteen million postmarketing safety reports in the FDA Adverse Event Reporting System, FAERS. The results of the analysis indicate that patients who received BoNT injections to treat hyperhidrosis, facial wrinkles, migraine prophylaxis, spasticity, and spasms, had a significantly lower number of depression reports when compared to patients undergoing different treatments for the same conditions. These findings suggest that the antidepressant effect of BoNT is significant, and, surprisingly, is observed for a broad range of injection sites.

Machine learning guided association of adverse drug reactions with in vitro target-based pharmacology

BACKGROUND

Adverse drug reactions (ADRs) are one of the leading causes of morbidity and mortality in health care. Understanding which drug targets are linked to ADRs can lead to the development of safer medicines.

METHODS

Here, we analyse in vitro secondary pharmacology of common (off) targets for 2134 marketed drugs. To associate these drugs with human ADRs, we utilized FDA Adverse Event Reports and developed random forest models that predict ADR occurrences from in vitro pharmacological profiles.

FINDINGS

By evaluating Gini importance scores of model features, we identify 221 target-ADR associations, which co-occur in PubMed abstracts to a greater extent than expected by chance. Amongst these are established relations, such as the association of in vitro hERG binding with cardiac arrhythmias, which further validate our machine learning approach. Evidence on bile acid metabolism supports our identification of associations between the Bile Salt Export Pump and renal, thyroid, lipid metabolism, respiratory tract and central nervous system disorders. Unexpectedly, our model suggests PDE3 is associated with 40 ADRs.

INTERPRETATION

These associations provide a comprehensive resource to support drug development and human biology studies.

FUNDING

This study was not supported by any formal funding bodies.

Molecular basis of mood and cognitive adverse events elucidated via a combination of pharmacovigilance data mining and functional enrichment analysis

Drug-induced Mood- and Cognition-related adverse events (MCAEs) are often only detected during the clinical trial phases of drug development, or even after marketing, thus posing a major safety concern and a challenge for both pharmaceutical companies and clinicians. To fill some gaps in the understanding and elucidate potential biological mechanisms of action frequently associated with MCAEs, we present a unique workflow linking observational population data with the available knowledge at molecular, cellular, and psychopharmacology levels. It is based on statistical analysis of pharmacovigilance reports and subsequent signaling pathway analyses, followed by evidence-based expert manual curation of the outcomes. Our analysis: (a) ranked pharmaceuticals with high occurrence of such adverse events (AEs), based on disproportionality analysis of the FDA Adverse Event Reporting System (FAERS) database, and (b) identified 120 associated genes and common pathway nodes possibly underlying MCAEs. Nearly two-thirds of the identified genes were related to immune modulation, which supports the critical involvement of immune cells and their responses in the regulation of the central nervous system function. This finding also means that pharmaceuticals with a negligible central nervous system exposure may induce MCAEs through dysregulation of the peripheral immune system. Knowledge gained through this workflow unravels putative hallmark biological targets and mediators of drug-induced mood and cognitive disorders that need to be further assessed and validated in experimental models. Thereafter, they can be used to substantially improve in silico/in vitro/in vivo tools for predicting these adversities at a preclinical stage.

Relevance of physicochemical properties and functional pharmacology data to predict the clinical safety profile of direct oral anticoagulants

Direct oral anticoagulants (DOACs) have rapidly become the drug class of choice for anticoagulation therapy in secondary care. It is known that gastrointestinal hemorrhage are potential side effects of the DOAC drug class. In this study we have investigated the relevance of molecular structure and on/off-target pharmacology as a predictor of adverse drug reactions (ADRs) for the DOAC drug class. Use of the Reaxys MedChem module allowed for data mining of all possible reported off-target effects of the DOAC class members. For the first time, the MHRA Yellow card database in combination with prescribing rates in the United Kingdom (data for n = 30 566 936 DOAC Rx (up to 2017) and ADR data n = 22 275 (up to 2018)) were used for our data comparison of DOACs. From the underlying reported data, we were able to rank the DOACs in terms of the likely adverse events we would expect to observe. We identified potential risks of ADRs based on the DOACs pharmacology including the expected GI hemorrhage, but also the unexpected risk of stroke, pulmonary embolism and kidney injury. Statistically significant (P < .001) differences were found between all DOACs and their total number of ADRs. Although the risks are small, strong statistical correlation between observed pharmacology and national ADR data is observed in three out of the five areas of concern.

Human Cell-Based in vitro Phenotypic Profiling for Drug Safety-Related Attrition

Ensuring the safety of new drugs is critically important to regulators, pharmaceutical researchers and patients alike. Even so, unexpected toxicities still account for 20–30% of clinical trial failures, in part due to the persistence of animal testing as the primary approach for de-risking new drugs. Clearly, improved methods for safety attrition that incorporate human-relevant biology are needed. This recognition has spurred interest in non-animal alternatives or new approach methodologies (NAMs) including in vitro models that utilize advances in the culture of human cell types to provide greater clinical relevance for assessing risk. These phenotypic assay systems use human primary and induced pluripotent stem cell-derived cells in various formats, including co-cultures and advanced cellular systems such as organoids, bioprinted tissues, and organs-on-a-chip. Despite the promise of these human-based phenotypic approaches, adoption of these platforms into drug discovery programs for reducing safety-related attrition has been slow. Here we discuss the value of large-scale human cell-based phenotypic profiling for incorporating human-specific biology into the de-risking process. We describe learnings from our experiences with human primary cell-based assays and analysis of clinically relevant reference datasets in developing in vitro-based toxicity signatures. We also describe how Adverse Outcome Pathway (AOP) frameworks can be used to integrate results from diverse platforms congruent with weight-of-evidence approaches from risk assessment to improve safety-related decisions in early discovery.

Proton-pump inhibitor use is associated with a broad spectrum of neurological adverse events including impaired hearing, vision, and memory

Proton-pump inhibitors, PPIs, are considered effective therapy for stomach acid suppression due to their irreversible inhibition of the hydrogen/potassium pump in the gastric parietal cells. They are widely prescribed and are considered safe for over-the-counter use. Recent studies have shown an association between PPI use and Alzheimer dementia, while others have disputed that connection. We analyzed over ten million United States Food and Drug Administration Adverse Event Reporting System reports, including over forty thousand reports containing PPIs, and provided evidence of increased propensity for memory impairment among PPI reports when compared to histamine-2 receptor antagonist control group. Furthermore, we found significant associations of PPI use with a wide range of neurological adverse reactions including, migraine, several peripheral neuropathies, and visual and auditory neurosensory abnormalities.

Comparison of Zebrafish Larvae and hiPSC Cardiomyocytes for Predicting Drug-Induced Cardiotoxicity in Humans

Cardiovascular drug toxicity is responsible for 17% of drug withdrawals in clinical phases, half of post-marketed drug withdrawals and remains an important adverse effect of several marketed drugs. Early assessment of drug-induced cardiovascular toxicity is mandatory and typically done in cellular systems and mammals. Current in vitro screening methods allow high-throughput but are biologically reductionist. The use of mammal models, which allow a better translatability for predicting clinical outputs, is low-throughput, highly expensive, and ethically controversial. Given the analogies between the human and the zebrafish cardiovascular systems, we propose the use of zebrafish larvae during early drug discovery phases as a balanced model between biological translatability and screening throughput for addressing potential liabilities. To this end, we have developed a high-throughput screening platform that enables fully automatized in vivo image acquisition and analysis to extract a plethora of relevant cardiovascular parameters: heart rate, arrhythmia, AV blockage, ejection fraction, and blood flow, among others. We have used this platform to address the predictive power of zebrafish larvae for detecting potential cardiovascular liabilities in humans. We tested a chemical library of 92 compounds with known clinical cardiotoxicity profiles. The cross-comparison with clinical data and data acquired from human induced pluripotent stem cell cardiomyocytes calcium imaging showed that zebrafish larvae allow a more reliable prediction of cardiotoxicity than cellular systems. Interestingly, our analysis with zebrafish yields similar predictive performance as previous validation meta-studies performed with dogs, the standard regulatory preclinical model for predicting cardiotoxic liabilities prior to clinical phases.

Pharmacogenetics in the clinical analysis laboratory: clinical practice, research, and drug development pipeline

Introduction: Over the last decade, the spread of next-generation sequencing technology along with the rising cost in health management in national health systems has led to widespread use/abuse of pharmacogenetic tests (PGx) in the practice of many clinical disciplines. However, given their clinical significance, it is important to standardize these tests for having an interaction with the clinical analysis laboratory (CAL), in which a PGx service can meet these requirements. Areas covered: A diagnostic test must meet the criteria of reproducibility and validity for its utility in the clinical routine. This present review mainly describes the utility of introducing PGx tests in the CAL routine to produce correct results useful for setting up personalized drug treatments. Expert opinion: With a PGx service, CALs can provide the right tool to help clinicians to make better choices about different categories of drugs and their dosage and to manage the economic impact both in hospital-based settings and in National Health Services, throughout electronic health records. Advances in PGx also allow a new approach for pharmaceutical companies in order to improve drug development and clinical trials. As a result, CALs can achieve a powerful source of epidemiological, clinical, and research findings from PGx tests.

Retrospective analysis reveals significant association of hypoglycemia with tramadol and methadone in contrast to other opioids

Tramadol is one of the most commonly used analgesics worldwide, classified as having a low abuse potential by U.S. Drug Enforcement Agency, and often recommended in pain management guidelines. Its pain-relieving mechanism of action is attributed to mild μ-opioid receptor agonism, serotonin and norepinephrine mediated nociception modulation, and N-methyl-D-aspartate receptor, NMDAR, antagonism. However, recent case reports and case-control studies have shown an association between tramadol use and hypoglycemia. The growing concern over increasing tramadol use and unexpected side effects warranted a further comparative and quantitative analysis of tramadol adverse reactions. In this study we analyzed over twelve million reports from United States Food and Drug Administration Adverse Event Reporting System and provided evidence of increased propensity for hypoglycemia in patients taking tramadol when compared to patients taking other opioids, serotonin-norepinephrine reuptake inhibitors, and drugs affecting NMDAR activity. Additionally, we identified that only methadone from the opioid cohort behaves similarly to tramadol and has an association with hypoglycemia.

Analysis of postmarketing safety data for proton-pump inhibitors reveals increased propensity for renal injury, electrolyte abnormalities, and nephrolithiasis

Proton pump inhibitors, PPIs, are widely prescribed and sold globally. Although initially intended for time-limited treatment of acute disorders, such as gastric ulcers and esophagitis, PPIs are now commonly used for prolonged durations and are considered safe for over the counter access. Recent studies have raised concern over associations between PPI use and acute kidney injury, chronic kidney disease, end-stage renal disease, and electrolyte abnormalities. The growing concern over potentially serious adverse drug reactions warrants an evaluation of post marketing surveillance data. In this study of over ten million FDA Adverse Event Reporting System records, we provided evidence of kidney injury and electrolyte imbalances in an alarming number of patients taking PPIs. Additionally, we assessed differences between specific PPIs and observed significant electrolyte and renal abnormalities for each individual drug with varying magnitudes.

Evaluation of Postmarketing Reports from Industry-Sponsored Programs in Drug Safety Surveillance

INTRODUCTION AND OBJECTIVE

Adverse event reports from industry-sponsored programs, such as patient support programs, have contributed to a rise in the number of individual case safety reports in the US Food and Drug Administration Adverse Event Reporting System database. This study aimed to characterize individual case safety reports from industry-sponsored program and non-industry-sponsored program sources and compare their usefulness in safety signal detection.

METHODS

Individual case safety reports of six drug and biological products were identified in the Food and Drug Administration Adverse Event Reporting System database between the date of Food and Drug Administration product approval and the first quarter of 2017. A random subset of industry-sponsored program and non-industry-sponsored program individual case safety reports were then compared to identify differences in reporters, outcomes, data completeness, and usefulness. The 'usefulness' of individual case safety reports was assessed by manually reviewing the availability of key information in the narrative (e.g., temporality, comorbidities).

RESULTS

Compared with non-industry-sponsored program reports, more industry-sponsored program reports were associated with a serious outcome (51.4% vs. 58.8%, p = 0.02) and were reported by consumers (35.5% vs. 50.4%, p < 0.01). Industry-sponsored program reports tended to contain more data elements than non-industry-sponsored program reports (i.e., age, sex, indication for use), but completeness was variable across products. No significant difference in usefulness was identified between non-industry-sponsored program and industry-sponsored program individual case safety reports (30.6% vs. 28.5%, p = 0.42). Useful reports that contained at least one serious, unlabeled adverse event represented only 4% and 6.2% of the non-industry-sponsored program and industry-sponsored program report cohorts, respectively.

CONCLUSIONS

Our study suggests that reports obtained from industry-sponsored programs in the Food and Drug Administration Adverse Event Reporting System database contain more data elements but are similar to non-industry-sponsored program reports with regard to 'usefulness' in signal detection.

The Alzheimer's comorbidity phenome: mining from a large patient database and phenome-driven genetics prediction

Objective

Alzheimer’s disease (AD) is a severe neurodegenerative disorder and has become a global public health problem. Intensive research has been conducted for AD. But the pathophysiology of AD is still not elucidated. Disease comorbidity often associates diseases with overlapping patterns of genetic markers. This may inform a common etiology and suggest essential protein targets. US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) collects large-scale postmarketing surveillance data that provide a unique opportunity to investigate disease co-occurrence pattern. We aim to construct a heterogeneous network that integrates disease comorbidity network (DCN) from FAERS with protein–protein interaction (PPI) to prioritize the AD risk genes using network-based ranking algorithm.

Materials and Methods

We built a DCN based on indication data from FAERS using association rule mining. DCN was further integrated with PPI network. We used random walk with restart ranking algorithm to prioritize AD risk genes.

Results

We evaluated the performance of our approach using AD risk genes curated from genetic association studies. Our approach achieved an area under a receiver operating characteristic curve of 0.770. Top 500 ranked genes achieved 5.53-fold enrichment for known AD risk genes as compared to random expectation. Pathway enrichment analysis using top-ranked genes revealed that two novel pathways, ERBB and coagulation pathways, might be involved in AD pathogenesis.

Conclusion

We innovatively leveraged FAERS, a comprehensive data resource for FDA postmarket drug safety surveillance, for large-scale AD comorbidity mining. This exploratory study demonstrated the potential of disease-comorbidities mining from FAERS in AD genetics discovery.

Safety differentiation: emerging competitive edge in drug development

With increasing expectations to provide evidence of drug efficacy, safety, and cost-effectiveness, best-in-class drugs are a major value driver for the pharmaceutical industry. Superior safety is a key differentiation criterion that could be achieved through better risk:benefit profiles, safety margins, fewer contraindications, and improved patient compliance. To accomplish this, comparative safety assessments using innovative and adaptive nonclinical and clinical outcome-based approaches should be undertaken, and continuous strategic adjustments must be made as the risk:benefit profiles evolve. Key success criteria include scientific expertise and integration between all disciplines during the full extent of the drug development process.

Detecting a potential safety signal of antidepressants and type 2 diabetes: a pharmacovigilance-pharmacodynamic study

AIMS

Recent data suggest that antidepressants are associated with incident diabetes but the possible pharmacological mechanism is still questioned. The aim of the present study was to evaluate antidepressant's risk for reporting diabetes using disproportionality analysis of the FDA adverse events spontaneous reporting system (FAERS) database and to investigate possible receptor/transporter mechanisms involved.

METHODS

Data from 2004 to 2017 were analysed using OpenVigil2 and adjusted reporting odds ratio (aROR) for reporting diabetes was calculated for 22 antidepressants. Events included in the narrow scope of the SMQ 'hyperglycaemia/new-onset diabetes mellitus' were defined as cases and all the other events as non-cases. The pharmacodynamic profile was extracted using the PDSP and IUPHAR/BPS databases and the occupancy on receptors (serotonin, alpha adrenoreceptors, dopamine, muscarinic, histamine) and transporters (SERT, NET, DAT) was estimated. The relationship between aROR for diabetes and receptor occupancy was investigated with Pearson's correlation coefficient (r) and univariate linear regression.

RESULTS

Six antidepressants were associated with diabetes: nortriptyline with aROR [95% CI] of 2.01 [1.41-2.87], doxepin 1.97 [1.31-2.97], imipramine 1.82 [1.09-3.06], sertraline 1.47 [1.29-1.68], mirtazapine 1.33 [1.04-1.69] and amitriptyline 1.31 [1.09-1.59]. Strong positive correlation coefficients between occupancy and aROR for diabetes were identified for the receptors M₁ , M₃ , M₄ , M₅ and H₁ .

CONCLUSION

Most of the tricyclic antidepressants, mirtazapine and sertraline seem to be associated with reporting diabetes in FAERS. Higher degrees of occupancy on muscarinic receptors and H₁ may be a plausible pharmacological mechanism. Further clinical assessment and pharmacovigilance data is needed to validate this potential safety signal.

In vitro secondary pharmacological profiling: An IQ-DruSafe industry survey on current practices

INTRODUCTION

In 2015, IQ DruSafe conducted a survey of its membership to identify industry practices related to in vitro off target pharmacological profiling of small molecules.

METHODS

An anonymous survey of 20 questions was submitted to IQ-DruSafe representatives. Questions were designed to explore screening strategies, methods employed and experience of regulatory interactions related to in vitro secondary pharmacology profiling.

RESULTS

The pharmaceutical industry routinely utilizes panels of in vitro assays to detect undesirable off-target interactions of new chemical entities that are deployed at all stages of drug discovery and early development. The formats, approaches and size of panels vary between companies, in particular i) choice of assay technology; ii) test concentration (single vs. multiple concentrations) iii) rationale for targets and panels selection (taking into account organizational experience, primary target, therapeutic area, availability at service providers) iv) threshold level for significant interaction with a target and v) data interpretation. Data are generated during the early phases of drug discovery, principally before in vivo GLP studies (i.e., hit-to-lead, lead optimization, development candidate selection) and used to contextualize in vivo non-clinical and clinical findings. Data were included in regulatory documents, and around half of respondents experienced regulatory questions about the significance of the results.

CONCLUSION

While it seems that in vitro secondary pharmacological profiling is generally considered valuable across the industry, particularly as a tool in early phases of drug discovery for small molecules, there is only loose consensus on testing paradigm, the required interpretation and suitable follow up strategies to fully understand potential risk.

Population scale retrospective analysis reveals distinctive antidepressant and anxiolytic effects of diclofenac, ketoprofen and naproxen in patients with pain

Currently approved monoamine modulating antidepressant and anxiolytic pharmaceutics fail in over one third of patients due to delayed and variable therapeutic effect, adverse reactions preceding the therapeutic action, and adherence issues. Even with adequate adherence to the regimen and tolerability, one third of the patients do not respond to any class of antidepressants. There is a strong correlation between treatment resistant depression and increase in inflammatory cytokines in plasma and cerebrospinal fluid. Furthermore, epidemiological studies suggest that depression and anxiety are commonly comorbid with pain and inflammation. While a link between pain, inflammation and depression has been suggested it remains unclear which anti-inflammatory treatment may be beneficial to patients with depression and anxiety due to pain. Here, we analyzed 430,783 FDA adverse effect reports of patients treated for pain to identify potential antidepressant and anxiolytic effects of various anti-inflammatory medications. Patients treated for depression or patients taking any known antidepressants were excluded. The odds ratio analysis of 139,072 NSAID reports revealed that ketoprofen was associated with decreased reports of depression by a factor of 2.32 (OR 0.43 and 95% Confidence Interval [0.31, 0.59]) and decreased reports of anxiety by a factor of 2.86 (OR 0.35 [0.22, 0.56]), diclofenac with decreased depression reports by a factor of 2.22 (OR 0.45 [0.40, 0.49]) and anxiety by a factor of 2.13 (OR 0.47 [0.41, 0.54]), while naproxen decreased depression reports by a factor of 1.92 (OR 0.52 [0.49, 0.57]) and anxiety by a factor of 1.23 (OR 0.81 [0.75, 0.88]). Other NSAIDs did not exhibit any noticeable antidepressant and/or anxiolytic effect.

Big Data in Drug Discovery

Interpretation of Big Data in the drug discovery community should enhance project timelines and reduce clinical attrition through improved early decision making. The issues we encounter start with the sheer volume of data and how we first ingest it before building an infrastructure to house it to make use of the data in an efficient and productive way. There are many problems associated with the data itself including general reproducibility, but often, it is the context surrounding an experiment that is critical to success. Help, in the form of artificial intelligence (AI), is required to understand and translate the context. On the back of natural language processing pipelines, AI is also used to prospectively generate new hypotheses by linking data together. We explain Big Data from the context of biology, chemistry and clinical trials, showcasing some of the impressive public domain sources and initiatives now available for interrogation.

The benefits of data mining

Careful analysis of a database populated by physicians and patients sheds new light on the side effects of drugs.