Exploring the FDA adverse event reporting system to generate hypotheses for monitoring of disease characteristics

Polypharmacology prediction: the long road toward comprehensively anticipating small-molecule selectivity to de-risk drug discovery

INTRODUCTION

Small molecules often bind to multiple targets, a behavior termed polypharmacology. Anticipating polypharmacology is essential for drug discovery since unknown off-targets can modulate safety and efficacy - profoundly affecting drug discovery success. Unfortunately, experimental methods to assess selectivity present significant limitations and drugs still fail in the clinic due to unanticipated off-targets. Computational methods are a cost-effective, complementary approach to predict polypharmacology.

AREAS COVERED

This review aims to provide a comprehensive overview of the state of polypharmacology prediction and discuss its strengths and limitations, covering both classical cheminformatics methods and bioinformatic approaches. The authors review available data sources, paying close attention to their different coverage. The authors then discuss major algorithms grouped by the types of data that they exploit using selected examples.

EXPERT OPINION

Polypharmacology prediction has made impressive progress over the last decades and contributed to identify many off-targets. However, data incompleteness currently limits most approaches to comprehensively predict selectivity. Moreover, our limited agreement on model assessment challenges the identification of the best algorithms - which at present show modest performance in prospective real-world applications. Despite these limitations, the exponential increase of multidisciplinary Big Data and AI hold much potential to better polypharmacology prediction and de-risk drug discovery.

Computational drug repositioning identifies niclosamide and tribromsalan as inhibitors of Mycobacterium tuberculosis and Mycobacterium abscessus

Tuberculosis (TB) is still a major global health challenge, killing over 1.5 million people each year, and hence, there is a need to identify and develop novel treatments for Mycobacterium tuberculosis (M. tuberculosis). The prevalence of infections caused by nontuberculous mycobacteria (NTM) is also increasing and has overtaken TB cases in the United States and much of the developed world. Mycobacterium abscessus (M. abscessus) is one of the most frequently encountered NTM and is difficult to treat. We describe the use of drug-disease association using a semantic knowledge graph approach combined with machine learning models that has enabled the identification of several molecules for testing anti-mycobacterial activity. We established that niclosamide (M. tuberculosis IC90 2.95 μM; M. abscessus IC90 59.1 μM) and tribromsalan (M. tuberculosis IC90 76.92 μM; M. abscessus IC90 147.4 μM) inhibit M. tuberculosis and M. abscessus in vitro. To investigate the mode of action, we determined the transcriptional response of M. tuberculosis and M. abscessus to both compounds in axenic log phase, demonstrating a broad effect on gene expression that differed from known M. tuberculosis inhibitors. Both compounds elicited transcriptional responses indicative of respiratory pathway stress and the dysregulation of fatty acid metabolism.

Ocular toxicities in chimeric antigen receptor T-cell therapy: a real-world study leveraging FAERS database

Aim: The purpose of this study was to comprehensively explore the ocular toxicity associated with chimeric antigen receptor (CAR) T-cell therapy. Materials & methods: Data were assembled from the US FDA's Adverse Event Reporting System (FAERS) database from 2017 to 2023. Information component and reporting odds ratio methods were used for signal detection in total/categorized CAR T-cell therapy. Results: A total of 17 positive signals (preferred term) were detected, yet none of them were documented in the product information. Some adverse events were with death outcomes and overlapped a lot with cytokine-release syndrome. Conclusion: The ocular adverse events associated with CAR-T cell therapy are noteworthy, and it is imperative to maintain increased alertness and institute early intervention strategies.

Safety profile and signal detection of phosphodiesterase type 5 inhibitors for erectile dysfunction: a Food and Drug Administration Adverse Event Reporting System analysis

Background

Phosphodiesterase type 5 inhibitors (PDE5Is) are generally well tolerated but have been associated with uncommon and significant adverse events (AEs).

Aim

This study aims to investigate and compare the characteristics of AEs associated with PDE5Is used for erectile dysfunction and identify any safety signals in a postmarketing surveillance database between 2010 and 2021.

Methods

A descriptive analysis was conducted for all AEs reported to the Food and Drug Administration Adverse Event Reporting System for 4 PDE5Is-avanafil, sildenafil, tadalafil, and vardenafil-indicated for erectile dysfunction between January 2010 and December 2021. The frequency of the most reported AEs and outcomes were identified. A disproportionality analysis based on proportional reporting ratio (PRR) and reporting odds ratio (ROR) was conducted for the most common and clinically important AEs to identify signals to gain insights into potential differences in safety profiles.

Outcomes

The outcome measures of the study are frequency of reported AEs and outcomes following AE.

Results

A total of 29 236 AEs were reported for PDE5Is during the study period. The most reported AE was "drug ineffective" with 7115 reports (24.3%). Eight safety signals were detected across the 4 drugs. Key signals were sexual disorders (PRR, 3.13 [95% CI, 2.69-3.65]; ROR, 3.24 [95% CI, 2.77-3.79]) and death (PRR, 3.17 [2.5-4.01]; ROR, 3.211 [2.52-4.06]) for sildenafil, priapism (PRR, 3.63 [2.11-6.24]; ROR, 3.64 [2.12-6.26]) for tadalafil, and drug administration error (PRR, 2.54 [1.84-3.52]; ROR, 2.6 [1.86-3.63]) for vardenafil. The most reported outcomes were other serious events with 6685 events (67.2%) and hospitalization with 1939 events (19.5%).

Clinical Implications

The commonly reported AEs and detected signals may guide clinicians in treatment decision making for men with erectile dysfunction.

Strengths and Limitations

This is the first comprehensive report and disproportionality analysis on all types of AEs associated with PDE5Is used for erectile dysfunction in the United States. The findings should be interpreted cautiously due to limitations in the Adverse Event Reporting System, which includes self-reports, duplicate and incomplete reports, and biases in reporting and selection. Therefore, establishing a causal relationship between the reported AEs and the use of PDE5Is is uncertain, and the data may be confounded by other medications and indications.

Conclusion

PDE5Is demonstrate significantly increased risks of reporting certain clinically important AEs. While these events are not common, it is imperative to continually monitor PDE5I use at the levels of primary care to national surveillance to ensure safe utilization.

Considerations on the use of different comparators in pharmacovigilance: A methodological review

Pharmacovigilance studies based on spontaneous reporting systems use disproportionality analysis methods to identify drug-event combinations with higher-than-expected reporting. Enhanced reporting is deemed as a proxy for a detected signal and is used to generate drug safety hypotheses, which can then be tested in pharmacoepidemiologic studies or randomized controlled trials. Higher-than-expected reporting means that the reporting rate of a drug-event combination of interest is disproportionately higher than the rate in a specific comparator or reference set. Currently, it is unclear which comparator is the most appropriate for use in pharmacovigilance. Moreover, it is also unclear how the selection of a comparator may affect the directionality of the various reporting and other biases. This paper reviews commonly used comparators chosen for signal detection studies (active comparator, class-exclusion comparator, and full data reference set). We give an overview of the advantages and disadvantages of each method based on examples from the literature. We also touch upon the challenges related to the derivation of general recommendations for the selection of comparators when mining spontaneous reports for pharmacovigilance.

Exploring the association between selective serotonin reuptake inhibitors and rhabdomyolysis risk based on the FDA pharmacovigilance database

Rhabdomyolysis is a syndrome potentially fatal and has been associated with selective serotonin reuptake inhibitors (SSRIs) treatment in a few case reports. Herein, we purpose to establish the correlation between SSRIs use and rhabdomyolysis using the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS) database. We conducted an analysis on reports that were submitted to the FAERS database during the period between January 1, 2004, and December 31, 2022. Four algorithms, including reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian confidence propagation neural network (BCPNN), and empirical Bayes geometric mean (EBGM), were employed to quantify the signals of rhabdomyolysis associated with SSRIs. In total, 16,011,277 non-duplicated reports were obtained and analyzed. Among 33,574 reports related to rhabdomyolysis, SSRIs were classified as primary suspected drug in 889 cases. Disproportionality analysis identified a positive signal between rhabdomyolysis and SSRIs (ROR: 2.86, 95% CI 2.67-3.05; PRR: 2.84, χ²: 1037.16; IC_0.25 = 1.39; EBGM_0.5 = 2.64). Among six SSRIs, fluvoxamine had the strongest signal (ROR: 11.64, 95% CI 8.00-16.93; PRR: 11.38, χ²: 265.51; IC_0.25 = 2.41; EBGM_0.5 = 8.31), whereas no significant signal of rhabdomyolysis was detected for paroxetine (ROR: 1.83, 95% CI 1.55-2.15; PRR: 1.82, χ²: 53.82; IC_0.25 = 0.73; EBGM_0.5 = 1.59). After excluding cases co-administered with statins, the signal of rhabdomyolysis associated with SSRIs remains significant. Our analysis reveals that there are differences in safety signals among six SSRIs in respect to the risk of rhabdomyolysis, with fluvoxamine displaying the highest risk signal, while paroxetine did not show a significant signal. Given the potentially lethal nature of rhabdomyolysis, healthcare professionals should inform patients of the potential risk of rhabdomyolysis associated with SSRIs prior to initiating treatment.

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.

Variation in adverse drug events of opioids in the United States

Background: The United States (US) ranks high, nationally, in opioid consumption. The ongoing increase in the misuse and mortality amid the opioid epidemic has been contributing to its rising cost. The worsening health and economic impact of opioid use disorder in the US warrants further attention. We, therefore, assessed commonly prescribed opioids to determine the opioids that were over-represented versus under-represented for adverse drug events (ADEs) to better understand their distribution patterns using the Food and Drug Administration's Adverse Event Reporting System (FAERS) while correcting for distribution using the Drug Enforcement Administration's Automation of Reports and Consolidated Orders System (ARCOS). Comparing the ratio of the percentage of adverse drug events as reported by the FAERS relative to the percentage of distribution as reported by the ARCOS database is a novel approach to evaluate post-marketing safety surveillance and may inform healthcare policies and providers to better regulate the use of these opioids. Methods: We analyzed the adverse events for 11 prescription opioids, when correcting for distribution, and their ratios for three periods, 2006-2010, 2011-2016, and 2017-2021, in the US. The opioids include buprenorphine, codeine, fentanyl, hydrocodone, hydromorphone, meperidine, methadone, morphine, oxycodone, oxymorphone, and tapentadol. Oral morphine milligram equivalents (MMEs) were calculated by conversions relative to morphine. The relative ADEs of the selected opioids, opioid distributions, and ADEs relative to distribution ratios were analyzed for the 11 opioids. Results: Oxycodone, fentanyl, and morphine accounted for over half of the total number of ADEs (n = 667,969), while meperidine accounted for less than 1%. Opioid distributions were relatively constant over time, with methadone repeatedly accounting for the largest proportions. Many ADE-to-opioid distribution ratios increased over time, with meperidine (60.6), oxymorphone (11.1), tapentadol (10.3), and hydromorphone (7.9) being the most over-represented for ADEs in the most recent period. Methadone was under-represented (<0.20) in all the three periods. Conclusion: The use of the FAERS with the ARCOS provides insights into dynamic changes in ADEs of the selected opioids in the US. There is further need to monitor and address the ADEs of these drugs.

Current status and future directions for a neurotoxicity hazard assessment framework that integrates in silico approaches

Neurotoxicology is the study of adverse effects on the structure or function of the developing or mature adult nervous system following exposure to chemical, biological, or physical agents. The development of more informative alternative methods to assess developmental (DNT) and adult (NT) neurotoxicity induced by xenobiotics is critically needed. The use of such alternative methods including in silico approaches that predict DNT or NT from chemical structure (e.g., statistical-based and expert rule-based systems) is ideally based on a comprehensive understanding of the relevant biological mechanisms. This paper discusses known mechanisms alongside the current state of the art in DNT/NT testing. In silico approaches available today that support the assessment of neurotoxicity based on knowledge of chemical structure are reviewed, and a conceptual framework for the integration of in silico methods with experimental information is presented. Establishing this framework is essential for the development of protocols, namely standardized approaches, to ensure that assessments of NT and DNT based on chemical structures are generated in a transparent, consistent, and defendable manner.

Drug Discovery of Spinal Muscular Atrophy (SMA) from the Computational Perspective: A Comprehensive Review

Spinal muscular atrophy (SMA), one of the leading inherited causes of child mortality, is a rare neuromuscular disease arising from loss-of-function mutations of the survival motor neuron 1 (SMN1) gene, which encodes the SMN protein. When lacking the SMN protein in neurons, patients suffer from muscle weakness and atrophy, and in the severe cases, respiratory failure and death. Several therapeutic approaches show promise with human testing and three medications have been approved by the U.S. Food and Drug Administration (FDA) to date. Despite the shown promise of these approved therapies, there are some crucial limitations, one of the most important being the cost. The FDA-approved drugs are high-priced and are shortlisted among the most expensive treatments in the world. The price is still far beyond affordable and may serve as a burden for patients. The blooming of the biomedical data and advancement of computational approaches have opened new possibilities for SMA therapeutic development. This article highlights the present status of computationally aided approaches, including in silico drug repurposing, network driven drug discovery as well as artificial intelligence (AI)-assisted drug discovery, and discusses the future prospects.

Retrospective analysis of adverse events associated with non-stimulant ADHD medications reported to the united states food and drug administration

Attention deficit hyperactivity disorder (ADHD) is one of the most common neurobehavioral disorders in children and although stimulant medications remain first line to treat the disorder, some families prefer nonstimulants. The goal is to analyze the adverse events (AE) associated with nonstimulant medications using post-marketing drug surveillance data. We aim to increase awareness and aide patient education. A retrospective study of adverse drug events with atomoxetine, clonidine, and guanfacine was performed using the Federal Drug Administration Adverse Event Reporting System (FAERS) Database. Results show that the most commonly reported AEs, as defined by FAERS, were ineffectiveness (9.91-14.15%) fatigue (8.93%), and somnolence (8.8-10.16%). Of those taking atomoxetine, suicidal and self-injurious ideation was reported to a similar degree amongst all age groups. Suicidal ideation was listed within the top 20 most reported AEs for all three medications. It is more likely that some patients will experience milder side effects. We suggest providing these data to patients to help overcome the stigma of starting medication, especially if they prefer nonstimulants. Serious AEs are still reported to a small degree, thus monitoring and consistent patient education remains important. We also recommend educating a wider demographic of patients about recognizing potential development of suicidal thoughts.

A Pharmacovigilance Study of Adverse Drug Reactions Reported for Cardiovascular Disease Medications Approved Between 2012 and 2017 in the United States Food and Drug Administration Adverse Event Reporting System (FAERS) Database

PURPOSE

Between 2012 and 2017, the FDA approved 29 therapies for a cardiovascular disease (CVD) indication. Due to the limited literature on patient safety outcomes for recently approved CVD medications, this study investigated adverse drug reports (ADRs) reported in the FDA Adverse Event Reporting System (FAERS).

METHODS

A disproportionality analysis of spontaneously reported ADR was conducted. Reports in FAERS from Quarter 1, 2012, through Quarter 1, 2019, were compiled, allowing a 2-year buffer following drug approval in 2017. Top 10 reported ADRs and reporting odds ratios (ROR; confidence interval (CI)), a measure of disproportionality, were analyzed and compared to drugs available prior to 2012 as appropriate.

RESULTS

Of 7,952,147 ADR reports, 95,016 (1.19%) consisted of reports for newly approved CVD medications. For oral anticoagulants, apixaban had significantly lower reports for anemia and renal failure compared to dabigatran and rivaroxaban but greater reports for neurological signs/symptoms, and arrhythmias. Evaluating heart failure drugs, sacubitril/valsartan had greater reports for acute kidney injury, coughing, potassium imbalances, and renal impairment but notably, lower for angioedema compared to lisinopril. Assessing familial hypercholesterolemia drugs, alirocumab had greater reports for joint-related-signs/symptoms compared to other agents in this category. A newer pulmonary arterial hypertension treatment, selexipag, had greater reports of reporting for bone/joint-related-signs/symptoms but riociguat had greater reports for hemorrhages and vascular hypotension.

CONCLUSION

Pharmacovigilance studies allow an essential opportunity to evaluate the safety profile of CVD medications in clinical practice. Additional research is needed to evaluate these reported safety concerns for recently approved CVD medications.

Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches

As expenditure on drug development increases exponentially, the overall drug discovery process requires a sustainable revolution. Since artificial intelligence (AI) is leading the fourth industrial revolution, AI can be considered as a viable solution for unstable drug research and development. Generally, AI is applied to fields with sufficient data such as computer vision and natural language processing, but there are many efforts to revolutionize the existing drug discovery process by applying AI. This review provides a comprehensive, organized summary of the recent research trends in AI-guided drug discovery process including target identification, hit identification, ADMET prediction, lead optimization, and drug repositioning. The main data sources in each field are also summarized in this review. In addition, an in-depth analysis of the remaining challenges and limitations will be provided, and proposals for promising future directions in each of the aforementioned areas.

A compound attributes-based predictive model for drug induced liver injury in humans

Drug induced liver injury (DILI) is one of the key safety concerns in drug development. To assess the likelihood of drug candidates with potential adverse reactions of liver, we propose a compound attributes-based approach to predicting hepatobiliary disorders that are routinely reported to US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). Specifically, we developed a support vector machine (SVM) model with recursive feature extraction, based on physicochemical and structural properties of compounds as model input. Cross validation demonstrates that the predictive model has a robust performance with averaged 70% of both sensitivity and specificity over 500 trials. An independent validation was performed on public benchmark drugs and the results suggest potential utility of our model for identifying safety alerts. This in silico approach, upon further validation, would ultimately be implemented, together with other in vitro safety assays, for screening compounds early in drug development.

Systems biology based drug repositioning for development of cancer therapy

Drug repositioning is a powerful method that can assists the conventional drug discovery process by using existing drugs for treatment of a disease rather than its original indication. The first examples of repurposed drugs were discovered serendipitously, however data accumulated by high-throughput screenings and advancements in computational biology methods have paved the way for rational drug repositioning methods. As chemotherapeutic agents have notorious side effects that significantly reduce quality of life, drug repositioning promises repurposed noncancer drugs with little or tolerable adverse effects for cancer patients. Here, we review current drug-related data types and databases including some examples of web-based drug repositioning tools. Next, we describe systems biology approaches to be used in drug repositioning for effective cancer therapy. Finally, we highlight examples of mostly repurposed drugs for cancer treatment and provide an overview of future expectations in the field for development of effective treatment strategies.

Risk of bronchospasm and coronary arteriospasm with sugammadex use: a post marketing analysis

Introduction:

Sugammadex is used for the reversal of neuromuscular blockade caused by rocuronium bromide and vecuronium bromide. As part of the post licensing phase of drug development, adverse events related to the use of sugammadex are still being uncovered and being reported. The potential association between sugammadex and adverse events bronchospasm and coronary arteriospasm using a retrospective pharmacovigilance signal analysis was carried out.

Methods:

Food and Drug Administration’s Adverse Event Reporting System database was used to run disproportionality analyses to investigate the potential association of sugammadex with bronchospasm or coronary arteriospasm. In this analysis we report the adverse event signal using frequentist methods of Relative reporting ratio (RRR), proportional reporting ratio (PRR), reporting odds ratio (ROR) and the Bayesian based Information Component metric.

Results:

A statistically significant disproportionality signal is found between sugammadex and bronchospasm (n = 44; chi-squared = 2993.87; PRR = 71.95 [95% CI: 54.00–95.85]) and sugammadex and coronary arteriospasm (n = 6; chi-squared = 209.39; PRR = 43.82 [95% CI: 19.73–97.33]) as per Evans criteria. Both statistically significant disproportionality signals persisted when stratified by gender. Based upon dynamic cumulative PRR graph, the PRR value has steadily increased and the 95% CI narrowed since December 2012.

Conclusion:

The results of the pharmacovigilance analysis highlight a statistically significant disproportionality signal between sugammadex usage and bronchospasm and coronary arteriospasm adverse events. Physicians need to be aware of these adverse events when using sugammadex. The results of the pharmacovigilance signal analysis highlight a statistically significant disproportionality signal between sugammadex usage and bronchospasm and coronary arteriospasm adverse events. Physicians need to be aware of these adverse events when using sugammadex.

In Silico Toxicology Data Resources to Support Read-Across and (Q)SAR

A plethora of databases exist online that can assist in in silico chemical or drug safety assessment. However, a systematic review and grouping of databases, based on purpose and information content, consolidated in a single source, has been lacking. To resolve this issue, this review provides a comprehensive listing of the key in silico data resources relevant to: chemical identity and properties, drug action, toxicology (including nano-material toxicity), exposure, omics, pathways, Absorption, Distribution, Metabolism and Elimination (ADME) properties, clinical trials, pharmacovigilance, patents-related databases, biological (genes, enzymes, proteins, other macromolecules etc.) databases, protein-protein interactions (PPIs), environmental exposure related, and finally databases relating to animal alternatives in support of 3Rs policies. More than nine hundred databases were identified and reviewed against criteria relating to accessibility, data coverage, interoperability or application programming interface (API), appropriate identifiers, types of in vitro, in vivo,-clinical or other data recorded and suitability for modelling, read-across, or similarity searching. This review also specifically addresses the need for solutions for mapping and integration of databases into a common platform for better translatability of preclinical data to clinical data.

Extended Multitarget Pharmacology of Anticancer Drugs

Multitarget pharmacology of small-molecule cancer drugs significantly contributes to their mechanism of action, side effects, and emergence of drug resistance and opens ways to repurpose, combine, or customize drug therapy. In most cases, the set of targets affected at therapeutic concentrations is not fully characterized and/or the interaction efficacy values are not accurately quantified. We collected information about multiple targets for each cancer drug along with their experimental effective concentrations or binding activities from multiple sources. All multitarget activity values for each drug then were used to build two proximity network pharmacology maps of anticancer drugs and targets of those drugs, respectively. Together with the network map, we showed that the majority of the cancer drugs had substantial multitarget pharmacology based on our current knowledge. In addition, most of the cancer drugs simultaneously affect macromolecular targets from different classes and types. The target subset can further be accentuated and personalized by patient sample-specific expression data. The network maps of cancer drugs and targets as well as all quantified activity data were integrated into a freely available database, CancerDrugMap (http://ruben.ucsd.edu/dnet/maps/drugnet.html). The identified multitarget pharmacology of cancer drugs is essential for improving the efficacy of individually prescribed drugs and drug combinations and minimization of adverse effects.

Polycystic ovary syndrome (PCOS), an inflammatory, systemic, lifestyle endocrinopathy

Polycystic ovary syndrome (PCOS) is an endocrine disorder, afflicting females of reproductive age. This syndrome leads to infertility, insulin resistance, obesity, and cardiovascular problems, including a litany of other health issues. PCOS is a polygenic, polyfactorial, systemic, inflammatory, dysregulated steroid state, autoimmune disease, manifesting largely due to lifestyle errors. The advent of biochemical tests and ultrasound scanning has enabled the detection of PCOS in the affected females. Subsequently, a huge amount of insight on PCOS has been garnered in recent times. Interventions like oral contraceptive pills, metformin, and hormone therapy have been developed to bypass or reverse the ill effects of PCOS. However, lifestyle correction to prevent aberrant immune activation and to minimize the exposure to inflammatory agents, appears to be the sustainable therapy of PCOS. This holistic review with multiple hypotheses might facilitate to devise better PCOS management approaches.

Sources of Safety Data and Statistical Strategies for Design and Analysis: Postmarket Surveillance

BACKGROUND

Safety data are continuously evaluated throughout the life cycle of a medical product to accurately assess and characterize the risks associated with the product. The knowledge about a medical product's safety profile continually evolves as safety data accumulate.

METHODS

This paper discusses data sources and analysis considerations for safety signal detection after a medical product is approved for marketing. This manuscript is the second in a series of papers from the American Statistical Association Biopharmaceutical Section Safety Working Group.

RESULTS

We share our recommendations for the statistical and graphical methodologies necessary to appropriately analyze, report, and interpret safety outcomes, and we discuss the advantages and disadvantages of safety data obtained from passive postmarketing surveillance systems compared to other sources.

CONCLUSIONS

Signal detection has traditionally relied on spontaneous reporting databases that have been available worldwide for decades. However, current regulatory guidelines and ease of reporting have increased the size of these databases exponentially over the last few years. With such large databases, data-mining tools using disproportionality analysis and helpful graphics are often used to detect potential signals. Although the data sources have many limitations, analyses of these data have been successful at identifying safety signals postmarketing. Experience analyzing these dynamic data is useful in understanding the potential and limitations of analyses with new data sources such as social media, claims, or electronic medical records data.

Relevance of the Weber effect in contemporary pharmacovigilance of oncology drugs

Background

Numerous reporting biases have been known to affect spontaneous reporting databases. The Weber effect, which constitutes a peak in adverse event (AE) reporting of a drug at the end of second year after regulatory approval followed by a continuous decline thereafter, has been considered an important bias for a long time. The existence of this bias in AE reporting of oncology drugs remains an underevaluated area, prompting a targeted examination.

Methods

The US Food and Drug Administration (USFDA) Adverse Event Reporting System (FAERS) was studied for AE reporting patterns of 5 years of 15 new molecular entities (NMEs) and biologics used in oncology. This 5-year period started from the USFDA date of approval for the NMEs and biologics. The number of AEs reported for each of the drugs was plotted against time (years). The AE reporting patterns were specifically examined for the existence of the Weber effect. In addition, AE reporting rate patterns of 5 years of seven NMEs and biologics used in oncology were examined.

Results

A total of 50,630 AE reports were logged in to the FAERS for all 15 drugs examined for AE reporting patterns. We observed five distinct AE reporting patterns for 15 drugs; however, none of the AE patterns were identical to the Weber effect. We did not observe a consistent AE reporting rate pattern for the seven drugs examined for AE reporting rates. With the exception of one drug (cetuximab), none of the drugs exhibited a second-year peak in AE reporting rates. This peak was not followed by continuous decline in AE reporting rate thereafter.

Conclusion

This study does not support the existence of the Weber effect in AE reporting of oncology drugs. The contemporary AE reporting of oncology drugs does not exhibit a consistent pattern.

Big Data Transforms Discovery-Utilization Therapeutics Continuum

Enabling omic technologies adopt a holistic view to produce unprecedented insights into the molecular underpinnings of health and disease, in part, by generating massive high-dimensional biological data. Leveraging these systems-level insights as an engine driving the healthcare evolution is maximized through integration with medical, demographic, and environmental datasets from individuals to populations. Big data analytics has accordingly emerged to add value to the technical aspects of storage, transfer, and analysis required for merging vast arrays of omic-, clinical-, and eco-datasets. In turn, this new field at the interface of biology, medicine, and information science is systematically transforming modern therapeutics across discovery, development, regulation, and utilization.

Drugs and Diseases Interacting with Cigarette Smoking in US Prescription Drug Labelling

The US Food and Drug Administration (FDA) draft guidance for industry on drug interaction studies recommends, but does not mandate, that both cigarette smokers and non-smokers can be used to study drug metabolism in clinical trials, and that important results related to smoking should be included in drug labelling to guide medication usage. This study aimed to provide a comprehensive review of drugs or diseases interacting with smoking, as presented in all US drug labelling. The 62,857 drug labels deposited in the FDA Online Label Repository were searched using the keywords 'smoke', 'smoker(s)', 'smoking', 'tobacco' and 'cigarette(s)' on 19 June 2014. The resultant records were refined to include only human prescription drug labelling, for manual examination. For 188 single-active-ingredient drugs and 36 multiple-active-ingredient drugs, the labelling was found to contain smoking-related information. The pharmacokinetics of 29 and 21 single-active-ingredient drugs were affected and unaffected, respectively, by smoking. For the remaining drugs, the provided information related to smoking affecting efficacy, safety or diseases but not pharmacokinetics. Depending on the nature of specific drugs, the perturbation in pharmacokinetic parameters in smokers ranged from 13 to 500%, in comparison with non-smokers. Dosage modifications in smokers are necessary for four drugs and may be necessary for six drugs, but are unnecessary for seven drugs although the pharmacokinetic parameters of four of them are affected by smoking. Cigarette smoking is a risk factor for 16 types of diseases or adverse drug reactions. For one single-active-ingredient contraceptive drug and 10 multiple-active-ingredient contraceptive drugs, a black box warning (the FDA's strongest safety warning) is included in the labelling for increased risks of heart attacks and strokes in female smokers, and "women are strongly advised not to smoke" when using these drugs. This study presents the first comprehensive overview of cigarette smoking interacting with drugs and/or diseases, as presented in US drug labelling.

Mining hidden knowledge for drug safety assessment: topic modeling of LiverTox as a case study

Background

Given the significant impact on public health and drug development, drug safety has been a focal point and research emphasis across multiple disciplines in addition to scientific investigation, including consumer advocates, drug developers and regulators. Such a concern and effort has led numerous databases with drug safety information available in the public domain and the majority of them contain substantial textual data. Text mining offers an opportunity to leverage the hidden knowledge within these textual data for the enhanced understanding of drug safety and thus improving public health.

Methods

In this proof-of-concept study, topic modeling, an unsupervised text mining approach, was performed on the LiverTox database developed by National Institutes of Health (NIH). The LiverTox structured one document per drug that contains multiple sections summarizing clinical information on drug-induced liver injury (DILI). We hypothesized that these documents might contain specific textual patterns that could be used to address key DILI issues. We placed the study on drug-induced acute liver failure (ALF) which was a severe form of DILI with limited treatment options.

Results

After topic modeling of the "Hepatotoxicity" sections of the LiverTox across 478 drug documents, we identified a hidden topic relevant to Hy's law that was a widely-accepted rule incriminating drugs with high risk of causing ALF in humans. Using this topic, a total of 127 drugs were further implicated, 77 of which had clear ALF relevant terms in the "Outcome and management" sections of the LiverTox. For the rest of 50 drugs, evidence supporting risk of ALF was found for 42 drugs from other public databases.

Conclusion

In this case study, the knowledge buried in the textual data was extracted for identification of drugs with potential of causing ALF by applying topic modeling to the LiverTox database. The knowledge further guided identification of drugs with the similar potential and most of them could be verified and confirmed. This study highlights the utility of topic modeling to leverage information within textual drug safety databases, which provides new opportunities in the big data era to assess drug safety.