Unveiling the Burden of Drug-Induced Impulsivity: A Network Analysis of the FDA Adverse Event Reporting System.

INTRODUCTION: Impulsivity induced by dopaminergic agents, like pramipexole and aripiprazole, can lead to behavioral addictions that impact on social functioning and quality of life of patients and families (e.g., resulting in unemployment, marital problems, anxiety). These secondary effects, interconnected in networks of signs and symptoms, are usually overlooked by clinical trials, not reported in package inserts, and neglected in clinical practice. OBJECTIVE: This study explores the syndromic burden of impulsivity induced by pramipexole and aripiprazole, pinpointing key symptoms for targeted mitigation. METHODS: An event-event Information Component (IC) on the FDA Adverse Event Reporting System (FAERS) (January 2004 to March 2022) identified the syndrome of events disproportionally co-reported with impulsivity, separately for pramipexole and aripiprazole. A greedy-modularity clustering on composite network analyses (positive pointwise mutual information [PPMI], Ising, Φ) identified sub-syndromes. Bayesian network modeling highlighted possible precipitating events. RESULTS: Suspected drug-induced impulsivity was documented in 7.49% pramipexole and 4.50% aripiprazole recipients. The highest IC concerned obsessive-compulsive disorder (reporting rate = 26.77%; IC median = 3.47, 95% confidence interval [CI] = 3.33-3.57) and emotional distress (21.35%; 3.42, 3.26-3.54) for pramipexole, bankruptcy (10.58%; 4.43, 4.26-4.55) and divorce (7.59%; 4.38, 4.19-4.53) for aripiprazole. The network analysis identified delusional jealousy and dopamine dysregulation sub-syndromes for pramipexole, obesity-hypoventilation and social issues for aripiprazole. The Bayesian network highlighted anxiety and economic problems as potentially precipitating events. CONCLUSION: The under-explored consequences of drug-induced impulsivity significantly burden patients and families. Network analyses, exploring syndromic reactions and potential precipitating events, complement traditional techniques and clinical judgment. Characterizing the secondary impact of reactions will support informed patient-centered decision making.

The evolving role of disproportionality analysis in pharmacovigilance.

INTRODUCTION: From 2009 to 2015, the IMI PROTECT conducted rigorous studies addressing questions about optimal implementation and significance of disproportionality analyses, leading to the development of Good Signal Detection Practices. The ensuing period witnessed the independent exploration of research paths proposed by IMI PROTECT, accumulating valuable experience and insights that have yet to be seamlessly integrated. AREAS COVERED: This state-of-the-art review integrates IMI PROTECT recommendations with recent acquisitions and evolving challenges. It deals with defining the object of study, disproportionality methods, subgrouping, masking, drug-drug interaction, duplication, expectedness, the debated use of disproportionality results as risk measures, integration with other types of data. EXPERT OPINION: Despite the ongoing skepticism regarding the usefulness of disproportionality analyses and individual case safety reports, their ability to timely detect safety signals regarding rare and unpredictable adverse reactions remains unparalleled. Moreover, recent exploration into their potential for characterizing safety signals revealed valuable insights concerning potential risk factors and the patient's perspective. To fully realize their potential beyond hypothesis generation and achieve a comprehensive evidence synthesis with other kinds of data and studies, each with their unique limitations and contributions, we need to investigate methods for more transparently communicating disproportionality results and mapping and addressing pharmacovigilance biases.

Emerging Toxicities of Antibody-Drug Conjugates for Breast Cancer: Clinical Prioritization of Adverse Events from the FDA Adverse Event Reporting System.

BACKGROUND: Antibody-drug conjugates (ADCs) are gaining widespread use in the treatment of breast cancer, although toxicity remains an underexplored issue in the real-world clinical setting. Individual case safety reports collected in large pharmacovigilance databases can advance our knowledge on their safety profile in routine clinical practice. OBJECTIVE: We prioritized adverse events (AEs) reported with ADCs approved for breast cancer using the Food and Drug Administration Adverse Event Reporting System (FAERS). METHODS: We assessed clinical priority of AEs reported in FAERS (February 2013-March 2022) for trastuzumab emtansine (T-DM1), trastuzumab deruxtecan (T-DXd), and sacituzumab govitecan (SG) by attributing a score to each AE disproportionally reported with ADCs. Four criteria were assessed: clinical relevance, reporting rate, reported case fatality rate, and stability of disproportionality signals (consistency of the reporting odds ratio across multiple analyses using three different comparators). RESULTS: We retained 6589 reports (77.4% referring to T-DM1 as suspect), and 572 AEs generated a disproportionality signal in at least one analysis. The majority of these AEs (62%) were classified as moderate clinical priorities (e.g., interstitial lung disease with T-DXd, thrombocytopenia, peripheral neuropathy with T-DM1, febrile neutropenia, and large intestine perforation with SG). Three AEs emerged as high clinical priorities (6 points): septic shock and neutropenic colitis with SG (N = 8 and 13, with median onset 13 and 10 days, respectively), without co-reported immunosuppressive agents; and pulmonary embolism with T-DM1 (N = 31, median onset 109 days, 52% with reported metastasis). CONCLUSION: The heterogeneous spectrum of post-marketing toxicities for ADCs used in breast cancer, as emerging from the FAERS, is largely in line with preapproval evidence. Although causality cannot be proved, we call for increased awareness by oncologists on potential serious unexpected reactions, including early onset of septic shock and neutropenic colitis with SG, and late emergence of pulmonary embolism with T-DM1.

The Reporting of a Disproportionality Analysis for Drug Safety Signal Detection Using Individual Case Safety Reports in PharmacoVigilance (READUS-PV): Development and Statement.

BACKGROUND AND AIM: Disproportionality analyses using reports of suspected adverse drug reactions are the most commonly used quantitative methods for detecting safety signals in pharmacovigilance. However, their methods and results are generally poorly reported in published articles and existing guidelines do not capture the specific features of disproportionality analyses. We here describe the development of a guideline (REporting of A Disproportionality analysis for drUg Safety signal detection using individual case safety reports in PharmacoVigilance [READUS-PV]) for reporting the results of disproportionality analyses in articles and abstracts. METHODS: We established a group of 34 international experts from universities, the pharmaceutical industry, and regulatory agencies, with expertise in pharmacovigilance, disproportionality analyses, and assessment of safety signals. We followed a three-step process to develop the checklist: (1) an open-text survey to generate a first list of items; (2) an online Delphi method to select and rephrase the most important items; (3) a final online consensus meeting. RESULTS: Among the panel members, 33 experts responded to round 1 and 30 to round 2 of the Delphi and 25 participated to the consensus meeting. Overall, 60 recommendations for the main body of the manuscript and 13 recommendations for the abstracts were retained by participants after the Delphi method. After merging of some items together and the online consensus meeting, the READUS-PV guidelines comprise a checklist of 32 recommendations, in 14 items, for the reporting of disproportionality analyses in the main body text and four items, comprising 12 recommendations, for abstracts. CONCLUSIONS: The READUS-PV guidelines will support authors, editors, peer-reviewers, and users of disproportionality analyses using individual case safety report databases. Adopting these guidelines will lead to more transparent, comprehensive, and accurate reporting and interpretation of disproportionality analyses, facilitating the integration with other sources of evidence.

The REporting of A Disproportionality Analysis for DrUg Safety Signal Detection Using Individual Case Safety Reports in PharmacoVigilance (READUS-PV): Explanation and Elaboration.

In pharmacovigilance, disproportionality analyses based on individual case safety reports are widely used to detect safety signals. Unfortunately, publishing disproportionality analyses lacks specific guidelines, often leading to incomplete and ambiguous reporting, and carries the risk of incorrect conclusions when data are not placed in the correct context. The REporting of A Disproportionality analysis for drUg Safety signal detection using individual case safety reports in PharmacoVigilance (READUS-PV) statement was developed to address this issue by promoting transparent and comprehensive reporting of disproportionality studies. While the statement paper explains in greater detail the procedure followed to develop these guidelines, with this explanation paper we present the 14 items retained for READUS-PV guidelines, together with an in-depth explanation of their rationale and bullet points to illustrate their practical implementation. Our primary objective is to foster the adoption of the READUS-PV guidelines among authors, editors, peer reviewers, and readers of disproportionality analyses. Enhancing transparency, completeness, and accuracy of reporting, as well as proper interpretation of their results, READUS-PV guidelines will ultimately facilitate evidence-based decision making in pharmacovigilance.

Enhancing Transparency in Defining Studied Drugs: The Open-Source Living DiAna Dictionary for Standardizing Drug Names in the FAERS.

INTRODUCTION: In refining drug safety signals, defining the object of study is crucial. While research has explored the effect of different event definitions, drug definition is often overlooked. The US FDA Adverse Event Reporting System (FAERS) records drug names as free text, necessitating mapping to active ingredients. Although pre-mapped databases exist, the subjectivity and lack of transparency of the mapping process lead to a loss of control over the object of study. OBJECTIVE: We implemented the DiAna dictionary, systematically mapping individual free-text instances to their corresponding active ingredients and linking them to the World Health Organization Anatomical Therapeutic Chemical (WHO-ATC) classification. METHODS: We retrieved all drug names reported to the FAERS (2004-December 2022). Using existing vocabularies and string editing, we automatically mapped free text to ingredients. We manually revised the mapping and linked it to the ATC classification. RESULTS: We retrieved 18,151,842 reports, with 74,143,411 drug entries. We manually checked the first 14,832 terms, up to terms occurring over 200 times (96.88% of total drug entries), to 6282 unique active ingredients. Automatic unchecked translations extend the standardization to 346,854 terms (98.94%). The DiAna dictionary showed a higher sensitivity compared with RxNorm alone, particularly for specific drugs (e.g., rimegepant, adapalene, drospirenone, umeclidinium). The most prominent drug classes in the FAERS were immunomodulating (37.40%) and neurologic drugs (29.19%). CONCLUSION: The DiAna dictionary, as a dynamic open-source tool, provides transparency and flexibility, enabling researchers to actively shape drug definitions during the mapping phase. This empowerment enhances accuracy, reproducibility, and interpretability of results.