Detection algorithms and attentive points of safety signal using spontaneous reporting systems as a clinical data source
Y Noguchi, T Tachi, H Teramachi - Briefings in bioinformatics, 2021 - academic.oup.com
Continuous evaluation of drug safety is needed following approval to determine adverse
events (AEs) in patient populations with diverse backgrounds. Spontaneous reporting …
events (AEs) in patient populations with diverse backgrounds. Spontaneous reporting …
Lessons to be learnt from real-world studies on immune-related adverse events with checkpoint inhibitors: a clinical perspective from pharmacovigilance
E Raschi, M Gatti, F Gelsomino, A Ardizzoni… - Targeted Oncology, 2020 - Springer
The advent of immune checkpoint inhibitors (ICIs) caused a paradigm shift both in drug
development and clinical practice; however, by virtue of their mechanism of action, the …
development and clinical practice; however, by virtue of their mechanism of action, the …
Incidence of venous and arterial thromboembolic events reported in the tofacitinib rheumatoid arthritis, psoriasis and psoriatic arthritis development programmes and …
P Mease, C Charles-Schoeman, S Cohen… - Annals of the …, 2020 - ard.bmj.com
Objectives Tofacitinib is a Janus kinase inhibitor for the treatment of rheumatoid arthritis
(RA), psoriatic arthritis (PsA) and ulcerative colitis, and has been investigated in psoriasis …
(RA), psoriatic arthritis (PsA) and ulcerative colitis, and has been investigated in psoriasis …
Text mining for adverse drug events: the promise, challenges, and state of the art
Text mining is the computational process of extracting meaningful information from large
amounts of unstructured text. It is emerging as a tool to leverage underutilized data sources …
amounts of unstructured text. It is emerging as a tool to leverage underutilized data sources …
Predicting adverse drug reactions through interpretable deep learning framework
Abstract Background Adverse drug reactions (ADRs) are unintended and harmful reactions
caused by normal uses of drugs. Predicting and preventing ADRs in the early stage of the …
caused by normal uses of drugs. Predicting and preventing ADRs in the early stage of the …
The coming of age of AI/ML in drug discovery, development, clinical testing, and manufacturing: The FDA Perspectives
SK Niazi - Drug Design, Development and Therapy, 2023 - Taylor & Francis
Artificial intelligence (AI) and machine learning (ML) represent significant advancements in
computing, building on technologies that humanity has developed over millions of years …
computing, building on technologies that humanity has developed over millions of years …
Cyclin-dependent kinase 4/6 inhibitors and interstitial lung disease in the FDA adverse event reporting system: a pharmacovigilance assessment
E Raschi, M Fusaroli, A Ardizzoni, E Poluzzi… - Breast Cancer Research …, 2021 - Springer
Purpose We assessed pulmonary toxicity of cyclin-dependent kinase (CDK) 4/6 inhibitors by
analyzing the publicly available FDA Adverse Event Reporting System (FAERS). Methods …
analyzing the publicly available FDA Adverse Event Reporting System (FAERS). Methods …
Detection of drug–drug interactions through data mining studies using clinical sources, scientific literature and social media
S Vilar, C Friedman, G Hripcsak - Briefings in bioinformatics, 2018 - academic.oup.com
Drug–drug interactions (DDIs) constitute an important concern in drug development and
postmarketing pharmacovigilance. They are considered the cause of many adverse drug …
postmarketing pharmacovigilance. They are considered the cause of many adverse drug …
Disproportionality analysis for pharmacovigilance signal detection in small databases or subsets: recommendations for limiting false-positive associations
O Caster, Y Aoki, LM Gattepaille, B Grundmark - Drug Safety, 2020 - Springer
Introduction Uncovering safety signals through the collection and assessment of individual
case reports remains a core pharmacovigilance activity. Despite the widespread use of …
case reports remains a core pharmacovigilance activity. Despite the widespread use of …
[HTML][HTML] Application of artificial intelligence and machine learning in early detection of adverse drug reactions (ADRs) and drug-induced toxicity
S Yang, S Kar - Artificial Intelligence Chemistry, 2023 - Elsevier
Adverse drug reactions (ADRs) and drug-induced toxicity are major challenges in drug
discovery, threatening patient safety and dramatically increasing healthcare expenditures …
discovery, threatening patient safety and dramatically increasing healthcare expenditures …