Santiago Vilar

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BACKGROUND Adverse drug events (ADE) cause considerable harm to patients, and consequently their detection is critical for patient safety. The US Food and Drug Administration maintains an adverse event reporting system (AERS) to facilitate the detection of ADE in drugs. Various data mining approaches have been developed that use AERS to detect signals(More)
OBJECTIVE Data-mining algorithms that can produce accurate signals of potentially novel adverse drug reactions (ADRs) are a central component of pharmacovigilance. We propose a signal-detection strategy that combines the adverse event reporting system (AERS) of the Food and Drug Administration and electronic health records (EHRs) by requiring signaling in(More)
BACKGROUND Drug-drug interactions (DDIs) are responsible for many serious adverse events; their detection is crucial for patient safety but is very challenging. Currently, the US Food and Drug Administration and pharmaceutical companies are showing great interest in the development of improved tools for identifying DDIs. METHODS We present a new(More)
OBJECTIVE Electronic health records (EHRs) contain information to detect adverse drug reactions (ADRs), as they contain comprehensive clinical information. A major challenge of using comprehensive information involves confounding. We propose a novel data-driven method to identify ADR signals accurately by adjusting for confounders. MATERIALS AND METHODS(More)
Drug-drug interactions (DDIs) constitute an important problem in postmarketing pharmacovigilance and in the development of new drugs. The effectiveness or toxicity of a medication could be affected by the co-administration of other drugs that share pharmacokinetic or pharmacodynamic pathways. For this reason, a great effort is being made to develop new(More)
BACKGROUND Adverse drug events (ADEs) detection and assessment is at the center of pharmacovigilance. Data mining of systems, such as FDA's Adverse Event Reporting System (AERS) and more recently, Electronic Health Records (EHRs), can aid in the automatic detection and analysis of ADEs. Although different data mining approaches have been shown to be(More)
Activation of G protein-coupled receptors (GPCRs) is a complex phenomenon. Here, we applied Induced Fit Docking (IFD) in tandem with linear discriminant analysis (LDA) to generate hypotheses on the conformational changes induced to the β(2)-adrenergic receptor by agonist binding, preliminary to the sequence of events that characterize activation of the(More)
Small molecule drugs are the foundation of modern medical practice, yet their use is limited by the onset of unexpected and severe adverse events (AEs). Regulatory agencies rely on postmarketing surveillance to monitor safety once drugs are approved for clinical use. Despite advances in pharmacovigilance methods that address issues of confounding bias,(More)
Although potential drug-drug interactions (PDDIs) are a significant source of preventable drug-related harm, there is currently no single complete source of PDDI information. In the current study, all publically available sources of PDDI information that could be identified using a comprehensive and broad search were combined into a single dataset. The(More)