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The distribution of compounds between blood and brain is a very important consideration for new candidate drug molecules. In this paper, we describe the derivation of two linear discriminant analysis (LDA) models for the prediction of passive blood-brain partitioning, expressed in terms of logBB values. The models are based on computationally derived(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)
The search for new compounds with a given biological activity requires enormous effort in terms of manpower and cost. This effort arises from the large number of compounds that need to be synthesized and subsequently biologically evaluated. For this reason the pharmaceutical industry has shown great interest in theoretical methods that enable the rational(More)
Cytidine deaminase (EC, CDA), an enzyme of the pyrimidine salvage pathways, is responsible for the degradation and inactivation of several cytidine-based antitumor drugs such as cytarabine, gemcitabine, decitabine, and azacytidine. Thus, CDA inhibitors are highly sought after as compounds to be co-administered with said drugs to improve their(More)
The important and diverse biological functions of adrenergic receptors, a subclass of G protein-coupled receptors (GPCRs), have made the search for compounds that selectively stimulate or inhibit the activity of different adrenergic receptor subtypes an important area of medicinal chemistry. We previously synthesized 2-, 5-, and 6-fluoronorepinehprine (FNE)(More)
The numerical encoding of chemical structure with Topological Indices (TIs) is currently growing in importance in Medicinal Chemistry and Bioinformatics. This approach allows the rapid collection, annotation, retrieval, comparison and mining of chemical structures within large databases. TIs can subsequently be used to seek quantitative structure-activity(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)
A more nuanced approach to drug design is to use multiple drugs in combination to target interacting or complementary pathways. Drug combination treatments have shown higher efficacy, fewer side effects, and less toxicity compared to single-drug treatment. In this Review, we focus on the use of high-throughput biological measurements (genetics, transcripts,(More)
G protein-coupled receptors (GPCRs) regulate a wide range of physiological functions and hold great pharmaceutical interest. Using the β(2)-adrenergic receptor as a case study, this article explores the applicability of docking-based virtual screening to the discovery of GPCR ligands and defines methods intended to improve the screening performance. Our(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)