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Drug Mechanisms (drug + mechanism)

Distribution by Scientific Domains

Medical Sciences	75%

Selected Abstracts

Combined Use of PCA and QSAR/QSPR to Predict the Drugs Mechanism of Action.

MOLECULAR INFORMATICS, Issue 4 2009
An Application to the NCI ACAM Database
Abstract During the years the National Cancer Institute (NCI) accumulated an enormous amount of information through the application of a complex protocol of drugs screening involving several tumor cell lines, grouped into panels according to the disease class. The Anti-cancer Agent Mechanism (ACAM) database is a set of 122 compounds with anti-cancer activity and a reasonably well known mechanism of action, for which are available drug screening data that measure their ability to inhibit growth of a panel of 60 human tumor lines, explicitly designed as a training set for neural network and multivariate analysis. The aim of this work is to adapt a methodology (previously developed for the analysis of DNA minor groove binders) for the analysis of NCI ACAM database, using Principal Component Analysis (PCA) and QSAR/QSPR for the prediction of the mechanism of action of anti-cancer drugs. The entire database was splitted in a training set of 60 structures and a test set of 48 ones, and each set was expressed in form of a matrix on which further procedures were performed. Three statistical parameters were calculated: First Attempt of Prediction (FAP) expresses the percentage of correct predictions at first attempt, Total Attempt of Prediction (TAP) expresses the total percentage of correct predictions across all the three attempts, Non-Classified (NC) expresses the percentage of compounds whose mechanism of action has failed to be predicted. The predictive ability of this approach is variable, but the results obtained are generally good; using 50% Growth Inhibiting concentration (GI50) values as training data, we were able to assign a correct mechanism of action with a good degree of reliability (more than 79%). [source]

1,026 Experimental treatments in acute stroke

ANNALS OF NEUROLOGY, Issue 3 2006
Victoria E. O'Collins B.Sci
Objective Preclinical evaluation of neuroprotectants fostered high expectations of clinical efficacy. When not matched, the question arises whether experiments are poor indicators of clinical outcome or whether the best drugs were not taken forward to clinical trial. Therefore, we endeavored to contrast experimental efficacy and scope of testing of drugs used clinically and those tested only experimentally. Methods We identified neuroprotectants and reports of experimental efficacy via a systematic search. Controlled in vivo and in vitro experiments using functional or histological end points were selected for analysis. Relationships between outcome, drug mechanism, scope of testing, and clinical trial status were assessed statistically. Results There was no evidence that drugs used clinically (114 drugs) were more effective experimentally than those tested only in animal models (912 drugs), for example, improvement in focal models averaged 31.3 ± 16.7% versus 24.4 ± 32.9%, p > 0.05, respectively. Scope of testing using Stroke Therapy Academic Industry Roundtable (STAIR) criteria was highly variable, and no relationship was found between mechanism and efficacy. Interpretation The results question whether the most efficacious drugs are being selected for stroke clinical trials. This may partially explain the slow progress in developing treatments. Greater rigor in the conduct, reporting, and analysis of animal data will improve the transition of scientific advances from bench to bedside. Ann Neurol 2006 [source]

Chronic neuropathic pain: mechanisms, drug targets and measurement

FUNDAMENTAL & CLINICAL PHARMACOLOGY, Issue 2 2007
Nanna B. Finnerup
Abstract Neuropathic pain is common in many diseases or injuries of the peripheral or central nervous system, and has a substantial impact on quality of life and mood. Lesions of the nervous system may lead to potentially irreversible changes and imbalance between excitatory and inhibitory systems. Preclinical research provides several promising targets for treatment such as sodium and calcium channels, glutamate receptors, monoamines and neurotrophic factors; however, treatment is often insufficient. A mechanism-based treatment approach is suggested to improve treatment. Valid and reliable tools to assess various symptoms and signs in neuropathic pain and knowledge of drug mechanisms are prerequisites for pursuing this approach. The present review summarizes mechanisms of neuropathic pain, targets of currently used drugs, and measures used in neuropathic pain trials. [source]

Protein Kinase Target Discovery From Genome-Wide Messenger RNA Expression Profiling

MOUNT SINAI JOURNAL OF MEDICINE: A JOURNAL OF PERSONALIZED AND TRANSLATIONAL MEDICINE, Issue 4 2010
Avi Ma'ayan
Abstract Genome-wide messenger RNA profiling provides a snapshot of the global state of the cell under different experimental conditions such as diseased versus normal cellular states. However, because measurements are in the form of quantitative changes in messenger RNA levels, such experimental data does not provide direct understanding of the regulatory molecular mechanisms responsible for the observed changes. Identifying potential cell signaling regulatory mechanisms responsible for changes in gene expression under different experimental conditions or in different tissues has been the focus of many computational systems biology studies. Most popular approaches include promoter analysis, gene ontology, or pathway enrichment analysis, as well as reverse engineering of networks from messenger RNA expression data. Here we present a rational approach for identifying and ranking protein kinases that are likely responsible for observed changes in gene expression. By combining promoter analysis; data from various chromatin immunoprecipitation studies such as chromatin immunoprecipitation sequencing, chromatin immunoprecipitation coupled with paired-end ditag, and chromatin immunoprecipitation-on-chip; protein-protein interactions; and kinase-protein phosphorylation reactions collected from the literature, we can identify and rank candidate protein kinases for knock-down, or other types of functional validations, based on genome-wide changes in gene expression. We describe how protein kinase candidate identification and ranking can be made robust by cross-validation with phosphoproteomics data as well as through a literature-based text-mining approach. In conclusion, data integration can produce robust candidate rankings for understanding cell regulation through identification of protein kinases responsible for gene expression changes, and thus rapidly advancing drug target discovery and unraveling drug mechanisms of action. Mt Sinai J Med 77:345,349, 2010. © 2010 Mount Sinai School of Medicine [source]