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Early Drug Discovery (early + drug_discovery)
Selected AbstractsAssessment of the sensitivity of the computational programs DEREK, TOPKAT, and MCASE in the prediction of the genotoxicity of pharmaceutical moleculesENVIRONMENTAL AND MOLECULAR MUTAGENESIS, Issue 3 2004Ronald D. Snyder Abstract Computational models are currently being used by regulatory agencies and within the pharmaceutical industry to predict the mutagenic potential of new chemical entities. These models rely heavily, although not exclusively, on bacterial mutagenicity data of nonpharmaceutical-type molecules as the primary knowledge base. To what extent, if any, this has limited the ability of these programs to predict genotoxicity of pharmaceuticals is not clear. In order to address this question, a panel of 394 marketed pharmaceuticals with Ames Salmonella reversion assay and other genetic toxicology findings was extracted from the 2000,2002 Physicians' Desk Reference and evaluated using MCASE, TOPKAT, and DEREK, the three most commonly used computational databases. These evaluations indicate a generally poor sensitivity of all systems for predicting Ames positivity (43.4,51.9% sensitivity) and even poorer sensitivity in prediction of other genotoxicities (e.g., in vitro cytogenetics positive; 21.3,31.9%). As might be expected, all three programs were more highly predictive for molecules containing carcinogenicity structural alerts (i.e., the so-called Ashby alerts; 61% ± 14% sensitivity) than for those without such alerts (12% ± 6% sensitivity). Taking all genotoxicity assay findings into consideration, there were 84 instances in which positive genotoxicity results could not be explained in terms of structural alerts, suggesting the possibility of alternative mechanisms of genotoxicity not relating to covalent drug-DNA interaction. These observations suggest that the current computational systems when applied in a traditional global sense do not provide sufficient predictivity of bacterial mutagenicity (and are even less accurate at predicting genotoxicity in tests other than the Salmonella reversion assay) to be of significant value in routine drug safety applications. This relative inability of all three programs to predict the genotoxicity of drugs not carrying obvious DNA-reactive moieties is discussed with respect to the nature of the drugs whose positive responses were not predicted and to expectations of improving the predictivity of these programs. Limitations are primarily a consequence of incomplete understanding of the fundamental genotoxic mechanisms of nonstructurally alerting drugs rather than inherent deficiencies in the computational programs. Irrespective of their predictive power, however, these programs are valuable repositories of structure-activity relationship mutagenicity data that can be useful in directing chemical synthesis in early drug discovery. Environ. Mol. Mutagen. 43:143,158, 2004. © 2004 Wiley-Liss, Inc. [source] Simulation modelling of human intestinal absorption using Caco-2 permeability and kinetic solubility data for early drug discoveryJOURNAL OF PHARMACEUTICAL SCIENCES, Issue 10 2008Simon Thomas Abstract Measurement of permeation across a monolayer of the human adenocarcinoma cell line, Caco-2, is a popular surrogate for a compound's permeation across the human intestinal epithelium. Taken alone, however, Caco-2 permeability has certain limitations in the prediction of the extent of absorption of an orally-administered compound, because it does not take into account confounding factors such as solubility and dissolution in the gastrointestinal (GI) tract fluids. A simulation model is described that uses Caco-2 permeability measured in the apical to basolateral direction plus kinetic solubility in buffered solution (both measured at pH 7.4) to predict human intestinal absorption. The model features novel treatment of time-varying fluid volume in the GI tract, as a consequence of secretions into, and absorption of fluid from, the upper part of the GI tract. The model has been trained and cross-validated with data for 120 combinations of compound and dose. It has superior predictive power to recently published simulation and quantitative structure property relationship models, and is suitable for high-throughput screening during lead identification and lead optimisation in drug discovery. © 2008 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 97:4557,4574, 2008 [source] Analytical strategies for identifying drug metabolitesMASS SPECTROMETRY REVIEWS, Issue 3 2007Chandra Prakash Abstract With the dramatic increase in the number of new chemical entities (NCEs) arising from combinatorial chemistry and modern high-throughput bioassays, novel bioanalytical techniques are required for the rapid determination of the metabolic stability and metabolites of these NCEs. Knowledge of the metabolic site(s) of the NCEs in early drug discovery is essential for selecting compounds with favorable pharmacokinetic credentials and aiding medicinal chemists in modifying metabolic "soft spots". In development, elucidation of biotransformation pathways of a drug candidate by identifying its circulatory and excretory metabolites is vitally important to understand its physiological effects. Mass spectrometry (MS) and nuclear magnetic resonance (NMR) have played an invaluable role in the structural characterization and quantification of drug metabolites. Indeed, liquid chromatography (LC) coupled with atmospheric pressure ionization (API) MS has now become the most powerful tool for the rapid detection, structure elucidation, and quantification of drug-derived material within various biological fluids. Often, however, MS alone is insufficient to identify the exact position of oxidation, to differentiate isomers, or to provide the precise structure of unusual and/or unstable metabolites. In addition, an excess of endogenous material in biological samples often suppress the ionization of drug-related material complicating metabolite identification by MS. In these cases, multiple analytical and wet chemistry techniques, such as LC-NMR, enzymatic hydrolysis, chemical derivatization, and hydrogen/deuterium-exchange (H/D-exchange) combined with MS are used to characterize the novel and isomeric metabolites of drug candidates. This review describes sample preparation and introduction strategies to minimize ion suppression by biological matrices for metabolite identification studies, the application of various LC-tandem MS (LC-MS/MS) techniques for the rapid quantification and identification of drug metabolites, and future trends in this field. © 2007 Wiley Periodicals, Inc., Mass Spec Rev [source] High-throughput metabolic stability studies in drug discovery by orthogonal acceleration time-of-flight (OATOF) with analogue-to-digital signal capture (ADC)RAPID COMMUNICATIONS IN MASS SPECTROMETRY, Issue 12 2010David G. Temesi Screening assays capable of performing quantitative analysis on hundreds of compounds per week are used to measure metabolic stability during early drug discovery. Modern orthogonal acceleration time-of-flight (OATOF) mass spectrometers equipped with analogue-to-digital signal capture (ADC) now offer performance levels suitable for many applications normally supported by triple quadruple instruments operated in multiple reaction monitoring (MRM) mode. Herein the merits of MRM and OATOF with ADC detection are compared for more than 1000 compounds screened in rat and/or cryopreserved human hepatocytes over a period of 3 months. Statistical comparison of a structurally diverse subset indicated good agreement for the two detection methods. The overall success rate was higher using OATOF detection and data acquisition time was reduced by around 20%. Targeted metabolites of diazepam were detected in samples from a CLint determination performed at 1,µM. Data acquisition by positive and negative ion mode switching can be achieved on high-performance liquid chromatography (HPLC) peak widths as narrow as 0.2,min (at base), thus enabling a more comprehensive first pass analysis with fast HPLC gradients. Unfortunately, most existing OATOF instruments lack the software tools necessary to rapidly convert the huge amounts of raw data into quantified results. Software with functionality similar to open access triple quadrupole systems is needed for OATOF to truly compete in a high-throughput screening environment. Copyright © 2010 John Wiley & Sons, Ltd. [source] High throughput analysis for early drug discovery, edited by J. N. Kyranos.BIOMEDICAL CHROMATOGRAPHY, Issue 7 2005116pp, Amsterdam, Elsevier science, ISBN: 0-12-431165-2., US$ 160.00, £100.00 No abstract is available for this article. [source] | ||||||||||