1,474 research outputs found

    Refinement of Computational Access to Molecular Physicochemical Properties: From Ro5 to bRo5

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    [Image: see text] There is a need of computational tools to rank bRo5 drug candidates in the very early phases of drug discovery when chemical matter is unavailable. In this study, we selected three compounds: (a) a Ro5 drug (Pomalidomide), (b) a bRo5 orally available drug (Saquinavir), and (c) a polar PROTAC (CMP 98) to focus on computational access to physicochemical properties. To provide a benchmark, the three compounds were first experimentally characterized for their lipophilicity, polarity, IMHBs, and chameleonicity. To reproduce the experimental information content, we generated conformer ensembles with conformational sampling and molecular dynamics in both water and nonpolar solvents. Then we calculated Rgyr, 3D PSA, and IMHB number. An innovative pool of strategies for data analysis was then provided. Overall, we report a contribution to close the gap between experimental and computational methods for characterizing bRo5 physicochemical properties

    Leveraging chromatography based physicochemical properties for efficient drug design

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    Applications of chromatography derived lipophilicity, polarity, and 3D concepts such as conformational states, exposed polarity and intramolecular hydrogen bonds (IMHB), are discussed along with recently developed methods for incorporating these concepts into drug design strategies. In addition, the drug design process is described with examples and practices used at Pfizer, as well as experimental and computed parameters used for parallel optimization of properties leading to drug candidate nominations

    Block relevance (BR) analysis and polarity descriptors in property-based drug design

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    Block Relevance (BR) analysis is a tool to interpret QSPR/PLS models which can provide the information content of any physicochemical determinant used in property-based drug discovery; its application for the characterization of experimental polarity descriptors is discussed

    ADME Profiling in Drug Discovery and a New Path Paved on Silica

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    The drug discovery and development pipeline have more and more relied on in vitro testing and in silico predictions to reduce investments and optimize lead compounds. A comprehensive set of in vitro assays is available to determine key parameters of absorption, distribution, metabolism, and excretion, for example, lipophilicity, solubility, and plasma stability. Such test systems aid the evaluation of the pharmacological properties of a compound and serve as surrogates before entering in vivo testing and clinical trials. Nowadays, computer-aided techniques are employed not just in the discovery of new lead compounds but embedded as part of the entire drug development process where the ADME profiling and big data analyses add a new layer of complexity to those systems. Herein, we give a short overview of the history of the drug development pipeline presenting state-of-the-art ADME in vitro assays as established in academia and industry. We will further introduce the underlying good practices and give an example of the compound development pipeline. In the next step, recent advances at in silico techniques will be highlighted with special emphasis on how pharmacogenomics and in silico PK profiling can enhance drug monitoring and individualization of drug therapy
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