Computational prediction of human drug metabolism.
Computational prediction of human drug metabolism.
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DOI：
10.1517/17425255.1.2.303
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发表时间：
2005-08-01
影响因子：
4.3
通讯作者：
Nikolskaya, Tatiana
中科院分区：
文献类型：
--
作者：
Ekins, Sean;Andreyev, Sergey;Nikolskaya, Tatiana
制药和生物技术行业、监管机构和学术界迫切需要提高选择用于临床试验的分子的成功率。尽管吸收、分布、代谢、排泄和毒性（ADME/Tox）特性是有助于成功药物发现和开发的许多组成部分中的一些，但它们代表了我们目前可以通过计算建模的体外和体内数据的因素。了解异生物质在人体内可能的毒性和代谢命运在早期药物发现中特别重要。因此，需要计算方法来揭示新分子的结构和生物活性之间的关系。包括高通量技术、数据库、ADME/Tox建模和系统生物学建模在内的众多技术的融合正在导致系统ADME/Tox的基础。实验结果可以与预测相结合，以全局模拟和理解分子在人类中可能的完整影响。MetaDrug（GeneGo，Inc.）主要成分的开发和早期应用软件将被描述，其中包括基于规则的代谢物预测，主要药物代谢酶的定量结构-活性关系模型，以及人类蛋白质-异生物质相互作用的广泛数据库。这代表了预测药物代谢的组合方法。MetaDrug可以很容易地用于可视化I期和II期代谢途径，以及解释来自微阵列的高通量数据作为相互作用的对象的网络。这将最终有助于假设生成和早期分类可能对关键蛋白质和细胞功能具有不良预测性质或测量影响的分子。
There is an urgent requirement within the pharmaceutical and biotechnology industries, regulatory authorities and academia to improve the success of molecules that are selected for clinical trials. Although absorption, distribution, metabolism, excretion and toxicity (ADME/Tox) properties are some of the many components that contribute to successful drug discovery and development, they represent factors for which we currently have in vitro and in vivo data that can be modelled computationally. Understanding the possible toxicity and the metabolic fate of xenobiotics in the human body is particularly important in early drug discovery. There is, therefore, a need for computational methodologies for uncovering the relationships between the structure and the biological activity of novel molecules. The convergence of numerous technologies, including high-throughput techniques, databases, ADME/Tox modelling and systems biology modelling, is leading to the foundation of systems-ADME/Tox. Results from experiments can be integrated with predictions to globally simulate and understand the likely complete effects of a molecule in humans. The development and early application of major components of MetaDrug (GeneGo, Inc.) software will be described, which includes rule-based metabolite prediction, quantitative structure-activity relationship models for major drug metabolising enzymes, and an extensive database of human protein-xenobiotic interactions. This represents a combined approach to predicting drug metabolism. MetaDrug can be readily used for visualising Phase I and II metabolic pathways, as well as interpreting high-throughput data derived from microarrays as networks of interacting objects. This will ultimately aid in hypothesis generation and the early triaging of molecules likely to have undesirable predicted properties or measured effects on key proteins and cellular functions.