Development of Novel Discrimination Model and Its Application to Predicting P-gp Substrate

新型判别模型的开发及其在预测 P-gp 底物中的应用

• 批准号: 15590129
• 负责人: YAMASHITA Fumiyoshi
• 金额: $ 2.24万
• 依托单位: Kyoto University
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 2003
• 资助国家: 日本
• 起止时间: 2003 至 2004
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-15590129/
• 关键词: structure-activity relationship; chemical space; information visualization; pattern recognition; drug-likeness; P-glycoprotein; orally active drugs; P糖タンパク質; 化合物超空間; トランスポータ

With the advent of combinatorial chemistry and high-throughput screening in drug discovery, it is increasingly important how to design a library of compounds. In particular, ADME(absorption, distribution, metabolism, and excretion) is a critical issue in drug development, because it is closely related with safety and efficacy of drugs. Computer-based prediction of ADME properties is expected to reduce the rate of attrition in the late stage of drug development and optimize drug screening and testing by looking at promising compounds. To this end, molecular structural features responsible for ADME processes should be elucidated. P-glycoprotein(P-gp) is an efflux transporter that expresses many organs. The transporter is responsible, for example, for suppression of entry of xenobiotics in the intestine and active excretion in the liver and kidney. Many of drugs are known to be recognized by P-gp, resulting in insufficient bioavailability and short duration of therapeutic effect. Therefor … More e, it is one of the important issues to develop the method of discriminating whether aimed compounds are P-gp substrates or not. Conventionally used pattern recognition algorithms, such as discrimination analysis and neural network, need categorical information (substrate or non-substrate) for all the compounds subjected to the analysis. Unfortunately, not so many literatures are available to clearly show that the compounds are non-substrate. The limited information makes it difficult to perform a large-scale data analysis. In this study, a novel discrimination analysis method has been proposed based on the chemical space concept. Chemical space is hyper-dimensional space consisting various independent chemical attributes (or molecular descriptors). Assuming that P-gp substrates form a cluster in entire chemical space, we developed a method for visualizing the cluster of P-gp in the chemical space downsized to 3-dimension. The loss of information associated with projection into 3-dimensional space can be minimized by finding the loading vectors that minimize, the variation ratio of test compounds to the entire chemicals. We realized that this mathematical problem is one of generalized eigenvalue/eigenvector problems. By using this method, we analyzed molecular features of P-gp substrates. When the analysis was performed using topological descriptors of compounds as molecular descriptors, it was found that 〜200 P-gp substrates localized in only 1/60 of the entire chemical space comprising 〜8,000 bioactive compounds. The same method was applied to mapping of orally active drugs. Seven hundreds sixty orally active drugs distributed approximately 1/12 of the entire chemical space consisting of 130,000 organic compounds listed in available chemical directory. The method developed in this study provides intuitive understanding of common features of target molecules by visualizing a large-scale data based on chemical space concept, and therefore contributes to accelerating drug discovery and development. Less

随着组合化学和高通量药物筛选技术的出现，如何设计化合物库变得越来越重要。特别是ADME（吸收、分布、代谢和排泄）是药物开发中的关键问题，因为它与药物的安全性和有效性密切相关。基于计算机的ADME性质预测有望降低药物开发后期的损耗率，并通过寻找有前途的化合物来优化药物筛选和测试。为此，应阐明负责ADME过程的分子结构特征。P-糖蛋白（P-gp）是一种表达许多器官的外排转运蛋白。该转运蛋白负责例如抑制外源性物质进入肠和在肝脏和肾脏中的主动排泄。许多药物被P-gp识别，导致生物利用度不足和疗效持续时间短。因此 ...更多信息 因此，建立鉴别目标化合物是否为P-gp底物的方法是目前研究的重要课题之一。常规使用的模式识别算法，如判别分析和神经网络，需要进行分析的所有化合物的分类信息（底物或非底物）。不幸的是，没有那么多的文献可以清楚地表明这些化合物是非底物的。有限的信息使得难以进行大规模的数据分析。本文提出了一种基于化学空间概念的判别分析方法。化学空间是由各种独立的化学属性（或分子描述符）组成的超维空间。假设P-gp底物在整个化学空间中形成一个簇，我们开发了一种方法，用于可视化P-gp在化学空间中的簇缩小到3维。通过找到最小化测试化合物与整个化学物质的变异比的负载向量，可以最大限度地减少与投影到三维空间相关的信息损失。我们认识到这个数学问题是一个广义特征值/特征向量问题。利用这种方法，我们分析了P-gp底物的分子特征。当使用化合物的拓扑描述符作为分子描述符进行分析时，发现1200个P-gp底物仅位于包含128，000个生物活性化合物的整个化学空间的1/60中。同样的方法也适用于口服活性药物的绘图。760种口服活性药物分布在整个化学空间的大约1/12，包括130，000种在可用化学目录中列出的有机化合物。本研究开发的方法通过基于化学空间概念的大规模数据可视化，提供了对靶分子共同特征的直观理解，因此有助于加速药物发现和开发。少

项目成果

QSAR analysis of the inhibition of recombinant CYP 3A4 activity by structurally diverse compounds using a genetic algorithm-combined partial least squares method

使用遗传算法结合偏最小二乘法对结构多样的化合物抑制重组 CYP 3A4 活性进行 QSAR 分析

• 发表时间: 2003
• 期刊: Pharmaceutical Research 20(9)
• 作者: Suchada Wanchana;Fumiyoshi Yamashita;Mitsuru Hashida
• 通讯作者: Mitsuru Hashida

Effect of polycyclic aromatic hydrocarbons on generation and efflux of glutathione conjugates in primary cultured alveolar epithelial cells.

• DOI: 10.2133/dmpk.19.407
• 发表时间: 2004
• 期刊: Drug metabolism and pharmacokinetics
• 影响因子: 2.1
• 作者: Keiko Nagayoshi;Takayuki Nemoto;Shumpei Yokoyama;F. Yamashita;M. Hashida

Two-and three-dimensional QSAR of carrier-mediated transport of beta-lactam antibiotics in Caco-2 cells

Caco-2 细胞中载体介导的 β-内酰胺抗生素转运的二维和三维 QSAR

• 发表时间: 2004
• 期刊: Journal of Pharmaceutical Sciences 93(12)
• 作者: Suchada Wanchana;Fumiyoshi Yamashita;Hideto Hara;Shin-Ichi Fujiwara;Miki Akamatsu;Mitsuru Hashida
• 通讯作者: Mitsuru Hashida

化合物群表示装置,化合物群表示方法,プログラム,及びコンピュータ読み取り可能な記録媒体

复合组显示装置、复合组显示方法、程序以及计算机可读记录介质

• 发表时间: 2005

In silico approaches for predicting ADME properties of drugs.

• DOI: 10.2133/dmpk.19.327
• 发表时间: 2004
• 期刊: Drug metabolism and pharmacokinetics
• 影响因子: 2.1
• 作者: F. Yamashita;M. Hashida