| Budget Amount *help |
¥3,500,000 (Direct Cost: ¥3,500,000)
Fiscal Year 2004: ¥800,000 (Direct Cost: ¥800,000)
Fiscal Year 2003: ¥2,700,000 (Direct Cost: ¥2,700,000)
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| Research Abstract |
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
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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
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