Predicting the Volume of Distribution of Drugs and Toxicants with Data Mining Methods

16世纪早期的一位医生帕拉塞尔苏斯（Paracelsus）说过这样一句话：“万物皆毒，无毒则无物；只有剂量允许某些东西不有毒”（http://en.wikipedia.org/wiki/Paracelsus）。尽管在过去的几十年里，药理学取得了巨大的进步，但目前，对于病人服用药物的剂量、频率和持续时间，以最大限度地发挥其治疗效果，最大限度地减少其副作用，仍然很难找到好的答案。这些问题是药代动力学和药效学相关领域的核心问题。药代动力学关注的是药物如何被人体处理，即药物输入参数（如剂量中的药物量和剂量频率）与药物在体内浓度随时间的关系。相比之下，药效学关注的是药物如何影响人体，即药物浓度与药物治疗和不良反应之间随时间的关系。这个项目的重点是一个重要的药代动力学问题：如何估计药物的分布体积，它代表了药物进入人体系统后的分布体积。估计药物的分布量很重要，因为它可以预测体内给定剂量的药物的血浆浓度，并影响药物的半衰期，而半衰期对于确定临床医生应该给患者开的正确剂量方案非常重要。该项目旨在开发新的计算数据挖掘方法来预测药物的分布量。这个项目的数据挖掘上下文是回归任务，其中系统被赋予一组代表一组对象的实例，其中每个实例由一个目标（响应）属性（或因变量）和一组描述对象的预测器属性（特征或自变量）组成。然后，系统发现一个回归模型，该模型根据实例的预测属性值来预测实例的目标属性值。在这个项目中，要分类的对象是化合物或药物，要预测的目标属性是药物的分布体积，预测属性是指药物的几类分子和物理化学性质。该项目将要开发的数据挖掘方法将与用于预测药物分布量的传统数据分析方法进行比较。

Paracelsus, a physician in the early 16th century, is credited with the phrase: "All things are poison, and nothing is without poison; only the dose permits something not to be poisonous" (http://en.wikipedia.org/wiki/Paracelsus). Despite significant advances in pharmacology in the last decades, at present it is still very difficult to find good answers to the questions of how much, how often and for how long a drug should be given to a patient, in order to maximize its therapeutic effect and minimize its adverse effects. These problems are the central concern of the related areas of pharmacokinetics and pharmacodynamics. Pharmacokinetics is concerned with how a drug is processed by the body, i.e., the relationship between drug input parameters (e.g. amount of drug in a dose and dose frequency) and the concentration of the drug in the body with time. In contrast, pharmacodynamics is concerned with how a drug affects the body, i.e., the relationship between drug concentration and the therapeutic and adverse effects of the drug with time.This project focuses on an important pharmacokinetics problem: how to estimate the volume of distribution of a drug, which represents the volume into which a drug is distributed once it has entered systemically into the body. Estimating a drug's volume of distribution is important because it predicts the drug's plasma concentration for a given amount of drug in the body and it influences the drug's half-life, which in turn is very important to determine the correct dosage regimen that clinicians should prescribe to patients.This project aims at developing new computational data mining methods to predict the volume of distribution of drugs. The data mining context for this project is the regression task, where the system is given a set of instances representing a set of objects, where each instance consists of a target (response) attribute (or dependent variable) and a set of predictor attributes (features or independent variables) describing an object. Then the system discovers a regression model that predicts the value of the target attribute for an instance based on the values of its predictor attributes. In this project, the objects to be classified will be chemical compounds or medical drugs, the target attribute to be predicted will be a drug's volume of distribution and the predictor attributes will refer to several types of molecular and physicochemical properties of drugs. The data mining methods to be developed in the project will be compared against traditional data analysis methods used for predicting a drug's volume of distribution.