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

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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.