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Review Article

Therapeutic Targets: Progress of Their Exploration and Investigation of Their Characteristics

Bioinformatics and Drug Design Group, Department of Computational Science, National University of Singapore, Singapore, Singapore

Abstract

I. Introduction

II. Distribution of Therapeutic Targets with Respect to Disease Classes

A. General Distribution Pattern

B. Targets for the Treatment of Diseases in Multiple Classes

C. Research Targets

III. Current Trends in Exploration of Therapeutic Targets

A. Targets of Investigational Agents in United States Patents Approved in 2000 through 2004

B. Progress and Difficulties in Target Exploration

C. Targets of Subtype-Specific Drugs

IV. Characteristics of Therapeutic Targets

A. What Constitutes a Therapeutic Target?

B. Protein Families Represented by Therapeutic Targets

C. Structural Folds

D. Biochemical Classes

E. Human Proteins Similar to Therapeutic Targets

F. Associated Pathways

G. Tissue Distribution

H. Chromosome Locations

V. Can Druggable Proteins Be Predicted from Their Sequence?

A. ''Rules'' for Guiding the Search for Druggable Proteins

B. Prediction of Druggable Proteins by a Statistical Learning Method

Modern drug discovery is primarily based on the search and subsequent testing of drug candidates acting on a preselected therapeutic target. Progress in genomics, protein structure, proteomics, and disease mechanisms has led to a growing interest in and effort for finding new targets and more effective exploration of existing targets. The number of reported targets of marketed and investigational drugs has significantly increased in the past 8 years. There are 1535 targets collected in the therapeutic target database compared with 500 targets reported in a 1996 review. Knowledge of these targets is helpful for molecular dissection of the mechanism of action of drugs and for predicting features that guide new drug design and the search for new targets. This article summarizes the progress of target exploration and investigates the characteristics of the currently explored targets to analyze their sequence, structure, family representation, pathway association, tissue distribution, and genome location features for finding clues useful for searching for new targets. Possible "rules" to guide the search for druggable proteins and the feasibility of using a statistical learning method for predicting druggable proteins directly from their sequences are discussed.

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