Will the real drug targets please stand up ?

Genomics, proteomics, and bioinformatics are important fields that help advance drug development. Genomics studies entire genomes and can identify disease-associated genes. Proteomics identifies the proteins expressed in a sample and how they differ between healthy and diseased tissues. Bioinformatics uses computers to store and analyze biochemical and biological data, especially related to genomics. These fields help discover new drug targets, validate existing targets, select drug candidates, study mechanisms of action and toxicity. Integrating omics data from genomics to proteomics provides a more comprehensive understanding of biological systems compared to individual fields alone.

Protein microarrays are high-throughput methods that allow researchers to study protein interactions and functions on a large scale. There are three main types of protein microarrays: analytical microarrays use antibodies to detect specific proteins in samples; functional microarrays examine protein-protein and other molecular interactions; and reverse-phase protein microarrays profile protein expression levels and post-translational modifications by immobilizing cell or tissue lysates. Protein microarrays have applications in diagnostics, proteomics, studying protein functions, and analyzing antibodies.

This document discusses the definition of what constitutes a drug. It argues that trying to divide drugs into "good" and "bad" categories is futile, as there is no drug that is perfectly safe and effective. All drugs have benefits and risks. The document provides examples of commonly used substances like caffeine, nicotine, alcohol, and cannabis that have biological effects and are therefore drugs, though they are often not thought of as such. It concludes that a broad definition of a drug is any compound that interacts with a biological system to produce a biological response.

This document discusses the SELEX (Systematic Evolution of Ligands by EXponential enrichment) process for identifying aptamers (nucleic acid molecules) that bind to specific targets. It provides details on how SELEX has been used to produce aptamers that bind to membrane-bound receptors and whole cells/organisms. The document also describes a protocol for selecting RNA aptamers that act as allosteric modulators of nicotinic acetylcholine receptors.

The basic aspects of drug discovery starts from target discovery and validation further going to lead identification and optimization. In this particular slide discussion is regarding the target discovery and the tools that have been utilized in this process.

Molecular docking is a method that predicts the preferred orientation of one molecule to another when bound to form a stable complex. It involves finding the best "fit" between a small molecule ligand and a protein receptor binding site. The key stages are target selection and preparation, ligand selection and preparation, docking, and evaluation. Docking software uses scoring functions to evaluate the strength of interaction and identify the best binding orientation. Applications include virtual screening in drug discovery and predicting enzyme-substrate interactions in bioremediation.

Molecular targets are cellular or tissue structures that are intended to be visualized by means of molecular imaging. Different biological structures can potentially serve as imaging targets. These Targets ranging from gene mutations, mRNA levels, protein levels, DNA, RNA and enzyme activities.

Traditional drug design involved origins from natural sources through accidental discoveries, not based on specific targets. Methods included random screening, trial and error using plant materials, ethnopharmacology observing indigenous drug uses, and serendipitous discoveries like penicillin. Rational drug design is target-based, using the known structure and function of targets. Methods include ligand-based approaches like quantitative structure-activity relationships (QSAR) and pharmacophore modeling, and structure-based approaches like molecular docking and de novo design using a target's 3D structure. Both traditional and rational methods have contributed to modern drug discovery.

Wendy Cornell, Director, Chemistry Modeling and Informatics at Merck, moderates an expert panel discussion on targets in drug discovery. The panelists are: Jamie Baumgartner, Ph.D., Senior Director of in vitro Pharmacology at MDS Pharma Services, Brian Lightbody, Vice President of Business Development of Small Molecule Drug Discovery for MicroCal Products Group, GE Healthcare, and Patrick Zarrinkar, Vice President of Technology Development at Ambit BioSciences

The document discusses structure-based drug design (SBDD). It first provides background on drug design and SBDD. It then describes some key aspects of SBDD, including using the 3D structure of the biological target obtained from techniques like X-ray crystallography and NMR spectroscopy. It also discusses ligand-based and receptor-based drug design approaches. The document then outlines the typical steps involved in SBDD, including target selection, ligand selection, target preparation, docking, evaluating results, and discusses some molecular docking techniques and scoring functions used to predict binding.

Computer Aided Drug Design uses computational methods to help streamline the drug discovery process. Key steps include identifying drug targets, generating molecular structures, evaluating interactions between potential drug compounds and targets through docking simulations, and developing quantitative structure-activity relationship (QSAR) models to predict compound activity and guide synthetic efforts. The overall goal is to reduce the cost and time needed to develop new pharmaceutical agents in a rational, mechanism-based manner.

Comparative modeling predicts the 3D structure of a target protein sequence based on its alignment to template protein structures of known structure. It consists of four main steps: fold assignment, alignment of the target and template sequences, building a model based on the alignment, and predicting errors in the model. Comparative modeling is often used to facilitate functional characterization of a protein when its experimental structure is unknown, as it can provide a useful 3D structural model for proteins related to templates.