CHAPTER 40 – Use of Information Technology in the Search for New PET Tracers

The true potential of positron emission tomography (PET) for studying various receptor systems and biochemical pathways has barely been tapped due to lack of suitable radiotracers. To overcome this, a means of rationalizing the selection of molecules for development as PET radiotracers is needed. Huge libraries of compounds exist within the pharmaceutical industry as well as in the public domain. The problem is to develop a way of “filtering” or interrogating these huge compound data bases to find lead molecules for PET. Information technology offers the potential to integrate and make most efficient use of the information available. We have set out to create an information technology resource for PET science. At the core of this is a PET chemistry data base, which already contains detailed information on more than 500 radiolabeled compounds. The main function of this resource is to serve as a base from which we can explore the application of computational chemistry methods to the selection and development of new and improved PET tracers. An overview of computational methods currently used to establish relationships between chemical structures and biological activity is presented. From this, an approach that utilizes components from a number of computational methods that could provide a rational approach to PET tracer selection is outlined.

[1]  D E Koshland,et al.  Molecular recognition analyzed by docking simulations: the aspartate receptor and isocitrate dehydrogenase from Escherichia coli. , 1993, Proceedings of the National Academy of Sciences of the United States of America.

[2]  I. Kuntz Structure-Based Strategies for Drug Design and Discovery , 1992, Science.

[3]  F I Carroll,et al.  Synthesis, ligand binding, QSAR, and CoMFA study of 3 beta-(p-substituted phenyl)tropane-2 beta-carboxylic acid methyl esters. , 1991, Journal of medicinal chemistry.

[4]  D. Maddalena,et al.  Prediction of receptor properties and binding affinity of ligands to benzodiazepine/GABAA receptors using artificial neural networks. , 1995, Journal of medicinal chemistry.

[5]  J M Blaney,et al.  Molecular modeling software and methods for medicinal chemistry. , 1990, Journal of medicinal chemistry.

[6]  F. Allen,et al.  The Cambridge Crystallographic Data Centre: computer-based search, retrieval, analysis and display of information , 1979 .

[7]  G R Marshall,et al.  Three-dimensional quantitative structure-activity relationship of angiotesin-converting enzyme and thermolysin inhibitors. II. A comparison of CoMFA models incorporating molecular orbital fields and desolvation free energies based on active-analog and complementary-receptor-field alignment rules. , 1993, Journal of medicinal chemistry.

[8]  E. C. Franklin The Electrical Conductivity of Aqueous Solutions , 1908 .

[9]  A. Hopfinger Computer-assisted drug design. , 1985, Journal of medicinal chemistry.

[10]  O Casher,et al.  Chemical collaboratories using World-Wide Web servers and EyeChem-based viewers. , 1995, Journal of molecular graphics.

[11]  J. Gálvez,et al.  Pharmacological distribution diagrams: a tool for de novo drug design. , 1996, Journal of molecular graphics.

[12]  F E Cohen,et al.  Computer-assisted drug discovery--a review. , 1993, Gene.

[13]  G J Williams,et al.  The Protein Data Bank: a computer-based archival file for macromolecular structures. , 1977, Journal of molecular biology.

[14]  R. Myers Quantification of brain function using PET , 1996 .

[15]  G. S. Johnson,et al.  An Information-Intensive Approach to the Molecular Pharmacology of Cancer , 1997, Science.

[16]  David A. Fletcher,et al.  The United Kingdom Chemical Database Service , 1996, J. Chem. Inf. Comput. Sci..