Chapter 5: Designing Multi-Target Drugs: In Vitro Panel Screening – Biological Fingerprinting
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Published:28 Mar 2012
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Special Collection: 2012 ebook collection , 2011-2015 industrial and pharmaceutical chemistry subject collectionSeries: Drug Discovery
J. S. Mason, in Designing Multi-Target Drugs, ed. J. R. Morphy and C. J. Harris, The Royal Society of Chemistry, 2012, ch. 5, pp. 66-85.
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The design of multi-target drugs requires an understanding of the polypharmacology of compounds, both desired and undesired/unexpected. In vitro panel screening, enabling the use of biological fingerprints, is a key experimental step towards this. Molecules are also usually described by their chemical structure and by fingerprints derived from this. These range from 2D structure based, that only represent the underlying structure that gives rise to the properties recognised by a biological target, to 3D pharmacophores or molecular interaction fields. These latter descriptions much better represent how the protein binding sites would ‘see’ a molecule; however, all of these have many limitations, including the need for conformational approximations for the 3D structure-based approaches. More recently, experimental profiling data has been generated for broad set of drugs and preclinical compounds that enables a molecule to be described by a fingerprint of binding affinity to a diverse set of biological targets (pharmacological and ‘anti-targets’ such as CYP450 metabolic enzymes). These results show that small changes in structure can cause large changes in broad biological profile, and that a structure-based analysis/clustering of compounds, for selecting different hits, leads or clinical candidates, often does not provide a differentiation that is relevant in biological space. The data show that ‘selective’ versus ‘non-selective’ compounds, and the type of off-target effects, are not evident from a ‘chemotype’ approach. In vitro panel screening and the concept of ‘biological fingerprints’ as a better way to describe compounds of biological interest is described in this chapter, focusing on the power of these descriptors versus structure-based descriptors to differentiate compounds and enable the selection of the best lead compounds.