Druggability

Druggability is a term used in drug discovery to describe the suitability of a protein or protein complex to be targeted by a drug or drug-like molecule, in a way that this interaction will alter the proteins function and correct disease causing behavior. The term is typically used for designating tractability by a small molecular weight drug,^[1] although druggability can be achieved using biotherapeutics such as monoclonal antibody. Druggability is now an ubiquitously used term that is referred to in different contexts, but always meaning the suitability of a target for drug discovery.^{[citation needed]}

Drug discovery comprises a number of stages that lead from a biological hypothesis to an approved drug. Target identification is typically the starting point of the modern drug discovery process. A protein is highlighted through experimental techniques if it is shown to cause disease, either due to a change in its behaviour brought about by e.g. a mutation; or through the introduction of foreign protein via a parasitic infection. The importance of a protein in the pathology of a disease alone is not sufficient to make it a successful drug target. Other considerations need to be taken into account including the feasibility of developing a chemically or biologically accessible drug against it.^{[citation needed]}

Types of druggability

Druggability is implied or predicted using different methods that rely on evolutionary relationships, 3D-structural properties or other descriptors^[2]

Precedence-based

Using precedence, or homology, based druggability, protein is classed as "druggable" if it is a member of a protein family ^[3] that is targeted by approved drugs, e.g. G protein-coupled receptors, Nuclear receptors, etc. While this is a useful approximation of druggability, this definition has limitations for two main reasons: a) it highlights only historically successful proteins, ignoring the possibility of a perfectly druggable, but yet undrugged protein family; and b) assumes that all protein family members are equally druggable.^{[citation needed]}

Structure-based

This relies on the availability of experimentally determined 3D structures or high quality homology models. A number of methods exist for this assessment of druggability but all of them consist of three main components:^{[citation needed]}

1. Identifying cavities or pockets on the structure
2. Calculating physicochemical and geometric properties of the pocket
3. Assessing how these properties fit a training set of known druggable targets, typically using machine learning algorithms

Early work on introducing introduced some of the parameters of structure-based druggability came from Abagyan and coworkers^[4] and then Fesik and coworkers,^[5] the latter by assessing the correlation of certain physicochemical parameters with hits from an NMR-based fragment screen. There has since been a number of publications reporting related methodologies.^[6][7][8]

There are several commercial tools and databases for structure-based druggability assessment. A publicly available database of pre-calculated druggability assessments for all structural domains within the Protein Data Bank (PDB) is provided through the ChEMBL's DrugEBIlity portal.^{[citation needed]}

Structure-based druggability is usually used to identify suitable binding pocket for a small molecule, however, some studies have assessed 3D structures for the availability of grooves suitable for binding helical mimetics.^[9] This is an increasingly popular approach in addressing the druggability of Protein-Protein-Interactions.^{[citation needed]}

Other types of druggability

As well as using 3D structure and family precedence, it is possible to estimate druggability using other properties of a protein such as features derived from the amino-acid sequence (Feature-based druggability)^[2] which is applicable to assessing small-molecule based druggability or biotherapeutic-based druggability or the properties of ligands or compounds known to bind the protein (Ligand-based druggability).^[10][11]

The importance of training sets

All methods for assessing druggability are highly dependent on the training sets used to develop them. This highlights an important caveat in all the methods discussed above: which is that they have learned from the successes so far. The training sets are typically either databases of curated drug targets;^[12][13] screened targets databases(ChEMBL, BindingDB, PubChem etc); or on manually compiled sets of 3D structure known by the developers to be druggable. As training sets improve and expand, the boundaries of druggability may also be expanded.

Using druggability assessment in drug discovery

Utilising druggability assessments at large scale for aiding drug discovery is exemplified in the TDR Targets database^[10] where entire parasitic genomes were assessed for their druggability and biological essentiality to the pathogen in order to aid tropical disease drug discovery.^[10]

References

1. ^ Owens, Joanna (2007). "Determining druggability". Nature Reviews Drug Discovery 6 (3): 187–187. doi:10.1038/nrd2275. 
2. ^ ^{a b} Al-Lazikani, Bissau; Gaulton, Anna; Paolini, Gaia; Lanfear, Jerry; Overington, John; Hopkins, Andrew (2007). "The Molecular Basis of Predicting Druggability". Chemical Biology. 1–3. pp. 804–823. doi:10.1002/9783527619375.ch14b. ISBN 9783527619375. 
3. ^ Hopkins, Andrew L.; Groom, Colin R. (2002). "Opinion: The druggable genome". Nature Reviews Drug Discovery 1 (9): 727–30. doi:10.1038/nrd892. PMID 12209152. 
4. ^ An, J; Totrov, M; Abagyan, R (2004). "Comprehensive identification of "druggable" protein ligand binding sites". Genome informatics. International Conference on Genome Informatics 15 (2): 31–41. PMID 15706489. 
5. ^ Hajduk, Philip J.; Huth, Jeffrey R.; Fesik, Stephen W. (2005). "Druggability Indices for Protein Targets Derived from NMR-Based Screening Data". Journal of Medicinal Chemistry 48 (7): 2518–25. doi:10.1021/jm049131r. PMID 15801841. 
6. ^ Halgren, Thomas A. (2009). "Identifying and Characterizing Binding Sites and Assessing Druggability". Journal of Chemical Information and Modeling 49 (2): 377–89. doi:10.1021/ci800324m. PMID 19434839. 
7. ^ Schmidtke, Peter; Barril, Xavier (2010). "Understanding and Predicting Druggability. A High-Throughput Method for Detection of Drug Binding Sites". Journal of Medicinal Chemistry 53 (15): 5858–67. doi:10.1021/jm100574m. PMID 20684613. 
8. ^ Gupta, Anvita; Gupta, Arun Kumar; Seshadri, Kothandaraman (2009). "Structural models in the assessment of protein druggability based on HTS data". Journal of Computer-Aided Molecular Design 23 (8): 583–92. doi:10.1007/s10822-009-9279-y. 
9. ^ Jochim, Andrea L.; Arora, Paramjit S. (2010). "Systematic Analysis of Helical Protein Interfaces Reveals Targets for Synthetic Inhibitors". ACS Chemical Biology 5 (10): 919–23. doi:10.1021/cb1001747. PMC 2955827. PMID 20712375. http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2955827. 
10. ^ ^{a b c} Agüero, Fernán; Al-Lazikani, Bissan; Aslett, Martin; Berriman, Matthew; Buckner, Frederick S.; Campbell, Robert K.; Carmona, Santiago; Carruthers, Ian M. et al. (2008). "Genomic-scale prioritization of drug targets: the TDR Targets database". Nature Reviews Drug Discovery 7 (11): 900–7. doi:10.1038/nrd2684. PMID 18927591. 
11. ^ Barelier, Sarah; Krimm, Isabelle (2011). "Ligand specificity, privileged substructures and protein druggability from fragment-based screening". Current Opinion in Chemical Biology. doi:10.1016/j.cbpa.2011.02.020. 
12. ^ Overington, John P.; Al-Lazikani, Bissan; Hopkins, Andrew L. (2006). "How many drug targets are there?". Nature Reviews Drug Discovery 5 (12): 993–6. doi:10.1038/nrd2199. PMID 17139284. 
13. ^ Knox, C.; Law, V.; Jewison, T.; Liu, P.; Ly, S.; Frolkis, A.; Pon, A.; Banco, K. et al. (2010). "DrugBank 3.0: a comprehensive resource for 'Omics' research on drugs". Nucleic Acids Research 39 (Database issue): D1035–41. doi:10.1093/nar/gkq1126. PMC 3013709. PMID 21059682. http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3013709. 

External links

• ChEMBL. "DrugEBIlity". https://www.ebi.ac.uk/chembl/drugebility/structure. 
• Agüero et al. "TDR Targets Database". http://tdrtargets.org/.