Druggability

Druggability is a term used in drug discovery to describe a biological target (such as a protein) that is known to or is predicted to bind with high affinity to a drug. Furthermore, by definition, the binding of the drug to a druggable target must alter the function of the target with a therapeutic benefit to the patient. The concept of druggability is most often restricted to small molecules (low molecular weight organic substances)[1] but also has been extended to include biologic medical products such as therapeutic monoclonal antibodies.

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. Candidate targets may be selected based on a variety of experimental criteria. These criteria may include disease linkage (mutations in the protein are known to cause a disease), mechanistic rationale (for example, the protein is part of a regulatory pathway that is involved in the disease process), or genetic screens in model organisms.[2] Disease relevance alone however is insufficient for a protein to become a drug target. In addition, the target must be druggable.

Prediction of druggability

If a drug has already been identified for a target, that target is by definition druggable. If no known drugs bind to a target, then druggability is implied or predicted using different methods that rely on evolutionary relationships, 3D-structural properties or other descriptors.[3]

Precedence-based

A protein is predicted to be "druggable" if it is a member of a protein family[4] for which other members of the family are known to be targeted by drugs (i.e., "guilt" by association). While this is a useful approximation of druggability, this definition has limitations for two main reasons: (1) it highlights only historically successful proteins, ignoring the possibility of a perfectly druggable, but yet undrugged protein family; and (2) assumes that all protein family members are equally druggable.

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:[5][6][7][8]

  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 some of the parameters of structure-based druggability came from Abagyan and coworkers[9] and then Fesik and coworkers,[10] 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.[5][11][12]

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.[13]

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.[14] This is an increasingly popular approach in addressing the druggability of protein-protein interactions.[15]

Predictions based on other properties

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)[3] 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).[16][17]

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;[18][19] 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.

Undruggable targets

Only 2% of human proteins interact with currently approved drugs. Furthermore, it is estimated that only 10-15% of human proteins are disease modifying while only 10-15% are druggable (there is no correlation between the two), meaning that only between 1-2.25% of disease modifying proteins are likely to be druggable. Hence it appears that the number of new undiscovered drug targets is very limited.[20][21][22]

A potentially much larger percentage of proteins could be made druggable if protein–protein interactions could be disrupted by small molecules. However the majority of these interactions occur between relatively flat surfaces of the interacting protein partners and it is very difficult for small molecules to bind with high affinity to these surfaces.[23][24] Hence these types of binding sites on proteins are generally thought to be undruggable but there has been some progress (by 2009) targeting these sites.[25][26]

gollark: So do it.
gollark: That is more than 20.
gollark: ~play the letter g
gollark: restoremathbotbees
gollark: <@319753218592866315> OH WAIT

References
  1. Owens J (2007). "Determining druggability". Nature Reviews Drug Discovery. 6 (3): 187–187. doi:10.1038/nrd2275.
  2. Dixon SJ, Stockwell BR (December 2009). "Identifying druggable disease-modifying gene products". Current Opinion in Chemical Biology. 13 (5–6): 549–55. doi:10.1016/j.cbpa.2009.08.003. PMC 2787993. PMID 19740696.
  3. Al-Lazikani B, Gaulton A, Paolini G, Lanfear J, Overington J, Hopkins A (2007). "The Molecular Basis of Predicting Druggability". In Wess G, Schreiber SL, Kapoor TM (eds.). Chemical Biology: From Small Molecules to Systems Biology and Drug Design. 1–3. Weinheim: Wiley-VCH. pp. 804–823. ISBN 3-527-31150-5.
  4. Hopkins AL, Groom CR (September 2002). "The druggable genome". Nature Reviews Drug Discovery. 1 (9): 727–30. doi:10.1038/nrd892. PMID 12209152.
  5. Halgren TA (February 2009). "Identifying and characterizing binding sites and assessing druggability". J Chem Inf Model. 49 (2): 377–89. doi:10.1021/ci800324m. PMID 19434839.
  6. Nayal M, Honig B (February 2006). "On the nature of cavities on protein surfaces: Application to the identification of drug-binding sites". Proteins. 63 (4): 892–906. doi:10.1002/prot.20897. PMID 16477622.
  7. Seco J, Luque FJ, Barril X (March 2009). "Binding Site Detection and Druggability Index from First Principles". J. Med. Chem. 52 (8): 2363–71. doi:10.1021/jm801385d. PMID 19296650.
  8. Bakan A, Nevins N, Lakdawala AS, Bahar I (July 2012). "Druggability Assessment of Allosteric Proteins by Dynamics Simulations in the Presence of Probe Molecules". J. Chem. Theory Comput. 8 (7): 2435–47. doi:10.1021/ct300117j. PMC 3392909. PMID 22798729.
  9. An J, Totrov M, Abagyan R (2004). "Comprehensive identification of "druggable" protein ligand binding sites". Genome Inform. 15 (2): 31–41. PMID 15706489.
  10. Hajduk PJ, Huth JR, Fesik SW (April 2005). "Druggability indices for protein targets derived from NMR-based screening data". J. Med. Chem. 48 (7): 2518–25. doi:10.1021/jm049131r. PMID 15801841.
  11. Schmidtke P, Barril X (August 2010). "Understanding and predicting druggability. A high-throughput method for detection of drug binding sites". J. Med. Chem. 53 (15): 5858–67. doi:10.1021/jm100574m. PMID 20684613.
  12. Gupta A, Gupta AK, Seshadri K (May 2009). "Structural models in the assessment of protein druggability based on HTS data". J. Comput.-Aided Mol. Des. 23: 583–592. doi:10.1007/s10822-009-9279-y. PMID 19479324.
  13. "DrugEBIlity Portal". ChEMBL. European Bioinformatics Institute.
  14. Jochim AL, Arora PS (October 2010). "Systematic analysis of helical protein interfaces reveals targets for synthetic inhibitors". ACS Chem. Biol. 5 (10): 919–23. doi:10.1021/cb1001747. PMC 2955827. PMID 20712375.
  15. Kozakov D, Hall DR, Chuang GY, Cencic R, Brenke R, Grove LE, Beglov D, Pelletier J, Whitty A, Vajda S (August 2011). "Structural conservation of druggable hot spots in protein–protein interfaces". PNAS. 108 (33): 13528–13533. doi:10.1073/pnas.1101835108. PMC 3158149. PMID 21808046.
  16. Agüero F, Al-Lazikani B, Aslett M, Berriman M, Buckner FS, Campbell RK, Carmona S, Carruthers IM, Chan AW, Chen F, Crowther GJ, Doyle MA, Hertz-Fowler C, Hopkins AL, McAllister G, Nwaka S, Overington JP, Pain A, Paolini GV, Pieper U, Ralph SA, Riechers A, Roos DS, Sali A, Shanmugam D, Suzuki T, Van Voorhis WC, Verlinde CL (November 2008). "Genomic-scale prioritization of drug targets: the TDR Targets database". Nature Reviews Drug Discovery. 7 (11): 900–7. doi:10.1038/nrd2684. PMC 3184002. PMID 18927591.
  17. Barelier S, Krimm I (August 2011). "Ligand specificity, privileged substructures and protein druggability from fragment-based screening". Current Opinion in Chemical Biology. 15 (4): 469–74. doi:10.1016/j.cbpa.2011.02.020. PMID 21411360.
  18. Overington JP, Al-Lazikani B, Hopkins AL (December 2006). "How many drug targets are there?". Nature Reviews Drug Discovery. 5 (12): 993–6. doi:10.1038/nrd2199. PMID 17139284.
  19. Knox C, Law V, Jewison T, Liu P, Ly S, Frolkis A, Pon A, Banco K, Mak C, Neveu V, Djoumbou Y, Eisner R, Guo AC, Wishart DS (January 2011). "DrugBank 3.0: a comprehensive resource for 'omics' research on drugs". Nucleic Acids Res. 39 (Database issue): D1035–41. doi:10.1093/nar/gkq1126. PMC 3013709. PMID 21059682.
  20. Kwon B (2011-05-16). "Chemical biologist targets 'undruggable' proteins linked to cancer in quest for new cures". Brent Stockwell interview. Medical Xpress. Retrieved 2012-05-17.
  21. Stockwell, Brent Roark (2011). The Quest for the Cure: The Science and Stories Behind the Next Generation of Medicines. New York: Columbia University Press. ISBN 0-231-15212-4.
  22. Stockwell B (October 2011). "Outsmarting Cancer. A biologist talks about what makes disease-causing proteins so difficult to target with drugs". Sci. Am. 305 (4): 20. PMID 22106796.
  23. Buchwald P (October 2010). "Small-molecule protein-protein interaction inhibitors: therapeutic potential in light of molecular size, chemical space, and ligand binding efficiency considerations". IUBMB Life. 62 (10): 724–31. doi:10.1002/iub.383. PMID 20979208.
  24. Morelli X, Bourgeas R, Roche P (August 2011). "Chemical and structural lessons from recent successes in protein-protein interaction inhibition (2P2I)". Current Opinion in Chemical Biology. 15 (4): 475–81. doi:10.1016/j.cbpa.2011.05.024. PMID 21684802.
  25. Verdine GL, Walensky LD (December 2007). "The challenge of drugging undruggable targets in cancer: lessons learned from targeting BCL-2 family members". Clin. Cancer Res. 13 (24): 7264–70. doi:10.1158/1078-0432.CCR-07-2184. PMID 18094406.
  26. Arkin MR, Whitty A (June 2009). "The road less traveled: modulating signal transduction enzymes by inhibiting their protein-protein interactions". Current Opinion in Chemical Biology. 13 (3): 284–90. doi:10.1016/j.cbpa.2009.05.125. PMID 19553156.

Further reading
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