Clinical Needs as a Starting Point for Different Strategies in Computational Drug Development

Abstract

Traditionally, the first step in the development of drugs is the definition of the target, by choice of a biological structure involved in a disease or by recognition of a molecule with some degree of a biological activity that presents itself as druggable and endowed with therapeutic potential. The complexity of the pathophysiological mechanisms of disease and of the structures of the molecules involved creates several challenges in this drug discovery process. These difficulties also come from independent operation of the different parts involved in drug development, with little interaction between clinical practitioners, academic institutions and large pharmaceutical companies. Research in this area is purpose specific, performed by specialized researchers in each field, without major inputs from clinical practitioners on the relevance of such strategy for future therapies. Translational research can shift the way these relationships operate towards a process in which new therapies can be generated by linking experimental discoveries directly to unmet clinical needs. Computational chemistry methods provide valuable insights on experimental findings and pharmacological and pathophysiological mechanisms, allow the virtual construction of new possibilities for the synthesis of new molecular entities, and pave the way for informed cost-effective decisions on expensive research projects. This text focus on the current computational methods used in drug design, how they can be used in a translational research model that starts from clinical practice and research-based theorization by medical practitioners and moves to applied research in a computational chemistry setting, aiming the development of new drugs for clinical use.

Key words

drug design - computational chemistry - clinical expertise - translational research

• References

• 1 Lindsay MA. Target discovery. Nat Rev Drug Discov 2003; 2: 831-838
• 2 David GT. Scientific process, pharmacology and drug discovery. Curr Opin Pharm 2011; 11: 528-533
• 3 Raza M. A role for physicians in ethnopharmacology and drug discovery. J Ethnopharm 2006; 104: 297-301
• 4 Sliwoski G, Kothiwale S, Meiler J. et al. Computational methods in drug discovery. Pharmacol Rev 2014; 66: 334-395
• 5 Ratti E, Trist D. Continuing evolution of the drug discovery process in the pharmaceutical industry. Pure Appl Chem 2001; 73: 67-75
• 6 Kalyaanamoorthy S, Chen YP. Structure-based drug design to augment hit discovery. Drug Discov Today 2011; 16: 831-839
• 7 Ashburn TT, Thor KB. Drug repositioning: Identifying and developing new uses for existing drugs. Nat Rev Drug Discov 2004; 3: 673-683
• 8 Keiser MJ, Setola V, Irwin JJ. et al. Predicting new molecular targets for known drugs. Nature 2009; 462: 175-181
• 9 Cressey D. Traditional drug-discovery model ripe for reform. Nature 2011; 471: 17-18
• 10 Bennani YL. Drug discovery in the next decade: Innovation needed ASAP. Drug Discov Today 2011; 16: 779-792
• 11 Stevens AJ, Phil D, Jensen JJ. et al. The role of public-sector research in the discovery of drugs and vaccines. N Engl J Med 2011; 364: 535-541
• 12 Fishburn CS. Translational research: The changing landscape of drug discovery. Drug Discov. Today 2013; 18: 487-494
• 13 Woolf SH. The meaning of translational research and why it matters. JAMA 2008; 299: 211-213
• 14 Ledford H. Translational research: The full cycle. Nature 2008; 453: 843-845
• 15 Eckert H, Bajorath J. Molecular similarity analysis in virtual screening: Foundations, limitations and novel approaches. Drug Discov Today 2007; 12: 225-233
• 16 Campillos M, Kuhn M, Gavin AC. et al. Drug target identification using side-effect similarity. Science 2008; 321: 263-266
• 17 Chiang AP, Butte AJ. Systematic evaluation of drug-disease relationships to identify leads for novel drug uses. Clin Pharmacol Ther 2009; 86: 507-510
• 18 Suthram S, Dudley JT, Chiang AP. et al. Network-based elucidation of human disease similarities reveals common functional modules enriched for pluripotent drug targets. PLoS Comput Biol. 2010; 6: e1000662
• 19 Pérot S, Sperandio O, Miteva MA. et al. Druggable pockets and binding site centric chemical space: A paradigm shift in drug discovery. Drug Discov Today 2010; 15: 656-667
• 20 Koutsoukas A, Simms B, Kirchmair J. et al. From in silico target prediction to multi-target drug design: Current databases, methods and applications. J Proteomics 2011; 74: 2554-2574
• 21 Bender A, Glen RC. Molecular similarity: A key technique in molecular informatics. Org Biomol Chem 2004; 2: 3204-3211
• 22 Wermuth G, Ganellin CR, Lindberg P. et al. Glossary of terms used in medicinal chemistry (IUPAC Recommendations 1998). Pure Appl Chem 1998; 70: 1129-1143
• 23 Leach AR, Gillet VJ, Lewis RA. et al. Three-dimensional pharmacophore methods in drug discovery. J Med Chem 2010; 53: 539-558
• 24 Wolber G, Seidel T, Bendix F. et al. Molecule-pharmacophore superpositioning and pattern matching in computational drug design. Drug Discov Today 2008; 13: 23-29
• 25 Shim J, Welsh WJ, Cartier E. et al. Molecular Interaction of the Antagonist N-(Piperidin-1-yl)-5-(4-chlorophenyl)-1-(2,4-dichlorophenyl)-4-methyl-1H-pyrazole-3-carboxamide with the CB1 Cannabinoid Receptor. J Med Chem 2002; 45: 1447-1459
• 26 Sprous DG, Palmer RK, Swanson JT. et al. QSAR in the pharmaceutical research setting: QSAR models for broad, large problems. Curr Top Med Chem 2010; 10: 619-637
• 27 Tropsha A, Golbraikh A. Predictive QSAR modeling workflow, model applicability domains, and virtual screening. Curr Pharm Des 2007; 13: 3494-3504
• 28 Clark RD. Prospective ligand- and target-based 3D QSAR: State of the art 2008. Curr Top Med Chem 2009; 9: 791-810
• 29 Daniel R, Kholodovych V, Tomasini MD. et al. In silico design of anti-atherogenic biomaterials. Biomaterials 2013; 34: 7950-7959
• 30 Gillman PK. Is there sufficient evidence to suggest cyclobenzaprine might be implicated in causing serotonin toxicity?. Am J Emerg Med 2009; 27: 509-510
• 31 Rognan D. Structure-based approaches to target fishing and ligand profiling. Mol Inf 2010; 29: 176-187
• 32 Brooijmans N, Kuntz ID. Molecular recognition and docking algorithms. Annu Rev Biophys Biomol Struct 2003; 32: 335-373
• 33 Alonso H, Blinyuk AA, Gready JE. Combining docking and molecular dynamic simulations in drug design. Med Res Rev 2006; 26: 531-568
• 34 Yang JS, Chun K, Park JE. et al. Structure based optimization of chromen-based TNF-a converting enzyme (TACE) inhibitors on S10 pocket and their quantitative structure–activity relationship (QSAR) study. Bioorg Med Chem 2010; 18: 8618-8629
• 35 Lipsky PE, van der Heijde DM, St Clair EW. et al. Infliximab and methotrexate in the treatment of rheumatoid arthritis. Anti-Tumor Necrosis Factor Trial in Rheumatoid Arthritis with Concomitant Therapy Study Group. N Eng J Med 2000; 343: 1594-1602
• 36 Machold KP, Smolen JS. Adalimumab - a new TNF-alpha antibody for treatment of inflammatory joint disease. Expert Opin Biol Ther 2003; 3: 351-360
• 37 Cavasotto CN, Phatak SS. Homology modeling in drug discovery: Current trends and applications. Drug Discov Today 2009; 14: 676-683
• 38 Baker D, Sali A. Protein structure prediction and structural genomics. Science 2001; 294: 93-96
• 39 Saravanan P, Avinash H, Dubey VK. et al. Targeting essential cell wall lipase rv3802c for potential therapeutics against Tuberculosis. J Mol Graphics Model 2012; 38: 235-242
• 40 Oprea TI, Matter H. Integrating virtual screening in lead discovery. Curr Opin Chem Biol 2004; 8: 349-358
• 41 Kuhn M, Campillos M, Letunic I. et al. A side effect resource to capture phenotypic effects of drugs. Mol Syst Biol 2010; 6: 343
• 42 Liu T, Lin Y, Wen X. et al. BindingDB: A web-accessible database of experimentally determined protein–ligand binding affinities. Nucleic Acids Res 2007; 35: D198-D201
• 43 Hendlich M, Bergner A, Günther J. et al. Relibase: Design and development of a database for comprehensive analysis of protein–ligand interactions. J Mol Biol 2003; 326: 607-620
• 44 Irwin JJ, Shoichet BK. ZINC − a free database of commercially available compounds for virtual screening. J Chem Inf Model 2005; 45: 177-182
• 45 Wang F, Eric Knabe W, Li L. et al. Design, synthesis, biochemical studies, cellular characterization, and structure-based computational studies of small molecules targeting the urokinase receptor. Bio Med Chem 2012; 20: 4760-4773