Ligand based drug design and discovery - computational aspects.pptx

Ligand based drug design and discovery - computational aspects

1. Ligand Based Drug Design and Discovery Asst Prof Dr Anand Gaurav Faculty of Pharmaceutical Sciences, UCSI University, Kuala Lumpur, Malaysia 1

2. Contents Drug discovery Traditional vs Modern Drug Discovery Computational Drug Design Ligand Based Drug Design (LBDD) and Structure Based Drug Design (SBDD) LBDD or SBDD? LBDD Basics LBDD Types QSAR Pharmacophore Modeling Scaffold Hopping 2

3. Drug Discovery Duelen , Robin & Corvelyn , Marlies & Tortorella, Ilaria & Leonardi, Leonardo & Chai, Yoke & Sampaolesi , Maurilio. (2019). Medicinal Biotechnology for Disease Modeling, Clinical Therapy, and Drug Discovery and Development. 10.1007/978-3-030-22141-6_5. 3 The drug discovery and design process in modern times is highly dependent on Ehrlich’s assumption , in which drugs work as “magic bullets” modulating one target of particular relevance to a disease.

4. Traditional vs Modern Drug Discovery Traditional Drug Discovery Modern Drug Discovery 4

5. Computational Drug Design The Nobel Prize in Chemistry 2013 was awarded jointly to Martin Karplus, Michael Levitt, and Arieh Warshel for their pioneering contributions in the development of multiscale models for complex biochemical systems This was a big recognition of the important role that theory and computational methods play as a direct and necessary complement to experiments 5

6. Computational Drug Design 6

7. Computational Drug Design Ou -Yang, S., Lu, J., Kong, X. et al. Computational drug discovery. Acta Pharmacol Sin 33, 1131–1140 (2012). https://doi.org/10.1038/aps.2012.109 7

8. Ligand Based Drug Design (LBDD) and Structure Based Drug Design (SBDD) 8

9. LBDD or SBDD? 9

10. LBDD Basics Ligand based drug design is an approach used in the absence of the receptor 3D information and it relies on knowledge of molecules that bind to the biological target of interest 3D quantitative structure activity relationships (3D QSAR) and pharmacophore modeling are the most important and widely used tools in ligand-based drug design They can provide predictive models suitable for lead identification and optimization 10

11. LBDD Types Ligand-based drug design is inherently a complicated problem as this approach is restricted to considering only one side of the actual biochemical process It has been shown in many cases that receptor molecules and/or ligands undergo significant conformational changes to facilitate their interaction 11

12. QSAR 12

13. QSAR Basics 13

14. QSAR Workflow 14

15. Descriptors 15

16. Selection of appropriate descriptors 16

17. Model development 17

18. 2D QSAR Model 18

19. 2D Descriptors 19

20. 3D QSAR Models 20

21. Model Validation 21

22. Advantages & Disadvantages of QSAR Advantages Able to predict activities of many compounds with little to no prior experimental data on activity. Can reveal which molecular properties may be worth investigating further. Regarded as a “green chemistry” approach since chemical waste is not generated when performing in silico predictions. In vivo and in vitro experimentation can be very expensive and time-consuming. QSAR modelling reduces the need for testing on animals and/or on cell cultures and saves time. Disadvantages Does not provide an in-depth insight on the mechanism of biological action. Some risk of highly inaccurate predictions of pharmacological or biological activity. 22

23. QSAR Methods Reviews • Exploring QSAR: Fundamentals and Applications in Chemistry and Biology by Corwin Hansch et al (ISBN-13:9780841229877) • QSAR: Hansch Analysis and Related Approaches by R Mannhold et al (ISBN: 978-3-527-61683-1) • Practical guide How to use and report (Q)SARs (https://echa.europa.eu/documents/10162/13655/pg_report_qsars_en.pdf) • Quantitative structure—activity relationships (QSAR) (DOI: 10.1016/0169-7439(89)80083-8) • Best Practices for QSAR Model Development, Validation, and Exploitation (DOI: 10.1002/minf.201000061) • Predictive QSAR Modeling Workflow, Model Applicability Domains, and Virtual Screening (DOI: 10.2174/138161207782794257) • How not to develop a quantitative structure-activity or structure-property relationship (QSAR/QSPR). (DOI: 10.1080/10629360902949567) • QSAR Modeling: Where Have You Been? Where Are You Going To? (DOI: 10.1021/jm4004285) • Descriptor Selection Methods in Quantitative Structure–Activity Relationship Studies: A Review Study (DOI: 10.1021/cr3004339) • New approaches to QSAR: Neural networks and machine learning (DOI: 10.1007/BF02174529) • Machine Learning: An Artificial Intelligence Approach (ISBN: 366212405X, 9783662124055) 23

24. Pharmacophore Modeling 24

25. Pharmacophore Different molecules with very different chemical nature may still have same activity and mechanism of action, common denominator of such molecules is the PHARMACOPHORE An ensemble of steric and electronic features that is necessary to ensure the optimal supramolecular interactions with a specific biological target and to trigger (or block) its biological response Typical pharmacophore features include hydrophobic centroids , aromatic rings , hydrogen bond acceptors or donors , cations , and anions . These pharmacophoric points may be located on the ligand itself or may be projected points presumed to be located in the receptor. 25

26. Pharmacophore Modeling Steps 26

27. Tools Available 27

28. Pharmacophore Modeling Applications 28

29. Pharmacophore Based Virtual Screening 29

30. Pharmacophore References A. R. Leach, V. J. Gillet, R. A. Lewis, R. Taylor Three-Dimensional Pharmacophore Methods in Drug Discovery J. Med. Chem. 2010, 53, 539-558 (http://dx.doi.org/10.1021/jm900817u) T. Seidel, G. Ibis, F. Bendix, G. Wolber Strategies for 3D pharmacophore-based virtual screening Drug Disc. Today: Technologies 2010, 7, e221-e228 (http://dx.doi.org/10.1016/j.ddtec.2010.11.004) G. Hessler, K.-H. Baringhaus The scaffold hopping potential of pharmacophores Drug Disc. Today: Technologies 2010, 7, e263-e269 (http://dx.doi.org/10.1016/j.ddtec.2010.09.001) M. Hein, D. Zilian, C. A. Sotriffer Docking compared to 3D-pharmacophores: the scoring function challenge Drug Disc. Today: Technologies 2010, 7, e2229-e236 (http://dx.doi.org/10.1016/j.ddtec.2010.12.003) F. Caporuscio, A. Tafi Pharmacophore Modelling: A Forty Year Old Approach and its Modern Synergies Curr. Med. Chem. 2011, 18, 2543-2553 I. Wallach Pharmacophore Interference and its Application to Computational Drug Discovery Drug Dev. Res. 2011, 72, 17-25 (http://dx.doi.org/10.1002/ddr.20398) 30

31. Pseudoreceptors 31

32. Pseudoreceptor modeling Pseudoreceptor models provide an entry point for receptor-based drug design approaches for cases in which high-resolution structures of targets are lacking. The methodology of pseudoreceptor model generation involves three fundamental tasks. First, the presumed key interaction sites (anchor points) of the ligand–receptor complex are defined Second, the core pseudoreceptor model is assembled around these hypotheses Third and last, model coordinates are optimized to gain more accurate calculated binding energies in validation studies 32 Tanrikulu , Y., Schneider, G. Pseudoreceptor models in drug design: bridging ligand- and receptor-based virtual screening.  Nat Rev Drug Discov   7,  667–677 (2008).

33. Pseudoreceptor modeling steps 33 Tanrikulu , Y., Schneider, G. Pseudoreceptor models in drug design: bridging ligand- and receptor-based virtual screening.  Nat Rev Drug Discov   7,  667–677 (2008).

34. Pseudoreceptor construction routes 34 Tanrikulu , Y., Schneider, G. Pseudoreceptor models in drug design: bridging ligand- and receptor-based virtual screening.  Nat Rev Drug Discov   7,  667–677 (2008). Grid-based ( a ); Isosurface -based ( b ); Partition-based ( c ); Atom-based ( d ); Peptide-based ( e ); and Fragment-based ( f ) Pseudoreceptor models around a structural alignment of flurbiprofen (light grey) and ibuprofen (magenta).

35. Scaffold Hopping 35

36. Scaffold Hopping Scaffold Hopping: “Identification of structurally novel compounds by modifying the central core structure of the molecule” Patent reasons: move away from competitor compounds Provide alternate lead series if problems arise due to difficult chemistry or poor ADME properties Descriptors for scaffold hopping Reduced graphs Topological pharmacophore keys 3D descriptors 36 Hans-Joachim Böhm et al. Scaffold hopping, Drug Discovery Today: Technologies, Volume 1, Issue 3, 2004, Pages 217-224,

37. Some Success Stories 37

38. Thanks 38