Insilico drug designing

1. Insilico drug designing

2. Modern drug discovery process

3. Drug discovery technologies Target identification Genomics, gene expression profiling and proteomics Target Validation Gene knock-out, inhibition assay Lead Identification High throughput screening, fragment based screening, combinatorial libraries Lead Optimization Medicinal chemistry driven optimization, X-ray crystallography, QSAR, ADME profiling (bioavailability) Pre Clinical Phase Pharmacodynamics (PD), Pharmacokinetics (PK), ADME, and toxicity testing through animals Clinical Phase Human trials

6. Bioinformatics tools in DD Comparison of Sequences: Identify targets Homology modelling: active site prediction Systems Biology: Identify targets Databases: Manage information In silico screening (Ligand based, receptor based): Iterative steps of Molecular docking. Pharmacogenomic databases: assist safety related issues

8. Insilico methods in Drug Discovery Molecular docking Virtual High through put screening. QSAR (Quantitative structure-activity relationship) Pharmacophore mapping Fragment based screening

10. Molecular Docking: classification Docking or Computer aided drug designing can be broadly classified Receptor based methods- make use of the structure of the target protein. Ligand based methods- based on the known inhibitors

11. Receptor based methods Uses the 3D structure of the target receptor to search for the potential candidate compounds that can modulate the target function. These involve molecular docking of each compound in the chemical database into the binding site of the target and predicting the electrostatic fit between them. The compounds are ranked using an appropriate scoring function such that the scores correlate with the binding affinity. Receptor based method has been successfully applied in many targets

12. Ligand based strategy In the absence of the structural information of the target, ligand based method make use of the information provided by known inhibitors for the target receptor. Structures similar to the known inhibitors are identified from chemical databases by variety of methods, Some of the methods widely used are similarity and substructure searching, pharmacophore matching or 3D shape matching. Numerous successful applications of ligand based methods have been reported

20. QSAR QSAR is statistical approach that attempts to relate physical and chemical properties of molecules to their biological activities. Various descriptors like molecular weight, number of rotatable bonds LogP etc. are commonly used. Many QSAR approaches are in practice based on the data dimensions. It ranges from 1D QSAR to 6D QSAR.

21. Pharmacophore mapping It is a 3D description of a pharmacophore, developed by specifying the nature of the key pharmacophoric features and the 3D distance map among all the key features. A Pharmacophore map can be generated by superposition of active compounds to identify their common features. Based on the pharmacophore map either de novo design or 3D database searching can be carried out.

23. Increased application of structure based drug designing is facilitated by: Growth of targets number Growth of 3D structures determination (PDB database) Growth of computing power Growth of prediction quality of protein-compound interactions

24. Summary: role of Bioinformatics? Identification of homologs of functional proteins (motif, protein families, domains) Identification of targets by cross species examination Visualization of molecular models Docking, vHTS QSAR, Pharmacophore mapping

25. Example: use of Bioinformatics in Drug discoveryIdentification of novel drug targets against human malaria

26. Malaria � A global problem! Malaria causes at least 500 million clinical cases and more than one million deaths each year. A child dies of malaria every 30 seconds. Out of four Plasmodium species causing human malaria, P.falciparum poses most serious threat: because of its virulence, prevalence and drug resistance. Malaria takes an economic toll - cutting economic growth rates by as much as 1.3% in countries with high disease rates. There are four types of human malaria: Plasmodium falciparum Plasmodium vivax Plasmodium malariae Plasmodium ovale.

27. Approximately half of the world's population is at risk of malaria, particularly those living in lower-income countries. Today, there are 109 malaria affected countries in 4 regions

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30. http://malaria.who.int/docs/adpolicy_tg2003.pdfhttp://malaria.who.int/docs/adpolicy_tg2003.pdf

31. Problems with the existing drugs Drug resistance is most common problem Adverse effects (Shock and cardiac arrhythmias caused by Chloroquine) Poor patient compliance (Quinine tastes very unpleasant, causes dizziness, nausea etc.) High cost of production for some effective drugs (Atovaquine). Urgent need for identification of novel drug targets which are effective and affordable.

32. Strategies for drug target identification in P. falciparum Parasite culture for functional assays are difficult and expensive. Making computational approaches more relevant. Malaria remains a neglected disease- very few stake holders! Availability of the genomic data of P.falciparum and H.sapiens has facilitated the effective application of comparative genomics. Comparative genomics helps in the identification and exploitation of different characteristic features in host and the parasite. Identification of specific metabolic pathways in P. falciparum and targeting the crucial proteins is an attractive approach of target based drug discovery.

33. Comparison of proteomes helps in identifying important indispensible parasite proteins Out of 5334 predicted proteins in P. falciparum, 60% didn�t show any similarity to known proteins. Hence assigning a physiological functional role to these hypothetical proteins using bioinformatics approach still remains a challenge.

37. Targets identified by comparison of proteins models Identification of two proteasomal proteins of prokaryotic origin, not present in hosts. The protein degradation is an important process in parasite development inside host RBCs.

39. ATP Dependent Protease Machinery ClpQY (PfHslUV system) The HslUV complex in prokaryotes is composed of an HslV threonine protease and HslU ATP-dependent protease, a chaperone of Clp/Hsp100 family. HslV (ClpQ) subunits are arranged in form of two-stacked hexameric rings and are capped by two HslU (ClpY) hexamers at both ends. HslU (ClpY) hexamer recognizes and unfold peptide substrates with an ATP dependent process, and translocates them into HslV for degradation.

41. ATP Dependent Protease machineries ClpQY (PfHslUV system) The HslUV complex in prokaryotes is composed of an HslV threonine protease and ATP-dependent protease HslU, a chaperone of clp/Hsp100 family. HslV subunits are arranged in the form of two-stacked hexameric rings and are capped by two HslU hexamers at both ends. In an ATP dependent process, HslU hexamer recognizes and unfold peptide substrates and translocate them into HslV for degradation.

43. PfClpQ is almost identical in all P.falciparum speciesPfClpQ is almost identical in all P.falciparum species

54. THESE NETWORKS CAN BE USED FOR FINDING THE DRUG TARGETS THESE CAN ALSO BE USED FOR ANNOTATION OF PROTEINS AND GENES BY COMPARING THEM BY INTERACTOME STUDIES THESE NETWORKS CAN BE USED FOR PATHWAY ANNOTATION BETTER THAN OTHER STUDIES AS THEY ARE BASED ON THE MICROARRAY DATA

55. Tools used: Sequence analysis: Pairwise and multiple sequence alignments, Pfam. Molecular modelling: Modeller Docking: Tripos FlexX, GOLD, Arguslab PP network: R package and Visant

56. Molecular docking hands on Download and install Arguslab in windows Load a PDB file, practice Arguslab tools Follow the tutorial at http://www.arguslab.com/tutorials/tutorial_docking_1.htm