m leads Structure-based Drug Discovery - Small molecule compound databases Drug Discovery & Development (DD&D) Structure-based drug discovery

Transcription

1 tructure-based rug iscovery tructure-based drug discovery h.. homas M. rimurer n ilico screening and drug discovery - mall molecule compound databases - Molecular descriptors - ompound filtering harmacophore perception technology - igand based drug design - Receptor based drug design ite irected rug iscovery - knowledge based approach for hit and lead identification rug iscovery & evelopment (&) mall molecule ligand Key rotein ock rotein ligand complex in vivo efficacy rug iscovery & evelopment (&) High hroughput creenign (H) ompound ( molecules) ssay n vitro screening &: Expensive, time consuming, with numerous bottlenecks & low success rate ew Webster s ictionary: drug any substance used in the composition of a medicine m leads rug ariable -6 months 6-9 months 2-8 months 9-2 months arget selection Hit dentification Hit to ead ead reclinical development rug iscovery & evelopment (&) n ilico hit and lead generation irtual 0 20 ompound ( molecules) arget selection arget n vitro screening rug ocused m comp. Hits/ead leads with good drug like rug like filters potential ME: dsorption, istribution, Metabolism & Excretion Hit dentification Hit to ead ead reclinical development hemical atabases & Molecular hemical ibraries roprietary n-house M rug 5 x 0 4 ombinatorial ublicly () atural irtual MW = log =. olar urface rea = 7 Å 2 umber of rotatable bonds = 8 umber of donor atoms = 2 umber of acceptor atoms = 6 umber of rings = 4 Etc. Molecular weight (MW) log olar urface rea () umber of Rotatable Bonds

2 hemical atabases & Molecular hemical ibraries l roprietary n-house M l rug 5 x 04 BR E: ombinatorial hemical atabases & Molecular 00 roprietary n-house M ub-structure fingerprints Macro fingerprints harmacophore Multiplets BR E: ombinatorial R-H.= hemical ibraries R4...l- δ- onic bond (alt bridge) harge-charge interaction H M RH Reinforced H-bonds charge-charge interaction δ RH irtual H π π aromatic interaction ation-π π-aromatic interaction hemical atabases & Molecular hemical ibraries roprietary n-house M onformations (multiple) harmacophore features hape onor cidic romatic &: More than lock & key Excluded volume ublicly () atural H-H=H-H2-H2-R interaction onformations (multiple) harmacophore features hape cceptor ombinatorial H δ R harge-dipole interaction R-H2-H2-H2- H- H rug 5 x 04 Hydrogen bond interaction δ R2H... < δ hemical atabases & Molecular roprietary n-house H rosidian lead R9 ranked nr. 5 form the top among BR E: harmacophore a set of structural features in a molecule that is recognized at a receptor site and is responsible for that molecule's biological activity" igand protein interaction forces ub-structure fingerprints Macro fingerprints harmacophore Multiplets ublicly () atural 00 irtual δ- uery reana lead R9 rug 5 x 04 irtual RH...---R ublicly () atural 2 hemical ibraries rotein rotein ligand complex igand rug 5 x 04 ombinatorial ublicly () atural irtual 2 ub-structure fingerprints Macro fingerprints harmacophore Multiplets ock Key Molecular weight (MW) log olar urface rea () umber of Rotatable Bonds bsorption istribution Metabolism Excretion in vivo efficacy n ilico prediction of ME/ helps to avoid bad drug candidates 2

3 mproving the odds in discriminating drug-like form non drug-like ompound iltering ompound is less likely absorbed when: > 5 H Bond onors (expressed as sum of H's H's) M.W. > 500 og > 5 (Mlog > 4.5) > 0 H Bond acceptors (expressed as sum of 's and 's) Basic iltering based on ipinski s rule of 5: Ref.:.. ipinski et al, dv. rug el. Rev., 997, 2, -25. More elaborate filtering based on physiochemical properties and advanced classification methods eural etwork rug-like Random 05 M 5 x 0 rug 5 x 04 tatistical methods (Multivariate Regression) ecision rees Hidden Markov Models (HMM) eural etworks () etc.. rug-like on drug-like ntegration of n-ilico trategies igand-based drug design harmacophore perception hape & electrostatic properties ead/caffold hopping Molecular connectivity's R ME/ n ilico screening and drug discovery - mall molecule compound databases - Molecular descriptors - ompound filtering harmacophore perception technology - igand based drug design - Receptor based drug design Receptor based drug design harmacophore perception tructure Based ocusing High hroughput ocking e ovo esign ragment based approach etc.. ite irected rug iscovery - knowledge based approach for hit and lead identification igand Based rug esign rug iscovery & evelopment (&) mall molecule ligand rotein rotein ligand complex igand pharmacophore 2 * romat / hydrophob * cceptor * teric fearture * Basic site Bond path constrains Key ock in vivo efficacy

4 Receptor Based rug esign Receptor binding site features ositive site onor site cceptor site site Receptor based drug design raction of ccepted n ilico screening and drug discovery - mall molecule compound databases - Molecular descriptors - ompound filtering harmacophore perception technology - igand based drug design - Receptor based drug design ite irected rug iscovery - knowledge based approach for hit and lead identification ite-irected rug iscovery ite-irected rug iscovery rug discovery in 7M receptors knowledge-based, efficient approach for Hit and ead eneration Know-how on 7M receptor - ligand interactions enerate and screen target-tailored mini- harmacophore and R driven ead ptimization harmacophore (search tool) for in silico screening of large (0 7 ) chemical arget validation & target selection Hit / ead generation ead arget validation & target selection Hit / ead generation ead he rocess hysicochemical analysis to identify pocket-related receptors Know How on 7M receptor-ligand interactions Related targets ssociated igands 7M models arget validation & target selection n-house harmacophore (search tool) for in silico screening ead Hit / ead generation enerate and screen target-tailored mini- 0 7 Hits ailored n-vitro screening ead Highly knowledge-based Residues process: utative facing protein-coupled the ligand-binding receptor R44 pocket R- - which residues? - HRE their relative importance? R-H.. = which physicochemical descriptors? W R K 2H RM.. < antagonism / agonism R R K M H. : H W H E E HRHB HRE H -H=H-H 2-H 2-R R H... - R-H 2-H 2-H 2- H- H H (simplified example) Binding pocket turned into pseudo-sequence M- M- M- M- M- HM K WH HMKWH turned into physicochemical barcode 4

5 dentification of pocket-related receptors with drug-like ligands hysicochemical analysis of binding pockets with known drug-like ligand(s) with no or limited small molecule ligand(s) ocket-related receptor(s) llows for 7M target jumping ocket information M H. H W H E arget site W K features R igand features utative protein-coupled receptor R44 W K R K M H. H W H E E arget receptor harmacophore based on both target receptor pocket and small molecule recognition (in related receptors) harmacophore mutational analysis etc.? search tool for in silico screening of large (07) chemical igand information. arget receptor 7Ms with no useful ligands with drug-like ligand(s) 7Ms with small molecule ligands n-house compound Highly enriched R (40.000) ibrary ocket-related receptor(s) mall arget-tailored Mini (with no or limited small molecule ligand) - eveloped against 20 R targets n-house compound database ibrary ommercial stock-available (07) ocused (0.000) Maintained and pre-computed in-house Kinases Metalloproteases n-silico screening, pharmacophore perception, docking, lead hopping development of target focused (charry picking) on a regular bases Methyltransferases on channels.. - ontinuesly improved and enriched atabases collected from 0 companies - ibraries being updated two to three times a year - ompounds can be acquired on a weekly basis trategy for generating iterative series of 7M target-tailored mini- to identify hits arget Jumping Exemplified with RH2 / 7M receptor binding pockets (yellow) RH2 utative protein-coupled receptor R44 W K R K protein-coupled M H utative receptor R44. H W H E W E K R K M H. M- M- M- M- M- H W HM K WH H E E Random sequences (blue) harmacophores improved throughout process / 2 n house arget receptor n house HMKWH (with no known small molecule ligand) High content Receptor assays Refine query 2 ibrary #2 ibrary # ibrary #4 ibrary # Hits from Hit caffold mprove E.g. agonism 07 expansion jumping drug-like vs. cmps. properties antagonism More/better back-up series ocket information hem. eries hem. hem. eries B eries hem. eries hem. eries E hem. eries igand information. ocket-related receptor(s) with drug-like ligand(s) 7Ms with no useful ligands R-rich Hit series RH2 hemical tractability? HMKWH Hits ibrary # M- M- M- M- M- HM K WH 7Ms with small molecule ligands elf rganizing Map (M) - cluster algorithm based on neural networks and artificial learning. ots (i.e. 7M receptor binding pockets) close to each other are similar 5

6 enerate and screen target-tailored mini- RH2 and binding pocket relationships M H. H W H E arget site W K features R igand features olar olar ositive W K charged R M H. H W H E M W W H K R. M M W H harmacophore n silico screening.2 million compds ositive charged ompound retrieval ~600 ~40 known ligands RH2 receptor receptor /2 RH2 Unrelated by traditional phylogenetics 0 % hit rate < 0µ µm Related by binding pocket properties electivity vs. related targets - provides an enriched Hit rate % H 0 nm 9 / 2 igand uery x actives 6 n top Br 0 0 H igand uery p50 (RH2) 80 4 random 90 arget pocket information tells you which ligand features are really important ombined ligand and RH2 pocket information 5 optimum umber of active 00 ffinity on RH2 receptor (target) Hits identified by harmacophore H H candesartan Known ligands 0 mm umber of screened 00 % ffinity on related and 2 receptors Receptor relationships translates into ligand relationships iverse structural classes onclusions & utlooks n ilico E REE are helpful tools for efficient drug design and development; RU REE can help to speed-up the & process and save funds allocated for real H; omputational ided rug iscovery () can guide organic chemistry synthesis efforts (e.g. n ilico combinatorial ); RU REE helps to cherry-pick ligands and offers binding mode analysis against different targets; RH2 harmacophore hit osartan ligand imilar harmacophore roperties 6