# 1 The application of computational drug design to real life problems Jan Kelder Molecular Design & Informatics N.V. Organon Bioinformatics IV CMBI Nijmegen: Computational Drug Discovery 1 Juni 2006

# 2 Drug targets  Nuclear hormone receptors  G-protein coupled receptors (GPCRs)  Ion channel receptors  Serine proteases  Kinases and Phosphatases  Phosphodiesterases  and many more

# 3 Drug target families A. L. Hopkins, Nature Rev. Drug Disc. 1, (2002)

# 4 Drugs on the market by target families A. L. Hopkins, Nature Rev. Drug Disc. 1, (2002)

# 5 Drug discovery  Molecular Modification  Screening (MTS and HTS)  Virtual Screening - 3D databases  Structure-Based Drug Design

# 6 Molecular modification towards combined 5-HT2 and H1 antagonism phenbenzamine (Antergan ®) mianserin (Tolvon ®) cyproheptadine (Periactin ®) H1 antagonist H1 + 5-HT2 antagonist antidepressant no antidepressant activity H1 + 5-HT2 antagonist

# 7 Molecular modification mianserin (Tolvon ®)cyproheptadine (Periactin ®)

# 8 Molecular modification

# 9 Molecular modification towards combined 5-HT2 and H1 antagonism tripelennamine (Azaron ®) mirtazapine (Remeron ®) cyproheptadine (Periactin ®) H1 antagonist H1 + 5-HT2 antagonist antidepressant no antidepressant activity H1 + 5-HT2 antagonist

# 10 Molecular modification mianserinmirtazapine Noradrenalin (NA) NA uptake blocker alpha-2 antagonist alpha-1 antagonist Serotonin (5-HT) 5-HT2A-2C antagonist Histamine H1 antagonist

# 11 Molecular modification

# 12 Molecular modification mianserin mirtazapine

# 13 5-HT GPCR subtypes 5-HT7 HUMAN 5-HT1A HUMAN 5-HT1B HUMAN 5-HT1D HUMAN 5-HT1E HUMAN 5-HT1F HUMAN 5-HT5A HUMAN 5-HT5B MOUSE 5-HT4 HUMAN 5-HT6 HUMAN 5-HT2B HUMAN 5-HT2A HUMAN 5-HT2C HUMAN

# 14 Molecular modification towards selective 5-HT2C antagonism (S)-(+)-mianserin (R)-(+)-Org 3363 Org Org GC 94 SDZ SER-082 (+)

# 15 (R)-(+) Org 3363 and (+) - SDZ SER-082 : Two selective 5-HT2C antagonists (R)-(+) Org 3363 (= Org 36743) SDZ SER-082 (+)

# 16 Fit of (R)-(+) Org 3363 and (+) enantiomer of SDZ SER-82

# 17 Drug discovery  Molecular Modification  Screening (MTS and HTS)  Virtual Screening - 3D databases  Structure-Based Drug Design

# 18 HTS Compound library HTS Hit Optimization Lead Optimization Confirmed Hit validated activity / structure purified sample In vitro optimization on potency & selectivity Lead fulfill potency / selectivity criteria and show activity in in vitro, ex vivo, or in vivo proof of principle model Development compound ADMETADMET High Throughput Screening

# 19 High throughput screening 200,000 Confirmed actives Retesting solid (+ LC-MS) Retesting Purification/Resynthesis Lead compounds 0-20 High Throughput Screening

# compounds/plate up to 150 plates/day 384-wells plate Orally active LH agonist: Robot screening for LH receptor agonists

# 21 HTS on human luteinizing hormone receptor agonists Confirmed hit: EC50 = 1.4  M Lead compound Org 41841: EC50 = 0.03  M (= 30 nM) Optimized compound Org 42599: EC50 = 3.1 nM Not orally activeOrally active Orally active

# 22 LMW LH agonists: Org selected for development

# 23 Drug discovery  Molecular Modification  Screening (MTS and HTS)  Virtual Screening - 3D databases  Structure-Based Drug Design

# 24 Decision Tree program above below sea-level

# 25 Decision Tree program

# 26 N N X R4 R7 R6 R3 R2 R1 Y Synthesize first set of compounds based on LH agonist Lead Org (at least 50 compounds) Test LH receptor activity Calculate Molecular Descriptors: (molecular weight, lipophilicity, polar surface) Build Decision Tree which separates active from inactive compounds Decision Tree program Calculated molecular descriptors activeinactive

# 27 Decision Tree pEC50 > 7.5 (n = 201) 23 actives(> 7.5) 23/23 correctly classified(100 %) 178 inactives (< 7.5)173/178 correctly classified( 97 % ) N N X R4 R7 R6 R3 R2 R1 Y

# 28 N N X R4 R7 R6 R3 R2 R1 Y Select substitution site on molecular scaffold (R2 this time) Design virtual library of compounds Calculate Molecular Descriptors of all virtual compounds Apply Decision Tree and predict active and inactive compounds Select, synthesize and test active compounds Decision Tree program

# 29 Virtual library design Selection of amines based on availability (ACD database) and predicted potency (pEC50 LH-CHO > 8.0; decision tree model derived from 250 analogues) +

# 30 Virtual library design ACD Select Reagents Generate Library Predict Actives 65 predicted actives 1934 library compounds 1934 amines

# 31 Virtual library for substitution at R2 derived from 1934 amines: 65 actives 1869 inactives N N X R4 R7 R6 R3 R2 R1 Y

# 32 Predicted LMW LH agonists 16/26 correctly predicted > 8.0 (62 %) 23/26 correctly predicted > 7.5 (88 %)

# 33 3D-database pharmacophore searches 5-HT2C and 5-HT2A antagonists X-ray structure of mesulergine

# 34 3D query derived from mesulergine 6-Membered aromatic ring at a distance of 5.18 Angstrom of a basic N atom (type 14D) with a tolerance of 1.0 Two aliphatic carbon atoms connected to the basic N atom Exclusion sphere placed in the direction where the basic N atom can be protonated at a distance of 7.0 Angstrom with a radius of 5.3 Angstrom A second exclusion sphere is placed at a distance of 7.0 Angstrom of the basic N atom in the direction of the N-CH3 bond with a radius of 4.5 Angstrom

# 35 3D-database pharmacophore searches 5-HT2C and 5-HT2A antagonists Chembase: D structures 9229 hits (11.6 % of 79716) 1500 hits available for testing 979 hits used for testing after elimination of 521 compounds tested before on 5-HT2C receptor binding HT2C ligands found (11.5 % of 979) HT2A ligands found (21.5 % of 979)

# 36 Comparison between MTS screen and 3D-database pharmacophore search Chembase: Mesulergine (5-HT2C) Ketanserin (5-HT2A) # hits > 95 % competition # hits > 95 % comp. MTS screening 49 (4.9 %) 83 (8.3 %) (1000 compounds) 3D pharmacophore 113 (11.5 %) 211 (21.5 %) screening (979 compounds) 3D pharmacophore 283 (18.9 %) 470 (31.3 %) screening (1500 compounds) 3.9 x 3.8 x

# 37 Results 3D-database pharmacophore searches 5-HT2C antagonists Org 9283 Two compounds were selected that showed already interesting 5-HT2C antagonistic potency and selectivity (Org 9283 and Org 20659) Org 9283 has been chosen as the lead compound for developing selective 5-HT2C antagonists as potential antidepressants/anxiolytics WO EP

# 38 Drug discovery  Molecular Modification  Screening (MTS and HTS)  Virtual Screening - 3D databases  Structure-Based Drug Design

# 39 Protein Data Bank structures X-ray structures 3194 NMR structures

# 40 Drug targets  Nuclear hormone receptors  G-protein coupled receptors (GPCRs)  Ion channel receptors  Serine proteases  Kinases and Phosphatases  Phosphodiesterases  and many more

# 41 NR4A2-NOT NR4A3-NOR1 NR4A1-NGFI NR5A1-SF1 NR5A2-FTF NR6A1-GCNF NR2F1-COTF NR2F2-ARP1 NR2F6-EAR2 NR2E3-PNR NR2B1-RRXA NR2B2-RRXB NR2A2-HN4G NR2E1-TLX NR2C1-TR2-11 NR2C2-TR4 NR2B3-RRXG NR2A1-HNF4 NR0B1-DAX1 NR0B2-SHP NR1C1-PPAR NR1C2-PPAS NR1C3-PPAT NR1D1-EAR1 NR1D2-BD73 NR1I3-CAR NR1H2-NER NR1H3-LXR NR1H4-FAR NR1I1-VDR NR1B3-RRG1 NR1F3-RORG NR1F2-RORB NR1F1-ROR1 NR1A2-THB1 NR1A1-THA1NR1I2-PXR NR1B2-RRB2 NR1B1-RRA1 NR3C1-GCR NR3C4-ANDR NR3C3-PRGR NR3A1-ESTR NR3A2-ERBT NR3B1-ERR1 NR3B2-ERR2 NR3C2-MCR NR3B2-ERR3 Hormone receptors Dimerisation Lipid metabolism Drug metabolism Cholesterol metabolism Cell growth Development 48 nuclear receptors

# 42 NR4A2-NOT NR5A2-FTF NR2B1-RRXA NR2B2-RRXB NR2A1-HNF4 NR1C1-PPAR NR1C2-PPAS NR1C3-PPAT NR1H2-NER NR1H3-LXR NR1H4-FAR NR1I1-VDR NR1B3-RRG1 NR1F2-RORB NR1F1-ROR1 NR1A2-THB1 NR1I2-PXR NR1B1-RRA1 NR3C1-GCR NR3C4-ANDR NR3C3-PRGR NR3A1-ESTR NR3A2-ERBT NR3B2-ERR3 25 X-rays LBD NR3C2-MCR

# 43 DCA/BFE FE FE DAX1 Heterodimers: CAR, RXR, RAR, TR, PPAR, HNF4, ER Heterodimers: SF1 Drug targets: Nuclear hormone receptors (typical and atypical) LBDDBD

# 44 ER  nuclear receptor domains AB D F E C LBD DBD DCA/BFE

# 45 Ligand binding domains (LBD) nuclear hormone receptors  Progesterone receptor (PR)1A28, 1E3K  Androgen receptor (AR)1I37, 1I38, 1E3G  Estrogen receptor (ER)1A52, 1ERE, 1ERR, 1QKM  1QKN, 1QKT, 1QKU, 3ERD  3ERT, 1G50, 1HJ1  Glucocorticoid receptor (GR)1M2Z, 1NHZ, 1P93  Mineralocorticoid receptor (MR)1Y9R, 1YA3  Vitamin D3 receptor (VDR)1DB1, 1IE8, 1IE9  Retinoic acid receptor (RAR)1EXA, 1EXX, 1FCX, 1FCY  1FCZ, 2LBD, 3LBD, 4LBD  Retinoid X receptor (RXR)1LBD, 1FBY, 1G1U, 1G5Y  1DKF, 1FM6, 1FM9  Peroxisome proliferator-activated rec.1K74, 1K7L, 1KKQ, 1PRG (PPAR)2PRG, 3PRG, 4PRG, 1GWX  2GWX, 3GWX

# 46 Steroid hormone receptors Progesterone Testosterone Dihydrotestosterone Estradiol Aldosterone Corticosterone Calcitriol etc H-bond donor HD H-bond donor HD2 Progesterone

# 47 LBD nuclear progesterone receptor in complex with progesterone PDB code 1A28 P.B. Sigler and S.P. Williams, Nature 393, (1998) Q R T

# 48 Synthetic Steroidal Progestogens progesterone (1933) ethisterone (1938)northisterone (1956) norethynodrel (1957) lynestrenol (1962)norgestrel (1966) desogestrel (1981) gestodene (1987) norgestimate (1986)

# 49 Synthetic Steroidal Progestogens drospirenone (2000) etonogestrel (1999)

# 50 LBD nuclear progesterone receptor in complex with etonogestrel (model) PDB code 1A28 Q R T

# 51 LBD nuclear androgen receptor in complex with dihydrotestosterone PDB code 1I37 J.S. Sack et al., Proc. Nat. Acad. Sci. USA 98, (2001) Q R T

# 52 Non-steroidal androgens Kaken (WO ) Ligand (WO ) R = Cl, H Univ. of Tennessee

# 53 LBD nuclear androgen receptor in complex with Kaken compound (MD simulation)

# 54 Kaken compound (MD minimum) + dihydrotestosterone (DHT)

# 55 Homology modelling  In case no experimental 3D structure of the LBD of a nuclear receptor is available homology modelling can be tried  Template selection  Sequence alignment between target and template  Model building  Optimization of the model  Validation  Ligand docking

# 56 Homology model LBD nuclear vitamin D3 receptor vs. experimental structure  Homology model (green) LBD VDR based on LBD PPAR  u X-ray structure VDR (blue) u Alignment: D. R. Boer et al, Thesis University of Utrecht (2001) 33 % similarity for residues % similarity for residues

# 57 LBDs nuclear vitamin D3 receptor and PR in complex with calcitriol and progesterone PDB code 1DB1 PDB code 1A28

# 58 Drug targets  Nuclear hormone receptors  G-protein coupled receptors (GPCRs)  Ion channel receptors  Serine proteases  Kinases and Phosphatases  Phosphodiesterases  and many more

# 59 Bovine rhodopsin X-ray model K. Palczewski et al., Science 289, (2000) PDB code 1F88

# 60 X-ray models GPCR and G-protein  ß 

# 61 Ligand binding domains (LBD) G-protein coupled receptors(GPCRs)  Follicle Stimulating Hormone (FSH) receptor  Luteinizing Hormone (LH) receptor  Thyroid Stimulating Hormone (TSH) receptor  Serotonin (5-HT) receptors  and many more

# 62 TRANSMEMBRANE REGION hLH receptor + Org (MD)

# 63 Lead optimization LH agonist LH receptor homology model

# 64 TRANSMEMBRANE REGION hLH receptor + Org (MD)

# 65 LO LMW LH agonists

# 66

# 67 Luteinizing hormone (LH) + LMW LH agonist Org 41841

# 68 LH receptor activation LHR TM domain EC domain LH/hCG LMW LH agonist

# 69 LFR/FLR chimeric receptors Chimeric receptors respond as expected mU/ml

# 70 LTR/TLR chimeric receptors Org binds in TM domain of LH receptor M

# 71 Ligand binding domains (LBD) G-protein coupled receptors(GPCRs  Follicle Stimulating Hormone receptor  Luteinizing Hormone receptor  Thyroid Stimulating Hormone receptor  Serotonin (5-HT) receptors  and many more

# 72 Serotonin GPCRs Acetylcholine Noradrenalin Adrenalin Dopamin Serotonin Histamin Opioid etc Acidic residue Asp H-bond acceptor Ser/Thr Serotonin

# 73 5-HT2C GPCR transmembrane model based on bacteriorhodopsin

# 74 Mutation studies 5-HT2C receptor serotonin Org WildtypeS219AF327A 5-HT2Cmutantmutant receptorreceptorreceptor pKipKipKi tryptamine

# 75 5-HT2C GPCR model based on bovine rhodopsin D S

# 76 5-HT GPCRs: Structure based query l TM3: CxxxxxxDxxxxxxxxxxxxxxxxDRY l TM5: xxxxxxxxSxxxFxxPxx TM5: xxxxxxxxTxxxFxxPxx

# 77 5-HT GPCRs 1869 GPCRs 879 GPCRs (unique) 71 GPCRs 12 5-HT GPCRs 59 GPCRs3 new Structure based query

# 78 Conclusions  The PDB forms a rich source of experimental structures that expands rapidly  Homology modeling is useful in cases where experimental structures are not yet available and good templates exist  Knowledge of how ligands bind to proteins can be utilised to suggest annotations of unknown protein sequences