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DRUG DISCOVERY AND DEVELOPMENT M. Hanafi Puslit Kimia LIPI Kawasan PUSPIPTEK, Serpong.

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Presentation on theme: "DRUG DISCOVERY AND DEVELOPMENT M. Hanafi Puslit Kimia LIPI Kawasan PUSPIPTEK, Serpong."— Presentation transcript:

1 DRUG DISCOVERY AND DEVELOPMENT M. Hanafi Puslit Kimia LIPI Kawasan PUSPIPTEK, Serpong

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3 Target Identification And Validation Search of Lead Structure Optimization of Lead Structure Preclinical Development Research Phases in Drug Development Idea Lead Structure Candidate for Development Product

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7 DEVELOPMENT of NOVEL DRUGS from NATURAL PRODUCT 1.Screening of Natural Compounds for Biological Activity : Soil, plants, fungi, etc 2. Isolation and Purification of Active Principle 3. Determination of Structure : NMR, IR, MS 4. Structure-Activity relationships(SAR) : Identification of Pharmacophore 5. Synthesis of Analogues : Increase activity, reduce side effects 6. Receptor Theories : binding site information 7. Design and Synthesis of Novel Drug Structure

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14 Vincristine (R = -CHO) – Vinblastine (R = -CH 3 ) Vinca rosea (Catharanthus roseus) (Apocynaceae) Camptothecin Camptotheca acuminata Topotecan Lead compounds from Natural Products

15 Discovery from Natural Products Lovastatin Aspergillus tereus Anticholesterol - Streptomycesp sp Cytotoxic to P338, KB Phenazine carbioxylate Pseudomonas pycocyaneae Calanone Callophyllum tesmanii Sulochrin - Antidiabetes Aspergillus terreus

16 Gingerol Curcumin Piperine Lead Compounds

17 Rational drug design X-ray crystallography has developed so that the determination of the 3-D crystal structures of proteins and receptors is coming easier. X-ray crystallography has developed so that the determination of the 3-D crystal structures of proteins and receptors is coming easier. The Protein Data Bank (see has data for hundreds of published structures which are all freely available The Protein Data Bank (see has data for hundreds of published structures which are all freely available Coupled with advances in computing power and molecular modelling the so-called rational or structure- based drug design. Coupled with advances in computing power and molecular modelling the so-called rational or structure- based drug design.

18 Diagram 1. Natural Product Drug Development from new information to new therapy (Guo et al., 2006)

19 Influencing Bio-molecular Processes Target = enzyme, receptor, nucleic acid, … Ligand = substrate, hormone, other messenger,...

20 Protein BcL-xL -

21 α-Glukosidase Visualisasi enzim α-Glukosidase Binding site prediction Positon of ligand in enzym target

22 Enzym HMG-CoA Reductase

23 Virtual Screening by in Silico Docking

24 New Technologies and should Enable Parallel Process and Faster Time to Market at Lower Cost

25 Drugs Fail Because of two Major Reason 39 % fail due to deficiencies in Absorption, Distribution, Metabolism & Elimination (ADME) 30% fail due to lack of efficacy 11% fail due to animal toxicity 10% fail due to adverse effects in man 5% fail due to commercial reason 5% miscellaneous

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28 H-bonding, electrostatic and hydrophobic interactions can be identified and, hopefully, optimised by “in silico” design. hydrogen bonding hydrophobic π-stacking interaction Design of DHODH Inhibitors

29 Properties of orally Available Drug-like Compounds The Lipinski : Rule of five criteria Molecular weight 500 Da Molecular weight 500 Da Log P ≤ 5 Log P ≤ 5 Hydrogen bond donors (OH and NH) ≤5 Hydrogen bond donors (OH and NH) ≤5 Hydrogen bond acceptors (lone-pairs of hetero-atoms, like O and N) Hydrogen bond acceptors (lone-pairs of hetero-atoms, like O and N) Number of heavy atoms 10–70 Number of heavy atoms 10–70

30 Curcumin PGV-0 HGV-0 PGV-1 HGV-1

31 CompoundHeLaT47DRajiMCF-7Myeloma Curcumin *6 PGV *3 PGV-1ND1.5ND2.5*ND Cytotoxic effect of curcumin, PGV-0 and PGV-1 on some cell ’ s types (IC 50,  M) * Concentrations to induce cell apoptosis as indicated by PARP cleavage Log P

32 For “direct analogues”, a new lead must normally promise improvements in properties over an existing drug to be pursued. They are sometimes known as “me-too compounds”. For example ACE inhibitors: Direct and structural analogues Captopril Log P 3.09 Enalapril Log P 0.24

33 Since the discovery of captopril many new ACE inhibitors have been discovered. The active site model of ACE was significantly improved, and the development of enalaprilat (enalapril) showed that carboxylates could be used as the zincbinding motif if the structure benefited from additional hydrophobic binding. Success inspires competition IC IC Log P Enalapril Log P 3.09 Log P -0.1 Log P -0.52

34 DEVELOPMENT OF LOVASTATIN FoR ANTICHOLESTEROL DEVELOPMENT OF LOVASTATIN FoR ANTICHOLESTEROL

35 Find and Optimized a Lead Compound: Lovastatin » Minimise energy of structure : Chem3D, Gaussian, Mopac, » QSAR (hub. Struktur Aktivits) : HyperChemPro » Direct Ligand Design (HMG-CoA rductase): Arguslab 4.0 » Synthesis » Bioaactivity Test

36 METHODOLOGY Sintesis Activity evaluation In vivo Drug Design Hyperchem &Docking Active Anticholesterol compound QSAR Parameter Identificatio n Evaluation Results Total cholesterol (mg/dl) Evaluation Results: HDL (mg/dl)

37 HIPOTESIS “… makin mudah menembus dinding usus halus” = makin tinggi aktivitasnya “Perubahan Polaritas/Sterik

38 DESAIN 2: Mengisi pusat aktif enzim [docking] Tabernero et al. J. Biol. Chem., 2003 Lovastatin fit terhadap enzim melalui 4 buah interaksi:

39 Log P 3.77 Log P 5.73 Log P 5.68 SIMVASTATIN & LOVASTATIN DERIVATIVES AND LOG P Log P 4.8 Log P 4.6

40 Interaction Dehydrolovastatin (grey) and the active site of HMG-CoA reductase (dark)NOCompounds Interaction Energy (kcal / mol) Log P 1 Substrat (HMG-CoA) - 10, ,5055 2Dehydrolovastin Lovastatin (1) - 9, Simvastatin (2) - 8,86 - 8, Buthyl ester (Lovastatin) - 9,91 4,92 INTERACTION ENERGY WITH HMG CoA REDUCTASE AND LOG P ArgusLab 4.0 HyperChem 7.0

41 Synthesis Dehydrolovastatin 88,3 % (EtOH) Heksan:EtOAC (4:1) Lovastatin

42 ParameterNormal control Hiperlipi- demic Simvastatin (7,2 mg/ 200 g bw) Lipistatin (7,2 mg/ 200 g bw) Lipistatin (14,4 mg/ 200 g bw) Total cholesterol (mg/dl) (%) 111,79156,66112,03 (28,49%) 106,64 (31,93 %) 105,54 (32,55 %) Trigliseride (mg/dl) (%) 106,29172,53102,28 (40,72%) 103,85 (40,0%) 94,79 (45,06%) LDL-cholesterol (mg/dl) (%) 32,3472,9930,23 (58,58%) 25,00 (65,75%) 28,77 (60,58%) HDL- cholesterol (mg/dl) (%) 58,2049,1661,34 (24,77%) 60,87 (23,82%) 57,81 (17,60%) Evaluation Results of Antihiperlipidemic Activity on Rat for Lipistatin and Simvastatin

43 Development of UK-3A analog potential for Breast cancer treatment Lipinski Rule Hyperchem Program MW < 500 g/mol; log P < +5 Virtual Interaction (molecular docking) ArgusLab program Structure Analog Design UK3A in silico

44 Sel Normal vs Sel Kanker Sel Payudara Normal Protein-protein anti-apoptosis (a.l. Bcl-xL) diinhibisi oleh protein-protein pro-apoptosis yang sama banyaknya Protein-protein anti-apoptosis (a.l. Bcl-xL) diinhibisi oleh protein-protein pro-apoptosis yang sama banyaknya Sel Kanker Payudara Protein-protein anti-apoptosis (a.l. Bcl-xL) berlebih, sehingga ada yang tidak terinhibisi Protein-protein anti-apoptosis (a.l. Bcl-xL) berlebih, sehingga ada yang tidak terinhibisiAkibat: Sel payudara rusak tidak alami apoptosis; terus tumbuh dan membelah tidak terkendali (kanker) Simstein et al,

45 Inhibisi Bcl-xL dengan Obat 45 OBAT Bila kelebihan Bcl-xL diinhibisi, sel rusak akan alami apoptosis secara spesifik >> tidak jadi kanker Ricci, et al, Ghobrial, et al, Ferreira, et al, 2002.

46 Optimum Conformation(E min )- Chem3D Ultra Konformasi PDBGE Konformasi PDOGE Chem3D

47 HyperChem Pro (QSAR Parameter) & ArgusLab 4.0 (Ebinding) 47 HyperChem Pro 7.0 ArgusLab 4.0 Interaction of Protein BcL-xL & Analog UK-3

48 Analog UK-3A : PSMOE UK-3A Ring opening (Analog UK-3A) DEVELOPMENT OF ANALOG UK-3A POTENTIAL FOR BREAST CANCER TREATMENT PSMOE BcL-xL Protein

49 QSAR Parameter & Cytotoxic Test Results Log P Eb inding = -7.1 kcal/mol IC 50 = >100 g/ml Log P 1.61 E binding = kcal/mol P388 : IC 50 = 38 g/ml Log P 1.67 E binding = kcal/mol Log P 1.30, E binding = kcal/mol KB :IC 50 = 0.23 mg/ml YMB-1:IC 50 = mg/ml HClg/MeOH

50 Cytotoxic Test Results to P388, KB and YMB-1 IC g/ml (P388) IC g/ml (KB) IC g/ml (YMB-1) IC g/ml (P388) IC g/ml ( KB ) IC g/ml (YMB-1) E binding =-9.66 kcal/mol), Log P 1.5 E binding = kcal/mol); Log P 1.62

51 IC  g/ml (P388) IC  g/ml (KB) IC  g/ml (YMB-1) Log P 3.32 Ebinding

52 SAR Parameter & Cytotoxic Test Results P388, KB and YMB-1 P388 :IC 50 = 7.75 g/ml KB :IC 50 = 0.6 g/ml YMB-1:IC 50 =2.97 g/ml Log P 3.29 E binding = kcal/mol

53 Calanone derivatives and Its Cytotoxic Activity Calanone Ester Calanol Log P 2.32 Against colon cancer cells HCT116: IC 50 = 1.29 µg/mL P388 : IC 50 = 7,5 µg/mL Log P 0.43 Against colon cancer cells HCT116: IC 50 > 20 µg/mL L1210 : 59.4 µg/mL P388 : IC 50 = 15 Cisplatin IC 50 = 1.02 µg/ml Log P Against colon cancer cells HCT116: IC 50 > 20 µg/mL L1210 : 70.0 µg/mL P388 : IC 50 = 15

54 Molegro Virtual Docking (MVD) Alignment of analog compound to ligand Determination of binding site “pocket” in the enzyme Calculation of docking energy value of compound candidate to fill the “pocket” Compound candidate synthesis

55 Inhibitor α-Glukosidase

56 Sulochrine Derivatives

57 LiganSimilarity Score IC50 LiganSimilarity Score Salacinol B C E S Benzophenone Sulochrin dioxybenzene Similarity Calculation Score of the ligan to MVD

58 KESIMPULAN 1.Tanaman Obat dapat dijadukan sumber Ide (Lead Compound) 2.Protein/Enzim tertentu dapat digunakan untuk stimulasi interaksi dengan ligan 3. Drug design sangat membantu dalam mempercepat dalam pengembangan obat 4. Parameter QSAR (Log P) dan Energi dcking dapat dijadikan indikator Optimasi lead Compound

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60 QSAR PARAMETER PARAMETERCalanonCalanol C.Octanoat e C. 2,2-di- Me-butirat C.Phe- propionat Taxol Log P Refractivity (A o ) Polarizability (A o ) Surface area (approx) Surface area (grid) Volume Geometry Optimazatio n(kcal/mpl) Molecular dynamic(kca l)

61 Citra Interaksi Substrat (bola) dengan Pusat Aktif HMG-CoA reduktase (kawat) Citra Interaksi Lovastatin terdehidrasi (kawat abu-abu) dengan pusat aktif HMG-CoA reduktase (kawat gelap)

62 Drug targets Drug targets are most often proteins, but nucleic acids, may also be attractive targets for some diseases. TARGET MECHANISM TARGET MECHANISM Enzyme Inhibitor : reversible or irreversible Enzyme Inhibitor : reversible or irreversible Receptor* : Agonist or antagonist Receptor* : Agonist or antagonist Nucleic acid : Intercalator (binder), modifier Nucleic acid : Intercalator (binder), modifier (alkylating agent) or substrate mimic. (alkylating agent) or substrate mimic. Ion channels* : Blockers or openers Ion channels* : Blockers or openers Transporters* :Uptake inhibitors Transporters* :Uptake inhibitors *present in the cell membranes

63 Rational Drug Design Physiological target where drugs act. 1.Enzymes : where new molecules are made in tissue 2. Receptors where circulating messengers, eg. Biogenic amines and peptides, act to alter cellular activity 3. Transport systems the selectivity permit access through membranes into and out of cells, eg. ion channels, transporter moleculed 4. Cell replication and protein synthesis controlled by DNA and RNA 5. Storage sites where molecules are kept in an inactive form for subsequent release and re-uptake, eg. Blood platelets, neurons

64 1. The antibiotic chloramphenicol is very bitter, but the palmitate ester does not get absorbed by the tongue so much when taken orally and so is more palatable. The succinate ester on the other hand makes it more soluble making intravenous formulation more effective. Once absorbed the esters are quickly hydrolysed. 2. The ACE inhibitor enalaprilat is potent in vitro, but is poorly absorbed and so not very effective in vivo. The ethyl ester enalapril, however, is absorbed much better but is a weak ACE inhibitor. It is hydrolyzed to the carboxylic acid by esterase enzymes in the blood, which is where ACE is found. Prodrugs - examples

65 Drug developmentStructure-based drug design65 Drug candidates Bind to specific protein, usually receptors or enzymes Bind to specific protein, usually receptors or enzymes Ease of absorption, distribution, metabolism, and excretion (ADME) Ease of absorption, distribution, metabolism, and excretion (ADME)

66 The Contribution of IT to Drug Discovery is Increasing

67 67 Drug designing based on 3D structural information Binding model: Lock and key Binding model: Lock and key Small molecule: complement in shape and electronic structure Small molecule: complement in shape and electronic structure Molecule features obey Lipinski’s rule Molecule features obey Lipinski’s rule –Mw < 500 –Σ hydrogen bond donors < 5 –Σ hydrogen bond donor acceptors <10 –Partition coefficient (log P) < 5

68 Candidate drugs Apoptosis:  P53  Bax  Bcl-2  Akt/PKA  NF  B  Caspase-3 Cell cycle:  CyclinD1  NF  B  COX-2  Kinase  Transcription factors Metastasis and Angiogenesis Tumor Promotion

69 α-Amilase structureStructure-based drug design69 A domain C domain B domain Binding pocket Ca 2+ α-Amilase structure PDB 7TAA

70 HIV protease structureStructure-based drug design70 Binding pocket D25 Catalytic residues HIV protease structure PDB 1HSH


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