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DRUG DISCOVERY AND DEVELOPMENT

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Presentation on theme: "DRUG DISCOVERY AND DEVELOPMENT"— Presentation transcript:

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

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

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7 DEVELOPMENT of NOVEL DRUGS from NATURAL PRODUCT
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 Lead compounds from Natural Products
Vincristine (R = -CHO) – Vinblastine (R = -CH3) Vinca rosea (Catharanthus roseus) (Apocynaceae) Camptothecin Camptotheca acuminata Topotecan

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

16 Lead Compounds Curcumin Piperine Gingerol

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. 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.

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

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

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

31 Cytotoxic effect of curcumin, PGV-0 and PGV-1 on some cell’s types (IC50 , M)
Compound HeLa T47D Raji MCF-7 Myeloma Curcumin 15.76 20 14 20* 6 PGV-0 7.60 10 3 10* PGV-1 ND 1.5 2.5* Log P 2.56 3.19 2.94 * Concentrations to induce cell apoptosis as indicated by PARP cleavage

32 Direct and structural analogues
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: Captopril Enalapril Log P 0.24 Log P 3.09

33 Success inspires competition
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. IC50 75 18.33 IC50 4.08 1 Log P -0.92 Log P -0.1 Enalapril Log P 3.09 Log P -0.52

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

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

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

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

40 Interaction Energy (kcal / mol)
HyperChem 7.0 ArgusLab 4.0 Interaction Dehydrolovastatin (grey) and the active site of HMG-CoA reductase (dark) INTERACTION ENERGY WITH HMG CoA REDUCTASE AND LOG P NO Compounds Interaction Energy (kcal / mol) Log P 1 Substrat (HMG-CoA) - 10,5055 2 Dehydrolovastin - 9.95 4.80 3 Lovastatin (1) - 9,48 3.77 4 Simvastatin (2) - 8,86 5.73 5 Buthyl ester (Lovastatin) - 9,91 4,92 40

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

42 Evaluation Results of Antihiperlipidemic Activity
on Rat for Lipistatin and Simvastatin Parameter Normal control Hiperlipi-demic Simvastatin (7,2 mg/ 200 g bw) Lipistatin (14,4 mg/ Total cholesterol (mg/dl) (%) 111,79 156,66 112,03 (28,49%) 106,64 (31,93 %) 105,54 (32,55 %) Trigliseride (mg/dl) 106,29 172,53 102,28 (40,72%) 103,85 (40,0%) 94,79 (45,06%) LDL-cholesterol (mg/dl) 32,34 72,99 30,23 (58,58%) 25,00 (65,75%) 28,77 (60,58%) HDL-cholesterol (mg/dl) 58,20 49,16 61,34 (24,77%) 60,87 (23,82%) 57,81 (17,60%) 42

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

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 Sel Kanker Payudara Protein-protein anti-apoptosis (a.l. Bcl-xL) berlebih, sehingga ada yang tidak terinhibisi Akibat: Sel payudara rusak tidak alami apoptosis; terus tumbuh dan membelah tidak terkendali (kanker) Simstein et al, 2003.

45 Inhibisi Bcl-xL dengan Obat
Bila kelebihan Bcl-xL diinhibisi, sel rusak akan alami apoptosis secara spesifik >> tidak jadi kanker Ricci, et al, 2006. Ghobrial, et al, 2005. Ferreira, et al, 2002.

46 Optimum Conformation(Emin)- Chem3D Ultra 10
Konformasi PDBGE Konformasi PDOGE

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

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

49 QSAR Parameter & Cytotoxic Test Results
HClg/MeOH Log P -1.18 Ebinding = -7.1 kcal/mol IC50 = >100 mg/ml Log P 1.61 Ebinding = kcal/mol P388 : IC50 = 38 mg/ml Log P 1.30, Ebinding = kcal/mol KB :IC50 = mg/ml YMB-1:IC50 = mg/ml Log P 1.67 Ebinding = kcal/mol 49

50 Cytotoxic Test Results to P388, KB and YMB-1
Ebinding=-9.66 kcal/mol), Log P 1.5 IC50 34 mg/ml (P388) IC mg/ml (KB) IC mg/ml (YMB-1) Ebinding= kcal/mol); Log P 1.62 IC50 38 mg/ml (P388) IC mg/ml (KB) IC mg/ml (YMB-1) 50

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

52 SAR Parameter & Cytotoxic Test Results P388, KB and YMB-1
Log P 3.29 Ebinding = kcal/mol P388 :IC50 = 7.75 mg/ml KB :IC50 = 0.6 mg/ml YMB-1:IC50 =2.97 mg/ml 52

53 Calanone derivatives and Its Cytotoxic Activity
Ester Calanol Log P -0.42 Log P 0.43 Against colon cancer cells HCT116: IC50 > 20 µg/mL L1210 : 59.4 µg/mL P388 : IC50 = 15 Against colon cancer cells HCT116: IC50 > 20 µg/mL L1210 : 70.0 µg/mL P388 : IC50 = 15 Log P 2.32 Against colon cancer cells HCT116: IC50 = 1.29 µg/mL P388 : IC50 = 7,5 µg/mL Cisplatin IC50 = 1.02 µg/ml

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 Similarity Calculation Score of the ligan to MVD
Similarity Score IC50 Salacinol 4 B 22.4 5 C 2 E 7 S3 6 1 Benzophenone-6 Sulochrin 80.4 dioxybenzene

58 KESIMPULAN Tanaman Obat dapat dijadukan sumber Ide (Lead Compound)
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

59 TERIMA KASIH

60 QSAR PARAMETER PARAMETER Calanon Calanol C.Octanoate C. 2,2-di-Me-butirat C.Phe-propionat Taxol Log P 0.43 -0.42 2.32 1.96 1.2 2.25 Refractivity (Ao) 133.1 133.7 170.5 161.2 176.4 233.6 Polarizability (Ao) 45.4 49.7 61.7 58.07 62.2 87.8 Surface area (approx) 312.1 432.9 576.1 393.1 437.8 122.2 Surface area (grid) 477.5 603.5 634.8 574.8 646.9 819.4 Volume 861.8 1068.4 1163.6 1056.8 1172.5 1532.1 Geometry Optimazation(kcal/mpl) 146.2 31.2 146.0 149.2 148.4 287.9 Molecular dynamic(kcal) 200.5 80.5 224.3 219.0 219.9 400.5

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 Enzyme Inhibitor : reversible or irreversible Receptor* : Agonist or antagonist Nucleic acid : Intercalator (binder), modifier (alkylating agent) or substrate mimic. Ion channels* : Blockers or openers Transporters* :Uptake inhibitors *present in the cell membranes

63 Rational Drug Design Physiological target where drugs act. 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 Prodrugs - examples 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.

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

66 The Contribution of IT to Drug Discovery is Increasing

67 Drug designing based on 3D structural information
Binding model: Lock and key Small molecule: complement in shape and electronic structure 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 Metastasis and Angiogenesis
Tumor Promotion Apoptosis: P53 Bax Bcl-2 Akt/PKA NFB Caspase-3 Cell cycle: CyclinD1 NFB COX-2 Kinase Transcription factors Candidate drugs Metastasis and Angiogenesis

69 Structure-based drug design
α-Amilase structure A domain C domain B domain Binding pocket Ca2+ PDB 7TAA α-Amilase structure Structure-based drug design

70 HIV protease structure
Binding pocket D25 D25 Catalytic residues HIV protease structure Structure-based drug design PDB 1HSH


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