Metin

Discovery of Novel CYP 17 Inhibitors through Homology Modeling and Structure-Based Drug Design

Metin Türkay İ. Halil Kavaklı Koç University, İstanbul, Turkey

Outline Introduction Prostate Cancer

Computational Studies Molecular Dynamics Simulations Molecular Docking Studies Analysis of Computational Results

Experimental Studies Construction of Expression Plasmids Expression Mammalian Cells Activity Assays Toxicity Assays

Lead Optimization QSAR

Summary & Conclusions

Structure-based drug design 3-dimensional structure of the target protein •by x-ray crystallography or NMR spectroscopy •by homology modelling

Identify and model the binding pocket

Screening of compounds •Experimental high-throughput screening •Computational virtual screening Potency optimization •Chemistry based libraries •Structure based optimization

Facts about Prostate Cancer • Most common age-related cause of cancer death among males. • Age and family history are risk factors. • Clinical studies indicated that high levels of androgens is common.

Androgens • Role of androgens – Sex-specific organ development and control • Role of androgen in prostate cancer – Major growth factor for prostate cancer cells; especially testosterone and dihydrotestosterone • Androgens and risk – Testosterone levels are proportional to prostate cancer risk

Testesterone

Dihydrotestesterone

Treatment of Prostate Cancer Hypothalamus

Estrogens

• Anti-androgens for androgen receptors • GnRH analogs and antagonists • Prostatechtomy and Orchiechtomy

Gonadorelin

Gonadorelin Analogs

Pituitary

LH

Adrenal

FSH

Testes

4-DIONE TESTO

Prostate (Carcinoma)

Antiandrogens

Androgen Synthesis Pathway

Target – CYP17 • Cytochrome p450 monooxygenase 17 α hydroxylase / 17, 20 lyase • Microsomal enzyme • Expressed in adrenal tissues and testicles

Target – CYP17 – Structure

2c17

Target Analysis Molecular Dynamics RMSD - Simulation 4 3,5 3 RMSD

• Molecular Dynamics simulation is done with NAMD using CHARMM force field parameters • ~10ns, with 20ps intervals.

2,5 2 1,5 1 0,5 0 0

2

4

6 Time (ns)

8

10

Target Analysis Molecular Dynamics

Target Analysis Molecular Dynamics

In-Silico Drug-Protein Interaction Tests • Virtual screening was done using Autodock – A drug candidate should decrease the energy of the ligand – protein complex when docked to the active site.

• Compound libraries (total 1,500,000) – – – –

Maybridge Ambinter WDI NCI Database

• First, analyze the natural substrates

Docking of Substrates -10.12 kcal/mol

-10.39 kcal/mol

-9.88 kcal/mol

-10.42 kcal/mol

Virtual Screening

Unsuccessful dockings

Compound Library

Virtual Screening

Successful dockings

Virtual Screening Molecules

Docking Energy (kcal/mol)

20543

-15,08

4216

-14,06

7435

-13,94

26310

-13,89

21614

-13,7

Virtual Screening

Steroidal

Non-Steroidal

Detailed Docking Analysis Non-Steroidal Molecules

ID

Virtual Screening

Detailed Docking

N01

-11,54 kcal/mol

-11.99 kcal/mol

N02

-12,47 kcal/mol

-13.24 kcal/mol

N03

-11,01 kcal/mol

-11,93 kcal/mol

N04

-10,96 kcal/mol

-11,30 kcal/mol

N05

-10,72 kcal/mol

-11,30 kcal/mol

N06

-10,04 kcal/mol

-10,81 kcal/mol

N07

-10.96 kcal/mol

-11,02 kcal/mol

N08

-9,12 kcal/mol

-11,11 kcal/mol

N09

-11,58 kcal/mol

-11,77 kcal/mol

N10

-11,52 kcal/mol

-11,93 kcal/mol

N11

-11,35 kcal/mol

-11,83 kcal/mol

N12

-10,75 kcal/mol

-10.82 kcal/mol

N13

-10,96 kcal/mol

-11,77 kcal/mol

N14

-11,88 kcal/mol

-12,25 kcal/mol

N15

-11,58 kcal/mol

-12,07 kcal/mol

N16

-10,57 kcal/mol

-12,26 kcal/mol

N17

-10,17 kcal/mol

-11,91 kcal/mol

N18

-12,20 kcal/mol

-12,43 kcal/mol

N19

-11,30 kcal/mol

-11,72 kcal/mol

N20

-11,30 kcal/mol

12,76 kcal/mol

Detailed Docking Analysis

Steroidal Molecules ID

Virtual Screening

Detailed Docking

S1

-10,52 kcal/mol

-11,91 kcal/mol

S2

-11,08 kcal/mol

-11,72 kcal/mol

S3

-11,10 kcal/mol

-11,63 kcal/mol

S4

-10,83 kcal/mol

-11,36 kcal/mol

S5

-10,03 kcal/mol

-10,21 kcal/mol

S6

-10,09 kcal/mol

-11,12 kcal/mol

S7

-10,51 kcal/mol

-11,14 kcal/mol

Summary of Virtual Screening Results • 20 nonsteroidal and 7 steroidal molecules were further analyzed for docking interactions. • In detailed docking runs 9 of these compounds were eliminated since they lack reactive species close to heme iron. • 18 compounds were subjected to experimental analysis.

Inhibition Tests • Intact cells transfected with CYP17 expression vector are used for inhibition tests. – Adsorption – Distribution – Metabolism – Excression

Experimental Studies

Inhibition (%)

100 50 0 0

20

40 N15 Concentration (µM)

60

80

Relative Luminescence (%)

Cell Viability Assay N15 Concentration vs Inhibition

100 80 60

N15 S3

40 20 0

50 µM

100 µM

200 µM

N15

70

36,2

35,4

S3

23,4

16,3

12,5

Candidate Concentration (µM)

CYP17 Expression Plasmid Expression of CYP17 protein • Mammalian expression vector is constructed. – pcDNA4+cyp17 for mammalian cell expression

CYP17 Expression

HEK-293t cells with pcDNA4+CYP17

Initial Inhibition Tests

Inhibition Assays – IC50 % Inhibition vs. S3 concentration 100

120

% inhibition of CYP17

% inhibition of CYP17

% Inhibition vs. N15 concentration 100 80 60 40 20 0 0

20

40

60

N15 concentration(uM)

IC50 = 35.65 µM

80

80 60 40 20 0 -20

0

20

40

60

S3 concentration (uM)

IC50 = 46.30 µM

80

100

Cell Viability Assays

Lead Optimization • 2 simultaneous approaches – Expertise • Analyze the docking positions of the lead compound • Modify the molecular structure in such a way that the interaction between the active site and drug candidate is increased

– QSAR Analysis • A novel data mining and classification approach

LEAD STRUCTURE Alkyl group for hydophobic tail

H bond donor/acceptor or coordination with heme iron Aromatic group to interact with heme group

Electron donor for coordination with heme iron

Optimization of Molecules Based on N15

OPTIMIZED STRUCTURES O

O N H

N H

NH2

O

O N H

O

O O

OH

OH

QSAR • Quantitative effort of understanding the correlation between the chemical structure of a molecule and its biological and chemical activities – i.e. biotransformation ability, reaction ability, solubility or target activity

• main idea – molecules that are structurally similar should have similar activities also

• Attention! – studying chemical structures of the candidate molecules Æ predict the drug activity

• HOW? – Detect the most significant chemical and structural descriptors of the drug candidates that affect the drug activity

Outline of our approach • Construct molecular structures of each drug, optimize their energy by minimization Ædetermine their confirmation in 3-D space (Marvin Sketch) • Generate chemical descriptors (E-Dragon) • Selected the most significant ones among these descriptors (PLS study, MINITAB) • Classify the drugs in terms of activity(MILP based hyperboxes method) • Perform significance analysis to improve classification accuracy

CLASSIFICATION CONCEPT SVM

MIP

x2

x1

x1

OPTIMIZATION MODEL ∨ YPB l

∀i

il

∨ YPC k

∨ YPB l

∀i

ik

⇔ ∨ YPCik

il

∀i

k

YBl ⇒ ∨ YBClk ∀l k

YBClk ⇒ ∨ YPBil ∀lk i

YBClk ⇒ ∨ YPCik ∀lk i

∧ YPBN ∧ YPBM n

m

ilmn

ilm

⇒ YPBM ilm ⇒ YPCik

YPCik ⇒ YP1ik ¬YPCik ⇒ YP2ik

∀ilm ∀ilk

∀ik ∉ Dik ∀ik ∈ Dik

Classification of drugs… • Apply the MILP based hyper-boxes method – By using the selected descriptors from the previous step

• The strength of hyper-boxes classification method Æ ability to use more than one hyper-box to define a class – Æ prevents overlapping in the classes

• Training and test sets are assigned by 10-fold cross validation !RESULT! The candidate drugs are classified as having low or high IC50 value

% accuracy

CYP17

7-attribute

10-attribute

15-attribute

MILP based hyper-boxes

100.00

100.00

100.00

Bayes Network

81.25

81.25

81.25

Naive Bayes

62.50

71.88

53.13

Naive Bayes Updatable

62.50

71.88

53.13

Lojistic

71.88

56.25

62.50

Multilayer Perceptron

62.50

71.88

59.38

SimpleLogistic

75.00

75.00

81.25

SMO

81.25

81.25

81.25

IB1

59.38

59.38

81.25

Logit Boost

71.88

62.50

62.50

Multi Class Classifier

71.88

56.25

62.50

Threshold Selector

43.75

40.63

62.50

LMT

75.00

75.00

81.25

RandomForest

75.00

68.75

65.63

OneR

75.00

71.88

75.00

OTHER TARGETS % accuracy

ACHE

% accuracy

7-attr

10-attr

100

91.89

89.19

Bayes Network

79.28

77.48

78.38

Naive Bayes

80.18

80.18

81.08

Naive Bayes Simple

81.08

80.18

81.98

Naive Bayes Updatable

80.18

80.18

81.08

Lojistic

79.28

84.68

80.18

Multilayer Perceptron

82.88

81.08

81.08

SimpleLogistic

83.78

82.88

79.28

SMO (WEKA SVM)

79.28

80.18

80.18

IB1

70.27

80.18

77.48

IBk

70.27

80.18

77.48

Logit Boost

82.88

81.08

82.88

Multi Class Classifier

79.28

84.68

80.18

Threshold Selector

47.75

68.47

60.36

LMT

83.78

82.88

79.28

RandomForest

80.18

80.18

81.98

OneR

81.08

72.97

72.97

MILP based hyper-boxes

15-attri

COX-2

7-attr

10-attr

15-attr

MILP based hyper-boxes

98.13

97.2

90.65

Bayes Network

67.2

67.2

66.88

Naive Bayes

71.66

70.06

64.65

Naive Bayes Simple

72.29

70.06

64.65

Naive Bayes Updatable

71.66

70.06

64.65

Lojistic

72.29

70.38

70.06

Multilayer Perceptron

72.61

72.29

75.16

SimpleLogistic

72.29

71.97

68.47

SMO (WEKA SVM)

71.02

69.43

69.43

IB1

69.11

71.02

70.06

IBk

69.11

71.02

70.06

Logit Boost

71.66

70.06

70.7

Multi Class Classifier

72.29

70.38

70.06

Threshold Selector

68.47

65.29

64.65

LMT

71.34

71.02

68.15

RandomForest

71.97

74.2

70.06

OneR

70.7

70.38

70.06

NUMBER OF ATTRIBUTES

Optimization of Molecules Based on N15

Optimization of Molecules Based on N15 Derivative Molecule

Improvement in Docking Energy

-0.29 kcal/mol

-0,53 kcal/mol

-0,96 kcal/mol

Summary & Conclusions

• Structure-Based Drug Design for Prostate Cancer case. • Use of computational model of target protein, CYP17. • Inhibition tests on intact cells. • Novel inhibitory compounds were identified. • Lead compounds with good inhibition and no toxic properties. • Lead optimization – QSAR: computational – IC50

Acknowledgements • M. Emre Özdemir • Pelin Armutlu • Şule Özdaş

Questions?