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CADD and Molecular Modeling : Importance in Pharmaceutical Development. Dr. Sanjeev Kumar Singh Department of Bioinformatics Alagappa University e-mail- [email protected] Working at the Intersection. Structural Biology Biochemistry Medicinal Chemistry Toxicology Pharmacology

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cadd and molecular modeling importance in pharmaceutical development

CADD and Molecular Modeling : Importance in Pharmaceutical Development

Dr. Sanjeev Kumar Singh

Department of Bioinformatics

Alagappa University

e-mail- [email protected]

working at the intersection
Working at the Intersection
  • Structural Biology
  • Biochemistry
  • Medicinal Chemistry
  • Toxicology
  • Pharmacology
  • Biophysical Chemistry
  • Information Technology
structural biology
Structural Biology
  • Fastest growing area of biology
  • Protein and nucleic acid structure and function
  • How proteins control living processes
medicinal chemistry
Medicinal Chemistry
  • Organic Chemistry
  • Applied to disease
  • Example: design new enzyme inhibitor drugs

doxorubicin (anti-cancer)

pharmacology
Pharmacology
  • Biochemistry of Human Disease
  • Different from Pharmacy: distribution of pharmaceuticals, drug delivery systems
new ideas from nature
New Ideas From Nature
  • Natural Products Chemistry
  • Chemical Ecology
    • During the next two decades: the major activity in organismal biology
  • Examples: penicillin, taxol (anti-cancer)
bio chem informatics
Bio/Chem-informatics
  • The collection, representation and organisation of chemical data to create chemical information, to which theories can be applied to create chemical knowledge.

Aim

  • To examine how computational techniques can be used to assist in the design of novel bioactive compounds.
  • To give an idea of how computational techniques can similarly be applied to other emerging areas such as Bio-informatics, Cheminformatics & Pharmainformatics.
overview
Overview
  • Drug discovery process
  • How do drugs work?
  • Overview of Computer-Aided Drug Design
pharmaceutical agrochemical industry
Pharmaceutical/Agrochemical Industry
  • Identification of novel compounds with useful and commercially valuable biological properties.
    • vastly complex,
    • multi-disciplinary task
    • many stages over extended periods of time
  • Risk
    • most novel compounds do not result in a drug.
    • those that do may cause unexpected, long-term side-effects.
slide10

Why CADD…?

  • Drug Discovery today are facing a serious challenge because of the increased cost and enormous amount of time taken to discover a new drug, and also because of rigorous competition amongst different pharmaceutical companies.
drug discovery development
Drug Discovery & Development

Identify disease

Find a drug effective

against disease protein

(2-5 years)

Isolate protein

involved in

disease (2-5 years)

Scale-up

Preclinical testing

(1-3 years)

Human clinical trials

(2-10 years)

File IND

Formulation

File NDA

FDA approval

(2-3 years)

drug development process

develop

assay

10,000’s

compounds

lead

identification

lead

optimisation

clinical

trials

1 drug

to market

Drug Development Process

On average it takes 12 -15 years and costs ~$500 -800 million to bring a drug to market

technology is impacting this process
Technology is impacting this process

GENOMICS, PROTEOMICS & BIOPHARM.

Potentially producing many more targets

and “personalized” targets

HIGH THROUGHPUT SCREENING

Identify disease

Screening up to 100,000 compounds a

day for activity against a target protein

VIRTUAL SCREENING

Using a computer to

predict activity

Isolate protein

COMBINATORIAL CHEMISTRY

Rapidly producing vast numbers

of compounds

Find drug

MOLECULAR MODELING

Computer graphics & models help improve activity

Preclinical testing

IN VITRO & IN SILICO ADME MODELS

Tissue and computer models begin to replace animal testing

slide15

X-ray or

Homology

Med Chem/Combichem

Gene sequence data

LibmakerTM

Designed libraries

Skelgen™

Designed Templates

Library synthesis

Screening

Ligand binding data

Pharmacophore

Model

Automating the CADD Process

slide16

Database

filtering

QSAR

Alignment

Computer Aided

Drug Design

(CADD)

ADMET

Similarity

analysis

Biophores

VHTS

diversity

selection

Combinatorial libraries

de novo design

Phases of CADD

Target discovery

Lead discovery

Target

Identification

Target

Validation

Lead

Identification

Lead

Optimization

SAVING 12 – 15 years, Costs: 500 - 800 million US $

how drugs work
How Drugs Work

+

Substrate

Enzyme

Enzyme-substrate

complex

Lock-and-key model

methodologies and strategies of cadd
Methodologies and strategies of CADD:
  • Structure based drug design (SBDD) “DIRECT DESIGN”
    • Followed when the spatial structure of the target is known.
  • Ligand based drug design (LBDD) “INDIRECT DESIGN”
    • Followed when the structure of the target is unknown.
c omputer a ided d rug d esign
Computer-Aided Drug Design
  • 3-D target structure unknown (LBDD)
    • Random screening if no actives are known
    • Similarity searching
    • Pharmacophore mapping
    • QSAR (2D & 3D) etc.
    • Combinatorial library design etc.
  • Structure-based drug design (SBDD)
    • Molecular Docking
    • De novo design
in pharmacophore
In Pharmacophore…
  • Pharmacoporic Studies on ACE inhibitors
  • Pharmacological Studies on HIV-1RT
    • Nucleosidic Inhibitors
    • Non-Nucleosidic Inhibitors
    • Interaction Energy – Potency Correlation
what is pharmacophore
What is Pharmacophore…?
  • Pharmacophore model
    • Set of points in space defining the binding of ligands with target.
    • Key factors in developing such a model are the determination of functional groups essential for binding, their correspondence from one ligand to another, and the common spatial arrangement of these groups when bound to the receptor

The pharmacophore model of HIV protease.

pharmacophore
Pharmacophore…..?
  • “a molecular framework that carries (phoros) the essential features responsible for a drug’s (pharmacon) biological activity” Paul Erlich, early 1990
  • “a set of structural features in a molecule that is recognized at a receptor site and is responsible for that molecule’s activity” Peter Gund, 1977
basic features
Basic Features
  • A set of features common to a series of active molecules
  • What are the features…?
    • HBD
    • HBA
    • +ve &-ve charged groups and
    • Hydrophobic regions
  • Functional groups or molecules with similar physical and chemical properties
  • Bioisosteres - substituents or groups that have chemical or physical similarities and which produce broadly similar biological properties
pharmacophore model
Pharmacophore model
  • Set of points in space defining the binding of ligands with target.
  • Key factors in developing such a model are the determination of functional groups essential for binding, their correspondence from one ligand to another, and the common spatial arrangement of these groups when bound to the receptor.
slide25
ACE
  • Angiotension converting enzyme
  • Converts angiotensinI to angiotension II
  • Inhibits bradykinin (vasodilator)
  • Vasoconstriction
ace inhibitor
ACE-inhibitor
  • Orally available

& potent drug

ace distance map
ACE distance map
  • 4 points defined
  • Five distances defined
slide28

Acceptor

Charged negative

Donor

Hydrophobic core

slide29

Pharmacophoric Features of Nucleosidic HIV-1RT Inhibitors

deoxy nucleoside

triphosphate (dNTP)

3\'-azido thymidine (AZT)

2\',3\' dideoxy nucleoside

3\'-nitro nucleoside

2\',3\'- didehydro dideoxy nucleoside

MESP contours for nucleosidic drugs. Red coloured contours indicate a value of -.01 for electrostatic potential and yellow contours indicate a value of -0.05

slide30

Concluding remarks on Nucleosidic inhibitors

  • Different substituents at the 3 position show similar sugar ring puckering and only slight differences in nucleosidic base disposition and interactions protein.
  • MESP plots have clearly indicated that the charge environment of the drugs is complementary to the receptor charge environment.Positive potential areas have been observed in the active site of HIV-1RT where DNA binding occurs.
  • Pharmacophoric Features of Nucleosidic HIV-1RT Inhibitors.
  • Arpita Yadav* and Sanjeev Kumar Singh Bioorg. & Med. Chem. 11, 2003, 1801.
slide31

Threshold interaction energy of NRTI’s (nucleosidic inhibitors for Reverse transcriptase) to undergo competitive inhibition

3.58 Å

2\'3\' dideoxy thymidine

-13.33 kcal/mol

-14.13 kcal/mol

AZT -16.71 kcal/mol

3’-Nitro nucleoside

-21.30 kcal/mol

2\'3\'-didehydro 2\'3\'-dideoxy thymidine

-12.39 kcal/mol

Correlation of interaction energy with potency

slide32

Concluding remarks on interaction energy studies

  • Correlation graph indicates the requirement of a threshold binding energy ~12 kcal/mol for the drug to be able to undergo competitive inhibition efficiently. Less than this binding energy/ interaction energy will make the drug ineffective or very high concentrations will be required for inhibition of enzyme. Which may lead to cytotoxicity.
  • vThreshold interaction energy of NRTI’s (nucleosidic inhibitors for Reverse transcriptase) to undergo competitive inhibition
  • Arpita Yadav* and Sanjeev Kumar Singh Bioorg. & Med. Chem. letts. 14, 2004, 2677-2680
slide33

Common binding mode for structurally and chemically diverse non- nucleosidic HIV-1RT inhibitors

Pyrrolyl hetro aryl sulfone with lysine

slide34

Concluding remarks of Non nucleosidic inhibitors

  • Conformational study of non-nucleosidic drugs indicated that each drug has a ‘V’- shaped conformation.
  • Each drug has a -NH group in a position that it can make H- bond with the carbonyl group of lysine 101 in conformity with earlier studies on pyrrolyl hetero aryl sulfone. This indicates the importance of lysine 101 in binding NNRTI’s.
  • Common binding mode for structurally and chemically diverse non- nucleosidic HIV-1RT inhibitors"

Arpita Yadav* and Sanjeev Kumar Singh, THEOCHEM, 723, 2005, 205-209.

disco dis tance co mparisons
DISCO: DIStance COmparisons
  • Generate some number of low-energy conformations for each active compound
  • The resulting conformations are represented by the positions of potential pharmacophore points.
    • Hydrogen-bond donors and acceptors; charged atoms; ring centroids; and centres of hydrophobic regions.
quantitative structure activity relationships qsar
Quantitative Structure-Activity Relationships (QSAR)
  • A QSAR relates a numerical description of molecular structure or properties to known biological activity
    • Activity = f (molecular descriptors)
    • Success of QSAR: right descriptors + right method (form of f )
  • A QSAR should be
    • explanatory (for structures with activity data)
    • predictive (for structures without activity data)
  • A QSAR can be used to explain or optimise:
    • localised properties of molecules such as binding properties
    • whole molecule properties such as uptake and distribution
3d qsar
3D QSAR
  • CoMFA and CoMSIA
    • Molecules are described by the values of molecular fields calculated at points in a 3D grid
    • The molecular fields are usually steric and electrostatic
    • Partial least squares (PLS) analysis used to correlate the field values with biological activity
    • A common pharmacophore is required.
using the model
Using the Model
  • The PLS results are presented as contour plots
  • Steric Bulk:
    • Green = Steric Favourable
    • Yellow = Steric Unfavourable
  • Electrostatics:
    • Red = Electronegative Favourable
    • Blue = Electronegative Unfavourable
3d qsar comfa study on aminothiazole derivatives as cyclin dependent kinase 2 inhibitors
3D-QSAR CoMFA Study on Aminothiazole Derivatives as Cyclin Dependent Kinase 2 Inhibitors
  • In this work we performed CoMFA study carried out on 47 aminothiazole derivatives as inhibitors of this protein kinase.
  • The models could be usefully employed to design selective CDK2 inhibitors and to find novel scaffolds through screening of chemical databases.

Allignment

slide40

CoMFA Electrostatic Contours

CoMFA Steric Contours

  • Green contours stand for points where sterically bulkier groups are anticipated to increase the biological activity.
  • The yellow contours are used to underscore the points where bulkier groups could lower the biological property.
  • The electrostatic red plots show where the presence of a negative charge is expected to enhance the activity.
  • The blue contours indicate where introducing or keeping positive charges are expected to better the observed activity.
  • 3D-QSAR CoMFA Study on Aminothiazole Derivatives as Cyclin Dependent Kinase 2 Inhibitors. Nigus Dessalew, Sanjeev Kumar Singh* and P.V. Bharatam QSAR Comb. Sci., 26(1), 2007, 85-91.
qsar work
QSAR WORK…
  • The developed model showed a strong correlative and predictive capability having a cross validated correlation co-efficient of 0.747 for CDK4 and 0.755 for CDK2 inhibitions.
  • 3D-QSAR CoMFA studies on Indenopyrazole as CDK2 Inhibitors. Sanjeev Kumar Singh*, Nigus Dessalew, and P. V. Bharatam Eur. J. of Med. Chem., 41, 2006, 1310-1319.
  • The conventional and predictive correlation coefficients were found to be respectively 0.943 and 0.508 for CDK1 and 0.957 and 0.585 for CDK2.
  • 3D-QSAR CoMFA Study on Oxindole Derivatives as Cyclin Dependent Kinase 1 (CDK1) and Cyclin Dependent Kinase 2 (CDK2) Inhibitors. Sanjeev Kumar Singh*, Nigus Dessalew, and P. V. Bharatam, Med. Chem. 3(1), 2007, 75-84.
structure based drug design
Structure Based Drug Design

Determine Protein Structure

Identify Interaction Sites

Discovery or design of molecules that interact with biochemical targets of known 3D structure

De Novo Design

3D Database

Evaluate Structure

Synthesize Candidate

Test Candidate

Lead Compound

structure based drug design1
Structure based drug design
  • Molecular database mining
    • Compounds with best complementarity to binding site are selected.
    • DOCK, Autodock, Flex X etc.
  • De novo drug designing
    • Virtual modeling and optimization of structure
    • LUDI, CLIX, CAVEAT, LeapFrog etc.
structural targets
Structural Targets
  • 3D structure of target receptors determined by
    • X-ray crystallography
    • NMR
    • Homology modeling
  • Protein Data Bank
    • Archive of experimentally determined 3D structures of biological macromolecules
molecular docking
Molecular docking
  • Virtual screening approach to predict receptor-ligand binding modes
  • Scoring method used
    • to detect correct bound conformation during docking process
    • to estimate binding affinities of candidate molecule after completion of docking
docking algorithms
Docking algorithms
  • Molecular flexibility
    • both ligand and protein rigid
    • flexible ligand and rigid protein
    • both ligand and protein flexible
  • search algorithm
    • use to explore optimal positions of the ligand within the active site
  • scoring function
    • value should correspond to preferred binding mode
  • efficiency very important for database searching
scoring function
Scoring function
  • Ligand-receptor binding is driven by
    • Electrostatics (including h-bonding)
    • Dispersion of vdw’s forces
    • Hydrophobic interaction
    • Desolvation of ligand and receptor
  • Molecular mechanics
    • Attempt to calculate interaction energy directly
docking
Docking

X-ray structure of complex

slide51

Ligand database

Target Protein

Molecular docking

Ligand docked into protein’s active site

how do my ligands dock into the protein
How do my ligands dock into the protein?
  • Various approaches, including:
    • Shape (DOCK program)
    • incremental search methods (Flex X)
    • Monte Carlo/Simulated annealing (AUTODOCK, FLO)
    • Genetic algorithms (GOLD)
    • Molecular dynamics
    • Systematic search (Glide, Open Eye)
  • Two key issues
    • sampling
    • scoring/evaluating possible configurations/poses
collaboration with
Collaboration with…
  • Prof. Shandhar Ahamad, National Institute of Biomedical Innovation, Japan
  • Dr. Nigus Desselaw Addis Ababa University, Ethiopia
  • Prof. J. Kastner, University of Stuttgart, Germany
  • Prof. K. Dharmalingam, Madurai Kamaraj Uni., Madurai
  • Dr. Arpita Yadav, CSJM University Kanpur
ad