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Cadd and molecular modeling for M.Pharm

The document discusses the role of computer-aided drug design (CADD) in pharmaceutical development, highlighting its significance in improving the efficiency of drug discovery. CADD utilizes molecular modeling, structure-based drug design (SBDD), and quantitative structure-activity relationships (QSAR) among other techniques to reduce the time and cost associated with bringing new drugs to market. It addresses the challenges in drug discovery, such as high costs and lengthy processes, while emphasizing the potential of computational techniques to streamline the development of novel bioactive compounds.

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Drug Design Overview• natural-products•3D Visualization•
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CADD and Molecular Modeling : Importance in Pharmaceutical Development
TRADITIONAL DRUG DESIGN Lead generation: Natural ligand / Screening Biological Testing Synthesis of New Compounds Drug Design Cycle If promising Pre-Clinical Studies
Structure-based Drug Design (SBDD) Molecular Biology & Protein Chemistry 3D Structure Determination of Target and Target-Ligand Complex Modelling Structure Analysis and Compound Design Biological Testing Synthesis of New Compounds If promising Pre-Clinical Studies Drug Design Cycle Natural ligand / Screening
 SBDD:  drug targets (usually proteins)  binding of ligands to the target (docking) ↓ “rational” drug design (benefits = saved time and $$$)
Ligand database Target Protein Molecular docking Ligand docked into protein’s active site
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  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.
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 Identify disease Isolate protein involved in disease (2-5 years) Find a drug effective against disease protein (2-5 years) Preclinical testing (1-3 years) Formulation Human clinical trials (2-10 years) Scale-up FDA approval (2-3 years)
Drug Development Process On average it takes 12 -15 years and costs ~$500 -800 million to bring a drug to market develop assay lead optimisation lead identification clinical trials to market 10,000’s compounds 1 drug
Cont…
Technology is impacting this process Identify disease Isolate protein Find drug Preclinical testing GENOMICS, PROTEOMICS & BIOPHARM. HIGH THROUGHPUT SCREENING MOLECULAR MODELING VIRTUAL SCREENING COMBINATORIAL CHEMISTRY IN VITRO & IN SILICO ADME MODELS Potentially producing many more targets and “personalized” targets Screening up to 100,000 compounds a day for activity against a target protein Using a computer to predict activity Rapidly producing vast numbers of compounds Computer graphics & models help improve activity Tissue and computer models begin to replace animal testing
Automating the CADD Process Gene sequence data X-ray or Homology Screening Library synthesis Med Chem/Combichem LibmakerTM Designed libraries Ligand binding data Pharmacophore Model Skelgen™ Designed Templates
Target Identification Target Validation Lead Identification Lead Optimization Target discovery Lead discovery Phases of CADD SAVING 12 – 15 years, Costs: 500 - 800 million US $ VHTS Similarity analysis Database filtering Computer Aided Drug Design (CADD) de novo design diversity selection Biophores Alignment Combinatorial libraries ADMET QSAR
How Drugs Work SubstrateEnzyme + Enzyme-substrate complex Lock-and-key model
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.
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
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.
Donor Hydrophobic core Charged negative Acceptor
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)  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  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  The PLS results are presented as contour plots  Steric Bulk:  Green = Steric Favourable  Yellow = Steric Unfavourable  Electrostatics:  Red = Electronegative Favourable  Blue = Electronegative Unfavourable
Combinatorial Chemistry • By combining molecular “building blocks”, we can create very large numbers of different molecules very quickly. • Usually involves a “scaffold” molecule, and sets of compounds which can be reacted with the scaffold to place different structures on “attachment points”.
Example Combinatorial Library NH R1 R2 R3 Scaffold “R”-groups R1 = OH OCH3 NH2 Cl COOH R2 = phenyl OH NH2 Br F CN R3 = CF3 NO2 OCH3 OH phenoxy Examples NH OH CN OH NH OH O CH3 NH C OH OHO CF3 NH C OH OHO O For this small library, the number of possible compounds is 5 x 6 x 5 = 150
Combinatorial Chemistry Issues • Which R-groups to choose • Which libraries to make – “Fill out” existing compound collection? – Targeted to a particular protein? – As many compounds as possible? • Computational profiling of libraries can help – “Virtual libraries” can be assessed on computer
5. Molecular Modeling • 3D Visualization of interactions between compounds and proteins “Docking” compounds into proteins computationally
3D Visualization • X-ray crystallography and NMR Spectroscopy can reveal 3D structure of protein and bound compounds • Visualization of these “complexes” of proteins and potential drugs can help scientists understand the mechanism of action of the drug and to improve the design of a drug • Visualization uses computational “ball and stick” model of atoms and bonds, as well as surfaces • Stereoscopic visualization available
“Docking” compounds into proteins computationally
6. In Vitro & In Silico ADME models • Traditionally, animals were used for pre- human testing. However, animal tests are expensive, time consuming and ethically undesirable • ADME (Absorbtion, Distribution, Metabolism, Excretion) techniques help model how the drug will likely act in the body • These methods can be experemental (in vitro) using cellular tissue, or in silico, using computational models
In Silico ADME Models • Computational methods can predict compound properties important to ADME, e.g. – LogP, a liphophilicity measure – Solubility – Permeability – Cytochrome p450 metabolism • Means estimates can be made for millions of compouds, helping reduce “atrittion” – the failure rate of compounds in late stage
Structure Based Drug Design Determine Protein Structure Identify Interaction Sites De Novo Design 3D Database Evaluate Structure Synthesize Candidate Test Candidate Lead Compound Discovery or design of molecules that interact with biochemical targets of known 3D structure
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  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
X-ray crystallography
NMR
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  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  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 X-ray structure of complex
Ligand database Target Protein Molecular docking Ligand docked into protein’s active site
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
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