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Computer Aided Drug Design
Hanoch Senderowitz Department of Chemistry Bar Ilan University BIU-Valencia Workshop April 2010
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Computer Aided Drug Discovery
Structure/ Sequence Structure-based Modeling Ligand-based Modeling Known Ligands Screening Virtual Library Scoring Virtual Hits Binding Assays 3D Optimization Predix has developed a novel computational technology (PREDICT™) for predicting the 3D structure of any G-protein coupled receptor. Our approach allows for the rapid and efficient discovery of lead compounds using only the protein primary sequence, making it a true bridge from genomic information to drugs. In silico Leads Chemistry Biology Drug Candidate
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Homology (Comparative) Modeling
Given a sequence of amino acids, predict the 3D structure of the protein Template selection Multiple sequence alignment Multiple structure alignment Model generation External servers In-house tools Energy minimization Molecular dynamics Virtual co-crystallization Model refinement Predix has developed a novel computational technology (PREDICT™) for predicting the 3D structure of any G-protein coupled receptor. Our approach allows for the rapid and efficient discovery of lead compounds using only the protein primary sequence, making it a true bridge from genomic information to drugs. Model “health” Agreement with available data Enrichment experiments Model validation
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In Silico Screening Library Generation Docking BMA
Start: 2D representation of commercially available compounds Filtration: Ligands and/or binding site characteristics End: Multiple 3D conformations of ~100K compounds Library Generation Docking Multiple docking tools BMA Selection of the most plausible binding mode Predix has developed a novel computational technology (PREDICT™) for predicting the 3D structure of any G-protein coupled receptor. Our approach allows for the rapid and efficient discovery of lead compounds using only the protein primary sequence, making it a true bridge from genomic information to drugs. Scoring Multiple scoring functions Consensus scoring algorithms Selection Selection of virtual hits Biological assays
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Ligand-Based Screening
Pharnacophore: A 3D arrangement of function groups which is responsible for the biological activity Obtained by the superposition of active (and inactive) compounds A Database can be screened against pharmacophore Acceptor Donor Excluded volume Aromatic ring Shape based on largest active compound
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In Silico Screening Track Record
Predix has developed a novel computational technology (PREDICT™) for predicting the 3D structure of any G-protein coupled receptor. Our approach allows for the rapid and efficient discovery of lead compounds using only the protein primary sequence, making it a true bridge from genomic information to drugs. (1) Conformational analysis; (2) IC50 from functionality assay; (3) Pharma collaboration; (4) Pharmacophore screening; (5) Ki estimated from IC50 OM Becker et al, PNAS 101 (2004),
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The Cystic Fibrosis Disease
CF is the most common lethal genetic disease among Caucasians The number of CF patients is estimated at 70,000 worldwide, about 30,000 of which are in the US In 2008, the median survival age of was ~37 years CF results in pathologies in multiple organs Depressed lung function, lung infection, inflammation and advanced lung disease Currently, there is no cure for CF and the only treatment is symptomatic Airways Liver Pancreas Intestine Reproductive Tract Skin Predix has developed a novel computational technology (PREDICT™) for predicting the 3D structure of any G-protein coupled receptor. Our approach allows for the rapid and efficient discovery of lead compounds using only the protein primary sequence, making it a true bridge from genomic information to drugs.
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The Molecular Basis of CF
Normal lung CF is caused by mutations to the Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) which is the largest Cl- channel in the body Most common disease causing mutation is DF508 DF508-CFTR does not fold properly: Most channels does not reach the cell surface; those that do have impaired Cl- conductance In absence of proper Cl- conductance the salt/water balance in the airways is disrupted leading to dehydration of the mucus layer lining the airways. The dehydrated mucus layer becomes colonized by bacteria leading to chronic lung disease, lung failure and death CFTR is a relevant target for developing CF therapeutics but its structure is unknown CF lung Predix has developed a novel computational technology (PREDICT™) for predicting the 3D structure of any G-protein coupled receptor. Our approach allows for the rapid and efficient discovery of lead compounds using only the protein primary sequence, making it a true bridge from genomic information to drugs.
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Model of Full Length Structure of CFTR
wt-CFTR DF508-CFTR Site is mostly linear and aligned by hydrophilic and aromatic moieties Site sufficiently large for drug like compounds Site supports specific interactions Site small and linear and aligned mainly by hydrophilic groups Site sufficiently large for drug like compounds Site supports specific interactions
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~300 compounds from in silico screen
In vitro Screening Compounds tested in vitro in functional, electro-physiology assays in two cell lines Assays measure channel conductance ~300 compounds from in silico screen YFP Fluorescence Quenching FRT DF508 (rat) and A549 DF508 (human) pSAR FRT DF508 Ussing chamber In-house or at ChanTest FRT Cells: 21 structure-based hits at 10 mM corresponds to a hit rate of 6.6% A549 Cells: 12 structure-based hits at 10 mM corresponds to a hit rate of 3.9% Similar screening campaigns reported in the literature yielded hit rates of % pSAR = Purchased SAR, i.e., purchasable analogs Hits represent multiple scaffolds In these assays, hits activity is similar to the best known CFTR corrector (Corr-4a) Several hits show dual mechanism acting as both correctors and potentiators Most promising hits entered lead optimization
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Lead Optimization: The Art of Balance
Permeability Binding hERG CYP Solubility BBB Efficacy Predix has developed a novel computational technology (PREDICT™) for predicting the 3D structure of any G-protein coupled receptor. Our approach allows for the rapid and efficient discovery of lead compounds using only the protein primary sequence, making it a true bridge from genomic information to drugs.
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Binding MM-GBSA simulations on a model system (Urokinase-type plasminogen activator (uPA)) Good correlating when simulation initiated from crystal structure (R2 = 0.75) Poorer correlation when the binding mode could only be approximated (R2 = 0.60) Poor correlation observed when only a model of the protein is available and /or when the binding mode is obtained through docking simulations Challenges Improved docking and scoring methods Improved treatment of entropy
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When Binding is Improved…
The hERG gene encodes a potassium channel conducting the repolarizing IKr current of the cardiac action potential. Drug related hERG inhibition could lead to a sudden cardiac death N+ “Classic” hERG pharmacophore Privileged structures for e.g., GPCRs Astemizole (potent hERG binder) Binding to primary target often goes hand in hand with hERG binding Solution: hERG model
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When hERG is Reduced… Permeability Affinity Hydrophobicity
Due to the hydrophobic nature of the hERG binding site, increased polarity may reduce hERG binding. Increased polarity will also lead to: Increased solubility Decreased permeation through biological membranes Decreased permeation through the Blood Brain Barrier Affinity hERG binding Permeability Hydrophobicity
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Last But (Certainly) not Least
Cyp inhibition may lead to toxicity via drug-drug interactions Cyp binding sites are large and promiscuous but are otherwise similar to “regular” binding sites CYP450-3A4 (PDB code 2v0m) Cavity size: 950 Å3 to 2000 Å3 CYP450-2D6 (PDB code 2f9q) Cavity size: 540 Å3
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Optimization in Chemoinformatics and Drug Design
Drug Discovery is a multi-objective optimization problem Successful drug candidates necessarily represent a compromise between numerous, sometimes competing objectives Many other problems in chemoinformatics and drug design could be casted into the form of an optimization problem Synthesis design QSAR/QSPR Docking & scoring Predix has developed a novel computational technology (PREDICT™) for predicting the 3D structure of any G-protein coupled receptor. Our approach allows for the rapid and efficient discovery of lead compounds using only the protein primary sequence, making it a true bridge from genomic information to drugs. Optimization Engine Multiobjective QSAR Conformational search Classification Models Consensus scoring Diversity analysis
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The Target Function and Variables
Define a target function (f) and corresponding variables f = f(x1,x2,x3…xn) Target function and variables related to the scientific problem Target function and variables define a multi-dimensional surface Energy Cartesian/internal coordinate 1 Cartesian/internal coordinate 2 Predix has developed a novel computational technology (PREDICT™) for predicting the 3D structure of any G-protein coupled receptor. Our approach allows for the rapid and efficient discovery of lead compounds using only the protein primary sequence, making it a true bridge from genomic information to drugs.
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Monte Carlo/Simulated Annealing (MC/SA) Based Optimization Engine
Metropolis Test “Trial” Random Move DE NO YES DE < 0 or exp(-DE/RT) > X[0,1] ? X[0,1] is a random number in the range 0 to 1 Predix has developed a novel computational technology (PREDICT™) for predicting the 3D structure of any G-protein coupled receptor. Our approach allows for the rapid and efficient discovery of lead compounds using only the protein primary sequence, making it a true bridge from genomic information to drugs. Tmax MC Tmin MC Steps Temperature
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Quantitative Structure Activity Relationship (QSAR) Quantitative Structure Property Relationship (QSPR) Correlate specific biological activity for a set of compounds with their structure-derived molecular descriptors by means of a mathematical model The nature of correlation, activity and descriptors are unlimited BBB permeability = f (hydrophobicity, H-bonding potential) Metabolic stability = f (presence/absence of specific fragments) Protein crystallizability = f (amino acid composition, secondary structure) Predix has developed a novel computational technology (PREDICT™) for predicting the 3D structure of any G-protein coupled receptor. Our approach allows for the rapid and efficient discovery of lead compounds using only the protein primary sequence, making it a true bridge from genomic information to drugs.
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Descriptors Calculation Descriptors Selection
QSAREngine Descriptors selection Outliers removal Consensus model Validation Predictions Generation of multiple models Model(s) validation and selection Outlier Removal Consensus Prediction Average SD Multiple Divisions Model Selection Model Derivation Linear (MLR) Non-linear (kNN) Test Set Y-Scrambling Avoid chance correlation Training Set Dataset Descriptors Calculation Descriptors Selection Internal Set External Set Predix has developed a novel computational technology (PREDICT™) for predicting the 3D structure of any G-protein coupled receptor. Our approach allows for the rapid and efficient discovery of lead compounds using only the protein primary sequence, making it a true bridge from genomic information to drugs.
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QSAR Model for Metabolic Stability in Human Liver Microsomes (HLM)
Metabolism alters chemicals to speed their removal from the body and is performed primarily in the liver by the Cytochromes HLM experiments measure compounds resistance to metabolism Compounds incubated with HLM (vesicles containing drug-metabolizing enzymes) and their t1/2 half life determined Dataset 290 in-house compounds and 58 literature compounds Descriptors 41 descriptors including fragment counts Outliers removal 50 outliers removed External test set 102 compounds Algorithm kNN, MLR Consensus model 190 kNN model Predix has developed a novel computational technology (PREDICT™) for predicting the 3D structure of any G-protein coupled receptor. Our approach allows for the rapid and efficient discovery of lead compounds using only the protein primary sequence, making it a true bridge from genomic information to drugs.
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The Grand Challenge How can we reliably and consistently predict the pharmacological profile of bio-active compounds? Basic scientific research Practical applications in drug design How can we make better drugs? Permeability Binding hERG CYP Solubility BBB Efficacy Predix has developed a novel computational technology (PREDICT™) for predicting the 3D structure of any G-protein coupled receptor. Our approach allows for the rapid and efficient discovery of lead compounds using only the protein primary sequence, making it a true bridge from genomic information to drugs.
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Acknowledgments EPIX Pharmaceuticals Lab members Dr. Efrat Noy Dr. Merav Fichman Gal Fradin Yocheved Beim Funding CFFT Predix has developed a novel computational technology (PREDICT™) for predicting the 3D structure of any G-protein coupled receptor. Our approach allows for the rapid and efficient discovery of lead compounds using only the protein primary sequence, making it a true bridge from genomic information to drugs.
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