Computer Aided Drug Design

1. Computer Aided Drug Design Hanoch Senderowitz Department of Chemistry Bar Ilan University BIU-Valencia Workshop April 2010

2. Computer Aided Drug Discovery Structure/ Sequence Structure-based Modeling Ligand-based Modeling Known Ligands Screening Virtual Library Scoring Virtual Hits Binding Assays 3D Optimization In silico Leads Chemistry Biology Drug Candidate

3. Homology (Comparative) Modeling • Given a sequence of amino acids, predict the 3D structure of the protein • Multiple sequence alignment • Multiple structure alignment Template selection • External servers • In-house tools Model generation • Energy minimization • Molecular dynamics • Virtual co-crystallization Model refinement • Model “health” • Agreement with available data • Enrichment experiments Model validation

4. In Silico Screening • 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 Scoring • Multiple scoring functions • Consensus scoring algorithms Selection • Selection of virtual hits • Biological assays

5. Donor Excluded volume Aromatic ring Aromatic ring Donor Shape based on largest active compound Acceptor 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

6. In Silico Screening Track Record (1) Conformational analysis; (2) IC50 from functionality assay; (3) Pharma collaboration; (4) Pharmacophore screening; (5) Ki estimated from IC50 OM Becker et al, PNAS101 (2004), 11304-11309

7. Airways Liver Pancreas Intestine Reproductive Tract Skin 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

8. 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

9. wt-CFTR DF508-CFTR Model of Full Length Structure of CFTR • Site small and linear and aligned mainly by hydrophilic groups • Site sufficiently large for drug like compounds • Site supports specific interactions • Site is mostly linear and aligned by hydrophilic and aromatic moieties • Site sufficiently large for drug like compounds • Site supports specific interactions

10. ~300 compounds from in silico screen YFP Fluorescence QuenchingFRT DF508 (rat) and A549 DF508 (human) pSAR FRT DF508Ussing chamberIn-house or at ChanTest In vitro Screening • Compounds tested in vitro in functional, electro-physiology assays in two cell lines • Assays measure channel conductance • 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 0.04-1.1% 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

11. CYP BBB Efficacy Solubility Permeability Binding hERG Lead Optimization: The Art of Balance

12. Binding • MM-GBSA simulations on a model system (Urokinase-type plasminogen activator (uPA)) • Good correlating when simulationinitiated 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

13. Astemizole (potent hERG binder) “Classic” hERG pharmacophore Privileged structures for e.g., GPCRs N+ 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 Binding to primary target often goes hand in hand with hERG binding Solution: hERG model

14. Permeability Affinity Hydrophobicity hERG binding When hERG is Reduced… • 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

15. 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

16. 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 Docking & scoring QSAR/QSPR Optimization Engine Multiobjective QSAR Conformational search Classification Models Consensus scoring Diversity analysis

17. Energy Cartesian/internal coordinate 2 Cartesian/internal coordinate 1 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

18. Random Move “Trial” DE Metropolis Test NO DE < 0 or exp(-DE/RT) > X[0,1] ? YES X[0,1] is a random number in the range 0 to 1 Tmax Temperature Temperature Temperature Temperature MC Tmin MC Steps MC Steps MC Steps MC Steps Monte Carlo/Simulated Annealing (MC/SA) Based Optimization Engine

19. 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)

20. Descriptors selection • Outliers removal • Generation of multiple models • Model(s) validation and selection • Consensus model • Validation • Predictions Descriptors Calculation Descriptors Selection Dataset Outlier Removal Internal Set External Set Multiple Divisions Y-Scrambling • Avoid chance correlation Training Set Test Set Model Derivation • Linear (MLR) • Non-linear (kNN) Model Selection Consensus Prediction • Average • SD QSAREngine

21. 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

22. CYP Efficacy Permeability Binding BBB Solubility hERG 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?

23. Acknowledgments • EPIX Pharmaceuticals • Lab members • Dr. Efrat Noy • Dr. Merav Fichman • Gal Fradin • Yocheved Beim • Funding • CFFT