Computer Aided Drug DesignHanoch Senderowitz

Department of Chemistry

Bar Ilan University

BIU-Valencia Workshop

April 2010

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Computer Aided Drug Discovery

Structure/Sequence

Leads

Virtual Hits

Structure-basedStructure-basedModelingModeling

ScoringScoring

ScreeningScreening

Drug Candidate

Binding Assays 3D Optimization3D Optimization

ChemistryBiology

In silico

Virtual Library

Known LigandsLigand-basedLigand-basedModelingModeling

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Homology (Comparative) Modeling

• Given a sequence of amino acids, predict the 3D structure of the protein

Template selectionTemplate selection• Multiple sequence alignment• Multiple structure alignment

Model generationModel generation• External servers• In-house tools

Model refinementModel refinement• Energy minimization• Molecular dynamics• Virtual co-crystallization

Model validationModel validation• Model “health”• Agreement with available data• Enrichment experiments

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In Silico Screening

Library GenerationLibrary Generation• Start: 2D representation of commercially available

compounds• Filtration: Ligands and/or binding site characteristics• End: Multiple 3D conformations of ~100K compounds

DockingDocking• Multiple docking tools

BMABMA • Selection of the most plausible binding mode

ScoringScoring • Multiple scoring functions• Consensus scoring algorithms

SelectionSelection • Selection of virtual hits• Biological assays

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• 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

Ligand-Based Screening

Acceptor

Donor

Excluded volumeAromatic ring

Shape based on largest active compound

Aromatic ring

Donor

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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, PNAS 101 (2004), 11304-11309

# Target IndicationComp.Tested

Hits Hit Rate Best Hit

(Ki)

Biogenic amine GPCR

1 5-HT1AAnxiety, Depression

78 16 21% 1 nM

2 5-HT4Alzheimer's Disease

93 19 21% 21 nM

3 5-HT2BPulmonary Hypertension

* From the 5HT4 screen 13 nM

4 5-HT6 Obesity 30(1) 5 17% 14 nM

5 D2 CNS 42 7 17% 58 nM

Ion Channels

6Kv1.5/4.3 (Ikur/Ito)

A-fib; Brugada 100 20 20% 100 nM (2)

Lipid GPCR

7 S1P1 MS, RA 140 7 5% 0.7 uM (2)

Purinergic GPCR

8 P2Y2 Cystic Fibrosis 167 16 10% 10 nM

# Target IndicationComp.Tested

Hits Hit Rate Best Hit

(Ki)

Peptide GPCR

9 NK1 Depression 53 8 15% 56 nM

10 NK2 Asthma 452(3) 47 10% 54 nM

11 V2 Cirrhosis, CHF 223(4) 7 3% 1.5 uM

12 B1 Inflamation, Pain 75 3 4% ~6 uM(5)

13 B2 Inflamation, Pain 136 6 4% ~8 uM(5)

Cannabinoid GPCR

14 CB1 Obesity 129 28 14% 496 nM

Chemokine GPCR

15 CCR3 Inflammation 43 5 12% ~10 uM(5)

16 CCR2 Inflammation, RA 158 12 8% 399 nM(2)

17 CXCR2 COPD, RA 130 8 6% 82 nM(4)

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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

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The Molecular Basis of CF

• 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 F508

• F508-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

Normal lung

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Model of Full Length Structure of CFTR

wt-CFTR F508-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

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In vitro Screening

~300 compounds from in silico screen

YFP Fluorescence QuenchingFRT F508 (rat) and A549 F508 (human)

pSARFRT F508 Ussing chamberIn-house or at ChanTest

• FRT Cells: 21 structure-based hits at 10 M corresponds to a hit rate of 6.6%

• A549 Cells: 12 structure-based hits at 10 M 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

• Compounds tested in vitro in functional, electro-physiology assays in two cell lines Assays measure channel conductance

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Lead Optimization: The Art of Balance

Permeability BindinghERG

CYP

Solubility

BBB Efficacy

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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

Binding to primary target often goes hand in hand with hERG binding

N+

“Classic” hERG pharmacophore

Astemizole (potent hERG binder)

Privileged structures for e.g., GPCRs

Solution: hERG model

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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

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

Synthesis design

Docking & scoringQSAR/QSPR

Consensus scoringClassification Models

Diversity analysis

Multiobjective QSAR Conformational searchOptimization Engine

• 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

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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

Ener

gy

Cartesian/internal coordinate 1

Cartes

ian/in

terna

l

coor

dinate

2

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Monte Carlo/Simulated Annealing (MC/SA) Based Optimization Engine

Metropolis Test

“Trial”Random Move

E

NO

YES

E < 0or exp(-E/RT) > X[0,1]

??X[0,1] is a random number in the range 0 to 1

Tmax

MC

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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)

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QSAREngine

Outlier Removal

Consensus Prediction• Average • SD

Multiple Divisions

Model SelectionModel Derivation• Linear (MLR)• Non-linear (kNN)

Test SetY-Scrambling• Avoid chance correlation

Training Set

Dataset Descriptors Calculation Descriptors Selection

Internal Set External Set

1. Descriptors selection2. Outliers removal

5. Consensus model6. Validation7. Predictions

3. Generation of multiple models4. Model(s) validation and selection

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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

R2 = 0.8127

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in)

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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?

Perm

eabi

lity

Binding

hERG

CYP

Solubility

BBB

Efficacy

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Acknowledgments

• EPIX Pharmaceuticals

• Lab members

• Dr. Efrat Noy

• Dr. Merav Fichman

• Gal Fradin

• Yocheved Beim

• Funding

• CFFT