Structure-Based Drug Design:Past, Present and FuturePerspectives
Cele Abad-ZapateroUniversity of Illinois at ChicagoCenter for Pharmaceutical BiotechnologyACA Crystallography School, July 12, 2007
Biomedical applications of MacromolecularStructure
Perutz solved the structure of hemoglobin, analyzed it to understand its function inatomic detail, studied the role of mutations in disease (i.e. sickle cell anemia),and thought about using the structural information for the purposes of drug discovery.
Hemoglobin Sculpture by Julian Voss Andreae. Progressive oxidation of the sculpture withthe passage of time: oxygen acting on hemoglogin.
Max F. Perutz (1914-2002)
AE1 E2 E3
B C M
Biologicalaction
Putative therapeutic agentsfor targets: E1,E2,E3
The concept of ‘Target-driven’ Drug Design
Biological processes/actions are normally linked to metabolic cascades
Related to Biological Function
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Structure-Based Drug Design Cycle (SBDD)
Targetprotein
A
Inhibitor(lead)
enz/inh complex
Study interactionsand design better(i.e. more potent) inhibitor B.
B
synthesize
Crystallize/soak compound Bwith target protein
©2005Abbott Laboratories
Clinicalcandidate
Brief history of IMCA
PastIMCA Consortium:
Technological breakthrough.Routine access to state-of-art SR for
the sole purpose of SBDD.
Sociological Milestone:Corporate organizations sharing a common resource to compete in the marketplace.
SBDD: Improvements made last 20years
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• Easier to solve structures of unknown targets; MR and ab initio: MAD, SAD and other phasing methods
• Quality of the structures: vastly improved quality of data.
•Throughput: number of structures of complex available
• Speed of bioinformatics and cheminformatic tools available
• Number of structures of targets: Structure Genomics Initiatives
•Speed of synthesis: libraries or parallel synthesis.
Most of them improved dramatically due to the routine access to SR by an increased number of laboratories (academia, industry)
Present• Content of this session:
-Ligand Design-Vaccines-High Throughput, robot-driven
operations-Therapies based on Protein Kinase
targets-…….
Future• Connected to the Future of Structural
Biology:Quo vadis Structural Biology?
• Novel ways to enhance and improveSBDD:
• ESBDD: Efficient Structure Based Drug Discovery• eSBDD: expanded Structure-Based Drug
Discovery
Quo vadis Structural Biology?• Harrison, S. (2004) Nat. Struct. Mol. Biol. 11: 12-15.
- Cellular and Molecular Biology will meet.
• Stevens, R. (2004) Nat. Struct. Mol. Biol. 293-295.• Dauter, Z. (2006) Acta Cryst. D62, 1-11.
-’High throughput’ and ‘discovery-driven’ paths will continue to enrich structural biology.
• Abad-Zapatero, C. Acta Cryst. (2007) D63, 630-634.-Include concepts of ‘dissipative structures’ and non-equilibrium thermodynamics to put structural biology in theright framework for a real extension to ‘systems biology’(i.e. beyond prot/gene interactions and networks).
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Biological Systems are:Complex ‘Dissipative Structures’(Landauer, 1961; I. Prigogine: 70’s)
‘Dissipative Structures’:Transient structures maintained far from thermodynamicequilibrium by the flows of matter and energy from thesurroundings.
Belousov-Zhabotinski (BZ) reaction:one of the simplest, best known oscillatorychemical processes, exhibits most propertiesof dissipative structures.Sulfuric acid, Malonic acid, cerium ammoniun nitrate and sodium bromate
single crystalX-ray diffraction
XFEL
Sample complexity/order
EastWest
Single/ComplexMoleculediffraction
Tissue, Organsamples‘tissular’Biology
DEI and relatedtechniques
Cryo-EM Fiber diff
Real 11 x 3 x 3 meter crystals of CaSO4.2H2O from the Naica mine caves in Chihuahua, MexicoGarcia-Ruíz et al. Geology (2007) 35: 327-330.Formation of natural gypsum megacrystals in Naica, Mexico
East: Growing suitable crystals will always be an issue!
Happycrystallographer
East: Single Particle Diffraction: Significant progress with soft x-rays FEL (FLASH)
H.N. Chapman et al. Nature Physics (2006) 2: 839
Intense focused FEL pulse (25 fs) gives a high resolution low-noise coherent diffraction pattern of an object before it explodes.
Structure of the object obtained, with no evidence of radiation damage.
diffraction pattern
Recovered image Diffraction pattern
(after first pulse)
Z. Zhong et al., Nucl. Instrum. Meth. in Phys. Res. A. 450(2000) 556-567
West: Diffraction Enhanced Imaging (DEI): Enhanced Contrast compared to Conventional Radiography.
Improved contrastin softer tissues (ie. lungs)by varying the angle ofthe ‘analyzer’ crystal.
radiography
single crystalX-ray diffraction
XFEL
Sample complexity/order
EastWest
Singleparticle/moleculediffraction withsoft/hard X-rays
tissue, organsamples‘tissular’biology
DEI and relatedtechniques
Cryo-EM Fiber diff
Soft and X-FEL
DEI , relatedmethods
systems biology
Future• Connected to the Future of Structural
Biology:Quo vadis Structural Biology?
• Novel ways to enhance and improveSBDD:
• ESBDD: Efficient Structure Based Drug Discovery• eSBDD: expanded Structure-Based Drug
Discovery
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Year Launched vs. MW for approved or marketedoral drugs 1937-1995: SBDD under scrutiny
Figure 1 of Proudfoot, J. R. “The Evolution of Synthetic Oral Drug Properties,” Biorg. & Med.
Chem. Lett. 2005, 15, 1087
MW ≈ 350
SBDD “Begins”
Issues still remaining in currentSBDD
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Often:
•Target poorly validated
• Designed compounds have poor PK, bioavailability
•Toxicity of designed compounds: target related or compound related
Quite often:
• Potency is the only factor considered when pursuing optimization of a series.
• Decision making is based on non-objective criteria.
• Still remains a multidimensional problem including variables for which we donot have a full understandingADMET: Absorption, Distribution, Metabolism, Excretion,Toxicity.
Most of these issues are biology related.
What are the critical variables for agood inhibitor to be a clinicalcandidate?
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• MW
• PSA (polar surface area)
• Potency
Can we reduce the number of variables? Yes, but how?
Can we find the key variables that determine bioavailabity?
Can we develop numerical ‘figures-of-merit’ for decision making?
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Efficient SBDD (ESBDD): Thebasics
Introduction and definition of Binding Efficiency Indices BEI, SEI1
Examples illustrating the ideas/concepts of ligand efficiency usingPTP1B, FBPase 2
What is possible vs. what is questionable Fragment, lead selection Comparison of different series Monitoring progress using graphs and novel SAR tables Druggability of different targets Where do some marketed drugs lay?
Efficient Structure-Based Drug Design (ESBDD)
1Abad-Zapatero & Metz. Drug Discovery Today (2005) 10:464-469. 2Abad-Zapatero, C. Exp. Op. Drug Discovery (2007) 2(4): 469-488.
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Binding Efficiency Index (BEI):Potency/kDalton
BEI :BEI :For a 1 nM compound (KFor a 1 nM compound (Kii = 1.0 x 10 = 1.0 x 10-9-9 M), pK M), pKii = 9.00 = 9.00
MarketedMarketed oral drugs, mean MW * oral drugs, mean MW *== 337 Da. 337 Da.
BEI = BEI = pKpKii or pIC or pIC5050 / MW (kD) / MW (kD)∴∴ BEI BEI ≅≅ 9.00 / 0.333 kDalton 9.00 / 0.333 kDalton BEI BEI ≅≅ 27 27 Example for a 1nM compound
and acceptable MW
* Wenlock et al. (2003). J. Med. Chem. 46, 1250-1256.
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Surface Efficiency Index (SEI): Potency/PSA
If PSA :If PSA :< 100 < 100 ÅÅ2 2 ,, then % F (bioavailability human) > 50 %then % F (bioavailability human) > 50 %≅≅100 100 ÅÅ2 2 ,, then % F (bioavailability human) then % F (bioavailability human) ≅≅ 50 % 50 %>> 100 100 ÅÅ22,, then % F (bioavailability human) < 50 %*then % F (bioavailability human) < 50 %*
For a 1 nM compound, pKFor a 1 nM compound, pKii = 9.0 = 9.0 SEI = pKSEI = pKii or pIC or pIC5050 / (PSA / 100 / (PSA / 100 ÅÅ22))∴∴ SEI = 9.0 / (50 SEI = 9.0 / (50 ÅÅ2 2 / 100 / 100 ÅÅ22))
SEI = 18 SEI = 18 Example for a 1 nM compoundand acceptable PSA of 50 Å2
* Palm, K. et al. Pharm. Res. 1997, 14, 568
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Are ligand efficiency indices a reincarnation of“Lipinski’s Rules?”
Lipinski’s Rules: BEI and SEI Indices:
1. ClogP < 5 1. ClogP not used explicitly
2. N Count + 2. N Count not used explicitly
3. O Count < 10 3. O Count not used explicitly
4. HB Donors < 5 4. HB Donors not used explicitly
5. MW < 500 5. MW: continuous scale in KDa
∴ BEI and SEI indices are different and do not use broad cutoffscompared to Lipinski’s rules. BEI provides a continuous numerical scalefor one of Lipinski’s variables (MW) and implicitly (via PSA), SEI providesan analogous scale for all the others. Reduce number of variables
NOTE: BEI/SEI = 10·(PSA/MW) . Independent of the target : depends on physico-chemical properties of ligand alone
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Mapping of 92 marketed, oral drugs in the SEI, BEIplane1 (preliminary analysis of Andrews data)
0
5
10
15
20
25
30
35
40
45
BEI
0 5 10 15 20 25 30 35 40 45
SEI
Centroid of entire distribution (N = 122) near (SEI, BEI) = (18, 27)
1) Data provided by Tudor Oprea as a private communication to Yvonne Martin
50%
90%95%
Abad-Zapatero & Metz. Drug Discovery Today (2005) 10:464-469.
0
2
4
6
8
10
12
14
16
18
20
22
24
26
28
30
BEI
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
SEI
A
B
C
D
E
G
H
I
J
Class
Bivariate Fit of BEI By SEISEI-BEI Plane for Different Targets
NNNOONOFClIressa
NOHOFClHaloperidol
SNOONOOO
0.750 0.320 0.200 0.150 0.110
0.090
ONNClSOO
Lines of decreasingPSA/MW ratios (slopes)Less polar: more drug-like
Abad-Zapatero, C. Exp. Op. Drug Discovery (2007) 2(4): 469-488.
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Ligand efficiency Indices and SBDD
How can we incorporate these ideas How can we incorporate these ideas into the SBDD process to make it more into the SBDD process to make it more efficient?efficient?
How can we use these ideas How can we use these ideas proactively to improve the SBDD cycle?proactively to improve the SBDD cycle?
Individual inputIdea Generators
Generate 3-D structure
1
Structure of Target/Inhibitor(1 example or an entire seriesbinding in a similar mode/pose)
3
Estimate Ki, IC50from known poses
Compute EfficiencyIndices SEI, BEI
5Use experimental datato ‘scale’ to theoretical Ki,IC50 values‘Parameterize’
4
0
5
10
15
20
25
30
BE
I (M
alac
hite
)
0 5 10 15
SEI (Malachite)
Bivariate Fit of BEI (Malachite) By SEI (Malachite)
6
Img. 2
7 Select most Efficient
R=0.85 σ= ± 0.5 pKi
Img. 1
Efficient Structure-Based Drug Design
Compounds with favorable SEI, BEI values
Predicted orknown 2
Future• Connected to the Future of Structural
Biology:Quo vadis Structural Biology?
• Novel ways to expand SBDD:• ESBDD: Efficient Structure Based Drug Discovery• eSBDD: expanded Structure-Based Drug
DiscoveryAn extended suite of Methods to address Biological
ProblemsNew sociological framework:Industrial Biomedical Science Consortium (IBSC-CAT)
IMCA extension: Industrial Biomedical ScienceAssociation CATAccess to multiple beamlines within a SR facility: various experimental techniques relatedto an expanded ‘Structural Biology ‘paradigm (molecular, organelle, cellular, ‘tissular‘) to address biological issues. IMCA and beyond!
IBSA-CAT
AbbottGSKBPfizerVertex3-DLillySGX…..
Experimental facility Legal frameworkIndustrialusers
Imaging(tissues,cells)
Protein Cryst.
XAFS
spec,µdiff.
Summary of FuturePerspectives
• Connected to the Future of StructuralBiology:
Expanded structural framework: molecular, cellular,tissular, ‘dissipative structures’, complex ‘systemsbiology’ within non-equilibrium thermodynamics.
• Novel ways to make SBDD more effective:ESBDD: Efficient Structure-Based Drug
Discovery. Future: numerically, statistically, driven by robustdescriptors.
eSBDD: extended Structure-Based DrugDiscovery.An expanded suite of methods to address BiologicalProblems.New sociological/association framework:Industrial Biomedical Science Association (IBSA-CAT):Proprietary access to multiple beamlines within a SR facility.
Thank you for your attention
Questions?
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Human Fraction Absorbed vs. Polar Surface Area(PSA)*
* Palm, K. et al. Pharm. Res. 1997, 14, 568
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SEI
BE
I
pKi/(PSA/100)
pKi/(MW/1000)
The Amazing SEI/BEI plane
Potency/per unit of exposed PSA
Pot
ency
/ dal
ton
Note: BEI/SEI = 10·(PSA/MW) : Independent of the target !
Decreasing BEI/SEIratios (slopes)
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MW
10·
PS
A
pKi/(PSA/100)
pKi/(MW/1000)
The Amazing SEI/BEI plane
Potency/per unit of exposed PSA
Pot
ency
/ dal
ton
Decreasing (10*PSA)/MWratios (slopes)
more polar(less drug-like)
less polar(more drug-like)
