bio leap innocos europe, paris
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BRINGING LEADING EDGE PHARMACEUTICAL CAPABILITIES TO COSMETICSNovel IP Faster time to market Collaboration opportunities David Pompliano, CEO, Bioleap IncTRANSCRIPT
DrugDiscoveryDoneDifferentlyDavidL.Pompliano,PhD
CEO
What we do
BioLeap delivers custom-made, pre-clinical drug leads and candidates, in collaboration or as a service.
Enabled by computational fragment-based methods, we design novel compounds of unrestricted chemical diversity that bind to their target with predictable affinities.
By minimizing unproductive guesswork, we achieve pre-clinical milestones in shorter times and provide better value compared to current methods.
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Many targets do not yield to the “process”
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Targets Validated Targets
Hits Leads Candidates
>360 Predicted essential
~160 26
KO’s 70 HTS’s Medicinal
chem
5 0
Combi chem
The GSK Antibacterial Experience
Source: Payne, Gwynn, Holmes & Pompliano Nature Rev. Drug Discov., 2007 6, 29-40.
Lead identification and optimization is a time-consuming and inefficient process with low probability of success
There has got to be a better way.
BioLeap changes the approach from trial-correlate to design- confirm.
BioLeap’s hypothesis-driven design process
reduces compound attrition.
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Fragment-based ligand design
1. Find small, but highly specific, fragments 2. Link them together (synergistic binding)
HT screening hit: asking too much all at once
Tight-binding drugs are composed of weak-binding fragments
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TheBioLeapTechnology
ChemicaldiversityDesign‐centeredprocess
Predic@verankingofcompounds
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Insufficient chemical diversity for screening (or an unwillingness to work on weak hits)
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Chemical diversity: combinatorial chemistry with fragments known to bind to the target
Chemotype substitution sites for Imatinib # of chemical moieties
# of substitutions
per site
# of sites
= Possible Combinations
100 3 5 = 2.4T
100 3 4 = 8.1B
100 3 3 = 27M
BioLeap custom-builds novel-structure ligands from fragment building blocks that calculations show already bind to the target.
We DON’T screen !
Fragment Binding Map
BioLeap’s technology enables our chemists to expand their role as drug designers
Constrained Fragment Annealing
protein- centric
ligand- centric
BioLeap’s 3D Design Tools
Drug Designers
We create a map of where, and with what affinity, small chemical building blocks bind
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Immerse protein Anneal µ
Isolated binding sites revealed
Lowest free energy, highest affinity site
A thermodynamically-principled model upon which to frame molecular design hypotheses
movie
Tools to rapidly assemble diverse fragments into novel compounds of predictable binding affinity
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Predictable binding using BioLeap’s in silico annealing process
J.Med.Chem.2002,45,2994‐3008
No predictability using conventional docking
A Critical Assessment of Docking Programs and Scoring Functions Gregory L. Warren,*,† C. Webster Andrews,‡ Anna-Maria Capelli,# Brian Clarke,| Judith LaLonde,†,§ Millard H. Lambert,‡ Mika Lindvall,^,b Neysa Nevins,† Simon F. Semus,† Stefan Senger,^ Giovanna Tedesco,# Ian D. Wall,| James M. Woolven,^ Catherine E. Peishoff,† and Martha S. Head† GlaxoSmithKline Pharmaceuticals, 1250 South Collegeville Road, Collegeville, Pennsylvania 19426, GlaxoSmithKline, Five Moore Drive, Research Triangle Park, North Carolina 27709, GlaxoSmithKline, Centre via Alessandro, Fleming 4, 37135, Verona, Italy, GlaxoSmithKline, New Frontiers Science Park, Third Avenue, Harlow, Essex CM19 5AW, U.K., and GlaxoSmithKline, Gunnels Wood Road, Stevenage, Hertfordshire SG1 2NY, U.K. Received April 17, 2005 Docking is a computational technique that samples conformations of small molecules in protein binding sites; scoring functions are used to assess which of these conformations best complements the protein binding site. An evaluation of 10 docking programs and 37 scoring functions was conducted against eight proteins of seven protein types for three tasks: binding mode prediction, virtual screening for lead identification, and rank-ordering by affinity for lead optimization. All of the docking programs were able to generate ligand conformations similar to crystallographically determined protein/ligand complex structures for at least one of the targets. However, scoring functions were less successful at distinguishing the crystallographic conformation from the set of docked poses. Docking programs identified active compounds from a pharmaceutically relevant pool of decoy compounds; however, no single program performed well for all of the targets. For prediction of compound affinity, none of the docking programs or scoring functions made a useful prediction of ligand binding affinity.
GSK molecular modelers conclude that computational methods are not predictive
15 J. Med. Chem. 2006, 49, 5912-5931
In a blinded test with big pharma, BioLeap correctly ranked 87% of predicted binding affinities
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-5
-4
-3
-2
-1
0 -50 -40 -30 -20 -10 0
Expe
rimen
talpIC
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PredictedFreeEnergy
BioLeap controls key attrition factors
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• Biology: Target affinity and selectivity
• Developability: Physicochemical properties
For MW, lower is better:
Source: J. Med Chem. 2003, 46, 1250-6.
BioLeap’s methodology is target-class independent
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• Kinases – Mapkap-k2 (5 variants) – P38 (3 variants) – cAbl (2 variants) – Ckit – PhoQ Histidine kinase – Proprietary kinases (3) – JAK2/JAK3
• Proteases and Hydrolytic Enzymes
– Elastase: PPE, HNE serine proteases – Peptide deformylase – T4 lysozyme – peptidyl t-RNA hydrolase
• Nuclear Hormone Receptors – ROR-alpha – LXR
• Oxygenases/Reductases – Dihydrofolate reductase – CpI hydrogenase – Cox1/Cox2 – IDO
• Receptors – EPO receptor – NOGO
• Macromolecular Interactions – Protein/DNA complex – P53/MDM2 – BPTI (trypsin proteinase inhibitor) – FABP4 – Fcrn (peptide mimetic)
• Other Classes – NS5B RNA polymerase – M2 proton pump – Amino transferase – Keap1 – Arginase
In silico validation vs. known ligand Results confirmed experimentally, or in progress
BioLeap transforms economics of drug discovery
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Comparison of Time and Resources Required to Produce a Development Candidate
BioLeap
• 150 compounds • 30 months • Minimal infrastructure • Library independent • Broad diversity
Pharma
• 2,000 compounds • 48 months • Big infrastructure • Library dependent • Limited diversity
Year 1 Year 2 Year 3 Year 4
3 Cycles Design / Test
2 Cycles Design / Test
Safety pharm.
HTS Assay Development Reagent Prep
HTS Hit Confirmation
Hit to Lead Chemistry
Lead Optimization Safety pharm.
Lead Candidate
Lead Candidate
Custom-built, ligand-efficient compounds with a past and a future.
• Targets for which HTS methods have failed to produce new lead compounds
• Targets where lead optimization efforts have stalled for a lack of understanding of the structure-activity relationship in the lead series
• Expand/bust a patent
• Develop a fast follower of an early stage clinical compounds
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Extras
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Leads are hard to find, and then the trouble starts
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Discovery~8y Development~5.5y
Findingalead
LeadoptoproduceDC
“ValleyofDeath”
• Clairvoyance• Tenacity• Regulatorystability
What if the ideal position of the fragment is not consistent with chemical synthesis?
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Only two obvious connections
Ligand-centric design: force constraints
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Constrain link to amide
Constrain bond to N-atom and fuse
Design ligand Calculate FE with applied constraints
Constrained Fragment Annealing
Alternative designs are possible: choose based on ranking and synthetic feasibility
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Control physicochemical properties and mode of target engagement
Accessvastchemicaldiversitybylinkingcombina@onsof
op@malfragments
Non‐obviousideasforleadop@miza@on
Circumven@ngtheSARparadox,avoidingpenal@esfrom@ghtly‐boundwaters
Automatedsearchforfragmentssa@sfyingbondgeometries Designforselec@vity,reduced
muta@onresistance
Exploitmoie@eswithstronginterac@onswiththebackboneorconservedaminoacidresidues
The goal of HTS
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