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Design of compound libraries for fragment screening (Feb 2012 version)

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Slimmed down fragment screening library talk presented at University of Adelaide (Dec 2011) and Pharmaxis (Feb 2012). Includes dingo and Maria Sharapova (losing finalist at 2012 Australian Open). ...

Slimmed down fragment screening library talk presented at University of Adelaide (Dec 2011) and Pharmaxis (Feb 2012). Includes dingo and Maria Sharapova (losing finalist at 2012 Australian Open). The photo for the title slide is of a range finder from the Admiral Graf Spee and was taken in Montevideo.

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    Design of compound libraries for fragment screening (Feb 2012 version) Design of compound libraries for fragment screening (Feb 2012 version) Presentation Transcript

    • Design of Compound Libraries for Fragment Screening Peter W. Kenny pwk.pub.2008@gmail.com | http://fbdd-lit.blogspot.com
    • Achtung! Spitfire! A Brief History of Screening: In the Beginning… Stuka on wikipedia
    • “Why can’t we pray for something good, like a tighter bombing pattern, for example? Couldn’t we pray for a tighter bombing pattern?” , Heller, Catch 22, 1961 … and then there was HTS B52 on wikipedia
    • So, Maria, why do you think it is that the Russians are so much better than the Germans at tennis these days? Actually we started to beat them at their national sport almost 70 years ago and... .... as Uncle Joe was so fond of saying, quantity has a quality all of its own. Even the stars of tennis have heard of HTS....
    • The HTS was YOUR idea so don’t try blaming me! Unfortunately HTS is not a panacea
    • FBDD Essentials Screen fragments Synthetic Elaboration Target Target & fragment hit Target & lead
    • Linking Fragment Elaboration Tactics Merging Growing
    • Why fragments? • Access to larger chemical space • Counter the advantage of competitors’ large compound collections • Ligands are assembled from proven molecular recognition elements • A smart way to do Structure-Based Design • Control resolution at which chemical space is sampled
    • PTP1B (Diabetes/Obesity): Fragment elaboration Elaboration by Hybridisation: Literature SAR was mapped onto intial fragment hit (green). Note overlay of aromatic rings of elaborated fragment (blue) and difluorophosphonate (red). See Black et al BMCL 2005, 15, 2503-2507 | http://dx.doi.org/doi:10.1016/j.bmcl.2005.03.068 Inactive at 200mM 15 mM 3000 mM 3 mM 150 mM (Conformational lock) 130 mM (3-Phenyl substituent)
    • Fragment-based lead discovery: Generalised workflow Target-based compound selection Analogues of known binders Generic screening library Measure Kd or IC50 Screen Fragments Synthetic elaboration of hits SAR Protein Structures Milestone achieved! Proceed to next project
    • A model for molecular complexity This model is equally relevant to conventional and fragment-based screening. See Hann, Leach & Harper J. Chem. Inf. Comput. Sci., 2001, 41, 856-864 | http://dx.doi.org/10.1021/ci000403i Success landscape
    • Degree of substitution as measure of molecular complexity The prototypical benzoic acid can be accommodated at both sites and, provided that binding can be observed, will deliver a hit against both targets See Blomberg et al JCAMD 2009, 23, 513-525 | http://dx.doi.org/10.1007/s10822-009-9264-5 | This way of thinking about molecular complexity is similar to the ‘needle’ concept introduced by Roche researchers. See Boehm et al J. Med. Chem. 2000, 43, 2664-2774 | http://dx.doi.org/10.1021/jm000017s
    • Ligand Efficiency LE= DGº/NonHyd Hopkins, Groom & Alex, DDT 2004, 9, 430-431 Binding Efficiency Index BEI= pIC50/(MW/kDa) Abad-Zapaftero & Metz, DDT 2005, 10, 430-431 Ligand Lipophilicity Efficiency LLE = pIC50 - ClogP Leeson & Springthorpe , NRDD 2007, 6, 881-890. Measured binding is scaled Measured binding is offset Binding Efficiency Measures
    • Fragment screening requirements • Assay capable of reliably quantifying weak (~mM) binding • Library of compounds with low molecular complexity and good (~mM) aqueous solubility •
    • Screening Library Design Requirements • Precise specification of substructure – Count substructural elements (e.g. chlorine atoms; rotatable bonds; terminal atoms; reactive centres…) – Define generic atom types (e.g. anionic centers; hydrogen bond donors) – SMARTS notation is particularly useful • Meaningful measure of molecular similarity – Structural neighbours likely to show similar response in assay
    • Measures of Diversity & Coverage • • • • • • • • • • • • • • • 2-Dimensional representation of chemical space is used here to illustrate concepts of diversity and coverage. Stars indicate compounds selected to sample this region of chemical space. In this representation, similar compounds are close together. The title slide for this talk shows the optical range finder that was salvaged from the pocket battleship Admiral Graf Spee and can be seen in Montevideo.
    • Coverage, Diversity & Library Design •• • • • •• • • • • • •
    • Neighborhoods and library design
    • Hits, non-hits & lipophilicity: Survival of the fattest* Mean Std Err Std Dev Hits 2.05 0.08 1.10 Non-Hits 1.35 0.03 1.24 *Analysis of historic screening data & quote: Niklas Blomberg, AZ Molndal Comparison of ClogP for hits and non-hits from fragment screens run at AstraZeneca
    • Sample Availability Molecular Connectivity Physical Properties screening samples Close analogs Ease of synthetic elaboration Molecular complexity Ionisation Lipophilicity Solubility Molecular recognition elementsMolecular shape 3D Pharmacophore Privileged substructures Undesirable substructures Molecular size 3D Molecular Structure Fragment selection criteria Why I don’t use the rule of 3: http://fbdd-lit.blogspot.com/2011/01/rule-of-three-considered-harmful.html
    • Acceptable diversity And coverage? Assemble library in soluble form Add layer to core Incorporate layer Yes No Select core Core and layer library design Compounds in a layer are selected to be diverse with respect to core compounds. The ‘outer’ layers typically contain compounds that are less attractive than the ‘inner’ layers. This approach to library design can be applied with Flush or BigPicker programs (Dave Cosgrove, AstraZeneca, Alderley Park) using molecular similarity measures calculated from molecular fingerprints. See Blomberg et al JCAMD 2009, 23, 513-525 | http://dx.doi.org/10.1007/s10822-009-9264-5
    • ClogP: Charged library compounds ClogP: Neutral library compoundsNon-hydrogen atoms GFSL05: Size and lipophilicity profiles Rotatable bonds
    • 61 17 13 4 4 1 0 Breakdown of GFSL05 by charge type Neutral Anion Cation Ionisation states are identified using AZ ionisation and tautomer model. Multiple forms are generated for acids and bases where pKa is thought to be close to physiological pH. See Kenny & Sadowski Methods and Principles in Medicinal Chemistry 2005, 23, 271-285 | http://dx.doi.org/10.1002/3527603743.ch11
    • GFSL05: Numbers of neighbours within library as function of similarity (Tanimoto coefficient; foyfi fingerprints) 0.90 0.85 0.80
    • FBDD Blogs These are ‘crosslinked’ and both will direct you to LinkedIn & facebook groups Practical Fragments: http://practicalfragments.blogspot.com FBDD Literature: http://fbdd-lit.blogspot.com
    • Spare slides
    • A (small) selection of literature General • Erlanson, McDowell & O’Brien, Fragment-Based Drug Discovery, J. Med. Chem., 2004, 47, 3463- 3482 | http://dx.doi.org/10.1021/jm040031v • Congreve et al. Recent Developments in Fragment-Based Drug Discovery, J. Med. Chem., 2008 51, 3661–3680 | http://dx.doi.org/10.1021/jm8000373 • Albert et al, An integrated approach to fragment-based lead generation: philosophy, strategy and case studies from AstraZeneca's drug discovery programmes. Curr. Top. Med. Chem. 2007, 7, 1600-1629 | http://www.ingentaconnect.com/content/ben/ctmc/2007/00000007/00000016/art00006 • Hann, Leach & Harper, Molecular Complexity and Its Impact on the Probability of Finding Leads for Drug Discovery, J. Chem. Inf. Comput. Sci., 2001, 41, 856-864 | | http://dx.doi.org/10.1021/ci000403i Screening Libraries • Blomberg et al, Design of compound libraries for fragment screening, JCAMD 2009, 23, 513-525 | http://dx.doi.org/10.1007/s10822-009-9264-5 • Schuffenhauer et al, Library Design for Fragment Based Screening, Curr. Top. Med. Chem. 2005, 5, 751-762 | http://www.ingentaconnect.com/content/ben/ctmc/2005/00000005/00000008/art00003 • Baurin et al, Design and Characterization of Libraries of Molecular Fragments for Use in NMR Screening against Protein Targets, J. Chem. Inf. Comput. Sci., 2004, 44, 2157-2166 | http://dx.doi.org/10.1021/ci049806z • Colclough et al, High throughput solubility determination with application to selection of compounds for fragment screening. Bioorg, Med. Chem. 2008, 16, 6611-6616 | http://dx.doi.org/doi:10.1016/j.bmc.2008.05.021 • Kenny & Sadowski, Structure modification in chemical databases. Methods and Principles in Medicinal Chemistry 2005, 23, 271-285 | http://dx.doi.org/10.1002/3527603743.ch11
    • Scheme for fragment based lead optimisation
    • 20% 10% 30% 40% 50% log(S/M) Aqueous solubility: Percentiles for measured log(S/M) as function of ClogP Data set is partitioned by ClogP into bins and the percentiles and mean ClogP is calculated for each. This way of plotting results is particularly appropriate when dynamic range for the measurement is low. Beware of similar plots where only the mean or median value is shown for the because this masks variation and makes weak relationships appear stronger than they actually are. See Colclough et al Bioorg. Med. Chem. 2008, 16, 6611-6616 | http://dx.doi.org/doi:10.1016/j.bmc.2008.05.021 Measure solubility for neutral (at pH 7.4) fragments for which ClogP > 2.2
    • GFSL05 project • Strategic requirement: – Readily accessible source of compounds for a range of fragment screening applications (NMR, Biochemical Assay, HTS at 10 x normal concentration) • Tactical objective: – Assemble 20k structurally diverse compounds with properties that are appropriate for fragment screening as 100mM DMSO stocks
    • GFSL05: Overview • Molecular recognition considerations – Requirement for at least one charged center or acceptably strong hydrogen bond donor or acceptor • Substructural requirements defined as SMARTS – Progressively more permissive filters to apply core and layer design – Restrict numbers of non-hydrogen atoms (size) and extent of substitution (complexity) – Filters to remove undesirable functional groups (acyl chloride) and to restrict numbers of others (nitro, chloro) – ‘Prototypical reaction products’ for easy follow up • Control of lipophilicity (ClogP) dependent on ionisation state – Solubility measurement for more lipophilic neutrals • Tanimoto coefficient calculated using foyfi fingerprints (Dave Cosgrove) as primary similarity measure – Requirement for neighbour availability in core and layer design
    • APGNMR07: Overview • General – Designed for NMR screening (especially 2D protein detect) – 1200 Compounds – Derived in part from existing AZ NMR libraries and GFSL05 – Molecules smaller on average than those in GFSL05 – Stock solutions: 200mM in d6-DMSO • Partitioning of library for cocktailing – Groups (200) of 6 compounds defined – Allows screening in mixtures of 6 or 12 – Acid:Base:Neutral = 1:1:4
    • GFSL05 APGNMR07 Lipophilicity profiles for GFSL05 and APGNMR07
    • GFSL05: Numbers of available neighbours as function of similarity (Tanimoto coefficient; foyfi fingerprints) and sample weight >10mg >20mg 0.90 0.85 0.80 0.90 0.85 0.80