The Application of Non-Combinatorial Chemistry to Lead Discovery


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The Application of Non-Combinatorial Chemistry to Lead Discovery. The evolution of purified non-combinatorial libraries as the way forward in parallel synthesis. Given at Lab automation 2002 Palm Springs

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The Application of Non-Combinatorial Chemistry to Lead Discovery

  1. 1. The Application of Non-Combinatorial Chemistry to Lead Discovery Dr Graham F. Smith Library Design and Production Group Pfizer Global R & D Sandwich, UK See Drug Discovery Today, 2001, volume 6, No. 15, p779-785
  2. 2. Parallel synthesis is a powerful technique The application of automation, parallelisation and miniaturization of Organic Chemistry has radically changed Chemistry Departments in Pharmaceutical companies. Combinatorial Chemistry is only one possible use of parallel synthesis
  3. 3. Our Groups Initial Goals <ul><li>To create a technology group which could make large numbers of HPLC pure single compounds </li></ul><ul><li>To create systems and processes which allow single compounds from an array to be selected and made in parallel </li></ul><ul><li>To combine the above with compound design at the individual compound level </li></ul><ul><ul><ul><li>These are then combined to make a library </li></ul></ul></ul><ul><li>To perform all of the above in a high throughput environment for file enrichment and later “lead optimisation” </li></ul>
  4. 4. Non Combinatorial Chemistry <ul><li>Parallel synthesis of single compounds </li></ul><ul><li>Not all combinations of monomers </li></ul><ul><li>No artificial constraints on array shape or dimensions (e.g 2000 from 200 X 150 x 30) </li></ul><ul><li>Allows “cherry picking” of desired products from an array based on: </li></ul><ul><ul><ul><li>diversity </li></ul></ul></ul><ul><ul><ul><li>similarity </li></ul></ul></ul><ul><ul><ul><li>calculated property e.g. Rule of 5 </li></ul></ul></ul><ul><ul><ul><li>predicted activity </li></ul></ul></ul><ul><ul><ul><li>gut feeling, crazy hunch, any of the above in combination….. </li></ul></ul></ul><ul><li>Allows maximum diversity or similarity from a given number of compounds </li></ul><ul><li>Uses all the locally available monomers </li></ul>
  5. 5. Diversity <ul><li>File enrichment for hit seeking requires maximal diversity in drug space and limited redundancy </li></ul><ul><ul><ul><li>populate with low diversity and small cluster sizes </li></ul></ul></ul><ul><li>Hit follow up requires more similarity and redundancy </li></ul><ul><ul><ul><li>populate with high diversity and large cluster sizes </li></ul></ul></ul><ul><li>Need metrics which map chemical similarity to biological similarity </li></ul>
  6. 6. Large clusters high similarity Cluster algorithm from Mike Miller - PGRD Groton USA Small clusters low similarity Spotfire Shenghua Shi
  7. 7. IT for Non Combinatorial Chemistry - LiCRA <ul><li>Li brary C reation R egistration and A nalysis </li></ul><ul><li>Oracle database, batch based not array (plate) based </li></ul><ul><li>MACCS database for structures </li></ul><ul><li>VB interface </li></ul><ul><ul><ul><li>Migration to Java and xml Q1 2002 </li></ul></ul></ul><ul><ul><ul><li>Zhenwei Peng </li></ul></ul></ul><ul><li>Inputs simple and general </li></ul><ul><ul><ul><li>SDF file with batch numbers </li></ul></ul></ul><ul><ul><ul><li>comma separated file with batch numbers & starting materials </li></ul></ul></ul><ul><ul><ul><li>e.g. Batch#1,benzylamine#1,benzoic acid#1 </li></ul></ul></ul><ul><ul><ul><li>synthesis plate format & monomer racks are created on the fly by analysis of library product composition </li></ul></ul></ul><ul><li>Output = complete registration file to corporate DB </li></ul><ul><ul><ul><li>structure, batch#, product weight, chemist, precursors etc. </li></ul></ul></ul><ul><li>Stores, analyses and reports history of synthesis and QC data </li></ul>
  8. 8. LDP - Technology <ul><li>96 well 1ml Teflon plates </li></ul><ul><li>Robbins FlexChem solid phase filter plates </li></ul><ul><li>Tecan Genesis 8 needle x 6 </li></ul><ul><ul><ul><ul><ul><li>dispensing </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>LLE </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>SPE </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>resin washing </li></ul></ul></ul></ul></ul><ul><li>Hamilton SPE x 2 </li></ul><ul><li>Bohdan tube weighing ( for products) x 3 </li></ul><ul><li>Genevac (HT12 & series 2) x 6 </li></ul><ul><li>Gilson 215 based HPLC x 16 </li></ul><ul><ul><ul><li>UV fraction detection </li></ul></ul></ul><ul><ul><ul><li>9ml 48 Well plate bar coded collection plates </li></ul></ul></ul>
  9. 14. Technology in use (2) <ul><li>MicroMass Platform MS or TOF x 6 </li></ul><ul><li>4 way Mux recently added for pre QC </li></ul><ul><li>ELSD (Polymer labs & Aztec) </li></ul><ul><li>Beckman ORCA 2M track robot based fraction selector (Anachem ~$1MM) </li></ul><ul><ul><ul><ul><li>Molecular ion detection </li></ul></ul></ul></ul><ul><ul><ul><ul><li>ELSD quantification </li></ul></ul></ul></ul><ul><ul><ul><ul><li>standard at beginning and end of every plate </li></ul></ul></ul></ul><ul><ul><ul><ul><li>multi detector rule based purity decision </li></ul></ul></ul></ul><ul><ul><ul><ul><li>on the fly product plate reformatting (red, amber, green) </li></ul></ul></ul></ul><ul><ul><ul><ul><li>on line progress monitoring from remote PC </li></ul></ul></ul></ul><ul><ul><ul><ul><li>~60,000 crude fraction samples in 2002 </li></ul></ul></ul></ul><ul><ul><ul><ul><li>24 x 7 operation with maintenance (1 FTE) </li></ul></ul></ul></ul>
  10. 17. Unforeseen advantages of Non Combinatorial Chemistry and HPLC library purification <ul><li>HPLC purification of plates ordered by clogP gives easier purification method development </li></ul><ul><li>Large monomers sets define bigger and better protocols after several iterations </li></ul><ul><li>Can take inputs from combinatorial and non combinatorial designs together </li></ul><ul><li>Leads are easily followed by discovery chemistry </li></ul><ul><li>Much larger monomer set = diversity </li></ul><ul><ul><ul><li>weighing more monomers manually intensive </li></ul></ul></ul><ul><ul><ul><li>inventory of more monomers needs resources (IT, $$ and FTE) </li></ul></ul></ul><ul><li>Less reliance on the activity or chemical success of single monomers in a library </li></ul>
  11. 18. Summary <ul><li>Non combinatorial chemistry is easily achieved within our LiCRA IT system </li></ul><ul><li>High Throughput semi prep reverse phase HPLC is capable of >50,000 product samples per annum delivering ~6mg per sample </li></ul><ul><li>Optimisation of library derived leads is more efficient with non combinatorial chemistry </li></ul><ul><li>File enrichment is more efficiently achieved with non combinatorial chemistry </li></ul><ul><li>Sparse matrix, cherry picked libraries produce HTS actives </li></ul><ul><ul><ul><li>“ hit rate” greater than the average screening file </li></ul></ul></ul><ul><ul><ul><ul><li>no false positives due to “artefacts” or mixtures </li></ul></ul></ul></ul><ul><ul><ul><li>these actives have opportunity of efficient // synthesis follow up from a large chemically validated VL </li></ul></ul></ul>
  12. 19. Acknowledgements <ul><li>Mark Gardner </li></ul><ul><li>Chris Selway </li></ul><ul><li>Mike Stace </li></ul><ul><li>Andrew Morrell </li></ul><ul><li>Ian Parsons </li></ul><ul><li>Jason Robson </li></ul><ul><li>Derek Hearn </li></ul><ul><li>Andrew Berridge </li></ul><ul><li>Mark Lord </li></ul><ul><li>Mike Snarey </li></ul><ul><li>Frank Pullen </li></ul><ul><li>Martin Edwards </li></ul><ul><li>Jeremy Everett </li></ul><ul><li>Nick Terrett </li></ul><ul><li>Adrian Wright </li></ul><ul><li>Andy Kemp (Aitken Scientific) </li></ul><ul><li>Ian Hibbard (Anachem) </li></ul><ul><li>Grant Cameron (Anachem) </li></ul>