Insights into Using High-Throughput Screening for Agrochemical Leads

1. What have we learnt about using HT screening as a source of agrochemical leads? John Delaney

2. Pharmaceuticals v Pesticides • Both interact with the same sorts of target – e.g. enzyme, receptor • Different economics • Pharmaceuticals – what price a life or a quality of life? • Pesticides – what price a bushel of wheat? • Quantities also differ somewhat…

3. A typical pharmaceutical delivery system

4. and another…

5. A typical pesticide delivery system

6. In vivo screening

7. In vivo high throughput screening

8. In vitro high throughput screening

9. The short answer… • The quality of the chemical input to a screen matters • Proper analysis of the screen results matters • Logistics/cycle times matter

10. What do we mean by input quality? • For the purposes of this talk I won’t cover sample integrity, important though that is • The guiding principle is … “If this compound were to hit on my screen, would I consider it a lead worth working on?” • If the answer is no, why did you screen it in the first place?

11. What do we look for in a lead (beyond potency) ? • A lead is a hit that is … • Novel • Distinct • Interesting

12. Novel • Is this compound similar to something I already know a lot about? • There are no prizes for re-discovering a well worked area of chemistry • We look for compounds that are dissimilar to anything in our corporate database – this assumes that we know everything there is to know about our corporate database!

13. Distinct • Are the compounds in the collection you’re testing different from each other? • A bunch of similar hits might only constitute one lead area • Every slot taken by an close analogue is a slot that could have been used to try a different area of chemistry

14. Interesting • Easy (and worthwhile) to define ‘uninteresting’ • Non-specific, toxic crap – e.g. organo-mercurics, acid chlorides, nitro-phenols • Compounds with poor physical properties • Harder to define ‘interesting’ – what makes a compound ‘agchem-like’ ?

15. Interesting = Right physical properties? • Bioavailability – a combination of potency, stability and mobility • All three affected by the physical properties of the molecule • We know that certain combinations of phys props severely compromise mobility • We know that the presence of certain chemical groups can affect stability

16. Physical properties of agrochemicals Not so very different from Lipinski’s ‘rule of five’ • MWT between 200 and 500 • clogP < 4 • Basic pKa < 9 (big difference from pharma) • H-bond donors (OH,NH) < 3 Mobility – like Lipinski refers to passive transport only (Colin Tice, Pest Manag Sci 57:3-16 (2001)

17. How do we ensure that our input is good? • Apply rigorous filters to compounds we buy in • Designing decent properties directly into our own libraries • Encouraging signs that this is working – more leads from the same number of hits • We can cope with collections offered in a variety of formats – individual, plates or whole collections

18. Analysis • Analysis of hits traditionally done ‘by hand’ • This becomes difficult as the number of screens and the number of compounds fed through them rise • Automation and standardisation part of the answer

19. Standardisation • Is each assay a unique case? Really? • Recording and storing data in a standard form greatly eases the task of developing analysis tools • Expect some up-front grief…

20. How do we analyse a bunch of hits? • Grouping similar structures together • Pulling relevant data from other sources • Turning raw biology into breakpoints • Flexible display of structures, activities and physical properties

21. Clustering • We tend to use Daylight substructural fingerprints as our molecular descriptor, the Tanimoto coefficient as our measure of similarity, and modified Jarvis-Patrick non-hierachical cluster analysis to group compounds – since you ask… • Unashamedly chemistry driven! • Groupings tend to chime with chemists’ intuition

22. Excel as a tool for data analysis • Ubiquitous – this is the way our chemists do most their data analysis • May not be the best tool for doing this kind of work, but… • Its short-comings can be addressed through programming effort • Take a general purpose tool and make it specific

23. The D1 batch system • Batch screening of compounds on in-vitro targets • A framework for collating data, analysis and driving analogue acquisition • Excel based – familiar to chemists • Incorporates clustering and data visualisation using AVS • Keeps track of what was done when and why

25. Cycle times • Cycle times can be surprisingly long • Often leads from the first stage of screening need ‘amplifying’ • Rapid follow-up with analogues key • We would like library design to be an iterative process – delays in getting results compromise the effectiveness of this • Effects of changes to compound selection procedures

26. Analogue ordering • Potential for chaos here • Centralise the actual ordering process • ‘Supersearch’ searches a hierarchy of databases and automatically eliminates duplicate compounds • Automatic annotation of database – “why was this compound ordered from Maybridge?” • Ordering done by adding an MFCD number to a spreadsheet

27. Conclusions • Good chemical input doesn’t just happen, you’ve got to work at it • Analysis can be made easier and faster – automate where possible, but consider the people doing the analysis • Cycle times are still a worry – some progress with making analogue ordering easier • And remember…

28. You’re only as good as your weakest link