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 … 1.Novel 2.Distinct 3.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