what have we learnt about using ht screening as a source of agrochemical leads? john delaney
Post on 02-Apr-2015
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What have we learnt about using HT screening as a source of agrochemical
leads?
John Delaney
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…
A typical pharmaceutical delivery system
and another…
A typical pesticide delivery system
In vivo screening
In vivo high throughput screening
In vitro high throughput screening
The short answer…
The quality of the chemical input to a screen matters
Proper analysis of the screen results matters
Logistics/cycle times matter
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?
What do we look for in a lead (beyond potency) ?
A lead is a hit that is …
1. Novel
2. Distinct
3. Interesting
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!
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
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’ ?
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
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)
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
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
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…
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
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
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
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
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
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
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…
You’re only as good as your weakest link
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