09 December 2024

They may be cons, but they’re our CONS

Practical Fragments has written repeatedly about various assay artifacts (vide infra). Different technologies are susceptible to different interference mechanisms, making general rules difficult. Earlier this year we wrote about the Metal Ion Interference Set, or MIIS: a collection of a dozen salts that could be used to assess the sensitivity of assays to metal contaminants. In a recent open-access JACS Au paper, Huabin Hu (Uppsala University), Jonathan Baell (Monash University), and collaborators extend the concept to small molecules.
 
The researchers have compiled a Collection Of useful Nuisance compounds, or CONS, perhaps with a nod to “Chemical con artists foil drug discovery” published a decade ago, which we highlighted here. The 103 members of the CONS are divided into three categories.
 
The first set contains five aggregators: molecules that have been shown to form colloidal clusters that non-specifically interfere with biological assays, as discussed here.
 
The largest set, at 67 members, consists of PAINS, or pan-assay interference compounds, which we first wrote about in 2010. These are themselves divided into various subcategories: non-specific electrophiles such as curcumin and an isothiazolone, redox cyclers such as quinones, contaminants such as the decomposition products of certain fused tetrahydroquinolines, miscellaneous, metal chelators, and additional mechanisms including optical interference and singlet oxygen quenchers, which are particularly problematic in AlphaScreen assays.  
 
The last set consists of 31 compounds that can cause problems in phenotypic assays. Some of these non-specifically disrupt cell membranes. Others have well-defined but toxic effects, such as interfering with tubulin or intercalating into DNA. Such bioactivity is not always a bad thing: some of these molecules, such as topotecan and colchicine, are approved drugs, but it’s useful to be aware of whether these types of activities will affect your assay.
 
One criticism of the PAINS concept is that it lumps together multiple mechanisms. (Pete Kenny wrote about this recently.) Another criticism is that, by focusing on chemical substructures, true hits may be unfairly deprioritized based on structure alone. What’s nice about the CONS list is that the potentially interfering mechanisms of each molecule are documented and categorized so they can be considered when establishing an assay. For example, you may not care whether a compound interferes in a phenotypic assay if you are performing a screen on an isolated enzyme.
 
The entire set of compounds is available from Enamine, and additional vendors are provided in a supplementary table. If you’re doing a lot of assays, particularly on new targets and mechanisms, it may be worth testing the CONS to understand what kinds of false positives might occur.

3 comments:

Jonathan Baell said...

Thanks for the shout out Dan. We hope these will be useful to others wanting to benchmark the susceptibility of their assays to disruptive compounds, and which sort

Jonathan Baell said...

Oh, and please contact Irena I.yavnyuk@enamine.net about an assay-ready set of these CONs if interested

Peter Kenny said...

Hi Dan, I think that my post that you linked gives a pretty accurate picture of my view of the PAINS substructure model. I’ve always been well aware of the problems caused by bad chemical matter and spent a lot of time working up HTS output between 1995 and 2005 (I also wrote the SMARTS-matching software that was used for the Zeneca ‘de-crapper’ and the first two articles cited in my critique of the ill-considered Ecstasy and Agony of Assay Interference Compounds editorial were to articles referencing HARPick and Flush). The fundamental problem with the PAINS substructural model is that the rhetoric is not supported by data and I don’t think we're going to solve the problems caused by crappy chemical matter using crappy cheminformatics.

On a related note, it’s almost exactly ten years since Teddy’s ‘PAINS Shaming, part deux’ post that caused me to take a more forensic look at the basis of the PAINS substructure model (I was staying in Puerto Iguazú while participating in the discussion of that post).