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"2.1.9. Interference Compounds. Active compounds from
any source must be examined for known classes of assay interference compounds
and this analysis must be provided in the General Experimental section.
Compounds shown to display misleading assay readouts by a variety of mechanisms
include, but are not limited to, aggregation, redox activity, fluorescence,
protein reactivity, singlet-oxygen quenching, the presence of impurities,
membrane disruption, and their decomposition in assay buffer to form reactive
compounds. Many of these compounds have been classified as Pan Assay
Interference Compounds (PAINS; see Baell & Holloway, J. Med. Chem. 2010,
53, 2719-2740 and webinar at bit.lyj/mcPAINS). Provide firm experimental
evidence in at least two different assays that reported compounds with
potential PAINS liability are specifically active and their apparent activity
is not an artifact."
The term 'known classes of assay interference compounds' must be defined more precisely in order to be usable by both authors submitting manuscripts and reviewers of those manuscripts. Specifically, the term 'known classes of assay interference compounds' implies the existence of a body of experimental data in the public domain for which specific substructural features have been proven to cause the observed assay interference. The JMC Guidelines for Authors (viewed 22-Jan-2017) imply that any assay result for a compound with 'potential PAINS liability' should necessarily be treated as less informative than would be the case if the compound did not have 'potential PAINS liability'. I shall term this as 'devaluing' the assay result.
The PAINS acronym stands for Pan Assay INterference compoundS and it was introduced in a 2010 JMC article (BH2010) that is cited in the Guidelines for Authors (viewed 22-Jan-2017). The PAINS filters introduced in the BH2010 study are based on analysis of frequent-hitter behavior in a panel of 6 AlphaScreen assays. Each PAINS filter consists of a substructural pattern and is associated with an enrichment factor that quantifies the frequent hitter behavior. Compounds that quench or scavenge singlet oxygen have the potential to interfere with AlphaScreen assays but individual PAINS substructural patterns were not evaluated for their likelihood of being associated with singlet oxygen quenching or scavenging. For example, the BH2010 study makes no mention of studies ( 1 | 2 | 3 | 4 ) linking singlet oxygen quenching/scavenging to the presence of a thiocarbonyl group which is a substructural element present in rhodanines.
I argue that a high hit rate against a small panel of assays that all use a single detection technology is inadmissible as evidence for pan-assay interference. I also argue that the results of screening against this assay panel can only be invoked to devalue the result from an AlphaScreen assay. In a cheminformatic context, the applicability domain of a model based on analysis of results from this assay panel is restricted to activity measured in AlphaScreen assays. Furthermore, it is questionable whether it is valid to invoke the results of screening against this assay panel to devalue a concentration response from an AlphaScreen assay because the results for each assay of the panel were obtained at a single concentration (i.e. no concentration response).
The BH2010 study does present some supporting evidence that compounds matching PAINS substructural patterns are likely to interfere with assays. In a cheminformatic context, this supporting evidence can be considered to extend the applicability domain of PAINS filters. However, supporting evidence is only presented for some of the substructural patterns and much of that supporting evidence is indirect and circumstantial. For example, the observation that rhodanines as a structural class have been reported as active against a large number of targets is, at best, indirect evidence for frequent hitter behavior which is characterized by specific compounds showing activity in large numbers of assays. There is not always a direct correspondence between PAINS substructural patterns and those used in analyses that are presented as supporting evidence. For example, the BH2010 study uses substructural patterns for rhodanines that specify the nature of C5 (either saturated or with exocyclic carbon-carbon bond). However, the sole rhodanine definition given in the BMS2006 study specifies an exocyclic carbon-carbon double bond. This means that it is not valid to invoke the BMS2006 study to devalue the result of every assay performed on any rhodanine.
(3)
All data and molecular structures used to carry out a QSAR/QSPR study are to be
reported in the paper and/or in its supporting information or should be readily
available without infringements or restrictions. The use of proprietary data is
generally not acceptable."
Given JMC's stated unacceptability of analysis based on proprietary data, to use such analysis to define editorial policy would appear to contradict that editorial policy.
To sum up:
I rest my case.
The PAINS acronym stands for Pan Assay INterference compoundS and it was introduced in a 2010 JMC article (BH2010) that is cited in the Guidelines for Authors (viewed 22-Jan-2017). The PAINS filters introduced in the BH2010 study are based on analysis of frequent-hitter behavior in a panel of 6 AlphaScreen assays. Each PAINS filter consists of a substructural pattern and is associated with an enrichment factor that quantifies the frequent hitter behavior. Compounds that quench or scavenge singlet oxygen have the potential to interfere with AlphaScreen assays but individual PAINS substructural patterns were not evaluated for their likelihood of being associated with singlet oxygen quenching or scavenging. For example, the BH2010 study makes no mention of studies ( 1 | 2 | 3 | 4 ) linking singlet oxygen quenching/scavenging to the presence of a thiocarbonyl group which is a substructural element present in rhodanines.
I argue that a high hit rate against a small panel of assays that all use a single detection technology is inadmissible as evidence for pan-assay interference. I also argue that the results of screening against this assay panel can only be invoked to devalue the result from an AlphaScreen assay. In a cheminformatic context, the applicability domain of a model based on analysis of results from this assay panel is restricted to activity measured in AlphaScreen assays. Furthermore, it is questionable whether it is valid to invoke the results of screening against this assay panel to devalue a concentration response from an AlphaScreen assay because the results for each assay of the panel were obtained at a single concentration (i.e. no concentration response).
The BH2010 study does present some supporting evidence that compounds matching PAINS substructural patterns are likely to interfere with assays. In a cheminformatic context, this supporting evidence can be considered to extend the applicability domain of PAINS filters. However, supporting evidence is only presented for some of the substructural patterns and much of that supporting evidence is indirect and circumstantial. For example, the observation that rhodanines as a structural class have been reported as active against a large number of targets is, at best, indirect evidence for frequent hitter behavior which is characterized by specific compounds showing activity in large numbers of assays. There is not always a direct correspondence between PAINS substructural patterns and those used in analyses that are presented as supporting evidence. For example, the BH2010 study uses substructural patterns for rhodanines that specify the nature of C5 (either saturated or with exocyclic carbon-carbon bond). However, the sole rhodanine definition given in the BMS2006 study specifies an exocyclic carbon-carbon double bond. This means that it is not valid to invoke the BMS2006 study to devalue the result of every assay performed on any rhodanine.
The data (results from 6 AlphaScreen assays and associated chemical structures) that form the basis of the analysis in the BH2010 study are not disclosed and must therefore be considered to be proprietary. Furthermore, some of the supporting evidence that compounds matching PAINS filters are likely to interfere with assays is itself based on analysis (e.g. BMS2006 and Abbott2007) of proprietary data. The JMC Guidelines for Authors (viewed 22-Jan-2017) make it clear that the use of proprietary data is unacceptable:
"2.3.5.2
Proprietary Data. Normally, the use of proprietary data for computational
modeling or analysis is not acceptable because it is inconsistent with the ACS
Ethical Guidelines. All experimental data and molecular structures used to
generate and/or validate computational models must be reported in the paper,
reported as supporting information, or readily available without infringements
or restrictions. The Editors may choose to waive the data deposition
requirement for proprietary data in a rare case where studies based on very
large corporate data sets provide compelling insight unobtainable otherwise.
2.3.6
QSAR/QSPR and Proprietary Data. The following are general requirements for
manuscripts reporting work done in this area:
To sum up:
- Analysis of the screening results for the BH2010 assay panel can only be invoked invoked to devalue or otherwise invalidate the result from an AlphaScreen assay.
- Additional supporting evidence is only provided in BH2010 for some of the PAINS filters. In these cases, the evidence is not generally presented in a manner that would allow a manuscript reviewer to assess risk of assay interference in an objective manner.
- Most of the analysis presented in the BH2010 study has been performed on proprietary data. To base JMC editorial policy on analysis of proprietary data would appear to contradict the Journal's policy on the use of proprietary data.
I rest my case.
1 comment:
Right on. The relativity of "pan-assay" is basic and essential. Prof. Baell does, I think, acknowledge this, at least according to a conference call I remember from 2011 or so. Apparently simple answers are craved, like Ro5, and overused.
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