Comment on The Ecstasy and Agony of Assay Interference

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Perspective

Comment on ‘The Ecstasy and Agony of Assay Interference Compounds’ Peter W. Kenny J. Chem. Inf. Model., Just Accepted Manuscript • DOI: 10.1021/acs.jcim.7b00313 • Publication Date (Web): 19 Oct 2017 Downloaded from http://pubs.acs.org on October 21, 2017

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Journal of Chemical Information and Modeling

Comment on ‘The Ecstasy and Agony of Assay Interference Compounds’ Peter W. Kenny* Berwick-on-Sea, North Coast Road, Blanchisseuse, Saint George, Trinidad and Tobago.

ABSTRACT

A recent editorial (Aldrich et al, The Ecstasy and Agony of Assay Interference Compounds, J. Chem. Inf. Model., 2017, 57, 387-390) is examined critically. When assessing assay hits from screening, it is important to draw a distinction between false positives, that have no effect on target function, and compounds that affect target function through an undesirable mechanism of action. Observation of frequent-hitter behavior for a compound should be regarded as circumstantial evidence, rather than definitive proof, that the compound has interfered with assay readouts or acted through an undesirable mechanism of action. The applicability domain of published (Baell & Holloway, J. Med. Chem. 2010, 53, 2719-2740) Pan Assay INterference compoundS (PAINS) filters is limited by the narrow scope of the proprietary data used to derive them. It is suggested that journal guidelines for authors should not prescribe, as those for Journal of Medicinal Chemistry appear to do, that activity in assays reported for compounds that match PAINS filters be treated any differently to that for compounds that do not match PAINS filters. It is argued that use of models based on proprietary data in the evaluation of manuscripts would contradict the editorial policy of any journal that deemed the use of proprietary data to be unacceptable in modeling studies.

ACS Paragon Plus Environment

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High-throughput screening (HTS) and combinatorial chemistry were touted in the early nineteen nineties as technologies that would transform drug discovery although it soon became clear to many of the pharmaceutical industry scientists who were analyzing HTS output that neither was likely to prove to be a panacea. Before long, terms such as ‘false positives’, ‘frequent-hitters’, ‘bad actors’ and ‘uglies’ came to be used to describe some of the output from HTS. The Flush1 molecular similarity software was used at Zeneca from the mid-nineteen nineties for analysis of HTS output while scientists at Rhône-Poulenc Rorer cleaned the process of combinatorial library design with HARPick.2 Although poor physicochemical properties were partially blamed3 for the unattractive nature and promiscuous behavior of many HTS hits, it was also recognized that some of the problems were likely to be due to the presence of particular substructures in the molecular structures of offending compounds. In particular, medicinal chemists working up HTS results became wary of compounds whose molecular structures suggested reactivity, instability, accessible redox chemistry or strong absorption in the visible spectrum as well as solutions that were brightly colored. While it has always been relatively easy to opine that a molecular structure ‘looks ugly’, it is much more difficult to demonstrate that a compound is actually behaving badly in an assay. A recent Editorial,4 published simultaneously in a number of American Chemical Society (ACS) journals, highlights the problems caused by bad behavior of compounds in assays and its specific focus is to provide new standards to, “ensure that all compounds for which activity is reported demonstrate activity commensurate with expectations”. Although the Editorial4 does make a number of useful recommendations, in particular for experimental studies to assess impact of colloidal aggregation,5 it is still important to examine it critically given the claim that, “These new standards will bring clarity to medicinal chemistry and chemical biology and further ensure the already high level of science published in ACS


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journals”. One particular issue that is highlighted in this Perspective concerns the use of predictive models in the evaluation of manuscripts and it can be argued that new standards will also be needed for any predictive models used for this purpose. Frequent-hitter behavior in HTS, especially when observed against diverse targets using assays with different readouts, has always been seen as indicative of bad behavior in assays and a compound that is a hit in many assays should always be treated with caution. Hit patterns against assays (e.g. compounds hit targets in different classes that share a feature such as a catalytic cysteine residue) can also be informative. Nevertheless, compounds can behave badly in an assay without being promiscuous and frequent-hitter behavior may reflect conserved molecular recognition characteristics that result from related targets (e.g. kinases) using the same cofactor (e.g. adenosine triphosphate). Generally, it is necessary to account for hit rates in the individual assays when quantifying frequent-hitter behavior and it is important to be aware of potential pitfalls6 when attempting to link frequent-hitter behavior to the presence of specific substructures in molecular structures. One of the challenges in defining rules like these is to capture the different contexts in which substructures occur in molecular structures. For example, a carboxylic acid and its methyl ester will both match the SMARTS7 pattern ‘OC=O’ but most medicinal chemists would anticipate different behavior by the two compounds in assay buffer at neutral pH. It is also important to bear in mind that pharmacological promiscuity is a function of both affinity and concentration. Commenting on compounds that behave badly in assays, the Editorial4 states, “Many of these false hits are Pan Assay INterference compoundS (PAINS) or colloidal aggregators”. It is unclear why colloidal aggregators5 and PAINS8 are considered to be different classes of bad actor since many drug discovery scientists would consider ‘pan assay interference’ to be an apposite description of promiscuity resulting from colloidal aggregation. However, there


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is a more useful categorization that can be made of undesirable behavior of compounds in assays. Given the potential ambiguity of the terms ‘interference’, ‘artifact’ and ‘false positive’ in the context of assays for biological activity, I shall also use the term ‘bad behavior’ and categorize the ways that compounds behave badly in assays simply as ‘Type 1’ and ‘Type 2’. The defining characteristic of Type 1 behavior is that the assay result gives an incorrect indication of the extent to which the compound affects the function of the target. The term ‘interference’ can safely be used to describe Type 1 behavior since the assay gives a false result. Both “spectroscopic interference compounds” and “compounds that inhibit reporter enzymes” discussed in the Editorial4 would be considered to be exhibiting Type 1 behavior as would AlphaScreen9 assay hits that result from quenching or scavenging of singlet oxygen. Type 1 behavior is usually revealed when compounds are tested in orthogonal assays with different readouts. In some cases, it may be possible to measure spectra for compounds in order to assess, and even correct for, interference.10 Experimental difficulties caused by Type 1 behavior usually increase with concentration and it is especially important to assess for interference when using biochemical assays to screen fragments.10 Type 1 behavior, which can lead to false negatives as well as false positives, should be seen primarily as a problem with the assay rather than the compounds. Type 2 behavior is characterized by compounds affecting target function by an undesirable mechanism of action (MoA). It can be argued that a hit which engages a target by an undesirable MoA should not be labelled ‘false’ nor should the term ‘assay interference’ be used in this context. Simply using an orthogonal assay with a different readout is unlikely to shed light on the problem because the effect of compound on target function is real. Target engagement by colloidal aggregates5 would generally be considered to be an undesirable


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MoA, not least because it would be necessary for the aggregates to assemble at the site of action after dosing for the drug to function in vivo. However, formation of a covalent11,12 bond between drug and target is a perfectly acceptable way in which to engage targets and it would be incorrect to dismiss this MoA as assay interference. Furthermore, selectivity can still be achieved even when binding is irreversible.13 Type 2 behavior should be seen primarily as a problem with the compounds rather than the assay. The Editorial4 states “Measuring and publishing full concentration-response curves is a simple but crucial way to retain focus on only the most interesting molecules; much can be learned from the steepness of the curve and how well it is sampled”. Requiring that full concentration-response curves be published for active compounds is indeed likely to improve the quality of the medicinal chemistry and chemical biology literature and it would be logical to also make this requirement for any claim that a compound has behaved badly in an assay.

The Editorial4 also recommends the use of, “reporter-free

methods such as isothermal titration calorimetry, surface plasmon resonance, or related techniques”. Using techniques like these allows bad behavior in assays to be characterized even when frequent-hitter behavior has not been observed and structure-activity relationships (SARs) have not yet been established. Surface plasmon resonance14,15 (SPR) is a particularly appropriate method with which to characterize MoA since association, dissociation and stoichiometry can all be observed directly. Isothermal titration calorimetry (ITC) is less suitable than SPR for this purpose because processes other than association of ligand with target (e.g. hydrogen peroxide production by a redox cycler) may contribute to the observed heat change.


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The term ‘PAINS’ (Pan Assay INterference compoundS) that was introduced in the Baell & Holloway 2010 (BH2010) study8 does not appear to be defined with sufficient precision to bring clarity to medicinal chemistry or chemical biology. BH20108 states “We conclude that at least 10 of these compound classes should be considered to be pan assay interference compounds, for which we have coined the acronym PAINS, and avoided as development options when furnished as screening hits”. This implies that the term ‘PAINS’ can be applied to both compounds and classes of compound and that compounds can be labelled as PAINS on the basis of class membership even if they do not interfere with any assays. If it is acceptable to describe compounds that do not actually exhibit pan assay interference as PAINS then the term ‘potential PAINS’, used in both the Editorial4 and BH2010,8 would appear to be ambiguous. The Nelson et al (N2017) study16 defines PAINS more stringently than BH2010 “PAINS, or pan-assay interference compounds, are compounds that have been observed to show activity in multiple types of assays by interfering with the assay readout rather than through specific compound/target interactions”. This definition restricts the PAINS label to compounds for which frequent hitter behavior has actually been observed in assays of different types and shown to be caused by interference with assay readouts. BH20108 does not actually present experimental data to support a view that frequent-hitter behavior observed in the assay panel was caused by Type 1 or Type 2 behavior. The Baell & Walters 2014 (BW2014) study17 asserts, “Most PAINS function as reactive chemicals rather than discriminating drugs” although no evidence is presented in support of this claim.


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The PAINS filters introduced in BH20108 are, in essence, simple models for predicting frequent-hitter behavior of compounds in assays which is assumed to be caused by assay interference. The PAINS filters8 are based on analysis of frequent-hitter behavior against a panel of six AlphaScreen9 assays and compounds were tested at a single concentration (between 10 µM and 30 µM) in each assay. A panel of six assays using the same readout would appear to represent a suboptimal design of an experiment to observe pan assay interference, especially since similar analysis18 had been performed four years previously on the results from a panel of 362 assays. BH20108 does not actually provide examples of hits against the assay panel that were shown definitively to result from either interference with the AlphaScreen9 readout or to be due to an undesirable MoA. The choice of a panel of six AlphaScreen9 assays for analysis limits the applicability domain of PAINS filters8 to prediction of frequent-hitter behavior in assays using this readout at concentrations in the 10 µM to 30 µM range. Furthermore, the proprietary nature of the data means that the analysis can neither be verified nor reproduced. PAINS filters8 do not appear to be generally predictive of frequent-hitter behavior when results from larger numbers of assays are analysed.19,20 The large-scale analysis also highlights the importance of substructural context and it has been observed20 that, “the same PAINS substructure was often found in consistently inactive and frequently active compounds”. BH20108 notes that chemical classes represented by some of the PAINS filters8 are becoming increasing prevalent in the literature as screening hits although this not equivalent to establishing that individual compounds in these chemical classes exhibit frequent-hitter behavior. BH20108 does not appear to have thoroughly evaluated individual PAINS filters for likelihood of singlet oxygen quenching or scavenging despite this being a known8 mechanism for AlphaScreen9 interference. For example, the singlet oxygen reactivity reported21,22 for compounds with thiocarbonyl groups


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in their molecular structures does not appear to have been considered as relevant to the frequent-hitter behavior of rhodanines in the BH20108 assay panel. There is considerable variation in the quantity of data on which individual PAINS filters8 are based and it has been observed19 that, “more than half of the PAINS alerts were derived from one or two compounds only”. Individual PAINS filters8 also differ with respect to literature evidence that the classes of compound that they specify actually exhibit Type 1 or Type 2 behavior in assays and, in some cases, the scope of the literature evidence appears to have been exaggerated. For example, BW201417 states that a study,23 “warns researchers that these types of compound undergo light-induced reactions that irreversibly modify proteins” although the more appropriate citation would be of an earlier article24 which presents three specific examples of rhodanines exhibiting photochemically-enhanced binding to a single target protein. While this MoA would rightly be regarded as unacceptable by most medicinal chemists, the rhodanines in question have extended π-systems and there is no basis for extrapolation of their photochemical behavior to all rhodanines. Despite the criticisms made of PAINS filters8 there may well be situations in which they are still useful and drug discovery scientists using them for HTS triage may have additional information available (e.g. historic screening results). Prudent and effective use of predictive models requires that users be aware of the limitations (e.g. in applicability domain) of the models that they are using. A drug discovery scientist who has observed a rhodanine as a hit when screened at 10 µM in an AlphaScreen9 assay is likely to find it useful to know that frequent-hitter behavior has been observed at this concentration in assays using this readout for some rhodanines, even when their structures are unknown. However, this information would be less relevant if a well-behaved concentration response corresponding to an IC50 value of 1 µM had been observed for the rhodanine in an AlphaScreen9 assay and would be


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practically irrelevant had a well-behaved concentration response, corresponding to a KD value of 1 µM, been observed by SPR. Drug discovery guidelines, such as the Rule of 5,3 are typically established using data analysis and strengths of trends observed in data indicate how rigidly guidelines should be adhered to. Data sets used in assay interference studies vary with respect to their information content. The data set used in BH20108 lies near one end of the spectrum in that the data are proprietary and all six assays in the panel use the same readout. The data set associated with the Dahlin et al 2015 study25 which discloses results from assays that had been designed to probe different interference mechanisms sits near the other end of the spectrum. Arguments presented in favor of open data typically focus on reproducibility and transparency but there is another strong rationale for disclosing data relevant to assay interference. The most compelling evidence that a compound has behaved badly in an assay against a particular target is provided by the observation that close structural analogs have behaved badly in similar assays against related targets. To better appreciate the potential challenges of implementing the new standards for potential assay interference compounds that were introduced in the Editorial,4 it may be useful to first examine the standards that have been already been implemented by Journal of Medicinal Chemistry (JMC). The current (revised January 2017; viewed September 5, 2017) JMC guidelines26 for authors make specific provision for the possibility that claimed biological activity may be due to assay interference “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.” This requirement raises the question of what constitutes ‘known’ in the context of assay interference and it would be logical to mandate that claims for interference and claims for


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biological activity be supported by comparable standards of evidence. It would be inaccurate to describe a chemotype that is claimed to show frequent-hitter behavior in a proprietary data set as a ‘known class of assay interference compounds’. Current (revised January 2017; viewed September 5, 2017) JMC guidelines26 require that authors: “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.”

There are two criticisms that can be made of using PAINS filters8 in the evaluation of manuscripts and I would suggest that each criticism is quite sufficient to preclude their use for this purpose. First, the PAINS filters8 have a limited applicability domain (assays using AlphaScreen9 readout) and are predictors of frequent-hitter behavior rather than assay interference. Second, they have been derived from analysis of proprietary data and JMC guidelines26 for authors (revised January 2017; viewed September 5, 2017) state that, “The use of proprietary data is generally not acceptable”. Even though the PAINS filters8 are freely available, it can be argued that their use in evaluation of manuscripts would constitute use of proprietary data in a manner that would be unacceptable to JMC. As such, to mandate that compounds matching PAINS filters8 be treated any differently from compounds not matching PAINS filters would appear to contradict JMC editorial policy. It is not only JMC that considers use of proprietary data to be unacceptable and a 2006 editorial27 in Journal of Chemical Information and Modeling is more explicit in this regard “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 be readily available, without infringements or restrictions. The use of proprietary data is generally not acceptable because it is inconsistent with the ACS Ethical Guidelines for publications.”


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It must be stressed that there is no suggestion that PAINS filters are unique in having been derived from proprietary data and the same criticism could be made of any requirement that models derived from proprietary data be used in the evaluation of manuscripts. The Editorial4 mandates actions for compounds with potential assay interference liability although it is unclear how this potential is to be assessed “In light of these concerns, the participating ACS journals plan to uphold the standards above to ensure that all compounds for which activity is reported demonstrate activity commensurate with expectations (i.e., the compound is binding to the expected pocket and accompanied by thorough SAR). 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. For compounds with potential assay interference liability, firm experimental evidence must be presented from at least two different assays, both of which report that the compounds are specifically active and that the apparent activity is not an artifact.” Persuading authors to examine active compounds for known classes of assay interference compounds is likely to improve the quality of the published literature although it is important to be clear about the term ‘known’ for consistent review of manuscripts. I would suggest that the term ‘known’ be only applied in this context to compound classes for which publicly available data is available that unequivocally demonstrates that structurally-diverse members of the compound class have actually been observed to interfere with assays. If predictive models are to be adopted for the purpose of evaluation of manuscripts then these models should also be subject to standards. Manuscript reviewers make decisions that may trigger requests for additional experimental work or lead to rejection of manuscripts so it is important that these decisions are made in an objective and consistent manner. Given that the


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use of proprietary data in modeling studies is unacceptable to at least two ACS journals, I would argue that editorial decisions should not be influenced by predictions made using models derived from proprietary data. Simply performing two different assays is unlikely to detect interference resulting from an unacceptable MoA (type 2 behavior) so it may be more appropriate to require that the second assay be of a label-free nature (e.g. SPR) and able to directly detect binding to target. Given the difficulties of assessing potential assay interference liability in a consistent manner from molecular structure that have been highlighted in this Perspective and elsewhere,19,20 ACS journal Editors may wish to consider mandating the same standard of evidence for all biological activity reported to be novel, regardless of perceived potential of compounds for assay interference.

Figure 1. Thiazolidine-2,4-dione and rhodanine structures Editors of journals may set editorial policy but they are still heavily reliant on manuscript reviewers to ensure that editorial policy is applied in the manner intended. The Pouliot & Jeanmart 2016 (PJ2016) study,28 cited in the Editorial4 in support of the assertion that the issue of PAINS is also relevant to phenotypic screening, illustrates how manuscript reviewers might over-interpret published data analysis and claims that compounds and classes of compound exhibit assay interference. PJ201628 states that the thiazolidine-2,4-dione (TZD) 1


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shown in Figure 1 has been claimed in the patent literature to be “good antifungal against

Candida albicans” and notes that, “one should be aware of the thiol reactivity associated with this type of molecule, as highlighted by ALARM NMR and glutathione assays”. However, the evidence presented for thiol reactivity applies to rhodanines rather than TZDs. Neither PJ201628 nor BW201417 cites a study29 in which four rhodanines and three TZDs, capable of functioning as Michael acceptors, were all shown to have no measurable reactivity toward glutathione.

PJ201628 cites BH20108 as evidence that, “rhodanines are promiscuous

compounds” although it is unclear what relevance the promiscuity of rhodanines in the BH20108 panel of six AlphaScreen9 assays has to TZDs. While proven thiol reactivity of a specific rhodanine capable of functioning as a Michael acceptor would raise legitimate concerns that the corresponding TZD could behave in an analogous manner, this would not be the case if the ‘activity’ in the AlphaScreen9 assays of the BH20108 panel was due to quenching or scavenging of singlet oxygen resulting from the presence of the thiocarbonyl group in the rhodanine molecular structure. Cysteine reactivity resulting from Michael addition is not a plausible explanation for frequent-hitter behavior of rhodanines in the BH20108 assay panel because rhodanines lacking an exocyclic carbon-carbon double bond were also claimed to exhibit frequent-hitter behavior.8 Substructural context needs to be accounted for when considering the possibility of rhodanines and TZDs functioning as Michael acceptors. Values of pKa measured for 2 (6.4)30 and 3 (5.6)31 suggest that members of these compound classes that lack a substituent on nitrogen are likely to exist predominantly in anionic forms under typical assay conditions. This may influence the likelihood of compounds like these functioning as Michael acceptors. Bad behavior by compounds in assays remains a significant issue in drug discovery and adopting some of the standards (e.g. label-free measurement of affinity and publication of full concentration-response curves) recommended in the Editorial4 should lead to improved


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reporting of biological activity in the medicinal chemistry and chemical biology literature. However, it is important to make a clear distinction between knowledge and belief when setting standards in this manner. Frequent-hitter behavior is circumstantial evidence for assay interference and should not be considered as definitive proof that a compound has behaved badly in a particular assay. Given that PAINS filters8 are based on analysis of proprietary data and have been reported19,20 to be less predictive of frequent-hitter behavior when used outside their narrow applicability domain, it is recommended that they should not be used in the evaluation of manuscripts. Consistent and objective assessment of potential for assay interference based solely on chemical structure does not appear to be generally feasible at present. As such, the Editors may wish to consider requiring the same standards of evidence in support of all claims for novel biological activity regardless of perceived potential for assay interference by compounds. Although there is a lack of clarity in terminology (e.g. whether or not a compound using an undesirable MoA to engage its target can correctly be described as ‘false hit’), the main obstacle to gaining a greater understanding of bad behavior of compounds in assays is the dearth of relevant experimental studies.24,25,32,33,34 The Editors may wish to encourage (e.g. by providing open access) publication of studies specifically designed to provide deeper understanding of bad behavior in assays and consider ways in which historic screening data from industry might be drawn into the public domain. When assessing manuscripts, more weight could be given to the quality, relevance and novelty of the experimental data that the studies would introduce to the public domain. FIGURES AUTHOR INFORMATION Corresponding Author


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*Email, [email protected] ABBREVIATIONS ACS, American Chemical Society; HTS, high-throughput screening; ITC, isothermal titration calorimetry; JMC, Journal of Medicinal Chemistry; MoA, mechanism of action; PAINS, pan assay interference compounds; SAR, structure-activity relationship; SMARTS, SMILES arbitrary target specification; SMILES, simplified molecular-input line-entry system; SPR, surface plasmon resonance; TZD, thiazolidine-2,4-dione. REFERENCES 1.

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21

Comment on The Ecstasy and Agony of Assay Interference

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