The ecstasy of discovering a new
hit from screening can lead to a highly productive research effort
to discover new bioactive compounds. However, in too many cases this
ecstasy is followed by the agony of realizing that the compounds are
not active against the desired target. Many of these false hits are
Pan Assay INterference compoundS (PAINS)[1] or colloidal aggregators.[2] Whether the
screen is conducted in silico or in the laboratory and whether screening
libraries, natural products, or drugs are used, all discovery efforts
that rely on some form of screening to identify bioactivity are susceptible
to this phenomenon. Studies that omit critical controls against experimental
artifacts caused by PAINS may waste years of research effort as useless
compounds are progressed.[3−8] The American Chemical Society (ACS) is eager to alert the scientific
community to this problem and to recommend protocols that will eliminate
the publication of research articles based on compounds with artificial
activity. This editorial aims to summarize relevant concepts and to
set the framework by which relevant ACS journals will address this
issue going forward.Alarmingly, up to 80–100% of initial
hits from screening can be artifacts if appropriate control experiments
are not employed. The source of this artificial behavior has been
thoroughly summarized in the literature.[9−12] Misleading assay results can
arise through a variety of mechanisms including covalent protein reactivity,[13] redox activity, interference with assay spectroscopy,[14−16] membrane disruption,[17] decomposition
in buffers,[18] and the formation of colloidal
aggregates.[2,19,20] If not properly controlled, colloidal aggregation is perhaps the
most common artifact from high-throughput screening: between 1 and
3% of molecules in many screening libraries will aggregate at relevant
concentrations and up to 95% of “hits” identified from
a screen can be assigned as aggregates,[21] and the colloids that they form inhibit,[20−22] or occasionally
activate, proteins.[23] PAINS molecules can
be synthetic in origin or derived from natural products; the latter
have been termed Invalid Metabolic PanaceaS, or IMPS.[24] Even marketed drugs can aggregate and may also contain
PAINS chemotypes. Over 60 FDA-approved and worldwide drugs contain
PAINS chemotypes,[25] and about the same
number have been shown to aggregate.[26] Although
some drugs can contain PAINS and can aggregate at micromolar concentrations,
such examples do not imply that any molecule that acts via a PAINS
or aggregation mechanism can become a drug. Hence, noting or “flagging”
any PAINS-containing hits and performing detailed follow-up experiments
are essential to validate that the function of the molecule is as
expected prior to discarding it from further consideration.[27] However, it is important to realize that no
PAINS-containing drug has ever been developed starting from a protein-reactive
PAINS target-based screening hit.[28]Publicly available filters can help to identify PAINS and aggregators
(e.g., http://zinc15.docking.org/patterns/home, http://www.cbligand.org/PAINS/, http://fafdrugs3.mti.univ-paris-diderot.fr/, http://advisor.docking.org), but these tools will not comprehensively identify all compounds
with PAINS-like or colloidal behavior, and they may also inappropriately
label a compound as an artifact when it is not.[29,30] Any in silico filter should therefore be augmented by experimental
follow-up, a detailed practical guide for which has recently been
published.[31] Such validation experiments
include classic dose response curves, lack of incubation effects,
imperviousness to mild reductants, and specificity versus counter-screening
targets. If a molecule is flagged as a potential PAINS or aggregator
using published patterns but is well-behaved by these criteria, it
may be a true, well-behaved ligand. Ultimately, genuine SAR combined
with careful mechanistic study provides the most convincing evidence
for a specific interaction.[30,32] Covalent and spectroscopic
interference molecules act via specific physical mechanisms, for which
controls are known (see section ). Colloidal aggregation, fortunately, is readily identified
by rapid mechanistic tests and by counter-screening (see section ).While
this editorial focuses on target-based screening, the issue of PAINS
is also relevant to phenotypic screening and to drug repurposing studies,
and it is obvious that rational interpretation and optimization of
cellular activity with an inherently reactive chemotype may be difficult
if not impossible.[33] Further, membrane
perturbation becomes an additional promiscuity mechanism[17] and is very likely a contributing reason for
the prevalence of IMPS in scientific databases and literature.[24] Whether PAINS and/or IMPS motifs are present,
the common requirement of comprehensive and logical SAR is of paramount
importance for any phenotypic screening hit, and optimization to well
under micromolar levels of activity should be demonstrated.
Controls
for Artifactual Assay Activity
Irreversible Inhibitors
Unless one is specifically screening for selective covalent modifiers,
irreversible inhibitors—either acting themselves through a
reactive center or representing the activity of an impurity—are
typically undesired artifacts. A rapid counter-screen for irreversible
inhibition is to incubate the target protein at 5× its normal
assay concentration and the hit at 5× its apparent IC50, and after incubation, dilute them 10-fold (other IC50 ratios may of course be chosen). If inhibition is rapidly reversible,
the inhibition on dilution should drop to about 33% of full inhibition
on dilution (about 40% of the value at 5× the IC50). If dilution changes the inhibition little, it supports covalent
activity. Legitimate slow off-rate inhibition is another alternative,
but such molecules are rare among initial screening hits. This experiment
will only work for soluble proteins, but related experiments to measure
off-rate may be adapted for membrane proteins. More generally, a time-dependent
increase in apparent inhibitory potency suggests irreversible binding.
Lack of dissociation determined by direct measurement of ligand kinetics
using biophysical methods such as surface plasmon resonance also demonstrates
irreversibility. Inhibitors with electrophiles need not react with target proteins to be problematic. Their reversible reactions with cellular thiolates, such as glutathione, can render them inactive in cells.[99] It may be necessary to use several techniques to
differentiate between covalent/nonreversible, covalent/reversible,
and pseudoirreversible inhibitors.
PAINS Molecules
The chemotypes represented by these molecules often occur among
promiscuous molecules that fail to progress. Most PAINS are dominated
by a few chemotypes that are readily recognized.[29] Several in silico tools are available to identify these
groups, including at http://www.cbligand.org/PAINS/, http://zinc15.docking.org/patterns/home, and http://fafdrugs3.mti.univ-paris-diderot.fr/. PAINS molecules act through several interference mechanisms, including
all those described herein, and there is no single diagnostic test
for the entire suite of bad actors. We recommend counter-screening
the molecule against unrelated targets, as well as determining whether
it competes with a ligand known to bind to the site and whether its
concentration–response curves are well-behaved (e.g., has a
Hill coefficient close to 1, or a strong mechanistic reason to differ
from 1). PAINS frequently make it through to peer-reviewed publications,
as protein reactivity can be subtle and selectivity over counter-screens
may be exhibited. Therefore, thorough and logical SAR is the most
important criterion that distinguishes a PAIN from a non-PAIN. As
for any screening hit, literature review for evidence of chemotype
promiscuity is essential, and in this context, Badapple[34] is an excellent resource that merits special
mention.
Spectroscopic Interference Compounds and Compounds
That Inhibit Reporter Enzymes
Compounds that absorb light
or fluoresce in a region used to measure activity, or compounds that
inhibit a reporter enzyme, like luciferase,[12,14−16] can appear to be active, but in fact are simply interfering
with the assay. Spectroscopic interference should change linearly
with concentration, following Beer’s law, rather than log-linearly
as in a single site isotherm. Inhibitors of the reporter enzyme require
a counter-screen. For all assay detection methods, it is critical
to determine the propensity of screening hits to interfere with the
detection signal by running an artifact or interference assay measuring
the effect of the compound on the signal detection reagents.
Colloidal Aggregation
Perhaps the largest single source
of artifacts in early discovery is colloidal aggregation by small
molecules.[26] These particles, typically
between 50 and 1000 nm in radius, nonspecifically adsorb protein,
partially denaturing them. About two percent of molecules in a typical
screening deck will aggregate at relevant concentrations, ensuring
that hits reflecting colloid formation dominate in screens, both virtual
and empirical, which do not control for them. Fortunately, molecules
that act as aggregators can sometimes be recognized computationally
(http://advisor.docking.org),[2,21,22] and better
still, this mechanism may be readily controlled experimentally:If activity can be
attenuated by small amounts of nonionic detergent, the compound is
likely an aggregator. A typical protocol involves 0.01% v/v freshly
prepared Triton X-100 or 0.025% v/v Tween-80[35] for membrane or cell assays.Direct observation of particles in the 50 to 1000 nm size range
by dynamic light scattering (DLS). Formation of particles does not
guarantee promiscuous inhibition, but it is a worrying sign.For cell-based assays,
colloidal particles can be precipitated by centrifugation of the medium
before the assay is run. If the compound is much more effective before
spin-down, it suggests colloidal aggregation. As an aside, it is critical
to demonstrate that the compound is active at concentrations substantially
lower than those producing cellular toxicity to show that the apparent
activity is not simply due to cell death. In activity assays in which
cytotoxicity is the desired end-point (e.g., anticancer assays), the
compound should show high selectivity for cancer over normal cells.Noncompetitive inhibition
with high Hill slopes. There are classical reasons for noncompetitive
inhibition and for cooperative binding, but the latter is rare in
early discovery and the two together suggest aggregation.Attenuation of inhibition
by increasing target concentration. Except when the receptor concentration
to Ki ratio is high,[36−38] increasing
receptor concentration should not affect inhibition for well-behaved
inhibitors. However, inhibitory activity will be much reduced for
colloidal aggregators, and an increase in the steepness of the response
curve will be observed. This experiment can only be used for soluble
proteins.Potential
aggregators can be counter-screened for inhibition of enzymes like
AmpC β-lactamase, trypsin, or malate dehydrogenase, which are
highly sensitive to compound aggregation.[39] These enzymes are convenient because perturbations like detergent
addition, which are not well-tolerated by some systems, are readily
tolerated by these enzymes.Regardless
of whether or not one suspects that a molecule is a bad actor, detailed
biophysical testing of new inhibitors for mechanism is always useful
and can accelerate a drug discovery campaign. There is an understandable
tendency to fall in love with early hits, but hard experience[40] shows that early hits can be fool’s gold
and distract from more promising molecules that emerge later. 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.[41] A step further is to measure
the full binding constant, either through determination of the Ki by kinetic analysis, by radioligand displacement,
or by reporter-free methods such as isothermal titration calorimetry,
surface plasmon resonance, or related techniques. Here too, full curves
should be measured and reported.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.
Other issues that need to be considered in this context are the purity
of the compound, stability in assay buffers, cysteamine or glutathione
(GSH) reactivity, and a review of the literature for previous activities
reported for the compound or compound class. The goal here is not
to eliminate a priori all compounds that may resemble PAINS or colloidal
aggregators—this cannot always be done to 100% confidence,
and even molecules that appear to have progressable SAR can still
be artifacts.[32] Rather, the goal is to
ensure that compounds with an inbuilt tendency toward this behavior
are well-vetted before publication, or indeed before submission for
publication. This will diminish the number of articles that mislead
the field. These new standards will bring clarity to medicinal chemistry
and chemical biology and further ensure the already high level of
science published in ACS journals.
Authors: Jonathan Bisson; James B McAlpine; J Brent Friesen; Shao-Nong Chen; James Graham; Guido F Pauli Journal: J Med Chem Date: 2015-10-27 Impact factor: 7.446
Authors: Melanie L Aprahamian; Svetlana B Tikunova; Morgan V Price; Andres F Cuesta; Jonathan P Davis; Steffen Lindert Journal: J Chem Inf Model Date: 2017-11-16 Impact factor: 4.956
Authors: Sarah E Huff; Faiz Ahmad Mohammed; Mu Yang; Prashansa Agrawal; John Pink; Michael E Harris; Chris G Dealwis; Rajesh Viswanathan Journal: J Med Chem Date: 2018-01-05 Impact factor: 7.446
Authors: Rayyan Alam; Allen T Barbarovich; Wagma Caravan; Mirna Ismail; Angela Barskaya; David W Parkin; Brian J Stockman Journal: Chem Biol Drug Des Date: 2018-06-19 Impact factor: 2.817
Authors: Brian J Stockman; Abinash Kaur; Julia K Persaud; Maham Mahmood; Samantha F Thuilot; Melissa B Emilcar; Madison Canestrari; Juliana A Gonzalez; Shannon Auletta; Vital Sapojnikov; Wagma Caravan; Samantha N Muellers Journal: J Vis Exp Date: 2019-06-30 Impact factor: 1.355
Authors: Samantha N Muellers; Juliana A Gonzalez; Abinash Kaur; Vital Sapojnikov; Annie Laurie Benzie; Dean G Brown; David W Parkin; Brian J Stockman Journal: ACS Infect Dis Date: 2019-02-01 Impact factor: 5.084
Authors: Beika Zhu; Rong Luo; Peng Jin; Tao Li; Hayeon C Oak; Stefanie Giera; Kelly R Monk; Parnian Lak; Brian K Shoichet; Xianhua Piao Journal: J Biol Chem Date: 2019-10-18 Impact factor: 5.157
Authors: Merlin Bresinsky; Jessica M Strasser; Bernadette Vallaster; Peng Liu; William M McCue; Jessica Fuller; Alexander Hubmann; Gurpreet Singh; Kathryn M Nelson; Matthew E Cuellar; Carrie M Wilmot; Barry C Finzel; Karen H Ashe; Michael A Walters; Steffen Pockes Journal: ACS Pharmacol Transl Sci Date: 2022-01-05