Keywords: COVID-19; SARS-CoV-2; Antiviral; Drug Discovery, Algorithm;
Bioinformatics; In-Silico; COMPARE Analysis; Drug Repurposing;
Introduction
The surprising outbreak of Covid-19 pandemic alarmed the world about the
need for an agile approach to quickly tackle and mitigate unprecedented
pathogenic diseases. Accelerated drug discovery by repurposing existing
therapeutics is the most practical strategy because fast development of
totally new drugs and vaccines can be hampered by the requirement of
lengthy safety studies and regulatory processes. Most of all, newly
developed vaccines and therapeutics can quickly lose their value if the
pathogen is progressively mutating over the course of time, making fresh
development of safe and effective vaccines and therapeutics a very
difficult goal to attain. This study elaborates on the effectiveness of
COMPARE analysis, an algorithm used in anticancer drug screening, for
uncovering and repurposing effective compounds against emerging viruses
like SARS-CoV-2. COMPARE algorithm was originally introduced by the
Developmental Therapeutic Program (DTP) of the National Cancer Institute
(NCI) and is publicly accessible (1, 2). The DTP and its Japanese
version, the Disease Oriented Screening (DOS) (3), were established
nearly three decades ago in the hope of identifying the most effective
compounds against specific cancers. The concept of these programs is
based on relating in vitro growth inhibition of tested compounds (GI50)
on a panel of diverse human cancer cell types with performance against
corresponding clinical cancers (Figure 1).
Apart from the original objective of these programs, mining the
collected data using bioinformatics can uncover new facts on
pharmacological modes, biological traits, and entirely new prospects for
anticancer drug-discovery (4). The author previously used the Japanese
DOS program to establish molecules with strong inhibitory effects
against the enzyme, telomerase (5, 6). In this study, the author
demonstrates that COMPARE analysis using the current DTP data repository
can be also useful for fast and low cost antiviral drug discovery.
Indeed, by seeding compounds with presumed effects against SARS-CoV-2
virus in COMPARE, several correlated compounds (preys) with approved
status for clinical use were easily identified. The value of this
efficient resource seems to have been overlooked during the rush for
discovering active antiviral drugs that are urgently needed to combat
new epidemics like Covid-19.
Methods
The current version of DTP data repository comprises an aggregate of
more than 88000 compounds tested on a panel of 59 different human cancer
cell lines using sulforhodamine B assay (7). Within the repository, GI50
data from ~55000 compounds is publically accessible. The
SRB assay is by design a quantitative reporter on protein mass and is
directly correlated with cell growth and division rate. The used cell
panel is highly diverse and represents a variety of cancers representing
leukemia, melanoma, lung, colon, brain, ovary, breast, prostate, and
kidney malignancies. The full description of the cell panel and the
screening method are described in details in the DTP link
https://dtp.cancer.gov/discovery_development/nci-60/methodology.htm.
Typically, all cell lines are grown on RPMI 1640 growth medium
supplemented with 5% fetal bovine serum. Chemo-sensitivity is performed
using a 48-h assay and then GI50 values are collected for each tested
compound on the 59 different cell lines.
Data Collection was conducted by accessing the NCI’s DTP link at
https://dtp.cancer.gov/databases_tools/compare.htm, then clicking
on the tab [New Technology PUBLIC COMPARE]. This will open the
COMPARE algorithm page https://nci60.cancer.gov/publiccompare/.
Running the COMPARE analysis was performed by identifying the NSC number
(National Service Center number) for each drug molecule from the PubChem
website (https://pubchem.ncbi.nlm.nih.gov/). For seeding a
compound in COMPARE algorithm, the corresponding NSC code is entered in
the ”NSCs - any delimiter” tab, also the ”GI50 data endpoint” box is
checked. With these parameters, ”SEARCH AS CONFIGURED BELOWS” can be
clicked. After COMPARE algorithm finishes identifying the fingerprint of
the seeded compound, it will ask for the option of Pearson minimum
correlation to be selected in order to call and present the most
correlated compounds. Once Pearson value is selected (e.g.,
>0.5), the algorithm will return a list of the correlated
compounds ranked in a table with actual structures, CAS numbers (when
available), SMILE codes and NSC codes. The results can be converted to
excel. In order to easily convert the collected NSCs of the correlated
compounds to useful information about their commercial availability,
detailed identity and origin, the list is copied from excel sheet and
then inserted into the search window of the site: Enhanced NCI Database
Browser 2.2 (https://cactus.nci.nih.gov/ncidb2.2/). This site can
convert a batch of NSC numbers to the corresponding molecules with
useful details on identifiers and sources.
Results and Discussion
While the SRB assay is considered simple and straightforward, in effect,
it accurately and categorically reports on the numerous events involved
in the cellular growth machinery where hundreds of intra and
extracellular events are involved. For example, it is reported that more
than 850 genes are operating in only cell cycle regulation activities
(8). Any exogenous compound that changes the readout from the SRB assay
is in effect a compound that interferes with one or more of the numerous
events involved in cell growth. Meanwhile, the use of 59 different cell
lines from various tissues warrants sufficient heterogeneity of the
modes of interactions between each tested compound and the cell panel.
It is well established in pharmacology that the molecular modes of
interactions between drug molecules and biological targets are intrinsic
properties produced by the various chemical attributes and descriptors
of drug molecules like electron density, polarity, charge, hydrogen
bonding, aromaticity, size and conformation (9). When a given compound
is tested on the cell panel, its impact on the growth rate of each cell
line is governed by a plethora of interactions and events like binding
to membrane receptors and transporters, signaling, checkpoints,
suppression or activation of enzymes, nucleic acids synthesis and the
entire machineries involved in cell growth and division. Once the growth
inhibitory values are plotted as mean graphs, a signature response (a
pharmacological code or fingerprint) unique to each compound is
generated. Figures 1 and 2 are descriptions on how the variable GI50
values from the cell panel encrypt a unique fingerprint for each tested
compound. COMPARE algorithm was developed to enable the identification
and stratification of compounds presenting similar mean graph
fingerprints. What has been clearly observed is that the similarity of
fingerprints is a reflection of similarities in the pharmacological
mode(s) of action. This is because of the unique set of interactions
between a given compound and a given type of cancer cells. In other
words, for every biological readout, e.g., cell growth rate, there is a
labyrinth of known and unknown multitude of events, all acts at variable
levels in response to interaction(s) with the applied exogenous
compound. The DTP’s large pool of molecules (~55 000
thus far) and the heterogeneity of the cell panel both form a rich
source for identifying drug similarities in pharmacological activities.
Therefore, any compound with unknown pharmacological effect can be used
as a seed molecule in COMPARE algorithm to enable the discovery of
compounds that share similar fingerprints and hence have similar mode(s)
of action. This approach has been used to shed light on the modes of
actions of new anti-cancer drugs (4-6, 10). The current study shows that
the same COMPARE-based approach can also be exploited for discerning
previously unknown pharmacological activities of existing drugs as long
as the screened drug molecules interfere with cell growth processes.
Since viruses are known to affect host cells and hijack cellular
machineries particularly those machineries involved in cell cycle and
protein synthesis, the author demonstrates here that COMPARE analysis
can also be useful for antiviral drug discovery and can be applied to
uncover drugs suitable for repurposing against the SARS-CoV-2 virus.
Seed compounds with presumed effect against Covid-19 were chosen from
ongoing clinical experience and reports from professional sites like
Medscape and Cure ID. Among these compounds, chloroquine,
hydroxychloroquine and ritonavir (11) were found enlisted in the
publically accessible ~55000 chemicals screened by the
DTP. Each of the three candidates was used as a seed “a bait” and a
Pearson correlation factor of >0.55 was set for each
screening attempt. Using the above criteria, the algorithm typically
returned 100-250 compounds per seed. Only compounds that are clinically
relevant or considered as an active principle in commonly used medicinal
plants are shown in this study. Because hydroxychloroquine as a seed
yielded only two clinically relevant compounds (lapachol, an active
principle in antimalarial and antiviral medicinal plants (12), and
ethacizine, an antiarrhythmic and psychotropic agent (13)), a second
cycle of COMPARE analysis was conducted on each of these two compounds.
As shown in table 1, several known natural and synthetic compounds were
extracted based on mean graph similarities. They all belong to one of
the following pharmacological classes: anti-viral, anti-parasitic,
anti-arrhythmic, and psychotropic agents. Interestingly, the
pharmacological classes of the extracted compounds are the same classes
that were described in recent screening studies seeking potentially
effective therapeutics for viral infections like SARS-CoV-2 (14-17). In
particular, it is striking that the extracted compounds in this study
are very similar to the compounds identified by the recent extensive
QSAR based anti Covid-19 screening study conducted by a consortium of 49
research centers (17), confirming that the COMPARE-based repurposing
strategy presented here is capable of achieving comparable results but
in a way that is easier, faster and at a far lower cost as it uses an
already established database. The results also validate the concept that
the cell type-dependent variability of the GI50 values in the DTP
repository is indeed correlated pharmacologically at the molecular level
and reflects a unique pharmacological pattern for each compound. It is
noteworthy that although the resulting hits share common pharmacological
effects, they do not always share common pharmacophore structures,
indicating that the conventional view of using dedicated pharmacophores
in QSAR in-silico drug discovery studies may not always be
relevant and the results from COMPARE screening have more biological
relevance. Observations on the extracted compounds are summarized in
table 1. Of particular interest are the currently evaluated compounds
for the treatment of Covid-19, like retinol (16, 18, 19), methotrexate
(20, 21), and didanosine (21). Interesting to see also that the
psychotropic compounds ethacizine, a phenothiazine with antiarrhythmic
effects, and thiothixene, a thioxanthene are in the list. This type of
compounds was found to be effective in inhibiting virus entry and
virus-cell fusion (22) This finding further validates the vision that
COMPARE is sophisticated enough to enable the identification of
compounds with clinical relevance even when they belong to different
pharmacological classes. Also interesting to notice that flucloxacillin
is among the extracted molecules being a synthetic penicillin. This
suggests that the currently trialed penicillin antibiotics on Covid-19
patients (e.g., piperacillin and amoxicillin, CURE ID updates)) might be
assisting the patients via additional mechanisms beyond their
anti-bacterial effects. Finally, although this study is not providing
direct experimental evidence on the anti SARS-CoV-2 effect of the
compounds in Table 1, the fact that all the identified compounds are
reportedly involved in antiviral effects proves that this approach is
valid and accurate in guiding drug repurposing efforts.
Conclusion
Despite the fact that the purpose of the DTP repository and COMPARE
analysis is anticancer drug discovery, it is demonstrated here that
these unique assets of bioinformatics are valuable also in guiding drug
repurposing to combat new viral diseases like SARS-CoV-2. Unlike
QSAR-based in-silico screening methods, the COMPARE–based
approach is biologically more relevant, easier, faster, and more
economical. Moreover, this approach is flexible and can continuously
evolve to respond to mutational changes or potential surge of
infections. A variety of compounds were singled out using this approach.
Some are existing drugs and can be quickly evaluated on volunteer
patients. Others are components of existing medicinal plants already
known for antiviral activities. The findings also warrant the need for
extending the DTP repository to include a wider range of clinically
approved therapeutics so that more possibilities are allowed. Finally,
the study urges health response authorities to consider COMPARE analysis
and DTP as an additional tool in the fight against current and future
viral outbreaks.
Abbreviations
DTP, Developmental Therapeutic Program; NCI, National Cancer Institute;
SRB, Sulforhodamine B; DOS; Disease Oriented Screening, QSAR,
Quantitative Structure Activity Relationship; GI50, 50-percent
growth-inhibitory concentration.
Acknowledgement
The author is grateful to Dr Mark W. Kunkel of the NCI for the helpful
information on the latest versions of COMPARE algorithm.
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