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Analysis of Ivermectin as Potential Inhibitor of SARS-CoV-2 Using Resonant Recognition Model

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Abstract: With worldwide spread of COVID-19 disease caused by SARS-CoV-2 virus, everyone is trying to find effective cure. One of the quickest approaches is to test existing already approved and safe drugs for possibility of their activity against SARS-CoV-2 virus. One of such possible drugs is ivermectin, which is FDA approved broad spectrum anti parasitic agent. Recently it has been shown that ivermectin has broad range of in vitro antiviral activities and it has been shown that ivermectin is an inhibitor of SARS-CoV-2. We have previously used our recently developed extended Resonant Recognition Model (RRM) for small molecules and we have proposed hat drugs like hydroxychloroquine, chloroquine and remdesivir can interfere with SARS-CoV-2 viral infection. Here, we have used an extended RRM model for small molecules to analyze the possibility for ivermectin, an already FDA-approved drug, to interfere with SARS-CoV-2 viral activity, which could lead to an effective cure.
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Irena Cosic (Correspondence)
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Published at: http://www.ijsciences.com/pub/issue/2021-01/
DOI: 10.18483/ijSci.2433; Online ISSN: 2305-3925; Print ISSN: 2410-4477
Analysis of Ivermectin as Potential Inhibitor
of SARS-CoV-2
Using Resonant Recognition Model
Irena Cosic1, Drasko Cosic1, Ivan Loncarevic2
1AMALNA Consulting, Black Rock, 3193, Australia
2QuantBioRes - QBR A/S, Copenhagen, 2860, Denmark
Running Title: Ivermectin as Potential Drug for COVID-19
Abstract: With worldwide spread of COVID-19 disease caused by SARS-CoV-2 virus, everyone is trying to find
effective cure. One of the quickest approaches is to test existing already approved and safe drugs for possibility of
their activity against SARS-CoV-2 virus. One of such possible drugs is ivermectin, which is FDA approved broad
spectrum anti parasitic agent. Recently it has been shown that ivermectin has broad range of in vitro antiviral
activities and it has been shown that ivermectin is an inhibitor of SARS-CoV-2. We have previously used our
recently developed extended Resonant Recognition Model (RRM) for small molecules and we have proposed hat
drugs like hydroxychloroquine, chloroquine and remdesivir can interfere with SARS-CoV-2 viral infection. Here,
we have used an extended RRM model for small molecules to analyze the possibility for ivermectin, an already
FDA-approved drug, to interfere with SARS-CoV-2 viral activity, which could lead to an effective cure.
Keywords: Drug Design, Small Molecules, Resonant Energy, Bioelectromagnetism, Resonant Recognition Model,
SARS-Cov-2, COVID-19, Ivermectin
Introduction
With the huge demand/development for new effective
drugs there is large need for computational methods
capable of quick preselection of potential drugs
before they are chemically and biologically tested.
With rapid development of biotechnology and
specially pharmacology, there is busy time for drug
developments. Most of these developments are based
on trial and error, without thinking out of the box
within biochemical experiments, which are time and
resource consuming. So, there is large demand for
computational rational drug preselection. The most of
existing computational models are based on 3D and
electrostatic fit (docking) between small molecule
and related substrate. Such approaches are not
considering general functionality of substrates with
similar biological function, nor long distance specific
recognition between small molecules and their
substrates. Particularly, with the latest outbreak of
coronavirus SARS-CoV-2 causing the COVID-19
pandemic, there is urgent need to preselect number of
potential small molecule drugs that are already
approved for other purposes and are clinically tested
on a range of infected humans with a variety of
success. The already established Resonant
Recognition Model (RRM) proposes that selectivity
of biological interactions and functions between
proteins, DNA/RNA, is based on resonant
electromagnetic energy between interacting
macromolecules at the specific frequency for the
specific interaction/function. However, this approach
cannot be applied for selective specific interactions
between proteins and small molecules (potential
drugs), as small molecules are not linear sequential
molecules. Recently, we have extended the RRM
model to small molecules interaction with proteins
proposing that energy frequencies of free electrons in
small molecules are the most relevant for their
resonant recognition and interaction with proteins [1].
This extended RRM model has been tested for a
couple of natural examples to explain and support the
model. In addition, extended RRM model has been
applied to already approved drugs, including
remdesivir, chloroquine and hydroxychloroquine [1]
and we have proposed that those small molecules are
potential COVID-19 drugs. Another already
approved safe drug, which has been tested for its
activity against SARS-C0V-2 virus is ivermectin,
which we have analyzed here also using extended
RRM model.
Ivermectin is FDA approved broad spectrum anti
parasitic agent used to treat many types of parasite
infestations [2]. Recently it has been shown that
ivermectin has a broad range of in vitro antiviral
activities [3-6]. Due to COVID-19 pandemic
ivermectin has been also tested in vitro as potential
inhibitor of SARS-CoV-2 replication and it has been
Analysis of Ivermectin as Potential Inhibitor of SARS-CoV-2 Using Resonant Recognition Model
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shown that it indeed is an inhibitor of SARS-CoV-2
[7]. We have shown previously using our recently
developed extended Resonant Recognition Model
(RRM) for small molecules that drugs like
hydroxychloroquine, chloroquine and remdesivir can
interfere with SARS-CoV-2 viral infection [1]. Here,
we have used extended RRM model for small
molecules to analyze possibility for ivermectin,
already FDA-approved drug, to interfere with SARS-
CoV-2 viral activity, which could lead to a cure.
Methods
Extended Resonant Recognition Model for Small
Molecules
We have previously established Resonant
Recognition Model (RRM) for analyzes of protein
interactions with other proteins, DNA/RNA, which
takes into account functionality of those
macromolecules and is based on periodicities
(frequencies) within distribution of free electron
energies along these macromolecules [8-20]. With
electron charge moving through protein, DNA/RNA
backbone passing different free electron energies
within different side groups (amino acids or
nucleotides) electromagnetic radiation or absorption
of frequency related to distribution of energies of free
electrons along macromolecular backbone will be
produced. The frequencies of these electromagnetic
radiation or absorption depend on charge velocity,
which is estimated to be at 7.87x105m/s [8-15]. For
this velocity and the distance between amino acids in
protein backbone, which is 3.8Å, the frequency range
obtained for protein interactions was estimated to be
in the range of 1013Hz up to 1015Hz. Therefore, the
estimated range for both amino acid and nucleotide
macromolecules includes far infra-red, infra-red and
visible up to ultra-violet light spectrum. From the
comparison with number of published experimental
results [8-12,16-24], the strong linear correlation has
been established between frequencies, as calculated
using the RRM model and experimentally measured
characteristic frequencies, with the slope factor of
K=201 [8-12,16-20]. This correlation can be
represented as following:
λ = K / frrm
where λ is the wavelength of light radiation in
nanometres (nm), which can influence particular
biological process, frrm is RRM numerical frequency
and K is coefficient of this linear correlation.
However, this already established RRM approach
cannot be applied for interactions between proteins
and small molecules, as small molecules are not
linear sequential molecules. Still, we propose that
small molecules also recognize proteins on the
distance and interact with proteins through
electromagnetic resonant energy transfer enabling
specific biological activity. To expand the idea of
electromagnetic resonant recognition to small
molecules and their interaction with proteins, RRM
model has been extended by calculating
electromagnetic frequencies of free electron energies
within the small molecule and comparing these
frequencies with RRM characteristic frequencies for
relevant interacting proteins [1].
For that purpose, we have proposed that energies of
free electrons in small molecules are the most
relevant for such resonant energy transfer and can be
calculated as the electron-ion interaction pseudo-
potential (EIIP) of small molecule using the
following semi-empirical formula as developed by
Veljkovic [25-27]:
< k + q |w| k > = 0.25 x Z x sin (π x 1.04 x Z) / (2 x π)
where q is change of momentum of delocalized
electron in the interaction with potential w (EIIP) in
Rydberg (Ry = 2.18 x 10-18 [J]), while Z is average
valence number over the whole small molecule.
The corresponding electromagnetic wavelength for
this energy can be calculated using de Broglie
formula as follows:
λ = (h x c) / E in vacuum, where c is speed
of light (c = 2.998 x 108 [m/s])
λ = (h x v) / E in other materials, where v is
speed of light in other materials
where λ is wavelength of light [nm], h is Planck
constant (h = 6.626 x 10-34 [Js]), E is energy [J] of
free electrons within small molecule.
All biological processes and interactions are taking
place within biological materials and thus speed of
light will depend on refraction index within
biological materials:
v = c / n
where n is refraction index of biological materials.
For water refraction index is 1.33, while for
biological materials refraction index is: for cell
membranes 1.46-1.60, for cytoplasm 1.35-1.39 and
for proteins 1.36-1.55 [28].
Bearing in mind all the above, we have hypothesized
that wavelengths (frequencies) produced by energies
of free electrons within small molecules are critical
for small molecule biological functions and their
recognition and interaction with proteins and we have
proposed that small molecules are recognizing and
interacting with proteins through resonance at the
Analysis of Ivermectin as Potential Inhibitor of SARS-CoV-2 Using Resonant Recognition Model
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same characteristic frequency (wavelength) [1]. To be
able to compare resonant frequencies of small
molecules, with resonant RRM frequencies, we can
convert λb[nm] into the RRM frequency, as related
RRM frequency for small molecules can be presented
using the formula below:
fsm = K[nm] / λb[nm]
where K=201 is coefficient in [nm] previously semi-
empirically identified to characterize the relationship
between RRM frequencies and related
electromagnetic radiation wavelengths, as explained
above [8-11].
Thus, the RRM resonant frequency of small
molecules can be calculated as follows:
fsm = (K x E)/ (h x (c / n))
where: fsm is numerical RRM frequency
corresponding to electromagnetic radiation
resonances due to energies of free electrons within
small molecules, K=201 is coefficient in nm
previously semi empirically identified to characterize
the relationship between RRM frequencies and
related electromagnetic radiation wavelengths, E is
energy of free electrons within small molecules, h is
Planck constant, c is speed of light, n is refraction
index in biological materials.
The hypothesis, that wavelengths (frequencies)
produced by energies of free electrons within small
molecules are critical for small molecules biological
functions and their recognition and interaction with
proteins, has been already tested in couple of natural
examples and some potential COVID-19 drugs [1].
Results
The first thing to do is identify and recognize
SARS-CoV-2 spike proteins, which are on the
surface of coronavirus [29]. We are proposing,
based on our earlier experience with HIV virus
[30-32], that all strains of SARS-CoV-2 spike
proteins would have one common component that
characterize their recognition and interaction with
host cells. To find out this characteristic
component we have utilized the RRM model,
which is capable to analyze functional
characteristics, as common periodicities
(frequencies) for all proteins with the same
function/recognition. When we have applied the
RRM analysis to spike proteins from different
coronaviruses, from UniProt database, as listed in
Appendix, we have found the common component
that can characterize the spikes recognition and
interaction with host cells at the most prominent
common RRM frequency of 0.2827+0.0009, as
presented in Figure 1.
Figure 1. RRM cross-spectrum of spike proteins. The common RRM characteristic frequency is at 0.2827±0.0009.
It is interesting to note that there is one unique
common characteristic for all analyzed spike
proteins from many different coronaviruses. This
would mean that all coronaviruses have one and
the same RRM characteristic frequency at
0.2827±0.0009, characterizing viral recognition
and interaction with host cells. Thus, there is a
possibility to interfere with this frequency and
prevent the very first step of viral infection by
using small molecule drugs. One of such
candidates is ivermectin, which has been already
approved for treatment of parasites and has already
shown to inhibit replication of SARS-CoV-2 in
vitro [7].
There are two variants of ivermectin:
ivermectinB1A with chemical formula (C48H74O14)
and ivermectinB1B with chemical formula
Analysis of Ivermectin as Potential Inhibitor of SARS-CoV-2 Using Resonant Recognition Model
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(C47H72O14).
Using the chemical formula for ivermectinB1A
small molecule (C48H74O14), we have calculated,
as described in Methods section, energy of free
electrons within ivermectinB1A small molecule to
be at Ea=0.0870Ry. For this energy and refraction
index for proteins, which is between 1.36-1.55
[28], we have calculated the corresponding RRM
frequency for ivermectinB1A small molecule to be
between 0.26-0.30, as highlighted in green in
Figure 2.
Figure 2. RRM cross-spectrum of spike proteins. The common RRM characteristic frequency is at 0.2827±0.0009.
RRM frequency range for ivermectinB1A small molecule between 0.26-0.30 highlighted in green.
It is important to notice that this RRM frequency
range for ivermectinB1A small molecule overlaps
the characteristic RRM frequency for SARS-CoV-
2 spike proteins, which is at 0.2827±0.0009. As
there is an overlap between characteristic RRM
frequency for coronavirus spike proteins including
SARS-CoV-2 and RRM frequency for
ivermectinB1A small molecules, we propose that
ivermectinB1A could interact with SARS-CoV-2
spike proteins and could be efficient in the first
phase of infection by neutralising activity of spike
proteins on the surface of coronavirus and thus
preventing initial viral infection. It is also
interesting to note that range of RRM
characteristic frequencies for ivermectinB1A is
exactly the same as previously calculated [1] for
hydroxychloroquine indicating that although these
two molecules have different chemical formulas
they can have the same biological activity.
Using the chemical formula for ivermectinB1B
small molecule (C47H72O14), we have calculated,
as described in Methods section, energy of free
electrons within ivermectinB1B small molecule to
be at Eb=0.0852Ry. For this energy and refraction
index for proteins, which is between 1.36-1.55
[28], we have calculated the corresponding RRM
frequency for ivermectinB1B small molecule to be
between 0.25-0.29, as highlighted in green in
Figure 3.
Figure 3. RRM cross-spectrum of spike proteins. The common RRM characteristic frequency is at 0.2827±0.0009.
RRM frequency range for ivermectinB1B small molecule between 0.25-0.29 highlighted in green.
It is important to notice that this RRM frequency range for ivermectinB1B small molecule overlaps
Analysis of Ivermectin as Potential Inhibitor of SARS-CoV-2 Using Resonant Recognition Model
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characteristic RRM frequency for SARS-CoV-2
spike proteins, which is at 0.2827±0.0009. As
there is overlap between characteristic RRM
frequency for coronavirus spike proteins including
SARS-CoV-2 and RRM frequency for
ivermectinB1B small molecules, we propose that
ivermectinB1B could interact with SARS-CoV-2
spike proteins and thus could be efficient in the
first phase of infection by neutralising activity of
spike proteins on the surface of coronavirus and
thus preventing initial viral infection.
Discussion and Conclusion
Previously, we have proposed a completely new
approach for preselection of bioactive drugs (small
molecules) based on electromagnetic energy
resonances between small molecules and
substrates/proteins considering long distance
selective resonance, as well as common biological
functionality of related substrates/proteins [1].
This extended RRM model opens new horizons for
biochemistry and pharmaceutical industry in
analyzes of small molecule protein interaction,
consequently in preselection of new drugs and
drug design in general.
We have previously presented two natural
examples of small molecules interactions with
related receptors, namely: cannabinoids with
cannabinoid receptors and kainic acid (kainate)
with glutamate kainic receptors. In addition, we
presented application to already approved drugs,
which are also potential COVID-19 drugs
including remdesivir, chloroquine and
hydroxychloroquine for their ability to interact
with viral proteins and thus interfere with viral
infection [1]. These examples have shown that
electromagnetic frequencies of free electrons
within the small molecules are overlapping the
characteristic RRM frequencies of related
receptors/proteins, indicating that interactions
between small molecules and related
receptors/proteins are indeed based on resonant
electromagnetic energy at a specific frequency for
a specific interaction.
Here, we have applied the extended RRM model
to ivermectin with the aim to find out if ivermectin
is potential candidate to interact and possibly
inhibit SARS-CoV-2’s activity. During this
analysis we have found that the RRM frequency
corresponding to energy of free electrons within
ivermectin corresponds to RRM frequency
characterizing coronavirus spike proteins including
SARS-CoV-2. Thus, we can propose that
ivermectin could interfere with activity of spike
proteins and as such could be efficient treatment
for the first phase of SARS-CoV-2 infection by
neutralising activity of spike proteins on the
surface of coronavirus and preventing initial viral
infection, in the similar way as for
hydroxychloroquine [1].
Our findings are in complete agreement with
experimental results, which have shown that
ivermectin has ability to inhibit SARS-CoV-2
virus [7]. We have shown here that the extension
of the RRM model, which is aimed generally to
aid developments within pharmaceutical industry,
can satisfy demand for pre-selective computational
methods capable of quick preselection of small
molecules before they are chemically and
biologically tested. The extended RRM model,
which represents quick preselection method that
could significantly narrow down a number of
experimentally tested potential molecular
candidates is capable to enormously save resources,
time and costs that are involved in discovery of
new drugs, and could provide quicker and better
health outcomes.
Consequently, the extended RRM model is
opening new horizons for, not only understanding
protein, DNA/RNA selectivity of interactions, but
also for understanding selectivity of interactions
between small molecules and related
receptors/proteins. This could lead to new
directions for pharmaceutical industry, drug design
and biochemistry in general.
Contributions
Conceptualization, I.C.; Methodology, I.C. and
D.C.; Software, D.C.; Resources I.L.; Writing
Original Draft PreparationReview and Editing,
I.C., D.C. and I.L.
Competing Interests
Authors declare they have no competing interests.
Funding
This research received no external funding.
Acknowledgement
The authors would like to thank Miss Amy Cosic
for editing this manuscript and Mr. Anthony
Slingsby for proofreading this manuscript.
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Appendix
The protein sequences from UniProt Database analyzed using the
RRM model:
Cleaved, active spike proteins: K9N5Q8, Q5MQD0, Q14EB0,
Q0ZME7, P36334, P59594,
P11224, P11225, Q8BB25, Q9IKD1, P05135, P11223, Q0Q466,
P36300, Q65984, Q7T6T3, P15423, Q6Q1S2, P18450, P33470,
P07946, P27655, P24413, Q01977, P10033, Q91AV1 and
QHD43416.1.
... Each biological function is driven by proteins that selectively interact with other proteins, DNA/RNA regulatory segments or small molecules. Through extensive use of RRM model, it has been shown that proteins and their targets share the same matching RRM characteristic frequency [13][14][15][16][17][18][19][20][21][22][23][24]. The matching of periodicities within the distribution of energies of free electrons along the interacting proteins can be regarded as the resonant recognition and as such is highly selective. ...
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Background The Crigler-Najjar syndrome is extremely rare genetic disease, that is manifested by severe jaundicedue to lack of UDP glucuronosyltransferase 1-A1 (UDP) activity. The main treatment is to use the blue lightphototherapy, during the prolong time, during the day every day. Methods Here, we analyzed human UDP’s correlation with the blue light phototherapy using the nonlinear physico-mathematical model: Resonant Recognition Model (RRM), which proposes that protein activation is electromagnetic in nature within the frequency range of infrared and visible light. ResultsWe found that human UDP’s are characterized by specific RRM frequency that is related to the blue light radiation. This could be the explicit explanation, why phototherapy with the blue light could replace lack of UDP activity. Conclusion However, the blue light treatment is less effective with ageing, due to decrease of the blue lightpenetration through skin. Thus, there is a need for alternative treatments. Here, we propose to design de-novopeptide, using this specific RRM frequency. Such peptide, according to RRM, is proposed to have the samebiological function as UDP glucuronosyltransferase 1-A1 and thus can be used for alternative treatment of Crigler-Najjar syndrome.
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To establish a new lineage in the human population, avian influenza A viruses (AIV) must overcome the intracellular restriction factor MxA. Partial escape from MxA restriction can be achieved when the viral nucleoprotein (NP) acquires the critical human-adaptive amino acid residues 100I/V, 283P, and 313Y. Here, we show that introduction of these three residues into the NP of an avian H5N1 virus renders it genetically unstable, resulting in viruses harboring additional single mutations, including G16D. These substitutions restored genetic stability yet again yielded viruses with varying degrees of attenuation in mammalian and avian cells. Additionally, most of the mutant viruses lost the capacity to escape MxA restriction, with the exception of the G16D virus. We show that MxA escape is linked to attenuation by demonstrating that the three substitutions promoting MxA escape disturbed intracellular trafficking of incoming viral ribonucleoprotein complexes (vRNPs), thereby resulting in impaired nuclear import, and that the additional acquired mutations only partially compensate for this import block. We conclude that for adaptation to the human host, AIV must not only overcome MxA restriction but also an associated block in nuclear vRNP import. This inherent difficulty may partially explain the frequent failure of AIV to become pandemic.
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Background: It has been shown that there are electromagnetic resonances in biological molecules (proteins, DNA and RNA) in the wide range of frequencies including THz, GHz, MHz and KHz. These resonances could be important for biological function of macromolecules, as well as could be used in development of devices like molecular computers. As experimental measurements of macromolecular resonances are timely and costly there is a need for computational methods that can reliably predict these resonances. We have previously used the Resonant Recognition Model (RRM) to predict electromagnetic resonances in tubulin and microtubules. Consequently, these predictions were confirmed experimentally. Methods: The RRM is developed by authors and is based on findings that protein, DNA and RNA electromagnetic resonances are related to the free electron energy distribution along the macromolecule. Results: Here, we applied the Resonant Recognition Model (RRM) to predict possible electromagnetic resonances in telomerase as an example of protein, telomere as an example of DNA and TERT mRNA as an example of RNA macromolecules. Conclusion: We propose that RRM is a powerful model that can computationally predict protein, DNA and RNA electromagnetic resonances.