Treatment with metformin glycinate reduces SARS-CoV-2 viral load: An in vitro model and randomized, double-blind, Phase IIb clinical trial

Abstract

The health crisis caused by the new coronavirus SARS-CoV-2 highlights the need to identify new treatment strategies for this viral infection. During the past year, over 400 coronavirus disease (COVID-19) treatment patents have been registered; nevertheless, the presence of new virus variants has triggered more severe disease presentations and reduced treatment effectiveness, highlighting the need for new treatment options for the COVID-19. This study evaluates the Metformin Glycinate (MG) effect on the SARS-CoV-2 in vitro and in vivo viral load. The in vitro study was conducted in a model of Vero E6 cells, while the in vivo study was an adaptive, two-armed, randomized, prospective, longitudinal, double-blind, multicentric, and phase IIb clinical trial. Our in vitro results revealed that MG effectively inhibits viral replication after 48 h of exposure to the drug, with no cytotoxic effect in doses up to 100 µM. The effect of the MG was also tested against three variants of interest (alpha, delta, and epsilon), showing increased survival rates in cells treated with MG. These results are aligned with our clinical data, which indicates that MG treatment reduces SARS-CoV2-infected patients´ viral load in just 3.3 days and supplementary oxygen requirements compared with the control group. We expect our results can guide efforts to position MG as a therapeutic option for COVID-19 patients.

Full text

Biomedicine & Pharmacotherapy 152 (2022) 113223
Available online 2 June 2022
0753-3322/© 2022 The Authors. Published by Elsevier Masson SAS. This is an open access article under the CC BY license
(http://creativecommons.org/licenses/by/4.0/).
Treatment with metformin glycinate reduces SARS-CoV-2 viral load: An in
vitro model and randomized, double-blind, Phase IIb clinical trial
Claudia Ventura-L´
opez
a
,
1
, Karla Cervantes-Luevano
a
,
1
, Janet S. Aguirre-S´
anchez
b
,
Juan C. Flores-Caballero
b
, Carolina Alvarez-Delgado
a
, Johanna Bernaldez-Sarabia
a
,
Noemí S´
anchez-Campos
a
, Laura A. Lugo-S´
anchez
c
, Ileana C. Rodríguez-V´
azquez
c
,
Jose G. Sander-Padilla
c
, Yulia Romero-Antonio
c
, María M. Arguedas-Nú˜
nez
c
, Jorge Gonz´
alez-
Canudas
c
, Alexei F. Licea-Navarro
a
,
*
a
Departamento de Innovaci´
on Biom´
edica, CICESE, Carretera Ensenada-Tijuana 3918, Zona Playitas, Ensenada, BC 22860, Mexico
b
The American British Cowdray Medical Center I.A.P. (Centro M´
edico ABC), Mexico
c
Laboratorio Silanes S.A. de C.V., CdMx, Mexico
ARTICLE INFO
Keywords:
SARS-CoV-2 viral load
COVID-19 treatment
Metformin glycinate
SARS-CoV-2 variants
ABSTRACT
The health crisis caused by the new coronavirus SARS-CoV-2 highlights the need to identify new treatment
strategies for this viral infection. During the past year, over 400 coronavirus disease (COVID-19) treatment
patents have been registered; nevertheless, the presence of new virus variants has triggered more severe disease
presentations and reduced treatment effectiveness, highlighting the need for new treatment options for the
COVID-19. This study evaluates the Metformin Glycinate (MG) effect on the SARS-CoV-2 in vitro and in vivo viral
load. The in vitro study was conducted in a model of Vero E6 cells, while the in vivo study was an adaptive, two-
armed, randomized, prospective, longitudinal, double-blind, multicentric, and phase IIb clinical trial. Our in vitro
results revealed that MG effectively inhibits viral replication after 48 h of exposure to the drug, with no cytotoxic
effect in doses up to 100 µM. The effect of the MG was also tested against three variants of interest (alpha, delta,
and epsilon), showing increased survival rates in cells treated with MG. These results are aligned with our clinical
data, which indicates that MG treatment reduces SARS-CoV2-infected patients´viral load in just 3.3 days and
supplementary oxygen requirements compared with the control group. We expect our results can guide efforts to
position MG as a therapeutic option for COVID-19 patients.
1. Introduction
A novel coronavirus was recently discovered and termed SARS-CoV-
2. Human infection can cause coronavirus disease 2019 (COVID-19), for
which, at this point, over 510 million cases have resulted in more than 6
million deaths in over 40 countries; in Mexico, there are over 5 million
cases with more than 300,000 deaths [1]. SARS-CoV-2 infects different
organs and causes a systemic disease; as clearance of the virus occurs,
the symptoms tend to worsen, implicating an aberrant immune response
as pathogenesis of infection [2]. COVID-19 has presented one of the
most signicant challenges to humanity concerning the development of
diagnostic systems, vaccines, and new treatments. A detailed search in
the Orbit database indicated that between January 2020 to August 2021,
439 relevant patents and applications were registered relating to the
treatment of the infection caused by the coronavirus SARS-CoV-2,
including compounds of traditional Chinese medicine, as well as a
wide variety of treatments comprising placenta-derived natural killer
cells, ILC3 cells derived from a population of hematopoietic stem or
* Corresponding author.
E-mail addresses: cventura@cicese.mx (C. Ventura-L´
opez), kcervates@cicese.mx (K. Cervantes-Luevano), janetaguirre@yahoo.com (J.S. Aguirre-S´
anchez),
juancarlos18@hotmail.com (J.C. Flores-Caballero), alvarezc@cicese.mx (C. Alvarez-Delgado), jbernald@cicese.mx (J. Bernaldez-Sarabia), lsanchez@cicese.mx
(N. S´
anchez-Campos), llugo@silanes.com.mx (L.A. Lugo-S´
anchez), icrodriguez@silanes.com.mx (I.C. Rodríguez-V´
azquez), jgsander@silanes.com.mx (J.G. Sander-
Padilla), yromero@silanes.com.mx (Y. Romero-Antonio), marguedas@silanes.com.mx (M.M. Arguedas-Nú˜
nez), jogonzalez@silanes.com.mx (J. Gonz´
alez-
Canudas), alicea@cicese.mx (A.F. Licea-Navarro).
1
These authors contribute equally to this work.
Contents lists available at ScienceDirect
Biomedicine & Pharmacotherapy
journal homepage: www.elsevier.com/locate/biopha
https://doi.org/10.1016/j.biopha.2022.113223
Received 20 March 2022; Received in revised form 25 May 2022; Accepted 30 May 2022

Biomedicine & Pharmacotherapy 152 (2022) 113223
2
progenitor cells, antibodies, an alpaca nanobody, modied viruses,
bacteria (Pseudomonas), recombinant plasmids, oligomannuronic acid
phosphate, chitosan, ketoamide-based compounds, extracellular poly-
saccharide metabolite of yeast, "halogen or halogenated" compounds for
inhibiting ion channel activity of SARS-CoV-2E protein, thio-
imidazolidinone to dysregulate Angiotensin-converting enzyme (ACE)
and TMPRSS2 (Spike) proteins, GP73 inhibitor to reduce the enrichment
of ACE2 in the cell membrane preventing the virus infection, xanthine
oxidase inhibitor and an active oxygen scavenger that modies disulde
bonds in viral receptors, altering the afnity between the viral receptor
and the spike protein (S), inhibiting the virus proliferation, membrane
fusion inhibitor polypeptide, viral replication inhibitors, pinocytosis
inhibitors of the virus in host cells, activity inhibitor of virus protease
3CL pro and viral RNA-dependent RNA polymerase (RdRp) inhibitor.
Treatments involving drug repositioning also stand out, such as
Remdesivir, Ivermectin, and their respective modications. However,
the presence of mutations or substitutions in amino acids of the S protein
give place to new virus variants with implications for public health such,
as an increase in transmissibility, more severe disease presentations, and
reduced effectiveness of treatments or vaccines; highlighting the need to
identify new treatment strategies for these viral infections.
Metformin (Met) is a biguanide widely used to treat type 2 diabetes
[3], a disease that increases the risk of mortality in patients with
COVID-19 infection. Recently, metformin glycinate (MG), a new drug
derived from Met, has been shown to have better bioavailability, ab-
sorption, and safety prole than Met, with comparable anti-
hyperglycemic effects [4,5]. Met and MG have different mechanisms of
action: Met has been shown to selectively inhibit mitochondrial complex
I (NADH dehydrogenase), leading to less oxygen consumption rate,
lower ATP levels, and the activation of AMP-activated protein kinase
(AMPK) [6].
On the other hand, four lines of work characterize the mechanism of
action of MG. They are a function of different proteins: Goodpasture
antigen-binding Protein (GPBP), Liver Kinase B1 (LKB1), AMPK, and Akt
substrate of 160 kDa (AS160), which are essential in regulating the ac-
tivity of the ceramide transporter (CERT), energy, and glucose meta-
bolism [6,7]. So far, based on the evidence [8–14] it has been
determined that: it inhibits kinase activity GPBP/CERT activity.
GPBP/CERT and LKB1 synergistically enhance their kinase activity,
AMPK increases GPBP/CERT activity, increases the activity of LKB1
(metformin does not), and inhibits the cross-activation of GPBP/CERT
and LKB1. Provides a different prole modulation of the immune
response, especially with the migration of M1 to M2 by inhibiting the
synthesis of Interleukin 10 (IL10), translocate the Glucose transporter
(GLUT4) more efciently, consequently acts via the VAPA-VAMP2
interaction, and participates in the regulation of AS160. Metformin
glycinate is the only commercially available inhibitor of kinase activity
CERT with safety and efcacy studies.
In this study, we established a human cell culture model for infection
of lung cells H1299 with SARS-CoV-2 clinically isolated. Employing this
system, we determined the SARS-CoV-2 viral load at different times after
infection. In the context of our drug repositioning hypothesis, we tested
the capacity of MG to inhibit infection by SARS-CoV-2 in an in vitro
model of Vero E6 cells. Furthermore, we studied the efcacy and safety
of MG for the treatment of hospitalized patients with acute severe res-
piratory syndrome secondary to SARS-CoV-2, in a randomized, double-
blind, phase IIb clinical trial. The fact that MG reduces the protein
secretion pathway led us to hypothesize it could also inhibit the secre-
tion of viral particles from infected cells and thus be a candidate for drug
repositioning against SARS-CoV-2.
2. Materials and methods
2.1. Viral isolation and cell culture
Cell line H1299 (carcinoma; non-small cell lung cancer) and Vero E6
cell line were obtained from ATCC. Cells were maintained in a Dulbec-
co’s Modied Eagles Medium (DMEM) medium (Corning) containing 10
% of fetal bovine serum (FBS) (Biowest), and 1 % antibiotic/antimycotic
(Gibco, 10,000 units/mL of penicillin, 10,000 µg/mL of streptomycin,
and 25 µg/mL of Fungizone).
Nasopharyngeal swabs were obtained from patients and identied as
positive for SARS-CoV2 infection after RNA extraction with QIAamp
Viral RNA Mini Kit (Qiagen) and positive amplication of RNA-
dependent RNA polymerase (RdRP) gene by qPCR using qPCRBIO
probe 1 step Go No-ROX (PCR biosystems). For viral isolation, the Vero
E6 cell line was used at conuency in a T25 cm
2
ask in a modied
protocol [15]. Briey: complete media was removed, and the monolayer
was washed twice with phosphate buffer solution (PBS), trypsinized and
counted; for infection, a total of 3 ×10
6
cells were seeded in DMEM 2 %
FBS +1 % antibiotic/antimycotic (infection media) for each nasopha-
ryngeal swab, a control of mocked cell was seeded in parallel. After 24 h,
cells reached 80 % conuence and the monolayer was infected with 50
µL of the nasopharyngeal swab in 800 µL of infection media, ask were
incubated at 37 ◦C and 5 % CO
2
and manually moved every 20 min for 2
h; after incubation supernatant was removed and 5 mL of new infection
media were added. Cultures were monitored every 24 h for cytopathic
effects (CPE). Isolated supernatant was used for sequencing and further
experiments; Tissue Culture Infectious Dose 50 % (TCID
50
) and Multi-
plicity of Infection (MOI) were calculated in Vero E6 cells [16–18].
SARS-CoV2 variants were identied by whole-genome sequencing using
Illumina COVIDSeq Assay (Illumina), from viral amplications of
nasopharyngeal swabs with low Cq value; complete viral sequences
were assembled and characterized using the Illumina® DRAGEN COVID
Lineage App version 3.5.4 (Illumina) and submitted to GISAID.
2.2. In vitro effect of metformin glycinate
For viral load assays in H1299 cells, a total of 5 ×10
4
cells/well were
seeded 24 h before infection with SARS-CoV-2 MX/BC1/2020 at a MOI
of 100:1 particle per cell for 2 h in DMEM 2 %FBS +1 % antibiotic/
antimycotic (infection media). After incubation, the supernatant with
the inoculum was removed and 500 µL of new infection media was
added containing 0, 0.1, 1 or 10 µM of MG. At 24 and 48 h after the
drug’s addition, the cell supernatant was collected and centrifuged for 5
min at 300 g to remove debris and stored at −80 ◦C. Cell monolayers
were collected by scraping and resuspending in 500 µL of infection
media.
2.2.1. Dose effect of MG on SARS-CoV-2 viral load and cell survival
Both cell monolayers and supernatant were used to isolate RNA using
the QIAamp Viral RNA Mini Kit (Qiagen). The obtained RNA was used as
a template in the reverse transcription and amplication of the SARS-
CoV-2 E gene [19]. For this amplication, the qPCRBIO probe 1 step
Go No-ROX (PCR biosystems) kit was used in a nal volume of 20 µL
contained 1X reaction buffer, 0.4 µM of the forward
(5′-ACAGGTACGTTAATAGTTAATAGCGT-3′) and reverse
(5′-ATATTGCAGCAGTACGCACACA-3′) primers, 0.2 µM Probe
(5′-FAM-ACACTAGCCATCCTTACTGCGCTTCG-BHQ1-3′), 0.2 ×of
RTase Go, and 5 µL of RNA. Amplication conditions were,
Retro-transcription at 50 ◦C for 15 min, denaturation at 95 ◦C for 2 min,
followed by 45 cycles for 15 s at 95 ◦C, 30 s at 60 ◦C acquiring the
uorescence at this step. The RT-qPCR reactions were conducted in
duplicate for all samples using a 96-well and optical adhesive lm
(Bio-Rad, CA, USA) on a CFX96 Real-Time PCR Detection System
(Bio-Rad). The determination of the copy numbers was obtained by
extrapolation of Cq values in the linear regression curve. The standard
curve was obtained from 5 serial dilutions (dilution factor 1:10) of a
Synthetic RNA transcribed in vitro, ranging from 10
6
to 10
2
molecules
SARS-CoV-2 per µL. The synthesized RNA corresponds to a 1000 bp
sequence with the SARS-CoV-2 RdRP and E genes inserted. For the
statistical analysis, a factorial ANOVA was used to establish differences
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opez et al.

Biomedicine & Pharmacotherapy 152 (2022) 113223
3
between drug concentration, and signicance was established at P <
0.05.
To evaluate cell death due to the active principle, toxicity controls
were set up in parallel with the infection experiments without infection.
In short, 0.1, 1.0,10 and 100 µM of MG were added to 5 ×10
4
H1299
cells/well in cell culture conditions using 10 % DMSO as a dead control,
and PBS as a negative control. The nal viability of cells after 48 h of
exposure was measured in a parallel experiment by a colorimetric
method [20,21] using the CellTiter 96® AQueous One Solution Cell
Proliferation Assay (Promega). Half-maximal inhibitory concentration
(IC50) values were tting using a dose-response curve in GraphPad
prism 9.
2.2.2. Inhibition of SARS-CoV-2 variants cytopathic effects by MG
A total of 1.5 ×10
4
Vero E6 cells/well were seeded in a 96 well plate
for 24 h in infection media as pre-conditioning. After this time, media
was removed and cells were pre-incubated with 300 µM of MG for 1 h
prior to virus infection with 0.01 pfu/cell of isolated viral strains B.1.387
(D614G); B.1.429 +B.1.427 (Epsilon); B.1.1.7 (alpha) or B.1.617.2
(delta). After 96 h incubation, cytopathic effect on cells was measured
by neutral red uptake assay and viral mediated cell death was compared
among variants. The nucleoside GS-441524 were used as a positive
control for inhibition of viral replication in the same conditions. Dif-
ferences between the media was evaluated by unpaired student t-test
with p <0.0001 with the software GraphPad prism 9.
2.3. Clinical study
An adaptive, two-armed, randomized, prospective, longitudinal,
double-blind, multicentric and phase IIb clinical trial was performed,
with the main objective of evaluating the efcacy and safety of MG
treatment vs placebo (Treatment A: Metformin Glycinate 620 mg,
administered orally, twice a day for 14 days, Treatment B: Placebo,
administered orally, twice a day for 14 days) in patients with severe
acute respiratory syndrome secondary to SARS-CoV-2 infection and
diagnosis of type 2 diabetes mellitus. The study was conducted from July
2020 to March 2021, with a follow-up period of 14 days. The evaluation
criteria were: a) Comparing the viral load between groups; b) analyzing
the viral load in the same group at days 0 (basal), 2, 5, 7, 9, and at the
end of the study; c) comparing the use of supplementary oxygen, arti-
cial mechanical ventilation, duration of hospital stay, normalization of
body temperature, oxygen saturation, and number of deaths, between
groups; d) evaluating the change in basal and nal levels of serum
creatinine, aspartate aminotransferase, and creatine kinase MB (CK-
MB); e) and prevalence of grades 3 and 4 adverse events at the beginning
and end of the study.
2.3.1. Study subjects
Twenty patients, over 18 years of age, both genders with coronavirus
infection, severe acute respiratory syndrome (SARS-CoV)-2 conrmed
by polymerase chain reaction (PCR) test ≤4 days before randomization,
hospitalized and with radiographic evidence of pulmonary inltrates,
were recruited. Patients with multiorgan failure, with mechanical
ventilation during the randomization and pregnant women were
excluded from the study. Patients were randomly and blindly distributed
into “group A” (treatment group that received 620 mg of MG, twice a
day) and “group B” (placebo), with 10 patients in each group. All pa-
tients signed their informed consent form. This study was conducted in
the American British Cowdray Medical Center and was accepted by the
Ethical Research Committee of the institution: with approval number
ABC-20-16. The study was registered in the Clinical Trials System (NC
T04625985) in October 2020, during the enrollment of participants,
as well as in the Clinical Trials National Registry (RNEC by its Spanish
acronym) with the number 203301410A0085 registered on June 30 of
2020.
2.3.2. Randomization and blinding
Randomization of the treatment group was performed by the prin-
cipal investigator using a randomization list generated by randomizer.
org. Allocation was done by simple random sampling, balanced by
treatment. During the enrollment and randomization, no participants
were excluded, all 20 participants were randomized and started the
study treatments. The blinding of the study was carried out by homol-
ogating the tablets and the packaging. The packages were identied by
kit number, both the patient and the principal investigator were un-
aware of the allocation of participants. Breaking of blinding of the study
was not required.
2.3.3. Statistical analysis
The efcacy of the treatment was statistically analyzed in “all treated
patients” (ATP), a group of all the randomized patients that received at
least one dose of MG, and that had a basal and subsequent reading.
Missing data were managed using the “last observation carried forward”
method. The comparison between the groups of viral load, oxygen
supplementation and days of hospital stay was carried out using Stu-
dent’s t-tests for independent samples. The comparison between the
groups of the laboratory variables was carried out using the non-
parametric Mann Whitney U test. Qualitative variables were analyzed
using Fisher’s exact-tests. All tests were performed with IBM SPSS Sta-
tistics for Windows, version 25 (IBM Corp., Armonk, N.Y., USA). Values
of p <0.05 were considered signicant.
The sample size was selected to evaluate the primary efcacy in 10
patients infected by SARS-CoV2 vs 10 patients with personalized treat-
ment for COVID-19 according to the criteria of the treating physician.
Using an adaptive model.
3. Results
3.1. In vitro effect of metformin glycinate
3.1.1. Dose effect and IC50
To test the antiviral effect, three doses of MG 0.1, 1, and 10 µM were
added to H1299 cells for 24 and 48 h. Quantication of the extracellular
viral load was performed in at least 6 independent replicates. At 24 h
after exposition to the drug, no signicant differences were observed
neither in the cell culture nor in the supernatant (data not shown). As
shown in Fig. 1, the major inhibition effect for the presence of virus in
the supernatant and cell-associated virus was observed at 48 h. MG
shows at 48 h, a 98 % decrease in the viral load in the cell-associated
virus (Fig. 1A); and a 86 % decrease of the viral load in the superna-
tant medium (Fig. 1B). The IC50 value to decrease the cell-associated
virus load was 189.8 µM of MG (Fig. 1C), and the IC50 value to
decrease the supernatant viral load was 355.4 µM of MG (Fig. 1D).
3.1.2. MG inhibits cytopathic effects mediated by SARS-CoV2 variants
After 48 h of exposure, the drug MG potently inhibits SARS-CoV-2
replication, and cytopathic effects in vitro with doses up to 100 µM
showing a survival percentage of 100 % (Fig. 2A). Furthermore, the
effect of MG was tested against three variants of concern: B.1.387
(D614G), B.1.429 +B.1.427 (Epsilon), B.1.1.7 (alpha) and B.1.617.2
(delta) showing a signicant cell survival after 96 h incubation
compared to infected cells with no MG administration (Fig. 2B).
3.2. In vivo effect of metformin glycinate
3.2.1. Demographic and basal characteristics from the clinical study
patients
The study included 20 patients (Fig. 3 shows the selection and
allocation of the participants) recruited from July 2020 to March 2021,
with a follow-up period of 14 days. The mean age was 47.48 (42.83 in
group A; 49.38 in group B), 85 % of the patients were male and the
average BMI was 28.54 kg/m
2
. Table 1 shows the basal parameters and
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Biomedicine & Pharmacotherapy 152 (2022) 113223
4
Fig. 1. Metformin glycinate (MG) Inhibition effect on SARS-CoV-2 clinical isolated (MX/BC1/2020). Metformin Glycinate effect on the SARS-CoV-2
(MOI =100) viral load (RNA copies per mL) determined 48 h after infection in (A) supernatant and (B) whole cells (carcinoma; non-small cell lung cancer; Cell
line H1299). Half-maximal inhibitory concentration (IC50) of MG on cell viability in whole cells (C) and supernatant (D).
Fig. 2. Evaluation of cytotoxicity of MG in H1299 cells and inhibition of cytopathic effects in Vero E6. (A) Cell viability of H1299 cells after 48 h infection
with SARS-CoV-2 clinical isolated (MX/BC1/2020) exposed to different concentration (0.1, 1.0,10 and 100 µM) of MG. 10 % DMSO was used as a dead control, and
PBS a negative control (NT). (B) Cell viability of Vero E6 cells treated with MG (300 µM) and infected with 0.01pfu/cell of isolated viral strains B.1.387(D614G);
B.1.429 +B.1.427 (Epsilon); B.1.1.7 (alpha) or B.1.617.2 (delta).
C. Ventura-L´
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Biomedicine & Pharmacotherapy 152 (2022) 113223
5
clinical characteristics of the patients. The comorbidities detected were:
diabetes 20.0 % (10 % per treatment group), hypertension 20.0 %
(group A, 5.0 %; group B, 15.0 %), dyslipidemia 15.0 % (group A, 5.0 %,
group B, 10.0 %). There were no important differences between groups,
except in the percentage of oxygen saturation, where group B had 93.0
and group A had 89 (the mean percentage was 92.0).
3.2.2. Comparison of the basal main biochemical and immunological
parameters between groups of patients from the clinical trial
Table 2 shows the biochemical data for both groups. Essentially,
there were no differences between the groups, except in the levels of
alkaline phosphatase, where group A had a basal level of 90.5 UI/L and
B group had 66.5 UI/L (mean from both groups was 70.5 UI/L), which
represents a statistically signicant difference (p =0.011).
3.2.3. Basal covid-related symptoms from each group of patients
Table 3 shows that both groups of patients entered the study with
similar covid-related symptoms. There were no signicant differences in
these variables, which included cough, fever, dyspnea, headache, my-
algias/arthralgias, diarrhea and a disturbance to the general condition
(loss of appetite, fatigue, and weight loss). Additionally, the frequency of
other symptoms, such as pharyngitis, tachycardia, abdominal pain,
nasal congestion, and abdominal distention, among others, were
studied. We did not nd signicant differences in the basal frequency or
percentage of these symptoms between groups.
3.2.4. Comparison of other basal biochemical and immunological
parameters between groups of treatment
In addition to analyzing the main biochemical and immunological
parameters shown in Table 2, we compared the levels of other variables
indicative of inammation, tissue damage, and severe disease. The re-
sults in Table 4 show that both groups of patients entered the study with
very similar basal levels of immunoglobulins (IgG and IgM), reactive C
protein (RCP), lactate dehydrogenase (DHL), and creatine phosphoki-
nase (CPK).
3.2.5. Comparison of biochemical and immunological parameters in the
same group at the beginning and end of the clinical study
There were statistically signicant changes in the levels of AST,
lymphocytes, neutrophils, D dimer, CRP, DHL, and IgG in the group
treated with MG in the beginning and end of the study. In the control
group, we observed signicant differences at the basal and nal state in
ALT, ferritin, CRP, and IgG (Table 5).
3.2.6. Evaluation of the need for external oxygen supplementation
Supplementary oxygen requirements were evaluated daily for each
Fig. 3. Flowchart of the selection and allocation of study participants. *No patients were excluded from the statistical analysis since the data obtained was
sufcient to be included in the nal analysis.
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Biomedicine & Pharmacotherapy 152 (2022) 113223
6
patient during their hospitalization, using the system shown in Table 6.
At the end of the study, the points were added and the mean values were
compared between groups.
3.2.7. Evaluation of the main efcacy variables between groups
We analyzed three main variables indicative of treatment efcacy:
duration of hospitalization, oxygen requirement, and reduction of the
percentage of viral load. In the MG-treated group, we observed a sig-
nicant reduction in the viral load (93.2 %) from the beginning to the
end of the study. In the control group, there was only a 78.3 % decrease
in the viral load, importantly (Table 7).
Analyzing the number of days necessary to demonstrate an unde-
tectable viral load, it was found that, group A needed only 3.3 days while
group B 5.6 days (p =0.043) (Table 8), the average hospitalization was
8.8 days for group A and 9.8 days for group B, in relation to the need for
supplemental oxygen intake, it was found that group A required a lower
intake on days 2, 5 and 7 than group B (5.9 vs 10.6 points). The differ-
ences were statistically signicant for all the variables except for the
days of hospitalization (Table 7).
It was also determined that patients in group B are 6 times more
likely to have a detectable viral load after 4 days (OR =6.667)
compared to group A (Table 8).
Finally, in relation to safety, the number and type of adverse events
Table 1
Basal demographic and clinical characteristics in each group.
Variable Treatment A
n=10
Treatment B
n=10
Total n =20 p
Age (years) 42.83 (32.2;
65.0)
49.38 (45.7;
61.2)
47.48
(39.83;
63.0)
0.247
Sex
Male (n, %) 8 (40.0) 9 (45.0) 17 (85.0) 1.000
Female (n, %) 2 (10.0) 1 (5.0) 3 (15.0)
Weight (kg) 84.50 (71.5;
95.0)
85.50 (74.5;
95.0)
84.50
(73.25;
95.0)
0.684
Height (m) 1.72 (1.65;
1.79)
1.75
(1.71–1.77)
1.74 (1.68;
1.78)
0.579
BMI (kg/m
2
) 29.1 (22.75;
31.93)
27.72 (25.51;
30.81)
28.54
(24.83;
31.0)
0.684
Clinical
characteristics
SBP (mmHg) 113.0 (109.2;
125.0)
120.0 (118.0;
128.0)
120.0
(112.0;
125.0)
0.165
DBP (mmHg) 70 (65.0;
73.0)
70 (69.0; 78.0) 70.0 (68.0;
76.0)
0.579
CF (lpm) 79 (67.0;
84.0)
70 (66.0; 85.0) 70.0 (67.0;
84.0)
0.684
RF (rpm) 20 (18.0;
20.0)
19 (18.0; 20.0) 20.0 (18.0;
20.0)
0.393
O2 saturation (%) 89 (88.0;
95.0)
93 (90.0;
96.0)
92.0 (88.0;
96.0)
0.165
Temperature (◦C) 36.5 (36.0;
37.5)
36.2 (36.0;
36.7)
36.4 (36.0;
37.4)
0.529
Comorbidities
Diabetes (n, %) 2 (10) 2 (10) 4 (20) 1.000
Hypertension (n, %) 1 (5) 3 (15) 4 (20) 0.582
Dyslipidemias (n,
%)
1 (5) 2 (10) 3 (15) 1.000
Respiratory disease
(n, %)
2 (10) 0 (0) 2 (10) 0.474
Autoimmune
disease (n, %)
1 (5) 1 (5) 2 (10) 1.000
Tabaquism (n, %) 1 (5) 1 (5) 2 (10) 1.000
SBP: systolic blood pressure; DBP: diastolic blood pressure; CF: cardiac fre-
quency; RF: respiratory frequency; BMI: body mass index. Medians and inter-
quartile range were used for continuous variables, comparisons between the
groups were made through the Mann Whitney U test and for qualitative vari-
ables in frequencies and percentages the Fisher’s exact-tests were used.
Table 2
Basal main biochemical parameters between groups.
Variable Treatment A Treatment B Total p
Glucose (mg/dL) 151.5 (124.9;
184.3)
148.9 (135.0;
187.7)
148.9
(129.4;
177.3)
0.796
HBA1c (%) 5.95 (5.65;
6.50)
5.8 (4.40;
6.10)
5.8 (5.4; 6.1) 0.529
Total cholesterol
(mg/dL)
151.5 (143.0;
189.2)
156.5 (147.2;
173.5)
154.5
(147.0;
176.5)
0.796
HDL cholesterol
(mg/dL)
37.5 (26.7;
42.7)
37.5 (32.2;
43.0)
37.0 (29.7;
42.5)
0.529
LDL cholesterol
(mg/dL)
91.0 (75.2;
125.2)
102.5 (86.7;
105.2)
97.5 (86.7;
107.2)
0.684
Triglycerides (mg/
dL)
169.0 (138.5;
180.0)
137.0 (100.0;
207.5)
157.5
(104.7;
176.5)
0.529
ALT (U/L) 62.5 (30.0;
83.0)
44.0 (30.7;
63.0)
53.0 (30.7;
66.2)
0.436
AST (U/L) 33.8 (23.2;
92.0)
26.4 (21.8;
35.0)
27.6 (22.1;
50.6)
0.089
GGT (U/L) 89.5 (56.7;
112.7)
99.0 (36.0;
134.7)
94.5 (39.2;
129.5)
0.780
Serum creatinine
(mg/dl)
0.73 (0.61;
0.81)
0.81 (0.73;
0.93)
0.79 (0.67;
0.87)
0.156
Total bilirubin (mg/
dl)
0.29 (0.16;
0.48)
0.37 (0.28;
0.52)
0.31 (0.27;
0.50)
0.315
Albumin (g/L) 3.4 (3.8; 3.8) 3.3 (3.0; 3.4) 3.4 (3.0; 3.6) 0.089
Alkaline
phosphatase
(UI/L)
90.5 (71.2;
114.5)
66.5 (56.0;
73.2)
70.5 (64.2;
91.2)
0.011
Leukocytes (Cell/
uL)
12.6 (9.3;
15.0)
10.1 (6.6;
12.9)
10.8 (6.7;
13.6)
0.579
Erythrocytes (Cell/
uL)
4.8 (4.3; 5.2) 4.6 (4.2; 5.0) 4.8 (4.2; 5.1) 0.315
Hemoglobin (g/dl) 15.2 (13.5;
15.6)
14.3 (13.4;
15.1)
14.5 (13.5;
15.4)
0.315
Hematocrit (%) 43.4 (40.1;
44.6)
42.6 (39.9;
43.8)
42.9 (40.4;
44.2)
0.247
MCV (fL) 89.6 (84.4;
91.7)
91.6 (87.3;
92.9)
90.4 (87.3;
92.3)
0.247
MCH (pc) 31.2 (29.3;
31.7)
30.9 (29.7;
31.8)
31.0 (29.7;
31.8)
0.684
Platelets (10
9
/L) 257.0 (205.2;
339.7)
268.5 (214.5;
307.5)
266.0
(211.7;
307.5)
1.000
Lymphocytes (%) 8.1 (4.0; 11.8) 8.2 (6.7; 11.6) 8.2 (6.0;
11.6)
0.971
Neutrophiles (%) 85.2 (83.4;
89.3)
87.2 (82.5;
89.2)
86.1 (83.2;
89.2)
0.529
Monocytes (%) 4.0 (3.4; 5.7) 3.8 (2.7; 4.4) 3.9 (3.3; 5.0) 0.280
HBA1c: glycated hemoglobin; ALT: alanine aminotransferase; AST: aspartate
aminotransferase; GGT: gamma-glutamyl transferase; MCV: mean corpuscular
volume; MCH: mean corpuscular hemoglobin. Medians and interquartile range
were used for continuous variables, the Mann Whitney U test was for compari-
sons between treatment groups, qualitative variables frequencies and percent-
ages were used, comparisons were made using the Fisher’s exact-tests.
Table 3
Covid-related variables.
Variable Treatment
A
Treatment
B
Total p
n=10 n =10 n =20
Cough (n, %) 8 (40) 7 (35) 15 (75) 1.000
Fever (n, %) 9 (45) 6 (30) 15 (75) 0.303
Dyspnea (n, %) 4 (20) 7 (35) 11 (55) 0.370
Headache (n, %) 7 (35) 3 (15) 10 (50) 0.179
Myalgias/arthralgias (n, %) 6 (30) 6 (30) 12 (60) 1.000
Diarrhea (n, %) 4 (20) 2 (10) 6 (30) 0.628
General condition disturbances
(n, %)
5 (25) 7 (35) 12 (60) 0.650
%, percentage. Data presented as frequencies and percentages. Differences be-
tween groups were analyzed with the Fisher’s exact-tests.
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Biomedicine & Pharmacotherapy 152 (2022) 113223
7
Table 4
Other biochemical and immunological parameters at the beginning of the trial
(basal).
Variable Treatment A Treatment B Total p
n=10 n =10 n =20
D dimer (ng/mL) 533.0 (233.5;
703.5)
480.0 (233.5;
703.5)
511.5 (322.5;
756.5)
0.739
Ferritin (ng/mL) 1335.0 (298.5;
2102.0)
1473.5 (991.2;
2003.0)
1473.5
(754.5;
1987.5)
0.853
Procalcitonin
(ng/mL)
0.04 (0.02;
0.13)
0.08 (0.04;
0.22)
0.06 (0.04;
0.17)
0.481
RCP (mg/L) 1.83 (0.47;
2.30)
4.16 (1.47;
11.13)
2.28 (0.74;
7.58)
0.063
DHL (UI/L) 326.5 (255.2;
464.0)
257.5 (237.7;
334.7)
305.5 (241.7;
366.0)
0.105
Interleukin 6
(pg/mL)
13.7 (2.20;
67.35)
8.9 (2.07;
30.62)
10.1 (2.17;
47.0)
0.631
Vitamin D (ng/
mL)
19.1 (15.2;
22.8)
23.2 (19.3;
28.0)
20.1 (18.4;
24.3)
0.043
IgG (mg/dl) 1012.0 (753.0;
1106.7)
969.0 (819.0;
1151.7)
1012.0
(819.0;
1116.5)
0.912
IgM (mg/dl) 91.7 (66.4;
119.0)
82.5 (69.0;
115.7)
91.1 (69.1;
115.1)
0.631
CPK (U/L) 41.0 (28.5;
72.7)
35.5 (25.7;
122.7)
39.0 (28.8;
80.5)
0.684
ng/mL, nanograms per milliliter; mg/L, milligrams per liter; IU/L, international
units per liter; CRP, C-reactive protein; IgG, immunoglobulin G; IgM, immuno-
globulin M; CPK, creatinine phostokinase. Medians and interquartile range were
used for continuous variables and comparisons between treatment groups were
made with the Mann Whitney U test.
Table 5
Biochemical and immunological parameters at the beginning and end of the trial.
Variable Treatment A Treatment B
Basal Final p +Basal Final p +p*
Glucose (mg/dL) 151.5 (124.9; 184.3) 110.3 (78.3;136.2) .173 148.9 (135.0; 187.7) 139.0 (126.8; 173.4) .345 .093
Total cholesterol (mg/dL) 151.5 (143.0; 189.2) 160.0 (132.2; 174.5) .400 156.5 (147.2; 173.5) 185.5 (164.0;203.0) .069 .142
HDL cholesterol (mg/dL) 37.5 (26.7; 42.7) 35.5 (21.0; 42.7) .917 37.5 (32.2; 43.0) 51.5 (36.0; 54.2) .050 .081
LDL cholesterol (mg/dL) 91.0 (75.2; 125.2) 92.5 (74.7; 114.7) .599 102.5 (86.7; 105.2) 122.5 (46.9; 138.7) .484 .414
Triglycerides (mg/dL) 169.0 (138.5; 180.0) 147.5 (113.2; 246.0) .599 137.0 (100.0; 207.5) 114.0 (99.2; 251.25) .944 .573
ALT (U/L) 62.5 (30.0; 83.0) 85.5 (35.0; 165.2) .075 44.0 (30.7; 63.0) 92.0 (78.0;110.0) .017 .662
AST (U/L) 33.8 (23.2; 92.0) 31.5 (22.1; 49.7) .028 26.4 (21.8; 35.0) 22.2 (17.9; 29.8) .889 .491
GGT (U/L) 89.5 (56.7; 112.7) 84 (33.7; 148.5) 1.000 99.0 (36.0; 134.7) 128.5 (102.7; 175.0) .262 .142
Serum creatinine (mg/dl) 0.73 (0.61; 0.81) 0.74 (0.60; 0.84) .600 0.81 (0.73; 0.93) 0.84 (0.72;0.92) .575 .142
Total bilirubin (mg/dl) 0.29 (0.16; 0.48) 0.35 (0.23; 0.58) .528 0.37 (0.28; 0.52) 0.47 (0.36; 0.73) .069 .228
Albumin (g/L) 3.4 (3.8; 3.8) 3.15 (3.07; 3.48) .345 3.3 (3.0; 3.4) 3.48 (2.95; 3.77) .440 .662
Alkaline phosphatase (UI/L) 90.5 (71.2; 114.5) 69.0 (56.7; 76.5) .116 66.5 (56.0; 73.2) 62.0 (59.5; 79.5) .726 .755
Leukocytes (Cell/uL) 12.6 (9.3; 15.0) 9.4 (6.9; 13.3) .345 10.1 (6.6; 12.9) 9.9 (6.7; 13.8) .674 .852
Erythrocytes (C´
el/uL) 4.8 (4.3; 5.2) 5.0 (4.17; 7.96) .345 4.6 (4.2; 5.0) 4.6 (4.4; 4.9) .441 .282
Hemoglobin (g/dl) 15.2 (13.5; 15.6) 14.8 (12.7; 15.3) .293 14.3 (13.4; 15.1) 14.5 (13.8; 15.6) 1.000 .852
Hematocrit (%) 43.4 (40.1; 44.6) 43.7 (38.7; 44.8) .674 42.6 (39.9; 43.8) 41.3 (38.7; 44.7) .208 .491
MCV (fL) 89.6 (84.4; 91.7) 88.5 (84.8; 91.5) .345 91.6 (87.3; 92.9) 89.4 (85.4; 92.1) .208 .573
MCH (pc) 31.2 (29.3; 31.7) 30.8 (27.2; 32.2) .917 30.9 (29.7; 31.8) 31.3 (29.8; 32.8) .671 .662
Platelets (10
9
/L) 257.0 (205.2; 339.7) 260 (190.7; 346.7) .916 268.5 (214.5; 307.5) 324.5 (228.5; 441.7) .208 .491
Lymphocytes (%) 8.1 (4.0; 11.8) 17.0 (5.7; 36.52) .028 8.2 (6.7; 11.6) 8.0 (5.7; 12.4) .263 .491
Neutrophiles (%) 85.2 (83.4; 89.3) 76.2 (42.3; 88.7) .046 87.2 (82.5; 89.2) 85.5 (80.9; 87.8) .293 .755
Monocytes (%) 4.0 (3.4; 5.7) 7.0 (4.7; 11.0) .173 3.8 (2.7; 4.4) 3.9 (3.5; 5.8) .207 .181
D Dimer (ng/mL) 480.0 (233.5; 703.5) 634.0 (171.7; 1315.0) .028 480.0 (233.5; 703.5) 356.0 (153.5; 1585.09 .237 .491
Ferritin (ng/mL) 1401.5 (360.2; 2523.0) 1289.5 (260.25; 3483.2) .600 1709.0 (1199.2; 2179.0) 1381.0 (1205.2; 1899.2) .036 .852
Procalcitonin (ng/mL) 0.08 (0.04; 0.22) 0.04 (0.02;0.21) .916 0.08 (0.04; 0.22) 0.04 (0.03;0.05) .018 .755
CRP (mg/L) 4.16 (1.47; 11.13) 0.10 (0.02;0.41) .046 4.16 (1.47; 11.13 0.09 (0.05; 1.10) .012 .662
DHL (UI/L) 257.5 (237.7; 334.7) 256.5 (223.7; 295.7) .028 257.5 (237.7; 334.7) 244.5 (211.0; 296.5) .208 .852
Interleukin 6 (pg/mL) 8.9 (2.07; 30.62) 2.10 (2.0; 26.2) .346 8.9 (2.07; 30.62) 2.4 (1.82; 23.8) .050 .662
Vitamin D (ng/mL) 23.2 (19.3; 28.0) 19.6 (18.5; 31.0) .249 23.2 (19.3; 28.0) 29.2 (22.1; 36.7) .484 .345
IgG (mg/dl) 969.0 (819.0; 1151.7) 781.0 (550.0; 873.2) .028 969.0 (819.0; 1151.7) 800.5 (643.7; 997.7) .017 .662
IgM (mg/dl) 82.5 (69.0; 115.7) 220.1 (81.0; 133.99 .116 82.5 (69.0; 115.7) 98.2 (80.9; 147.8) .327 .573
CPK (U/L) 35.5 (25.7; 122.7) 47.0 (20.7; 76.2) .833 35.5 (25.7; 122.7) 48.5 (25.7; 76.5) .674 .950
ng/mL, nanograms per milliliter; mg/L, milligrams per liter; IU/L, international units per liter; CRP, C-reactive protein; IgG, immunoglobulin G; IgM, immunoglobulin
M; CPK, creatinine phostokinase. Medians and interquartile range were used for continuous variables * Mann Whitney U test was use to observe differences between
treatment groups.
†Wilcoxon test was used for comparisons of changes within groups – 6 cases where no laboratories were performed were excluded from the count.
Table 6
Point system for evaluating the need for supplementary oxygen.
Need for supplementary oxygen Points
1 Not hospitalized 0
2 Hospitalized without oxygen supplementation 1
3 Hospitalized with the requirement of supplementary oxygen (not high
ux, non-invasive ventilation).
2
4 Hospitalized with the requirement of non-invasive ventilation and/or
high ux oxygen).
4
5 Hospitalized with the requirement of extracorporeal membrane
oxygenation and/or invasive mechanical ventilation.
8
6 Death 10
Table 7
Description of the main efcacy variables.
Variable Treatment A Treatment B P*
Days of hospitalization 8.8 ±6.1 9.8 ±5.4 0.352
Oxygen need (points) 5.9 ±4.6 10.6 ±6.2 0.030
LV% Reduction 93.2 ±15.4 78.3 ±62.7 0.013
Expression of results as mean and standard deviation. * Student’s t-tests for
independent samples were used to compare treatment groups. LV% Percentage
viral load.
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opez et al.

Biomedicine & Pharmacotherapy 152 (2022) 113223
8
found were similar in both treatment groups, all of them of mild
severity. There were no patients with hypoglycemia.
4. Discussion
The potential role of metformin in COVID-19 disease has been
elucidated in diverse research highlighting its antioxidant, anti-
inammatory, immunomodulatory, and antiviral effects [22–24]. Our
results demonstrate that MG has anti-viral action against the
SARS-CoV-2 clinical isolate in vitro, with a single dose able to control
viral replication within 48 h. SARS–CoV-2 is a positive single-strain RNA
virus (+ssRNA) and depends on cellular membranes in all steps of the
viral life cycle and immunologically have similar characteristics con-
cerning host immune response with other +ssRNA viruses, which might
offer some insight into the treatment of COVID-19 [25,26].
Plus-stranded RNA viruses share the characteristic of remodeling
intracellular membranes in order to create membrane replication fac-
tories or replication organelles, which are vesicles where viral RNA
replication occurs [27,28]. These vesicles not only represent the site of
viral replication but also act as one of the strategies of viral immune
evasion mechanisms, shielding the viral RNA from cellular innate im-
mune sensors [29–32]. We hypothesize that MG inhibits the replication
of SARS-CoV-2 by the inhibition of protein synthesis as has been
described for other RNA viruses. Sphingomyelin (SM) is required for the
replication of some RNA viruses as the hepatitis C virus (HCV); the
biosynthesis starts in the endoplasmic reticulum (ER) and gives rise to
ceramide, which is transported from the ER to the Golgi by the action of
ceramide transfer protein (CERT), where it can be converted to a SM. For
HCV, it has been demonstrated that inhibition of SM biosynthesis, either
by using small-molecule inhibitors or by knockout (KO) of CERT, sup-
pressed HCV replication in a genotype-independent manner. This
reduction in HCV replication was rescued by exogenous SM or ectopic
expression of the CERT protein, but not by ectopic expression of
nonfunctional CERT mutants. Observing low numbers of DMVs in stable
replicon cells treated with a SM biosynthesis inhibitor or in CERT-KO
cells transfected with either HCV replicon or with constructs that
drive HCV protein production in a replication-independent system
indicated the signicant importance of SM to DMVs. The degradation of
SM of the in vitro-isolated DMVs affected their morphology and
increased the vulnerability of HCV RNA and proteins to RNase and
protease treatment, respectively [33,34].
The SARS-CoV-2 can induce cell death after 48–72 h of infection;
therefore, the cell viability is a surrogate measure of viral replication in
vitro. Pre-treatment of cells with a single-dose of 300 µM MG was able to
reduce cell death among different SARS-CoV2 variants, indicating that
MG cellular mechanism is kept despite differences in SARS-CoV2 spike.
The in vitro results are in line with our clinical study where we
demonstrated that MG reduces the viral load of Covid-19 patients to
undetectable levels in only 3.3 days. This reduction of infection is
associated with shorter hospitalization and less dependence on
supplementary oxygen. Except for a mildly elevated GGT in both groups,
in the basal and nal state, most of the biochemical parameters evalu-
ated at the beginning and end of the clinical study were normal for the
MG and control group. The only two-biochemical markers that changed
signicantly with MG treatment were AST and LDH levels. AST levels
were signicantly reduced at the end of the study in the group treated
with MG (33.8–31.5 U/L, basal vs nal, respectively), but both levels are
in the normal range (8–48 U/L). Similarly, LDH levels decreased in the
MG group from 257.5 to 256.5 IU/L, both of which are within the
normal range for this enzyme. D dimer was above the normal range in
both groups at the beginning of the study (480.0 ng/mL for both
groups), as can be expected for Covid-19 patients [35]. Interestingly, D
dimer increased signicantly only in the MG group at the end of the
treatment (480.0–634.0 ng/mL), while a non-signicant decrease was
observed for the control group (480.0–356.0 ng/mL).
Even though the majority of patients from both groups entered the
study with moderate disease, we did not observe the expected elevated C
reactive protein level for Covid patients (20–50 mg/L) [36]. In fact, both
groups had normal CRP levels at the beginning (4.16 mg/L, both groups)
and these levels decreased signicantly in all patients at the end of the
study (0.10 and 0.09 mg/L, MG and control groups, respectively). This
could be explained by the fact that none of the patients that entered this
study presented with severe Covid symptoms. Basal fasting glucose
levels were elevated in both groups (151.5 and 148.9 mg/dL, respec-
tively). This is not surprising, as 50 % of hospitalized Covid patients
develop acute hyperglycemia, and 7 % of this population has diabetes
[37]. At the end of the study, glucose levels in the MG-treated group
were close to normal (110.3 mg/dL), as was expected from the
anti-hyperglycemic mode of action of the drug.
5. Conclusions
In the present study, we demonstrated the in vitro effect of metformin
glycinate on the viral load of SARS-CoV-2. The antiviral effect of MG was
supported by in vivo results according to biochemical and immunolog-
ical parameters; 620 mg of MG administered orally every 12 h signi-
cantly and safely decreased viral load in Covid 19 patients. Although
these results strongly suggest that MG could be repositioned for the
treatment of SARS-CoV2, further clinical studies need to be assessed to
establish the safety and efcacy of MG in large populations. The hos-
pitalization condition of the patients was one of the limitations of the
clinical study, which made the collection of complete data and follow-up
of subjects more complicated. A longer follow-up could elucidate if the
biochemical alterations found in this study (reduced AST and reduced
DHL) are clinically relevant or are the only biochemical modications.
Finally, the longer follow-up could determine if the ability to decrease
viral replication of MG would have an implication on subacute and/or
chronic complications of the COVID-19 patients.
CRediT authorship contribution statement
Claudia Ventura-Lopez: Formal analysis, Investigation, Methodol-
ogy Writing – original draft. Karla Cervantes-Luevano: Formal anal-
ysis, Data curation, Methodology. Janet S. Aguirre-S´
anchez: Formal
analysis, Data curation, Methodology. Juan C. Flores-Caballero:
Formal analysis, Methodology. Carolina Alvarez-Delgado: Data cura-
tion, Writing – original draft. Johanna Bernaldez-Sarabia: Supervi-
sion, Project administration. Noemí S´
anchez-Campos: Data curation.
Laura A. Lugo-S´
anchez: Methodology, Writing – review & editing.
Ileana C. Rodríguez-V´
azquez: Methodology, Writing – review &
editing. Jose G. Sander-Padilla: Methodology, Writing – review &
editing. Yulia Romero-Antonio: Methodology, Writing – review &
editing. María M. Arguedas-Nú˜
nez: Methodology, Writing – review &
editing. Jorge Gonz´
alez-Canudas: Conceptualization, Resources, Su-
pervision, Writing – review & editing. Alexei F. Licea-Navarro: Fund-
ing acquisition, Project administration, Supervision, Writing – review &
Table 8
Comparison of negative viral load by treatment group.
Variable Treatment
A
Treatment
B
P OR (95 %IC)
n=10 n =10
Negative viral load
(days)
3.3 ±2.16 5.6 ±0.89 0.029
Negative viral load
<3.3 days (n, %)
4.0 (40.0) 0.0 (0.0) 0.043 6.667
(0.596–74.490)
Negative viral load
>4.0 days (n, %)
6.0 (60.0) 10.0
(100.0)
Expression of results as mean and standard deviation. * For quantitative vari-
ables the Student’s t-tests for independent samples was used to compare treat-
ment groups. For qualitative variable the Fisher’s exact-tests was used for the
comparison between treatment groups.
C. Ventura-L´
opez et al.

Biomedicine & Pharmacotherapy 152 (2022) 113223
9
editing.
Funding
Funding for this work came partially from CICESE Grant 685-101.
Declaration of Competing Interest
The authors declare that they have no known competing nancial
interests or personal relationships that could have appeared to inuence
the work reported in this paper.
Data availability
Data will be made available on request.
Acknowledgments
We are grateful to Dr. Javier Perez-Robles, Dr. Jahaziel Gasperin and
M.A. Itandehui Betanzo Guti´
errez for support during samples
processing.
References
[1] World Health Organization, Coronavirus disease situation, 2022. 〈https://covid19.
who.int/〉, (Accessed 27 April 2022).
[2] T.S. Fung, D.X. Liu, Human coronavirus: host-pathogen interaction, Annu. Rev.
Microbiol. 73 (2019) 529–557, https://doi.org/10.1146/annurev-micro-020518-
115759.
[3] R.J. Ford, M.D. Fullerton, S.L. Pinkosky, E.A. Day, J.W. Scott, J.S. Oakhill, A.
L. Bujak, B.K. Smith, J.D. Crane, R.M. Blümer, K. Marcinko, B.E. Kemp, H.
C. Gerstein, G.R. Steinberg, Metformin and salicylate synergistically activate liver
AMPK, inhibit lipogenesis and improve insulin sensitivity, Biochem. J. 468 (2015)
125–132, https://doi.org/10.1042/BJ20150125.
[4] National Library of Medicine (U.S.), Metformin Glycinate on Metabolic Control and
Inammatory Mediators in Type 2 Diabetes (COMET), Identier NCT01386671,
2018. Available from: 〈https://clinicaltrials.gov/ct2/show/study/NCT01386671〉.
[5] J. Gonz´
alez-Canudas, Comet Group, 146-LB: efcacy and safety of metformin
glycinate vs. metformin hydrochloride in metabolic control and inammatory
mediators in Type 2 diabetes mellitus patients (T2DM), ADA (2019) 68, https://
doi.org/10.2337/db19-146-LB.
[6] P. Rada, A. Mosquera, J. Mutan´
e, F. Ferrandiz, L. Rodríguez-Ma˜
nas, F. de Pablo,
J. Gonz´
alez-Canudas, A.M. Malverde, Differential effects of metformin glycinate
and hydrochloride in glucose production, AMPK phosphorylation and insulin
sensitivity in hepatocytes from non-diabetic and diabetic mice, Food Chem.
Toxicol. 123 (2019) 470–480, https://doi.org/10.1016/j.fct.2018.11.019.
[7] D.G. Hardie, F.A. Ross, S.A. Hawley, AMPK: a nutrient and energy sensor that
maintains energy homeostasis, Nat. Rev. Mol. Cell Biol. 13 (2012) 251–262,
https://doi.org/10.1038/nrm3311.
[8] L.J. Foster, M.L. Weir, D.Y. Lim, Z. Liu, W.S. Trimble, A. Klip, A functional role for
VAP-33 in insulin-stimulated GLUT4 trafc, Trafc 1 (2000) 512–521, https://doi.
org/10.1034/j.1600-0854.2000.010609.x.
[9] L. Florin, A. Pegel, E. Becker, A. Hausser, M.A. Olayioye, H. Kaufmann,
Heterologous expression of the lipid transfer protein CERT increases therapeutic
protein productivity of mammalian cells, J. Biotechnol. 141 (2009) 84–90, https://
doi.org/10.1016/j.jbiotec.2009.02.014.
[10] A. Raya, F. Revert-Ros, P. Martinez-Martinez, S. Navarro, E. Rosello, B. Vieites,
F. Granero, J. Forteza, J. Saus, Goodpasture antigen-binding protein, the kinase
that phosphorylates the goodpasture antigen, is an alternatively spliced variant
implicated in autoimmune pathogenesis, J. Biol. Chem. 275 (2000) 40392–40399,
https://doi.org/10.1074/jbc.M002769200.
[11] M. Kawano, K. Kumagai, M. Nishijima, K. Hanada, Efcient trafcking of ceramide
from the endoplasmic reticulum to the Golgi apparatus requires a VAMP-associated
protein-interacting FFAT motif of CERT, J. Biol. Chem. 281 (2006) 30279–30288,
https://doi.org/10.1074/jbc.M605032200.
[12] S. Saito, H. Matsui, M. Kawano, K. Kumagai, N. Tomishige, K. Hanada, S. Echigo,
S. Tamura, T. Kobayashi, Protein phosphatase 2Cepsilon is an endoplasmic
reticulum integral membrane protein that dephosphorylates the ceramide
transport protein CERT to enhance its association with organelle membranes,
J. Biol. Chem. 283 (2008) 6584–6593, https://doi.org/10.1074/jbc.M707691200.
[13] D.C. Prosser, D. Tran, P.Y. Gougeon, C. Verly, J.K. Ngsee, FFAT rescues VAPA-
mediated inhibition of ER-to-Golgi transport and VAPB-mediated ER aggregation,
J. Cell Sci. 121 (2008) 3052–3061, https://doi.org/10.1242/jcs.028696.
[14] Y. Zhang, Y. Wang, C. Bao, Y. Xu, H. Shen, J. Chen, J. Yan, Y. Chen, Metformin
interacts with AMPK through binding to gamma subunit, Mol. Cell. Biochem. 368
(2012) 69–76, https://doi.org/10.1007/s11010-012-1344-5.
[15] F.J. Díaz, W. Aguilar-Jim´
enez, L. Fl´
orez-´
Alvarez, G. Valencia, K. Laiton-Donato,
C. Franco-Mu˜
noz, D. ´
Alvarez-Díaz, M. Mercado-Reyes, M.T. Rugeles, Isolation and
characterization of an early SARS-CoV-2 isolate from the 2020 epidemic in
Medellín, Colombia. Aislamiento y caracterizaci´
on de una cepa temprana de SARS-
CoV-2 durante la epidemia de 2020 en Medellín, Colombia, Biomed.: Rev. Inst.
Nac. Salud 40 (2020) 148–158, https://doi.org/10.7705/biomedica.5834.
[16] L.J. Reed, H. Muench, A simple method of estimating fty per cent endpoints, Am.
J. Epidemiol. 27 (1983) 493–497, https://doi.org/10.1093/oxfordjournals.aje.
a118408.
[17] L.A. Sachs, D. Schnurr, S. Yagi, M.E. Lachowicz-Scroggins, J.H. Widdicombe,
Quantitative real-time PCR for rhinovirus, and its use in determining the
relationship between TCID50 and the number of viral particles, J. Virol. Methods
171 (2011) 212–218, https://doi.org/10.1016/j.jviromet.2010.10.027.
[18] M.A. Ramakrishnan, Determination of 50 % endpoint titer using a simple formula,
World J. Virol. 5 (2016) 85–86, https://doi.org/10.5501/wjv.v5.i2.85.
[19] V.M. Corman, O. Landt, M. Kaiser, R. Molenkamp, A. Meijer, D.K. Chu, T. Bleicker,
S. Brünink, J. Schneider, M.L. Schmidt, D.G. Mulders, B.L. Haagmans, B. Van der
Veer, S. Van den Brink, L. Wijsman, G. Goderski, J.L. Romette, J. Ellis, M. Zambon,
M. Peiris, H. Goossens, C. Reusken, M.P. Koopmans, C. Drosten, Detection of 2019
novel coronavirus (2019-nCoV) by real-time RT-PCR, Eur. Surveill. 25 (2020),
2000045, https://doi.org/10.2807/1560-7917.ES.2020.25.3.2000045 (Erratum
in: Euro Surveill. 2020 Apr;25(14): Erratum in: Euro Surveill. 2020 Jul;25(30):
Erratum in: Euro Surveill. 2021 Feb;26(5)).
[20] A.H. Cory, T.C. Owen, J.A. Barltrop, J.G. Cory, Use of an aqueous soluble
tetrazolium/formazan assay for cell growth assays in culture, Cancer Commun. 3
(1991) 207–212, https://doi.org/10.3727/095535491820873191.
[21] T.L. Riss, R.A. Moravec, Comparison of MTT, XTT, and a novel tetrazolium
compound for MTS for in vitro proliferation and chemosensitivity assays, Mol. Biol.
Cell 3 (Suppl.) (1992) S184a.
[22] L. Ojeda-Fern´
andez, A. Foresta, G. Macaluso, P. Colacioppo, M. Tettamanti,
A. Zambon, S. Genovese, I. Fortino, O. Leoni, M.C. Roncaglioni, M. Baviera,
Metformin use is associated with a decrease in the risk of hospitalization and
mortality in COVID-19 patients with diabetes: a population-based study in
Lombardy, Diabetes Obes. Metab. 24 (5) (2022) 891–898, https://doi.org/
10.1111/dom.14648.
[23] S.M. Samuel, E. Varghese, D. Büsselberg, Therapeutic potential of metformin in
COVID-19: reasoning for its protective role, Trends Microbiol. 29 (10) (2021)
894–907, https://doi.org/10.1016/j.tim.2021.03.004.
[24] E. Varghese, S.M. Samuel, A. Liskova, P. Kubatka, D. Büsselberg, Diabetes and
coronavirus (SARS-CoV-2): molecular mechanism of metformin intervention and
the scientic basis of drug repurposing, PLoS Pathog. 17 (6) (2021), e1009634,
https://doi.org/10.1371/journal.ppat.1009634.
[25] N. Altan-Bonnet, Lipid tales of viral replication and transmission, Trends Cell Biol.
27 (2017) 201–213, https://doi.org/10.1016/j.tcb.2016.09.011.
[26] H. Alothaid, M. Aldughaim, K. El Bakkouri, S. AlMashhadi, A.A. Al-Qahtani,
Similarities between the effect of SARS-CoV-2 and HCV on the cellular level, and
the possible role of ion channels in COVID19 progression: a review of potential
targets for diagnosis and treatment, Channels 14 (2020) 403–412, https://doi.org/
10.1080/19336950.2020.1837439.
[27] D. Egger, B. Wolk, R. Gosert, L. Bianchi, H.E. Blum, D. Moradpour, K. Bienz,
Expression of hepatitis C virus proteins induces distinct membrane alterations
including a candidate viral replication complex, J. Virol. 76 (2002) 5974–5984,
https://doi.org/10.1128/jvi.76.12.5974-5984.2002.
[28] H. Wang, A.W. Tai, Mechanisms of cellular membrane reorganization to support
hepatitis C virus replication, Viruses 8 (2016) 142, https://doi.org/10.3390/
v8050142.
[29] M. Kikkert, Innate immune evasion by human respiratory RNA viruses, J. Innate
Immun. 12 (2020) 4–20, https://doi.org/10.1159/000503030.
[30] I. Romero-Brey, R. Bartenschlager, Membranous replication factories induced by
plus-strand RNA viruses, Viruses 6 (2014) 2826–2857, https://doi.org/10.3390/
v6072826.
[31] A. Garcia-Sastre, Ten strategies of interferon evasion by viruses, Cell Host Microbe
22 (2017) 176–184, https://doi.org/10.1016/j.chom.2017.07.012.
[32] Y.T. Kao, M.M.C. Lai, C.Y. Yu, How dengue virus circumvents innate immunity,
Front. Immunol. 7 (2018) 2860, https://doi.org/10.3389/mmu.2018.02860.
[33] Y. Amako, G.H. Syed, A. Siddiqui, Protein kinase D negatively regulates hepatitis C
virus secretion through phosphorylation of oxysterol-binding protein and ceramide
transfer protein, J. Biol. Chem. 286 (2011) 11265–11274, https://doi.org/
10.1074/jbc.M110.182097.
[34] H. Gewaid, H. Aoyagi, M. Arita, K. Watashi, R. Suzuki, S. Sakai, K. Kumagai,
T. Yamaji, M. Fukasawa, F. Kato, T. Hishiki, A. Mimata, Y. Sakamaki, S. Ichinose,
K. Hanada, M. Muramatsu, T. Wakita, H. Aizaki, Sphingomyelin is essential for the
structure and function of the double-membrane vesicles in hepatitis C virus RNA
replication factories, J. Virol. 94 (2020), https://doi.org/10.1128/JVI.01080-20
(e01080 – 20).
[35] Y. Yao, J. Cao, Q. Wang, Q. Shi, K. Liu, Z. Luo, X. Chen, S. Chen, K. Yu, Z. Hang, D-
dimer as a biomarker for disease severity and mortality in COVID-19 patients: a
case control study, J. Intensive Care 8 (2020) 49, https://doi.org/10.1186/s40560-
020-00466-z.
[36] N. Ali, Elevated level of C-reactive protein may be an early marker to predict risk
for severity of COVID-19, J. Med. Virol. 92 (2020) 2409–2411, https://doi.org/
10.1002/jmv.26097.
[37] A. Ceriello, Hyperglycemia and COVID-19: what was known and what is really
new? Diabetes Res. Clin. Pract. 167 (2020), 108383 https://doi.org/10.1016/j.
diabres.2020.108383.
C. Ventura-L´
opez et al.