viruses

Article Modeling the Molecular Impact of SARS-CoV-2 Infection on the Renin-Angiotensin System

Fabrizio Pucci 1, Philippe Bogaerts 2 and Marianne Rooman 1,*

1 Computational Biology and Bioinformatics, Université Libre de Bruxelles, CP 165/61, Roosevelt Ave. 50, 1050 Brussels, Belgium; [email protected] 2 Biosystems Modeling and Control, Université Libre de Bruxelles, CP 165/61, Roosevelt Ave. 50, 1050 Brussels, Belgium; [email protected] * Correspondence: [email protected]

Academic Editors: Amber M. Smith and Ruian Ke



 Received: 15 October 2020; Accepted: 25 November 2020; Published: 30 November 2020

Abstract: SARS-CoV-2 infection is mediated by the binding of its spike protein to the angiotensin-converting enzyme 2 (ACE2), which plays a pivotal role in the renin-angiotensin system (RAS). The study of RAS dysregulation due to SARS-CoV-2 infection is fundamentally important for a better understanding of the pathogenic mechanisms and risk factors associated with COVID-19 coronavirus disease and to design effective therapeutic strategies. In this context, we developed a mathematical model of RAS based on data regarding protein and peptide concentrations; the model was tested on clinical data from healthy normotensive and hypertensive individuals. We used our model to analyze the impact of SARS-CoV-2 infection on RAS, which we modeled through a downregulation of ACE2 as a function of viral load. We also used it to predict the effect of RAS-targeting drugs, such as RAS-blockers, human recombinant ACE2, and angiotensin 1–7 peptide, on COVID-19 patients; the model predicted an improvement of the clinical outcome for some drugs and a worsening for others. Our model and its predictions constitute a valuable framework for in silico testing of hypotheses about the COVID-19 pathogenic mechanisms and the effect of drugs aiming to restore RAS functionality.

Keywords: SARS-CoV-2; renin angiotensin system; mathematical modeling; RAS-blockers; acute respiratory distress syndrome

1. Introduction Since December 2019, the world has been facing a global viral pandemic of the novel severe acute respiratory syndrome coronavirus 2, “SARS-CoV-2”; this pandemic has, to date, caused millions of people to be infected and hundreds of thousands to die [1]. First detected in the city of Wuhan (China) [2–5], SARS-CoV-2 spread rapidly throughout the world. The coronavirus family, to which SARS-CoV-2 belongs, includes a number of viruses, such as SARS-CoV and MERS-CoV, which have been implicated in serious epidemics that cause acute respiratory distress syndrome (ARDS). There is not yet consensus on the origin of SARS-CoV-2 [6–9], but the primary hypothesis is that it originated from bat (Rhinolophus affisor) or pangolin (Manis javanica), since the genomes of these two viral species share high sequence identity with SARS-CoV-2. Coronaviral genomes encode a series of structural proteins, one of which is the spike glycoprotein or S-protein that protrudes from the membrane surface [9]. Similar to the SARS-CoV virus that was identified in 2003, the S-protein of SARS-CoV-2 has been shown to bind to the angiotensin-converting enzyme 2 (ACE2), so that it can be used as an entry receptor to the cell [9–13]. This protein plays a pivotal role in the renin-angiotensin system (RAS) signaling pathway [14] by cleaving angiotensin

Viruses 2020, 12, 1367; doi:10.3390/v12121367 www.mdpi.com/journal/viruses Viruses 2020, 12, 1367 2 of 20

I and II peptides to generate angiotensin 1–9 and the biologically active peptide angiotensin 1–7, respectively [15,16]. ACE2 is highly expressed in type II alveolar cells of lung, epithelial cells of oral mucosa, colon enterocytes, myocardial cells, and kidney proximal tubule cells. The protective role of ACE2 in severe ARDS is also widely recognized [17,18]. Indeed, it has been shown, both in in vitro and in vivo mouse models, that a loss of ACE2 expression causes increased production of angiotensin II and that this contributes to lung failure [18]. It has already been established years ago that the SARS-CoV spike protein interferes with RAS due to its binding to ACE2 [19], thus causing ACE2 downregulation; this has opened up a number of interesting means of tackling SARS-CoV infection through RAS modulation. Indeed, injection of a soluble form of recombinant human ACE2 (rhACE2, GSK2586881) into mice infected with SARS-CoV appears to have a double role [18]: it slows the viral infection by binding to the S-protein and rescues ACE2 activity, thus causing angiotensin II reduction and protecting lung from severe failure. rhACE2 has been tested in phase II trials for its ability to ameliorate ARDS [20]. Although rhACE2 treatment is well tolerated by patients and offers a significant reduction in the angiotensin II level, clinical distress severity was not reduced in a recent pilot study [20]. Further studies are needed to understand the biological differences between the responses of animal models and humans. Since SARS-CoV-2 also targets ACE2 receptors when it infects cells, it is logical to hypothesize that rhACE2 might help reduce the severity of COVID-19 disease [21]. Indeed, it has been shown that rhACE2 inhibits SARS-CoV-2 infection in vitro and that this inhibition depends both on the initial quantity of the virus and on the rhACE2 concentration [22]. Following these exciting results, a clinical trial with exogenous submission of rhACE2 recently started [23]. A number of other clinical trials are also underway that target the dysregulated RAS to restore its functionality [24–28]. Hypertension and cardiovascular disease have been shown to be risk factors in cases of SARS-CoV-2 infection. This brings into question what might be the potential effects on the COVID-19 development of the RAS-targeting drugs that are used to treat hypertension and cardiovascular disease. RAS-targeting drugs fall into three categories: (i) angiotensin-converting enzyme inhibitors (ACE-I), (ii) angiotensin receptor blockers (ARBs), and (iii) direct renin inhibitors (DRIs) (Figure1). Several recent studies on large patient cohorts [29–31] concluded that there is only a weak correlation between treatment with drugs from these categories and any substantial increase in the risk of COVID-19.

MAS - Ang 1-7

SARS- MAS CoV-2

RE Ang 1-7 ARB DRI NEP ACE2

AT1R - CHYM AT1R AngII PRA Ang I Ang II ACE AGT AT2R AT2R- AngII Ang IV ACE-I

Figure 1. Schematic representation of RAS. In the unperturbed system, soluble proteins that are explicitly considered in the model are in blue grey, the peptides in light blue, and the peptide-bound membrane proteins in medium blue. The activities and enzymes considered only through reaction rates are in green. The feedback loop is indicated in blue. In the perturbed system, the drugs are in orange and SARS-CoV-2 in dark red. Viruses 2020, 12, 1367 3 of 20

Despite these interesting findings, there is not yet a detailed understanding of how SARS-CoV-2 infection leads to a dysregulation of RAS and, in severe cases, to ARDS. It is of fundamental importance that we gain better insights into the perturbed RAS in order to properly elucidate the pathogenic mechanisms and associated risk factors of SARS-CoV-2 infection; this, in turn, will enable novel therapeutic strategies to be designed and tested so that disease progression can be inhibited.

2. Methods

2.1. Modeling the Renin-Angiotensin System RAS has been widely studied both experimentally [32–34] and computationally [35–38]. It plays a key role in the regulation of a large series of physiological systems including the renal, lung, and cardiovascular systems. Consequently, its dysregulation is related to multiple pathological conditions such as hypertension and ARDS, just to mention a few [39–43]. There are two different types of RAS: the circulating RAS that is localized in the plasma and is involved in the regulation of the cardiovascular system and the tissue-localized systems that act intracellularly or interstitially within different organs in association with the systemic RAS or independently of it. Here, we focus on the local RAS within the pulmonary circulation and model its network of biochemical reactions, as schematically depicted in Figure1. When the blood pressure decreases, the juxtaglomerular kidney cells that sense changes in renal perfusion pressure secret an aspartic protease protein called renin (RE, EC 3.4.23.15). The activity of this enzyme, called plasma renin activity (PRA), is the common measure used in clinical practice to set up the diagnosis and treatment design of essential hypertension. The dynamics of the renin concentration can be modeled as:

d[RE] Log 2 = β [RE] (1) dt − hre where hre is renin’s half-life and β its production rate. The latter is not constant, but depends on other elements of RAS, which we will discuss later in the section. The role of renin is to cleave the N-terminus of a protein from the serine protease inhibitor family called angiotensinogen (AGT) to form the decapeptide hormone angiotensin I (AngI). The dynamics of the angiotensinogen can be written as:

d[AGT] Log 2 = kagt cre[RE] [AGT] (2) dt − − hagt where the reaction rate cre relates the renin concentration to its activity, kagt is AGT’s production rate, and hagt its half-life. The AngI peptide is further cleaved by different enzymes:

The angiotensin-converting enzyme (ACE, EC3.4.15.1) is a zinc metalloproteinase located mainly • in the capillaries of lung and in the endothelial cells. It catalyzes the transformation of AngI into the octapeptide angiotensin II (AngII). Chymase (CHY, EC 3.4.21.39), a serine protease that is mainly localized in blood vessels and heart, • also catalyzes the transformation of AngI into AngII. Neprilysin (NEP, EC3.4.24.11), another zinc metalloproteinase that is expressed in a wide variety • of tissues, catalyzes the transformation of AngI into the heptapeptide hormone angiotensin-(1-7) (Ang1-7).

The dynamics of AngI can thus be described as:

d[AngI] Log 2 = cre[RE] cace + cchy + cnep [AngI] [AngI] (3) dt − − hangI   Viruses 2020, 12, 1367 4 of 20

where cace, cchy, and cnep are the reaction rates associated with the corresponding enzymatic reactions. The role of AngII in RAS is central since it has a vasoconstriction effect, enhances blood pressure, and triggers inflammatory processes and fibrosis. In lung, the capillary blood vessels are among the sites that have the highest ACE expression and production of AngII. Its dysregulation has frequently been related to a wide series of chronic and acute diseases such as pulmonary fibrosis and ARDS. AngII’s effects are mediated by two G-protein coupled receptors (GPCR) called angiotensin II type 1 (AT1R) and type 2 (AT2R). In addition, it can be cleaved by different enzymes. For example, ACE2 generates Ang1-7 peptides, and aminopeptidase A (APA, EC 3.4. 11.7) generates other peptides such as angiotensin III (AngIII), which is further cleaved to AngIV. In our model, we skipped all the details about the enzymatic reactions AngII-AngIII-AngIV and kept only a single equation for their transformation. The dynamics of AngII and AngIV can thus be written as:

d[AngII] = c + c [AngI] dt ace chy   Log 2 cace2 + cangIV + cat1r + cat2r [AngII] [AngII] (4) − − hangII
d[AngIV] Log 2 = cangIV [AngII] [AngIV] (5) dt − hangIV where hangII and hangIV are the half-lives of the peptides and cace2, cangIV, cat1r, and cat2r the rates of the enzymatic reactions. The dynamics of the peptide-bound form of the GPCRs are expressed as:

d[AT1R-AngII] Log 2 = cat1r[AngII] [AT1R-AngII] (6) dt − hat1r

d[AT2R-AngII] Log 2 = cat2r[AngII] [AT2R-AngII] (7) dt − hat2r where [AT1R-AngII] and [AT1R-AngII] are the concentrations of the bound forms of the receptors and hat1r and hat2r their half-lives. Until now, we have modeled the ACE/AngII/AT1R regulatory axis of RAS. Since the last two decades, it became clear that there is another RAS axis that acts as a counterregulator of the first axis [44]. The key role of this second axis is played by the Ang1-7 peptide that is mainly produced from AngII by the ACE2 enzyme and binds to the transmembrane GPCR called MAS. However, Ang1-7 can also be obtained as an enzymatic product from AngI via the catalytic activity of NEP and, to a lesser extent, from Ang1-9 via ACE and NEP. We overlooked the Ang1-9-related enzymatic reactions in our model, as they contribute less to Ang1-7 formation [33,34]. The dynamical equations for the Ang1-7 peptide and the MAS-bound receptor are as follows:

d[Ang1-7] Log 2 = cnep[AngI] + cace2[AngII] cmas[Ang1-7] [Ang1-7] (8) dt − − hang1 7 − d[MAS-Ang1-7] Log 2 = cmas[Ang1-7] [MAS-Ang1-7] (9) dt − hmas Let us now go back to Equation (1) in which we simply expressed the renin production as a baseline term β. To describe the autoregulatory nature of RAS, this term has to depend on the production of other species, thus introducing a feedback regulation. It is known that this feedback depends on AT1R bound to AngII. Following other models [37,38], we express β as::

N δ [AT1R-AngII]0 β = β0 + 1 (10)  [AT1R-AngII] ! −    Viruses 2020, 12, 1367 5 of 20

N where β0 is a constant parameter to be identified and [AT1R-AngII]0 the equilibrium concentration for healthy normotensive humans. δ is a positive number that we fixed to 0.8 [37]. Technical details on the procedure used to solve the model and on model stability are given in Sections 2.6 and 2.7.

2.2. Modeling Blood Pressure Blood pressure is well known to be increased by the concentration of AngII bound to AT1R. It has also been described to be decreased by the concentration of MAS bound to Ang1-7 and of AT2R bound to AngII, but the precise mechanism is not yet known [45–47]. Therefore, we did not introduce in our model a feedback between these concentrations and renin production, as we did for AT1R-AngII, and modeled the diastolic blood pressure (DBP) simply from the AT1R-AngII concentration:

DBP = P0 + P1[AT1R-AngII] (11)

We identified the two parameters P0 and P1 by fixing DBP equal to 80 mmHg for normotensive N individuals and to 110 mmHg for hypertensive individuals. Hence, P0 + P1[AT1R-AngII]0 = 80 mmHg H and P0 + P1[AT1R-AngII]0 = 110 mmHg, where the N and H superscripts denote the concentration in normotensive and hypertensive individuals and the 0 subscript the equilibrium concentrations.

2.3. Modeling RAS-Blocker Effects Since dysregulated RAS with high levels of AngII is related to essential hypertension, a wide range of RAS-targeting drugs have been developed in the last forty years [48]. They can be classified into three different categories based on their pharmacological target [49]:

Angiotensin-converting enzyme inhibitors (ACE-I) that bind to ACE and thus inhibit the formation • of angiotensin II and the associated vasoconstriction and inflammatory cascades. Examples of this type of drug are enalapril, lisinopril, and captopril. Angiotensin receptor blockers (ARB) that block the binding of AngII to AT1R and thus act in • antagonism with AngII. Examples are candesartan, losartan, and valsartan. Direct renin inhibitors (DRI) that act on renin and thus inhibit the conversion of AGT to AngI. • Examples are aliskiren, enalkiren, and remikiren.

We modeled the action of these three types of drugs by modifying the reaction rates associated with their targets as:

c c (1 γACE-I) ace −→ ace × − c c (1 γARB) at1r −→ at1r × − c c (1 γDRI) (12) re −→ re × − where γACE-I, γARB, and γDRI are parameters describing the drug activity.

2.4. Modeling SARS-CoV-2 Infection Since ACE2 is the entry point of SARS-CoV-2 [19], it is downregulated upon infection, and this impacts substantially the local and systemic RASs. In order to model the downregulation effect due to the virus, we modified the ACE2 reaction rate with the function γCoV:

c c (1 γCoV(C )) (13) ace2 −→ ace2 × − t

We chose γCoV to be a sigmoid function of the cycle threshold value Ct, which is inversely related to the viral load [50]: 1 = γCoV aC b (14) 1 + e t− Viruses 2020, 12, 1367 6 of 20

where a and b are positive real numbers. Ct values of 31.5, 27.6, and 23.8 correspond to mild, moderate, and severe disease, respectively, and Ct > 40 to undetected viral infection [51]. We thus chose the inflection point of the sigmoid at Ct = 31.5 and imposed γCoV > 0.99 for Ct > 40. Using these relations, we identified the values of the parameters a and b. They are reported in Table1.

2.5. Modeling ARDS Severity To model ARDS severity and how the lungs of SARS-CoV-2 patients evolve in response to RAS dysregulation, we introduced a phenomenological relation to estimate the PAO2/FIO2 ratio, defined as the ratio between the partial pressure of arterial oxygen (PAO2) and the fraction of inspired oxygen (FIO2). This quantity plays a key role in the assessment of ARDS patients [52,53]. The normal range of PAO2/FIO2 is between 400 and 500 mmHg. Mild and moderate ARDS are characterized by PAO2/FIO2 values in the range [200–300] mmHg and [100–200] mmHg, respectively. ARDS is severe for values below 100 mmHg. We predicted the PAO2/FIO2 ratio as a function of the AngII and Ang1-7 concentrations:

[AngII] [Ang1-7] PAO2/FIO2 = A + A + (15) 0 1 − [AngII] [Ang1-7]  0 0  where A0 and A1 are two parameters that we identified on the basis of our model by comparing the baseline RAS with the same system in which ACE2 is knocked out. In the former case, we fixed PAO2/FIO2 = 450 mmHg and, in the latter, PAO2/FIO2 = 50 mmHg.

2.6. Solving the RAS Model The mathematical model of RAS described in Equations (1)–(10) is a system of ordinary differential equations (ODEs), which are linear except for the feedback loop of Equation (10). We collected from the literature the values of the equilibrium concentrations of all proteins and peptides except renin and MAS bound to Ang1-7, for normotensive and hypertensive humans (Table2). From these values, we fixed the parameters that appear in the phenomenological relations (11) and (15) for DBP and PAO2/FIO2 (Table1). We also collected the values of the half-life of all proteins and peptides but MAS; we assumed the latter to be equal to that of the other membrane receptors (Table1). Moreover, we estimated the value of reaction rate cre from [36,54]. Using these concentration and parameter values, we solved the system of nine ODEs (Equations (1)–(9)) in the stationary state to identify the unknown parameters and concentrations. However, these equations have 12 unknowns: kagt, β0, cace, cace2, cangIV, cat1r, cat2r, cmas, cchy, cnep,[RE]0, and [MAS-Ang1-7]0. We had thus to assume three additional relations, which are:

cmas = cat2r (16)

cchy = 0 (17)

cnep = 0 (18)

Since no quantitative data related to the MAS receptor can be found in the literature, we hypothesized the first relation assuming MAS and AT2R to be equally expressed and the affinity of Ang1-7 for MAS to be similar to the affinity of AngII for AT2R [46]. Moreover, we assumed cchy = 0 and cnep = 0, but discuss the effect of non-vanishing values in Section4. By imposing these three additional relations, we solved the system of nine ODEs in the stationary state. The values obtained for [RE]0,[MAS-Ang1-7]0, kagt, β0, cace, cace2, cangIV, cat1r, and cat2r for normotensive and hypertensive humans, are given in Table2. Viruses 2020, 12, 1367 7 of 20

Table 1. Half-lives of the species involved in RAS and other parameters of the model. “Fitted” means fitted on experimental data.

Parameter Unit Values Reference

hagt min 600 [35] hang1–7 min 0.5 [35] hangI min 0.5 [35] hangII min 0.5 [35] hangIV min 0.5 [35] hat1r min 12 [35] hat2r min 12 [35] hre min 12 [35] hmas min 12 - cre 1/min 20 [36,54]

A0 mmHg 450 Fitted A1 mmHg 267 Fitted P0 mmHg 73.6 Fitted P1 mmHg mL/fmol 0.43 Fitted a - 0.53 Fitted b - 16.7 Fitted

Table 2. Equilibrium concentrations of the species involved in RAS and production and reaction rate parameters, for healthy normotensive and hypertensive humans. “Solved” means solved from the model.

Parameter Unit Normotensive Hypertensive Reference [AGT] fmol/mL 6 105 6 105 [55] 0 × × [AngI]0 fmol/mL 70 110 [56,57] [AngII]0 fmol/mL 28 156 [56,57] [Ang1-7]0 fmol/mL 36 92 [56–58] [AngIV]0 fmol/mL 1 1 [59] [AT1R-AngII]0 fmol/mL 15 85 [37] [AT2R-AngII]0 fmol/mL 5 27 [37]

[RE]0 fmol/mL 9.43 25.25 Solved [MAS-Ang1-7]0 fmol/mL 6.43 15.92 Solved kagt fmol/(mL min) 881.82 1198.22 Solved β0 fmol/(mL min) 0.54 2.21 Solved cace 1/min 1.31 3.21 Solved cace2 1/min 1.80 0.82 Solved cangIV 1/min 0.05 0.01 Solved cat1r 1/min 0.03 0.03 Solved cat2r 1/min 0.01 0.01 Solved

2.7. Stability of the RAS Model The system of nine ODEs (Equations (1)–(9)) can be summarized in the form:

dx(t) = f (x(t), θ) (19) dt where x(t) is the vector containing the nine state variables, i.e., the concentrations of all proteins and peptides at time t, θ is the vector with all the production, kinetic, and half-life parameters, and f represents the vector that corresponds to the right-hand sides of Equations (1)–(9). In order to analyze N H the stability of the two steady states x0 and x0 for normotensive and hypertensive individuals, respectively, we computed the eigenvalues of the Jacobian matrix:

∂ f (x, θ) J(x0) = (20) ∂x x=x0

Viruses 2020, 12, 1367 8 of 20

N H where x0 stands for either x0 or x0 . In both the normotensive and hypertensive cases, seven strictly negative real values were obtained, together with two complex conjugate eigenvalues with strictly negative real parts. Both steady states N H x0 and x0 are therefore stable. The nonzero imaginary parts of the two complex conjugate eigenvalues are responsible for some damped oscillations in transient responses to parameter changes, but the overshoots are limited. It is interesting to note that the imaginary part is more than three times lower in the hypertensive case, hence leading to more damped responses in comparison with the normotensive case. To quantify the state variable transients and the aforementioned overshoots, we simulated step responses corresponding to a 10% increase in the normal baseline for renin production β0. We observed some damped oscillations during the transient phase of the normotensive case, with very limited overshoots, e.g., 1.3% for the RE concentration. In the hypertensive case, the imaginary part of the complex conjugate eigenvalues is so low that the overshoots become almost undetectable (0.025%).

3. Results The main objective of this paper is to investigate the effect of RAS-targeting drugs and SARS-CoV-2 infection, both individually and in combination, on the RAS of normotensive and hypertensive individuals. The robustness and predictive power of our model was first assessed by investigating the effects on RAS of three types of antihypertensive drugs: (i) ACE-I, (ii) ARB, and (iii) DRI (described in Section 2.3). This assessment included a comparison of model simulations with patient clinical data. Following the confirmation of model robustness and accuracy, ACE2 downregulation due to viral infection was introduced into the model to quantitatively predict how RAS is perturbed in COVID-19.

3.1. Model Predictions and Clinical Data on RAS-Blocker Drugs The effect of enalapril, an ACE-I type drug, on plasma ACE activity and on plasma levels of AngI and AngII has been measured in normotensive individuals who received a single oral dose of 20 mg [60]. To compare these data with model predictions, we first fitted the γACE-I parameter introduced in Equation (12) to the ACE activity values during enalapril administration divided by the pre-treatment activity (measured by an antibody-trapping assay). Once γACE-I was set, we used our model to predict the dynamical response of RAS to this inhibitor drug. The time-dependent values of the AngI and AngII concentrations, normalized by their concentration at time 0, are shown in Figure2a,b, both for our model predictions and experimental enalapril data; there is very good agreement between the two curves, without any further parameter fitting. The excellent correspondence between model prediction and experimental data is also clear from the root mean squared deviation (rmsd) between model prediction and experimental data on all time points following drug administration, as shown in Table3. Our model, thus, captures the known dynamics of ACE inhibition, (i.e., increased AngI levels and decreased AngII levels); this has the effect of lowering the concentration of AngII bound to AT1R and, thus, also lowers the blood pressure (Equation (11)). To study the effect of ARB antihypertensive drugs on RAS, we considered data from [61], which measured the effects of different types of AT1R blocking molecules on the plasma levels of AngII in normotensive individuals. Specifically, the study participants received a single 50 mg dose of losartan, 80 mg of valsartan, or 150 mg of irbesartan. First, we fitted the γARB parameter (defined in Equation (12)) to the in vitro ability of the administered drug to induce the AngII receptor blockade, as measured by an AT1R radioreceptor binding assay [61]. We then used our model to predict the time-dependent AngI level, which was normalized by its concentration prior to drug administration. The results were evaluated through the rmsd between experimental and predicted values of AngI/AngI0 at different time points after drug administration. The results, which are detailed in Table3, clearly show that our model accurately predicts the RAS response to ARBs. Viruses 2020, 12, 1367 9 of 20

Figure 2. Dynamical response of RAS to ACE-I (enalapril) administration. Comparison between the computational prediction (green) and the experimental data (brown) of normalized AngI (a) and AngII (b) as a function of time (in hours) after the single dose administration. Continuous lines are obtained through data interpolation. (c) Measured DBP averaged over more than ten ACE-I types as a function of the normalized dosage ξACE-I (dosage divided by maximal dosage) (brown points) and predicted DBP as a function of ξACE-I values considering γACE-I = 0.5 ξACE-I (continuous green 1/4 line) and γACE-I = 0.4 ξACE-I (dashed green line). (d) Predicted effect of the combination of ACE-I and ARB on DPB values as a function of the normalized drug dosages ξACE-I and ξARB, considering γACE-I = 0.5 ξACE-I and γARB = 0.5 ξARB.

We also studied the effect of DRI-type drugs using experimental data that describe PRA activity and RE, AngI, and AngII concentrations, when different doses of aliskiren were administered orally to normotensive individuals [62]. We used the PRA activity data to fit the γDRI parameter (introduced in Equation (12)), and we used our model to calculate the normalized AngI and AngII levels as a function of time. Here also, the results from our model and the experimental concentration data agree very well, as shown in Table3. In summary, the rmsd between predicted and experimental values of normalized AngI and AngII levels, averaged over all tested drugs, dosages, and a total of 38 time points, is 0.57 and 0.18, respectively (Table3). These values should be compared with average experimental values of 1.7 and 0.5, respectively, demonstrating excellent agreement between experimental data and model predictions. It should be noted that all reported experimental data were obtained after administration of single doses of RAS-targeting drugs. However, for hypertensive patients receiving long-term treatment, the expression of some enzymes involved in RAS could be either up- or down-regulated; we will return to this point in Section4.

1

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Table 3. Comparison between model predictions and experimental values of AngI and AngII levels normalized by their value before the administration of the drugs. Range is the interval of experimental values, and rmsd is the root mean square deviation between experimental and predicted values, computed over all time points; Np is the number of time points. ACE-I, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker; DRI, direct renin inhibitor.

Drugs Class Dose [AngI](t)/[AngI]0 [AngII](t)/[AngII]0 Np Ref. (mg) rmsd (Range) rmsd (Range) Enalapril ACE-I 20 1.31 [1.0–9.2] 0.09 [0.2–1.0] 5 [60] Losartan ARB 50 0.61 [1.0–2.1] - 3 [61] Valsartan ARB 850 0.83 [1.0–2.2] - 3 [61] Irbesartan ARB 150 0.97 [1.0–4.4] - 3 [61] Aliskiren DRI 40 0.13 [0.4–1.1] 0.14 [0.5–1.0] 6 [62] Aliskiren DRI 80 0.15 [0.4–1.0] 0.16 [0.4–1.0] 6 [62] Aliskiren DRI 160 0.26 [0.2–1.0] 0.20 [0.3–1.0] 6 [62] Aliskiren DRI 640 0.29 [0.1–1.0] 0.29 [0.1–1.0] 6 [62] Mean 0.57 0.18

Finally, we compared model predictions against clinical data from large cohorts of patients describing the effect of ACE-I and ARB drug administration on blood pressure [63,64]. We first analyzed the response to ACE-I drugs alone. We plot measured DBP values averaged over more than ten ACE-I drug types as a function of the normalized dosage ξACE-I [63] in Figure2c, as well as predicted DBP values. We first considered a linear relation between γACE-I and the normalized drug dosage (γACE-I = 0.5 ξACE-I). Despite this simplification, chosen to limit the number of parameters and thus overfitting issues, the curve reproduces the experimental data reasonably well. We also defined a non-linear relationship between these two quantities by introducing additional parameters: 1/4 γACE-I = 0.4 ξACE-I. We thus obtained a better fit as shown in Figure2c. We then studied the effect of the combined administration of the two drugs, ARB and ACE-I, on blood pressure, plotting the predicted DBP values as a function of both ξACE-I and ξARB (see Figure2d). We found that combined administration of ARB and ACE-I reduces DBP by 4 mmHg when compared with ARB monotherapy and by 12 mmHg when compared with ACE-I monotherapy. These predictions should be compared with clinical DBP values of 3 mmHg for combined administration compared to either monotherapy [64]. Thus, our model again provides an excellent prediction of experimental clinical data; further improvements to the model’s predictive strength are possible by fixing the γARB value at the maximum dose to be slightly lower than the corresponding γACE-I value.

3.2. RAS in COVID-19 It is known that ACE2 is the cellular receptor of the spike glycoprotein of SARS-CoV-2 [9–13] and that it triggers the entry of SARS-COV-2 into the host cell. Although ACE2 is expressed in a variety of tissues [65–67], it is expressed mainly in the alveolar epithelial cells of lung, in the gastrointestinal tract, and in the kidney proximal tubular cells. Here, we used our model to predict how RAS is perturbed by the SARS-CoV-2 virus. Simulation results of AngII and Ang1-7 concentrations and of the physiological value of PAO2/FIO2 as a function of SARS-CoV-2 viral load are presented in Figure3 and in Table4. We observe that the AngII level increases with increasing viral load, with a much stronger increase for hypertensive than for normotensive patients. The AngII level is predicted to increase by approximately 15% for patients with moderate and severe COVID-19 (Table4); this prediction is in very good agreement with the experimental value of 16% found in [68], but in poorer agreement with the value of 35% resulting from a study of only 12 patients [69]. We also observe that our model predicts a severe reduction of the Ang1-7 level, due to ACE2 downregulation; this reduction is the same for hypertensive and normotensive patients. Viruses 2020, 12, 1367 11 of 20

The overall result of the model is that RAS becomes imbalanced upon SARS-CoV-2 infection, with the harmful AngII axis upregulated and the counteracting Ang1-7 axis severely downregulated. This imbalance can be related to multiple clinical manifestations of COVID-19. More specifically, increased AngII levels cause hyperinflammation, which, in turn, increases plasma proinflammatory cytokine levels (in particular, IL-6) [70,71]. In addition, thrombotic events are observed, since AngII promotes the expression of plasminogen activator inhibitor-1 (PAI-1) and tissue-factors (TFs) [72,73]. Ang1-7, which normally counteracts these various effects [44], is downregulated by SARS-CoV-2 infection, such that COVID-19 clinical manifestations become increasingly severe as the disease develops. Moreover, our model predicts severe ARDS with PAO2/FIO2 < 100 mmHg for normotensive and hypertensive patients whose Ct values are smaller than 24.1 and 27.0, respectively. Our model predicts moderate ARDS, characterized by a PAO2/FIO2 ratio in the range of 100–200 mmHg, for normotensive and hypertensive patients having 24.1 < Ct < 29.3 and 27.0 < Ct < 29.7, respectively, and mild ARDS, characterized by a PAO2/FIO2 ratio in the range of 200-300 mmHg for normotensive and hypertensive patients having 29.3 < Ct < 31.4 and 29.7 < Ct < 31.6, respectively. Our modeling approach suggests a weak relationship between hypertension and ARDS severity resulting from SARS-CoV-2 infection. The mean value of the PAO2/FIO2 ratio over the entire Ct range is approximately 20 mmHg lower for hypertensive than for normotensive patients. Indeed, the large difference in AngII levels between normotensive and hypertensive patients is partially compensated by the absence of any difference in Ang1-7 levels.

CoV( (a) AngII / AngII(0) (b) 1.0 Monitoring the acute1.5 respiratory distressNormo syndrome and its severity 0.8 To model how the lungs1.4 of the infected patients evolveHyper in response to the modulation of the RAS system, we introduced a phenomenological relation to estimate the PaO2/FiO2 ratio, 0.6 1.3 defined as the ratio between the partial pressure of arterial oxygen (PaO2) and the fraction of 1.2 0.4 inspired oxygen. This quantity plays a key role in the assessment of ARDS patients [34, 35]. The normal range of1.1PaO2/FiO2 is between 400 and 500 mmHg. Mild and moderate ARDS 0.2 are characterizedCt by PaO2/FiO2 values in the range [200–300]Ct mmHg and [100-200] mmHg, 1.0 0.0 respectively. ARDS is severe for values below 100 mmHg. 20 25 30 35 40 20 25 30 35 40 We predicted the PaO2/FiO2 ratio as a function of the AngII and Ang1-7 concentrations:

Ang1-7 / Ang1-7(0) [AngII] [Ang1-7] (c) PaO2/FiO2 = A + A +(d) (15) 0 1 [AngII] [Ang1-7] 1.0 ✓ 0 0 ◆ Normo where A0 and A1 are400 two parametersNormo that we identified from our model by comparing the 0.8 Hyper baseline RAS with the same systemHyper in which ACE2 is knocked out. In the former we fixed 0.6 PaO2/FiO2= 450 mmHg300 and in the latter PaO2/FiO2= 50 mmHg. 0.4 200 Solving the RAS model 0.2 100 xx should thisC partt not be in the Results section? With theC paragrapht of Philippe ? xx 0.0 The mathematical0 model of the RAS system described in Eqs (1)-(11) is a system of 25 30 35 40 20 ordinary di↵erential equations20 (ODEs),25 which30 are35 linear except40 for the feedback loop of Eq. (11). We collected from the literature the values of the equilibrium concentrations of all Figure 3. Simulated responseproteins of RAS to and viral peptides infection. ( fora) The bothγCoV normotensivefunction used to and model hypertensive the effect humans (Table 1), except of the infection as a function ofreninCt, the and cycleMAS thresholdbound of to theAng1-7 virus. (.b– Fromd) Predictions these values, obtained we from fixed our the parameters that appear in model for the normalized levelsthe of phenomenologicalAngII and Ang1-7 and relations for the physiological (12) and (15)PAO2/F for DBPIO2 value, and as PaO2/FiO2 a (2). function of Ct, for normotensiveWe (blue) also and got hypertensive the values (red) of the individuals. half-life of all proteins and peptides but MAS; we assumed the latter to be equal to that of the other membrane receptors (Table 2). Moreover, we estimated the value of reaction rate cre from [32, 33]. Using these concentration and parameter values, we solved the system of 9 ODEs (1)-(11) at the stationary state to identify the unknown parameters and concentrations. However, these equations have 12 unknowns: kagt, 0, cace, cace2, cangIV , cat1r, cat2r, cmas, cchy, cnep, [RE] and [MAS-Ang1-7]. We had thus to assume three additional relations to be able to solve the system. These are:

cmas = cat2r (16)

cchy = 0 (17)

cnep = 0 (18)

Since no quantitative data related to the MAS receptor can be found in the literature, we hypothesized the first relation assuming MAS and AT2R to be equally expressed and the anity of Ang1-7 for MAS to be similar to the anity of AngII for AT2R [46]. Moreover, we assumed cchy = 0 and cnep = 0, but carefully discussed the e↵ect of non-vanishing values in the Discussion section. Imposing these three additional relations, we solved the system of 9 ODEs (1)-(11) at the stationary state. The values obtained for [RE] and [MAS-Ang1-7], kagt, 0, cace, cace2, cangIV , cat1r and cat2r for normotensive and hypertensive humans are given in Table 1.

7 Viruses 2020, 12, 1367 12 of 20

Table 4. Prediction of biochemical and clinical features of SARS-CoV-2 patients.

Uninfected Mild Moderate Severe Ct 40.0 31.5 27.6 23.8 Normotensive [AngII] (fmol/mL) 28 32 36 38 [Ang1-7] (fmol/mL) 36 21 5 1 PAO2/FIO2 (mmHg) 450 300 145 98 DBP (mmHg) 80 81 82 82 Hypertensive [AngII] (fmol/mL) 156 186 221 231 [Ang1-7] (fmol/mL) 92 55 15 2 PAO2/FIO2 (mmHg) 450 292 115 60 DBP (mmHg) 110 117 125 128

3.3. Impact of RAS-Modulating Drugs on COVID-19 Severity We analyzed the effect of administering a selection of drugs to normotensive and hypertensive patients who were infected with SARS-CoV-2. More specifically, we considered RAS-blocking drugs that are already commonly used to treat hypertension, as well as drugs that are currently undergoing clinical trials in the context of COVID-19, such as rhACE2 and Ang1-7.

Antihypertensive RAS-blocking drugs: We combined the effect of each of the three RAS-blocking • ACE-I, ARB, and DRI drugs, which were modeled by the enzyme-inhibiting γ functions (introduced in Equation (12)), with the ACE2-inhibiting Ct-dependent γCoV function (defined in Equation (14)), which mimics SARS-CoV-2 infection. The PAO2/FIO2 values predicted by our model are presented in Figure4.

Our model predicts that administration of ACE-I and DRI drugs protect from the adverse effects of ARDS, especially for hypertensive patients, while ARB drugs are predicted to worsen ARDS severity, especially for normotensive patients. Model predictions for ACE inhibitors are in agreement with clinical data, which indicate that treatment with ACE inhibitors is associated with better survival among COVID-19 patients [31, 74]. Indeed, only 3% of non-surviving COVID-19 patients that were monitored were treated with ACE-I drugs compared to 9% of surviving COVID-19 patients [31]. Moreover, in a meta-analysis [74], hypertensive patients treated with ACE-I drugs were associated with a reduced mortality of 35% when compared to patients who were not treated with ACE-I drugs. In another clinical analysis [75], older patients who were treated with ACE-I drugs had a 40% lower risk of hospitalization than those who were not treated with ACE-I drugs. No data are currently available to validate our model prediction that COVID-19 attenuation due to ACE-I drug treatment is stronger in hypertensive than in normotensive patients. Furthermore, no data are currently available to validate our model prediction that DRI and ACE-I drug treatments cause similar levels of COVID-19 disease attenuation. In contrast to DRI and ACE-I drugs, our model predicts that ARB drug treatment worsens COVID-19 severity, with the effect being stronger for normotensive compared to hypertensive patients. Here, the agreement between model predictions and clinical data is less clear, with some clinical data in agreement with our model prediction [31,75], while other clinical data suggest that ARB drug treatment does not affect hospitalization risk [75] or mortality [74,76]. This lack of agreement must be further investigated with additional clinical data. Moreover, we performed a quantitative prediction of the drug effects on disease severity by calculating the RAS peptide concentrations, PAO2/FIO2 values, and DPB for moderate COVID-19 patients. The results are presented in Table5. Viruses 2020, 12, 1367 13 of 20

Administration of ACE-I drugs, modeled by γACE I = 0.5, increases the PAO2/FIO2 value − by approximately 50 and 70 mmHg for normotensive and hypertensive patients, respectively. An equivalent administration of DRI drugs increases this ratio even more, by 70 and 150 mmHg, while ARB administration decreases it by 140 and 30 mmHg for normotensive and hypertensive patients, respectively. The opposite effect of ARBs administration compared to ACE-I and DRI drugs can be attributed to the substantial increase in AngII concentration, which is only partially balanced by a relatively small increase in Ang1-7 concentration, given that ACE2 is downregulated in SARS-CoV-2 infection. Note that a number of ARB drugs, including valsartan and losartan, are currently being tested in clinical trials, with the hope that they will rescue RAS in COVID-19 patients [25–27]. Our model predicts that this will not be theMonitoring case. the acute respiratory distress syndromeMonitoring and its severity the acute respiratory distress syndrome and its severity To model how the lungs of thePucci infectedet al. patients evolve in responseTo model to howthe modulation the lungs of of the the infected patients evolve in response to the modulationPage 7 of of the20 Finally, as shown in TableRAS5,system, the blood we introduced pressure a phenomenological is predicted relation to toRAS estimate be system, unaffected the PaO2/FiO2 we introduced byratio, aphenomenological the administration relation to estimate the PaO2/FiO2 ratio, Pucci et al. defined as the ratio between the partial pressure of arterial oxygendefined (PaO2) as the andratio the betweenPage fraction 7 of the20 of partial pressure of arterial oxygen (PaO2) and the fraction of of either ACE-I, ARB, or DRIinspired to normotensive oxygen. This quantity COVID-19 plays a key role patients, in the assessmentinspired but of ARDSoxygen. to be patients This reduced quantity [34, 35]. plays by a approximately key role in the assessment of ARDS patients [34, 35]. 10–20 mmHg by administrationThe normal to hypertensive range of PaO2/FiO2 patients.is between 400 and 500 mmHg.The normal Mild and range moderate of PaO2/FiO2 ARDS is between 400 and 500 mmHg. Mild and moderate ARDS are characterized by PaO2/FiO2 values in the rangeAngiotensin [200–300]are mmHgcharacterized receptor and [100-200]blockers by PaO2/FiO2 mmHg,(ARB) thatvalues block in the the range binding [200–300] of AngII mmHgto AT1R and [100-200] mmHg, respectively. ARDS is severe for values below 100• mmHg. respectively. ARDS is severe for values below 100 mmHg. and thus act in antagonism with AngII. Examples are candesartan, losartan AngiotensinWe predicted receptor theblockersPaO2/FiO2 (ARB)ratio that asblock a function the binding of the AngII of AngIIWeand predictedtoAng1-7AT1R theconcentrations:PaO2/FiO2 ratio as a function of the AngII and Ang1-7 concentrations: • (b) (a)AngII and valsartan. and thus act in antagonism with . Examples[ areAngII candesartan,] [Ang1-7 losartan] [AngII] [Ang1-7] PaO2/FiO2 = A + A Direct+ renin inhibitors (DRI)PaO2/FiO2 that(15) act= onA renin+ A and thus inihibit+ the conver- (15) and valsartan. 0 1 [AngII• ] [Ang1-7] 0 1 [AngII] [Ang1-7] ✓ sion0 of AGT to AngI0 ◆ . Examples are aliskiren, enalkiren✓ and0 remikiren.0 ◆ PucciDirectwhereet renin al. A inhibitorsand A are (DRI) two thatparameters act on that renin we and identified thus inihibit fromwhere our theA model conver-and byA comparingare two parameters the that we identifiedPage from 7 of our20 model by comparing the • 0 1 We modeled the action0 of1 these three types of drugs by modifying the reaction sion ofbaselineAGT to RASAngI with. Examples the same are systemMonitoring aliskiren, in which enalkiren theACE2 acuteis and knocked respiratory remikiren.baseline out. RAS In distress the with former the syndromesame we fixed system and in itswhich severityACE2 is knocked out. In the former we fixed We modeledPaO2/FiO2 the action= 450of these mmHg three and intypes the latterof drugsPaO2/FiO2rates by associated modifying= 50PaO2/FiO2 mmHg.to the their reaction targets= 450 mmHg as: and in the latter PaO2/FiO2= 50 mmHg. To model how the lungs of the infected patients evolve in response to the modulation of the rates associated to their targets as: Solving the RAS model RAS system, we introduced aSolving phenomenological the RAS relation model to estimate the PaO2/FiO2 ratio, Angiotensindefined receptor as the blockersratio between (ARB) the partial thatblock pressure the of bindingarterial oxygen of AngII (PaO2)to andAT1R the fraction of xx should this part• not be in theinspired Results oxygen. section? This Withc quantityace thexx paragraph playscace should a(1 key this of role Philippe partACE-I in not the) ? be assessment xx in the Results of ARDS section? patients With [34, the 35]. paragraph of Philippe ? xx The mathematicaland model thusThe actof the normalin RASantagonism range system of PaO2/FiO2 described with!AngII inThe Eqsis. between⇥ Examples mathematical (1)-(11) 400 is and are a system model 500 candesartan, mmHg. of the Mild RAS losartan and system moderate described ARDS in Eqs (1)-(11) is a system of c c (1 ) c c (1 ) Ct ace !ordinaryace ⇥ di↵erential ACE-Iand equations valsartan.are (ODEs), characterized which areC byt PaO2/FiO2 linearat1r except!ordinaryvalues forat1r the di⇥ in↵ feedbackerential the rangeARB equations loop [200–300] of Eq. (ODEs), mmHg which and [100-200] are linear mmHg, except for the feedback loop of Eq. c (11).c We collected(1 from) therespectively. literature the ARDS values is of severe thec equilibrium for(11). valuesc We below collected concentrations(1 100 mmHg.from) the of all literature the values of the equilibrium concentrations(13) of all at1r at1r DirectARB renin inhibitors (DRI) thatre act onre renin andDRI thus inihibit the conver- proteins! and⇥ peptides for both normotensiveWe predictedthe andPaO2/FiO2 hypertensiveproteins!ratio humans and as⇥ a functionpeptides (Table 1), of for the except bothAngII normotensiveand Ang1-7 concentrations: and hypertensive humans (Table 1), except c c (1• ) (13) reninre ! andreMAS⇥ boundsionDRI to ofAng1-7AGT.to FromAngI these. Examples values, we are fixedrenin(c) aliskiren, the andparametersMAS enalkirenbound that to appear andAng1-7 remikiren. in. From these values, we fixed the parameters that appear in the phenomenological relations (12) and (15)where for DBPACE-I and PaO2/FiO2the, ARB phenomenologicaland (2).DRI [areAngII relations parameters] [Ang1-7 (12) describingand] (15) for the DBP drug and activity. PaO2/FiO2 (2). We modeled the action of thesePaO2/FiO2 three types= A of0 + drugsA1 by modifying+ the reaction (15) We also got the values of the half-life of all proteins and peptidesWe also but gotMAS the; we[AngII values assumed] of the the[Ang1-7 half-life] of all proteins and peptides but MAS; we assumed the where ACE-I, ARB and DRI are parameters describing the drug activity. ✓ 0 0 ◆ latter to berates equal associated to that of the to other their membrane targets receptors as: (Tablelatter 2). to beMoreover, equal to we that estimated of the other membrane receptors (Table 2). Moreover, we estimated where A0 and AModeling1 are two CoViD-19 parameters infection that we identified from our model by comparing the the value of reaction rate cre frombaseline [32, RAS 33]. with the same systemthe value in of which reactionACE2 rateis knockedcre from out. [32, 33].In the former we fixed Since ACE2 is the entry point of SARS-CoV-2 [19], it is downregulated upon in- Modeling CoViD-19Using infection these concentration andPaO2/FiO2 parameter= values,450 mmHg we solved and in theUsing the system latter these ofPaO2/FiO2 concentration 9 ODEs (1)-(11)= 50and mmHg. parameter values, we solved the system of 9 ODEs (1)-(11) Since ACE2 atis the stationary entry point state of SARS-CoV-2to identify the [ unknown19],fection, it is parameters downregulated and thisat and the impacts concentrations. stationary upon substantially in- state However, to identify on the the local unknown and systemic parameters RAS and systems. concentrations. In However, fection, andthese this impactsequations substantially havecace 12 unknowns:c onSolvingace thek(1 localagt the, 0 and,ACE-I RASordercace systemic, c modelto)ace2 model, cangIV RASthese the, systems.cat equations downregulation1r, cat2r In, havecmas, 12cchy unknowns: e↵, ectcnep, duek toagt the, 0, virus,cace, cace we2, modifiedcangIV , cat the1r, cACE2at2r, cmas, cchy, cnep, [RE] and [MAS-Ang1-7]. We! had thus⇥ to assume three additional[RE] and relations [MAS-Ang1-7 to be able]. We to solve had thus to assume three additional relations to be able to solve rate with the function as: order to modelthe system. the downregulation These are:cat1r e↵ectxxcat should due1r to(1 this the part virus,ARB not) we be modifiedin thethe Results system. the CoV section?ACE2 These are: With the paragraph of Philippe ? xx rate with the function as: ! The⇥ mathematical model of the RAS system described in Eqs (1)-(11) is a system of CoV c c (1 ) (13) Ct re ordinaryre c dimas↵erential= DRIcat equations2r (ODEs), which are linear except(16) for thecmas feedback= cat loop2r of Eq. (16) ! ⇥ cace2 cace2 (1 CoV(Ct)) (14) (11). Wecchy collected= 0 from the! literature⇥ the values of the(17) equilibriumcchy concentrations= 0 of all (17) cace2 cace2 (1 CoV(Ct)) proteins and peptides for both normotensive(14) and hypertensive humans (Table 1), except ! ⇥where ACE-I, ARB andcnepDRI=are 0 parameters describing the drug(18) activity.cnep = 0 (18) Figure 4. Impact of different RAS-blockingrenin drugs and MAS inbound normotensiveThis function to Ang1-7 depends. From (blue) these onthe values, and virus we hypertensive fixedcycle the threshold parameters value (red) thatC appeart,whichisinversely in the phenomenological relations (12) and (15) for DBP and PaO2/FiO2 (2). This functionSince depends no quantitative on the virus data cyclerelated threshold to the MASrelated valuereceptorC tot,whichisinversely the canSince viral be found no load quantitative in [69 the]. literature, data related we to the MAS receptor can be found in the literature, we We also got the values of the half-life of all proteins and peptides but MAS; we assumed the SARS-CoV-2 patients.related Predicted to thehypothesized viral loadPModelingA the [O2/F69]. first relationCoViD-19IO2 assumingvalue infection asMAS aand functionAT2R to be equallyhypothesized of the expressed cycle the andfirst threshold the relation anity assuming valueMAS Candt andAT2R to be equally expressed and the anity of Ang1-7 for MAS to be similarlatter to the toa benity equal of toAngII that offor theAT2Rof otherAng1-7[46]. membrane Moreover,for MAS receptorsto we be assumed similar (Table to the 2). a Moreover,nity of AngII we estimatedfor AT2R [46]. Moreover, we assumed Since ACE2 is the entry point of SARS-CoV-2 [19], it is downregulated upon in- (a) γACE I,(b) γARB, and (c) γ=DRI 0 andfunctionsc = 0, but thatthe carefullymodel value of discussed reaction theMonitoring administration the rate e↵creect thefromc ofacute non-vanishing [32,= respiratory0 33]. and ofc the values distress= corresponding 0, butin syndrome the carefully and discussed its drugs. severity the e↵ect of non-vanishing values in the − chy nep chy nep Monitoring theDiscussion acute respiratoryfection, section. and distress this syndrome impactsUsing these and substantiallyTo itsconcentration modelseverity how on andDiscussion the the parameter lungs local ofsection. and the values, infected systemic we solved patients RAS the systemsystems. evolve ofin 9 response In ODEs (1)-(11) to the modulation at the stationary state to identify the unknown parameters and concentrations. However, Table 5. PredictedTo effects model onhowAngII theImposing lungsorderand of these the toAng1-7 three infected model additional patients thelevels, downregulation relations, evolvePAO2/Fof in we responsethe solved RASI eO2↵ theect to system, system, the and dueImposing modulation ofto DBP we 9 the ODEs introduced these upon virus, (1)-(11) three we drug additional a at modified phenomenological the administration relations, the ACE2 we solvedrelation the to system estimate of 9 ODEs the (1)-(11) at the stationary state. The values obtainedthese equations for [RE]have and [MAS-Ang1-7 12 unknowns:stationary], kkagtagt, , state.0,0,cacecace The, ,cacecace values2,2,cangIVcangIV obtained, , cat1r for, cat [RE2r], andcmas [,MAS-Ang1-7cchy, cnep, ], kagt, 0, cace, cace2, cangIV , of the RAS system, werate introduced with the functiona phenomenological[RE] and [MAS-Ang1-7as:PaO2/FiO2 relation]. We had toratio, thus estimate to defined assume the as three the additional ratio between relations the to partial be able topressure solve of arterial to normotensive and hypertensivecat1r and cat2r for COVID-19 normotensive andpatients. hypertensiveCoV The humans drug arecat given1r administrationsand incat Table2r for 1. normotensive are and modeled hypertensive humans by are given in Table 1. PaO2/FiO2 ratio, defined as the ratiothe between system. These theoxygen partial are: (PaO2) pressure and of the arterial fraction of inspired oxygen. This quantity plays a key role γACE I, γARB, γDRI,oxygenγrhACE (PaO2)2 = and0.5 the, fraction and η ofAng inspired17 = oxygen.25 Thisfmol/(mLin the quantity assessment plays min) of a key ARDS and role patients moderate [34, 35]. SARS-CoV-2 The normal range of PaO2/FiO2 − cace2 cace2 (1 CoV(Ct)) cmas = cat2r (14) (16) in the assessment of ARDS patients! [34, 35].⇥ The normalis between range 400 of andPaO2/FiO2 500 mmHg. Mild and moderate ARDS are characterized by infection by γCoV = 27.6. cchy = 0 (17) is between 400 and 500 mmHg. Mild and moderatePaO2/FiO2 ARDS are characterizedvalues in the range by [200–300] mmHg and [100-200] mmHg, respectively. cnep = 0 (18) This function depends on the7 virus cycle threshold value Ct,whichisinversely7 DrugsPaO2/FiO2 values in the No range Drugs [200–300] ACE-ImmHg andARDS [100-200] ARB is mmHg,severe DRI for respectively. values rhACE2 below 100 mmHg. Ang1–7 ARDS is severe for valuesrelated below to 100themmHg. viralSince load no quantitative [69]. We predicted data related the toPaO2/FiO2 the MAS receptorratio can as a be function found in of the the literature,AngII and we Ang1-7 con- MAS AT2R We predicted the PaO2/FiO2 ratio ashypothesized a function the of firstthe AngII relationand assumingAng1-7 con-and to be equally expressed and the anity Normotensive—Moderateof Ang1-7 for MAScentrations:to Infection be similar to the anity of AngII for AT2R [46]. Moreover, we assumed centrations: Monitoring the acutecchy = respiratory 0 and cnep distress= 0, but syndrome carefully anddiscussed its severity the e↵ect of non-vanishing values in the [AngII]/[AngII]0 To model1.29 how theDiscussion lungs 1.10 of section. the infected 1.98 patients 0.99 evolve 1.10 in response[AngII to 1.29] the modulation[Ang1-7] [AngII] [Ang1-7] PaO2/FiO2 = A0 + A1 + (15) Imposing these three additional relations, we solved[AngII the system]0 [ ofAng1-7 9 ODEs]0 (1)-(11) at the [Ang1-7]/[Ang1-7PaO2/FiO2]0 =ofA0 the+0.15A RAS1 system, 0.13+ we introduced 0.23 a phenomenological 0.11(15) 0.68✓ relation 0.64to estimate the◆ [AngIIstationary] [Ang1-7 state.] The values obtained for [RE] and [MAS-Ang1-7], kagt, 0, cace, cace2, cangIV , ✓ 0 0 ◆ PAO2/FIO2 (mmHg)PaO2/FiO2 145ratio,cat1r 188and definedcat2r for as normotensive the 0 ratio 216 between and hypertensive the 337 partial humans pressure are 278 given of in Tablearterial 1. where A0 and A1 are two parameters that we identified from our model by com- DBPwhere (mmHg)A0 and A1 areoxygen two parameters 82 (PaO2) thatand thewe 81 identified fractionparing 80 fromof theinspired our baseline model80 oxygen. RAS by com- withThis 81 the quantity same system plays 82 a in key which roleACE2 is knocked out. In paring the baseline RASin the with assessment the same system of ARDS in which patientsACE2 [is34 knocked, 35]. The out. normal In range of PaO2/FiO2 Hypertensive—Moderatethe former Infection we fixed PaO2/FiO2= 450 mmHg and in the latter PaO2/FiO2= 50 the former we fixed PaO2/FiO2is between= 400 450 and mmHg 500 and mmHg. inmmHg. thelatter MildPaO2/FiO2 and moderate= 50 ARDS are characterized by mmHg. [AngII]/[AngII]0 PaO2/FiO21.42values1.12 in the range 1.55 [200–300] 0.77 mmHg and 1.14 [100-200]7 mmHg, 1.42 respectively. ARDS is severe for values below 100 mmHg. [Ang1-7]/[Ang1-7]0 0.16 0.13 0.18 0.09 0.70 0.36 PAO2/FIO2 (mmHg)We 115predicted the PaO2/FiO2 185 83ratio as 268 a function of332 the AngII and 167Ang1-7 con- DBP (mmHg)centrations: 125 114 101 102 115 125 [AngII] [Ang1-7] PaO2/FiO2 = A + A + (15) 0 1 [AngII] [Ang1-7] ✓ 0 0 ◆

where A0 and A1 are two parameters that we identified from our model by com- paring the baseline RAS with the same system in which ACE2 is knocked out. In the former we fixed PaO2/FiO2= 450 mmHg and in the latter PaO2/FiO2= 50 mmHg. Viruses 2020, 12, 1367 14 of 20

Other RAS-targeting drugs: We used our model to test the potential of other drugs that are • currently in clinical trials to restore the functional activity of the perturbed RAS upon viral infection. First, we modeled how the administration of an exogenous supplement of rhACE2 (GSK2586881) affects RAS by modifying the reaction rate cace2 defined in Equation (13). This rate already includes the function γCoV that mimics SARS-CoV-2 infection, and we simply added a second function γrhACE2 associated with the effects of rhACE2 administration:

c c (1 + γrhACE2 γCoV(C )) (21) ace2 −→ ace2 × − t

Our model predicts an increase in PAO2/FIO2 following the administration of exogenous rhACE2, thus predicting an alleviation of disease severity, as shown in Figure5 and Table5. Specifically, PAO2/FIO2 is predicted to increase by approximately 200 mmHg when γrhACE2 is fixed at 0.5. Our model also predicts, as expected, a reduction in AngII level and an increase in Ang1-7 level. These predictions are in agreement with both animal and in vitro studies [18,22], whereby rhACE2 is observed to alleviate virus-related ARDS severity through a double action. First, by rhACE2 binding to the virus spike protein, interaction with endogenous ACE2 is prevented, and infection is slowed down. Second, rhACE2 administration increases ACE2 activity, thus causing a reduction in AngII level and an increase in Ang1-7 level; this protects lung against severe failure. Current clinical trial data concerning the administration of different doses of rhACE2 (0.1, 0.2, 0.4, and 0.8 mg/kg) to SARS-CoV patients at different time intervals (2, 4, and 18 h) are only in partial agreement with our model predictions [20]. Specifically, while clinical data followed the predicted decrease in [AngII] and the predicted increase in [Ang1-7], there was no sustained increase in PAO2/FIO2 compared with placebo. It has been suggested that the drug concentrations used in these clinical trials were too low to have a measurable effect on the respiratory system and that drug administration via infusion would have been more sustained [20]. More experimental and clinical data are clearly needed to further investigate the effect of rhACE2 on coronavirus-related ARDS. Another method of boosting the second RAS axis, ACE2/Ang1-7/MAS, which is downregulated by SARS-CoV-2 infection, is to administer Ang1-7 peptides as a means of triggering anti-inflammatory and anti-fibrotic mechanisms. We modeled Ang1-7 peptide administration by introducing a new parameter, the production rate ηAng17, to the dynamical Equation (8) of [Ang1-7]; this allows the model to describe the exogenous Ang1-7 level, which is added to the endogenous Ang1-7 baseline. As shown in Figure5b and Table5, our model predicts a clear alleviation of COVID-19 severity, with PAO2/FIO2 increasing by 50 and 130 mmHg for hypertensive and normotensive patients, respectively, upon administration of ηAng17 = 25 fmol/(mL min) Ang1-7 in infusion. Note that COVID-19 alleviation is significantly stronger in normotensive compared to hypertensive patients for the same drug concentrations; a slightly stronger concentration of Ang1-7 must be administered to hypertensive patients for an equivalent effect. Our model predicts a quantitative reduction in ARDS severity in COVID-19 patients, in agreement with the known anti-inflammation and anti-fibrosis nature of Ang1-7. Model predictions nicely agree with data from animal studies without the need for any additional fitting. For example, administration of Ang1-7 by infusion to acid-injured rats suffering from ARDS increases the baseline Ang1-7 level by a factor 2.5, leading to an increase in PAO2/FIO2 of approximately 70 mmHg [77]. However, while the PAO2/FIO2 value increases linearly in our model as a function of Ang1-7 concentration, it reaches a plateau in rats; this suggests that our model is probably oversimplified, since PAO2/FIO2 is not a linear function of Ang1-7 concentration. Further work on this aspect of our model will be possible when more data become available. Monitoring the acute respiratory distress syndromeMonitoring and its severity the acute respiratory distress syndrome and its severity ToCurrent model how the clinical lungs of trial the infected data concerningpatients evolve inthe responseTo administration model to howthe modulation the lungs of of di of the↵ theerent infected doses patients of evolverhACE2 in response to the modulation of the (0.1,RAS system, 0.2, 0.4 we andintroduced 0.8 mg/kg)a phenomenological to SARS-CoV relation toRAS estimate infected system, the patientsPaO2/FiO2 we introduced atratio, a di phenomenological↵erent time relationintervals to estimate the PaO2/FiO2 ratio, defined as the ratio between the partial pressure of arterial oxygendefined (PaO2) as the andratio the between fraction the of partial pressure of arterial oxygen (PaO2) and the fraction of (2,inspired 4, and oxygen. 18 Thish), arequantity only plays in a partial key role in agreement the assessmentinspired with of ARDSoxygen. our patients This model quantity [34, predictions 35]. plays a key role [20]. in the Specif- assessment of ARDS patients [34, 35]. Viruses 2020, 12, 1367 ically,The normal while range clinical of PaO2/FiO2 datais followed between 400 the and predicted 500 mmHg.The normal Mild decrease and range moderate of inPaO2/FiO2 [AngII ARDS15 of]is 20 and between the 400 predicted and 500 mmHg. Mild and moderate ARDS are characterized by PaO2/FiO2 values in the range [200–300]are mmHgcharacterized and [100-200] by PaO2/FiO2 mmHg, values in the range [200–300] mmHg and [100-200] mmHg, increaserespectively. in ARDS [Ang1-7 is severe], there for values was below no 100 sustained mmHg. respectively. increase ARDS in PaO2/FiO2 is severe for valuescompared below 100 mmHg. with We predicted the PaO2/FiO2 ratio as a function of the AngII and Ang1-7 concentrations: placebo.We predicted It has the PaO2/FiO2 been suggested(a) ratio as a that function the of the drugAngII concentrationsand Ang1-7 concentrations: used(b) in these clinical tri- Pucci et al. als were too low to have a measurable e↵ect on the respiratoryPage 15 of 20 system and that[AngII drug] [Ang1-7] [AngII] [Ang1-7] PaO2/FiO2 = A0 + A1 + (15) PaO2/FiO2 = A0 + A1 + (15) [AngII] [Ang1-7] [AngII] [Ang1-7] ✓ 0 0 ◆ administration via infusion would✓ have0 been more0 ◆ sustained [20]. More experimen- where A0 and A1 are two parameters that we identified from our model by comparing the talwhere andA0 clinicaland A1 are data two parameters are clearly that we needed identified to from further our model investigate by comparing the the e↵ect of rhACE2 on baseline RAS with the same system in which ACE2 is knocked out. In the former we fixed coronavirus-relatedbaseline RAS with the same ARDS. system in which ACE2 is knocked out. In the former we fixed rhACE2 (GSK2586881).PaO2/FiO2 We= 450 modeled mmHg andits e in↵ect the on latter thePaO2/FiO2 RAS system= 50PaO2/FiO2 by mmHg. modifying= 450 mmHg and in the latter PaO2/FiO2= 50 mmHg. the c coecientAnother defined inmethod Eq. (14) of which boosting already the mimics second the SARS-CoV-2 RAS axis, ACE2/Ang1-7/MAS, which is ace2 Solving the RAS model infection, as:downregulatedSolving the RAS by model SARS-CoV-2 infection, is to administer Ang1-7 peptides as a means ofxx triggering should this part anti-inflammatory not be in the Results section? and With anti-fibrotic thexx paragraph should mechanisms.this of Philippe part not ? be xx in the We Results modeled section?Ang1-7 With the paragraph of Philippe ? xx The mathematical model of the RAS system described inThe Eqs mathematical (1)-(11) is a system model of the RAS system described in Eqs (1)-(11) is a system of cace2 peptidecace2 administration(1 + by(C introducingt)) a new parameter,(20) ⌘ , to dynamical equation Ct !ordinary⇥ di↵erentialrhACE2 equations CoV (ODEs), which areCt linear exceptordinary for the di↵ feedbackerentialAng equations loop17 of Eq. (ODEs), which are linear except for the feedback loop of Eq. (9)(11). of We [Ang1-7 collected]; from this the allowed literature the the model values of to the describe equilibrium(11). We the collected concentrations exogenous from the of allAng1-7 literature thelevel, values which of the is equilibrium concentrations of all proteins and peptides for both normotensive and hypertensive humans (Table 1), except FigureWe thus 5. Impact introducedproteins on the aP andA newO2/F peptides gammaIO2 value for function both of the normotensive administrationrhACE2 associated and of hypertensiverhACE2 toand the humansAng1-7 e↵ect ofin (Table normotensive 1), except added to the endogenous Ang1-7 baseline. Asrenin shown and MAS inbound Fig. to 5.bAng1-7 and. From Table these 5, values, our model we fixed the parameters that appear in rhACE2 administration.renin and MAS bound to Ang1-7. From these values, we fixed the parameters that appear in (blue) and hypertensivepredicts (red) a clear SARS-CoV-2 alleviation patients. of (a) COVID-19 Predicted PAO2/F severity,theIO2 phenomenologicalvalues with asPaO2/FiO2 a function relations of (12) andincreasing (15) for DBP by and 50 PaO2/FiO2 (2). The predictionsthe phenomenological of our model are relations shown (12) in Fig. and (15)5 and for Table DBP and5. We PaO2/FiO2 observe (2). an Ct and γrhACE2.(andb) PredictedWe 130 also mmHg got P theAO2/F values forIO2 hypertensiveof thevalues half-life as a of function all and proteins of normotensiveC andt and peptidesηAngWe17 also but. patients, gotMAS the; we values assumed respectively, of the the half-life of uponall proteins admin- and peptides but MAS; we assumed the increase of thelatterPaO2/FiO2 to be equalvalue to that upon of the intake other membrane of exogenous receptorsrhACE2 (Tablelatter, and 2). to bethusMoreover, equal a to we that estimated of the other membrane receptors (Table 2). Moreover, we estimated 4. Discussion istration of ⌘Ang17 =25 fmol/(ml min) Ang1-7the valuein of infusion. reaction rate Notecre from that [32, 33]. the COVID-19 weakening ofthe value disease of reaction severity. rate Thecre increasefrom [32, 33].of PaO2/FiO2 is of about 200 alleviationUsing these is concentration significantly and parameter stronger values, in normotensive we solved theUsing system these compared of concentration 9 ODEs (1)-(11) to and hypertensive parameter values, patients we solved the system of 9 ODEs (1)-(11) ThemmHg spike when proteinrhACE2 ofvaries SARS-CoV-2 in the interval interferes [0-0.5]. with We also RAS observe by binding a reduction to of the ACE2 receptor, at the stationary state to identify the unknown parametersat and the concentrations. stationary state However, to identify the unknown parameters and concentrations. However, the AngII levelfor and the an same increase drug of the concentrations;Ang1-7 level. a slightlythese stronger equations concentration have 12 unknowns: ofkagtAng1-7, 0, cace,mustcace2, c beangIV , cat1r, cat2r, cmas, cchy, cnep, a key element of RAS.these Despite equations recent have 12 progress unknowns: inkagt understanding, 0, cace, cace2, c COVID-perturbedangIV , cat1r, cat2r, cmas RAS, cchy, andcnep, how its functionalityThese predictions canadministered[RE] be and are restored, [MAS-Ang1-7 in agreement to more]. hypertensive Wework hadwith thus animal is to urgently assume patients and threein needed vitro for additionalstudies an[RE in equivalent] theand relations [18 [ contextMAS-Ang1-7, 27 to], be e of↵ able].ect. the We to solve had current thus to assume three additional relations to be able to solve the system. These are: COVID-19where pandemic.rhACE2 theadministrationOur system. model These has are: predicts led to an a improvement quantitative of the reduction disease condition in ARDS severity in COVID-19 pa- through a double action. First, its binding to the S-protein of the virus xx S or S1 ? cmas = cat2r (16) We here presenttients, a simple in agreement computational with approach thecmas known= to modelingcat2r anti-inflammation RAS evolution in and the(16) context anti-fibrosis nature of xx prevents interaction with endogenous ACE2 and slows down the viral infection. cchy = 0 (17) of SARS-CoV-2 infection.Ang1-7. Inspired Model by predictions a number ofc nicelychy existing= agree0 RAS models with data [35–38 from], we animal searched(17) studies the without the Second, rhACE2 administration increases the ACE2 activity, thus causing a reduction cnep = 0 (18) literature for measuredneed half-lives of any additional and concentrations fitting.cnep of angiotensin= For 0 example, peptides administration and their receptors(18) of Ang1-7 in by infusion of the AngII level and an increase of the Ang1-7 level, which results in the protection healthy normotensiveto acid-injured and hypertensive rats individuals su↵ering and from then ARDS identified increasesSince the no unknown quantitative baseline production dataAng1-7 related and tolevel the MAS byreceptor a factor can be found in the literature, we of the lung fromSince severe no quantitative failure. data related to the MAS receptor can be found in the literature, we reaction rate parametershypothesized from the the first model. relation As assuming an initialMAS test,and AT2R weto compared be equallyhypothesizedour expressed model the andfirst predictions the relation anity assuming of MAS and AT2R to be equally expressed and the anity However, our2.5, predictions leading and to the an data increase described in PaO2/FiO2 above do not agreeof withAng1-7 approximately clinicalMAS 70 mmHg [77]. However,AngII AT2R how the administrationof Ang1-7 of RAS-blockingfor MAS to be similar drugs to the would anity affect of AngIIAngforpeptideAT2Rof [46]. concentrations Moreover,for to we be assumed similar and blood to the anity of for [46]. Moreover, we assumed while the PaO2/FiO2 value increases linearlycchy = in 0 and ourcnep model= 0, but as carefullya function discussed of theAng1-7 e↵ect of non-vanishing values in the trials clinical datacchy = [20 0] and regardingcnep = the 0, but administration carefully discussed of rhACE2 the e↵atect di of↵erent non-vanishing doses values in the pressure with relevant experimental data; we found good quantitativeDiscussion agreement section. between our model (0.1 mg/kg, 0.2concentration,Discussion mg/kg, section. 0.4 mg/kg it reaches and 0.8 mg/kg) a plateau and intervals in rats; (2, this4, and suggests 18 h) that our model is probably and experimental data, without the need for further parameter fitting.Imposing We then these modeled three additional the effect relations, we solved the system of 9 ODEs (1)-(11) at the Imposing these three additional relations,ANGII we solved the system ofANG1-7 9 ODEs (1)-(11) at the to CoViD patientsoversimplified, are less positive. since A dropPaO2/FiO2 in [ ] andis an not increase astationary linear in [ state. function] The values of Ang1-7 obtained forconcentration. [RE] and [MAS-Ang1-7], kagt, 0, cace, cace2, cangIV , of SARS-CoV-2 infectionstationary on state. RAS The through values obtained the downregulation for [RE] and [MAS-Ang1-7 of ACE2], ,kagt which, 0, cace we, cace related2, cangIV to, the was found, similar to what we found, but no sustained increase of PaO2/FiO2cat1r and catwas2r for normotensive and hypertensive humans are given in Table 1. SARS-CoV-2 viralFurther load.cat1r and c workat2r for normotensive on this aspect and hypertensive of our humans model are will given be in Table possible 1. when more data become observed for these patients compared with placebo, in contrast with what happens A focal pointavailable. of our work was to investigate how a series of RAS-targeting drugs affected from animal model, . However there is the possibility that drug concentrations COVID-19 patients. We found that the administration of two antihypertensive drugs, ACE-I and DRI, were not enough substained and that maybe only trough its continuous infusions tended to reduce the severity of COVID-19, while ARB drugs worsened it. Clinical data generally could reach a more e↵ective result [20]. More experimental data are needed to support the model’s4 predictions Conclusion for the administration and of Perspectives ACE-I drugs, but they are either absent or 7 further investigate e↵ect of rhACE2 on ARDS and preturbed7 RAS system due to partially contradict the model’s prediction for DRI and ARB administration. Additionally, we modeled the SARS-CoV-2 infection. a potential treatmentThe that spike is currently protein under of SARS-CoV-2 clinical trial in interferes COVID-19 with patients: the administration RAS system of by binding to the Another way to maintain an high level of the ACE2/Ang1-7/MasR negative rhACE2 or Ang1-7ACE2by drugreceptor, infusion. a Our key model element predicts of improved RAS. Despite clinical recent outcomes progress in these incases, understanding the counter-regulation in CoViD is to administrate the Ang1-7 peptide to trigger anti- in agreement with a series of experimental data on animal models. inflammatoryCOVID-perturbed and antifibrotic mechanisms. RAS In system our computation and how we its model functionality this ad- can be restored, more work Itministration is important introducingis to urgently note that, the needed despite parameter its in simplicity,⌘ thedescribing context our the model endogenously of the has current excellentAng1-7 COVID-19 accuracyquan- in reproducing pandemic. clinicaltity and that experimental is addedWe to data the here normalon present the perturbed baseline a simple quantity. RAS. computationalFurthermore, We can see the the resultsmodel’s approach of predictions the to modeling of changes RAS system evolu- in COVID-19computation severitytion in Fig due in5.b the to and drug context in Table administration XXX of SARS-CoV-2 where are a clear blind improvement infection. predictions, of Inspired without the dis- the by fitting a number of any of existing RAS additionalease severity parameters.models is observed [41, with 42, an increase 52, 59],PaO2/FiO2 we searchedbetween the 70 andliterature 140 mmHg for measured half-lives and con- Manyfor hypertensive challengescentrations andremain normotensve in ourof angiotensin current patients understanding respectivley peptides andof RASand forperturbation administration their receptors in in COVID-19 in healthy patients. normotensive and Importantly, more data regarding angiotensin peptide concentrations upon SARS-CoV-2 infection infusion of 25hypertensive fmol/ml that means individuals, almost doubling and the then control identified values Ang1-7 the0 Note unknown production and reaction are urgently needed, since currently available data are often inconsistent or conflicting so that that the improvementrate parameters is significantly from more the pronounced model. in As normotensive an initial patients test of our model, we compared its reliablethan comparisons in hypertensive between ones for model equal predictions drug concentrations and experimental and to reach data the cannot same ef- be made. Even in healthyfects individuals, the Ang1-7 angiotensinconcentration peptide administrated levels can to hypertensive vary substantially patients due have to to their be low circulating concentrations,slightly increased. the experimental techniques used to measure them,18 and inter-patient variability. WhenIn agreement developing with our clinical model, data we on humanchose not ARDS to andconsider with the two anti-inflammation enzymes that are active in RAS throughand the anti-fibrosis cancellation nature of their of Ang1-7 reaction, our rates: resultsCHY predictand quantitativelyNEP (see Equations an improvenent (17)–(18)). The CHY enzyme is expressed in mast cells present in interstitial lung connective tissues, and it cleaves AngI to form Viruses 2020, 12, 1367 16 of 20

AngII. The addition of this enzymatic reaction in the model would not really influence the predictions since it would essentially be a reparametrization of ACE activity and of ACE-I action. It might, nevertheless, be interesting to add the CHY enzymatic reaction, which yields ACE-independent synthesis of AngII and has been suggested (although debated) to be upregulated in the case of long-term ACE-I administration [78]; this would enable an explanation of why ACE-I fails to inhibit AngII formation after some time [78,79]. The NEP enzyme is expressed in a wide range of tissues, being particularly abundant in kidney, and it cleaves AngI to form Ang1-7. It influences the counterregulatory RAS axis through its connection to Ang1-7 levels, thus affecting COVID-19 severity. However, NEP’s role is far from clear, and the literature contains contradictory findings. Experimental data from rats with ARDS suggest that NEP is severely downregulated in both plasma and lung tissues [80]. Note that NEP also cleaves natriuretic peptides, which have both anti-inflammatory and anti-fibrotic effects [81]. Therefore, the combined administration of NEP-inhibiting and ARB drugs has been suggested to treat SARS-CoV-2 patients [82]. Our future work will include building more complexity into our model by explicitly considering the communication between local and systemic RASs [33,34] and by including the interaction between RAS and the immune system [83]. This model extension is necessary for an improved quantitative understanding of RAS dysregulation upon a variety of perturbations, including SARS-CoV-2 infection. Our model and its predictions provide a valuable and robust framework for in silico testing of hypotheses regarding COVID-19’s pathogenic mechanisms and the effect of drugs that are aimed at restoring RAS functionality. Our work also opens a broader discussion on the role of the full RAS in COVID-19, a topic that has received little attention to date, perhaps due to the current focus on the ACE2 enzyme, which, although very important as it is directly targeted by the virus, constitutes only one part of a much more complex system.

Author Contributions: Conceptualization, F.P., P.B. and M.R.; formal analysis and investigation F.P. and M.R.; methodology and validation, F.P., P.B. and M.R.; writing–original draft preparation, F.P. and M.R.; writing–review and editing F.P., P.B. and M.R. All authors have read and agreed to the published version of the manuscript. Funding: This work is funded by the F.R.S.-FNRS Fund for Scientific Research through a COVID—Exceptional Research Project. Acknowledgments: We thank Filippo Annoni and Fabio Taccone for enlightening discussions. F.P. and M.R. are Scientific Collaborator and Research Director, respectively, at the F.R.S.-FNRS Fund for Scientific Research. Conflicts of Interest: The authors declare that they have no conflict of interest. Data availability: The code used to generate all the results of this paper is freely available on the GitHub repository (https://github.com/3BioCompBio/RASinCOVID).

References

1. Dong, E.; Du, H.; Gardner, L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect. Dis. 2020, 20, 533–534. [CrossRef] 2. Chan, J.F.W.; Yuan, S.; Kok, K.H.; To, K.K.W.; Chu, H.; Yang, J.; Xing, F.; Liu, J.; Yip, C.C.Y.; Poon, R.W.S.; et al. A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: A study of a family cluster. Lancet 2020, 395, 514–523. [CrossRef] 3. Huang, C.; Wang, Y.; Li, X.; Ren, L.; Zhao, J.; Hu, Y.; Zhang, L.; Fan, G.; Xu, J.; Gu, X.; et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 2020, 395, 497–506. [CrossRef] 4. Chen, N.; Zhou, M.; Dong, X.; Qu, J.; Gong, F.; Han, Y.; Qiu, Y.; Wang, J.; Liu, Y.; Wei, Y.; et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: A descriptive study. Lancet 2020, 395, 507–513. [CrossRef] 5. Wu, F.; Zhao, S.; Yu, B.; Chen, Y.M.; Wang, W.; Song, Z.G.; Hu, Y.; Tao, Z.W.; Tian, J.H.; Pei, Y.Y.; et al. A new coronavirus associated with human respiratory disease in China. Nature 2020, 579, 265–269. [CrossRef] 6. Andersen, K.G.; Rambaut, A.; Lipkin, W.I.; Holmes, E.C.; Garry, R.F. The proximal origin of SARS-CoV-2. Nat. Med. 2020, 26, 450–452. [CrossRef] Viruses 2020, 12, 1367 17 of 20

7. Benvenuto, D.; Giovanetti, M.; Ciccozzi, A.; Spoto, S.; Angeletti, S.; Ciccozzi, M. The 2019-new coronavirus epidemic: Evidence for virus evolution. J. Med. Virol. 2020, 92, 455–459. [CrossRef] 8. Zhang, T.; Wu, Q.; Zhang, Z. Probable pangolin origin of SARS-CoV-2 associated with the COVID-19 outbreak. Curr. Biol. 2020, 30, 1346–1351.e2 [CrossRef] 9. Zhou, P.; Yang, X.L.; Wang, X.G.; Hu, B.; Zhang, L.; Zhang, W.; Si, H.R.; Zhu, Y.; Li, B.; Huang, C.L.; et al. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature 2020, 579, 270–273. [CrossRef] 10. Hoffmann, M.; Kleine-Weber, H.; Schroeder, S.; Krüger, N.; Herrler, T.; Erichsen, S.; Schiergens, T.S.; Herrler, G.; Wu, N.H.; Nitsche, A.; et al. SARS-CoV-2 cell entry depends on ACE2 and TMPRSS2 and is blocked by a clinically proven protease inhibitor. Cell 2020, 181, 271–280.e8 [CrossRef] 11. Zhang, H.; Penninger, J.M.; Li, Y.; Zhong, N.; Slutsky, A.S. Angiotensin-converting enzyme 2 (ACE2) as a SARS-CoV-2 receptor: molecular mechanisms and potential therapeutic target. Intensive Care Med. 2020, 46, 586–590. [CrossRef][PubMed] 12. Cao, Y.; Li, L.; Feng, Z.; Wan, S.; Huang, P.; Sun, X.; Wen, F.; Huang, X.; Ning, G.; Wang, W. Comparative genetic analysis of the novel coronavirus (2019-nCoV/SARS-CoV-2) receptor ACE2 in different populations. Cell Discov. 2020, 6, 1–4. [CrossRef][PubMed] 13. Lu, R.; Zhao, X.; Li, J.; Niu, P.; Yang, B.; Wu, H.; Wang, W.; Song, H.; Huang, B.; Zhu, N.; et al. Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding. Lancet 2020, 395, 565–574. [CrossRef] 14. Burrell, L.M.; Johnston, C.I.; Tikellis, C.; Cooper, M.E. ACE2, a new regulator of the renin–angiotensin system. Trends Endocrinol. Metab. 2004, 15, 166–169. [CrossRef] 15. Donoghue, M.; Hsieh, F.; Baronas, E.; Godbout, K.; Gosselin, M.; Stagliano, N.; Donovan, M.; Woolf, B.; Robison, K.; Jeyaseelan, R.; et al. A novel angiotensin-converting enzyme–related carboxypeptidase (ACE2) converts angiotensin I to angiotensin 1-9. Circ. Res. 2000, 87, e1–e9. [CrossRef] 16. Tipnis, S.R.; Hooper, N.M.; Hyde, R.; Karran, E.; Christie, G.; Turner, A.J. A human homolog of angiotensin- converting enzyme cloning and functional expression as a captopril-insensitive carboxypeptidase. J. Biol. Chem. 2000, 275, 33238–33243. [CrossRef] 17. Imai, Y.; Kuba, K.; Rao, S.; Huan, Y.; Guo, F.; Guan, B.; Yang, P.; Sarao, R.; Wada, T.; Leong-Poi, H.; et al. Angiotensin-converting enzyme 2 protects from severe acute lung failure. Nature 2005, 436, 112–116. [CrossRef] 18. Kuba, K.; Imai, Y.; Rao, S.; Gao, H.; Guo, F.; Guan, B.; Huan, Y.; Yang, P.; Zhang, Y.; Deng, W.; et al. A crucial role of angiotensin converting enzyme 2 (ACE2) in SARS coronavirus–induced lung injury. Nat. Med. 2005, 11, 875–879. [CrossRef] 19. Li, W.; Moore, M.J.; Vasilieva, N.; Sui, J.; Wong, S.K.; Berne, M.A.; Somasundaran, M.; Sullivan, J.L.; Luzuriaga, K.; Greenough, T.C.; et al. Angiotensin-converting enzyme 2 is a functional receptor for the SARS coronavirus. Nature 2003, 426, 450–454. [CrossRef] 20. Khan, A.; Benthin, C.; Zeno, B.; Albertson, T.E.; Boyd, J.; Christie, J.D.; Hall, R.; Poirier, G.; Ronco, J.J.; Tidswell, M.; et al. A pilot clinical trial of recombinant human angiotensin-converting enzyme 2 in acute respiratory distress syndrome. Crit. Care 2017, 21, 1–9. [CrossRef] 21. Gheblawi, M.; Wang, K.; Viveiros, A.; Nguyen, Q.; Zhong, J.C.; Turner, A.J.; Raizada, M.K.; Grant, M.B.; Oudit, G.Y. Angiotensin-converting enzyme 2: SARS-CoV-2 receptor and regulator of the renin-angiotensin system: Celebrating the 20th anniversary of the discovery of ACE2. Circ. Res. 2020, 126, 1456–1474. [CrossRef][PubMed] 22. Monteil, V.; Kwon, H.; Prado, P.; Hagelkrüys, A.; Wimmer, R.A.; Stahl, M.; Leopoldi, A.; Garreta, E.; Del Pozo, C.H.; Prosper, F.; et al. Inhibition of SARS-CoV-2 infections in engineered human tissues using clinical-grade soluble human ACE2. Cell 2020, 181, 905–913.e7. [CrossRef][PubMed] 23. NCT04287686. Recombinant Human Angiotensin-Converting Enzyme 2 (rhACE2) as a Treatment for Patients with COVID-19. Available online: https://clinicaltrials.gov/ct2/show/NCT04287686 (accessed on 27 February 2020). 24. NCT04332666. Angiotensin-(1,7) Treatment in COVID-19: The ATCO Trial (ATCO). Available online: https://www.clinicaltrials.gov/ct2/show/NCT04332666 (accessed on 3 April 2020). Viruses 2020, 12, 1367 18 of 20

25. NCT04335786. Valsartan for Prevention of Acute Respiratory Distress Syndrome in Hospitalized Patients With SARS-COV-2 (COVID-19) Infection Disease. Available online: https://clinicaltrials.gov/ct2/show/ NCT04335786 (accessed on 6 April 2020). 26. NCT04312009. Losartan for Patients with COVID-19 Requiring Hospitalization. Available online: https: //clinicaltrials.gov/ct2/show/NCT04312009 (accessed on 17 March 2020). 27. NCT04311177. Losartan for Patients with COVID-19 Not Requiring Hospitalization. Available online: https://clinicaltrials.gov/ct2/show/NCT04311177 (accessed on 17 March 2020). 28. NCT04318418. ACE Inhibitors, Angiotensin II Type-I Receptor Blockers and Severity of COVID-19 (CODIV-ACE). Available online: https://clinicaltrials.gov/ct2/show/NCT04318418 (accessed on 24 March 2020). 29. Reynolds, H.R.; Adhikari, S.; Pulgarin, C.; Troxel, A.B.; Iturrate, E.; Johnson, S.B.; Hausvater, A.; Newman, J.D.; Berger, J.S.; Bangalore, S.; et al. Renin–angiotensin–aldosterone system inhibitors and risk of Covid-19. N. Engl. J. Med. 2020, 382, 2441–2448. [CrossRef][PubMed] 30. Mancia, G.; Rea, F.; Ludergnani, M.; Apolone, G.; Corrao, G. Renin–angiotensin–aldosterone system blockers and the risk of Covid-19. N. Engl. J. Med. 2020, 382, 2431–2440. [CrossRef][PubMed] 31. Mehra, M.R.; Desai, S.S.; Kuy, S.; Henry, T.D.; Patel, A.N. Cardiovascular disease, drug therapy, and mortality in COVID-19. N. Engl. J. Med. 2020, 382, e102. [CrossRef] 32. Paul, M.; Poyan Mehr, A.; Kreutz, R. Physiology of local renin-angiotensin systems. Physiol. Rev. 2006, 86, 747–803. [CrossRef] 33. Raizada, M.K.; Phillips, M.I.; Sumners, C. Cellular and Molecular Biology of the Renin-Angiotensin System; CRC Press: Boca Raton, FL, USA, 1993. 34. Casarini, D.E.; Arita, D.Y.; Cunha, T.S.; Colucci, J.A. New Aspects of the Renin Angiotensin System in Cardiovascular and Renal Diseases; Bentham: Sharjah, UAE, 2016. 35. Hallow, K.M.; Lo, A.; Beh, J.; Rodrigo, M.; Ermakov, S.; Friedman, S.; de Leon, H.; Sarkar, A.; Xiong, Y.; Sarangapani, R.; et al. A model-based approach to investigating the pathophysiological mechanisms of hypertension and response to antihypertensive therapies: extending the Guyton model. Am. J. Physiol. Regul. Integr. Comp. Physiol. 2014, 306, R647–R662. [CrossRef] 36. Versypt, A.N.F.; Harrell, G.K.; McPeak, A.N. A pharmacokinetic/pharmacodynamic model of ACE inhibition of the renin-angiotensin system for normal and impaired renal function. Comput. Chem. Eng. 2017, 104, 311–322. [CrossRef] 37. Leete, J.; Gurley, S.; Layton, A.T. Modeling sex differences in the renin angiotensin system and the efficacy of antihypertensive therapies. Comput. Chem. Eng. 2018, 112, 253–264. [CrossRef] 38. Leete, J.; Layton, A.T. Sex-specific long-term blood pressure regulation: Modeling and analysis. Comput. Biol. Med. 2019, 104, 139–148. [CrossRef] 39. Ruiz-Ortega, M.; Lorenzo, O.; Ruperez, M.; Esteban, V.; Suzuki, Y.; Mezzano, S.; Plaza, J.; Egido, J. Role of the renin-angiotensin system in vascular diseases: Expanding the field. Hypertension 2001, 38, 1382–1387. [CrossRef][PubMed] 40. De Man, F.S.; Tu, L.; Handoko, M.L.; Rain, S.; Ruiter, G.; François, C.; Schalij, I.; Dorfmüller, P.; Simonneau, G.; Fadel, E.; et al. Dysregulated renin–angiotensin–aldosterone system contributes to pulmonary arterial hypertension. Am. J. Respir. Crit. Care Med. 2012, 186, 780–789. [CrossRef][PubMed] 41. Jia, G.; Aroor, A.R.; Hill, M.A.; Sowers, J.R. Role of renin-angiotensin-aldosterone system activation in promoting cardiovascular fibrosis and stiffness. Hypertension 2018, 72, 537–548. [CrossRef][PubMed] 42. Te Riet, L.; van Esch, J.H.; Roks, A.J.; van den Meiracker, A.H.; Danser, A.J. Hypertension: Renin–angiotensin– aldosterone system alterations. Circ. Res. 2015, 116, 960–975. [CrossRef][PubMed] 43. Kobori, H.; Nangaku, M.; Navar, L.G.; Nishiyama, A. The intrarenal renin-angiotensin system: From physiology to the pathobiology of hypertension and kidney disease. Pharmacol. Rev. 2007, 59, 251–287. [CrossRef] 44. Simões e Silva, A.; Silveira, K.; Ferreira, A.; Teixeira, M. ACE2, angiotensin-(1-7) and M as receptor axis in inflammation and fibrosis. Br. J. Pharmacol. 2013, 169, 477–492. [CrossRef] 45. Povlsen, A.L.; Grimm, D.; Wehland, M.; Infanger, M.; Krüger, M. The vasoactive Mas receptor in essential hypertension. J. Clin. Med. 2020, 9, 267. [CrossRef] Viruses 2020, 12, 1367 19 of 20

46. Santos, R.A.; e Silva, A.C.S.; Maric, C.; Silva, D.M.; Machado, R.P.; de Buhr, I.; Heringer-Walther, S.; Pinheiro, S.V.B.; Lopes, M.T.; Bader, M.; et al. Angiotensin-(1–7) is an endogenous ligand for the G protein-coupled receptor Mas. Proc. Natl. Acad. Sci. USA 2003, 100, 8258–8263. [CrossRef] 47. Carey, R.M. AT2 receptors: Potential therapeutic targets for hypertension. Am. J. Hypertens. 2017, 30, 339–347. 48. Zaman, M.A.; Oparil, S.; Calhoun, D.A. Drugs targeting the renin–angiotensin–aldosterone system. Nat. Rev. Drug Discov. 2002, 1, 621–636. [CrossRef] 49. Williams, B. Drug discovery in renin–angiotensin system intervention: Past and future. Ther. Adv. Cardiovasc. Dis. 2016, 10, 118–125. [CrossRef][PubMed] 50. Borg, I.; Rohde, G.; Löseke, S.; Bittscheidt, J.; Schultze-Werninghaus, G.; Stephan, V.; Bufe, A. Evaluation of a quantitative real-time PCR for the detection of respiratory syncytial virus in pulmonary diseases. Eur. Respir. J. 2003, 21, 944–951. [CrossRef][PubMed] 51. Zheng, S.; Fan, J.; Yu, F.; Feng, B.; Lou, B.; Zou, Q.; Xie, G.; Lin, S.; Wang, R.; Yang, X.; et al. Viral load dynamics and disease severity in patients infected with SARS-CoV-2 in Zhejiang province, China, January-March 2020: Retrospective cohort study. BMJ 2020, 369, m1443. [CrossRef][PubMed] 52. Villar, J.; Pérez-Méndez, L.; Blanco, J.; Añón, J.M.; Blanch, L.; Belda, J.; Santos-Bouza, A.; Fernández, R.L.; Kacmarek, R.M. A universal definition of ARDS: The PaO2/FiO2 ratio under a standard ventilatory setting—a prospective, multicenter validation study. Intensive Care Med. 2013, 39, 583–592. [CrossRef] 53. Ware, L.B.; Matthay, M.A. The acute respiratory distress syndrome. New Engl. J. Med. 2000, 342, 1334–1349. [CrossRef] 54. Streatfeild-James, R.M.; Williamson, D.; Pike, R.N.; Tewksbury, D.; Carrell, R.W.; Coughlin, P.B. Angiotensinogen cleavage by renin: Importance of a structurally constrained N-terminus. FEBS Lett. 1998, 436, 267–270. [CrossRef] 55. Katsurada, A.; Hagiwara, Y.; Miyashita, K.; Satou, R.; Miyata, K.; Ohashi, N.; Navar, L.G.; Kobori, H. Novel sandwich ELISA for human angiotensinogen. Am. J. Physiol. Ren. Physiol. 2007, 293, F956–F960. [CrossRef] 56. Chappell, M.C. Biochemical evaluation of the renin-angiotensin system: The good, bad, and absolute? Am. J. Physiol. Heart Circ. Physiol. 2016, 310, H137–H152. [CrossRef] 57. Pendergrass, K.D.; Pirro, N.T.; Westwood, B.M.; Ferrario, C.M.; Brosnihan, K.B.; Chappell, M.C. Sex differences in circulating and renal angiotensins of hypertensive mRen. Lewis but not normotensive Lewis rats. Am. J. Physiol. Heart Circ. Physiol. 2008, 295, H10–H20. [CrossRef] 58. Sullivan, J.C.; Rodriguez-Miguelez, P.; Zimmerman, M.A.; Harris, R.A. Differences in angiotensin (1–7) between men and women. Am. J. Physiol.-Heart Circ. Physiol. 2015, 308, H1171–H1176. [CrossRef] 59. Nussberger, J.; Brunner, D.B.; Waeber, B.; Brunner, H.R. Specific measurement of angiotensin metabolites and in vitro generated angiotensin II in plasma. Hypertension 1986, 8, 476–482. [CrossRef][PubMed] 60. Nussberger, J.; Brunner, D.; Keller, I.; Brunner, H.R. Measurement of converting enzyme activity by antibody-trapping of generated angiotensin II: Comparison with two other methods. Am. J. Hypertens. 1992, 5, 393–398. [CrossRef][PubMed] 61. Mazzolai, L.; Maillard, M.; Rossat, J.; Nussberger, J.; Brunner, H.R.; Burnier, M. Angiotensin II receptor blockade in normotensive subjects: A direct comparison of three AT1 receptor antagonists. Hypertension 1999, 33, 850–855. [CrossRef][PubMed] 62. Nussberger, J.; Wuerzner, G.; Jensen, C.; Brunner, H.R. Angiotensin II Suppression in humans by the orally active renin inhibitor aliskiren (SPP100) comparison with enalapril. Hypertension 2002, 39, e1–e8. [CrossRef] 63. Heran, B.S.; Wong, M.M.; Heran, I.K.; Wright, J.M. Blood pressure lowering efficacy of angiotensin converting enzyme (ACE) inhibitors for primary hypertension. Cochrane Database Syst. Rev. 2008.[CrossRef] 64. Doulton, T.W.; He, F.J.; MacGregor, G.A. Systematic review of combined angiotensin-converting enzyme inhibition and angiotensin receptor blockade in hypertension. Hypertension 2005, 45, 880–886. [CrossRef] 65. Chen, L.; Li, X.; Chen, M.; Feng, Y.; Xiong, C. The ACE2 expression in human heart indicates new potential mechanism of heart injury among patients infected with SARS-CoV-2. Cardiovasc. Res. 2020, 116, 1097–1100. [CrossRef] 66. Xu, H.; Zhong, L.; Deng, J.; Peng, J.; Dan, H.; Zeng, X.; Li, T.; Chen, Q. High expression of ACE2 receptor of 2019-nCoV on the epithelial cells of oral mucosa. Int. J. Oral Sci. 2020, 12, 1–5. [CrossRef] Viruses 2020, 12, 1367 20 of 20

67. Sungnak, W.; Huang, N.; Bécavin, C.; Berg, M.; Queen, R.; Litvinukova, M.; Talavera-López, C.; Maatz, H.; Reichart, D.; Sampaziotis, F.; et al. SARS-CoV-2 entry factors are highly expressed in nasal epithelial cells together with innate immune genes. Nat. Med. 2020, 26, 681–687. [CrossRef] 68. Liu, N.; Hong, Y.; Chen, R.G.; Zhu, H.M. High rate of increased level of plasma Angiotensin II and its gender difference in COVID-19: An analysis of 55 hospitalized patients with COVID-19 in a single hospital, WuHan, China. medRxiv 2020.[CrossRef] 69. Liu, Y.; Yang, Y.; Zhang, C.; Huang, F.; Wang, F.; Yuan, J.; Wang, Z.; Li, J.; Li, J.; Feng, C.; et al. Clinical and biochemical indexes from 2019-nCoV infected patients linked to viral loads and lung injury. Sci. China Life Sci. 2020, 63, 364–374. [CrossRef] 70. Merad, M.; Martin, J.C. Pathological inflammation in patients with COVID-19: A key role for monocytes and macrophages. Nat. Rev. Immunol. 2020, 20, 355–362. [CrossRef][PubMed] 71. Satou, R.; Penrose, H.; Navar, L.G. Inflammation as a regulator of the renin-angiotensin system and blood pressure. Curr. Hypertens. Rep. 2018, 20, 100. [CrossRef][PubMed] 72. Vaughan, D.E.; Lazos, S.A.; Tong, K. Angiotensin II regulates the expression of plasminogen activator inhibitor-1 in cultured endothelial cells. A potential link between the renin-angiotensin system and thrombosis. J. Clin. Investig. 1995, 95, 995–1001. [CrossRef][PubMed] 73. Vaughan, D.E. PAI-1 and atherothrombosis. J. Thromb. Haemost. 2005, 3, 1879–1883. [CrossRef] 74. Ssentongo, A.; Ssentongo, P.; Heilbrunn, E.S.; Lekoubou, A.; Du, P.; Liao, D.; Oh, J.S.; Chinchilli, V.M. Renin-angiotensin-aldosterone system inhibitors and mortality in patients with hypertension hospitalized for COVID-19: A systematic review and meta-analysis. medRxiv 2020.[CrossRef] 75. Khera, R.; Clark, C.; Lu, Y.; Guo, Y.; Ren, S.; Truax, B.; Spatz, E.S.; Murugiah, K.; Lin, Z.; Omer, S.B.; et al. Association of Angiotensin-Converting Enzyme Inhibitors and Angiotensin Receptor Blockers with the Risk of Hospitalization and Death in Hypertensive Patients with Coronavirus Disease-19. medRxiv 2020. [CrossRef] 76. Baral, R.; White, M.; Vassiliou, V.S. Impact of hospitalised patients with COVID-19 taking Renin-Angiotensin- Aldosterone System inhibitors: A systematic review and meta-analysis. medRxiv 2020.[CrossRef] 77. Zambelli, V.; Bellani, G.; Borsa, R.; Pozzi, F.; Grassi, A.; Scanziani, M.; Castiglioni, V.; Masson, S.; Decio, A.; Laffey, J.G.; et al. Angiotensin-(1-7) improves oxygenation, while reducing cellular infiltrate and fibrosis in experimental Acute Respiratory Distress Syndrome. Intensive Care Med. Exp. 2015, 3, 1–17. [CrossRef] 78. Chester, A.; Borland, J. Chymase-dependent angiotensin II formation in human blood vessels. J. Hum. Hypertens. 2000, 14, 373–376. [CrossRef] 79. Athyros, V.G.; Mikhailidis, D.P.; Kakafika, A.I.; Tziomalos, K.; Karagiannis, A. Angiotensin II reactivation and aldosterone escape phenomena in renin–angiotensin–aldosterone system blockade: Is oral renin inhibition the solution? Expert Opin. Pharmacother. 2007, 8, 529–535. [CrossRef][PubMed] 80. Hashimoto, S.; Amaya, F.; Oh-hashi, K.; Kiuchi, K.; Hashimoto, S. Expression of neutral endopeptidase activity during clinical and experimental acute lung injury. Respir. Res. 2010, 11, 1–10. [CrossRef][PubMed] 81. Bayes-Genis, A.; Morant-Talamante, N.; Lupón, J. Neprilysin and natriuretic peptide regulation in heart failure. Curr. Heart Fail. Rep. 2016, 13, 151–157. [CrossRef][PubMed] 82. Acanfora, D.; Ciccone, M.M.; Scicchitano, P.; Acanfora, C.; Casucci, G. Neprilysin inhibitor–angiotensin II receptor blocker combination (sacubitril/valsartan): Rationale for adoption in SARS-CoV-2 patients. Eur. Heart-J.–Cardiovasc. Pharmacother. 2020.[CrossRef][PubMed] 83. Crowley, S.D.; Rudemiller, N.P. Immunologic effects of the renin-angiotensin system. J. Am. Soc. Nephrol. 2017, 28, 1350–1361. [CrossRef][PubMed]

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