# COVID-19 and the Importance of Being Prepared: A Multidisciplinary Strategy for the Discovery of Antivirals to Combat Pandemics

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## Abstract

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## 1. Introduction

## 2. Materials and Methods

#### 2.1. QSAR Strategy

#### 2.2. Creation of a Data Set of Anti-Protease Compounds: Molecular Docking Studio

#### 2.3. Molecular Docking Simulation

#### 2.4. Topological Descriptors and Statistical Modeling Methods

#### 2.4.1. Linear Discriminant Analysis (LDA)

_{Class_6LU7}, which discriminates between molecules capable and incapable of interacting with the main protease of the virus. An important aspect of building a robust LDA model is the selection of the most significant variables or descriptors to characterize the compounds so that their contribution to the discrimination is high. To select the best descriptors, we followed a forward stepwise algorithm based on p-value. Therefore, at each step, the variable with a more favorable p-value < 0.05 was chosen. The process ends when the algorithm cannot introduce any more descriptors with a p-value < 0.05. Therefore, at each step, the variable that adds the most to the separation of the groups is entered into the discriminant function. Another parameter to assess the significance of the selected descriptor is the Fisher-Snedecor parameter; higher values indicate a better descriptor. The quality of the discriminant function is assessed by the Wilks’ lambda parameter [41]. In general, the Wilks’ lambda can take values between 0 and 1; the smaller the value the better the prediction. Statistica was the software used for developing linear discriminant models [42].

#### 2.4.2. Multilinear Regression Analysis (MLRA)

_{reg_6LU7}[43] using a forward stepwise variable selection procedure in which variables are sequentially entered into the model depending on the p-value selected (threshold: p < 0.05). Subsequently, the best subseries of six descriptors with respect to the property (6LU7-docking score) are identified. Therefore, from almost 2000 descriptors calculated, only six top descriptors were selected for modeling docking score values against Mpro (6LU7). Statistical parameters indicating the quality of the regression are, among others, correlation coefficient, r

^{2}, the F (Fisher-Snedecor), and p-values.

#### 2.4.3. Artificial Neural Network Analysis (ANN): Classification and Regression

#### 2.5. QSAR Models and Validation

#### 2.5.1. Classification Matrix and External Validation

_{Class}models is evaluated by the classification matrices, which sort all cases from the model into categories by determining whether the predicted value matches the actual value.

#### 2.5.2. Relative Operating Characteristic Curve (ROC) Curve

#### 2.6. Pharmacological Distribution Diagrams

_{a}= a/(i + 1), where “a” is the number of active compounds in the range divided by the total number of active compounds and “i” is the number of inactive compounds in the interval divided by the total number of inactive compounds. The expectancy of inactivity is defined in a symmetrical way, as E

_{i}= i/(a + 1).

#### 2.7. Virtual Screening

#### 2.8. In Vitro Testing

_{10}TCID

_{50,}were calculated by the Karber [48] method. The MRC-5 cells were grown in Eagle’s MEM containing fetal bovine serum and antibiotics. We used three different in vitro methods to determine the efficacy of the six potential antiviral compounds (see below). All these dilutions were inoculated in three wells each and all experiments were done in duplicate. Both negative controls which consisted of cell cultures not infected with the virus, and positive controls, which consisted of cell cultures infected with the virus but not treated with any drug, have been considered in the experiments.

- a.
- Pre-treatment of cells: After discarding its growth medium, the cell monolayers were covered with 100 µL of a solution containing 50 µg/mL of a given compound). Each compound was tested in separate cell monolayers. After incubation at room temperature for 60 min, the monolayers were washed with sterile PBS. Immediately, the washed monolayers were inoculated with serial 10-fold dilutions of 229E (prepared in MEM). The plates were then incubated at 37 °C in a 5% CO
_{2}incubator and examined daily under an inverted microscope for the appearance of virus-induced cytopathic effects (CPE). After 5 days of incubation, 229E titers in pre-treated and untreated monolayers were calculated and compared. - b.
- Co-treatment of cells with antivirals and virus: Serial 10-fold dilutions of 229E were prepared separately in 1:10 dilutions of each antiviral. From each virus dilution, 100 µL was used to infect MRC-5 monolayers. The titers of 229E prepared in antiviral solutions and 229E prepared in MEM (control) were calculated and compared after 5 days of incubation at 37 °C in a 5% CO
_{2}incubator. - c.
- Post-infection treatment of cells with antivirals: Cell monolayers were inoculated with serial 10-fold dilutions of the virus followed by incubation at 37 °C for 2 h for viral attachment to the cells. After virus attachment, the monolayers were washed and treated separately with 100 µL of a given antiviral. The titers of 229E were calculated after 5 days of incubation at 37 °C in a 5% CO
_{2}incubator and compared with the control virus titer.

## 3. Results and Discussion

#### 3.1. Molecular Docking Simulation on Mpro

#### 3.2. SARS-CoV-2 Main Protease QSAR Models and Validation

#### 3.2.1. Discriminant Models and Validation

_{Class_6LU7})”, focuses on identifying the mathematical pattern that discriminates between drugs with low and high docking scores against the protease. The second model was developed using the same data and machine learning methods. To be precise, artificial neural networks were used to generate a predictive model capable of classifying potential protease inhibitors based on the docking score values. Table 1 shows the statistics and descriptors for the two classification models. Detailed information regarding training and external test sets for classification models is given in Tables S1–S4.

_{Class_6LU7}and ANN

_{Class_6LU7}models report higher sensitivity than specificity. However, its ability to identify inactive drugs is above 94%. The results obtained after external validation were even better than those presented by the selected models (Table 2); higher than 97% correct classification was found for test sets for both models indicating that the models are robust and reliable. The same classification matrices were obtained for training and test data sets of both models, which indicates that there is no difference between linear and non-linear statistical techniques to predict anti-SARS-CoV-2 activity by means of docking score value of 6LU7 protease.

_{Class_6LU7}equation, a higher number of paths of order 8 in a molecule results in a greater chance of exhibiting anti-SARS-CoV-2 activity. Figure 3 shows that compounds with MPC08 value greater than 3.7 are considered active by the DF

_{Class_6LU7}model (for instance adrabetadex and benzoyl-arginine-alanine-methyl ketone) while those with lower values are labeled as inactive (agmatine and NCX701). Agmatine has a MPC08 value of zero, as no path of order 8 is present in its structure. It appears that there is an optimal number of paths of order 8 above which it is possible to establish a correlation with anti-SARS-CoV-2 activity. This is probably because NCX701 contains a path of order 8 in its structure, despite being labeled as inactive. This chemo-mathematical feature may also be correlated with biochemistry when considering a possible correlation with the steric space of the molecules inside the catalytic pocket of the protein. A higher volume of the ligands indicates a higher probability to establish interactions.

#### 3.2.2. Regression Models and Validation

_{reg_6LU7}and an artificial neural network regression model ANN

_{reg_6LU7}. The same data sets used for the discriminant models (training and test set) were employed, but this time the objective was to predict the docking scores of the interactions with 6LU7 protease. Detailed information regarding training and external test sets for both MLR and ANN regression models is given in Tables S5–S8.

#### 3.2.3. ROC Curve

_{Class_6LU7}models. For this discriminant equation, the area under the curve (AUC) is greater than 0.96 for all models, which suggests a 96% chance that the models will correctly distinguish between an active and inactive/decoy compound.

#### 3.2.4. Pharmacological Distribution Diagram

_{Class_6LU7}values between 1 and 8.25 show the range of the highest expectancy of harboring anti-SARS2 drugs. Drugs that do not present anti-SARS-CoV-2 activity, generally have DF values between −12 and 0 although we found some areas with slight overlapping between active and inactive throughout the PDD. Finally, drugs with DF values greater than 9 and less than −12 were considered outliers.

_{Class_6LU7}and ANN

_{Class_6LU7}); (2) should have a predicted docking score lower than −5 kcal/mol (MLR

_{reg_6LU7}and/or ANN

_{reg_6LU7}), and finally (3) be commercially available. Molecular structures of the final pool of compounds selected by virtual screening are shown in Figure 7.

#### 3.3. Antiviral Activity of Chemicals against Human Coronavirus 229E

## 4. Conclusions

## Supplementary Materials

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

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**Figure 1.**Search algorithm used to develop in silico strategy for the repositioning of potential Mpro inhibitors against SARS-CoV-2.

**Figure 2.**(

**A**) Binding pocket for Mpro crystallized protein (PDB:6LU7) docking studio. (

**B**) Interaction of Mpro co-crystallized ligand (inhibitor N3) with key catalytic residues.

**Figure 4.**A descriptor directly correlated with the prediction of docking score of SARS-CoV-2 protease by MLR

_{reg_6LU7}.

**Figure 5.**ROC curve for DF

_{Class_6LU7}and ANN

_{Class_6LU7.}TPF: true positive fraction; FPF: false positive fraction.

**Figure 8.**Docking pose (

**A**,

**B**) and amino acid interaction (

**C**) of top-rank compound on 6LU7: Molport-046-067-769.

**Table 1.**Development of classification models to predict the anti-SARS-CoV-2 activity (protease inhibition activity).

Statistical Method | Model | Model Parameters |
---|---|---|

LDA | $D{F}_{Class\_6LU7}=\left(2.917\times MPC08\right)-10.550$ | N = 206 λ = 0.305 F = 417.37 p < 0.00001 |

ANN | ANN_{Class_6LU7}MLP 1 *-2-2 | Training algorithm: BFGS 8 Error function: Entropy Hidden activation function: Tanh Output activation function: Softmax |

Model | External Validation | ||||||
---|---|---|---|---|---|---|---|

% of Correct Classification | Active | Inactive | % of Correct Classification | Active | Inactive | ||

Active | 100.0 | 80 | 0 | 100.0 | 26 | 0 | |

DF_{Class_6LU7} | Inactive | 94.4 | 7 | 119 | 95.2 | 2 | 40 |

Average | 97.2 | 97.6 | |||||

Active | 100.0 | 80 | 0 | 100.0 | 26 | 0 | |

ANN_{Class_6LU7} | Inactive | 94.4 | 7 | 119 | 95.2 | 2 | 40 |

Average | 97.2 | 97.6 |

Statistical Method | Model | Model Parameters |
---|---|---|

MLR | $\begin{array}{cc}\hfill Dockingscore& \left(6LU7\right)\hfill \\ & =1.041\hfill \\ & -\left(0.614\times {\mathrm{SpDiam}}_{\mathrm{EA}\left(\mathrm{bo}\right)}\right)\hfill \\ & -\left(1.765\times \mathrm{Eig}{09}_{\mathrm{EA}\left(\mathrm{bo}\right)}\right)\hfill \\ & -\left(6.403\times \mathrm{nRNR}2\right)\hfill \\ & +\left(7.069\times \mathrm{N}-068\right)\hfill \\ & +\left(0.115\times \mathrm{CATS}2{\mathrm{D}}_{{05}_{\mathrm{LL}}}\right)\hfill \\ & -\left(0.463\times \mathrm{nLevel}1\right)\hfill \end{array}$ | N = 206 r ^{2} = 0.884F = 255.927 p < 0.00001 SEE = 1.415 q ^{2} = 0.741 |

ANN | ANN _{reg_6LU7}MLP 4 *-2-1 | N = 206 r ^{2} = 0.887q ^{2} = 0.764Training algorithm: BFGS 73 Error function: SOS Hidden activation function: Tanh Output activation function: Logistic |

^{2}: correlation coefficient; SEE: Standard error of estimate; q

^{2}, cross-validation correlation coefficient. * Input network: SM4_B(m), Eig09_EA(bo), CATS2D_05_LL, s2_relPathLength.

Descriptor Type | Descriptor Name | Descriptor Definition |
---|---|---|

2D matrix-based descriptors | SM4_B(m) | Spectral moment of order 4 from Burden matrix weighted by mass |

Atom-centered fragments | N-068 | Al3-N |

Chirality descriptors | nLevel1 | Number of neighboring atoms of the chiral center (level 1) |

Chirality descriptors | s2_relPathLength | Maximum path length of the substituent 2 normed by the heavy atoms |

Edge adjacency indices | Eig09_EA(bo) | Eigenvalue nº 9 from edge adjacency matrix weighted by bond order |

Edge adjacency indices | SpDiam_EA(bo) | Spectral diameter from edge adjacency matrix weighted by bond order |

Functional group counts | nRNR2 | Number of tertiary amines (aliphatic) |

Pharmacophore descriptors | CATS2D_05_LL | CATS2D Lipophilic-Lipophilic at lag 05 |

Drug | DF_{Class_6LU7} | P.A. | ANN_{Class_6LU7} | Conf. Levels | Docking ScoreMLR_{reg_6LU7} | Docking ScoreANN_{reg_6LU7} |
---|---|---|---|---|---|---|

Docetaxel | 7.630 | 1.000 | 1 | 0.873 | −7.853 | −6.813 |

Ginsenoside | 8.379 | 1.000 | 1 | 0.873 | −5.319 | −4.300 |

Josamycin | 5.473 | 0.996 | 1 | 0.873 | −6.158 | −6.718 |

Molport-046-067-769 | 4.598 | 0.990 | 1 | 0.872 | −8.628 | −8.842 |

Molport-046-568-802 | 3.557 | 0.972 | 1 | 0.871 | −8.213 | −8.584 |

Pepstatin A | 1.758 | 0.853 | 1 | 0.854 | −6.163 | −5.975 |

**Table 6.**Potential anti-SARS-CoV-2 compounds selected by Molecular Topology and docking score for Mpro (PDB:6LU7).

Compound | Docking Score | Interaction with Indicated Amino Acids |
---|---|---|

Inhibitor N3 (co-crystallized ligand) | −8.019 | Glu166 (3× H, salt bridge) Gly143 (H) Thr26 (H) Asn142(2× H) Gln189 (H) |

Molport-046-067-769 | −7.514 | Glu166 (2× H, salt bridge) Gly143 (H) Thr26 (H) Gln189 (H) His41 (H) |

Pepstatin A | −7.155, | Glu166 (2× H, salt bridge) Gln189 (H) His164 (H) Ala191(H) |

Docetaxel | −6.916 | Glu166 (4× H, salt bridge) Gln189 (H) |

Molport-046-568-802 | −6.361 | Glu166 (H) Asn142 (H) Thr26 (H) Gln189 (H) |

Ginsenoside | −5.319 | Gln189 (H) Glu166 (2× H) Gly170(H) |

Josamycin | −3.995 | Glu166 (2× H, salt bridge) |

Compound | Virus Titers Shown as Log_{10} TCID_{50}/100 µL (Per Cent Virus Inactivation) | |||
---|---|---|---|---|

Stock Virus | Pre-Treatment | Co-Treatment | Post-Treatment | |

Josamycin | 5.7 | 2.83 (99.87) | 3.1 (99.75) | 4.0 (98.00) |

Pepstatin | 5.7 | 3.5 (99.37) | 3.6 (99.20) | 4.5 (93.69) |

Docetaxel | 5.5 | 3.0 (99.68) | 3.5 (99.00) | 4.5 (90.00) |

Molport-046-067-769 | 5.5 | 2.83 (99.78) | 2.60 (99.87) | 3.83 (97.86) |

Molport-046-568-802 | 5.5 | 2.66 (99.85) | 3.16 (99.54) | 4.1 (96.01) |

Ginsenocide Rh1 | 5.5 | 2.83 (99.78) | 2.05 (99.96) | 3.5 (99.00) |

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Galvez-Llompart, M.; Zanni, R.; Galvez, J.; Basak, S.C.; Goyal, S.M.
COVID-19 and the Importance of Being Prepared: A Multidisciplinary Strategy for the Discovery of Antivirals to Combat Pandemics. *Biomedicines* **2022**, *10*, 1342.
https://doi.org/10.3390/biomedicines10061342

**AMA Style**

Galvez-Llompart M, Zanni R, Galvez J, Basak SC, Goyal SM.
COVID-19 and the Importance of Being Prepared: A Multidisciplinary Strategy for the Discovery of Antivirals to Combat Pandemics. *Biomedicines*. 2022; 10(6):1342.
https://doi.org/10.3390/biomedicines10061342

**Chicago/Turabian Style**

Galvez-Llompart, Maria, Riccardo Zanni, Jorge Galvez, Subhash C. Basak, and Sagar M. Goyal.
2022. "COVID-19 and the Importance of Being Prepared: A Multidisciplinary Strategy for the Discovery of Antivirals to Combat Pandemics" *Biomedicines* 10, no. 6: 1342.
https://doi.org/10.3390/biomedicines10061342