key: cord-1016377-mky2gs2p
authors: Sankhe, Runali; Rathi, Ekta; Manandhar, Suman; Kumar, Avinash; Pai, K. Sreedhara Rangnath; Kini, Suvarna; Kishore, Anoop
title: Repurposing of existing FDA approved drugs for Neprilysin inhibition: an in-silico study
date: 2020-08-12
journal: J Mol Struct
DOI: 10.1016/j.molstruc.2020.129073
sha: 3ddeb2023935e6aa21813de624262a70823f1934
doc_id: 1016377
cord_uid: mky2gs2p

Neprilysin (NEP) is a neutral endopeptidase with diverse physiological roles in the body. NEP's role in degradation of diverse classes of peptides such as amyloid beta, natriuretic peptide, substance P, angiotensin, endothelins, etc., is associated with pathologies of alzheimer's, kidney and heart disease, obesity, diabetes, and certain malignancies. Hence the functional inhibition of NEP in the above systems can be a good therapeutic target. In the present study, in-silico drug repurposing approach was used to identify NEP inhibitors. Molecular docking was carried out using GLIDE tool and 2934 drugs from the ZINC12 database were screened using high throughput virtual screening (HTVS) followed by standard precision (SP), and extra precision (XP) docking. Based on the XP docking score and ligand interaction, the top 8 hits were subjected to free ligand binding energy calculation, to filter out 4 hits (ZINC000000001427, ZINC000001533877, ZINC000000601283, and ZINC000003831594). Further, induced fit docking-standard precision (IFD-SP) and molecular dynamics (MD) studies were performed. The results obtained from MD +studies suggest that ZINC000000601283-NEP and ZINC000003831594-NEP complexes were most stable for 20ns simulation period as compared to ZINC000001533877-NEP and ZINC000000001427-NEP complexes. Interestingly, ZINC000000601283 and ZINC000003831594 showed similarity in binding with reported NEP inhibitor sacubitrilat. Findings from this study suggest that ZINC000000601283 and ZINC000003831594 may act as NEP inhibitors. In future studies, the role of ZINC000000601283 and ZINC000003831594 in NEP inhibition should be tested in biological systems to evaluate therapeutic effect in NEP associated pathological conditions.

Abstract: Neprilysin (NEP) is a neutral endopeptidase with diverse physiological roles in the body. NEP's role in degradation of diverse classes of peptides such as amyloid beta, natriuretic peptide, substance P, angiotensin, endothelins, etc., is associated with pathologies of alzheimer's, kidney and heart disease, obesity, diabetes, and certain malignancies. Hence the functional inhibition of NEP in the above systems can be a good therapeutic target. In the present study, in-silico drug repurposing approach was used to identify NEP inhibitors.

Molecular docking was carried out using GLIDE tool and 2934 drugs from the ZINC12 database were screened using high throughput virtual screening (HTVS) followed by standard precision (SP), and extra precision (XP) docking. Based on the XP docking score and ligand interaction, the top 8 hits were subjected to free ligand binding energy calculation, to filter out 4 hits (ZINC000000001427, ZINC000001533877, ZINC000000601283, and ZINC000003831594). Further, induced fit docking-standard precision (IFD-SP) and molecular dynamics (MD) studies were performed. The results obtained from MD +studies suggest that ZINC000000601283-NEP and ZINC000003831594-NEP complexes were most stable for 20ns simulation period as compared to ZINC000001533877-NEP and ZINC000000001427-NEP complexes.

Interestingly, ZINC000000601283 and ZINC000003831594 showed similarity in binding with reported NEP inhibitor sacubitrilat.

Findings from this study suggest that ZINC000000601283 and ZINC000003831594 may act as NEP inhibitors. In future studies, the role of ZINC000000601283 and ZINC000003831594

in NEP inhibition should be tested in biological systems to evaluate therapeutic effect in NEP associated pathological conditions. [4] . The progression of various pathological conditions such as kidney and heart disease [4] , obesity [5] , diabetes [6] [7] , few malignancies such as colon cancer, lung cancer and melanomas [8] [9] [10] [11], etc. is associated the peptidase activity of NEP. In 2015, the U.S. Food and Drug Administration (FDA) approved sacubitril/valsartan, combination of a neprilysin inhibitor and an angiotensin receptor blocker (ARB) respectively, commonly known as angiotensin receptor Neprilysin inhibitor (ARNi), for heart failure with reduced ejection fraction [12] . Further, in 2017, clinical trials involving sacubitril/valsartan treatment groups performed well in the renal failure population as compared to treatment with an ARB (Valsartan) alone [13] . Therefore, NEP has gained considerable attention in the last decade for its peptide degrading property, and whose inhibition has therapeutic potential in multiple diseases. But the known and available NEP inhibitors are limited. Hence, drug repurposing using different in-silico tools can aid in speeding up the process of drug discovery for the development of new NEP inhibitors.

The role of NEP has been extensively studied in various diseases. The study report of the PARADIGM trial highlighted the role of NEP inhibitors in the population of heart failure with reduced ejection fraction [14] . In an in-vivo study of subtotal nephrectomy, the renoprotective effect of sacubitril/valsartan was found to be stronger as compared to valsartan alone [15] . According to the result of the U.K. HARP-III trial, the combination of sacubitril/valsartan is effective and is well-tolerated in the chronic kidney disease population [16] . Similarly, various studies are focussed on the importance of NEP on chronic kidney and cardiovascular diseases. NEP inhibition in streptozotocin-induced diabetic mice, improved outcomes of cardiac function for heart failure with reduced ejection fraction. In diabetic nephropathy, the combination of the NEP inhibitor thiorphan, with an angiotensin receptor blocker and an angiotensin-converting enzyme II activator showed significant improvement in the condition, by modulating components of the renin-angiotensin system and natriuretic peptide system [6] . The activation of the leptin-aldosterone-neprilysin axis contributes to the pathogenesis of cardiac complications in obese patients [17] . In obesity and type 2 diabetes, NEP inhibition showed improvement in insulin sensitivity and glycaemic control. The inhibition results in modulation of several peptides with glucoregulatory properties such as bradykinin, cholecystokinin, glycogen like peptide, glucose-dependent insulinotropic peptide, secretin, and vasoactive intestinal polypeptide, leading to improved glucose homeostasis and weight loss [18] . A study conducted to evaluate the effect of NEP on nociception, concluded that NEP inhibition can be a good strategy for pain management [19] . In cancers, such as colon cancer [9] [10], lung cancer [11] [20] , and melanomas [8] , the increased levels of NEP is correlated with neoplastic progression. The peptidase activity of NEP and its interaction with Akt/ focal adhesion kinase is assumed to contribute to the pathogenesis of colon cancer [21] . In aggressive melanomas, CD10 (NEP) is the biomarker [8] for detection. The recent finding, also highlighted the role of ARNi to enhance anti-inflammatory and natriuretic peptide system in COVID-19 patients [22] [23] . Additionally, the use of ARNi is also recommended for patients suffering from COVID-19 [24] . All these findings highlighted the need for designing novel NEP inhibitors. But, de novo drug development is resource intensive and time consuming. Hence, drug discovery by repurposing the existing drugs can be an attractive strategy, with the benefit of with reduced developmental risk, especially in the case of NEP inhibitors.

The computation repurposing is known as 'in-silico drug repurposing'. In 2019, in the U.S., approximately 30% of drugs approved was through the drug repurposing approach [25] . The concept of drug repurposing has been already practiced in cardiovascular disease, cancer, obesity, erectile dysfunction, smoking cessation, stress, psychosis, etc. [26] . Drug repurposing using already approved drugs reduces the time and money on preliminary screening, toxicity studies, clinical trials, bulk manufacturing and formulation development.

On the other hand, the establishment of new drug candidates requires lots of time and resources. A good example is the case of allopurinol which was originally approved for cancer and is now available for the treatment of gout [25] .

In this context, we decided to find out a series of inhibitors for NEP using in-silico drug repurposing. The protein structure of the extracellular domain of NEP with sacubitralat (the active metabolite of sacubitril) was used in the current study. The inhibitor binding pocket in the protein structure of the extracellular domain of human NEP (PDB ID: 5JMY) has already been revealed by Schiering, Nikolaus, et al. [27] . The inhibitor binding pocket contains the catalytically essential triad of HIS583, HIS587, and GLU646. For our drug repurposing study, the structures of 2934 FDA approved drugs were downloaded from the Zinc 12 database. Based on the binding pocket of NEP inhibitors, the high throughput virtual screening of existing FDA approved drugs was done to find out new series of NEP inhibitors.

To the best of our knowledge, this is the first study based on drug repurposing approach that is being reported and employed for the development of NEP inhibitors using receptorinhibitor complex.

In the current study, the Maestro Molecular platform (Version 12.1) by Schrodinger, LLC was used to perform molecular docking and simulation studies on an HP desktop system with Linux Ubuntu 18.04.1 LTS platform, Intel Haswell graphics card, 8GB Ram and Intel core i3-4160 processor.

X-ray crystallographic structure of an extracellular domain of human NEP (PDB ID: 5JMY) was downloaded from the RCSB protein data bank. The PDB ID: 5JMY has a resolution of 2 Å. Prior to docking and simulation studies, the biological unit of protein was prepared using 'Protein Preparation Wizard' in Schrodinger suite [28] . During the process of protein preparation, the protein was subjected to import and refine, review and modify, and minimize processes. In protein preparation wizard, missing side chains and residues were filled using the Prime tool. The active site and catalytically important residues were retained in the protein structure. The non-protein water molecules beyond 5 Å were deleted and stages were generated for hetero atoms. To generate low energy state protein, energy minimization was done using OPLS3e (Optimized potential for liquid stimulation) force field and the prepared protein was used for molecular modelling. To generate a grid around ligand, the receptor grid generation workflow was used by keeping all functional residues in the grid [29] .

The structures of 2934 FDA approved drugs from Zinc 12 database were downloaded [30] .

For ligand preparation, the LigPrep tool was employed. The lowest energy 3D structures with correlated chiralities were generated at pH 7.0 ± 2.0 under the OPLS3e force field. In this process, all the ligands were pre-processed, which includes generation of tautomers, ionization state at pH 7.0 ± 2.0 using Epik, addition of hydrogen bond, charged group neutralization, and ligand geometry were optimized [29] .

All the molecular docking studies were carried out by using the ligand docking tool GLIDE [33] .

For the assessment of the ADME profile, the QikProp tool from the maestro modeling platform was used [33] . The QikProp tool helps in the prediction of the druggable property of best four hits based on ADME analysis. During this process, various descriptors such as molecular weight, cardiotoxicity (QPlogHERG), predicted octanol/water partition coefficient (QPlogPo/w), permeability (QPPCaco), polar surface area (PSA), percentage human oral absorption (% Oral Absorption) , and Lipinski rule of five were calculated.

IFD-SP was carried out using the induced-fit docking module from Maestro molecular modelling platform [34] . Based on the XP GLIDE docking score, binding energy, crucial residues involved and ADME analysis, four (ZINC000000001427, ZINC000001533877, ZINC000000601283, and ZINC000003831594) drugs were selected for IFD-SP docking. In IFD, based on the B-factor, side chains were trimmed with receptor and Vander Waals scaling of 0.70 and 0.50 respectively and a maximum of 20 poses were set for each ligand.

Further prime side-chain prediction and minimization were performed in which refinement of all residues within 5Å of the ligands pose and side chains were performed [35] . This process allows the ligand structure and conformation to accommodate nearby reorienting side chains.

The ligands and residues are minimized. In induced-fit protein structure, all the ligands were rigorously docked and IFD score for each was calculated using the formula:

IFD Score: 1.0*Prime_Energy + 9.057*GLIDE Score + 1.428*GLIDE_Ecoul

The flexibility of the receptor is restricted in grid-based docking systems like XP and IFD.

These do not mimic the actual biological systems, where the protein and drug are solvated in water. Hence to tackle this problem, MD simulation was performed. Based on the GLIDE docking score, free binding energy, and IFD score, four drugs were selected for MD simulation for 20ns. For MD simulation, three steps were performed, viz., system builder, minimization, and MD simulation. The docked complex of protein and ligand were selected, and the system model was made by predefined SPC solvent under orthorhombic boundary conditions. Next, the system model was subjected to energy minimization until a gradient threshold reached 25 kcal/mol/Å, balanced at 300 K temperature, and 1 bar pressure via NPT ensemble. In the final step, Minimized ligand-protein complex were subjected to MD simulation [36].

For optimization of ADME and biological properties of top two selected compounds (ZINC000000601283 and ZINC000003831594), the bioisostere replacement of functional group was performed. The bioisosteric replacement tool from Maestro molecular modelling platform was employed to create bioisosteric structures of better potency and ADME profile.

Further, the results of the generated bioisosteres were analysed through interaction of ligands with crucial amino acid residue, XP GLIDE docking score, binding energy, and ADME analysis [37].

NEP was prepared at a neutral pH of 7.0 ± 0.2. Two α-helical subdomains were present in the extracellular domain. Both α-helical subdomains of NEP are connected with the linker region and essential catalytic triad are present in the central cavity of both subdomains. In the central cavity, the catalytically important zinc atom is coordinated with the side chains of amino acid residues HIS583, HIS587, and GLU646 [38] [27] . In present protein, co-crystallized ligand, sacubitrilat is bound to the active site NEP and showed crucial interactions with HIS583, HIS587, GLU646, and fourth coordination is provided by the carboxylate oxygen adjacent to the P1 methyl of the sacubitrilat. To generate a receptor grid, receptor grid generation workflow was used and the cubic box of specific dimensions was generated around the cocrystalized ligand sacubitrilat to perform molecular docking studies. was towards the shallow S1 pocket of NEP protein [27] . The charge positive interaction with ARG717 and polar interaction with ASN542 were found to be common in sacubitrilat and selected eight drugs. Even in this study, all the eight drugs showed hydrophobic interactions with PHE544. Sacubitrilat also showed interactions with ASN542, ARG717, ARG110, and ARG102. Our eight selected drugs showed interactions with any three of the abovementioned residues. In-silico docking studies also showed that all the eight drugs showed interaction with HIS711 which then formed a hydrogen bond with zinc, causing the Table 2 .

Around

The Prime-MMGBSA was employed to calculate the binding energy of the top eight drugs with selected docked poses. All the eight drugs showed stability in the docked pose with ΔG binding energy > -34 Kcal/mol (Described in Table 1 ). The ΔG binding energy of cocrystallized drug sacubitrilat was found to be -96.51Kcal/mol. Both, co-crystalized ligand and selected eight drugs were found to be stability with docked pose of ligands. This finding indicates that, selected drug may act as NEP inhibitor.

After Also, in the body, the ligand binding site on the proteins conforms to the ligand shape and binding mode. IFD was conducted to resolve the shortcomings of rigid docking protocols.

IFD has two main applications, first is it generates the most accurate active complex structure of ligand, which is not possible in virtual molecular docking with rigid protein structure.

Second, IFD avoids false-negative results of virtual docking. In virtual docking screening of the ligands was done with the single conformation of ligands. However, in IFD, 20

confirmers were generated for each ligand. Hence IFD-SP was carried for ZINC000000001427, ZINC000001533877, ZINC000000601283, and ZINC000003831594

and a maximum of 20 conformers were generated for each ligand based on molecular docking and binding energy. Further, the IFD score and ligand interaction were analyzed for selected drugs. The IFD score and 3D ligand interactions are given in Figure 1 .

ZINC000000001427 showed similar non-bonding interactions as predicted in XP docking. ZINC000003831594 also showed new hydrophobic interaction with PHE689 and MET579, with hydrophobic interaction missing with TYR545. It also showed similar hydrophobic interaction patterns with other amino acid residues as predicted in XP docking.

Then, ADME properties of the four drugs were analyzed using the Quikprop module. The ADME profile was assessed using various descriptor calculations such as molecular weight, QPlogHERG, QPlogPo/w, QPPCaco, % human oral absorption, PSA and Lipinski rule of five (Given in Table 3 ). All the selected drugs obey the Lipinski rule of five.

The concept of molecular dynamics is used to simulate several atoms to the system with biological relevance. In includes the explicit solvent representation with the entire protein.

The main advantage of MD stimulation is that it represents the actual conditions of the biological system. It provides a highly dynamic protein structure, and the ligand-protein complex is solvated with water, as happening in 

The ZINC000000601283 (Indomethacin, a non-steroidal anti-inflammatory drug) and ZINC000003831594 (Tyropanoic acid, radiocontrast agent) were found to be more stable in increased % oral absorption of bioisostere structures of both drugs may increase the activity towards NEP as compared to their parent drugs. Therefore, the designed compounds could be further evaluated for NEP activity as they have shown preferable ADME profile in comparison of parent drugs. The other ADME parameters of bioisostere structure 1 and 2 of ZINC000000601283 and ZINC000003831594 are given in Table 6 .

The SwissADME free web server tool [43] was also used to determine the metabolism and excretion pattern of ZINC000003831594 and its bioisostere structures. After oral administration, ZINC000003831594 is readily absorbed from small intestine. 2hr after oral administration, ZINC000003831594 is rapidly excreted into bile which may limit its potential in targeting NEP. SwissADME analysis of ZINC000003831594 showed that, the metabolism of the drug is mediated by CYP3A4, CTP2D6 and CYP2C19 enzymes. However, both bioisosteric structures of ZINC000003831594 are inhibitors of CYP3A4 enzyme and their metabolism occurs by CYP1A2, CYP2C19, CYP2C9, and CYP2D6. This observation assumes importance, since CYP3A4 metabolises more than 50% of drugs and endogenous compounds and their biliary excretion [44] [45] [46] . Hence, both the bioisosteres of ZINC000003831594 may overcome the problem associated with the rapid excretion of the parent drug, due to their ability to inhibit CYP3A4 enzyme. Inhibition of metabolism may further prolong their biological activity.

In the present study, the in-silico drug repurposing approach was used to identify FDA approved drugs for NEP inhibition using the ZINC 12 database. 2934 FDA approved drugs were retrieved from the ZINC 12 database. Initially, all the drugs are subjected to HTVS, SP, screened for ADME analysis. All four selected drugs showed acceptable ADME profile in terms of molecular weight, QPlogPo/w, QPPCaco, percentage human oral absorption, PSA,

and Lipinski rule of five as compared to standard drug sacubitrilat. The QPlogHERG was estimated as -1.014 for sacubitrilat whereas -4.031, -6.747, -6.120, and -2.581 for ZINC000000001427, ZINC000001533877, ZINC000000601283, and ZINC000003831594

respectively. The ADME analyses indicate that, the selected four drugs might be less cardiotoxic when compared to sacubitrilat, with a lesser tendency to block HERG K + channel.

Further, the IFD-SP analysis was done for the top four selected drugs. Based on the binding pattern, ligand interaction, and IFD-SP score, the best conformer was selected for each of the four selected drugs and opted for MD simulation. In MD simulation, all ligand-protein complexes showed acceptable RMSD values. But significant ligand-protein stability was observed in complexes 3 and 4 as compared to complexes 1 and 2. Further the bioisosteres replacement approach was used to enhance the biological activity ZINC000000601283 and ZINC000003831594 against NEP.

Based on the results obtained from present study, ZINC000000601283, and ZINC000003831594 showed almost similar type of interaction compared to sacubitrilat with desired ADME profiles. Hence ZINC000000601283, and ZINC000003831594 and their bioisosteric structures might be act as potential inhibitors of NEP. However, further in-vitro and in-vivo studies using models of NEP inhibition needs to be evaluated to confirm the insilico predictions. 

Suman Manandhar Data Curation and Reviewing

Avinash Kumar Software

K. Sreedhara Rangnath Pai Reviewing

Suvarna Kini Reviewing

Anoop Kishore Supervision, Reviewing and Editing

☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. 

The author declares that there is no conflict of interest.

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