Chemoinformatic Screening for the Selection of Potential Senolytic Compounds from Natural Products

biomolecules

Article Chemoinformatic Screening for the Selection of Potential Senolytic Compounds from Natural Products

Oscar Salvador Barrera-Vázquez 1 , Juan Carlos Gómez-Verjan 2 and Gil Alfonso Magos-Guerrero 1,*

1 Department of Pharmacology, School of Medicine, Universidad Nacional Autónoma de México (UNAM), Mexico City 04510, Mexico; [email protected] 2 Dirección de Investigación, Instituto Nacional de Geriatría, Mexico City 10200, Mexico; [email protected] * Correspondence: [email protected]; Tel.: +52-(55)-5623-2165

Abstract: Cellular senescence is a cellular condition that involves significant changes in gene expression and the arrest of cell proliferation. Recently, it has been suggested in experimental models that the elimination of senescent cells with pharmacological methods delays, prevents, and improves multiple adverse outcomes related to age. In this sense, the so-called senoylitic compounds are a class of drugs that selectively eliminates senescent cells (SCs) and that could be used in order to delay such adverse outcomes. Interestingly, the first senolytic drug (navitoclax) was discovered by using chemoinformatic and network analyses. Thus, in the present study, we searched for novel senolytic compounds through the use of chemoinformatic tools (fingerprinting and network pharmacology) over different chemical databases (InflamNat and BIOFACQUIM) coming from natural products (NPs) that have proven to be quite remarkable for drug development. As a result of

screening, we obtained three molecules (hinokitiol, preussomerin C, and tanshinone I) that could be considered senolytic compound candidates since they share similarities in structure with senolytic

Citation: Barrera-Vázquez, O.S.; leads (tunicamycin, ginsenoside Rb1, ABT 737, rapamycin, navitoclax, timosaponin A-III, digoxin, Gómez-Verjan, J.C.; Magos-Guerrero, roxithromycin, and azithromycin) and targets involved in senescence pathways with potential use in G.A. Chemoinformatic Screening for the treatment of age-related diseases. the Selection of Potential Senolytic Compounds from Natural Products. Keywords: chemoinformatics; senescence; natural products; senolytics; database; aging Biomolecules 2021, 11, 467. https://doi.org/ 10.3390/biom11030467 1. Introduction Academic Editor: Antonio Evidente Cellular senescence is a biological condition that involves signiﬁcant changes in gene expression and the loss of proliferative potential. Interestingly, during aging, immune clear- Received: 25 February 2021 ance defects lead to the accumulation of senescent cells, which are thought to limit tissue by Accepted: 17 March 2021 Published: 22 March 2021 releasing the so-called senescence-associated secretory phenotype (SASP), thus creating a mild but chronic proinﬂammatory microenvironment that may impair the normal function

Publisher’s Note: MDPI stays neutral of surrounding tissue [1]. Such an environment has been associated with several diseases with regard to jurisdictional claims in such as cancer, cardiovascular disease, obesity, type 2 diabetes, sarcopenia, pulmonary published maps and institutional affil- fibrosis, osteoarthritis, atherosclerosis, and neurological disorders [1]. Moreover, senescent iations. cells have upregulated pro-survival/antiapoptotic mechanisms such as the PI3K/Akt and Bcl2 pathways [2]. Studies in experimental models have shown that targeting senescent cells through different genetic or pharmacological methods may delay, prevent, and/or improve multiple adverse outcomes related to age [3]. Senolytics are a class of drugs that are distinguished by selectively killing senes- Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. cent cells (SCs). The first reported compounds with senolytic properties were dasatinib, This article is an open access article quercetin, fisetin, and navitoclax; however, over the years, this group of drugs has been distributed under the terms and slowly growing larger. It should be noted that in different preclinical models, treatment conditions of the Creative Commons with senolytics allows the pro-survival Senescent Cell Anti-Apoptotic Pathways (SCAPs) Attribution (CC BY) license (https:// to be temporarily deactivated, thereby causing the apoptosis of SCs in damaged tissues [4]. creativecommons.org/licenses/by/ Moreover, in different preclinical studies, senolytics appear to restore progenitor dysfunc- 4.0/). tion, attenuate tissue inflammation, and alleviate age-related metabolism and disease

Biomolecules 2021, 11, 467. https://doi.org/10.3390/biom11030467 https://www.mdpi.com/journal/biomolecules Biomolecules 2021, 11, 467 2 of 17

dysfunction in all cell and tissue types [5]. Senolytics have been shown to delay, prevent, or alleviate frailty, cancers, and cardiovascular, neuropsychiatric, liver, kidney, muscu- loskeletal, and pulmonary diseases and disorders, among others. The first pilot trials of senolytics suggest that they decrease senescent cells, reduce inflammation, and improve human frailty deficits [4,6]. Moreover, early trials with two senolytic compounds (quercetin and dasatinib) have demonstrated positive effects on clinical assessments of patients with idiopathic pulmonary fibrosis [7]. Natural products (NPs) have played an essential role in drug discovery; it is estimated that around 30% of the drugs currently used in clinics for the last 39 years are NP-derived or NP botanicals [8,9]. Additionally, different NPs have been shown to be quite helpful in therapeutics since they are known to possess exceptional selectivity for different cell targets such as quercetin [10]. Additionally, computational analyses have been very valuable for drug discovery and screening, improving the drug discovery success rate, and diminishing the number of experimental approaches needed. In this sense, the first senolytic drug (navitoclax) was discovered using chemoinformatic and network analyses [11]. Therefore, in the present work, we searched the literature for reported senolytic compounds in order to determine their physicochemical properties and use them, employing different methodologies based on fingerprinting and network pharmacology. The aim was to search for novel senolytic compounds in natural products across two databases: InflamNat (657 molecules from https://pubs.acs.org/doi/10.1021/acs.jcim.8b00560, Accessed on 8 June 2020) [12], and BIOFACQUIM, (422 molecules from https://biofacquim.herokuapp.com/). Accessed on 6 July 2020 [13].

2. Materials and Methods 2.1. Creation of a Senolytic Lead Dataset 2.1.1. Collection of Senolytic Compounds Review of the bibliography published in Pubmed and Scopus up to September 2020 concerning compounds reported as senolytic drugs to create a dataset.

2.1.2. Selection of Senolytic Leads from the Dataset Based on Hierarchical Analysis and Chemical Space Analysis The Simplified Molecule Input Line Entry System (SMILES) of the compounds from the dataset was searched in Pubchem, accessed on 3 August 2018 to 30 September 2020 (https://pubchem.ncbi.nlm.nih.gov/)[14] and the SwissADME server, accessed on 30 September 2020 (http://www.swissadme.ch)[15]. Osiris Data Warrior software V5.2.1 was used in order to analyze the physicochemical properties and molecular descriptors such as G protein-coupled receptors (GPCR) ligand (GPCR. Ligand), ion channel modulator (ion.channel.modulator), kinase inhibitor (kinase.inhibitor), nuclear receptor ligand (nuclear.receptor.ligand), protease inhibitor (protease.inhibitor), enzyme inhibitor (enzyme.inhibitor), number of violations (nviolations), number of atoms (natoms), log k p values in [cm/s] (log.Kp..cm.s.), Lipinski’s Rule violations (Lipinski..violations), Ghose Filter violations (Ghose..violations), Veber Rule violations (Veber..violations), Egan Rule violations (Egan..violations), Muegge’s Rule violations (Muegge..violations), bioavailability score (bioavailability.score), molecular weight (molweight), P: conc (octanol)/conc (water) (cLogP), S: water solubility in mol/L (cLogS), hydrogen acceptor (H.acceptors), hydrogen donors (H.donors), total surface area (total.surface.area), polar surface area (polar.surface.area), druglikeness (druglikeness), shape index (shape.index), molecular flexibility (molecular.flexibility), electronegative atoms (electronegative.atoms), rotatable bonds (rotatable.bonds), aromatic rings (aromatic.rings), aromatic atoms (aromatic.atoms), sp3 atoms (sp3 atoms), and symmetric atoms (symmetric.atoms) of the compounds previously obtained from the created dataset [16]. First, we performed a cluster analysis of all physicochemical and molecular descriptors using the K-means algorithm and a distance matrix from senolytic compounds. Such analysis was performed with the complexheatmap package [17] with R-studio (RStudio Biomolecules 2021, 11, 467 3 of 17

PBC250, Boston, MA, USA), version 3.4. Next, the chemical space and the distribution of the data were analyzed using Principal Component Analysis (PCA) to evaluate whether any of the parameters (molecular descriptors) were more critical in distributing the chemical space on the cluster analysis. PCA is defined as an orthogonal linear transformation technique that can transform the data into a new coordinate system. We used a two- dimensional system in our analysis [18]. Briefly, the variance of the data maximized on the first coordinate was called the first principal component. The second variance was maximized on the second coordinate; we used the factoextra package with R-Studio version 3.4.

2.2. Selection of Drug-Likeness of NPs from the BIOFACQUIM Database and the InflamNat Dataset Based on the Quantitative Estimate of Drug-Likeness (QED) 2.2.1. Data Collection for NPs We obtained a drug-likeness estimator based on the Quantitative Estimate of Druglik- ness (QED) for compounds from the InflamNat and BIOFACQUIM databases [12,13] by using DruLito software (http://www.niper.gov.in/pi_dev_tools/DruLiToWeb/DruLiTo_ index.html), accessed on 7 December 2020 [19]. The Spatial Data File (SDF) files required for this software were obtained from the PubChem (https://pubchem.ncbi.nlm.nih.gov/)[14] and ZINC15 [20] servers, accessed on 11 January 2021. We analyzed only those molecules available on both servers.

2.2.2. Determination of the Drug-Likeness of NPs To determine drug-likeness, we used the Quantitative Estimate of Drug-likeness (QED), an integrated score, to evaluate the compounds’ preference to be considered a hit. QED is a method for the quantiﬁcation of drug-likeness, considering the main molecular properties together. The QED score uses the molecular descriptors: molecular weight (Da), Ghose-Crippen-Viswanadhan octanol-water partition coefﬁcient (AlogP), the number of H- acceptors (HBA), the number of H-donors (HBD), the number of rotatable bonds, total polar surface area (TPSA), and aromatic bond count. A histogram and boxplot were performed to compare the distribution of the molecular descriptors, both senolytic compounds and NP properties. We selected the NPs with high QED scores (over 0.5).

2.3. The Selection of Putative Senolytic Compounds Is Based on a Comparison of the Fingerprints of the Drug-Like NPs and Senolytic Leads Fingerprints of Drug-Like NPs and Senolytic Leads To determine their fingerprints and perform a comparison, we used ChemmineR and rcdk in R-Studio version 3.4 [21]. We used the extended value with a default length of 1024 (number of bits), taking rings and atomic properties of the senolytic compound dataset as leads and the NPs obtained from databases as tested compounds. Then, we performed a cluster analysis by using the Tanimoto coefficient [21] to compare the drug-likeness of NPs versus the senolytic compounds. The molecules were then classified into three groups using as a parameter the molecular distances by Ward’s clustering method, which are the most popular hierarchical clustering algorithms used in drug discovery [22]. To confirm the clustering results, the Dunn index and the silhouette coefficient were used [21]. After the different clustering methods were performed, the cluster containing the senolytic lead was analyzed in order to obtain the putative senolytic molecules and construct a compound-target network.

2.4. Selection of the Best Putative Senolytic Molecules through Their Multitarget Capacity Compound-Target Network Generation To explore the possible interaction of the drug-like molecules with targets involved in the senescence processes, we generated a compound-target network (by using Compound Spring Embedder (CoSE) layout based on the Cytoscape software, (National Institute of General Medical Sciences of the National Institutes of Health, Bethesda, MD, USA), Biomolecules 2021, 11, x 4 of 17

To explore the possible interaction of the drug-like molecules with targets involved in the senescence processes, we generated a compound-target network (by using Com- pound Spring Embedder (CoSE) layout based on the Cytoscape software, (National Insti- Biomolecules 2021, 11, 467 tute of General Medical Sciences of the National Institutes of Health, Bethesda, Maryland4 of 17, United States of America), 3.8.2 version [23]) with information on targets obtained from the BindingDB database (https://www.bindingdb.org/bind/index.jsp) [24], accessed on 12 January3.8.2 version 2021. [This23]) database with information of measured on targets binding obtained affinities from focus thees on BindingDB the interactions database of proteins(https://www.bindingdb.org/bind/index.jsp considered to be drug targets with small)[24 drug], accessed-like molecules on 12 January [24]. Interestingly, 2021. This thisdatabase database of measured has been binding previously afﬁnities used focusesin search on of the anti interactions-inflammatory of proteins drugs considered from NPs [to18 be]. The drug network targets withwas constructed small drug-like from molecules several pathways [24]. Interestingly, involved thisin senescence database has present been inpreviously Wikipathways used in such search as DNA of anti-inﬂammatory damage/telomere drugs stress from-induced NPs [senescence18]. The network (DD/TSIS) was, pconstructedentose phosphate from several pathway pathways and g involvedlycolysis in senescencesenescent cells present (PPPsc) in Wikipathways, oncogene-induced such as senescenceDNA damage/telomere (OIS), DNA stress-induced damage response senescence (DDR) (DD/TSIS),, oxidative pentosestress-induced phosphate senescence pathway (OSIS)and glycolysis, glycolysis in senescent in senescence cells (PPPsc),(Gis), senescence oncogene-induced-associated senescence secretory (OIS),phenotype DNA damage(SASP), intrinsicresponse pathways (DDR), oxidative for apoptosis stress-induced (IPFA) and senescence the cell c (OSIS),ycle (CC) glycolysis [25,26] and in senescence information (Gis), on BindingDBsenescence-associated with compounds secretory to phenotype select the (SASP),multitarget intrinsic putative pathways senolytic for apoptosis molecules (IPFA). For moreand the details cell cycle concerning (CC) [25 the,26 data] and on information targets and on respective BindingDB pharmacological with compounds functions to select and the themultitarget genes from putative the senolytic senolytic network, molecules. see the For Supplementary more details concerning Materials. the The data methodology on targets ofand this respective work is summarized pharmacological in Figure functions 1. and the genes from the senolytic network, see the Supplementary Materials. The methodology of this work is summarized in Figure1.

Figure 1. The workflow of the methodology for identifying putative senolytic molecules. (A) Phase 1: Creating a senolytic leadFigure dataset 1. The from workflow Pubmed of the and methodology Scopus databases for identifying from the putative information senolytic obtained molecules. up to September(A) Phase 1: 2020. Creating The leadsa senolytic were lead dataset from Pubmed and Scopus databases from the information obtained up to September 2020. The leads were determined by hierarchical structural clustering using the complexheatmap (K-means) with R-Studio through their molecular determined by hierarchical structural clustering using the complexheatmap (K-means) with R-Studio through their mo- descriptors. (B) Phase 2: Selection of drug-like natural products (NPs) from the BIOFACQUIM and Inflamnat datasets based lecular descriptors. (B) Phase 2: Selection of drug-like natural products (NPs) from the BIOFACQUIM and Inflamnat da- ontasets the based Quantitative on the Quantitative Estimate of Drug-likenessEstimate of Drug (QED).-likeness The molecules(QED). The were molecules searched were in thesearched Pubchem in the server Pubchem to obtain server their to structuralobtain their information. structural information. Data filtering Data of NPs filtering to obtain of NPs drug-like to obtain molecules drug-like by molecules QED was by searched QED was for thesearched reported for targetsthe re- inported BindingDB. targets in (C BindingDB.) Phase 3: Selection (C) Phase of 3: putative Selection senolytic of putative compounds senolytic basedcompounds on a comparison based on a comparison of the fingerprints of the finger- of the drug-likeprints of the NPs drug and-like senolytic NPs and leads senolytic employing leads the employing Tanimoto the coefficient Tanimoto and coefficient the Silhouette and the clustering Silhouette (used clustering to corroborate (used to thecorroborate drug-like the molecules drug-like from molecules NPs). from (D) Phase NPs). 4: (D Selection) Phase 4: of Selection the best of putative the best senolytic putative molecules senolytic molecules by creating by networks creating with Compound Spring Embedder (CoSE) layout using Cytoscape software. The selected drug-like molecules, their pharmacological targets, and the network that contained targets involved in senescence were used in order to construct a compound-target senescence network. Biomolecules 2021, 11, 467 5 of 17

3. Results 3.1. Creation of a Senolytic Lead Dataset The search for senolytic compounds in Scopus and Pubmed showed 79 compounds with senolytic activity. Hierarchical clustering and PCA analysis performed with molecular descriptors can be seen in Figure2 and Supplementary Figure S1. Interestingly, we found two main clusters of senolytic compounds according to their 1-D and 2-D molecular descriptors focused on molecular.ﬂexibility, H.Donors, Veber..violations, Muegge..violations, Lipinski..violations, nviolations, H.Acceptors, polar.surface.area, electronegative.atoms, sp3.atoms, molweight, natoms, total.surface.area, rotatable.bonds, Egan..violations, and Ghose..violations. The cluster with the greatest similarity between the molecules was cluster one (marked in yellow in Figure2). This cluster includes navitoclax, ABT-737, tunicamycin A, B, C, and D, digoxin, rapamycin, ginsenoside Rb1, roxithromycin, timosaponin A-III, and azithromycin. We used these compounds as senolytic leads for the rest of the study. Table1 summarizes the main characteristics of the compounds in cluster one, including their pharmacological activity.

Table 1. Main characteristics of the molecules of cluster one.

Pubchem Compound K-Means Senolytics Pharmacological Activity References ID(CID) Coefﬁcient Navitoclax 24978538 Inhibitor of Bcl-2 and Bcl-xL 0.44 [11,27] ABT 737 11228183 Inhibitor of Bcl-2 and Bcl-xL 0.58 [28] Disturbs the endoplasmic reticulum (ER) homeostasis and causes the accumulation of Tunicamycin 56927848 0.34 [29] misfolded or unfolded proteins in the ER, inducing cell death Affects the Wnt/β-catenin signalling pathway by Ginsenoside downregulating β-catenin/T-cell factor-dependent 9898279 0.31 [30] Rb1 transcription and expression of its target genes ATP-binding cassette G2 and P-glycoprotein Enhances autophagosome formation of T cells by suppressing S6RP phosphorylation, which is a Azithromycin 447043 0.32 [31] downstream target of the mammalian target of rapamycin (mTOR) pathway Inhibitor of TGF-β1-induced activation of ERK and Roxithromycin 6915744 0.23 [32] AKT and down-regulation of caveolin-1 The inductor of selective cytotoxic activity that Timosaponin 15953793 involves inhibition of mTOR, induction of ER stress, 0.29 [33] A-III and protective autophagy Digoxin 2724385 Positive inotropic and negative chronotropic agent 0.21 [34] Inhibitor of mTOR complex 1 (mTORC1), which phosphorylates substrates including S6 kinase 1 (S6K1), eIF4E-binding protein 1 (4E-BP1), Rapamycin 5284616 −0.2 [35] transcription factor EB (TFEB), unc-51-like autophagy-activating kinase 1 (Ulk1), and growth factor receptor-bound protein 10 (GRB-10) Biomolecules 2021, 11, 467 6 of 17 Biomolecules 2021, 11, x 6 of 17

FigureFigure 2. Hierarchical 2. Hierarchical structural structural clustering clustering of of the the previously previously reportedreported senolytic compounds compounds.. A heatmap A heatmap of hierarchical of hierarchical clustering was generated employing the complexheatmap package in R-Studio separated by K-means. The histogram at clustering was generated employing the complexheatmap package in R-Studio separated by K-means. The histogram at the top represents a frequency distribution of each molecular descriptor (turquoise). Density plots at the top represent the the topdistribution represents of a frequencyeach molecular distribution descriptor of (blue each). Violin molecular plots descriptoron the right(turquoise). show the molecular Density descriptors’ plots at the distribution top represent for the distributioneach senolytic of each compound molecular and descriptor their probability (blue). Violindensity. plots Boxplots on the on the right right show show the the molecular distribution descriptors’ of numerical distribution data from for each senolytic compound and their probability density. Boxplots on the right show the distribution of numerical data from molecular descriptors in each senolytic compound. As seen in the dendrogram at the left of the figure, there are two main clusters; we focus on the first, framed in red lines due to its similarity coefficient. Biomolecules 2021, 11, x 7 of 17

Biomolecules 2021, 11, 467 7 of 17 molecular descriptors in each senolytic compound. As seen in the dendrogram at the left of the figure, there are two main clusters; we focus on the first, framed in red lines due to its similarity coefficient.

3.2.3.2. SelectionSelection ofof Drug-LikeDrug-Like NPsNPs BasedBased onon QEDQED FromFrom 1079 1079 NP NP molecules molecules obtained obtained from from thethe InﬂamNatInflamNat (657 (657 molecules) molecules) and and BIO- BIO- FACQUIMFACQUIM (422 (422 molecules)molecules) datasets, datasets, wewe onlyonly usedused 562562 moleculesmolecules analyzedanalyzed inin DruLitoDruLito softwaresoftware toto obtainobtain thethe physicochemicalphysicochemical parametersparameters requiredrequired for for QED. QED. The The comparison comparison of of thethe physicochemicalphysicochemical parametersparameters and and the the whole whole senolyitic senolyitic dataset dataset (79 (79 molecules) molecules) is is shown shown ininFigures Figure3s and3 and4 . In4. both In both ﬁgures, figures the molecular, the molecular descriptor descriptor values of values the NPs of and the senolytic NPs and compoundssenolytic compounds are distributed are distrib withinuted a similar within range. a similar range.

FigureFigure 3. 3.Distribution Distribution ofof molecularmolecular descriptorsdescriptors forfor senolyticsenolytic compoundscompounds andand NPs:NPs: molecularmolecular weightweight (Da),(Da), Ghose-Crippen-Ghose-Crippen- Viswanadhan octanol-water partition coefficient (AlogP), the number of H-acceptors (HBA), the number of H-donors Viswanadhan octanol-water partition coefﬁcient (AlogP), the number of H-acceptors (HBA), the number of H-donors (HBD), the number of rotatable bonds, total polar surface area (TPSA), aromatic bond count, and Quantitative Estimate of (HBD), the number of rotatable bonds, total polar surface area (TPSA), aromatic bond count, and Quantitative Estimate of Drug-likeness (wQED). Vertical lines represent frequency values, while horizontal lines represent the intervals of the data. Drug-likenessThe abscissa axis (wQED). represents Vertical the linescalculated represent values frequency for each values, compound while and horizontal each molecular lines represent descriptor, the intervalsand the ordinate of the data. axis Therepresents abscissa the axis fre representsquency of thethe calculatedvalues. NPs values (n = 562) for each and compoundsenolytic comp ando eachunds molecular (n = 79). descriptor, and the ordinate axis represents the frequency of the values. NPs (n = 562) and senolytic compounds (n = 79). Biomolecules 2021, 11, x 8 of 17 Biomolecules 2021, 11, 467 8 of 17

Figure 4. Boxplots showing the distribution and summary statistics of the molecular descriptors of NPs and senolytic Figure 4. Boxplots showing the distribution and summary statistics of the molecular descriptors of NPs and senolytic compounds: molecular weight range (Da), Ghose-Crippen-Viswanadhan octanol-water partition coefﬁcient (AlogP), the compounds: molecular weight range (Da), Ghose-Crippen-Viswanadhan octanol-water partition coefficient (AlogP), the numbernumber of of H-acceptors H-acceptors (HBA), (HBA), the the number number of of H-donors H-donors (HBD), (HBD), the the number number of of rotatable rotatable bonds, bonds, total total polar polar surface surface area area (TPSA),(TPSA), aromatic aromatic bond bond count count of senolyticof senolytic compounds compounds and and NPs. NPs The. The abscissa abscissa axis axis represents represents the the calculated calculated values values for for each each compoundcompound and and each each molecular molecular descriptor, descriptor, and and the the ordinate ordinate axis axis represents represents the the frequency frequency of theof the values. values. NPs NPs (n =(n 562) = 562) and and senolyticsenolytic compounds compounds (n (=n 79).= 79).

AfterAfter QED, QED, molecules molecules with with a scorea score greater greate thanr than 0.5 0.5 were were considered considered drug-like, drug-like pro-, pro- ducingducing only only 345 345 molecules molecules (61.39%) (61.39%) of of the the 562 562 NPs, NPs from, from which which we we obtained obtained Total Total Num- Num- berber of of NP NP Molecules Molecules thatthat ViolatedViolated the Rule Rule (( (weightedweighted Quantitative Quantitative Estimate Estimate of of Druglike- Drug- likenessness(wQED (wQED)) < 0.5 <) 0.5)) = 217 = 217 and and Total Total Number Number of NP of Molecule NP Moleculess that Passed that Passed the Rule the (wQED Rule (wQED> 0.5) = > 345. 0.5) = 345. 3.3. Selection of Putative Senolytic Compounds 3.3. Selection of Putative Senolytic Compounds From 345 drug-like molecules obtained by QED, only 53 molecules were considered From 345 drug-like molecules obtained by QED, only 53 molecules were considered in BindingDB; they showed reported targets. We performed a comparison of the 53 in BindingDB; they showed reported targets. We performed a comparison of the 53 mol- molecules with each of the 17 senolytic leads obtained from cluster one (Figure2) through ecules with each of the 17 senolytic leads obtained from cluster one (Figure 2) through fingerprints, the Tanimoto coefficient, and Ward’s method. Figure5 is an example of this fingerprints, the Tanimoto coefficient, and Ward’s method. Figure 5 is an example of this analysis with rapamycin. The optimal number of clusters was determined by the Elbow analysis with rapamycin. The optimal number of clusters was determined by the Elbow method (Figure5A) , resulting in three clusters as an optimal number for cluster analysis. method (Figure 5A), resulting in three clusters as an optimal number for cluster analysis. The Dunn index and the silhouette coefficient (Sc) corroborate the cluster analysis for the 53 The Dunn index and the silhouette coefficient (Sc) corroborate the cluster analysis for the drug-like molecules and the senolytic leads from group 1 (Figure5D). The number of drug- 53 drug-like molecules and the senolytic leads from group 1 (Figure 5D). The number of like molecules clustered with senolytic leads from group 1 is 17 for navitoclax (Sc: 0.23), drug-like molecules clustered with senolytic leads from group 1 is 17 for navitoclax (Sc: 16 for ABT 737 (Sc: 0.23), 17 for tunicamycin (Sc: 0.24), 14 for ginsenoside (Sc: 0.29), 16 for azithromycin0.23), 16 for (Sc:ABT 0.27) 737 (S andc: 0.23) roxithromycin, 17 for tunicamycin (Sc: 0.27), (S 14c: for0.24) timosaponin, 14 for ginsenoside A-III (Sc: (Sc: 0.29), 0.29), Biomolecules 2021, 11, x 9 of 17

Biomolecules 2021, 11, 467 9 of 17

16 for azithromycin (Sc: 0.27) and roxithromycin (Sc: 0.27), 14 for timosaponin A-III (Sc: 0.29), 11 for digoxin (Sc: 0.39), and 11 for rapamycin (Sc: 0.38). The mean of Sc was highest 11 for digoxin (Sc: 0.39), and 11 for rapamycin (Sc: 0.38). The mean of Sc was highest in three clusters, reflecting a better similarity between the drug-like molecules and the in three clusters, reﬂecting a better similarity between the drug-like molecules and the senolytic leads (Figure 5D). Supplementary Figure 2 shows the complete analysis for each senolytic leads (Figure5D). Supplementary Figure S2 shows the complete analysis for each of the 17 senolytic leads from group 1. Finally, the mean of the Sc of the cluster analysis of the 17 senolytic leads from group 1. Finally, the mean of the Sc of the cluster analysis performed with the 17 senolytic leads from group 1 was 0.27. Table 2 shows the results performed with the 17 senolytic leads from group 1 was 0.27. Table2 shows the results after eliminating repeated molecules. after eliminating repeated molecules.

FigureFigure 5 5.. StepsSteps in in the the selection selection of of putative putative senolytics senolytics comparing comparing 53 53 drug drug-like-like NPs NPs with with the the senolytic senolytic lead lead r rapamycin.apamycin. ( (AA)) TheThe dashed line line representsrepresents the the suitable suitable number number of clusters of clusters determined determined by the by Elbowthe Elbow method. method. (B) Cluster (B) Cluster analysis analysis of Ward’s of Ward’smethod, method, the senolytic the senolytic cluster is cluster shaped is in shaped red and in the red senolytic and themolecule senolytic as molecule 1. (C) Cluster as 1. ( plotC) Cluster using Kmeans plot using showed Kmeans the showedsame molecules the same in molecules the senolytic in the cluster senolytic marked cluster in blue. marked (D) Silhouette in blue. (D cluster) Silhouette representation cluster representation to corroborate to the corroborate previously thedescribed previously cluster described analysis cluster methods. analysis * means methods. matrix * of means distances matrix used of in distances clustering used by Ward’sin clustering method by in Ward subﬁgure’s method B. in subfigure B.

Table 2. Drug-like molecules obtained by fingerprint analysis and their pharmacological activity.

Compound Resulting from Pharmacological Activity Re- References ZINC ID Fingerprint Analysis ported Cacospongionolide B ZINC26966472 Anti-inflammatory agent [36] Carnosol ZINC3871891 Antineoplastic agent [37] Antiviral, anti-mutagenic, anti- [38–40] Dihydrotanshinone I ZINC2585546 cancer agent Epoxyazadiradione ZINC58576553 Anti-inflammatory agent [41] Farnesiferol B ZINC29134693 Anti-oxidant agent [42] Biomolecules 2021, 11, 467 10 of 17

Table 2. Drug-like molecules obtained by ﬁngerprint analysis and their pharmacological activity.

Compound Resulting from ZINC ID Pharmacological Activity Reported References Fingerprint Analysis Cacospongionolide B ZINC26966472 Anti-inflammatory agent [36] Carnosol ZINC3871891 Antineoplastic agent [37] Antiviral, anti-mutagenic, anti-cancer Dihydrotanshinone I ZINC2585546 [38–40] agent Epoxyazadiradione ZINC58576553 Anti-inflammatory agent [41] Farnesiferol B ZINC29134693 Anti-oxidant agent [42] Anti-inflammatory and Friedelin ZINC4097720 [43] antipyretic agent Fuscoside B ZINC72123265 Anti-inflammatory agent [44,45] Gibberellic acid ZINC3860467 Anti-inflammatory agent [46] Gliotoxin ZINC3875454 Anti-inflammatory agent [47] Hinokitiol ZINC95911093 Anti-cancer agent [48] Neurolenin B ZINC100090140 Anti-inflammatory agent [49] Penicillic acid ZINC3874657 Antibiotic [50] Preussomerin C ZINC34383300 Cytotoxic and anti-nematodal agent [51,52] Anti-oxidant and Tanshinone I ZINC2558154 [40,53] anti-inflammatory agent Anti-oxidant and Tanshinone IIA ZINC1650576 [54] anti-inflammatory agent Anti-cancer, anti-inflammation, Triptolide ZINC6483512 [55] anti-obesity, and anti-diabetic Anti-inflammatory and Ursolic acid ZINC31356858 [56] antihyperlipidemic agent

3.4. Network Analysis to Obtain the Best Putative Senolytic Molecules through Their Multitarget Capacity We obtained a compound-target network (Figure6) related to senescence that shows 861 nodes and 1227 edges (or links), where 17 nodes represent the drug-like molecules obtained by QED analysis. It should be noted that three molecules have the most signiﬁ- cant number of targets involved in any of the senescence pathways used. These putative senolytic molecules are represented in Figure6 as yellow nodes and their cellular targets as red nodes. The putative senolytic molecule hinokitiol showed a connection to the senescence network through the inhibition of histone deacetylase 4 and 5 (HDCA4 and 5) [57], targets involved in the cell cycle (CC). Preussomerin C inhibits JUN protein, a target involved in oxidative stress-induced senescence (OSIS) [58], and tanshinone I modulates the target RAD51, which is involved in DNA damage response (DDR) [59] (Figure6). Table3 summarizes the main characteristics of these putative senolytic compounds obtained from this analysis. BiomoleculesBiomolecules 20212021, 11,,11 x , 467 11 11of 17 of 17

FigureFigure 6. 6.A Anetwork network of ofputative putative senolytic senolytic compounds compounds with with their their biological biological targets. targets. (A ()A Interactions) Interactions of oftanshinone tanshinone I with I with its itsphamarcological phamarcological targets: targets: TOP1, TOP1, CA1,CES1, CA1,CES1, ACHE, ACHE, PTPN6 PTPN6 and and RAD51 RAD51.. (B) ( BInteractions) Interactions of ofpreussomerin preussomerin C with C with its its phamarcologicalphamarcological targets: targets: CCR1 CCR1,CCR2,CCR3,CCR6,CCR7,CCR8,,CCR2,CCR3,CCR6,CCR7,CCR8, CCR9, CCR9, CXCL10, CXCL10, FOS FOS and and JUN JUN.. ( (CC)) Interactions Interactions of hinoki- hinokitiol tiolwith with its its phamarcological phamarcological targets: targets: HDAC1, HDAC1, HDAC2, HDAC2, HDAC4, HDAC4, HDAC5,HDAC6, HDAC5,HDAC6, HDAC8, HDAC8, and and TYR. TYR The. The compound-target compound- targetnetwork network was was generated generated with with CoSE CoSE Layout Layout in Cytoscape in Cytoscape software, software, version versi 3.8.2on 3.8.2 [23 ].[23]. The The network network was was constructed constructed with with several pathways involved in senescence represented with the nodes of different colors and compounds (Methods): several pathways involved in senescence represented with the nodes of different colors and compounds (Methods): Nodes Nodes represent compounds or targets related to senescence according to their color. An edge represents a relation ob- tainedrepresent from BindingDB. compounds Abbreviations: or targets related DNA to topoisomerase senescence according 1 (TOP1), to carbonic their color. anhydrase An edge 1 (CA1), represents liver acarboxylesterase relation obtained 1 (CES1),from BindingDB. acetylcholinesterase Abbreviations: (ACHE), DNA tyrosine topoisomerase-protein phosphatase 1 (TOP1), carbonic non-receptor anhydrase type 6 1 (PTPN6), (CA1), liver DNA carboxylesterase repair protein 1 RA(CES1),D51 homolog acetylcholinesterase 1 (RAD51), CC (ACHE), chemokine tyrosine-protein receptor 1–3,6– phosphatase9 (CCR1–3, 6 non-receptor–9), C-X-C motif type chemokine 6 (PTPN6), ligand DNA 10 repair (CXCL10 protein), JunRAD51 Proto- homologOncogene, 1 (RAD51),AP-1 Transcription CC chemokine Factor receptor Subunit 1–3,6–9 (JUN), (CCR1–3,Fos Proto 6–9),-Oncogene, C-X-C motifAP-1 chemokineTranscription ligand Factor 10 Subunit (CXCL10), (FOS),Jun Proto-Oncogene,histone deacetylase AP-1 1,2,4 Transcription–6,8 (HDAC1,2,4 Factor–6,8), Subunit tyrosine (JUN),-protein Fos phosphatase Proto-Oncogene, non-receptor AP-1 Transcription type 6 (TYR), Factor DNA Subunitdam- age/telomere(FOS), histone stress deacetylase-induced senescence 1,2,4–6,8 (HDAC1,2,4–6,8),(DD/TSIS), pentose tyrosine-protein phosphate pathway phosphatase and glycolysis non-receptor in senescent type cells 6 (TYR), (PPPsc), DNA oncogenedamage/telomere-induced senescence stress-induced (OIS), senescenceDNA damage (DD/TSIS), response pentose(DDR), oxidative phosphate stress pathway-induced and senescence glycolysis (OSIS), in senescent glycoly- cells sis in senescence (Gis), senescence-associated secretory phenotype (SASP), intrinsic pathways for apoptosis (IPFA) and (PPPsc), oncogene-induced senescence (OIS), DNA damage response (DDR), oxidative stress-induced senescence (OSIS), the cell cycle (CC). glycolysis in senescence (Gis), senescence-associated secretory phenotype (SASP), intrinsic pathways for apoptosis (IPFA) and the cell cycle (CC). The putative senolytic molecules with the most significant number of targets were hinokitiol, preussomerin C, and tanshinone. Hinokitiol showed a connection to the senescence network through the targets; hinokitiol inhibits histone deacetylase 4 (HDCA4) and 5 [58], which are involved in the cell cycle (CC). Preussomerin C showed a connection to oxidative stress-induced senescence (OSIS) through the target JUN, acting as an inhibitor [59], and tanshinone I was linked to DNA damage response (DDR) through the target RAD51 as a modulator [59] (Figure 6). Hinokitiol is a monoterpenoid proven to be helpful as an antimicrobial, antifungal, antiviral, antiproliferative, anti-inflammatory, and antiplasmodial agent [60,61]. It can be isolated from the roots of the Hinoki tree, Hiba arboruitae (Japanese cypress) [62,63]. Preussomerin C belongs to the Preussomerins family and Biomolecules 2021, 11, x 12 of 17

Biomolecules 2021, 11, x 12 of 17

Biomolecules 2021, 11, x 12 of 17 exhibits a wide range of bioactivities, such as antibacterial, antifungal, nematicidal, and exhibitscytotoxic a activitywide range [63– 6of6 ]bioactivities,. Finally, tanshinones such as antibacterial, are the main antifungal, bioactive compoundsnematicidal, from and cytotoxicSalvia miltiorrhiza activity Bunge[63–66 (Danshen)]. Finally, trootsanshinones. They are are extensively the main bioactive used in traditional compounds Chinese from exhibitsSalviamedicine miltiorrhiza a andwide have range Bunge been of (Danshen)reportedbioactivities, to roots inhibit such. They migrationas antibacterial, are extensively, invasion antifungal, used, and in gelatinase traditional nematicidal, activity Chinese and in cytotoxicmedicinelung cancer activityand cell have line [6 been3 CL1–66] reported-.5 Finally, [53]. Table totanshinones inhibit 3 summarizes migration are the some, invasionmain characteristics bioactive, and gelatinase compounds of the activity three from pu- in Salvialungtative cancer miltiorrhizasenolytic cell compoundsline Bunge CL1 (Danshen)-5 [5 obtained3]. Table roots from 3 .summarizes They the arecompound extensively some- targetcharacteristics used network in traditional .of the three Chinese pu- medicinetative senolytic and have compounds been reported obtained to inhibit from themigration compound, invasion-target, and network gelatinase. activity in Biomolecules 2021, 11, 467 12 of 17 Table 3. Main characteristicslung cancer of thecell best line putative CL1-5 [5senolytic3]. Table compounds 3 summarizes obtained some by multitargetcharacteristics capacity of the. three putative senolytic compounds obtained from the compound-target network. Table 3. Main characteristics of the best putative senolytic compounds obtained byTargets multitarget of capacity. Pharmacological TargetsSenolytic of Senolytic CTableandidateTable 3. Main 3. Main characteristics characteristicsStructure of the of thebest best putative putativeSource senolytic senolytic compounds compounds obtained obtained by bymultitarget multitarget capacity capacity.References. PharmacologicalActivity CSompoundenolytic Senolytic Candidate Structure Source References Activity CTargetsNompoundetworkTargets of of

Senolytic PharmacologicalPharmacological SNenolyticetworkSenolytic

Senolytic Candidate Structure Source Source ReferencesReferences

Candidate The roots of the ActivityActivity CompoundCompound NetworkNetwork TheHinoki roots tree,of the Hinokitiol Anti-cancer HibaHinoki arboruitae tree, 7 [48] ZINC95911093Hinokitiol Antiagen-cancert TheHiba(Japanese roots arboruitae of cy- the 7 [48] ZINC95911093 The roots of the agent Hinokipress). tree, HinokitiolHinokitiol Hinoki(Japanese tree, cy-Hiba Anti-cancer Hiba arboruitae Anti-cancer agent7 7[48] [48] ZINC95911093ZINC95911093 arboruitaepress). agent (Japanese(Japanese cypress). cypress).

Endophytic Cytotoxic and Preussomerin C fungusEndophytic Lasiodip- antiCytotoxic-nematodal and 10 [51,52] PreussomerinZINC34383300 C funguslodia theobromae Lasiodip- Endophytic fungus Cytotoxicagent and Preussomerin C Endophytic anti-nematodal 10 [51,52] ZINC34383300 lodiaLasiodiplodiaZJ theobromae-HQ1 Cytotoxicanti-nematodal and 10 [51,52] PreussomerinZINC34383300 C fungus Lasiodip- agent theobromaeZJ-HQ1ZJ-HQ1 anti-nematodalagent 10 [51,52] ZINC34383300 lodia theobromae agent ZJ-HQ1

Anti- SalviaSalvia miltiorrhiza miltior- Anti-inflamma- inflammatory, TanshinoneTanshinone I I rhiza(Danshen (Danshen or tory, anti-coagu- Salvia miltior- Anti-anti-coagulant,inflamma- 6 [40,53] ZINC2558154 Tanshen in 6 [40,53] ZINC2558154Tanshinone I rhizaor Tanshen (Danshen in lanttory,and, andanti anti-neoplasic anti-coagu--ne- Chinese) 6 [40,53] ZINC2558154 Salviaor Chinese)Tanshen miltior- in lantAntioplasic, -andinflamma- antiagent-ne- Tanshinone I rhiza (Danshen tory, anti-coagu- Chinese) oplasic agent 6 [40,53] ZINC2558154 or Tanshen in lant, and anti-ne- The putative senolyticChinese) molecules oplasic with agent the most significant number of targets were 4. Discussionhinokitiol, preussomerin C, and tanshinone. Hinokitiol showed a connection to the senes- 4. DiscussioncenceDrug network discovery through based the on targets; natural hinokitiol products inhibits has histonea long deacetylaseand successful 4 (HDCA4) history. and Inter- 5 [58], estingly,whichDrug arewhen discovery involved natural based in product the cellon natural cycleresearch (CC). products is Preussomerin combined has a withlong C showed andcomputer successful a connection-aided history. drug to oxidativedesign Inter- 4. Discussion estingly,techniques,stress-induced when an actualnatural senescence amount product (OSIS)of researchsuccess through becomes is combined the target evident with JUN,, mainly computer acting when as- anaided we inhibitor explor drug edesign [ 59natu-], and techniques,raltanshinone proDrugducts’ discovery an chemical Iactual was linkedbased amount space toons in DNA ofnatural order success damage to products identifybecomes response hasbioactive evident a (DDR)long, compounds,mainly and through successful when the emphasizing we target history. explor RAD51e Inter- natu-drug as a estingly,raldiscoverymodulator products’ when [67 chemical] [. 59 nInatural] (Figurethis space context,product6).s Hinokitiolin torderresearchhe first to senolyticsidentify isis a combined monoterpenoid bioactive were with discovered compounds, computer proven using to-aided emphasizing be helpfulbioinformatics drug design as drug an an- techniques,discoveryapproachestimicrobial, [ 6 anto7] . antifungal,actual identifyIn this amount context,agents antiviral, of that t successhe transientlyfirst antiproliferative, becomessenolytics disable evident were anti-inflammatory, the discovered, mainlySCAP networkswhen using we and bioinformatics explorthat antiplasmodial allowe natu- se- ralapproachesnescent agentproducts’ cells [60 ,61to chemicalto ].identify survive It can space be agentsthe isolated shostile in that order from microenvironmenttransiently to the identify roots disable bioactive of the they Hinokithe compounds, SCAP create tree, networks andHiba emphasizingkill arboruitae the that non allow(Japanese-senes- drug se- discoverynescentcentcypress) cells cells around[6 [627 ]to., 63 Insurvive ]. themthis Preussomerin context, [ the4]. hostile the C firstmicroenvironment belongs senolytics to the Preussomerinswere they discovered create family and using kill and bioinformaticsthe exhibits non-senes- a wide approachescentrange cellsIn this ofaround bioactivities,to study, identify them we agents [4 identified such]. that as antibacterial, transiently 79 compounds disable antifungal, in the Pubmed SCAP nematicidal, andnetworks Scopus and that cytotoxic reported allow se- activ- as nescentsenolytics,ityIn [63 cells this–66 and ]. to study, Finally, surviveonce wwe tanshinonese the identifiedperformed hostile aremicroenvironment 79the the compounds cluster main analysis, bioactive in they Pubmed we compounds create found and and two Scopusfrom kill groups theSalvia reportednon with miltiorrhiza-senes- simi- as centsenolytics,laritiesBunge cells in around (Danshen)their and molecularonce them we roots. [4 performed] .descriptors. They are the extensively The cluster most analysis, representative used in we traditional found cluster, two Chinese bygroups similarities, medicine with simi- in- and have been reported to inhibit migration, invasion, and gelatinase activity in lung cancer laritiescludedIn in thisthe their followin study, molecular wg edrugs: identified descriptors. navitoclax, 79 compoundsThe ABT most-737, representative ginsenoside in Pubmed cluster,Rb1, and tunicamycin Scopus by similarities, reported A, B, in- asC cell line CL1-5 [53]. Table3 summarizes some characteristics of the three putative senolytic senolytics,cludedand D, thetimosaponin and followin once gwe AIII,drugs: performed digoxin, navitoclax, therapamycin, cluster ABT- 737,analysis, azithromycin, ginsenoside we found andRb1, two roxithromycin. tunicamycin groups with A, Thesesimi- B, C compounds obtained from the compound-target network. laritiesanddrugs D, arein timosaponin their involved molecular in AIII, cellular descriptors. digoxin, senescence rapamycin, The most [2,25,26] representativeazithromycin,. This cluster cluster,and was roxithromycin. bycorroborated similarities, These with included the following drugs: navitoclax, ABT-737, ginsenoside Rb1, tunicamycin A, B, C drugs4. Discussion are involved in cellular senescence [2,25,26]. This cluster was corroborated with and D, timosaponin AIII, digoxin, rapamycin, azithromycin, and roxithromycin. These drugs areDrug involved discovery in cellular based onsenescence natural products [2,25,26]. has This a longcluster and was successful corroborated history. with Inter- estingly, when natural product research is combined with computer-aided drug design techniques, an actual amount of success becomes evident, mainly when we explore natural products’ chemical spaces in order to identify bioactive compounds, emphasizing drug discovery [67]. In this context, the first senolytics were discovered using bioinformatics approaches to identify agents that transiently disable the SCAP networks that allow senescent cells to survive the hostile microenvironment they create and kill the non-senescent cells around them [4]. Biomolecules 2021, 11, 467 13 of 17

In this study, we identified 79 compounds in Pubmed and Scopus reported as senolytics, and once we performed the cluster analysis, we found two groups with similarities in their molecular descriptors. The most representative cluster, by similarities, included the following drugs: navitoclax, ABT-737, ginsenoside Rb1, tunicamycin A, B, C and D, timosaponin AIII, digoxin, rapamycin, azithromycin, and roxithromycin. These drugs are involved in cellular senescence [2,25,26]. This cluster was corroborated with PCA analysis. It is important to mention that all of them have different senolytic action mechanisms. For instance, navitoclax and ABT-737 can inhibit Bcl-2 and Bcl-xL, triggering the activation of caspases and exposure to phosphatidylserine in a Bak/Bax-dependent manner [68], and they share a similar sulfonylbenzamide structure. Tunicamycin binds irreversibly to the translocase enzyme [69]. Ginsenoside Rb1 is a glycosylated triterpenoid saponin that affects the Wnt/β-catenin signalling pathway by downregulating β-catenin/T-cell factor-dependent transcription and the expression of its target genes (ATP-binding cassette G2 and P-glycoprotein) [30,70]. Macrolides (azithromycin and roxithromycin) are inhibitors of protein synthesis via binding to the 50S ribosomal subunit of bacteria at the peptidyl transferase center, which enhances autophagosome formation of T cells by suppressing S6RP phosphorylation, thereby protecting against chronic inflammation [31,32,71,72]. Tim- osaponin AIII is a steroidal saponin with a selective cytotoxic activity that involves the inhibition of mTOR, the induction of ER stress, and protective autophagy [34]. Digoxin is a cardiac glycoside with a steroid-like structure used in order to treat cardiac arrhythmias and congestive heart failure [73], and rapamycin, a multitarget drug, is an inhibitor of mTOR complex 1 (mTORC1), which phosphorylates substrates including S6 kinase 1 (S6K1), eIF4E-binding protein 1 (4E-BP1), transcription factor EB (TFEB), unc-51-like autophagy- activating kinase 1 (Ulk1), and growth factor receptor-bound protein 10 (GRB-10). These substrates are involved in several processes including autophagy, cancer, neurological diseases, and aging [19,35]. To evaluate whether a compound is drug-like, Lipinski’s rule of five is traditionally used. However, the rule does not apply to substrates of biological transporters or natural products [74] which do not necessarily fall into Lipinski’s rule, and many approved drugs are a clear example of that (16% of oral drugs violate at least one of the criteria and 6% fail two or more) [74]. For this reason, Lipinski’s rule of five was replaced by the Quantitative Estimate of Drug-likeness (QED) as criteria for evaluating the drug-likeness of an NP [74]. This estimation allowed us to refine our search for NPs that could be candidates for drugs. In this context, the selection criteria were more tolerant in the QED, despite the fact that with other criteria, the molecular properties of some NPs could be outside the selection parameters, as occurs with Lipinski’s rule. QED allowed us to avoid bias and enrich our selection of NPs, obtaining a more realistic and graduated result. In reality, many approved drugs violate the criteria of this rule [74]. The distribution of the molecular descriptors between both datasets was similar, even though the senolytic dataset contained molecules of different natures, ranging from natural to synthetic products. Thus, these data indicate that natural products can be used as a reliable source for drug discovery. Although there are many clustering methods, only some are used in practice; two were used in this study: hierarchical grouping and the K-means method. They have been used successfully in fingerprint analysis [21]. In this study, fingerprint analysis allowed us to obtain 17 molecules due to their similarity with lead molecules from cluster one, indicating that the physicochemical properties of the 17 compounds have a greater similarity with the properties of senolytics. Different clustering methods validated these analyses. The construction of a compound-target network helped us to select which compounds might be better senolytic candidates due to their therapeutic targets. All these data helped us to verify that the similarity in chemical structure may exhibit a similar function at the biological level, avoiding high costs in experiments, because some NPs (hinokitiol, preussomerin C, and tanshinone I) showed targets involved in some pathways related to senescence such as the cell cycle, oxidative stress-induced senescence (OSIS), and DNA Biomolecules 2021, 11, 467 14 of 17

damage response (DDR), which are linked to several conditions, disorders, and age-related diseases [4,75]. It ought to be noted, however, that these three remaining molecules may still need experimental validation to demonstrate their properties as senolytics. We should not overlook that many of these drugs’ targets have not yet been fully explored, so many putative senolytics obtained from fingerprint analysis may also show senolytic activity due to their structural similarity. However, the criterion we used in our work was to consider those NPs that showed similarity with senolytic leads and had targets reported and involved in senescence. This study helped us to identify which compounds of those previously selected by fingerprints possess multitarget capacity, in agreement with those proposed by Kirkland et al. (2020), who indicate that a senolytic is a compound that can interact with different nodes in different ways, increasing the specificity for senescent cells [4]. The present study identified three compounds by evaluating the multitarget structure and functionality, which increases the possibility of selective action on senescent cells. These new molecules open a field of pre-clinical study of great interest, which is why the use of computational tools is necessary to expand knowledge through the discovery of new candidates from NPs, and their evaluation in pre-clinical studies.

5. Conclusions Our results show that the compounds hinokitiol, preussomerin C, and tanshinone-I can be used as senolytics due to their multitarget potential and their chemical characteristics of similarity. Therefore, they may be used in medicine to delay some age-related conditions such as cancer or neurodegenerative diseases. Nevertheless, further experimental analysis must be performed to validate such activities and demonstrate effectiveness and low toxicity. These agents may lead to the delay, prevention, or treatment of senescence and age-related conditions in humans, if clinical trials continue. Additionally, compounds from cluster one (tunicamycin, ginsenoside Rb1, ABT 737, rapamycin, navitoclax, rimosaponin A-III, digoxin, roxithromycin, and azithromycin) may be used as lead compounds for future chemoinformatic analysis due to the similarity in their molecular descriptors.

Supplementary Materials: The following are available online at https://www.mdpi.com/2218-273 X/11/3/467/s1: Figure S1. Principal component analysis of senolytic compounds. Figure S2. Results of fingerprinting of senolytic compounds. Table S1. Raw results for senolytic analyses. Author Contributions: Conceptualization, O.S.B.-V., J.C.G.-V. and G.A.M.-G.; methodology, O.S.B.-V. and J.C.G.-V.; software, O.S.B.-V. and J.C.G.-V.; validation, G.A.M.-G. and J.C.G.-V.; formal analysis, G.A.M.-G. and J.C.G.-V.; investigation, O.S.B.-V. and J.C.G.-V.; resources, G.A.M.-G.; data curation, J.C.G.-V.; writing—original draft preparation, O.S.B.-V., J.C.G.-V., and G.A.M.-G.; writing—review and editing, G.A.M.-G. and J.C.G.-V.; visualization, O.S.B.-V. and J.C.G.-V., and G.A.M.-G.; supervi- sion, G.A.M.-G. and J.C.G.-V.; project administration, G.A.M.-G. and J.C.G.-V. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The data that support the findings of this study are available in the supplementary material of this article. Acknowledgments: Oscar Salvador Barrera-Vázquez receives a postdoctoral fellowship from the DGAPA-UNAM. This paper is part of registered projects at the Department of Pharmacology, School of Medicine, UNAM. We thank Gerardo Mauro Najera Cortes for the database construction for senolytics. This paper is part of a registered protocol on geroprotectors at the Instituto Nacional de Geriatria (INGER). Conflicts of Interest: The authors declare no conflict of interest. Biomolecules 2021, 11, 467 15 of 17

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