# Expanding the Knowledge of the Molecular Effects and Therapeutic Potential of Incomptine A for the Treatment of Non-Hodgkin Lymphoma: In Vivo and Bioinformatics Studies, Part III

Normand García-Hernández, Fernando Calzada, Elihú Bautista, José Manuel Sánchez-López, Miguel Valdes, Claudia Velázquez, Elizabeth Barbosa

► To cite this version:

Normand García-Hernández, Fernando Calzada, Elihú Bautista, José Manuel Sánchez-López, Miguel Valdes, et al.. Expanding the Knowledge of the Molecular Effects and Therapeutic Potential of Incomptine A for the Treatment of Non-Hodgkin Lymphoma: In Vivo and Bioinformatics Studies, Part III. Pharmaceuticals, 2025, 18 (9), pp.1263. 10.3390/ph18091263. hal-05462248

HAL Id: hal-05462248

https://ut3-toulouseinp.hal.science/hal-05462248v1

Submitted on 16 Jan 2026

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
Article

# Expanding the Knowledge of the Molecular Effects and Therapeutic Potential of Incomptine A for the Treatment of Non-Hodgkin Lymphoma: In Vivo and Bioinformatics Studies, Part III

Normand García-Hernández <fnref n="1" />,* ID, Fernando Calzada <fnref n="2" />,* ID, Elihú Bautista <fnref n="3" /> ID, José Manuel Sánchez-López <fnref n="4" />,<fnref n="5" /> ID, Miguel Valdes <fnref n="2" />,<fnref n="6" />,<fnref n="7" /> ID, Claudia Velázquez <fnref n="8" /> ID and Elizabeth Barbosa <fnref n="6" /> ID

<figure><img src="image_2.png" /></figure>

check for
updates

Academic Editor: Eleni Gavriilaki

Received: 29 July 2025

Revised: 19 August 2025

Accepted: 19 August 2025

Published: 25 August 2025

**Citation:** García-Hernández, N.; Calzada, F.; Bautista, E.; Sánchez-López, J.M.; Valdes, M.; Velázquez, C.; Barbosa, E. Expanding the Knowledge of the Molecular Effects and Therapeutic Potential of Incomptine A for the Treatment of Non-Hodgkin Lymphoma: In Vivo and Bioinformatics Studies, Part III. *Pharmaceuticals* **2025**, *18*, 1263. https://doi.org/10.3390/ph18091263

**Copyright:** © 2025 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license
(https://creativecommons.org/
licenses/by/4.0/).

Abstract

**Background/Objectives:** Non-Hodgkin lymphoma (NHL) is a group of blood cancers that arise in the lymphatic nodes and other tissues after an injury to the DNA of B/T lineage and NK lymphocytes. Recently, we reported that incomptine A (IA) has in vivo antilymphoma properties. This research aimed to evaluate the effects of IA in the treatment of NHL using antilymphoma activity, Tandem Mass Tag (TMT), and bioinformatics approaches. **Methods:** The antilymphoma activity of IA was tested on male Balb/c mice inoculated with U-937 cells. Also, TMT, gene ontology enrichment, Reactome pathway, Kyoto Encyclopedia of Gene and Genomes pathway, molecular docking, toxicoinformatic, and pharmaceutical analyses were performed. **Results:** By TMT analysis of the altered levels of proteins present in the lymph nodes of Balb/c mice with NHL and treated with IA, we identified 106 significantly differentially expressed proteins (DEPs), including Il1rap, Ifi44, Timd4, Apoa4, and Fabp3 as well as Myh3, Eno 2, and H4c11. Among these, the Fhl1 result was the most important cluster altered and a potential core target of IA for the treatment of NHL. Network pharmacology studies have revealed that DEPs are associated with processes such as muscle contraction, glycolysis, hemostasis, epigenetic regulation of gene expression, transport of small molecules, neutrophil extracellular trap formation, adrenergic signaling in cardiomyocytes, systemic lupus erythematosus, alcoholism, and platelet activation, signaling, and aggregation. Computational studies revealed strong binding affinities with six proteins associated with cancer, positive pharmacokinetic properties, and no

<footnote n="1">¹ Unidad de Investigación Médica en Genética Humana, UMAE Hospital Pediatría 2° Piso, Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Av. Cuauhtémoc 330, Col. Doctores, Mexico City 06725, Mexico</footnote>

<footnote n="2">² Unidad de Investigación Médica en Farmacología, UMAE Hospital de Especialidades, 2° Piso CORSE, Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Av. Cuauhtémoc 330, Col. Doctores, Mexico City 06725, Mexico; valdesguevaramiguel@gmail.com</footnote>

<footnote n="3">³ CONAHCYT-División de Biología Molecular, Instituto Potosino de Investigación Científica y Tecnológica A. C, San Luis Potosí 78216, Mexico; francisco.bautista@ipicyt.edu.mx</footnote>

<footnote n="4">⁴ Hospital Infantil de Tlaxcala, Investigación y Enseñanza, 20 de Noviembre S/M, San Matías Tepetomatitlan, Apetatitlan de de Antonio Carvajal, Tlaxcala 90606, Mexico; cienciaflosan@gmail.com</footnote>

<footnote n="5">5 Phagocytes Architecture and Dynamics, IPBS, UMR5089 CNRS-Université Toulouse 3, 205 Route de Narbonne, 31077 Toulouse, France</footnote>

<footnote n="6">⁶ Instituto Politécnico Nacional, Sección de Estudios de Posgrado e Investigación, Escuela Superior de Medicina, Plan de San Luis y Salvador Díaz Mirón S/N, Col. Casco de Santo Tomás, Miguel Hidalgo, Mexico City 11340, Mexico; rebc78@yahoo.com.mx</footnote>

<footnote n="7">7 Laboratorio de Inmunología, Departamento de Sistemas Biológicos, Universidad Autónoma Metropolitana, Unidad Xochimilco, Calz. del Hueso 1100, Col. Villa Quietud, Coyoacán, Mexico City 04960, Mexico</footnote>

<footnote n="8">⁸ Área Académica de Farmacia, Instituto de Ciencias de la Salud, Universidad Autónoma del Estado de Hidalgo, Circuito exHacienda La Concepción S/N, Carretera Pachuca-Atocpan, San Agustín Tlaxcala 42076, Mexico; cvg09@yahoo.com</footnote>

<footnote>* Correspondence: normandgarcia@gmail.com (N.G.-H.); fercalber10@gmail.com (F.C.)</footnote>
toxicity. **Conclusions:** Our contribution suggests that IA may be a compound with potential therapeutic effects against NHL.

**Keywords:** incomptine A; non-Hodgkin lymphoma; antilymphoma activity; Tandem Mass Tag; network pharmacology; molecular docking; toxicoinformatic; pharmaceutical analysis

# 1. Introduction

Non-Hodgkin lymphoma (NHL) is a kind of blood cancer that generally develops in lymph nodes or in lymphatic tissue present in the organs of the human body such as the skin, intestines, or stomach. NHL is characterized by the malignant transformation of NK, T, or B cells and is associated with an increased proliferation and reduction in apoptosis [1]. NHL affects people of all ages, but is rare in children. There were 544,352 new cases and 259,793 deaths around the world in 2020; among these, 112,576 were women and 147,217 were men [1,2]. In Mexico, it was reported that NHL was the ninth leading cause of cancer for both sexes and was the fourth cause of death among neoplasms of lymphatic tissue and blood-forming and related organs [3]. Treatment is according to the subtype and stage of disease; usually, chemotherapy, stem cell transplantation, immunotherapy, and radiation therapy are the principal ways to treat NHL. In relation to chemotherapy, the drugs currently employed are effective but expensive, and they show several side effects such as mouth sores, nausea, vomiting, diarrhea, constipation, bladder irritation, and blood in the urine [4,5]. Methotrexate, an antimetabolite compound, is the first-line drug to treat NHL in Mexico; however, it causes hepatotoxicity, nephrotoxicity, dermatologic toxicity, and genotoxic damage, among others [5]. Therefore, it is important to understand the processes involved in the development of NHL and search for novel drugs that are easy to obtain, cheap, with low side effects, more effective, and that have therapeutic potential to treat several neoplasms. In this sense, recently the use of proteomic, network pharmacology, and computational studies has attracted increasing attention for finding novel drugs. Also, the integration of studies that include in vivo and in vitro activity associated with proteomic studies, network pharmacology, and in silico approaches have provided an important research strategy to know and elucidate the mode of action, find new targets, discover new biomarkers of the disease, and determine pharmacokinetic properties and toxicity, among others [6–15].

Incomptine A (IA), a heliangolide sesquiterpene lactone isolated from the roots and leaves of *Decachaeta incompta* (DC) R. M. King and H. Robinson [16], has been reported with several biological properties such as antiprotozoal, antibacterial, antipropulsive, anti-inflammatory, allelopathic, spermatic, antimicrobial, and phytotoxic effects as well as cytotoxic and antitumor activity (Figures 1 and 2). In relation to its cytotoxic properties, IA exhibited dose-dependent activity against four subtypes of NHL cells (U-937, Garage, SU-DHL-2, and REC-1), three subtypes of leukemia cells (HI-60, K562, and REH), and six subtypes of breast cancer cells (4T1, MDA-MB-231, SK-BR-3, T-47D, MCF7, and MCF10A). Also, it regulates NF-κB expression, induces apoptosis, induces the production of reactive oxygen species, and induces the differential protein expression of cytoskeleton proteins and glycolytic enzymes in U-937 cells and non-Hodgkin lymphoma in mice [10,16]. Additionally, IA could affect the antiapoptotic function of hexokinase II in 4T1 breast cancer cells [17].
<figure><img src="image_3.png" /><figcaption>**Figure 1.** Pharmacological activities reported for incomptine A (IA).</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>**Figure 2.** *Decachaeta incompta* (DC) R. M. King and H. Robinson.</figcaption></figure>

As a part of our study of the antitumor properties of **IA** and search for new natural anticancer products [10,17], we reported the molecular effects and therapeutic potential of **IA** for the treatment of NHL using antilymphoma activity, Tandem Mass Tag (TMT), gene ontology enrichment, Reactome pathway, Kyoto Encyclopedia of Gene and Genomes pathway, molecular docking, and toxicoinformatic and pharmaceutical analysis.

## 2. Results

### 2.1. Isolation and Identification of Sesquiterpene Lactone Incomptine A from Decachaeta Incompta

Dichloromethane extract from *Decachaeta incompta* (DCEDi) leaves was analyzed to detect the presence of sesquiterpene lactone **IA** by HPLC-DAD, using as standard an authentic sample obtained previously [16]. The results showed the presence of the helian-golide **IA** at 44.9 min (Figure 3) (Table 1). Incomptine A was obtained as white crystals from CH₂Cl₂-hexane with Rf of 0.60 and mp of 176–177 °C. **IA** was identified by comparison of the ¹H and ¹³C NMR data (Table 2) with the spectroscopic data obtained from the authentic sample. In the ¹³C-NMR spectra, 17 signals were observed. The ¹H and ¹³C NMR (Table 2) showed the presence of the α, β-unsaturated γ-lactone by the signals for an exocyclic methylene (δ<sub>H</sub> 6.40, 1H, dd, *J* = 2.0, 0.5 Hz and 5.80, 1H, d, *J* = 2.0 Hz) and a γ-lactone
carbonyl (δ<sub>C</sub> 169.7, C). These spectra showed signals for a trisubstituted double bond (δ<sub>H</sub> 5.31, 1H, dq, *J* = 11.0, 1.5 Hz, CH; δ<sub>C</sub> 126.1, CH, δ<sub>C</sub> 136.0, C), a disubstituted double bond (δ<sub>H</sub> 5.55, 1H, dd, *J* = 11.5, 7.5 Hz, and δ<sub>H</sub> 6.14, 1H, d, *J* = 11.5 Hz; CH; δ<sub>C</sub> 128.3, δ<sub>C</sub> 132.3; CH), an epoxy group (δ<sub>H</sub> 3.26, 1H, dd, *J* = 7.5, 1.0 Hz, CH; δ<sub>C</sub> 60.4, CH, δ<sub>C</sub> 61.2, C), two methyl groups (δ<sub>H</sub> 1.41, 3H, s; δ<sub>C</sub> 19.7, CH and δ<sub>H</sub> 1.88, 3H, s; δ<sub>C</sub> 23.7, CH), and one acetyl group (δ<sub>H</sub> 2.02, 3H, s, δ<sub>C</sub> 20.7, CH; δ<sub>C</sub> 169.2, C). Finally, all the signals of the ¹H and ¹³C NMR spectra obtained were compared with those reported in the literature [16] and were confirmed by MNova from MestreLab (version 14.0). Also, a presumptive identification was made by thin-layer chromatography (Figure 4); the same retention factor was obtained comparing with an authentic sample [16].

<figure><img src="image_5.png" /><figcaption>**Figure 3.** Chromatographic profile (240 nm) of dichloromethane extract from Decachaeta incompta (DCEDI) leaves (A) and incomptine A (B) standard. The x-axis indicates the retention time in minutes, while the y-axis indicates the peak % signal intensity.</figcaption></figure>
<table><caption>**Table 1.** HPLC coupled to diode array, retention time, and percentage in area of incomptine A identified of the dichloromethane extract from Decachaeta incompta leaves.</caption><thead><tr><th>Compound</th><th>R.T. * (min)</th><th>Area (%)</th><th>Molecular Weight (g/mol)</th><th>Molecular Formula</th></tr></thead><tbody><tr><td>Incomptine A</td><td>44.95</td><td>10.42</td><td>304.35</td><td>C<sub>17</sub>H<sub>20</sub>O<sub>5</sub></td></tr></tbody></table>

<table><caption>**Table 2.** ¹H and ¹³C NMR data (500 MHz and 125 MHz) of incomptine A (IA) in CDCl₃.</caption><thead><tr><th rowspan="2">Position</th><th colspan="2">Incomptine A</th></tr><tr><th>δ<sub>H</sub>, mult. (J in Hz)</th><th>δ<sub>C</sub> (ppm)</th></tr></thead><tbody><tr><td>1</td><td>3.26 dd (7.5, 1.0)</td><td>60.4</td></tr><tr><td>2</td><td>5.55 dd (11.5, 7.5)</td><td>128.3</td></tr><tr><td>3</td><td>6.14 d (11.5)</td><td>132.3</td></tr><tr><td>4</td><td>-</td><td>136.0</td></tr><tr><td>5</td><td>5.31 dq (11.0, 1.5)</td><td>126.1</td></tr><tr><td>6</td><td>5.01 dd (11.0, 1.5)</td><td>75.7</td></tr><tr><td>7</td><td>2.94 quin (3.0, 1.5)</td><td>50.1</td></tr><tr><td>8</td><td>5.20 ddd (4.5, 3.0, 2.0)</td><td>77.0</td></tr><tr><td>9</td><td>2.70 dd (14.5, 4.5)</td><td>43.0</td></tr><tr><td>9'</td><td>1.38 ddd (14.5, 3.0, 0.5)</td><td></td></tr><tr><td>10</td><td>-</td><td>61.2</td></tr><tr><td>11</td><td>-</td><td>137.2</td></tr><tr><td>12</td><td>-</td><td>169.7</td></tr><tr><td>13</td><td>6.40 dd (2.0, 0.5)</td><td>124.8</td></tr><tr><td>13'</td><td>5.80 d (2.0)</td><td></td></tr><tr><td>14</td><td>1.41 s</td><td>19.7</td></tr><tr><td>15</td><td>1.88 s</td><td>23.7</td></tr><tr><td>Ac (C=O)</td><td>-</td><td>169.2</td></tr><tr><td>Ac (CH<sub>3</sub>)</td><td>2.02 s</td><td>20.7</td></tr></tbody></table>

<figure><img src="image_6.png" /><figcaption>**Figure 4.** Thin-layer chromatography of presumptive identification of incomptine A (IA) in dichloromethane extract from *Decachaeta incompta* (DCEDI) leaves. (A) DCEDI leaves; (B) incomptine A; (C) incomptine B. Mobile phase: CHCl₃: EtOAc (7.5: 2.5, v/v); adsorbent: silica gel 60F₂₅₄ pre-coated TLC plates (Merck, Darmstadt); detection: compounds and extract were visualized by spraying with a 10 % solution of H₂SO₄ in water and then heating at 120 °C.</figcaption></figure>

<footnote>* R.T.: Retention time.</footnote>
## 2.2. *In Silico Prediction of Pharmacokinetics and Toxicological and Physicochemical Properties of Incomptine A and Methotrexate*

The absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of incomptine (IA) and methotrexate (MTX) were predicted utilizing ADMETlab, SwissADME, admetSAR, and PROTOX web servers (accessed on 13 July 2025). It is important to highlight that in recent years, the bioinformatic analysis to predict the ADMET properties of compounds under drug development has increased and has become an indispensable tool in the development of new drug candidates [13,18,19]. In this context, ADMET properties were calculated for IA and MTX to compare them (Table 3). The analysis suggests that IA has better oral absorption than MTX. In relation to the metabolism of IA and MTX, several CYP450 isoforms were evaluated to know if the molecules were substrates and/or inhibitors of them. Results suggest that both compounds can be metabolized by CYP3A4; they did not act like inhibitors of any CYP isoforms evaluated. In respect to distribution, the results suggest that IA has a better volume of distribution than MTX, and their excretion results suggest that IA may have a higher half-life than MTX. Finally, results of toxicity suggest that MTX may be more toxic and cause hepatotoxicity, neurotoxicity, and respiratory toxicity. In addition, the median lethal dose to humans predicted that MTX is more lethal than IA, in agreement with their category classes, I and IV, respectively.

<table><caption>**Table 3.** Expected pharmacokinetic and toxicological properties for incomptine A (IA) and methotrexate (MTX).</caption><thead><tr><th colspan="6">Pharmacokinetics Properties</th></tr></thead><tbody><tr><th colspan="3">Absorption</th><th colspan="3">Metabolism</th></tr><tr><td></td><th>IA</th><th>MTX</th><td></td><th>IA</th><th>MTX</th></tr><tr><td>Gastrointestinal absorption</td><td>High</td><td>Low</td><td>CYP2C9 substrate</td><td>No</td><td>No</td></tr><tr><td>Hematoencephalic barrier</td><td>Yes</td><td>No</td><td>CYP2D6 substrate</td><td>No</td><td>No</td></tr><tr><td>Caco-2 permeability</td><td>High</td><td>Low</td><td>CYP3A4 substrate</td><td>Yes</td><td>Yes</td></tr><tr><td>p-glycoprotein substrate</td><td>Yes</td><td>Yes</td><td>CYP2C9 inhibitor</td><td>No</td><td>No</td></tr><tr><td>p-glycoprotein inhibitor</td><td>No</td><td>No</td><td>CYP2D6 inhibitor</td><td>No</td><td>No</td></tr><tr><td>Log Kp (skin permeation)</td><td>-6.89 cm/s</td><td>-10.39 cm/s</td><td>CYP3A4 inhibitor</td><td>No</td><td>No</td></tr><tr><td></td><td></td><td></td><td>CYP1A2 inhibitor</td><td>No</td><td>No</td></tr><tr><td></td><td></td><td></td><td>CYP2C19 inhibitor</td><td>No</td><td>No</td></tr><tr><th colspan="3">Distribution</th><th colspan="3">Excretion</th></tr><tr><td>Mitochondrial</td><td>Yes</td><td>Yes</td><td>CL</td><td>4.64 mL/min/Kg</td><td>2.41 mL/min/Kg</td></tr><tr><td>Protein plasma binding</td><td>75.9%</td><td>63.4%</td><td>T<sub>1/2</sub></td><td>0.78 h</td><td>0.39 h</td></tr><tr><td>Volume distribution</td><td>1.38 L/Kg</td><td>0.32 L/Kg</td><td></td><td></td><td></td></tr><tr><th colspan="3">Toxicity</th><th colspan="3">Toxicity</th></tr><tr><td>Hepatotoxicity</td><td>Inactive</td><td>Active</td><td>Carcinogenicity</td><td>Inactive</td><td>Inactive</td></tr><tr><td>Neurotoxicity</td><td>Inactive</td><td>Active</td><td>Immunotoxicity</td><td>Inactive</td><td>Inactive</td></tr><tr><td>Nephrotoxicity</td><td>Inactive</td><td>Inactive</td><td>Mutagenicity</td><td>Inactive</td><td>Inactive</td></tr><tr><td>Respiratory toxicity</td><td>Active</td><td>Active</td><td>Cytotoxicity</td><td>Inactive</td><td>Inactive</td></tr><tr><td>Cardiotoxicity</td><td>Inactive</td><td>Inactive</td><td></td><td></td><td></td></tr><tr><td>Predicted rats LD<sub>50</sub></td><td>2.68 mol/Kg</td><td>3.49 mol/Kg</td><td></td><td></td><td></td></tr><tr><td>Predicted human LD<sub>50</sub></td><td>1330 mg/kg</td><td>3.0 mg/kg</td><td></td><td></td><td></td></tr><tr><td>Expected toxicity class *</td><td>IV</td><td>I</td><td></td><td></td><td></td></tr></tbody></table>

Predictions were obtained from ADMETlab, SwissADME, admetSAR, and PROTOX web servers. T<sub>1/2</sub>: Half lifetime; * Toxicity classes are defined according to the Globally Harmonized System of Classification and Labeling of Chemicals (GHS). LD<sub>50</sub> is expressed in mg/kg. Class I: fatal by ingestion (LD<sub>50</sub> ≤ 5); Class II: fatal by ingestion (5 < LD<sub>50</sub> ≤ 50); Class III: fatal by ingestion (50 < LD<sub>50</sub> ≤ 300); Class IV: fatal by ingestion (300 < LD<sub>50</sub> ≤ 2000); Class V: fatal by ingestion (2000 < LD<sub>50</sub> ≤ 5000); Class VI: fatal by ingestion (LD<sub>50</sub> > 5000).

Physicochemical properties are important parameters measured in medicinal chemistry to know the probability of the molecules submitted for study to be a candidate for the development of new drugs and their possibility of being administered orally (Table 4). According to the results, IA proved to be a better candidate for an orally administered drug than MTX because it may have a better solubility than MTX (LogP and LogS); moreover,
the solubility class is better for **IA** than **MTX** according to the ADMET results, which indicated better absorption for **IA**. Drug-likeness analysis showed that **IA** fulfills all the important characteristics for an orally administered drug satisfactorily in comparison with **MTX**, which violates three important filters (Veber, Egan, and Muegge).

<table><caption>**Table 4.** Physicochemical properties for **IA** and **MTX**.</caption><thead><tr><th colspan="3">Physicochemical Properties</th></tr><tr><th></th><th>IA</th><th>MTX</th></tr></thead><tbody><tr><td>Molecular formula</td><td>C<sub>17</sub>H<sub>22</sub>O<sub>5</sub></td><td>C<sub>20</sub>H<sub>22</sub>N<sub>8</sub>O<sub>5</sub></td></tr><tr><td>Molecular weight</td><td>306.35 g/mol</td><td>454.44 g/mol</td></tr><tr><td>TPSA</td><td>65.13 Å<sup>2</sup></td><td>210.54 Å<sup>2</sup></td></tr><tr><td>Lipophilicity (LogP)</td><td>2.33</td><td>-0.32</td></tr><tr><td>Water solubility (LogS)</td><td>-2.71</td><td>-2.41</td></tr><tr><td>Solubility class</td><td>Soluble</td><td>Very soluble</td></tr><tr><td>Number of rotating links</td><td>2</td><td>10</td></tr><tr><td>Number of H-bond donors</td><td>0</td><td>5</td></tr><tr><td>Number of H-bond acceptors</td><td>5</td><td>9</td></tr></tbody></table><table><thead><tr><th colspan="3">Drug-Likeness</th></tr><tr><th></th><th>IA</th><th>MTX</th></tr></thead><tbody><tr><td>Lipinsky</td><td>Yes</td><td>Yes</td></tr><tr><td>Ghose</td><td>Yes</td><td>Yes</td></tr><tr><td>Veber</td><td>Yes</td><td>No, 1 violation</td></tr><tr><td>Egan</td><td>Yes</td><td>No, 1 violation</td></tr><tr><td>Muegge</td><td>Yes</td><td>No, 1 violation</td></tr><tr><td>PAINS</td><td>0</td><td>0</td></tr></tbody></table>

Predictions were obtained from ADMETlab and SwissADME.

## 2.3. Cytotoxic Activity of Incomptine A and Methotrexate

The cytotoxic activities of incomptine A (IA) and methotrexate (MTX) were assayed on U-937 cells (histiocytic lymphoma) by MTT test. To compare the cytotoxic activity of the sesquiterpene lactone IA, we included MTX as the positive control, considering that it is an antimetabolite drug used in Mexico for the treatment of non-Hodgkin lymphoma [5]. The results for cytotoxic activity on the U-937 cells showed that IA and MTX had dose-dependent cytotoxic effects (Figure 5). In addition, IA showed a better cytotoxic activity compared with MTX (Table 5).

<figure><img src="image_7.png" /></figure>

**Figure 5.** Cytotoxic activity of IA and MTX against U-937 cells (histiocytic lymphoma). The graphs show the % growth inhibition of the U-937 cell line treated with IA (A) and MTX (B) at different concentrations after 24 h of exposure. Data were analyzed using Graph Pad Prism 6.03 (n = 3 and *p* < 0.05, GraphPad, La Jolla, CA, USA); CC₅₀ was defined as the treatment concentration at which 50% reduction in cellular proliferation was observed. The assays were performed in triplicate, and these data were used to obtain the CC₅₀ by linear regression analysis.
<table><caption>**Table 5.** Median cytotoxic concentration calculated against U-937 cell line from IA and MTX.</caption><thead><tr><th>Compound</th><th>CC<sub>50</sub> (µM) <sup>a</sup></th></tr></thead><tbody><tr><td>Incomptine A (IA)</td><td>0.29 ± 0.01 <sup>*</sup></td></tr><tr><td>Methotrexate (MTX)</td><td>1.60 ± 0.02</td></tr></tbody></table>

<sup>a</sup> U-937 cell line (histiocytic lymphoma); CC<sub>50</sub>: median cytotoxic concentration causing 50% cell death. Calculated by linear regression analysis of percentage mortality against concentration. Data are expressed as mean ± SEM (n = 3 and *p* < 0.05).

## 2.4. Antilymphoma Activity of Sesquiterpene Lactone, Incomptine A, and Methotrexate

To evaluate the antilymphoma effect of sesquiterpene lactone, incomptine A (IA), and methotrexate (MTX), the weight of the lymph nodes of male mice with NHL were obtained, and the percentages of inhibition of lymph nodes after treatment were calculated. Table 6 shows the antilymphoma activity in mice treated with IA and MTX. Both compounds had strong antitumor activity, with ED<sub>50</sub> values of 7.5 mg/kg and 1.4 mg/kg, respectively. MTX was more potent than IA.

<table><caption>**Table 6.** Results of the antilymphoma activity after the treatment with IA and MTX on male Balb/c mice with NHL.</caption><thead><tr><th>Compound</th><th>% Inhibition (10 mg/kg) in Male Balb/c Mice <sup>a</sup></th><th>ED<sub>50</sub> (mg/kg) <sup>b</sup></th></tr></thead><tbody><tr><td>Incomptine A (IA)</td><td>62.0 ± 1.5</td><td>7.5 ± 0.01</td></tr><tr><td>Methotrexate (MTX)</td><td>ND <sup>*</sup></td><td>1.4 ± 0.02</td></tr></tbody></table>

<sup>a</sup> Results are expressed as average ± SEM (n = 6 and *p* < 0.05). <sup>*</sup> ND: not determined; MTX at doses ≥ 1.25 mg/kg caused mortality in male Balb/c mice. <sup>b</sup> ED50: median effective dose that caused 50% of mice to have the desired pharmacological effect.

In addition, the proteomics study was performed at a dose of 10 mg/kg; the antilymphoma activity was studied in these conditions. IA showed 62% growth inhibition of lymph nodes in male Balb/c mice. In the case of MTX, it was not determined, because a dose of 10 mg/kg caused 100% mortality in the first week of the test. Therefore, for the proteomic study, for MTX, the percentage of growth inhibition in lymph nodes was obtained at a dose of 1.25 mg/kg; the result was 33.4% growth inhibition in male Balb/c mice and 20% mortality.

## 2.5. Identification of Differentially Expressed Proteins in Lymph Nodes from Male Balb/C Mice with Non-Hodgkin Lymphoma and Treated with Incomptine A

To delve deeper and explore the changes induced in the proteome of the lymph nodes from male Balb/c mice with non-Hodgkin lymphoma and treated with IA (LNNHLTIA) or MTX (LNNHLTMTX), compared with lymph nodes of male Balb/c mice with non-Hodgkin lymphoma (LNNHL), the differentially expressed proteins (DEPs) were identified by a TMT-based labeling approach coupled with UHPLC-MS/MS.

The lymph nodes were clustered as pools of all mice in each group, including the 10 mg/kg and 1.25 mg/kg IA and MTX treatments, respectively. The lymph node samples were lysed, and then the protein was alkylated and digested. The peptide samples were labeled with TMT reagent and subjected to UHPLC-MS/MS; six raw MS files with 20,453 peptides were compared against the mouse protein database. Among these, a total of 2717 proteins were identified and quantified in the lymph node samples. The DEPs were quantified according to the values of fold change (FC); FC > 1.5 was considered upregulated, while FC < 0.66 was considered downregulated (Table 7). The raw proteomics data have been deposited into the ProteomeXchange Consortium via the PRIDE, through px-submission-tool (version 2.7.3) partner repository under the dataset identifier PXD060392.
<table><caption>**Table 7.** Differentially expressed proteins for the treatment of IA or MTX in lymph nodes from male Balb/c mice with non-Hodgkin lymphoma.</caption><thead><tr><th>C-(DMSO) vs</th><th>Downregulated FC &lt; 0.66<br>(1/1.5)</th><th>Upregulated FC &gt; 1.5</th></tr></thead><tbody><tr><td>MTX</td><td>57</td><td>7</td></tr><tr><td>IA</td><td>66</td><td>40</td></tr></tbody></table>

Data represents the fold change (FC) with the number of DEPs. IA at a dose of 10 mg/kg, MTX at a dose of 1.25 mg/kg and negative control, male Balb/c mice with NHL. DEPs were compared with mouse protein.

In total, we obtained 106 significant DEPs, including 66 downregulated and 40 upregulated; among these, IA induced downregulation in 66 proteins and upregulation in 40. Also, 42 were altered by IA only (Table 8), and 64 overlapped in IA and MTX (Table 9). MTX induced downregulation in 57 and upregulation in 7 proteins (Table 8). In addition, the 106 DEPs gained were used for the next step of research on potential targets and the effect of IA in the treatment of NHL.

<table><caption>**Table 8.** Proteins altered for IA only (10 mg/kg) in lymph nodes from male mice with NHL and treated with IA or MTX (1.25 mg/kg) and identified by TMT.</caption><thead><tr><th>Protein ID</th><th>Protein Name</th><th>Gene Name</th><th>IA (FC)</th><th>MTX (FC)</th></tr></thead><tbody><tr><td>P04202</td><td>Transforming growth factor beta 1</td><td>Tgfb1</td><td>0.643493</td><td>WC</td></tr><tr><td>P97434</td><td>Myosin phosphatase Rho interacting protein</td><td>Mprip</td><td>0.637507</td><td>WC</td></tr><tr><td>Q9EQN3</td><td>Domain family member 4</td><td>Tsc22d4</td><td>0.621895</td><td>WC</td></tr><tr><td>P49717</td><td>DNA replication licensing factor MCM4</td><td>Mcm4</td><td>0.605720</td><td>WC</td></tr><tr><td>P56375</td><td>Acylphosphatase-2</td><td>Acyp2</td><td>0.612446</td><td>WC</td></tr><tr><td>Q64339</td><td>Ubiquitin-like protein ISG15</td><td>Isg15</td><td>0.596993</td><td>WC</td></tr><tr><td>Q8CI51</td><td>PDZ and LIM domain protein 5</td><td>Pdlim5</td><td>0.610126</td><td>WC</td></tr><tr><td>Q9QXV3</td><td>Inhibitor of growth protein 1</td><td>Ing1</td><td>0.612365</td><td>WC</td></tr><tr><td>Q9WVR4</td><td>RNA-binding protein</td><td>Fxr2</td><td>0.604267</td><td>WC</td></tr><tr><td>P15089</td><td>Mast cell carboxypeptidase A</td><td>Cpa3</td><td>1.684613</td><td>WC</td></tr><tr><td>P57746</td><td>V-type proton ATPase subunit D</td><td>Atp6v1d</td><td>1.692460</td><td>WC</td></tr><tr><td>Q00623</td><td>Apolipoprotein A-I</td><td>Apoa1</td><td>1.714051</td><td>WC</td></tr><tr><td>Q61490</td><td>CD 106 antigen</td><td>Alcam</td><td>1.592873</td><td>WC</td></tr><tr><td>Q64314</td><td>Hematopoietic progenitor cell antigen CD34</td><td>Cd34</td><td>1.557006</td><td>WC</td></tr><tr><td>Q8SZ7</td><td>Guanine nucleotide-binding protein G(I)/G(S)/G(O)</td><td>Gng5</td><td>1.847469</td><td>WC</td></tr><tr><td>Q8C3X2</td><td>Coiled-coil domain-containing protein 90B</td><td>Ccdc90b</td><td>1.602073</td><td>WC</td></tr><tr><td>Q9CQI7</td><td>U2 small nuclear ribonucleoprotein B</td><td>Snrpb2</td><td>1.583845</td><td>WC</td></tr><tr><td>Q9D0M1</td><td>Phosphoribosyl pyrophosphate synthase-associated protein 1</td><td>Prpsap1</td><td>1.605011</td><td>WC</td></tr><tr><td>Q9D7M1</td><td>Glucose-induced degradation protein 8 homolog</td><td>Gid8</td><td>1.630454</td><td>WC</td></tr><tr><td>Q9JL35</td><td>High mobility group nucleosome-binding domain containing protein 5</td><td>Hmgn5</td><td>1.665424</td><td>WC</td></tr><tr><td>O09167</td><td>Large ribosomal subunit protein eL21</td><td>Rpl21</td><td>2.107700</td><td>WC</td></tr><tr><td>O35387</td><td>HCLS1-associated protein X-1</td><td>Hax1</td><td>1.777345</td><td>WC</td></tr><tr><td>O70494</td><td>Transcription factor Sp3</td><td>Sp3</td><td>1.687230</td><td>WC</td></tr><tr><td>P84244</td><td>Histone H3.3</td><td>H3f3a; H3f3b</td><td>2.804078</td><td>WC</td></tr><tr><td>Q8CGP2</td><td>Histone H2B type 1-P</td><td>Hist1h2bp</td><td>2.244255</td><td>WC</td></tr><tr><td>P12246</td><td>Serum amyloid P-component</td><td>Apcs</td><td>1.636670</td><td>WC</td></tr><tr><td>P15864</td><td>Histone H1.2</td><td>H1-2 or Hist1h1c</td><td>2.578941</td><td>WC</td></tr><tr><td>P27661</td><td>Histone H2AX</td><td>H2ax or H2afx</td><td>2.641841</td><td>WC</td></tr><tr><td>P28184</td><td>Metallothionein-3</td><td>Mt3</td><td>1.630307</td><td>WC</td></tr><tr><td>P51910</td><td>Apolipoprotein D</td><td>Apod</td><td>1.835178</td><td>WC</td></tr><tr><td>P62806</td><td>Histone H4</td><td>H4c11 or Hist1h4a</td><td>3.246594</td><td>WC</td></tr><tr><td>P97315</td><td>Cysteine and glycine-rich protein 1</td><td>Csrp1</td><td>1.620372</td><td>WC</td></tr><tr><td>P97864</td><td>Caspase-7</td><td>Casp7</td><td>1.611143</td><td>WC</td></tr><tr><td>Q80UW8</td><td>DNA-directed RNA polymerases I, II, and III subunit RPABC1</td><td>Polr2e</td><td>1.715163</td><td>WC</td></tr><tr><td>Q8BTS4</td><td>Nuclear pore complex protein Nup54</td><td>Nup54</td><td>1.595948</td><td>WC</td></tr><tr><td>Q8BX10</td><td>Serine/threonine-protein phosphatase PGAM5, mitochondrial</td><td>Pgam5</td><td>1.578568</td><td>WC</td></tr><tr><td>Q8CGP5</td><td>Histone H2A type 1-F</td><td>Hist1h2af</td><td>2.449976</td><td>WC</td></tr><tr><td>Q8JZY2</td><td>COMM domain-containing protein 10</td><td>Commd10</td><td>1.674767</td><td>WC</td></tr><tr><td>Q9D0A3</td><td>Arpin</td><td>Arpin</td><td>1.560642</td><td>WC</td></tr><tr><td>Q9D820</td><td>Prolyl-tRNA synthetase associated domain-containing protein 1</td><td>Prorsd1</td><td>1.790119</td><td>WC</td></tr><tr><td>Q9QZQ8</td><td>Core histone macro-H2A.1</td><td>Macroh2a1 or H2afy</td><td>2.275530</td><td>WC</td></tr><tr><td>Q9Z2D6</td><td>Methyl-CpG-binding protein 2</td><td>Mecp2</td><td>1.942897</td><td>WC</td></tr></tbody></table>

FC, fold change; WC, without change.
<table><caption>**Table 9.** Proteins altered by IA (10 mg/kg) and MTX (1.25 mg/kg) in lymph nodes from male mice with NHL and identified by TMT.</caption><thead><tr><th>Protein ID</th><th>Protein Name</th><th>Gene Name</th><th>IA (FC)</th><th>MTX (FC)</th></tr></thead><tbody><tr><td>O09165</td><td>Calsequestrin-1</td><td>Casq1</td><td>0.116192</td><td>0.168875</td></tr><tr><td>O70250</td><td>Phosphoglycerate mutase 2</td><td>Pgam2</td><td>0.130508</td><td>0.153858</td></tr><tr><td>P13412</td><td>Troponin I, fast skeletal muscle</td><td>Tnni2</td><td>0.124295</td><td>0.155632</td></tr><tr><td>P20801</td><td>Troponin C, skeletal muscle</td><td>Tnnc2</td><td>0.138595</td><td>0.178338</td></tr><tr><td>P17183</td><td>Gamma-enolase</td><td>Eno2</td><td>0.151492</td><td>0.204480</td></tr><tr><td>Q9QZ47</td><td>Troponin T, fast skeletal muscle</td><td>Tnnt3</td><td>0.168688</td><td>0.187661</td></tr><tr><td>P97457</td><td>Myosin regulatory light chain 2, skeletal muscle isoform</td><td>Mylpf</td><td>0.160783</td><td>0.185690</td></tr><tr><td>P13542</td><td>Myosin-8</td><td>Myh8</td><td>0.167594</td><td>0.188003</td></tr><tr><td>Q9WUZ7</td><td>SH3 domain-binding glutamic acid-rich protein</td><td>Sh3bgr</td><td>0.181164</td><td>0.214274</td></tr><tr><td>P05977</td><td>Myosin light chain 1/3, skeletal muscle isoform</td><td>Myl1</td><td>0.180140</td><td>0.223749</td></tr><tr><td>Q5SX39</td><td>Myosin-4</td><td>Myh4</td><td>0.188800</td><td>0.191696</td></tr><tr><td>P58774</td><td>Tropomyosin beta chain</td><td>Tpm2</td><td>0.183496</td><td>0.197290</td></tr><tr><td>P32848</td><td>Parvalbumin alpha</td><td>Pvalb</td><td>0.186871</td><td>0.197277</td></tr><tr><td>Q5SX40</td><td>Myosin-1</td><td>Myh1</td><td>0.193077</td><td>0.224988</td></tr><tr><td>Q9JKS4</td><td>LIM domain-binding protein 3</td><td>Ldb3</td><td>0.210207</td><td>0.245489</td></tr><tr><td>P07310</td><td>Creatine kinase M-type</td><td>Ckm</td><td>0.205044</td><td>0.218269</td></tr><tr><td>P58771</td><td>Tropomyosin alpha-1 chain</td><td>Tpm1</td><td>0.211997</td><td>0.215467</td></tr><tr><td>Q9JK37</td><td>Myozenin-1</td><td>Myoz1</td><td>0.212286</td><td>0.240133</td></tr><tr><td>Q8R429</td><td>Sarcoplasmic/endoplasmic reticulum calcium ATPase 1</td><td>Atp2a1</td><td>0.241364</td><td>0.246236</td></tr><tr><td>Q62234</td><td>Myomesin-1</td><td>Myom1</td><td>0.235461</td><td>0.252929</td></tr><tr><td>Q6P8J7</td><td>Creatine kinase S-type, mitochondrial</td><td>Ckmtd2</td><td>0.237430</td><td>0.265434</td></tr><tr><td>Q8R1X6</td><td>Spartin</td><td>Spg20</td><td>0.255282</td><td>0.309371</td></tr><tr><td>P51667</td><td>Myosin regulatory light chain 2, ventricular/cardiac muscle isoform</td><td>My12</td><td>0.244251</td><td>0.292434</td></tr><tr><td>P09542</td><td>Myosin light chain 3</td><td>My13</td><td>0.238113</td><td>0.2115191</td></tr><tr><td>Q5XKE0</td><td>Myosin-binding protein C, fast-type</td><td>Mybpc2</td><td>0.257520</td><td>0.274625</td></tr><tr><td>Q9WUB3</td><td>Glycogen phosphorylase, muscle form</td><td>Pygm</td><td>0.250124</td><td>0.280577</td></tr><tr><td>P13541</td><td>Myosin-3</td><td>Myh3</td><td>0.245456</td><td>0.259989</td></tr><tr><td>A2ASS6</td><td>Titin</td><td>Ttn</td><td>0.278499</td><td>0.310760</td></tr><tr><td>P21550</td><td>Beta-enolase</td><td>Eno3</td><td>0.287361</td><td>0.301657</td></tr><tr><td>P23927</td><td>Alpha-crystallin B chain</td><td>Cryab</td><td>0.280105</td><td>0.307071</td></tr><tr><td>Q9JI91</td><td>Alpha-actinin-2</td><td>Actn2</td><td>0.282126</td><td>0.349733</td></tr><tr><td>O88990</td><td>Alpha-actinin-3</td><td>Actn3</td><td>0.309565</td><td>0.335477</td></tr><tr><td>P04247</td><td>Myoglobin</td><td>Mb</td><td>0.327717</td><td>0.314839</td></tr><tr><td>Q9JLH8</td><td>Tropomodulin-4</td><td>Tmod4</td><td>0.342717</td><td>0.419507</td></tr><tr><td>P97447</td><td>Four and a half LIM domains protein 1</td><td>Fhl1</td><td>0.359150</td><td>0.385025</td></tr><tr><td>P11404</td><td>Fatty acid-binding protein, heart</td><td>Fabp3</td><td>0.375626</td><td>0.505352</td></tr><tr><td>P68134</td><td>Actin, alpha skeletal muscle</td><td>Acta1</td><td>0.401163</td><td>0.351810</td></tr><tr><td>Q9JIF9</td><td>Myotilin</td><td>Myot</td><td>0.393725</td><td>0.442669</td></tr><tr><td>P48962</td><td>ADP/ATP translocase 1</td><td>Slc25a4</td><td>0.434997</td><td>0.292006</td></tr><tr><td>Q9DAZ9</td><td>Abscission/NoCut checkpoint regulator</td><td>Zfyve19</td><td>0.463985</td><td>0.424291</td></tr><tr><td>Q64345</td><td>Interferon-induced protein with tetratricopeptide repeats 3</td><td>Ifit3</td><td>0.408482</td><td>0.500078</td></tr><tr><td>Q9R0Y5</td><td>Adenylate kinase isoenzyme 1</td><td>Ak1</td><td>0.394418</td><td>0.342173</td></tr><tr><td>Q7TQ48</td><td>Sarcalumenin</td><td>Srl</td><td>0.417223</td><td>0.452027</td></tr><tr><td>Q3TJD7</td><td>PDZ and LIM domain protein 7</td><td>Pdlim7</td><td>0.423444</td><td>0.581780</td></tr><tr><td>Q8VHX6</td><td>Filamin-C</td><td>Flnc</td><td>0.460515</td><td>0.528664</td></tr><tr><td>Q91Z83</td><td>Myosin-7</td><td>Myh7</td><td>0.450777</td><td>0.432883</td></tr><tr><td>A2ABU4</td><td>Myomesin-3</td><td>Myom3</td><td>0.447776</td><td>0.505737</td></tr><tr><td>Q8BV66</td><td>Interferon-induced protein 44</td><td>Ifi44</td><td>0.549142</td><td>0.481085</td></tr><tr><td>P15307</td><td>Proto-oncogene c-Rel</td><td>Rel</td><td>0.513074</td><td>0.548115</td></tr><tr><td>P04919</td><td>Band 3 anion transport protein</td><td>Slc4a1</td><td>0.506732</td><td>0.390399</td></tr><tr><td>Q99NB8</td><td>Ubiquilin-4</td><td>Ubqln4</td><td>0.453602</td><td>0.576117</td></tr><tr><td>Q9CQC7</td><td>NADH dehydrogenase [ubiquinone] 1 beta subcomplex subunit 4</td><td>Ndufb4</td><td>0.581219</td><td>0.459827</td></tr><tr><td>P45591</td><td>Cofilin-2</td><td>Cfl2</td><td>0.597159</td><td>0.624516</td></tr><tr><td>Q9DA08</td><td>SAGA-associated factor 29 homolog</td><td>Ccdc101</td><td>0.594209</td><td>0.656104</td></tr><tr><td>Q9D1L0</td><td>Coiled-coil-helix-coiled-coil-helix domain-containing protein 2</td><td>Chchd2</td><td>0.580712</td><td>0.635534</td></tr><tr><td>Q9CXW2</td><td>28S ribosomal protein S22, mitochondrial</td><td>Mrps22</td><td>0.635789</td><td>0.602915</td></tr><tr><td>Q9R059</td><td>Four and a half LIM domains protein 3</td><td>Fhl3</td><td>0.645077</td><td>0.581828</td></tr><tr><td>P06728</td><td>Apolipoprotein A-IV</td><td>Apoa4</td><td>1.522175</td><td>1.508959</td></tr><tr><td>P01645</td><td>Ig kappa chain V-V region HP 93G7</td><td>KV5AC</td><td>1.748303</td><td>1.688753</td></tr><tr><td>P47955</td><td>Large ribosomal subunit protein P1</td><td>Rplp1</td><td>1.574290</td><td>1.558623</td></tr><tr><td>Q501J7</td><td>Phosphatase and actin regulator 4</td><td>Phactr4</td><td>1.603299</td><td>1.505982</td></tr><tr><td>Q6U7R4</td><td>T-cell immunoglobulin and mucin domain-containing protein 4</td><td>Timd4</td><td>1.725275</td><td>1.539719</td></tr><tr><td>Q61730</td><td>Interleukin-1 receptor accessory protein</td><td>Il1rap</td><td>1.665689</td><td>1.502210</td></tr><tr><td>P26645</td><td>Myristoylated alanine-rich C-kinase substrate</td><td>Marcks</td><td>1.891390</td><td>1.679438</td></tr></tbody></table>

FC, fold change.
## 2.6. *Bioinformatic Analysis*

### 2.6.1. Protein-Protein Interaction Network of Incomptine A Potential Targets for the Treatment of Non-Hodgkin Lymphoma

In order to find the most important protein clusters altered and potential core targets of incomptine A (IA) in the treatment of non-Hodgkin lymphoma (NHL) in male Balb/c mice, a total of 106 DEPs (Tables 8 and 9), containing 66 downregulated and 40 upregulated proteins, were investigated by the protein-protein interaction (PPI) enrichment networks using Cytoscape as well as the String database (Figure 6). A large number of interactions were found to be enriched, including Fhl1, Il1rap, Ifi44, Timd4, Apoa4, Fabp3, Myh3, Eno 2, and H4c11. Among these, Fhl1 resulted as the most important DEP; it was shown directly or indirectly interacting with 101 other DEPs, containing other DEPs with fewer interactions such as Il1rap, Ifi44, Timd4, Apoa4, Fabp3, Myh3, Eno 2, and H4c11. Fhl1 was a downregulated protein, as were Fabp3, Ifi44, Myh3, and Eno 2. H4c11, Apoa4, Il1rap, and Timd4 were upregulated proteins. Also, four DEPs, including Prorsd1, Ccdc90b, Gid8, and Spg20 proteins, showed no connection with other proteins.

<figure><img src="image_8.png" /><figcaption>**Figure 6.** PPI network map of protein clusters and core targets in male Balb/c mice with non-Hodgkin lymphoma treated with incomptine A. The green color shows downregulated proteins and the red/cream color shows upregulated proteins. PPI, protein-protein interaction.</figcaption></figure>
## 2.6.2. Results of the Enrichment Study Performed with G: Profiler, Gene Ontology, Reactome, and Kyoto Encyclopedia of Genes and Genomes

To visually disclose the enrichment pathways of the 106 DEPs, g: Profiler, gene ontology (GO), Reactome (REAC), and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were carried out for a bioinformatics analysis. A large number of pathways were enriched according to g: Profiler analysis of the GO, REAC, and KEGG databases (Figure 7A).

<figure><img src="image_9.png" /></figure>

<table><caption>**Figure 7.** Results of bioinformatics analysis using g: Profiler. (A) Significant enriched terms in the GO, REAC, and KEGG databases. GO: MF, gene ontology molecular function; GO: BP, gene ontology process; GO: CC, gene ontology cellular component; KEGG, Kyoto Encyclopedia of Genes and Genomes; REAC, Reactome. (B) List of top 30 significantly changed terms enriched by GO, KEGG, and REAC databases.</caption><thead><tr><th>ID</th><th>Source</th><th>Term ID</th><th>Term Name</th><th>Padj (query_1)</th></tr></thead><tbody><tr><td>20</td><td>GO:CC</td><td>GO:0030016</td><td>myofibril</td><td>2.070x10<sup>-42</sup></td></tr><tr><td>21</td><td>GO:CC</td><td>GO:0015629</td><td>actin cytoskeleton</td><td>5.185x10<sup>-27</sup></td></tr><tr><td>1</td><td>GO:MF</td><td>GO:0003779</td><td>actin binding</td><td>5.093x10<sup>-23</sup></td></tr><tr><td>9</td><td>GO:BP</td><td>GO:0003012</td><td>muscle system process</td><td>1.430x10<sup>-22</sup></td></tr><tr><td>27</td><td>REAC</td><td>REAC:R-MMU-390522</td><td>Striated Muscle Contraction</td><td>3.107x10<sup>-20</sup></td></tr><tr><td>10</td><td>GO:BP</td><td>GO:0030239</td><td>myofibril assembly</td><td>4.367x10<sup>-16</sup></td></tr><tr><td>28</td><td>REAC</td><td>REAC:R-MMU-397014</td><td>Muscle contraction</td><td>4.678x10<sup>-14</sup></td></tr><tr><td>2</td><td>GO:MF</td><td>GO:0044877</td><td>protein-containing complex binding</td><td>7.997x10<sup>-10</sup></td></tr><tr><td>24</td><td>KEGG</td><td>KEGG:04814</td><td>Motor proteins</td><td>2.545x10<sup>-9</sup></td></tr><tr><td>3</td><td>GO:MF</td><td>GO:0005198</td><td>structural molecule activity</td><td>4.810x10<sup>-8</sup></td></tr><tr><td>11</td><td>GO:BP</td><td>GO:0046034</td><td>ATP metabolic process</td><td>1.550x10<sup>-6</sup></td></tr><tr><td>26</td><td>KEGG</td><td>KEGG:05410</td><td>Hypertrophic cardiomyopathy</td><td>2.191x10<sup>-5</sup></td></tr><tr><td>12</td><td>GO:BP</td><td>GO:0007507</td><td>heart development</td><td>2.730x10<sup>-5</sup></td></tr><tr><td>25</td><td>KEGG</td><td>KEGG:05414</td><td>Dilated cardiomyopathy</td><td>2.769x10<sup>-5</sup></td></tr><tr><td>4</td><td>GO:MF</td><td>GO:0000146</td><td>microfilament motor activity</td><td>1.477x10<sup>-4</sup></td></tr><tr><td>22</td><td>GO:CC</td><td>GO:0000786</td><td>nucleosome</td><td>2.748x10<sup>-4</sup></td></tr><tr><td>5</td><td>GO:MF</td><td>GO:0031492</td><td>nucleosomal DNA binding</td><td>7.346x10<sup>-4</sup></td></tr><tr><td>13</td><td>GO:BP</td><td>GO:0051128</td><td>regulation of cellular component organization</td><td>1.719x10<sup>-3</sup></td></tr><tr><td>14</td><td>GO:BP</td><td>GO:0043501</td><td>skeletal muscle adaptation</td><td>3.179x10<sup>-3</sup></td></tr><tr><td>15</td><td>GO:BP</td><td>GO:0061615</td><td>glycolytic process through fructose-6-phosphate</td><td>3.774x10<sup>-3</sup></td></tr><tr><td>16</td><td>GO:BP</td><td>GO:0006007</td><td>glucose catabolic process</td><td>5.207x10<sup>-3</sup></td></tr><tr><td>17</td><td>GO:BP</td><td>GO:0006979</td><td>response to oxidative stress</td><td>5.675x10<sup>-3</sup></td></tr><tr><td>6</td><td>GO:MF</td><td>GO:0046983</td><td>protein dimerization activity</td><td>7.998x10<sup>-3</sup></td></tr><tr><td>7</td><td>GO:MF</td><td>GO:0005509</td><td>calcium ion binding</td><td>1.708x10<sup>-2</sup></td></tr><tr><td>18</td><td>GO:BP</td><td>GO:0006091</td><td>generation of precursor metabolites and energy</td><td>2.370x10<sup>-2</sup></td></tr><tr><td>29</td><td>REAC</td><td>REAC:R-MMU-212165</td><td>Epigenetic regulation of gene expression</td><td>2.637x10<sup>-2</sup></td></tr><tr><td>23</td><td>GO:CC</td><td>GO:0000015</td><td>phosphopyruvate hydratase complex</td><td>3.626x10<sup>-2</sup></td></tr><tr><td>19</td><td>GO:BP</td><td>GO:0014874</td><td>response to stimulus involved in regulation of muscle adaptation</td><td>4.855x10<sup>-2</sup></td></tr><tr><td>34</td><td>KEGG</td><td>KEGG:04261</td><td>Adrenergic signaling in cardiomyocytes</td><td>4.867x10<sup>-2</sup></td></tr><tr><td>8</td><td>GO:MF</td><td>GO:0004111</td><td>creatinine kinase activity</td><td>4.998x10<sup>-2</sup></td></tr></tbody></table>

In total, 190 terms were significantly enriched with 179, 8, and 3 related with GO, KEGG, and REAC, respectively. In the case of GO, 25, 115, and 39 terms corresponded with
molecular function (MF), biological process (BP), and cellular component (CC), respectively. The top 30 significantly enriched terms by GO, KEGG, and REAC are displayed in Figure 7B.

### 2.6.3. Results of the Gene Ontology Enrichment Study

Gene ontology (GO) enrichment analysis was further utilized to explore the enriched biological process (BP), cellular component (CC), and molecular function (MF) terms. For the BP terms, muscle system process and muscle contraction were the most abundant among the top 10 biological processes (Figure 8). In CC terms, contractile fiber, myofibril, sarcomere, and actin cytoskeleton were the most abundant among the top 10 cellular components. Finally, the most abundant terms were actin binding and actin filament binding among the top 10 MF terms (Figure 8).

<figure><img src="image_10.png" /><figcaption>**Figure 8.** Gene ontology (GO) enrichment analysis of DEPs obtained from male Balb/c mice with non-Hodgkin lymphoma treated with incompbine A. BP, biological process; CC, cellular component; MF, molecular function.</figcaption></figure>

### 2.6.4. Gene Ontology Enrichment Analysis and Kyoto Encyclopedia of Genes and Genomes Pathway Analysis

The GO and KEGG enrichment analyses were carried out through the DAVID database to forecast the pathway and the functional annotation enrichment related to incompbine A (IA) for the treatment of NHL in male Balb/c mice. The full analysis of 106 DEPs performed through Gene ontology (GO) functional analysis yielded 179 entries, with 115 associated with biological process (BP), 39 under cellular component (CC), and 25 under molecular
function (MF). These pathways showed the 10 most important pathways that are affected by the 105 DEPs. Figure 9 shows the top 10 BP, CC, MF, and KEGG terms. The primary BP included muscle system process and muscle contraction (A). In the case of CC, it included contractile fiber and myofibril (B). Primary MF comprised actin binding (C).

<figure><img src="image_11.png" /><figcaption>**Figure 9.** GO enrichment analysis and KEGG pathway analysis in male Balb/c mice with non-Hodgkin lymphoma. (A) Gene ontology (GO) in terms of biological process; (B) GO in terms of cellular component; (C) GO in terms of molecular function; (D) KEGG pathway analysis.</figcaption></figure>

KEGG analysis was associated with cytoskeleton in muscle cells and motor proteins (D). Necroptosis (Figure 10A), neutrophil extracellular trap formation, cardiac muscle contraction, and dilated cardiomyopathy (Figure 10B), among others, are some pathways affected by downregulated proteins such as Pgam5, Slc25a4, and H2ax as well as Myl2, Myl3, Atp2a1, and Tpm2.
<figure><img src="image_12.png" /><figcaption>**Figure 10.** Correlation between DEPs and pathways in KEGG analysis: (A) necroptosis; (B) cardiac muscle contraction and dilated cardiomyopathy.</figcaption></figure>

2.6.5. Results of the Reactome Enrichment Analysis

The analysis of Reactome (REAC) exhibited nine important networks: glycolysis, glucose metabolism, metabolism of carbohydrates, transport of small molecules, striated muscle contraction, muscle contraction, hemostasis, platelet activation signaling and aggregation, and transcriptional regulation by small RNAs, among others, were enriched in several DEPs (Figure 11).

<figure><img src="image_13.png" /><figcaption>**Figure 11.** Correlation between DEPs and networks in REAC analysis.</figcaption></figure>

Among these, glycolysis, glucose metabolism, and metabolism of carbohydrates were enriched in three downregulated genes, Eno2, Eno3, and Pgam2. Transport of small
molecules was enriched in five genes; among these, Mb, Slc4a1, and Slc25a4 were downregulated, and Apod and Apoa4 were upregulated. Muscle contraction and striated muscle contraction were enriched in 14 downregulated genes: Tmod4, Tnnc2, Myh3, Myh8, Tpm1, Mybpc2, Myl3, Actn3, Myl1, Tpm2, Myl2, Acta1, Tnnt3, and Tnn12. Hemostasis was enriched in three downregulated genes containing Tgfb1, Actn2, and Atp2a1. Platelet activation signaling and aggregation was enriched in one downregulated gene, Tgfb1. Finally, transcriptional regulation by small RNAs was enriched in four upregulated proteins containing H4c11, Polr2e, H3f3b, and H2ax.

### 2.6.6. Molecular Docking Studies of the Sesquiterpene Lactone, Incomptine A Against Six Differentially Expressed Proteins in Non-Hodgkin Lymphoma in Mice

We carried out molecular docking studies with the aim of deepening the antilymphoma properties of the sesquiterpene lactone, incomptine A (IA). In this sense, we considered the previous results obtained here, containing those of the TMT, GO, KEGG, REAC and PPI analyses. Molecular docking studies were performed to obtain additional information about the interactions between sesquiterpene lactone, IA with six DEPs; methotrexate (MTX), an antilymphoma drug currently used for the treatment of NHL in Mexico, was used as a control ligand. The complete analysis of molecular docking studies suggested that IA presents more affinity to all DEPs selected for this study than MTX, including Apoa4 (−6.35 Kca/mol), Fabp3 (−7.17 Kca/mol), Fhl1 (−6.86 Kca/mol), IFi44 (−6.55 Kca/mol), Il1rap (−7.15 Kca/mol), and Timd4 (−7.24 Kca/mol), in comparison with MTX, Apoa4 (−4.27 Kca/mol), Fabp3 (−4.84 Kca/mol), Fhl1 (−4.92 Kca/mol), IFi44 (−5.72 Kca/mol), Il1rap (−6.02 Kca/mol), and Timd4 (−5.11 Kca/mol), respectively. Furthermore, it is important to denote that in almost all proteins, the binding site is the same for IA and MTX, and they share several aminoacidic residues with mostly polar interactions (Table 9). The only protein where IA did not share the same binding position with MTX was IFi44 (Table 10, Figure 12).

<table><caption>**Table 10.** Protein–ligand interactions from molecular docking, ΔG (kcal/mol), and Ki (μM).</caption><thead><tr><th rowspan="3">Protein</th><th colspan="6">Ligands</th></tr><tr><th colspan="3">Incomptine A (IA)</th><th colspan="3">Methotrexate (M)</th></tr><tr><th>ΔG</th><th>Ki</th><th>H-BR</th><th>NPI</th><th>ΔG</th><th>Ki</th><th>H-BR</th><th>NPI</th></tr></thead><tbody><tr><td>Apoa4</td><td>-6.35</td><td>22.12</td><td>Phe303, Thr357, Leu358, Glu359, Lys361, Gly361</td><td>Met194, Leu197, Leu263, Val300, Arg304, Lys360</td><td>-4.27</td><td>744.4</td><td>Glo308, Gly311, Glu312, Asn315, Glu349, Asn352, Ser353, Ser356, Glu359 Asn60, Thr61, Glu62, Asn64, Phe65, Phe71, Asp72, Glu73, Thr75, Ala76, Asp78</td><td>Arg348, Met355</td></tr><tr><td>Fabp3</td><td>-7.17</td><td>5.53</td><td>Phe17, Tyr20, Thr54, Lys59, Thr61, Thr75, Ala76, Asp77, Arg79, Gln96</td><td>Met21, Leu24, Val26, Leu105, Leu116, Leu118</td><td>-4.84</td><td>284.07</td><td>Arg100, Cys101, Tyr117, Gly119, Thr120, Val121, Trp122, Gly145, Glu146, Phe148</td><td>Lys59, Ile63, Val74</td></tr><tr><td>Fhl1</td><td>-6.86</td><td>9.38</td><td>Gly103, Thr120, Trp122, Cys126, Thr128, Ser130, Lys133, Glu146, Asp147</td><td>Lys102, Phe127, Phe148</td><td>-4.92</td><td>284.1</td><td>Ile356, Tyr357, Asn358, Lys363, Leu366, Ser378, Leu381, Val382, Val383, Met398, Leu399, Ser402, Ala403, Glu406</td><td>Lys102, Phe105, Lys118</td></tr><tr><td>IFi44</td><td>-6.55</td><td>15.9</td><td>Thr8, Gln10, Gln11, Lys58, Cys59, Val60, Gln82, Glu83, Phe86</td><td>Cys14, Leu81</td><td>-5.72</td><td>64.22</td><td>Thr31, Met32, Arg33, Gln34, Ile35, Gln36, Phe144, Pro145, Pro161, Asn162, Arg227, Thr228, Val229</td><td>Leu377</td></tr><tr><td>Il1rap</td><td>-7.15</td><td>5.74</td><td>Gln34, Gln36, Arg143, Arg143</td><td>Ile35, Met142, Phe144, Pro145</td><td>-6.02</td><td>38.51</td><td>Ala23, Ser24, Glu25, Thr37, Leu38, Pro39, Cys40, His41, Ser96, Ala124, Asn126, Val127, Cys310, Gln312,</td><td>Val37, Met142, Arg143</td></tr><tr><td>Timd4</td><td>-7.24</td><td>4.9</td><td>Arg113, Phe120, Asn121, Asp122, Val123, Leu300, Gly303, Thr306</td><td>Lys124, Phe299, Arg302</td><td>-5.11</td><td>180.54</td><td>Asp26, Lys215, Leu311, Arg313, His314</td><td>Asp26, Lys215, Leu311, Arg313, His314</td></tr></tbody></table>

Apoa4: apolipoprotein A-IV, Fabp3: fatty acid-binding, Fhl1: four and a half LIM domains protein 1, IFi44: interferon-induced protein 44, Il1rap: interleukin-1 receptor accessory protein, Timd4: T-cell immunoglobulin and mucin domain-containing protein 4.
<figure><img src="image_14.png" /><figcaption>**Figure 12.** The interaction mode between incompine A (1) and methotrexate (2) and six potential targets: (A) apolipoprotein A-IV, (B) fatty acid-binding, (C) four and a half LIM domains protein 1, (D) interferon-induced protein 44, (E) interleukin-1 receptor accessory protein, and (F) T-cell immunoglobulin and mucin domain-containing protein 4.</figcaption></figure>
### 3. Discussion

Non-Hodgkin lymphomas (NHLs) are the 10th most common malignant neoplasm that are an important cause of mortality and morbidity around the world. In this sense, nearly 544,352 persons are diagnosed with NHL annually, resulting in nearly 259,793 deaths in 2020 [1,20–22]. In the last ten years, NHLs have had a rise in mortality rate and incidence with 112, 576 cases in women and 147,217 cases in men [2]. In the case of Mexico, it has been reported that NHLs were the ninth cause of cancer for both genders and were the fourth cause of death [3,4].

The first-line treatments for NHLs are chemotherapy and chemoimmunotherapy, including drugs such as prednisone, cisplatin, fludarabine, doxorubicin, vincristine, methotrexate, cyclophosphamide, etoposide, and rituximab. However, several of these drugs show limited efficacy, and some types of aggressive lymphomas relapse and progress into refractory lymphoma [23–25]. They also have several side effects such as kidney injury, nephrotoxicity, hepatotoxicity, and myelosuppression, among others [3,5]. In this sense, the investigation of new antitumor drugs with the best efficacy and safety is very important around the world and remains a challenge.

Part of our ongoing research involves new anti-NHL drugs isolated from Mexican medicinal plants. Previously, we reported the antilymphoma activity in a mouse model at a dose of 5 mg/kg of the sesquiterpene lactone incomptine A (IA) [10]. To expand on these findings, in this work, the antilymphoma activity of the IA was evaluated at a dose of 10 mg/kg using in vitro, in vivo, and bioinformatics approaches, enhancing the translatability of our prior findings, and we assessed the impact of IA on additional aspects of NHL.

First, the dichloromethane extract of the leaves of *Decachaeta incompta* was subject to sequential chromatographic methods, including column chromatography and thin-layer chromatography (TLC) over silica gel, to afford a sesquiterpene lactone that was identified as IA by HPLC (Figure 3 and Table 1), TLC (Figure 4), and NMR methods (Table 2). The retention time, retention factor, and NMR data were the same as those for the authentic sample provided by Dr. Bautista [16].

Once the identification of IA was made, an in silico ADMET (absorption, distribution, metabolism, excretion, and toxicity) analysis was performed [18]. In silico techniques represent a fast and economic strategy that helps to reduce time and costs in research for the development of new drugs [26–28]. ADMET and physicochemical properties were calculated in order to compare the results of IA against MTX, considering that the first is a potential drug and the second is one drug used currently in Mexico for the treatment of cancers such as NHL. Incomptine A exhibits favorable drug-like properties. An ADMET evaluation was carried out; this kind of in silico study brings valuable information for new molecules, including several parameters that must be determined when a new drug is in development [17,27,28]. Considering the constant advancement and development of computer software for in silico techniques, these have been transformed into a valuable tool that allows researchers to determine these and other parameters reducing costs and time and avoiding the use of animals [28]. The complete analysis of the ADMET calculation suggests that IA may have better absorption when it is administered orally than MTX, which also is reflected in a better distribution of the drug. In respect to metabolism, this parameter was similar to MTX, and the excretion calculated for IA suggests that it may have a longer T₁/₂ than MTX. These results may support the important activity described in this study that IA is more active than MTX for NHL treatment. Moreover, the toxicities of both molecules were determined; in this sense, we observe that IA may have lower toxicity than MTX, with a predicted human lethal dose 50 (LD₅₀) of 3 mg/kg for IA in comparison with 1330 mg/kg for MTX. This result suggests a wide therapeutic window for the use of
**IA** in comparison with **MTX** and classifies **IA** in Class IV (300 < LD50 ≤ 2000 mg/kg) in comparison with **MTX**, which is collocated in Class I, which is considered fatal by ingestion (LD50 ≤ 5 mg/kg). **MTX** is known as one of the most widely used anticancer agents [29–31]. It is important to highlight that the result of toxicity obtained in this study of **IA** was in agreement with the in vivo toxicity reported previously and confirms the predicted findings by bioinformatics [32]. In contrast, it is also known that the use of **MTX** may generate side effects such as nephrotoxicity, muscle pain, red eyes, swollen gums, and hair loss, among others [33,34]. It also has been reported that high doses of **MTX** may be toxic to humans and mice [5,35]. This is according to some predictions obtained from ADMET studies and represents an advantage in the use of **IA** as a drug for the treatment of cancer. Commonly, the route of administration of **MTX** is intravenous, which is painful for the patients [36]; in comparison, **IA** represents one important alternative because our results suggest better oral absorption with important pharmacological anticancer activity, and perhaps with lower side effects, than **MTX**. Finally, the drug likeness analysis supports the information showed in the ADMET study, because it suggests that **IA** fulfills the necessary criteria to be an important candidate for the development of an orally administered drug.

After the ADMET analysis of **IA** was made, its cytotoxic activity was determined using U-937 cells (histiocytic lymphoma) that are associated as a cause of NHL [37]. The results showed that **IA** was nearly six times most active than **MTX** (Table 5 and Figure 5), used as positive control. Cytotoxic activity of the sesquiterpene lactone, **IA**, was more significant than those reported for other natural products with important cytotoxic properties such as farnesol, nerol, citral, and geraniol [38]. The cytotoxic activity here reported for **IA** is in agreement with previous reports about its cytotoxic properties on several cancer cell lines, including four subtypes of NHL cells (U-937, Garage, SU-DHL-2, and REC-1), three subtypes of leukemia cells (Hl-60, K562, and REH), and six subtypes of breast cancer cells (4T1, MDA-MB-231, SK-BR-3, T-47D, MCF7, and MCF10A) [10,17]. In addition, it is important to highlight that it has been reported that **IA** was not cytotoxic to the MCF10A normal human mammary breast cell line [17]. These results of ADMET and cytotoxicity suggest that **IA** has antitumor potential [39–43].

Then, antilymphoma activity was tested by comparing the effect of **IA** and the anti-
lymphoma drug **MTX**, which was chosen considering that currently it is used in Mexico
to treat NHL [3]. The results showed that **IA** was less active that **MTX** (Table 6); however,
the activity of **IA** was similar to other natural products with significant antilymphoma
properties, including flavonoid glycosides and sesquiterpenoids [10,35,38]. In addition, **IA**
is a sesquiterpene lactone, a group of compounds with an α-methylene-γ-lactone moiety in
their structure that have been reported as a new promising form of cancer therapy based
on their anticancer activity. The most important examples include eupatolide, deoxyele-
phantopin, and parthenolide. In the last case, it is undergoing cancer clinical trials [39]. It is
important to mention that although **MTX** was more potent in this study, it caused mortality
in the treated mice at doses ≥ 1.25 mg/kg; in particular, a dose of 10 mg/kg caused 100%
mortality in the first week of the test. Also, it has been reported to have serious side
effects including pancreatitis, cirrhosis, aplastic anemia, leukopenia, and gastrointestinal
bleeding [43].

The next step of this work was to delve deeper and explore the changes induced in
the proteome of the lymph nodes from male Balb/c mice with non-Hodgkin lymphoma
and treated with IA at doses of 10 mg/kg. Recently, we reported a proteomic study in a
mouse model at a dose of 5 mg/kg of incomptine A [10]. To expand on these findings, in
this work, the antilymphoma activity of the IA was evaluated at a dose of 10 mg/kg using
in vitro, in vivo, and bioinformatics approaches, enhancing the translatability of our prior
findings and assessing the impact of IA on additional aspects of NHL.
The results of antilymphoma activity showed that IA caused 62% growth inhibition of lymph nodes in male Balb/c mice. The case of MTX was not determinate because the dose of 10 mg/kg caused 100% mortality. Therefore, the proteomic study for MTX of the lymph nodes was obtained at a dose of 1.25 mg/kg (Table 6). TMT proteomics technology was used to identify the differentially expressed proteins (DEPs) of the lymph nodes from male Balb/c mice with non-Hodgkin lymphoma and treated with IA (LNNHLTIA) or MTX (LNNHLTMTX) and from male Balb/c mice with non-Hodgkin lymphoma (LNNHL). A total of 2717 proteins were identified and quantified in the LNNHLTIA in this study, among which 106 proteins showed significant expression differences (Tables 8 and 9), including 66 downregulated proteins and 40 upregulated proteins. In general, IA induced more downregulation than upregulation of proteins in the lymph nodes of mice with NHL, 62% and 38%, respectively, in agreement with our previous study. Also, the number of DEPs at a dose of 10 mg/kg was 40% higher compared with those obtained at a dose of 5 mg/kg [10].

All DEPs altered by IA treatment were subjected to PPI analysis, showing that the four and a half LIM domains protein 1 (Fhl1) was the most important core protein (Figure 6); it showed direct or indirect interaction with other proteins, including Il1rap, Ifi44, Timd4, Apoa4, and Fabp3 as well as Myh3, Eno 2, and H4c11. The last three were reported recently to be possibly associated with the appearance of NHL [10]. According to the PPI network, GO, KEGG, and REAC analyses, the DEPs including Fhl1, Il1rap, Ifi44, Timd4, Apoa4, Fabp3, Myh3, Eno 2, and H4c11 may be associated with several processes, including muscle contraction, glycolysis, hemostasis, epigenetic regulation of gene expression, transport of small molecules, neutrophil extracellular trap formation, adrenergic signaling in cardiomyocytes, systemic lupus erythematosus, alcoholism, and platelet activation, signaling, and aggregation (Figures 7–11).

Four and a half LIM domains protein 1 (Fhl1) resulted in the most important cluster altered and a potential core target of IA for the treatment of NHL in male Balb/c mice. In PPI, it showed direct interaction relationships with 40 downregulated proteins and 2 upregulated proteins. It also had an indirect interaction relationship with 59 DEPs. Fhl1 is widely expressed in humans, particularly in skeletal and cardiac muscle cells. Fhl1 participates in the development of the skeletal muscle and myocardium as well in the regulation of gene transcription, thyroid function, blood glucose levels, myoblast differentiation, and other biological process. Abnormal expression of Fhl1 is associated with several diseases such as skeletal muscle disease, reductive myopathy, hypertrophic cardiomyopathy, and viral infections (chikungunya and cashmere). It also regulates cell proliferation, differentiation, apoptosis, adhesion, migration, transcription, and other cellular processes, and it plays an important role in cell growth. In tumors, the expression of Fhl1 is upregulated or downregulated and plays a role in promoting or inhibiting tumor development. The expression of Fhl1 is downregulated in several cancers such as lung, prostate, breast, ovarian, colon, thyroid, brain, kidney, liver, and skin (melanoma), as well as oral cancers. In contrast, the expression of Fhl1 is upregulated in cancers such as laryngeal carcinomas, gastric signet ring cell carcinoma, lung adenocarcinoma, gastric cancer, acute promyelocytic leukemia, and glioblastoma, among others. It is important to highlight that considering the significant differences observed in the expressions of Fhl1 between different tumors, Fhl1 is advised as a prognostic marker and could be a potential target for cancer therapy research including NHL [44].

In relation to the proteins, Il1rap, Ifi44, Timd4, Apoa4, Fabp3, Myh3, Eno 2, and H4c11 showed ≤ 24 direct or indirect interaction relationships with other DEPs. These eight DEPs have been reported to be associated with one or more cancers including pancreatic, kidney, tongue, colorectal, breast, bone, cervical, stomach, small-cell lung, non-small-cell lung, head, neck, lung, liver, and nasopharyngeal cancer, among others, as well as myeloblastic,
chronic myeloid, acute myeloid, and acute lymphoblastic leukemias. It is important to note that Il1rap, Ifi44, Timd4, Fabp3, and Eno 2 have been reported as potential therapeutic targets and potential prognostic biomarkers for several cancers [45–60].

Molecular docking assay was performed to obtain additional information about the in-
teraction between the sesquiterpene lactone **IA** and the six DEPs implicated in NHL, Il1rap,
Apoa4, Fabp3, IFi44, Timd4, and Fhl1. This technique has gained significant importance in
the development of novel treatments for various diseases. This computational technique
predicts the binding affinity of ligands to receptor proteins. The assay yields free Gibbs
energy (ΔG) values, which indicate the exergonic nature of the reaction. A more negative
ΔG value suggest a higher probability of ligand–protein union and interaction [61,62].

The results of molecular docking showed that **IA** has better affinity to all the proteins used in this study in comparison with **MTX** because its ΔG values were lower compared with those obtained from **MTX**. Moreover, in almost all proteins, the binding site as well as the polar interactions between **IA** and **MTX** were the same. This kind of interaction is important because it gives stability to the binding position. Our results suggest that both molecules bind on the same position of the proteins, and perhaps, **IA** may have better activity than **MTX** for the treatment of NHL. Also, this is the first report that involves Il1rap, Apoa4, Fabp3, IFi44, Timd4, and Fhl1 with NHL.

In agreement with the GO, REAC, and KEGG pathway as well as TMT, PPI network, and molecular docking analyses, the sesquiterpene lactone **IA** showed direct or indirect interaction with 106 depositions. Among these, Fhl1 was the most important core protein. In this context, Fhl1 could be a potential therapeutic target and potential prognostic biomarker in NHL. In addition, these results support the effects of **IA** in several processes such as muscle contraction, glycolysis, hemostasis, epigenetic regulation of gene expression, transport of small molecules, neutrophil extracellular trap formation, adrenergic signaling in cardiomyocytes, systemic lupus erythematosus, alcoholism, and platelet activation, signaling, and aggregation, among others. Also, the results of ADMET and antilymphoma activity provide additional evidence that support the therapeutic antitumor potential of **IA**. In addition, the information obtained in this work indicates that all approaches used can be employed to reliably find potential therapeutic targets and potential prognostic biomarkers to several cancers, including NHL. Additional assays, including immunohistochemistry, Western blotting, ELISA, and miRNA expression, among others, are mandatory to validate the results obtained here. Also, other assays are required to explore the way **IA** promotes lymphoma cell death in NHL, such as apoptosis (i.e., caspase activity, TUNEL staining, and annexin V/PI flow cytometry) and the effect on the glycolytic pathway (i.e., expression of aldolase A, lactate dehydrogenase, and hexokinase II). Given the aforementioned, we propose that **IA** can be considered a promising molecule with an exceptional pharmacological profile. Its potential application in the future as a novel pharmacological treatment for NHL, or as a foundation for the development of new molecule derivates to enhance its efficacy in treating this disease, warrants continued investigation of this molecule to acquire more information from it.

**4. Materials and Methods**

4.1. *Isolation of Incomptine A from Dichloromethane Extract of Decachaeta Incompta*

The sesquiterpene lactone **IA** was obtained from the aerial parts of *Decachaeta incompta*
(Asteraceae), collected in February 2022 in the State of Oaxaca, Mexico. The leaves of
the plant were botanically identified by the MS Abigail Aguilar Contreras biology of the
Instituto Mexicano del Seguro Social. A sample ( voucher 15311) was deposited at the
Herbarium, IMSSM. The phytochemical procedure was performed according to a protocol
previously described [16]. **IA** was identified by NMR spectra and HPLC-DAD and by
comparison with an authentic sample having a purity near 99%. Dimethyl sulfoxide (DMSO) Sigma Aldrich (St. Louis, MO, USA), was used to dissolve **IA** and **MTX** (Sigma Aldrich).

## 4.2. Chemicals and Instrumentation

Triethylammonium bicarbonate buffer (1.0 M, pH 8.5 ± 0.1), tris (2-carboxyethyl) phosphine hydrochloride solution (0.5 M, pH 7.0), iodoacetamide (IAA), formic acid (FA), acetonitrile (ACN), methanol were purchased from Sigma Aldrich (St. Louis, MO, USA), trypsin from bovine pancreas Promega (Madison, WI, USA), ultrapure water (Millipore, Burlington, MA, USA), TMT 6-plex Isobaric Label Reagent, and Pierce Quantitative Colorimetric Peptide Assay Thermo Scientific (Waltham, MA, USA). were used in this work. An Ultimate 3000 nano UHPLC system was coupled to a Q Exactive HF MS equipped with a Nano spray Flex Ion Source Thermo Scientific (Waltham, MA, USA). TMT-based Quantification Analytical Service was provided by Creative Proteomics (Shirley, NY, USA).

## 4.3. Cell Culture Conditions

U-937 cells were acquired from the American Type Culture Collection (CRL-1593,2, histiocytic lymphoma). To develop the mice model, U-937 cells were cultured at 37 °C in RPMI 1640 culture medium (GIBCO Cat: 11875-093) supplemented with 5% fetal bovine serum (GIBCO Cat: 16000044), streptomycin (100 µg/mL)/penicillin (100 U/mL), and 5% CO2. In vivo tests were realized using cell cultures at a density of 2.5 × 10⁶ cells in T75 flasks (Invitrogen, Waltham, MA, USA). Cell viability was determined by the trypan blue exclusion test. Cells were resuspended in fresh medium 24 h before treatments to ensure exponential growth.

The cytotoxic activity of incomptine A (IA) against U-937 cells was assessed using the colorimetric 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-tetrazolium bromide (MTT) test, Sigma Aldrich (St. Louis, MO, USA). Exponentially growing cells of U-937 cell lines were seeded in 96-well plates at a density of 5.0 × 10³ cells per well in 100 µL and treated with five serial concentrations between 0.1 µm and 4.0 µm of IA or MTX for 24 h under 5% CO₂ and 95% O₂ at 37 °C. The compounds were dissolved in DMSO; the final concentration of DMSO used was 0.1% (v/v) for each sample. Cells (U-937) treated with 0.1% DMSO served as the control group. After incubation for specified times, MTT reagent (10 µL, 5 mg dissolved in 1 mL of PBS) was added to each well and incubated for 4 h. The plates were centrifuged (10 min at 350 × g), and the purple formazan crystals of metabolized yellow tetrazolium salt by viable cells were dissolved in 150 µL of DMSO. Absorbance was quantified at 570 nm using the ELISA plate reader. Results were expressed as a percentage of viability, with 100% representing control cells treated with 0.1% DMSO alone. Then, the CC₅₀ was determined. This was defined as the treatment concentration at which 50% reduction in cellular proliferation was observed. This was calculated graphically using the curve-fitting algorithm of the computer software Prism 5.03 (GraphPad, La Jolla, CA, USA). Values were calculated as means ± S.E.M. from three independent experiments, each performed in triplicate. Statistical analysis of the data was performed using one-way ANOVA, as well as Dunnett's multiple comparison tests, with a value of *p* < 0.05 to establish a significant difference between the study groups.

## 4.4. Animals

To induce the lymphoma model, healthy male Balb/c mice (25 ± 3g) were provided by the animal house of the Centro Medico Nacional Siglo XXI, from Instituto Mexicano del Seguro Social. The research had ethical authorization by the National Committee of Scientific Research from the Instituto Mexicano del Seguro Social with registration numbers: R-2018-785-111 and R-2020-3601-186. Mice were maintained in polyvinyl cages at 22 °C
and light–dark periods of 12 h, with ad libitum access to food and water in agreement with Mexican Official Norma, NOM-062-ZOO-1999 [63].

### 4.4.1. Antilymphoma Test

Briefly, healthy male Balb/c mice were divided into 9 groups with 5 mice per group: LNNHL1, LNNHL, LNNHLTIA [LNNHLTIAa, LNNHLTIAb, LNNHLTIAc, and being LNNHLTIAd], and LNNHLTMTX [LNNHLTMTXa, LNNHLTMTXb, and LNNHLTMTXc]. For comparison, LNNHL1, designated as the healthy group, was treated with tween 80 in water (2% v/v), and LNNHL was used as negative control only, inoculated with $1 \times 10^6$ U-937 cells. LNNHLTIA and LNNHLTMTX groups were injected intraperitoneally with $1 \times 10^6$ U-937 cells and treated orally for 9 days with incompotine A (IA, 2.5, 5.0, 7.5, and 10 mg·kg$^{-1}$) or methotrexate (MTX, 1.25, 2.5, and 3.75 mg·kg$^{-1}$), respectively. Animals were maintained under observation for 30 days, recording their daily survival and weight. Mice were sacrificed, and axillary and inguinal lymph nodes were removed and weighted. Antilymphoma activity was obtained by comparing the total lymph node weight of each group against the negative control, and the percentage of inhibition and the 50% effective inhibitory doses (ED$_{50}$) were calculated. Lymph nodes obtained of the groups LNNHLTIA, LNNHLTMTX, and LNNHL were used in the TMT test.

The ED$_{50}$ was calculated graphically using the curve-fitting algorithm of the computer software Prism 5.03 (GraphPad, La Jolla, CA, USA). Values were calculated as means ± S.E.M. ($n = 5$). Statistical analysis of the data was performed using one-way ANOVA as well as Dunnett's multiple comparison tests with a value of $p < 0.05$ to establish a significant difference between the study groups.

### 4.4.2. Non-Hodgkin's Lymphoma Protein Expression Induced Through IA or MTX

To evaluate the effects in the proteome of the non-Hodgkin lymphoma experimental model, we clustered the lymph nodes as pools of all mice in each group, concerning IA and MTX treatments of 10 mg/kg and 1.25 mg/kg, respectively. Then, tissue was lysed using a TissueLyser after being centrifuged at low temperature for 15 min at 12,000 rpm, and the protein concentration was performed with a BCA kit. Following that, 100 µg of protein was treated with TCEP (10 mM) for 1 h at 56 °C and then alkylated with IAA (20 mM) for 1h in dark at room temperature. Then, trypsin was added (1:50) and incubated at 37 °C overnight. The peptides were brought to almost dryness and re-dissolved with TEAB (100 mM). After that, each sample of peptides was treated with anhydrous acetonitrile (41 µL) and centrifuged. The supernatant was treated with TMT Reagent using the following: (C-)/TMT, MTX/TMT, and IA/TMT. Finally, the reaction was incubated for 1 h at room temperature; then, it was treated with 5% hydroxylamine (8 µL) and incubated for another 15 min.

### 4.4.3. Nano UHPLC-MS/MS Analyses

The labeled peptides obtained in Section 4.4.2 were subjected to analysis by UHPLC-MS/MS using an Ultimate 3000 nano UHPLC system coupled to a Q Exactive HF mass spectrometer equipped with a Nano spray Flex Ion Source (Thermo Scientific, Waltham, MA). Briefly, the sample (2 µg) was injected onto a trap column (PepMap C18, 100 Å, $100 \mu m \times 2 cm$, 5 µm) and subjected to fractionation using an analytical column (PepMap C18, 100 Å, $75 \mu m \times 50 cm$, 2 µm). A linear gradient was used: 5 to 7% buffer B for 2 min, from 7% to 20% buffer B for 80 min, from 20% to 40% buffer B for 35 min, then from 40% to 90% buffer B for 4 min. Mobile phase A: 0.1% formic acid in water; B: 0.1% formic acid in 80% acetonitrile. For labeled samples, the full scan was performed between 350 and 1650 m/z at a resolution of 200 to 120,000 Th and a gain control of 3 to 6. The MS/MS scan was operated in Top 15 mode using a resolution of 200 to 30,000 Th; automatic gain
control target 1 × 10⁵; charge state exclusion: unassigned, 1, > 6; dynamic exclusion 40 s, normalized collision energy at 32%; isolation window of 1.2 Th.

#### 4.4.4. Protein Identification

The six raw MS files were subjected to analysis and compared against mouse protein database using Maxquant (v2.6.7.0), ProteoWizard (version 3), and Proteome Discoverer 3.2 (ThermoFisher, Waltham, MA, USA). The parameters were set as follows: The protein modifications were oxidation (M, variable), carbamidomethylation (C, fixed), and TMT-6Plex. Enzyme specificity was set to trypsin, the precursor ion mass tolerance was set to 10 ppm, the maximum missed cleavages were set to 2, and MS/MS tolerance was 0.6 Da. The distribution of all the proteins identified according to the protein mass (kDa), the distribution of 20,453 peptides identified according to the length, and the distribution of 2717 of the proteins identified according to sequence coverage, were performed. Data analytical report was carried out by Analytical Service from Creative Proteomics (New York, NY, USA) with *p* < 0.05 (indicates > 95% confidence of change in protein concentration respect of the magnitude of change) selected to designate differentially expressed proteins.

#### 4.4.5. Differentially Expressed Protein Analysis

A total of 2717 proteins were identified for this work, with (C-)/TMT and **MTX**/**TMT** used to determine protein differential expression between treatments MTX/TMT and **IA**/**TMT** establishing the ratios MTX/C, IA/C, and IA/MTX. It is important to highlight that fold-change cutoffs at protein level accepted with biological meaning are defined as 1.5 times up or down. Therefore, the screening criteria for the relative quantitation of proteins in this work were as follows: fold change < 0.67 was considered downregulated, and fold-change > 1.5 was considered upregulated. Each comparison was analyzed using enrichment processes analysis and cell signaling pathways.

### 4.5. Bioinformatic Methodology

The log ratio data matrix was used to feed the algorithms for the analysis of cellular process enrichment networks and signaling pathways through programming language R v4.2.2 and RStudio v3.1.4, applying the clusterProfiler v4.2.2, MSigDB in R (Molecular Signatures Database, accessed 24 May 2025), enrich plot, and ggplo2 packages (accessed 24 May 2025). Also, we used symbol. mouse ID data (org.Mm.eg.db, accessed 24 May 2025), KEGG (Kyoto Encyclopedia of Genes and Genomes), gene ontology (GO), biological process (BP), molecular function (MF), and cellular component (CC), and Reactome databases. The enrichGO, compareCluster, and enrichKEGG functions were applied. The results were plotted with the functions "emapplot", "aplot_list", "dotplot", and "cnetplot" for visualization. Dotplot and three onthologies plots enrichment was performed with the ClusterProfiler package of the R v4.2.2 tool, following the steps proposed by the developer, using the GO databases (BP, MF, CC) and FDR < 0.05 as the value of statistical significance. Additionally, enrichment networks were performed in Cytoscape plug-in using the ClueGo (Version 2.5.10) as well as STRING plugins (confidence cutoff of 0.4) for both analyses, following the developer's manual using KEGG and GO databases and setting a *p* < 0.05 as the statistical significance criterion. In parallel, we performed a functional enrichment analysis, also known as over-representation analysis (ORA), using the g: Profiler tool available online, for which we used the parameters preset by the program. The log ratio (log1.5) is the log of the fold-changes, i.e., log1.5 (condition 1/condition 2). Log ratios are used/plotted in graphs for the exploratory analysis of pathways and functional enrichment of cellular processes in each of the different treatments, generating a log ratio values matrix with each protein. These data are better to show because they center around zero, giving reductions a negative
value and increments a positive value, and because the log ratios are symmetric around zero [64].

### 4.5.1. Comparison of Shared Processes and Molecules

Differentially expressed proteins obtained for each treatment were analyzed with the g: Profiler tool available online. Graphs and pathway enrichment tables and figures were generated by comparing with KEGG, GO, and Reactome databases. Enrichment plot images generated in the x-axis show the number of different enriched processes according to each database, while the y-axis plots the significance value obtained in ascending order (–log10 adj-*p*-value); a dashed cutoff at the top of the graph divides the most significant enriched cellular processes, and a black circle with a number of different processes of interest are highlighted according to each database, according to the order in which they appear in a general list of enriched processes. At the bottom is the list of the representative processes marked and arranged by significance value, the first having the highest value. The list is labeled by ID (number in the general list of the process), resource (database with which the process was enriched), term ID (identification of the process according to each platform), term name (name of the enriched process according to each database), and adj-*p*-value (significance value of the process) shown with number and color scale and arranged in a descending order. Finally, to know how many molecules and cellular processes are shared between the different conditions, the modulated proteins data and their change values were used to analyze them with the DiVenn v2.0 online tool [65–67]. All annotations to refer proteins are based on the UNIQID gene name or UNIPROT-ID. http://geneontology.org/; https://reactome.org/; https://www.uniprot.org/; or https://www.genome.jp/kegg/pathway.html (accessed on 24 May 2025).

### 4.5.2. In Silico Physicochemical, Pharmacokinetic, and Toxicological Properties

To determine the physicochemical, pharmacokinetic, and toxicological properties of IA and MTX, ADMETlab [68], SwissADME [19], admetSAR [69], and PROTOX [70] servers were used. These servers work using the SMILES code for each chemical structure; therefore, the IA structure was drawn on ChemDraw software (version 20.0.0.22), and the SMILES code was obtained (code: C=C1C(=O)O[C@@@H]2/C=C(/C)CC[C@H]3O[C@@@]3(C)C[C@@@H](OC(C)=O)[C@H]12), on the other hand, MTX SMILES code was obtained from PubChem library (CID 126941, and SMILES code: CN(Cc1cnc2nc(N)nc(N)c2n1)c1ccc(C(=O)N[C@@@H](CCC(=O)O)C(=O)O)cc1). Both codes were used on in silico servers to predict the properties previously described for IA and MTX.

### 4.5.3. Molecular Docking Studies

Blind docking experiments were carried out in order to determine the probability of the ligands MTX and IA to bind to the proteins proposed in the study. The apolipoprotein A-IV (Apoa4, ID: P06728), fatty acid-binding (Fabp3, ID: P11404), four and a half LIM domains protein 1 (Fhl1, ID: P97447), interferon-induced protein 44 (Ifi44, ID: Q8BV66), interleukin-1 receptor accessory protein (Il1rap, ID: Q61730), and T-cell immunoglobulin and mucin domain-containing protein 4 (Timd4, IDQ6U7R4) were used as targets of the study. The crystals were retrieved from the Uniprot database (https://www.uniprot.org/) (accessed on 29 May 2025). Proteins were optimized; for this, the molecules of water and ions were eliminated and only those important for catalytic activity were preserved, the polar hydrogen atoms were added, and proteins were ionized considering a basic environment (pH = 7.4). Gasteiger charges were assigned; the computed output topologies from the previous steps were used as input files for docking simulations.

For ligands optimization, the two-dimensional (2D) structure of methotrexate (CID: 126941) was retrieved from the chemical library PubChem (https://pubchem.ncbi.nlm.nih.gov/) (accessed on 29 May 2025), for incompbine-A, and the 2D structure of the proposed
ligand was drawn. Both ligands were optimized and submitted to energetic and geometrical minimization using Avogadro software (Avogadro: an open-source molecular builder and visualization tool. Version 1.2.0) [71].

Docking analysis was carried out using Autodock 4.2 software [72]. The search parameters were as follows: A grid-base procedure was employed to generate the affinity maps delimiting a grid box of 126 × 126 × 126 Å³ in each space coordinate, with a grid-point spacing of 0.375 Å. The grid centers (x, y, z) for each protein search were −28.512, 7.344, 21.938 for Apoa4; 0.815, 2.902, 1.116 for Fabp3; −1.118, −0.813, −5.054 for Fhl1; 5.872, −0.066, −7.999 for Ifi44; 30.545, 10.244, −27.227 for Il1rap; and −12.49, −4.782, −4.185 for Timd4. Lamarckian genetic algorithm was employed as scoring function with a randomized initial population of 100 individuals and a maximum number of energy evaluations of 1 × 10⁷ cycles. The analysis of the interactions in the protein–ligand complex was visualized with Drug Discovery Version 20.1.0.19295 (https://www.3ds.com/products/biovia/discovery-studio (accessed on 29 May 2025)) [73].

**5. Conclusions**

In conclusion, we used GO, REAC, and KEGG pathways as well as TMT, PPI network, ADMET, and molecular docking analyses and identified 106 differentially expressed lymph node proteins in NHL. We likewise found that Fhl1 was the most important core protein expressed in this study, suggesting that it plays an important role in the pathology of NHL. Also, the results suggest that Fhl1 could be a potential therapeutic target and potential prognostic biomarker of NHL. In addition, we provide additional evidence that supports **IA** as a potential agent for the treatment of NHL. Computational techniques such as PPI network, GO, REAC, ADMET, molecular docking, and KEGG pathway associated with TMT may be used as tools to find potential targets of drugs in NHL. Additionally, our findings could have potential clinical value in the diagnosis and therapeutic treatment of NHL beyond enriching the existing information of the pharmacological activity of IA on NHL. This study confirms that **IA** is a sesquiterpene lactone with several targets and several pathways. However, our research was limited by the lack of experimental validation, particularly in the bioinformatics approaches, and in the translatability of mouse NHL models to human disease. Moreover, clinical studies should be conducted focusing on Fhl1 as a key hub gene for early NHL diagnosis and as a potential target in precision medicine for NHL. Therefore, future studies are needed to investigate the role and impact of Fhl1 on patients with NHL and to elucidate its precise actions underlaying their effects. Furthermore, systematic studies are necessary to validate the protein targets, changes at the protein level in treated lymph nodes, and role of specific proteins in NHL. These studies should include immunohistochemistry, Western blotting, ELISA, and miRNA expression, among others.

**Author Contributions:** Conceptualization, F.C. and N.G.-H.; Methodology, E.B. (Elihú Bautista), E.B. (Elizabeth Barbosa), C.V., J.M.S.-L. and M.V.; Software, J.M.S.-L. and M.V.; Validation, N.G.-H. and F.C.; Formal analysis, N.G.-H. and F.C.; Investigation, E.B. (Elihú Bautista), E.B. (Elizabeth Barbosa), C.V., J.M.S.-L. and M.V.; Resources, F.C. and N.G.-H.; Data curation, C.V. and E.B. (Elizabeth Barbosa); Writing—original draft preparation, F.C., M.V., J.M.S.-L. and N.G.-H.; Writing—review and editing, F.C. and N.G.-H.; Visualization, F.C.; Supervision, N.G.-H.; Project administration and funding acquisition, F.C. and N.G.-H. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Instituto Mexicano del Seguro Social, contract grant sponsor Fondo de Investigación en Salud: FIS/IMSS/PROT/EMER18/1846 (Approval no: R-2018-785-111). This research did not receive external funding.
**Institutional Review Board Statement:** Study was approved by the National Ethics Committee of the Instituto Mexicano del Seguro Social, protocol codes R-2018-785-111 (9 October 2018) and R-2020-3601-186 (12 August 2020).

**Informed Consent Statement:** This research study did not involve humans.

**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author(s).

**Acknowledgments:** The authors would like to thank Donovan García García for the technical support given to create and upload the mass spectrometry proteomics data to BS.

**Conflicts of Interest:** Authors declare they have no conflicts of interest.

## References

1. Rapier-Sharman, N.; Clancy, J.; Pickett, B.E. Joint secondary transcriptomic analysis of non-Hodgkin's B-cell lymphomas predicts reliance on pathways associated with the extracellular matrix and robust diagnostic biomarkers. *J. Bioinform. Syst. Biol.* **2022**, *5*, 119-135. [CrossRef] [PubMed]

2. Sung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, L.; Jemal, A.; Bray, F. Glabal cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. *CA Cancer J. Clin.* **2021**, *71*, 209-249. [PubMed]

3. Hernández-Ruiz, E.; Alvarado-Ibarra, M.; Lien-Chang, L.E.; Banda-Garcia, L.; Aquino-Salgado, J.L.; Barragan-Ibanez, G.; Ramirez-Romero, E.F.; Nolasco-Cancino, C.; Herrera-Olivares, W.; Morales-Adrian, J.J.; et al. Epidemiology and clinical characteristics of non-Hodgkin lymphoma in Mexico. *World J. Oncol.* **2021**, *12*, 28-33. [CrossRef]

4. Palacio-Mejia, L.S.; Hernandez-Avila, J.E.; Hernandez-Avila, M.; Dyer-Leal, D.; Barranco, A.; Quezada-Sanchez, A.D.; Alvarez-Aceves, M.; Cortes-Alcala, R.; Fernandez-Wheatley, J.L.; Ordonez-Hernandez, I.; et al. Leading causes of excess mortality in Mexico during the COVID-19 pandemic 2020-2021: A death certificates study in a middle-income country. *Lancet Reg. Health Am.* **2022**, *13*, 100303. [CrossRef]

5. Howard, S.C.; McCormick, J.; Pui, C.H.; Buddington, R.K.; Harvey, R.D. Preventing and managing toxicities of high-dose methotrexate. *Oncologist* **2016**, *21*, 1471-1482. [CrossRef]

6. Zeng, J.; Luo, Q.; Wang, X.; Xie, W.; Dong, S.; Fu, H.; Wei, Y.; Liu, T. Network pharmacology- and molecular docking-base investigation of the therapeutical potential and mechanism of daucosterol against multiple myeloma. *Transl. Cancer Res.* **2023**, *12*, 1006-1020. [CrossRef]

7. Zhu, C.; Cheng, H.; Li, N.; Liu, T.; Ma, Y. Isobaric tags for relative and absolute quantification-based proteomics reveals candidate proteins of fat deposition in Chinese indigenous sheep with morphologically different tails. *Front. Genet.* **2021**, *12*, 710449. [CrossRef]

8. Zhang, H.-J.; Lan, H.-Q.; Wang, M.-Y.; Wang, C.-F.; Wei, L.-G.; Xu, C. Transcriptome analysis reveals norathyriol prolongs the lifespan via regulation metabolism in *C. elegans*. *Metabolites* **2024**, *14*, 716. [CrossRef]

9. Wang, S.; Hong, H.; Yuan, Y.; Yin, Y.; Zeng, J.; Xiao, J. Exploring the mechanism of action of quercetin for the treatment of cervical high-risk human papilloma virus using network pharmacology and molecular docking. *Chin. Med. Nat. Prod.* **2024**, *4*, e35-e42. [CrossRef]

10. Calzada, F.; García-Hernández, N.; Bautista, E.; Sánchez-López, J.M.; Valdes, M.; Velázquez, C.; Barbosa, E. Understanding the molecular mechanisms of incomptine A in treating non-Hodgkin lymphoma associated with U-937 cells: Bioinformatics approaches, Part I. *Pharmaceuticals* **2024**, *18*, 5. [CrossRef]

11. Merzouki, O.; Arrousse, N.; Ech-chihbi, E.; Alanazi, A.S.; Mabrouk, E.H.; Hefnawy, M.; El Moussaoui, A.; Touijer, H.; El Barnossi, A.; Taleb, M. Enveironmentally friendly synthesis of new mono. And bis-pyrazole derivatives; in vitro antimicrobial, antifungal, and antioxidant activity; and in silico studies: DFT, ADMETox, and molecular docking. *Pharmaceuticals* **2025**, *18*, 167. [CrossRef] [PubMed]

12. Kalitin, N.; Koroleva, N.; Lushnikova, A.; Babaeva, M.; Samoylenkova, N.; Savchenko, E.; Smirnova, G.; Borisova, Y.; Kostarev, A.; Karamysheva, A.; et al. N-glycoside of indolo [2, 3-a] pyrrolo [3, 4-c] carbazole LC1269 exerts anti-glioblastoma effects by G2 cell cycle arrest and CDK1 activity modulation: Molecular docking studies, biological investigations, and ADMET prediction. *Pharmaceuticals* **2024**, *17*, 1642. [CrossRef] [PubMed]

13. Bulbul, M.V.; Mermer, A.; Kolbasi, B.; Kocabas, F.; Kalender, S.M.; Kirectepe Aydin, K.A.; Demiorcan, T.; Keskin, I. mTor pathway inhibition, anticancer activity and in silico calculations of novel hydazone derivatives in two- and three-dimensional cultured type 1 endometrial cancer cells. *Pharmaceuticals* **2024**, *17*, 1562. [CrossRef] [PubMed]

14. Kolberg, L.; Raudvere, U.; Kuzmin, I.; Adler, P.; Vilo, J.; Peterson, H. g: Profiler-interoperable web service for functional enrichment analysis and gene identifier mapping (2023 update). *Nucleic Acids Res.* **2023**, *51*, W207-W212. [CrossRef]
15. Li, D.-F.; Cui, Z.-H.; Wang, L.-Y.; Zhang, K.-H.; Cao, L.-T.; Zheng, S.-J.; Zhang, L.-X. Tandem mass tag (TMT)-based proteomic analysis of *Cryptosporidium andersoni* oocysts before and after excystation. *Parasit. Vectors* **2021**, *14*, 608. [CrossRef]

16. Bautista, E.; Calzada, F.; Yepez-Mulia, L.; Chavez-Soto, M.; Ortega, A. Incomptines C and D, two heliangolides from *Decachaeta incompta* and their antiprotozoal activity. *Planta Med.* **2012**, *78*, 1698–1701. [CrossRef]

17. Arietta-García, A.G.; Calzada, F.; Ramírez-Sánchez, I.; Bautista, E.; García-Hernandez, N.; Ordoñez-Razo, R.M. Advances in the properties of incomptine A: Cytotoxic activity and downregulation of hexokinase II in breast cancer cell lines. *Int. J. Mol. Sci.* **2023**, *24*, 12406. [CrossRef]

18. Taslimi, P.; Akhundova, F.; Kurbanova, M.; Türkan, F.; Tuzum, B.; Sujayev, A.; Sadeghian, N.; Maharramov, A.; Farzaliyev, V.; Gülcin, I. Biological activity and molecular docking study of some bicyclic structures: Antidiabetic and anticholinergic potentials. *Polycycl. Aromat. Comp.* **2022**, *42*, 6003–6016. [CrossRef]

19. Daina, A.; Michielin, O.; Zoete, V. SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friedlieness of small molecules. *Sci. Rep.* **2017**, *7*, 42717. [CrossRef]

20. Ferlay, J.; Colombet, M.; Soerjomataram, I.; Parkin, D.M.; Piñeros, M.; Znaor, A.; Bray, F. Cancer statistics for the year 2020: An overview. *Int. J. Cancer* **2021**, *149*, 778–789. [CrossRef]

21. Lasater, E.A.; Amin, D.N.; Bannerji, R.; Mali, R.S.; Barret, K.; Rys, R.N.; Oeh, J.; Lin, E.; Sterne-Weiler, T.; Ingalla, E.R.; et al. Targeting MCL-1 and BCL-2 with polatuzumab, vedotin and venetoclax overcomes treatment resistance in R/R non-Hodgkin lymphoma: Results from preclinical models and a phase IB study. *Am. J. Hematol.* **2023**, *98*, 449–463. [CrossRef]

22. Cho-Vega, J.H.; Rassidakis, G.Z.; Admirand, J.H.; Oyarzo, M.; Ramalingam, P.; Paraguaya, A.; Medeiros, L.J. MCL-1 expression in B-cell non-Hodgkin's lymphomas. *Hum. Pathol.* **2004**, *35*, 1095–1100. [CrossRef]

23. Zare-Shahneh, F.; Valiyari, S.; Barandaran, B.; Abdolalizadeh, J.; Bandehagh, A.; Azadmehr, A.; Hajiaghaee, R. Inhibitory and cytotoxic activities of *Salvia officinalis* L. extract on human lymphoma and leukemia cells by induction of apoptosis. *Adv. Pharm. Bull.* **2013**, *3*, 51–55.

24. Cvetkovic', Z.; Markovic', O.; Marinkovic', G.; Pejic', S.; Vucic', V. Tumor microenvironment, inflammation, and inflammatory prognostic indices in diffuse large B-cell lymphomas: A Narrative review. *Int. J. Mol. Sci.* **2025**, *26*, 5670. [CrossRef]

25. Swerdlow, S.H.; Campo, E.; Pileri, S.A.; Harris, N.L.; Stein, H.; Siebert, R.; Advani, R.; Ghielmini, M.; Salles, G.A.; Zelenetz, A.D.; et al. The 2016 revision of the World Health Organization classification of lymphoid neoplasms. *Blood J. Am. Soc. Hematol.* **2016**, *127*, 2375–2390. [CrossRef] [PubMed]

26. Liu, Y.; Lawless, M.; Li, M.; Fairman, K.; Embry, M.; Mitchell, C.A. Prediction of physicochemical and pharmacokinetic properties of botanical constituents by computational models. *J. Appl. Toxicol.* **2024**, *44*, 1236–1245. [CrossRef] [PubMed]

27. Khan, T.; Lawrence, A.; Azad, I.; Raza, S.; Joshi, S.; Khan, A. Computational drug design and prediction of important parameters using in silico methods—A review. *Curr. Comput. Aided Drug Des.* **2019**, *15*, 384–397. [CrossRef] [PubMed]

28. Cheng, F.; Li, W.; Liu, G.; Tang, Y. In silico ADMET prediction: Recent advances, current challenges and future trends. *Curr. Top. Med. Chem.* **2013**, *13*, 1273–1289. [CrossRef]

29. Widemann, B.; Adamson, P. Understanding and managing methotrexate nephrotoxicity. *Oncologist* **2006**, *11*, 694–703. [CrossRef]

30. Rizvi, S.; Shahzad, Y.; Saleh, A.; Muhammad, N. Dose issues in cancer chemotherapy. *Oncology* **2020**, *98*, 520–527. [CrossRef]

31. Guellec, C.; Blasco, H.; Benz, I.; Hulin, A. Therapeutic drug monitoring of methotrexate after its administration in high-dose protocols. *Therapie* **2010**, *65*, 163–169.

32. Calzada, F.; Bautista, E.; Hidalgo-Figueroa, S.; Garcia-Hernandez, N.; Barbosa, E.; Velazquez, C.; Ordoñez-Razo, R.M.; Arietta-Garcia, A.G. Antilymphoma effect of incomptine A: In Vivo, in silico, and toxicological studies. *Molecules* **2021**, *26*, 6646. [CrossRef] [PubMed]

33. Widemann, B.; Balis, F.; Kempf-Bielak, B.; Bielak, S.; Pratt, C.; Ferari, S.; Bacci, G.; Craft, A.; Adamson, P. High-dose methotrexate-induced nephrotoxicity in patients with osteosarcoma. *Cancer* **2004**, *100*, 2222–2223. [CrossRef] [PubMed]

34. Darmon, M.; Ciroldi, M.; Thiery, G.; Schelmmer, B.; Azoulay, E. Clinical review: Specific aspects of acute renal failure in cancer patients. *Crit. Care* **2006**, *10*, 211. [CrossRef] [PubMed]

35. Calzada, F.; Solares-Pascasio, J.I.; Valdes, M.; Garcia-Hernandez, N.; Velazquez, C.; Ordoñez-Razo, R.M.; Barbosa, E. Antilymphoma potential of the ethanol extract and rutin obtained of the leaves from *Schinus mole* Linn. *Phcog. Res.* **2018**, *10*, 119–123. [CrossRef]

36. Campbell, M.; Perrier, D.; Dorr, R.; Alberts, D.; Finley, P. Methotrexate: Bioability and pharmacokinetics. *Cancer Treat. Rep.* **1985**, *69*, 833–838.

37. Zubair, A.C.; Ali, S.A.; Rees, R.C.; Goepel, J.R.; Winfield, D.A.; Goysns, M.H. Analysis of the colonization of unirradiated and irradiated SCID mice by human lymphoma and non-malignant lymphoid cells. *Leuk. Lymphoma* **1996**, *22*, 463–471. [CrossRef]

38. Calzada, F.; Ramírez-Santos, J.; Ordoñez-Razo, R.M.; Valdes, M.; Velazquez, C.; Barbosa, E. Anti-lymphoma activity of acyclic terpénoïds and its structure-activity relationship: In vitro, in vitro, and in silico studies. *Int. J. Mol. Sci.* **2025**, *26*, 5683. [CrossRef]

39. Babaei, G.; Aliarab, A.; Abroon, S.; Rasmi, Y.; Aziz, S.G. Application of sesquiterpene lactone: A new promising from cancer therapy based on anticancer activity. *Biomed. Pharmacother.* **2018**, *105*, 239–246. [CrossRef]
40. Abdolmohamma, M.H.; Fouladdel, S.; Shafiee, A.; Amin, G.; Ghaffari, S.; Azizi, E. Anticancer effects and cell cycle analysis on human breast cancer T47D cells treated with extracts of *Astrophaucus persicus* (Boiss). Drude in comparison to doxorubicin. *J. Pharm. Sci.* **2008**, *16*, 112–118.

41. Perumal, A.; AlSalhi, M.S.; Kanakarajan, S.; Devanesan, S.; Selvaraj, R.; Tamizhazhagan, V. Phytochemical evaluation and anticancer activity of rambutan (*Nephelium lappaceum*) fruit endocarp extracts against human hepatocellular carcinoma (HepG-2) cells. *Saudi J. Biol. Sci.* **2021**, *28*, 1816–1825. [CrossRef] [PubMed]

42. Suffness, M.; Douros, J. Current status of the NCI plant and animal product program. *J. Nat. Prod.* **1982**, *45*, 1–14. [CrossRef] [PubMed]

43. Shetty, A.; Cho, W.; Alazawi, W.; Syn, W.K. Methotrexate hepatotoxicity and the impact of nonalcoholic fatty liver disease. *Am. J. Med. Sci.* **2017**, *354*, 172–181. [CrossRef] [PubMed]

44. Tao, Y.; Wu, Y.; Shen, R.; He, S.; Miao, X. Role of four and half LIM domain protein 1 in tumors (Review). *Oncol. Lett.* **2024**, *29*, 37. [CrossRef]

45. Frenay, J.; Belleye, P.-S.; Oudot, A.; Helbling, A.; Petitot, C.; Ferrand, C.; Collin, B.; Dias, A.M.M. IL-1RAP, a key therapeutic target in cancer. *Int. J. Mol. Sci.* **2022**, *23*, 14918. [CrossRef]

46. Pan, H.; Wang, H.; Huang, W.; Dai, Y.; Yang, M.; Liang, H.; Wu, X.; Zhang, L.; Huang, W.; Yuan, L.; et al. Interferon-induced protein 44 correlated with immune infiltration serves as a potential prognostic indicator in head and neck squamous cell carcinoma. *Frotn. Oncol.* **2020**, *10*, 557157. [CrossRef]

47. Li, H.; Zhang, J.; Zhang, Y.; Li, Z.; Han, Y.; Lun, Y. A Pan-cancer study of tumor associated efferocytosis core genes and preliminary exploration of TIMD4 in renal cell carcinoma. *J. Cell. Mol. Med.* **2025**, *29*, e70671. [CrossRef]

48. Darwish, N.M.; Al-Hail, M.K.; Mohamed, Y.; Saady, R.A.; Mohsen, S.; Zar, A.; Al-Mansoori, L.; Pederson, S. Apolipoproteins in the commonest cancers. *Cancers* **2023**, *15*, 5565. [CrossRef]

49. Tang, Z.; Shen, Q.; Xie, H.; Zhou, X.; Li, J.; Feng, J.; Liu, H.; Wang, W.; Zhang, S.; Ni, S. Elevated expression of FABP3 and FABP4 cooperatively correlates with porp prognosis in non-small cell lung cancer (NSCLC). *Oncotarget* **2016**, *7*, 46253–46261. [CrossRef]

50. Toydemir, R.M.; Rutherford, A.; Whitby, F.G.; Jorde, L.B.; Carey, J.C.; Bamshad, M.J. Mutations in embryonic myosin heavy chain (MYH3) cause Freeman-Sheldon syndrome and Sheldon-Hall syndrome. *Nat. Genet.* **2006**, *38*, 561–565. [CrossRef]

51. Hu, G.; Yao, H.; Wei, Z.; Li, L.; Yu, Z.; Li, J.; Luo, X.; Guo, Z. A bioinformatic approach to identify a disufidptosis-related gene signature for prognostic implication in colon adenocarcinoma. *Sci. Rep.* **2023**, *13*, 12403.

52. Lee, D.-Y.; Kang, Y.; Im, N.-R.; Kim, B.; Kwon, T.-K.; Jung, K.-Y.; Baek, S.-K. Actin-associated gene expression is aassociated with early regional metastasis of tongue cancer. *Laryngoscope* **2021**, *131*, 813–819. [CrossRef] [PubMed]

53. Yu, X.-J.; Yang, M.-J.; Zhou, B.; Wang, G.-Z.; Huang, Y.-C.; Wu, L.-C.; Cheng, X.; Wen, Z.-S.; Huang, J.-Y.; Zhang, Y.-D.; et al. Characterization of somatic mutations in air pollution-related lung cancer. *EBioMedicine* **2015**, *2*, 583–590. [CrossRef] [PubMed]

54. Sun, J.; Li, S.; Wang, F.; Fan, C.; Wang, J. Idetification of key pathways and genes in PTEN mutation prostae cancer by bioinformatics analysis. *BMC Med. Genet.* **2019**, *20*, 191. [CrossRef]

55. Pan, D.; Chen, J.; Feng, C.; Wu, W.; Wang, Y.; Tong, J.; Zhou, D. Preferential localization of MUC1 glycoprotein in exosomes secreted by non-small cell lung carcinoma cells. *Int. J. Mol. Sci.* **2019**, *20*, 323. [CrossRef]

56. Zhang, Q.; Hu, H.; Chen, S.-Y.; Liu, C.-J.; Hu, F.-F.; Yu, J.; Wu, Y.; Guo, A.-Y. Transcriptome and regulatory network analyses of CD-19-CART-T immunotherapy for B-ALL. *Genom. Proteom. Bioinform.* **2019**, *17*, 190–200. [CrossRef]

57. Yan, G.; Dai, M.; Poulet, S.; Wang, N.; Boudreault, J.; Daliah, G.; Ali, S.; Lebrun, J.-J. Combined in vitro/in vivo genome-wide CRISPR screens in triple negative breast cancer identify cancer stemness regulators in paclitaxel resistence. *Oncogenesis* **2023**, *12*, 51. [CrossRef]

58. Liu, C.-C.; Wang, H.; Wang, W.-D.; Wang, L.; Liu, W.-J.; Wang, J.-H.; Geng, Q.-R. ENO2 promotes cell proliferation, glycolys, and glucocorticoid-resistance inn acute lymphoblastic leukemia. *Cell. Physiol. Biochem.* **2018**, *46*, 1525–1535. [CrossRef]

59. Isgró, M.A.; Bottoni, P.; Scatena, R. Neuron-specif enolase as a biomarker: Biochemical and clinical aspects. In *Advances in Cancer Biomarkers; Advances in Experimental Medicine and Biology* 867; Springer Science: Dordrecht, The Netherlands, 2015; Volume 867, pp. 126–143.

60. Gao, L.; Yang, F.; Tang, D.; Xu, Z.; Tang, Y.; Tang, Y.; Yang, D.; Sun, D.; Chen, Z.; Teng, Y. Mediation of PKM2-dependent glycolytic and non-glycolytic pathways by ENO2 in head and neck cancer development. *J. Exp. Clin. Cancer Res.* **2023**, *42*, 1. [CrossRef]

61. Agu, P.; Afiukwa, C.; Orji, O.; Ezeh, E.; Ofole, I.; Ogbu, C.; Uwuja, E.; Aja, P. Molecular docking as a tool for the discovery of molecular targets of nutraceuticals in diseases management. *Sci. Rep.* **2023**, *7*, 13384–13421. [CrossRef]

62. Ferreira, L.; N dos Santos, R.; Oliva, C.; Anridopulo, A. Molecular Docking and Structure-Based Drugs Design Strategies. *Molecules* **2013**, *20*, 13384–13421. [CrossRef]

63. NOM-062-ZOO-1999; Norma Oficial Mexicana. Especificaciones Técnicas para la Producción, Cuidado y uso de Animales de Laboratorio. Diario Oficial de la Federación: Mexico City, Mexico, 2001. Available online: https://www.gob.mx/cms/uploads/attachment/file/203498/NOM-062-ZOO-1999_220801.pdf (accessed on 13 January 2025).
64. Wu, T.; Hu, E.; Xu, S.; Chen, M.; Guo, P.; Dai, Z.; Feng, T.; Zhou, L.; Tang, W.; Zhan, L.; et al. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. *Innovation* **2021**, *2*, 100141. [CrossRef] [PubMed]

65. Raudvere, U.; Kolberg, L.; Kuzmin, I.; Arak, T.; Adler, P.; Peterson, H.; Vilo, J. g: Profiler: A web server for functional enrichment analysis and conversions of gene lists (2019 update). *Nucleic Acids Res.* **2019**, *47*, W191–W198. [CrossRef] [PubMed]

66. Sun, L.; Dong, S.; Ge, Y.; Fonseca, J.P.; Robinson, Z.T.; Mysore, K.S.; Mehta, P. DiVenn: An Interactive and integrated web-based visualization tool for comparing gene lists. *Front. Genet.* **2019**, *10*, 421. [CrossRef] [PubMed]

67. Oliveros, J.C. Venny. An interactive Tool for Comparing Lists with Venn's Diagrams. Available online: https://bioinfogp.cnb.csic.es/tools/venny/index.html (accessed on 11 May 2025).

68. Xiong, G.; Wu, Z.; Yi, J.; Fu, L.; Yang, Z.; Hsieh, C.; Yin, M.; Zeng, X.; Wu, C.; Lu, A.; et al. ADMETlab 2.0: An integrative online plataform for accurate and comprehensive predictions of ADMET properties. *Nucleic Acids Res.* **2021**, *49*, W5–W14. [CrossRef]

69. Cheng, F.X.; Li, W.H.; Zhou, Y.D.; Shen, J.; Wu, Z.R.; Liu, G.X.; Lee, P.W.; Tang, Y. admetSAR: A Comprehensive source and free tool for assessment of chemical ADMET properties. *J. Chem. Inf. Model.* **2012**, *52*, 3099–3105. [CrossRef]

70. Banerjee, P.; Kemmler, E.; Dunkel, M.; Preissner, R. ProTox 3.0: A webserver for the prediction of toxicity of chemicals. *Nucleic Acids Res.* **2024**, *52*, W513–W520. [CrossRef]

71. Hanwell, M.; Curtis, D.; Lonie, D.; Vandermeersch, T.; Zurek, E.; Hutchison, G. Avogadro: An advanced semantic chemical editor, visualization, and analysis platform. *J. Cheminform.* **2012**, *4*, 17. [CrossRef]

72. Morris, G.; Lindstrom, W.; Sanner, M.; Belew, R.; Goodshell, D.; Olson, A. Autodock4 and AutodockTools4: Automated docking with selective receptor flexibility. *J. Comput. Chem.* **2009**, *30*, 2785–2791. [CrossRef]

73. Drug Discovery. *BIOVIA Discovery Studio Visualizer*; Version 20.1.0.19295; Dassault Systems BIOVIA: Waltham, MA, USA, 2025; Available online: https://www.3ds.com/products/biovia/discovery-studio (accessed on 22 May 2025).

**Disclaimer/Publisher's Note:** The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.