Biological evaluation of some novel 1,3-bis substituted-2-isopropylamidines by in silico molecular dynamics and simulation studies†

Pradeep Kumar P. S.^a, Jeevan Chakravarthy A. S.*^b, Shriraksha A^c and Sunil K*^a
aDepartment of Chemistry, Sri Siddhartha Institute of Technology, SSAHE, Tumakuru 572105, India. E-mail: sunilk999@gmail.com
^bDepartment of Chemistry, BMSIT&M affiliated to VTU, Avalahalli, Yelahanka, Bangalore 560064, India. E-mail: jeechakravarthy@gmail.com
^cDepartment of Biochemistry, Bengaluru City University, Bengaluru 560001, India

First published on 12th August 2024

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Introduction

Amidines are a versatile type of organic compound that are structural analogues of carboxylic acids and esters.¹ Their profound biological importance is evident in the presence of several naturally occurring biological cores (Scheme 1).² A wide spectrum of properties, such as anti-inflammatory, diuretic, and anti-diabetic properties, exhibited by this class is attributed to their direct structural relationship with biomolecules.³ Amidines are extensively used as potential precursors in the synthesis of a wide class of biologically important heterocycles, including imidazoles,⁴ 1,2,4-triazoles,⁵ 1,2,3-triazoles,⁶ isoquinolines, quinolines, quinazolines, 1,2,4-oxadiazoles, thiazoles, and thiadiazoles.⁷

In recent years, several synthetic routes have been developed for the synthesis of amidines due to their wide applicability in many fields. Recent synthetic developments include metal-free multicomponent synthesis of sulfonyl amidines by the reaction of enamines generated in situ with azides.⁸ They are also synthesized from N-arylbromodifluoroacetimidoyl chloride or N-aryltrifluoroacetimidoyl chloride under mild reaction conditions with amines,⁹ one-pot reactions of alicyclic amines, cyclic ketones and 4-azidoquinolin-2(1H)-ones by tandem conversion of nitriles,¹⁰ and the selective conversion of lithium amides generated in situ (Scheme 2a–c).¹¹ In recent decades, extensive work has been carried out on the theoretical and practical applications of heterocycles and their synthesis and bio-evaluation.¹²

The broad scope of heterocycles encouraged us to develop a novel path for their synthesis and the evaluation of their pharmacological properties. Hence, in this study, the following problems were addressed.

Cancer stands second with respect to high mortality rates worldwide;¹³ more than 10 million deaths were reported due to cancer in 2020. Past decades have seen rapid technological and conceptual progress in various fields to fight cancer, with efforts ranging from massive next-generation sequencing, high-resolution microscopy, molecular immunology, flow cytometry, analysis and sequencing of individual cells, new cell culture techniques, and the development of animal models to the synthesis of novel small molecules with anticancer potency.¹⁴ Although there have been consistent efforts in developing analytical techniques and new drugs to target cancer, chemoresistance poses one of the major setbacks to effective cancer therapy, creating demand for new drugs.

Similarly, HIV1-protease, a vital enzyme in HIV replication, is involved in the generation of mature virions that infect CD₄⁺ cells by cleavage of Gag and Pol polyproteins; therefore, it is a potent target to tackle AIDS. Although HIV does not cause any diseases directly, the weakened immune system exposes the infected to the risk of various types of diseases, such as cancer and bacterial and viral infections.¹⁵

A constant search for vaccines against the RNA virus, especially the virus behind the global pandemic of 2020, i.e., SARS-CoV-2, is in high demand due to the mutagenic nature of the virus. The viral strains continue to infect people of all ages and genders. Scientific investigations have revealed that the coronaviral genome encodes a main protease and a papain-like protease, both of which are involved in the generation of mature virions. Thus, targeting one of these cysteine-like proteases can help in the reduction of SARS-CoV-2 infections considerably.¹⁶

Acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) play vital roles in cholinergic signaling. BChE has been associated with the hallmarks of Alzheimer's, such as amyloidogenesis; in addition, it has been reported to compensate for AChE when its levels go down. Despite numerous strategies for the treatment of Alzheimer's disease, there has been no cure for it, leaving palliative care as the only option.^17,18

Methicillin-resistant Staphylococcus aureus is a global cause of concern due to its resistance to the majority of treatment options available. Most antibiotics targeting S. aureus contain beta-lactams that inhibit the four types of penicillin-binding proteins (PBPs) found in MRSA. The fifth type, i.e., PBP2a, is not inhibited by beta-lactams, allowing the synthesis of bacterial cell walls.¹⁹ We aim to analyze the ability of our ligands to bind to PBP2a, and thus, inhibit bacterial growth.

Alzheimer's disease is a neurodegenerative disease that has a disruptive cholinergic system that results in the release of low levels of acetylcholine. Reports have shown that the breakdown of acetylcholine can be prevented by targeting the enzyme that causes neurotransmitter hydrolysis, which can then improve the conditions of patients diagnosed with Alzheimer's disease.²⁰

The use of amidines as chemical cores has progressed over several years in biomedical research. This inspired us to synthesize amidines following a new approach. In this present work, we report the synthesis of a set of novel amidines by the tandem conversion of amines to amidines. The presence of a pre-tested pharmaceutically active core in the synthesized molecules led us to conduct a biological evaluation based on their interactions with different proteins of pathological significance using a bioinformatics approach.

Results and discussion

In recent years, the use of amidines as reactants, intermediates, and application products has been widely reported. Langer et al. reported the use of amidines as an intermediate in the total synthesis of dabigatran, a direct thrombin inhibitor.²¹ Glomb et al. identified and synthesized a novel amidine cross-link structure, N₁,N₂- bis-(5-amino-5-carboxypentyl)-2-hydroxy-acetamidine (glyoxal lysine amidine, GLA), that was formed from glyoxal through the isomerization cascade; it is used to quantify AGE in processed food proteins.²² Yudin et al. incorporated the amidine functionality into cyclic peptides to serve as a pH-sensitive conformational probe for them and to study the effect of hydrogen bonding on the macrocyclic conformation of proteins.²³ Ramachary et al. used amidines, along with ynone moieties, which are Michael acceptors in formal [4+2]-cycloaddition reactions, in the Ca(OTf)₂-catalyzed regioselective synthesis of tricyclic azepines.²⁴ Hong et al. reported the synthesis of novel 10π-electron cyclic amidines with excellent fluorescence properties and great potential for biological imaging. The reported amidines were successfully used as fluorescent probes for the localization of the NK-1 receptor in living systems in fluorescence–structure relationship studies.²⁵ An amidine-based ionic liquid was reported by Lucio et al. that could be used as an electrolyte in Al batteries.²⁶ Yang et al. reported the synthesis of a set of amidine derivatives of avibactam and their use as antibacterial agents by in vitro inhibition of β-lactamase, in combination with an antibiotic.²⁷

Given the wide use of the amidine functionality, in the present work, a simple and convenient methodology for the synthesis of novel amidines is proposed via two different approaches, i.e., a step-wise and a tandem one-pot route. (Scheme 3)

Method 1 involves two steps, including (step a) the synthesis and isolation of N- arylisobutyryl amides 3 from the reaction of corresponding amines 1 and isobutyrylchloride 2 in DCM using triethylamine as a base and (step b) the synthesis of 1,3-bis(het)aryl-2- isopropylamidines 5 by amidation of in situ-generated iminylchloride that was formed by the reaction of amides 3 with POCl₃ using amine 4. All reactions were monitored by TLC aliquots. Workup and column chromatography purification produced bis(het)aryl-2-isopropylamidines 5a–e in more than 80% yields.

Method 2 involves the conversion of amide 3 formed by the reaction of amine 1 and isobutyryl chloride 2 via sequential conversion to iminylchloride by the addition of POCl₃. This reaction was followed by the addition of arylamine 4 in a tandem one-pot synthesis process to obtain the novel 1,3-bis(het)aryl-2-isopropylamidine 5.

Both the methods yielded the novel amidine 5 in greater than 80% yields, however, better yields under minimal reaction conditions were obtained from the one-pot method. The yields of amidines by both methods are furnished in Table 1. Each reaction was carried out for a minimum of three trials and optimum isolated yields were reported.

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The synthesized novel amidine 5 was further evaluated for its physiological profile using molecular docking and simulation of molecular dynamics.

Biological activity prediction

The probable molecular targets of compounds 5a–e were predicted using online PASS Targets.²⁸ The top three targets for each compound were chosen for further analysis. Values of Pa above 0.3 were considered for molecular docking with targets specific to the disease, and the results are represented in Fig. 1. Compounds 5a, 5b, and 5c showed Pa values of 0.336, 0.337, and 0.333 respectively, for the treatment of preneoplastic and neoplastic conditions. It was observed that compound 5b also showed Pa > 0.3 for the treatment of neurodegenerative diseases; it was also anticipated to be effective for interactions with the P-glycoprotein. Compounds 5d and 5e had almost equal Pa values (0.30–0.39) for neurodegenerative diseases and P-glycoprotein. Except for compound 5c, all compounds were predicted to have antiviral activity against SARS coronavirus and retroviruses.

ADMET studies

The drug likeliness of all five synthesized compounds 5a–e was analyzed for ADMET properties in silico using online tools, swiss-ADME and pkCSM.^28,29 Results are summarized in Table 2. All the synthesized compounds, except 5c, showed high gastrointestinal absorptivity, which obeyed Lipinski's rule of five. Furthermore, compounds, 5d and 5e, could permeate the blood–brain barrier. Fig. 2 shows the boiled egg and radar map of compounds 5a–e, where the pink area defines the oral bioavailability of the query compound. All the molecules were found to have the same oral bioavailability value of 0.55. The toxicity profiles of all five compounds are shown in Table 3.

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Validation of the docking model

The molecular docking models were validated by extracting and re-docking the co-crystallized ligand into the docking site with the same grid parameters using Autodock Vina. This approach was adopted to ensure that the ligand of interest (i.e., the inhibitor) binds to the active site without deviation from the co-crystallized complex. Fig. 3 describes the superimposed ligands with their RMSD values.

Molecular docking

For a better understanding of the interaction of compounds with proteins of interest, molecular docking was performed using Autodock Vina and the results are summarized in Table 4. Cancer is a class of diseases, which involves the uncontrolled proliferation of cells. There have been many chemotherapeutic strategies to inhibit cell differentiation and proliferation. However, the cells develop resistance to drugs causing them to have little to no effect on the target cells. Thus, the development of new drugs against the same target is a challenge. The novel amidines 5a–e synthesized here were tested as ligands for their anti-cancer properties through molecular docking against the 3D structures of p38 α, cdk2, and vegfr-2 as targets.

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Various stress and non-stress stimuli can activate p38 MAPK signaling that regulates numerous cellular processes. Reports suggest many pro-oncogenic mechanisms by p38 in various cancers, bringing into light the prospects of developing drugs targeting p38.³⁰ Docking studies for all five ligands 5a–e were carried out for p38 alpha inhibitors at a resolution of 1.74 Å. Docking of the ligands with p38 alpha showed binding affinities ranging from −7.2 kcal mol⁻¹ to −9.7 kcal mol⁻¹. The ligand 5c showed the least docking score of −9.5 kcal mol⁻¹. The molecular docking of ligand 5c with 6YJC revealed the active site amino acids to have more hydrophobic interactions with the ligand as shown in Fig. 4a.

The amino acid residues, Glu(71), Asp(168), and Thr(106), could interact hydrophobically with the ligand 5c. A recent study exploring the dynamic back pocket of p38α revealed a type II inhibitor of the same type to bind to the abovementioned pockets, thereby inhibiting the activity of p38α.³¹

Targeting CDK can help accentuate other modalities of cancer treatment, while decreasing cell proliferation because it is involved in many oncogenic pathways. Different ligand-binding modes were observed for the cdk2 protein (3PXQ) with a resolution of 1.90 Å. Ligand 5b had the lowest docking score of −10.5 kcal mol⁻¹. The 3D orientation of the best conformation is shown in Fig. 4b. Molecular docking with cdk2 revealed that most of the interacting residues in the active site are hydrophobic in nature. Although all five ligands have promising inhibitory interactions with the receptor, ligand 5b displays the highest binding energy of −10.0 kcal mol⁻¹ and is thus likely to be a potent inhibitor of cdk2 as shown in Table 4. Inhibitory small molecules were studied to bind at the allosteric pockets mentioned above via hydrophobic interactions with Tyr(15), Ile(35), Leu (55), and Lys(56) with a predicted docking score of −10.2 kcal mol⁻¹ (Fig. 4c).^32,33

Despite advances in the development of the inhibitors of vegfr, their potential toxicity and low clinical efficacy limit their use. Thus, there is a need for the development of new vegfr inhibitors. Reports show that blocking vegfr2 could effectively suppress tumor growth and metastasis.^34,35 A docking study was performed on all the synthesized ligands 5 for vegfr-2 (3VHE–1.55 Å resolutions). Among the ligands tested for docking analysis, (E)-N′-(2-chlorobenzyl)-N-[4-(trifluoromethyl) phenylisobutyrimidamide] (ligand 5c) had the least binding energy of −9.2 kcal mol⁻¹. The docking scores for different ligands with various receptors are summarized in Table 4. In this study, all the ligands 5 were docked deeply into the binding pocket of 3VHE with hydrophobic interactions. The ligand 5c showed hydrophobic interactions with the following amino acid residues: Val(848), Glu(885), Ile(888), Leu(889), Ile(892), Val(899), His(1026), and Asp(1046), as depicted in Fig. 4c.

HIV-affected individuals can be at a higher risk of contracting SARS-CoV-2 or any other infection primarily due to their weakened immune systems. Reports suggest that the use of antivirals that target proteases might alter the risk of infection.³⁶ Docking with HIV1 protease and SARS CoV-2 main protease revealed ligand 5b to have the lowest docking score of −7.5 and −8.5 kcal mol⁻¹, respectively. PyMol images showed that ligand 5b interacts hydrophobically with the active site residues of SARS CoV-2 main protease as shown in Fig. 4f. The ligand 5b shows high binding affinity with HIV1 protease as well with a docking score of −7.5 kcal mol⁻¹. Hydrogen bonding, as shown in Fig. 4d, is seen with Ile(50), and is surrounded by Ile(84), Asp(25), Gly(49), and Phe(53) between HIV1 protease and ligand 5b.

MRSA is resistant to most penicillin-based antibiotics due to structural modifications in the PBPs. The presence of the mecA gene in PBP2a renders the organism resistant to β-lactams.³⁷ Based on the molecular docking scores summarized in Table 4, ligand 5b has the lowest docking score of −8.5 kcal mol⁻¹. This ligand displayed hydrogen bond interaction (2.5 Å) with only the Lys(148) residue. Interactions with His(293), Glu(294), Val (256), Val(277), and Arg(241) were mostly hydrophobic as seen in Fig. 4e.

Acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) play a vital role in cholinergic signalling. BChE is associated with the hallmarks of Alzheimer's, such as amyloidogenesis, and has been shown to compensate for AChE when its levels decrease. Despite having numerous strategies for the treatment of Alzheimer's disease, there is no cure, and only palliative care is available.^38,39

Among the esterase receptors, BChE showed the lowest docking score of −10.0 kcal mol⁻¹ with ligand 5c, whereas AChE shows an average docking score of −5.5 kcal mol⁻¹ with the same ligand as indicated by Table 4. No hydrogen bond interactions were observed between ligands and AChE or BChE receptors as shown in Fig. 4g and h. Docking results indicated that BChE modulation was more favorable with ligand 5c.

Simulations of molecular dynamics

Molecular dynamic simulations are a strong technique that is similar to physical experiments. Based on the docking results, the ligand2-cdk2 complex was considered for molecular dynamic simulations. Group properties, such as RMSD, RMSF, and MM-PBSA free energy calculations, were performed using trajectory files. The free binding energy of the docked complex was estimated using molecular mechanics Poisson–Boltzmann surface area (gmx-MMPBSA). Results are presented in Table 5. The total energy of the complex (ΔG) was found to be −6.48 kJ mol⁻¹, indicating stable conformation of the ligand with cdk2.

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Root mean square deviation (RMSD) was determined to illustrate the dynamic stability of proteins when they were bound to the ligand of interest. The RMSD of proteins fluctuated for 12 ns as observed in Fig. 5. The RMSD tends to increase along the simulation but becomes stable toward the end of the 100-ns-long simulation.

RMSF analysis showed residual fluctuations during the simulation time. The computed RMSF showed major fluctuations between the amino acid residues number 150 and 160, which can be attributed to the presence of loops in the protein structure as depicted in Fig. 6. However, no major fluctuations were observed near the active site pockets. Most of the residues were found to have RMSF values less than 0.2 nm indicating them to exist in rigid complex states.

Hydrogen bond analysis provides insight into the interaction power for the stabilization of protein and ligand. Fig. 7 shows that the hydrogen bond of the simulated complex was observed to have a stable trend. Fewer hydrogen bonds were seen at the start of the simulations, however, strong hydrogen bonding was observed after 20 ns until the end of the simulation, indicating stable binding.

The presence of non-binding interactions also accounts for the stability of the protein structure. One such parameter is hydrophobic interaction, which can be described by the SASA (solvent-accessible surface region). The SASA of the protein–ligand complex during the 100-ns-long simulation is depicted in Fig. 8. The plot of the SASA showed stabilization at an average of 160 nm², confirming the compactness of the complex.

The radius of gyration (R_g) provides insights into the compactness of protein–ligand interaction. The average R_g value of the system was found to be 2.04 nm as shown in Fig. 9. The low R_g value indicates that the protein–ligand interaction showed maximum compactness throughout the simulation.

The obtained results indicate that all the ligands bind to the active site pockets of the receptors in a similar position and orientation. In all the receptor–ligand interactions discussed above, hydrophobic interactions were crucial in stabilizing the ligands at the binding site. A high number of hydrophobic interactions at the active site of the ligand–receptor interface tend to increase the biological activity of the ligand. The existing relationship between the free binding energy and the affinity of small organic molecules contributes to the prediction and interpretation of the activity of the small molecule toward the target receptors.⁴⁰ The overall free energy of binding of the ligands studied here indicated a good affinity of the ligands for the given receptors. ADME studies and molecular dynamics simulation of compound 5b with that of cdk2 receptor suggested that the compound could be a potential anti-cancer agent. In this study, we considered the molecule with the best docking score for further studies. However, reports have suggested molecules with low binding affinity to work efficiently with minimal side effects. Further studies using in vivo techniques are required to further develop the application of the proposed compounds in cancer therapeutics.

General procedure

Method 1

Method 2

Conclusion

In the present work, we report a simple and straightforward method for the synthesis of some biologically important amidines. We also evaluated them as potent candidates for therapeutic work. The result of the work carried out here indicates that compound 5b is a promising candidate and acts as a good modulator of proteins that play a vital role in cancer cell differentiation and proliferation. The present work forms a solid theoretical platform for the wet lab evaluation of compound 5b in future studies.

Data availability

The data underlying this study are available in the published article and its ESI.†

Conflicts of interest

There are no conflicts to declare.

References

1. The Chemistry of Carboxylic Acid and Esters, ed. P. Saul, Patai's Chemistry of Functional Groups, John Wiley and Sons, 1969, 1157 Search PubMed .
2. A. A. Aly, S. Brase and M. A.-M. Gomaa, ARKIVOC, 2018, vi, 85–138 Search PubMed .
3. (a) Y. Huang, W. Yi, Q. Sun and F. Yia, Adv. Synth. Catal., 2018, 360, 3074–3082 CrossRef CAS ; (b) Y. Liu, X. Hu, Y. Wu, W. Zhang, X. Chen, X. You and L. Hu, Eur. J. Med. Chem., 2018, 150, 771–782 CrossRef CAS PubMed ; (c) C. C. Santos, J. R. Lionel, R. B. Peres, M. M. Batista, P. B. Silva, G. M. Oliveira, C. F. Silva, D. G. J. Batista, S. M. O. Souza, C. H. Andrade, B. J. Neves, R. C. Braga, D. A. Patrick, S. M. Bakunova, R. R. Tidwell and M. N. C. Soeiro, Antimicrob. Agents Chemother., 2018, 62(2), e02205–17 CrossRef PubMed .
4. H. Xu, H. Chen, X. Hu, G. Xuan, P. Li and Z. Zhang, J. Org. Chem., 2022, 87, 16204–16212 CrossRef CAS PubMed .
5. D. Wang, X. Wan, Y. Zhou, J. Liu, J. Cai and G.-J. Deng, Asian J. Org. Chem., 2023, 12, e202200674 CrossRef CAS .
6. S. Maracic, P. Grbcic, S. Shammugam, M. R. Stojkovic, K. Pavelic, M. Sedic, S. K. Pavelic and S. Raic-Malic, Molecules, 2021, 26, 7060 CrossRef CAS PubMed .
7. (a) L. C. R. Carvalho, E. Fernandes and M. M. B. Marques, Chem. – Eur. J., 2011, 17, 12544–12555 CrossRef CAS PubMed ; (b) T. Shimbayashi, G. Matsushita, A. Nanya, A. Eguchi, K. Okamoto and K. Ohe, ACS Catal., 2018, 8, 7773–7780 CrossRef CAS ; (c) E. Vitaku, D. T. Smith and J. T. Njardarson, J. Med. Chem., 2014, 57, 10257–10274 CrossRef CAS PubMed ; (d) N. Zumbrägel, M. Sako, S. Takizawa, H. Sasai and H. Gröger, Org. Lett., 2018, 20, 4723–4727 CrossRef PubMed ; (e) W. Guo, M. Zhao, W. Tan, L. Zheng, K. Tao and X. Fan, Org. Chem. Front., 2019, 6, 2120 RSC .
8. Z. Alassad, A. AboRaed, M. S. Mizrachi, M. H. Pérez-Temprano and A. Milo, J. Am. Chem. Soc., 2022, 144, 20672–20679 CrossRef CAS PubMed .
9. Y. M. Wu, M. Zhang and Y.-Q. Li, J. Fluor. Chem., 2006, 127, 1168–1174 CrossRef CAS .
10. E. M. El-Sheref, H. N. Tawfeek, A. A. Hassan, S. Bräse, M. A. I. Elbastawesy, H. A. M. Gomaa, Y. A. Mostafa and B. G. M. Youssif, Front. Chem., 2022, 10, 1039176 CrossRef CAS PubMed .
11. (a) W. Guo, M. Zhao, W. Tan, L. Zheng, K. Tao and X. Fan, Org. Chem. Front., 2019, 6, 2120–2141 RSC ; (b) S. H. Cho, J. Y. J. Y. Kim, J. Kwak and S. Chang, Chem. Soc. Rev., 2011, 40, 5068–5083 RSC ; (c) D. Elorriaga, B. Parra-Cadenas, A. Antiñolo, F. Carrillo-Hermosilla and J. García-Álvarez, Green Chem., 2022, 24, 800–812 RSC .
12. (a) S. Arya, N. Kumar, P. Roy and S. M. Sondhi, Eur. J. Med. Chem., 2013, 59, 7–14 CrossRef CAS PubMed ; (b) M. D. Carrion, B. Rubio-Ruiz, F. Franco-Montalban, P. Amoia, M. C. Zuccarini, C. De Simone, M. E. Camacho, R. Amoroso and C. Maccallini, Eur. J. Med. Chem., 2023, 248, 115112 CrossRef CAS PubMed ; (c) A. E. M. Abdallah, S. A. Abdel-Latif and G. H. Elgemeie, ACS Omega, 2023, 8, 19587–19602 CrossRef CAS PubMed ; (d) O. A. Abbas, O. M. El-Roudi and S. A. Abdel-Latif, Appl. Organomet. Chem., 2023, 37(e7236), 1–20 Search PubMed ; (e) A. M. Abu-Dief, R. M. El-Khatib, T. El-Dabea, S. A. Abdel-Latif, I. O. Barnawi, F. S. Aljohani, K. Al-Ghamdi and M. A. E. A. A. A. El-Remaily, Appl. Organomet. Chem., 2024, 38(e7463), 1–32 Search PubMed ; (f) W. Omara, S. A. Abdel-Latif, H. M. E. Azzazy and N. S. Abdel-Kader, Appl. Organomet. Chem., 2024, 38(e7439), 1–10 Search PubMed ; (g) B. Wang, H. Li, M. Liu, J. Lin, C. Yang, Z. Zhao, D. Li and J. Yang, New J. Chem., 2024, 48, 13654–13658 RSC ; (h) N. S. Abdel-Kadera, S. A. Abdel-Latif, A. L. El-Ansarya and M. A. Hemeda, Polycycl. Aromat. Compd., 2023, 44(6), 3601–3632 CrossRef ; (i) H. M. El-Shafeai, E. Abdel-Latif, A. A. Fadda, M. R. Elmorsy and M. A. Ismail, Russ. J. Gen. Chem., 2024, Vol. 94(1), 184–192 CrossRef .
13. World Health Organization. WHO Report on Cancer: Setting Priorities, Investing Wisely and Providing Care for All. 2020. License: CC BY-NC-SA 3.0 IGO. https://apps.who.int/iris/handle/10665/330745.
14. P. Piña-Sánchez, A. Chávez-González, M. Ruiz-Tachiquín, E. Vadillo, A. Monroy-García, J. J. Montesinos, R. Grajales, M. Gutiérrez de la Barrera and H. Mayani, Can. Cont., 2021, 28, 1–21 Search PubMed .
15. E. Lewitus, Y. Li, H. Bai, P. Pham and M. Rolland, mBio, 2024, 15(3), 2150 CrossRef PubMed .
16. S. Steiner, A. Kratzel, G. T. Barut, R. M. Lang, E. A. Moreira, L. Thomann, J. N. Kelly and V. Thiel, Nat. Rev. Microbiol., 2024, 22, 206–225 CrossRef CAS PubMed .
17. J. G. Fernández-Bolaños and Ó. López, Expert Opin. Ther. Pat., 2022, 32(8), 913–932 CrossRef PubMed .
18. S. Darvesh, Curr. Alzheimer Res., 2016, 13(10), 1173–1177 CrossRef CAS PubMed .
19. S. S. Ambade, V. K. Gupta, R. P. Bhole, P. B. Khedekar and R. V. Chikhale, Molecules, 2023, 28(20), 7008 CrossRef CAS PubMed .
20. H. T. M. Ferreira-Vieira, I. R. Guimaraes, F. Silva and F. Ribeiro, Curr. Neuropharmacol., 2016, 14(1), 101–115 CrossRef PubMed .
21. S. Jopp, R. Molenda, E. R. D. Seiler, M. F. Maitz, A. Villinger, P. Ehlers and P. Langer, ChemistrySelect, 2019, 4, 13802–13805 CrossRef CAS .
22. (a) M. D. Eggen and M. A. Glomb, J. Agric. Food Chem., 2021, 69, 7960–7968 CrossRef CAS PubMed ; (b) S. Huh, S. D. Appavoo and A. K. Yudin, Org. Lett., 2020, 22, 9210–9214 CrossRef CAS PubMed .
23. T. P. Reddy, J. Gujral, P. Roy and D. B. Ramachary, Org. Lett., 2020, 22, 9653–9657 CrossRef CAS PubMed .
24. G. Li, M. Zhao, J. Xie, Y. Yao, L. Mou, X. Zhang, X. Guo, W. Sun, Z. Wang, J. Xu, J. Xue, T. Hu, M. Zhang, M. Li and L. Hong, Chem. Sci., 2020, 11, 3586 RSC .
25. A. J. Lucio, I. Efimov, O. N. Efimov, C. J. Zaleski, S. Viles, B. B. Ignatiuk, A. P. Abbott, A. R. Hillman and K. S. Ryder, Chem. Commun., 2021, 57, 9834 RSC .
26. Y. Gao, Y. Liu, Z. Iqbal, J. Sun, J. Ji, L. Zhai, D. Tang, J. Ji, L. He, Y. Mu, H. Yang and Z. Yang, ChemistrySelect, 2021, 6, 1174–1178 CrossRef CAS .
27. A. Martínez-Limón, M. Joaquin, M. Caballero, F. Posas and E. De Nadal, Int. J. Mol. Sci., 2020, 21, 1913 CrossRef PubMed .
28. P. J. P. Tjitda, F. O. Nitbani, A. A. Parikesit, M. I. T. Bessi and T. D. Wahyuningsih, Trop. J. Nat. Prod. Res., 2024, 8(2), 6208–6217 CAS .
29. A. Daina, O. Michielin and V. Zoete, Sci. Rep., 2017, 7(1), 42717 CrossRef PubMed .
30. S. Röhm, M. Schröder, J. E. Dwyer, C. S. Widdowson, A. Chaikuad, B.-T. Berger, A. C. Joerger, A. Krämer, J. Harbig, D. Dauch, M. Kudolo, S. Laufer, M. C. Bagley and S. Knapp, Eur. J. Med. Chem., 2020, 208, 112721 CrossRef PubMed .
31. E. B. Faber, N. Wang and G. I. Georg, Biol. Reprod., 2020, 103, 357–367 CrossRef PubMed .
32. E. B. Faber, D. Tian, D. Burban, N. M. Levinson, J. E. Hawkinson and G. I. Georg, ACS Chem. Biol., 2020, 15, 1759–1764 CrossRef CAS PubMed .
33. F. M. Behelgardi, S. Zahri, Z. G. Shahvir, F. Mashayekhi, L. Mirzanejad and S. M. Asghari, Mol. Biol. Rep., 2020, 47, 2061–2071 CrossRef PubMed .
34. H. Watanabe, E. Ichihara, H. Kayatani, G. Makimoto, K. Ninomiya, K. Nishii, H. Higo, C. Ando, S. Okawa, T. Nakasuka, H. Kano, N. Hara, A. Hirabae, Y. Kato, T. Ninomiya, T. Kubo, K. Rai, K. Ohashi, K. Hotta, M. T. Y. Maeda and K. Kiura, Cancer Sci., 2021, 112, 1853–1864 CrossRef CAS PubMed .
35. P. Vizcarra, M. J. Pérez-Elías, C. Qereda, A. Moreno, M. J. Vivancos, F. Dronda and J. L. Casado, Lancet HIV, 2020, 7(8), e554–e564 CrossRef PubMed .
36. J. Fishovitz, J. A. Hermoso, M. Chang and S. Mobashery, IUBMB Life, 2014, 66, 572–577 CrossRef CAS PubMed .
37. M. E. Gondokesumo and I. M. Kurniawan, J. Basic Clin. Physiol. Pharmacol., 2019, 30(6), 20190282 CrossRef CAS PubMed .
38. M. Işık and S. Beydemir, J. Biomol. Struct. Dyn., 2020, 39, 6515–6523 CrossRef PubMed .
39. J. G. Fernández-Bolaños and Ó. López, Expert Opin. Ther. Pat., 2020, 32(8), 913–932 CrossRef PubMed .
40. P. C. Agu, C. A. Afiukwa, O. U. Orji, E. M. Ezeh, I. H. Ofoke, C. O. Ogbu, E. I. Ugwuja and P. M. Aja, Sci. Rep., 2023, 13(1), 13398 CrossRef CAS PubMed .

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Footnote

† Electronic supplementary information (ESI) available. See DOI: https://doi.org/10.1039/d4nj03384a

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