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Article

Structural Parameters of the Interaction between Ciprofloxacin and Human Topoisomerase-II β Enzyme: Toward New 19F NMR Chemical Shift Probes

by
Thais Aparecida Sales
1,
Mateus Aquino Gonçalves
1 and
Teodorico Castro Ramalho
1,2,*
1
Department of Chemistry, Federal University of Lavras, Lavras CEP 37203-202, Brazil
2
Department of Chemistry, Faculty of Science, University of Hradec Králové, 500 03 Hradec Králové, Czech Republic
*
Author to whom correspondence should be addressed.
Magnetochemistry 2022, 8(12), 181; https://doi.org/10.3390/magnetochemistry8120181
Submission received: 26 October 2022 / Revised: 25 November 2022 / Accepted: 29 November 2022 / Published: 7 December 2022
(This article belongs to the Special Issue NMR Spectroscopy and Imaging in Biological Chemistry and Medicine)
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Abstract

New tools for cancer diagnosis are being studied since early diagnosis can be crucial for a successful treatment. In this context, the use of NMR probes constitutes an efficient method of diagnosis. In this study, we investigated the use of ciprofloxacin to indirectly label the overexpression of topoisomerase-II enzymes by changes in 19F NMR chemical shifts of ciprofloxacin. Increased topoisomerase-II expression has been associated with cancer occurrence, mainly with aggressive forms of breast cancer, thus constituting a promising molecular target for new tumor cell identifiers. Using DFT calculations, we performed a spectroscopy analysis of ciprofloxacin in different chemical environments and evaluated the solvent and enzymatic effects. Our results show that ciprofloxacin forms a stable complex with the enzyme, and the main intermolecular interactions between ciprofloxacin and human topoisomerase-IIβ are hydrogen bonds, followed by π-π stacking and electrostatic interactions. Additionally, a shift of 6.04 ppm occurs in the 19F NMR signal when ciprofloxacin interacts with the human topoisomerase-IIβ enzyme, and this parameter may be an indirect marker indicating the overexpression of these enzymes in the body.

1. Introduction

Fluoroquinolones (FQ), introduced more than 20 years ago, are a quinolone derivative class of molecules known for their antibacterial activity [1]. The broad-spectrum commercialized antibacterial agent ciprofloxacin (CPX) is representative of the FQs [2,3,4]. These compounds exert antibacterial activity through the inhibition of two bacterial enzymes: DNA gyrase and topoisomerase II [5,6]. The latter is considered to be the primary target of several anticancer agents, such as doxorubicin and etoposide [7,8,9]. Researchers continue to investigate the development of new anticancer drugs based on evidence indicating increased levels of topoisomerase II in several types of proliferating cancer cells, including gallbladder cancer [10], aggressive breast cancer [10,11,12], epithelial ovarian cancer [10,13,14,15], lymphomas and sarcomas [16,17,18], and colon cancer [10]. Increased levels of this enzyme associated with aggressive breast cancer are related to increased expression of the oncogene HER2 neu, predicted disease-related death, lymph node metastasis, and advanced tumor stage [19].
Currently, cancer is one of the deadliest diseases in the world [20,21,22,23], and one factor that contributes to numerous deaths is the difficulty in diagnosis [24,25]. An early diagnosis can be influenced by three main factors: awareness of search for healthcare, clinical and diagnostic evaluation, and access to treatment [26]. Regarding the latter, it is important to stress that access barriers are mainly a problem in underdeveloped countries. In developed countries, a prognosis is reached in more than 70% of cases, while in underdeveloped countries only 20–50% of patients receive an early diagnosis, which compromises the chance of a cure [27]. In this sense, the research, development, and implementation of fast, simple, and low-cost tools can help change this reality [28,29,30]. New research into diagnostic tools is aimed at developing systems that are increasingly capable of locating species in different environments, with high specificity and resolution [28,31,32,33,34]. For this purpose, many spectroscopic techniques have been explored, such as nuclear magnetic resonance (NMR) [29,35,36,37,38,39,40,41,42]. Molecules that interact with key enzymes can act as spectroscopic probes [43]. These molecules are of great interest due to their high sensitivity and ease of operation, enabling rapid location within live systems [44].
In CPX molecules, the presence of a fluorine atom allows the application of 19F NMR spectroscopy techniques. The large chemical shift range, together with the high sensitivity of 19F NMR nuclei, makes the use of 19F NMR an extremely attractive proposition [45]. Additionally, considering the scarcity of naturally occurring fluorine compounds, 19F NMR offers an attractive option for investigating the interactions between proteins and other biomolecules, as well as the structure and mechanisms of action of fluorinated inhibitors [46]. Moreover, another advantage of 19F NMR is that this technique is particularly useful for studying large proteins that cannot be easily probed by conventional NMR experiments [47].
Computational methods have been widely employed to predict the spectroscopic properties of different compounds for various purposes [48,49,50]. Theoretical methods offer a fast, efficient and practical way to investigate changes in the NMR properties of different compounds, which can be caused by several factors, such as changes in the chemical environment or structure of the molecule that can occur due to interactions with biological macromolecules [29,51]. In that context, the aim of this study is to theoretically investigate the behavior of CPX in the human topoisomerase-II β (hTOPO-II) active site, evaluating how this interaction affects the 19F NMR chemical shift of CPX to propose the use of CPX as a spectroscopic NMR probe for cancer diagnosis.

2. Methodology

2.1. Molecular Dynamics (MD) Simulations

The theoretical analysis in this study was performed with the DNA topoisomerase II β enzyme. FQs are known inhibitors of both DNA gyrase and topoisomerase. However, considering our proposal to use this drug as an NMR probe in humans, we have determined that there is no need for a study on the interaction of ciprofloxacin with DNA gyrase since this enzyme is not present in large eukaryotes [8,52,53]. The first Molecualr Dynamics (MD) simulation was performed with CPX in the active site of the hTOPO-II enzyme. For the simulation, the crystallographic structure of hTOPO-II in complex with DNA (PDB-ID 5ZAD) was obtained from Protein Data Bank [54] while CPX topology and charge data were sourced from the Automated Topology Builder (ATB) Repository [55]. The simulation was performed by employing the GROMACS® Package [56] using the Gromos 54a7 force field [57]. The system CPX:hTOPO-IIβ was solvated inside a cubic box with the SPC water model. A steepest descent algorithm was employed for the minimization step, stopping minimization when the maximum force was under 10.0 kJ/mol. A heading step of 1 ps was performed in the NVT ensemble, and for equilibrium simulation in the NPT ensemble, the temperature and pressure, respectively, were controlled by the v-rescale thermostat (300 K) and Berendsen barostat (1 bar). The last simulation step was the performance of 10 ns of MD simulation using the Parrinello-Rahman barostat and v-rescale thermostat. Coordinates, velocities, and energies were saved at 10.0 ps of simulation, obtaining 1000 frames at the end of simulation. For both steps, the leap-frog integrator was adopted.
Finally, to select the best conformations, the optimal wavelet signal compression algorithm (OWSCA) [58] was used. This algorithm is based on a wavelet compression strategy, in which an optimization algorithm is applied to compress the maximum number of wavelet coefficients, instead of using heuristically chosen parameters. A second MD simulation of free CPX in a water box (CPX:explicit water system) was also performed under the same conditions mentioned above for comparison of 19FNMR chemical shifts.

2.2. 19F NMR Chemical Shift (δ) Calculations

All 19F NMR shielding constant calculations of this step were performed using the GAUSSIAN 09 software package [59] at the DFT level, with the B3LYP functional and Dunning basis set [60] with diffuse function [61,62] aug-cc-pVDZ, and by applying the gauge-including atomic orbital (GIAO) method [63]. These levels of theory were selected based on previous parametrization studies performed on NMR calculations [64] for the CPX molecule [49]. Calculations were made for CPX in the selected frames of two MD systems using the ONIOM model [65]. In the CPX:explicit water system, the first solvation shell was maintained, and the obtained values were compared with the results obtained for the CPX:hTOPO-II system. Additionally, 19F NMR shielding constants were calculated for CPX in a vacuum (CPX:vacuum) and, using the IEF-PCM solvation model [66], when employing water as solvent (CPX: implicit water). For both systems, the initial structures were generated from a conformational analysis in Spartan 14® software using molecular mechanics. After this step, the ten lowest energy conformations obtained were subjected to geometry optimization calculations in Gaussian software at the B3LYP/aug-cc-pVDZ level of theory. Then, NMR calculations were performed in the same way as in the previous systems.
The theoretical 19F NMR chemical shifts were calculated in ppm according to Equation (1) [64]. The chemical shifts were expressed relative to the external chemical shift reference CF3COOH. Theoretical results obtained were compared with experimental data, where measurements were carried out using the same reference compound [67,68].
δ t e o r = σ r e f c a l − σ C P X c a l
where σ r e f c a l and σ C P X c a l are the isotropic NMR shieldings of the reference compound (CF3COOH) and the CPX frame, respectively. To analyze the agreement between theoretical values for chemical shifts and the experimental 19F NMR chemical shift data, the Δδ calculation was performed using Equation (2), as follows [64]:
Δ δ = δ e x p −   δ c a l

3. Results and Discussion

It is well-known that CPX, like all FQs, is a bacterial topoisomerase inhibitor [6,69]. However, due to the presence of these enzymes in the human body, many experimental researches have been focused recently on the potential of this drug and its derivatives to inhibit human topoisomerases [12,70,71,72,73,74,75,76]. Theoretical investigations have already been conducted in order to better understand the mechanisms of action and the main differences between the interactions of TOPO-II in the two organisms [77,78]. A previous study that investigated the interaction of thirteen FQs with human topoisomerases using molecular docking techniques showed that CPX is able to form a hydrogen bond with the hTOPO-IIβ active site on the amino acid Asp 479 [79]. The study found that the binding affinity was −9.62 kcal·mol−1. Another recent theoretical investigation explored how CPX binds to different sites of the hTOPO-IIβ enzyme [77]. Through molecular docking calculations, the authors showed that CPX has a similar interaction energy in both human and bacterial enzymes and that CPX preferentially interacts in the same locale as the chemotherapeutic agent etoposide. The study found that the interaction energy of CPX was −71.62 kcal·mol−1 and that CPX was able to form hydrogen bonds with Glu477, Tyr 821, Gln778, and Asp 479 amino acid residues.
All the studies referenced above utilized the molecular docking technique in their investigations. Molecular docking is an important computational technique in structural biology and computer-aided drug design [80]. The main goal of this type of computational simulation is to evaluate the most feasible binding geometries of a ligand to a target protein whose three-dimensional structure is known [81,82]. Despite their fundamental importance in this research field, docking studies only provide a static view of the interactions between the ligand and the protein. MD simulations, on the other hand, are used to analyze the dynamic behavior of these interactions as well as of the entire system, helping to reproduce the biological events in a computer simulation [83,84]. Here, the main proposal is to investigate the possibility of using the well-known antibiotic ciprofloxacin as a 19F NMR chemical shift probe to localize the overexpression of hTPO-IIβ, which is associated with cancer incidence [10,85]. For this, a dynamic analysis of the system is of crucial importance.

3.1. MD Simulations

In order to analyze the influence of the chemical environment on the conformational change of CPX, two MD simulations were performed. One simulation was carried out with CPX in the hTOPO-II active site (CPX:hTOPO-II system) and the other was performed with CPX only in a water box (CPX:explicit water). With the analysis of the root mean square deviation (RMSD) of CPX in both systems (Figure 1), it was possible to observe that the systems reached equilibrium at around 2000 ps of simulation, and this time was used as the starting time for the selection of representative frames using an OWSCA algorithm. As shown in Figure 1, there was a slightly higher flexibility of CPX in the aqueous system when compared to molecules in the enzyme active site. Once in the active site, the molecule has greater conformational restriction due to the presence of the surrounding amino acids, with which it engaged in intermolecular interactions. Additionally, the RMSD levels for CPX in the hTOPO-II active site were around 0.1 nm (1 Å), indicating high stability of the structures [86,87].
Figure 1 also shows the root mean square fluctuation (RMSF). Together with the RMSD, the relative RMSF provides information about the fluctuation of each residue in the simulation. Understanding the relationship between the flexibility of the residues and the interaction with the ligand facilitates the identification of regions with great flexibility. Generally, the flexibility of the terminal residue and surface loop regions is higher and the protein core is more limited [88]. As can be seen, the fluctuation of residues around 400–600 is more restricted than at other points, which may indicate that CPX forms a stable connection in this region. The total energy variation obtained for CPX in both systems, CPX:hTOPO-II and CPX:explicit water, is shown in Figure 2 (A and B). The values remained balanced over the course of the simulation, showing a stabilization of both systems. Regarding the ligand–protein interaction energy, also shown in Figure 2, in the CPX:hTOPO-II system, the average value of the total interaction energy was equal to −94.27 ± 1.02 kJ.mol−1, which corresponds to the sum of the short-range electrostatic (coulombic) interactions, −36.27 ± 0.77 kJ·mol−1, and the short-range Lennard-Jones interactions, whose average value was equal to −58 ± 0.67 kJ·mol−1.
The hydrogen bonds formed between CPX and hTOPO-II β were the main interactions responsible for the stability of the molecule in the enzyme active site, as shown in Table 1., This details the main residues that participated in the intermolecular interactions for the representative conformations selected by the OWSCA algorithm. Additionally, the number of hydrogen bonds formed during the MD simulation for all frames is shown in Figure 3. By analyzing the figure, it can be observed that the CPX shows three hydrogen bonds with hTOPO-IIβ, two of which are quite frequent during most of the simulation time.
Figure 4A shows the hydrogen bonds formed for frame 8, at 3200 ps of simulation, which is the point when the greatest number of hydrogen interactions can occur. The residues that participate in the interaction are Glu 519, Asn 520, and Ala 521. Figure 4B also shows the π-π stacking interactions between CPX and amino acids residues Arg 503, Lis 505, and Gly 504.
In the next step, the chemical shift calculation was performed for the representative configurations in both systems. For the CPX:explicit water system, the first solvation shell was maintained in order to represent the presence of explicit solvent molecules in the NMR calculation. For the CPX:hTOPO-II system, amino acid residues participating in hydrogen interactions with CPX were maintained in order to represent the change in the chemical environment of the molecule inside the active site.

3.2. Spectroscopic Parameters: 19F- Chemical Shifts (δ)

Fluorinated compounds have a wide range of applications, including anti-inflammatory drugs, anesthesiology, and cancer therapy. Di- and trifluoromethyl groups can considerably improve the profile of bioactive compounds by increasing their uptake and permeability as they exhibit unique properties such as high electronegativity, lipophilicity, and high steric demand [89]. 19F NMR spectroscopy is a rapidly emerging tool and an attractive option for studies of new spectroscopic probes for biological use [90,91,92,93]. The main advantages include its high sensitivity, the very low background signal, the scarce natural occurrence of fluorinated compounds, and the high magnetic moment that results in a 19F NMR sensitivity similar to that of 1H [94]. The fluorinated compound chosen for this work is a widely marketed and prescribed antibiotic drug throughout the world [49,95]. This means that CPX is safe for in vivo use and that much information related to its pharmacodynamics and pharmacokinetics is already well-known [96,97,98]. The repositioning drug strategy, which consists of proposing new uses for existing drugs [99], is a growing field of research. The implementation of known compounds for new applications saves a considerable amount of time and resources related to the study of the bioavailability, toxicity, and implementation of these compounds [100,101,102].
Theoretical calculations of 19F NMR chemical shifts were performed to investigate whether the specific interaction of CPX with the hTOPO-IIβ enzyme can be used as a biologic human topoisomerase identifier. Table 2 contains the average of the calculated values for the theoretical 19F NMR shifts in all tested systems. The data show a high similarity between the experimental value and the theoretical value obtained for CPX in the CPX:explicit water system. The low Δδ value indicates that the method and the level of theory selected are very accurate for this type of calculation [49]. Secondly, the data show that the value obtained in the calculation using the implicit solvation model, CPX:implicit water, is far from the experimental value. It is worth mentioning that results for the system CPX:implicit are similar to values obtained for CPX in a vacuum. Such results indicate that explicit solvation is adequate for representing the solvent effect on CPX. It can also be inferred that the explicit presence of the water molecules in the calculation is important since it creates the proper hydrogen bonding network of water molecules for calculating 19F NMR spectroscopic parameters [103]. As mentioned, the fluorine nucleus has a high sensitivity when compared to the 13C and 15N nuclei, being almost as sensitive as 1H [104]. In this context, although solvent exposure effects can be difficult to observe in nuclei such as 13C and 15N NMR, for the 19F nucleus, solvent-induced isotopic shifts can be as high as 0.25 ppm, offering an efficient way to probe solvent exposure [105].
Analysis of the effect on the 19F NMR chemical shifts caused by the interaction of CPX with hTOPO-II β (Table 2 and Figure 5 and Figure 6) shows that there was a variation of 6.03 ppm in relation to the experimental value for CPX in aqueous solution. NMR spectroscopy is a technique extremely sensitive to conformational effects as well as molecular structure effects, both of which can be directly affected by modifications in the chemical environment [106]. Interactions that are able to alter the electronic distribution or even the HOMO-LUMO boundary orbitals can be factors that modify the chemical shift of molecules [107]. Analysis of the figures reveals that the interactions of CPX with hTOPO-II caused a modification in the electronic density (Figure 6) and the frontier orbitals of CPX (Figure 7), which provides a possible explanation for the change in the fluorine chemical shift. This variation in the 19F NMR chemical shift of CPX when it was interacting with the enzyme, represented in Figure 7, can provide important information regarding the occurrence of the ligand in the free form, and in the complexed form with the human topoisomerase-IIβ enzyme. The characteristic signal of CPX when complexed with the enzyme thus constitutes an interesting form of indirect labeling of these proteins, helping to identify their overproduction in the body and, consequently, in cancer cell mapping [8,18,85].
The results of this study lead us to propose CPX as a possible candidate for 19F NMR probing, which can be utilized in cancer diagnosis [29,45]. The application of fluorine probes is advantageous considering that the natural occurrence of fluorine in biological systems is scarce and the signals from 19F NMR spectroscopy will not find any overlapping background signals to compete with the fluorine probes, making the spectra simple and easy to analyze [46,47]. In comparison, the enzyme concentration in tumor cells is higher [10]. Several previous studies have already proven the efficacy of CPX in inhibiting hTOPO-II, leading to the anti-proliferative and cytotoxic activities of this molecule against several malignant cells [74,108]. Accordingly, we can expect that the CPX probe will be efficient and able to reach the desired location. Finally, our study is the first attempt to investigate the use of 19F NMR of CPX as a probe for cancer diagnosis, providing a starting point for further exploration of this new possibility. Additional experimental studies must be carried out in order to obtain more information on the effective implementation of a probe for this purpose.

4. Conclusions

The results of this study show that the interaction of ciprofloxacin with human topoisomerase-II β can alter the 19F NMR chemical shift signal of ciprofloxacin, when compared to the same parameter for the free molecule in water. Thus, this well-known antimicrobial agent constitutes a possible 19F NMR chemical shift probe for cancer diagnosis, capable of indirectly labeling the overexpression of human topoisomerase-IIβ enzyme in the body, and, consequently, able to assist in the detection of cancer cells.
Considering the results of this study and the low toxicity of this commercially used drug, ciprofloxacin shows promise as an ally in cancer diagnosis. Our results may stimulate new experimental and full-dimensional theoretical investigations that could assess the validity of this assumption. Moreover, our theoretical findings add to the overall understanding of the interaction between ciprofloxacin and the human topoisomerase-II β enzyme and may provide new insights into how it exerts its anti-carcinogenic effect. The results of this study may thus contribute to the development of new tools for cancer diagnosis.

Author Contributions

T.A.S. was responsible for the methodology, planning and execution of experiments, data validation and analysis, and writing. M.A.G. was responsible for the selection of the molecular dynamics structures and for the implementation of the algorithm used in the selections. T.C.R. was responsible for supervision, analysis, visualization, data validation, project and resource management, formal analysis, and writing review. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Fundação de Amparo ao Ensino e Pesquisa de Minas Gerais (FAPEMIG), and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior/Ministério da Defesa (CAPES/MD) (88882.446445/2019-01).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Acknowledgments

The authors wish to thank the Brazilian financial agencies Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Fundação de Amparo ao Ensino e Pesquisa de Minas Gerais (FAPEMIG), and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior/Ministério da Defesa (CAPES/MD) for financial support, and the Federal University of Lavras (UFLA) and Minas Gerais State University (UEMG) for providing the physical infrastructure.

Conflicts of Interest

Authors declare no conflict of interest.

References

  1. Pitman, S.K.; Hoang, U.T.P.; Wi, C.H.; Alsheikh, M.; Hiner, D.A.; Percival, K.M. Revisiting oral fluoroquinolone and multivalent cation drug-drug interactions: Are they still relevant? Antibiotics 2019, 8, 108. [Google Scholar] [CrossRef] [PubMed]
  2. Qin, P.; Su, B.; Liu, R. Probing the binding of two fluoroquinolones to lysozyme: A combined spectroscopic and docking study. Mol. Biosyst. 2012, 8, 1222. [Google Scholar] [CrossRef] [PubMed]
  3. Suaifan, G.A.R.Y.; Mohammed, A.A.M. Fluoroquinolones structural and medicinal developments (2013–2018): Where are we now? Bioorganic Med. Chem. 2019, 27, 3005–3060. [Google Scholar] [CrossRef] [PubMed]
  4. Ali, R.; Alminderej, F.M.; Messaoudi, S.; Saleh, S.M. Ratiometric ultrasensitive optical chemisensor film based antibiotic drug for Al(III) and Cu(II) detection. Talanta 2021, 221, 121412. [Google Scholar] [PubMed]
  5. Abdel-Aziz, M.; Park, S.E.; Abuo-Rahma, G.E.D.A.A.; Sayed, M.A.; Kwon, Y. Novel N-4-piperazinyl-ciprofloxacin-chalcone hybrids: Synthesis, physicochemical properties, anticancer and topoisomerase I and II inhibitory activity. Eur. J. Med. Chem. 2013, 69, 427–438. [Google Scholar] [CrossRef]
  6. Majalekar, P.P.; Shirote, P.J. Fluoroquinolones: Blessings or Curses. Curr. Drug Targets 2020, 21, 1354–1370. [Google Scholar] [CrossRef]
  7. Jacob, D.A.; Mercer, S.L.; Osheroff, N.; Deweese, J.E. Etoposide Quinone Is a Redox-Dependent Topoisomerase II Poison. Biochemistry 2011, 50, 5660–5667. [Google Scholar] [CrossRef]
  8. Idowu, T.; Schweizer, F. Ubiquitous nature of fluoroquinolones: The oscillation between antibacterial and anticancer activities. Antibiotics 2017, 6, 26. [Google Scholar] [CrossRef]
  9. Bisacchi, G.; Hale, M. A “Double-Edged” Scaffold: Antitumor Power within the Antibacterial Quinolone. Curr. Med. Chem. 2016, 23, 520–577. [Google Scholar] [CrossRef]
  10. Heestand, G.M.; Schwaederle, M.; Gatalica, Z.; Arguello, D.; Kurzrock, R. Topoisomerase expression and amplification in solid tumours: Analysis of 24,262 patients. Eur. J. Cancer 2017, 83, 80–87. [Google Scholar] [CrossRef]
  11. Suresh, N.; Suresh, A.; Yerramsetty, S.; Bhadra, M.P.; Alvala, M.; Sekhar, K.V.G.C. Anti-proliferative activity, molecular modeling studies and interaction with calf thymus DNA of novel ciprofloxacin analogues. J. Chem. Sci. 2018, 130, 1–11. [Google Scholar] [CrossRef]
  12. Beberok, A.; Wrześniok, D.; Rok, J.; Rzepka, Z.; Respondek, M.; Buszman, E. Ciprofloxacin triggers the apoptosis of human triple-negative breast cancer MDA-MB-231 cells via the p53/Bax/Bcl-2 signaling pathway. Int. J. Oncol. 2018, 52, 1727–1737. [Google Scholar] [CrossRef] [PubMed]
  13. Chekerov, R.; Klaman, I.; Zafrakas, M.; Könsgen, D.; Mustea, A.; Petschke, B.; Lichtenegger, W.; Sehouli, J.; Dahl, E. Altered Expression Pattern of Topoisomerase IIα in Ovarian Tumor Epithelial and Stromal Cells after Platinum-Based Chemotherapy. Neoplasia 2006, 8, 38. [Google Scholar] [CrossRef] [PubMed]
  14. Faggad, A.; Darb-Esfahani, S.; Wirtz, R.; Sinn, B.; Sehouli, J.; Könsgen, D.; Lage, H.; Weichert, W.; Noske, A.; Budczies, J.; et al. Topoisomerase IIα mRNA and protein expression in ovarian carcinoma: Correlation with clinicopathological factors and prognosis. Mod. Pathol. 2009, 22, 579–588. [Google Scholar] [CrossRef] [PubMed]
  15. Bai, Y.; Li, L.D.; Li, J.; Lu, X. Targeting of topoisomerases for prognosis and drug resistance in ovarian cancer. J. Ovarian Res. 2016, 9, 1–12. [Google Scholar] [CrossRef] [PubMed]
  16. Zhou, H.; Tang, J.; Zhang, J.; Chen, B.; Kan, J.; Zhang, W.; Zhou, J.; Ma, H. A red lysosome-targeted fluorescent probe for carboxylesterase detection and bioimaging. J. Mater. Chem. B 2019, 7, 2989–2996. [Google Scholar] [CrossRef]
  17. Kirk, E.; Hevenern, T.A.; Verstak, K.E.L.; Daniel, L.; Riggsbee, J.W.M. Recent developments in topoisomerase-targeted cancer chemotherapy. Acta Pharm. Sin. B 2018, 8, 844–861. [Google Scholar]
  18. Cinelli, M.A. Topoisomerase 1B poisons: Over a half-century of drug leads, clinical candidates, and serendipitous discoveries. Med. Res. Rev. 2019, 39, 1294–1337. [Google Scholar] [CrossRef]
  19. Verma, A. Prions, prion-like prionoids, and neurodegenerative disorders. Ann. Indian Acad. Neurol. 2016, 19, 169–174. [Google Scholar] [CrossRef]
  20. Pilleron, S.; Soto-Perez-de-Celis, E.; Vignat, J.; Ferlay, J.; Soerjomataram, I.; Bray, F.; Sarfati, D. Estimated global cancer incidence in the oldest adults in 2018 and projections to 2050. Int. J. Cancer 2021, 148, 601–608. [Google Scholar] [CrossRef]
  21. Dan, N.; Setua, S.; Kashyap, V.; Khan, S.; Jaggi, M.; Yallapu, M.; Chauhan, S.; Dan, N.; Setua, S.; Kashyap, V.K.; et al. Antibody-Drug Conjugates for Cancer Therapy: Chemistry to Clinical Implications. Pharmaceuticals 2018, 11, 32. [Google Scholar] [CrossRef] [PubMed]
  22. Bray, F.; Ferlay, J.; Soerjomataram, I.; Siegel, R.L.; Torre, L.A.; Jemal, A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. Cancer J. Clin. 2018, 68, 394–424. [Google Scholar] [CrossRef]
  23. Kassab, A.E.; Gedawy, E.M. Novel ciprofloxacin hybrids using biology oriented drug synthesis (BIODS) approach: Anticancer activity, effects on cell cycle profile, caspase-3 mediated apoptosis, topoisomerase II inhibition, and antibacterial activity. Eur. J. Med. Chem. 2018, 150, 403–418. [Google Scholar] [CrossRef] [PubMed]
  24. Pashayan, N.; Pharoah, P.D.P. The challenge of early detection in cancer. Science 2020, 368, 589–590. [Google Scholar] [CrossRef] [PubMed]
  25. Holtedahl, K. Challenges in early diagnosis of cancer: The fast track. Scand. J. Prim. Health Care 2020, 38, 251–252. [Google Scholar] [CrossRef] [PubMed]
  26. World Health Organization. Module 3: Early Detection. Cancer Control: Knowledge into Action: WHO Guide for Effective Programmes 2007, 1–50. [Google Scholar] [PubMed]
  27. Unger-Saldaña, K. Challenges to the early diagnosis and treatment of breast cancer in developing countries. World J. Clin. Oncol. 2014, 5, 465. [Google Scholar] [CrossRef] [PubMed]
  28. Lue, N.; Kang, J.W.; Yu, C.-C.; Barman, I.; Dingari, N.C.; Feld, M.S.; Dasari, R.R.; Fitzmaurice, M. Portable Optical Fiber Probe-Based Spectroscopic Scanner for Rapid Cancer Diagnosis: A New Tool for Intraoperative Margin Assessment. PLoS ONE 2012, 7, e30887. [Google Scholar] [CrossRef]
  29. Pereira, B.T.L.L.; Gonçalves, M.A.; Mancini, D.T.; Kuca, K.; Ramalho, T.C. First attempts of the use of 195Pt NMR of phenylbenzothiazole complexes as spectroscopic technique for the cancer diagnosis. Molecules 2019, 24, 3970. [Google Scholar] [CrossRef]
  30. da Rocha, E.P.; Rodrigues, H.A.; da Cunha, E.F.F.; Ramalho, T.C. Probing kinetic and thermodynamic parameters as well as solvent and substituent effects on spectroscopic probes of 2-amino-1,4-naphthoquinone derivatives. Comput. Theor. Chem. 2016, 1096, 17–26. [Google Scholar] [CrossRef]
  31. Wan, X.; Wang, H.; Shi, B.; Guo, Y.; Liu, S.Y.; Wang, X. An enzyme activated fluorescent probe for LTA4H activity sensing and its application in cancer screening. Talanta 2023, 253, 123887. [Google Scholar] [CrossRef]
  32. Zhou, X.; Bai, D.; Yu, H.; Fu, Y.; Song, L.; Wu, Y.; Chen, K.; Li, J.; Yang, Y.; Chen, H.; et al. Detection of rare CTCs by electrochemical biosensor built on quaternary PdPtCuRu nanospheres with mesoporous architectures. Talanta 2023, 253, 123955. [Google Scholar] [CrossRef] [PubMed]
  33. Gab, M.; Allah, A.; Sarhan, M.A.; Elshennawy, M.N. Edge U-Net: Brain tumor segmentation using MRI based on deep U-Net model with boundary information. Expert Syst. Appl. 2023, 213, 118833. [Google Scholar]
  34. Kim, J.H.; An, Y.J.; Kim, T.M.; Kim, J.E.; Park, S.; Choi, S.H. Ex vivo NMR metabolomics approach using cerebrospinal fluid for the diagnosis of primary CNS lymphoma: Correlation with MR imaging characteristics. Cancer Med. 2022, 00, 1–11. [Google Scholar] [CrossRef] [PubMed]
  35. Saleem, H.; Maryam, A.; Bokhari, S.A.; Ashiq, A.; Rauf, S.A.; Khalid, R.R.; Qureshi, F.A.; Siddiqi, A.R. Design, synthesis, characterization and computational docking studies of novel sulfonamide derivatives. EXCLI J. 2018, 17, 169–180. [Google Scholar]
  36. Larkin, J.R.; Anthony, S.; Johanssen, V.A.; Yeo, T.; Sealey, M.; Yates, A.G.; Smith, C.F.; Claridge, T.D.W.; Nicholson, B.D.; Moreland, J.A.; et al. Metabolomic Biomarkers in Blood Samples Identify Cancers in a Mixed Population of Patients with Nonspecific Symptoms. Clin. Cancer Res. 2022, 28, 1651–1661. [Google Scholar] [CrossRef]
  37. Derveauex, E.; Thomeer, M.; Mesotten, L.; Reekmans, G.; Adriaensens, P. Detection of Lung Cancer via Blood Plasma and 1H-NMR Metabolomics: Validation by a Semi-Targeted and Quantitative Approach Using a Protein-Binding Competitor. Metabolites 2021, 11, 537. [Google Scholar] [CrossRef] [PubMed]
  38. Kwon, H.N.; Lee, H.; Park, J.W.; Kim, Y.H.; Park, S.; Kim, J.J. Screening for Early Gastric Cancer Using a Noninvasive Urine Metabolomics Approach. Cancers 2020, 12, 2904. [Google Scholar] [CrossRef]
  39. Michálková, L.; Horník, Š.; Sýkora, J.; Habartová, L.; Setnička, V. Diagnosis of pancreatic cancer via1H NMR metabolomics of human plasma. Analyst 2018, 143, 5974–5978. [Google Scholar] [CrossRef]
  40. Mikkonen, J.J.W.; Singh, S.P.; Akhi, R.; Salo, T.; Lappalainen, R.; González-Arriagada, W.A.; Lopes, M.A.; Kullaa, A.M.; Myllymaa, S. Potential role of nuclear magnetic resonance spectroscopy to identify salivary metabolite alterations in patients with head and neck cancer. Oncol. Lett. 2018, 16, 6795–6800. [Google Scholar] [CrossRef]
  41. Erben, V.; Bhardwaj, M.; Schrotz-King, P.; Brenner, H. Metabolomics Biomarkers for Detection of Colorectal Neoplasms: A Systematic Review. Cancers 2018, 10, 246. [Google Scholar] [CrossRef] [PubMed]
  42. Yang, B.; Liao, G.; Wen, X.; Chen, W.; Cheng, S.; Stolzenburg, J.U.; Ganzer, R.; Neuhaus, J. Nuclear magnetic resonance spectroscopy as a new approach for improvement of early diagnosis and risk stratification of prostate cancer. J. Zhejiang Univ. B 2017, 18, 921–933. [Google Scholar] [CrossRef] [PubMed]
  43. Tang, Z.-Y.; Zhang, Y.; Chen, Y.-T.; Yu, Q.; An, L.-K. The first small fluorescent probe as Tyrosyl-DNA phosphodiesterase 1 (TDP1) substrate. Dye. Pigment. 2019, 169, 45–50. [Google Scholar] [CrossRef]
  44. Zhou, J.; Ma, H. Design principles of spectroscopic probes for biological applications. Chem. Sci. 2016, 7, 6309–6315. [Google Scholar] [CrossRef] [PubMed]
  45. Stanley, P.D. Principles and Topical Applications of 19F NMR Spectrometry. In Organofluorines. The Handbook of Environmental Chemistry; Neilson, A.H., Ed.; Springer: Berlin/Heidelberg, Germany, 2002; Volume 3N, pp. 1–61. [Google Scholar]
  46. .Marsh, E.N.G.; Suzuki, Y. Using 19F NMR to Probe Biological Interactions of Proteins and Peptides. ACS Chem. Biol. 2014, 9, 1242–1250. [Google Scholar] [CrossRef]
  47. Gimenez, D.; Phelan, A.; Murphy, C.D.; Cobb, S.L. 19F NMR as a tool in chemical biology. Beilstein J. Org. Chem. 2021, 17, 293–318. [Google Scholar] [CrossRef] [PubMed]
  48. Jawaria, R.; Hussain, M.; Khalid, M.; Khan, M.U.; Tahir, M.N.; Naseer, M.M.; Braga, A.A.C.; Shafiq, Z. Synthesis, crystal structure analysis, spectral characterization and nonlinear optical exploration of potent thiosemicarbazones based compounds: A DFT refine experimental study. Inorg. Chim. Acta 2019, 486, 162–171. [Google Scholar] [CrossRef]
  49. Koch, A.; Stamboliyska, B.; Mikhova, B.; Breznica-Selmani, P.; Mladenovska, K.; Popovski, E. Calculations of 13C NMR chemical shifts and F–C coupling constants of ciprofloxacin. Magn. Reson. Chem. 2019, 57, S75–S84. [Google Scholar] [CrossRef]
  50. Ghosh, D.; Rhodes, S.; Winder, D.; Atkinson, A.; Gibson, J.; Ming, W.; Padgett, C.; Landge, S.; Aiken, K. Spectroscopic investigation of bis-appended 1,2,3-triazole probe for the detection of Cu(II) ion. J. Mol. Struct. 2017, 1134, 638–648. [Google Scholar] [CrossRef]
  51. Ramalho, T.C.; Taft, C.A. Thermal and solvent effects on the NMR and UV parameters of some bioreductive drugs. J. Chem. Phys. 2005, 123, 054319. [Google Scholar] [CrossRef]
  52. Pudipeddi, A.; Vasudevan, S.; Shanmugam, K.; Mohan, S.S.; Vairaprakash, P.; Neelakantan, P.; Balraj, A.S.; Solomon, A.P. Design, dynamic docking, synthesis, and in vitro validation of a novel DNA gyrase B inhibitor. J. Biomol. Struct. Dyn. 2022, 4, 1–14. [Google Scholar] [CrossRef] [PubMed]
  53. Khan, T.; Sankhe, K.; Suvarna, V.; Sherje, A.; Patel, K.; Dravyakar, B. DNA gyrase inhibitors: Progress and synthesis of potent compounds as antibacterial agents. Biomed. Pharmacother. 2018, 103, 923–938. [Google Scholar] [CrossRef] [PubMed]
  54. Berman, H.M.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T.N.; Weissig, H.; Shindyalov, I.N.; Bourne, P.E. The Protein Data Bank. Nucleic Acids Res. 2000, 28, 235–242. [Google Scholar] [CrossRef] [PubMed]
  55. Malde, A.K.; Zuo, L.; Breeze, M.; Stroet, M.; Poger, D.; Nair, P.C.; Oostenbrink, C.; Mark, A.E. An Automated force field Topology Builder (ATB) and repository: Version 1.0. J. Chem. Theory Comput. 2011, 7, 4026–4037. [Google Scholar] [CrossRef] [PubMed]
  56. Abraham, M.J.; Murtola, T.; Schulz, R.; Páll, S.; Smith, J.C.; Hess, B.; Lindah, E. Gromacs: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 2015, 1–2, 19–25. [Google Scholar] [CrossRef]
  57. Christen, M.; Hünenberger, P.H.; Bakowies, D.; Baron, R.; Bürgi, R.; Geerke, D.P.; Heinz, T.N.; Kastenholz, M.A.; Kräutler, V.; Oostenbrink, C.; et al. The GROMOS software for biomolecular simulation: GROMOS05. J. Comput. Chem. 2005, 26, 1719–1751. [Google Scholar] [CrossRef]
  58. Gonçalves, M.A.; Santos, L.S.; Prata, D.M.; Peixoto, F.C.; da Cunha, E.F.F.; Ramalho, T.C. Optimal wavelet signal compression as an efficient alternative to investigate molecular dynamics simulations: Application to thermal and solvent effects of MRI probes. Theor. Chem. Acc. 2017, 136, 15. [Google Scholar] [CrossRef]
  59. Frisch, M.J.; Trucks, G.W.; Schlegel, H.B.; Scuseria, G.E.; Robb, M.A.; Cheeseman, J.R.; Scalmani, G.; Barone, V.; Mennucci, B.; Petersson, G.A.; et al. Gaussian 09; Gaussian, Inc.: Wallingford, CT, USA, 2009. [Google Scholar]
  60. Dunning, T.H., Jr. Gaussian basis sets for use in correlated molecular calculations. I. The atoms boron through neon and hydrogen. J. Chem. Phys. 1998, 90, 1007. [Google Scholar] [CrossRef]
  61. Kendall, R.A.; Dunning, T.H., Jr.; Harrison, R.J. Electron affinities of the first-row atoms revisited. Systematic basis sets and wave functions. J. Chem. Phys. 1998, 96, 6796. [Google Scholar] [CrossRef]
  62. Woon, D.E., Jr. Gaussian basis sets for use in correlated molecular calculations. III. The atoms aluminum through argon. J. Chem. Phys. 1998, 98, 1358. [Google Scholar] [CrossRef]
  63. Wolinski, K.; Hinton, J.F.; Pulay, P. Efficient implementation of the gauge-independent atomic orbital method for NMR chemical shift calculations. J. Am. Chem. Soc. 2002, 112, 8251–8260. [Google Scholar] [CrossRef]
  64. Mejía-Urueta, R.; Mestre-Quintero, K.; Vivas-Reyes, R. DFT-GIAO Calculation of Properties of 19 F NMR and Stability Study of Environmentally Relevant Perfluoroalkylsulfonamides (PFASAmide). Artic. J. Braz. Chem. Soc. 2011, 22, 2268–2274. [Google Scholar]
  65. Dapprich, S.; Komáromi, I.; Byun, K.S.; Morokuma, K.; Frisch, M.J. A new ONIOM implementation in Gaussian98. Part I. The calculation of energies, gradients, vibrational frequencies and electric field derivatives. J. Mol. Struct. Theochem. 1999, 461–462, 1–21. [Google Scholar] [CrossRef]
  66. Marenich, A.V.; Cramer, C.J.; Truhlar, D.G. Universal Solvation Model Based on Solute Electron Density and on a Continuum Model of the Solvent Defined by the Bulk Dielectric Constant and Atomic Surface Tensions. J. Phys. Chem. B 2009, 113, 6378–6396. [Google Scholar] [CrossRef]
  67. Trefi, S.; Gilard, V.; Malet-Martino, M.; Martino, R. Generic ciprofloxacin tablets contain the stated amount of drug and different impurity profiles: A 19F, 1H and DOSY NMR analysis. J. Pharm. Biomed. Anal. 2007, 44, 743–754. [Google Scholar] [CrossRef]
  68. Zhang, F.-F.; Jiang, M.-H.; Sun, L.-L.; Zheng, F.; Dong, L.; Shah, V.; Shen, W.-B.; Ding, Y. Quantitative analysis of sitagliptin using the 19F-NMR method: A universal technique for fluorinated compound detection. Analyst 2015, 140, 280–286. [Google Scholar] [CrossRef]
  69. Ezelarab, H.A.A.; Abbas, S.H.; Hassan, H.A.; Abuo-Rahma, G.E.D.A. Recent updates of fluoroquinolones as antibacterial agents. Arch. Pharm. 2018, 351, 1800141. [Google Scholar] [CrossRef]
  70. Fief, C.A.; Hoang, K.G.; Phipps, S.D.; Wallace, J.L.; Deweese, J.E. Examining the Impact of Antimicrobial Fluoroquinolones on Human DNA Topoisomerase IIα and IIβ. ACS Omega 2019, 4, 4049–4055. [Google Scholar] [CrossRef]
  71. Beberok, A.; Wrześniok, D.; Minecka, A.; Rok, J.; Delijewski, M.; Rzepka, Z.; Respondek, M.; Buszman, E. Ciprofloxacin-mediated induction of S-phase cell cycle arrest and apoptosis in COLO829 melanoma cells. Pharmacol. Rep. 2018, 70, 6–13. [Google Scholar] [CrossRef]
  72. Perrone, C.E.; Takahashi, K.C.; Williams, G.M. Inhibition of human topoisomerase IIα by fluoroquinolones and ultraviolet A irradiation. Toxicol. Sci. 2002, 69, 16–22. [Google Scholar] [CrossRef][Green Version]
  73. Swedan, H.K.; Kassab, A.E.; Gedawy, E.M.; Elmeligie, S.E. Design, synthesis, and biological evaluation of novel ciprofloxacin derivatives as potential anticancer agents targeting topoisomerase II enzyme. J. Enzym. Inhib. Med. Chem. 2023, 38, 118–137. [Google Scholar] [CrossRef] [PubMed]
  74. Mazandaran, K.E.; Mirshokraee, S.A.; Didehban, K.; Houshdar Tehrani, M.H. Design, Synthesis and Biological Evaluation of Ciprofloxacin- Peptide Conjugates as Anticancer Agents. Iran. J. Pharm. Res. IJPR 2019, 18, 1823. [Google Scholar]
  75. Mohammed, H.H.H.; Abd El-Hafeez, A.A.; Ebeid, K.; Mekkawy, A.I.; Abourehab, M.A.S.; Wafa, E.I.; Alhaj-Suliman, S.O.; Salem, A.K.; Ghosh, P.; Abuo-Rahma, G.E.D.A.; et al. New 1,2,3-triazole linked ciprofloxacin-chalcones induce DNA damage by inhibiting human topoisomerase I& II and tubulin polymerization. J. Enzym. Inhib. Med. Chem. 2022, 37, 1346–1363. [Google Scholar]
  76. Mohammed, H.H.H.; Abbas, S.H.; Hayallah, A.M.; Abuo-Rahma, G.E.D.A.; Mostafa, Y.A. Novel urea linked ciprofloxacin-chalcone hybrids having antiproliferative topoisomerases I/II inhibitory activities and caspases-mediated apoptosis. Bioorg. Chem. 2021, 106, 104422. [Google Scholar] [CrossRef]
  77. Sales, T.A.; Ramalho, T.C. Ciprofloxacin/Topoisomerase-II complex as a promising dual UV–Vis/fluorescent probe: Accomplishments and opportunities for the cancer diagnosis. Theor. Chem. Acc. 2022, 141, 1–10. [Google Scholar] [CrossRef]
  78. Cowen, T.; Karim, K.; Piletsky, S. No Title. Anal. Chim. Acta 2016, 936, 62–74. [Google Scholar] [CrossRef]
  79. Jadhav, A.K.; Karuppayil, S.M. Molecular docking studies on thirteen fluoroquinolines with human topoisomerase II a and b. Silico Pharmacol. 2017, 5, 1–12. [Google Scholar] [CrossRef] [PubMed]
  80. Patel, J.R.; Joshi, H.V.; Shah, U.A.; Patel, J.K. A Review on Computational Software Tools for Drug Design and Discovery. Indo. Glob. J. Pharm. Sci. 2022, 12, 53–81. [Google Scholar] [CrossRef]
  81. Pajeva, I.; Tsakovska, I.; Pencheva, T.; Alov, P.; Al Sharif, M.; Lessigiarska, I.; Jereva, D.; Diukendjieva, A. In silico Studies of Biologically Active Molecules. Stud. Comput. Intell. 2021, 934, 421–451. [Google Scholar]
  82. Caballero, J. The latest automated docking technologies for novel drug discovery. Expert Opin. Drug Discov. 2020, 16, 625–645. [Google Scholar] [CrossRef] [PubMed]
  83. Frye, L.; Bhat, S.; Akinsanya, K.; Abel, R. From computer-aided drug discovery to computer-driven drug discovery. Drug Discov. Today Technol. 2021, 39, 111–117. [Google Scholar] [CrossRef] [PubMed]
  84. Shukla, R.; Tripathi, T. Molecular Dynamics Simulation in Drug Discovery: Opportunities and Challenges. In Innovations and Implementations of Computer Aided Drug Discovery Strategies in Rational Drug Design; Singh, S.K.., Ed.; Springer: Singapore, 2021; pp. 295–316. [Google Scholar]
  85. McClendon, A.K.; Osheroff, N. DNA Topoisomerase II, Genotoxicity, and Cancer. Mutat. Res. Mol. Mech. Mutagen. 2007, 623, 83–97. [Google Scholar] [CrossRef] [PubMed]
  86. Sales, T.A.; Marcussi, S.; da Cunha, E.F.F.; Kuca, K.; Ramalho, T.C. Can inhibitors of snake venom phospholipases A2 lead to new insights into anti-inflammatory therapy in humans? A theoretical study. Toxins 2017, 9, 341. [Google Scholar] [CrossRef] [PubMed]
  87. Schreiner, W.; Karch, R.; Knapp, B.; Ilieva, N. Relaxation estimation of RMSD in molecular dynamics immunosimulations. Comput. Math. Methods Med. 2012, 2012, 173521. [Google Scholar] [CrossRef] [PubMed]
  88. Bhardwaj, P.; Biswas, G.P.; Mahata, N.; Ghanta, S.; Bhunia, B. Exploration of binding mechanism of triclosan towards cancer markers using molecular docking and molecular dynamics. Chemosphere 2022, 293, 133550. [Google Scholar] [CrossRef]
  89. Chekmenev, E.Y.; Chow, S.K.; Tofan, D.; Weitekamp, D.P.; Ross, B.D.; Bhattacharya, P. Fluorine-19 NMR chemical shift probes molecular binding to lipid membranes. J. Phys. Chem. B 2008, 112, 6285–6287. [Google Scholar] [CrossRef] [PubMed][Green Version]
  90. Maxwell, R.J.; Workman, P.; Griffiths, J.R. Demonstration of tumor-selective retention of fluorinated nitroimidazole probes by 19F magnetic resonance spectroscopy in vivo. Int. J. Radiat. Oncol. 1989, 16, 925–929. [Google Scholar] [CrossRef] [PubMed]
  91. Peterson, K.L.; Srivastava, K.; Pierre, V.C. Fluorinated paramagnetic complexes: Sensitive and responsive probes for magnetic resonance spectroscopy and imaging. Front. Chem. 2018, 6, 160. [Google Scholar] [CrossRef] [PubMed]
  92. Ojugo, A.S.E.; Mcsheehy, P.M.J.; Mcintyre, D.J.O.; Mccoy, C.; Stubbs, M.; Leach, M.O.; Judson, I.R.; Grifths, J.R. Measurement of the extracellular pH of solid tumours in mice by magnetic resonance spectroscopy: A comparison of exogenous 19 F and 31 P probes. NMR Biomed. Int. J. Devoted Dev. Appl. Magn. Reson. Vivo 1999, 12, 495–504. [Google Scholar]
  93. Ye, Y.; Liu, X.; Zhang, Z.; Wu, Q.; Jiang, B.; Jiang, L.; Zhang, X.; Liu, M.; Pielak, G.J.; Li, C. 19FNMR Spectroscopy as a Probe of Cytoplasmic Viscosity and Weak Protein Interactions in Living Cells. Chem. A Eur. J. 2013, 19, 12705–12710. [Google Scholar] [CrossRef]
  94. Ulrich, A.S. Solid state 19F NMR methods for studying biomembranes. Prog. Nucl. Magn. Reson. Spectrosc. 2005, 46, 1–21. [Google Scholar] [CrossRef]
  95. Centers for Disease Control and Prevention. Outpatient Antibiotic Prescriptions—United States; CDC: Atlanta, GA, USA, 2017.
  96. Crump, B.; Wise, R.; Dent, J. Pharmacokinetics and tissue penetration of ciprofloxacin. Antimicrob. Agents Chemother. 1983, 24, 784–786. [Google Scholar] [CrossRef] [PubMed]
  97. Brunner, M.; Staß, H.; Möller, J.G.; Schrolnberger, C.; Erovic, B.; Hollenstein, U.; Zeitlinger, M.; Georg Eichler, H.; Müller, M. Target Site Concentrations of Ciprofloxacin after Single Intravenous and Oral Doses. Antimicrob. Agents Chemother. 2002, 46, 3724. [Google Scholar] [CrossRef] [PubMed]
  98. Yuan, F.; Hu, C.; Hu, X.; Wei, D.; Chen, Y.; Qu, J. Photodegradation and toxicity changes of antibiotics in UV and UV/H2O2 process. J. Hazard. Mater. 2011, 185, 1256–1263. [Google Scholar] [CrossRef]
  99. Ashburn, T.T.; Thor, K.B. Drug repositioning: Identifying and developing new uses for existing drugs. Nat. Rev. Drug Discov. 2004, 3, 673–683. [Google Scholar] [CrossRef]
  100. Mucke, H.A. The case of galantamine: Repurposing and late blooming of a cholinergic drug. Futur. Sci. OA 2015, 1. [Google Scholar] [CrossRef]
  101. Mullard, A. Drug repurposing programmes get lift off. Nat. Rev. Drug Discov. 2012, 11, 505–506. [Google Scholar] [CrossRef]
  102. Durães, F.; Pinto, M.; Sousa, E.; Durães, F.; Pinto, M.; Sousa, E. Old Drugs as New Treatments for Neurodegenerative Diseases. Pharmaceuticals 2018, 11, 44. [Google Scholar] [CrossRef]
  103. Edmond, L.Y.; Gerig, J.T. Origins of Fluorine NMR Chemical Shifts in Fluorine-Containing Proteins. J. Am. Chem. Soc. 2000, 122, 4408–4417. [Google Scholar]
  104. Modo, M. 19F Magnetic Resonance Imaging and Spectroscopy in Neuroscience. Neuroscience 2021, 474, 37–50. [Google Scholar] [CrossRef]
  105. Evanics, F.; Kitevski, J.L.; Bezsonova, I.; Forman-Kay, J.; Prosser, R.S. 19F NMR studies of solvent exposure and peptide binding to an SH3 domain. Biochim. Biophys. Acta Gen. Subj. 2007, 1770, 221–230. [Google Scholar] [CrossRef] [PubMed]
  106. Gonçalves, M.A.; Gonçalves, A.S.; Franca, T.C.C.; Santana, M.S.; da Cunha, E.F.F.; Ramalho, T.C. Improved Protocol for the Selection of Structures from Molecular Dynamics of Organic Systems in Solution: The Value of Investigating Different Wavelet Families. J. Chem. Theory Comput. 2022, 18, 5810–5818. [Google Scholar] [CrossRef] [PubMed]
  107. Rayene, K.; Imane, D.; Abdelaziz, B.; Leila, N.; Fatiha, M.; Abdelkrim, G.; Bouzid, G.; Ismahan, L.; Brahim, H.; Rabah, O. Molecular modeling study of structures, Hirschfield surface, NBO, AIM, RDG, IGM and 1HNMR of thymoquinone/hydroxypropyl-β-cyclodextrin inclusion complex from QM calculations. J. Mol. Struct. 2022, 1249, 131565. [Google Scholar] [CrossRef]
  108. Jaber, D.F.; Jallad, M.A.N.; Abdelnoor, A.M. The effect of ciprofloxacin on the growth of B16F10 melanoma cells. J. Cancer Res. Ther. 2017, 13, 956–960. [Google Scholar] [PubMed]
Magnetochemistry 08 00181 g001
Figure 1. RMSF (left) and RMSD (right) for ciprofloxacin molecule inside the active site (CPX:hTOPO-II) and out of the active site of the topoisomerase-II enzyme (CPX: explicit water).
Figure 1. RMSF (left) and RMSD (right) for ciprofloxacin molecule inside the active site (CPX:hTOPO-II) and out of the active site of the topoisomerase-II enzyme (CPX: explicit water).
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Figure 2. Energy graphs extracted from MD simulations. (A,B) Total energy variation for the CPX:hTOPO-II and CPX:explicit water systems, respectively. (C) Interaction energy graph for the CPX:hTOPO-II complex. In C, the black line corresponds to Coulombic-type interactions while the red line corresponds to Lennard-Jones-type interaction energy.
Figure 2. Energy graphs extracted from MD simulations. (A,B) Total energy variation for the CPX:hTOPO-II and CPX:explicit water systems, respectively. (C) Interaction energy graph for the CPX:hTOPO-II complex. In C, the black line corresponds to Coulombic-type interactions while the red line corresponds to Lennard-Jones-type interaction energy.
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Figure 3. Hydrogen bonds formed between CPX and hTOPO-II β during the molecular dynamics simulation.
Figure 3. Hydrogen bonds formed between CPX and hTOPO-II β during the molecular dynamics simulation.
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Figure 4. Intermolecular interactions during molecular dynamics simulation. (A) Hydrogen bonds formed between CPX and the amino acids Glu 519, Asn 520, and Ala 521 at 3200 ps of simulation. (B) π-π stacking interactions between CPX and amino acids Arg 503, Lis 505, and Glu 504 at 7900 ps of simulation.
Figure 4. Intermolecular interactions during molecular dynamics simulation. (A) Hydrogen bonds formed between CPX and the amino acids Glu 519, Asn 520, and Ala 521 at 3200 ps of simulation. (B) π-π stacking interactions between CPX and amino acids Arg 503, Lis 505, and Glu 504 at 7900 ps of simulation.
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Figure 5. HOMO–LUMO frontier orbital representations for ciprofloxacin molecule in two different environments: in the enzyme active site (binding CPX), and in water (free CPX).
Figure 5. HOMO–LUMO frontier orbital representations for ciprofloxacin molecule in two different environments: in the enzyme active site (binding CPX), and in water (free CPX).
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Figure 6. Electronic density variation of ciprofloxacin molecule when binding to the topoisomerase-II enzyme and in a free form in water solvent.
Figure 6. Electronic density variation of ciprofloxacin molecule when binding to the topoisomerase-II enzyme and in a free form in water solvent.
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Figure 7. Representation of the variation of the 19F chemical shift for the CPX molecule in the hTOPO-II active site.
Figure 7. Representation of the variation of the 19F chemical shift for the CPX molecule in the hTOPO-II active site.
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Table 1. Intermolecular interactions between human topoisomerase-II β enzyme and ciprofloxacin molecule during molecular dynamics simulation.
Table 1. Intermolecular interactions between human topoisomerase-II β enzyme and ciprofloxacin molecule during molecular dynamics simulation.
FrameTime (ps)ResidueInteraction Type
12000Asn 520HBond
22200Asn 520HBond
32300Leu 507HBond
42400Asn 520HBond
52600Asn 520; Gln 516HBond
63000Asn 520HBond
73100Asn 520HBond
83200Glu 519; Asn 520; Ala 521HBond
93700Asn 520; Ala 521HBond
103900Asn 520; Ala 521HBond
114200Asn 520HBond
124400Asn 520; Ala 521HBond
134700Asn 520; Ala 521HBond
145100Asn 520; Ala 521HBond
155500Ala 521HBond
167000--
177300Lys 505π-π; Electrostatic HBond
187500--
197700Arg 503HBond
207900Arg 503; Lis 505; Gly 504π-π
218000--
228250Lys 505HBond; π-π
238800--
249000Ile 506HBond
259200Ile 506HBond
269400Ile 506HBond
279500-
289800Ile 506HBond
2910,000Ile 506HBond
Table 2. Experimental vs. theoretically computed 19F NMR chemical shifts at the DFT/B3LYP/aug-cc-pVDZ level for a CPX molecule.
Table 2. Experimental vs. theoretically computed 19F NMR chemical shifts at the DFT/B3LYP/aug-cc-pVDZ level for a CPX molecule.
System19F δppmΔδppm
CPX:aqueous solution (experimental)−43.700.00
CPX:explicit water−43.54−0.16
CPX:hTOPO-II−49.736.03
CPX:vacuum−55.1111.41
CPX:implicit water−56.2012.50
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Sales, T.A.; Gonçalves, M.A.; Ramalho, T.C. Structural Parameters of the Interaction between Ciprofloxacin and Human Topoisomerase-II β Enzyme: Toward New 19F NMR Chemical Shift Probes. Magnetochemistry 2022, 8, 181. https://doi.org/10.3390/magnetochemistry8120181

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Sales TA, Gonçalves MA, Ramalho TC. Structural Parameters of the Interaction between Ciprofloxacin and Human Topoisomerase-II β Enzyme: Toward New 19F NMR Chemical Shift Probes. Magnetochemistry. 2022; 8(12):181. https://doi.org/10.3390/magnetochemistry8120181

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Sales, Thais Aparecida, Mateus Aquino Gonçalves, and Teodorico Castro Ramalho. 2022. "Structural Parameters of the Interaction between Ciprofloxacin and Human Topoisomerase-II β Enzyme: Toward New 19F NMR Chemical Shift Probes" Magnetochemistry 8, no. 12: 181. https://doi.org/10.3390/magnetochemistry8120181

APA Style

Sales, T. A., Gonçalves, M. A., & Ramalho, T. C. (2022). Structural Parameters of the Interaction between Ciprofloxacin and Human Topoisomerase-II β Enzyme: Toward New 19F NMR Chemical Shift Probes. Magnetochemistry, 8(12), 181. https://doi.org/10.3390/magnetochemistry8120181

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