Discovery of some phenylhydrazones as potential antimalarials: An integrated computational approach on PfATP6 and PfDHFR mutant proteins

Discovery of some phenylhydrazones as potential antimalarials: An integrated computational approach on PfATP6 and PfDHFR mutant proteins | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Discovery of some phenylhydrazones as potential antimalarials: An integrated computational approach on PfATP6 and PfDHFR mutant proteins Cedric Dzidzor Kodjo Amengor, Prince Danan Biniyam, Patrick Gyan, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4057743/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Plasmodium falciparum resistance to artemisinins and anti-folate pyrimethamine has hampered WHO efforts in the global eradication of malaria. Several studies have linked artemisinin and pyrimethamine resistance to mutations in the PfATP6 (calcium ATPase) and PfDHFR (dihydrofolate reductase) genes, respectively. However, the mechanism of resistance of Plasmodium falciparum to artemisinins and dihydrofolates has not been fully explored. Hence, new medicines for malaria are urgently needed to find a solution to the increasing demand for antimalarials with improved activity and better safety profiles. In our previous report, the phenylhydrazones PHN3 and PHN6 were shown to possess antimalarial activity on the ring stage of Plasmodium falciparum . Hence, this earlier report was leveraged to form the basis for the in silico design of 72 phenylhydrazone analogues for this study. Methods In this study, computational molecular docking and dynamics via AutoDock tools were used as rational approaches to predict better clinical candidates. We also evaluated all the designed analogues of PHN3 and PHN6 in silico to determine their physicochemical, pharmacokinetic and safety profiles. P. falciparum dihydrofolate reductase (PfDHFR) and P. falciparum ATPase6 (PfATP6) were the protein targets employed in the present study. The structure of the malarial PfATP6 mutant protein (L263E) was modelled from the wild-type PfATP6 structure using PyMOL. Molecular dynamics simulation was carried out following docking experiments to better understand the interactions of the mutant proteins with the optimized ligand complex. Results Hence, we elucidated the binding affinity and efficacy of phenylhydrazone-based compounds on the PfATP6 and PfDHFR proteins in the presence of the L263E and qm-PfDHFR mutations, respectively, with artemisinin and pyrimethamine as standards. Moreover, we identified possible hit candidates through virtual screening of 72 compounds that could inhibit the wild-type and mutant PfATP6 and PfDHFR proteins. We observed that the binding affinity of artemisinin for PfATP6 is affected by L263E mutations. Here, the computational interpretation of Plasmodium resistance to artemisinin and pyrimethamine reinforced the identification of novel compounds (B24 and B36) that showed good binding affinity and efficacy with wt-PfATP6, the L263E mutant, wt-PfDHFR and the PfDHFR quadruple mutant proteins in molecular docking and molecular dynamics studies. It is also worth noting that CN, COCH 3 , COOH, and CONH 2 were better electron withdrawing group replacements for the NO 2 groups in the phenylhydrazone scaffolds in the minimization of toxicity. Twelve of the designed analogues demonstrated favourable physicochemical, pharmacokinetic, and drug-like characteristics, suggesting that they could be promising drug candidates for further investigation. Conclusions These results suggest that the B24 and B36 protein complexes are stable and less likely to induce structural instability in the studied proteins. The binding of B24 and B36 to the active sites of the two Plasmodium proteins was not significantly affected by the mutations. Additionally, when bound to both targets, B24 and B36 exhibited inhibition constants (Ki) below 5 µM for all the proteins docked, indicating that they inhibited the PfATP6 and PfDHFR targets more successfully than did artemisinin and pyrimethamine. The two in silico hit compounds identified represent potential clinical candidates for the design of novel antimalarials. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Background In regard to killing humans, no other animal comes close to the mosquito. This disease is known as malaria, as it has been transmitted by this female anopheles mosquito over the years and has high morbidity and mortality rates [ 1 , 2 ]. Although there has been a breakthrough with regard to the systematic use of malaria vaccines for the prevention of malaria in underdeveloped and highly endemic regions since 2019, the continuous use of oral artemisinin-based combination therapies cannot be implemented [ 3 , 4 ]. Nevertheless, there has been an astronomical increase in malaria mortality globally due to treatment failure with first-line antimalarial agents, such as artemisinins, especially artemisinin combination therapies [ 5 , 6 ]. ACTs have continuously been used to target various species of Plasmodium , especially P. vivax and P. falciparum [ 7 ]. However, the most potent antimalarial noted for its quick reduction in parasitemia since the 1980s has gradually lost its ground due to the popularity of resistant strains [ 8 ]. Notably, the current low rate of parasitaemia clearance has provided the driving force for the continuous exposure of a high number of parasites to artemisinin derivatives [ 9 ]. This phenomenon is increasing in Southeast Asia, where resistance to artemisinin combined with piperaquine has been documented [ 10 ]. The current trend of increasing Plasmodium resistance is a serious global public health threat and presents a wide gap in the complete eradication of malaria [ 11 , 12 ]. Resistance to artemisinins is caused by mutations in certain key genes involved in the biochemistry and morphology of the parasite. A key protein is the pfK13 propeller domain, which allows the parasite to enter a latent state. Plasmodium falciparum Kelch 13 (PfK13) protein is also linked to artemisinin resistance. PfK13 is vital for asexual erythrocytic development, but its role in Plasmodium survival has not been fully established [ 13 , 14 ]. A newly identified validated target for artemisinins, the P. falciparum 3D7 calcium-transporting ATPase protein (PfATP6) PfSERCA or PfATPase6, was fully reported in 2022 [ 15 ]. PfATP6 is a calcium ATPase gene encoded by Plasmodium falciparum and has been hypothesized to be a target of artemisinins [ 16 , 17 ]. This presents new opportunities in malaria drug discovery, where a theoretical framework has been established in this work in which any compound that can bind to PfATP6 in a suitable pose has the potential to inhibit the function of the PfATP6-encoded mutant Plasmodium falciparum [ 18 , 19 ]. On the other hand, the metabolism of folate has been established as essential for the survival of the parasite because it is crucial for the biosynthesis of its cell walls and other organelles. Dihydrofolate reductase (DHFR) transforms dihydrofolate to tetrahydrofolate, which is responsible for maintaining the cell division pool in organisms such as Plasmodium [ 20 ]. Therefore, inhibiting this folate pathway with drugs such as pyrimethamine and cycloguanil would terminate this essential aspect of the parasite’s survival [ 21 , 22 ]. Docking studies have been used to explore the binding interactions of compounds that inhibit the activity of the DHFR enzyme in Plasmodium [ 23 , 24 ]. Hence, by extension, this could provide an impetus for the discovery of hit compounds for malaria drug discovery campaigns. Methodology Protein structure prediction and preparation: P. falciparum dihydrofolate reductase (PfDHFR) and P. falciparum ATPase6 (PfATP6) were the protein targets employed in the present study. The crystal structures of the two variants of PfDHFR, wild type (PDB ID: 3QGT) and quadruple mutant (PDB ID: 1J3K), were obtained from a protein data bank [ 25 ], while the tertiary structure of wild-type PfATP6 (wt-PfATP6) was predicted using the SWISS-MODEL server ( https://swissmodel.ExPASy.org/ ) based on its primary sequence. The structure of the malarial PfATP6 mutant protein (L263E) was then modelled from the wild-type PfATP6 structure using PyMOL ( https://pymol.org/2/ ) [ 26 ]. Additionally, the protein targets were prepared using the protein preparation tool UCSF Chimera software and finally subjected to optimization and minimization via the Swiss PDB Viewer 4.1.0 [ 27 ]. Ligand Preparation The ligands used in this study were optimized PHN6 [A01-A036] and PHN3 [B01-B036]. They were optimized by reducing the imine group and simply substituting different electron-withdrawing groups for the nitro groups in PHN3 and PHN6. The ligands were first sketched in Xdrawchem, and then their 3-dimensional coordinates were generated using Chemdraw3d software. Hydrogen and Gasteiger charges were added to the design using the AutoDock tool [ 28 ], followed by energy minimization utilizing the general Amber force field (GAFF) incorporated into the Avogadro program. Physicochemical and pharmacokinetic prediction The pharmacokinetic and toxicity predictions of each analogue were performed using software/web-based servers that have proven to be remarkably sensitive and accurate [ 29 ]. The SwissADME and ADMETSAR web services were used to assess the physicochemical characteristics of the compounds, including molecular weight, molar refractivity, topological polar surface area, number of hydrogen bond donors and acceptors, number of reversible bonds, partition coefficient and pharmacokinetic features such as blood‒brain barrier penetration (BBB), plasma protein binding (PPB), human intestinal absorption (HIA), and P-glycoprotein substrate and inhibitor. The ProTox-II [ 30 ] and OSIRINS property explorer programs were utilized to evaluate the hepatotoxicity and carcinogenicity of the compounds. In addition, acute oral toxicity, Ames toxicity and possible cardiotoxicity of the ligands, including suppression of the human either-a-go-go-related gene (hERG), were assessed. Protein–Ligand Docking The AutoDock tool 1.5.6 was utilized to perform docking runs [ 31 ]. For every ligand, ten distinct conformations were generated and clustered, each of which was scored using AutoDock scoring functions and ranked based on docked energy. The Lamarckian genetic algorithm implemented in AutoDock was employed [ 32 ]. To ensure reproducibility, three technical runs of each docking experiment were conducted. The maximum RMS tolerance for conformational cluster analysis was set to 2.00 Å. PyMOL ( https://pymol.org/2/ ) and Discovery Studio visualizer ( https://discover.3ds.com/discovery-studio-visualizer-download ) were used for postdocking analysis. Molecular dynamics Using Gromacs version 2023.2, which is compiled on a Linux operating system, molecular dynamics simulations were carried out following docking experiments to better comprehend the interactions of the wild-type and mutant proteins with the optimized ligand complexes. The CHARMM36 all-atom force field and TIP3 water model were used to prepare the protein topology file. However, we relied on the CHARMM general force field to prepare the ligand topology file. ( https://cgenff.silcsbio.com/ ). A dodecahedron box was defined and filled with simple-point-charge water molecules by setting a minimal distance of 1.0 between the solute and the box. After adding sodium and chloride ions in the appropriate amounts to neutralize the systems, the steepest descent technique was employed to minimize energy consumption in 50,000 steps. Equilibration of the systems was carried out using constant-temperature, constant-pressure (NPT) and constant-temperature, constant-volume (NVT) ensembles for 1000 ps each. A V-rescale thermostat was used for equilibration with a reference temperature of 300 K. Finally, 50 ns and 10 ns MD were performed for the PfDHFR and PfATP6 targets, respectively, with a time step of 2 femtoseconds. During the simulation, the Leap-Frog integrator was used, long-range electrostatics were calculated by the particle‒mesh Ewald method, all bond lengths were constrained by the linear constraint solver algorithm, and the energy information and trajectory information were collected every 10 ps. The built-in GROMACS tools were used to calculate the root mean square deviation, root mean square fluctuation, radius of gyration and number of hydrogen bonds for the protein‒ligand complexes. Excel was utilized for analysis and graph plotting. Results and Discussion ADME prediction and toxicity: We previously synthesized an existing library of phenylhydrazones with potent antimalarial activity against chloroquine - sensitive and chloroquine - resistant P. falciparum (3D7 and Dd2 ) strains [ 33 ]. However, because of the presence of imine and nitro groups, the most potent compounds, PHN3 and PHN6, showed unfavourable toxicity predictions. Here, by simply swapping the nitro groups on PHN3 and PHN6 with other electron-withdrawing groups (CN, CHO, COCH 3 , COOH, CONH 2 and SO 3 H) and reducing the imine group, we designed 72 analogues, as presented in Fig. S1 . Since the majority of compounds fail to reach clinical trials due to their undesirable pharmacokinetic profiles [ 34 ] and poor drug-like and safety properties pose significant obstacles in the drug development process [ 36 ], we evaluated all the designed analogues of PHN3 and PHN6 in silico to determine their physicochemical, pharmacokinetic and safety profiles. This phase was critical because it made it possible to choose analogues that are optimal, nontoxic, and have good pharmacokinetic and physicochemical characteristics. In accordance with SwissADME and admetsar, 12 designed analogues demonstrated favourable physicochemical, pharmacokinetic, and drug-like characteristics, suggesting that they could be promising drug candidates for further investigation. We observed that all analogues containing sulfonic acid and aldehyde groups were eliminated due to their violation of the Brenk rule, making them inappropriate electron withdrawing groups to substitute for the nitro groups in the phenylhydrazone scaffold. Analogues bearing various combinations of CN, COCH 3 , COOH, and CONH 2 groups exhibited good physicochemical and pharmacokinetic properties and could be suitable substitutes for nitro groups in phenylhydrazone scaffolds. However, few of them were predicted to inhibit various forms of cytochrome P450 enzymes according to SwissADME and the Admetsar server and were eliminated because their inhibition of these enzymes could induce or inhibit the metabolism of other drugs, resulting in clinically significant drug–drug interactions [ Table S4, Table S5 and Table S6 ]. Drugs are actively transported across biological membranes by the membrane protein p-glycoprotein (P-gp) [ 37 ]. P-gp can facilitate or restrict the absorption, distribution, excretion, and toxicity of many medications [ 38 ]. Several pharmacokinetic drug‒drug interactions can also occur when two compounds are combined , one of which is a substrate and the other is an inhibitor of the transporter [ 39 ]. The propensity of the optimal analogues to produce drug‒drug interactions or be effluxed from the cell is limited because they are not p-gp substrates or inhibitors [ 40 ]. The compounds were also very soluble, had good absorption in the human intestine and exhibited high gastrointestinal absorption. They did not cross the blood brain barrier. Furthermore, each of these drugs conforms to the Lipinski, Ghose, Verber, Egan and Muegge rules for orally active drugs and hence has the potential to be developed into oral medications. Apart from hepatotoxicity, drug-induced cardiotoxicity is another often-reported adverse event that has resulted in drug withdrawal [ 41 ]; one source of drug-induced cardiotoxicity is inhibition of hERG (human ether -à-go-go- related gene ) K + channels, which produces a type of fatal arrhythmia known as torsade de pointes or long QT syndrome [ 42 ]. Analysis of the optimal analogues suggested that they are neither cardiotoxic nor hepatotoxic (Table S4 and Table S5 ). The best analogues were further used for molecular docking and dynamics studies for protein‒ligand characterization. Validation of the docking protocol: Initial verification of the docking methodology was carried out using pyrimethamine (inhibitor) and its binding protein (PDB ID 3QGT) prior to investigating the binding modes and energies of the designed compounds with PfATP6 and its mutant protein. The inhibitor was removed from P. falciparum dihydrofolate reductase (PDB ID 3QGT) and redocked into the active site using autodock tools. Using PyMOL 2.3 software, these poses were superimposed on top of the reference inhibitor, as shown in Fig S2. The estimated root mean square deviation (RMSD), which was 0.03, indicates little variation. The docked conformation was also evaluated using Discovery Studio Visualizer, which verified the presence of comparable active site residues that facilitate hydrophobic contact and hydrogen bonding between the inhibitor and binding protein. A docking approach with a greater degree of reliability was ensured by the computed root mean square deviation (RMSD) in conjunction with similar interactions since a value of less than 2 angstroms indicates good reproducibility of the cocrystallized structure [ 43 ]. Molecular docking studies The receptor protein PfATP6, also known as PfSERCA, has been demonstrated to be a common target of artemisinin-based antimalarials [ 44 ]. A total of 1228 amino acids constitute this 139 kDa protein. Since the protein's three-dimensional structure is unavailable, we predicted the PfATP6 three-dimensional structure using the Swiss model server [ 45 ]. After evaluating the built model with PROCHEK ( https://saves.mbi.ucla.edu/ ), the malarial PfATP6 mutant type L263E was then modelled. Fig S3 Summary of the Ramachandran plots of the studied proteins. In the docking procedure, the grid coordinates were generated by enclosing the residues at positions 263, 264, 267, 977, 981, 985, 1039, 1040, 1041 and 1042 for the malarial PfATP6 proteins and at positions ASP54, CYS15, ILE14, LEU164, ASN108, PHE58, PRO113, ILE112 and MET55 for the PfDHFR proteins in a receptor grid box. These residues have been investigated and confirmed as the active site residues mediating the binding of artemisinin to PfATP6 and pyrimethamine to PfDHFR [ 46 , 47 ]. Additionally, the chemical structures of artemisinin and pyrimethamine were adequately prepared and docked into the binding pockets of PfATP6 and PfDHFR as control drugs, as shown in Fig. 3 and Fig. 4 . Based on the molecular interactions between the control drugs and wild-type proteins, it can be inferred that the main mechanism of inhibition is van der Waals interactions. For instance, conventional hydrogen bonds with LEU1040 and ASN1039 and hydrophobic contacts with LYS260, PHE264, ILE1041 and LEU1046 stand out as fundamental amino acids involved in the inhibition of wild-type PfATP6 by artemisinin. In addition, pyrimethamine has been shown to form hydrogen bonds with the amino acids ILE14, CYS15, ASP54, ILE164, and THR185 and hydrophobic interactions with the amino acids ALA16, MET55, PHE58 and ILE112 for the catalytic inhibition of wild-type PfDHFR. However, analysis of the interactions of artemisinin and pyrimethamine with the PfATP6 and PfDHFR mutant proteins revealed fewer hydrogen bonds and fewer hydrophobic interactions. For example, no conventional hydrogen bonds with active site residues were observed after the analysis of all the conformations of artemisinin with the PfATP6 mutant protein. Furthermore, pyrimethamine formed three hydrogen bonds with active site residues during the analysis of its best docked pose with the qm-PfDHFR complex, while the wild-type PfDHFR and the same drug exhibited six conventional hydrogen bonds with active site residues. In addition, the molecular docking simulation revealed that the interaction of artemisinin and pyrimethamine with the mutant proteins resulted in greater free binding energy and inhibition constant (Ki) values than those of the wild-type proteins. These findings suggest that the propensity of Plasmodium falciparum to switch amino acid residues at these positions may modify the binding mechanism, perhaps reducing the susceptibility of the organism to artemisinin and pyrimethamine. Table S1 shows the estimated free energy of binding for the compounds and control drugs against the targets, which presented the best scoring of the compounds. According to the obtained results, the designed compounds present suitable affinity towards the four proteins, with compounds B24 and B36 having the best performance against the studied proteins. When the designed compounds were docked with wild-type and mutant proteins, all compounds hit the four targets at the active site mainly via van der Waals interactions. The interactions of the control drugs with the targets were compared with those of the designed compounds to obtain further insight into the role of molecular interactions in the ligand‒receptor complex. Interestingly, the compounds engaged with both wild-type PfATP6 and PfDHFR and their mutants via a greater number of conventional hydrogen bonds as well as some notable hydrophobic interactions with the active site residues, as indicated in Figs. 4 and 5 . Additionally, when bound to both targets, B24 and B36 exhibited lower inhibition constants (Ki) (below 5 µM) for all the studied proteins, indicating that it is possible that they will inhibit these targets more successfully than the control drugs. Molecular dynamics study To assess the stability of the malarial protein‒ligand complexes formed from the docking runs, molecular dynamics (MD) simulations were carried out using the gromacs 2023 program on a Linux system [ 48 ]. The steps included the preparation of protein and ligand topologies with the pdbgmx tool and CHARMM, respectively; solvation; the addition of ions; energy minimization; NVT and NPT equilibration; and production and analysis. The optimal docked poses of B24 and B36 were taken into consideration because these ligands displayed the best van der Waal interaction with the target active site residues, had lower binding free energies, and had lower Ki values than the control drugs. We first examined the stability of artemisinin and pyrimethamine upon binding to their target proteins and compared the results with those of the proposed compounds (B24 and B36). The root mean square deviation (RMSD) of the wild-type and mutant malarial PfATP6 and DHFR proteins with respect to the control drugs are shown in Fig. S4 and Fig. S5. The average backbone RMSD of artemisinin bound to malarial PfATP6 and its mutant protein throughout the whole simulation period was 0.54126 and 0.630925 nm, respectively; thus, a greater fluctuation was observed for the L263E-artemisinin complex than for the wild-type malarial PfATP6, as indicated in Fig. S3. The RMSD for the backbone was calculated for pyrimethamine in complex with the wild-type and quadruple mutant proteins over a 50 ns simulation. The average RMSD of pyrimethamine-qmDHFR was slightly lower (0.141858 nm) than that of pyrimethamine-wild-type protein (0.206869 [Table S1 ]). However, the RMDS for the entire 50 ns simulation remained below 0.4 nm, as illustrated in Fig. S3 and Table S1 . Hydrogen bond analysis revealed that fewer hydrogen bonds formed between the control drugs and mutant proteins during the simulation period (Figs. S6 and S7). This was also evident for the malarial PfATP6 mutant treated with artemisinin, in which there were few and sporadic hydrogen bonds (S7 Fig). This indicates that there is possibly poor complementarity between artemisinin and this target protein. Figure 7 shows the backbone RMSD analysis of B24 and B36 bound to the dihydrofolate reductase quadruple mutant enzyme and the PfATP6 mutant, respectively. The average RMSD of qmDHFR was 0.151612 and 0.195304 for B24 and B36, respectively, and 0.660084 and 0.356839 nm for the malarial PfATP6 mutant protein. This suggests that B24 and B36 protein complexes are stable and less likely to induce structural instability with these proteins. Analysis of the number of hydrogen bonds formed during the entire simulation revealed that B24 and B36 formed a maximum of eight and five hydrogen bonds with the mutant PfATP6 protein and a maximum of six and seven hydrogen bonds with the quadruple mutant DHFR. Additionally, the formation of hydrogen bonds between B24 and B36 and the mutant proteins was frequent and prominent. The ability of the two compounds to maintain a stable number of hydrogen bonds during the simulation period could suggest a strong binding affinity towards the mutant protein compared to the control drugs. The results of the gmx hbond analysis of the two compounds and the mutant proteins are shown in Fig. 8 . Conclusion From docking analysis to molecular dynamics simulation, our designed compounds were carefully investigated. The two candidates B24 and B36 showed greater binding affinity and stability with the wild-type and mutant Plasmodium falciparum ATP6 and dihydrofolate reductase proteins. Additionally, the best two compounds showed promising results in drug-likeness and ADMET profiling and hence represent candidates for progress through the drug discovery pipeline for biological assays mentioned in this work. Declarations The authors declare that this work is the origination of our own research Ethics approval and consent to participate No ethical approval was required since human or animal subjects and their parts were used in this study. Author Information Authors and Affiliation 1 Department of Pharmaceutical Chemistry, School of Pharmacy, University of Health and Allied Sciences, Ho-Ghana. Cedric Dzidzor Kodjo Amengor, Prince Danan Biniyam and Patrick Gyan 2 Department of Chemistry and Biochemistry, Hattiesburg Campus, 118 College Drive Hattiesburg , the University of Southern Mississippi, USA. Francis Klenam Kekessie . Corresponding Author Information Correspondence to [email protected] Competing Interests The authors declare that they have no competing interests. Authors’ contributions CDKA and PDB were involved in the conception and design of the study. PDB managed the experimental procedure and performed computer simulations. FKK and PG participated in the statistical analysis. CDKA, PDB and PG drafted and critically revised the manuscript. All the authors have read and approved the manuscript. Consent for publication All authors declare that no conflict of interest exists for this research. Funding The study was financed by the Computational Medicinal Chemistry Unit, School of Pharmacy, University of Health and Allied Sciences. Acknowledgement The authors wish to extend their gratitude to the Computational Medicinal Chemistry Unit, School of Pharmacy for providing the space and resources for the study. References Institute of Medicine (US) Committee on the Economics of Antimalarial Drugs; Arrow KJ, Panosian C, Gelband H, editors. Saving Lives, Buying Time: Economics of Malaria Drugs in an Age of Resistance. 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Molecular Docking Study, and Biological Evaluation of New 4-(2,5-Dimethyl-1 H -pyrrol-1-yl)- N ’-(2-(substituted)acetyl)benzohydrazides as Dual Enoyl ACP Reductase and DHFR Enzyme Inhibitors. Antibiotics. 2023;12(4):763. https://doi.org/10.3390/antibiotics12040763 . https:// . Retrieved on 20th February, 2024, 9:30 am GMT. https://pymol.org/2/. Retrieved on 20th. February, 2024, 9:30 am GMT. Swiss PDB. -viewer 4.1.0. Retrieved on 21st February, 2024, 10:30 am. GMT. AutoDock tool ( http://autodock.scripps.edu/resources/adt ). 2:30 pm. GMT. Roman D, Larisa M, Roman C, Som Mélanie, Schmutz E, Hernandez P, Wick T, Casalini G, Perale V, Ostafe, Adriana Isvoran. Computational Assessment of the Pharmacological Profiles of Degradation Products of Chitosan. Front Bioeng Biotechnol. 2019;7. https://doi.org/10.3389/fbioe.2019.00214 . Banerjee P, Andreas O, Eckert AK, Schrey, Preissner R. ProTox-II: A Webserver for the Prediction of Toxicity of Chemicals. Nucleic Acids Res. 2018;46(W1):W257–63. https://doi.org/10.1093/nar/gky318 . Morris GM, Huey R, Lindstrom W, Michel F, Sanner RK, Belew DS, Goodsell, Olson AJ. AutoDock4 and AutoDockTools4: Automated Docking with Selective Receptor Flexibility. J Comput Chem. 2009;30(16):2785–91. https://doi.org/10.1002/jcc.21256 . Chen T, Shu X, Zhou H, Beckford FA, and Mustafa Misir. Algorithm Selection for Protein–Ligand Docking: Strategies and Analysis on ACE. Sci Rep. 2023;13(1):8219. https://doi.org/10.1038/s41598-023-35132-5 . Amengor CD, Kodjo PD, Biniyam AA, Brobbey. Francis Klenam Kekessie, Felix Kwame Zoiku, Sherif Hamidu, Patrick Gyan, and Billy Mawunyo Abudey. 2024. ‘N-Substituted Phenylhydrazones Kill the Ring Stage of Plasmodium Falciparum’. Biomed Res Int. 2024;February1–13. https://doi.org/10.1155/2024/6697728 . Sun D, Gao W. Hongxiang Hu, and Simon Zhou. 2022. ‘Why 90% of Clinical Drug Development Fails and How to Improve It?’ Acta Pharmaceutica Sinica B . Chinese Academy of Medical Sciences. https://doi.org/10.1016/j.apsb.2022.02.002 . Lipinski CA. Drug-like Properties and the Causes of Poor Solubility and Poor Permeability. J Pharmacol Toxicol Methods. 2000;44(1):235–49. https://doi.org/10.1016/S1056-8719(00)00107-6 . Ahmed Juvale I, Imtiyaz, Azzmer Azzar Abdul Hamid, Khairul Bariyyah Abd Halim, and, Tarmizi Che A. Has. 2022. ‘P-Glycoprotein: New Insights into Structure, Physiological Function, Regulation and Alterations in Disease’. Heliyon 8 (6): e09777. https://doi.org/10.1016/j.heliyon.2022.e09777 . Kammala A, Benson M, Ganguly E, Richardson L, Menon R. (2022). Functional role and regulation of permeability-glycoprotein (P‐gp) in the fetal membrane during drug transportation. Am J Reprod Immunol, 87(2), e13515. Sajid A, Lusvarghi S, Murakami M, Chufan EE, Abel B, Gottesman MM, Durell SR, Ambudkar SV. (2020). Reversing the direction of drug transport mediated by the human multidrug transporter P-glycoprotein. Proceedings of the National Academy of Sciences , 117 (47), 29609–29617. Zhao D, Chen J, Chu M, Long X, Wang J. (2020). Pharmacokinetic-based drug–drug interactions with anaplastic lymphoma kinase inhibitors: a review. Drug Des Devel Ther, 1663–81. Senarathna SMDK, Ganga M, Page-Sharp, and Andrew Crowe. The Interactions of P-Glycoprotein with Antimalarial Drugs, Including Substrate Affinity, Inhibition and Regulation. PLoS ONE. 2016;11(4):e0152677. https://doi.org/10.1371/journal.pone.0152677 . Seal S, Spjuth O, Hosseini-Gerami L, García-Ortegón M, Singh S, Bender A, Carpenter AE. 2024. ‘Insights into Drug Cardiotoxicity from Biological and Chemical Data: The First Public Classifiers for FDA Drug-Induced Cardiotoxicity Rank’. Journal of Chemical Information and Modelling , February. https://doi.org/10.1021/acs.jcim.3c01834 . Amengor CD, Kodjo E, Orman CA, Danquah IO, Ben PD, Biniyam, and Benjamin Kingsley Harley. Pyridine-N-Oxide Alkaloids from Allium Stipitatum and Their Synthetic Disulfide Analogues as Potential Drug Candidates against Mycobacterium Tuberculosis: A Molecular Docking, QSBAR, and ADMET Prediction Approach. Biomed Res Int. 2022;2022(October):1–14. https://doi.org/10.1155/2022/6261528 . Aziz M, Sarfraz M, Ibrahim MK, Ejaz SA, Zehra T, Ogaly HA, Arafat M, Fatimah AM, Al-Zahrani, Chen, Li. Evaluation of Anticancer Potential of Tetracene-5,12-Dione (A01) and Pyrimidine-2,4-Dione (A02) via Caspase 3 and Lactate Dehydrogenase Cytotoxicity Investigations. PLoS ONE. 2023;18(12):e0292455. https://doi.org/10.1371/journal.pone.0292455 . Dohutia C, Chetia D, Gogoi K, Bhattacharyya DR, and Kishore Sarma. Molecular Docking, Synthesis and in Vitro Antimalarial Evaluation of Certain Novel Curcumin Analogues. Brazilian J Pharm Sci. 2018;53(4). https://doi.org/10.1590/s2175-97902017000400084 . Waterhouse A, Bertoni M, Bienert S, Studer G, Tauriello G, Gumienny R, Florian T, Heer, et al. SWISS-MODEL: Homology Modelling of Protein Structures and Complexes. Nucleic Acids Res. 2018;46(W1):W296–303. https://doi.org/10.1093/nar/gky427 . Kyei L, Kwane EN, Gasu GB, Ampomah JO, Mensah. and Lawrence Sheringham Borquaye. 2022. ‘An In Silico Study of the Interactions of Alkaloids from Cryptolepis Sanguinolenta with Plasmodium Falciparum Dihydrofolate Reductase and Dihydroorotate Dehydrogenase’. Journal of Chemistry 2022 (May): 1–26. https://doi.org/10.1155/2022/5314179 . Shandilya A, Chacko S, Jayaram B, Ghosh I. A Plausible Mechanism for the Antimalarial Activity of Artemisinin: A Computational Approach. Sci Rep. 2013;3(1):2513. https://doi.org/10.1038/srep02513 . Sami M, Suppl C. (May): C19–23. Additional Declarations No competing interests reported. Supplementary Files SupplementarydocumentBMC.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4057743","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":278614305,"identity":"2dbaf2c9-db17-489b-a376-68faf4e06977","order_by":0,"name":"Cedric Dzidzor Kodjo Amengor","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYDADfgYeMM3YwMDARpwWyQaStRgcIFYLf/vpxMcFv2zyjW/kHvz4g8FGdsMB9msP8GmROJO72XhmX5rltht5ydI8DGnGGw7wlBvgdQ9D7jZp3p7DBmY3cgykGRgOJwK1pEng1cL/dvtvkBbjGTnGP38w/CdCi0TuNmaeH4cNDCRyzCR4GA4AtbAfw6tF4sbbzdK8DWkGEmfemFnzGCQbzzzMw4ZXC39/7sbPPH9sDPjbc4xv/qiwk+073v4MrxYwYGyDuxOImXnwBhgU/EHhsT8gQssoGAWjYBSMIAAAqSpJ7rWpUVEAAAAASUVORK5CYII=","orcid":"","institution":"University of Health and Allied Sciences","correspondingAuthor":true,"prefix":"","firstName":"Cedric","middleName":"Dzidzor Kodjo","lastName":"Amengor","suffix":""},{"id":278614306,"identity":"b2170a4c-6c9f-4e9e-93ab-ee0a31345e9f","order_by":1,"name":"Prince Danan Biniyam","email":"","orcid":"","institution":"University of Health and Allied Sciences","correspondingAuthor":false,"prefix":"","firstName":"Prince","middleName":"Danan","lastName":"Biniyam","suffix":""},{"id":278614307,"identity":"1214adac-7257-4b5a-a27f-6ce05d4eb65a","order_by":2,"name":"Patrick Gyan","email":"","orcid":"","institution":"University of Health and Allied Sciences","correspondingAuthor":false,"prefix":"","firstName":"Patrick","middleName":"","lastName":"Gyan","suffix":""},{"id":278614308,"identity":"5044fd1b-e4ac-4fc6-96ff-573093103555","order_by":3,"name":"Francis Klenam Kekessie","email":"","orcid":"","institution":"University of Southern Mississippi","correspondingAuthor":false,"prefix":"","firstName":"Francis","middleName":"Klenam","lastName":"Kekessie","suffix":""}],"badges":[],"createdAt":"2024-03-09 15:31:51","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4057743/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4057743/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52651345,"identity":"01aaf222-b993-40ef-89ee-d65a52fb1ea6","added_by":"auto","created_at":"2024-03-14 05:39:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":38269,"visible":true,"origin":"","legend":"\u003cp\u003eStructures of common antimalarials in clinical use.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4057743/v1/77d99f2dbba2120a384cdfc4.png"},{"id":52651344,"identity":"480b3e9c-ce6a-49e6-a28c-23a4e6ef049e","added_by":"auto","created_at":"2024-03-14 05:39:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":25313,"visible":true,"origin":"","legend":"\u003cp\u003eStructures of the phenylhydrazone antimalarial ligands. Fig. 2a: PHN3 (left) and Fig. 2b: PHN6 (right)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4057743/v1/71ab8bd274a5ca757296f525.png"},{"id":52651353,"identity":"346689ed-44ef-4c8e-8dac-4f0bb48cecec","added_by":"auto","created_at":"2024-03-14 05:39:25","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":178979,"visible":true,"origin":"","legend":"\u003cp\u003eInteractions of (a) wild-type malarial PfATP6 with artemisinin and (b) the malarial PfATP6 mutant (L263E) with artemisinin.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4057743/v1/c7e97c8096ec022bfe71b180.png"},{"id":52652141,"identity":"738b583b-94aa-45ea-9875-0564d79956d2","added_by":"auto","created_at":"2024-03-14 05:47:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":157348,"visible":true,"origin":"","legend":"\u003cp\u003eInteractions of (a) wild-type PfDHFR with pyrimethemine and (b) the PfDHFR quadruple mutant with pyrimethamine.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4057743/v1/dd8f278c5b3efacd8a54874f.png"},{"id":52651347,"identity":"9aa1fb21-370a-4e66-939e-1c32a3814e56","added_by":"auto","created_at":"2024-03-14 05:39:24","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":147179,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction of (a) the PfATP6 mutant with B24 and (b) the PfATP6 mutant with B36\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4057743/v1/e5a947068b4e34ad32eeb6f1.png"},{"id":52651348,"identity":"0e0cecb4-d343-4132-8230-be1f926eceac","added_by":"auto","created_at":"2024-03-14 05:39:24","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":174680,"visible":true,"origin":"","legend":"\u003cp\u003eInteractions of (a) qm-PfDHFR with B24 and (b) qm-PfDHFR with B36\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4057743/v1/0eb3636f7787cc8ae36c2e68.png"},{"id":52651349,"identity":"71c82285-791c-4cd5-814b-f97f27c09c62","added_by":"auto","created_at":"2024-03-14 05:39:24","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":90923,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 7a (left). Backbone root mean square deviation (RMSD) of qm-PfDHFR in complex with pyrimethamine (green), B24 (orange) and B36 (indigo). Figure 7b (right). Backbone root mean square deviation (RMSD) of the malarial PfATP6 mutant (L263E) in complex with artemisinin (black), B24 (orange) and B36 (indigo).\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4057743/v1/217d5ee9f6bd6a8633541c2b.png"},{"id":52652142,"identity":"1f4540b9-4a35-4a18-81ea-e58d2774ccae","added_by":"auto","created_at":"2024-03-14 05:47:24","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":130620,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 8a (left). Analysis of hydrogen bonds formed between qm-PfDHFR and compoundsB24 (orange),B36 (indigo) and pyrimethamine (green). Figure 7b (right). Analysis of hydrogen bonds formed between L2633 and compound B24 (orange), B36 (indigo) and artemisinin (black).\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-4057743/v1/74fa4c492ada6ed4a0aea509.png"},{"id":56846443,"identity":"7baeb15e-b22c-4068-8782-ddf844773416","added_by":"auto","created_at":"2024-05-21 08:10:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1337304,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4057743/v1/2c151d73-158a-4277-90d4-318a6da366fc.pdf"},{"id":52651351,"identity":"21db4c57-8652-4ea7-a3b0-be19d66106e2","added_by":"auto","created_at":"2024-03-14 05:39:24","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1129975,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarydocumentBMC.docx","url":"https://assets-eu.researchsquare.com/files/rs-4057743/v1/8d5636006db782fc30d2cca9.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Discovery of some phenylhydrazones as potential antimalarials: An integrated computational approach on PfATP6 and PfDHFR mutant proteins","fulltext":[{"header":"Background","content":"\u003cp\u003eIn regard to killing humans, no other animal comes close to the mosquito. This disease is known as malaria, as it has been transmitted by this female anopheles mosquito over the years and has high morbidity and mortality rates [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e]. Although there has been a breakthrough with regard to the systematic use of malaria vaccines for the prevention of malaria in underdeveloped and highly endemic regions since 2019, the continuous use of oral artemisinin-based combination therapies cannot be implemented [\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e]. Nevertheless, there has been an astronomical increase in malaria mortality globally due to treatment failure with first-line antimalarial agents, such as artemisinins, especially artemisinin combination therapies [\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e]. ACTs have continuously been used to target various species of \u003cem\u003ePlasmodium\u003c/em\u003e, especially P. \u003cem\u003evivax\u003c/em\u003e and P. \u003cem\u003efalciparum\u003c/em\u003e [\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, the most potent antimalarial noted for its quick reduction in parasitemia since the 1980s has gradually lost its ground due to the popularity of resistant strains [\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e]. Notably, the current low rate of parasitaemia clearance has provided the driving force for the continuous exposure of a high number of parasites to artemisinin derivatives [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e]. This phenomenon is increasing in Southeast Asia, where resistance to artemisinin combined with piperaquine has been documented [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e]. The current trend of increasing \u003cem\u003ePlasmodium\u003c/em\u003e resistance is a serious global public health threat and presents a wide gap in the complete eradication of malaria [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e]. Resistance to artemisinins is caused by mutations in certain key genes involved in the biochemistry and morphology of the parasite.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA key protein is the pfK13 propeller domain, which allows the parasite to enter a latent state. \u003cem\u003ePlasmodium falciparum\u003c/em\u003e Kelch 13 (PfK13) protein is also linked to artemisinin resistance. PfK13 is vital for asexual erythrocytic development, but its role in \u003cem\u003ePlasmodium\u003c/em\u003e survival has not been fully established [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e]. A newly identified validated target for artemisinins, the \u003cem\u003eP. falciparum\u003c/em\u003e 3D7 calcium-transporting ATPase protein (PfATP6) PfSERCA or PfATPase6, was fully reported in 2022 [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e]. PfATP6 is a calcium ATPase gene encoded by \u003cem\u003ePlasmodium falciparum and\u003c/em\u003e has been hypothesized to be a target of artemisinins [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]. This presents new opportunities in malaria drug discovery, where a theoretical framework has been established in this work in which any compound that can bind to PfATP6 in a suitable pose has the potential to inhibit the function of the PfATP6-encoded mutant \u003cem\u003ePlasmodium falciparum\u003c/em\u003e [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]. On the other hand, the metabolism of folate has been established as essential for the survival of the parasite because it is crucial for the biosynthesis of its cell walls and other organelles. Dihydrofolate reductase (DHFR) transforms dihydrofolate to tetrahydrofolate, which is responsible for maintaining the cell division pool in organisms such as \u003cem\u003ePlasmodium\u003c/em\u003e [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]. Therefore, inhibiting this folate pathway with drugs such as pyrimethamine and cycloguanil would terminate this essential aspect of the parasite\u0026rsquo;s survival [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]. Docking studies have been used to explore the binding interactions of compounds that inhibit the activity of the DHFR enzyme in \u003cem\u003ePlasmodium\u003c/em\u003e [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]. Hence, by extension, this could provide an impetus for the discovery of hit compounds for malaria drug discovery campaigns.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eProtein structure prediction and preparation: \u003cem\u003eP. falciparum\u003c/em\u003e dihydrofolate reductase (PfDHFR) and P. falciparum ATPase6 (PfATP6) were the protein targets employed in the present study. The crystal structures of the two variants of PfDHFR, wild type (PDB ID: 3QGT) and quadruple mutant (PDB ID: 1J3K), were obtained from a protein data bank [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e], while the tertiary structure of wild-type PfATP6 (wt-PfATP6) was predicted using the SWISS-MODEL server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://swissmodel.ExPASy.org/\u003c/span\u003e\u003c/span\u003e) based on its primary sequence. The structure of the malarial PfATP6 mutant protein (L263E) was then modelled from the wild-type PfATP6 structure using PyMOL (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pymol.org/2/\u003c/span\u003e\u003c/span\u003e) [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]. Additionally, the protein targets were prepared using the protein preparation tool UCSF Chimera software and finally subjected to optimization and minimization via the Swiss PDB Viewer 4.1.0 [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003eLigand Preparation\u003c/h2\u003e\n\u003cp\u003eThe ligands used in this study were optimized PHN6 [A01-A036] and PHN3 [B01-B036]. They were optimized by reducing the imine group and simply substituting different electron-withdrawing groups for the nitro groups in PHN3 and PHN6.\u003c/p\u003e\n\u003cp\u003eThe ligands were first sketched in Xdrawchem, and then their 3-dimensional coordinates were generated using Chemdraw3d software. Hydrogen and Gasteiger charges were added to the design using the AutoDock tool [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e], followed by energy minimization utilizing the general Amber force field (GAFF) incorporated into the Avogadro program.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003ePhysicochemical and pharmacokinetic prediction\u003c/h2\u003e\n\u003cp\u003eThe pharmacokinetic and toxicity predictions of each analogue were performed using software/web-based servers that have proven to be remarkably sensitive and accurate [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e]. The SwissADME and ADMETSAR web services were used to assess the physicochemical characteristics of the compounds, including molecular weight, molar refractivity, topological polar surface area, number of hydrogen bond donors and acceptors, number of reversible bonds, partition coefficient and pharmacokinetic features such as blood‒brain barrier penetration (BBB), plasma protein binding (PPB), human intestinal absorption (HIA), and P-glycoprotein substrate and inhibitor. The ProTox-II [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e] and OSIRINS property explorer programs were utilized to evaluate the hepatotoxicity and carcinogenicity of the compounds. In addition, acute oral toxicity, Ames toxicity and possible cardiotoxicity of the ligands, including suppression of the human either-a-go-go-related gene (hERG), were assessed.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003eProtein\u0026ndash;Ligand Docking\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eThe\u003c/strong\u003e AutoDock tool 1.5.6 was utilized to perform docking runs [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]. For every ligand, ten distinct conformations were generated and clustered, each of which was scored using AutoDock scoring functions and ranked based on docked energy. The Lamarckian genetic algorithm implemented in AutoDock was employed [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e]. To ensure reproducibility, three technical runs of each docking experiment were conducted. The maximum RMS tolerance for conformational cluster analysis was set to 2.00 \u0026Aring;. PyMOL (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pymol.org/2/\u003c/span\u003e\u003c/span\u003e) and Discovery Studio visualizer (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://discover.3ds.com/discovery-studio-visualizer-download\u003c/span\u003e\u003c/span\u003e) were used for postdocking analysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n\u003ch2\u003eMolecular dynamics\u003c/h2\u003e\n\u003cp\u003eUsing Gromacs version 2023.2, which is compiled on a Linux operating system, molecular dynamics simulations were carried out following docking experiments to better comprehend the interactions of the wild-type and mutant proteins with the optimized ligand complexes. The CHARMM36 all-atom force field and TIP3 water model were used to prepare the protein topology file. However, we relied on the CHARMM general force field to prepare the ligand topology file. (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cgenff.silcsbio.com/\u003c/span\u003e\u003c/span\u003e). A dodecahedron box was defined and filled with simple-point-charge water molecules by setting a minimal distance of 1.0 between the solute and the box. After adding sodium and chloride ions in the appropriate amounts to neutralize the systems, the steepest descent technique was employed to minimize energy consumption in 50,000 steps. Equilibration of the systems was carried out using constant-temperature, constant-pressure (NPT) and constant-temperature, constant-volume (NVT) ensembles for 1000 ps each. A V-rescale thermostat was used for equilibration with a reference temperature of 300 K. Finally, 50 ns and 10 ns MD were performed for the PfDHFR and PfATP6 targets, respectively, with a time step of 2 femtoseconds. During the simulation, the Leap-Frog integrator was used, long-range electrostatics were calculated by the particle‒mesh Ewald method, all bond lengths were constrained by the linear constraint solver algorithm, and the energy information and trajectory information were collected every 10 ps. The built-in GROMACS tools were used to calculate the root mean square deviation, root mean square fluctuation, radius of gyration and number of hydrogen bonds for the protein‒ligand complexes. Excel was utilized for analysis and graph plotting.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results and Discussion","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003eADME prediction and toxicity:\u003c/h2\u003e\n\u003cp\u003eWe previously synthesized an existing library of phenylhydrazones with potent antimalarial activity against chloroquine\u003cem\u003e-\u003c/em\u003esensitive and chloroquine\u003cem\u003e-\u003c/em\u003eresistant \u003cem\u003eP. falciparum\u003c/em\u003e (3D7 and \u003cem\u003eDd2\u003c/em\u003e) strains [\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e]. However, because of the presence of imine and nitro groups, the most potent compounds, PHN3 and PHN6, showed unfavourable toxicity predictions. Here, by simply swapping the nitro groups on PHN3 and PHN6 with other electron-withdrawing groups (CN, CHO, COCH\u003csub\u003e3\u003c/sub\u003e, COOH, CONH\u003csub\u003e2\u003c/sub\u003e and SO\u003csub\u003e3\u003c/sub\u003eH) and reducing the imine group, we designed 72 analogues, as presented in Fig. \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e. Since the majority of compounds fail to reach clinical trials due to their undesirable pharmacokinetic profiles [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e] and poor drug-like and safety properties pose significant obstacles in the drug development process [\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e], we evaluated all the designed analogues of PHN3 and PHN6 \u003cem\u003ein silico\u003c/em\u003e to determine their physicochemical, pharmacokinetic and safety profiles.\u003c/p\u003e\n\u003cp\u003eThis phase was critical because it made it possible to choose analogues that are optimal, nontoxic, and have good pharmacokinetic and physicochemical characteristics. In accordance with SwissADME and admetsar, 12 designed analogues demonstrated favourable physicochemical, pharmacokinetic, and drug-like characteristics, suggesting that they could be promising drug candidates for further investigation. We observed that all analogues containing sulfonic acid and aldehyde groups were eliminated due to their violation of the Brenk rule, making them inappropriate electron withdrawing groups to substitute for the nitro groups in the phenylhydrazone scaffold. Analogues bearing various combinations of CN, COCH\u003csub\u003e3\u003c/sub\u003e, COOH, and CONH\u003csub\u003e2\u003c/sub\u003e groups exhibited good physicochemical and pharmacokinetic properties and could be suitable substitutes for nitro groups in phenylhydrazone scaffolds. However, few of them were predicted to inhibit various forms of cytochrome P450 enzymes according to SwissADME and the Admetsar server and were eliminated because their inhibition of these enzymes could induce or inhibit the metabolism of other drugs, resulting in clinically significant drug\u0026ndash;drug interactions [\u003cem\u003eTable\u003c/em\u003e S4, Table S5 and Table \u003cem\u003eS6\u003c/em\u003e]. Drugs \u003cem\u003eare\u003c/em\u003e actively transported across biological membranes by the membrane protein p-glycoprotein (P-gp) [\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e]. P-gp \u003cem\u003ecan\u003c/em\u003e facilitate or restrict the absorption, distribution, excretion, and toxicity of many medications [\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e]. \u003cem\u003eSeveral\u003c/em\u003e pharmacokinetic drug‒drug interactions \u003cem\u003ecan also occur\u003c/em\u003e when two compounds are \u003cem\u003ecombined\u003c/em\u003e, one of which is a substrate and the other \u003cem\u003eis\u003c/em\u003e an inhibitor of the transporter [\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e]. The propensity of the optimal analogues to produce drug‒drug \u003cem\u003einteractions\u003c/em\u003e or be effluxed \u003cem\u003efrom\u003c/em\u003e the cell is limited because they \u003cem\u003eare\u003c/em\u003e not p-gp substrates \u003cem\u003eor\u003c/em\u003e inhibitors [\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e]. The compounds were also very soluble, \u003cem\u003ehad good absorption in the human intestine\u003c/em\u003e and exhibited high gastrointestinal absorption. They did not cross \u003cem\u003ethe\u003c/em\u003e blood brain barrier. Furthermore, each of \u003cem\u003ethese drugs\u003c/em\u003e conforms to \u003cem\u003ethe\u003c/em\u003e Lipinski, Ghose, Verber, Egan and Muegge \u003cem\u003erules\u003c/em\u003e for orally active drugs \u003cem\u003eand\u003c/em\u003e hence has the potential to be developed into oral medications. Apart from hepatotoxicity, drug-induced cardiotoxicity is another often-reported adverse event that has resulted in drug withdrawal [\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e]; one source of drug-induced cardiotoxicity is inhibition of hERG (human \u003cem\u003eether\u003c/em\u003e-\u0026agrave;-go-go-\u003cem\u003erelated gene\u003c/em\u003e) K\u003csup\u003e+\u003c/sup\u003e channels, which produces a type of fatal arrhythmia known as torsade de pointes or long QT syndrome [\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e]. Analysis of the optimal analogues \u003cem\u003esuggested that\u003c/em\u003e they are neither \u003cem\u003ecardiotoxic\u003c/em\u003e nor hepatotoxic (Table \u003cem\u003eS4\u003c/em\u003e and Table \u003cem\u003eS5\u003c/em\u003e). The best analogues were further used for molecular docking and dynamics studies for protein‒ligand characterization.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003eValidation of the docking protocol:\u003c/h2\u003e\n\u003cp\u003eInitial verification of the docking methodology was carried out using pyrimethamine (inhibitor) and its binding protein (PDB ID 3QGT) prior to investigating the binding modes and energies of the designed compounds with PfATP6 and its mutant protein. The inhibitor was removed from P. \u003cem\u003efalciparum\u003c/em\u003e dihydrofolate reductase (PDB ID 3QGT) and redocked into the active site using autodock tools. Using PyMOL 2.3 software, these poses were superimposed on top of the reference inhibitor, as shown in Fig S2. The estimated root mean square deviation (RMSD), which was 0.03, indicates little variation. The docked conformation was also evaluated using Discovery Studio Visualizer, which verified the presence of comparable active site residues that facilitate hydrophobic contact and hydrogen bonding between the inhibitor and binding protein. A docking approach with a greater degree of reliability was ensured by the computed root mean square deviation (RMSD) in conjunction with similar interactions since a value of less than 2 angstroms indicates good reproducibility of the cocrystallized structure [\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003eMolecular docking studies\u003c/h2\u003e\n\u003cp\u003eThe receptor protein PfATP6, also known as PfSERCA, has been demonstrated to be a common target of artemisinin-based antimalarials [\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e]. A total of 1228 amino acids constitute this 139 kDa protein. Since the protein's three-dimensional structure is unavailable, we predicted the PfATP6 three-dimensional structure using the Swiss model server [\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e]. After evaluating the built model with PROCHEK (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://saves.mbi.ucla.edu/\u003c/span\u003e\u003c/span\u003e), the malarial PfATP6 mutant type L263E was then modelled. Fig S3 Summary of the Ramachandran plots of the studied proteins.\u003c/p\u003e\n\u003cp\u003eIn the docking procedure, the grid coordinates were generated by enclosing the residues at positions 263, 264, 267, 977, 981, 985, 1039, 1040, 1041 and 1042 for the malarial PfATP6 proteins and at positions ASP54, CYS15, ILE14, LEU164, ASN108, PHE58, PRO113, ILE112 and MET55 for the PfDHFR proteins in a receptor grid box. These residues have been investigated and confirmed as the active site residues mediating the binding of artemisinin to PfATP6 and pyrimethamine to PfDHFR [\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e]. Additionally, the chemical structures of artemisinin and pyrimethamine were adequately prepared and docked into the binding pockets of PfATP6 and PfDHFR as control drugs, as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBased on the molecular interactions between the control drugs and wild-type proteins, it can be inferred that the main mechanism of inhibition is van der Waals interactions. For instance, conventional hydrogen bonds with LEU1040 and ASN1039 and hydrophobic contacts with LYS260, PHE264, ILE1041 and LEU1046 stand out as fundamental amino acids involved in the inhibition of wild-type PfATP6 by artemisinin. In addition, pyrimethamine has been shown to form hydrogen bonds with the amino acids ILE14, CYS15, ASP54, ILE164, and THR185 and hydrophobic interactions with the amino acids ALA16, MET55, PHE58 and ILE112 for the catalytic inhibition of wild-type PfDHFR. However, analysis of the interactions of artemisinin and pyrimethamine with the PfATP6 and PfDHFR mutant proteins revealed fewer hydrogen bonds and fewer hydrophobic interactions. For example, no conventional hydrogen bonds with active site residues were observed after the analysis of all the conformations of artemisinin with the PfATP6 mutant protein. Furthermore, pyrimethamine formed three hydrogen bonds with active site residues during the analysis of its best docked pose with the qm-PfDHFR complex, while the wild-type PfDHFR and the same drug exhibited six conventional hydrogen bonds with active site residues. In addition, the molecular docking simulation revealed that the interaction of artemisinin and pyrimethamine with the mutant proteins resulted in greater free binding energy and inhibition constant (Ki) values than those of the wild-type proteins. These findings suggest that the propensity of \u003cem\u003ePlasmodium falciparum\u003c/em\u003e to switch amino acid residues at these positions may modify the binding mechanism, perhaps reducing the susceptibility of the organism to artemisinin and pyrimethamine.\u003c/p\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e shows the estimated free energy of binding for the compounds and control drugs against the targets, which presented the best scoring of the compounds. According to the obtained results, the designed compounds present suitable affinity towards the four proteins, with compounds B24 and B36 having the best performance against the studied proteins.\u003c/p\u003e\n\u003cp\u003eWhen the designed compounds were docked with wild-type and mutant proteins, all compounds hit the four targets at the active site mainly via van der Waals interactions. The interactions of the control drugs with the targets were compared with those of the designed compounds to obtain further insight into the role of molecular interactions in the ligand‒receptor complex. Interestingly, the compounds engaged with both wild-type PfATP6 and PfDHFR and their mutants \u003cem\u003evia\u003c/em\u003e a greater number of conventional hydrogen bonds as well as some notable hydrophobic interactions with the active site residues, as indicated in Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. Additionally, when bound to both targets, B24 and B36 exhibited lower inhibition constants (Ki) (below 5 \u0026micro;M) for all the studied proteins, indicating that it is possible that they will inhibit these targets more successfully than the control drugs.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003eMolecular dynamics study\u003c/h2\u003e\n\u003cp\u003eTo assess the stability of the malarial protein‒ligand complexes formed from the docking runs, molecular dynamics (MD) simulations were carried out using the gromacs 2023 program on a Linux system [\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e]. The steps included the preparation of protein and ligand topologies with the pdbgmx tool and CHARMM, respectively; solvation; the addition of ions; energy minimization; NVT and NPT equilibration; and production and analysis. The optimal docked poses of B24 and B36 were taken into consideration because these ligands displayed the best van der Waal interaction with the target active site residues, had lower binding free energies, and had lower Ki values than the control drugs. We first examined the stability of artemisinin and pyrimethamine upon binding to their target proteins and compared the results with those of the proposed compounds (B24 and B36). The root mean square deviation (RMSD) of the wild-type and mutant malarial PfATP6 and DHFR proteins with respect to the control drugs are shown in Fig. S4 and Fig. S5. The average backbone RMSD of artemisinin bound to malarial PfATP6 and its mutant protein throughout the whole simulation period was 0.54126 and 0.630925 nm, respectively; thus, a greater fluctuation was observed for the L263E-artemisinin complex than for the wild-type malarial PfATP6, as indicated in Fig. S3. The RMSD for the backbone was calculated for pyrimethamine in complex with the wild-type and quadruple mutant proteins over a 50 ns simulation. The average RMSD of pyrimethamine-qmDHFR was slightly lower (0.141858 nm) than that of pyrimethamine-wild-type protein (0.206869 [Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e]). However, the RMDS for the entire 50 ns simulation remained below 0.4 nm, as illustrated in Fig. S3 and Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eHydrogen bond analysis revealed that fewer hydrogen bonds formed between the control drugs and mutant proteins during the simulation period (Figs. S6 and S7). This was also evident for the malarial PfATP6 mutant treated with artemisinin, in which there were few and sporadic hydrogen bonds (S7 Fig). This indicates that there is possibly poor complementarity between artemisinin and this target protein. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e shows the backbone RMSD analysis of B24 and B36 bound to the dihydrofolate reductase quadruple mutant enzyme and the PfATP6 mutant, respectively. The average RMSD of qmDHFR was 0.151612 and 0.195304 for B24 and B36, respectively, and 0.660084 and 0.356839 nm for the malarial PfATP6 mutant protein. This suggests that B24 and B36 protein complexes are stable and less likely to induce structural instability with these proteins.\u003c/p\u003e\n\u003cp\u003eAnalysis of the number of hydrogen bonds formed during the entire simulation revealed that B24 and B36 formed a maximum of eight and five hydrogen bonds with the mutant PfATP6 protein and a maximum of six and seven hydrogen bonds with the quadruple mutant DHFR. Additionally, the formation of hydrogen bonds between B24 and B36 and the mutant proteins was frequent and prominent. The ability of the two compounds to maintain a stable number of hydrogen bonds during the simulation period could suggest a strong binding affinity towards the mutant protein compared to the control drugs. The results of the gmx hbond analysis of the two compounds and the mutant proteins are shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eFrom docking analysis to molecular dynamics simulation, our designed compounds were carefully investigated. The two candidates B24 and B36 showed greater binding affinity and stability with the wild-type and mutant \u003cem\u003ePlasmodium falciparum\u003c/em\u003e ATP6 and dihydrofolate reductase proteins. Additionally, the best two compounds showed promising results in drug-likeness and ADMET profiling and hence represent candidates for progress through the drug discovery pipeline for biological assays mentioned in this work.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe authors declare that this work is the origination of our own research\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo ethical approval was required since human or animal subjects and their parts were used in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eDepartment of Pharmaceutical Chemistry, School of Pharmacy, University of Health and Allied Sciences, Ho-Ghana.\u003c/p\u003e\n\u003cp\u003eCedric Dzidzor Kodjo Amengor, Prince Danan Biniyam and Patrick Gyan\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003eDepartment of Chemistry and Biochemistry, \u003cem\u003eHattiesburg\u003c/em\u003e Campus, 118 College Drive \u003cem\u003eHattiesburg\u003c/em\u003e, the University of\u0026nbsp;Southern\u0026nbsp;Mississippi, USA.\u003c/p\u003e\n\u003cp\u003eFrancis Klenam Kekessie\u003csup\u003e.\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAuthor\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eInformation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to\u0026nbsp;\u003ca href=\"mailto:[email protected]\"\[email protected]\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCDKA and PDB were involved in the conception and design of the study. PDB managed\u0026nbsp;the\u0026nbsp;experimental procedure and performed computer simulations. FKK and PG participated in the statistical analysis. CDKA, PDB and PG drafted and critically revised the manuscript. All the authors have read and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare that no conflict of interest exists for this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was financed by the Computational Medicinal Chemistry Unit, School of Pharmacy, University of Health and Allied Sciences.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors wish to extend their gratitude to the Computational Medicinal Chemistry Unit, School of Pharmacy for providing the space and resources for the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eInstitute of Medicine (US) Committee on the Economics of Antimalarial Drugs; Arrow KJ, Panosian C, Gelband H, editors. 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(May): C19\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4057743/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4057743/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePlasmodium falciparum\u003c/em\u003e resistance to artemisinins and anti-folate pyrimethamine has hampered WHO efforts in the global eradication of malaria. Several studies have linked artemisinin and pyrimethamine resistance to mutations in the PfATP6 (calcium ATPase) and PfDHFR (dihydrofolate reductase) genes, respectively. However, the mechanism of resistance of \u003cem\u003ePlasmodium falciparum \u003c/em\u003eto artemisinins and dihydrofolates has not been fully explored. Hence, new medicines for malaria are urgently needed to find a solution to the increasing demand for antimalarials with improved activity and better safety profiles. In our previous report, the phenylhydrazones PHN3 and PHN6 were shown to possess antimalarial activity on the ring stage of \u003cem\u003ePlasmodium falciparum\u003c/em\u003e. Hence, this earlier report was leveraged to form the basis for the \u003cem\u003ein silico\u003c/em\u003e design of 72 phenylhydrazone analogues for this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, computational molecular docking and dynamics \u003cem\u003evia\u003c/em\u003e AutoDock tools were used as rational approaches to predict better clinical candidates. We also evaluated all the designed analogues of PHN3 and PHN6 \u003cem\u003ein silico\u003c/em\u003e to determine their physicochemical, pharmacokinetic and safety profiles. \u003cem\u003eP. falciparum \u003c/em\u003edihydrofolate reductase (PfDHFR) and \u003cem\u003eP. falciparum\u003c/em\u003e ATPase6 (PfATP6) were the protein targets employed in the present study. The structure of the malarial PfATP6 mutant protein (L263E) was modelled from the wild-type PfATP6 structure using PyMOL. Molecular dynamics simulation was carried out following docking experiments to better understand the interactions of the mutant proteins with the optimized ligand complex.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHence, we elucidated the binding affinity and efficacy of phenylhydrazone-based compounds on the PfATP6 and PfDHFR proteins in the presence of the L263E and qm-PfDHFR mutations, respectively, with artemisinin and pyrimethamine as standards. Moreover, we identified possible hit candidates through virtual screening of 72 compounds that could inhibit the wild-type and mutant PfATP6 and PfDHFR proteins. We observed that the binding affinity of artemisinin for PfATP6 is affected by L263E mutations. Here, the \u003cem\u003ecomputational \u003c/em\u003einterpretation of \u003cem\u003ePlasmodium\u003c/em\u003e resistance to artemisinin and pyrimethamine reinforced the identification of novel compounds (B24 and B36) that showed good binding affinity and efficacy with wt-PfATP6, the L263E mutant, wt-PfDHFR and the PfDHFR quadruple mutant proteins in molecular docking and molecular dynamics studies. It is also worth noting that CN, COCH\u003csub\u003e3\u003c/sub\u003e, COOH, and CONH\u003csub\u003e2 \u003c/sub\u003ewere better electron withdrawing group replacements for the NO\u003csub\u003e2\u003c/sub\u003e groups in the phenylhydrazone scaffolds in the minimization of toxicity. Twelve of the designed analogues demonstrated favourable physicochemical, pharmacokinetic, and drug-like characteristics, suggesting that they could be promising drug candidates for further investigation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThese results suggest that the B24 and B36 protein complexes are stable and less likely to induce structural instability in the studied proteins. The binding of B24 and B36 to the active sites of the two \u003cem\u003ePlasmodium\u003c/em\u003e proteins was not significantly affected by the mutations. Additionally, when bound to both targets, B24 and B36 exhibited inhibition constants (Ki) below 5 µM for all the proteins docked, indicating that they inhibited the PfATP6 and PfDHFR targets more successfully than did artemisinin and pyrimethamine. The two \u003cem\u003ein silico\u003c/em\u003e hit compounds identified represent potential clinical candidates for the design of novel antimalarials.\u003c/p\u003e","manuscriptTitle":"Discovery of some phenylhydrazones as potential antimalarials: An integrated computational approach on PfATP6 and PfDHFR mutant proteins","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-14 05:39:19","doi":"10.21203/rs.3.rs-4057743/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"377909f7-92e4-42c3-82f3-3ca33b6aa85f","owner":[],"postedDate":"March 14th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-21T08:02:30+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-14 05:39:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4057743","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4057743","identity":"rs-4057743","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}