In-silico Evaluation of Phenylpiperazinyl Derivatives as Inhibitors of P. falciparum Dihydrofolate Reductase-Thymidylate Synthase: A Potential New Antimalarial Drug Candidate

doi:10.21203/rs.3.rs-5185852/v1

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In-silico Evaluation of Phenylpiperazinyl Derivatives as Inhibitors of P. falciparum Dihydrofolate Reductase-Thymidylate Synthase: A Potential New Antimalarial Drug Candidate

https://doi.org/10.21203/rs.3.rs-5185852/v1

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Resistance to antifolates targeting dihydrofolate reductase (DHFR) in Plasmodium falciparum poses a significant challenge to malarial control initiatives, with some standard drugs active chemical compounds exhibiting associated toxicity concerns. It becomes expedient to explore new antifolate inhibitors for malaria control. Herein, hypothetical compound A1 (piperazine-1,4-diylbis(phenylmethanone) and it's modified mono- and di-alcohol derivatives A2 and A3 were screened virtually for toxicity and inhibitory efficiencies against bifunctional DHFR-thymylate protease enzyme to obtain a safer and potent antimalarial drug compound. Additionally, the physicochemical properties, drug-likeness, toxicity and binding energy of these compounds were compared with ten standard reference antimalaria drugs using Molinspiration, SwissADME, Protox II webserver and PyRx 0.8 AutoDock Vina Wizard including Discovery Studio 2020 respectively. Compounds A1, A2 and A3 were found to be non-toxic, competing favourably with nine of the standard reference existing drugs each unfortunately showing some level of immunotoxicity. Interestingly, compound A1 exhibited comparable inhibitory activity with Artesunate (binding score of -9.20 kcal/mol) towards amino acid residues Ala₁₆, Leu₄₀, and Ser₁₀₈, responsible for DHFR's reduction to tetrahydrofolate reductase (THFR). Similarly, A2 and A3 showed binding scores of (-8.8 kcal/mol) and (-8.2 kcal/mol) respectively, both higher against DHFR-TS enzyme compared to Mefloquine at (-7.9 kcal/mol) as the only screened standard reference displaying non-toxicity against human cells. Furthermore, the three hypothetical compounds A1-3 obeyed the rule of five and exhibited high gastrointestinal absorption indicative of good drug-like properties and ease of absorption by the body. Density functional theory (DFT) studies revealed a binding trend which is a function of electron affinity (EA) and electronic chemical potential (µ) values which were complementary to the binding energy trend against P. falciparum DHFR where A1 exhibits the highest binding energy and A3 has better pharmacokinetic properties. The results obtained from this study showed that the investigated compounds, particularly A1 and A3, can be explored as potential non-toxic candidates for antimalarial drugs development.

Antimalaria

non-toxic

Plasmodium falciparum

piperazine-1

4-diylbis(phenylmethanone)

dihydrofolate reductase-thymidylate synthase (DHFR-TS).

Malaria remains a significant global infectious disease characterized by severe fever, presenting an ongoing concern for public health [1]. In 2022, the World Health Organization (WHO) reported an estimated global burden of malaria in 2020, ranging between 200 to 300 million cases, resulting in approximately 627,000 deaths [2]. Plasmodium falciparum, the most widespread and formidable species of the parasite particularly affects Africa [3]. In the recent times there have been emergence of drug-resistant strains of P. falciparum, creating a distressing situation due to the diminished effectiveness of current medications (Fig. 1). These challenges have inspired initiatives to explore new drugs or repurposing of existing chemical compounds as strategies to more enduring medical intervention. An innovative approach to addressing drug resistance in malaria involves investigating alternative biological compounds that can affect different target sites. Drug resistance in malaria parasites, especially resistance to antifolates that target dihydrofolate reductase (DHFR), presents a formidable barrier to malaria control [4].

DHFR is a component of the multifunctional enzyme dihydrofolate reductase-thymidylate synthase (DHFR-TS), which in P. falciparum is a confirmed target for antifolate antimalarials. The enzyme DHFR exists as a dimer, and its interactions between domains are significantly influenced by the junction region and plasmodium-specific sequences [5]. Unfortunately, well-known inhibitors such as pyrimethamine and cycloguanil, including their derivatives, have encountered widespread resistance at positions 108 and 16, as well as changes in the overall configuration of the enzyme's main chain [6]. Combinations of inhibitors with sulfa-drugs, which target dihydropteroate synthase (DHPS) in the folate synthesis pathway, are not immune to the threat of resistance.

In-silico studies showed that piperazine derivatives could target Plasmodium plasmepsin II enzyme. Thirteen new phenylpiperazines derivatives activities against P. falciparum [7] were investigated. Similarly, phenyl piperazines analogs were investigated for in-vitro and toxicity activities against P. falciparum Colombian FCR-3 strain and Leishmania infantum (Fig. 2a) [8]. A series of 27 flavonoid derivatives having various substitution patterns on both the piperazinyl and flavone units, were evaluated against P. falciparum to observe effects on the compounds' antiplasmodial activity [9]. The most potent compounds demonstrated in-vitro micromolar to submicromolar activity levels range against both chloroquine-sensitive and -resistant strains of P. falciparum. Specifically, the compounds that exhibited the highest activity featured a 2,3,4-trimethoxybenzylpiperazinyl chain attached to the flavone at the 7-phenol group (Fig. 2b) [9–11]. Thus, herein the aim of the study is to investigate the potentials of phenylpiperazinyl derivatives as alternative safe and potent antifolate target enzyme inhibitor towards obtaining lead candidates in malaria.

Hypothetical compounds, piperazine-1,4-diylbis(phenylmethanone) A1, its reduced mono- and di-alcohol derivative A2 and A3 respectively (Fig. 3) were conceptualized and drawn using ChemDraw 14. The strategy adopted was desgined to obtain insights into the binding interactions between the ligand compounds A1-3 and the specific amino acid residues of the enzyme's active sites which are essential for antifolate targets.

2.1 Toxicity prediction of hypothetical compounds A1-3

The hypothetical compounds A1-3 underwent toxicity testing by inputting their SMILES representation drawn using ChemDraw 14.0 and saved as an .sdf file onto Protox II web server (https://tox-new.charite.de/protox_II/). The extracted data from the web server provided in-silico information on hepatoxicity, carcinogenicity, immunotoxicity, mutagenicity and cytotoxicity [12].

2.2 Conversion of selected reference antimalarial active compounds into pdbqt format

Ten antimalarial drugs were selected; artesunate, doxycycline, tafenoquine, amodiaquine, artemeter, lumefantrine, primaquine, piperaquine, mefloquine and chloroquine for use in this study. Their structures were obtained from PubChem (https://pubchem.ncbi.nlm.nih.gov/) and docked as ligands for comparative purposes. The structures of these drugs active compounds were downloaded and saved in the .sdf format, and their corresponding SMILES representations were then uploaded onto the Protox II web server (https://pubchem.ncbi.nlm.nih.gov/) for virtual toxicity profiles screening and compliance with drug-likeness rules [13].

2.3 Selection of P. falciparum dihydrofolate reductase-thymidylate synthase receptor

The crystal structure of the P. falciparum dihydrofolate reductase-thymidylate synthase (DHFR-TS) protein molecule, with a resolution of 2.60 Å, was acquired from the Protein Data Bank at rcsb.org (https://www.rcsb.org/) in the pdb format. Thereafter, it was processed using BIOVIA Discovery Studio DS 2020 to eliminate any unwanted ligands and water molecules. Additionally, polar hydrogen atoms were added to the structure as required.

2.4 In-silico drug-likeness and ADME predictions

The compounds A1-3 were investigated for drug-likeness using SwissADME (http://www.swissadme.ch/index.php) to predict crucial adsorption, distribution, metabolis and excretion (ADME) parameters. The SMILES representations of these compounds were uploaded onto the web server. The extracted data were processed to obtain results which were crucial to gain insights on their potential drug-likeness.

2.5 Bioactivity scores of Phenylpiperazinyl derivatives A1 – A3

Vital physicochemical parameters amd bioactivity score predictions of the hypothetical compounds A1-3 was carried out using molinspiration webserver (http://https://www.molinspiration.com/). This analysis provides the basis for evaluation of bioactive compounds compliance with Pearson correlation co-efficient towards identifying potential lead drug candidates. The SMILES representations of the compounds were submitted to the web server, and the resulting data were analysed.

2.6 DFT Studies of Phenylpiperazinyl derivatives A1 – A3

The conformational search was performed using Spartan 14 Conformer Distribution with Molecular Mechanics/MMFF, and the most stable conformers were chosen [14]. Geometry optimization and Frequency calculation of the best conformer for each piperazines were simulated without constraints using the density functional theory (DFT) method with the functional (B3LYP) and 6-31G** (basis set) [15, 16]. The optimized structures, the Frontier molecular orbitals, dipole moment and electrostatic potential maps were generated using the software from the TDDFT calculations. Global reactivity descriptors like electronegativity (χ), chemical potential (µ), electrophilicity (ω), global hardness (η) and global softness (S) were derived from the EHOMO and ELUMO, respectively [17].

2.7 Molecular docking study

Evaluation of the inhibitory potential of the designed compounds A1–3, and the selected reference drugs active chemical compunds were investigated through docking simulations performed using the PyRx 0.8 AutoDock Vina Wizard. The macromolecules were initially converted to Autodock format, and a flexible ligand to rigid protein approach was employed. All possible binding sites on the target protein were explored during the docking process. The docking calculations were performed within a cubic grid of dimensions 90 × 75 × 60 centered on the protein, encompassing the entire protein structure. This process lasted approximately one hour. A grid spacing of 1.00 Å was utilized to generate the grid maps using the autogrid module of AutoDock Tools. Each ligand underwent nine independent runs to ensure accuracy. Based on the identified potential binding sites, energetically favorable binding conformations were selected using AutodockVina [18]. The binding modes, along with their respective binding affinities and RSB (upper and lower) values, were obtained to guide the selection of the highest scoring binding conformation for each ligand. The binding mode with the best binding affinity was chosen. The ligand-protein complexes were analyzed using DS Visualizer. All software applications were executed on PC-based machines running the Microsoft Windows 10 operating system.

The in-silico evaluation of phenylpiperazinyl derivatives A1, A2, and A3 as potential inhibitors of Plasmodium falciparum dihydrofolate reductase-thymidylate synthase (DHFR-TS) offers promising prospects for antimalarial drug development. Using tools like Protox II for toxicity prediction, SwissADME for drug-likeness and ADME analysis, molecular docking for binding studies, and Density Functional Theory (DFT) for chemical reactivity, the compounds were comprehensively analyzed. Table 1a highlights the favorable toxicity profiles of A1-A3, with low risks compared to standard antimalarial drugs (Table 1b). ADME predictions (Table 2a and 2b) show compliance with the Rule of Five, good oral bioavailability, and brain permeability. Figure 7’s BOILED-Egg model confirms high absorption potential. Bioactivity scores (Figure 8) indicate moderate to high pharmacological activity, and molecular docking studies (Figures 11-15) reveal strong binding affinities. DFT analysis (Table 3) and MEP surfaces (Figure 9) highlight the derivatives' reactive sites

3.1 Toxicity Prediction of Designed Drug-Candidate A1-3

3.2 In-silico Drug-likeness and ADME Predictions

The summary of physicochemical an drug-likeness results presented in Table 2a and Figure 4 indicate that the hypothetical compounds A1-A3 exhibit characteristics in compliance with the Rule of Five (RO5), and showing zero violation to drug-likeness rules [13].

Compounds A1-3 act as inhibitors of CYP2D6 while serving as substrates for CYP1A2, CYP2C9 and CYP3A4 as presented in Table 2b.

BOILED-Egg plot, also used in this study, has proven straightforward interpretation and efficient translation to molecular design in a variety of drug discovery initiatives. This graphical output (Figure 7) is an intuitive method to simultaneously predict two key ADME parameters, i.e. the passive gastrointestinal absorption (HIA) and brain access (BBB) relying on WLOGP and TPSA physicochemical descriptors for lipophilicity and apparent polarity [23]. The egg-shaped classification plot includes the yolk (physicochemical space for highly probable BBB permeation) and the white albumen (physicochemical space for highly probable HIA absorption). The outside grey region stands for molecules with properties implying predicted low absorption and limited brain penetration. Furthermore, it provides information on active efflux from the CNS or to the gastrointestinal lumen (an important active efflux mechanism involved in those biological barriers) by colour-coding in which the blue dots signifies a P-gp substrates (PGP+) while red dots for P-gp non-substrate (PGP−) [24].

3.3 Bioactivity Score for Phenylpiperazinyl derivatives A1 – A3

The candidacy of drug leads can be assessed by evaluating their bioactivity scores. Bioactivity scores for organic molecules are interpreted as active (when the bioactivity score > 0), moderately active (when the bioactivity score lies between −5.0 and 0.0), and inactive (when the bioactivity score < −5.0). All the compounds A1-3 (Figure 5) were found to exhibit high or moderate bioactivity across various parameters as shown in Figure 8.

3.4 Structural Analysis of Reactivity of Compounds A1-3 Using DFT Studies

The molecular electrostatic potential (MEP) surfaces mapped over the total density of the geometrically optimized molecules A1-3 are presented in Figure 9a-c.

3.5 Molecular docking study

Dihydrofolate reductase (DHFR) is a crucial enzyme in folate metabolism, responsible for reducing dihydrofolate to tetrahydrofolate, a necessary cofactor for the synthesis of purines, thymidylate, and certain amino acids. Inhibition of DHFR disrupts deoxyribonucleic acid (DNA) synthesis and cell division, making it an important target for antimalarial drugs. Drugs like methotrexate, pyrimethamine, and trimethoprim have historically been used to inhibit DHFR (Pal et al., 2023). However, drug resistance has driven the need to discover new inhibitors, often through computational methods and in silico docking studies like those represented in Figure 11-15.

The toxicity profiles of phenylpiperazinyl derivatives A1, A2, and A3, along with ten reference antimalarial drugs, were evaluated using in-silico methods (Protox II). This analysis predicted low risks of hepatotoxicity, carcinogenicity, immunotoxicity, mutagenicity, and cytotoxicity for A1-A3, unlike nine of the ten reference drugs. Mefloquine, the only exception, showed no toxicity violations. Further analysis of drug-likeness and ADME properties revealed that A1-A3 complied with the Rule of Five, displaying favorable oral bioavailability and gastrointestinal absorption.

4.1 Toxicity Prediction of Designed Drug-Candidate A1-3

The toxicity profiles (hepatoxicity, carcinogenicity, immunogenicity, mutagenicity, and cytotoxicity) [12] of the test compounds A1, A2 and A3 and also the ten standard reference drugs; artesunate, doxycycline, tafenoquine, amodiaquine, artemeter, lumefantrine, primaquine, piperaquine, mefloquine, including chloroquine were obtained through in-silico investigations using Protox II and are as outlined in Table 1 (a-b). Interestingly this analysis reveals that while all the hypothetical compounds A1-3 exhibited non-toxicity, nine out of the ten reference drugs active compounds exhibited one or more violations. The only exception being Mefloquine (Table 1a – b). Therefore, mefloquine was adopted for further virtual studies alongside the hypothetical lead compounds A1-3.

4.2 In-silico Drug-likeness and ADME Predictions

The results presented in Table 2a and Fig. 4 indicate that the hypothetical compounds A1-A3 exhibit characteristics in compliance with the Rule of Five (RO5), and showing zero violation to drug-likeness rules [13]. Additionally, compounds A1-3 presented suitable number of hydrogen bond donors (0–2 for nitrogen-hydrogen and oxygen-hydrogen bonds), and for hydrogen bond acceptors (4–7 for nitrogen or oxygen atoms). These characteristics fall within the recommended ranges (< 5 and < 10, respectively). Their molecular weights were within range and therefore align with the guideline of 150 to 500 g/mol.

The observed topological polar surface area (TPSA) values range from 40.62 to 46.94 which conform to the acceptable range of 20 to 130 Å². Additionally, the number of rotatable bonds in compounds A1-3 does not exceed 9 (Fig. 4). Moreover, the negative log Kp values (-6.46 to -7.19) (Fig. 5) predicts that compounds A1, A2, A3 have appropriate lower permeability through human cells [19].

Interestingly, all the three designed compounds A1, A2, and A3 exhibit excellent gastrointestinal (GI) absorption rates and were permeable to blood-brain barrier (BBB) intercellular movement. These characteristics conform to potential drug leads for both gastrointestinal and nervous system interventions (Table 2b). These occurrence could be attributed to specific structural features, such as the presence of the oxygen atoms of the carbonyl and the hydroxy groups in addition to the nitrogen atoms of the central piperazinyl moiety. Furthermore, while compound A1 is a non-substrate, A2 and A3 as well as the Mefloquine reference drug were identified as substrates for P-glycoprotein (P-gp) based on previous studies [20; 19]. All three lead compounds A1-3 demonstrated good oral bioavailability, with a value of 0.55 comparable with the standard drugs, with no violation [13]. The inhibition of cytochrome P450 (CYP) isoenzymes has been recognised as a major factor contributing to pharmacokinetics-related drug-drug interactions [21]. These cytochromes play crucial roles in the metabolism and elimination of approximately 25% of clinically utilized drugs, involving the addition or removal of specific functional groups through hydroxylation, demethylation and dealkylation processes [22].

Compounds A1-3 act as inhibitors of CYP2D6 while serving as substrates for CYP1A2, CYP2C9 and CYP3A4 as presented in Table 2b. We observe in Fig. 6 the compliance of compounds A1–3 with physicochemical space for oral bioavailability where LIPO (lipophilicity): -0.7 < XLOG3 < + 5.0; SIZE: 150 g/mol < MV < 500 g/mol; POLAR (polarity): 20 A² < TPSA < 130 A²; INSOLU (insolubility): -6 < Log S (ESOL) < 0; INSATU (insaturation): 0.25 < Fraction Csp³ < 1; FLEX (flexibility): 0 < No. of rotatable bonds < 9.

BOILED-Egg plot has proven straightforward interpretation and efficient translation to molecular design in a variety of drug discovery initiatives. This graphical output (Fig. 7) is an intuitive method to simultaneously predict two key ADME parameters, i.e. the passive gastrointestinal absorption (HIA) and brain access (BBB) relying on WLOGP and TPSA physicochemical descriptors for lipophilicity and apparent polarity [23]. The egg-shaped classification plot includes the yolk (physicochemical space for highly probable BBB permeation) and the white albumen (physicochemical space for highly probable HIA absorption). The outside grey region stands for molecules with properties implying predicted low absorption and limited brain penetration. Furthermore, it provides information on active efflux from the CNS or to the gastrointestinal lumen (an important active efflux mechanism involved in those biological barriers) by colour-coding in which the blue dots signifies a P-gp substrates (PGP+) while red dots for P-gp non-substrate (PGP−) [24]. BOILED-Egg plot (Fig. 7) of test compounds releaved compounds A1 (Molecule 1) to be P-gp non-substrate (PGP−) with active influx/efflux from the CNS, while compounds A2 and A3 (Molecule 2 and 3) were both P-gp substrates (PGP+) with active CNS efflux/influx mechanism.

4.3 Bioactivity Score for Phenylpiperazinyl derivatives A1 – A3

The candidacy of drug leads can be assessed by evaluating their bioactivity scores. All the compounds A1-3 (Fig. 5) were found to exhibit high or moderate bioactivity across various parameters. Bioactivity scores for organic molecules are interpreted as active (when the bioactivity score > 0), moderately active (when the bioactivity score lies between − 5.0 and 0.0), and inactive (when the bioactivity score < − 5.0). Specifically, compound A1 displays high activity in one out of the six parameters, with bioactivity scores 0.03 glycoprotein receptors (GPCR) while A2 and A3 only exhibited moderate activities. All the compounds A1-3 demonstrated moderate bioactivity as kinase inhibitors (KI) (with scores ranging from − 0.03 to -0.08), which suggests their potential to inhibit cancer cells [24]. They also exhibited moderate bioactivity as protease inhibitor (PI) (with scores of -0.03 to -0.04) suggesting their potential to impede the maturation of new HIV cells [25].

Additionally, we observed these ligan compounds A1-3 having potentials as moderate nuclear receptors (with scores of 0.18 and 0.26, respectively) and ion channel modulators (ICM) (scores ranging between − 0.08 and − 0.21) showing their ability to interact with hydrophobic molecules such as fatty acids, cholesterol, and lipophilic hormones [26–27]. Furthermore, A1-3 can regulate moderately metabolic enzymes and promoter proteins [28]. We observed their propensity to serve as enzyme inhibitor (with a score of -0.05 to -0.18), implying capability to bind to additional sites on the enzyme [10]. Among the hypothetical ligand compounds, A1 exhibited overall best activity especially as GCPR, ICM, KI, PI and EI, while A2 exhibits the least bioactivity score especially as ion channel modulators (ICM) and nuclear reactor ligand.

4.4 Structural Analysis of Reactivity of Compounds A1-3 Using DFT Studies

The chemical structure of a candidate drug compound influences its effectivenesss. Therefore, the chemical reactivity of the test compounds A1-3 were investigated using DFT analysis. The optimized structures and the calculated infrared spectra (IR) of A1-3 by DFT method using B3LYP functional and 6-31G** basis set are shown in Figs. 9 and 10. Also the obtained calculated parameters are displayed in Table 3. The results revealed that compound A1 has the highest electron affinity (EA) whereas A2 indicated the lowest ionization potential (IP). Ionization potential (IP) and electron affinity (EA) are the eigenvalues of HOMO and LUMO energy levels [29]. Consequently, the obtained IP and EA for the test compounds are the negative numerical values of EH and EL respectively (Table 3). The high EA of compound A1 suggest it has strong affinity to accept electron from other species, whereas compound A2 will readily lose its electron [30] relative to others owing to its lower IP value. This observation points to the fact that the binding energy trend of each compounds A1-3 is a function of EA energy of the molecules.

Furthermore, compound A3 relatively has the highest value of the global chemical hardness (η), chemical electrophilicity (ω) and electronic chemical potential (µ) whereas compound A2 displayed the highest chemical softness S value. This shows that compound A3 is less reactive unlike compound A2 which displayed the lowest chemical hardness and higest softness values. Additionally, the high µ value observed for A3 is suggestive of its good electron acceptor potential comparatively. Generally, a reagent with a high electronic chemical potential µ is a good electron acceptor, whereas a reagent with small electronic chemical potential µ is a good electron donor [31]. The µ values of compounds A1-3 confirms the binding energy trend of the test molecules with (P. falciparum dihydrofolate reductase-thymidylate synthase (PDB ID: 3UM8) receptor for which compound A1 exhibits the higest binding energy.

The electron acceptor ability of test molecules A1-3 were illustrated through conventional hydrogen bonding with the target. For instance, in compound A1, one of the carbonyl groups form hydrogen bonding with Ala₁₆ of the receptor 3UM8 by accepting electrons from the amino group on the residue (Fig. 11). However, compound A3 also shows a non-conventional hydrogen bonding by donating electrons to the carbonyl on Ile₁₆₄ residue (Fig. 14). The electrophilicity ω of the studied scaffolds also support this explanation with A1 having the highest ω value of 2.393 eV.

The molecular electrostatic potential (MEP) surfaces mapped over the total density of the geometrically optimized molecules A1-3 are presented in Fig. 9a-c. The MEP maps revealed the charge distribution around the molecules and describes enhanced electrophilicity or nucleophilicity of the studied molecules [32]. The colour map ranging from the region of red to blue depicts increase in nucleophilicity of the surface. The red regions are areas of nucleophilic attack on the molecular surface while blue regions are available for electrophilic attack. The green regions show areas of neutral charge. The hydrogen bonding shown by A1 and A3 with the target molecule 3UM8 was due to its electrophilic attack on the receptor at residues Ala₁₆ and Ile₁₆₄ respectively. The optimised structures of A1–3 and their calculated infrared spectra are also presented in Figs. 10a-c.

4.5 Molecular docking study

Dihydrofolate reductase (DHFR) is a crucial enzyme in folate metabolism, responsible for reducing dihydrofolate to tetrahydrofolate, a necessary cofactor for the synthesis of purines, thymidylate, and certain amino acids. Inhibition of DHFR disrupts DNA synthesis and cell division, making it an important target for antimalarial drugs. Drugs like methotrexate, pyrimethamine, and trimethoprim have historically been used to inhibit DHFR (Pal et al., 2023). However, drug resistance has driven the need to discover new inhibitors, often through computational methods and in silico docking studies like those represented in Fig. 11–15.

The findings from the docking simulations of ligands A1-3 and reference drugs active compounds against P. falciparum dihydrofolate reductase-thymidylate synthase are summarized in Table 4. The binding energies for compound A1 (-9.20 kcal/mol) is effectively comparable with the best reference drug, Artesunate (-9.20 kcal/mol). Furthermore, the reduced derivative A2 though performing below three of the reference drugs Artesunate, Doxycycline and Tafenoquine, nevertheless it performs better than seven other reference drugs active compounds.

The extracted 3D structures reveal interactions of the best ligand and reference drug with P. falciparum dihydrofolate reductase (PDB ID: 3UM8) are presented in Fig. 11. This reveals the binding of specific amino acid residues responsible for the reduction of DHFR to THFR for protein’s translation, normal mutations and growth (Alanine to Valine), for reproduction (Phenylalanine and Leucine), for resistance, invasion and survival (Ileucine and Asparagine) and reproduction (mutations of Threonine to Serine /Asparagine). Table 5 and Fig. 11 revealed unique interactions within the A1-3UM8 and Artesunate-3UM8 complexes exhibiting inhibition of similar amino acid residues Ala₁₆, Leu₄₀ and Ser₁₀₈. Other variant residues are Leu₄₆ and Val₁₉₅ for A1-3UM8, and Ile₁₆₄ and Phe₅₈ for Artesunate-3UM8 interactions. Interactions shown by derivative A2-3 and the non-toxic reference drug, Mefolquine are also presented in Fig. 12.

The A1 derivative forms multiple interactions with DHFR (Fig. 11), as seen in the bond length, residues, hydrophobic surface, and solvent accessibility interactions. Hydrophobic residues like leucine (Leu) and alanine (Ala) play a central role in stabilizing the interaction, with Leu 40 and Ala 16 being particularly important. Serine (Ser) at position 195 is also highlighted, suggesting a hydrogen-bonding interaction, which contributes to the overall stability of the complex. The bond lengths reflect proximity to key active site residues, indicating effective binding within the enzyme’s active pocket. The hydrophobic surface shown around the ligand suggests that the interaction is predominantly non-polar, which is beneficial for binding affinity and inhibition.

Artesunate, a well-known antimalarial drug, forms specific interactions with DHFR (Fig. 12), as indicated by the bond length and residue interactions. Leucine (Leu 195) and isoleucine (Ile 46) seem to contribute to hydrophobic interactions that stabilize the binding. The image indicates that artesunate’s interaction with DHFR involves both hydrophobic regions and solvent-accessible areas. The solvent accessibility surface interaction, particularly the exposed hydrophilic areas, suggests that artesunate could form additional interactions with surrounding water molecules or other active-site residues, enhancing the drug's binding affinity.

For the A2 derivative (Fig. 13), there are significant binding interactions with residues such as Leu 40 and Ile 46. The positioning of these residues, coupled with their hydrophobic properties, suggests that the binding of A2 is largely driven by van der Waals forces and hydrophobic effects. The relatively close bond lengths imply a strong interaction between the drug and the DHFR active site. This could enhance A2’s ability to inhibit DHFR by blocking the active site, preventing the binding of natural substrates necessary for folate metabolism.

The A3 derivative appears to engage in more complex interactions compared to A1 and A2. In addition to hydrophobic residues like Leu 40 and Ile 46, this derivative shows enhanced solvent area accessibility interactions, as seen in the visualization (Fig. 14). This suggests that A3 may form water-mediated hydrogen bonds or interact with nearby polar residues in the enzyme, potentially leading to greater specificity and binding strength. The hydrophobicity of A3 is balanced by its interactions with solvent-accessible regions, which may contribute to improved solubility and bioavailability.

Mefloquine, another well-established antimalarial drug, interacts primarily with hydrophobic residues such as Leu 195 and Ile 46 as shown in Fig. 15. The bond length measurements indicate close interactions, which could contribute to the strong binding affinity observed with DHFR. The solvent accessibility surface interaction reveals that mefloquine has portions exposed to the surrounding solvent, which may aid in its transport and diffusion within the biological system. The hydrophobicity of the interaction suggests that the drug remains well-anchored in the DHFR active site, preventing substrate access and inhibiting the enzyme's activity.

4.6 Significance of A1 – A3 bond lengths, residues, hydrophobicity and solvent interactions

The bond lengths between drug derivatives and DHFR active site residues are crucial in determining the strength of the interaction. Shorter bond lengths typically indicate stronger interactions, which can translate to better inhibition of DHFR. Residues such as leucine, isoleucine, alanine, and serine frequently participate in these interactions, either through van der Waals forces or hydrogen bonding. Hydrophobic residues like Leu and Ile are particularly significant in stabilizing the drug-enzyme complex by creating a favorable non-polar environment within the binding pocket. Hydrogen bonds involving serine residues, on the other hand, contribute to specificity and binding strength.

Hydrophobic interactions dominate the binding of these derivatives to DHFR, as evidenced by the brown areas surrounding the ligands in the images. Hydrophobicity plays a key role in drug binding since it often correlates with higher affinity and reduced off-target interactions. The balance between hydrophobicity and solvent-accessibility is crucial for determining a drug's bioavailability and solubility. Compounds that interact strongly with hydrophobic residues but still maintain some degree of solvent interaction, like A3 and mefloquine, have better pharmacokinetic properties, such as absorption and distribution within the body.

This study explores the development of novel antimalarial compounds in response to growing resistance in Plasmodium falciparum against antifolates targeting dihydrofolate reductase (DHFR). Three hypothetical compounds—piperazine-1,4-diylbis(phenylmethanone) (A1), and its mono- and di-alcohol derivatives (A2, A3)—were virtually screened for their toxicity, drug-likeness, and inhibitory potential against bifunctional DHFR-thymidylate synthase (DHFR-TS). These compounds were compared to ten standard antimalarial drugs using computational tools such as Molinspiration, SwissADME, Protox II, and PyRx AutoDock Vina.

The results showed that compounds A1, A2, and A3 are non-toxic, although they exhibit some immunotoxicity. A1 demonstrated the strongest inhibitory activity, with a binding energy of -9.20 kcal/mol, comparable to Artesunate, binding to key amino acid residues Ala16, Leu40, and Ser108, which are responsible for DHFR’s enzymatic reduction. A2 and A3 also exhibited strong binding scores of -8.8 kcal/mol and − 8.2 kcal/mol, respectively, outperforming Mefloquine (-7.9 kcal/mol), which was the only reference compound with non-toxicity against human cells.

All three compounds obeyed Lipinski’s rule of five, displaying good drug-like properties and high gastrointestinal absorption. Density Functional Theory (DFT) studies revealed that A1 had the highest binding energy, while A3 showed better pharmacokinetic properties, including absorption and distribution. The study concludes that compounds A1 and A3 hold significant promise as non-toxic, effective candidates for antimalarial drug development. They could provide safer alternatives for malaria treatment, particularly in overcoming resistance issues with existing antifolates. These findings lay the groundwork for future in vitro and in vivo testing to advance these compounds toward clinical use.

#HA: Number of Hydrogen Bond Acceptors

#HD: Number of Hydrogen Bond Donors

#rot_b: Number of Rotatable Bonds

Å: Ångström (unit of length, 10^-10 meters)

ADME: Absorption, Distribution, Metabolism, and Excretion

B3LYP: Becke, 3-parameter, Lee-Yang-Parr (functional used in DFT calculations)

BBB: Blood-Brain Barrier

BOILED-Egg: Brain Or IntestinaL Estimate D Permeation Graph

CNS: Central Nervous System

CYP: Cytochrome P450

DFT: Density Functional Theory

DHFR: Dihydrofolate Reductase

DHPS: Dihydropteroate Synthase

DNA: Deoxyribonucleic Acid

EA: Electron Affinity

EGap: Energy Gap

EH: Energy of HOMO

EHOMO: Energy of the Highest Occupied Molecular Orbital

EL: Energy of LUMO

ELUMO: Energy of the Lowest Unoccupied Molecular Orbital

ESI: Electrospray Ionization

ESOL: Estimation of Aqueous Solubility

GPCR: G-Protein Coupled Receptor

HIA: Human Intestinal Absorption

HOMO: Highest Occupied Molecular Orbital

IP: Ionization Potential

IR: Infrared Spectroscopy

KI: Kinase Inhibitor

LIPO: Lipophilicity

Log S: Logarithm of Solubility

LUMO: Lowest Unoccupied Molecular Orbital

MEP: Molecular Electrostatic Potential

MMFF: Merck Molecular Force Field

MV: Molecular Volume

NMR: Nuclear Magnetic Resonance

PAINS: Pan-Assay Interference Compounds

PI: Protease Inhibitor

P-gp: P-glycoprotein

PDB: Protein Data Bank

P. falciparum: Plasmodium falciparum

Protox II: Toxicity Prediction Webserver

RO5: Rule of Five

RSB: Root-Mean-Square Deviation (upper and lower bounds)

S: Global Softness

SMILES: Simplified Molecular Input Line Entry System

THFR: Tetrahydrofolate Reductase

TPSA: Total Polar Surface Area

TS: Thymidylate Synthase

WHO: World Health Organization

WLOGP: Wildman-Crippen LogP (octanol-water partition coefficient)

XLOGP: Logarithm of the Partition Coefficient (Octanol/Water)

ZPE: Zero-Point Energy

χ: Electronegativity

ω: Electrophilicity

Ω: Electrophilicity Index

ω-: Negative Electrophilicity

ω+: Positive Electrophilicity

μ: Chemical Potential

η: Global Hardness

The author (s) declared that this article does not reflect the policies as well as views of anyone or organization.

Ethical approval and consent to participate

Not applicable

Consent for publication

Not applicable

Availability of data and material

Not applicable

Competing interest

The authors of the article stated that there is no competing interest.

Funding

No funding was received for the preparation of this manuscript.

Authors’ contribution statements.

OSA carried out virtual screening studies; LAA and OBF designed and supervised the studies; and LAA provide instrumentation for experimental studies, MOR carried out the DFT analysis and interpretation. OSA and MOR wrote the first manuscript draft. KO-F, LAA and OBF critically reviewed and edited the manuscript.

Acknowledgements

The authors wish to appreciate the Department of Chemistry University of Lagos for supporting this research.

Clinical trial number

Not applicable

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Tables 1 to 3 are available in the Supplementary Files section

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In-silico Evaluation of Phenylpiperazinyl Derivatives as Inhibitors of P. falciparum Dihydrofolate Reductase-Thymidylate Synthase: A Potential New Antimalarial Drug Candidate

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Abstract

Figures

1.0 Background

2.0 Methods

2.1 Toxicity prediction of hypothetical compounds A1-3

2.2 Conversion of selected reference antimalarial active compounds into pdbqt format

2.3 Selection of P. falciparum dihydrofolate reductase-thymidylate synthase receptor

2.4 In-silico drug-likeness and ADME predictions

2.5 Bioactivity scores of Phenylpiperazinyl derivatives A1 – A3

2.6 DFT Studies of Phenylpiperazinyl derivatives A1 – A3

2.7 Molecular docking study

3.0 Results

4.0 Discussion of results

4.1 Toxicity Prediction of Designed Drug-Candidate A1-3

4.2 In-silico Drug-likeness and ADME Predictions

4.3 Bioactivity Score for Phenylpiperazinyl derivatives A1 – A3

4.4 Structural Analysis of Reactivity of Compounds A1-3 Using DFT Studies

4.5 Molecular docking study

4.6 Significance of A1 – A3 bond lengths, residues, hydrophobicity and solvent interactions

5.0 Conclusions

Abbreviations

Declarations

References

Tables

Additional Declarations

Supplementary Files

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