In silico analysis of doxorubicin analogues conjugated to polysorbate 80 for sustained and targeted delivery to the brain

In silico analysis of doxorubicin analogues conjugated to polysorbate 80 for sustained and targeted delivery to the brain | 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 In silico analysis of doxorubicin analogues conjugated to polysorbate 80 for sustained and targeted delivery to the brain D. K. S. Lekshmi, Arsha S Nair, A. Jayakrishnan, Sandhya Karakkadparambil Sankaran This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5910276/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 Drug discovery for the treatment of central nervous system (CNS) diseases is a highly complex affair, especially due to the blood brain barrier (BBB) restricting the entry of many drugs. In the present study, we utilized in silico studies to screen twelve derivatives of doxorubicin (DOX) against seven tumor proteins. The DOX ligands were modelled and optimized using Density Functional Theory (DFT), and the binding affinity was evaluated. Seven tumor proteins were selected for docking and their PDB IDs were: 3LGL, 2IG0, 3LGF, 4RG2, 3PG7, 3LH0 , and 4HBM . Later, an attempt was made to deliver these modified DOX ligands into the brain using a non-ionic surfactant, polysorbate 80 (PS80). Modified DOX ligands-PS80 conjugated structures were made using Semi empirical methods. After categorizing 12 DOX ligands and 12 DOX-PS80 conjugated ligands, the top-ranked three DOX ligands ( R6 , R4 , and R9 ) and their conjugates with PS80 ( R6-PS80 , R4-PS80 , and R9-PS80 ) underwent comprehensive molecular dynamics (MD) analysis. The best DOX ligands and their conjugated structures with PS80 were subjected to a thorough examination for structural stability (RMSD), intermolecular hydrogen bond (H-bond) interactions, effect of solvent accessibility (SASA), and compactness (Rg). The results demonstrated that these ligands and conjugated ligands exhibited folding behavior and were incredibly stable. Furthermore, we employed MM/GBSA to figure out the docking scores of the top three DOX-PS80-conjugated ligands. In silico pharmacokinetic parameters of all compounds were analyzed using the ADMET AI server. It is shown that attaching to PS80 could potentially decrease the overall toxicity of DOX ligands. These findings indicate that the modified DOX ligands, when combined with PS80, have the potential to cross the BBB and could be used for further in vitro and in vivo evaluation. Doxorubicin analogues Polysorbate 80 Molecular docking ADMET Molecular dynamics MM/GBSA in silico Blood brain barrier CNS disorders Drug Targeting Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1. Introduction Cancer has emerged as the world's second main cause of death. Although early detection of cancer can save lives, it is not always feasible. A tumor could be malignant, pre-carcerous, or benign. Brain tumors are the main cause of cancer mortality in children and remain a challenge to treat, despite advancements in surgery and adjuvant therapies. [ 1 ] Brain tumors can cause various psychiatric symptoms, including depression, personality changes, abulia, hallucinations (both auditory and visual), mania, panic attacks, and forgetfulness. [ 2 ] Certain individuals with brain tumors, particularly those with very small tumors, do not exhibit any symptoms. The main hurdle to healing many neurological disorders is the BBB, a polarized layer of endothelial cells that hinders the central nervous system (CNS) from receiving systemically introduced diagnostic or therapeutic substances. [ 3 ] BBBplays a crucial function in preventing pathogenic organisms and undesired substances from entering the brain, thus making it difficult to target therapeutic agents for CNS illnesses. [ 4 – 6 ] This barrier is made up of an array of distinct kinds of cells, among which are microglial cells, astrocytes, pericytes, and endothelial cells. [ 7 ] Despite breakthroughs in comprehending the molecular structure and physiology of the BBB, as well as therapeutic approaches to treat CNS diseases, effective delivery of potential therapies remains a challenge. [ 8 ] The brain endothelium is the fundamental morphological and functional component of the BBB. [ 9 ] The tight junctions (TJ) between adjacent endothelial cells, produced by complex transmembrane proteins, hinder drug transfer into the brain via bloodstream. [ 6 – 10 ] Over the years, considerable research has been reported on the utilization of polymeric, liposomal, and inorganic nanoparticulate (NP) drug delivery systems for transferring an array of therapeutic agents across the BBB. [ 11 – 18 ] The NP approach entails many challenges, specifically when targeting the brain. [ 16 – 19 ] It has been reported that NPs coated with the non-ionic surfactant polysorbate 80 (PS80) may penetrate the BBB and ferry an array of drugs across the BBB. [ 20 – 22 ] Even though PS80-coated NPs exhibited BBB permeability, additional challenges persist, like the toxicity of polycyanoacrylates or the metals or inorganic substances utilized as NPs and their deleterious repercussions. It is a potential threat that NPs and their breakdown products will accumulate in the brain capillaries. The drug content in NPs is typically only a small fraction of the total weight of the NPs. Additionally, the processes used to fabricate and incorporate drugs into the NPs often involve organic solvents, which can be challenging to eliminate completely. This is evident even when using biocompatible and biodegradable NPs made from poly(lactic acid) or poly(glycolic acid). Glutathione-pegylated liposomal DOX (2B3-101) has been explored as a novel treatment for individuals suffering brain cancer. It relies on a previously commercialized pegylated liposomal DOX, enhanced with a glutathione coating that safely improves drug transport across the BBB. Glutathione-pegylated liposomal DOX facilitates the effective ferry of DOX to brain tumors and could serve as a viable new therapeutic option for the treatment of brain malignancies. [ 23 ] It has been demonstrated that PS80-coated NPs containing DOX may penetrate through the intact BBB and achieve therapeutic concentrations in the brain. [ 24 ] Moreover, interaction with nanoparticles might mitigate the adverse outcomes of toxic drugs like DOX. [ 25 ] We are aware that DOX is not the best drug to treat brain tumors. The key objective of the present research is to demonstrate that DOX-PS80 conjugated ligands can effectively cross the BBB for the treatment of brain tumor. We designed twelve DOX ligands and optimized them via DFT. To find the best drug out of twelve DOX ligand, we docked them with seven tumor proteins against brain tumor utilizing molecular docking analysis. To gain further insight into drug delivery to the brain using best DOX ligands, we conjugated them with PS80. Further, Molecular Dynamics simulation studies have been done for the top-ranked DOX ligands and their DOX-PS80 conjugated ligands. Afterwards, the ADMET properties of all compounds were evaluated to obtain valuable insights for drug design. 2. Methods and Materials 2.1 Preparation of Proteins The X ray structures of seven tumor proteins were used as receptors in the current study. The corresponding Protein Data Bank codes are: 3LGL, 2IG0, 3LGF, 4RG2, 3PG7, 3LH0 , and 4HBM . The experimental findings reveal that all of these proteins have good resolution, no mutations and have better R values. The structures of the target proteins were acquired from RCSB PDB. [ 26 ] The protein structures were thoroughly examined for any missing residues or repetitions on the amino acid residues using the Spdbv software. [ 27 ] Visualization were performed on Discovery Studio (DS) visualization software, [ 28 ] pyMOL [ 29 ] and docking were performed on Autodock tools. [ 30 ] All PDB files of downloaded raw proteins were opened in DS software 2024 and subsequently we have eliminated water molecules, solvent, inhibitors and ions from it. The proteins underwent docking tests with twelve DOX ligands after being saved in PDB format. The 3D structures of the target proteins taken for the present study are shown in Fig. 1 . 2.2 Preparation of Ligands DOX is an anthracycline antibiotic produced by Streptomyces peucetius. It inhibits topoisomerase II, which is responsible for DNA and RNA production. This medicine is used to treat a wide range of cancers, including carcinomas, sarcomas, leukemias, and lymphomas. [ 31 ] DOX's therapeutic use is limited by a number of issues, including inherent and acquired drug resistance and dose-dependent cardiotoxicity. Hundreds of DOX compounds have been reported in an attempt to develop analogues with decreased host toxicity and improved antitumor activity. [ 32 ] The reference drug DOX ( R1 ) was acquired from PubChem. [ 33 ] Based on the sdf file of DOX ( R1 ) obtained from PubChem, and we used Chemcraft software to model other structures and optimize these structures using DFT in log format. DOX ( R1 ) and its eleven analogues ( R2-R12 ) have been created as potential antitumor drugs by adding the following alkylating or latent alkylating substituents, R, on the 3'-position of the daunosamine sugar. [ 32 ] We chose electron withdrawing and electron donating groups as functional groups to modify these structures. The modelled structures are summarized in Chart 1 . 2.3 Density Functional Theory Studies (DFT) The modeled structures of twelve DOX ligands were optimized using B3LYP/6-31G(d,p) density functional theory (DFT). Frequency calculations were performed to conform geometrical optimization. The computations are carried out using the Gaussian software G9 program package. [ 34 ] The optimized 3D structures of twelve DOX ligands are shown in Fig. 2 . 2.4 Preparation of PS80, and twelve DOX-PS80 conjugated ligands PS80 is a nonionic surfactant that is utilized extensively in the production of protein pharmaceuticals. [ 35 ] Nanoparticles (NPs) wrapped with PS80, had been demonstrated to penetrate the BBB and transport several medications to the brain. [ 20 – 22 ] In our study, we modelled the structure of PS80, and twelve DOX-PS80 conjugated ligands using Chemsketch software and optimized these structures using PM6 Semi empirical method in log format. The computational efficiency of the fundamental PM6 approach enables computations that would be unfeasible with DFT or Hatree-Fock (HF), such as protein shape optimizations or large-system vibrational studies. [ 36 ] The computations were carried out using the Gaussian software G9 program package. The optimized 3D structures of DOX-PS80 conjugated ligands are shown in Fig. 3 . 2.5 ADMET studies Computational modeling studies are the preferred method for assessing the pharmacokinetics and drug likeness of novel chemical entities. [ 37 , 38 ] The focus lately has been on using artificial intelligence (AI) and machine learning (ML)-based prediction software packages to predict the absorption (A), distribution (D), metabolism (M), excretion (E), and toxicity (T) (ADMET) properties of novel therapeutic molecules among various computational resources. [ 38 , 39 ] Many compounds were hazardous and failed in in vitro testing, preventing their development into drugs. In our work, we utilized an ADMET AI web server to gain a more profound comprehension of the ADMET characteristics of twelve DOX ligands, PS80, and twelve DOX-PS80 conjugated ligands. The ADMET scores of each compound were compared, and the best results are reported. 2.6 Molecular Docking Studies Molecular docking is important in SBDD because it predicts the binding orientation and affinity of a drug within a target binding site. This approach is intended to precisely predict the experimental binding mode and affinity. [ 40 ] In our work, docking was conducted using Autodock Tools. Before docking, we removed water molecules, inhibitors, and heteroatoms from the seven target proteins. The protein structures were meticulously inspected for missing residues and repetitions in the amino acid sequences using Spdbv software. Docking studies with twelve DOX ligands were performed on the selected protein structures in their PDB formats that were acquired using Autodock Tools. The default grid box size was set to 40 Å × 40 Å × 40 Å, with the grid center corresponding to the coordinates of the reference inhibitors in the target proteins. The chosen inhibitors centroid was circumscribed by a spherical, transparent binding sphere, which was subsequently used to figure out the binding site traits. The findings were then evaluated to determine which binding modes received the greatest scores. Further, DS visualization software and pyMOL were utilized to obtain 2D and 3D perspectives. The conformations were examined to determine interactions such as van der Waals and hydrogen bonding between target protein receptors, and ligands. 2.7 Molecular Dynamics Simulation Molecular dynamics (MD) simulations of the top-docked protein-ligand complexes were implemented with Gromacs 2021 software. [ 41 ] SwissParam [ 42 ] furnished the forcefield for all ligands, while the CHARMM36 all-atom forcefield was put to use for the protein. The dodecahedron box was composed of solvent water and, the SPC water model was processed. NVT, NPT, and energy minimization were executed via the 5000-step steepest descent approach. The system was adjusted to equilibrate at 300 K and 1 atm of pressure via a modified Berendsen thermostat and a V-rescale for temperature coupling. The MD integrator was configured to utilize the leap frog approach to update the atoms positions and velocities. MD simulations were conducted for 100 ns with 50,000 frames per simulation. RMSD, RMSF, Rg, SASA, and H bonds, can compute root mean square deviation, root mean square fluctuation, radius of gyration, solvent accessible surface area, and intermolecular hydrogen bonds in xvg format. The QtGrace software was utilized to plot the data. 2.8 MM/GBSA calculations MM/GBSA was employed for determining the binding energy and decomposition free energy contributions of protein-ligand complexes. [ 43 ] This approach has been proven to balance computational efficiency and accuracy, particularly when interacting with large systems. [ 44 ] The gmx_MMGBSA tool has been utilized to find out the binding energy of three DOX-PS80 conjugated ligands. Using this tool, we calculated the Gibbs binding free energy (ΔG) from the last 5000 to 10000 frames (with a 500 interval) of the trajectory file. The formula for calculating ΔG was, ΔG = ΔH-TΔS = ΔE MM + ΔG solv –TΔS Where, ΔE MM - total gas-phase molecular mechanics ΔG solv - summation of polar and nonpolar solvation free energies TΔS - conformational entropy upon ligand bonding. 3. Results and Discussion 3.1 Molecular Docking Analysis Table 1 Docking scores of Protein 3PG7 with best DOX ligands DOX Ligands Molecular Formula Binding Energy (kcal/mol) R4 C 34 H 32 BrNO 14 S -11.10 R6 C 34 H 32 ClNO 14 S -12.02 R9 C 34 H 32 FNO 14 S -10.19 In this work, seven tumor proteins, along with their docking scores with DOX ( R1 ) and its elven analogues ( R2-R12 ), were evaluated and their comprehensive molecular interactions were examined. The structure of DOX analogue is not available; based on the sdf file of DOX ( R1 ) obtained from PubChem, we utilized Chemcraft software to model other structures and subsequently optimized it with DFT. DOX was modified to enhance the antitumor efficiency and to reduce its toxicity. We chose electron withdrawing and electron donating groups as functional groups to modify these structures. The binding affinity of the modelled DOX ligands with the target proteins that were acquired using Autodock Tools is summarized ( Table SI1 ). The Autodock softwares docking analysis results between seven tumor proteins and twelve DOX ligands revealed binding energies ranging from − 5.70 kcal/mol to -12.02 kcal/mol. Out of the twelve DOX ligands, ten DOX ligands exhibited binding affinity ranging from − 8.16 kcal/mol to -12.02 kcal/mol for specific target proteins. The ligand R6 exhibited the most significant binding affinity with the protein 3PG7 , at -12.02 kcal/mol. Ligand R6 demonstrated the best with four of the identified proteins: 2IG0 (-8.16 kcal/mol), 4RG2 (-10.53 kcal/mol), 3PG7 (-12.02 kcal/mol), and 4HBM (-9.50 kcal/mol). Ligand R4 had the best outcomes with two of the proteins tested: 3PG7 (-11.10 kcal/mol) and 4RG2 (-10.06 kcal/mol). Ligand R9 demonstrated the best results with 3PG7 (-10.19 kcal/mol). Ligand R8 displayed the best binding affinity with two of the chosen proteins: 3LGF (-9.35kcal/mol) and 3LGL (-7.68 kcal/mol). Ligand R7 interacted most strongly with 3LGF (-9.69 kcal/mol) and 4HBM (-8.07 kcal/mol). Ligand R12 exhibited better binding affinity with 2IG0 (-7.29 kcal/mol). On comparing docking results with the reference drug ligand R1 , Ligands R6 and R12 had enhanced binding affinities for the protein 2IG0 compared to ligand R1 . Ligands R2 , R3 , R5 , R6 , R7 , and R8 demonstrated better binding affinities for the protein 3LGF over the reference drug ligand R1 . For protein 3LGL , ligand R8 exhibited better docking scores than ligands R1 . For the protein 4RG2 , ligands R4 , R6 , and R9 revealed higher docking scores than R1 . When it pertained to the protein 3PG7 , ligands R2 , R3 , R4 , R5 , R6 , R7 , R8 , R9 , R10 , R11 , and R12 possessed a higher binding affinity than ligand R1 . Ligand R2 bound to the protein 3LH0 more strongly than ligand R1 . When it came to protein 4HBM , ligands R2 , R3 , R4 , R5 , R6 , R7 , R8 , R9 , R10 , R11 , and R12 exhibited better docking scores than the reference drug ligand R1 . Later, we attempted to deliver these twelve DOX ligands into the brain using PS80. We made the DOX ligands with PS80 conjugated structures using semi empirical method. DOX ligands were conjugated with PS80, since they are unable to cross the BBB under normal conditions. Furthermore, DOX ligands conjugated very well with PS80. Among these twelve DOX ligands docked with seven tumor proteins, ligands R6 , R4 and R9 exhibited notable binding affinity with protein 3PG7 ( Table 1 ) . The 3D structure of best docked complexes is summarized in Fig. 4 and the 2D interaction of protein 3PG7 with best three DOX ligands are summarized in Fig. 5 . Furthermore, the corresponding binding energies of these ligands exceed − 10.00 kcal/mol. We have observed that ligand R6 interacts with the TYR1618, TYR1587, and TYR1668 residues of 3PG7 via hydrogen bonding (Fig. 5 a). A pi-sigma interaction can also be seen for the LEU1679 residue. Numerous van der Waals and a pi-sulfur interactions can also be observed. Furthermore, VAL1606, ILE1620, PHE1633, LEU1638, LEU1676 and PHE1642 also participated alkyl, pi-alkyl and pi-pi stacked interactions. Ligand R4 showed interactions with amino acids (TYR1650, TYR1618, TYR1668 and TRP1641) of 3PG7 , resulting in the presence of a hydrogen bond, as well as hydrophobic interactions (alkyl, pi-alkyl, pi-pi stacked and pi-sigma) with VAL1622, ILE1620, PHE1645, ALA1649, LEU1679, LEU1638, PHE1642, and PHE1633 residues (Fig. 5 b). A pi-sulfur interaction can be seen for TYR1587 residue. Certain van der Waals interactions were also discernible. Ligand R9 interacted with amino acids LEU1679, GLY1678 and TYR1618 of 3PG7 (Fig. 5 c), resulting in an H-bond and a hydrophobic contact (alkyl, pi-alkyl and pi-sigma) with amino acids LEU1685, VAL1656, ILE1658, LEU1624 and VAL1622, as well as various van der Waals interactions can also be observed. The 3D interaction diagram with a surface color by hydrogen bond type indicated that the H-bond donors and H-bond acceptors of best three DOX ligands docked with protein 3PG7 and the structure is given in SI. 3.2 ADMET Analysis The pharmacokinetic parameters of a drug have a significant impact concerning its toxicity, brain penetration, carcinogenicity and bioavailability. In silico pharmacokinetic parameters of all compounds were analyzed via the ADMET AI server and the best results are reported. The radial plots from ADMET- AI server ( Figure SI2 and Figure SI3 ) summarize the important Admet features, such as bioavailability, solubility, toxicity, BBB, and hERG safety. The ADMET AI prediction clearly suggested that all of these compounds are non-toxic and BBB safe. The ADMET AI prediction of all these compounds were compared and tabulated ( Table SI2 , Table SI3 and Table SI4 ). It is important to notice that DOX-PS80 conjugated ligands exhibited a higher bioavailability value when compared with free DOX ligands. The BBB penetration scores of the DOX-PS80 conjugated ligands were better than the BBB penetration scores of free DOX ligands. This implies that these twelve DOX-PS80 conjugated ligands are CNS active compounds (BBB > 0.40). Additionally, these twelve conjugated ligands showed good human intestinal absorption (HIA) scores of 1.00, indicating the high absorption capacity of the drug. CYP (cytochrome P450) inhibition prediction of all compounds exhibited values in the range of 0 to 1 indicating a strong inhibiting tendency. The CYP enzyme plays a crucial role in the drug efficacy as it is responsible for drug metabolism. Further, all compounds exhibited less carcinogenicity and also showed a high excretion rate in terms half-life. These findings indicated that these twelve DOX-PS80 conjugated ligands are non-toxic and have the potential to cross the BBB. 3.3 Molecular Dynamic Simulation To assess the compactness, flexibility, conformations, and stability of ligand-bound proteins, we utilized the CHARMM36 all-atom forcefield to run MD simulations. To accomplish this, we chose the top three DOX ligands and three DOX-PS80 conjugated ligands that docked with protein 3PG7 , and we also assessed the structural changes that the ligands induced in the protein. The root-mean-square deviation (RMSD) profile of docked complexes, illustrating how stable the protein-ligand complex is, is shown in Fig. 6 . The RMSD graph of Fig. 6 demonstrated that every complex exhibited fluctuations beneath 0.5 nm. RMSD values gradually varied between 0.1 nm and 0.3 nm until 15 ns, indicating the rigidity of the protein-ligand complexes. After 20 ns, proteins with R6 , R9 , and R4-PS80 had RMSD < 0.25 nm, indicating that they were quite rigid. The RMSD of Protein with R6-PS80 displayed stability up to 60 ns and there was a slight fluctuation within 0.35nm. However, in contrast to the others, protein 3PG7 with R4 and R9-PS80 exhibited slight variations. Rg conveys insight information on the moment of inertia of atoms of proteins from the center of mass, which in turn indicates stability and compactness. All six docked complexes exhibited only very slight fluctuations, as shown in Fig. 7 . Rg values for all six docked complexes varied from 2 nm to 2.1 nm. Throughout the 100 ns simulation run, the complexes Rg values were consistent, suggesting that the formed complexes were more rigid and compact. SASA measured the volume expansion of the protein throughout the interaction with the ligand, which varied to various extents in each system. Consequently, it is employed to figure out which specific regions of a protein should be permeable to a water solvent. A higher SASA value signifies a greater level of volume expansion of a protein, and minimal changes are expected over the simulation period. The influence of solvent molecules on the residues of the conjugated ligands-protein complexes, ligands-protein complexes are clearly illustrated from the SASA plots. The SASA values were altered when the protein 3PG7 interacted with three DOX-PS80 conjugated ligands and three DOX ligands. The average SASA values for the six complexes were in the range of 140 to 165 nm 2 , as illustrated in Fig. 8 . Higher SASA values indicated a stronger solvation effect and a larger molecular size of the formed complexes. Furthermore, we assessed the hydrogen bonds that formed between proteins-ligands complexes, proteins-conjugated ligands complexes across the simulation. Hydrogen bond analysis is vital to comprehending the stability of ligand-protein and conjugated-ligand-protein interactions. Conformational stability is always improved by having the maximum number of hydrogen bonds. H-bonds serve a substantial role in determining selectivity and binding affinity. From Fig. 9a to 9f , ligand R6 has an average H-bond of 2, ligand R4 has 3, ligand R9 shows 2, R6-PS80 conjugated ligands exhibit 6, R4-PS80 conjugated ligand displays 2 and R9-PS80 conjugated ligand exhibit 4. The maximum H-bond that appeared for ligand R6 is 8 (Fig. 9a), ligand R4 has 7 H-bond (Fig. 9b), ligand R9 displays 10 H-bond (Fig. 9c), R6-PS80 conjugated ligand shows 15 H-bond (Fig. 9d), R4-PS80 conjugated ligand exhibits 8 H-bond (Fig. 9e) and R9-PS80 conjugated ligand shows 10 H-bonds (Fig. 9f) during the simulation. 3.4 MM/GBSA Analysis Table 2 Total binding free energy and its components of protein 3PG7 with three DOX-PS80 conjugated ligands Parameters R6-PS80_3PG7 R4-PS80_3PG7 R9-PS80_3PG7 Van der Waals energy -169.36 -149.64 -121.15 Electrostatic energy -48.09 -34.11 -28.33 Generalized born model 106.15 93.20 76.89 Surface area -22.50 -21.25 -17.12 Total binding free energy -133.72 -111.81 -89.70 The total binding free energy and its components of protein 3PG7 with three DOX-PS80 conjugated ligands are illustrated in Table 2 . In the present study, the observed binding free energy for the R6-PS80_3PG7 complex was − 133.72 kcal/mol. The binding free energy for the R4-PS80_3PG7 complex was − 111.81 kcal/mol, while the R9-PS80_3PG7 complex showed a binding free energy of -89.70 kcal/mol. The R6-PS80 conjugated ligand exhibited higher binding affinity to the protein 3PG7 than the R4-PS80 and R9-PS80 conjugated ligands. Van der Waals and electrostatic energy serve a vital part in energy minimization, while de-solvation energy (generalized Born model) increases overall energy. The 2D interaction diagram of protein 3PG7 with three DOX-PS80 conjugated ligands are shown in Fig. 10 . We have observed that the R6-PS80_3PG7 complex formed four H-bonds with amino acids such as TYR1618, VAL1653, PHE1645, and TRP1641. It also had halogen bond interaction with amino acid ASP1600, as well as salt bridge interaction with LYS1640 residue. π-π interaction (PHE-1633 and PHE1592 residues, respectively) were also present in this complex (Fig. 10 a). Figure 10 b shows that the interaction of the R4-PS80_3PG7 complex, which contained six H-bonds through amino acids ARG1684, VAL1656, TYR1657, TYR1659, and TYR1668. Halogen bond interaction (HIS1672) and salt bridge interaction (LYS1640) were also present in this complex. Additionally, amino acids such as GLU1667, and LYS1670 also participated π-π interaction. In the R9-PS80_3PG7 complex, there were four hydrogen bonds involved in amino acids VAL1653, ARG1590, ARG1684, and TYR1668 (Fig. 10 c). Moreover, GLU1667, LYS1640, LYS1670 TRP1641, and PHE1637 residues were involved in salt bridge and π-π interactions. 4. CONCLUSION The present study examined twelve DOX ligands targeting seven tumor proteins through molecular docking. DOX ligands were modified with electron withdrawing and electron donating groups and optimized using DFT. DOX-PS80 conjugated structures were made using semi-empirical methods. DOX ligands were conjugated with PS80, since they were unable to cross the BBB under normal conditions. In addition, ADMET results disclosed that all compounds were non-toxic and CNS active. They could be utilized as feasible lead molecules in future drug discovery. Among twelve DOX ligands, ligands R6 (-12.02 kcal/mol), R4 (-11.10 kcal/mol) and R9 (-10.19 kcal/mol) exhibited notable binding affinities with protein 3PG7 . The top-ranked three DOX ligands ( R6 , R4 , and R9 ) and their conjugation with PS80 ( R6-PS80 , R4-PS80, and R9-PS80 ) underwent comprehensive molecular dynamics analysis for understanding the structural stability (RMSD), intermolecular H-bond interactions, the effect of solvent accessibility (SASA), and compactness (Rg) variables. From the MD results, it was explicit that these ligands and conjugated ligands exhibited folding behavior and were incredibly stable. These findings suggested that these modified DOX ligands, when combined with PS80, have the potential to cross the BBB and could be used in the treatment of brain tumors. Further studies will be carried out to improve the BBB range by conjugating DOX analogues with suitable nanoparticles. Declarations CRediT authorship contribution statement Lekshmi D Karunan S .: DFT calculation, molecular Docking, Dynamics simulation and wrote the paper Arsha S Nair.: Molecular docking and DFT calculation A. Jayakrishnan.: Review the paper and editing the manuscript Sandhya K.S.: Core concept, editing and review the paper. All authors have read and approved the final manuscript. Supporting Information . All data generated or analysed during this study are included in supplementary information SI. Acknowledgements The authors are also thankful to CLIFF high-performing computing facility, University of Kerala, for submitting molecular dynamics simulations. ORCiD ID’s A. 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Pharmacokinetics, brain delivery, and efficacy in brain tumor-bearing mice of glutathione pegylated liposomal doxorubicin (2B3-101). PloS one , 9 (1), e82331.DOI: https://doi.org/10.1371/journal.pone.0082331 Ambruosi, A., Khalansky, A. S., Yamamoto, H., Gelperina, S. E., Begley, D. J., & Kreuter, J. (2006). Biodistribution of polysorbate 80-coated doxorubicin-loaded [14C]-poly (butyl cyanoacrylate) nanoparticles after intravenous administration to glioblastoma-bearing rats. Journal of drug targeting , 14 (2), 97-105.DOI: https://doi.org/10.1080/10611860600636135 Wohlfart, S., Gelperina, S., & Kreuter, J. (2012). Transport of drugs across the blood–brain barrier by nanoparticles. Journal of controlled release , 161 (2), 264-273.DOI: https://doi.org/10.1016/j.jconrel.2011.08.017 Berman, H. M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T. N., Weissig, H., ... & Bourne, P. E. (2000). The protein data bank. 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Morphine facilitates doxorubicin penetration in the central nervous system: a new prospect for therapy of brain tumors. Journal of neuro-oncology , 104 (2), 619-620. DOI 10.1007/s11060-010-0518-9 Farquhar, D., Newman, R. A., Zuckerman, J. E., & Andersson, B. S. (1991). Doxorubicin analogs incorporating chemically reactive substituents. Journal of medicinal chemistry , 34 (2), 561-564.DOI: https://doi.org/10.1021/jm00106a013 Kim, S., Chen, J., Cheng, T., Gindulyte, A., He, J., He, S., ... & Bolton, E. E. (2023). PubChem 2023 update. Nucleic acids research , 51 (D1), D1373-D1380.DOI: https://doi.org/10.1093/nar/gkac956 Frisch, M. J., Trucks, G. W., Schlegel, H. B., Scuseria, G. E., Robb, M. A., Cheeseman, J. R., ... & Fox, D. J. (1988). Gaussian 09, Revision A. 02, Gaussian, Inc., Wallingford, CT, 2016. Chem. Chem. Phys. Accept. Manuscr , 37 , 785-789. Cheng, Z. (2024). Solution Stability of Polysorbate 80 Under Stress Conditions. Authorea Preprints .DOI: 10.22541/au.170664838.88937951/v1 Kromann, J. C., Christensen, A. S., Steinmann, C., Korth, M., & Jensen, J. H. (2014). A third-generation dispersion and third-generation hydrogen bonding corrected PM6 method: PM6-D3H+. PeerJ , 2 , e449.DOI: https://doi.org/10.7717/peerj.449 Komura, H., Watanabe, R., & Mizuguchi, K. (2023). The trends and future prospective of in silico models from the viewpoint of ADME evaluation in drug discovery. Pharmaceutics , 15 (11), 2619.DOI: 10.3390/pharmaceutics15112619 Pradeepkiran, J. A., Sainath, S. B., & Shrikanya, K. V. L. (2021). In silico validation and ADMET analysis for the best lead molecules. In Brucella Melitensis (pp. 133-176). Academic Press.DOI: 10.1016/B978-0-323-85681-2.00008-2 Swanson, K., Walther, P., Leitz, J., Mukherjee, S., Wu, J. C., Shivnaraine, R. V., & Zou, J. (2024). ADMET-AI: a machine learning ADMET platform for evaluation of large-scale chemical libraries. Bioinformatics , 40 (7), btae416.DOI: https://doi.org/10.1093/bioinformatics/btae416 Guedes, I. A., de Magalhães, C. S., & Dardenne, L. E. (2014). Receptor–ligand molecular docking. Biophysical reviews , 6 , 75-87. DOI 10.1007/s12551-013-0130-2 Abraham, M. J., Murtola, T., Schulz, R., Páll, S., Smith, J. C., Hess, B., & Lindahl, E. (2015). GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX , 1 , 19-25. DOI: https://doi.org/10.1016/j.softx.2015.06.001 Bugnon, M., Goullieux, M., Röhrig, U. F., Perez, M. A., Daina, A., Michielin, O., & Zoete, V. (2023). SwissParam 2023: a modern web-based tool for efficient small molecule parametrization. Journal of Chemical Information and Modeling , 63 (21), 6469-6475. DOI: 10.1021/acs.jcim.3c01053 Ks, S., & Nair, A. S. (2023). Insights on the interaction of SARS-CoV-2 variant B. 1.617. 2 with antibody CR3022 and analysis of antibody resistance. Journal of Genetic Engineering and Biotechnology , 21 (1), 35.DOI: https://doi.org/10.1186/s43141-023-00492-y Valdés-Tresanco, M. S., Valdés-Tresanco, M. E., Valiente, P. A., & Moreno, E. (2021). gmx_MMPBSA: a new tool to perform end-state free energy calculations with GROMACS. Journal of chemical theory and computation , 17 (10), 6281-6291. DOI:10.1021/acs.jctc.1c00645 Chart Chart 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files lekshmiPS80SuppInformRevision2.docx TOC.png TOC chart1.png Chart 1 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-5910276","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":409049406,"identity":"c34efa7f-8206-4426-8bc1-63c765c8419b","order_by":0,"name":"D. K. S. 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study\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5910276/v1/1981d921d00a8f9bd2cc7110.png"},{"id":75329596,"identity":"a08bd2d9-8a57-454d-b891-c67abe53e662","added_by":"auto","created_at":"2025-02-03 12:03:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":307979,"visible":true,"origin":"","legend":"\u003cp\u003eOptimized 3D structures of twelve DOX ligands\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5910276/v1/8f375679c36b93eaaa247547.png"},{"id":75330591,"identity":"59e48139-d46d-480c-93ae-613b3b3399d8","added_by":"auto","created_at":"2025-02-03 12:11:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":390045,"visible":true,"origin":"","legend":"\u003cp\u003eOptimized 3D structures of DOX-PS80 conjugated ligands\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5910276/v1/f7f60a5fc11a5e6e7eefa9b2.png"},{"id":75329564,"identity":"60c7dd2e-10a7-4eb8-85e8-97aca80e0952","added_by":"auto","created_at":"2025-02-03 12:03:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":925215,"visible":true,"origin":"","legend":"\u003cp\u003e3D structure of the best docked complex of protein \u003cstrong\u003e3PG7\u003c/strong\u003e with a) \u003cstrong\u003eR6\u003c/strong\u003e b)\u003cstrong\u003e R4\u003c/strong\u003e c) \u003cstrong\u003eR9\u003c/strong\u003e, d) \u003cstrong\u003eR6-PS80\u003c/strong\u003e e) \u003cstrong\u003eR4-PS80\u003c/strong\u003e f) \u003cstrong\u003eR9-PS80\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5910276/v1/b4459e19cd152fd711fc17a0.png"},{"id":75329555,"identity":"d1732b50-54a3-441f-abd6-cbf6eb2f193d","added_by":"auto","created_at":"2025-02-03 12:03:13","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":682886,"visible":true,"origin":"","legend":"\u003cp\u003e2D interaction of\u003cstrong\u003e 3PG7\u003c/strong\u003e with best three DOX ligands a) \u003cstrong\u003eR6\u003c/strong\u003e b) \u003cstrong\u003eR4\u003c/strong\u003e c) \u003cstrong\u003eR9\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5910276/v1/0d9219caa88402a3b3124ab5.png"},{"id":75330820,"identity":"b213585e-5396-4bd4-9de1-3820d3a7e5d7","added_by":"auto","created_at":"2025-02-03 12:19:14","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":216801,"visible":true,"origin":"","legend":"\u003cp\u003eRMSD of \u003cstrong\u003e3PG7\u003c/strong\u003e with DOX ligands and \u003cstrong\u003e3PG7\u003c/strong\u003e with DOX-PS80 conjugated ligands\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5910276/v1/92a9290495a879e3d45423d4.png"},{"id":75329585,"identity":"18364bfb-3a23-4a71-a06f-934cae2a278b","added_by":"auto","created_at":"2025-02-03 12:03:14","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":199148,"visible":true,"origin":"","legend":"\u003cp\u003eRg of\u003cstrong\u003e 3PG7\u003c/strong\u003e with DOX ligands and \u003cstrong\u003e3PG7\u003c/strong\u003e with DOX-PS80 conjugated ligands\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-5910276/v1/f3ae4eabb3e7987675d2ed3b.png"},{"id":75329549,"identity":"c6d3f1d6-b159-4e66-8143-bd32cbbf8fd9","added_by":"auto","created_at":"2025-02-03 12:03:12","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":294011,"visible":true,"origin":"","legend":"\u003cp\u003eSASA of \u003cstrong\u003e3PG7\u003c/strong\u003e with DOX ligands and \u003cstrong\u003e3PG7\u003c/strong\u003e with DOX-PS80 conjugated ligands\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-5910276/v1/9f0c393400d9323890085dce.png"},{"id":75330593,"identity":"23e33f2c-573b-43ab-aa0b-0906519e8358","added_by":"auto","created_at":"2025-02-03 12:11:13","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":190142,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of hydrogen bonds appear for protein \u003cstrong\u003e3PG7\u003c/strong\u003ewith a) \u003cstrong\u003eR6\u003c/strong\u003e b) \u003cstrong\u003eR4\u003c/strong\u003e c) \u003cstrong\u003eR9\u003c/strong\u003e d) \u003cstrong\u003eR6-PS80\u003c/strong\u003e e) \u003cstrong\u003eR4-PS80\u003c/strong\u003e f) \u003cstrong\u003eR9-PS80\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-5910276/v1/83ecfe2cc5a13d9d1185ba41.png"},{"id":75329571,"identity":"ce06dc45-274f-4d0d-b1a9-ca02ca8fa9cf","added_by":"auto","created_at":"2025-02-03 12:03:13","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":966338,"visible":true,"origin":"","legend":"\u003cp\u003e2D interaction of \u003cstrong\u003e3PG7\u003c/strong\u003e with DOX-PS80 conjugated ligands a) \u003cstrong\u003eR6-PS80\u003c/strong\u003e, b) \u003cstrong\u003eR4-PS80\u003c/strong\u003e c) \u003cstrong\u003eR9-PS80\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-5910276/v1/e3b1e272b912cd74561cc159.png"},{"id":75811237,"identity":"ce62eb71-6a2f-4748-bb13-8815db4caf64","added_by":"auto","created_at":"2025-02-08 22:16:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5726043,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5910276/v1/ea15003f-254a-4579-874a-37e4f358662e.pdf"},{"id":75330596,"identity":"00f47ae0-ada3-48c8-9182-63958d8f4acb","added_by":"auto","created_at":"2025-02-03 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12:03:12","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":87427,"visible":true,"origin":"","legend":"\u003cp\u003eChart 1\u003c/p\u003e","description":"","filename":"chart1.png","url":"https://assets-eu.researchsquare.com/files/rs-5910276/v1/16035912a413b32e00f9938f.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"In silico analysis of doxorubicin analogues conjugated to polysorbate 80 for sustained and targeted delivery to the brain","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCancer has emerged as the world's second main cause of death. Although early detection of cancer can save lives, it is not always feasible. A tumor could be malignant, pre-carcerous, or benign. Brain tumors are the main cause of cancer mortality in children and remain a challenge to treat, despite advancements in surgery and adjuvant therapies.\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e Brain tumors can cause various psychiatric symptoms, including depression, personality changes, abulia, hallucinations (both auditory and visual), mania, panic attacks, and forgetfulness.\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e Certain individuals with brain tumors, particularly those with very small tumors, do not exhibit any symptoms. The main hurdle to healing many neurological disorders is the BBB, a polarized layer of endothelial cells that hinders the central nervous system (CNS) from receiving systemically introduced diagnostic or therapeutic substances.\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eBBBplays a crucial function in preventing pathogenic organisms and undesired substances from entering the brain, thus making it difficult to target therapeutic agents for CNS illnesses.\u003csup\u003e[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e This barrier is made up of an array of distinct kinds of cells, among which are microglial cells, astrocytes, pericytes, and endothelial cells.\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e Despite breakthroughs in comprehending the molecular structure and physiology of the BBB, as well as therapeutic approaches to treat CNS diseases, effective delivery of potential therapies remains a challenge.\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e The brain endothelium is the fundamental morphological and functional component of the BBB.\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e The tight junctions (TJ) between adjacent endothelial cells, produced by complex transmembrane proteins, hinder drug transfer into the brain \u003cem\u003evia\u003c/em\u003e bloodstream.\u003csup\u003e[\u003cspan additionalcitationids=\"CR7 CR8 CR9\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eOver the years, considerable research has been reported on the utilization of polymeric, liposomal, and inorganic nanoparticulate (NP) drug delivery systems for transferring an array of therapeutic agents across the BBB.\u003csup\u003e[\u003cspan additionalcitationids=\"CR12 CR13 CR14 CR15 CR16 CR17\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e The NP approach entails many challenges, specifically when targeting the brain.\u003csup\u003e[\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e It has been reported that NPs coated with the non-ionic surfactant polysorbate 80 (PS80) may penetrate the BBB and ferry an array of drugs across the BBB.\u003csup\u003e[\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e Even though PS80-coated NPs exhibited BBB permeability, additional challenges persist, like the toxicity of polycyanoacrylates or the metals or inorganic substances utilized as NPs and their deleterious repercussions. It is a potential threat that NPs and their breakdown products will accumulate in the brain capillaries. The drug content in NPs is typically only a small fraction of the total weight of the NPs. Additionally, the processes used to fabricate and incorporate drugs into the NPs often involve organic solvents, which can be challenging to eliminate completely. This is evident even when using biocompatible and biodegradable NPs made from poly(lactic acid) or poly(glycolic acid).\u003c/p\u003e \u003cp\u003eGlutathione-pegylated liposomal DOX (2B3-101) has been explored as a novel treatment for individuals suffering brain cancer. It relies on a previously commercialized pegylated liposomal DOX, enhanced with a glutathione coating that safely improves drug transport across the BBB. Glutathione-pegylated liposomal DOX facilitates the effective ferry of DOX to brain tumors and could serve as a viable new therapeutic option for the treatment of brain malignancies.\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e It has been demonstrated that PS80-coated NPs containing DOX may penetrate through the intact BBB and achieve therapeutic concentrations in the brain.\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e Moreover, interaction with nanoparticles might mitigate the adverse outcomes of toxic drugs like DOX.\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e We are aware that DOX is not the best drug to treat brain tumors. The key objective of the present research is to demonstrate that DOX-PS80 conjugated ligands can effectively cross the BBB for the treatment of brain tumor. We designed twelve DOX ligands and optimized them \u003cem\u003evia\u003c/em\u003e DFT. To find the best drug out of twelve DOX ligand, we docked them with seven tumor proteins against brain tumor utilizing molecular docking analysis. To gain further insight into drug delivery to the brain using best DOX ligands, we conjugated them with PS80. Further, Molecular Dynamics simulation studies have been done for the top-ranked DOX ligands and their DOX-PS80 conjugated ligands. Afterwards, the ADMET properties of all compounds were evaluated to obtain valuable insights for drug design.\u003c/p\u003e"},{"header":"2. Methods and Materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Preparation of Proteins\u003c/h2\u003e \u003cp\u003eThe X ray structures of seven tumor proteins were used as receptors in the current study. The corresponding Protein Data Bank codes are: \u003cb\u003e3LGL, 2IG0, 3LGF, 4RG2, 3PG7, 3LH0\u003c/b\u003e, and \u003cb\u003e4HBM\u003c/b\u003e. The experimental findings reveal that all of these proteins have good resolution, no mutations and have better R values. The structures of the target proteins were acquired from RCSB PDB.\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e The protein structures were thoroughly examined for any missing residues or repetitions on the amino acid residues using the Spdbv software.\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e Visualization were performed on Discovery Studio (DS) visualization software,\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e pyMOL\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e and docking were performed on Autodock tools.\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e All PDB files of downloaded raw proteins were opened in DS software 2024 and subsequently we have eliminated water molecules, solvent, inhibitors and ions from it. The proteins underwent docking tests with twelve DOX ligands after being saved in PDB format. The 3D structures of the target proteins taken for the present study are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Preparation of Ligands\u003c/h2\u003e \u003cp\u003eDOX is an anthracycline antibiotic produced by Streptomyces peucetius. It inhibits topoisomerase II, which is responsible for DNA and RNA production. This medicine is used to treat a wide range of cancers, including carcinomas, sarcomas, leukemias, and lymphomas.\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e DOX's therapeutic use is limited by a number of issues, including inherent and acquired drug resistance and dose-dependent cardiotoxicity. Hundreds of DOX compounds have been reported in an attempt to develop analogues with decreased host toxicity and improved antitumor activity.\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe reference drug DOX (\u003cb\u003eR1\u003c/b\u003e) was acquired from PubChem.\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e Based on the sdf file of DOX (\u003cb\u003eR1\u003c/b\u003e) obtained from PubChem, and we used Chemcraft software to model other structures and optimize these structures using DFT in log format. DOX (\u003cb\u003eR1\u003c/b\u003e) and its eleven analogues (\u003cb\u003eR2-R12\u003c/b\u003e) have been created as potential antitumor drugs by adding the following alkylating or latent alkylating substituents, R, on the 3'-position of the daunosamine sugar.\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e We chose electron withdrawing and electron donating groups as functional groups to modify these structures. The modelled structures are summarized in Chart \u003cspan refid=\"Str1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Density Functional Theory Studies (DFT)\u003c/h2\u003e \u003cp\u003eThe modeled structures of twelve DOX ligands were optimized using B3LYP/6-31G(d,p) density functional theory (DFT). Frequency calculations were performed to conform geometrical optimization. The computations are carried out using the Gaussian software G9 program package.\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e The optimized 3D structures of twelve DOX ligands are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Preparation of PS80, and twelve DOX-PS80 conjugated ligands\u003c/h2\u003e \u003cp\u003ePS80 is a nonionic surfactant that is utilized extensively in the production of protein pharmaceuticals.\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e Nanoparticles (NPs) wrapped with PS80, had been demonstrated to penetrate the BBB and transport several medications to the brain.\u003csup\u003e[\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e In our study, we modelled the structure of PS80, and twelve DOX-PS80 conjugated ligands using Chemsketch software and optimized these structures using PM6 Semi empirical method in log format. The computational efficiency of the fundamental PM6 approach enables computations that would be unfeasible with DFT or Hatree-Fock (HF), such as protein shape optimizations or large-system vibrational studies.\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e The computations were carried out using the Gaussian software G9 program package. The optimized 3D structures of DOX-PS80 conjugated ligands are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 ADMET studies\u003c/h2\u003e \u003cp\u003eComputational modeling studies are the preferred method for assessing the pharmacokinetics and drug likeness of novel chemical entities.\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e The focus lately has been on using artificial intelligence (AI) and machine learning (ML)-based prediction software packages to predict the absorption (A), distribution (D), metabolism (M), excretion (E), and toxicity (T) (ADMET) properties of novel therapeutic molecules among various computational resources.\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e Many compounds were hazardous and failed in \u003cem\u003ein vitro\u003c/em\u003e testing, preventing their development into drugs. In our work, we utilized an ADMET AI web server to gain a more profound comprehension of the ADMET characteristics of twelve DOX ligands, PS80, and twelve DOX-PS80 conjugated ligands. The ADMET scores of each compound were compared, and the best results are reported.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Molecular Docking Studies\u003c/h2\u003e \u003cp\u003eMolecular docking is important in SBDD because it predicts the binding orientation and affinity of a drug within a target binding site. This approach is intended to precisely predict the experimental binding mode and affinity.\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e In our work, docking was conducted using Autodock Tools. Before docking, we removed water molecules, inhibitors, and heteroatoms from the seven target proteins. The protein structures were meticulously inspected for missing residues and repetitions in the amino acid sequences using Spdbv software. Docking studies with twelve DOX ligands were performed on the selected protein structures in their PDB formats that were acquired using Autodock Tools. The default grid box size was set to 40 \u0026Aring; \u0026times; 40 \u0026Aring; \u0026times; 40 \u0026Aring;, with the grid center corresponding to the coordinates of the reference inhibitors in the target proteins. The chosen inhibitors centroid was circumscribed by a spherical, transparent binding sphere, which was subsequently used to figure out the binding site traits. The findings were then evaluated to determine which binding modes received the greatest scores. Further, DS visualization software and pyMOL were utilized to obtain 2D and 3D perspectives. The conformations were examined to determine interactions such as van der Waals and hydrogen bonding between target protein receptors, and ligands.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Molecular Dynamics Simulation\u003c/h2\u003e \u003cp\u003eMolecular dynamics (MD) simulations of the top-docked protein-ligand complexes were implemented with Gromacs 2021 software.\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e SwissParam\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e furnished the forcefield for all ligands, while the CHARMM36 all-atom forcefield was put to use for the protein. The dodecahedron box was composed of solvent water and, the SPC water model was processed. NVT, NPT, and energy minimization were executed \u003cem\u003evia\u003c/em\u003e the 5000-step steepest descent approach. The system was adjusted to equilibrate at 300 K and 1 atm of pressure \u003cem\u003evia\u003c/em\u003e a modified Berendsen thermostat and a V-rescale for temperature coupling. The MD integrator was configured to utilize the leap frog approach to update the atoms positions and velocities. MD simulations were conducted for 100 ns with 50,000 frames per simulation. RMSD, RMSF, Rg, SASA, and H bonds, can compute root mean square deviation, root mean square fluctuation, radius of gyration, solvent accessible surface area, and intermolecular hydrogen bonds in xvg format. The QtGrace software was utilized to plot the data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 MM/GBSA calculations\u003c/h2\u003e \u003cp\u003eMM/GBSA was employed for determining the binding energy and decomposition free energy contributions of protein-ligand complexes.\u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e This approach has been proven to balance computational efficiency and accuracy, particularly when interacting with large systems.\u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e The gmx_MMGBSA tool has been utilized to find out the binding energy of three DOX-PS80 conjugated ligands. Using this tool, we calculated the Gibbs binding free energy (ΔG) from the last 5000 to 10000 frames (with a 500 interval) of the trajectory file.\u003c/p\u003e \u003cp\u003eThe formula for calculating ΔG was,\u003c/p\u003e \u003cp\u003eΔG\u0026thinsp;=\u0026thinsp;ΔH-TΔS\u0026thinsp;=\u0026thinsp;ΔE\u003csub\u003eMM\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;ΔG\u003csub\u003esolv\u003c/sub\u003e \u0026ndash;TΔS\u003c/p\u003e \u003cp\u003eWhere, ΔE\u003csub\u003eMM\u003c/sub\u003e - total gas-phase molecular mechanics\u003c/p\u003e \u003cp\u003eΔG\u003csub\u003esolv\u003c/sub\u003e - summation of polar and nonpolar solvation free energies\u003c/p\u003e \u003cp\u003eTΔS - conformational entropy upon ligand bonding.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Molecular Docking Analysis\u003c/h2\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDocking scores of Protein \u003cstrong\u003e3PG7\u003c/strong\u003e with best DOX ligands\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDOX Ligands\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMolecular Formula\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBinding Energy (kcal/mol)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eR4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003csub\u003e34\u003c/sub\u003eH\u003csub\u003e32\u003c/sub\u003eBrNO\u003csub\u003e14\u003c/sub\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-11.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eR6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003csub\u003e34\u003c/sub\u003eH\u003csub\u003e32\u003c/sub\u003eClNO\u003csub\u003e14\u003c/sub\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-12.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eR9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003csub\u003e34\u003c/sub\u003eH\u003csub\u003e32\u003c/sub\u003eFNO\u003csub\u003e14\u003c/sub\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-10.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eIn this work, seven tumor proteins, along with their docking scores with DOX (\u003cstrong\u003eR1\u003c/strong\u003e) and its elven analogues (\u003cstrong\u003eR2-R12\u003c/strong\u003e), were evaluated and their comprehensive molecular interactions were examined. The structure of DOX analogue is not available; based on the sdf file of DOX (\u003cstrong\u003eR1\u003c/strong\u003e) obtained from PubChem, we utilized Chemcraft software to model other structures and subsequently optimized it with DFT. DOX was modified to enhance the antitumor efficiency and to reduce its toxicity. We chose electron withdrawing and electron donating groups as functional groups to modify these structures. The binding affinity of the modelled DOX ligands with the target proteins that were acquired using Autodock Tools is summarized (\u003cstrong\u003eTable SI1\u003c/strong\u003e). The Autodock softwares docking analysis results between seven tumor proteins and twelve DOX ligands revealed binding energies ranging from \u0026minus;\u0026thinsp;5.70 kcal/mol to -12.02 kcal/mol. Out of the twelve DOX ligands, ten DOX ligands exhibited binding affinity ranging from \u0026minus;\u0026thinsp;8.16 kcal/mol to -12.02 kcal/mol for specific target proteins. The ligand \u003cstrong\u003eR6\u003c/strong\u003e exhibited the most significant binding affinity with the protein \u003cstrong\u003e3PG7\u003c/strong\u003e, at -12.02 kcal/mol. Ligand \u003cstrong\u003eR6\u003c/strong\u003e demonstrated the best with four of the identified proteins: \u003cstrong\u003e2IG0\u003c/strong\u003e (-8.16 kcal/mol), \u003cstrong\u003e4RG2\u003c/strong\u003e (-10.53 kcal/mol), \u003cstrong\u003e3PG7\u003c/strong\u003e (-12.02 kcal/mol), and \u003cstrong\u003e4HBM\u003c/strong\u003e (-9.50 kcal/mol). Ligand \u003cstrong\u003eR4\u003c/strong\u003e had the best outcomes with two of the proteins tested: \u003cstrong\u003e3PG7\u003c/strong\u003e (-11.10 kcal/mol) and \u003cstrong\u003e4RG2\u003c/strong\u003e (-10.06 kcal/mol). Ligand \u003cstrong\u003eR9\u003c/strong\u003e demonstrated the best results with \u003cstrong\u003e3PG7\u003c/strong\u003e (-10.19 kcal/mol). Ligand \u003cstrong\u003eR8\u003c/strong\u003e displayed the best binding affinity with two of the chosen proteins: \u003cstrong\u003e3LGF\u003c/strong\u003e (-9.35kcal/mol) and \u003cstrong\u003e3LGL\u003c/strong\u003e (-7.68 kcal/mol). Ligand \u003cstrong\u003eR7\u003c/strong\u003e interacted most strongly with \u003cstrong\u003e3LGF\u003c/strong\u003e (-9.69 kcal/mol) and \u003cstrong\u003e4HBM\u003c/strong\u003e (-8.07 kcal/mol). Ligand \u003cstrong\u003eR12\u003c/strong\u003e exhibited better binding affinity with \u003cstrong\u003e2IG0\u003c/strong\u003e (-7.29 kcal/mol).\u003c/p\u003e\n \u003cp\u003eOn comparing docking results with the reference drug ligand \u003cstrong\u003eR1\u003c/strong\u003e, Ligands \u003cstrong\u003eR6\u003c/strong\u003e and \u003cstrong\u003eR12\u003c/strong\u003e had enhanced binding affinities for the protein \u003cstrong\u003e2IG0\u003c/strong\u003e compared to ligand \u003cstrong\u003eR1\u003c/strong\u003e. Ligands \u003cstrong\u003eR2\u003c/strong\u003e, \u003cstrong\u003eR3\u003c/strong\u003e, \u003cstrong\u003eR5\u003c/strong\u003e, \u003cstrong\u003eR6\u003c/strong\u003e, \u003cstrong\u003eR7\u003c/strong\u003e, and \u003cstrong\u003eR8\u003c/strong\u003e demonstrated better binding affinities for the protein \u003cstrong\u003e3LGF\u003c/strong\u003e over the reference drug ligand \u003cstrong\u003eR1\u003c/strong\u003e. For protein \u003cstrong\u003e3LGL\u003c/strong\u003e, ligand \u003cstrong\u003eR8\u003c/strong\u003e exhibited better docking scores than ligands \u003cstrong\u003eR1\u003c/strong\u003e. For the protein \u003cstrong\u003e4RG2\u003c/strong\u003e, ligands \u003cstrong\u003eR4\u003c/strong\u003e, \u003cstrong\u003eR6\u003c/strong\u003e, and \u003cstrong\u003eR9\u003c/strong\u003e revealed higher docking scores than \u003cstrong\u003eR1\u003c/strong\u003e. When it pertained to the protein \u003cstrong\u003e3PG7\u003c/strong\u003e, ligands \u003cstrong\u003eR2\u003c/strong\u003e, \u003cstrong\u003eR3\u003c/strong\u003e, \u003cstrong\u003eR4\u003c/strong\u003e, \u003cstrong\u003eR5\u003c/strong\u003e, \u003cstrong\u003eR6\u003c/strong\u003e, \u003cstrong\u003eR7\u003c/strong\u003e, \u003cstrong\u003eR8\u003c/strong\u003e, \u003cstrong\u003eR9\u003c/strong\u003e, \u003cstrong\u003eR10\u003c/strong\u003e, \u003cstrong\u003eR11\u003c/strong\u003e, and \u003cstrong\u003eR12\u003c/strong\u003e possessed a higher binding affinity than ligand \u003cstrong\u003eR1\u003c/strong\u003e. Ligand \u003cstrong\u003eR2\u003c/strong\u003e bound to the protein \u003cstrong\u003e3LH0\u003c/strong\u003e more strongly than ligand \u003cstrong\u003eR1\u003c/strong\u003e. When it came to protein \u003cstrong\u003e4HBM\u003c/strong\u003e, ligands \u003cstrong\u003eR2\u003c/strong\u003e, \u003cstrong\u003eR3\u003c/strong\u003e, \u003cstrong\u003eR4\u003c/strong\u003e, \u003cstrong\u003eR5\u003c/strong\u003e, \u003cstrong\u003eR6\u003c/strong\u003e, \u003cstrong\u003eR7\u003c/strong\u003e, \u003cstrong\u003eR8\u003c/strong\u003e, \u003cstrong\u003eR9\u003c/strong\u003e, \u003cstrong\u003eR10\u003c/strong\u003e, \u003cstrong\u003eR11\u003c/strong\u003e, and \u003cstrong\u003eR12\u003c/strong\u003e exhibited better docking scores than the reference drug ligand \u003cstrong\u003eR1\u003c/strong\u003e. Later, we attempted to deliver these twelve DOX ligands into the brain using PS80. We made the DOX ligands with PS80 conjugated structures using semi empirical method. DOX ligands were conjugated with PS80, since they are unable to cross the BBB under normal conditions. Furthermore, DOX ligands conjugated very well with PS80.\u003c/p\u003e\n \u003cp\u003eAmong these twelve DOX ligands docked with seven tumor proteins, ligands \u003cstrong\u003eR6\u003c/strong\u003e, \u003cstrong\u003eR4\u003c/strong\u003e and \u003cstrong\u003eR9\u003c/strong\u003e exhibited notable binding affinity with protein \u003cstrong\u003e3PG7 (\u003c/strong\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e. The 3D structure of best docked complexes is summarized in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e and the 2D interaction of protein \u003cstrong\u003e3PG7\u003c/strong\u003e with best three DOX ligands are summarized in Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. Furthermore, the corresponding binding energies of these ligands exceed \u0026minus;\u0026thinsp;10.00 kcal/mol. We have observed that ligand \u003cstrong\u003eR6\u003c/strong\u003e interacts with the TYR1618, TYR1587, and TYR1668 residues of \u003cstrong\u003e3PG7\u003c/strong\u003e via hydrogen bonding (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ea). A pi-sigma interaction can also be seen for the LEU1679 residue. Numerous van der Waals and a pi-sulfur interactions can also be observed. Furthermore, VAL1606, ILE1620, PHE1633, LEU1638, LEU1676 and PHE1642 also participated alkyl, pi-alkyl and pi-pi stacked interactions. Ligand \u003cstrong\u003eR4\u003c/strong\u003e showed interactions with amino acids (TYR1650, TYR1618, TYR1668 and TRP1641) of \u003cstrong\u003e3PG7\u003c/strong\u003e, resulting in the presence of a hydrogen bond, as well as hydrophobic interactions (alkyl, pi-alkyl, pi-pi stacked and pi-sigma) with VAL1622, ILE1620, PHE1645, ALA1649, LEU1679, LEU1638, PHE1642, and PHE1633 residues (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eb). A pi-sulfur interaction can be seen for TYR1587 residue. Certain van der Waals interactions were also discernible. Ligand \u003cstrong\u003eR9\u003c/strong\u003e interacted with amino acids LEU1679, GLY1678 and TYR1618 of \u003cstrong\u003e3PG7\u003c/strong\u003e (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ec), resulting in an H-bond and a hydrophobic contact (alkyl, pi-alkyl and pi-sigma) with amino acids LEU1685, VAL1656, ILE1658, LEU1624 and VAL1622, as well as various van der Waals interactions can also be observed. The 3D interaction diagram with a surface color by hydrogen bond type indicated that the H-bond donors and H-bond acceptors of best three DOX ligands docked with protein \u003cstrong\u003e3PG7\u003c/strong\u003e and the structure is given in SI.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 ADMET Analysis\u003c/h2\u003e\n \u003cp\u003eThe pharmacokinetic parameters of a drug have a significant impact concerning its toxicity, brain penetration, carcinogenicity and bioavailability. \u003cem\u003eIn silico\u003c/em\u003e pharmacokinetic parameters of all compounds were analyzed \u003cem\u003evia\u003c/em\u003e the ADMET AI server and the best results are reported. The radial plots from ADMET- AI server (\u003cstrong\u003eFigure SI2 and Figure SI3\u003c/strong\u003e) summarize the important Admet features, such as bioavailability, solubility, toxicity, BBB, and hERG safety. The ADMET AI prediction clearly suggested that all of these compounds are non-toxic and BBB safe.\u003c/p\u003e\n \u003cp\u003eThe ADMET AI prediction of all these compounds were compared and tabulated (\u003cstrong\u003eTable SI2\u003c/strong\u003e, \u003cstrong\u003eTable SI3\u003c/strong\u003e and \u003cstrong\u003eTable SI4\u003c/strong\u003e). It is important to notice that DOX-PS80 conjugated ligands exhibited a higher bioavailability value when compared with free DOX ligands. The BBB penetration scores of the DOX-PS80 conjugated ligands were better than the BBB penetration scores of free DOX ligands. This implies that these twelve DOX-PS80 conjugated ligands are CNS active compounds (BBB\u0026thinsp;\u0026gt;\u0026thinsp;0.40). Additionally, these twelve conjugated ligands showed good human intestinal absorption (HIA) scores of 1.00, indicating the high absorption capacity of the drug. CYP (cytochrome P450) inhibition prediction of all compounds exhibited values in the range of 0 to 1 indicating a strong inhibiting tendency. The CYP enzyme plays a crucial role in the drug efficacy as it is responsible for drug metabolism. Further, all compounds exhibited less carcinogenicity and also showed a high excretion rate in terms half-life. These findings indicated that these twelve DOX-PS80 conjugated ligands are non-toxic and have the potential to cross the BBB.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Molecular Dynamic Simulation\u003c/h2\u003e\n \u003cp\u003eTo assess the compactness, flexibility, conformations, and stability of ligand-bound proteins, we utilized the CHARMM36 all-atom forcefield to run MD simulations. To accomplish this, we chose the top three DOX ligands and three DOX-PS80 conjugated ligands that docked with protein \u003cstrong\u003e3PG7\u003c/strong\u003e, and we also assessed the structural changes that the ligands induced in the protein. The root-mean-square deviation (RMSD) profile of docked complexes, illustrating how stable the protein-ligand complex is, is shown in Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e. The RMSD graph of Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e demonstrated that every complex exhibited fluctuations beneath 0.5 nm. RMSD values gradually varied between 0.1 nm and 0.3 nm until 15 ns, indicating the rigidity of the protein-ligand complexes. After 20 ns, proteins with \u003cstrong\u003eR6\u003c/strong\u003e, \u003cstrong\u003eR9\u003c/strong\u003e, and \u003cstrong\u003eR4-PS80\u003c/strong\u003e had RMSD\u0026thinsp;\u0026lt;\u0026thinsp;0.25 nm, indicating that they were quite rigid. The RMSD of Protein with \u003cstrong\u003eR6-PS80\u003c/strong\u003e displayed stability up to 60 ns and there was a slight fluctuation within 0.35nm. However, in contrast to the others, protein \u003cstrong\u003e3PG7\u003c/strong\u003e with \u003cstrong\u003eR4\u003c/strong\u003e and \u003cstrong\u003eR9-PS80\u003c/strong\u003e exhibited slight variations. Rg conveys insight information on the moment of inertia of atoms of proteins from the center of mass, which in turn indicates stability and compactness. All six docked complexes exhibited only very slight fluctuations, as shown in Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e. Rg values for all six docked complexes varied from 2 nm to 2.1 nm. Throughout the 100 ns simulation run, the complexes Rg values were consistent, suggesting that the formed complexes were more rigid and compact. SASA measured the volume expansion of the protein throughout the interaction with the ligand, which varied to various extents in each system. Consequently, it is employed to figure out which specific regions of a protein should be permeable to a water solvent. A higher SASA value signifies a greater level of volume expansion of a protein, and minimal changes are expected over the simulation period. The influence of solvent molecules on the residues of the conjugated ligands-protein complexes, ligands-protein complexes are clearly illustrated from the SASA plots. The SASA values were altered when the protein \u003cstrong\u003e3PG7\u003c/strong\u003e interacted with three DOX-PS80 conjugated ligands and three DOX ligands. The average SASA values for the six complexes were in the range of 140 to 165 nm\u003csup\u003e2\u003c/sup\u003e, as illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e. Higher SASA values indicated a stronger solvation effect and a larger molecular size of the formed complexes.\u003c/p\u003e\n \u003cp\u003eFurthermore, we assessed the hydrogen bonds that formed between proteins-ligands complexes, proteins-conjugated ligands complexes across the simulation. Hydrogen bond analysis is vital to comprehending the stability of ligand-protein and conjugated-ligand-protein interactions. Conformational stability is always improved by having the maximum number of hydrogen bonds. H-bonds serve a substantial role in determining selectivity and binding affinity. From \u003cstrong\u003eFig.\u0026nbsp;9a to 9f\u003c/strong\u003e, ligand \u003cstrong\u003eR6\u003c/strong\u003e has an average H-bond of 2, ligand \u003cstrong\u003eR4\u003c/strong\u003e has 3, ligand \u003cstrong\u003eR9\u003c/strong\u003e shows 2, \u003cstrong\u003eR6-PS80\u003c/strong\u003e conjugated ligands exhibit 6, \u003cstrong\u003eR4-PS80\u003c/strong\u003e conjugated ligand displays 2 and \u003cstrong\u003eR9-PS80\u003c/strong\u003e conjugated ligand exhibit 4. The maximum H-bond that appeared for ligand \u003cstrong\u003eR6\u003c/strong\u003e is 8 (Fig. 9a), ligand \u003cstrong\u003eR4\u003c/strong\u003e has 7 H-bond (Fig. 9b), ligand \u003cstrong\u003eR9\u003c/strong\u003e displays 10 H-bond (Fig. 9c), \u003cstrong\u003eR6-PS80\u003c/strong\u003e conjugated ligand shows 15 H-bond (Fig. 9d), \u003cstrong\u003eR4-PS80\u003c/strong\u003e conjugated ligand exhibits 8 H-bond (Fig. 9e) and \u003cstrong\u003eR9-PS80\u003c/strong\u003e conjugated ligand shows 10 H-bonds (Fig. 9f) during the simulation.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 MM/GBSA Analysis\u003c/h2\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTotal binding free energy and its components of protein \u003cstrong\u003e3PG7\u003c/strong\u003e with three DOX-PS80 conjugated ligands\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR6-PS80_3PG7\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR4-PS80_3PG7\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR9-PS80_3PG7\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVan der Waals energy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-169.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-149.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-121.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElectrostatic energy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-48.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-34.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-28.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGeneralized born model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e106.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e93.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e76.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSurface area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-22.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-21.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-17.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal binding free energy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-133.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-111.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-89.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eThe total binding free energy and its components of protein \u003cstrong\u003e3PG7\u003c/strong\u003e with three DOX-PS80 conjugated ligands are illustrated in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. In the present study, the observed binding free energy for the \u003cstrong\u003eR6-PS80_3PG7\u003c/strong\u003e complex was \u0026minus;\u0026thinsp;133.72 kcal/mol. The binding free energy for the \u003cstrong\u003eR4-PS80_3PG7\u003c/strong\u003e complex was \u0026minus;\u0026thinsp;111.81 kcal/mol, while the \u003cstrong\u003eR9-PS80_3PG7\u003c/strong\u003e complex showed a binding free energy of -89.70 kcal/mol. The \u003cstrong\u003eR6-PS80\u003c/strong\u003e conjugated ligand exhibited higher binding affinity to the protein \u003cstrong\u003e3PG7\u003c/strong\u003e than the \u003cstrong\u003eR4-PS80\u003c/strong\u003e and \u003cstrong\u003eR9-PS80\u003c/strong\u003e conjugated ligands. Van der Waals and electrostatic energy serve a vital part in energy minimization, while de-solvation energy (generalized Born model) increases overall energy. The 2D interaction diagram of protein \u003cstrong\u003e3PG7\u003c/strong\u003e with three DOX-PS80 conjugated ligands are shown in Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e. We have observed that the \u003cstrong\u003eR6-PS80_3PG7\u003c/strong\u003e complex formed four H-bonds with amino acids such as TYR1618, VAL1653, PHE1645, and TRP1641. It also had halogen bond interaction with amino acid ASP1600, as well as salt bridge interaction with LYS1640 residue. \u0026pi;-\u0026pi; interaction (PHE-1633 and PHE1592 residues, respectively) were also present in this complex (Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003ea). Figure \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003eb shows that the interaction of the \u003cstrong\u003eR4-PS80_3PG7\u003c/strong\u003e complex, which contained six H-bonds through amino acids ARG1684, VAL1656, TYR1657, TYR1659, and TYR1668. Halogen bond interaction (HIS1672) and salt bridge interaction (LYS1640) were also present in this complex. Additionally, amino acids such as GLU1667, and LYS1670 also participated \u0026pi;-\u0026pi; interaction. In the \u003cstrong\u003eR9-PS80_3PG7\u003c/strong\u003e complex, there were four hydrogen bonds involved in amino acids VAL1653, ARG1590, ARG1684, and TYR1668 (Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003ec). Moreover, GLU1667, LYS1640, LYS1670 TRP1641, and PHE1637 residues were involved in salt bridge and \u0026pi;-\u0026pi; interactions.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. CONCLUSION","content":"\u003cp\u003eThe present study examined twelve DOX ligands targeting seven tumor proteins through molecular docking. DOX ligands were modified with electron withdrawing and electron donating groups and optimized using DFT. DOX-PS80 conjugated structures were made using semi-empirical methods. DOX ligands were conjugated with PS80, since they were unable to cross the BBB under normal conditions. In addition, ADMET results disclosed that all compounds were non-toxic and CNS active. They could be utilized as feasible lead molecules in future drug discovery. Among twelve DOX ligands, ligands \u003cstrong\u003eR6\u003c/strong\u003e (-12.02 kcal/mol), \u003cstrong\u003eR4\u003c/strong\u003e (-11.10 kcal/mol) and \u003cstrong\u003eR9\u003c/strong\u003e (-10.19 kcal/mol) exhibited notable binding affinities with protein \u003cstrong\u003e3PG7\u003c/strong\u003e. The top-ranked three DOX ligands (\u003cstrong\u003eR6\u003c/strong\u003e, \u003cstrong\u003eR4\u003c/strong\u003e, and \u003cstrong\u003eR9\u003c/strong\u003e) and their conjugation with PS80 (\u003cstrong\u003eR6-PS80\u003c/strong\u003e,\u003cstrong\u003e\u0026nbsp;R4-PS80,\u0026nbsp;\u003c/strong\u003eand\u003cstrong\u003e\u0026nbsp;R9-PS80\u003c/strong\u003e) underwent comprehensive molecular dynamics analysis for understanding the structural stability (RMSD), intermolecular H-bond interactions, the effect of solvent accessibility (SASA), and compactness (Rg) variables. From the MD results, it was explicit that these ligands and conjugated ligands exhibited folding behavior and were incredibly stable. These findings suggested that these modified DOX ligands, when combined with PS80, have the potential to cross the BBB and could be used in the treatment of brain tumors. \u0026nbsp;Further studies will be carried out to improve the BBB range by conjugating DOX analogues with suitable nanoparticles.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Lekshmi D Karunan S\u003c/strong\u003e.: DFT calculation, molecular Docking, Dynamics simulation and wrote \u0026nbsp;the paper\u0026nbsp;\u003cstrong\u003eArsha S Nair.:\u003c/strong\u003e Molecular docking and DFT calculation\u0026nbsp;\u003cstrong\u003eA. Jayakrishnan.:\u0026nbsp;\u003c/strong\u003eReview the paper and editing the manuscript\u0026nbsp;\u003cstrong\u003eSandhya K.S.:\u0026nbsp;\u003c/strong\u003eCore concept, editing and review the paper. All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupporting Information\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in supplementary information SI.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are also thankful to CLIFF high-performing computing facility, University of Kerala, for submitting molecular dynamics simulations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eORCiD ID\u0026rsquo;s\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA. Jayakrishnan.:\u0026nbsp;\u003c/strong\u003e0000-0003-2738-6161\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSandhya K.S.:\u0026nbsp;\u003c/strong\u003e0000-0002-3628-0521\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLekshmi D Karunan S.:\u0026nbsp;\u003c/strong\u003e0009-0006-6923-8019\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eArsha S Nair.:\u0026nbsp;\u003c/strong\u003e0009-0006-0817-5069\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSingh, S. K., Clarke, I. D., Terasaki, M., Bonn, V. E., Hawkins, C., Squire, J., \u0026amp; Dirks, P. B. (2003). 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DOI:10.1021/acs.jctc.1c00645\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Chart ","content":"\u003cp\u003e Chart 1 is available in the Supplementary Files section.\u003c/p\u003e\n"}],"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":"Doxorubicin analogues, Polysorbate 80, Molecular docking, ADMET, Molecular dynamics, MM/GBSA, in silico, Blood brain barrier, CNS disorders, Drug Targeting","lastPublishedDoi":"10.21203/rs.3.rs-5910276/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5910276/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDrug discovery for the treatment of central nervous system (CNS) diseases is a highly complex affair, especially due to the blood brain barrier (BBB) restricting the entry of many drugs. In the present study, we utilized \u003cem\u003ein silico\u003c/em\u003e studies to screen twelve derivatives of doxorubicin (DOX) against seven tumor proteins. The DOX ligands were modelled and optimized using Density Functional Theory (DFT), and the binding affinity was evaluated. Seven tumor proteins were selected for docking and their PDB IDs were: \u003cb\u003e3LGL, 2IG0, 3LGF, 4RG2, 3PG7, 3LH0\u003c/b\u003e, and \u003cb\u003e4HBM\u003c/b\u003e. Later, an attempt was made to deliver these modified DOX ligands into the brain using a non-ionic surfactant, polysorbate 80 (PS80). Modified DOX ligands-PS80 conjugated structures were made using Semi empirical methods. After categorizing 12 DOX ligands and 12 DOX-PS80 conjugated ligands, the top-ranked three DOX ligands (\u003cb\u003eR6\u003c/b\u003e, \u003cb\u003eR4\u003c/b\u003e, and \u003cb\u003eR9\u003c/b\u003e) and their conjugates with PS80 (\u003cb\u003eR6-PS80\u003c/b\u003e, \u003cb\u003eR4-PS80\u003c/b\u003e, and \u003cb\u003eR9-PS80\u003c/b\u003e) underwent comprehensive molecular dynamics (MD) analysis. The best DOX ligands and their conjugated structures with PS80 were subjected to a thorough examination for structural stability (RMSD), intermolecular hydrogen bond (H-bond) interactions, effect of solvent accessibility (SASA), and compactness (Rg). The results demonstrated that these ligands and conjugated ligands exhibited folding behavior and were incredibly stable. Furthermore, we employed MM/GBSA to figure out the docking scores of the top three DOX-PS80-conjugated ligands. \u003cem\u003eIn silico\u003c/em\u003e pharmacokinetic parameters of all compounds were analyzed using the ADMET AI server. It is shown that attaching to PS80 could potentially decrease the overall toxicity of DOX ligands. These findings indicate that the modified DOX ligands, when combined with PS80, have the potential to cross the BBB and could be used for further in vitro and in vivo evaluation.\u003c/p\u003e","manuscriptTitle":"In silico analysis of doxorubicin analogues conjugated to polysorbate 80 for sustained and targeted delivery to the brain","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-03 12:03:06","doi":"10.21203/rs.3.rs-5910276/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":"03063d20-42a2-44c5-bbac-0d69856236e7","owner":[],"postedDate":"February 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-08T22:08:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-03 12:03:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5910276","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5910276","identity":"rs-5910276","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}