title: Towards better modelling of drug-loading in solid lipid nanoparticles: Molecular dynamics, docking experiments and Gaussian Processes machine learning.
abstract: This study represents one of the series applying computer-oriented processes and tools in digging for information, analysing data and finally extracting correlations and meaningful outcomes. In this context, binding energies could be used to model and predict the mass of loaded drugs in solid lipid nanoparticles after molecular docking of literature-gathered drugs using MOE® software package on molecularly simulated tripalmitin matrices using GROMACS®. Consequently, Gaussian processes as a supervised machine learning artificial intelligence technique were used to correlate the drugs' descriptors (e.g. M.W., xLogP, TPSA and fragment complexity) with their molecular docking binding energies. Lower percentage bias was obtained compared to previous studies which allows the accurate estimation of the loaded mass of any drug in the investigated solid lipid nanoparticles by just projecting its chemical structure to its main features (descriptors).
authors.affiliation: Department of Pharmaceutics and Industrial Pharmacy, Faculty of Pharmacy, Ain Shams University, Cairo, Egypt; Bioinformatics Program, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt; Department of Pharmaceutical Technology, Faculty of Pharmacy and Biotechnology, German University in Cairo (GUC), Cairo, Egypt. Electronic address: r_hathout@yahoo.com.
authors.forename: Rania M
authors.initials: RM
authors.lastname: Hathout
authors.affiliation: Department of Pharmaceutics and Industrial Pharmacy, Faculty of Pharmacy, Ain Shams University, Cairo, Egypt.
authors.forename: Abdelkader A
authors.initials: AA
authors.lastname: Metwally
mesh_terms: D001185:Artificial Intelligence; D003474:Curcumin / Q000737:chemistry; D004337:Drug Carriers / Q000737:chemistry*; D016503:Drug Delivery Systems; D006860:Hydrogen Bonding; D008055:Lipids / Q000737:chemistry*; D000069550:Machine Learning; D008962:Models, Theoretical; D062105:Molecular Docking Simulation; D056004:Molecular Dynamics Simulation; D053758:Nanoparticles / Q000737:chemistry; D016011:Normal Distribution; D011136:Polysorbates / Q000737:chemistry; D012984:Software; D014280:Triglycerides / Q000737:chemistry
chemical_list: D004337:Drug Carriers; D008055:Lipids; D011136:Polysorbates; D014280:Triglycerides; C005859:tripalmitin; D003474:Curcumin
keywords: Computational pharmaceutics; Descriptors; Docking; Gaussian; Lipid nanoparticles; Machine learning; Molecular dynamics