Predicting Oral Disintegrating Tablet Formulations by Neural Network Techniques

Oral Disintegrating Tablets (ODTs) is a novel dosage form that can be dissolved on the tongue within 3min or less especially for geriatric and pediatric patients. Current ODT formulation studies usually rely on the personal experience of pharmaceutical experts and trial-and-error in the laboratory, which is inefficient and time-consuming. The aim of current research was to establish the prediction model of ODT formulations with direct compression process by Artificial Neural Network (ANN) and Deep Neural Network (DNN) techniques. 145 formulation data were extracted from Web of Science. All data sets were divided into three parts: training set (105 data), validation set (20) and testing set (20). ANN and DNN were compared for the prediction of the disintegrating time. The accuracy of the ANN model has reached 85.60%, 80.00% and 75.00% on the training set, validation set and testing set respectively, whereas that of the DNN model was 85.60%, 85.00% and 80.00%, respectively. Compared with the ANN, DNN showed the better prediction for ODT formulations. It is the first time that deep neural network with the improved dataset selection algorithm is applied to formulation prediction on small data. The proposed predictive approach could evaluate the critical parameters about quality control of formulation, and guide research and process development. The implementation of this prediction model could effectively reduce drug product development timeline and material usage, and proactively facilitate the development of a robust drug product.Comment: This is a post-peer-review, pre-copyedit version of an article published in Asian Journal of Pharmaceutical Sciences. The final authenticated version is available online at: https://doi.org/10.1016/j.ajps.2018.01.00

Activated carbon as a carrier for amorphous drug delivery:effect of drug characteristics and carrier wettability

Recent research on porous silica materials as drug carriers for amorphous and controlled drug delivery has shown promising results. However, due to contradictory literature reports on toxicity and high costs of production, it is important to explore alternative safe and inexpensive porous carriers. In this study, the potential of activated carbon (AC) as an amorphous drug carrier was investigated using paracetamol (PA) and ibuprofen (IBU) as model drugs. The solution impregnation method was used for drug loading, with loading efficiency determined by UV spectroscopy and drug release kinetics studied using USP II dissolution apparatus. The physical state of the drug in the complex was characterised using differential scanning calorimetry and X-ray diffractions techniques, whilst sites of drug adsorption were studied using Fourier transform infrared spectroscopy and N2 adsorption techniques. In addition, the cytotoxicity of AC on human colon carcinoma (Caco-2) cells was assessed using the MTT assay. Results presented here reveal that, for PA/AC and IBU/AC complexes, the saturation solubility of the drug in the loading solvent appears to have an effect on the drug loading efficiency and the physical state of the drug loaded, whilst drug release kinetics were affected by the wettability of the activated carbon particles. Furthermore, activated carbon microparticles exhibited very low cytotoxicity on Caco-2 cells at the concentrations tested (10–800 μg/mL). This study, therefore, supports the potential of activated carbon as a carrier for amorphous drug delivery

Deep learning for in vitro prediction of pharmaceutical formulations

Current pharmaceutical formulation development still strongly relies on the traditional trial-and-error approach by individual experiences of pharmaceutical scientists, which is laborious, time-consuming and costly. Recently, deep learning has been widely applied in many challenging domains because of its important capability of automatic feature extraction. The aim of this research is to use deep learning to predict pharmaceutical formulations. In this paper, two different types of dosage forms were chosen as model systems. Evaluation criteria suitable for pharmaceutics were applied to assessing the performance of the models. Moreover, an automatic dataset selection algorithm was developed for selecting the representative data as validation and test datasets. Six machine learning methods were compared with deep learning. The result shows the accuracies of both two deep neural networks were above 80% and higher than other machine learning models, which showed good prediction in pharmaceutical formulations. In summary, deep learning with the automatic data splitting algorithm and the evaluation criteria suitable for pharmaceutical formulation data was firstly developed for the prediction of pharmaceutical formulations. The cross-disciplinary integration of pharmaceutics and artificial intelligence may shift the paradigm of pharmaceutical researches from experience-dependent studies to data-driven methodologies

Investigating the role of cholesterol in the formation of non-ionic surfactant based bilayer vesicles:thermal analysis and molecular dynamics

The aim of this research was to investigate the molecular interactions occurring in the formulation of non-ionic surfactant based vesicles composed monopalmitoyl glycerol (MPG), cholesterol (Chol) and dicetyl phosphate (DCP). In the formulation of these vesicles, the thermodynamic attributes and surfactant interactions based on molecular dynamics, Langmuir monolayer studies, differential scanning calorimetry (DSC), hot stage microscopy and thermogravimetric analysis (TGA) were investigated. Initially the melting points of the components individually, and combined at a 5:4:1 MPG:Chol:DCP weight ratio, were investigated; the results show that lower (90 C) than previously reported (120-140 C) temperatures could be adopted to produce molten surfactants for the production of niosomes. This was advantageous for surfactant stability; whilst TGA studies show that the individual components were stable to above 200 C, the 5:4:1 MPG:Chol:DCP mixture show ∼2% surfactant degradation at 140 C, compared to 0.01% was measured at 90 C. Niosomes formed at this lower temperature offered comparable characteristics to vesicles prepared using higher temperatures commonly reported in literature. In the formation of niosome vesicles, cholesterol also played a key role. Langmuir monolayer studies demonstrated that intercalation of cholesterol in the monolayer did not occur in the MPG:Chol:DCP (5:4:1 weight ratio) mixture. This suggests cholesterol may support bilayer assembly, with molecular simulation studies also demonstrating that vesicles cannot be built without the addition of cholesterol, with higher concentrations of cholesterol (5:4:1 vs 5:2:1, MPG:Chol:DCP) decreasing the time required for niosome assembly. © 2013 Elsevier B.V

Synthesis of Carbon Onion and Its Application as a Porous Carrier for Amorphous Drug Delivery

Given the great potential of porous carrier-based drug delivery for stabilising the amorphous form of drugs and enhancing dissolution profiles, this work is focussed on the synthesis and application of carbon onion or onion-like carbon (OLC) as a porous carrier for oral amorphous drug delivery, using paracetamol (PA) and ibuprofen (IBU) as model drugs. Annealing of nanodiamonds at 1100 °C produced OLC with a diamond core that exhibited low cytotoxicity on Caco-2 cells. Solution adsorption followed by centrifugation was used for drug loading and results indicated that the initial concentration of drug in the loading solution needs to be kept below 11.5% PA and 20.7% IBU to achieve complete amorphous loading. Also, no chemical interactions between the drug and OLC could be detected, indicating the safety of loading into OLC without changing the chemical nature of the drug. Drug release was complete in the presence of sodium dodecyl sulphate (SDS) and was faster compared to the pure crystalline drug, indicating the potential of OLC as an amorphous drug carrier

Carbon nanowalls grown by microwave plasma enhanced chemical vapor deposition during the carbonization of polyacrylonitrile fibers

We used microwave plasma enhanced chemical vapor deposition (MPECVD) to carbonize an electrospun polyacrylonitrile (PAN) precursor to form carbon fibers. Scanning electron microscopy, Raman spectroscopy, and Fourier transform infrared spectroscopy were used to characterize the fibers at different evolution stages. It was found that MPECVD-carbonized PAN fibers do not exhibit any significant change in the fiber diameter, whilst conventionally carbonized PAN fibers show a 33% reduction in the fiber diameter. An additional coating of carbon nanowalls (CNWs) was formed on the surface of the carbonized PAN fibers during the MPECVD process without the assistance of any metallic catalysts. The result presented here may have a potential to develop a novel, economical, and straightforward approach towards the mass production of carbon fibrous materials containing CNWs

Comparison of Three Molecular Simulation Approaches for Cyclodextrin-Ibuprofen Complexation

Cyclodextrins are widely used for the solubilisation of poorly soluble drugs in the formulations. However, current cyclodextrin formulation development strongly depends on trial-and-error in the laboratory, which is time-consuming and high cost. The aim of this research was to compare three modeling approaches (Docking, molecular dynamics (MD), and quantum mechanics (QM)) for cyclodextrin/drug complexation. Ibuprofen was used as a model drug. Binding free energy from three simulation methods was calculated as an important parameter to compare with the experimental results. Docking results from AutoDock Vina program showed that the scoring of complexation capability between ibuprofen and cyclodextrins is alpha (α), gamma (γ), beta (β), and HP-beta-cyclodextrins, which indicated similar ranking with the results from phase, solubility diagram experiments. MD simulation indicated that ibuprofen could form the stable complexes with β-, γ-, and HP-β-cyclodextrins, but not for alpha cyclodextrin. Binding free energies from the MD simulation for β-, γ-, and HP-β-cyclodextrins were −3.67, −0.67, and −3.87 kcal/mol, individually. The enthalpies of QM simulation for β-, γ-, and HP-β-cyclodextrins were −17.22, −14.75, and −20.28 kcal/mol, respectively. Results from all three modeling approaches showed similar ranking between ibuprofen and four cyclodextrin molecules as the experimental data. However, MD simulation with entropy calculation had the closest value to experimental data for β and HP-beta-cyclodextrins. Thus, MD simulation with MM-PBSA method may be fit to in silico screen for cyclodextrin formulations