In-vitro and in-vivo evaluation of carrageenan/methylcellulose polymeric systems for transscleral delivery of macromolecules.

In this study, polymeric dispersions composed of methylcellulose (MC) and either kappa carrageenan (KC) or iota carrageenan (IC) were proposed as a platform for transscleral delivery of macromolecules. The additive effects of the two polymers were investigated using oscillatory rheometer and FT-IR spectroscopy. Mechanical spectra demonstrated a conformation dependent association of the two polymers at 37 °C in the presence of selected counter ions. The polymer association was also confirmed by the shifts in MC peaks at 1049.5, 1114 and 1132.9 cm(-1) in the presence of carrageenans, which corresponds to the stretching vibrations of C-O-C bonds of the polysaccharides. The MC-IC polymeric system displayed the highest bio-adhesion, owing to the relatively high negative charge. However, the MC-IC system did not affect the in-vitro scleral permeability of sodium fluorescein and 10 kDa FITC-dextran. Nonetheless, the formulation properties had a substantial impact on the results of the in-vivo studies. The efficacy of transscleral drug delivery was determined using rats with altered connexin 43 (Cx43) levels, a gap junction protein, in the choroid. Periocular injection of Cx43 antisense oligonucleotides (AsODN) incorporated in the MC-IC system lead to a significant reduction in the Cx43 levels in the choroid of rats at 24 h of treatment. AsODN incorporated in phosphate buffered saline (PBS) also demonstrated a trend towards reduced Cx43 levels; however this was not statistically significant owing to great variability between treated animals. Consequently the in-vivo data suggests the transscleral route to be of value in delivering therapeutics to the choroid. Moreover this study identified a new polymeric system based on MC and IC which provides aqueous loading of therapeutics and prolonged retention at the site of administration.

Preparation of niosomes as an ocular delivery system for naltrexone hydrochloride: physicochemical characterization.

Recent reports have demonstrated that topical and systemic application of naltrexone markedly improves the characteristic signs of diabetic keratopathy; most notably, impaired corneal sensation and delayed wound repair. The aim of this study was to prepare and characterise non-ionic surfactant vesicles (niosomes) for the ocular drug delivery of naltrexone hydrochloride. The niosomes were prepared using the thin film hydration method and characterised using polarized light microscopy, cryo-scanning electron microscopy (Cryo-SEM), percent drug entrapment efficiency (EE %), laser light diffraction and differential scanning calorimetry (DSC). Two classes of non-ionic surfactants (sorbitan esters and polyoxyethylene alkyl ethers) were investigated. The results revealed that tuning of cholesterol concentrations can significantly alter the niosome's physical properties including sizes, EE% and membrane fluidity (thermo-responsiveness). The prepared vesicles were in the range of 7.0 +/- 1.0 to 14.6 +/- 0.8 microm in size. The prepared niosomes showed different abilities to accommodate cholesterol. This was highly dependent on the structure and continuity of the hydrophobic chains of the used surfactants. Span 60-based vesicles containing 30% mol/mol of cholesterol showed the highest EE%. The microstructure and lamellarity of the niosomes were studied using Cryo-SEM. Typical concentric multilayered structures (onion or rose-like) were seen suggesting the formation of multilamellar vesicles. DSC-studies conducted on Span 60-based niosomes containing 30% mol/mol cholesterol revealed liquid-gel transition (T(m) and entropy of 43.5 degrees C and 0.82 kcal/mol, respectively). Such transition reflects potential thermo-responsive properties, which is desirable for ocular delivery.

W/O microemulsions for ocular delivery: evaluation of ocular irritation and precorneal retention.

Water-in-oil microemulsions (w/o ME) capable of undergoing a phase-transition to lamellar liquid crystals (LC) or bicontinuous ME upon aqueous dilution were formulated using Crodamol EO, Crill 1 and Crillet 4, an alkanol or alkanediol as cosurfactant and water. The hypothesis that phase-transition of ME to LC may be induced by tears and serve to prolong precorneal retention was tested. The ocular irritation potential of components and formulations was assessed using a modified hen's egg chorioallantoic membrane test (HET-CAM) and the preocular retention of selected formulations was investigated in rabbit eye using gamma scintigraphy. Results showed that Crill 1, Crillet 4 and Crodamol EO were non-irritant. However, all other cosurfactants investigated were irritant and their irritation was dependent on their carbon chain length. A w/o ME formulated without cosurfactant showed a protective effect when a strong irritant (0.1 M NaOH) was used as the aqueous phase. Precorneal clearance studies revealed that the retention of colloidal and coarse dispersed systems was significantly greater than an aqueous solution with no significant difference between ME systems (containing 5% and 10% water) as well as o/w emulsion containing 85% water. Conversely, a LC system formulated without cosurfactant displayed a significantly greater retention compared to other formulations.

Prediction of corneal permeability using artificial neural networks.

The purpose of this study was to develop a simple model for prediction of corneal permeability of structurally different drugs as a function of calculated molecular descriptors using artificial neural networks. A set of 45 compounds with experimentally derived values of corneal permeability (log C) was used to develop, test and validate a predictive model. Each compound was encoded with 1194 calculated molecular structure descriptors. A genetic algorithm was used to select a subset of descriptors that best describe corneal permeability coefficient log C and a supervised network with radial basis transfer function (RBF) was used to correlate calculated molecular descriptors with experimentally derived measures of corneal permeability. The best model, with 4 input descriptors and 12 hidden neurones was chosen, and the significance of the selected descriptors to corneal permeability was examined. Strong correlation of predicted with experimentally derived log C values (correlation coefficient greater than 0.87 and 0.83 respectively) was obtained for the training and testing data sets. The developed model could be useful for the rapid prediction of the corneal permeability of candidate drugs based on molecular structure alone as it does not require experimentally derived data.

Molecular descriptors that influence the amount of drugs transfer into human breast milk.

Most drugs are excreted into breast milk to some extent and are bioavailable to the infant. The ability to predict the approximate amount of drug that might be present in milk from the drug structure would be very useful in the clinical setting. The aim of this research was to simplify and upgrade the previously developed model for prediction of the milk to plasma (M/P) concentration ratio, given only the molecular structure of the drug. The set of 123 drug compounds, with experimentally derived M/P values taken from the literature, was used to develop, test and validate a predictive model. Each compound was encoded with 71 calculated molecular structure descriptors, including constitutional descriptors, topological descriptors, molecular connectivity, geometrical descriptors, quantum chemical descriptors, physicochemical descriptors and liquid properties. Genetic algorithm was used to select a subset of the descriptors that best describe the drug transfer into breast milk and artificial neural network (ANN) to correlate selected descriptors with the M/P ratio and develop a QSAR. The averaged literature M/P values were used as the ANN's output and calculated molecular descriptors as the inputs. A nine-descriptor nonlinear computational neural network model has been developed for the estimation of M/P ratio values for a data set of 123 drugs. The model included the percent of oxygen, parachor, density, highest occupied molecular orbital energy (HOMO), topological indices (chiV2, chi2 and chi1) and shape indices (kappa3, kappa2), as the inputs had four hidden neurons and one output neuron. The QSPR that was developed indicates that molecular size (parachor, density) shape (topological shape indices, molecular connectivity indices) and electronic properties (HOMO) are the most important for drug transfer into breast milk. Unlike previously reported models, the QSPR model described here does not require experimentally derived parameters and could potentially provide a useful prediction of M/P ratio of new drugs only from a sketch of their structure and this approach might also be useful for drug information service. Regardless of the model or method used to estimate drug transfer into breast milk, these predictions should only be used to assist in the evaluation of risk, in conjunction with assessment of the infant's response.

Role of genetic algorithms and artificial neural networks in predicting the phase behavior of colloidal delivery systems.

PURPOSE: A genetic neural network (GNN) model was developed to predict the phase behavior of microemulsion (ME), lamellar liquid crystal (LC), and coarse emulsion forming systems (W/O EM and O/W EM) depending on the content of separate components in the system and cosurfactant nature. METHOD: Eight pseudoternary phase triangles, containing ethyl oleate as the oil component and a mixture of two nonionic surfactants and n-alcohol or 1,2-alkanediol as a cosurfactant, were constructed and used for training, testing, and validation purposes. A total of 21 molecular descriptors were calculated for each cosurfactant. A genetic algorithm was used to select important molecular descriptors, and a supervised artificial neural network with two hidden layers was used to correlate selected descriptors and the weight ratio of components in the system with the observed phase behavior. RESULTS: The results proved the dominant role of the chemical composition, hydrophile-lipophile balance, length of hydrocarbon chain, molecular volume, and hydrocarbon volume of cosurfactant. The best GNN model, with 14 inputs and two hidden layers with 14 and 9 neurons, predicted the phase behavior for a new set of cosurfactants with 82.2% accuracy for ME, 87.5% for LC, 83.3% for the O/W EM, and 91.5% for the W/O EM region. CONCLUSIONS: This type of methodology can be applied in the evaluation of the cosurfactants for pharmaceutical formulations to minimize experimental effort.

Characterizing colloidal structures of pseudoternary phase diagrams formed by oil/water/amphiphile systems.

Two pseudoternary phase diagrams were constructed using ethyl oleate, water, and a surfactant blend containing poly (oxyethylene 20) sorbitan monooleate and sorbitan monolaurate with or without the cosurfactant 1-butanol. Two colloidal regions were identified in the cosurfactant-free phase diagram; a microemulsion (ME) and a region containing lamellar liquid crystals (LC). The addition of 1-butanol increased the area in which systems formed microemulsions and eliminated the formation of any liquid crystalline phases. Samples that form the colloidal regions of both systems were investigated by freeze-fracture transmission electron microscopy and by viscosity and conductivity measurements. The three techniques were compared and evaluated as characterisation tools for such colloidal systems and also to identify transitions between the colloidal systems formed. A droplet ME was present at a low water volume fraction (phi w) in both systems (phi w < 0.15) as revealed by electron microscopy. At higher phi w values, LC structures were observed in micrographs of samples taken from the cosurfactant-free system while the structure of samples from the cosurfactant-containing system was that of a bicontinuous ME. The viscosity of both systems increased with increasing phi w to 0.15 and flow was Newtonian. However, formation of LC in the cosurfactant-free system resulted in a dramatic increase in viscosity that was dependent on phi w and a change to pseudoplastic flow. In contrast, the viscosity of the bicontinuous ME was independent of phi w. Three different methods were used to estimate the percolation threshold from the conductivity data for the cosurfactant-containing system. The use of nonlinear curve fitting was found to be most useful yielding a value close to 0.15 for the phi w.

Effects of alcohols and diols on the phase behaviour of quaternary systems.

The aim of the current study was to investigate the effect of different co-surfactants on the phase behaviour of the pseudoternary system water:ethyl oleate:nonionic surfactant blend (sorbitan monolaurate/polyoxyethylene 20 sorbitan mono-oleate). Four aliphatic alcohols (1-propanol, 1-butanol, 1-hexanol and 1-octanol) and four 1, 2-alkanediols (1,2-propanediol, 1,2-pentanediol, 1,2-hexanediol and 1,2-octanediol) were used. The co-surfactant-free system forms two different colloidal structures, a water-in-oil microemulsion (w/o ME) and lamellar liquid crystals (LC) and two coarse dispersions, water-in-oil (w/o EM) and oil-in-water (o/w EM) emulsions. Microemulsion region area (%ME), liquid crystalline region area (%LC), amount of amphiphile blend required to produce a balanced microemulsion (%AMPH) and amount of water solubilised (%W) were used as assessment criteria to evaluate the co-surfactants. Seven calculated physico-chemical descriptors were used to represent the different co-surfactants. 1-butanol, 1,2-hexanediol and 1, 2-octanediol produced balanced MEs capable of solubilising a high percentage of both oil and water. A similarity was observed between the descriptors attributed to 1-butanol and 1,2-hexanediol. The requirements of a co-surfactant molecule to produce a balanced microemulsion were: HLB value 7.0-8.0, a carbon backbone of 4-6 atoms, percentage carbon of 60-65%, percentage oxygen of 20-30%, logP value 0.2-0.9 and log 1/S (S: aqueous solubility) close to zero.

Use of artificial neural networks to predict quaternery phase systems from limited experimental data.

The aim of the present work was to develop a method for predicting the phase behaviour of four component systems consisting of oil, water and two surfactants from a limited number of screening experiments. Investigations were conducted to asses the potential of artificial neural networks (ANNs) with back-propagation training algorithm to predict the phase behaviour of four component systems. Three inputs only (percentages of oil and water and HLB of the surfactant blend) and four outputs (oil in water emulsion, water in oil emulsion, microemulsion, and liquid crystals containing regions) were used. Samples used for training represented about 15% of the sampling space within the tetrahedron body. The network was trained by performing optimization of the number and size of the weights for neuron interconnections. The lowest error was obtained with 15 hidden neurons and after 4,500 training cycles. The trained ANN was tested on validation data and had an accuracy of 85.2-92.9%. In most cases the errors in the prediction were confined to points lying along the boundaries of regions and for the extrapolated predictions outside the ANNs 'experience'. This approach is shown to be highly successful and the ANNs have proven to be a useful tool for the prediction of the phase behaviour of quaternary systems with less experimental effort.