Quantitative and qualitative prediction of corneal permeability for drug-like compounds (original) (raw)
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Prediction of corneal permeability using artificial neural networks
2003
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 coef...
Prediction of the Corneal Permeability of Drug-Like Compounds
Pharmaceutical Research, 2010
Purpose To develop a computational model for optimisation of low corneal permeability, which is a key feature in ocular drug development. Methods We have used multivariate analysis to build corneal permeability models based on a structurally diverse set of 58 drug-like compounds. Results According to the models, the most important parameters for permeability are logD at physiologically relevant pH and the number of hydrogen bonds that can be formed. Combining these descriptors resulted in models with Q 2 and R 2 values ranging from 0.77 to 0.79. The predictive capability of the models was verified by estimating the corneal permeability of an external data set of 11 compounds and by using predicted permeability values to calculate the aqueous humour concentrations in the steady-state of seven compounds. The predicted values correlated well with experimental values. Conclusion The developed models are useful in early drug development to predict the corneal permeability and steadystate drug concentration in aqueous humor without experimental data.
Journal of Biomedicine and Biotechnology, 2011
This study was undertaken to determinein vivopermeability coefficients for fluoroquinolones and to assess its correlation with the permeability derived using reported models in the literature. Further, the aim was to develop novel QSPR model to predict corneal permeability for fluoroquinolones and test its suitability on other training sets. Thein vivopermeability coefficient was determined using cassette dosing (N-in-One) approach for nine fluoroquinolones (norfloxacin, ciprofloxacin, lomefloxacin, ofloxacin, levofloxacin, sparfloxacin, pefloxacin, gatifloxacin, and moxifloxacin) in rabbits. The correlation between corneal permeability derived usingin vivostudies with that derived from reported models was determined. Novel QSPR-based model was developed usingin vivocorneal permeability along with other molecular descriptors. The suitability of developed model was tested onβ-blockers (n=15). The model showed better prediction of corneal permeability for fluoroquinolones(r2>0.9)as...
E-Journal of Chemistry, 2011
Parallel artificial membrane permeation assays (PAMPA) have been extensively utilized to determine the drug permeation potentials. In the present work, the permeation of miscellaneous drugs measured as flux by PAMPA (logF) of 94 drugs, are predicted by quantitative structure property relationships modeling based on a variety of calculated theoretical descriptors, which screened and selected by genetic algorithm (GA) variable subset selection procedure. These descriptors were used as inputs for generated artificial neural networks. After generation, optimization and training of artificial neural network (5:3:1), it was used for the prediction of logF for the training, test and validation sets. The standard error for the GA-ANN calculated logF for training, test and validation sets are 0.17, 0.028 and 0.15 respectively, which are smaller than those obtained by GA-MLR model (0.26, 0.051 and 0.22, respectively). Results obtained reveal the reliability and good predictably of neural netw...
Mechanisms of corneal drug penetration III: Modeling of molecular transport
Journal of Pharmaceutical Sciences, 1988
D A model relating the parameters of permeability coefficient in the cornea with partition coefficient and molecular weight of the penetrating species is presented. The development of the model is unique in that it includes the availability of a "pore" pathway with the corresponding kinetic data to substantiate this premise. The "pore" pathway is applied to small hydrophilic compounds and assumes that an aqueous diffusional space is available for transport of these compounds. This is in contrast to an alternate "partitioning" mechanism which is the most probable route of transport for larger or more lipophilic entities. The model is consistent with published data from this and other laboratories.
Journal of Pharmaceutical Sciences, 1984
0 Current understanding of the mechanism of corneal penetration by organic molecules proposes the epithelial layer as the rate-limiting membrane for water-soluble compounds and the stromal layer as rate limiting for lipid-soluble compounds. This suggests that the relationship between corneal permeability and the logarithm of oil/water partition coefficients, for a series of drugs, should not be the typical, single, continuous, parabolic-shaped curve. Corneal penetration studies have been conducted in unanesthetized albino rabbits using various organic compounds, representing five orders of magnitude in partition coefficient, at a constant concentration of 4 X I o-s M dispensed in either a 1-or 90-centipoise (cps) solution. It has been shown that for non-ionizable compounds, a pair of bell-shaped curves were generated, one for lipid-soluble and one for water-soluble compounds. Small water-soluble species demonstrate very high apparent permeabilities, which may relate to the presence of aqueous pores or other paracellular drug movement. Penetration of ionizable compounds does not appear to correlate well with the structural relationships invoked for un-ionized compounds. Consistent with the proposed mechanisms of corneal penetration, oil-soluble drug substances show no improvement in drug bioavailability when dosed from a 90-cps solution, and water-soluble drugs show a modest improvement in ocular drug bioavailability. Small water-soluble substances demonstrate no improvement due to their already high bioavailability. The importance of nonproductive absorption and precorneal drainage on bioavailability is addressed.
Drug Delivery and Translational Research, 2019
In this work, topical matrix patches of diclofenac sodium (DS) were formulated by the solvent casting method using different ratios of chitosan (CTS) and kappa carrageenan (KC). Propylene glycol and tween 80 were used as a plasticizer and permeation enhancer, respectively. The drug matrix film was cast on a polyvinyl alcohol backing membrane. All the patches were evaluated for their physicochemical characteristics (thickness, folding endurance, flatness, drug content, tensile strength, bioadhesion, moisture content, and moisture uptake), along with their in vitro release and in vitro skin permeation studies. Franz diffusion cells were used to conduct the in vitro permeation studies. The artificial neural network (ANN) model was applied to simultaneously predict the DS release and the ex vitro skin permeation kinetics. The formulated patches showed good physicochemical properties. Out of all the studied patches, F6 presented sustained permeation in 32 h and was selected as the best formulation. The ANN model accurately predicted both the kinetic release and the skin permeability of DS from each formulation. This performance was demonstrated by the obtained R 2 = 0.9994 and R 2 = 0.9798 for release and permeation kinetics modeling, respectively, with root mean square error (RMSE) = 3.46 × 10 −5 .
CON4EI: Bovine corneal opacity and permeability test method
Toxicology Letters, 2016
Assessment of ocular irritation potential is an international regulatory requirement in the safety evaluation of industrial and consumer products. None in vitro ocular irritation assays are capable of fully categorizing chemicals as stand-alone. Therefore, the CEFIC-LRI-AIMT6-VITO CON4EI consortium assessed the reliability of eight in vitro test methods and computational models as well as established a tiered-testing strategy. One of the selected assays was Bovine Corneal Opacity and Permeability (BCOP). In this project, the same corneas were used for measurement of opacity using the OP-KIT, the Laser Light-Based Opacitometer (LLBO) and for histopathological analysis. The results show that the accuracy of the BCOP OP-KIT in identifying Cat 1 chemicals was 73.8% while the accuracy was 86.3% for No Cat chemicals. BCOP OP-KIT false negative results were often related to an in vivo classification driven by conjunctival effects only. For the BCOP LLBO, the accuracy in identifying Cat 1 chemicals was 74.4% versus 88.8% for No Cat chemicals. The BCOP LLBO seems very promising for the identification of No Cat liquids but less so for the identification of solids. Histopathology as an additional endpoint to the BCOP test method does not reduce the false negative rate substantially for in vivo Cat 1 chemicals.
Modelling the permeability of polymers: a neural network approach
Journal of Membrane Science, 1994
In this short communication, the prediction of the permeability of carbon dioxide through different polymers using a neural network is studied. A neural network is a numeric-mathematical construction that can model complex non-linear relationships. Here it is used to correlate the IR spectrum of a polymer to ita permeability. The underlying assumption is that the chemical information hidden in the IR spectrum is sufficient for the prediction. The best neural network investigated so far does indeed show predictive capabilities.
Journal of pharmaceutical and …, 2000
Artificial neural networks (ANNs) are biologically inspired computer programs designed to simulate the way in which the human brain processes information. ANNs gather their knowledge by detecting the patterns and relationships in data and learn (or are trained) through experience, not from programming. An ANN is formed from hundreds of single units, artificial neurons or processing elements (PE), connected with coefficients (weights), which constitute the neural structure and are organised in layers. The power of neural computations comes from connecting neurons in a network. Each PE has weighted inputs, transfer function and one output. The behavior of a neural network is determined by the transfer functions of its neurons, by the learning rule, and by the architecture itself. The weights are the adjustable parameters and, in that sense, a neural network is a parameterized system. The weighed sum of the inputs constitutes the activation of the neuron. The activation signal is passed through transfer function to produce a single output of the neuron. Transfer function introduces non-linearity to the network. During training, the inter-unit connections are optimized until the error in predictions is minimized and the network reaches the specified level of accuracy. Once the network is trained and tested it can be given new input information to predict the output. Many types of neural networks have been designed already and new ones are invented every week but all can be described by the transfer functions of their neurons, by the learning rule, and by the connection formula. ANN represents a promising modeling technique, especially for data sets having non-linear relationships which are frequently encountered in pharmaceutical processes. In terms of model specification, artificial neural networks require no knowledge of the data source but, since they often contain many weights that must be estimated, they require large training sets. In addition, ANNs can combine and incorporate both literature-based and experimental data to solve problems. The various applications of ANNs can be summarised into classification or pattern recognition, prediction and modeling. Supervised associating networks can be applied in pharmaceutical fields as an alternative to conventional response surface methodology. Unsupervised feature-extracting networks represent an alternative to principal component analysis. Non-adaptive unsupervised networks are able to reconstruct their patterns when presented with noisy samples and can be used for image recognition. The potential applications of ANN methodology in the pharmaceutical sciences range from interpretation of analytical data, drug and dosage form design through biopharmacy to clinical pharmacy.