Neural network-based prediction and optimization of estradiol release from ethylene–vinyl acetate membranes

Dissolution from Ethylene Vinyl Acetate Copolymer Long-Acting Implants: Effect of Model Active Ingredient Size and Shape

In recent pharmaceutical applications, an active pharmaceutical ingredient (API) can be mixed with a polymer material to yield a composite long-acting drug-delivery device. These devices boast higher patient compliance, localized drug delivery, and lower dosage concentrations, which can increase patient safety. As a laboratory-safe option, calcium carbonate (CaCO₃) was used as a drug surrogate to mimic the release kinetics of a low-solubility API. The release of CaCO₃ from a poly(ethylene vinyl acetate) (EVA) polymer matrix was studied in ultra-high-purity water. The geometry of CaCO_3, along with the manufacturing technique, was manipulated to study the implications on surrogate drug release. It was found that injection molding proved to yield higher burst release, due to higher pressures achievable during manufacturing. The extrusion process can affect the surface concentration of the pharmaceutical ingredient when extruded through a water bath, resulting in a lower initial burst concentration. Regarding CaCO₃ geometry, the particle size was more critical than the surface area in terms of CaCO₃ release. Larger particles showed a higher release rate, though they also displayed higher variability in release. These data can be used to engineer specific release profiles when designing composite formulations and manufacturing methods for pharmaceutical-drug-delivery applications.

Efficient Prediction of In Vitro Piroxicam Release and Diffusion From Topical Films Based on Biopolymers Using Deep Learning Models and Generative Adversarial Networks

The purpose of this study was to simultaneously predict the drug release and skin permeation of Piroxicam (PX) topical films based on Chitosan (CTS), Xanthan gum (XG) and its Carboxymethyl derivatives (CMXs) as matrix systems. These films were prepared by the solvent casting method, using Tween 80 (T80) as a permeation enhancer. All of the prepared films were assessed for their physicochemical parameters, their in vitro drug release and ex vivo skin permeation studies. Moreover, deep learning models and machine learning models were applied to predict the drug release and permeation rates. The results indicated that all of the films exhibited good consistency and physicochemical properties. Furthermore, it was noticed that when T80 was used in the optimal formulation (F8) based on CTS-CMX3, a satisfactory drug release pattern was found where 99.97% of PX was released and an amount of 1.18 mg/cm² was permeated after 48 h. Moreover, Generative Adversarial Network (GAN) efficiently enhanced the performance of deep learning models and DNN was chosen as the best predictive approach with MSE values equal to 0.00098 and 0.00182 for the drug release and permeation kinetics, respectively. DNN precisely predicted PX dissolution profiles with f₂ values equal to 99.99 for all the formulations.

Investigating the Release of a Hydrophobic Peptide from Matrices of Biodegradable Polymers: An Integrated Method Approach

The objectives of this work were: (1) to select suitable compositions of tyrosine-derived polycarbonates for controlled delivery of voclosporin, a potent drug candidate to treat ocular diseases, (2) to establish a structure-function relationship between key molecular characteristics of biodegradable polymer matrices and drug release kinetics, and (3) to identify factors contributing in the rate of drug release. For the first time, the experimental study of polymeric drug release was accompanied by a hierarchical sequence of three computational methods. First, suitable polymer compositions used in subsequent neural network modeling were determined by means of response surface methodology (RSM). Second, accurate artificial neural network (ANN) models were built to predict drug release profiles for fifteen polymers located outside the initial design space. Finally, thermodynamic properties and hydrogen-bonding patterns of model drug-polymer complexes were studied using molecular dynamics (MD) technique to elucidate a role of specific interactions in drug release mechanism. This research presents further development of methodological approaches to meet challenges in the design of polymeric drug delivery systems.

Design of skin penetration enhancers using replacement methods for the selection of the molecular descriptors

Transdermal delivery of certain drugs is challenging because of skin barrier resistance. This study focuses on the implementation of feature-selection algorithms to design chemical penetration enhancers. A database, consisting of 145 polar and nonpolar chemicals, was chosen for the investigation. Replacement, enhanced replacement and stepwise algorithms were applied to identify relevant structural properties of these compounds. The descriptors were calculated using Molecular Modeling Pro™ Plus. Based on the coefficient of determination, the replacement methods outperformed the stepwise approach in selecting the features that best correlated with the flux enhancement ratio. An artificial neural network model was built to map a subset of descriptors from sixty-one nonpolar enhancers onto the output vector. The R² value improved from 0.68, for a linear model, to 0.74, which shows that the improved framework might be effective in the design of compounds with user-defined properties.

Novel approaches to neural and evolutionary computing in pharmaceutical formulation: challenges and new possibilities

The development of commercial pharmaceutical formulations can involve extensive experimentation that generates a large amount of data. Understanding such data and discovering the key relationships within them can be a complex process, adding considerably to the time and expense taken to get a product to market. However, new computational techniques such as neural and evolutionary computing have the potential to accelerate the mining and modeling of data, and these methodologies are now being packaged in a way that makes them readily accessible to the product formulator. This article outlines the basis of these technologies and reviews their use in pharmaceutical formulation, showing that they have gained acceptance as practical research tools, especially when integrated with complementary optimization and visualization tools. Neural and evolutionary computing are gaining widespread acceptance in the field of pharmaceutical formulation, with results being comparable with, or better than, those from traditional statistical methods.

Application of artificial neural networks for the estimation of tumour characteristics in biological tissues

BACKGROUND

Artificial tactile sensing is a method in which the existence of tumours in biological tissues can be detected and computerized inverse analyses used to produce 'forward results'.

METHODS

Three feed-forward neural networks (FFNN) have been developed for the estimation of tumour characteristics. Each network provides one of the three parameters of the tumour, i.e. diameter, depth and tumour:tissue stiffness ratio. A resilient back-propagation (RP) algorithm with a leave-one-out (LOO) cross-validation approach is used for training purposes.

RESULTS

The proposed inverse approach based on neural networks is a reliable and efficient tool for diagnostic tests in order to accurately estimate the basic parameters of the tumour in the tissue.

CONCLUSION

There is a non-linear correlation between the tumour characteristics and their effects on the extracted features. In general, reliable estimation of tumour stiffness is obtained when the depth of tumour is small.

Repeated applications of a transdermal patch: Analytical solution and optimal control of the delivery rate

The integral transform technique was implemented to solve a mathematical model developed for percutaneous drug absorption. The model included repeated application and removal of a patch from the skin. Fick's second law of diffusion was used to study the transport of a medicinal agent through the vehicle and subsequent penetration into the stratum corneum. Eigenmodes and eigenvalues were computed and introduced into an inversion formula to estimate the delivery rate and the amount of drug in the vehicle and the skin. A dynamic programming algorithm calculated the optimal doses necessary to achieve a desired transdermal flux. The analytical method predicted profiles that were in close agreement with published numerical solutions and provided an automated strategy to perform therapeutic drug monitoring and control.