Aleksander Mendyk

KinetDS: An Open Source Software for Dissolution Test Data Analysis

Because drug quality is the focus for pharmaceutical industry and regulatory agencies, the in vitro dissolution test bec omes a standard tool for characterization of manufactured products. However, results of the dissolution test must be expressed in mathematical terms; this is realized by fitting various models to the cumulative dissolution curves. The models might be either mechanistic or empirical. The fitting process requires software (e.g., KinetDS) for automation and determination of possible release mechanisms of drug substances from the dosage forms. The software is FOSS (Free Open Source Software) and is available at http://sourceforge.net/projects/kinetds/.

Neural network as a decision support system in the development of pharmaceutical formulation-focus on solid dispersions

The aim of the study was to create simple, neural model of ketoprofen (Ket) dissolution from solid dispersions (SD) and physical mixtures (PM), which could be an aid in prospective development of pharmaceutical formulation. An application of artificial neural networks (ANNs) methodology was investigated using experimental data. Backpropagation (BP) ANNs with two hidden layers, hyperbolic tangent as the activation function and Hampels target function were studied. Neuro-fuzzy systems were also applied. As the input variables formulation type and preparation technology as well as qualitative and quantitative composition of SD and physical mixtures (PM) were selected. Direct incorporation of physicochemical properties of excipients (connectivity index, CI) enhanced ANNs model usability. Further improvement of neural model was achieved by input variables reduction performed on the basis of the sensitivity analysis. ANNs functions as decision support system in prospective ketoprofen SD formulation as well as data-mining tool were confirmed.

Virtual population generator for human cardiomyocytes parameters: in silico drug cardiotoxicity assessment

Background: The anatomical and histological parameters of the human ventricle depend on many factors including age and sex. Myocyte volume and electric capacitance are significant physiological parameters of left ventricle cardiomyocyte mathematical models. They allow the assessment of inter-individual variability during in vitro–in vivo extrapolation of the drug cardiotoxic effect. Objective: The current research was carried out to analyze the relationship between age, human left ventricle cardiomyocyte volume, and electric capacitance in a healthy population. Methods: In order to collect data describing cardiomyocyte volume and membrane area, literature searches were performed. It was assumed that the cardiomyocyte volume (VOL) and area (AREA) distribution have non-negative support and are skewed to the right. A log-linear model with constant variance was used. A simulation study was run to assess the influence of physiological parameters on action potential duration. Results: The coefficient of determination for the proposed model R2 = 0.95, that is, 95% of the variability observed in log cardiomyocyte volume can be explained by the estimated regression equation. To allow simple calculation and model performance validation, a simple Excel file was developed (Supplementary material). Conclusions: To the best of our knowledge, there is no other model available, combining age, cardiomyocyte volume, and area. The main limitations of the proposed models result from the assumptions made at the data analysis stage. The limited amount of information available in the literature and the lack of differentiation between sexes results in one common equation. The developed model is a part of the computational system for drug cardiotoxicity assessment.

Heuristic modeling of macromolecule release from PLGA microspheres

Dissolution of protein macromolecules from poly(lactic-co-glycolic acid) (PLGA) particles is a complex process and still not fully understood. As such, there are difficulties in obtaining a predictive model that could be of fundamental significance in design, development, and optimization for medical applications and toxicity evaluation of PLGA-based multiparticulate dosage form. In the present study, two models with comparable goodness of fit were proposed for the prediction of the macromolecule dissolution profile from PLGA micro- and nanoparticles. In both cases, heuristic techniques, such as artificial neural networks (ANNs), feature selection, and genetic programming were employed. Feature selection provided by fscaret package and sensitivity analysis performed by ANNs reduced the original input vector from a total of 300 input variables to 21, 17, 16, and eleven; to achieve a better insight into generalization error, two cut-off points for every method was proposed. The best ANNs model results were obtained by monotone multi-layer perceptron neural network (MON-MLP) networks with a root-mean-square error (RMSE) of 15.4, and the input vector consisted of eleven inputs. The complicated classical equation derived from a database consisting of 17 inputs was able to yield a better generalization error (RMSE) of 14.3. The equation was characterized by four parameters, thus feasible (applicable) to standard nonlinear regression techniques. Heuristic modeling led to the ANN model describing macromolecules release profiles from PLGA microspheres with good predictive efficiency. Moreover genetic programming technique resulted in classical equation with comparable predictability to the ANN model.

Gastroretentive drug delivery systems with L-dopa based on carrageenans and hydroxypropylmethylcellulose.

A comprehensive study was conducted to investigate the effects of carrageenans, and hydroxypropylmethylcellulose (HPMC) on the properties of hydrodynamically balanced systems (HBS) containing L-dopa as a model drug. The novel integrated approach included measurements of: solvent uptake, erosion, apparent density and changes in the internal structure of dosage forms during dissolution test by means of a USP4 compatible MRI. Differences in water ingress into the matrices with pure carrageenans (ι, κ, λ) or low viscous HPMC, were detected by non-invasive magnetic resonance imaging. Matrices based on carrageenans subjected to rapid hydration and erosion, were not able to maintain satisfactory floating properties for a sufficiently long period of time. The application of carrageenans in mixtures with HMC promoted water uptake by HBS formulations. The effect produced by varying the polymer blends composition on release of the L-dopa was also studied. Dissolution data was fitted to Korsmeyer-Peppas equation. For matrices containing mixtures of carrageenan and HPMC, the linear increase in the releasing rate constant, K, with the carrageenan content in the matrix was observed.

Prediction of the hERG potassium channel inhibition potential with use of artificial neural networks

New drug development is a complex and time consuming process. The procedure is informally divided into strongly inter-dependent phases beginning from chemical structure synthesis or bioengineering through ADME-Tox properties assessment, clinical trials, up to the market introduction. Recently more and more effort has been invested in the early toxicity assessment of the drugs being developed. Apart from relatively well known and widely researched groups of effects which include hepatotoxicity, immunotoxicity, genotoxicity new toxic effects have become deeply investigated. One of the possible and potentially dangerous cardiotoxic effects is triggered by drugs acquired long QT syndrome (LQTS) which can lead to the fatal ventricular arrhythmia what effected in withdrawal of several drugs from the market. In most drugs known causing the ECG (electrocardiography) interferences the effect results from inhibition of the fast potassium channels (encoded as hERG follow the gene name-human ERG). Therefore early prediction of the hERG channel-drug interaction potential has become a major pharmacological safety concern. The objective of this research was to develop a reliable empirical model for the - triggered by drugs - potassium channel inhibition prediction with use of the previously published and publicly available database. The input data consisted of in vitro research settings, drug chemical structure (molecular fingerprints) and physico-chemical parameters for all substances present in database. Artificial neural networks were chosen as the algorithms for the models development and back-propagation multi-layer perceptrons as well as neuro-fuzzy systems of Mamdani MISO (multiple input single output) type were tested. Classifiers were built on the training set containing 447 records, describing 175 various chemicals. Two test procedures were applied for the model performance assessment: standard 10-fold cross validation procedure and validation based on the external test set containing 45 records describing. Various activation functions were tested including hyperbolic tangent, sigma and fsr function. The performance of the best model estimated in 10-fold CV was 76% (78% for positive and 74% for negative output respectively). Neural network model with 3 and 2 cells in hidden layers respectively and sigma activation function properly predicted 89% instances from the external validation dataset. By neural model analysis it was possible to estimate quantitative relationship between cardiotoxicity risk potential of particular drug and its lipophilicity described as the logP value.

In vitro–in vivo extrapolation of drug-induced proarrhythmia predictions at the population level

Drug cardiotoxicity is a serious issue for patients, regulators, pharmaceutical companies and health service payers because they are all affected by its consequences. Despite the wide range of data they generate, existing approaches for cardiac safety testing might not be adequate and sufficiently cost-effective, probably as a result of the complexity of the problem. For this reason, translational tools (based on biophysically detailed, mathematical models) allowing for in vitro-in vivo extrapolation are gaining increasing interest. This current review describes approaches that can be used for cardiac safety assessment at the population level, by accounting for various sources of variability including kinetics of the compound of interest.

Generalized in vitro-in vivo relationship (IVIVR) model based on artificial neural networks

Background The aim of this study was to develop a generalized in vitro-in vivo relationship (IVIVR) model based on in vitro dissolution profiles together with quantitative and qualitative composition of dosage formulations as covariates. Such a model would be of substantial aid in the early stages of development of a pharmaceutical formulation, when no in vivo results are yet available and it is impossible to create a classical in vitro-in vivo correlation (IVIVC)/IVIVR. Methods Chemoinformatics software was used to compute the molecular descriptors of drug substances (ie, active pharmaceutical ingredients) and excipients. The data were collected from the literature. Artificial neural networks were used as the modeling tool. The training process was carried out using the 10-fold cross-validation technique. Results The database contained 93 formulations with 307 inputs initially, and was later limited to 28 in a course of sensitivity analysis. The four best models were introduced into the artificial neural network ensemble. Complete in vivo profiles were predicted accurately for 37.6% of the formulations. Conclusion It has been shown that artificial neural networks can be an effective predictive tool for constructing IVIVR in an integrated generalized model for various formulations. Because IVIVC/IVIVR is classically conducted for 2–4 formulations and with a single active pharmaceutical ingredient, the approach described here is unique in that it incorporates various active pharmaceutical ingredients and dosage forms into a single model. Thus, preliminary IVIVC/IVIVR can be available without in vivo data, which is impossible using current IVIVC/IVIVR procedures.

Analysis of pellet properties with use of artificial neural networks

The objective was to prepare neural models identifying relationships between formulation characteristics and pellet properties based on algorithmic approach of crucial variables selection and neuro-fuzzy systems application. The database consisted of information about 227 pellet formulations prepared by extrusion/spheronization method, with various model drugs and excipients. Cheminformatic description of excipients and model drugs was employed for numerical description of pellet formulations. Initial numbers of neural model inputs were up to around 3000. The inputs reduction procedure based on sensitivity analysis allowed to obtain less than 40 inputs for each model. The reduced models were subjects of fuzzy logic implementation resulting in logical rules tables providing human-readable rule sets applicable in future development of pellet formulations. Neural modeling enhanced knowledge about pelletization process and provided means for future computer-guided search for the optimal formulation.

Empirical modeling of the fine particle fraction for carrier-based pulmonary delivery formulations.

In vitro study of the deposition of drug particles is commonly used during development of formulations for pulmonary delivery. The assay is demanding, complex, and depends on: properties of the drug and carrier particles, including size, surface characteristics, and shape; interactions between the drug and carrier particles and assay conditions, including flow rate, type of inhaler, and impactor. The aerodynamic properties of an aerosol are measured in vitro using impactors and in most cases are presented as the fine particle fraction, which is a mass percentage of drug particles with an aerodynamic diameter below 5 μm. In the present study, a model in the form of a mathematical equation was developed for prediction of the fine particle fraction. The feature selection was performed using the R-environment package “fscaret”. The input vector was reduced from a total of 135 independent variables to 28. During the modeling stage, techniques like artificial neural networks, genetic programming, rule-based systems, and fuzzy logic systems were used. The 10-fold cross-validation technique was used to assess the generalization ability of the models created. The model obtained had good predictive ability, which was confirmed by a root-mean-square error and normalized root-mean-square error of 4.9 and 11%, respectively. Moreover, validation of the model using external experimental data was performed, and resulted in a root-mean-square error and normalized root-mean-square error of 3.8 and 8.6%, respectively.