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

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

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.Published versio

Data-driven modeling of the bicalutamide dissolution from powder systems

Low solubility of active pharmaceutical compounds (APIs) remains an important challenge in dosage form development process. In the manuscript, empirical models were developed and analyzed in order to predict dissolution of bicalutamide (BCL) from solid dispersion with various carriers. BCL was chosen as an example of a poor watersoluble API. Two separate datasets were created: one from literature data and another based on in-house experimental data. Computational experiments were conducted using artificial intelligence tools based on machine learning (AI/ML) with a plethora of techniques including artificial neural networks, decision trees, rule-based systems, and evolutionary computations. The latter resulting in classical mathematical equations provided models characterized by the lowest prediction error. In-house data turned out to be more homogeneous, as well as formulations were more extensively characterized than literature-based data. Thus, in-house data resulted in better models than literature-based data set. Among the other covariates, the best model uses for prediction of BCL dissolution profile the transmittance from IR spectrum at 1260 cm−1 wavenumber. Ab initio modeling–based in silico simulations were conducted to reveal potential BCL–excipients interaction. All crucial variables were selected automatically by AI/ML tools and resulted in reasonably simple and yet predictive models suitable for application in Quality by Design (QbD) approaches. Presented data-driven model development using AI/ML could be useful in various problems in the field of pharmaceutical technology, resulting in both predictive and investigational tools revealing new knowledge

Evolutionary algorithms in modeling aerodynamic properties of spray-dried microparticulate systems

Spray drying is a single step process in which solutions or dispersions are converted into dry particles. It is widely used in pharmaceutical technology to produce inhalable particles. Dry particle behavior during inhalation, described as the emitted dose (ED) and fine particle fraction (FPF), is determined in vitro by standardized procedures. A large number of factors influencing the spray drying process and particle interaction makes it difficult to predict the final product properties in advance. This work presents the development of predictive models based on experimental data obtained by aerodynamic assessment of respirable dry powders. Developed models were tested according to the 10-fold cross-validation procedure and yielded good predictive ability. Both models were characterized by normalized root-mean-square error (NRMSE) below 8.50% and coefficient of determination (R2) above 0.90. Moreover, models were analyzed to establish a relationship between spray drying process parameters and the final product quality measures. Presented work describes the strategy of implementing the evolutionary algorithms in empirical model’s development. Obtained models can be applied as an expert system during pharmaceutical formulation development. The models have the potential for product optimization and a knowledge extraction to improve final quality of the drug

Quantitative Assessment of the Physiological Parameters Influencing QT Interval Response to Medication: Application of Computational Intelligence Tools

Human heart electrophysiology is complex biological phenomenon, which is indirectly assessed by the measured ECG signal. ECG trace is further analyzed to derive interpretable surrogates including QT interval, QRS complex, PR interval, and T wave morphology. QT interval and its modification are the most commonly used surrogates of the drug triggered arrhythmia, but it is known that the QT interval itself is determined by other nondrug related parameters, physiological and pathological. In the current study, we used the computational intelligence algorithms to analyze correlations between various simulated physiological parameters and QT interval. Terfenadine given concomitantly with 8 enzymatic inhibitors was used as an example. The equation developed with the use of genetic programming technique leads to general reasoning about the changes in the prolonged QT. For small changes of the QT interval, the drug-related IKr and ICa currents inhibition potentials have major impact. The physiological parameters such as body surface area, potassium, sodium, and calcium ions concentrations are negligible. The influence of the physiological variables increases gradually with the more pronounced changes in QT. As the significant QT prolongation is associated with the drugs triggered arrhythmia risk, analysis of the role of physiological parameters influencing ECG seems to be advisable

Neural modeling of the in vitro release of drugs from sustained release dosage forms

Celem pracy było stworzenie modelu neuronowego umożliwiającego przewidywanie przebiegu profilu uwalniania różnych substancji leczniczych z doustnych postaci leku o przedłużonym uwalnianiu. Na podstawie doniesień z piśmiennictwa przygotowano bazę wiedzy dla modeli neuronowych zawierającą przebieg 151 profili uwalniania dla 92 formulacji. Do opisu składu jakościowego formulacji wykorzystano dwa zestawy deskryptorów molekularnych obliczonych przy pomocy programów: (1) Calculator Plugins firmy ChemAxon oraz (2) PaDEL. Za pomocą analizy wrażliwościowej modeli neuronowych dokonano selekcji zmiennych kluczowych, dzięki czemu zredukowany został rozmiar wektora wejściowego i poprawione zdolności generalizacyjne. Te ostatnie testowano przy wykorzystaniu procedury 10-krotnego wzajemnego sprawdzania. Modelowanie neuronowe przeprowadzono za pomocą dwóch programów: Nets2012 oraz R: monmlp uruchamianych na 21 stacjach roboczych skonfigurowanych w środowisko gridowe. Stworzone modele zorganizowano w struktury wyższego rzędu, tzw. komitety ekspertów. Błąd generalizacji RMSE dla najlepszego modelu wyniósł 13,84 a współczynnik determinacji R2 odpowiednio 0,82, co potwierdza dobrą jakość uzyskanego modelu. Wykazano, że uogólnione podejście do tworzenia reprezentacji numerycznej składu jakościowego formulacji za pomocą deskryptorów molekularnych stwarza możliwość zastosowania modeli do predykcji profili uwalniania dla nieznanych systemowi substancji leczniczych oraz pomocniczych.The aim of this study was to build a neural model that could predict dissolution profile of various drugs from sustained release oral dosage forms. The knowledge base for neural models based on the literature and contained 151 dissolution profiles for 92 different formulations. In order to describe qualitative composition of the formulations, two sets of molecular descriptors calculated by ChemAxon's Calculator Plugins and PaDEL respectively, were used. Employment of sensitivity analysis of the neural models allowed to select crucial variables for the problem, which resulted in the reduction of the input vector and improvement of generalization ability of the models. The latter was tested with use of the 10-fold cross validation procedure. Neural modeling was conducted using two programs: Nets2012 and R: monmlp running on the 21 workstations configured into the grid environment. The neural models were organized into the higher order structure, so-called expert committees. The best model achieved generalization error RMSE = 13.84 and the coefficient of determination R2 = 0.82, which confirmed its good performance. It was proved that generalized approach to the numerical representation of the qualitative composition of the formulation, by use of the molecular descriptors, allows prediction of the release profiles for unknown drugs and excipients

Computational intelligence for predicting biological effects of drug absorption in lungs

Recently, the lungs have been extensively examined as a route for delivering drugs (active pharmaceutical ingredients, APIs) into the bloodstream; this is mainly due to the possibility of the noninvasive administration of macromolecules such as proteins and peptides. The absorption mechanisms of chemical compounds in the lungs are still not fully understood, which makes pulmonary formulation composition development challenging. This manuscript presents the development of an empirical model capable of predicting the excipients’ influence on the absorption of drugs in the lungs. Due to the complexity of the problem and the not-fully-understood mechanisms of absorption, computational intelligence tools were applied. As a result, a mathematical formula was established and analyzed. The normalized root-mean-squared error (NRMSE) and R2 of the model were 4.57%, and 0.83, respectively. The presented approach is beneficial both practically by developing an in silico predictive model and theoretically by gaining knowledge of the influence of APIs and excipient structure on absorption in the lungs

Computational intelligence for prediction of biological effects of drugs absorption in lungs

Recently, the lungs have been extensively examined as a route for delivering drugs (active pharmaceutical ingredients, APIs) into the bloodstream; this is mainly due to the possibility of the noninvasive administration of macromolecules such as proteins and peptides. The absorption mechanisms of chemical compounds in the lungs are still not fully understood, which makes pulmonary formulation composition development challenging. This manuscript presents the development of an empirical model capable of predicting the excipients’ influence on the absorption of drugs in the lungs. Due to the complexity of the problem and the not-fully-understood mechanisms of absorption, computational intelligence tools were applied. As a result, a mathematical formula was established and analyzed. The normalized root-mean-squared error (NRMSE) and R2 of the model were 4.57%, and 0.83, respectively. The presented approach is beneficial both practically by developing an in silico predictive model and theoretically by gaining knowledge of the influence of APIs and excipient structure on absorption in the lungs

Computational intelligence for predicting biological effects of drug absorption in lungs

Recently, the lungs have been extensively examined as a route for delivering drugs (active pharmaceutical ingredients, APIs) into the bloodstream; this is mainly due to the possibility of the noninvasive administration of macromolecules such as proteins and peptides. The absorption mechanisms of chemical compounds in the lungs are still not fully understood, which makes pulmonary formulation composition development challenging. This manuscript presents the development of an empirical model capable of predicting the excipients’ influence on the absorption of drugs in the lungs. Due to the complexity of the problem and the not-fully-understood mechanisms of absorption, computational intelligence tools were applied. As a result, a mathematical formula was established and analyzed. The normalized root-mean-squared error (NRMSE) and R2 of the model were 4.57%, and 0.83, respectively. The presented approach is beneficial both practically by developing an in silico predictive model and theoretically by gaining knowledge of the influence of APIs and excipient structure on absorption in the lungs