SerotoninAI: Serotonergic System Focused, Artificial Intelligence-Based Application for Drug Discovery.

SerotoninAI is an innovative web application for scientific purposes focused on the serotonergic system. By leveraging SerotoninAI, researchers can assess the affinity (pKi value) of a molecule to all main serotonin receptors and serotonin transporters based on molecule structure introduced as SMILES. Additionally, the application provides essential insights into critical attributes of potential drugs such as blood-brain barrier penetration and human intestinal absorption. The complexity of the serotonergic system demands advanced tools for accurate predictions, which is a fundamental requirement in drug development. SerotoninAI addresses this need by providing an intuitive user interface that generates predictions of pKi values for the main serotonergic targets. The application is freely available on the Internet at https://serotoninai.streamlit.app/, implemented in Streamlit with all major web browsers supported. Currently, to the best of our knowledge, there is no tool that allows users to access affinity predictions for serotonergic targets without registration or financial obligations. SerotoninAI significantly increases the scope of drug development activities worldwide. The source code of the application is available at https://github.com/nczub/SerotoninAI_streamlit.

Artificial Intelligence-Based Quantitative Structure-Property Relationship Model for Predicting Human Intestinal Absorption of Compounds with Serotonergic Activity.

Oral medicines represent the largest pharmaceutical market area. To achieve a therapeutic effect, a drug must penetrate the intestinal walls, the main absorption site for orally delivered active pharmaceutical ingredients (APIs). Indeed, predicting drug absorption can facilitate candidate screening and reduce time to market. Algorithms are available with good prediction accuracy that however focus only on solubility. In this work, we focused on drug permeability looking at human intestinal absorption as a marker for intestinal bioavailability. Being of considerable therapeutic relevance, APIs with serotonergic activity were selected as a dataset. Due to process complexity, experimental data scarcity, and variability, we turned toward an artificial intelligence (AI)-based system, which is a hierarchical combination of classification and regression models. This combination of seemingly two models into a single system widens the space of molecules classified as highly permeable with high accuracy. The specialized and optimized system enables in silico and structure-based prediction with a high degree of certainty. Predictions in external validation allowed correct selection of the 38% of highly permeable molecules without any false positives. The proposed system based on AI represents a promising tool useful for oral drug screening at an early stage of drug discovery and development. Datasets and the obtained models are available on the GitHub platform (https://github.com/nczub/HIA_5-HT).

Investigating the Impact of Co-processed Excipients on the Formulation of Bromhexine Hydrochloride Orally Disintegrating Tablets (ODTs).

PURPOSE: Orodispersible tablets (orally disintegrating tablets, ODTs) have been used in pharmacotherapy for over 20 years since they overcome the problems with swallowing solid dosage forms. The successful formula manufactured by direct compression shall ensure acceptable mechanical strength and short disintegration time. Our research aimed to develop ODTs containing bromhexine hydrochloride suitable for registration in accordance with EMA requirements. METHODS: We examined the performance of five multifunctional co-processed excipients, i.e., F-Melt® C, F-Melt® M, Ludiflash®, Pharmaburst® 500 and Prosolv® ODT G2 as well as self-prepared physical blend of directly compressible excipients. We tested powder flow, true density, compaction characteristics and tableting speed sensitivity. RESULTS: The manufacturability studies confirmed that all the co-processed excipients are very effective as the ODT formula constituents. We noticed superior properties of both F-Melt's®, expressed by good mechanical strength of tablets and short disintegration time. Ludiflash® showed excellent performance due to low works of plastic deformation, elastic recovery and ejection. However, the tablets released less than 30% of the drug. Also, the self-prepared blend of excipients was found sufficient for ODT application and successfully transferred to production scale. Outcome of the scale-up trial revealed that the tablets complied with compendial requirements for orodispersible tablets. CONCLUSIONS: We proved that the active ingredient cannot be absorbed in oral cavity and its dissolution profiles in media representing upper part of gastrointestinal tract are similar to marketed immediate release drug product. In our opinion, the developed formula is suitable for registration within the well-established use procedure without necessity of bioequivalence testing.

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 water-soluble 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.

Physiologically based pharmacokinetic-quantitative systems toxicology and safety (PBPK-QSTS) modeling approach applied to predict the variability of amitriptyline pharmacokinetics and cardiac safety in populations and in individuals.

The physiologically based pharmacokinetic (PBPK) models allow for predictive assessment of variability in population of interest. One of the future application of PBPK modeling is in the field of precision dosing and personalized medicine. The aim of the study was to develop PBPK model for amitriptyline given orally, predict the variability of cardiac concentrations of amitriptyline and its main metabolite-nortriptyline in populations as well as individuals, and simulate the influence of those xenobiotics in therapeutic and supratherapeutic concentrations on human electrophysiology. The cardiac effect with regard to QT and RR interval lengths was assessed. The Emax model to describe the relationship between amitriptyline concentration and heart rate (RR) length was proposed. The developed PBPK model was used to mimic 29 clinical trials and 19 cases of amitriptyline intoxication. Three clinical trials and 18 cases were simulated with the use of PBPK-QSTS approach, confirming lack of cardiotoxic effect of amitriptyline in therapeutic doses and the increase in heart rate along with potential for arrhythmia development in case of amitriptyline overdose. The results of our study support the validity and feasibility of the PBPK-QSTS modeling development for personalized medicine.

The Relationship Between the Evolution of an Internal Structure and Drug Dissolution from Controlled-Release Matrix Tablets.

In the last decade, imaging has been introduced as a supplementary method to the dissolution tests, but a direct relationship of dissolution and imaging data has been almost completely overlooked. The purpose of this study was to assess the feasibility of relating magnetic resonance imaging (MRI) and dissolution data to elucidate dissolution profile features (i.e., kinetics, kinetics changes, and variability). Commercial, hydroxypropylmethyl cellulose-based quetiapine fumarate controlled-release matrix tablets were studied using the following two methods: (i) MRI inside the USP4 apparatus with subsequent machine learning-based image segmentation and (ii) dissolution testing with piecewise dissolution modeling. Obtained data were analyzed together using statistical data processing methods, including multiple linear regression. As a result, in this case, zeroth order release was found to be a consequence of internal structure evolution (interplay between region's areas-e.g., linear relationship between interface and core), which eventually resulted in core disappearance. Dry core disappearance had an impact on (i) changes in dissolution kinetics (from zeroth order to nonlinear) and (ii) an increase in variability of drug dissolution results. It can be concluded that it is feasible to parameterize changes in micro/meso morphology of hydrated, controlled release, swellable matrices using MRI to establish a causal relationship between the changes in morphology and drug dissolution. Presented results open new perspectives in practical application of combined MRI/dissolution to controlled-release drug products.

Roughness-controlled self-assembly of mannitol/LB agar microparticles by polymorphic transformation for pulmonary drug delivery.

Novel roughness-controlled mannitol/LB Agar microparticles were synthesized by polymorphic transformation and self-assembly method using hexane as the polymorphic transformation reagent and spray-dried mannitol/LB Agar microparticles as the starting material. As-prepared microparticles were characterized by Fourier transform infrared spectra (FTIR), X-ray diffraction spectra (XRD), differential scanning calorimetry (DSC), scanning electron microscopy (SEM), thermal gravimetric analysis (TGA), and Andersen Cascade Impactor (ACI). The XRD and DSC results indicate that after immersing spray-dried mannitol/LB Agar microparticles in hexane, ß-mannitol was completely transformed to α-mannitol in 1 h, and all the δ-mannitol was transformed to α form after 14 days. SEM shows that during the transformation the nanobelts on the spray-dried mannitol/LB Agar microparticles become more dispersed and the contour of the individual nanobelts becomes more noticeable. Afterward, the nanobelts self-assemble to nanorods and result in rod-covered mannitol/LB Agar microparticles. FTIR indicates new hydrogen bonds were formed among mannitol, LB Agar, and hexane. SEM images coupled with image analysis software reveal that different surface morphology of the microparticles have different drug adhesion mechanisms. Comparison of ACI results and image analysis of SEM images shows that an increase in the particle surface roughness can increase the fine particle fractions (FPFs) using the rod-covered mannitol microparticles as drug carriers. Transformed microparticles show higher FPFs than commercially available lactose carriers. An FPF of 28.6 ± 2.4% was achieved by microparticles transformed from spray-dried microparticles using 2% mannitol(w/v)/LB Agar as feed solution. It is comparable to the highest FPF reported in the literature using lactose and spray-dried mannitol as carriers.

Enhanced QSAR models for drug-triggered inhibition of the main cardiac ion currents.

The currently changing cardiac safety testing paradigm suggests, among other things, a shift towards using in silico models of cellular electrophysiology and assessment of a concomitant block of multiple ion channels. In this study, a set of four enhanced QSAR models have been developed: for the rapid delayed rectifying potassium current (IKr), slow delayed rectifying potassium current (IKs), peak sodium current (INa) and late calcium current (ICaL), predicting ion currents changes for the specific in vitro experiment from the 2D structure of the compounds. The models are a combination of both in vitro study parameters and physico-chemical descriptors, which is a novel approach in drug-ion channels interactions modeling. Their predictive power assessed in the enhanced, more demanding than standard procedure, 10-fold cross validation was reasonably high. Rough comparison with published pure in silico hERG interaction models shows that the quality of the model predictions does not differ from other models available in the public domain, however, it takes its advantage in accounting for inter-experimental settings variability. Developed models are implemented in the Cardiac Safety Simulator, a commercially available platform enabling the in vitro-in vivo extrapolation of the drugs proarrhythmic effect and ECG simulation. A more comprehensive assessment of the effects of the compounds on ion channels allows for making more informed decisions regarding the risk - and thus avoidance - of exclusion of potentially safe and effective drugs.

Computer-based prediction of the drug proarrhythmic effect: problems, issues, known and suspected challenges.

It is likely that computer modelling and simulations will become an element of comprehensive cardiac safety testing. Their role would be primarily the integration and the interpretation of previously gathered data. There are still unanswered questions and issues which we list and describe below. They include sources of data used for the development of the models as well as data utilized as input information, which can come from the in vitro studies and the quantitative structure-activity relationship models. The pharmacokinetics of the drugs in question play a crucial role as their active concentration should be considered, yet the question remains where is the right place to assess it. The pharmacodynamic angle includes complications coming from multiple drugs (i.e. active metabolites) acting in parallel as well as the type of interaction with (potentially) multiple affected channels. Once established, the model and the methodology of its use should be further validated, optimistically against individual data reported at the clinical level as the physiological, anatomical, and genetic parameters play a crucial role in the drug-triggered arrhythmia induction. All the abovementioned issues should be at least considered and-hopefully-resolved, to properly utilize the mathematical models for a cardiac safety assessment.

Integrated QSAR Models for Prediction of Serotonergic Activity: Machine Learning Unveiling Activity and Selectivity Patterns of Molecular Descriptors.

Understanding the features of compounds that determine their high serotonergic activity and selectivity for specific receptor subtypes represents a pivotal challenge in drug discovery, directly impacting the ability to minimize adverse events while maximizing therapeutic efficacy. Up to now, this process has been a puzzle and limited to a few serotonergic targets. One approach represented in the literature focuses on receptor structure whereas in this study, we followed another strategy by creating AI-based models capable of predicting serotonergic activity and selectivity based on ligands' representation by molecular descriptors. Predictive models were developed using Automated Machine Learning provided by Mljar and later analyzed through the SHAP importance analysis, which allowed us to clarify the relationship between descriptors and the effect on activity and what features determine selective affinity for serotonin receptors. Through the experiments, it was possible to highlight the most important features of ligands based on highly efficient models. These features are discussed in this manuscript. The models are available in the additional modules of the SerotoninAI application called "Serotonergic activity" and "Selectivity".

Slow delayed rectifying potassium current (IKs ) - analysis of the in vitro inhibition data and predictive model development.

The excitable cell membranes contain ion channels that allow the ions passage through the specific pores via a passive process. Assessment of the inhibition of the IKr (hERG) current is considered to be the main target during the drug development process, although there are other ionic currents for which drug-triggered modification can either potentiate or mask hERG channel blockade. Information describing the results of in vitro studies investigating the chemical-IKs current interactions has been developed in the current study. Based on the publicly available data sources, 145 records were collected. The final list of publications consists of 64 positions and refers to 106 different molecules connected with IKs current inhibition, with at least one IC50 value measured. Ultimately, 98 of the IC50 values expressed as absolute values were gathered. For 36 records the IC50 was expressed as a relative value. For the 11 remaining records, the inhibition was not clearly expressed. Based on the collected data the predictive models for the IC50 estimation were developed with the use of various algorithms. The extended Quantitative Structure-Activity Relationships (QSAR) methodology was applied and the in vitro research settings were included as independent variables, apart from the physico-chemical descriptors calculated with the use of the Marvin Calculator Plugins. The root mean squared error and normalized root mean squared error values for the best model (an expert system based on two independent artificial neural networks) were 0.86 and 14.04%, respectively. The model was further built into the ToxComp system, the ToxIVIVE tool specialized for cardiotoxicity assessment of drugs.

Application of automated machine learning in the identification of multi-target-directed ligands blocking PDE4B, PDE8A, and TRPA1 with potential use in the treatment of asthma and COPD.

Asthma and COPD are characterized by complex pathophysiology associated with chronic inflammation, bronchoconstriction, and bronchial hyperresponsiveness resulting in airway remodeling. A possible comprehensive solution that could fully counteract the pathological processes of both diseases are rationally designed multi-target-directed ligands (MTDLs), combining PDE4B and PDE8A inhibition with TRPA1 blockade. The aim of the study was to develop AutoML models to search for novel MTDL chemotypes blocking PDE4B, PDE8A, and TRPA1. Regression models were developed for each of the biological targets using "mljar-supervised". On their basis, virtual screenings of commercially available compounds derived from the ZINC15 database were performed. A common group of compounds placed within the top results was selected as potential novel chemotypes of multifunctional ligands. This study represents the first attempt to discover the potential MTDLs inhibiting three biological targets. The obtained results prove the usefulness of AutoML methodology in the identification of hits from the big compound databases.

Predictive model for L-type channel inhibition: multichannel block in QT prolongation risk assessment.

Evaluation of the proarrhythmic potential of an investigated compound is now an integral element of the safety profile required for the approval of new drugs. The human ether-à-go-go-related gene (hERG) channel blocking potency is regarded as a surrogate marker of the proarrhythmic risk at the early stages of the research and development process. However, there is no straight correlation between QT prolongation and TdP occurrence probability, and hERG inhibition potential can be an inadequate predictor of QT prolongation. The L-type calcium channel plays a pivotal role in cardiomyocytes' physiology. Thus the main aim of this study was to develop a predictive model for drug-triggered CaL channel inhibition and also the assessment of drug-multichannel interaction effects on the heart rate-corrected QT interval. The data set, consisting of 123 records describing in vitro experimental settings, measured IC50 values and calculated physico-chemical properties for 72 various chemicals, was collected. The models were tested in a modified 10-fold cross-validation procedure. The generalization ability of the best model was as follows: root mean squared error (RMSE) = 1.10, normalized root mean squared error (NRMSE) = 16.09%. Out of the 10 most important variables, 5 described conditions of the in vitro experiments thus their description and experiment's conditions standardization might be the key to the models better performance. The simulations performed with the ToxComp system showed that the hERG block alone causes concentration-dependent QT prolongation, whereas when multichannel block is regarded, the effect could be reversed. For that reason, the multichannel interaction of tested compounds should be taken into consideration, in order to make the proarrhythmic risk assessment more reliable.

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 R(2) = 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.

Do AutoML-Based QSAR Models Fulfill OECD Principles for Regulatory Assessment? A 5-HT1A Receptor Case.

The drug discovery and development process requires a lot of time, financial, and workforce resources. Any reduction in these burdens might benefit all stakeholders in the healthcare domain, including patients, government, and companies. One of the critical stages in drug discovery is a selection of molecular structures with a strong affinity to a particular molecular target. The possible solution is the development of predictive models and their application in the screening process, but due to the complexity of the problem, simple and statistical models might not be sufficient for practical application. The manuscript presents the best-in-class predictive model for the serotonin 1A receptor affinity and its validation according to the Organization for Economic Co-operation and Development guidelines for regulatory purposes. The model was developed based on a database with close to 9500 molecules by using an automatic machine learning tool (AutoML). The model selection was conducted based on the Akaike information criterion value and 10-fold cross-validation routine, and later good predictive ability was confirmed with an additional external validation dataset with over 700 molecules. Moreover, the multi-start technique was applied to test if an automatic model development procedure results in reliable results.

Formulation of Dosage Forms with Proton Pump Inhibitors: State of the Art, Challenges and Future Perspectives.

Since their introduction to pharmacotherapy, proton pump inhibitors (PPIs) have been widely used in the treatment of numerous diseases manifested by excessive secretion of gastric acid. Despite that, there are still unmet needs regarding their availability for patients of all age groups. Their poor stability hinders the development of formulations in which dose can be easily adjusted. The aim of this review is to describe the discovery and development of PPIs, discuss formulation issues, and present the contemporary solutions, possibilities, and challenges in formulation development. The review outlines the physicochemical characteristics of PPIs, connects them with pharmacokinetic and pharmacodynamic properties, and describes the stability of PPIs, including the identification of the most important factors affecting them. Moreover, the possibilities for qualitative and quantitative analysis of PPIs are briefly depicted. This review also characterizes commercial preparations with PPIs available in the US and EU. The major part of the review is focused on the presentation of the state of the art in the development of novel formulations with PPIs covering various approaches employed in this process: nanoparticles, microparticles, minitablets, pellets, bilayer, floating, and mucoadhesive tablets, as well as parenteral, transdermal, and rectal preparations. It also anticipates further possibilities in the development of PPIs dosage forms. It is especially addressed to the researchers developing new formulations containing PPIs, since it covers the most important formulary issues that need to be considered before a decision on the selection of the formula is made. It may help in avoiding unnecessary efforts in this process and choosing the best approach. The review also presents an up-to-date database of publications focused on the pharmaceutical technology of formulations with PPIs.

Puzzle out Machine Learning Model-Explaining Disintegration Process in ODTs.

Tablets are the most common dosage form of pharmaceutical products. While tablets represent the majority of marketed pharmaceutical products, there remain a significant number of patients who find it difficult to swallow conventional tablets. Such difficulties lead to reduced patient compliance. Orally disintegrating tablets (ODT), sometimes called oral dispersible tablets, are the dosage form of choice for patients with swallowing difficulties. ODTs are defined as a solid dosage form for rapid disintegration prior to swallowing. The disintegration time, therefore, is one of the most important and optimizable critical quality attributes (CQAs) for ODTs. Current strategies to optimize ODT disintegration times are based on a conventional trial-and-error method whereby a small number of samples are used as proxies for the compliance of whole batches. We present an alternative machine learning approach to optimize the disintegration time based on a wide variety of machine learning (ML) models through the H2O AutoML platform. ML models are presented with inputs from a database originally presented by Han et al., which was enhanced and curated to include chemical descriptors representing active pharmaceutical ingredient (API) characteristics. A deep learning model with a 10-fold cross-validation NRMSE of 8.1% and an R2 of 0.84 was obtained. The critical parameters influencing the disintegration of the directly compressed ODTs were ascertained using the SHAP method to explain ML model predictions. A reusable, open-source tool, the ODT calculator, is now available at Heroku platform.

Application and Multi-Stage Optimization of Daylight Polymer 3D Printing of Personalized Medicine Products.

Additive technologies have undoubtedly become one of the most intensively developing manufacturing methods in recent years. Among the numerous applications, the interest in 3D printing also includes its application in pharmacy for production of small batches of personalized drugs. For this reason, we conducted multi-stage pre-formulation studies to optimize the process of manufacturing solid dosage forms by photopolymerization with visible light. Based on tests planned and executed according to the design of the experiment (DoE), we selected the optimal quantitative composition of photocurable resin made of PEG 400, PEGDA MW 575, water, and riboflavin, a non-toxic photoinitiator. In subsequent stages, we adjusted the printer set-up and process parameters. Moreover, we assessed the influence of the co-initiators ascorbic acid or triethanolamine on the resin's polymerization process. Next, based on an optimized formulation, we printed and analyzed drug-loaded tablets containing mebeverine hydrochloride, characterized by a gradual release of active pharmaceutical ingredient (API), reaching 80% after 6 h. We proved the possibility of reusing the drug-loaded resin that was not hardened during printing and determined the linear correlation between the volume of the designed tablets and the amount of API, confirming the possibility of printing personalized modified-release tablets.

Curated Database and Preliminary AutoML QSAR Model for 5-HT1A Receptor.

Introduction of a new drug to the market is a challenging and resource-consuming process. Predictive models developed with the use of artificial intelligence could be the solution to the growing need for an efficient tool which brings practical and knowledge benefits, but requires a large amount of high-quality data. The aim of our project was to develop quantitative structure-activity relationship (QSAR) model predicting serotonergic activity toward the 5-HT1A receptor on the basis of a created database. The dataset was obtained using ZINC and ChEMBL databases. It contained 9440 unique compounds, yielding the largest available database of 5-HT1A ligands with specified pKi value to date. Furthermore, the predictive model was developed using automated machine learning (AutoML) methods. According to the 10-fold cross-validation (10-CV) testing procedure, the root-mean-squared error (RMSE) was 0.5437, and the coefficient of determination (R2) was 0.74. Moreover, the Shapley Additive Explanations method (SHAP) was applied to assess a more in-depth understanding of the influence of variables on the model's predictions. According to to the problem definition, the developed model can efficiently predict the affinity value for new molecules toward the 5-HT1A receptor on the basis of their structure encoded in the form of molecular descriptors. Usage of this model in screening processes can significantly improve the process of discovery of new drugs in the field of mental diseases and anticancer therapy.

Novel application of MRI technique combined with flow-through cell dissolution apparatus as supportive discriminatory test for evaluation of controlled release formulations.

Dissolution studies cannot distinguish phenomena occurring inside the dosage forms when studying formulation with similar dissolution profiles-such formulations can behave differently when considering their physical changes. The application of flow-through dissolution apparatus integrated with magnetic resonance imaging (MRI) system for discriminative evaluation of controlled release dosage forms with similar dissolution profiles was presented. Hydrodynamically balanced systems (HBS) containing L: -dopa and various grades hydroxypropyl methylcelluloses were prepared. The dissolution studies of L: -dopa were performed at high field (4.7 T) MR system with MR-compatible flow-through cell. MRI was done with 0.14 x 0.14 x 1-mm spatial resolution and temporal resolution of 10 min to record changes of HBS parameters during dissolution in 0.1 M HCl. Structural and geometrical changes were evaluated using the following parameters: total area of HBS cross-section, its Feret's diameter, perimeter and circularity, area of hydrogel layer, and "dry core" area. While the dissolution profiles of L: -dopa were similar, the image analysis revealed differences in the structural and geometrical changes of the HBS. The mechanism of drug release from polymeric matrices is a result of synergy of several different phenomena occurring during dissolution and may differ between formulations, yet giving similar dissolution profiles. A multivariate analysis was performed to create a model taking into account dissolution data, data from MRI, information about chemical structure, and polymer viscosity. It provided a single model for all the formulations which was confirmed to be competent. The presented method has merit as a potential Process Analytical Technology tool.