Development of an eco-friendly fluorescent probe for mefenamic acid sensing in pharmaceuticals and biofluids.

Mefenamic acid, renowned for its analgesic properties, stands as a reliable choice for alleviating mild to moderate pain. However, its versatility extends beyond pain relief, with ongoing research unveiling its promising therapeutic potential across diverse domains. A straightforward, environmentally friendly, and sensitive spectrofluorometric technique has been developed for the precise quantification of the analgesic medication, mefenamic acid. This method relies on the immediate reduction of fluorescence emitted by a probe upon interaction with varying concentrations of the drug. The fluorescent probe utilized, N-phenyl-1-naphthylamine (NPNA), was synthesized in a single step, and the fluorescence intensities were measured at 480 nm using synchronous fluorescence spectroscopy with a wavelength difference of 200 nm. Temperature variations and lifetime studies indicated that the quenching process was static. The calibration curve exhibited linearity within the concentration range of 0.50-9.00 µg/mL, with a detection limit of 60.00 ng/mL. Various experimental parameters affecting the quenching process were meticulously examined and optimized. The proposed technique was successfully applied to determine mefenamic acid in pharmaceutical formulations, plasma, and urine, yielding excellent recoveries ranging from 98% to 100.5%. The greenness of the developed method was evaluated using three metrics: the Analytical Eco-scale, AGREE, and the Green Analytical Procedure Index.

Expanding the Role of Chiral Drugs and Chiral Nanomaterials as a Potential Therapeutic Tool.

Chirality, the property of molecules having mirror-image forms, plays a crucial role in pharmaceutical and biomedical research. This review highlights its growing importance, emphasizing how chiral drugs and nanomaterials impact drug effectiveness, safety, and diagnostics. Chiral molecules serve as precise diagnostic tools, aiding in accurate disease detection through unique biomolecule interactions. The article extensively covers chiral drug applications in treating cardiovascular diseases, CNS disorders, local anesthesia, anti-inflammatories, antimicrobials, and anticancer drugs. Additionally, it explores the emerging field of chiral nanomaterials, highlighting their suitability for biomedical applications in diagnostics and therapeutics, enhancing medical treatments.

A hybrid evaluation of the intestinal absorption performance of compounds from molecular structure.

Intestinal absorption of compounds is significant in drug research and development. To evaluate this efficiently, a method combining mathematical modeling and molecular simulation was proposed, from the perspective of molecular structure. Based on the quantitative structure-property relationship study, the model between molecular structure and their apparent permeability coefficients was successfully constructed and verified, predicting intestinal absorption of drugs and interpreting decisive structural factors, such as AlogP98, Hydrogen bond donor and Ellipsoidal volume. The molecules with strong lipophilicity, less hydrogen bond donors and receptors, and small molecular volume are more easily absorbed. Then, the molecular dynamics simulation and molecular docking were utilized to study the mechanism of differences in intestinal absorption of drugs and investigate the role of molecular structure. Results indicated that molecules with strong lipophilicity and small volume interacted with the membrane at a lower energy and were easier to penetrate the membrane. Likewise, they had weaker interaction with P-glycoprotein and were easier to escape from it and harder to export from the body. More in, less out, is the main reason these molecules absorb well.

Dataset for quantum-mechanical exploration of conformers and solvent effects in large drug-like molecules.

We here introduce the Aquamarine (AQM) dataset, an extensive quantum-mechanical (QM) dataset that contains the structural and electronic information of 59,783 low-and high-energy conformers of 1,653 molecules with a total number of atoms ranging from 2 to 92 (mean: 50.9), and containing up to 54 (mean: 28.2) non-hydrogen atoms. To gain insights into the solvent effects as well as collective dispersion interactions for drug-like molecules, we have performed QM calculations supplemented with a treatment of many-body dispersion (MBD) interactions of structures and properties in the gas phase and implicit water. Thus, AQM contains over 40 global and local physicochemical properties (including ground-state and response properties) per conformer computed at the tightly converged PBE0+MBD level of theory for gas-phase molecules, whereas PBE0+MBD with the modified Poisson-Boltzmann (MPB) model of water was used for solvated molecules. By addressing both molecule-solvent and dispersion interactions, AQM dataset can serve as a challenging benchmark for state-of-the-art machine learning methods for property modeling and de novo generation of large (solvated) molecules with pharmaceutical and biological relevance.

DrugMetric: quantitative drug-likeness scoring based on chemical space distance.

The process of drug discovery is widely known to be lengthy and resource-intensive. Artificial Intelligence approaches bring hope for accelerating the identification of molecules with the necessary properties for drug development. Drug-likeness assessment is crucial for the virtual screening of candidate drugs. However, traditional methods like Quantitative Estimation of Drug-likeness (QED) struggle to distinguish between drug and non-drug molecules accurately. Additionally, some deep learning-based binary classification models heavily rely on selecting training negative sets. To address these challenges, we introduce a novel unsupervised learning framework called DrugMetric, an innovative framework for quantitatively assessing drug-likeness based on the chemical space distance. DrugMetric blends the powerful learning ability of variational autoencoders with the discriminative ability of the Gaussian Mixture Model. This synergy enables DrugMetric to identify significant differences in drug-likeness across different datasets effectively. Moreover, DrugMetric incorporates principles of ensemble learning to enhance its predictive capabilities. Upon testing over a variety of tasks and datasets, DrugMetric consistently showcases superior scoring and classification performance. It excels in quantifying drug-likeness and accurately distinguishing candidate drugs from non-drugs, surpassing traditional methods including QED. This work highlights DrugMetric as a practical tool for drug-likeness scoring, facilitating the acceleration of virtual drug screening, and has potential applications in other biochemical fields.

Prediction method of pharmacokinetic parameters of small molecule drugs based on GCN network model.

CONTEXT: Accurately predicting plasma protein binding rate (PPBR) and oral bioavailability (OBA) helps to better reveal the absorption and distribution of drugs in the human body and subsequent drug design. Although machine learning models have achieved good results in prediction accuracy, they often suffer from insufficient accuracy when dealing with data with irregular topological structures. METHODS: In view of this, this study proposes a pharmacokinetic parameter prediction framework based on graph convolutional networks (GCN), which predicts the PPBR and OBA of small molecule drugs. In the framework, GCN is first used to extract spatial feature information on the topological structure of drug molecules, in order to better learn node features and association information between nodes. Then, based on the principle of drug similarity, this study calculates the similarity between small molecule drugs, selects different thresholds to construct datasets, and establishes a prediction model centered on the GCN algorithm. The experimental results show that compared with traditional machine learning prediction models, the prediction model constructed based on the GCN method performs best on PPBR and OBA datasets with an inter-molecular similarity threshold of 0.25, with MAE of 0.155 and 0.167, respectively. In addition, in order to further improve the accuracy of the prediction model, GCN is combined with other algorithms. Compared to using a single GCN method, the distribution of the predicted values obtained by the combined model is highly consistent with the true values. In summary, this work provides a new method for improving the rate of early drug screening in the future.

DrugDomain: The evolutionary context of drugs and small molecules bound to domains.

Interactions between proteins and small organic compounds play a crucial role in regulating protein functions. These interactions can modulate various aspects of protein behavior, including enzymatic activity, signaling cascades, and structural stability. By binding to specific sites on proteins, small organic compounds can induce conformational changes, alter protein-protein interactions, or directly affect catalytic activity. Therefore, many drugs available on the market today are small molecules (72% of all approved drugs in the last 5 years). Proteins are composed of one or more domains: evolutionary units that convey function or fitness either singly or in concert with others. Understanding which domain(s) of the target protein binds to a drug can lead to additional opportunities for discovering novel targets. The evolutionary classification of protein domains (ECOD) classifies domains into an evolutionary hierarchy that focuses on distant homology. Previously, no structure-based protein domain classification existed that included information about both the interaction between small molecules or drugs and the structural domains of a target protein. This data is especially important for multidomain proteins and large complexes. Here, we present the DrugDomain database that reports the interaction between ECOD of human target proteins and DrugBank molecules and drugs. The pilot version of DrugDomain describes the interaction of 5160 DrugBank molecules associated with 2573 human proteins. It describes domains for all experimentally determined structures of these proteins and incorporates AlphaFold models when such structures are unavailable. The DrugDomain database is available online: http://prodata.swmed.edu/DrugDomain/.

Mechanisms in the photocatalytic breakdown of persistent pharmaceutical and pesticide molecules over TiO2-based photocatalysts: A review.

Titanium dioxide (TiO2) based photocatalysts have been widely used as a photocatalyst for the degradation of various persistent organic compounds in water and air. The degradation mechanism involves the generation of highly reactive oxygen species, such as hydroxyl radicals, which react with organic compounds to break down their chemical bonds and ultimately mineralize them into harmless products. In the case of pharmaceutical and pesticide molecules, TiO2and modified TiO2photocatalysis effectively degrade a wide range of compounds, including antibiotics, pesticides, and herbicides. The main downside is the production of dangerous intermediate products, which are not frequently addressed in the literature that is currently available. The degradation rate of these compounds by TiO2photocatalysis depends on factors such as the chemical structure of the compounds, the concentration of the TiO2catalyst, the intensity, the light source, and the presence of other organic or inorganic species in the solution. The comprehension of the degradation mechanism is explored to gain insights into the intermediates. Additionally, the utilization of response surface methodology is addressed, offering a potential avenue for enhancing the scalability of the reactors. Overall, TiO2photocatalysis is a promising technology for the treatment of pharmaceutical and agrochemical wastewater, but further research is needed to optimize the process conditions and to understand the fate and toxicity of the degradation products.

Multimodal Image Fusion Workflow Incorporating MALDI Imaging Mass Spectrometry and Microscopy for the Study of Small Pharmaceutical Compounds.

Multimodal imaging analyses of dosed tissue samples can provide more comprehensive insights into the effects of a therapeutically active compound on a target tissue compared to single-modal imaging. For example, simultaneous spatial mapping of pharmaceutical compounds and endogenous macromolecule receptors is difficult to achieve in a single imaging experiment. Herein, we present a multimodal workflow combining imaging mass spectrometry with immunohistochemistry (IHC) fluorescence imaging and brightfield microscopy imaging. Imaging mass spectrometry enables direct mapping of pharmaceutical compounds and metabolites, IHC fluorescence imaging can visualize large proteins, and brightfield microscopy imaging provides tissue morphology information. Single-cell resolution images are generally difficult to acquire using imaging mass spectrometry but are readily acquired with IHC fluorescence and brightfield microscopy imaging. Spatial sharpening of mass spectrometry images would thus allow for higher fidelity coregistration with other higher-resolution microscopy images. Imaging mass spectrometry spatial resolution can be predicted to a finer value via a computational image fusion workflow, which models the relationship between the intensity values in the mass spectrometry image and the features of a high-spatial resolution microscopy image. As a proof of concept, our multimodal workflow was applied to brain tissue extracted from a Sprague-Dawley rat dosed with a kratom alkaloid, corynantheidine. Four candidate mathematical models, including linear regression, partial least-squares regression, random forest regression, and two-dimensional convolutional neural network (2-D CNN), were tested. The random forest and 2-D CNN models most accurately predicted the intensity values at each pixel as well as the overall patterns of the mass spectrometry images, while also providing the best spatial resolution enhancements. Herein, image fusion enabled predicted mass spectrometry images of corynantheidine, GABA, and glutamine to approximately 2.5 µm spatial resolutions, a significant improvement compared to the original images acquired at 25 µm spatial resolution. The predicted mass spectrometry images were then coregistered with an H&E image and IHC fluorescence image of the µ-opioid receptor to assess colocalization of corynantheidine with brain cells. Our study also provides insights into the different evaluation parameters to consider when utilizing image fusion for biological applications.

Prediction of Human Liver Microsome Clearance with Chirality-Focused Graph Neural Networks.

In drug candidate design, clearance is one of the most crucial pharmacokinetic parameters to consider. Recent advancements in machine learning techniques coupled with the growing accumulation of drug data have paved the way for the construction of computational models to predict drug clearance. However, concerns persist regarding the reliability of data collected from public sources, and a majority of current in silico quantitative structure-property relationship models tend to neglect the influence of molecular chirality. In this study, we meticulously examined human liver microsome (HLM) data from public databases and constructed two distinct data sets with varying HLM data quantity and quality. Two baseline models (RF and DNN) and three chirality-focused GNNs (DMPNN, TetraDMPNN, and ChIRo) were proposed, and their performance on HLM data was evaluated and compared with each other. The TetraDMPNN model, which leverages chirality from 2D structure, exhibited the best performance with a test R2 of 0.639 and a test root-mean-squared error of 0.429. The applicability domain of the model was also defined by using a molecular similarity-based method. Our research indicates that graph neural networks capable of capturing molecular chirality have significant potential for practical application and can deliver superior performance.

Versatile Framework for Drug-Target Interaction Prediction by Considering Domain-Specific Features.

Predicting drug-target interactions (DTIs) is one of the crucial tasks in drug discovery, but traditional wet-lab experiments are costly and time-consuming. Recently, deep learning has emerged as a promising tool for accelerating DTI prediction due to its powerful performance. However, the models trained on limited known DTI data struggle to generalize effectively to novel drug-target pairs. In this work, we propose a strategy to train an ensemble of models by capturing both domain-generic and domain-specific features (E-DIS) to learn diverse domain features and adapt them to out-of-distribution data. Multiple experts were trained on different domains to capture and align domain-specific information from various distributions without accessing any data from unseen domains. E-DIS provides a comprehensive representation of proteins and ligands by capturing diverse features. Experimental results on four benchmark data sets in both in-domain and cross-domain settings demonstrated that E-DIS significantly improved model performance and domain generalization compared to existing methods. Our approach presents a significant advancement in DTI prediction by combining domain-generic and domain-specific features, enhancing the generalization ability of the DTI prediction model.

Potential of Powder Rheology for Detecting Unforeseen Cross-Contamination of Foreign Active Pharmaceutical Ingredients.

Unexpected cross-contamination by foreign components during the manufacturing and quality control of pharmaceutical products poses a serious threat to the stable supply of drugs and the safety of customers. In Japan, in 2020, a mix-up containing a sleeping drug went undetected by liquid chromatography during the final quality test because the test focused only on the main active pharmaceutical ingredient (API) and known impurities. In this study, we assessed the ability of a powder rheometer to analyze powder characteristics in detail to determine whether it can detect the influence of foreign APIs on powder flow. Aspirin, which was used as the host API, was combined with the guest APIs (acetaminophen from two manufacturers and albumin tannate) and subsequently subjected to shear and stability tests. The influence of known lubricants (magnesium stearate and leucine) on powder flow was also evaluated for standardized comparison. Using microscopic morphological analysis, the surface of the powder was observed to confirm physical interactions between the host and guest APIs. In most cases, the guest APIs were statistically detected due to characteristics such as their powder diameter, pre-milling, and cohesion properties. Furthermore, we evaluated the flowability of a formulation incorporating guest APIs for direct compression method along with additives such as microcrystalline cellulose, potato starch, and lactose. Even in the presence of several additives, the influence of the added guest APIs was successfully detected. In conclusion, powder rheometry is a promising method for ensuring stable product quality and reducing the risk of unforeseen cross-contamination by foreign APIs.

In Silico Assessment of Biomolecule Reactivity with Leachables.

Leachables in pharmaceutical products may react with biomolecule active pharmaceutical ingredients (APIs), for example, monoclonal antibodies (mAb), peptides, and ribonucleic acids (RNA), potentially compromising product safety and efficacy or impacting quality attributes. This investigation explored a series of in silico models to screen extractables and leachables to assess their possible reactivity with biomolecules. These in silico models were applied to collections of known leachables to identify functional and structural chemical classes likely to be flagged by these in silico approaches. Flagged leachable functional classes included antimicrobials, colorants, and film-forming agents, whereas specific chemical classes included epoxides, acrylates, and quinones. In addition, a dataset of 22 leachables with experimental data indicating their interaction with insulin glargine was used to evaluate whether one or more in silico methods are fit-for-purpose as a preliminary screen for assessing this biomolecule reactivity. Analysis of the data showed that the sensitivity of an in silico screen using multiple methodologies was 80%-90% and the specificity was 58%-92%. A workflow supporting the use of in silico methods in this field is proposed based on both the results from this assessment and best practices in the field of computational modeling and quality risk management.

Characterizing Extractables and Leachables Chemical Space to Support In Silico Toxicological Hazard Assessments.

This article describes the development of a representative dataset of extractables and leachables (E&L) from the combined Extractables and Leachables Safety Information Exchange (ELSIE) Consortium and the Product Quality Research Institute (PQRI) published datasets, representing a total of 783 chemicals. A chemical structure-based clustering of the combined dataset identified 142 distinct chemical classes with two or more chemicals across the combined dataset. The majority of these classes (105 chemical classes out of 142) contained chemicals from both datasets, whereas 8 classes contained only chemicals from the ELSIE dataset and 29 classes contain only chemicals from the PQRI dataset. This evaluation also identified classes containing chemicals that were flagged as potentially mutagenic as well as potent (strong or extreme) dermal sensitizers by in silico tools. The prevalence of alerting structures in the E&L datasets was approximately 9% (69 examples) for mutagens and 3% (25 examples) for potent sensitizers. This analysis showed that most (80%; 20 of 25) E&L predicted to be strong or extreme dermal sensitizers were also flagged as potential mutagens. Only two chemical classes, each containing three chemicals (alkyl bromides and isothiocyanates), were uniquely identified in the PQRI dataset and contained chemicals predicted to be potential mutagens and/or potent dermal sensitizers.

Oleogels: Versatile Novel Semi-Solid System for Pharmaceuticals.

Oleogels is a novel semi-solid system, focusing on its composition, formulation, characterization, and diverse pharmaceutical applications. Due to their stability, smoothness, and controlled release qualities, oleogels are frequently utilized in food, cosmetics, and medicinal products. Oleogels are meticulously formulated by combining oleogelators like waxes, fatty acids, ethyl cellulose, and phytosterols with edible oils, leading to a nuanced understanding of their impact on rheological characteristics. They can be characterized by methods like visual inspection, texture analysis, rheological measurements, gelation tests, and microscopy. The applications of oleogels are explored in diverse fields such as nutraceuticals, cosmetics, food, lubricants, and pharmaceutics. Oleogels have applications in topical, transdermal, and ocular drug delivery, showcasing their potential for revolutionizing drug administration. This review aims to enhance the understanding of oleogels, contributing to the evolving landscape of pharmaceutical formulations. Oleogels emerge as a versatile and promising solution, offering substantial potential for innovation in drug delivery and formulation practices.

Ultrasonic treatment of endocrine disrupting compounds, pharmaceuticals, and personal care products in water: An updated review.

Pharmaceuticals, personal care products (PPCPs), and endocrine-disrupting compounds (EDCs) have seen a recent sustained increase in usage, leading to increasing discharge and accumulation in wastewater. Conventional water treatment and disinfection processes are somewhat limited in effectively addressing this micropollutant issue. Ultrasonication (US), which serves as an advanced oxidation process, is based on the principle of ultrasound irradiation, exposing water to high-frequency waves, inducing thermal decomposition of H2O while using the produced radicals to oxidize and break down dissolved contaminants. This review evaluates research over the past five years on US-based technologies for the effective degradation of EDCs and PPCPs in water and assesses various factors that can influence the removal rate: solution pH, temperature of water, presence of background common ions, natural organic matter, species that serve as promoters and scavengers, and variations in US conditions (e.g., frequency, power density, and reaction type). This review also discusses various types of carbon/non-carbon catalysts, O3 and ultraviolet processes that can further enhance the degradation efficiency of EDCs and PPCPs in combination with US processes. Furthermore, numerous types of EDCs and PPCPs and recent research trends for these organic contaminants are considered.

Laplace Transform Based Modeling of Drug Delivery with Reversible Drug Binding in a Multilayer Tissue.

OBJECTIVE: Drug delivery from a drug-loaded device into an adjacent tissue is a complicated process involving drug transport through diffusion and advection, coupled with drug binding kinetics responsible for drug uptake in the tissue. This work presents a theoretical model to predict drug delivery from a device into a multilayer tissue, assuming linear reversible drug binding in the tissue layers. METHODS: The governing mass conservation equations based on diffusion, advection and drug binding in a multilayer cylindrical geometry are written, and solved using Laplace transformation. The model is used to understand the impact of various non-dimensional parameters on the amounts of bound and unbound drug concentrations as functions of time. RESULTS: Good agreement for special cases considered in past work is demonstrated. The effect of forward and reverse binding reaction rates on the multilayer drug binding process is studied in detail. The effect of the nature of the external boundary condition on drug binding and drug loss is also studied. For typical parameter values, results indicate that only a small fraction of drug delivered binds in the tissue. Additionally, the amount of bound drug rises rapidly with time due to early dominance of the forward reaction, reaches a maxima and then decays due to the reverse reaction. CONCLUSIONS: The general model presented here can account for other possible effects such as drug absorption within the device. Besides generalizing past work on drug delivery modeling, this work also offers analytical tools to understand and optimize practical drug delivery devices.

Large-Scale Screening of Pharmaceutical Compounds to Explore the Application Space of On-Tissue MALDI and MALDI-2 Mass Spectrometry.

The successful application of matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) in pharmaceutical research is strongly dependent on the detection of the drug of interest at physiologically relevant concentrations. Here we explored how insufficient sensitivity due to low ionization efficiency and/or the interaction of the drug molecule with the local biochemical environment of the tissue can be mitigated for many compound classes using the recently introduced MALDI-MSI coupled with laser-induced postionization, known as MALDI-2-MSI. Leveraging a MALDI-MSI screen of about 1,200 medicines/drug-like compounds from a broad range of medicinal application areas, we demonstrate a significant improvement in drug detection and the degree of sensitivity uplift by using MALDI-2 versus traditional MALDI. Our evaluation was made under simulated imaging conditions using liver homogenate sections as substrate, onto which the compounds were spotted to mimic biological conditions to the first order. To enable an evaluable detection by both MALDI and MALDI-2 for the majority of employed compounds, we spotted 1 µL of a 10 mM solution using a spotting robot and performed our experiments with a Bruker timsTOF fleX MALDI-2 instrument in both positive and negative ion modes. Specifically, we demonstrate using a large cohort of drug-like compounds that ∼60% of the tested compounds showed a more than 10-fold increase in signal intensity and ∼16% showed a more than 100-fold increase upon use of MALDI-2 postionization. Such increases in sensitivity could help advance pharmaceutical MALDI-MSI applications toward the single-cell level.

Drug-target interaction predictions with multi-view similarity network fusion strategy and deep interactive attention mechanism.

MOTIVATION: Accurately identifying the drug-target interactions (DTIs) is one of the crucial steps in the drug discovery and drug repositioning process. Currently, many computational-based models have already been proposed for DTI prediction and achieved some significant improvement. However, these approaches pay little attention to fuse the multi-view similarity networks related to drugs and targets in an appropriate way. Besides, how to fully incorporate the known interaction relationships to accurately represent drugs and targets is not well investigated. Therefore, there is still a need to improve the accuracy of DTI prediction models. RESULTS: In this study, we propose a novel approach that employs Multi-view similarity network fusion strategy and deep Interactive attention mechanism to predict Drug-Target Interactions (MIDTI). First, MIDTI constructs multi-view similarity networks of drugs and targets with their diverse information and integrates these similarity networks effectively in an unsupervised manner. Then, MIDTI obtains the embeddings of drugs and targets from multi-type networks simultaneously. After that, MIDTI adopts the deep interactive attention mechanism to further learn their discriminative embeddings comprehensively with the known DTI relationships. Finally, we feed the learned representations of drugs and targets to the multilayer perceptron model and predict the underlying interactions. Extensive results indicate that MIDTI significantly outperforms other baseline methods on the DTI prediction task. The results of the ablation experiments also confirm the effectiveness of the attention mechanism in the multi-view similarity network fusion strategy and the deep interactive attention mechanism. AVAILABILITY AND IMPLEMENTATION: https://github.com/XuLew/MIDTI.

Microchip isotachophoresis for green and sustainable pharmaceutical quality control: Method validation and application to complex pharmaceutical formulations.

Universal microchip isotachophoresis (µITP) methods were developed for the determination of cationic and anionic macrocomponents (active pharmaceutical ingredients and counterions) in cardiovascular drugs marketed in salt form, amlodipine besylate and perindopril erbumine. The developed methods are characterized by low reagent and sample consumption, waste production and energy consumption, require only minimal sample preparation and provide fast analysis. The greenness of the proposed methods was assessed using AGREE. An internal standard addition was used to improve the quantitative parameters of µITP. The proposed methods were validated according to the ICH guideline. Linearity, precision, accuracy and specificity were evaluated for each of the studied analytes and all set validation criteria were met. Good linearity was observed in the presence of matrix and in the absence of matrix, with a correlation coefficient of at least 0.9993. The developed methods allowed precise and accurate determination of the studied analytes, the RSD of the quantitative and qualitative parameters were less than 1.5% and the recoveries ranged from 98 to 102%. The developed µITP methods were successfully applied to the determination of cationic and anionic macrocomponents in six commercially available pharmaceutical formulations.