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.

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.

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.

Drug delivery to and through the skin.

Drug delivery technology has advanced significantly over >50 years, and has produced remarkable innovation, countless publications and conferences, and generations of talented and creative scientists. However, a critical review of the current state-of-the-art reveals that the translation of clever and sophisticated drug delivery technologies into products, which satisfy important, unmet medical needs and have been approved by the regulatory agencies, has - given the investment made in terms of time and money - been relatively limited. Here, this point of view is illustrated using a case study of technology for drug delivery into and through the skin and aims:  to examine the historical development of this field and the current state-of-the-art;  to understand why the translation of drug delivery technologies into products that improve clinical outcomes has been quite slow and inefficient; and  to suggest how the impact of technology may be increased and the process of concept to approved product accelerated.

Carbon and metal based magnetic porous materials - Role in drug removal: A Comprehensive review.

Development of effective adsorbents for the removal of contaminants from wastewater is indispensable due to increasing water scarcity and a lack of pure drinking water, which are prevailing as a result of rapid industrialization and population growth. Recently, the development of new adsorbents and their effective use without generating secondary waste is receiving huge consideration. In order to protect the environment from primary and secondary pollution, the development of adsorbents from wastes and their recycling have become conventional practices aimed at waste management. As a result, significant progress has been made in the synthesis of new porous carbon and metal-organic frameworks as adsorbents, with the objective of using them for the removal of pollutants. While many different kinds of pollutants are produced in the environment, drug pollutants are the most vicious because of their tendency to undergo significant structural changes, producing metabolites and residues with entirely different properties compared to their parent compounds. Chemical reactions involving oxidation, hydrolysis, and photolysis transform drugs. The resulting compounds can have detrimental effects on living beings that are present in soil and water. This review stresses the development of adsorbents with adjustable porosities for the broad removal of primary drug pollutants and their metabolites, which are formed as a result of drug transformations in environmental matrices. This keeps adsorbents from building up in the environment and prevents them from becoming significant pollutants in the future. Additionally, it stops secondary pollution caused by the deterioration of the used adsorbents. Focus on the development of effective adsorbents with flexible porosities allows for the complete removal of coexisting contaminants and makes a substantial contribution to wastewater management. In order to concentrate more on the development of flexible pore adsorbents, it is crucial to comprehend the milestones reached in the research and applications of porous magnetic adsorbents based on metal and carbon, which are discussed here.

MDF-DTA: A Multi-Dimensional Fusion Approach for Drug-Target Binding Affinity Prediction.

Drug-target affinity (DTA) prediction is an important task in the early stages of drug discovery. Traditional biological approaches are time-consuming, effort-consuming, and resource-consuming due to the large size of genomic and chemical spaces. Computational approaches using machine learning have emerged to narrow down the drug candidate search space. However, most of these prediction models focus on single feature encoding of drugs and targets, ignoring the importance of integrating different dimensions of these features. We propose a deep learning-based approach called Multi-Dimensional Fusion for Drug Target Affinity Prediction (MDF-DTA) incorporating different dimensional features. Our model fuses 1D, 2D, and 3D representations obtained from different pretrained models for both drugs and targets. We evaluated MDF-DTA on two standard benchmark data sets: DAVIS and KIBA. Experimental results show that MDF-DTA outperforms many state-of-the-art techniques in the DTA task across both data sets. Through ablation studies and performance evaluation metrics, we evaluate the importance of individual representations and the impact of each representation on MDF-DTA.

DiPPI: A Curated Data Set for Drug-like Molecules in Protein-Protein Interfaces.

Proteins interact through their interfaces, and dysfunction of protein-protein interactions (PPIs) has been associated with various diseases. Therefore, investigating the properties of the drug-modulated PPIs and interface-targeting drugs is critical. Here, we present a curated large data set for drug-like molecules in protein interfaces. We further introduce DiPPI (Drugs in Protein-Protein Interfaces), a two-module web site to facilitate the search for such molecules and their properties by exploiting our data set in drug repurposing studies. In the interface module of the web site, we present several properties, of interfaces, such as amino acid properties, hotspots, evolutionary conservation of drug-binding amino acids, and post-translational modifications of these residues. On the drug-like molecule side, we list drug-like small molecules and FDA-approved drugs from various databases and highlight those that bind to the interfaces. We further clustered the drugs based on their molecular fingerprints to confine the search for an alternative drug to a smaller space. Drug properties, including Lipinski's rules and various molecular descriptors, are also calculated and made available on the web site to guide the selection of drug molecules. Our data set contains 534,203 interfaces for 98,632 protein structures, of which 55,135 are detected to bind to a drug-like molecule. 2214 drug-like molecules are deposited on our web site, among which 335 are FDA-approved. DiPPI provides users with an easy-to-follow scheme for drug repurposing studies through its well-curated and clustered interface and drug data and is freely available at http://interactome.ku.edu.tr:8501.

Quantum-Informed Molecular Representation Learning Enhancing ADMET Property Prediction.

We examined pretraining tasks leveraging abundant labeled data to effectively enhance molecular representation learning in downstream tasks, specifically emphasizing graph transformers to improve the prediction of ADMET properties. Our investigation revealed limitations in previous pretraining tasks and identified more meaningful training targets, ranging from 2D molecular descriptors to extensive quantum chemistry simulations. These data were seamlessly integrated into supervised pretraining tasks. The implementation of our pretraining strategy and multitask learning outperforms conventional methods, achieving state-of-the-art outcomes in 7 out of 22 ADMET tasks within the Therapeutics Data Commons by utilizing a shared encoder across all tasks. Our approach underscores the effectiveness of learning molecular representations and highlights the potential for scalability when leveraging extensive data sets, marking a significant advancement in this domain.

Accelerated Predictive Stability Study of a Pediatric Drug Product for a Supplemental New Drug Application.

In this paper, we report two Accelerated Stability Assessment Program (ASAP) studies for a pediatric drug product. Whereas the first study using a generic design failed to establish a predictive model, the second one was successful after troubleshooting the first study and customizing the study conditions. This work highlighted important lessons learned from designing an ASAP study for formulations containing excipients that could undergo phase change at high humidity levels. The stability predictions by the second ASAP model were consistent with available long-term stability data of the drug product under various storage conditions in two different packaging configurations. The ASAP model was part of the justifications accepted by the health authority to submit a stability package with reduced long-term stability data from the primary stability batches for a Supplemental New Drug Application (sNDA).

Navigating Skin Delivery Horizon: An Innovative Approach in Pioneering Surface Modification of Ultradeformable Vesicles.

In the dynamic landscape of pharmaceutical advancements, the strategic application of active pharmaceutical ingredients to the skin through topical and transdermal routes has emerged as a compelling avenue for therapeutic interventions. This non-invasive approach has garnered considerable attention in recent decades, with numerous attempts yielding approaches and demonstrating substantial clinical potential. However, the formidable barrier function of the skin, mainly the confinement of drugs on the upper layers of the stratum corneum, poses a substantial hurdle, impeding successful drug delivery via this route. Ultradeformable vesicles/carriers (UDVs), positioned within the expansive realm of nanomedicine, have emerged as a promising tool for developing advanced dermal and transdermal therapies. The current review focuses on improving the passive dermal and transdermal targeting capacity by integrating functionalization groups by strategic surface modification of drug-loaded UDV nanocarriers. The present review discusses the details of case studies of different surface-modified UDVs with their bonding strategies and covers the recent patents and clinical trials. The design of surface modifications holds promise for overcoming existing challenges in drug delivery by marking a significant leap forward in the field of pharmaceutical sciences.

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.

Nanoformulations in Pharmaceutical and Biomedical Applications: Green Perspectives.

This study provides a brief discussion of the major nanopharmaceuticals formulations as well as the impact of nanotechnology on the future of pharmaceuticals. Effective and eco-friendly strategies of biofabrication are also highlighted. Modern approaches to designing pharmaceutical nanoformulations (e.g., 3D printing, Phyto-Nanotechnology, Biomimetics/Bioinspiration, etc.) are outlined. This paper discusses the need to use natural resources for the "green" design of new nanoformulations with therapeutic efficiency. Nanopharmaceuticals research is still in its early stages, and the preparation of nanomaterials must be carefully considered. Therefore, safety and long-term effects of pharmaceutical nanoformulations must not be overlooked. The testing of nanopharmaceuticals represents an essential point in their further applications. Vegetal scaffolds obtained by decellularizing plant leaves represent a valuable, bioinspired model for nanopharmaceutical testing that avoids using animals. Nanoformulations are critical in various fields, especially in pharmacy, medicine, agriculture, and material science, due to their unique properties and advantages over conventional formulations that allows improved solubility, bioavailability, targeted drug delivery, controlled release, and reduced toxicity. Nanopharmaceuticals have transitioned from experimental stages to being a vital component of clinical practice, significantly improving outcomes in medical fields for cancer treatment, infectious diseases, neurological disorders, personalized medicine, and advanced diagnostics. Here are the key points highlighting their importance. The significant challenges, opportunities, and future directions are mentioned in the final section.

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.

HCSRL: hyperledger composer system for reducing logistics losses in the pharmaceutical product supply chain using a blockchain-based approach.

Blockchain technology uses a secure and decentralised framework for transaction management and data sharing within supply chains. This is particularly crucial in the pharmaceutical industry, where product authenticity and traceability are paramount. Blockchain plays a pivotal role in preventing product loss and counterfeiting, while simultaneously enhancing transparency and efficiency throughout the supply chain. The research introduces a step-by-step approach to implementing a proof-of-concept (PoC) for Supply Chain Risk Management (SCRM) through blockchain technology. This PoC involves simulating a supply chain process to assess feasibility and measure key performance indicators. Engaging stakeholders and gathering feedback is integral to refining the blockchain-based SCRM system. The study rigorously evaluates the performance of the SCRM blockchain across various test scenarios, featuring differing numbers of organizations and clients. Multiple fabric networks are employed to assess the system's scalability and performance under diverse conditions. The results of these comprehensive tests inform practical deployment decisions and highlight areas for potential optimization and further development. So this research provides valuable insights into the application of blockchain in pharmaceutical supply chains, offering a roadmap for implementation and improving supply chain security, efficiency, and transparency.

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.