Pharmaceutical Compounds Research Articles

Thermodynamic Sorption Study of a Medicinal Plant Using the Standard Static Gravimetric Method for a Better Conservation

Aromatic and medicinal plants are a natural source of pharmaceutical compounds with curative and therapeutic properties. They have been used for centuries to treat various ailments and offer alternative options to conventional treatments. Among these plants, Marrubium vulgare L., which is widely used in traditional medicine for diabetes treatment, has antioxidant potential as well as anti-inflammatory, healing, and soothing properties, attracting increasing medical interest. In this context, the hygroscopic behavior of Marrubium vulgare L. is reported. The adsorption-desorption isotherms of Marrubium vulgare leaves were determined using the standard static gravimetric method at three temperatures (30, 40, and 50 °C) to ensure physicochemical and microbiological stability throughout the storage process. The results showed that the adsorption-desorption isotherms of all samples followed a sigmoidal pattern, consistent with other agricultural products discussed in the literature. The optimal moisture content for conservation was also determined. The GAB (Guggenheim-Anderson-de Boer) and double polynomial models were the most suitable for describing the sorption curves. The adsorption-desorption data were examined to determine the moisture content of the monolayer (3.4-9.7%), properties of sorbed water in porous structures and surfaces, total heat of wetting, net isosteric heat of sorption, spreading pressure, differential entropy, and enthalpy-entropy compensation. It was also observed that the spreading pressure and average pore radius increase with rising relative humidity and temperature, leading to the appearance of defects on the surface of Marrubium vulgare leaves. Compensation theory is essential to consider when evaluating the impact of temperature on the adsorption-desorption properties. The Gibbs free energy was positive for sorption, indicating that the process is non-spontaneous.

Optimization of Capillary Electrophoresis by Central Composite Design for Separation of Pharmaceutical Contaminants in Water Quality Testing

Many pharmaceutical active compounds are prepared as hydrochlorides for quick release in the gastrointestinal tract upon oral administration. Their inadvertent escape into the water environment requires efficient analytical separation for accurate quantitation to monitor their environmental fate. The purpose of this study is to demonstrate how best to optimize a capillary electrophoresis method for the separation of four model pharmaceutical hydrochlorides. Concentration of sodium dibasic phosphate in the background electrolyte solution, pH adjustment with HCl or NaOH, and applied voltage across the capillary were the three key factors chosen for optimization. The peak resolutions and total migration time were examined as the response indicators to complete a central composite design in response surface methodology. The examination revealed that CE separation was driven significantly by a linear regression model and minimally by a quadratic regression model, based on the coefficient of determination, the lack of fit, the total sum of squares, and the p values. Under optimal conditions of the background electrolyte concentration of 75 mM, pH 9, and the applied voltage of 10 kV, the model hydrochlorides were separated within five minutes in the migration order of metformin (first) > phenformin > mexiletine > ranitidine (last). The limits of UV detection/quantification attained under optimal CE conditions were 0.015/0.045, 0.020/0.060, 0.142/0.426, and 0.017/0.051, respectively.

FragMAX Facility for Crystallographic Fragment and Ligand Screening at MAX IV

ABSTRACTThe FragMAX facility at MAX IV Laboratory is a state‐of‐the‐art platform for crystallographic fragment screening, designed to support structure‐based drug and chemical tool compound discovery. This facility offers a comprehensive workflow, from high‐throughput crystal preparation and automated diffraction data collection at the BioMAX beamline to advanced data processing and analysis using custom software tools like FragMAXapp and FragMAXproc. Key components include an extensive relational SQLite database, various fragment libraries, laboratory automation equipment, and a range of bespoke software solutions. FragMAX has conducted numerous successful screening campaigns, serving both academic and industrial users. Users benefit from comprehensive support, and stringent data management. Here, we provide an overview of the different components of the facility and details of their practical implementation.

Degradation of X-Ray Contrast Media in Anaerobic Membrane Bioreactors

The presence of pharmaceutical compounds, including iodinated contrast media (ICM), in aquatic systems poses significant ecological and health risks due to their biological activity at low concentrations. This study investigated the removal efficiency of three selected ICM—diatrizoate, iohexol, and iodipamide—from synthetic hospital wastewater using anaerobic membrane bioreactors (MBRs) operated at varying sludge ages of 40, 70, and 100 days. The results indicated that the performance of the MBRs in removing organic compounds improved with increased sludge age. Diatrizoate exhibited the highest removal efficiency, achieving 72% at a sludge age of 40 days and nearly 90% at 70 and 100 days, with no substantial differences between the two higher sludge ages. In contrast, iohexol and iodipamide demonstrated relatively low and inconsistent removal efficiencies, reaching a maximum of 40%, with no observable dependency on sludge age. The findings underscore the importance of optimizing sludge age in biological treatment processes for effective ICM removal.

A python approach for prediction of physicochemical properties of anti-arrhythmia drugs using topological descriptors

In recent years, machine learning has gained substantial attention for its ability to predict complex chemical and biological properties, including those of pharmaceutical compounds. This study proposes a machine learning-based quantitative structure-property relationship (QSPR) model for predicting the physicochemical properties of anti-arrhythmia drugs using topological descriptors. Anti-arrhythmic drug development is challenging due to the complex relationship between chemical structure and drug efficacy. To address this, we applied machine learning techniques, specifically linear regression models combined with K-fold cross-validation, to predict critical properties such as Density, Boiling Point, Flash Point, Bioconcentration Factor (BCF), Organic Carbon Partition Coefficient (KOC), Polarizability, and Molar Volume. The models were developed using data from ten anti-arrhythmic drugs ( to ). We evaluated the models based on performance metrics such as R and and obtained significant results. Most accurate predictions are obtained for polarizability from models with H(G) and .

SP-DTI: Subpocket-Informed Transformer for Drug-Target Interaction Prediction.

Drug-target interaction (DTI) prediction is crucial for drug discovery, significantly reducing costs and time in experimental searches across vast drug compound spaces. While deep learning has advanced DTI prediction accuracy, challenges remain: (i) existing methods often lack generalizability, with performance dropping significantly on unseen proteins and cross-domain settings; (ii) current molecular relational learning often overlooks subpocket-level interactions, which are vital for a detailed understanding of binding sites. We introduce SP-DTI, a subpocket-informed transformer model designed to address these challenges through: (i) detailed subpocket analysis using the Cavity Identification and Analysis Routine (CAVIAR) for interaction modeling at both global and local levels, and (ii) integration of pre-trained language models into graph neural networks to encode drugs and proteins, enhancing generalizability to unlabeled data. Benchmark evaluations show that SP-DTI consistently outperforms state-of-the-art models, achieving a ROC-AUC of 0.873 in unseen protein settings, an 11% improvement over the best baseline. The model scripts are available at https://github.com/Steven51516/SP-DTI. For correspondence, please contact xiajun@westlake.edu.cn. Supplementary data are available online at Bioinformatics.

Modulation of PI3K/AKT signaling and DFT modeling via selected pharmaceutical compounds attenuates carrageenan-induced inflammation and oxidative stress in rats.

The main goal of the current study is to estimate the in vivo anti-inflammatory/antioxidant ability of four selected pharmaceutical compounds: bisoprolol (Biso), piracetam (Pirc), clopidogrel (Clop), and cinnarizine (Cinna). Indomethacin (Indo) was used as areference drug to perform a realistic comparison between the four compounds and the Indo in vivo through tracking PI3K/AKT signaling and computational chemistry via density functional theory (DFT) modeling to analyze the electrostatic potential across the molecule and provide insight into the regions for receptor binding of the studied compounds. To achieve the safe dose of these compounds, cytotoxicity was performed against isolated adipose tissue-derived mesenchymal stem cells (ADMSCs) using MTT assay. In vivo determination of anti-inflammatory/antioxidant biochemical and genetic parameters of the tested compounds against rats' paw carrageenan (Carg)-induced inflammation was assessed. The data showed that there was no significant different in cell viability of ADMSCs until dose 10 µg/ml, so we used this concentration for in vivo experiments. Carg high significantly increased the volume of the paw edema at 120 min and 180 min compared to the control group (p < 0.01). Cinna (10 mg/kg), relatively similar to Indo, was the most anti-inflammatory compound among others, followed by Clop and Pirc, where they decreased the volume of the paw edema significantly (p < 0.01) at 120 min and 180 min compared to the Carg-group. Microscopic examination confirmed the above results indicating that paw tissue of Carg-group shows edema formation and massive inflammation compared with control. In comparison to the control group, Carg high significantly increased the malondialdehyde (MDA) levels (p < 0.01), whereas, at the concentration 10 mg/kg of the tested compounds, the MDA concentrations significantly reduced, especially the Clop, Cinna, and Indo-treated groups. On the contrary, total antioxidant capacity (TAC) and 5-lipoxygenase (5-LOP) concentrations were significantly decreased in Carg-group (p < 0.01) compared with control. Cyclooxygenase-2 (COX-2), phosphoinositide 3-kinase (PI3k), and protein kinase B (AKT) gene expressions were high and significantly upregulated in Carg-group compared to control,whilethe tested compounds downregulated theirexpressions compared to the Carg-group. Moreover, COX-2, interleukins (IL-10/IL-6/IL-4), PI3k, and AKT protein concentrations were high and significantly increased in Carg-group compared to control, however thetestedcompounds were high and significantly decreased their concentrations compared to the Carg-group. DFT modeling aligned with the biochemical data and indicated that Cinna emerges as the most reactive drug with high polarizability (302.741 a.u.), a relative small FMOs energy gap (ΔE 5.002 eV), relative low molecular hardness (2.501 eV), relative high softness (0.400 eV-1), and distinct nucleophilic/electrophilic interaction sites, indicating strong specific interactions with biological receptor. In conclusion, this study revealed the ability of Cinna to potentially suppress inflammation in in vivo Carg-induced rat paw inflammation model through inhibition of PI3K/AKT signaling and DFT modeling mediated by oxidative stress and inflammatory mediators.

High Content Image Analysis of Cellular Responses of the Murine J774A.1 Cell Line and Primary Human Cells Alveolar Macrophages to an Extended Panel of Pharmaceutical Agents.

In vitro screening of macrophages for drug-induced effects, such as phospholipidosis, is useful for detecting potentially problematic compounds in the preclinical development of oral inhaled products. High-content image analysis (HCIA) is a multi-parameter approach for cytotoxicity screening. This study provides new insights into HCIA-derived response patterns of murine J774A.1 cells and primary human alveolar macrophages (hAM). Several compounds were compared with reference groups (cationic amphiphilic drugs and apoptosis inducers) at different concentrations (0.01 to 10µM). After incubation, cells were stained with fluorescence markers and HCIA was performed (Cytation™ 5 Cell Imaging System). Ten parameters were analysed: non-adherent cells, increased or reduced mitochondrial activity, membrane permeability, cell area, nuclear area, polynucleated cells, vacuole area, neutral and phospholipid content. A new system of response categorisation was developed for data analysis. Murine J774A.1 cells exhibited a drug-induced response pattern that was distinct to the corresponding pattern of hAM cells. Comparison with the literature revealed that primary cells (rat or human origin) have similar response patterns, while cell lines (mouse, rat or human) exhibited a different response pattern. Hierarchical clustering revealed toxicologically aligned clusters of compounds, suggesting potential use for understanding mechanisms of drug effects in cell lines and primary cells. Valuable information for selecting a suitable cell type for HCIA screening of macrophage responses to drug compounds is provided. All cell types were suitable for screening drug-induced phospholipidosis. Still, human primary alveolar macrophages responded differently to drug treatment compared to macrophage cell lines and may be required to evaluate broader response-patterns and mechanisms of toxicity.

Environmental bioremediation of pharmaceutical residues: microbial processes and technological innovations: a review.

The ubiquitous presence of pharmaceuticals and personal care products (PPCPs) in the environment has become a significant concern due to their persistence, bioaccumulation potential in biota, and diverse implications for human health and wildlife. This review provides an overview of the current state-of-the-art in environmental bioremediation techniques for reducing pharmaceutical residues, with a special emphasis on microbial physiological aspects. Numerous microorganisms, including algae, bacteria or fungi, can biodegrade various pharmaceutical compounds such as antibiotics, analgesics and beta-blockers. Some microorganisms are capable of transferring electrons within the cell, and this feature can be harnessed using Bio Electrochemical Systems (BES) to potentiate the degradation of pharmaceuticals present in wastewater. Moreover, researchers are evaluating the genetic modification of microbial strains to improve their degradation capacity and expand list of target compounds. This includes also discuss how environment changes, such as fluctuations in temperature or pH, may affect bioremediation efficiency. Furthermore, the presence of pharmaceuticals in the environment is emphasised as a major public health issue because it increases the chance for antibiotic-resistant bacteria emerging. This review combines existing information and outlines needed research areas for improving bioremediation technologies in the future.

Forced Degradation Studies on Anti-Cancer Drugs: A Comprehensive Review

Forced degradation studies are essential to understanding the stability, safety, and efficacy of anti-cancer drugs, playing a critical role in ensuring their reliability throughout the product lifecycle. These studies expose pharmaceutical compounds to controlled stress conditions—such as heat, light, oxidation, and hydrolysis—to simulate long-term storage and identify degradation pathways. Anti-cancer drugs, due to their intricate molecular structures and sensitivity, are particularly prone to degradation, making these studies indispensable in pharmaceutical development and regulatory compliance. This review explores the fundamental principles of forced degradation, highlights common degradation mechanisms, and discusses advanced analytical techniques used to evaluate the stability of anti-cancer drugs. Case studies of widely used therapies, including doxorubicin and paclitaxel, illustrate the challenges and mitigation strategies. Furthermore, this paper evaluates regulatory frameworks, such as ICH Q1A(R2), and examines future trends, such as artificial intelligence in predictive modelling. These insights emphasize the significance of forced degradation studies in ensuring the safety and effectiveness of anti-cancer drugs.

Synthetic versatility: the C-P bond odyssey.

C-P bond formation reactions have garnered significant attention due to the widespread presence of organophosphorus compounds in pharmaceuticals, phosphine-containing ligands, pesticides, and materials science. Consequently, various efficient methodologies have been established in recent decades for constructing C-P bonds. This review article traces the historical evolution of C-P bond research and explores the prospects of C-P bond formation. It contrasts biotechnological approaches with chemical synthesis, emphasizing the critical importance of precision and innovation in developing novel C-P structures. A forward-looking perspective is provided on the role of computational tools and machine learning in optimizing C-P bond synthesis and discovering new compounds. The article explores prospective avenues for reactions that form C-P bonds and advocates for enhanced interdisciplinary collaboration to propel scientific and technological advancements.

Facile synthesis of functionalized quinolinones in a green reaction medium and their photophysical properties.

A facile and green chemical approach was successfully developed to construct functionalized quinolinones utilizing substituted alcohols, alkyl acetoacetate, and α-bromo ketones. Various quinolinones bearing either electron-rich or electron-deficient groups at different positions were synthesized in moderate to good yields under mild reaction conditions. The plausible mechanistic pathway for this transformation is supported by experimental evidence and control experiments. This simple approach for synthesizing quinolinones could open new avenues for discovering novel biological and pharmaceutical compounds. The use of affordable nickel catalysts, mild reaction conditions, operational simplicity, and high atom economy are attractive features of this method. Furthermore, the synthetic efficiency has been demonstrated through gram-scale experiments. Our research also provides valuable insights into the photophysical properties of the synthesized derivatives. Notably, compound 6n exhibited the highest Stokes shift (216 nm) in DCM solvent. Furthermore, compounds 5d and 6j showed positive solvatochromism, displaying a stronger emission as the solvent polarity increased. Additionally, compound 6j displayed aggregation-induced emission (AIE) properties in a DMSO : water mixture, making it suitable for use as a security ink, highlighting its potential applications in various fields.

Palladium‐Catalyzed Site‐Selective C─H Alkoxylation Using Biorelevant Heterocyclic Directing Groups

AbstractThis study introduces a novel palladium‐catalyzed alkoxylation method via C─H activation, utilizing an intrinsic, weakly coordinating directing group (DG) derived from bioactive molecules. This approach is particularly relevant for the pharmaceutical and chemical industries, offering a versatile tool for functionalizing complex compounds. The success and site selectivity of this method are determined by the choice of catalytic systems and reaction conditions, including the catalyst, ligand, solvent, and temperature. The substrate scope demonstrates broad applicability, particularly for modifying bioactive scaffolds, highlighting the method's generalizability. This protocol provides a promising route for altering pharmaceutical compounds and other critical chemical products with exceptional regioselectivity. The discovery of an intrinsic DG, combined with the ability to fine‐tune reaction conditions, positions this methodology as a significant advancement for functionalization processes in pharmaceutical and chemical applications, paving the way for further exploration in these fields.

Relational Graph Convolution Network with Multi Features for Anti- COVID-19 Drugs Discovery using 3CLpro Potential Target

Background: The potential of graph neural networks (GNNs) to revolutionize the analysis of non-Euclidean data has gained attention recently, making them attractive models for deep machine learning. However, insufficient compound or molecular graphs and feature representations might significantly impair and jeopardize their full potential. Despite the devastating impacts of ongoing COVID-19 across the globe, for which there is no drug with proven efficacy that has been shown to be effective. As various stages of drug discovery and repositioning require the accurate prediction of drug-target interactions (DTI), here, we propose a relational graph convolution network (RGCN) using multi-features based on the developed drug compound-coronavirus target graph data representation and combination of features. During the implementation of the model, we further introduced the use of not only the feature module to understand the topological structure of drugs but also the structure of the proven drug target (i.e., 3CLpro) for SARS-CoV-2 that shares a genome sequence similar to that of other members of the betacoronavirus group such as SARS-CoV, MERS-CoV, bat coronavirus. Our feature comprises topological information in molecular SMILES and local chemical context in the SMILES sequence for the drug compound and drug target. Our proposed method prevailed with high and compelling performance accuracy of 97.30% which could be prioritized as the potential and promising prediction route for the development of novel oral antiviral medicine for COVID- 19 drugs. Objective: Forecasting DTI stands as a pivotal aspect of drug discovery. The focus on computational methods in DTI prediction has intensified due to the considerable expense and time investment associated with conducting extensive in vitro and in vivo experiments. Machine learning (ML) techniques, particularly deep learning, have found broad applications in DTI prediction. We are convinced that this study could be prioritized and utilized as the promising predictive route for the development of novel oral antiviral treatments for COVID-19 and other variants of coronaviruses. Methods: This study addressed the problem of COVID-19 drugs using proposed RGCN with multifeatures as an attractive and potential route. This study focused mainly on the prediction of novel antiviral drugs against coronaviruses using graph-based methodology, namely RGCN. This research further utilized the features of both drugs and common potential drug targets found in betacoronaviruses group to deepen understanding of their underlying relation. Results: Our suggested approach prevailed with a high and convincing performance accuracy of 97.30%, which may be utilized as a top priority to support and advance this field in the prediction and development of novel antiviral treatments against coronaviruses and their variants. Conclusion: We recursively performed experiments using the proposed method on our constructed DCCCvT graph dataset from our collected dataset with various single and multiple combinations of features and found that our model had achieved comparable best-averaged accuracy performance on T7 features followed by a combination of T7, R6, and L8. The proposed model implemented in this investigation turns out to outperform the previous related works.

Removing drug residues from water using biosorbents: a review of academic works

Water pollution by emerging contaminants (EC) is one of today's most significant environmental problems. Among these are pharmaceutical compounds, which are increasingly being discarded into watercourses. Due to the toxicity caused by residues of these substances, it is essential to develop techniques to remove these contaminants. Thus, through bibliographic research, this study investigates Brazilian academic works such as theses and dissertations that report studies on removing pharmaceutical residues from water using biosorption processes as a high-potential, low-cost alternative for decontamination. An advanced search was carried out in the Brazilian Digital Library of Theses and Dissertations, using the words "adsorption" and "pharmaceuticals," considering all the works documented on this database until 2024. After selective reading, the search resulted in 55 academic works on the field. Most of the research investigated the use of activated carbon produced from coconut shell waste, avocado pits, sugarcane bagasse, and sewage sludge as biosorbents, as well as chitosan-based composites, magnetic hybrid materials, wood sawdust, algae biomass, and grape pomace. As for the pharmaceutical residues, the most common contaminants studied were sodium diclofenac, amoxicillin, paracetamol, and ciprofloxacin. Although promising results have been reported using these alternatives for remediating water contaminated by pharmaceuticals, biosorption processes still require further development before they can be fully applied to treat wastewater or supply and spring waters.

A Brief Review on Gene Therapy

Gene therapy is modern technique for treatment and control of diseases which cannot be treated by conventional drug compounds. Through this technique disease like haemophilia, Glaucoma can be treated. Gene therapy includes the incorporation of foreign genes into body to treat genetic defects. Somatic cell gene therapy is better than the germ line gene therapy because of less problems in term of ethics and easiest in terms of technology. This review includes brief introduction of Gene therapy including history, types, approaches and recent advancement in the field of Gene therapy. Keywords: Gene therapy, gene expression.

Current-Regulated Selective Nickel-Catalyzed Electroreductive Cross-Electrophile Carbonylation to β/γ-Hydroxy Ketones.

The nickel catalyzed multi-component cross-electrophile carbonylation which emerges as a powerful and efficient method for constructing diverse ketones has attracted increasing attention of organic chemists. However, the selectivity of this reaction poses a significant challenge. In this work, we have developed a current-regulated selective nickel-catalyzed electroreductive cross-electrophile carbonylation, which offers a direct convergent synthesis of β/γ-hydroxy ketones, which represent pivotal structural motifs found in numerous natural products, bioactive molecules, pharmaceutical compounds, and essential building blocks. A diverse range of multi-substituted or stereospecific β/γ-hydroxyketones can be accessed with high chemo- and regioselectivity from epoxides, aryl iodides, and a simple CO source (ClCO2Pr). This electroreductive carbonylation strategy exhibits high functional group tolerance and can be applied in late-stage derivatization of drugs and natural products. Notably, chiral epoxides can be employed as reactants with chirality retention, enabling the synthesis of asymmetric β-hydroxy ketones. Our approach demonstrates a novel electrochemical selectivity-controlled strategy in multi-component cross-electrophile coupling.

Exploration of the shared pathways and common biomarkers in cervical and ovarian cancer using integrated bioinformatics analysis.

Searching for potential biomarkers and therapeutic targets for early diagnosis of gynecological tumors to improve patient survival. Microarray datasets of cervical cancer (CC) and ovarian cancer (OC) were downloaded from the Gene Expression Omnibus (GEO) database, then, differential gene expression between cancerous and normal tissues in the datasets was analyzed. Weighted gene co-expression network analysis (WGCNA) was performed to screen for co-expression modules associated with CC and OC. The screened shared genes were then further analyzed for functional pathway enrichment. Next, the least absolute shrinkage and selection operator (LASSO) with tenfold cross validation is used to further screened for common diagnostic biomarkers for the two diseases, and further validation is performed using two independent GEO datasets. Finally, the CIBERSORT algorithm was used to estimate the immune infiltration levels of CC and OC, and the correlation between immune cell infiltration and common biomarkers was explored. After crossing the common DEGs detected by "Limma" R package with the common module genes identified by WGCNA, 44 shared genes were obtained. Functional enrichment indicates that these shared genes are mainly related to DNA synthesis pathways. Lasso regression analysis revealed that EFNA1, TYMS, and WISP2 were co-diagnostic markers for CC and OC, and then based on their expression levels and diagnostic efficacy, EFNA1 was selected as the best co-marker for CC and OC. Immune infiltration analysis shows that the immune environment has a significant impact on the occurrence and development of CC and OC, and the expression of EFNA1 is related to changes in immune cells. Gene-drug interaction analyses identified 27 common drug compounds that interact with candidate genes. This study adopted bioinformatics methods to investigate the common pathways and identify diagnostic markers between CC and OC, suggesting that DNA synthesis and immune environment are closely related to the occurrence and development of CC and OC. EFNA1 may be a potential diagnostic indicator and therapeutic target for patients with CC and OC.

Development and Application of an In Vitro Drug Screening Assay for Schistosoma mansoni Schistosomula Using YOLOv5.

Schistosomiasis impacts over 230 million people globally, with 251.4 million needing treatment. The disease causes intestinal and urinary symptoms, such as hepatic fibrosis, hepatomegaly, splenomegaly, and bladder calcifications. While praziquantel (PZQ) is the primary treatment, its effectiveness against juvenile stages (schistosomula) is limited, highlighting the need for new therapeutic agents, repurposed drugs, or reformulated compounds. Existing microscopy methods for assessing schistosomula viability are labor-intensive, subjective, and time-consuming. An artificial intelligence (AI)-assisted culture system using YOLOv5 was developed to evaluate compounds against Schistosoma mansoni schistosomula. The AI model, based on object detection, was trained on 4390 images distinguishing between healthy and damaged schistosomula. The system was externally validated against human counters, and a small-scale assay was performed to demonstrate its potential for larger-scale assays in the future. The AI model exhibited high accuracy, achieving a mean average precision (mAP) of 0.966 (96.6%) and effectively differentiating between healthy and damaged schistosomula. External validation demonstrated significantly improved accuracy and counting time compared to human counters. A small-scale assay was conducted to validate the system, identifying 28 potential compounds with schistosomicidal activity against schistosomula in vitro and providing their preliminary LC50 values. This AI-powered method significantly improves accuracy and time efficiency compared to traditional microscopy. It enables the evaluation of compounds for potential schistosomiasis drugs without the need for dyes or specialized equipment, facilitating more efficient drug assessment.

Crystallization of n-Alkanes under Anisotropic Nanoconfinement in Lipid Bilayers.

Understanding crystallization behavior is integral to the design of pharmaceutical compounds for which the pharmacological properties depend on the crystal forms achieved. Very often, these crystals are based on hydrophobic molecules. One method for delivering crystal-forming hydrophobic drugs is by means of lipid nanoparticle carriers. However, so far, a characterization of the potential crystallization of fully hydrophobic molecules in a lipid environment has never been reported. In this work we investigate the crystallization behavior of two model hydrophobic chains, n-eicosane (C20) and n-triacontane (C30), in phospholipid bilayers. We combine static 2H nuclear magnetic resonance (NMR) spectroscopy and differential scanning calorimetry (DSC) and show that C30 molecules can indeed crystallize inside DMPC and POPC bilayers. The phase transition temperatures of C30 are slightly reduced inside DMPC, and rotator phase formation becomes a two-step process: Preorganized n-alkane chains assemble in rotator-phase crystallites just as fast as bulk C30, but further addition of molecules is notably slower. Under the same isothermal conditions, different crystal forms can be obtained by crystallization in the membrane and in bulk. In excess water conditions, homogeneous nucleation of C30 is observed. The initial anisotropic molecular arrangement of C30 molecules in the membrane is readily recovered upon reheating, showing reversibility. The shorter C20 molecules on the other hand become trapped in the DMPC membrane gel-phase upon cooling and do not crystallize. This work marks the first observation of the crystallization of hydrophobic chains inside a lipid bilayer environment. As such, it defines a fundamental starting point for studying the crystallization characteristics of various hydrophobic molecules in lipid membranes.