Search Results (380)

Nanocarriers have found numerous applications in pharmaceutical and food sectors due to their unique physical and chemical properties. In particular, liposomes are the most extensively studied kind of nanoparticles for these applications. They are spherical colloidal systems characterized by lipid membranes enclosing an aqueous core. This versatile structure enables the incorporation of hydrophilic, hydrophobic, and amphiphilic molecules, making them optimal candidates for the controlled release of drugs and enzymes. Despite numerous promising applications, liposomes face challenges such as low colloidal stability, inefficient drug encapsulation, and high production costs for large-scale applications. For this reason, innovative methods, such as microfluidics, electroporation, and supercritical CO₂, are currently being investigated to overcome these limitations. This review examines the recent applications of liposomes in enzyme encapsulation within the pharmaceutical and food sectors, emphasizing production challenges and emerging technological developments. Full article

Cancer remains one of the most formidable challenges to human health; hence, developing effective treatments is critical for saving lives. An important strategy involves reactivating tumor suppressor genes, particularly p53, by targeting their negative regulator MDM2, which is essential in promoting cell cycle arrest and apoptosis. Leveraging a drug repurposing approach, we screened over 24,000 clinically tested molecules to identify new MDM2 inhibitors. A key innovation of this work is the development and application of a selective cleaning algorithm that systematically filters assay data to mitigate noise and inconsistencies inherent in large-scale bioactivity datasets. This approach significantly improved the predictive accuracy of our machine learning model for pIC₅₀ values, reducing RMSE by 21.6% and achieving state-of-the-art performance (R² = 0.87)—a substantial improvement over standard data preprocessing pipelines. The optimized model was integrated with structure-based virtual screening via molecular docking to prioritize repurposing candidate compounds. We identified two clinical CB1 antagonists, MePPEP and otenabant, and the statin drug atorvastatin as promising repurposing candidates based on their high predicted potency and binding affinity toward MDM2. Interactions with the related proteins MDM4 and BCL2 suggest these compounds may enhance p53 restoration through multi-target mechanisms. Quantum mechanical (ONIOM) optimizations and molecular dynamics simulations confirmed the stability and favorable interaction profiles of the selected protein–ligand complexes, resembling that of navtemadlin, a known MDM2 inhibitor. This multiscale, accuracy-boosted workflow introduces a novel data-curation strategy that substantially enhances AI model performance and enables efficient drug repurposing against challenging cancer targets. Full article

Kinase is an enzyme responsible for cell signaling and other complex processes. Mutations or changes in kinase can cause cancer and other diseases in humans, including leukemia, neuroblastomas, glioblastomas, and more. Considering these concerns, inhibiting overexpressed or dysregulated kinases through small drug molecules is very important. In the past, many machine learning and deep learning approaches have been used to inhibit unregulated kinase enzymes. In this work, we employ a Graph Neural Network (GNN) to predict the inhibition activities of kinases. A separate Graph Convolution Network (GCN) and combined Graph Convolution and Graph Attention Network (GCN_GAT) are developed and trained on two large datasets (Kinase Datasets 1 and 2) consisting of small drug molecules against the targeted kinase using 10-fold cross-validation. Furthermore, a wide range of molecules are used as independent datasets on which the performance of the models is evaluated. On both independent kinase datasets, our model combining GCN and GAT provides the best evaluation and outperforms previous models in terms of accuracy, Matthews Correlation Coefficient (MCC), sensitivity, specificity, and precision. On the independent Kinase Dataset 1, the values of accuracy, MCC, sensitivity, specificity, and precision are 0.96, 0.89, 0.90, 0.98, and 0.91, respectively. Similarly, the performance of our model combining GCN and GAT on the independent Kinase Dataset 2 is 0.97, 0.90, 0.91, 0.99, and 0.92 in terms of accuracy, MCC, sensitivity, specificity, and precision, respectively. Full article

Transdermal drug delivery systems have recently been explored as an alternative to oral systems, which have many challenges. Due to the limitations of first-generation transdermal systems, second- and third-generation systems have been developed, among which microneedles have been the most remarkable products. Building on the advancements of nanotechnology, nanoneedles have recently been developed. Gene therapy molecules—such as DNA, RNA, siRNA, miRNA, and other nucleic acids—are typically delivered using viral or chemical carriers, but these methods face several challenges. In this context, nanoneedles offer a promising and efficient solution for delivering these large molecules. Nanoneedles are a biocompatible and reliable physical method for gene delivery, enabling transdermal administration by penetrating the skin barrier and delivering nucleic acids directly into cells. Their ability to penetrate cellular barriers with minimal invasiveness makes them advantageous for delivering genetic materials. This review will focus on the potential applications of nanoneedles in pharmaceutical contexts, especially in gene therapy. In addition, information on the properties, structure, and fabrication of nanoneedles is also provided. Full article

Bacterial natural ecological niches are characterized by variations in the availability of nutrients, resulting in a complex metabolism. Their impressive ability to adapt to changeable nutrient conditions is possible through the utilization of large amounts of substrates. Recent discoveries in bacterial metabolism have suggested the importance of polyamine metabolism in bacteria, particularly in those of the order Actinomycetales, in enabling them to survive in their natural habitats. This makes such enzymes promising targets to inhibit their growth. Since the polyamine metabolisms of soil bacteria of the genus Streptomyces and the human pathogenic Mycobacteria are surprisingly similar, target-based drug development in Streptomyces and Mycobacterium spp. is an alternative approach to the classical search for antibiotics. The recent development of drugs to treat epidemic diseases like tuberculosis (TB) has gained attention due to the occurrence of multidrug-resistant strains. In addition, drug repurposing plays a crucial role in the treatment of various complex diseases, such as malaria. With that notion, the treatment of TB could also benefit from this approach. For example, molecular chaperones, proteins that help other proteins to fold properly, are found in almost all living organisms, including the causative agents of TB. Therefore, targeting these molecules could help in the treatment of TB. We aim to summarize our knowledge of the nitrogen and carbon metabolism of the two closely related actinobacterial genera, Streptomyces and Mycobacterium, and of the identification of new potential drug targets. Full article

Mesoporous silica nanoparticles (MSNs) are gaining popularity in nanomedicine due to their large surface area, variable pore size, great biocompatibility, and chemical adaptability. In recent years, the combination of smart polymeric materials with MSNs has transformed the area of regulated drug administration, particularly under stimuli-responsive settings. Polymer-modified MSNs provide increased stability, longer circulation times, and, most crucially, the capacity to respond to diverse internal (pH, redox potential, enzymes, and temperature) and external (light, magnetic field, and ultrasonic) stimuli. These systems allow for the site-specific, on-demand release of therapeutic molecules, increasing treatment effectiveness while decreasing off-target effects. This review presents a comprehensive analysis of recent advancements in the development and application of polymer-functionalized MSNs for stimuli-triggered drug delivery. Key polymeric modifications, including thermoresponsive, pH-sensitive, redox-responsive, and enzyme-degradable systems, are discussed in terms of their design strategies and therapeutic outcomes. The synergistic use of dual or multiple stimuli-responsive polymers is also highlighted as a promising avenue to enhance precision and control in complex biological environments. Moreover, the integration of targeting ligands and stealth polymers such as PEG further enables selective tumor targeting and immune evasion, broadening the potential clinical applications of these nanocarriers. Recent progress in stimuli-triggered MSNs for combination therapies such as chemo-photothermal and chemo-photodynamic therapy is also covered, emphasizing how polymer modifications enhance responsiveness and therapeutic synergy. Finally, the review discusses current challenges, including scalability, biosafety, and regulatory considerations, and provides perspectives on future directions to bridge the gap between laboratory research and clinical translation. Full article

Human skin provides an effective route of delivery for selected drugs. Topical penetration of molecules is largely attributed to passive diffusion, and the degree of penetration can be represented by in silico, in vitro, and ex vivo models. Percutaneous absorption of pharmaceutical ingredients is a delicate balance between the molecular properties of the drug, the skin properties of the patients, and the formulation properties. Understanding this interplay can aid in the development of products applied to the skin. The kinetics of percutaneous absorption and an understanding of the rate-limiting steps involved can facilitate the optimization of these systems and enhance the degree to which skin drug delivery can be achieved. Solute–vehicle, vehicle–skin, and solute–skin interactions contribute notably to product release as well as the rate of absorption and diffusion across skin layers. These interactions alter the degree of permeation by interfering with the skin barrier or solubility and thermodynamic activity of the active pharmaceutical ingredient. This article aims to provide a concise understanding of some of the factors involved in the skin absorption of topical products, i.e., the pharmacokinetics of percutaneous absorption as well as the solute–vehicle–skin interactions that determine the rate of release of products and the degree of drug diffusion across the skin. Full article

With the rapid development of radical initiation technologies such as photocatalysis and electrocatalysis, radical reactions have become an increasingly attractive approach for constructing target molecules. However, designing efficient synthetic routes using radical reactions remains a significant challenge due to the inherent complexity and instability of radical intermediates. While computer-aided synthesis planning (CASP) has advanced retrosynthetic analysis for polar reactions, radical reactions have been largely overlooked in AI-driven approaches. In this study, we introduce RadicalRetro, the first deep learning-based retrosynthesis model specifically tailored for radical reactions. Our work is distinguished by three key contributions: (1) RadicalDB: A novel, manually curated database of 21.6 K radical reactions, focusing on high-impact literature and mechanistic clarity, addressing the critical gap in dedicated radical reaction datasets. (2) Model Innovation: By pretraining Chemformer on ZINC-15 and USPTO datasets followed by fine-tuning with RadicalDB, RadicalRetro achieves a Top-1 accuracy of 69.3% in radical retrosynthesis, surpassing the state-of-the-art models LocalRetro and Mol-Transformer by 23.0% and 25.4%, respectively. (3) Interpretability and Practical Utility: Attention weight analysis and case studies demonstrate that RadicalRetro effectively captures radical reaction patterns (e.g., cascade cyclizations and photocatalytic steps) and proposes synthetically viable routes, such as streamlined pathways for Tamoxifen precursors and glycoside derivatives. RadicalRetro’s performance highlights its potential to transform radical-based synthetic planning, offering chemists a robust tool to leverage the unique advantages of radical chemistry in drug synthesis. Full article

In today’s information-driven era, machine learning is revolutionizing medicinal chemistry, offering a paradigm shift from traditional, intuition-based, and often bias-prone methods to the prediction of chemical properties without prior knowledge of the basic principles governing drug function. This perspective highlights the growing importance of informatics in shaping the field of medicinal chemistry, particularly through the concept of the “informacophore”. The informacophore refers to the minimal chemical structure, combined with computed molecular descriptors, fingerprints, and machine-learned representations of its structure, that are essential for a molecule to exhibit biological activity. Similar to a skeleton key unlocking multiple locks, the informacophore points to the molecular features that trigger biological responses. By identifying and optimizing informacophores through in-depth analysis of ultra-large datasets of potential lead compounds and automating standard parts in the development process, there will be a significant reduction in biased intuitive decisions, which may lead to systemic errors and a parallel acceleration of drug discovery processes. Full article

Hematopoietic progenitor kinase 1 (HPK1), a negative regulator of T-cells, B-cells, and dendritic cells, has gained attention in antitumor immunotherapy research over the past decade. No HPK1 inhibitor has yet reached clinical approval, largely due to selectivity and drug-like limitations. Leveraging the available structural insights into HPK1, we conducted a rational hit identification using a structure-based virtual screening of over 600,000 drug-like molecules from ASINEX and OTAVA databases. A series of molecular docking studies, in vitro kinase assays, and molecular dynamics simulations were conducted to identify viable HPK1 inhibitor hits. This approach resulted in two promising novel hit scaffolds, 4H-Pyrido[1,2-a] thieno[2,3-d] pyrimidin-4-one (ISR-05) and quinolin-2(1H)-one (ISR-03), neither of which has previously been reported as an HPK1 inhibitor. ISR-05 and ISR-03 exhibited IC₅₀ values of 24.2 ± 5.07 and 43.9 ± 0.134 µM, respectively, in kinase inhibition assays. These hits constitute tractable starting points for future hit-to-lead optimization aimed at developing more effective HPK1 inhibitors for cancer therapy. Full article

Physiologically based pharmacokinetic (PBPK) modelling for biologics, such as monoclonal antibodies and therapeutic proteins, involves capturing complex processes, including target-mediated drug disposition (TMDD), FcRn-mediated recycling, and tissue-specific distribution. The Simcyp Designer Biologics PBPK Platform Model offers an intuitive and efficient platform for constructing mechanistic PBPK models with pre-defined templates and automated model assembly, reducing manual input and improving reproducibility. This tutorial provides a step-by-step guide to using the platform, highlighting features such as cross-species scaling, population variability simulations, and flexibility for model customization. Practical case studies demonstrate the platform’s capability to streamline workflows, enabling rapid, mechanistic model development to address key questions in biologics drug development. By automating critical processes, this tool enhances decision-making in translational research, optimizing the modelling and simulation of large molecules across discovery and clinical stages. Full article

Background: Juvenile scleroderma (JS) comprises a group of rare chronic autoimmune and fibrosing disorders in children, primarily presenting as juvenile localized scleroderma (jLS) or juvenile systemic sclerosis (jSS). While jLS predominantly affects the skin and subcutaneous tissues, jSS may involve multiple internal organs and is associated with increased morbidity and mortality. Due to the scarcity of pediatric-specific clinical trials, the current treatment strategies are largely empirical and often adapted from adult protocols. Objective: This narrative review aims to provide a comprehensive update on emerging systemic therapies for juvenile scleroderma, focusing on biologics, small molecule inhibitors, and advanced cellular interventions, to support the development of more personalized and effective pediatric treatment approaches. Methods: A literature search was conducted through PubMed and a manual bibliographic review, covering publications from 2001 to 2024. Only English-language studies involving pediatric populations were included, comprising randomized controlled trials, reviews, and case reports. Additional searches were performed for drugs that are specifically used in juvenile scleroderma. Results: Biologic agents such as tocilizumab, rituximab, and abatacept, along with small molecules including Janus kinase (JAK) inhibitors and imatinib, have demonstrated potential in managing refractory cases by reducing skin fibrosis and pulmonary involvement. Novel approaches—such as pamrevlumab, nintedanib, and chimeric antigen receptor (CAR-T) cell therapy—target fibrotic and autoimmune pathways but remain investigational in children. Autologous stem cell transplantation (ASCT) has also been explored in severe, treatment-resistant cases, although data are extremely limited. The overall evidence base is constrained by small sample sizes, a lack of controlled pediatric trials, and reliance on adult extrapolation. Conclusions: While innovative systemic therapies show promise for juvenile scleroderma, their widespread clinical application remains limited by insufficient pediatric-specific evidence. Large, multicenter, long-term trials are urgently needed to establish safety, efficacy, and optimal treatment algorithms that are tailored to the pediatric population. Full article

Reconstituted high-density lipoprotein nanoparticles (NPs), which mimic the structure and function of endogenous human plasma HDL, hold promise as a robust drug delivery system. These nanoparticles, when loaded with appropriate agents, serve as powerful tools for targeted drug delivery. The fundamental challenge lies in controlling and estimating the actual drug load and the efficiency of drug release at the target. In this report, we present a novel approach based on enhanced Förster Resonance Energy Transfer (FRET) to assess particle load and monitor payload release. The NPs are labeled with donor molecules embedded in the lipid phase, while the spherical core volume is filled with acceptor molecules. Highly enhanced FRET efficiency to multiple acceptors in the NP core has been observed at distances significantly larger than the characteristic Förster distance (R₀). To confirm that the observed changes in donor and acceptor emissions are a result of FRET, we developed a theoretical model for nonradiative energy transfer from a single donor to multiple acceptors enclosed in a spherical core volume. The load-dependent shortening of the fluorescence lifetime of the donor correlated with the presence of a negative component in the intensity decay of the acceptor clearly demonstrates that FRET can occur at a large distance comparable to the nanoparticle size (over 100 Å). Comparison of theoretical simulations with the measured intensity decays of the donor and acceptor fluorophores constitute a new method for evaluating particle load. The observed FRET efficiency depends on the number of acceptors in the core, providing a simple way to estimate the nanoparticle load efficiency. Particle disintegration and load release result in a distinct change in donor and acceptor emissions. This approach constitutes a novel strategy for assessing NP core load, monitoring NP integrity, and evaluating payload release efficiency to target cells. Significants: In the last decade, nanoparticles have emerged as a promising strategy for targeted drug delivery, with applications ranging from cancer therapy to ocular neurodegenerative disease treatments. Despite their potential, a significant issue has been the real-time monitoring of these drug delivery vehicles within biological systems. Effective strategies for monitoring NP payload loading, NP integrity, and payload release are needed to assess the quality of new drug delivery systems. In our study, we have found that FRET-enabled NPs function as an improved method for monitoring these aspects currently missing from current drug delivery efforts. Full article

The protozoan parasite Trypanosoma cruzi, causative agent of Chagas disease, affects millions of people in endemic Latin American countries and beyond. In Latin America, Chagas disease is an important cause of death and disability, for which vaccines are lacking and improved treatment options are required. Additionally, the factors governing the development of a variety of clinical manifestations during Chagas disease, ranging from complete lack of symptoms to severe irreversible chronic organ damage (mainly cardiac or digestive), remain largely unknown. Much remains to be learned regarding the biology of T. cruzi in order to enhance our understanding of these lines of inquiry. In this context, proteomic methods have been leveraged to investigate different parasite strains, life-cycle forms, subcellular compartments, macromolecular complexes, signaling events and secreted molecules. The factors driving morphological transformation during the life cycle, the composition and functions of the parasite’s organelles and secreted molecules as well as the determinants of pathogenicity have been explored via proteomic methods, yielding insights into the fundamental processes behind the parasite biology and informing drug design and vaccine development. Importantly, the correlation between the wide genetic and phenotypic variability displayed by T. cruzi has been examined through proteomic methods as well. Here, we review the literature on T. cruzi proteomics and discuss it in the light of its limitations and in the context of the parasite’s genetic diversity. Full article

Background/Objectives: Infliximab, ipilimumab, and nivolumab are three monoclonal antibodies that have been associated with hepatotoxicity. Three separate physiologically based pharmacokinetic (PBPK) models were developed in GastroPlus^® to simulate plasma and liver concentrations in patient populations after administration of either infliximab, ipilimumab, or nivolumab. Methods: The models include distribution and clearance mechanisms specific to large molecules, FcRn binding dynamics, and target-mediated drug disposition (TNF-α for infliximab, CTLA-4 for ipilimumab, and PD-1 for nivolumab). Results: The PBPK model for each large molecule was able to reproduce observed plasma concentration data in patient populations, including patients with rheumatoid arthritis and patients with solid tumors. Liver concentrations were predicted to be between 10% and 23% of the plasma concentrations for each of the three drugs, aligning with previously reported results. This lends further validity to the PBPK models and their ability to accurately predict hepatic concentrations in the absence of direct tissue measurements. Conclusions: These results can be used to drive liver toxicity predictions using the quantitative systems toxicology model, BIOLOGXsym™, which integrates hepatic interstitial concentrations with in vitro mechanistic toxicity data to predict the extent of liver toxicity for biologics. Full article