Search Results (50)

To explore the emerging role of natural ligands, specifically coumarin and curcumin, and their coordination with the transition metals ruthenium (Ru) and iridium (Ir) as photosensitizers (PSs) in photodynamic therapy (PDT) for cancer. This highlights the integration of natural compounds and transition metals to overcome limitations in photophysical properties, hypoxia tolerance, and clinical translation. Regarding PDT oncology, this examines an immunological effect on Ru/Ir complexes and natural ligand–metal hybrids. They induce immunogenic cell death (ICD) through reactive oxygen species (ROS) generation, calreticulin exposure, extracellular ATP release, and HMGB1 secretion. These damage-associated molecular patterns act as “danger signals” to recruit dendritic cells, prime CD8⁺ cytotoxic T-cells, and establish systemic antitumor immunity. This study compares natural ligand–metal complexes with conventional Ru(II)/Ir(III) complexes and clinical PSs to assess their translational potential as immune-activating agents in PDT oncology, as well as focusing on the integration of nanotechnology with natural ligand–metal complexes to enhance delivery, biocompatibility, and clinical translation. A narrative review was conducted of the literature published between 2010 and 2025 across multiple electronic databases, including WanFang Data, PubMed, ScienceDirect, Scopus, Web of Science, Springer Link, SciFinder, and CNKI, without language restrictions. Studies focusing on coumarin, curcumin, Ru(II), Ir(III), and PDT were analyzed. Extracted data included chemical structures, absorption and emission spectra, singlet oxygen yields, biological activities, and therapeutic outcomes. Comparative evaluation was performed between free natural ligands, their Ru(II)/Ir(III) complexes, and nanodelivery systems to assess efficacy, biocompatibility, and translational potential. Coumarin and curcumin exhibited intrinsic antioxidant, anti-inflammatory, and anticancer properties but were limited by short absorption/emission ranges, poor photostability, and low singlet oxygen yields, restricting preclinical application. Coordination with Ru(II) and Ir(III) significantly enhanced intersystem crossing, extended absorption into the near-infrared region, and improved singlet oxygen quantum yields (Φ_Δ up to ~0.78). These complexes demonstrated potent photocytotoxicity under normoxia and hypoxia, achieving IC₅₀ values in the nanomolar range, which indicated organelle-specific targeting (mitochondria, lysosomes, ER), induced ICD, and synergized with checkpoint blockade. Nanocarrier encapsulation further improved solubility and tumor selectivity, and reduced systemic toxicity. Coumarin- and curcumin-based Ru/Ir complexes represent promising next-generation or immune-activating PDT agents by combining natural pharmacological activity with superior photophysical performance. The ability to generate reactive oxygen species under hypoxia and achieve multimodal therapeutic effects positions them as strong candidates for clinical translation. Clinical approval of natural ligand–Ru/Ir complexes depends on rigorous safety, pharmacokinetic, and nanodelivery validation, but these complexes clearly extend PDT beyond local cytotoxicity toward durable immune protection. Future research should prioritize ligand engineering, nanotechnology integration, and translational models to bridge preclinical promise with safe and effective clinical applications. Full article

Background: The emergence of antifungal resistance and the limited number of clinically available antifungal drug classes necessitate the development of new agents with improved efficacy and safety. We investigated how linker architecture influences the antifungal activity and lead properties of hybrid triazole-thiazolidine derivatives. Methods: A focused library of triazole-thiazolidine hybrids incorporating alkylamine, amide, cyclic amine, 2-hydroxypropyl, and thiazepane linkers was synthesized and characterized. Antifungal activity was evaluated against reference strains and clinical isolates of Candida spp., Aspergillus fumigatus, dermatophytes, and Cryptococcus neoformans. Structure–activity relationships were analyzed by molecular docking. Selected compounds were further assessed by SCRAPPY profiling, fluorescence microscopy, mammalian-cell cytotoxicity assays, acute oral toxicity studies, and evaluation of microsomal stability and interactions with human CYP450 isoforms. Results: Linker architecture strongly influenced antifungal potency. Amide- and cyclic amine-containing hybrids were generally the most active, whereas simple alkylamide derivatives showed narrower activity profiles. Compound 28 emerged as the most promising lead, exhibiting sub-microgram MIC values against several Candida isolates, particularly C. parapsilosis, and retaining measurable activity against an azole-resistant C. albicans strain. Docking generated putative CYP51-binding models, while SCRAPPY profiling and fluorescence microscopy revealed an azole-like cellular response consistent with perturbation of sterol-associated homeostasis. Compound 28 was tolerated at 300 mg/kg in an acute oral study but showed concentration- and time-dependent cytotoxicity and rapid CYP3A4-mediated microsomal metabolism. Conclusions: Systematic variation of linker architecture identified compound 28 as a promising exploratory antifungal lead. Further optimization should focus on improving metabolic stability and cytotoxicity, together with direct target validation, pharmacokinetic characterization, and in vivo efficacy studies. Full article

Background/Objectives: This study applies the previously introduced structure-preserving hybrid framework for compartmental pharmacokinetic models and extends its clinical evaluation using published clinical datasets. The framework combines a mechanistic pharmacokinetic backbone with a bounded data-driven correction. The aim was to assess predictive performance in held-out subjects while keeping the main pharmacokinetic structure and avoiding a fully black-box model. Methods: This applied extension of the framework was tested in several numerical studies using published clinical pharmacokinetic datasets for polymyxin B, linezolid, and tacrolimus. For polymyxin B and linezolid, repeated subject-wise cross-validation with nested tuning was used to compare the mechanistic baseline with unconstrained and constrained hybrid corrections and a boosted-tree residual benchmark. The studies were designed to assess its behavior in a main application setting, across different drugs, under difficult fitting conditions, and under changes in correction strength and mechanistic parameters. Computational time was also assessed. Results: The results showed that the constrained correction remained close to the mechanistic baseline in the held-out analyses of polymyxin B and linezolid, but it did not significantly improve subject-level prediction. The unconstrained correction showed greater deterioration, while the boosted-tree benchmark gave mixed results and no significant subject-level improvement. The additional analyses showed that tighter correction bounds were generally selected and that the constrained hybrid still responds to changes in the mechanistic parameters in a sensible manner. Conclusions: Overall, the results suggest that the bounded data-driven correction can control the poorer performance seen with an unrestricted correction while keeping prediction close to the mechanistic baseline. It therefore provides a cautious way to combine mechanistic pharmacokinetic modeling with data-driven correction while preserving interpretability. Further external validation is still needed. Full article

Nanostructured drug-delivery materials—liposomes, polymeric nanoparticles, dendrimers, and inorganic carriers—have become central to pharmaceutical strategies for crossing the blood–brain barrier (BBB), where most candidate therapeutics fail to reach their targets. Their biological performance hinges on a coupled chain of vascular transport, BBB translocation, tissue diffusion, cellular uptake, and intracellular release, each of which is shaped by the nanocarrier’s size, surface chemistry, charge, and ligand functionalization. Physiologically based pharmacokinetic (PBPK) models describe this chain mechanistically but are limited by parameter uncertainty, simplified representations of the BBB, and coarse regional resolution. Machine learning (ML) can close these gaps by extracting nonlinear structure–transport–exposure relationships from heterogeneous experimental and clinical datasets. This review examines emerging ML–PBPK hybrid frameworks for predicting the brain biodistribution of nanostructured drug carriers. We compare regression, kernel, and deep learning approaches for parameter inference, model correction, and surrogate modeling; assess strategies for feature selection, uncertainty quantification, and interpretability; and discuss documented failure cases that bound the conditions under which these methods can be trusted. The review closes with recommendations on dataset standardization, software platform selection, and the responsible use of generative AI in pharmaceutical modeling, thus providing guidance for translating nanostructured material design into safer, more effective brain-targeted therapies. Full article

Cancers still remain one of the most significant challenges in medicine or pharmacy, accounting for nearly 10 million deaths annually and imposing a substantial socioeconomic burden worldwide. Although chemotherapy continues to play a central role in the treatment of many tumors, conventional anticancer therapies are frequently associated with poor selectivity, systemic toxicity, multidrug resistance, and unfavorable pharmacokinetic profiles. Paclitaxel (PTX), one of the most widely used antineoplastic agents, demonstrates remarkable clinical efficacy against breast, ovarian, lung, pancreatic, and several other malignancies. Nevertheless, its clinical application remains limited by poor aqueous solubility, non-specific biodistribution, dose-limiting toxicities, and the development of resistance mechanisms. Nanotechnology-based anticancer drug delivery systems have emerged as a promising strategy to address these limitations. Among them, polymeric nanosystems have attracted particular attention owing to their physicochemical properties, biocompatibility, controlled drug-release capabilities, and potential for tumor-targeted delivery. Natural, semi-synthetic, and synthetic polymers are extensively investigated as carriers for PTX, leading to the development of nanoparticles, micelles, nanogels, nanofibers, dendritic systems, and hybrid nanoplatforms. Nanosystems demonstrate enhanced therapeutic efficacy, reduced systemic toxicity, prolonged circulation times, and improved tumor accumulation in preclinical models. Despite encouraging laboratory results, the clinical translation of polymeric PTX nanocarriers (NCs) remains limited. Numerous barriers, including tumor heterogeneity, variability of the enhanced permeability and retention (EPR) effect, manufacturing complexity, regulatory challenges, scale-up difficulties, and discrepancies between animal models and human cancers, continue to hinder successful commercialization and widespread clinical adoption. This review critically discusses the current state of polymeric drug delivery systems (DDSs) that contain PTX, as well as the advantages and limitations of synthetic, natural, and semi-synthetic polymers used in DDS technologies. Furthermore, translational challenges and future perspectives of PTX-based DDSs were analyzed. Full article

Multifunctional nano-contrast agent carriers are redefining molecular imaging by combining high-fidelity visualization with targeted delivery, controlled release, and, increasingly, therapeutic action. This review encompasses the development of nano-contrast platforms from conventional dendrimer, liposome, chitosan, and silica systems to modular nano-contrast platforms for multimodal, multi-parametric, and activatable imaging in clinically relevant environments. We dissect engineering strategies that govern surface chemistry, ligand organization, stimulus responsiveness, and microenvironmental sensing, and relate them to theranostic performance, immune system engagement, and quantitative image readouts. Biodistribution, pharmacokinetics, and safety are discussed from both classical and model-informed perspectives, with design principles that favor predictable behavior, manufacturability, and regulatory acceptance. Current clinical translation, regulatory pathway evolution, and market dynamics are critically reviewed to elucidate that a few nano-contrast agents have reached patients despite a widespread experimental landscape. Finally, we discuss emerging trends, including biomimetic and ultrasmall carriers, metal–organic and hybrid frameworks, AI-assisted design, digital twins, and precision medicine workflows, which are likely to shape the next-generation nano-contrast theranostics. By systematically relating material selection and carrier architecture to imaging function and translational limitations, this review suggests concrete research priorities for taking nano-contrast agents from sophisticated prototypes to robust, patient-tailored tools. Full article

Bioanalytical methods to quantitate conjugated payloads are essential for assessing antibody-drug conjugate (ADC) stability and pharmacokinetics (PK). Dual-payload ADCs present analytical challenges; different linker chemistries can require complex digestion conditions to perform the cleavage. Developing separate methods for each linker combination can be time and resource demanding. Rat tritosomes—purified lysosomal fractions from Triton-treated rat liver—provide a comprehensive enzymatic mixture that mimics the lysosomal environment. The presented bioanalytical method combines immunoaffinity purification with tritosome-mediated digestion for simultaneous quantitation of dual-conjugated payloads. The method was applied to a model dual-payload ADC containing two different cytotoxic payloads, conjugated using different enzymatically cleavable linkers, with an unrelated DAR (drug-to-antibody ratio). Method validation in mouse plasma demonstrated excellent accuracy (bias ± 20%, LLOQ and ULOQ ± 25%) and precision (coefficient of variation CV% ≤ 20%, LLOQ and ULOQ ± 25%) across all concentration levels (lower to upper limit of quantitation, LLOQ to ULOQ) for both payloads, with 100% of quality control samples (QCs) meeting acceptance criteria for hybrid LC-MS/MS quantitation methods. This tritosome-based approach provides a unified, efficient platform for multi-payload ADC bioanalysis, eliminates linker-specific method optimization, and enables robust support for preclinical studies. The method has been tested for accuracy and precision on 4 different model ADCs and employed to quantify the conjugated payloads in in vivo samples from a homozygous hFcRn transgenic mouse model (Tg32) PK study, resulting in reliable data in accordance with total antibody measurements. Full article

Artificial intelligence (AI) is increasingly influencing nanopharmaceutical development by supporting the transition from empirical formulation screening toward predictive, data-driven, and translationally oriented design. Nanocarrier-based therapeutics are governed by nonlinear relationships among material composition, physicochemical attributes, manufacturing parameters, biological identity, pharmacokinetics, toxicity, and therapeutic performance. In this review, we examine how AI can contribute to nanopharmaceutical development from predictive formulation design to clinical translation. We synthesize current applications of machine learning, deep learning, physics-informed modeling, hybrid mechanistic–AI approaches, and automated optimization workflows, with emphasis on critical quality attribute modeling, multi-objective optimization, design of experiments, quality-by-design, process analytical technology, digital twins, and continuous manufacturing. We also discuss applications involving nano–bio interactions, pharmacokinetics, toxicity, immunogenicity, and precision nanomedicine. AI-based approaches can support rational nanocarrier design, identify nonlinear formulation–property relationships, guide optimization, improve process understanding, and integrate heterogeneous experimental, biological, and manufacturing datasets across diverse nanopharmaceutical platforms. These methods are particularly relevant for modeling protein corona formation, cellular uptake, intracellular trafficking, biodistribution, pharmacokinetics, toxicity, immunogenicity, and patient-specific responses. However, translational implementation remains limited by fragmented datasets, inconsistent reporting standards, limited interpretability, insufficient external validation, uncertain predictions, poorly defined applicability domains, and evolving regulatory expectations for adaptive computational models. Overall, AI should be viewed not only as an optimization tool, but also as a translational framework connecting formulation science, biological prediction, manufacturing control, and clinical implementation. Future progress will depend on standardized data infrastructures, explainable and externally validated models, uncertainty quantification, applicability-domain definition, hybrid mechanistic–AI frameworks, regulatory-ready documentation, and clinically relevant case studies. Full article

Background: Neglected diseases caused by protozoan parasites remain a major public health burden, particularly in low- and middle-income countries. Among the chemical motifs explored in antiparasitic drug discovery, guanidine-containing compounds have attracted considerable attention due to their strong cationic character, high capacity for hydrogen bonding, and versatility in interacting with biological targets. Methodology: This review summarizes advances reported in the last decade regarding guanidine derivatives with activity against pathogens associated with Chagas disease, human African trypanosomiasis, Leishmaniasis, tuberculosis, toxoplasmosis, dengue and schistosomiasis. Results: Evidence gathered from synthetic, natural, and drug-repurposing studies indicates that the guanidine, guanidine-containing and guanidine-related compounds contribute to modulating biological activity by changing electrostatic interactions, hydrogen-bonding networks, and physicochemical properties, with enzymes, nucleic acids, and membrane-associated targets essential for parasite survival. Across the analyzed studies, several emerging structure–activity relationship trends were identified, including the contribution of polycationic or dicationic architectures, the influence of halogenated or lipophilic substituents, and the dependence of biological activity on the complete molecular framework, including heterocyclic systems, macrocycles, peptide conjugates, hybrid scaffolds, and repurposed drugs. In addition to direct antiparasitic effects, certain guanidine-containing and guanidine-related compounds demonstrate immunomodulatory or host-protective properties, expanding the therapeutic relevance of this class. Despite promising in vitro results, protonation trapping, efflux pump susceptibility, and pharmacokinetic limitations such as poor oral absorption, high polarity, plasma protein binding and limited membrane permeability remain significant challenges for clinical translation. Nonetheless, the integration of medicinal chemistry, computational modeling, and biological screening continues to accelerate the identification of optimized scaffolds. Conclusions: Overall, guanidine-based compounds constitute a promising scaffold for the development of new therapeutic strategies targeting neglected parasitic diseases, and further structural optimization may enable the emergence of candidates with improved efficacy, selectivity, and drug-like properties. Full article

Antiretroviral drugs (ARVDs) remain the cornerstone of HIV/AIDS management, but their therapeutic efficacy and safety are highly influenced by bioconversion processes such as hepatic metabolism and enzymatic transformation. Variability in metabolic pathways, mediated by cytochrome P450 enzymes and other liver-based systems, contributes to interindividual differences in drug response, toxicity, and resistance. Recent advances in artificial intelligence, particularly artificial neural networks (ANNs), offer promising tools for modeling and optimizing these complex bioconversion processes. ANNs are capable of learning nonlinear relationships from high-dimensional datasets, making them ideal for predicting the pharmacokinetic parameters, enzyme–substrate interactions, and metabolic stability of ARVDs. This review explores the emerging role of ANNs in understanding and optimizing the metabolic transformation of antiretroviral agents. Key applications are discussed, including prediction of drug–enzyme interactions, in silico modeling of hepatic clearance, and simulation of enzyme kinetics. The integration of molecular descriptors, omics data, and clinical parameters into ANN models allows for improved prediction accuracy and personalized therapy. Furthermore, ANN-based tools can aid in early-stage drug development by identifying metabolic liabilities and guiding structural modifications to enhance metabolic stability. Despite their potential, challenges such as data scarcity, model interpretability, and standardization remain. Future research should focus on hybrid models combining ANN with mechanistic pharmacokinetics, the incorporation of real-world patient data, and validation against experimental outcomes. Overall, ANNs represent a powerful approach to optimizing ARVDs bioconversion, with the potential to improve efficacy, reduce toxicity, and support the development of next-generation antiretroviral therapies Full article

Pharmacokinetic modeling in Positron Emission Tomography (PET) imaging has become a cornerstone in cancer research, offering insights into tumor development and progression. These models facilitate the quantification of radiotracer distribution and metabolism, enabling precise measurement of physiological parameters essential for cancer diagnosis, staging, and treatment monitoring. However, accurate pharmacokinetic modeling depends on reliable input function acquisition and partial volume correction techniques to minimize biases in quantitative PET metrics. This review provides a comprehensive overview of current methodologies and advancements in pharmacokinetic modeling for PET oncology imaging. We discuss techniques for acquiring input functions, including arterial, venous, and image-derived input functions (IDIFs), along with population-based input functions (PBIFs). Their strengths, limitations, and clinical applications are critically evaluated. Additionally, we examine quantitative methods such as partial volume correction (PVC) that mitigate the spatial resolution limitations of PET, improving radiotracer quantification in small or heterogeneous tumors. Furthermore, we explore advanced kinetic modeling techniques, including compartmental models, graphical approaches, and data-driven methods, highlighting recent innovations such as machine learning and Bayesian modeling. Key areas for future research in PET pharmacokinetic modeling include integrating hybrid imaging modalities, developing robust patient-specific input functions, and leveraging machine learning to streamline modeling processes. These advancements aim to enhance the precision and clinical utility of PET imaging in oncology, leading to more personalized cancer treatment strategies. Full article

Ovarian cancer (OC) remains one of the leading causes of gynecologic cancer mortality, largely due to late diagnosis, frequent relapse, and the emergence of chemoresistance. An important but often-overlooked contributor to treatment failure is the heterogeneous penetration of anticancer drugs within tumors. Structural and biochemical barriers—including abnormal vasculature, elevated interstitial pressure, dense extracellular matrix, drug efflux transporters, and malignant ascites—generate steep intratumoral concentration gradients that conventional preclinical models fail to capture. As a result, systemic pharmacokinetic measurements frequently provide limited insight into tumor-level drug exposure. Patient-derived organoids (PDOs) have emerged as physiologically relevant 3D models that preserve the genetic, architectural, and functional characteristics of the original tumor. These systems enable controlled investigation of pharmacokinetic and pharmacodynamic processes, including drug penetration, metabolism, retention, and exposure–response relationships. Adding cell-free malignant ascites supernatant enhances PDOs’ ability to mimic the metastatic peritoneal microenvironment of OC. This review discusses recent advances in PDO technologies and examines how PDO-derived data can inform intratumoral pharmacokinetics and dosing strategies using physiologically based pharmacokinetic modeling and in vitro–in vivo extrapolation. Emerging hybrid platforms, including organoid-on-chip systems, vascularized co-cultures, and multi-omics integration, are crucial to improve translational prediction and support precision oncology. Full article

Lab-on-a-Chip (LoC) and microfluidic technologies are rapidly reshaping the development pipeline for nano drug delivery systems (DDSs) by enabling precise control of physicochemical properties, high-throughput screening, and integrated biological evaluation within miniaturized platforms. This review synthesizes recent advances in microfluidic principles, fabrication strategies, and sensing modalities that facilitate continuous flow synthesis, real-time characterization, and adaptive formulation of nanoparticles. We highlight how LoC-enabled systems improve monodispersity, reproducibility, and tunability of liposomes, polymeric nanoparticles, and metallic nanocarriers, while providing powerful tools for assessing pharmacokinetics, drug release, and systemic responses using organ-on-chip (OoC) models. Emerging trends, including AI-driven autonomous optimization, stimuli-responsive materials, 3D-printed hybrid architectures, and self-powered portable devices, are discussed in the context of future integrated nano-pharmaceutics platforms. Despite existing challenges related to biocompatibility, standardization, data integration, and translation to industrial and clinical applications, the synergistic evolution of LoC engineering and nanomedicine holds transformative potential for personalized and next-generation therapeutic strategies. Full article

Organ-on-a-chip (OoC) technologies, also termed microphysiological systems (MPSs), integrate microfluidics, engineered biomaterials, human-derived cells, and on-chip biosensing to model human physiology in microscale devices that deliver quantitative, time-resolved readouts. This review surveys the 2010–2025 literature, emphasizing how sensing, standardized sampling, and analytics enable clinical concordance and fit-for-purpose regulatory use. We synthesize advances in (i) materials, fabrication, and microfluidic design; (ii) organ- and disease-focused case studies; and (iii) translational benchmarks that align chip outputs with clinical pharmacokinetics, toxicology, and biomarker datasets. Across organ systems, platforms increasingly incorporate vascularization, immune components, and organoid hybrids, paired with real-time measurements of barrier integrity, metabolism, electrophysiology, and secreted biomarkers using impedance (TEER), electrochemical, and optical modalities. Representative benchmarking studies report cardiac OoCs achieving AUROC ≥ 0.85 for torsadogenic risk classification, and renal chips improving prediction of transporter-mediated clearance relative to conventional in vitro assays. We summarize validation approaches and regulatory developments relevant to new approach methodologies, including the FDA Modernization Act 2.0, and discuss how AI and multi-omics can automate signal and image analysis, harmonize cross-platform datasets, and support digital-twin workflows that couple OoC measurements to in silico models. Overall, biosensor-enabled OoCs are progressing toward quantitatively benchmarked platforms for safety pharmacology, ADME/PK–PD, and precision medicine. Full article

Coumarin-based compounds are recognized for their chemical versatility and diverse biological activities, yet clinical applications remain largely confined to 4-hydroxycoumarin anticoagulants. To bridge this translational gap, coumarin scaffolds have been increasingly employed in prodrug design to enable controlled activation, targeted delivery, and theranostic functionality. This review critically evaluates whether coumarin-based prodrugs fulfill their therapeutic promise or remain primarily preclinical tools across oncology, inflammation, infectious diseases, and cardiovascular disorders. Strategies including enzymatic-, pH-, redox-, and light-triggered activation, as well as subcellular targeting and multifunctional hybrids, are discussed. Preclinical studies demonstrate improved bioavailability, reduced off-target toxicity, and real-time fluorescence monitoring, yet most compounds remain at the in vitro or small-animal model stage. Despite their mechanistic and conceptual potential, clinical translation is constrained by molecular complexity, pharmacokinetics, safety, and regulatory challenges. Overall, coumarins constitute a versatile multifunctional platform whose therapeutic impact relies on rigorous in vivo validation and strategic optimization. Full article