Search Results (1,471)

Pregnane X receptor (PXR) is an important nuclear receptor that regulates diverse physiological functions, including drug metabolism. Although PXR activation is potentially involved in adverse events, the full scope of its impact has yet to be elucidated. In this study, we developed a machine learning model to predict the activity of PXR agonists and applied the model to drugs listed in the US Food and Drug Administration Adverse Event Reporting System database. Analysis of the predicted agonist–active drug interactions and adverse event reports revealed statistically significant risks (lnROR > 1 and −logp > 1.3) for multiple cardiac disorders. These findings suggest that PXR activity is involved in cardiovascular adverse effects and may contribute to drug safety through the early identification of risks. Full article

Arginine kinase is a key phosphagen kinase in invertebrates that facilitates rapid ATP regeneration by reversibly transferring phosphate groups between phosphoarginine and ADP. Structural studies have shown that the enzyme adopts distinct conformations in its ligand-free and ligand-bound states, known as the “open” and “closed” forms, respectively. These conformational changes are crucial for catalytic activity, enabling precise positioning of active-site residues and loop closure during phosphoryl transfer. Transition-state analog complexes have provided additional insights by mimicking intermediate states of catalysis, supporting the functional relevance of the open/closed structural model. Furthermore, studies across multiple species reveal how monomeric and dimeric forms of arginine kinase contribute to its allosteric regulation and substrate specificity. Beyond its metabolic role, arginine kinase is also recognized as a major allergen in crustaceans. Its structural uniqueness and absence in vertebrates make it a promising candidate for selective drug targeting. By integrating crystallographic data with functional context, this review highlights conserved features and species-specific variations of arginine kinase that may inform the design of inhibitors. Such molecules have the potential to serve both as antiparasitic agents and as novel therapeutics to manage crustacean-related allergic responses in humans. Full article

Multiple sclerosis (MS) is a chronic, immune-mediated disorder of the central nervous system (CNS) characterized by inflammation, demyelination, axonal degeneration, and gliosis. Its pathophysiology involves a complex interplay of genetic susceptibility, environmental triggers, and immune dysregulation, ultimately leading to progressive neurodegeneration and functional decline. Although significant advances have been made in disease-modifying therapies (DMTs), many patients continue to experience disease progression and unmet therapeutic needs. Drug repurposing—the identification of new indications for existing drugs—has emerged as a promising strategy in MS research, offering a cost-effective and time-efficient alternative to traditional drug development. Several compounds originally developed for other diseases, including immunomodulatory, anti-inflammatory, and neuroprotective agents, are currently under investigation for their efficacy in MS. Repurposed agents, such as selective sphingosine-1-phosphate (S1P) receptor modulators, kinase inhibitors, and metabolic regulators, have demonstrated potential in promoting neuroprotection, modulating immune responses, and supporting remyelination in both preclinical and clinical settings. Simultaneously, artificial intelligence (AI) is transforming drug discovery and precision medicine in MS. Machine learning and deep learning models are being employed to analyze high-dimensional biomedical data, predict drug–target interactions, streamline drug repurposing workflows, and enhance therapeutic candidate selection. By integrating multiomics and neuroimaging data, AI tools facilitate the identification of novel targets and support patient stratification for individualized treatment. This review highlights recent advances in drug repurposing and discovery for MS, with a particular emphasis on the emerging role of AI in accelerating therapeutic innovation and optimizing treatment strategies. Full article

Diabetes mellitus (DM) is one of the leading causes of death and disability worldwide and its prevalence continues to rise. Chronic hyperglycemia exposes patients to severe complications. Among these, diabetic vascular lesions are the most destructive. Their primary driver is the synergistic interaction between hyperglycemia-induced oxidative stress and chronic inflammation. This review systematically elucidates how multiple pathological pathways—namely, metabolic dysregulation, mitochondrial dysfunction, endoplasmic reticulum stress, and epigenetic reprogramming—cooperate to drive oxidative stress and inflammatory cascades. Confronting this complex pathological network, natural products, unlike conventional single-target synthetic drugs, exert multi-target synergistic effects, simultaneously modulating several key pathogenic networks. This enables the restoration of redox homeostasis and the suppression of inflammatory responses, thereby improving vascular function and delaying both microvascular and macrovascular disease progression. However, the clinical translation of natural products still faces multiple challenges and requires comprehensive mechanistic studies and rigorous validation to fully realize their therapeutic potential. Full article

Antimicrobial peptides (AMPs) provide a robust alternative to conventional antibiotics, combating escalating microbial resistance through their diverse functions and broad pathogen-targeting abilities. While current deep learning technologies enhance AMP generation, they face challenges in developing multifunctional AMPs due to intricate amino acid interdependencies and limited consideration of diverse functional activities. To overcome this challenge, we introduce a novel de novo multifunctional AMP design framework that enhances a Feedback Generative Adversarial Network (FBGAN) by integrating a global quantitative AMP activity regression module and a multifunctional-attribute integrated prediction module. This integrated approach not only facilitates the automated generation of potential AMP candidates, but also optimizes the network’s ability to assess their multifunctionality. Initially, by integrating an effective pre-trained regression and classification model with feedback-loop mechanisms, our model can not only identify potential valid AMP candidates, but also optimizes computational predictions of Minimum Inhibitory Concentration (MIC) values. Subsequently, we employ a combinatorial predictor to simultaneously identify and predict five multifunctional AMP bioactivities, enabling the generation of multifunctional AMPs. The experimental results demonstrate the efficiency of generating AMPs with multiple enhanced antimicrobial properties, indicating that our work can provide a valuable reference for combating multi-drug-resistant infections. Full article

Eukaryotic elongation factor 1A (eEF1A), the second most abundant intracellular protein, not only plays a key role in peptide elongation, but is also capable of numerous moonlighting functions. Within malignant cells, eEF1A is by no means a neutral bystander but instead actively participates in oncogenic transformations via a myriad of molecular pathways. Thus far, a broad range of small-molecule inhibitors have been identified, which, despite their structural diversity, suppress tumor growth by targeting eEF1A. Interestingly, just as eEF1A enables its oncogenic potential far beyond boosting protein translation, these targeted agents disrupt this oncoprotein via multiple axes distinct from mere protein synthesis inhibition. Whereas the oncogenic mechanisms of eEF1A has been well documented, there lacks a systemic survey of the eEF1A-targeting agents in terms of their mechanisms. Accordingly, the present work aims to examine their multifaceted modes of action more than just blocking protein synthesis. By unveiling these insights, our deepened knowledge of these eEF1A-binding inhibitors will inform the development of future eEF1A-targeted drugs for cancer treatment. Full article

Background: Chronic Kidney Disease (CKD) is a major global health issue, with diabetes being its primary cause and cardiovascular disease contributing significantly to patient mortality. Recently, two classes of medications—sodium–glucose cotransporter 2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP-1 RAs)—have shown promise in protecting both kidney and heart health beyond their effects on blood sugar control. Methods: We conducted a narrative review summarizing the findings of different clinical trials and mechanistic studies evaluating the effect of SGLT2i and GLP-1 RAs on kidney function, cardiovascular outcomes, and overall disease progression in patients with CKD and DKD. Results: SGLT2i significantly mitigate kidney injury by restoring tubuloglomerular feedback, reducing intraglomerular hypertension, and attenuating inflammation, fibrosis, and oxidative stress. GLP-1 RAs complement these effects by enhancing endothelial function, promoting weight and blood pressure control, and exerting direct anti-inflammatory and anti-fibrotic actions on renal tissues. Landmark trials—CREDENCE, DAPA-CKD, and EMPA-KIDNEY—demonstrate that SGLT2i reduce the risk of kidney failure and renal or cardiovascular death by 25–40% in both diabetic and non-diabetic CKD populations. Likewise, trials such as LEADER, SUSTAIN, and AWARD-7 confirm that GLP-1 RAs slow renal function decline and improve cardiovascular outcomes. Early evidence suggests that using both drugs together may offer even greater benefits through multiple mechanisms. Conclusions: SGLT2i and GLP-1 RAs have redefined the therapeutic landscape of CKD by offering organ-protective benefits that extend beyond glycemic control. Whether used individually or in combination, these agents represent a paradigm shift toward integrated cardiorenal-metabolic care. A deeper understanding of their mechanisms and clinical utility in both diabetic and non-diabetic populations can inform evidence-based strategies to slow disease progression, reduce cardiovascular risk, and improve long-term patient outcomes in CKD. Full article

Background/Objectives: All current FDA-approved antibody–drug conjugates (ADCs) are single-target and single-payload molecules that have limited efficacy in patients due to drug resistance. Therefore, our goal was to generate a novel ADC that was less susceptible to single points of resistance to reduce the likelihood of patient relapse. Methods: We developed a dual-targeting, dual-payload ADC by conjugating a bispecific EGFR x cMET antibody to two payloads (MMAF and SN38) that had separate mechanisms of action using a novel tri-functional linker. This dual-payload ADC was tested for potency and efficacy in dividing and nondividing in vitro cell models using multiple tumor cell types. Efficacy of the dual-payload ADC was confirmed using in vivo models. Results: Our ADC with dual MMAF and SN38 payloads was more efficacious in inhibiting cell proliferation than single-payload ADCs across multiple cancer cell lines. In addition, the dual-payload molecule inhibited nondividing cells, which were more resistant to traditional ADC payloads. The dual-payload ADC also exhibited more potent tumor growth inhibition in vivo compared to that of single-payload ADCs. Conclusions: Overall, the bispecific antibody conjugated with both the MMAF and SN38 payloads inhibited tumor growth more strongly than ADCs conjugated with MMAF or SN38 alone. Developing dual-payload ADCs could limit the impact of acquired resistance in patients as well as lower the effective dose of each payload. Full article

Drug resistance remains a critical barrier to effective treatment in several cancers, particularly triple-negative breast cancer (TNBC). Estrogen receptor α36 (ERα36), a variant of the estrogen receptor in ER-negative breast cancer cells, plays important roles in cancer cell proliferation. We investigated the role of ERα36 in regulating multidrug resistance protein 1 (MDR1) in MDA-MB-231 human breast cancer cells. The activation of ERα36 by BSA-conjugated estradiol (BSA-E2) increased cell viability under Adriamycin exposure, suggesting its involvement in promoting drug resistance. BSA-E2 treatment significantly reduced the intracellular rhodamine-123 levels by activating the MDR1 efflux function, which was linked to increased MDR1 transcription and protein expression. The mechanical ERα36-mediated BSA-E2-induced activation of EGFR and downstream signaling via c-Src led to an activation of the Akt/ERK pathways and transcription factors, NF-κB and CREB. Additionally, ERα36 is involved in activating Wnt/β-catenin pathways to induce MDR1 expression. The silencing of ERα36 inhibited the BSA-E2-induced phosphorylation of Akt and ERK, thereby reducing MDR1 expression via downregulation of NF-κB and CREB as well as Wnt/β-catenin signaling. These findings demonstrated that ERα36 promotes MDR1 expression through multiple non-genomic signaling cascades, including Akt/ERK-NF-κB/CREB and Wnt/β-catenin pathways, and highlight the role of ERα36 as a promising target to enhance chemotherapeutic efficacy in TNBC. Full article

Background/Objectives: Fibromyalgia syndrome (FMS) is a chronic pain condition in which executive function (EF) alterations have been reported, though strikingly, relationships between simple executive functions (EFs) (updating, inhibition, and mental flexibility) and high-order ones, such as planning and problem-solving, have not been addressed yet in this population. This research aimed to firstly explore how low-level EFs play a role in planning and problem-solving performances. Methods: Thirty FMS patients and thirty healthy participants completed a series of neuropsychological tests evaluating low- and high-order EFs. Clinical and emotional symptoms were assessed with self-report questionnaires, while pain and fatigue levels were measured with numerical scales. Importantly, specific drug restrictions were accounted for. Results: Patients scored lower in most neurocognitive tests, with statistical significance noted only for visuospatial working memory (WM) and two planning and problem-solving tests. Pain, fatigue, and sleep disturbances showed important effects on most of the cognitive outcomes. Multiple regression analyses reflected that planning and problem-solving were successfully and partially predicted by updating, inhibition, and mental flexibility (though differences emerged between tasks). Conclusions: Our study confirms the presence of cognitive impairments in FMS, especially in high-order EFs, supporting patients’ complaints. Clinical symptoms play a role in FMS dyscognition but do not explain it completely. For the first time, as far as the authors know, simple EF influences on planning and problem-solving tests have been described for FMS patients. These results might help in unraveling the dysexecutive profile in FMS to design more adjusted treatment options. Full article

Clinical and animal studies suggest that multiple brain systems are involved in mediating reward-motivated and related emotional behavior including the consumption of commonly used drugs and palatable food, and there is evidence that the repeated ingestion of or exposure to these rewarding substances may in turn stimulate these brain systems to produce an overconsumption of these substances along with co-occurring emotional disturbances. To understand this positive feedback loop, this review focuses on a specific population of hypothalamic peptide neurons expressing melanin-concentrating hormone (MCH), which are positively related to dopamine reward and project to forebrain areas that mediate this behavior. It also examines neurons expressing the peptide hypocretin/orexin (HCRT) that are anatomically and functionally linked to MCH neurons and the molecular systems within these peptide neurons that stimulate their development and ultimately affect behavior. This report first describes evidence in animals that exposure in adults and during adolescence to rewarding substances, such as the drugs alcohol, nicotine and cocaine and palatable fat-rich food, stimulates the expression of MCH as well as HCRT and their intracellular molecular systems. It also increases reward-seeking and emotional behavior, leading to excess consumption and abuse of these substances and neurological conditions, completing this positive feedback loop. Next, this review focuses on the model involving embryonic exposure to these rewarding substances. In addition to revealing a similar positive feedback circuit, this model greatly advances our understanding of the diverse changes that occur in these neuropeptide/molecular systems in the embryo and how they relate, perhaps causally, to the disturbances in behavior early in life that predict a later increased risk of developing substance use disorders. Studies using this model demonstrate in animals that embryonic exposure to these rewarding substances, in addition to stimulating the expression of peptide neurons, increases the intracellular molecular systems in neuroprogenitor cells that promote their development. It also alters the morphology, migration, location and neurochemical profile of the peptide neurons and causes them to develop aberrant neuronal projections to forebrain structures. Moreover, it produces disturbances in behavior at a young age, which are sex-dependent and occur in females more than in males, that can be directly linked to the neuropeptide/molecular changes in the embryo and predict the development of behavioral disorders later in life. These results supporting the close relationship between the brain and behavior are consistent with clinical studies, showing females to be more vulnerable than males to developing substance use disorders with co-occurring emotional conditions and female offspring to respond more adversely than male offspring to prenatal exposure to rewarding substances. It is concluded that the continued consumption of or exposure to rewarding substances at any stage of life can, through such peptide brain systems, significantly increase an individual’s vulnerability to developing neurological disorders such as substance use disorders, anxiety, depression, or cognitive impairments. Full article

Growth hormone (GH) signaling has been implicated in tumor progression and therapy resistance across multiple cancer types, yet its role in bladder cancer remains largely unexplored. In this study, we investigated the impact of GH and its receptor (GHR) on therapy resistance and disease progression in urothelial carcinoma (UC) through integrated transcriptomic and in vitro analyses. Transcriptomic profiling of The Cancer Genome Atlas bladder cancer cohort revealed that high tumoral GHR expression was associated with differential upregulation of genes involved in drug efflux, epithelial-to-mesenchymal transition (EMT), and extracellular matrix (ECM) remodeling. Notably, elevated GHR levels correlated with significantly reduced overall survival in patients with UC. In parallel, in vitro experiments demonstrated that GH promotes chemoresistance in UC cell lines via upregulation of ATP-binding cassette-containing (ABC) transporters and activation of EMT. GH also modulated ECM-remodeling-associated genes in a chemotherapy-dependent manner, including matrix metalloproteinases and tissue inhibitors of metalloproteinases. Importantly, these effects were abrogated by Pegvisomant, a GHR antagonist, indicating the functional relevance of GH/GHR signaling in the mediation of these phenotypes. Collectively, our findings support a mechanistic role for GH signaling in driving therapy resistance and tumor aggressiveness in bladder cancer and suggest GHR antagonism as a potential therapeutic strategy to improve treatment outcomes. Full article

The demand for rapid, portable, and cost-effective analytical tools has driven advances in smartphone-based microfluidic sensors. By combining microfluidic precision with the accessibility and processing power of smartphones, these devices offer real-time and on-site diagnostic capabilities. This review explores recent developments in smartphone-integrated microfluidic sensors, focusing on their design, fabrication, smartphone integration, and analytical functions with the applications in forensic science, agriculture, and environmental monitoring. In forensic science, these sensors provide fast, field-based alternatives to traditional lab methods for detecting substances like DNA, drugs, and explosives, improving investigation efficiency. In agriculture, they support precision farming by enabling on-demand analysis of soil nutrients, water quality, and plant health, enhancing crop management. In environmental monitoring, these sensors allow the timely detection of pollutants in air, water, and soil, enabling quicker responses to hazards. Their portability and user-friendliness make them particularly valuable in resource-limited settings. Overall, this review highlights the transformative potential of smartphone-based microfluidic sensors in enabling accessible, real-time diagnostics across multiple disciplines. Full article

Polypharmacology, the rational design of small molecules that act on multiple therapeutic targets, offers a transformative approach to overcome biological redundancy, network compensation, and drug resistance. This review outlines the scientific rationale for polypharmacology, highlighting its success across oncology, neurodegeneration, metabolic disorders, and infectious diseases. Emphasis is placed on how polypharmacological agents can synergize therapeutic effects, reduce adverse events, and improve patient compliance compared to combination therapies. We also explore how computational methods—spanning ligand-based modeling, structure-based docking, network pharmacology, and systems biology—enable target selection and multi-target ligand prediction. Recent advances in artificial intelligence (AI), particularly deep learning, reinforcement learning, and generative models, have further accelerated the discovery and optimization of multi-target agents. These AI-driven platforms are capable of de novo design of dual and multi-target compounds, some of which have demonstrated biological efficacy in vitro. Finally, we discuss the integration of omics data, CRISPR functional screens, and pathway simulations in guiding multi-target design, as well as the challenges and limitations of current AI approaches. Looking ahead, AI-enabled polypharmacology is poised to become a cornerstone of next-generation drug discovery, with potential to deliver more effective therapies tailored to the complexity of human disease. Full article

Background/Objectives: The treatment of multiple myeloma (MM) remains a challenge, as almost all patients will eventually relapse. Proteasome inhibitors are a cornerstone in the management of MM. Unfortunately, validated biomarkers predicting drug response are largely missing. Therefore, we aimed to identify genes associated with drug resistance or sensitization to proteasome inhibitors. Methods: We performed genome-wide CRISPR-Cas9 knockout (KO) screens in human KMS-28-BM myeloma cells to identify genetic determinants associated with resistance or sensitization to proteasome inhibitors. Results: We show that KO of KLF13 and PSMC4 induces drug resistance, while NUDCD2, OSER1 and HERC1 KO cause drug sensitization. Subsequently, we focused on top sensitization hit, NUDCD2, which acts as a co-chaperone of Hsp90 to regulate the LIS1/dynein complex. RNA sequencing showed downregulation of genes involved in the ERAD pathway and in ER-associated ubiquitin-dependent protein catabolic processes in both untreated and carfilzomib-treated NUDCD2 KO cells, suggesting that NUDCD2 depletion alters protein degradation. Furthermore, bortezomib-treated NUDCD2 KO cells showed a decreased expression of genes that have a function in oxidative phosphorylation and the mitochondrial membrane, such as Carnitine Palmitoyltransferase 1A (CPT1A). CPT1A catalyzes the uptake of long chain fatty acids into mitochondria. Mitochondrial lipid metabolism has recently been reported as a possible therapeutic target for MM drug sensitivity. Conclusions: These results contribute to the search for therapeutic targets that can sensitize MM patients to proteasome inhibitors. Full article