Hasil untuk "Therapeutics. Pharmacology"

J. Noebels, M. Avoli, M. Rogawski et al.

Agnese Gagliardi, Elena Giuliano, Eeda Venkateswararao et al.

Advances in nanotechnology have favored the development of novel colloidal formulations able to modulate the pharmacological and biopharmaceutical properties of drugs. The peculiar physico-chemical and technological properties of nanomaterial-based therapeutics have allowed for several successful applications in the treatment of cancer. The size, shape, charge and patterning of nanoscale therapeutic molecules are parameters that need to be investigated and modulated in order to promote and optimize cell and tissue interaction. In this review, the use of polymeric nanoparticles as drug delivery systems of anticancer compounds, their physico-chemical properties and their ability to be efficiently localized in specific tumor tissues have been described. The nanoencapsulation of antitumor active compounds in polymeric systems is a promising approach to improve the efficacy of various tumor treatments.

B. Furr, V. Jordan

E. O'Brien, N. Atkins, G. Stergiou et al.

H. Patel, Teeru Bihani

Breast cancer is the most frequently diagnosed cancer in women, with estrogen receptor positive (ER+) breast cancer making up approximately 75% of all breast cancers diagnosed. Given the dependence on active ER signaling in these tumors, the predominant treatment strategy has been to inhibit various aspects of this pathway including directly antagonizing ER with the use of selective estrogen receptor modulators (SERMs) and selective estrogen receptor degraders (SERDs). Interestingly, the dependence on ER for breast cancer growth is often retained even after progression through several lines of antiestrogen therapy, making ER a bonafide biomarker for this cancer subtype and driving the continued research and development of novel ER-targeted therapeutics to treat this patient population. This, combined with the continuous discovery of mechanisms underlying endocrine resistance, is resulting in a continually evolving treatment landscape for ER+ breast cancer. This review discusses various ER antagonists investigated for the treatment of breast cancer, outlining their pharmacological and tissue-specific mechanisms of action as well as their specified use within the ER+ breast cancer setting. In addition, mechanisms of resistance to SERMs and SERDs, the use of ER antagonists in combination therapy strategies, and the ongoing development of novel drugs are also reviewed in the context of the changing clinical landscape of ER+ breast cancer. Lastly, the role of SERMs and SERDs in non-breast cancer indications is also discussed.

T. Che, S. Majumdar, S. Zaidi et al.

Ethan B. Russo

The topic of Cannabis curries controversy in every sphere of influence, whether politics, pharmacology, applied therapeutics or even botanical taxonomy. Debate as to the speciation of Cannabis, or a lack thereof, has swirled for more than 250 years. Because all Cannabis types are eminently capable of cross-breeding to produce fertile progeny, it is unlikely that any clear winner will emerge between the “lumpers” vs. “splitters” in this taxonomical debate. This is compounded by the profusion of Cannabis varieties available through the black market and even the developing legal market. While labeled “strains” in common parlance, this term is acceptable with respect to bacteria and viruses, but not among Plantae. Given that such factors as plant height and leaflet width do not distinguish one Cannabis plant from another and similar difficulties in defining terms in Cannabis, the only reasonable solution is to characterize them by their biochemical/pharmacological characteristics. Thus, it is best to refer to Cannabis types as chemical varieties, or “chemovars.” The current wave of excitement in Cannabis commerce has translated into a flurry of research on alternative sources, particularly yeasts, and complex systems for laboratory production have emerged, but these presuppose that single compounds are a desirable goal. Rather, the case for Cannabis synergy via the “entourage effect” is currently sufficiently strong as to suggest that one molecule is unlikely to match the therapeutic and even industrial potential of Cannabis itself as a phytochemical factory. The astounding plasticity of the Cannabis genome additionally obviates the need for genetic modification techniques.

D. Maggs, P. Miller, Ron Ofri

Arfan Ghani

Epileptic seizures arise from abnormally synchronised neural activity and remain a major global health challenge, affecting more than 50 million people worldwide. Despite advances in pharmacological interventions, a significant proportion of patients continue to experience uncontrolled seizures, underscoring the need for alternative neuromodulation strategies. Rhythmic neural entrainment has recently emerged as a promising mechanism for disrupting pathological synchrony, but most existing systems rely on complex analogue electronics or high-power stimulation hardware. This study investigates a minimal digital custom-designed chip that generates a stable 6 Hz oscillation capable of entraining epileptic seizure activity. Using a publicly available EEG seizure dataset, we extracted and averaged analogue seizure waveforms, digitised them to emulate neural front-ends, and directly interfaced the digitised signals with digital output recordings acquired from the chip using a Saleae Logic analyser. The chip pulse train was resampled and low-pass-reconstructed to produce an analogue 6 Hz waveform, allowing direct comparison between seizure morphology, its digitised representation, and the entrained output. Frequency-domain and time-domain analyses demonstrate that the chip imposes a narrow-band 6 Hz rhythm that overrides the broadband spectral profile of seizure activity. These results provide a proof-of-concept for low-power digital custom-designed entrainment as a potential pathway toward simplified, wearable seizure-interruption devices for precision medicine and future healthcare devices.

Kyong Ju Lee, Yuan Ji

Phase I oncology trials aim to identify a safe dose - often the maximum tolerated dose (MTD) - for subsequent studies. Conventional designs focus on population-level toxicity modeling, with recent attention on leveraging pharmacokinetic (PK) data to improve dose selection. We propose the Precision Dose-Finding (PDF) design, a novel Bayesian phase I framework that integrates individual patient PK profiles into the dose-finding process. By incorporating patient-specific PK parameters (such as volume of distribution and elimination rate), PDF models toxicity risk at the individual level, in contrast to traditional methods that ignore inter-patient variability. The trial is structured in two stages: an initial training stage to update model parameters using cohort-based dose escalation, and a subsequent test stage in which doses for new patients are chosen based on each patient's own PK-predicted toxicity probability. This two-stage approach enables truly personalized dose assignment while maintaining rigorous safety oversight. Extensive simulation studies demonstrate the feasibility of PDF and suggest that it provides improved safety and dosing precision relative to the continual reassessment method (CRM). The PDF design thus offers a refined dose-finding strategy that tailors the MTD to individual patients, aligning phase I trials with the ideals of precision medicine.

Yuan Sang, Huiqing Zhao, Jiajun Wu et al.

Hyperosmolarity is a key contributor to nucleus pulposus cell (NPC) apoptosis during intervertebral disc degeneration (IVDD). Aquaporin 3 (AQP3), a membrane channel protein, regulates cellular osmotic balance by transporting water and osmolytes. Although AQP3 downregulation is associated with disc degeneration, its role in apoptosis under hyperosmotic conditions remains unclear. Here, we demonstrate that hyperosmolarity induces AQP3 depletion, suppresses the PI3K/AKT/mTOR signaling pathway, and promotes mitochondrial dysfunction and ROS accumulation in NPCs. Lentiviral overexpression of AQP3 restores this pathway, attenuates oxidative damage, and reduces apoptosis, preserving disc structure in IVDD rat models. In contrast, pharmacological inhibition of AQP3 exacerbates ECM catabolism and NP tissue loss. Our findings reveal that AQP3 deficiency under hyperosmolarity contributes to NPC apoptosis via suppression of PI3K/AKT/mTOR signaling, potentially creating a pathological cycle of disc degeneration. These results highlight AQP3 as a promising therapeutic target for IVDD.

Jules Bangard, Einar Holsbø, Kristian Svendsen et al.

Adverse drug interactions are a critical concern in pharmacovigilance, as both clinical trials and spontaneous reporting systems often lack the breadth to detect complex drug interactions. This study introduces a computational framework for adverse drug interaction detection, leveraging disproportionality analysis on individual case safety reports. By integrating the Anatomical Therapeutic Chemical classification, the framework extends beyond drug interactions to capture hierarchical pharmacological relationships. To address biases inherent in existing disproportionality measures, we employ a hypergeometric risk metric, while a Markov Chain Monte Carlo algorithm provides robust empirical p-value estimation for the risk associated to cocktails. A genetic algorithm further facilitates efficient identification of high-risk drug cocktails. Validation on synthetic and FDA Adverse Event Reporting System data demonstrates the method's efficacy in detecting established drugs and drug interactions associated with myopathy-related adverse events. Implemented as an R package, this framework offers a reproducible, scalable tool for post-market drug safety surveillance.

Linyan Zou

Major depressive disorder represents one of the most significant global health challenges of the 21st century, affecting millions of people worldwide and creating substantial economic and social burdens. While conventional antidepressant therapies have provided relief for many individuals, their limitations including delayed onset of action, significant side effects, and treatment resistance in a substantial portion of patients have prompted researchers and healthcare providers to explore alternative therapeutic approaches (Kasneci et al.). African traditional medicine, with its rich heritage of plant-based remedies developed over millennia, offers a valuable resource for developing novel antidepressant treatments that may address some of these limitations. This paper examines the integration of large language models with African natural products for depression support, combining traditional knowledge with modern artificial intelligence technology to create accessible, evidence-based mental health support systems. The research presented here encompasses a comprehensive analysis of African medicinal plants with documented antidepressant properties, their pharmacological mechanisms, and the development of an AI-powered support system that leverages DeepSeek's advanced language model capabilities. The system provides evidence-based information about African herbal medicines, their clinical applications, safety considerations, and therapeutic protocols while maintaining scientific rigor and appropriate safety standards. Our findings demonstrate the potential for large language models to serve as bridges between traditional knowledge and modern healthcare, offering personalized, culturally appropriate depression support that honors both traditional wisdom and contemporary medical understanding.

Yaroslav Balytskyi, Inna Hubenko, Alina Balytska et al.

Accurate identification of druggable pockets and their features is essential for structure-based drug design and effective downstream docking. Here, we present RAPID-Net, a deep learning-based algorithm designed for the accurate prediction of binding pockets and seamless integration with docking pipelines. On the PoseBusters benchmark, RAPID-Net-guided AutoDock Vina achieves 54.9% of Top-1 poses with RMSD < 2 A and satisfying the PoseBusters chemical-validity criterion, compared to 49.1% for DiffBindFR. On the most challenging time split of PoseBusters aiming to assess generalization ability (structures submitted after September 30, 2021), RAPID-Net-guided AutoDock Vina achieves 53.1% of Top-1 poses with RMSD < 2 A and PB-valid, versus 59.5% for AlphaFold 3. Notably, in 92.2% of cases, RAPID-Net-guided Vina samples at least one pose with RMSD < 2 A (regardless of its rank), indicating that pose ranking, rather than sampling, is the primary accuracy bottleneck. The lightweight inference, scalability, and competitive accuracy of RAPID-Net position it as a viable option for large-scale virtual screening campaigns. Across diverse benchmark datasets, RAPID-Net outperforms other pocket prediction tools, including PUResNet and Kalasanty, in both docking accuracy and pocket-ligand intersection rates. Furthermore, we demonstrate the potential of RAPID-Net to accelerate the development of novel therapeutics by highlighting its performance on pharmacologically relevant targets. RAPID-Net accurately identifies distal functional sites, offering new opportunities for allosteric inhibitor design. In the case of the RNA-dependent RNA polymerase of SARS-CoV-2, RAPID-Net uncovers a wider array of potential binding pockets than existing predictors, which typically annotate only the orthosteric pocket and overlook secondary cavities.

Tung-Lam Ngo, Ba-Hoang Tran, Duy-Cat Can et al.

Understanding the interaction between different drugs (drug-drug interaction or DDI) is critical for ensuring patient safety and optimizing therapeutic outcomes. Existing DDI datasets primarily focus on textual information, overlooking multimodal data that reflect complex drug mechanisms. In this paper, we (1) introduce MUDI, a large-scale Multimodal biomedical dataset for Understanding pharmacodynamic Drug-drug Interactions, and (2) benchmark learning methods to study it. In brief, MUDI provides a comprehensive multimodal representation of drugs by combining pharmacological text, chemical formulas, molecular structure graphs, and images across 310,532 annotated drug pairs labeled as Synergism, Antagonism, or New Effect. Crucially, to effectively evaluate machine-learning based generalization, MUDI consists of unseen drug pairs in the test set. We evaluate benchmark models using both late fusion voting and intermediate fusion strategies. All data, annotations, evaluation scripts, and baselines are released under an open research license.

Dan Feng, Jian Zhang, Huanmin Niu et al.

The elevated polyamines, amine-rich molecules with diverse functions in pathophysiology processes, are implicated in contributing to tumorigenesis and progression. Whether and how they affect the efficacy of chemotherapy is incompletely understood. Our screening assays reveal that the supplement with a low dose of spermidine (Spd), one of the polyamines, enhances ferroptosis in prostate cancer cells as evidenced by increased lipid peroxidation and intracellular Fe2+ levels in vitro. Combination treatment with Spd and a low dose of ferroptosis inducer erastin synergistically augments anti-tumor efficacy with undetectable toxicity in mice. Analysis of RNA-seq data indicates that heme oxygenase 1 (HMOX1), an enzyme that catalyzes the cleavage of heme to release Fe2+, is significantly upregulated in response to Spd and erastin cotreatment. Spd mediated the hypusine modification of the eukaryotic initiation factor 5A (EIF5A) promotes the translation of the nuclear factor erythroid 2-related factor 2 (NRF2), subsequently leading to elevation of HMOX1. Moreover, Spd and erastin significantly inhibit proteasome activity which results in a decrease in proteasomal degradation of NRF2, although many proteasome-related genes are induced either by Spd or Spd plus erastin. Thus, in addition to its pro-oncogenic activity, the supplement of Spd improves antitumor activity in combination with ferroptosis inducers and offers an optional approach to cancer treatment.

Lili Zhou, Ke Cheng, Linbin Chen et al.

BackgroundSuboptimal medication adherence remains a major cause of allograft failure after kidney transplantation. Previous studies have focused on isolated factors rather than integrated mechanisms. Based on the COM-B model, this study investigates the mediating roles of medication beliefs and regulatory emotional self-efficacy (RESE) between medication literacy, social support, and medication adherence.MethodsA cross-sectional survey included 351 kidney transplant recipients (KTRs) from a tertiary hospital in Changsha (April-July 2025). Participants completed a general information questionnaire, the Basel Assessment of Adherence to Immunosuppressive Medications Scale, the Chinese version of the RESE Scale, the Social Support Rating Scale, the Chinese Medication Literacy Scale, and the Beliefs about Medicines Questionnaire-Specific. Data were analyzed using SPSS and AMOS for descriptive, correlational, hierarchical regression, and mediation analyses (bootstrapping with 5000 samples).ResultsThe medication non-adherence rate in KTRs was 37.6%, primarily due to missed doses (33.3%). Medication literacy, social support, medication beliefs, and RESE were significantly correlated with adherence (p &lt; 0.01). After controlling for demographic variables, these factors explained 47.2% of the variance in adherence. Path analysis showed that medication literacy (β= -0.219) and social support (β= -0.180) directly reduced non-adherence and also indirectly improved adherence through medication beliefs and RESE. Specifically, medication literacy had indirect effects via medication beliefs (β= -0.034, 11.6%) and RESE (β= -0.039, 13.4%); social support exerted indirect effects through medication beliefs (β= -0.113, 35.0%) and RESE (β= -0.030, 9.3%). All bootstrap 95% CIs excluded zero.ConclusionMedication adherence among KTRs remains suboptimal. Within the COM-B framework, this study confirms that medication literacy and social support not only directly affect adherence but also exert indirect effects through the dual mediating pathways of medication beliefs and RESE. These findings suggest that clinical interventions should adopt a multidimensional approach, focusing not only on enhancing medication knowledge and support systems but also specifically addressing patients’ medication beliefs and emotional self-efficacy. A multi-path synergistic strategy is recommended to optimize intervention effectiveness.

Miguel V. Guerra, Juan Castro, Antonio Moreno et al.

Lysosomal storage diseases (LSDs) are characterized by the accumulation of undegraded substrates within lysosomes, often associated with oxidative stress and impaired lysosomal function. In this study, we investigate the role of the c-Abl/TFEB pathway in different LSDs: Gaucher, Niemann-Pick type A (NPA), and Niemann-Pick type C (NPC). Our findings identify c-Abl activation (p-c-Abl) as a common pathogenic mechanism in these disorders. We demonstrate that c-Abl phosphorylates TFEB at Tyr173, leading to its cytoplasmic retention. Using pharmacological models of Gaucher, NPA and NPC in SH-SY5Y neuronal cells and HeLa cells, we assess the effects of the c-Abl inhibitors Imatinib and Neurotinib, as well as the antioxidant α-Tocopherol (α-TOH), on TFEB nuclear translocation and p-c-Abl protein levels. Additionally, we explore the effects of c-Abl inhibitors in cholesterol accumulation in LSDs neuronal models. Our results show that treatment with c-Abl inhibitors or α-TOH promotes TFEB nuclear translocation, enhances lysosomal clearance, and reduces cholesterol accumulation in all three LSD models. These findings highlight the c-Abl/TFEB pathway as a potential therapeutic target for LSDs and potentially other neurodegenerative disorders associated with lysosomal dysfunction.

Yang Liu, Jie Gao, Xiaonan Zhang et al.

Messenger RNA (mRNA) vaccines and therapeutics are emerging as powerful tools against a variety of diseases, including infectious diseases and cancer. The design of mRNA molecules, particularly the untranslated region (UTR) and coding sequence (CDS) is crucial for optimizing translation efficiency and stability. Current design approaches generally focus solely on either the 5' UTR or the CDS, which limits their ability to comprehensively enhance translation efficiency and stability. To address this, we introduce LinearDesign2, an algorithm that enables the co-design of the 5' UTR and CDS. This integrated approach optimizes translation initiation efficiency (TIE), codon adaptation index (CAI), and minimum free energy (MFE) simultaneously. Comparative analyses reveal that sequences designed by LinearDesign2 exhibit significantly higher TIE than those designed by LinearDesign, with only a slight increase in MFE. Further, we validate the accuracy of the computational TIE metric using large-scale parallel translation experimental data. This study highlights the importance of a joint design strategy for the 5' UTR and CDS in optimizing mRNA performance, paving the way for more efficient mRNA vaccines and therapeutics.

Fatemeh Azari, Anne M. Robertson, Lori A. Birder

This study employs micro-computed tomography (micro-CT) to unravel the geometrical intricacies of the rat urinary bladder wall during various states of ex-vivo filling, contrasting markedly with the commonly held idealizations of uniform bladder geometry. Through precise 3D reconstructions at resolutions between 10-20 micrometers, the research meticulously documents the bladder's morphological transformations across different filling pressures. The findings illuminate substantial deviations from the theoretical model of a uniformly thick, spherical bladder, particularly underlined by variations in wall thickness and bladder volume during the transition from voided to filled states. These results are pivotal for refining mechanical models of bladder function, which have traditionally oversimplified the complex geometrical and biomechanical behavior of the bladder. Moreover, this study underscores the potential of micro-CT in providing a deeper understanding of bladder mechanics, essential for advancing therapeutic strategies for conditions like bladder outlet obstruction (BOO), thereby enhancing both surgical and pharmacological treatment paradigms.