The CIGB-552 peptide is a novel therapeutic alternative for the treatment of cancer. In this study, we aimed to determine the optimal formulation and route of administration for CIGB-552. Other objectives were to characterize the peptide’s pharmacokinetic profile in rats and conduct toxicity studies at different dosage regimens. The antitumor activity of CIGB-552 was evaluated by different routes of administration (intraperitoneal, subcutaneous) and also with different peptide formulations (Tartrate/mannitol, Tartrate/trehalose) using a TC-1 tumor model in C57BL/6 mice. The pharmacokinetic profile of the peptide was also characterized after subcutaneous administration in Sprague-Dawley rats using PK Solver software. In addition, the safety of the peptide was evaluated following single-dose and repeated-dose administration schedules in healthy BALB/c mice. In the tumor model, subcutaneous administration of CIGB-552 and its tartrate/trehalose formulation resulted in a significant reduction in tumor volume compared to untreated groups. CIGB-552 exhibited a typical extravascular delivery profile, with rapid absorption, rapid tissue distribution, and rapid blood clearance. The peptide’s half-life was 2.5 h, and peak plasma concentration (Cmax) was reached in approximately 15 min. Furthermore, CIGB-552 was shown to have nonlinear pharmacokinetics across the evaluated dose and exposure range. On the other hand, CIGB-552 administration in the evaluated regimens was safe, with toxicity and fatal outcomes observed only with the 60 mg/kg dose administered subcutaneously every two days until seven doses were completed in total. The peptide’s safety profile in repeated dosing, combined with evidence of no systemic accumulation, supports the development of new regimens involving higher and more frequent doses to enhance antitumor efficacy in clinical studies. CIGB-552 administered subcutaneously and formulated with tartrate/trehalose demonstrates potent antitumor activity in murine models. CIGB-552 has peak plasma concentration at 15 min and a half-life of 2.5 h in Sprague Dawley rats. No significant adverse effects were observed at therapeutic doses, with safety confirmed in acute and repeated-dose studies in mice.
Abstract Glioblastoma is a very aggressive brain tumor with few therapeutics’ options. Type I and II Interferons (IFNs) co-formulation HeberFERON has been used in cancer treatment, with promising results in high grade brain tumors. High throughput techniques in easy-to-handle models have been important to interrogate biomolecules changes, describe mechanisms and find pharmacodynamic biomarkers. This study aims to elucidate the effect of HeberFERON over the cell proteome in comparison to its individual IFNs components. Proteomic changes with HeberFERON in the glioblastoma-derived cell line U-87MG, in comparison with individual IFN-α2b and IFN-γ, were studied using a nanoLC instrument EasyLC coupled to Velos Pro mass spectrometer; Maxquant and Perseus were also used. Several enrichment tools, networking analysis and canSAR for drug targets were employed. Translation, RNA processing, mitotic cell cycle, cytoskeleton and chromosome organization, apoptosis, autophagy, DNA repair are enriched to limit cellular growing together with changes in immune response components, supporting HeberFERON as a multitarget treatment. This co-formulation is distinguished at modulating RNA splicing with SMN complex, cytoskeleton organization and microtubule-based movement, nuclear envelope breakdown, DNA conformational changes, and oxidative phosphorylation, with a better drawing of effects over a variety of systems inside the tumoral cell. Together with previous microarray experiment, informative genes and proteins as pharmacodynamic biomarkers for antiproliferative effects showed up (ex. STAT1/2, CENPE, ATRIP, MAP1B, LIMA1, VCP, several ribosomal, spliceosome and proteasomal complexes proteins). This study complements transcriptomic and phosphoproteomic previous experiments in this model and underscore HeberFERON as a glioblastoma therapeutic.
IntroductionSea lice are crustacean ectoparasites affecting Atlantic salmon production worldwide and impediments to industry growth. Chemical treatment has been the method of choice to control infestation with increasing resistance. Vaccination is an environmentally friendly alternative for sea lice control; however, obtaining high levels of lice reduction through active immunization has proven difficult. This study aimed to explore the efficacy of two sea lice vaccine prototypes under laboratory-controlled conditions.MethodsTherein, fish were vaccinated with two chimeric antigens, TT-P0 or P0-my32, using oil-adjuvanted vaccine formulations and a prime-boost vaccination protocol. Fish were experimentally challenged with copepodids at 2, 5, and 11 months post-prime vaccination.Results and discussionTT-P0 vaccinated fish had a significantly lower lice number at all three challenges, 88, 90, and 20%, respectively, compared to controls. The P0-my32 vaccine gave high protection at early time points post-vaccination, with 91 and 75.4% reduction at 3 and 6 months, respectively, fading off at 12 months (4.2% reduction vs. control). The TT-P0 group had a significantly lower lice number than controls at the 11-month challenge. A higher degree of protection coincided with higher circulating antibody levels against homologous antigens. This proof of concept study encourage the use of vaccination as a tool to reduce the lice burden in salmon, and preclinical and clinical testing at a large scale is needed to document the level of protection attained under field conditions.
As the global population ages, healthcare systems face increasing challenges in managing the complex health needs of older adults, including multimorbidity, cognitive decline, and frailty. Artificial intelligence (AI) holds significant potential to address these challenges by offering advanced tools for personalized health management, disease prediction, and real-time monitoring. This paper reviews key AI applications in gerontology, focusing on its role in analyzing multimodal data such as electronic health records, genomic data, medical imaging, and wearable device metrics. AI’s ability to integrate and analyze these diverse data types enhances the precision of disease management and treatment personalization, particularly in chronic disease care and cognitive function assessment. However, challenges related to data quality, privacy concerns, and model interpretability remain. This review highlights both the transformative potential and the limitations of AI in elderly healthcare, advocating for future research aimed at improving model transparency, scalability, and interdisciplinary integration to enhance geriatric care.
