Speakers

Title: Rational design and mechanistic characterization of novel antifungal agents targeting squalene epoxidase

Abstract: The increasing prevalence of antifungal resistance, particularly against terbinafine, represents a growing challenge in the treatment of dermatophytic infections. Resistance mechanisms, mainly associated with mutations in the squalene epoxidase (SQLE) enzyme, significantly compromise the efficacy of current therapies. In this context, the development of new antifungal agents with improved activity and reduced susceptibility to resistance is urgently needed. In this work, we report the rational design, synthesis, and comprehensive evaluation of a series of novel sulfonamide-based compounds targeting SQLE in Trichophyton species. The molecular design strategy was based on combining structural features of known inhibitors while reducing steric hindrance and modulating physicochemical properties to overcome resistance-associated limitations. Five compounds were synthesized and evaluated against clinical isolates of T. rubrum and T. mentagrophytes. Among them, compound 2 exhibited outstanding antifungal activity in the nanomolar range, surpassing the reference drug terbinafine. Structure–activity relationship analysis revealed that subtle structural modifications significantly influence biological activity, highlighting the importance of scaffold flexibility and aromatic substitution patterns. To gain mechanistic insight, an integrated computational strategy was employed, including homology modeling of fungal SQLE, molecular docking, and molecular dynamics simulations. These studies revealed that compound 2 adopts a stable binding mode within the hydrophobic active site, driven by favorable π–π and hydrophobic interactions with key residues such as Leu393 and Phe397, which are directly implicated in resistance mechanisms. Importantly, the proposed scaffold avoids the steric limitations associated with the naphthyl group present in terbinafine, enabling improved accommodation within the binding pocket and potentially reducing susceptibility to resistance. The combination of experimental and computational results establishes a clear correlation between molecular design, binding affinity, and biological activity. Overall, this study provides a robust framework for the development of next-generation antifungal agents and highlights the power of integrating synthetic chemistry, biological evaluation, and in silico approaches to address drug resistance. These findings pave the way for further optimization and future structural validation studies toward clinical translation.

Title: Analysis of Ligand Selection Criteria in Structure-Activity Relationship (SAR) Studies

Abstract:  Structure-activity relationship (SAR) studies help establish a link between a molecule’s chemical structure and its biological activity. They are essential in medicinal chemistry for optimizing the efficacy and selectivity of compounds. The selection of molecules is based on several criteria, including structural diversity, to explore different functional groups. It is also important to have reliable biological data covering a broad spectrum of activity. Physicochemical properties, such as molecular weight, lipophilicity, and polarity, must be taken into account. The quality of experimental data is essential to ensure reproducibility. Experimental approaches include synthesis and biological testing. In silico approaches, such as molecular docking and QSAR, complement this analysis. A sound selection strategy enables the identification of key pharmacophores; it thus helps optimize candidates and accelerate the discovery of new molecules

Title: Research in human physiology in the field of big data.

Abstract: In the age of big data, research in human physiology is undergoing a profound transformation driven by the integration of digital technologies and advanced analytical tools. The analysis of large volumes of data from diverse sources—including biological, clinical, and connected device data—enables a more nuanced and dynamic understanding of physiological functions. This approach facilitates the modeling of complex systems, the identification of new biomarkers, and improved prediction of pathological conditions. It thus paves the way for a more integrative approach to physiology, focused on precision medicine and biomedical innovation.

Title: Structure–Antimicrobial Activity Relationships of Recombinant Host Defence Peptides Against Drug‐Resistant Bacteria. 

Abstract: Host defence peptides (HDPs) represent a valuable class of antimicrobial agents with the potential to address the growing threat of antimicrobial resistance (AMR). Here, we have studied recombinant constructs based on a combination of HDPs fused to the GFP protein and multidomain proteins combining three or four HDPs in a single polypeptide, referred to as first and second generation antimicrobials, respectively. These recombinant peptides were tested against Gram-positive and Gram-negative bacteria associated with healthcare infections. In addition, in silico studies provided insight into the antimicrobial structure–activity relationships of these biomolecules. For the first generation of antimicrobials, amphipathicity mainly explains the average antimicrobial activity against the Gram-positive strains. In the case of the Gram-negative bacteria, it depends on the quantity and the exposed area of the Ser and Thr amino acids. For the second generation of antimicrobials, the order of domains is crucial to act against Gram-positive strains, preferably by positioning the most bioactive domain against the Gram-positive pathogen at the ends. 

Title: AI in Oncology: Next-Generation Intelligence from Diagnosis to Therapy

Abstract: Artificial Intelligence (AI) is reshaping oncology by enabling smarter, data-driven approaches from diagnosis to therapy. This keynote explores recent advances in AI for medical imaging, where deep learning enhances cancer detection and characterization, as well as predictive modeling using clinical and multi-source patient data for prognosis and treatment response. It also highlights the growing role of Natural Language Processing (NLP) in extracting knowledge from clinical records and supporting AI-driven treatment recommendation systems. Through real-world applications and recent research advances, the talk presents how multimodal AI integrating imaging, clinical, and textual data is paving the way toward more precise, personalized, and intelligent cancer care, while addressing key challenges in interpretability, ethics, and clinical adoption.

Title: Constructed Wetlands for Domestic Wastewater Treatment : New perspectives under Cirqua Project

Abstract: The CIRQUA project aims to enhance nature-based solutions (NBS), with a particular focus on constructed wetlands (CWs) for wastewater treatment and water recovery in rural areas. These systems are widely recognized as safe, decentralized, cost-effective, and environmentally sustainable solutions. Our objective is to contribute to improve water use efficiency and conserving water resources in the Mediterranean region, thereby strengthening resilience to climate change. CWs offer opportunities for valorizing non-conventional nutrient resources, supporting precision irrigation and fertilization practices that are essential for sustainable agriculture. CIRQUA project proposes to upgrade conventional CWs through the integration of advanced technological innovations, including nanostructured filtration materials, photocatalytic treatment modules, cultivation of nitrogen-fixing legumes, real-time monitoring sensors, automated operation systems, and precision irrigation strategies supported by artificial intelligence tools. These advancements are expected to significantly improve effluent quality, enabling the safe reuse of treated water and biomass for agricultural applications. By promoting resource recovery and reuse at the local scale, the CIRQUA approach supports the transition toward a circular economy while addressing environmental and water management challenges in rural contexts.