Marine-origin gelatin has been increasingly used as a safe alternative to bovine and porcine ones due to their structural similarity, avoiding the health-related problems and sociocultural concerns associated with using mammalian-origin materials. Another benefit of marine-origin gelatin is that it can be produced from fish processing-products enabling high production at low cost. Recent studies have demonstrated the excellent capacity of gelatin-methacryloyl (GelMA)-based hydrogels in a wide range of biomedical applications due to their suitable biological properties and tunable physical characteristics, such as tissue engineering applications, including the engineering of cartilage. In this study, fish gelatin was obtained from Greenland halibut skins by an acidic extraction method and further functionalized by methacrylation using methacrylic anhydride, developing a photosensitive gelatin-methacryloyl (GelMA) with a degree of functionalization of 58%. The produced marine GelMA allowed the fabrication of photo-crosslinked hydrogels by incorporating a photoinitiator and UV light exposure. To improve the biological performance, GelMA was combined with two glycosaminoglycans (GAGs): hyaluronic acid (HA) and chondroitin sulfate (CS). GAGs methacrylation reaction was necessary, rendering methacrylated HA (HAMA) and methacrylated CS (CSMA). Three different concentrations of GelMA were combined with CSMA and HAMA at different ratios to produce biomechanically stable hydrogels with tunable physicochemical features. The 20% (w/v) GelMA-based hydrogels produced in this work were tested as a matrix for chondrocyte culture for cartilage tissue engineering with formulations containing both HAMA and CSMA showing improved cell viability. The obtained results suggest these hybrid hydrogels be used as promising biomaterials for cartilage tissue engineering applications.

Corneal pathologies from infectious or noninfectious origin have a significant impact on the daily lives of millions of people worldwide. Despite the risk of organ rejection or infection, corneal transplantation is currently the only effective treatment. Finding safe and innovative strategies is the main goal of tissue-engineering-based approaches. In this study, the potential of gelatin methacryloyl (GelMA) hydrogels produced from marine-derived gelatin and loaded with ascorbic acid (as an enhancer of the biological activity of cells) was evaluated for corneal stromal applications. Marine GelMA was synthesized with a methacrylation degree of 75%, enabling effective photocrosslinking, and hydrogels with or without ascorbic acid were produced, encompassing human keratocytes. All the produced formulations exhibited excellent optical and swelling properties with easy handling as well as structural stability and adequate degradation rates that may allow proper extracellular matrix remodeling by corneal stromal cells. Formulations loaded with 0.5 mg/mL of ascorbic acid enhanced the biological performance of keratocytes and induced collagen production. These results suggest that, in addition to marine-derived gelatin being suitable for the synthesis of GelMA, the hydrogels produced are promising biomaterials for corneal regeneration applications.

Antimicrobial resistance (AMR) is a silent pandemic that presents an urgent threat to human health. Recently, polymyxins have been revived as a last-line therapeutic option, despite their toxicity. As such, there is a need for fast and reliable approaches to devise novel polymyxin analogues. In this work, machine learning was employed to devise a semi-quantitative model of the activity of polymyxin-like molecules. Four learning algorithms and ten families of molecular descriptors were explored. Top performance was observed for an AdaBoost model using the Kier and Hall topological indexes, allowing for the exploration of the systematic changes in the structure of polymyxin B.

Antimicrobial resistance (AMR) is a silent pandemic that represents an urgent threat to human health. Unfortunately, the antibiotic development pipeline is slow even though AMR has been escalating uncontrollably fast, namely amongst Gram-negative pathogens. Although out of use until recently due to their toxic side effects, polymyxins have been revived as a last-line therapeutic option since all other antibiotics are currently failing. In an attempt to ameliorate their toxicity and improve antimicrobial activity, many studies have been generating polymyxin analogues through different strategies, mostly empirical. As such, there is still a lack of faster and more reliable approaches to make analog design efficient in order to tackle AMR in a timely fashion. The solution to accelerate the discovery of new drugs probably lies in the use of in silico approaches, such as machine learning, due to their faster pace and time and cost efficiency. In this work, machine learning was applied to Quantitative Structure-Activity Relationship (QSAR) modeling with the objective of providing a working semi-quantitative model capable of predicting the activity of polymyxin-like molecules for a given species. For this, we applied four different learning algorithms and ten different families of molecular descriptors to our dataset of 408 molecule/microorganism pairs retrieved from PubChem. The AdaBoost model devised using the CKP set of descriptors was the best performer, with good accuracies and very low false negative and positive predictions. Preliminary exploration of the model's response to systematic changes in the structure of polymyxin B reveals a trend towards increased antimicrobial activity when exchanging some of its constituent amino acids for more lipophilic ones. Experimental studies are already underway based on this model's application and we believe it will become a crucial tool for drug development.