Topical ophthalmic drug delivery targeting the posterior segment of the eye has become a key area of interest due to its non-invasive nature, safety, ease of application, patient compliance, and cost-effectiveness. However, achievement of effective drug bioavailability in the posterior ocular segment is a significant challenge due to unique ocular barriers, including precorneal factors and anatomical barriers, like the cornea, the conjunctiva, and the sclera. Successful ocular drug delivery systems require increased precorneal residence time and improved corneal penetration to enhance intraocular bioavailability. A promising strategy to overcome these barriers is incorporating drug penetration enhancers (DPEs) into formulations. These compounds facilitate drug delivery by improving permeability across otherwise impermeable or poorly permeable membranes. At the ocular level, they act through three primary mechanisms: breaking tear film stability by interfering with the mucous layer; disrupting membrane components such as phospholipids and proteins; and loosening epithelial cellular junctions. DPEs offer significant potential to improve bioavailability and therapeutic outcomes, particularly for drugs targeting the posterior segment of the eye. This review is focused on analyzing the current literature regarding the use of penetration enhancers in topical ocular drug delivery, highlighting their mechanisms of action and potential to revolutionize ophthalmic treatments.

Electronic noses (E-noses), are usually composed by an array of sensors with different selectivities towards classes of VOCs (Volatile Organic Compounds). These devices have been applied to a variety of fields, including environmental protection, public safety, food and beverage industries, cosmetics, and clinical diagnostics. This work demonstrates that it is possible to classify eleven VOCs from different chemical classes using a single gas sensing biomaterial that changes its optical properties in the presence of VOCs. To accomplish this, an in-house built E-nose, tailor-made for the novel class of gas sensing biomaterials, was improved and combined with powerful machine learning techniques. The device comprises a delivery system, a detection system and a data acquisition and control system. It was designed to be stable, miniaturized and easy-to-handle. The data collected was pre-processed and features and curve fitting parameters were extracted from the original response. A recursive feature selection method was applied to select the best features, and then a Support Vector Machine classifier was implemented to distinguish the eleven distinct VOCs. The results show that the followed methodology allowed the classification of all the VOCs tested with 94.6% (± 0.9%) accuracy.