Artificial intelligence (AI) encompasses a broad spectrum of techniques that have been utilized by pharmaceutical companies for decades, including machine learning, deep learning, and other advanced computational methods. These innovations have unlocked unprecedented opportunities for the acceleration of drug discovery and delivery, the optimization of treatment regimens, and the improvement of patient outcomes. AI is swiftly transforming the pharmaceutical industry, revolutionizing everything from drug development and discovery to personalized medicine, including target identification and validation, selection of excipients, prediction of the synthetic route, supply chain optimization, monitoring during continuous manufacturing processes, or predictive maintenance, among others. While the integration of AI promises to enhance efficiency, reduce costs, and improve both medicines and patient health, it also raises important questions from a regulatory point of view. In this review article, we will present a comprehensive overview of AI’s applications in the pharmaceutical industry, covering areas such as drug discovery, target optimization, personalized medicine, drug safety, and more. By analyzing current research trends and case studies, we aim to shed light on AI’s transformative impact on the pharmaceutical industry and its broader implications for healthcare.

Fungal keratitis (FK), a severe eye infection that leads to vision impairment and blindness, poses a high risk to contact lens users, and Candida albicans remains the most common underpinning fungal pathogen in temperate climates. Patients are initially treated empirically (econazole 1% drops hourly for 24–48 h), and if there is no response, amphotericin B (AmB) 0.15% eye drops (extemporaneously manufactured to be stable for a week) are the gold-standard treatment. Here, we aim to develop a sustained-release AmB ocular film to treat FK with an enhanced corneal retention time. As there is a paucity of reliable in vitro models to evaluate ocular drug release and antifungal efficacy under flow, we developed a 3D-printed microfluidic device based on four chambers stacked in parallel, in which lenses previously inoculated with a C. albicans suspension were placed. Under the flow of a physiological fluid over 24 h, the release from the AmB-loaded film that was placed dry onto the surface of the wetted contact lenses was quantified, and their antifungal activity was assessed. AmB sodium deoxycholate micelle (dimeric form) was mixed with sodium alginate and hyaluronic acid (3:1 w/w) and cast into films (0.48 or 2.4%), which showed sustained release over 24 h and resulted in a 1.23-fold reduction and a 5.7-fold reduction in CFU/mL of C. albicans, respectively. This study demonstrates that the sustained delivery of dimeric AmB can be used for the treatment of FK and provides a facile in vitro microfluidic model for the development and testing of ophthalmic antimicrobial therapies.