Medicines

Background/Objectives: Though ototoxic, cisplatin is a mainstay of chemotherapy for a variety of cancers. One suggested mechanism of cisplatin ototoxicity involves damage to the spiral ganglion afferent neurons in the inner ear. There is a need for a high-throughput model to screen medications for efficacy against cisplatin and to develop a local therapeutic to mitigate cisplatin’s debilitating side effects. Microparticles encapsulating a therapeutic medication are an injectable and tunable method of sustained drug delivery, and thus a promising treatment. Methods: SH-SY5y human neuroblastoma cells were used as a cell line model for the spiral ganglion neurons. The cells were dosed with cisplatin and four potential therapeutics (melatonin, metformin, cyclosporine, and N-acetylcysteine), with cell viability measured by CCK-8 assay. The most promising therapeutic, N-acetylcysteine (NAC), was then encapsulated into multiple poly(lactic-co-glycolic acid) (PLGA) microparticle subtypes of varied lactide–glycolide (L:G) ratios and NAC amounts. The elution profile of each microparticle subtype was determined over two months. Results: Of the therapeutics screened, only cells dosed with 1 or 10 mM NAC prior to cisplatin injury demonstrated an improvement in cell viability (73.8%, p < 1 × 10⁻⁸) when compared to cells dosed with cisplatin alone. The 75:25 L:G microparticles demonstrated an increase in the amount of NAC released compared to the 50:50 L:G microparticles. Conclusions: NAC is a potential therapeutic agent for cisplatin toxicity when tested in a neuronal cell line model. NAC was encapsulated into PLGA microparticles and eluted detectable concentrations of NAC for 6 days, which is a first step towards otoprotection for the weeks long duration of chemotherapy treatment. This work describes a method of screening potential therapeutics and a strategy to develop local drug eluting treatments to protect against cisplatin ototoxicity.

Background: Glycation, a non-enzymatic reaction between sugars and biomolecules, leads to the formation of advanced glycation end-products (AGEs), which are implicated in the progression of chronic diseases. Connarus ruber (Poepp.) Planch (C. ruber), a traditional medicinal plant used for diabetes, has shown anti-glycation activity. This study aimed to identify the active components in C. ruber extract and elucidate their anti-glycation mechanisms. Methods: Using NMR and LC-MS analyses, we identified epicatechin and procyanidin A2 as major polyphenolic constituents. Collagen glycation assays were performed to evaluate the inhibitory effects of these compounds on fructose- and glyceraldehyde (GA)-induced glycation. Additionally, their cytoprotective effects were assessed using GA-induced cytotoxicity assays in dental pulp stem cells (DPSCs). Results: Both epicatechin and procyanidin A2 inhibited fructose- and GA-induced glycation in a dose-dependent manner, showing greater efficacy than aminoguanidine. Furthermore, these compounds significantly alleviated GA-induced cytotoxicity in DPSCs. Conclusions: These findings suggest that epicatechin and procyanidin A2 are candidate contributors to the anti-glycation and cytoprotective effects of C. ruber. The results support the potential of C. ruber extract as a source of therapeutic agents for glycation-related diseases and for enhancing stem cell viability.

Drug repurposing is the process of discovering new therapeutic indications for already existing drugs. By using already approved molecules with known safety profiles, this approach reduces the time, costs, and failure rates associated with traditional drug development, accelerating the availability of new treatments to patients. Artificial Intelligence (AI) plays a crucial role in drug repurposing by exploiting various computational techniques to analyze and process big datasets of biological and medical information, predict similarities between biomolecules, and identify disease mechanisms. The purpose of this review is to explore the role of AI tools in drug repurposing and underline their applications across various medical domains, mainly in oncology, neurodegenerative disorders, and rare diseases. However, several challenges remain to be addressed. These include the need for a deeper understanding of molecular mechanisms, ethical concerns, regulatory requirements, and issues related to data quality and interpretability. Overall, AI-driven drug repurposing is an innovative and promising field that can transform medical research and drug development, covering unmet medical needs efficiently and cost-effectively.