Capitalizing on existing knowledge, present and future scientists must focus on creating simple but effective systems that meet clinical needs without unnecessary complexity. This will require interdisciplinary collaboration among chemists, material scientists, biologists, and clinicians. Furthermore, the integration of artificial intelligence tools is anticipated to provide additional support. By leveraging artificial intelligence in drug discovery, machine learning models trained on experimental data from both the formulation lab and hospital setting would allow to identify combinations that might not be intuitively obvious and that may satisfy multiple needs. This approach would rationalize the research process, allowing experimental work to focus on the most promising formulations.