Given that formulations reported in the literature are disparate in their methods of preparation and available details, extracting literature-derived data to inform the design and development of new NP formulations is challenging. Nonetheless, there continues to be interest in these drug delivery systems. In fact, the number of articles investigating lipid and polymer (e.g., PLGA) NPs for cancer applications has grown over the years, alongside the approval of new products by various regulatory agencies (Figure ). While a useful next step would be the adoption of more universal reporting standards, such as those suggested by Lammers and Storm , it is also necessary to establish more tractable formulation datasets as a way to better understand how previously prepared drug delivery systems can be progressed or inspire new formulations. This will help to reframe decision making as a data-driven process, for example enabling selection of candidate materials based on predicted performance. In effect, the choice of materials need not be guided solely by factors such as accessibility and established biocompatibility, but rather best fit for the intended application. Nonetheless, there will always be a balance between the time and cost associated with employing a new material and the extent to which it improves formulation performance. In efforts to improve translatability, there also needs to be better strategies for systematically screening and optimizing nanoformulations at the preclinical level. This will help to streamline the development of formulations while mitigating the constraints associated with the traditional trial-and-error paradigm. For this purpose, data-driven approaches based on automation, AI and ML have emerged as a promising direction.