Merging data curation and machine learning to improve nanomedicines

Abstract

Nanomedicine design is often a trial-and-error process, and the optimization of formulations and in vivo properties requires tremendous benchwork. To expedite the nanomedicine research progress, data science is steadily gaining importance in the field of nanomedicine. Recently, efforts have explored the potential to predict nanomaterials synthesis and biological behaviors via advanced data analytics. Machine learning algorithms process large datasets to understand and predict various material properties in nanomedicine synthesis, pharmacologic parameters, and efficacy. “Big data” approaches may enable even larger advances, especially if researchers capitalize on data curation methods. However, the concomitant use of data curation processes needed to facilitate the acquisition and standardization of large, heterogeneous data sets, to support advanced data analytics methods such as machine learning has yet to be leveraged. Currently, data curation and data analytics areas of nanotechnology-focused data science, or 'nanoinformatics', have been proceeding largely independently. This review highlights the current efforts in both areas and the potential opportunities for coordination to advance the capabilities of data analytics in nanomedicine.

Introduction

The medical use of nanomaterials has shown promising advancement in the field of drug delivery. From the first FDA-approved nanodrug Doxil to the clinical use of lipid nanoparticles for mRNA vaccines, numerous nanoparticles can efficiently and safely deliver small molecules, proteins, nucleic acids, or other active pharmaceutical ingredients (APIs) [1], [2], [3]. Encapsulation in nanoparticles improves drug solubility, stability, and potentially facilitates the crossing of biological barriers and the selective targeting to disease sites, when these pharmacologic advantages have not or cannot be engineered into APIs [4]. However, designing nanocarriers and optimizing the delivery strategy can be time-intensive, and typically involves a substantial amount of trial and error. Clinical translation of nanomedicines often does not follow pre-clinical development, often due to the complexity and heterogeneity of nanoparticles[5] To address these issues, advanced data analytics methods promise to accelerate and improve the nanomedicine development processes.

While the prediction of nanomaterial behavior in vivo is challenging due to a lack of systematic studies across material types, artificial intelligence (AI) platforms promise to improve and streamline nanomedicine development. Since 2007, when the term ‘nanoinformatics’ was coined [6], [7], there has been an explosion of nanomaterial-related data science efforts, which has generated large datasets with nanomaterial characterizations [7]. However, there is a disconnect between the scientists curating the databases and those utilizing AI. As a result, some AI platforms are developed using small-scale, project-specific datasets that lack translational values. In addition, non-standardized reporting metrics make it difficult to compare different material entities, and non-centralized databases are inaccessible to research groups who are not equipped with data mining skills. Herein, the aims of this review are to (1) review the most recent AI studies that capture the epitome of nanomedicine platform development, (2) introduce public databases focused on nanomaterial characterizations, and, most importantly, (3) advocate for a collaboration between the research groups developing AI platforms and nano-informaticians in order to increase the clinical utility of nanomedicine.