A large-scale machine learning analysis of inorganic nanoparticles in preclinical cancer research.

Owing to their distinct physical and chemical properties, inorganic nanoparticles (NPs) have shown promising results in preclinical cancer therapy, but designing and engineering them for effective therapeutic purposes remains a challenge. Although a comprehensive database of inorganic NP research is not currently available, it is crucial for developing effective cancer therapies. In this context, machine learning (ML) has emerged as a transformative tool, but its adaptation to nanomedicine is hindered by inexistent or small datasets. Here we assembled a large database of inorganic NPs, comprising experimental datasets from 745 preclinical studies in cancer nanomedicine. Using descriptive statistics and explainable ML models we mined this database to gain knowledge of inorganic NP design patterns and inform future NP research for cancer treatment. Our analyses suggest that NP shape and therapy type are prominent features in determining in vivo efficacy, measured as a percentage of tumour reduction. Moreover, our database provides a large-scale open-access resource for discriminative ML that the broader nanotechnology community can utilize. Our work blueprints data mining for translational cancer research and offers evidence for standardizing NP reporting to accelerate and de-risk inorganic NP-based drug delivery, which may help to improve patient outcomes in clinical settings.

SDF-1 Bound Heparin Nanoparticles Recruit Progenitor Cells for Their Differentiation and Promotion of Angiogenesis after Stroke.

Angiogenesis after stroke is correlated with enhanced tissue repair and functional outcomes. The existing body of research in biomaterials for stroke focuses on hydrogels for the delivery of stem cells, growth factors, or small molecules or drugs. Despite the ability of hydrogels to enhance all these delivery methods, no material has significantly regrown vasculature within the translatable timeline of days to weeks after stroke. Here, two novel biomaterial formulations of granular hydrogels are developed for tissue regeneration after stroke: highly porous microgels (i.e., Cryo microgels) and microgels bound with heparin-norbornene nanoparticles with covalently bound SDF-1α. The combination of these materials results in perfused vessels throughout the stroke core in only 10 days, in addition to increased neural progenitor cell recruitment, maintenance, and increased neuronal differentiation.

SDF-1 Bound Heparin Nanoparticles Recruit Progenitor Cells for Their Differentiation and Promotion of Angiogenesis After Stroke.

Angiogenesis after stroke is correlated with enhanced tissue repair and functional outcomes. The existing body of research in biomaterials for stroke focuses on hydrogels for the delivery of stem cells, growth factors, or small molecules or drugs. Despite the ability of hydrogels to enhance all these delivery methods, no material has significantly regrown vasculature within the translatable timeline of days to weeks after stroke. Here we developed 2 novel biomaterials for tissue regeneration after stroke, a highly porous granular hydrogel termed Cryo microgels, and heparin-norbornene nanoparticles with covalently bound SDF-1α. The combination of these materials resulted in fully revascularized vessels throughout the stroke core in only 10 days, as well as increased neural progenitor cell migration and maintenance and increased neurons.