Paula Gardiner, Lisa McGonigal, Ariel Villa, Lara C Kovell, Pallavi Rohela, Andrew Cauley, Diana Rinker, Barbara Olendzki
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JMIR formative research
owing distinct physical chemical properties inorganic nanoparticles nps shown promising results preclinical cancer therapy designing engineering effective therapeutic purposes remains challenge although comprehensive database inorganic np research currently available crucial developing effective cancer therapies context machine learning ml emerged transformative tool adaptation nanomedicine hindered inexistent small datasets assembled large database inorganic nps comprising experimental datasets 745 preclinical studies cancer nanomedicine using descriptive statistics explainable ml models mined database gain knowledge inorganic np design patterns inform future np research cancer treatment analyses suggest np shape therapy type prominent features determining vivo efficacy measured percentage tumour reduction moreover database provides largescale openaccess resource discriminative ml broader nanotechnology community utilize work blueprints data mining translational cancer research offers evidence standardizing np reporting accelerate derisk inorganic npbased drug delivery may help improve patient outcomes clinical settings