Project 458773

The development of nanoparticles for personalised anti-cancer medicine has reached a breakneck pace in papers published and patents submitted. However, few of these nanoparticle candidates will survive the rigors of safety and efficacy testing, and regulatory approvals to reach early-stage clinical trials. A major roadblock in the clinical translation of nanoparticles is uncertainty related to their safety and therapeutic efficacy as they are "scaled-up" from preclinical animal models to human patients. This scale-up process for nanoparticle pharmacokinetics and pharmacodynamics is especially challenging for nanoparticles with new and clinically untested chemistries and functionalities. In the proposed research, we seek to overcome this challenge using computational modelling tools capable of predicting the safety and therapeutic efficacy of nanoparticles "in-humans" with cellular and animal data alone. Our approach uses a sophisticated mechanistically based modelling tool that incorporates key insights on nanoparticle's unique physicochemical properties and biological interactions with tumour cells to simulate/predict their pharmacokinetic profiles in human cancers. Importantly, the nanoparticle we will use to build these models is planned to enter clinical testing in the spring 2022, thereby provide crucial clinical measurements with which to evaluate and compare our model-based human predictions (using cellular and animal data) to real-life clinical observations in cancer patients. The impacts of our proposed research will reach across the entire spectrum of clinical translation for nanoparticles: from preclinical development and safety testing to clinical trial design and regulatory approval. Our methodology for preclinical model development and clinical validation will serve as a template from which other nanoparticle researchers may base their own in human predictions and hopefully accelerate the safe clinical translation of future nanoparticle products.