Artificial intelligence-integrated mechanistic modeling for rational design of nanoparticles to improve organ targeting and safety - PROJECT SUMMARY.
Nanoparticles (NPs) hold great promise as targeted drug delivery systems but tailoring their
pharmacokinetics (PK) to specifically target regions of interest remains a challenge. This limits
the clinical translation of NPs due to poor efficacy and safety concerns associated with off-target
accumulation of NP-based formulations. Due to the interactions of NPs with biological
components, driven by their structural properties, customizing the pharmacokinetics (PK) of NPs
requires a quantitative understanding of the effect of NP structural properties on their whole-body
biodistribution, which in turn also governs their safety profile. Therefore, to enable rational design
of NPs to achieve organ targeting and safety, we propose to leverage artificial intelligence to
develop a toxicology-integrated physiologically-based pharmacokinetic model (PBPK-Tox)
capable of accurately predicting the whole-body exposure and safety of novel nanomaterials,
based solely on their structural properties, dose, and route of administration. For this, we will (1)
develop the PBPK-Tox model based on diverse datasets from literature, (2) establish the
quantitative relationship between NP properties, exposure, and toxicity, and (3) experimentally
test the model predictions of rational design to target one or more organs. Our proposed modeling
framework will enable efficient preclinical development of novel nanomaterials (and accelerate
their clinical translation) by providing rational design guidelines through in-depth computational
investigation of biological and physicochemical variability on biodistribution and safety of NPs.
