An in vitro assay and artificial intelligence approach to determine rate constants of nanomaterial-cell interactions

Edward Price,Andre J Gesquiere

doi:10.1038/s41598-019-50208-x

Abstract

In vitro assays and simulation technologies are powerful methodologies that can inform scientists of nanomaterial (NM) distribution and fate in humans or pre-clinical species. For small molecules, less animal data is often needed because there are a multitude of in vitro screening tools and simulation-based approaches to quantify uptake and deliver data that makes extrapolation to in vivo studies feasible. Small molecule simulations work because these materials often diffuse quickly and partition after reaching equilibrium shortly after dosing, but this cannot be applied to NMs. NMs interact with cells through energy dependent pathways, often taking hours or days to become fully internalized within the cellular environment. In vitro screening tools must capture these phenomena so that cell simulations built on mechanism-based models can deliver relationships between exposure dose and mechanistic biology, that is biology representative of fundamental processes involved in NM transport by cells (e.g. membrane adsorption and subsequent internalization). Here, we developed, validated, and applied the FORECAST method, a combination of a calibrated fluorescence assay (CF) with an artificial intelligence-based cell simulation to quantify rates descriptive of the time-dependent mechanistic biological interactions between NMs and individual cells. This work is expected to provide a means of extrapolation to pre-clinical or human biodistribution with cellular level resolution for NMs starting only from in vitro data.

Highlights

In vitro assays and simulation technologies are powerful methodologies that can inform scientists of nanomaterial (NM) distribution and fate in humans or pre-clinical species
Simulation-based approaches that incorporate animal physiology[12], such as physiologically based pharmacokinetic models (PBPK), are a possible solution to this problem as they have proved successful for small molecules[13]
For NMs, rate constants for adsorption, desorption, internalization, and degradation more accurately represent uptake processes incident on a NM when exposed to cell environment. (b) Layout of calibrated fluorescence assay (CF) assay, including Cell System Interactions (CSI) compartment, Cell Kinetic Data (CKD) compartment, Media and Protein Effect (MPE) compartment, and Cell Control (CC) compartment

Summary

Introduction

In vitro assays and simulation technologies are powerful methodologies that can inform scientists of nanomaterial (NM) distribution and fate in humans or pre-clinical species. We developed, validated, and applied the FORECAST method, a combination of a calibrated fluorescence assay (CF) with an artificial intelligence-based cell simulation to quantify rates descriptive of the time-dependent mechanistic biological interactions between NMs and individual cells. Simulation-based approaches that incorporate animal physiology[12], such as physiologically based pharmacokinetic models (PBPK), are a possible solution to this problem as they have proved successful for small molecules[13] These simulations assume immediate diffusion of drug from blood to whole tissue based on partitioning coefficients, kp[14,15]. In recent NM PBPK simulations, kp has been replaced with a combination of endothelial penetration (optimized from animal data), total macrophage uptake (obtained in vitro), and macrophage/endothelium release rates (optimized from animal data)[23] These modifications resulted in inclusion of time-dependent NM transport from the blood supply into tissue cells. Of total macrophage release rates to animal data, and critical mechanisms involved in NM transport (adsorption, desorption, internalization)[24] remain unaccounted for

Methods

Results

Conclusion