Modeling Organ-on-Chip Microsystems: In-device Toxicokinetics and Metabolite Detection
Tasneem, Kazi Mahmuda
Organ-on-chip microsystems (OCMs) have proven useful to evaluate chemical toxicity in human cells cultured in 3D heterotypic microenvironments. The strength of such microsystems in toxicity screening and their ability to recreate cellular functionalities come with two challenges: accurate prediction of in-device cellular dosages and evaluation of cellular responses due to toxicant exposure. In this dissertation, these two research questions were computationally explored. OCMs are often fabricated from polydimethylsiloxane (PDMS), which has high affinity for small hydrophobic molecules. When potential toxicants are tested in such devices, hydrophobic chemicals may partition into PDMS and reduce the dose that reaches the cultured cells, severely impacting dose-response curves. In this work, we developed toxicokinetics models that demonstrate how to predict the impact of chemical-PDMS interactions to estimate in-device cellular exposures in OCMs. Chemical-PDMS interactions were assessed for the wider range of chemicals to describe their partitioning into PDMS, as well as chemical sequestration via reaction rates and chemical-specific carrying capacities were quantified. These readily measurable parameters were used in computational fluid dynamics models to predict in-device effects of chemical-PDMS interactions on chemical bioavailability. The measurements and modeling strategies could be widely adopted in the field of polymer-based microfluidics for the improved prediction of human-chemical dose responses using OCMs. In addition to quantifying in-device exposure, the efficacy of OCMs also relies on accurate detection of cellular responses through measurement like glucose consumption and lactate production in response to toxicants. One such available technique is downstream detection that was designed with the ability to plug in enzyme-modified sensor electrodes in microfluidic chamber for connecting with any cellular construct, allowing flexibility in metabolite detection across variety of OCMs. In this work, a computational framework was developed to evaluate the performance of downstream detection for cellular consumption and metabolite production. This predictive modeling approach will guide us for detailed understanding of the toxic effects on cells inside OCMs, enabling real-time monitoring of cellular responses using organ-on-chip microsystems.