Human Physiome On A Chip: Merging Tissue Engineering and Systems Pharmacology (2016)

In vitro models have been developed and utilized in various stages for the preclinical development. Compared to animal models, in vitro models have advantages such as high-throughput capability, low cost, well-controlled experimental parameters and fewer ethical concerns etc. The simplicity of the conventional in vitro models makes them incapable of achieving adequate physiological relevance for mimicking the human body, however, which is a dynamic system that has complex three-dimensional microenvironment, intracellular communications and organ interactions. Hence, there is an urgent need to develop more physiologically relevant in vitro systems for better simulating the human body in response of drugs and providing more reliable in-vitro in-vivo translation (IVIVT) from preclinical results to clinical outcomes. Our goal in developing a Microphysiological Systems (MPS) technology is to provide an improved approach for more predictive preclinical drug discovery via a highly integrated experimental/computational paradigm. Success will require quantitative characterization of MPSs and mechanistic analysis of experimental findings sufficient to translate resulting insights from in vitro to in vivo. We describe a systems pharmacology perspective on this problem, incorporating more mechanistic detail than traditional pharmacokinetic (PK) and pharmacokinetic/pharmacodynamic (PK/PD) models yet within broadly comprehensive scope. These Systems Pharmacology approaches offer new insight into design of experiments, data interpretation and organ-specific responses, which can be translated to in vivo responses, such as drug efficacy and toxicity. For example, ADME, pharmacodynamic and toxicodynamic properties of a drug can be experimentally investigated in multi-MPS platforms under various physiological conditions. Complex experimental results can be interpreted using mechanistic pharmacokinetic & pharmacodynamics (PK/PD) models allowing us to predict clinical outcomes more accurately than existing preclinical animal models. Support or Funding Information Financial support for this project was provided by the DARPA Microphysiological Systems Program (W911NF-12-2-0039), the NIH Microphysiological Systems Program (4-UH3-TR000496-03), and MIT Center for Environmental Health Sciences (NIEHS Grant P30-ES002109) Model structure for the four-MPS interactome (Gut MPS, Liver MPS, Kidney MPS, PD MPS and mixing chamber). In the four-MPS model, Qmixing is the sum of Qhepatic artery, Qgut, Qkidney and QPD. Simulations of the four-MPS interactome. (a) tmixing,80 for an endogenously produced molecule as a function of Qmixing and VPD. (b–c) tmixing,80 as a function of Qmixing and VPD, for cases of high intestinal drug permeability, and low intestinal drug permeability. (d) AUC0–48,PD for unbound drug as a function of Qmixing and VPD (e) AUC0–48,PD for unbound drug as function of fraction of drug unbound (fu) and P f. (f) Systemic total drug cleared percentage at 48 hours. Model structure for the four-MPS interactome (Gut MPS, Liver MPS, Kidney MPS, PD MPS and mixing chamber). In the four-MPS model, Qmixing is the sum of Qhepatic artery, Qgut, Qkidney and QPD. Simulations of the four-MPS interactome. (a) tmixing,80 for an endogenously produced molecule as a function of Qmixing and VPD. (b–c) tmixing,80 as a function of Qmixing and VPD, for cases of high intestinal drug permeability, and low intestinal drug permeability. (d) AUC0–48,PD for unbound drug as a function of Qmixing and VPD (e) AUC0–48,PD for unbound drug as function of fraction of drug unbound (fu) and P f. (f) Systemic total drug cleared percentage at 48 hours.