Quantitative Systems Pharmacology Approaches Applied to Microphysiological Systems (MPS): Data Interpretation and Multi-MPS Integration

Abstract

Our goal in developing 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 herein a systems pharmacology approach to MPS development and utilization that incorporates more mechanistic detail than traditional pharmacokinetic/pharmacodynamic (PK/PD) models. A series of studies illustrates diverse facets of our approach. First, we demonstrate two case studies: a PK data analysis and an inflammation response--focused on a single MPS, the liver/immune MPS. Building on the single MPS modeling, a theoretical investigation of a four-MPS interactome then provides a quantitative way to consider several pharmacological concepts such as absorption, distribution, metabolism, and excretion in the design of multi-MPS interactome operation and experiments.

Figure 1

PK modeling of HC metabolism in the liver/immune MPS. HC PK data measured from the liver/immune MPS experiment on culture day 3 to day 5 (a,b) and day 5 to day 7 (c,d) for low (a,c) and high (b,d) HSA concentrations were fitted to the mechanistic model. Black lines correspond to total HC concentrations and red lines to free HC. Error bars and dashed lines represent SEM of measured data and fitted curves, respectively (n = 3).

Figure 2

Modeling of the inflammatory responses in the liver/immune MPS. Net production of TNF-α appears transient (a) while that of IL-6 is sustained (c). Subsequent doses of LPS fail to stimulate TNF-α (b) and IL-6 (d) secretion. The discontinuity at the 48-hour timepoint reflects a medium change. Symbols and error bars are measured data and SEM (n = 3). Solid lines are model fitted curves.

Figure 3

A semimechanistic model of LPS-TLR4 binding, internalization, and trafficking predicts muting of inflammatory response on repeated dosing. Simulated liver/immune MPS inflammatory responses to LPS doses of 1 µg/ml (red), 0.1 µg/ml (blue), 0.01 µg/ml (black), and 0.001 µg/ml (cyan). LPS was added to the liver/immune MPS as a bolus dose at time 0 and again at the time of subsequent medium changes (48 and 96 hours), and the resulting temporal evolution of the concentrations of TNF-α (a) and IL-6 (b) were simulated.

Figure 4

Model structure for the four-MPS interactome (Gut MPS, Liver MPS, Kidney MPS, PD MPS, and mixing chamber). In the four-MPS model, Q_mixing is the sum of Q_{hepatic artery}, Q_gut, Q_kidney, and Q_PD.

Figure 5

Simulations of the four-MPS interactome. (a) t_mixing,80 for an endogenously produced molecule as a function of Q_mixing and V_PD. (b,c) t_mixing,80 for a drug administered to the apical side of the gut (“oral administration”), as a function of Q_mixing and V_PD, for cases of high intestinal drug permeability, ((b), P = 20 × 10⁻⁶ cm/s) and low intestinal drug permeability ((c), P = 1 × 10⁻⁶ cm/s). (d) AUC_0-48,PD for unbound drug as a function of Q_mixing and V_PD for orally administered drug. (e) AUC_0-48,PD for unbound drug as function of fraction of drug unbound (fu) and P for orally administered drug. (f) Systemic total drug cleared percentage at 48 hours following oral administration of drug as a function of fraction of drug unbound (fu) and hepatic metabolism rate constant (k_{metabolism,liver}).

Figure 6

Simulations of drug concentration in each MPS plus mixing chamber of the four-MPS interactome for the oral drug administration scenario and several combinations of parameter values; (red) high intestinal permeability coefficient P (20 × 10⁻⁶ cm/s) with high Q_mixing (60 mL/day); (blue) high P (20 × 10⁻⁶ cm/s) with low Q_mixing (5 mL/day); (magenta) low P (P = 1 × 10⁻⁶ cm/s) with high Q_mixing (60 mL/day).