Coupled systems of in vitro microfabricated organs-on-a-chip containing small populations of human cells are being developed to address the formidable pharmacological and physiological gaps between monolayer cell cultures, animal models, and humans that severely limit the speed and efficiency of drug development. These gaps present challenges not only in tissue and microfluidic engineering, but also in systems biology: how does one model, test, and learn about the communication and control of biological systems with individual organs-on-chips that are one-thousandth or one-millionth of the size of adult organs, or even smaller, i.e., organs for a milliHuman (mHu) or microHuman (μHu)? Allometric scaling that describes inter-species variation of organ size and properties provides some guidance, but given the desire to utilize these systems to extend and validate human pharmacokinetic and pharmacodynamic (PK/PD) models in support of drug discovery and development, it is more appropriate to scale each organ functionally to ensure that it makes the suitable physiological contribution to the coupled system. The desire to recapitulate the complex organ–organ interactions that result from factors in the blood and lymph places a severe constraint on the total circulating fluid (∼5 mL for a mHu and ∼5 μL for a μHu) and hence on the pumps, valves, and analytical instruments required to maintain and study these systems. Scaling arguments also provide guidance on the design of a universal cell-culture medium, typically without red blood cells. This review presents several examples of scaling arguments and discusses steps that should ensure the success of this endeavour.

Microphysiological systems (MPS), consisting of interacting organs-on-chips or tissue-engineered, 3D organ constructs that use human cells, present an opportunity to bring new tools to biology, medicine, pharmacology, physiology, and toxicology. This issue of Experimental Biology and Medicine describes the ongoing development of MPS that can serve as in-vitro models for bone and cartilage, brain, gastrointestinal tract, lung, liver, microvasculature, reproductive tract, skeletal muscle, and skin. Related topics addressed here are the interconnection of organs-on-chips to support physiologically based pharmacokinetics and drug discovery and screening, and the microscale technologies that regulate stem cell differentiation. The initial motivation for creating MPS was to increase the speed, efficiency, and safety of pharmaceutical development and testing, paying particular regard to the fact that neither monolayer monocultures of immortal or primary cell lines nor animal studies can adequately recapitulate the dynamics of drug–organ, drug–drug, and drug–organ–organ interactions in humans. Other applications include studies of the effect of environmental toxins on humans, identification, characterization, and neutralization of chemical and biological weapons, controlled studies of the microbiome and infectious disease that cannot be conducted in humans, controlled differentiation of induced pluripotent stem cells into specific adult cellular phenotypes, and studies of the dynamics of metabolism and signaling within and between human organs. The technical challenges are being addressed by many investigators, and in the process, it seems highly likely that significant progress will be made toward providing more physiologically realistic alternatives to monolayer monocultures or whole animal studies. The effectiveness of this effort will be determined in part by how easy the constructs are to use, how well they function, how accurately they recapitulate and report human pharmacology and toxicology, whether they can be generated in large numbers to enable parallel studies, and if their use can be standardized consistent with the practices of regulatory science.

Understanding blood-brain barrier responses to inflammatory stimulation (such as lipopolysaccharide mimicking a systemic infection or a cytokine cocktail that could be the result of local or systemic inflammation) is essential to understanding the effect of inflammatory stimulation on the brain. It is through the filter of the blood-brain barrier that the brain responds to outside influences, and the blood-brain barrier is a critical point of failure in neuroinflammation. It is important to note that this interaction is not a static response, but one that evolves over time. While current models have provided invaluable information regarding the interaction between cytokine stimulation, the blood-brain barrier, and the brain, these approaches—whether in vivo or in vitro—have often been only snapshots of this complex web of interactions. We utilize new advances in microfluidics, organs-on-chips, and metabolomics to examine the complex relationship of inflammation and its effects on blood-brain barrier function ex vivo and the metabolic consequences of these responses and repair mechanisms. In this study, we pair a novel dual-chamber, organ-on-chip microfluidic device, the NeuroVascular Unit, with small-volume cytokine detection and mass spectrometry analysis to investigate how the blood-brain barrier responds to two different but overlapping drivers of neuroinflammation, lipopolysaccharide and a cytokine cocktail of IL-1β, TNF-α, and MCP1,2. In this study, we show that (1) during initial exposure to lipopolysaccharide, the blood-brain barrier is compromised as expected, with increased diffusion and reduced presence of tight junctions, but that over time, the barrier is capable of at least partial recovery; (2) a cytokine cocktail also contributes to a loss of barrier function; (3) from this time-dependent cytokine activation, metabolic signature profiles can be obtained for both the brain and vascular sides of the blood-brain barrier model; and (4) collectively, we can use metabolite analysis to identify critical pathways in inflammatory response. Taken together, these findings present new data that allow us to study the initial effects of inflammatory stimulation on blood-brain barrier disruption, cytokine activation, and metabolic pathway changes that drive the response and recovery of the barrier during continued inflammatory exposure.

The reverse engineering of metabolic networks from experimental data is traditionally a labor-intensive task requiring a priori systems knowledge. Using a proven model as a test system, we demonstrate an automated method to simplify this process by modifying an existing or related model--suggesting nonlinear terms and structural modifications--or even constructing a new model that agrees with the system's time series observations. In certain cases, this method can identify the full dynamical model from scratch without prior knowledge or structural assumptions. The algorithm selects between multiple candidate models by designing experiments to make their predictions disagree. We performed computational experiments to analyze a nonlinear seven-dimensional model of yeast glycolytic oscillations. This approach corrected mistakes reliably in both approximated and overspecified models. The method performed well to high levels of noise for most states, could identify the correct model de novo, and make better predictions than ordinary parametric regression and neural network models. We identified an invariant quantity in the model, which accurately derived kinetics and the numerical sensitivity coefficients of the system. Finally, we compared the system to dynamic flux estimation and discussed the scaling and application of this methodology to automated experiment design and control in biological systems in real time.

The sophistication and success of recently reported microfabricated organs-on-chips and human organ constructs have made it possible to design scaled and interconnected organ systems that may significantly augment the current drug development pipeline and lead to advances in systems biology. Physiologically realistic live microHuman (μHu) and milliHuman (mHu) systems operating for weeks to months present exciting and important engineering challenges such as determining the appropriate size for each organ to ensure appropriate relative organ functional activity, achieving appropriate cell density, providing the requisite universal perfusion media, sensing the breadth of physiological responses, and maintaining stable control of the entire system, while maintaining fluid scaling that consists of ~5 mL for the mHu and ~5 μL for the μHu. We believe that successful mHu and μHu systems for drug development and systems biology will require low-volume microdevices that support chemical signaling, microfabricated pumps, valves and microformulators, automated optical microscopy, electrochemical sensors for rapid metabolic assessment, ion mobility-mass spectrometry for real-time molecular analysis, advanced bioinformatics, and machine learning algorithms for automated model inference and integrated electronic control. Toward this goal, we are building functional prototype components and are working toward top-down system integration.

Stem cells hold great promise as a means of treating otherwise incurable, degenerative diseases due to their ability both to self-renew and differentiate. However, stem cell damage can also play a role in the disease with the formation of solid tumors and leukaemias such as chronic myeloid leukaemia (CML), a hematopoietic stem cell (HSC) disorder. Despite recent medical advances, CML remains incurable by drug therapy. Understanding the mechanisms which govern chemoresistance of individual stem cell leukaemias may therefore require analysis at the single cell level. This task is not trivial using current technologies given that isolating HSCs is difficult, expensive, and inefficient due to low cell yield from patients. In addition, hematopoietic cells are largely non-adherent and thus difficult to study over time using conventional cell culture techniques. Hence, there is a need for new microfluidic platforms that allow the functional interrogation of hundreds of non-adherent single cells in parallel. We demonstrate the ability to perform assays, normally performed on the macroscopic scale, within the microfluidic platform using minimal reagents and low numbers of primary cells. We investigated normal and CML stem cell responses to the tyrosine kinase inhibitor, dasatinib, a drug approved for the treatment of CML. Dynamic, on-chip three-color cell viability assays revealed that differences in the responses of normal and CML stem/progenitor cells to dasatinib were observed even in the early phases of exposure, during which time normal cells exhibit a significantly elevated cell death rate, as compared to both controls and CML cells. Further studies show that dasatinib does, however, markedly reduce CML stem/progenitor cell migrationin situ.