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Drug-induced kidney injury, a major cause of acute kidney injury, results in progressive kidney disease and is linked to increased mortality in hospitalized patients. Primary injury sites of drug-induced kidney injury are proximal tubules. Clinically, kidney injury molecule-1, an established tubule-specific biomarker, is monitored to assess the presence and progression of injury. The ability to accurately predict drug-related nephrotoxicity preclinically would reduce patient burden and drug attrition rates, yet state-of-the-art in vitro and animal models fail to do so. In this study, we demonstrate the use of kidney injury molecule-1 measurement in the kidney microphysiological system as a preclinical model for drug toxicity assessment. To show clinical relevance, we use quantitative systems pharmacology computational models for in vitro-in vivo translation of the experimental results and to identify favorable dosing regimens for one of the tested drugs.

Microphysiological systems (MPS) hold promise for improving therapeutic drug approval rates by providing more physiological, human-based, in vitro assays for preclinical drug development activities compared to traditional in vitro and animal models. Here, we first summarize why MPSs are needed in pharmaceutical development, and examine how MPS technologies can be utilized to improve preclinical efforts. We then provide the perspective that the full impact of MPS technologies will be realized only when robust approaches for in vitro-in vivo (MPS-to-human) translation are developed and utilized, and explain how the burgeoning field of quantitative systems pharmacology (QSP) can fill that need.

Early assessment of adverse drug effects in humans is critical to avoid long-lasting harm. However, current approaches for early detection of adverse effects still lack predictive and organ-specific biomarkers to evaluate undesired responses in humans. Microphysiological systems (MPSs) are in vitro representations of human tissues and provide organ-specific translational insights for physiological processes. In this study, a brain MPS was utilized to assess molecular signatures of neurotoxic and non-neurotoxic compounds using targeted and untargeted molecular approaches. The brain MPS comprising of human embryonic stem (ES) cell-derived neural progenitor cells seeded on three-dimensional (3D), chemically defined, polyethylene glycol hydrogels was treated with the neurotoxic drug, bortezomib and the non-neurotoxic drug, tamoxifen over 14-days. Possible toxic effects were monitored with human N-acetylaspartic acid (NAA) kinetics, which correlates the neuronal function/health and DJ-1/PARK7, an oxidative stress biomarker. Changes in NAA levels were observed as early as 2-days post-bortezomib treatment, while onset detection of oxidative stress (DJ-1) was delayed until 4-days post-treatment. Separately, the untargeted extracellular metabolomics approach revealed distinct fingerprints 2-days post-bortezomib treatment as perturbations in cysteine and glycerophospholipid metabolic pathways. These results suggest accumulation of reactive oxygen species associated with oxidative stress, and disruption of membrane structure and integrity. The NAA response was strongly correlated with changes in a subset of the detected metabolites at the same time point 2-days post-treatment. Moreover, these metabolite changes correlated strongly with DJ-1 levels measured at the later time point (4-days post-treatment). This suggests that early cellular metabolic dysfunction leads to later DJ-1 leakage and cell death, and that early measurement of this subset of metabolites could predict the later occurrence of cell death. While the approach demonstrated here provides an individual case study for proof of concept, we suggest that this approach can be extended for preclinical toxicity screening and biomarker discovery studies.

Microphysiological systems (MPS) provide relevant physiological environments in vitro for studies of pharmacokinetics, pharmacodynamics and biological mechanisms for translational research. Designing multi-MPS platforms is essential to study multi-organ systems. Typical design approaches, including direct and allometric scaling, scale each MPS individually and are based on relative sizes not function. This study's aim was to develop a new multi-functional scaling approach for integrated multi-MPS platform design for specific applications. We developed an optimization approach using mechanistic modeling and specification of an objective that considered multiple MPS functions, e.g., drug absorption and metabolism, simultaneously to identify system design parameters. This approach informed the design of two hypothetical multi-MPS platforms consisting of gut and liver (multi-MPS platform I) and gut, liver and kidney (multi-MPS platform II) to recapitulate in vivo drug exposures in vitro. This allows establishment of clinically relevant drug exposure-response relationships, a prerequisite for efficacy and toxicology assessment. Design parameters resulting from multi-functional scaling were compared to designs based on direct and allometric scaling. Human plasma time-concentration profiles of eight drugs were used to inform the designs, and profiles of an additional five drugs were calculated to test the designed platforms on an independent set. Multi-functional scaling yielded exposure times in good agreement with in vivo data, while direct and allometric scaling approaches resulted in short exposure durations. Multi-functional scaling allows appropriate scaling from in vivo to in vitro of multi-MPS platforms, and in the cases studied provides designs that better mimic in vivo exposures than standard MPS scaling methods., United States. Defense Advanced Research Projects Agency. Microphysiological Systems Program (W911NF-12-2-0039), National Institutes of Health (U.S.). Microphysiological Systems Program (4-UH3-TR000496-03)

Microphysiological systems (MPS), consisting of tissue constructs, biomaterials, and culture media, aim to recapitulate relevant organ functions in vitro. MPS components are housed in fluidic hardware with operational protocols, such as periodic complete media replacement. Such batch-like operations provide relevant nutrients and remove waste products but also reset cell-secreted mediators (e.g. cytokines, hormones) and potentially limit exposure to drugs (and metabolites). While each component plays an essential role for tissue functionality, MPS-specific nutrient needs are not yet well-characterized nor utilized to operate MPSs at more physiologically-relevant conditions. MPS-specific nutrient needs for gut (immortalized cancer cells), liver (human primary hepatocytes) and cardiac (iPSC-derived cardiomyocytes) MPSs were experimentally quantified. In a long-term study of the gut MPS (10 days), this knowledge was used to design operational protocols to maintain glucose and lactate at desired levels. This quasi-steady state operation was experimentally validated by monitoring glucose and lactate as well as MPS functionality. In a theoretical study, nutrient needs of an integrated multi-MPS platform (gut, liver, cardiac MPSs) were computationally simulated to identify long-term quasi-steady state operations. This integrative experimental and computational approach demonstrates the utilization of quantitative multi-scale characterization of MPSs and incorporating MPS-specific information to establish more physiologically-relevant experimental operations., United States. Defense Advanced Research Projects Agency. Microphysiological Systems Program (W911NF-12-2-0039), National Institutes of Health (U.S.) (U24TR001951), National Institutes of Health (U.S.). Microphysiological Systems Program (4-UH3-TR000496-03)

Microphysiological systems (MPSs) are in vitro models that capture facets of in vivo organ function through use of specialized culture microenvironments, including 3D matrices and microperfusion. Here, we report an approach to co-culture multiple different MPSs linked together physiologically on re-useable, open-system microfluidic platforms that are compatible with the quantitative study of a range of compounds, including lipophilic drugs. We describe three different platform designs – “4-way”, “7-way”, and “10-way” – each accommodating a mixing chamber and up to 4, 7, or 10 MPSs. Platforms accommodate multiple different MPS flow configurations, each with internal re-circulation to enhance molecular exchange, and feature on-board pneumatically-driven pumps with independently programmable flow rates to provide precise control over both intra- and inter-MPS flow partitioning and drug distribution. We first developed a 4-MPS system, showing accurate prediction of secreted liver protein distribution and 2-week maintenance of phenotypic markers. We then developed 7-MPS and 10-MPS platforms, demonstrating reliable, robust operation and maintenance of MPS phenotypic function for 3 weeks (7-way) and 4 weeks (10-way) of continuous interaction, as well as PK analysis of diclofenac metabolism. This study illustrates several generalizable design and operational principles for implementing multi-MPS “physiome-on-a-chip” approaches in drug discovery.

A capability for analyzing complex cellular communication among tissues is important in drug discovery and development, and in vitro technologies for doing so are required for human applications. A prominent instance is communication between the gut and the liver, whereby perturbations of one tissue can influence behavior of the other. Here, we present a study on human gut-liver tissue interactions under normal and inflammatory contexts, via an integrative multi-organ platform comprising human liver (hepatocytes and Kupffer cells), and intestinal (enterocytes, goblet cells, and dendritic cells) models. Our results demonstrated long-term (>2 weeks) maintenance of intestinal (e.g., barrier integrity) and hepatic (e.g., albumin) functions in baseline interaction. Gene expression data comparing liver in interaction with gut, versus isolation, revealed modulation of bile acid metabolism. Intestinal FGF19 secretion and associated inhibition of hepatic CYP7A1 expression provided evidence of physiologically relevant gut-liver crosstalk. Moreover, significant non-linear modulation of cytokine responses was observed under inflammatory gut-liver interaction; for example, production of CXCR3 ligands (CXCL9,10,11) was synergistically enhanced. RNA-seq analysis revealed significant upregulation of IFNα/β/γ signaling during inflammatory gut-liver crosstalk, with these pathways implicated in the synergistic CXCR3 chemokine production. Exacerbated inflammatory response in gut-liver interaction also negatively affected tissue-specific functions (e.g., liver metabolism). These findings illustrate how an integrated multi-tissue platform can generate insights useful for understanding complex pathophysiological processes such as inflammatory organ crosstalk., National Institutes of Health (U.S.) (grant UH3TR00069), United States. Defense Advanced Research Projects Agency (grant Microphysiological Systems Program (W911NF-12-2-00))

Investigation of the pharmacokinetics (PK) of a compound is of significant importance during the early stages of drug development, and therefore several in vitro systems are routinely employed for this purpose. However, the need for more physiologically realistic in vitro models has recently fueled the emerging field of tissue-engineered 3D cultures, also referred to as organs-on-chips, or microphysiological systems (MPSs). We have developed a novel fluidic platform that interconnects multiple MPSs, allowing PK studies in multi-organ in vitro systems along with the collection of high-content quantitative data. This platform was employed here to integrate a gut and a liver MPS together in continuous communication, and investigate simultaneously different PK processes taking place after oral drug administration in humans (e.g., intestinal permeability, hepatic metabolism). Measurement of tissue-specific phenotypic metrics indicated that gut and liver MPSs can be fluidically coupled with circulating common medium without compromising their functionality. The PK of diclofenac and hydrocortisone was investigated under different experimental perturbations, and results illustrate the robustness of this integrated system for quantitative PK studies. Mechanistic model-based analysis of the obtained data allowed the derivation of the intrinsic parameters (e.g., permeability, metabolic clearance) associated with the PK processes taking place in each MPS. Although these processes were not substantially affected by the gut-liver interaction, our results indicate that inter-MPS communication can have a modulating effect (hepatic metabolism upregulation). We envision that our integrative approach, which combines multi-cellular tissue models, multi-MPS platforms, and quantitative mechanistic modeling, will have broad applicability in pre-clinical drug development., United States. Defense Advanced Research Projects Agency (Grant W911NF-12-2-0039), National Institutes of Health (U.S.) (Grant 4-UH3-TR000496-03)

Contemporary, computer-based mathematical modeling techniques make it possible to represent complex biological mechanisms in a manner that permits hypothesis testing in silico. This perspective shows how such approaches might be applied to bone remodeling and therapeutic research. Currently, the dominant conceptual model applied in bone research involves the dynamic balance between the continual build-up and breakdown of bone matrix by two cell types, the osteoblasts and osteoclasts, acting together as a coordinated, remodeling unit. This conceptualization has served extraordinarily well as a focal point for understanding how mutations, chemical mediators, and mechanical force, as well as external influences (e.g., drugs, diet) affect bone structure and function. However, the need remains to better understand and predict the consequences of manipulating any single factor, or combination of factors, within the context of this complex system's multiple interacting pathways. Mathematical models are a natural extension of conceptual models, providing dynamic, quantitative descriptions of the relationships among interacting components. This formalization creates the ability to simulate the natural behavior of a system, as well as its modulation by therapeutic or dietetic interventions. A number of mathematical models have been developed to study complex bone functions, but most include only a limited set of biological components needed to address a few specific questions. However, it is possible to develop larger, multiscale models that capture the dynamic interactions of many biological components and relate them to important physiological or pathological outcomes that allow broader study. Examples of such models include Entelos' PhysioLab platforms. These models simulate the dynamic, quantitative interactions among a biological system's biochemicals, cells, tissues, and organs and how they give rise to key physiologic and pathophysiologic outcomes. We propose that a similar predictive, dynamical, multiscale mathematical model of bone remodeling and metabolism would provide a better understanding of the mechanisms governing these phenomena as well as serve as an in silico platform for testing pharmaceutical and clinical interventions on metabolic bone disease.

This paper reports that, in pressurized heavy water nuclear reactors of the type standardly used in Canada (Canada deuterium uranium-pressurized heavy water reactors), the zirconium alloy pressure tubes of the core absorb deuterium produced by corrosion reactions. This deuterium weakens the tubes through hydrogen embrittlement. Thin palladium coatings on the outside of the zirconium are analyzed as a method for deuterium removal. This coating is expected to catalyze the reaction D{sub 2} + 1/2O{sub 2} {r reversible} D{sub 2}O when O{sub 2} is added to the annular (insulating) gas in the tubes. Major reductions in the deuterium concentration and, hence, hydrogen embrittlement are predicted. Potential problems such as plating the tube geometry, neutron absorption, catalyst deactivation, radioactive waste production, and oxygen corrosion are shown to be manageable. Also, a simple set of equations are derived to calculate the effect on diffusion caused by neutron interactions. Based on calculations of ordinary and neutron flux induced diffusion, a palladium coating of 1 {times} 10{sup {minus}6} m is recommended. This would cost approximately $60,000 per reactor unit and should more than double reactor lifetime. Similar coatings and similar interdiffusion calculations might have broad applications.

Analysis of cell motility effects in physiological processes can be facilitated by a mathematical model capable of simulating individual cell movement paths. A quantitative description of motility of individual cells would be useful, for example, in the study of the formation of new blood vessel networks in angiogenesis by microvessel endothelial cell (MEC) migration. In this paper we propose a stochastic mathematical model for the random motility and chemotaxis of single cells, and evaluate migration paths of MEC in terms of this model. In our model, cell velocity under random motility conditions is described as a persistent random walk using the Ornstein-Uhlenbeck (O-U) process. Two parameters quantify this process: the magnitude of random movement accelerations, alpha, and a decay rate constant for movement velocity, beta. Two other quantities often used in measurements of individual cell random motility properties—cell speed, S, and persistence time in velocity, Pv—can be defined in terms of the fundamental stochastic parameters alpha and beta by: S =square root (alpha/beta) and Pv = 1/beta. We account for chemotactic cell movement in chemoattractant gradients by adding a directional bias term to the O-U process. The magnitude of the directional bias is characterized by the chemotactic responsiveness, kappa. A critical advantage of the proposed model is that it can generate, using experimentally measured values of alpha, beta and kappa, computer simulations of theoretical individual cell paths for use in evaluating the role of cell migration in specific physiological processes. We have used the model to assess MEC migration in the presence of absence of the angiogenic stimulus acidic fibroblast growth factor (aFGF). Time-lapse video was used to observe and track the paths of cells moving in various media, and the mean square displacement was measured from these paths. To test the validity of the model, we compared the mean square displacement measurements of each cell with model predictions of that displacement. The comparison indicates that the O-U process provides a satisfactory description of the random migration at this level of comparison. Using nonlinear regression in these comparisons, we measured the magnitude of random accelerations, alpha, and the velocity decay rate constant, beta, for each cell path. We consequently obtained values for the derived quantities, speed and persistence time. In control medium, we find that alpha = 250 +/− 100 microns 2h-3 and beta = 0.22 +/− 0.03h-1, while in stimulus medium (control plus unpurified aFGF) alpha = 1900 +/− 720 microns 2h-3 and beta = 0.99 +/− 0.37h-1.(ABSTRACT TRUNCATED AT 400 WORDS)

Fas-induced apoptosis is a critical process for normal immune system development and function. Although many molecular components in the Fas signaling pathway have been identified, a systematic understanding of how they work together to determine network dynamics and apoptosis itself has remained elusive. To address this, we generated a computational model for interpreting and predicting effects of pathway component properties. The model integrates current information concerning the signaling network downstream of Fas activation, through both type I and type II pathways, until activation of caspase-3. Unknown parameter values in the model were estimated using experimental data obtained from human Jurkat T cells. To elucidate critical signaling network properties, we examined the effects of altering the level of Bcl-2 on the kinetics of caspase-3 activation, using both overexpression and knockdown in the model and experimentally. Overexpression was used to distinguish among alternative hypotheses for inhibitory binding interactions of Bcl-2 with various components in the mitochondrial pathway. In comparing model simulations with experimental results, we find the best agreement when Bcl-2 blocks the release of cytochrome c by binding to both Bax and truncated Bid instead of Bax, truncated Bid, or Bid alone. Moreover, although Bcl-2 overexpression strongly reduces caspase-3 activation, Bcl-2 knockdown has a negligible effect, demonstrating a general model finding that varying the expression levels of signal molecules frequently has asymmetric effects on the outcome. Finally, we demonstrate that the relative dominance of type I vs type II pathways can be switched by varying particular signaling component levels without changing network structure.

Internalization of targeted therapeutics is often needed for efficacy, but also alters drug penetration of a tissue. A new model explores the trade-offs of intracellular drug trafficking.

Our goal has been to provide a quantitative analysis of the random motility and chemotaxis of microvessel endothelial cells (MEC) in order to understand the role of these functions in the development of new capillaries. A major difference of our work from previous investigations has been our use of mathematical analysis to interpret experimental results, and to relate in vitro migration measurements to in vivo angiogenesis observations. In this paper we present some of our methods and their rationale, with recent results from both experiment and mathematical models.

Of fundamental interest are the mechanisms by which neurons and other electrically excitable cells generate oscillatory activity and establish synchronized rhythms. Through quantitative modeling we seek to identify those biophysical factors (cell structure, ionic channels, biochemical pathways, and coupling functions) which play the most significant roles in determining cellular responses and collective behavior. From a generalist’s point of view we also seek qualitative understanding of the underlying mathematical structure of models in order to characterize the robustness of our results.

The growth of new capillary blood vessels, or angiogenesis, is a prominent component of numerous physiological and pathological conditions. An understanding of the co-ordination of underlying cellular behaviors would be helpful for therapeutic manipulation of the process. A probabilistic mathematical model of angiogenesis is developed based upon specific microvessel endothelial cell (MEC) functions involved in vessel growth. The model focuses on the roles of MEC random motility and chemotaxis, to test the hypothesis that these MEC behaviors are of critical importance in determining capillary growth rate and network structure. Model predictions are computer simulations of microvessel networks, from which questions of interest are examined both qualitatively and quantitatively. Results indicate that a moderate MEC chemotactic response toward an angiogenic stimulus, similar to that measured in vitro in response to acidic fibroblast growth factor, is necessary to provide directed vascular network growth. Persistent random motility alone, with initial budding biased toward the stimulus, does not adequately provide directed network growth. A significant degree of randomness in cell migration direction, however, is required for vessel anastomosis and capillary loop formation, as simulations with an overly strong chemotactic response produce network structures largely absent of these features. The predicted vessel extension rate and network structure in the simulations are quantitatively consistent with experimental observations of angiogenesis in vivo. This suggests that the rate of vessel outgrowth is primarily determined by MEC migration rate, and consequently that quantitative in vitro migration assays might be useful tools for the prescreening of possible angiogenesis activators and inhibitors. Finally, reduction of MEC speed results in substantial inhibition of simulated angiogenesis. Together, these results predict that both random motility and chemotaxis are MEC functions critically involved in determining the rate and morphology of new microvessel network growth.

A probabilistic model for angiogenesis was developed to investigate the possible role of microvessel endothelial cell (MEC) Chemotaxis in determining microvessel network morphology and growth rate. The model simulates developing microvessels, providing theoretical pictures of the networks. The cell at the tip of a growing capillary is hypothesized to guide the path of the capillary according to a model of single cell migration, using experimentally measured values for MEC speed and persistence time. The simulations demonstrate that random motility alone cannot account for the directional growth of vessels observed in vivo. A moderate chemotactic response, like that we have measured in acidic fibroblast growth factor (Stokes et al., 1990), is necessary to provide directional growth of vessels similar to that observed in vivo.