Integrated gut/liver microphysiological systems elucidates inflammatory inter-tissue crosstalk.

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/ß/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. Biotechnol. Bioeng. 2017;114: 2648-2659. © 2017 Wiley Periodicals, Inc.

Integrated systems modeling of severe asthma: Exploration of IL-33/ST2 antagonism.

Asthma is a complex, heterogeneous disease with a high unmet medical need, despite therapies targeting a multitude of pathways. The ability to quantitatively integrate preclinical and clinical data on these pathways could aid in the development and testing of novel targets and therapeutics. In this work, we develop a computational model of asthma biology, including key cell types and mediators, and create a virtual population capturing clinical heterogeneity. The simulated responses to therapies targeting IL-13, IL-4Rα, IL-5, IgE, and TSLP demonstrate agreement with clinical endpoints and biomarkers of type 2 (T2) inflammation, including blood eosinophils, FEV1, IgE, and FeNO. We use the model to explore the potential benefit of targeting the IL-33 pathway with anti-IL-33 and anti-ST2. Model predictions are compared with data on blood eosinophils, FeNO, and FEV1 from recent anti-IL-33 and anti-ST2 trials and used to interpret trial results based on pathway biology and pharmacology. Results of sensitivity analyses on the contributions of IL-33 to the predicted biomarker changes suggest that anti-ST2 therapy reduces circulating blood eosinophil levels primarily through its impact on eosinophil progenitor maturation and IL-5-dependent survival, and induces changes in FeNO and FEV1 through its effect on immune cells involved in T2 cytokine production. Finally, we also investigate the impact of ST2 genetics on the conferred benefit of anti-ST2. The model includes representation of a wide array of biologic mechanisms and interventions that will provide mechanistic insight and support clinical program design for a wide range of novel therapies during drug development.

Assessment of Drug-Induced Toxicity Biomarkers in the Brain Microphysiological System (MPS) Using Targeted and Untargeted Molecular Profiling.

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.

Translational Assessment of Drug-Induced Proximal Tubule Injury Using a Kidney Microphysiological System.

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.

Maximizing the impact of microphysiological systems with in vitro-in vivo translation.

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.

Establishing quasi-steady state operations of microphysiological systems (MPS) using tissue-specific metabolic dependencies.

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.

Interconnected Microphysiological Systems for Quantitative Biology and Pharmacology Studies.

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.

Multi-functional scaling methodology for translational pharmacokinetic and pharmacodynamic applications using integrated microphysiological systems (MPS).

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.

Integrated Gut and Liver Microphysiological Systems for Quantitative In Vitro Pharmacokinetic Studies.

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.

In silico modeling and simulation of bone biology: a proposal.

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.

Preexisting autoantibodies predict efficacy of oral insulin to cure autoimmune diabetes in combination with anti-CD3.

We have previously developed a combination therapy (CT) using anti-CD3 monoclonal antibodies together with islet-(auto)antigen immunizations that can more efficiently reverse type 1 diabetes (T1D) than either entity alone. However, clinical translation of antigen-specific therapies in general is hampered by the lack of biomarkers that could be used to optimize the modalities of antigen delivery and to predict responders from nonresponders. To support the rapid identification of candidate biomarkers, we systematically evaluated multiple variables in a mathematical disease model. The in silico predictions were validated by subsequent laboratory data in NOD mice with T1D that received anti-CD3/oral insulin CT. Our study shows that higher anti-insulin autoantibody levels at diagnosis can distinguish responders and nonresponders among recipients of CT exquisitely well. In addition, early posttreatment changes in proinflammatory cytokines were indicative of long-term remission. Coadministration of oral insulin improved and prolonged the therapeutic efficacy of anti-CD3 therapy, and long-term protection was achieved by maintaining elevated insulin-specific regulatory T cell numbers that efficiently lowered diabetogenic effector memory T cells. Our validation of preexisting autoantibodies as biomarkers to distinguish future responders from nonresponders among recipients of oral insulin provides a compelling and mechanistic rationale to more rapidly translate anti-CD3/oral insulin CT for human T1D.

Effects of Bcl-2 levels on Fas signaling-induced caspase-3 activation: molecular genetic tests of computational model predictions.

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.