Safety Profiling of Tumor-targeted T Cell-Bispecific Antibodies with Alveolus Lung- and Colon-on-Chip.

Traditional drug safety assessments often fail to predict complications in humans, especially when the drug targets the immune system. Rodent-based preclinical animal models are often ill-suited for predicting immunotherapy-mediated adverse events in humans, in part because of the fundamental differences in immunological responses between species and the human relevant expression profile of the target antigen, if it is expected to be present in normal, healthy tissue. While human-relevant cell-based models of tissues and organs promise to bridge this gap, conventional in vitro two-dimensional models fail to provide the complexity required to model the biological mechanisms of immunotherapeutic effects. Also, like animal models, they fail to recapitulate physiologically relevant levels and patterns of organ-specific proteins, crucial for capturing pharmacology and safety liabilities. Organ-on-Chip models aim to overcome these limitations by combining micro-engineering with cultured primary human cells to recreate the complex multifactorial microenvironment and functions of native tissues and organs. In this protocol, we show the unprecedented capability of two human Organs-on-Chip models to evaluate the safety profile of T cell-bispecific antibodies (TCBs) targeting tumor antigens. These novel tools broaden the research options available for a mechanistic understanding of engineered therapeutic antibodies and for assessing safety in tissues susceptible to adverse events. Graphical abstract Figure 1. Graphical representation of the major steps in target-dependent T cell-bispecific antibodies engagement and immunomodulation, as performed in the Colon Intestine-Chip.

Performance assessment and economic analysis of a human Liver-Chip for predictive toxicology.

BACKGROUND: Conventional preclinical models often miss drug toxicities, meaning the harm these drugs pose to humans is only realized in clinical trials or when they make it to market. This has caused the pharmaceutical industry to waste considerable time and resources developing drugs destined to fail. Organ-on-a-Chip technology has the potential improve success in drug development pipelines, as it can recapitulate organ-level pathophysiology and clinical responses; however, systematic and quantitative evaluations of Organ-Chips' predictive value have not yet been reported. METHODS: 870 Liver-Chips were analyzed to determine their ability to predict drug-induced liver injury caused by small molecules identified as benchmarks by the Innovation and Quality consortium, who has published guidelines defining criteria for qualifying preclinical models. An economic analysis was also performed to measure the value Liver-Chips could offer if they were broadly adopted in supporting toxicity-related decisions as part of preclinical development workflows. RESULTS: Here, we show that the Liver-Chip met the qualification guidelines across a blinded set of 27 known hepatotoxic and non-toxic drugs with a sensitivity of 87% and a specificity of 100%. We also show that this level of performance could generate over $3 billion annually for the pharmaceutical industry through increased small-molecule R&D productivity. CONCLUSIONS: The results of this study show how incorporating predictive Organ-Chips into drug development workflows could substantially improve drug discovery and development, allowing manufacturers to bring safer, more effective medicines to market in less time and at lower costs.

Drug development is lengthy and costly, as it relies on laboratory models that fail to predict human reactions to potential drugs. Because of this, toxic drugs sometimes go on to harm humans when they reach clinical trials or once they are in the marketplace. Organ-on-a-Chip technology involves growing cells on small devices to mimic organs of the body, such as the liver. Organ-Chips could potentially help identify toxicities earlier, but there is limited research into how well they predict these effects compared to conventional models. In this study, we analyzed 870 Liver-Chips to determine how well they predict drug-induced liver injury, a common cause of drug failure, and found that Liver-Chips outperformed conventional models. These results suggest that widespread acceptance of Organ-Chips could decrease drug attrition, help minimize harm to patients, and generate billions in revenue for the pharmaceutical industry.

A microengineered Brain-Chip to model neuroinflammation in humans.

Species differences in brain and blood-brain barrier (BBB) biology hamper the translation of findings from animal models to humans, impeding the development of therapeutics for brain diseases. Here, we present a human organotypic microphysiological system (MPS) that includes endothelial-like cells, pericytes, glia, and cortical neurons and maintains BBB permeability at in vivo relevant levels. This human Brain-Chip engineered to recapitulate critical aspects of the complex interactions that mediate neuroinflammation and demonstrates significant improvements in clinical mimicry compared to previously reported similar MPS. In comparison to Transwell culture, the transcriptomic profiling of the Brain-Chip displayed significantly advanced similarity to the human adult cortex and enrichment in key neurobiological pathways. Exposure to TNF-α recreated the anticipated inflammatory environment shown by glia activation, increased release of proinflammatory cytokines, and compromised barrier permeability. We report the development of a robust brain MPS for mechanistic understanding of cell-cell interactions and BBB function during neuroinflammation.

Modeling alpha-synuclein pathology in a human brain-chip to assess blood-brain barrier disruption.

Parkinson's disease and related synucleinopathies are characterized by the abnormal accumulation of alpha-synuclein aggregates, loss of dopaminergic neurons, and gliosis of the substantia nigra. Although clinical evidence and in vitro studies indicate disruption of the Blood-Brain Barrier in Parkinson's disease, the mechanisms mediating the endothelial dysfunction is not well understood. Here we leveraged the Organs-on-Chips technology to develop a human Brain-Chip representative of the substantia nigra area of the brain containing dopaminergic neurons, astrocytes, microglia, pericytes, and microvascular brain endothelial cells, cultured under fluid flow. Our αSyn fibril-induced model was capable of reproducing several key aspects of Parkinson's disease, including accumulation of phosphorylated αSyn (pSer129-αSyn), mitochondrial impairment, neuroinflammation, and compromised barrier function. This model may enable research into the dynamics of cell-cell interactions in human synucleinopathies and serve as a testing platform for target identification and validation of novel therapeutics.

Human immunocompetent Organ-on-Chip platforms allow safety profiling of tumor-targeted T-cell bispecific antibodies.

Traditional drug safety assessment often fails to predict complications in humans, especially when the drug targets the immune system. Here, we show the unprecedented capability of two human Organs-on-Chips to evaluate the safety profile of T-cell bispecific antibodies (TCBs) targeting tumor antigens. Although promising for cancer immunotherapy, TCBs are associated with an on-target, off-tumor risk due to low levels of expression of tumor antigens in healthy tissues. We leveraged in vivo target expression and toxicity data of TCBs targeting folate receptor 1 (FOLR1) or carcinoembryonic antigen (CEA) to design and validate human immunocompetent Organs-on-Chips safety platforms. We discovered that the Lung-Chip and Intestine-Chip could reproduce and predict target-dependent TCB safety liabilities, based on sensitivity to key determinants thereof, such as target expression and antibody affinity. These novel tools broaden the research options available for mechanistic understandings of engineered therapeutic antibodies and assessing safety in tissues susceptible to adverse events.

A Novel Microphysiological Colon Platform to Decipher Mechanisms Driving Human Intestinal Permeability.

BACKGROUND & AIMS: The limited availability of organoid systems that mimic the molecular signatures and architecture of human intestinal epithelium has been an impediment to allowing them to be harnessed for the development of therapeutics as well as physiological insights. We developed a microphysiological Organ-on-Chip (Emulate, Inc, Boston, MA) platform designed to mimic properties of human intestinal epithelium leading to insights into barrier integrity. METHODS: We combined the human biopsy-derived leucine-rich repeat-containing G-protein-coupled receptor 5-positive organoids and Organ-on-Chip technologies to establish a micro-engineered human Colon Intestine-Chip (Emulate, Inc, Boston, MA). We characterized the proximity of the model to human tissue and organoids maintained in suspension by RNA sequencing analysis, and their differentiation to intestinal epithelial cells on the Colon Intestine-Chip under variable conditions. Furthermore, organoids from different donors were evaluated to understand variability in the system. Our system was applied to understanding the epithelial barrier and characterizing mechanisms driving the cytokine-induced barrier disruption. RESULTS: Our data highlight the importance of the endothelium and the in vivo tissue-relevant dynamic microenvironment in the Colon Intestine-Chip in the establishment of a tight monolayer of differentiated, polarized, organoid-derived intestinal epithelial cells. We confirmed the effect of interferon-γ on the colonic barrier and identified reorganization of apical junctional complexes, and induction of apoptosis in the intestinal epithelial cells as mediating mechanisms. We show that in the human Colon Intestine-Chip exposure to interleukin 22 induces disruption of the barrier, unlike its described protective role in experimental colitis in mice. CONCLUSIONS: We developed a human Colon Intestine-Chip platform and showed its value in the characterization of the mechanism of action of interleukin 22 in the human epithelial barrier. This system can be used to elucidate, in a time- and challenge-dependent manner, the mechanism driving the development of leaky gut in human beings and to identify associated biomarkers.

Modeling alcohol-associated liver disease in a human Liver-Chip.

Alcohol-associated liver disease (ALD) is a global health issue and leads to progressive liver injury, comorbidities, and increased mortality. Human-relevant preclinical models of ALD are urgently needed. Here, we leverage a triculture human Liver-Chip with biomimetic hepatic sinusoids and bile canaliculi to model ALD employing human-relevant blood alcohol concentrations (BACs) and multimodal profiling of clinically relevant endpoints. Our Liver-Chip recapitulates established ALD markers in response to 48 h of exposure to ethanol, including lipid accumulation and oxidative stress, in a concentration-dependent manner and supports the study of secondary insults, such as high blood endotoxin levels. We show that remodeling of the bile canalicular network can provide an in vitro quantitative readout of alcoholic liver toxicity. In summary, we report the development of a human ALD Liver-Chip as a powerful platform for modeling alcohol-induced liver injury with the potential for direct translation to clinical research and evaluation of patient-specific responses.

An information-theoretic approach for measuring the distance of organ tissue samples using their transcriptomic signatures.

MOTIVATION: Recapitulating aspects of human organ functions using in vitro (e.g. plates, transwells, etc.), in vivo (e.g. mouse, rat, etc.), or ex vivo (e.g. organ chips, 3D systems, etc.) organ models is of paramount importance for drug discovery and precision medicine. It will allow us to identify potential side effects and test the effectiveness of new therapeutic approaches early in their design phase, and will inform the development of better disease models. Developing mathematical methods to reliably compare the 'distance/similarity' of organ models from/to the real human organ they represent is an understudied problem with important applications in biomedicine and tissue engineering. RESULTS: We introduce the Transcriptomic Signature Distance (TSD), an information-theoretic distance for assessing the transcriptomic similarity of two tissue samples, or two groups of tissue samples. In developing TSD, we are leveraging next-generation sequencing data as well as information retrieved from well-curated databases providing signature gene sets characteristic for human organs. We present the justification and mathematical development of the new distance and demonstrate its effectiveness and advantages in different scenarios of practical importance using several publicly available RNA-seq datasets. AVAILABILITY AND IMPLEMENTATION: The computation of both TSD versions (simple and weighted) has been implemented in R and can be downloaded from https://github.com/Cod3B3nd3R/Transcriptomic-Signature-Distance. CONTACT: dimitris.manatakis@emulatebio.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The evolution of radiographic edema in ARDS and its association with clinical outcomes: A prospective cohort study in adult patients.

PURPOSE: To assess the longitudinal evolution of radiographic edema using chest X-rays (CXR) in patients with Acute Respiratory Distress Syndrome (ARDS) and to examine its association with prognostic biomarkers, ARDS subphenotypes and outcomes. MATERIALS AND METHODS: We quantified radiographic edema on CXRs from patients with ARDS or cardiogenic pulmonary edema (controls) using the Radiographic Assessment of Lung Edema (RALE) score on day of intubation and up to 10 days after. We measured baseline plasma biomarkers and recorded clinical variables. RESULTS: The RALE score had good inter-rater agreement (r = 0.83, p < 0.0001) applied on 488 CXRs from 129 patients, with higher RALE scores in patients with ARDS (n = 108) compared to controls (n = 21, p = 0.01). Baseline RALE scores were positively correlated with levels of the receptor for end-glycation end products (RAGE) in ARDS patients (p < 0.05). Baseline RALE scores were not predictive of 30- or 90-day survival. Persistently elevated RALE scores were associated with prolonged need for mechanical ventilation (p = 0.002). CONCLUSIONS: The RALE score is easily implementable with high inter-rater reliability. Longitudinal RALE scoring appears to be a reproducible approach to track the evolution of radiographic edema in patients with ARDS and can potentially predict prolonged need for mechanical ventilation.

Duodenum Intestine-Chip for preclinical drug assessment in a human relevant model.

Induction of intestinal drug metabolizing enzymes can complicate the development of new drugs, owing to the potential to cause drug-drug interactions (DDIs) leading to changes in pharmacokinetics, safety and efficacy. The development of a human-relevant model of the adult intestine that accurately predicts CYP450 induction could help address this challenge as species differences preclude extrapolation from animals. Here, we combined organoids and Organs-on-Chips technology to create a human Duodenum Intestine-Chip that emulates intestinal tissue architecture and functions, that are relevant for the study of drug transport, metabolism, and DDI. Duodenum Intestine-Chip demonstrates the polarized cell architecture, intestinal barrier function, presence of specialized cell subpopulations, and in vivo relevant expression, localization, and function of major intestinal drug transporters. Notably, in comparison to Caco-2, it displays improved CYP3A4 expression and induction capability. This model could enable improved in vitro to in vivo extrapolation for better predictions of human pharmacokinetics and risk of DDIs.

Transcriptional regulatory model of fibrosis progression in the human lung.

To develop a systems biology model of fibrosis progression within the human lung we performed RNA sequencing and microRNA analysis on 95 samples obtained from 10 idiopathic pulmonary fibrosis (IPF) and 6 control lungs. Extent of fibrosis in each sample was assessed by microCT-measured alveolar surface density (ASD) and confirmed by histology. Regulatory gene expression networks were identified using linear mixed-effect models and dynamic regulatory events miner (DREM). Differential gene expression analysis identified a core set of genes increased or decreased before fibrosis was histologically evident that continued to change with advanced fibrosis. DREM generated a systems biology model (www.sb.cs.cmu.edu/IPFReg) that identified progressively divergent gene expression tracks with microRNAs and transcription factors that specifically regulate mild or advanced fibrosis. We confirmed model predictions by demonstrating that expression of POU2AF1, previously unassociated with lung fibrosis but proposed by the model as regulator, is increased in B lymphocytes in IPF lungs and that POU2AF1-knockout mice were protected from bleomycin-induced lung fibrosis. Our results reveal distinct regulation of gene expression changes in IPF tissue that remained structurally normal compared with moderate or advanced fibrosis and suggest distinct regulatory mechanisms for each stage.

Host-Response Subphenotypes Offer Prognostic Enrichment in Patients With or at Risk for Acute Respiratory Distress Syndrome.

OBJECTIVES: Classification of patients with acute respiratory distress syndrome into hyper- and hypoinflammatory subphenotypes using plasma biomarkers may facilitate more effective targeted therapy. We examined whether established subphenotypes are present not only in patients with acute respiratory distress syndrome but also in patients at risk for acute respiratory distress syndrome (ARFA) and then assessed the prognostic information of baseline subphenotyping on the evolution of host-response biomarkers and clinical outcomes. DESIGN: Prospective, observational cohort study. SETTING: Medical ICU at a tertiary academic medical center. PATIENTS: Mechanically ventilated patients with acute respiratory distress syndrome or ARFA. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We performed longitudinal measurements of 10 plasma biomarkers of host injury and inflammation. We applied unsupervised latent class analysis methods utilizing baseline clinical and biomarker variables and demonstrated that two-class models (hyper- vs hypoinflammatory subphenotypes) offered improved fit compared with one-class models in both patients with acute respiratory distress syndrome and ARFA. Baseline assignment to the hyperinflammatory subphenotype (39/104 [38%] acute respiratory distress syndrome and 30/108 [28%] ARFA patients) was associated with higher severity of illness by Sequential Organ Failure Assessment scores and incidence of acute kidney injury in patients with acute respiratory distress syndrome, as well as higher 30-day mortality and longer duration of mechanical ventilation in ARFA patients (p < 0.0001). Hyperinflammatory patients exhibited persistent elevation of biomarkers of innate immunity for up to 2 weeks postintubation. CONCLUSIONS: Our results suggest that two distinct subphenotypes are present not only in patients with established acute respiratory distress syndrome but also in patients at risk for its development. Hyperinflammatory classification at baseline is associated with higher severity of illness, worse clinical outcomes, and trajectories of persistently elevated biomarkers of host injury and inflammation during acute critical illness compared with hypoinflammatory patients. Our findings provide strong rationale for examining treatment effect modifications by subphenotypes in randomized clinical trials to inform precision therapeutic approaches in critical care.

Mixed graphical models for integrative causal analysis with application to chronic lung disease diagnosis and prognosis.

MOTIVATION: Integration of data from different modalities is a necessary step for multi-scale data analysis in many fields, including biomedical research and systems biology. Directed graphical models offer an attractive tool for this problem because they can represent both the complex, multivariate probability distributions and the causal pathways influencing the system. Graphical models learned from biomedical data can be used for classification, biomarker selection and functional analysis, while revealing the underlying network structure and thus allowing for arbitrary likelihood queries over the data. RESULTS: In this paper, we present and test new methods for finding directed graphs over mixed data types (continuous and discrete variables). We used this new algorithm, CausalMGM, to identify variables directly linked to disease diagnosis and progression in various multi-modal datasets, including clinical datasets from chronic obstructive pulmonary disease (COPD). COPD is the third leading cause of death and a major cause of disability and thus determining the factors that cause longitudinal lung function decline is very important. Applied on a COPD dataset, mixed graphical models were able to confirm and extend previously described causal effects and provide new insights on the factors that potentially affect the longitudinal lung function decline of COPD patients. AVAILABILITY AND IMPLEMENTATION: The CausalMGM package is available on http://www.causalmgm.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

piMGM: incorporating multi-source priors in mixed graphical models for learning disease networks.

Motivation: Learning probabilistic graphs over mixed data is an important way to combine gene expression and clinical disease data. Leveraging the existing, yet imperfect, information in pathway databases for mixed graphical model (MGM) learning is an understudied problem with tremendous potential applications in systems medicine, the problems of which often involve high-dimensional data. Results: We present a new method, piMGM, which can learn with accuracy the structure of probabilistic graphs over mixed data by appropriately incorporating priors from multiple experts with different degrees of reliability. We show that piMGM accurately scores the reliability of prior information from a given expert even at low sample sizes. The reliability scores can be used to determine active pathways in healthy and disease samples. We tested piMGM on both simulated and real data from TCGA, and we found that its performance is not affected by unreliable priors. We demonstrate the applicability of piMGM by successfully using prior information to identify pathway components that are important in breast cancer and improve cancer subtype classification. Availability and implementation: http://www.benoslab.pitt.edu/manatakisECCB2018.html. Supplementary information: Supplementary data are available at Bioinformatics online.

Respiratory Microbiome Profiling for Etiologic Diagnosis of Pneumonia in Mechanically Ventilated Patients.

Etiologic diagnosis of bacterial pneumonia relies on identification of causative pathogens by cultures, which require extended incubation periods and have limited sensitivity. Next-generation sequencing of microbial DNA directly from patient samples may improve diagnostic accuracy for guiding antibiotic prescriptions. In this study, we hypothesized that enhanced pathogen detection using sequencing can improve upon culture-based diagnosis and that certain sequencing profiles correlate with host response. We prospectively collected endotracheal aspirates and plasma within 72 h of intubation from patients with acute respiratory failure. We performed 16S rRNA gene sequencing to determine pathogen abundance in lung samples and measured plasma biomarkers to assess host responses to detected pathogens. Among 56 patients, 12 patients (21%) had positive respiratory cultures. Sequencing revealed lung communities with low diversity (p < 0.02) dominated by taxa (>50% relative abundance) corresponding to clinically isolated pathogens (concordance p = 0.009). Importantly, sequencing detected dominant pathogens in 20% of the culture-negative patients exposed to broad-spectrum empiric antibiotics. Regardless of culture results, pathogen dominance correlated with increased plasma markers of host injury (receptor of advanced glycation end-products-RAGE) and inflammation (interleukin-6, tumor necrosis factor receptor 1-TNFR1) (p < 0.05), compared to subjects without dominant pathogens in their lung communities. Machine-learning algorithms identified pathogen abundance by sequencing as the most informative predictor of culture positivity. Thus, enhanced detection of pathogenic bacteria by sequencing improves etiologic diagnosis of pneumonia, correlates with host responses, and offers substantial opportunity for individualized therapeutic targeting and antimicrobial stewardship. Clinical translation will require validation with rapid whole meta-genome sequencing approaches to guide real-time antibiotic prescriptions.