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Xia M, Varmazyad M, Palacin IP, Gavlock DC, Debiasio R, LaRocca G, Reese C, Florentino R, Faccioli LAP, Brown JA, Vernetti LA, Schurdak ME, Stern AM, Gough A, Behari J, Soto-Gutierrez A, Taylor DL, Miedel M. Comparison of Wild-Type and High-risk PNPLA3 variants in a Human Biomimetic Liver Microphysiology System for Metabolic Dysfunction-associated Steatotic Liver Disease Precision Therapy. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.22.590608. [PMID: 38712213 PMCID: PMC11071381 DOI: 10.1101/2024.04.22.590608] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Metabolic dysfunction-associated steatotic liver disease (MASLD) is a worldwide health epidemic with a global occurrence of approximately 30%. The pathogenesis of MASLD is a complex, multisystem disorder driven by multiple factors including genetics, lifestyle, and the environment. Patient heterogeneity presents challenges for developing MASLD therapeutics, creation of patient cohorts for clinical trials and optimization of therapeutic strategies for specific patient cohorts. Implementing pre-clinical experimental models for drug development creates a significant challenge as simple in vitro systems and animal models do not fully recapitulate critical steps in the pathogenesis and the complexity of MASLD progression. To address this, we implemented a precision medicine strategy that couples the use of our liver acinus microphysiology system (LAMPS) constructed with patient-derived primary cells. We investigated the MASLD-associated genetic variant PNPLA3 rs738409 (I148M variant) in primary hepatocytes, as it is associated with MASLD progression. We constructed LAMPS with genotyped wild type and variant PNPLA3 hepatocytes together with key non-parenchymal cells and quantified the reproducibility of the model. We altered media components to mimic blood chemistries, including insulin, glucose, free fatty acids, and immune activating molecules to reflect normal fasting (NF), early metabolic syndrome (EMS) and late metabolic syndrome (LMS) conditions. Finally, we investigated the response to treatment with resmetirom, an approved drug for metabolic syndrome-associated steatohepatitis (MASH), the progressive form of MASLD. This study using primary cells serves as a benchmark for studies using patient biomimetic twins constructed with patient iPSC-derived liver cells using a panel of reproducible metrics. We observed increased steatosis, immune activation, stellate cell activation and secretion of pro-fibrotic markers in the PNPLA3 GG variant compared to wild type CC LAMPS, consistent with the clinical characterization of this variant. We also observed greater resmetirom efficacy in PNPLA3 wild type CC LAMPS compared to the GG variant in multiple MASLD metrics including steatosis, stellate cell activation and the secretion of pro-fibrotic markers. In conclusion, our study demonstrates the capability of the LAMPS platform for the development of MASLD precision therapeutics, enrichment of patient cohorts for clinical trials, and optimization of therapeutic strategies for patient subgroups with different clinical traits and disease stages.
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Aleman J, Ravikumar K, Wiegand C, Schurdak ME, Vernetti L, Gavlock D, Reese C, DeBiasio R, LaRocca G, Angarita YD, Gough A, Soto-Gutierrez A, Behari J, Yechoor V, Miedel MT, Stern AM, Banerjee I, Taylor DL. A metabolic-dysfunction associated steatotic liver acinus biomimetic induces pancreatic islet dysfunction in a coupled microphysiology system. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.22.590598. [PMID: 38712135 PMCID: PMC11071380 DOI: 10.1101/2024.04.22.590598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Preclinical and clinical studies suggest that lipid-induced hepatic insulin resistance is a primary defect that predisposes to dysfunction in pancreatic islets, implicating a perturbed liver-pancreas axis underlying the comorbidity of T2DM and MASLD. To investigate this hypothesis, we developed a human biomimetic microphysiological system (MPS) coupling our vascularized liver acinus MPS (vLAMPS) with primary islets on a chip (PANIS) enabling MASLD progression and islet dysfunction to be quantitatively assessed. The modular design of this system (vLAMPS-PANIS) allows intra-organ and inter-organ dysregulation to be deconvoluted. When compared to normal fasting (NF) conditions, under early metabolic syndrome (EMS) conditions, the standalone vLAMPS exhibited characteristics of early stage MASLD, while no significant differences were observed in the standalone PANIS. In contrast, with EMS, the coupled vLAMPS-PANIS exhibited a perturbed islet-specific secretome and a significantly dysregulated glucose stimulated insulin secretion (GSIS) response implicating direct signaling from the dysregulated liver acinus to the islets. Correlations between several pairs of a vLAMPS-derived and a PANIS-derived secreted factors were significantly altered under EMS, as compared to NF conditions, mechanistically connecting MASLD and T2DM associated hepatic factors with islet-derived GLP-1 synthesis and regulation. Since vLAMPS-PANIS is compatible with patient-specific iPSCs, this platform represents an important step towards addressing patient heterogeneity, identifying complex disease mechanisms, and advancing precision medicine.
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Negi V, Gavlock D, Miedel MT, Lee JK, Shun T, Gough A, Vernetti L, Stern AM, Taylor DL, Yechoor VK. Modeling mechanisms underlying differential inflammatory responses to COVID-19 in type 2 diabetes using a patient-derived microphysiological organ-on-a-chip system. LAB ON A CHIP 2023; 23:4514-4527. [PMID: 37766577 DOI: 10.1039/d3lc00285c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/29/2023]
Abstract
Background: COVID-19 pandemic has caused more than 6 million deaths worldwide. Co-morbid conditions such as Type 2 Diabetes (T2D) have increased mortality in COVID-19. With limited translatability of in vitro and small animal models to human disease, human organ-on-a-chip models are an attractive platform to model in vivo disease conditions and test potential therapeutics. Methods: T2D or non-diabetic patient-derived macrophages and human liver sinusoidal endothelial cells were seeded, along with normal hepatocytes and stellate cells in the liver-on-a-chip (LAMPS - liver acinus micro physiological system), perfused with media mimicking non-diabetic fasting or T2D (high levels of glucose, fatty acids, insulin, glucagon) states. The macrophages and endothelial cells were transduced to overexpress the SARS-CoV2-S (spike) protein with appropriate controls before their incorporation into LAMPS. Cytokine concentrations in the efflux served as a read-out of the effects of S-protein expression in the different experimental conditions (non-diabetic-LAMPS, T2D-LAMPS), including incubation with tocilizumab, an FDA-approved drug for severe COVID-19. Findings: S-protein expression in the non-diabetic LAMPS led to increased cytokines, but in the T2D-LAMPS, this was significantly amplified both in the number and magnitude of key pro-inflammatory cytokines (IL6, CCL3, IL1β, IL2, TNFα, etc.) involved in cytokine storm syndrome (CSS), mimicking severe COVID-19 infection in T2D patients. Compared to vehicle control, tocilizumab (IL6-receptor antagonist) decreased the pro-inflammatory cytokine secretion in T2D-COVID-19-LAMPS but not in non-diabetic-COVID-19-LAMPS. Interpretation: macrophages and endothelial cells play a synergistic role in the pathophysiology of the hyper-inflammatory response seen with COVID-19 and T2D. The effect of Tocilizumab was consistent with large clinical trials that demonstrated Tocilizumab's efficacy only in critically ill patients with severe disease, providing confirmatory evidence that the T2D-COVID-19-LAMPS is a robust platform to model human in vivo pathophysiology of COVID-19 in T2D and for screening potential therapeutics.
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Affiliation(s)
- Vinny Negi
- Diabetes and Beta Cell Biology Center, Division of Endocrinology and Metabolism, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Dillon Gavlock
- Drug Discovery Institute and Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Mark T Miedel
- Drug Discovery Institute and Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jeong Kyung Lee
- Diabetes and Beta Cell Biology Center, Division of Endocrinology and Metabolism, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Tongying Shun
- Drug Discovery Institute and Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Albert Gough
- Drug Discovery Institute and Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Lawrence Vernetti
- Drug Discovery Institute and Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Andrew M Stern
- Drug Discovery Institute and Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - D Lansing Taylor
- Drug Discovery Institute and Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Vijay K Yechoor
- Diabetes and Beta Cell Biology Center, Division of Endocrinology and Metabolism, University of Pittsburgh, Pittsburgh, PA, USA.
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Beaudoin JJ, Clemens L, Miedel MT, Gough A, Zaidi F, Ramamoorthy P, Wong KE, Sarangarajan R, Battista C, Shoda LKM, Siler SQ, Taylor DL, Howell BA, Vernetti LA, Yang K. The Combination of a Human Biomimetic Liver Microphysiology System with BIOLOGXsym, a Quantitative Systems Toxicology (QST) Modeling Platform for Macromolecules, Provides Mechanistic Understanding of Tocilizumab- and GGF2-Induced Liver Injury. Int J Mol Sci 2023; 24:9692. [PMID: 37298645 PMCID: PMC10253699 DOI: 10.3390/ijms24119692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 05/25/2023] [Accepted: 05/30/2023] [Indexed: 06/12/2023] Open
Abstract
Biologics address a range of unmet clinical needs, but the occurrence of biologics-induced liver injury remains a major challenge. Development of cimaglermin alfa (GGF2) was terminated due to transient elevations in serum aminotransferases and total bilirubin. Tocilizumab has been reported to induce transient aminotransferase elevations, requiring frequent monitoring. To evaluate the clinical risk of biologics-induced liver injury, a novel quantitative systems toxicology modeling platform, BIOLOGXsym™, representing relevant liver biochemistry and the mechanistic effects of biologics on liver pathophysiology, was developed in conjunction with clinically relevant data from a human biomimetic liver microphysiology system. Phenotypic and mechanistic toxicity data and metabolomics analysis from the Liver Acinus Microphysiology System showed that tocilizumab and GGF2 increased high mobility group box 1, indicating hepatic injury and stress. Tocilizumab exposure was associated with increased oxidative stress and extracellular/tissue remodeling, and GGF2 decreased bile acid secretion. BIOLOGXsym simulations, leveraging the in vivo exposure predicted by physiologically-based pharmacokinetic modeling and mechanistic toxicity data from the Liver Acinus Microphysiology System, reproduced the clinically observed liver signals of tocilizumab and GGF2, demonstrating that mechanistic toxicity data from microphysiology systems can be successfully integrated into a quantitative systems toxicology model to identify liabilities of biologics-induced liver injury and provide mechanistic insights into observed liver safety signals.
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Affiliation(s)
- James J. Beaudoin
- DILIsym Services Division, Simulations Plus Inc., Research Triangle Park, Durham, NC 27709, USA (S.Q.S.)
| | - Lara Clemens
- DILIsym Services Division, Simulations Plus Inc., Research Triangle Park, Durham, NC 27709, USA (S.Q.S.)
| | - Mark T. Miedel
- Department of Computational and Systems Biology, Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15219, USA (A.G.); (D.L.T.)
| | - Albert Gough
- Department of Computational and Systems Biology, Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15219, USA (A.G.); (D.L.T.)
| | - Fatima Zaidi
- Metabolon Inc., Durham, NC 27713, USA (P.R.); (K.E.W.); (R.S.)
| | | | - Kari E. Wong
- Metabolon Inc., Durham, NC 27713, USA (P.R.); (K.E.W.); (R.S.)
| | | | - Christina Battista
- DILIsym Services Division, Simulations Plus Inc., Research Triangle Park, Durham, NC 27709, USA (S.Q.S.)
| | - Lisl K. M. Shoda
- DILIsym Services Division, Simulations Plus Inc., Research Triangle Park, Durham, NC 27709, USA (S.Q.S.)
| | - Scott Q. Siler
- DILIsym Services Division, Simulations Plus Inc., Research Triangle Park, Durham, NC 27709, USA (S.Q.S.)
| | - D. Lansing Taylor
- Department of Computational and Systems Biology, Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15219, USA (A.G.); (D.L.T.)
| | - Brett A. Howell
- DILIsym Services Division, Simulations Plus Inc., Research Triangle Park, Durham, NC 27709, USA (S.Q.S.)
| | - Lawrence A. Vernetti
- Department of Computational and Systems Biology, Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15219, USA (A.G.); (D.L.T.)
| | - Kyunghee Yang
- DILIsym Services Division, Simulations Plus Inc., Research Triangle Park, Durham, NC 27709, USA (S.Q.S.)
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Liver Organoids as an In Vitro Model to Study Primary Liver Cancer. Int J Mol Sci 2023; 24:ijms24054529. [PMID: 36901961 PMCID: PMC10003131 DOI: 10.3390/ijms24054529] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 02/16/2023] [Accepted: 02/22/2023] [Indexed: 03/02/2023] Open
Abstract
Primary liver cancers (PLC), including hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA), are among the leading causes of cancer-related mortality worldwide. Bi-dimensional in vitro models are unable to recapitulate the key features of PLC; consequently, recent advancements in three-dimensional in vitro systems, such as organoids, opened up new avenues for the development of innovative models for studying tumour's pathological mechanisms. Liver organoids show self-assembly and self-renewal capabilities, retaining essential aspects of their respective in vivo tissue and allowing modelling diseases and personalized treatment development. In this review, we will discuss the current advances in the field of liver organoids focusing on existing development protocols and possible applications in regenerative medicine and drug discovery.
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Lefever DE, Miedel MT, Pei F, DiStefano JK, Debiasio R, Shun TY, Saydmohammed M, Chikina M, Vernetti LA, Soto-Gutierrez A, Monga SP, Bataller R, Behari J, Yechoor VK, Bahar I, Gough A, Stern AM, Taylor DL. A Quantitative Systems Pharmacology Platform Reveals NAFLD Pathophysiological States and Targeting Strategies. Metabolites 2022; 12:528. [PMID: 35736460 PMCID: PMC9227696 DOI: 10.3390/metabo12060528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 05/28/2022] [Accepted: 06/03/2022] [Indexed: 11/17/2022] Open
Abstract
Non-alcoholic fatty liver disease (NAFLD) has a high global prevalence with a heterogeneous and complex pathophysiology that presents barriers to traditional targeted therapeutic approaches. We describe an integrated quantitative systems pharmacology (QSP) platform that comprehensively and unbiasedly defines disease states, in contrast to just individual genes or pathways, that promote NAFLD progression. The QSP platform can be used to predict drugs that normalize these disease states and experimentally test predictions in a human liver acinus microphysiology system (LAMPS) that recapitulates key aspects of NAFLD. Analysis of a 182 patient-derived hepatic RNA-sequencing dataset generated 12 gene signatures mirroring these states. Screening against the LINCS L1000 database led to the identification of drugs predicted to revert these signatures and corresponding disease states. A proof-of-concept study in LAMPS demonstrated mitigation of steatosis, inflammation, and fibrosis, especially with drug combinations. Mechanistically, several structurally diverse drugs were predicted to interact with a subnetwork of nuclear receptors, including pregnane X receptor (PXR; NR1I2), that has evolved to respond to both xenobiotic and endogenous ligands and is intrinsic to NAFLD-associated transcription dysregulation. In conjunction with iPSC-derived cells, this platform has the potential for developing personalized NAFLD therapeutic strategies, informing disease mechanisms, and defining optimal cohorts of patients for clinical trials.
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Affiliation(s)
- Daniel E. Lefever
- Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA; (D.E.L.); (M.T.M.); (R.D.); (T.Y.S.); (M.S.); (L.A.V.); (A.S.-G.); (S.P.M.); (V.K.Y.); (I.B.); (A.G.)
| | - Mark T. Miedel
- Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA; (D.E.L.); (M.T.M.); (R.D.); (T.Y.S.); (M.S.); (L.A.V.); (A.S.-G.); (S.P.M.); (V.K.Y.); (I.B.); (A.G.)
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15260, USA; (F.P.); (M.C.)
| | - Fen Pei
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15260, USA; (F.P.); (M.C.)
| | - Johanna K. DiStefano
- Diabetes and Fibrotic Disease Unit, Translational Genomics Research Institute TGen, Phoenix, AZ 85004, USA;
| | - Richard Debiasio
- Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA; (D.E.L.); (M.T.M.); (R.D.); (T.Y.S.); (M.S.); (L.A.V.); (A.S.-G.); (S.P.M.); (V.K.Y.); (I.B.); (A.G.)
| | - Tong Ying Shun
- Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA; (D.E.L.); (M.T.M.); (R.D.); (T.Y.S.); (M.S.); (L.A.V.); (A.S.-G.); (S.P.M.); (V.K.Y.); (I.B.); (A.G.)
| | - Manush Saydmohammed
- Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA; (D.E.L.); (M.T.M.); (R.D.); (T.Y.S.); (M.S.); (L.A.V.); (A.S.-G.); (S.P.M.); (V.K.Y.); (I.B.); (A.G.)
| | - Maria Chikina
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15260, USA; (F.P.); (M.C.)
- Pittsburgh Liver Research Center, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Lawrence A. Vernetti
- Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA; (D.E.L.); (M.T.M.); (R.D.); (T.Y.S.); (M.S.); (L.A.V.); (A.S.-G.); (S.P.M.); (V.K.Y.); (I.B.); (A.G.)
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15260, USA; (F.P.); (M.C.)
| | - Alejandro Soto-Gutierrez
- Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA; (D.E.L.); (M.T.M.); (R.D.); (T.Y.S.); (M.S.); (L.A.V.); (A.S.-G.); (S.P.M.); (V.K.Y.); (I.B.); (A.G.)
- Pittsburgh Liver Research Center, University of Pittsburgh, Pittsburgh, PA 15261, USA
- Department of Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15261, USA
- McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA 15203, USA
| | - Satdarshan P. Monga
- Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA; (D.E.L.); (M.T.M.); (R.D.); (T.Y.S.); (M.S.); (L.A.V.); (A.S.-G.); (S.P.M.); (V.K.Y.); (I.B.); (A.G.)
- Pittsburgh Liver Research Center, University of Pittsburgh, Pittsburgh, PA 15261, USA
- Department of Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Ramon Bataller
- Division of Gastroenterology Hepatology and Nutrition, Department of Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15261, USA; (R.B.); (J.B.)
| | - Jaideep Behari
- Division of Gastroenterology Hepatology and Nutrition, Department of Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15261, USA; (R.B.); (J.B.)
- UPMC Liver Clinic, University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
| | - Vijay K. Yechoor
- Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA; (D.E.L.); (M.T.M.); (R.D.); (T.Y.S.); (M.S.); (L.A.V.); (A.S.-G.); (S.P.M.); (V.K.Y.); (I.B.); (A.G.)
- Division of Endocrinology and Metabolism, Department of Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15203, USA
| | - Ivet Bahar
- Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA; (D.E.L.); (M.T.M.); (R.D.); (T.Y.S.); (M.S.); (L.A.V.); (A.S.-G.); (S.P.M.); (V.K.Y.); (I.B.); (A.G.)
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15260, USA; (F.P.); (M.C.)
| | - Albert Gough
- Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA; (D.E.L.); (M.T.M.); (R.D.); (T.Y.S.); (M.S.); (L.A.V.); (A.S.-G.); (S.P.M.); (V.K.Y.); (I.B.); (A.G.)
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15260, USA; (F.P.); (M.C.)
| | - Andrew M. Stern
- Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA; (D.E.L.); (M.T.M.); (R.D.); (T.Y.S.); (M.S.); (L.A.V.); (A.S.-G.); (S.P.M.); (V.K.Y.); (I.B.); (A.G.)
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15260, USA; (F.P.); (M.C.)
| | - D. Lansing Taylor
- Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA; (D.E.L.); (M.T.M.); (R.D.); (T.Y.S.); (M.S.); (L.A.V.); (A.S.-G.); (S.P.M.); (V.K.Y.); (I.B.); (A.G.)
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15260, USA; (F.P.); (M.C.)
- Pittsburgh Liver Research Center, University of Pittsburgh, Pittsburgh, PA 15261, USA
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Kanabekova P, Kadyrova A, Kulsharova G. Microfluidic Organ-on-a-Chip Devices for Liver Disease Modeling In Vitro. MICROMACHINES 2022; 13:428. [PMID: 35334720 PMCID: PMC8950395 DOI: 10.3390/mi13030428] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 03/05/2022] [Accepted: 03/08/2022] [Indexed: 12/12/2022]
Abstract
Mortality from liver disease conditions continues to be very high. As liver diseases manifest and progress silently, prompt measures after diagnosis are essential in the treatment of these conditions. Microfluidic organs-on-chip platforms have significant potential for the study of the pathophysiology of liver diseases in vitro. Different liver-on-a-chip microphysiological platforms have been reported to study cell-signaling pathways such as those activating stellate cells within liver diseases. Moreover, the drug efficacy for liver conditions might be evaluated on a cellular metabolic level. Here, we present a comprehensive review of microphysiological platforms used for modelling liver diseases. First, we briefly introduce the concept and importance of organs-on-a-chip in studying liver diseases in vitro, reflecting on existing reviews of healthy liver-on-a-chip platforms. Second, the techniques of cell cultures used in the microfluidic devices, including 2D, 3D, and spheroid cells, are explained. Next, the types of liver diseases (NAFLD, ALD, hepatitis infections, and drug injury) on-chip are explained for a further comprehensive overview of the design and methods of developing liver diseases in vitro. Finally, some challenges in design and existing solutions to them are reviewed.
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Affiliation(s)
- Perizat Kanabekova
- School of Engineering and Digital Sciences, Nazarbayev University, Nur-Sultan 010000, Kazakhstan;
| | - Adina Kadyrova
- Department of Biological Sciences, School of Sciences and Humanities, Nazarbayev University, Nur-Sultan 010000, Kazakhstan;
| | - Gulsim Kulsharova
- School of Engineering and Digital Sciences, Nazarbayev University, Nur-Sultan 010000, Kazakhstan;
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