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Xia M, Varmazyad M, Pla-Palacín I, Gavlock DC, DeBiasio R, LaRocca G, Reese C, Florentino R, Faccioli LAP, Brown JA, Vernetti LA, Schurdak M, Stern AM, Gough A, Behari J, Soto-Gutierrez A, Taylor DL, Miedel MT. 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 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] [What about the content of this article? (0)] [Abstract] [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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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 Chip 2023; 23:4514-4527. [PMID: 37766577 DOI: 10.1039/d3lc00285c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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Dufva M. A quantitative meta-analysis comparing cell models in perfused organ on a chip with static cell cultures. Sci Rep 2023; 13:8233. [PMID: 37217582 DOI: 10.1038/s41598-023-35043-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 05/11/2023] [Indexed: 05/24/2023] Open
Abstract
As many consider organ on a chip for better in vitro models, it is timely to extract quantitative data from the literature to compare responses of cells under flow in chips to corresponding static incubations. Of 2828 screened articles, 464 articles described flow for cell culture and 146 contained correct controls and quantified data. Analysis of 1718 ratios between biomarkers measured in cells under flow and static cultures showed that the in all cell types, many biomarkers were unregulated by flow and only some specific biomarkers responded strongly to flow. Biomarkers in cells from the blood vessels walls, the intestine, tumours, pancreatic island, and the liver reacted most strongly to flow. Only 26 biomarkers were analysed in at least two different articles for a given cell type. Of these, the CYP3A4 activity in CaCo2 cells and PXR mRNA levels in hepatocytes were induced more than two-fold by flow. Furthermore, the reproducibility between articles was low as 52 of 95 articles did not show the same response to flow for a given biomarker. Flow showed overall very little improvements in 2D cultures but a slight improvement in 3D cultures suggesting that high density cell culture may benefit from flow. In conclusion, the gains of perfusion are relatively modest, larger gains are linked to specific biomarkers in certain cell types.
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Affiliation(s)
- Martin Dufva
- Department of Health Technology, Technical University of Denmark, 2800, Kgs Lyngby, Denmark.
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Zuo Q, Mogol AN, Liu YJ, Santaliz Casiano A, Chien C, Drnevich J, Imir OB, Kulkoyluoglu-Cotul E, Park NH, Shapiro DJ, Park BH, Ziegler Y, Katzenellenbogen BS, Aranda E, O'Neill JD, Raghavendra AS, Tripathy D, Madak Erdogan Z. Targeting metabolic adaptations in the breast cancer-liver metastatic niche using dietary approaches to improve endocrine therapy efficacy. Mol Cancer Res 2022; 20:923-937. [PMID: 35259269 DOI: 10.1158/1541-7786.mcr-21-0781] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 01/17/2022] [Accepted: 02/16/2022] [Indexed: 11/16/2022]
Abstract
Estrogen receptor-positive (ER+) metastatic tumors contribute to nearly 70% of breast cancer-related deaths. Most patients with ER+ metastatic breast cancer (MBC) undergo treatment with the estrogen receptor antagonist fulvestrant (Fulv) as standard-of-care. Yet, among such patients, metastasis in the liver is associated with reduced overall survival compared to other metastasis sites. The factors underlying the reduced responsiveness of liver metastases to ER-targeting agents remain unknown, impeding the development of more effective treatment approaches to improve outcomes for patients with ER+ liver metastases. We therefore evaluated site-specific changes in MBC cells and determined the mechanisms through which the liver metastatic niche specifically influences ER+ tumor metabolism and drug resistance. We characterized ER activity of MBC cells both in vitro, using a novel system of tissue-specific extracellular matrix hydrogels representing the stroma of ER+ tumor metastatic sites (liver, lung and bone), and in vivo, in liver and lung metastasis mouse models. ER+ metastatic liver tumors and MBC cells grown in liver hydrogels displayed upregulated expression of glucose metabolism enzymes in response to Fulv. Furthermore, differential ERα activity, but not expression, was detected in liver hydrogels. In vivo, increased glucose metabolism led to increased glycogen deposition in liver metastatic tumors, while a fasting-mimicking diet increased efficacy of Fulv treatment to reduce the metastatic burden. Our findings identify a novel mechanism of endocrine resistance driven by the liver tumor microenvironment. Implications: These results may guide the development of dietary strategies to circumvent drug resistance in liver metastasis, with potential applicability in other metastatic diseases.
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Affiliation(s)
- Qianying Zuo
- University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - Ayca Nazli Mogol
- University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - Yu-Jeh Liu
- University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | | | - Christine Chien
- University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - Jenny Drnevich
- University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - Ozan Berk Imir
- University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | | | | | - David J Shapiro
- University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - Ben Ho Park
- Vanderbilt University, Nashville, TN, United States
| | - Yvonne Ziegler
- University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | | | | | | | | | - Debu Tripathy
- The University of Texas MD Anderson Cancer Center, Houston, TX, United States
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Marti JLG, Beckwitt CH, Clark AM, Wells A. Atorvastatin facilitates chemotherapy effects in metastatic triple-negative breast cancer. Br J Cancer 2021; 125:1285-98. [PMID: 34462586 DOI: 10.1038/s41416-021-01529-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 07/12/2021] [Accepted: 08/12/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Metastatic triple-negative breast cancer (mTNBC) is treated mainly with chemotherapy. However, resistance frequently occurs as tumours enter dormancy. Statins have been suggested as effective against cancer but as they prolong and promote dormancy, it is an open question of whether the concomitant use would interfere with chemotherapy in primary and mTNBC. We examined this question in animal models and clinical correlations. METHODS We used a xenograft model of spontaneous metastasis to the liver from an ectopic tumour employing a mTNBC cell line. Atorvastatin was provided to sensitise metastatic cells, followed by chemotherapy. The effects of statin usage on outcomes in women with metastatic breast cancer was assessed respectively by querying a database of those diagnosed from 1999 to 2019. RESULTS Atorvastatin had limited influence on tumour growth or chemotherapy effects in ectopic primary tumours. Interestingly, atorvastatin was additive with doxorubicin (but not paclitaxel) when targeting liver metastases. E-cadherin-expressing, dormant, breast cancer cells were resistant to the use of either statins or chemotherapy as compared to wild-type cells; however, the combination of both did lead to increased cell death. Although prospective randomised studies are needed for validation, our retrospective clinical analysis suggested that patients on statin treatment could experience prolonged dormancy and overall survival; still once the tumour recurred progression was not affected by statin use. CONCLUSION Atorvastatin could be used during adjuvant chemotherapy and also in conjunction with metastatic chemotherapy to reduce mTNBC cancer progression. These preclinical data establish a rationale for the development of randomised studies.
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Ya S, Ding W, Li S, Du K, Zhang Y, Li C, Liu J, Li F, Li P, Luo T, He L, Xu A, Gao D, Qiu B. On-Chip Construction of Liver Lobules with Self-Assembled Perfusable Hepatic Sinusoid Networks. ACS Appl Mater Interfaces 2021; 13:32640-32652. [PMID: 34225454 DOI: 10.1021/acsami.1c00794] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Although various liver chips have been developed using emerging organ-on-a-chip techniques, it remains an enormous challenge to replicate the liver lobules with self-assembled perfusable hepatic sinusoid networks. Herein we develop a lifelike bionic liver lobule chip (LLC), on which the perfusable hepatic sinusoid networks are achieved using a microflow-guided angiogenesis methodology; additionally, during and after self-assembly, oxygen concentration is regulated to mimic physiologically dissolved levels supplied by actual hepatic arterioles and venules. This liver lobule design thereby produces more bionic liver microstructures, higher metabolic abilities, and longer lasting hepatocyte function than other liver-on-a-chip techniques that are able to deliver. We found that the flow through the unique micropillar design in the cell coculture zone guides the radiating assembly of the hepatic sinusoid, the oxygen concentration affects the morphology of the sinusoid by proliferation, and the oxygen gradient plays a key role in prolonging hepatocyte function. The expected breadth of applications our LLC is suited to is demonstrated by means of preliminarily testing chronic and acute hepatotoxicity of drugs and replicating growth of tumors in situ. This work provides new insights into designing more extensive bionic vascularized liver chips, while achieving longer lasting ex-vivo hepatocyte function.
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Affiliation(s)
- Shengnan Ya
- The Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, Anhui 230027, China
| | - Weiping Ding
- The Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, Anhui 230027, China
- Hefei National Lab for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei, Anhui 230027, China
| | - Shibo Li
- The Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, Anhui 230027, China
| | - Kun Du
- The Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, Anhui 230027, China
| | - Yuanyuan Zhang
- The Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, Anhui 230027, China
| | - Chengpan Li
- The Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, Anhui 230027, China
| | - Jing Liu
- School of Biology, Food and Environment Engineering, Hefei University, Hefei, Anhui 230601, China
| | - Fenfen Li
- The Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, Anhui 230027, China
- Hefei National Lab for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei, Anhui 230027, China
| | - Ping Li
- Department of Chinese Integrative Medicine Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230022, China
| | - Tianzhi Luo
- School of Engineering Science, University of Science and Technology of China, Hefei, Anhui 230027, China
| | - Liqun He
- School of Engineering Science, University of Science and Technology of China, Hefei, Anhui 230027, China
| | - Ao Xu
- Division of Life Sciences and Medicine, The First Affiliated Hospital of University of Science and Technology of China, Hefei, Anhui 230001, China
| | - Dayong Gao
- Department of Mechanical Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Bensheng Qiu
- The Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, Anhui 230027, China
- Hefei National Lab for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei, Anhui 230027, China
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Clark AM, Allbritton NL, Wells A. Integrative microphysiological tissue systems of cancer metastasis to the liver. Semin Cancer Biol 2021; 71:157-169. [PMID: 32580025 PMCID: PMC7750290 DOI: 10.1016/j.semcancer.2020.06.010] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 06/10/2020] [Accepted: 06/10/2020] [Indexed: 02/07/2023]
Abstract
The liver is the most commonly involved organ in metastases from a wide variety of solid tumors. The use of biologically and cellularly complex liver tissue systems have shown that tumor cell behavior and therapeutic responses are modulated within the liver microenvironment and in ways distinct from the behaviors in the primary locations. These microphysiological systems have provided unexpected and powerful insights into the tumor cell biology of metastasis. However, neither the tumor nor the liver exist in an isolated tissue situation, having to function within a complete body and respond to systemic events as well as those in other organs. To examine the influence of one organ on the function of other tissues, microphysiological systems are being linked. Herein, we discuss extending this concept to tumor metastases by integrating complex models of the primary tumor with the liver metastatic environment. In addition, inflammatory organs and the immune system can be incorporated into these multi-organ systems to probe the effects on tumor behavior and cancer treatments.
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Affiliation(s)
- Amanda M Clark
- Department of Pathology, University of Pittsburgh, Pittsburgh, PA 15261, USA; VA Pittsburgh Healthcare System, Pittsburgh, PA 15213, USA; UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA 15213, USA.
| | - Nancy L Allbritton
- Department of Bioengineering, University of Washington, Seattle, WA 98195, USA
| | - Alan Wells
- Department of Pathology, University of Pittsburgh, Pittsburgh, PA 15261, USA; VA Pittsburgh Healthcare System, Pittsburgh, PA 15213, USA; UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA 15213, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15260, USA; Department of Computational & Systems Biology, University of Pittsburgh, Pittsburgh, PA 15260, USA
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Saydmohammed M, Jha A, Mahajan V, Gavlock D, Shun TY, DeBiasio R, Lefever D, Li X, Reese C, Kershaw EE, Yechoor V, Behari J, Soto-Gutierrez A, Vernetti L, Stern A, Gough A, Miedel MT, Lansing Taylor D. Quantifying the progression of non-alcoholic fatty liver disease in human biomimetic liver microphysiology systems with fluorescent protein biosensors. Exp Biol Med (Maywood) 2021; 246:2420-2441. [PMID: 33957803 PMCID: PMC8606957 DOI: 10.1177/15353702211009228] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Metabolic syndrome is a complex disease that involves multiple organ systems including a critical role for the liver. Non-alcoholic fatty liver disease (NAFLD) is a key component of the metabolic syndrome and fatty liver is linked to a range of metabolic dysfunctions that occur in approximately 25% of the population. A panel of experts recently agreed that the acronym, NAFLD, did not properly characterize this heterogeneous disease given the associated metabolic abnormalities such as type 2 diabetes mellitus (T2D), obesity, and hypertension. Therefore, metabolic dysfunction-associated fatty liver disease (MAFLD) has been proposed as the new term to cover the heterogeneity identified in the NAFLD patient population. Although many rodent models of NAFLD/NASH have been developed, they do not recapitulate the full disease spectrum in patients. Therefore, a platform has evolved initially focused on human biomimetic liver microphysiology systems that integrates fluorescent protein biosensors along with other key metrics, the microphysiology systems database, and quantitative systems pharmacology. Quantitative systems pharmacology is being applied to investigate the mechanisms of NAFLD/MAFLD progression to select molecular targets for fluorescent protein biosensors, to integrate computational and experimental methods to predict drugs for repurposing, and to facilitate novel drug development. Fluorescent protein biosensors are critical components of the platform since they enable monitoring of the pathophysiology of disease progression by defining and quantifying the temporal and spatial dynamics of protein functions in the biosensor cells, and serve as minimally invasive biomarkers of the physiological state of the microphysiology system experimental disease models. Here, we summarize the progress in developing human microphysiology system disease models of NAFLD/MAFLD from several laboratories, developing fluorescent protein biosensors to monitor and to measure NAFLD/MAFLD disease progression and implementation of quantitative systems pharmacology with the goal of repurposing drugs and guiding the creation of novel therapeutics.
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Affiliation(s)
- Manush Saydmohammed
- University of Pittsburgh Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Anupma Jha
- University of Pittsburgh Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Vineet Mahajan
- University of Pittsburgh Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Dillon Gavlock
- University of Pittsburgh Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Tong Ying Shun
- University of Pittsburgh Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Richard DeBiasio
- University of Pittsburgh Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Daniel Lefever
- University of Pittsburgh Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Xiang Li
- University of Pittsburgh Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Celeste Reese
- University of Pittsburgh Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Erin E Kershaw
- Department of Medicine, Division of Endocrinology and Metabolism, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Vijay Yechoor
- Department of Medicine, Division of Endocrinology and Metabolism, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Jaideep Behari
- Department of Medicine, Division of Gastroenterology, Hepatology and Nutrition, Pittsburgh, PA 15261, USA
- UPMC Liver Clinic, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Alejandro Soto-Gutierrez
- Department of Pathology, University of Pittsburgh, Pittsburgh, PA 15261, USA
- Pittsburgh Liver Research Center, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Larry Vernetti
- University of Pittsburgh Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Andrew Stern
- University of Pittsburgh Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Albert Gough
- University of Pittsburgh Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Mark T Miedel
- University of Pittsburgh Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - D Lansing Taylor
- University of Pittsburgh Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA
- Pittsburgh Liver Research Center, University of Pittsburgh, Pittsburgh, PA 15261, USA
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15261, USA
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Gough A, Soto-Gutierrez A, Vernetti L, Ebrahimkhani MR, Stern AM, Taylor DL. Human biomimetic liver microphysiology systems in drug development and precision medicine. Nat Rev Gastroenterol Hepatol 2021; 18:252-268. [PMID: 33335282 PMCID: PMC9106093 DOI: 10.1038/s41575-020-00386-1] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/02/2020] [Indexed: 02/07/2023]
Abstract
Microphysiology systems (MPS), also called organs-on-chips and tissue chips, are miniaturized functional units of organs constructed with multiple cell types under a variety of physical and biochemical environmental cues that complement animal models as part of a new paradigm of drug discovery and development. Biomimetic human liver MPS have evolved from simpler 2D cell models, spheroids and organoids to address the increasing need to understand patient-specific mechanisms of complex and rare diseases, the response to therapeutic treatments, and the absorption, distribution, metabolism, excretion and toxicity of potential therapeutics. The parallel development and application of transdisciplinary technologies, including microfluidic devices, bioprinting, engineered matrix materials, defined physiological and pathophysiological media, patient-derived primary cells, and pluripotent stem cells as well as synthetic biology to engineer cell genes and functions, have created the potential to produce patient-specific, biomimetic MPS for detailed mechanistic studies. It is projected that success in the development and maturation of patient-derived MPS with known genotypes and fully matured adult phenotypes will lead to advanced applications in precision medicine. In this Review, we examine human biomimetic liver MPS that are designed to recapitulate the liver acinus structure and functions to enhance our knowledge of the mechanisms of disease progression and of the absorption, distribution, metabolism, excretion and toxicity of therapeutic candidates and drugs as well as to evaluate their mechanisms of action and their application in precision medicine and preclinical trials.
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Affiliation(s)
- Albert Gough
- University of Pittsburgh Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
- Pittsburgh Liver Research Center, University of Pittsburgh, Pittsburgh, PA, USA
| | - Alejandro Soto-Gutierrez
- University of Pittsburgh Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Pathology, University of Pittsburgh, Pittsburgh, PA, USA
- McGowan Institute of Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Pittsburgh Liver Research Center, University of Pittsburgh, Pittsburgh, PA, USA
| | - Lawrence Vernetti
- University of Pittsburgh Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
- Pittsburgh Liver Research Center, University of Pittsburgh, Pittsburgh, PA, USA
| | - Mo R Ebrahimkhani
- Department of Pathology, University of Pittsburgh, Pittsburgh, PA, USA
- McGowan Institute of Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Pittsburgh Liver Research Center, University of Pittsburgh, Pittsburgh, PA, USA
| | - Andrew M Stern
- University of Pittsburgh Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - D Lansing Taylor
- University of Pittsburgh Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.
- Pittsburgh Liver Research Center, University of Pittsburgh, Pittsburgh, PA, USA.
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Schurdak M, Vernetti L, Bergenthal L, Wolter QK, Shun TY, Karcher S, Taylor DL, Gough A. Applications of the microphysiology systems database for experimental ADME-Tox and disease models. Lab Chip 2020; 20:1472-1492. [PMID: 32211684 PMCID: PMC7497411 DOI: 10.1039/c9lc01047e] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Accepted: 01/07/2020] [Indexed: 05/04/2023]
Abstract
To accelerate the development and application of Microphysiological Systems (MPS) in biomedical research and drug discovery/development, a centralized resource is required to provide the detailed design, application, and performance data that enables industry and research scientists to select, optimize, and/or develop new MPS solutions, as well as to harness data from MPS models. We have previously implemented an open source Microphysiology Systems Database (MPS-Db), with a simple icon driven interface, as a resource for MPS researchers and drug discovery/development scientists (https://mps.csb.pitt.edu). The MPS-Db captures and aggregates data from MPS, ranging from static microplate models to integrated, multi-organ microfluidic models, and associates those data with reference data from chemical, biochemical, pre-clinical, clinical and post-marketing sources to support the design, development, validation, application and interpretation of the models. The MPS-Db enables users to manage their multifactor, multichip studies, then upload, analyze, review, computationally model and share data. Here we discuss how the sharing of MPS study data in the MS-Db is under user control and can be kept private to the individual user, shared with a select group of collaborators, or be made accessible to the general scientific community. We also present a test case using our liver acinus MPS model (LAMPS) as an example and discuss the use of the MPS-Db in managing, designing, and analyzing MPS study data, assessing the reproducibility of MPS models, and evaluating the concordance of MPS model results with clinical findings. We introduce the Disease Portal module with links to resources for the design of MPS disease models and studies and discuss the integration of computational models for the prediction of PK/PD and disease pathways using data generated from MPS models.
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Affiliation(s)
- Mark Schurdak
- Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania, USA. and Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Lawrence Vernetti
- Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania, USA. and Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Luke Bergenthal
- Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
| | - Quinn K Wolter
- Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
| | - Tong Ying Shun
- Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
| | - Sandra Karcher
- Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
| | - D Lansing Taylor
- Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania, USA. and Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Albert Gough
- Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania, USA. and Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
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Taylor DL, Gough A, Schurdak ME, Vernetti L, Chennubhotla CS, Lefever D, Pei F, Faeder JR, Lezon TR, Stern AM, Bahar I. Harnessing Human Microphysiology Systems as Key Experimental Models for Quantitative Systems Pharmacology. Handb Exp Pharmacol 2019; 260:327-367. [PMID: 31201557 PMCID: PMC6911651 DOI: 10.1007/164_2019_239] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Two technologies that have emerged in the last decade offer a new paradigm for modern pharmacology, as well as drug discovery and development. Quantitative systems pharmacology (QSP) is a complementary approach to traditional, target-centric pharmacology and drug discovery and is based on an iterative application of computational and systems biology methods with multiscale experimental methods, both of which include models of ADME-Tox and disease. QSP has emerged as a new approach due to the low efficiency of success in developing therapeutics based on the existing target-centric paradigm. Likewise, human microphysiology systems (MPS) are experimental models complementary to existing animal models and are based on the use of human primary cells, adult stem cells, and/or induced pluripotent stem cells (iPSCs) to mimic human tissues and organ functions/structures involved in disease and ADME-Tox. Human MPS experimental models have been developed to address the relatively low concordance of human disease and ADME-Tox with engineered, experimental animal models of disease. The integration of the QSP paradigm with the use of human MPS has the potential to enhance the process of drug discovery and development.
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Affiliation(s)
- D Lansing Taylor
- University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA.
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Albert Gough
- University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Mark E Schurdak
- University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Lawrence Vernetti
- University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Chakra S Chennubhotla
- University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Daniel Lefever
- University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA
| | - Fen Pei
- University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - James R Faeder
- University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Timothy R Lezon
- University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Andrew M Stern
- University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ivet Bahar
- University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
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