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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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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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Westberg J, Lefever D, Jason H. Clinical teaching in physician's assistant training programs. J Med Educ 1980; 55:173-180. [PMID: 6102154 DOI: 10.1097/00001888-198003000-00003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
This is a report on the clinical component of physician's assistant (PA) training programs. Given the significant role physicians played in establishing the new profession, it is not surprising many PA programs are built on the traditional model of medical education, with clinical training occurring after didactic and laboratory-based courses in the basic sciences. It is also not unexpected that programs rely heavily on physicians as clinical instructors. This situation, however, is changing. An increasing number of PA graduates are serving as role models and as instructors in patient care settings. These graduates are also assuming positions of leadership in PA programs. Like their counterparts in medical schools, the PA faculty members surveyed in this project are not adequately prepared for their work as teachers, but they do demonstrate an eagerness to enhance their skills and to continue to upgrade the quality of teaching in their programs. Two areas in which PA faculty would particularly like help are evaluation and faculty development.
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