Cargando…
Current Status and Challenges of Human Induced Pluripotent Stem Cell-Derived Liver Models in Drug Discovery
The pharmaceutical industry is in high need of efficient and relevant in vitro liver models, which can be incorporated in their drug discovery pipelines to identify potential drugs and their toxicity profiles. Current liver models often rely on cancer cell lines or primary cells, which both have maj...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8834601/ https://www.ncbi.nlm.nih.gov/pubmed/35159250 http://dx.doi.org/10.3390/cells11030442 |
Sumario: | The pharmaceutical industry is in high need of efficient and relevant in vitro liver models, which can be incorporated in their drug discovery pipelines to identify potential drugs and their toxicity profiles. Current liver models often rely on cancer cell lines or primary cells, which both have major limitations. However, the development of human induced pluripotent stem cells (hiPSCs) has created a new opportunity for liver disease modeling, drug discovery and liver toxicity research. hiPSCs can be differentiated to any cell of interest, which makes them good candidates for disease modeling and drug discovery. Moreover, hiPSCs, unlike primary cells, can be easily genome-edited, allowing the creation of reporter lines or isogenic controls for patient-derived hiPSCs. Unfortunately, even though liver progeny from hiPSCs has characteristics similar to their in vivo counterparts, the differentiation of iPSCs to fully mature progeny remains highly challenging and is a major obstacle for the full exploitation of these models by pharmaceutical industries. In this review, we discuss current liver-cell differentiation protocols and in vitro iPSC-based liver models that could be used for disease modeling and drug discovery. Furthermore, we will discuss the challenges that still need to be overcome to allow for the successful implementation of these models into pharmaceutical drug discovery platforms. |
---|