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Using Human Induced Pluripotent Stem Cell-derived Hepatocyte-like Cells for Drug Discovery
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[Applications of stem cell disease models in liver metabolic research and drug development]

Xiao-Shan Wu1,2, Shuang Li2, Qiu-Rong Ding3

  • 1School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.

Sheng Li Xue Bao : [Acta Physiologica Sinica]
|October 28, 2021
PubMed

Insights

Stem cell-derived disease models offer a powerful alternative to animal testing for drug development. These human models improve the prediction of drug efficacy and toxicity, reducing clinical trial failures.

Area of Science:

  • Biomedical Research
  • Stem Cell Biology
  • Pharmacology

Background:

  • New drug development faces high failure rates due to poor translation from animal models to human trials.
  • Current preclinical models often lack the genetic specificity required for accurate human disease simulation.

Purpose of the Study:

  • To review the development and application of stem cell-derived disease models for studying liver diseases.
  • To highlight the utility of these models in improving drug discovery and development processes.

Main Methods:

  • Utilizing human induced pluripotent stem cells (iPSCs) and adult stem cells to create patient-specific disease models.
  • Employing gene editing techniques to introduce specific genetic variations into stem cells.
  • Establishing cell banks for large-scale in vitro genetic studies, including Genome-Wide Association Studies (GWAS) and Quantitative Trait Locus (QTL) analyses.

Main Results:

  • Stem cell models provide unlimited, genetically relevant cell materials for disease simulation.
  • In vitro genetic studies on stem cell models can identify variants associated with drug sensitivity and cytotoxicity.
  • These models enhance the understanding of disease pathophysiology and aid in patient stratification for clinical trials.

Conclusions:

  • Stem cell-derived disease models represent a significant advancement in preclinical research.
  • These models offer a more accurate platform for drug development, potentially reducing failure rates.
  • Applications extend to personalized medicine by enabling the identification of patient subgroups likely to respond to specific therapies.

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