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An Individual Patient's "Body" on Chips-How Organismoid Theory Can Translate Into Your Personal Precision Therapy
Uwe Marx1,2, Enrico Accastelli2, Rhiannon David3
1Department of Medical Biotechnology, Institute of Biotechnology, Technische Universität Berlin, Berlin, Germany.
Frontiers in Medicine
|September 30, 2021
Summary
Organismoids are new in vitro models of the human body, created using self-assembly. These models can predict personalized therapies and disease progression for unprecedented precision in medicine.
Area of Science:
- Biotechnology and Regenerative Medicine
- In Vitro Modeling
- Personalized Medicine
Background:
- Early concepts of 'human-on-a-chip' aimed to emulate human physiology in vitro for personalized therapies.
- Recent advancements in human organoids demonstrate the feasibility of self-assembling miniature organ models from stem cells.
Purpose of the Study:
- To develop the 'human-on-a-chip' concept into a comprehensive 'organismoid theory'.
- To outline principles for creating and maintaining individual human body equivalents in vitro.
- To explore the application of organismoids in personalized therapy selection and disease modeling.
Main Methods:
- Development of organismoids: minute, physiological in vitro equivalents of a mature human body.
- Mimicking human ontogenesis through an artificially shortened process of self-assembly.
- Maintaining homeostasis in organismoids over extended periods and inducing diseases for study.
Main Results:
- Organismoids can be created to reflect individual human physiology and morphology.
- Methods are proposed for maintaining healthy organismoids and inducing disease states.
- The potential for organismoids to predict personalized therapeutic responses is discussed.
Conclusions:
- The organismoid theory offers a novel platform for precise, personalized medicine.
- This approach could significantly impact healthcare, particularly in the adoption of personalized T-cell therapies.
- Organismoids represent a significant advancement in in vitro human body modeling for predictive health applications.
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