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While primary cells are a good tool for modeling in vitro, their phenotype can alter in culture in isolation from normal neighboring cell types.
Successful research programs in Europe include the development and application of statistically designed experiments combined with multivariate data analysis and modeling in vitro and in vivo studies on a wide variety of chemicals such as petroleum hydrocarbons, aldehydes, food contaminants, industrial solvents, and mycotoxins.
Human pluripotent stem cells (hPSCs) are attractive sources for regenerative medicine and disease modeling in vitro.
One concern raised in translational medicine is to what extent these laboratory studies on theoretical modeling, in vitro characterization or in vivo assessment based on animal models, can be predictive of performance and therapeutic efficacy in human (Meijer et al., 2008; Agata et al., 2010).
Reprogramming of human somatic cells into induced pluripotent stem cells (iPSCs) provides a unique opportunity for disease modeling in vitro.
Despite the previously described advances in both technology and system pharmacology modeling, in vitro drug clearance prediction still faces some new challenges [ 25, 30].
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LMNA-related DCM was modeled in-vitro using patient-specific iPSC-CMs.
An interesting feature of muscle fiber cellularization is that it can be modelled in vitro using skeletal muscle myotubes [20].
It is difficult to model (in vitro) partially healed fractures or the contribution of soft tissues to stability accurately.
By such integration, scientists can model In vitro assays using In silico descriptor packages while making benefit of multi-learning features and automatics applicability domain estimation.
These effects can be seen in vivo as well as modeled in vitro.
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