Engineering tissues for in vitro applications

Khetani, Salman R.; Bhatia, Sangeeta N.

doi:10.1016/j.copbio.2006.08.009

Cited by 122 publications

(76 citation statements)

References 69 publications

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“…In addition, biomaterial approaches have been developed to generate surrogate, 3D matrices to house extracted cells with the goal of preserving cell function. [5][6][7][8][9] Analysis of cell populations within aggregates or 3D matrices had been limited to lowthroughput technologies, which typically require destruction of the sample (e.g., immunofluorescent staining and imaging, western blot, polymerase chain reaction (PCR)). However, newer, flow cytometry-based technologies are emerging that can rapidly and accurately analyze the composition and function of large particles ($50-1000 lm) without compromising cell viability.…”

Section: Introductionmentioning

confidence: 99%

Microfluidic sorting of microtissues

et al. 2012

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Increasingly, in vitro culture of adherent cell types utilizes three-dimensional (3D) scaffolds or aggregate culture strategies to mimic tissue-like, microenvironmental conditions. In parallel, new flow cytometry-based technologies are emerging to accurately analyze the composition and function of these microtissues (i.e., large particles) in a non-invasive and high-throughput way. Lacking, however, is an accessible platform that can be used to effectively sort or purify large particles based on analysis parameters. Here we describe a microfluidic-based, electromechanical approach to sort large particles. Specifically, sheath-less asymmetric curving channels were employed to separate and hydrodynamically focus particles to be analyzed and subsequently sorted. This design was developed and characterized based on wall shear stress, tortuosity of the flow path, vorticity of the fluid in the channel, sorting efficiency and enrichment ratio. The large particle sorting device was capable of purifying fluorescently labelled embryoid bodies (EBs) from unlabelled EBs with an efficiency of 87.3% 6 13.5%, and enrichment ratio of 12.2 6 8.4 (n ¼ 8), while preserving cell viability, differentiation potential, and long-term function. V C 2012 American Institute of Physics. [http://dx

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Section: Introductionmentioning

confidence: 99%

Microfluidic sorting of microtissues

et al. 2012

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“…In vitro testing and in vivo pharmacokinetics either in animals or in man is of paramount importance. These include in silico testing, high-throughput systems that are enzyme-or cell-based, including microfluidic systems of multiple cells in culture (Leeder et al, 1989;Johansson et al, 2004;Khetani and Bhatia, 2006;Li, 2007), and identification of mechanisms of on-target and off-target toxicity via covalent binding, alkylation protein, DNA (Baillie, 2006) or downstream adducts, biomarkers, or mechanism-based inactivation of cytochrome P450s (Jones et al, 2007). Animal models and surrogate animal testing are used for investigation of metabolite-mediated toxicity, genotoxicity studies, toxicity testing of embryonic-fetal development, and carcinogenicity studies in toxicological testing (Stevens,FIG.…”

Section: Discussionmentioning

confidence: 99%

Disparity in Intestine Disposition between Formed and Preformed Metabolites and Implications: A Theoretical Study

Sun

Pang²

2008

Drug Metab Dispos

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ABSTRACT:Metabolite in safety testing has been proposed for toxicity assessments. The question of how exposure of the synthetic metabolite compared with that of the formed metabolite was appraised kinetically by using physiologically based pharmacokinetic models, the (traditional) physiological model (TM), and segregated flow (SFM) models. The SFM differs from the TM and describes a partial (ϳ10% total) intestinal flow that perfuses the absorptive, metabolic, and secretory enterocyte layer to account for the higher extent of metabolism observed with oral versus systemic dosing of drugs. Theoretical solutions for the areas under the curve (AUC) of the formed metabolite after oral and intravenous administration of the precursor (AUC{mi,P}) and preformed, synthetic metabolite (AUC{pmi}) showed identical AUC iv {mi,P}, AUC po {mi,P}, and AUC po {pmi} for both the TM and SFM, whereas a larger AUC iv {pmi} existed for the SFM. The AUC{pmi} was influenced by metabolite parameters only: binding, absorptive (k a {mi}) and luminal degradation (k g {mi}) constants, intrinsic clearances for metabolism (CL int,met,I {mi}), apical efflux (CL int,sec,I {mi}), and basolateral transfer (CL d1 {mi} and CL d2 {mi}) for the metabolite. By contrast, the AUC{mi,P} was influenced additionally by precursor parameters: rate constants k a and k g , and CL int,met,I and CL int,sec,I , but not the basolateral transfer clearances. The drug parameters: CL int,met,I and k a increased whereas CL int,sec,I decreased AUC{mi,P}, and the effect of secretion was counterbalanced by reabsorption with high k a values. The simulated time courses for the metabolites and the AUC{pmi} and AUC{mi,P} resulting from intravenous and oral routes of administration of preformed metabolite and precursor differed, inferring that the kinetics of the preformed and formed metabolites are not identical.Metabolite-in-safety testing and the attainment of safe and efficacious drug use are key concerns in the drug development/surveillance paradigm. There has been a resurgence of interest in the testing of metabolites that are mediators of drug activity and toxicity due to the improvement in analytical methods for their detection, isolation, and characterization (Baillie et al., 2002). Metabolite testing is recommended, especially when metabolites are unique and identified only in humans, or when the metabolite exists at disproportionately higher levels in humans (Ͼ10%) than the animal species that was used for standard, nonclinical toxicology testing (Naito et al., 2007) (February, 2008 FDA Guidance for Industry, Safety Testing of Drug Metabolites. Pharmacology and Toxicology; http://www.fda.gov/cder/guidance/). It has been proposed that a metabolite is considered to be a major metabolite when the metabolite represents Ͼ10% (Davis-Bruno and Atrakchi, 2006) or 25% exposure (or area under the curve) of the precursor (Baillie et al., 2002). Others have suggested the estimate to be based on unbound concentration in the circulation or amounts in the excreta (Smith and Obach...

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“…[17][18][19] In these applications, PEG's resistance to protein adsorption and cell attachment are its chief strengths.20 PEG can also be easily photopolymerized into micro-scale gel forms. [21][22][23] In previous reports, photomasks have been used to selectively polymerize PEG hydrogels on flat silicon oxide surfaces.24,25 In a similar manner, high density arrays of microwells created by photopolymerized PEG hydrogel walls have been fabricated and used to guide cell attachment on glass, as was described above.…”

Section: Introductionmentioning

confidence: 99%

Micropallet arrays with poly(ethylene glycol) walls

et al. 2008

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Arrays of releasable micropallets with surrounding walls of poly(ethylene glycol) (PEG) were fabricated for the patterning and sorting of adherent cells. PEG walls were fabricated between the SU-8 pallets using a simple, mask-free strategy. By utilizing the difference in UV-transmittance of glass and SU-8, PEG monomer was selectively photopolymerized in the space surrounding the pallets. Since the PEG walls are composed of a cross-linked structure, the stability of the walls is independent of the pallet array geometry and the properties of the overlying solution. Even though surrounded with PEG walls, the individual pallets were detached from the array by the mechanical force generated by a focused laser pulse, with a release threshold of 6 μJ. Since the PEG hydrogels are repellent to protein adsorption and cell attachment, the walls localized cell growth to the pallet top surface. Cells grown in the microwells formed by the PEG walls were released by detaching the underlying pallet. The released cells/pallets were collected, cultured and clonally expanded. The micropallet arrays with PEG walls provide a platform for performing single cell analysis and sorting on chip.

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Engineering tissues for in vitro applications

Cited by 122 publications

References 69 publications

Microfluidic sorting of microtissues

Microfluidic sorting of microtissues

Disparity in Intestine Disposition between Formed and Preformed Metabolites and Implications: A Theoretical Study

Micropallet arrays with poly(ethylene glycol) walls

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