-
Categories
-
Pharmaceutical Intermediates
-
Active Pharmaceutical Ingredients
-
Food Additives
- Industrial Coatings
- Agrochemicals
- Dyes and Pigments
- Surfactant
- Flavors and Fragrances
- Chemical Reagents
- Catalyst and Auxiliary
- Natural Products
- Inorganic Chemistry
-
Organic Chemistry
-
Biochemical Engineering
- Analytical Chemistry
-
Cosmetic Ingredient
- Water Treatment Chemical
-
Pharmaceutical Intermediates
Promotion
ECHEMI Mall
Wholesale
Weekly Price
Exhibition
News
-
Trade Service
12, 2020 // -- In a recent study published in the international journal Science Advances, scientists from the University of Nottingham and others developed a 3D tumor model in the lab that could help understand the pathogenesis of ovarian cancer and develop new targeted therapies.
multicellular 3D micro-environment created by researchers to reproduce patterns of tumor cell growth in ovarian cancer and how it responds to chemotherapy drugs.
Photo Source: Alvaro Mata Researchers now need an improved version of the 3D cancer model to study tumor growth, progression, and response to new therapies in ovarian cancer patients, with 90 percent of successful cancer treatments tested in preclinical trials failing in early clinical trials and less than 5 percent of cancer drugs successfully used in clinical trials, and preclinical trials relying on 2D lab-grown cell cultures and animal models to predict patient responses to treatments.
However, traditional 2D cell culture does not simulate the key characteristics of tumor tissue, and differences between species can lead to many successful therapies in animal hosts becoming ineffective in human clinical trials, so researchers need to develop new experimental 3D cancer models to better reconstruct the tumor micro-environment of the human body, taking into account specific differences between patients.
This new hydrogel biomass is made up of a peptide and a special protein found in ovarian cancer, and its mechanism promotes the peptide to assemble these proteins into a molecular environment, thus simulating how it is presented in the patient's body tumor.
Researcher Professor Alvaro Mata said bioengineered self-assembled models could expand the researchers' experimental pool while helping to study tumor growth and progress in a biologically controlled way, in which the researchers were able to assemble peptide parent molecules with extracellular substate proteins into adjustable 3D models in the tumor microenta environment. Composite models can try to simulate the physical, biological and cellular characteristics of tumors in a patient's body, and then the researchers used chemotherapy drugs to test the response of laboratory-cultured tumors to confirm the function of multicellular structures, and they observed that tumors began to shrink, suggesting that the new peptide white blood cell biomass could help more effectively test new drugs and therapies for ovarian cancer.
Self-assembly is the process of assembling multiple components into more powerful structures, and biological systems rely on this process to control the assembly of molecules and cells into complex functional tissues with extraordinary characteristics known to researchers, such as the ability to grow, replicate, and perform powerful functions. Daniela Loessner, a final researcher at
, said the gold standard for the 3D cancer model is MatrixTm, which is currently on the market as a soluble substrate structure extracted from mouse sarcoma, and one of the main reasons Matrigel is popular is that it promotes cell-matrix interactions and promotes cancer and mechanism cell growth as a aggregate called cytosome.
However, this 3D cancer model still lacks control in simulating the tumor environment due to batch variation, unclear composition, and animal origin, which presents significant limitations for scientists in effectively screening and developing new cancer therapies, and the results of this paper demonstrate the ability of scientists to engineer 3D models that may be a complex, controllable and replaceable important biological material.
() Original source: Clara Louise Hedegaard, Carlos Redondo-Gómez, Bee Yi Tan, et al. Peptide-protein coassembling matrices as a biomimetic 3D model of ovarian cancer, Science Advances 02 Oct 2020: Vol. 6, no. 40, eabb3298 doi:10.1126/sciadv.abb3298.