Bh3 Mimetics
Bh3 Mimetics - Related Videos
07:03
Imaging Denatured Collagen Strands In vivo and Ex vivo via Photo-triggered Hybridization of Caged Collagen Mimetic Peptides
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Cited by 18 •
2014
This procedure demonstrates in vivo near IR fluorescence imaging of collagen remodeling activities in mice as well as ex vivo staining of collagens in tissue sections using caged collagen mimetic peptides that can be photo-triggered to hybridize with denatured collagen strands.
06:09
An In Ovo Model for Testing Insulin-mimetic Compounds
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Cited by 12 •
2018
In this study, we describe an in-ovo system as a promising tool to test insulin-mimetic compounds in a living organism. The main advantages include adequate throughput rates and acceptable costs, which enable the identification of phytochemicals with insulin-mimetic properties.
09:28
Engineering Molecular Recognition with Bio-mimetic Polymers on Single Walled Carbon Nanotubes
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Cited by 16 •
2017
We present a protocol for engineering the corona phase of near infrared fluorescent single walled carbon nanotubes (SWNTs) using amphiphilic polymers and DNA to develop sensors for molecular targets without known recognition elements.
09:39
Targeted Antibody Blocking by a Dual-Functional Conjugate of Antigenic Peptide and Fc-III Mimetics (DCAF)
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2019
Development of a dual-functional conjugate of antigenic peptide and Fc-III mimetics (DCAF) is novel for the elimination of harmful antibodies. Here, we describe a detailed protocol for the synthesis of DCAF1 molecule, which can selectively block 4G2 antibody to eliminate antibody dependent enhancement effect during Dengue virus infection.
03:56
Zebrafish Xenograft Assay: A Method to Assess the Anti-cancer Efficacy of Therapeutics on Tumorigenicity in Zebrafish In Vivo
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2023
This video describes the zebrafish xenograft assay to assess in vivo tumor formation and study the effect of therapeutic agents.
FAQs
Different BH3 mimetics can be distinguished by the anti-apoptotic BCL-2 family protein they target. A compound directed toward BCL-2, BCL-XL, or MCL-1 can therefore help reveal which survival protein a cancer cell relies on most. This selectivity makes the compounds useful for linking protein dependence with differences in apoptotic response across cancer models.
The critical mechanistic checkpoint is whether displaced pro-apoptotic factors can activate BAX and BAK. Once these effectors permeabilize the mitochondrial outer membrane, the apoptotic program proceeds toward caspase activation. Thus, binding to an anti-apoptotic protein is not the endpoint; the downstream mitochondrial response determines whether treatment produces programmed cancer-cell death.
A useful resistance analysis compares how cancer models respond when different anti-apoptotic proteins are targeted. If response changes across compounds directed at BCL-2, BCL-XL, or MCL-1, the comparison can help identify altered survival dependence or other resistance-associated behavior. This approach connects treatment response with the apoptotic regulators that remain protective.
Combination studies examine whether targeting anti-apoptotic protection can strengthen the effects of another anticancer treatment. By displacing activated pro-apoptotic factors, BH3 mimetics may help expose whether survival signaling is limiting apoptotic activation. These experiments can clarify complementary mechanisms and support strategies intended to improve the effectiveness of cancer treatment.
A response can provide evidence about apoptotic regulation and the survival dependency of a tumor model. Researchers can relate activity to the anti-apoptotic proteins being targeted and determine whether BAX and BAK-dependent mitochondrial permeabilization leads to caspase-dependent apoptosis. The resulting profile helps characterize how a cancer cell maintains survival and responds to treatment.
Researchers can compare compounds directed against BCL-2, BCL-XL, or MCL-1 across cancer models, then interpret differences in apoptotic response. Linking the targeted protein with mitochondrial permeabilization and caspase-dependent cell death provides a conceptual workflow for studying survival dependence. The comparison also creates a framework for examining resistance and combination-treatment effects.
Biomarker-guided strategies aim to match treatment selection with the survival proteins on which a tumor depends. In this context, biomarker information can help distinguish models that are more closely associated with BCL-2, BCL-XL, or MCL-1 activity. Such matching supports more precise investigation of treatment response, resistance, and suitable combination approaches.