Drug Resistance Generation
Drug Resistance Generation - Related Videos
04:33
Generating Chemoresistant Prostate Cancer Cells: A Procedure for Obtaining Drug-resistant Cancer Cells In Vitro
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2023
This video describes a protocol for generating taxane-resistant cell models of prostate cancer. These models can help study the pathways involved in progression to Docetaxel resistance in prostate cancer patients.
11:56
Nanomechanics of Drug-target Interactions and Antibacterial Resistance Detection
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Cited by 4 •
2013
Acquired resistance to antibiotics is a major public healthcare problem and is presently ranked by the WHO as one of the greatest threats to human life. Here we describe the use of cantilever technology to quantify antibacterial resistance, critical to the discovery of novel and powerful agents against multidrug resistant bacteria.
08:46
Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms
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Cited by 2 •
2015
Drug resistance to targeted therapeutics is widespread and the need to identify mechanisms of resistance--prior to or following clinical onset--is critical for guiding alternative clinical management strategies. Here, we present a protocol to couple derivation of drug-resistant lines in vitro with sequencing to expedite discovery of these mechanisms.
05:10
Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
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Cited by 4 •
2016
Here we describe a protocol using the web-based drug repurposing hypothesis generation tool: "RE:fine Drugs." This protocol can be modified to a user's preferences at the level of the query type (gene, drug or disease) and/or the range of available advanced options.
04:17
Dose Escalation: A Method for Developing Drug Resistance in Cancer Cells
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2023
The video describes the development of drug resistance in cancer cells by exposing cells to escalating doses of a drug after calculating IC50 values of the drug previously with MTT assay. The sample protocol describes the step-by-step process of developing drug-resistant cells.
FAQs
Changes in drug transport can alter how much anticancer compound reaches or remains within a cancer cell, while target modification can reduce the treatment’s ability to act on its intended molecular target. These adaptations may contribute to treatment failure through different routes, so resistant models help researchers determine which cellular changes are most relevant to a particular therapy.
Enhanced DNA repair may help cells recover from treatment-associated damage, whereas reduced apoptosis limits the programmed cell death that anticancer therapies can induce. Studying these mechanisms separately helps clarify whether resistance reflects improved damage recovery, impaired death signaling, or both. That distinction can guide investigations of combination therapies intended to restore treatment sensitivity.
Resistance may result from heritable adaptations that persist as cells reproduce, or from reversible changes that depend on continued treatment conditions. This distinction matters because a stable resistant population suggests lasting cellular alteration, while reversible resistance indicates that sensitivity could potentially return under different conditions. Comparing these patterns helps researchers interpret treatment response and design strategies to overcome resistance.
A model selected with one anticancer treatment can be examined for reduced sensitivity to other treatments, revealing cross-resistance. Such findings indicate that a shared cellular adaptation, such as altered transport, target modification, enhanced DNA repair, or reduced apoptosis, may affect multiple therapies. This information supports evaluation of treatment combinations and helps identify options less vulnerable to the same resistance mechanism.
The usual strategy applies anticancer treatment repeatedly or increases exposure gradually over successive selection periods. Cells that survive are retained as a resistant population for further investigation. Researchers can then examine the selected cells for molecular adaptations and compare their responses with untreated or drug-sensitive cells, linking the exposure history to mechanisms of treatment failure.
These models allow researchers to test whether a second treatment or a treatment combination can restore sensitivity in resistant cells. They also support searches for predictive biomarkers, which are measurable features associated with treatment response, and help evaluate approaches designed to delay resistance. The resulting evidence can connect cellular mechanisms of failure with strategies for improving anticancer therapy.