Drug Response Testing
Drug Response Testing - Related Videos
09:24
Generation of Microtumors Using 3D Human Biogel Culture System and Patient-derived Glioblastoma Cells for Kinomic Profiling and Drug Response Testing
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Cited by 20 •
2016
Patient-derived xenografts of glioblastoma multiforme can be miniaturized into living microtumors using 3D human biogel culture system. This in vivo-like 3D tumor assay is suitable for drug response testing and molecular profiling, including kinomic analysis.
09:58
Robotic Production of Cancer Cell Spheroids with an Aqueous Two-phase System for Drug Testing
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Cited by 37 •
2015
A protocol for robotic printing of cancer cell spheroids in a high throughput 96-well plate format using an aqueous two-phase system is presented.
01:21
Factors Affecting Drug Response: Overview
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2023
When it comes to infants and young children, they are typically administered smaller doses of medication in comparison to adults. This is primarily because their organ functions still need to fully develop, meaning their bodies are not as efficient at metabolizing or eliminating drugs. Additionally, their blood-brain barrier is more permeable than in adults. As a result, high concentrations of drugs can easily penetrate the central nervous system (CNS), potentially leading to neurological...
09:11
Alternating Magnetic Field-Responsive Hybrid Gelatin Microgels for Controlled Drug Release
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Cited by 10 •
2016
We present a facile method to fabricate a biodegradable gelatin-based drug release platform that is magneto-thermally responsive. This was achieved by incorporating superparamagnetic iron oxide nanoparticles and poly(N-isopropylacrylamide-co-acrylamide) within a spherical gelatin micro-network crosslinked by genipin, in conjunction with an alternating magnetic field application system.
08:26
Live Imaging of Drug Responses in the Tumor Microenvironment in Mouse Models of Breast Cancer
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Cited by 15 •
2013
We describe a method for imaging response to anti-cancer treatment in vivo and at single cell resolution.
FAQs
Dose-response measurements show how changing drug concentration alters treatment effect. Researchers expose cells, tissues, or tumor models to controlled concentrations, then compare outcomes such as viability, proliferation, apoptosis, or molecular changes. This relationship helps reveal whether an effect strengthens with increasing exposure and supplies evidence for evaluating drug activity rather than relying on a single concentration.
Sensitivity and resistance reflect different response patterns within cancer models. A sensitive population shows stronger treatment-associated changes, whereas a resistant population shows limited effects under the tested conditions. Measuring both phenotypes can identify heterogeneous responses and connect them with tumor biology, helping researchers investigate why a treatment may work in some cancer populations but not others.
Viability provides an outcome measure, while molecular changes add biological context to the treatment response. Considering both types of outcome can connect an observed drug effect with tumor biology. That connection supports interpretation of response patterns and may help reveal meaningful differences between cancer populations that show similar or different changes in viability.
A typical workflow selects a cancer model, exposes it to controlled concentrations of a drug, and measures treatment-associated outcomes. Researchers may assess cell viability, proliferation, apoptosis, or molecular changes, then examine the results for dose-response relationships. Comparing these measurements across models or cancer populations can reveal sensitivity and resistance patterns.
Response data can be linked to the biology of the tested tumor or cancer population. When treatment effects are considered together with molecular changes, researchers can look for biological features associated with sensitivity or resistance. These associations provide evidence for biomarker discovery, while also helping explain why the same drug may produce different outcomes in different cancer populations.
It supports preclinical drug development by showing how candidate treatments affect patient-derived cells, organoids, or tumor models under controlled conditions. Researchers can use the resulting response patterns to evaluate individual drugs or combinations, identify resistant populations, and generate evidence relevant to selecting therapies. The same strategy also contributes to efforts toward more personalized cancer treatment.