Drug Response Prediction
Drug Response Prediction - Related Videos
10:46
Rapid in vivo Drug Response Prediction Using Leukemia Cell Grafts in Zebrafish Embryos
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2025
This protocol provides step-by-step instructions for generating and troubleshooting human acute lymphoblastic leukemia (ALL) xenografts from cell lines and fresh patient material in transiently immunosuppressed zebrafish embryos, along with guidelines for drug response assessment using flow cytometry. The experimental pipeline can also be adapted for solid tumors.
12:17
An In Vitro Caseum Binding Assay that Predicts Drug Penetration in Tuberculosis Lesions
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Cited by 24 •
2017
Here we describe a rapid equilibrium dialysis (RED) method to measure drug binding to caseum from pulmonary tuberculosis lesions and cavities. The protocol is also used with a foamy macrophage-derived matrix that is an effective surrogate to caseum.
11:53
Predicting Gene Silencing Through the Spatiotemporal Control of siRNA Release from Photo-responsive Polymeric Nanocarriers
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Cited by 6 •
2017
We present a novel method that uses photo-responsive block copolymers for more efficient spatiotemporal control of gene silencing with no detectable off-target effects. Additionally, changes in gene expression can be predicted using straightforward siRNA release assays and simple kinetic modeling.
13:34
A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds
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Cited by 29 •
2016
We present a three-dimensional (3D) lung cancer model based on a biological collagen scaffold to study sensitivity towards non-small-cell-lung-cancer-(NSCLC)-targeted therapies. We demonstrate different read-out techniques to determine the proliferation index, apoptosis and epithelial-mesenchymal transition (EMT) status. Collected data are integrated into an in silico model for prediction of drug sensitivity.
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...
FAQs
The models analyze relationships between inputs such as genetic alterations, gene expression, cellular phenotypes, and prior treatment outcomes. Statistical or machine-learning methods use these patterns to estimate whether a biological system is likely to respond to a therapeutic compound or show resistance. This connects measurable molecular and cellular characteristics with predicted treatment outcomes.
Each measurement describes a different aspect of the biological system, so combining them provides a broader response profile than relying on one feature alone. Genetic alterations may be considered alongside gene activity, observable cellular behavior, and previous treatment results. Integrating these data helps models identify relationships associated with sensitivity or resistance more effectively.
Predictions prioritize compounds or treatment options according to their estimated response for a particular patient, cell, or tissue profile. This can reduce the number of possibilities requiring direct testing and make experimental planning more efficient. The same analysis may also reveal relationships that help clarify why biological systems respond differently to the same therapeutic compound.
A workflow begins by collecting biological measurements and, when available, prior treatment outcomes. These features are then analyzed with statistical or machine-learning models to identify associations with drug sensitivity or resistance. The resulting predictions can guide treatment selection, compound testing, or interpretation of response mechanisms in cells, tissues, patients, or engineered models.
Bioengineering applications include personalized treatment selection, drug screening, and evaluation of therapies in engineered tissue or organoid models. By linking biological profiles with predicted responses, researchers can use designed experimental systems to examine treatment behavior before relying on broader testing. This supports more targeted experiments and helps connect model-system results with therapeutic questions.
The approach can estimate drug sensitivity or resistance for a patient, cell, or tissue and help identify treatments that merit further consideration. In experimental settings, it can improve efficiency by narrowing testing priorities. Its analyses can also expose relationships among biological features and response outcomes, providing clues about mechanisms underlying variable treatment responses.