Application Note: Evaluating Antibody Drug Conjugates (ADCs) in vitro using 3D Tumor Spheroid Models

Overview

Antibody-drug conjugates (ADCs) are entities which combine highly specific monoclonal antibodies (mAbs) and cytotoxic chemotherapeutic drugs. This type of combination provides a highly targeted and powerful therapeutic for the treatment of several types of cancer.

The brisk increase in ADC development of recent years, has created a need for robust and reliable techniques for assessing novel candidate drugs. The 2D cell monolayer has long been used due to its simplicity, cost efficiencies, and scalability, however it can lack some critical features of the 3D tumor microenvironment.

Hence many researchers now use 3D advanced cell models, such as spheroids or organoids, to assess in vitro drug effects.

Traditional methods for measuring drug response in 3D models are often limited, as they often:

• Involve lengthy, time-consuming protocols, which require multiple rounds of optimization, fixation, and repetitive washes.
• Necessitate correlation of data from several different assays for each treatment condition, increasing the risk of data variability.
• Provide bulk measurement of cytotoxicity, without insights into spheroid cell type composition.

In this application note, we present a combined workflow approach of iQue^® High-Throughput Screening (HTS) by Cytometry and Incucyte^® Live-Cell Analysis, for quantifying the function of two Trastuzumab (anti-HER2) based ADCs.

Mix and read reagents combined with validated protocols, make experimentation simple and streamlined.

The iQue^® HTS Platform measured target cell counts and viability after spheroid dissociation, enabling profiling of different populations within a co-culture, to reveal differences in spheroid composition. This type of high-throughput analysis of dissociated cells, reveals mechanistic information on a drug’s activity. Thus improving drug discovery and biological research applications.

• Document type: Application Note
• Page count: 10
• Read time: 15 minutes

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