YOSHIDA Takashi1 YOSHIURA Takashi2 HACHIYA Kenji3 ITO Takeshi4
We have been developing a Drug Discovery Test System for Genome-based Drug Discovery. This system administers chemical compounds that serve as potential candidate drugs into live cells, which are the most basic components of all living organisms, records the changes in the amount and/or localization of target molecules inside cells with our CSU confocal scanner and a highly sensitive CCD camera, and processes and quantifies the captured high-resolution image data. This screening method enables the drug efficacy and adverse drug reactions of the candidate chemicals to be verified and the candidate drugs to be confirmed in live cells. This paper describes an image processing technology we have developed for our prototype Drug Discovery Test System.
|Figure 1 Analysis Flow|
We have been developing a prototype of a genomic drug test support system using our CSU confocal scanner. This system administers chemical compounds that serve as potential drug candidates into living cells, which are the most basic components of all living organisms, records the changes in the amount and localization of target molecules inside cells with the CSU confocal scanner and a highly sensitive CCD camera, and processes and quantifies the captured high-resolution image data. This screening method enables drug efficacy and adverse drug reactions of chemical components to be verified and drug candidates to be determined in living cells. This paper describes the image processing technology we have developed for our prototype of a genomic drug test support system.
|Figure 2 Input Image (First image)||Figure 3 Modeling (Extraction of
Even if interactions between protein and chemical compounds are observed in the test tube in the first screening process, they may not be verified because transporters that discharge chemicals and metabolizing enzymes such as cytochrome P450s exist in cells. For this reason, cultured cells which can serve as disease models are used for specimens in drug-discovery tests. Most of these cells can easily be cultured using immortalized cells. Specific areas of these cells are dyed using fluorescent reagent, images are obtained with an fluorescence microscope or a confocal microscope and processed to extract and quantify morphological changes of cells caused by chemical compounds.
|Figure 4 Tracking Results|
For actual specimens, cultured cells are sowed on a 96-well or 384-well micro well-plate. They are fluorescently dyed after being administered various concentrations of chemical compounds to observe morphological changes, etc. Yokogawa's image-capturing system (applying CSU) has the following features.
The following reports on this image processing technique together with some technical cases in which the characteristics of images obtained by the sensor (CSU system) are fully used.
The analysis flow of drug discovery tests consists of three steps as shown in Figure 1.
|Figure 5 Original Image||Figure 6 Cell and Dendrite Detection|
This chapter describes the image processing algorithms developed for drug discovery tests.
|Figure 7 Cell and Dendrite Matching|
|Figure 8 Original Image||Figure 9 Cell Area Detection|
The cell features data obtained by image processing usually contains varies greatly. Statistical processing is thus required to extract general trends from such data. Some of them are explained as below.
|Figure 10 Intracellular Granular
|Figure 11 Matching of Cells and Granules|
The Z'-factor is an index which expresses the validity of the assay to check if the use of drug causes significant difference in cell reactions. It represents distribution differences in the features of a particular cell between negative control and positive control. When the average value and standard deviation of negative control are denoted as μ n and n , respectively,and those of positive control are denoted as μp and p , respectively, the Z'- factor can be calculated as follows:
When an inequation 0.5 < Z' < 1.0 is formed, the quality of the assay can be regarded as excellent. If the Z'-factor is less than zero, it means that the assay needs to be reviewed.
Next, the EC50 (Effect Concentration 50) is explained. Generally speaking, when a certain drug has an effect on cells, the effect will increase as drug dose increases.
|Figure 12 Dose-Response Curve and EC50|
For example, when such drugs that could cause apoptosis (cell death) are used, the amount of dead cells will increase as the drug dose also increases. Then, subsequently, 100% of cells will die. The EC50 is the drug concentration level at which half of the maximum drug effect is observed. It is used as a guide to indicate the efficacy of drug concentration. If the effect of drug inhibits a certain function inside the cell, IC50 (Inhibitory Concentration 50) is used. It is the concentration of a drug which produces 50% of the maximal inhibition on the cell.
To obtain the EC50, first, it is necessary to focus on the features of cells that shows significant effects caused by drugs, and a drug's dose-response curve is drawn. Generally, the shape of the curve takes a sigmoidal form (Figure 12). Both the minimum and maximum values of the features on the curve are read, and the amount of the dosed drug for the median is obtained. This is equivalent to the EC50. The drug's dose-response curve generally includes variation in the characteristics of the specimen (cells) and experimental errors, and is therefore not an ideal response curve. So the sigmoid curve model is applied to the actually obtained drug's dose-response curve to estimate the EC50 value using a computer program performing a nonlinear least square fitting. At this point, it is recommendable to obtain the Z'-factor value to verify the validity of the evaluation process.
This paper reports on an image processing technique applied to the drug discovery tests using cultured cells. This image processing technique can be used for precisely measuring drug efficacy and toxicity of chemical compounds which can serve as potential drug candidates from the initial stage of drug development. We believe that it will contribute to reduce the development time for new genomic drugs as well as the development cost.
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