Speaker
Description
Concentration-response curves model the relationship between a concentration of a compound and the response it elicits in a biological system. Here, the viability of cells is considered as response. Typically, parametric models are fitted to the data. Modeling this relationship accurately is crucial for understanding the safety and potency of compounds, since one of the applications of these curves is the estimation of the effective concentration (EC), at which a certain pre-defined response is obtained. However, due to measurement errors or biological variability, it is often observed that measurements at individual concentration levels deviate from the typically assumed sigmoidal models. Such deviations in the data can compromise the accuracy of the estimation of EC values. Due to the usually very low number of measured concentrations, identifying these deviations is challenging.
Here, we simulated data for different scenarios of possible deviations at each individual concentration levels. We propose several statistical methods that can mitigate the impact of such deviations. Two methods try to identify the deviations and eliminate the data of the corresponding concentrations completely from the curve fitting process. Two methods use the iterative weighted least squares method to assign lower weights to deviations, and another weighted method uses weights obtained from an exponentially decreasing function. All these methods are then compared to a baseline method on how accurately they can estimate the true EC20 value. On average, the methods that eliminate the deviations are the most accurate, but they become considerably worse than any other method when correct data is eliminated instead of the true deviating data. To mitigate this, we recommend to combine one of them with a weighted method, which on average gives a less accurate estimation since it does not eliminate completely the deviating data, but the weighted method can help in validating the deviations that the other method has identified, reducing the risk of wrongly eliminating correct data from an already small dataset.
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