PURPOSE: The area under the curve (AUC) is commonly used to assess the extent of exposure of a drug. The same concept can be applied to generally assess pharmacodynamic responses and the deviation of a signal from its baseline value. When the initial condition for the response of interest is not zero, there is uncertainty in the true value of the baseline measurement. This necessitates the consideration of the AUC relative to baseline to account for this inherent uncertainty and variability in baseline measurements. METHODS: An algorithm to calculate the AUC with respect to a variable baseline is developed by comparing the AUC of the response curve with the AUC of the baseline while taking into account uncertainty in both measurements. Furthermore, positive and negative components of AUC (above and below baseline) are calculated separately to allow for the identification of biphasic responses. RESULTS: This algorithm is applied to gene expression data to illustrate its ability to capture transcriptional responses to a drug that deviate from baseline and to synthetic data to quantitatively test its performance. CONCLUSIONS: The variable nature of the baseline is an important aspect to consider when calculating the AUC.
PURPOSE: The area under the curve (AUC) is commonly used to assess the extent of exposure of a drug. The same concept can be applied to generally assess pharmacodynamic responses and the deviation of a signal from its baseline value. When the initial condition for the response of interest is not zero, there is uncertainty in the true value of the baseline measurement. This necessitates the consideration of the AUC relative to baseline to account for this inherent uncertainty and variability in baseline measurements. METHODS: An algorithm to calculate the AUC with respect to a variable baseline is developed by comparing the AUC of the response curve with the AUC of the baseline while taking into account uncertainty in both measurements. Furthermore, positive and negative components of AUC (above and below baseline) are calculated separately to allow for the identification of biphasic responses. RESULTS: This algorithm is applied to gene expression data to illustrate its ability to capture transcriptional responses to a drug that deviate from baseline and to synthetic data to quantitatively test its performance. CONCLUSIONS: The variable nature of the baseline is an important aspect to consider when calculating the AUC.
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Authors: Brett Tortelli; Hideji Fujiwara; Jessica H Bagel; Jessie Zhang; Rohini Sidhu; Xuntian Jiang; Nicole M Yanjanin; Roopa Kanakatti Shankar; Nuria Carillo-Carasco; John Heiss; Elizabeth Ottinger; Forbes D Porter; Jean E Schaffer; Charles H Vite; Daniel S Ory Journal: Hum Mol Genet Date: 2014-06-25 Impact factor: 6.150
Authors: Mehdi Farokhnia; Kelly M Abshire; Aaron Hammer; Sara L Deschaine; Anitha Saravanakumar; Enoch Cobbina; Zhi-Bing You; Carolina L Haass-Koffler; Mary R Lee; Fatemeh Akhlaghi; Lorenzo Leggio Journal: Int J Neuropsychopharmacol Date: 2021-07-14 Impact factor: 5.176