Non-Compartmental Analysis

This section provides a comprehensive tutorial on Non-Compartmental Analysis (NCA), a widely used method in pharmacokinetics to analyze drug concentration-time data without assuming any specific compartmental model. The tutorial covers how to perform NCA using Teoreler's user-friendly interface.

Two types of files .csv and .xlsx are accepted on Teoreler. Three different type of formats are accepted. The first format is the 'long' format where there are three columns - time, concentration and subject ID. The second format is the 'wide' format where there are multiple columns for concentration values for each subject and a single column for time. The third format is the 'summary' format where there are two columns - time and mean concentration values across all subjects. A template file of the accepted formats can be downloaded by clicking the 'download template' link provided.

Snapshots of the three formats are shown in the following image. The provided template file contains examples of all three formats and can be used as a reference for formatting the data correctly before uploading it to Teoreler for NCA.

Please note: If an excel (.xlsx / .xls) file contains multiple sheets, only the first sheet is considered for NCA.

A preview of the uploaded data is shown under the observed data tab, located under table in the 'Result' section. In this example, the NCA_Data_template.xlsx file was uploaded and the sheet named 'Format1' (the long format) is considered.

Please note: If there are multiple IDs, a 'Mean' ID is also created by taking the mean of the concentration values across all the IDs for each time point. This allows users to perform NCA on the mean concentration values across all subjects in addition to performing NCA on individual subject data.

Once the appropriate inputs are provided, the NCA can be performed by clicking on the 'Analyze' button. The results of the NCA will be displayed in a tabular format under the 'Analysis' tab in the 'Results' section. The table includes various pharmacokinetic parameters such as Cmax, Tmax, AUC, half-life, and clearance for each subject ID as well as for the mean concentration values across all subjects (if multiple IDs are present).

The plots show three different options - 1) A collation of individual plots based on subject IDs, 2) Kel plot and 3) Visual predictive check (VPC) plot. The collation of individual plots allows users to visualize the concentration-time profiles for each subject ID separately. The Kel plot is a semi-logarithmic plot of the natural logarithm of concentration versus time, which is used to estimate the elimination rate constant (Kel) and half-life. The VPC plot shows the mean observed data with 95% CI.