Proposed Workflow

1. Prepare and Upload Your Data

   Organize your data in a CSV file with columns: id, time, pH, and group. Optional columns include baseline, flowrate, and buffering. Download the template from the 'Load Data' tab to see the required format. Then upload your file or load a demo dataset.

2. Input the Correct Baseline Time and Visualize Your Data

   Set the baseline time (time of baseline measurement before administration) in the 'Load Data' tab. Your data will be adjusted accordingly. Review the data preview and visualization to ensure it looks correct. Check the 'Data Visualization' tab to see how your pH curve appears.

3. Run Direct Estimation Method

   Go to the 'Direct Estimation' tab. Set the pH threshold, start time, and end time for your analysis. Click 'Run Direct Estimation' to calculate parameters directly from your data such as time under pH threshold and minimum pH.

4. Run Model Fitting

   Go to the 'Model-based Estimation' tab. Choose your estimation method (Non-Linear Fixed Effects or Non-Linear Mixed Effects), select parameter options, and configure any group or covariate effects. Click 'Run Model-based Estimation' to fit the KPD model to your data.

5. Run Model-based Method

   After successful model fitting, review the diagnostics in the 'Model Fit Diagnostics' tab (individual fits and observed vs predicted plots). Set your parameters (pH threshold, start/end times) and click 'Extract Parameters' to obtain model-based parameter estimates including EDK50, KDE, KD, and KS.

Model-based Estimation Details

GatorpH uses a KPD (Kinetic-Pharmacodynamic) model for model-based estimation of oral pH dynamics. The KPD model is a mathematical model that describes the relationship between drug kinetics and pharmacodynamic response. It is particularly useful for modeling situations where the substance exposure data is not available, which is mostly the case in oral pH dynamics. The KPD model includes parameters such as EDK50 (apparent potency of the drug at steady state), KDE (elimination rate constant from virtual compartment K), KD (response degradation rate constant), and KS (response synthesis rate). By fitting this model to oral pH data, GatorpH can estimate these parameters, which can provide insights into the efficacy of different substances or formulations in modulating oral pH. For better identifability of KPD model parameters, it is important to have a baseline measurement before administration (see below).

After successful model fitting, ensure decent model diagnostics (e.g., individual fits, observed vs predicted plots) to assess the quality of the fit and reliability of parameter estimates. It is recommended to start with a simple model without group effects and then iteratively add group effects one at a time to see if it improves model fit without causing convergence issues.

GatorpH support 2 methods for model-based estimation, either non-linear mixed effects model (NLME) or non-linear model (NL) (fixed effects only)

The NLME model is recommended when you have data from at least 2 subjects and want to account for inter-subject variability in parameter estimates, which tends to be more accurate. The NL model can be used for pooled analysis (not recommended) or when you have data from only 1 subject.

How to Prepare Data for GatorpH

To use GatorpH, prepare a CSV/Excel file with the following columns:

• id: Unique identifier for each subject
• time: Time of measurement in minutes. This should represent time after administration (TAD), with the time of administration being 0. If no measurements were taken immediately after administration, you can set the baseline time to a negative value (e.g., -5) to impute a baseline measurement at time 0.
• pH: Measured pH value at the corresponding time point
• group: Group identifier for each subject (e.g., treatment group, control group). This is used for stratification and group-based analysis.
• Optional columns:
  • baseline: Baseline pH value for each subject. If not provided, a default value of 7 will be used.
  • flowrate: Salivary flow rate at the corresponding time point (if available). This is to be supported in future versions
  • buffering: Salivary buffering capacity at the corresponding time point (if available). This is to be supported in future versions

Definitions and Abbreviations

TAD: Time after administration. In GatorpH, this represents the original time variable from the uploaded dataset before adjustment for baseline time. This is the time variable that should be used when calculating parameters directly from the data without adjusting for baseline time.
EDK50: Apparent potency of drug at steady state (dose unit/time). This parameter combines substance clearance and potency, and can be used to compare the overall efficacy of different substances or formulations in modulating oral pH. It can be used to compare the efficacy of different substances or formulations in modulating oral pH, with lower EDK50 values indicating higher potency to reduce oral pH.
KDE: Elimination rate constant from virtual compartment K (1/time)
KD: Response degradation rate constant (1/time)
KS: Response synthesis rate (pH unit/time)
Area<pH_X: Area under the pH curve below pH threshold X between specified time interval
T<pH_X: Time under the pH threshold X between specified time interval
pH_min: Minimum pH reached for corresponding subject or group
T_min: Time to reach minimum pH for corresponding subject or group

Contact

For further assistance, please reach out to Omar Elashkar at omar.elashkar@ufl.edu or Kyle Kasparian at kyle.kasparian@ufl.edu , and for correspondence, please contact Abhisheak Sharma at abhisheak.sharma@ufl.edu