Abstract | HTTK Version | Prepare R to run the vignette | Clear the memory | eval = execute.vignette | Load the relevant libraries | Control run speed | Examples | Making a table | Plotting example | Suppressing Messages | TK Study Summary Statistics | Steady-state Plasma Concentration | Using more sophisticated Monte Carlo | Controlling the random number generator | IVIVE with HTTK | Monte Carlo Simulation of $C_{ss}$ | Solve the full TK time-course | Examine $C_{ss}$ | Adding a tissue | Adding a species | Export functions | Days to steady state histogram | Average vs. maximum concentration

Abstract | HTTK Version | Prepare for session | Clear the memory | eval = execute.vignette | Load the relevant libraries | Set up subpopulation specs | Generate populations

Abstract | HTTK Version | Prepare for session | Clear the memory | eval = execute.vignette | Load the relevant libraries

Abstract | HTTK Version | Prepare for session | Clear the memory | eval = execute.vignette | Load the relevant libraries | Plot functions | Figure Three | Figure Four | Figure Five | Figure Six | Figure Seven | Figure Eight | Figure Nine | Figure Ten | Figure Eleven

Abstract | HTTK Version | Prepare for session | Clear the memory | eval = execute.vignette | Load the relevant libraries | Load the data | Use HTTK to perform IVIVE | This loops through each study design in the table and runs solve_pbtk: | Make the plot

Abstract | HTTK Version | Prepare for session | Clear the memory | eval = execute.vignette | Load the relevant libraries | Updated Armitage 2014 Top Panel Figure 3 | Plot results

Abstract | HTTK Version | Prepare for session | Clear the memory | eval = execute.vignette | Load the relevant libraries | Define some needed code: | RMSE and $R^2$ | Multiple plot function | Calculates the $C_{ss}$ using uncertainty only: | This calculates the half-life using uncertainty only: | Figure 1: Histograms depicting the measured values | Figure 2: Distribution of Binding Affinities Employed in Bayesian $f_{up}$ Estimations. | Figure 3: Comparison of Bayesian $f_{up}$ and Uncertainty Estimations to Experimental Point | Figure 4: Comparison of Bayesian $Cl_{int}$ and Uncertainty Estimations to Experimental Point Estimates. | Figure 5: Uncertainty Assessment for TK Quantities half-life and $C_{ss}$. | Figure 6: Relative contributions of uncertainty and variability | Figure 7: High throughput risk-based prioritization | Final statistics:

Abstract | HTTK Version | Prepare for session | Clear the memory | eval = execute.vignette | Load the relevant libraries | Get metabolism and concentration data | ANALYSIS | Data summary for chemical properties | Exposure scenarios | Observations and Predictions | Write out the study-level figures: | Finish simulated concentration/time plots | Regressions | TABLE AND PLOT GENERATION | Figure 2: overall observed vs. predicted plot | Create and read out plots of overall cvt, cmax, and auc observed vs. pred | Figure 4: Cmax and AUC observed vs. Predicted Values | Figure 3: Separation by chemical class | Figures S1A-S1D: Separation by time quartile and physicochemical properties | Supplemental Table 2: Leave-one-out Chemical Sensitivity Analysis | Supplemental Table 1

Abstract | HTTK Version | Prepare for session | Clear the memory | eval = execute.vignette | Load the relevant libraries | Prepare custom functions | Number of chemicals | Aylward cord-blood data | Load/format the data | Convert the units: | Compare HTTK predictions with maternal:fetal observations from Aylward 2014 | Aylward Ratio figures: | Clean up repeated observations: | Cord Blood Ratio Predictions without Fup Adjustement: | Generate text for results section: | Cord Blood Ratio Predictions with Fup Adjustement: | Examine performance when excluding certain chemical classes: | Evaluation of Area Under the Curve Predictions | Predict AUC | Generate AUC Predicted vs. Observed Figure | Evaluation of Partition Coefficient Predictions | Predict partition coefficients | Create Observed vs. Predicted Plot | First indicate compound: | Then indicate tissues: | Compare httk partition coefficients with PKsim: | Chemical Prioritization on the Basis of Fetal Brain Concentration | Make predictions for fetal:maternal plasma ratio using three daily doses from week 13 to full term | Create final table holding all predictions for paper: | Error in fetal:maternal ratio (inverse of maternal:fetal ratio): | brain:plasma partitioning | brain:plasma uncertainty partitioning | Combine different ratio predicitons together for figure | Make prioritization figure: | Look at ionization state: | Evaluate any value added by PBPK model over just using partition coefficients: | Repeat analysis without fetal protein binding change: | Protein Binding Figures | Plot the fetal protein binding changes predicted: | Maternal-Fetal Predictions across HTTK Chemical Library: | Histogram of maternal-fetal ratio | Statistics on maternal-fetal ratio for full HTTK library | Examine Maternal-Fetal ratio Predictions without fetaul Fup correction: | Supplemental Histogram of maternal-fetal ratio without Fup correction | Statistics on maternal-fetal ratio for full HTTK library without Fup correction | Code used to create data distributed with vignette

Overview | HTTK Version | eval = execute.vignette | Data Import | Load libraries | IVIVE using 3-compartment steady state model | Examine Chemicals | In silico predictions of TK parameters | Examine missing chemicals | Predicting AEDs based on Css50 (in plasma) | Load ExpoCast predictions (SEEM3) | IVIVE using the Full Human Gestational Model | Compute half-lives | Simulate full-term human pregnancy model | Calculate AEDs by Target and Tissue | Figure 9: Priority Chemicals based on maternal-fetal BERs | Comparing IVIVE models based on BER prioritization | Table 8 | Figure 10: Understanding and Interpreting Maternal-Fetal BERs | Code used to create data distributed with vignette

Abstract | HTTK Version | Prepare for session | Session Preparation | Clear the memory | eval = execute.vignette | Load the relevant libraries | Function to calculate root mean squared log10 error | Analysis of Crizer et al. (2023) $Cl_{int}$ data | Methods | Evaluate Ratio Blood:Plasma Predictions | Evaluate Membrane Affinity Predictions | New In Vitro TK Parameters | Examine Kbloodair | HTTK Interspecies Clearance Differences | Remove Rat-specific HTTK data: | Add bioavailability predictions | Compare default httk against in vivo clearance data: | Add in Dawson 2023 predictions for later | Calculate human:species clearance ratios | Add non-PFAS clearances from literature | Evaluating Machine Learning Model for PFAS TK | New HTTK Model for PFAS | Text for manuscript | Other Analyses

Abstract | HTTK Version | Table of Contents (added by AG 01/02/2024) | Prepare for session | eval = FALSE | Set working directory and install packages | Result 1: Model Verification using In Vivo Data | Step One: Load Data | Display which chemicals are being looked at | Step Two: Clean and Format Data | Step Three: Simulate Data | Simulation Set-Up | Simulations: Nested For Loops | Step Four: Calculate Root Mean Squared Log10 Errors (RMSLE) | Step Five: Plot Simulations | Results 2: IVIVE | Step Two: Load Chemical Data | Step Three: Clean Data (Only Non-Volatiles and a few of the Semi-Volatiles from the validation study) | Step Four: Compute Q10.AC50 (and other parameters of interest) | Step Five: Conduct In Vitro to In Vivo Extrapolation | Save data | Step Six: Plot Results

Abstract | HTTK Version | Prepare for session | Clear the memory | eval = execute.vignette | Load the relevant libraries | Data Collection | Load and format the data | Run the in vitro distribution models | Manipulate data and prep for plotting | Main Paper Plots: | Figure 1: | Figure 2: | Figure 3: | Figure 4: | Figure 5: | Main Paper Calculations: | Over/underestimation | Effect of water solubility value for Cyclosporin A | Effect of experimentally measured nominal dose | Table S1. | Table S2. In vitro assay descriptors for the measurements | Table S3: | Table S4: | Table S5: | Table S6. | Table S7: | Table S8: | Table S9: | Table S10: | Table S11: | Figure S1: