Sensitivity analyses are important components of physiologically based pharmacokinetic (PBPK) model development and are required by regulatory agencies for PBPK submissions. They assess the impact of parametric uncertainty and variability on model estimates, aid model optimization by identifying parameters requiring calibration, and enable the testing of assumptions within PBPK models. One‐at‐a‐time (OAT) sensitivity analyses quantify the impact on a model output in response to changes in a single parameter while holding others fixed. Global sensitivity analysis (GSA) methods provide more comprehensive assessments by accounting for changes in all uncertain or variable parameters, though at a higher computational cost. This tutorial article presents a software package for conducting both OAT and GSA of PBPK models built in the Open Systems Pharmacology (OSP) Suite. The tool is accessible through either an R script or a graphical user interface, and the outputs consist of sensitivity metrics of pharmacokinetic (PK) parameters, such as C_max and AUC, evaluated with respect to model input parameters. Results are formatted according to regulatory standards. The OAT analysis methods comprise two‐way local sensitivity analyses and probabilistic uncertainty analyses, whereas the GSA methods include the Morris, Sobol, and EFAST methods. These analyses can be conducted on single PBPK models or pairs of models for the evaluation of the sensitivity of PK parameter ratios in drug–drug interaction studies. The practical application of the package is demonstrated through three illustrative case studies.

Sunscreen products constitute two distinct categories. Recreational sunscreens protect against high-intensity, episodic sun exposure, often applied over the entire body. In contrast, facial sunscreen products are designed for sub-erythemal, low-intensity daily sun exposure. Such different exposures necessitate distinctive product safety assessments. Building on earlier methods for predicting dermal disposition, a mechanistic model was developed to simulate plasma concentrations of seven organic sunscreen active ingredients: avobenzone, ensulizole, homosalate, octinoxate, octisalate, octocrylene, and oxybenzone, following facial application. In vitro permeation testing (IVPT) was performed with two different vehicles using a subset of the UV filters. These IVPT results, in addition to previously published IVPT data and published in vivo Maximal Usage Trial (MUsT) data for the UV filters, were used to train the mechanistic dermal model via a Bayesian Markov chain Monte Carlo (MCMC) method. An external validation of the trained model with real-world in vivo datasets demonstrated that the model's predicted UV filter plasma concentrations align well with experimental measurements and capture the observed inter-individual variability. Predictions of steady-state UV filter plasma concentrations under facial application scenarios at 5% concentration and at the maximal allowable concentrations were then generated by the trained model. Oxybenzone had the greatest predicted plasma concentration following facial application. Homosalate and octisalate predictions had high uncertainty associated with the absence of data. Several application scenarios pertaining to avobenzone, ensuilzole, octocrylene and octinoxate were identified in which median plasma concentration levels were at 0.5 ng/ml or below when applied in the recreational or facial product. Model limitations include uncertainty in vehicle/water partitioning, formulation metamorphosis, and UV filter systemic clearance, all of which can be refined with additional data. For UV filters, limiting exposure to facial application reduces human safety concerns based on FDA established thresholds.

Background: The development of a successful COVID-19 control strategy requires a thorough understanding of the trends in geographic and demographic distributions of disease burden. In terms of the estimation of the population prevalence, this includes the crucial process of unravelling the number of patients who remain undiagnosed. Objective: This study estimates the period prevalence of COVID-19 between March 1, 2020, and November 30, 2020, and the proportion of the infected population that remained undiagnosed in the Canadian provinces of Quebec, Ontario, Alberta, and British Columbia. Methods: A model-based mathematical framework based on a disease progression and transmission model was developed to estimate the historical prevalence of COVID-19 using provincial-level statistics reporting seroprevalence, diagnoses, and deaths resulting from COVID-19. The framework was applied to three different age cohorts (< 30; 30-69; and ≥70 years) in each of the provinces studied. Results: The estimates of COVID-19 period prevalence between March 1, 2020, and November 30, 2020, were 4.73% (95% CI 4.42%-4.99%) for Quebec, 2.88% (95% CI 2.75%-3.02%) for Ontario, 3.27% (95% CI 2.72%-3.70%) for Alberta, and 2.95% (95% CI 2.77%-3.15%) for British Columbia. Among the cohorts considered in this study, the estimated total number of infections ranged from 2-fold the number of diagnoses (among Quebecers, aged ≥70 years: 26,476/53,549, 49.44%) to 6-fold the number of diagnoses (among British Columbians aged ≥70 years: 3108/18,147, 17.12%). Conclusions: Our estimates indicate that a high proportion of the population infected between March 1 and November 30, 2020, remained undiagnosed. Knowledge of COVID-19 period prevalence and the undiagnosed population can provide vital evidence that policy makers can consider when planning COVID-19 control interventions and vaccination programs.

Systemic disposition of dermally applied chemicals is often formulation-dependent. Rapid evaporation of the vehicle can result in crystallization of active compounds, limiting their degree of skin penetration. In addition, the choice of vehicle can affect the permeant’s degree of penetration into the stratum corneum. The aim of this study is to build a predictive, mechanistic, dermal absorption model that accounts for vehicle-specific effects on the kinetics of permeant transport into skin. An existing skin penetration model is extended to explicitly include the effect of vehicle volatility over time. Using in vitro measurements of skin penetration by chemicals applied in both a saline and an ethanol solvent, the model is optimized to learn two vehicle-specific quantities: the solvent evaporation rate and the extent of permeant deposition into the upper stratum corneum immediately following application. The dermal disposition estimates of the trained model are subsequently compared against those of the original model using further in vitro measurements. The trained model showed a 1.5-fold improvement and a 19-fold improvement in overall goodness of fit among compounds tested in saline and ethanol solvents, respectively. The proposed model structure can thus form a basis for in vitro to in vivo extrapolations of dermal disposition for skin formulations containing volatile components.

The higher skin surface area to body weight ratio in children and the prematurity of skin in neonates may lead to higher chemical exposure as compared to adults. The objectives of this study were: (i) to provide a comprehensive review of the age-dependent anatomical and physiological changes in pediatric skin, and (ii) to construct and evaluate an age-dependent pediatric dermal absorption model. A comprehensive review was conducted to gather data quantifying the differences in the anatomy and physiology of child and adult skin. Maturation functions were developed for model parameters that were found to be age-dependent. A pediatric dermal absorption model was constructed by updating a MoBi implementation of the Dancik et al. 2013 skin permeation model with these maturation functions. Using a workflow for adult-to-child model extrapolation, the predictive performance of the model was evaluated by comparing its predicted rates of flux of diamorphine, phenobarbital and buprenorphine against experimental observations using neonatal skin. For diamorphine and phenobarbital, the model provided reasonable predictions. The ratios of predicted:observed flux in neonates for diamorphine ranged from 0.55 to 1.40. For phenobarbital, the ratios ranged from 0.93 to 1.26. For buprenorphine, the model showed acceptable predictive performance. Overall, the physiologically based pediatric dermal absorption model demonstrated satisfactory prediction accuracy. The prediction of dermal absorption in neonates using a model-based approach will be useful for both drug development and human health risk assessment.