OSP Suite Fact Sheet

Main modeling and simulation features:

• PBPK modeling of small molecules and biologics

• Species Extrapolation / First in Human dose prediction

• Parent-Metabolite Studies / Drug-Drug-Interaction

• Pediatric Study Design – PIP/PDP support

• Special Populations: Hepatic/Renal impairment / Obese / Elderly / (Pre)term neonates / Children / Pregnant women / more

• Formulations / Meal effects

• PBPK/PD, QSP as well as pathway, network and disease modeling

Model building blocks
Organisms	Pre-parameterized whole-body PBPK models including detailed integrated GI tract for Human Monkey Dog (beagle and mongrale) Minipig Rat Mouse Rabbit Allowing for full flexibility for parameterization of (anthropo)metrics, anatomical and physiological properties, protein expression levels ETC. Most important organs included. For each organ optional processes can be added: Metabolizing pathways Different active transporter types(influx, efflux, Pgp-like) Protein binding partners Biliary tract included, enables enterohepatic cycling Scaling of Individuals Scaling can be used to change the biometrics of an existing individual, i.e. an adult model may be scaled to an infant model while maintaining/scaling all specific modifications
Populations	Database for population simulations with distributions of anatomical and physiological parameters for European Caucasians (ICRP, 2002) US Caucasian (NHANES, 1997) US Asians (NHANES, 1997) US Africans (NHANES, 1997) Asian (Tanaka, 1996) Japanese (2015) Preterms (2015) Pregnant (Dallmann et al. 2017)
Protein Expression	The PK-Sim® library includes large-scale gene-expression data from publicly available sources which were downloaded, processed, stored and customized such that they can be directly utilized in PBPK model building. Public database which were imported are Whole genome expression arrays from ArrayExpress (European Informatics Institute, 2010, http://www.ebi.ac.uk/microarray-as/ae/) RT-PCR derived gene expression (Nishimura et al., 2003; Nishimura and Naito, 2005, 2006) Expressed sequence tags (EST) from UniGene (National Center for Biotechnology Information, 2010, http://www.ncbi.nlm.nih.gov/unigene).
Compounds	Full ADME characterization of drugs including Molecular weight Lipophilicity Protein binding Acid/base pKa Solubility Intestinal permeability Specific protein binding kinetics Enzyme specific metabolization kinetics Transporter specific transport kinetics Inhibition and induction parameters and for large therapeutic molecules (e.g. antibodies) Solute radius (calculated for molecular weight as per default) Dissociation constant for binding to FcRn Including a set of pre-parameterized standard compounds
Partition Coefficients	Prediction models for tissue partition coefficients PK-Sim 2003 Rodgers & Rowland Schmitt Poulin & Theil Berezhkovsky
Permeability	Prediction models for cellular permeabilities and intestinal permeability
Formulations	Dissolved Particle distribution Weibull Lint80 Table 1st order Zero order
Administration protocols	Administration routes: IV (Bolus and Infusion) Oral User defined (free choice of target organ/compartment) Administration Schemes: Single once daily, bi-daily, … complex (multi-)periodic schemes
Events	Meals Gallbladder emptying
Modeling tools
Parameter identification (PI)	A fully integrated PI Toolbox provides a straightforward means to adjust key model parameters automatically within user-defined ranges. It is possible to optimize multiple simulations, for example with different dose levels, and multiple observed data sets, simultaneously. A clear visualization of the optimization process and of the optimization results gives you full control and direct feedback whether the identification process was successful. Simultaneous optimization of multiple simulations Simultaneous optimization of multiple observed data sets LLOQ (Lower Limit of Quantification) values are taken into account Linking of multiple simulation parameters to one identification parameter (as absolute value or as a factor) Lin/Log scaling of identification parameters Lin/Log scaling of residuals Multiple optimizations with randomized start values Combining parameter identification with optimization for best suited partition coefficients/permeability methods Available optimization algorithms: Nelder-Mead Levenberg-Marquardt Monte-Carlo Visual feedback during optimization Time profile Predicted vs. Observed Error history: Total error vs number of evaluations Total error: current/best value Identification parameters: current/best value Export of parameters history to MS-Excel Visualization of optimization results Time profile Predicted vs. Observed Residuals vs. Time Histogram of Residuals Total error Number of evaluations Identification parameters: min/max/start/best value Warning if best values are "close to" boundaries Easy cloning of PI configuration within a project Replacing simulations in PI configuration without losing the settings Update simulations with optimized parameter values Export of PI to Matlab (optimization problem can be run in Matlab using any built-in algorithm) Calculation of time profile confidence intervals Confidence Interval: Corresponds to the model error, which is based on the uncertainty of estimated parameters. This uncertainty is based on an estimation of the difference between the mean value of used observed data compared with the mean value of the (unknown) total data. Visual Predictive Check Interval: Corresponds to the uncertainty based on the data error. The data error is the standard deviation of the distribution of the used observed data. Prediction Interval: Corresponds to the combination of the model error and the data error. It shows how much future measured data are expected to differ from the model predictions.
Sensitivity Analysis	Sensitivity of PK-Parameters (AUC, CMax, …) vs. simulation parameters. Because PBPK models can be complex and contain numerous input parameters, it would be useful to know which input parameters have the most impact on the output curves. The Sensitivity Analysis tool provides an answer to this question. For a chosen simulation, the relative impact of selected - or all - input parameters on the PK parameters of the output curves is calculated and displayed. In addition, the input parameters can be ranked by their impact on a certain PK parameter of an output. Results of Sensitivity Analysis can be shown as: Sensitivity table: Ranking of most sensitive simulation parameters. Most sensitive parameters comprise all parameters that contribute to 90% of total sensitivity.
Lab Journal	Automated documentation of modeling work in model history working journal documenting including labeling and commenting function Built-in working journal for manual annotation of models and simulations Roll-back / undo functionality
Model Editor	Full transparency and full edit access to all structural model properties
Simulation Tools
	Simulation creation by simple combining of previously defined building blocks Simulation of individuals and populations If a human individual or population is selected the growth of the human individual(s) during the simulation time will be taken into account when choosing this option. Based on the human growth and maturation functions available for most parameters in PK-Sim® (e.g. organ volumes, blood flow rates, organ composition, etc.) the parameters are updated along the time scale of the simulation. This is important for multiple drug administration to e.g. preterm and term neonates, for which the rapid changes in anatomical and physiological properties can influence the pharmacokinetics during the simulated study circle. Calculation of drug time courses in the most important organs for every subcompartment (Plasma, Endosome, Interstitial, Intracellular, Blood Cells) Calculation of the fraction of dose metabolized/excreted Plotting of all calculated time courses Plot settings (axes, styles, etc.) Individual simulations: Time profile plots Population simulations Time profile plots Box-Whisker plots Range plots Scatter plots Multiple plots per simulation Export of plotted/simulated results to Excel/CSV/PDF/Image Calculation of the most important PK-Parameters In all simulations AUC_tEnd AUC_inf %AUC(tlast-inf) AUC_tEnd_norm AUC_inf_norm AUC Ratio (AUCR) C_max C_max_norm C_max Ratio (Cmax_R) C_tEnd t_max Half-Life MRT In simulations with intravenous administration VSS(plasma) Vd(plasma) Vss(phys-chem) Total plasma clearance CL Total body clearance In simulations with oral administration Vss(plasma)/F Vd(plasma)/F Total plasma clearance/F Fraction absorbed Bioavailability In simulations with multiple administrations AUC_inf_tD1 AUC_inf_tD1_n ...tDi-tDj ...tDlast-tDEnd ...tDlast-1- tDlast C_trough_dDi C_trough_dlast Comparisons of calculated simulation results over multiple simulations (both individual and population simulations) Cloning of simulation Replacing of Building Blocks in already created simulations Synchronization between a building block used to create a simulation and the simulation Comparison between simulations Comparison between building blocks and simulations Comparison of building blocks can also be done between two simulations on the same kind of building block
Data
Data import	Import filters for MS Excel csv file Nonmem files
Model import	Import of SBML models

MoBi
Editing	Editing of PK-Sim simulations to the detail of all parameters, structural elements, transports, reactions, events, and more.
	Adding features to PK-Sim models, like tumors, complex molecular interactions, or non-standard drug applications
	Display and editing of a simulation as tree or diagram
Comparing	Result comparison charts
	Simulation and building block comparison, exportable list of differences
Merging	Merging of building blocks from different simulations
Simulating	Parameter identification and sensitivity analysis
	Re-sending simulations back to PK-Sim for population simulation
Documentation	Integrated working journal, sharable with PK-Sim, for documentation
	Automatic tracking of changes made in a history log file
Export	Export of simulated results as Excel file
	Various formats of model exports and listings, like XML, Excel
Import	Import of model parameters from Excel files
	Import of model files in SBML format for QSP model building
Building	Building models from scratch, like reaction pathways into a user-built spatial structure or for compartmental modeling
	Option to select frequently accessed parameters as favorites