ADME Workbench is a versatile software application providing flexible, robust pharmacokinetic modeling by integrating state-of-the-art absorption, distribution, metabolism and excretion methods. Designed for research applications in toxicology, pharmacology and biotechnology, ADME Workbench supports pharmacokinetic prediction from in vitro and/or in vivo data for drugs and environmental chemicals. The ADME Workbench user interface offers a highly optimized workflow for predictive pharmacokinetics, while allowing ample flexibility to adapt to specific research needs. The pharmacokinetic models used in ADME Workbench are based on research resulting from the PhRMA CPCDC Initiative on Predictive Models of Human Pharmacokinetics, and described in a series of articles published in the Journal of Pharmaceutical Sciences in 2011. Extension of the models and implementation in ADME Workbench has resulted from an ongoing scientific collaboration between Dr. Patrick Poulin and AEgis Technologies.
ADME Workbench assembles a number of state-of-the-art methods for PK prediction. This collection of methods allows a wide variety of data to be used for PK prediction. These methods are configured and data is specified in an intuitive user interface in which compound and PK data is entered using a familiar spreadsheet format. Predictions for compounds may be run in either single- or batch-mode. All models and scripts are delivered with the product, enabling you to inspect and modify the models to suit your particular research needs.
Current ADME Workbench capabilities include:
- Support for several state-of-the-art PK prediction techniques, including whole-body and lumped physiologically-based pharmacokinetic (PBPK) models, and allometry based on the Wajima approach
- PBPK models which include advanced GI uptake models (ACAT), support for both linear and nonlinear kinetics, and an advanced liver model
- Support for end-user customization of any of the provided PBPK models
- A choice of prediction methods depending of the availability of preclinical data either determined in vitro and/or in vivo in single or multiple species
- Ability to integrate values of ADME parameters predicted from a broad range of methods
- Ability to simulate a single compound or multiple compounds in batch mode
- Simulation of tissue concentration at the macro level (whole organ) and micro level (cell, interstitial fluid) either for the bound or unbound drug condition
AEgis Technologies provides PBPK modeling and computational biology consulting services to commercial, government, academic and nonprofit organizations throughout the world.
Among the PBPK modeling and analysis services we offer:
Model identification, evaluation and preparation
- Literature reviews to identify published models, in vivo data, or chemical-specific parameters
- Evaluation of suitability of a particular model for use in a specific study
- Migration of PBPK model code between various simulation tools: acslX/ACSL, Berkeley Madonna, MATLAB, MCsim
- Correction of programming errors and deficiencies; conformance to best practices
- Performance optimization of PBPK model code
Model verification and validation
- Verification of model equations against previously published model or published equations
- Dimensional analysis of model quantities
- Model semantic checks in accordance with the simulation tool being used (e.g., multiply defined variable, uninitialized variables)
- Verification of model parameters against published data sources
- Verification of parameters which are estimated by fitting to data
- Implementation of mass balance checks, parameter renormalization for MC, GSA, or MCMC studies
- Addition of new exposure routes, tissue compartments, metabolic pathways, etc to existing PBPK models
- Expansion of single-chemical PBPK models to support multiple chemicals (mixtures, interactions)
- Addition of pharmacodynamic models to PBPK models
- Development of statistical models for Bayesian (MCMC) estimation of model parameters
- Implementation of scripts to execute parameter estimation, sensitivity analysis, Monte-Carlo analysis
- Development of scripts to automate data visualization and presentation
- Development of scripts to allow computationally demanding analyses to be run in parallel on cluster systems
- Preparation, documentation and archival of model code and results of analyses
Summaries of some of our recent projects:
Computational modeling of paclitaxel diffusion into arterial tissue. Developed a finite-element model of paclitaxel diffusion from a drug-coated balloon catheter into surrounding arterial tissue for prevention of restenosis. Model was developed in COMSOL and accurately explained paclitaxel diffusion and residence in deep tissue layers.
Modification of existing VOC PBPK models to include micturition and multiple exposure routes. Existing VOC PBPK models were extended to include representation of chemical elimination through renal clearance and subsequent appearance in urine. Concentration in urine was modeled by taking urine production and bladder elimination schedule into account for purposes of dose inference. Models were also extended to support additional exposure routes.
Development and analysis of PBPK model for predicting nicotine kinetics. A published PBPK model of nicotine and cotinine kinetics was extended by adding exposure routes, tissue compartments and a PD model. Model parameters were estimated from published data using Bayesian/MCMC techniques. Model accurately predicts nicotine kinetics for exposure via IV, PO (gum), oral spray and smoking routes. PD model accurately predicts nicotine effects on heart rate, including tolerance effects. Sensitivity analysis and variability analysis were performed to determine confidence in parameter estimates.
Development of a platform for human health risk assessment of engineered nanomaterials. An proof-of-concept integrated platform for risk assessment of engineered nanomaterials was developed by integrating in vitro dosimetry, dose-response model, and inhaled deposition models. Resulting platform can produce hazard ranking of inhaled nanoparticle exposure by 1) estimating delivered in vitro dose to cells by accounting for diffusion and sedimentation effects; 2) producing BDM estimates of safe cellular exposure using the dose information computed in (1); and 3) extrapolating the safe cellular exposure to a corresponding equivalent human exposure by using an inhaled particle deposition dosimetry model.
PBPK modeling and population calibration of organophosphates (chlorpyrifos) PBPK model. A whole-body PBPK/PD model for ADME of organophosphate pesticides was extended and calibrated using data collected from Egyptian agriculture workers in 2010. Modeling efforts included addition of improved exposure and metabolism models. Sensitivity and uncertainty analysis of the model were performed to study population variability.
Model development and support for PBPK model of polychlorinated biphenyls and related breast cancer correlation study. Collaborated with subject matter experts to reconstruct lifetime toxicokinetic PCB profiles using PBPK models and completed a large-scale Monte-Carlo based study based on 2134 individuals. Developed framework to leverage large CLUMEQ cluster to significantly reduce runtime of massive Monte Carlo study.
Development of a probabilistic (Bayesian) approach to infer internal exposures from biomarkers and calculate biological limit values for lipophilic volatile organic compounds Developed a generic population PBPK model for volatile organic compounds (VOCs) in end-exhaled air. Model was calibrated using MCMC techniques and human data from controlled exposures. MCMC chains from the calibrated compounds can be used to generate predictions for other VOCs.
Evaluation of techniques for identifying uncertainty and variability in dose reconstruction for carbaryl exposures using a PBPK model of carbaryl kinetics in humans. Conducted study comparing the effectiveness of several dose-reconstruction techniques for carbaryl. Performed configuration and execution of Carbaryl dose-reconstruction using MCMC and other methods on 500 individuals.
- A framework incorporating the impact of exposure scenarios and application conditions on risk assessment of chemicals applied to skin. Yuri Dancik, John A Troutman and Joanna Jaworska. In Silico Pharmacology (2013), 1:10
- Prediction and Evaluation of Route Dependent Dosimetry of BPA in Rats at Different Life Stages Using a Physiologically Based Pharmacokinetic Model. Yang X, Doerge DR, Fisher JW. Toxicol Appl Pharmacol. 2013 Apr 5
- Application of Bayesian Population PBPK Modeling and Markov Chain Monte Carlo Simulations to Pesticide Kinetics Studied in Protected Marine Mammals: DDT, DDE, DDD in Harbour Porpoises. Weijs L, Yang RS, Das K, Covaci A, Blust R. Environ Sci Technol. 2013 Apr 6.
- Toward a new paradigm for the efficient in vitro-in vivo extrapolation of metabolic clearance in humans from hepatocyte data. Poulin P, Haddad S. J Pharm Sci. 2013 Mar 12
- September 24, 2013: AEgis Technologies Announces the Official Launch of ADME WorkBenchSoftware for Pharmacokinetics Prediction
- February 23, 2013: Nanogenesis to Introduce ADME WorkBench at Annual Society of Toxicology Conference In San Antonio on March 10-14, 2013
- September 16, 2013: AEgis Technologies Announces Official Launch of ADME WorkBench Software for Pharmacokinetics Prediction
|September 17 & 19, 2013||ADME WorkBench Webinar - What's new in the Latest Release of ADME WorkBench Details and Registration Information here -> http://conta.cc/19mpICl|
|May 12-15, 2013||ADME WorkBench Staff Exhibiting at the American Conference on Pharmacometrics (ACoP) 2013 in Ft. Lauderdale, FL|
|May 7, 2013||ADME Workbench Webinar #1 - “Best practices in human PK prediction: which method should I use?” (An introduction to ADME WorkBench)|
|March 10-14, 2013||ADME WorkBench Staff Exhibiting at the 52nd Annual Society of Toxicology Annual Meeting in San Antonio, TX|
Technical support, downloads and examples
The ADMEwb Technical Support team is available to address any Questions or Concerns you may have through a number of channels.
- Electronic Copies of our Documentation are available on our Documentation Page
- Frequently Asked Questions, and Demonstrations are posted on our Support FAQ and Examples Page
All Other Questions or Comments
Active ADMEwb users, and anyone looking for more information in addition to what is posted online is encouraged to contact our support team directly:
- Via email: firstname.lastname@example.org
- Or Telephone: +1-905-409-0111