Simcyp Animal is a whole-body PBPK/PD modeling platform for rat, dog, monkey, and mouse. It is based on the Simcyp Human Simulator with simplified interfaces and well-validated models. Simulations with Simcyp Animal can help identify key data requirements and inform the design of subsequent experiments. It can also increase confidence in in vitro-in vivo extrapolation (IVIVE) before moving to human simulations.
- Model and verify pre-clinical species data to confirm assumptions prior to making FIH predictions
- Prediction of dose for desired plasma and tissue concentration – time profiles in animals and humans
- Simulate exposure for different formulations and routes of administration
- Assess pharmacokinetic – pharmacodynamics relationships
- Evaluate the effect of food on oral drug absorption and pharmacokinetics
- Predict concentration-time profiles in plasma, tissues and organs
- Investigate the formation and kinetics of primary metabolites
Simcyp Animal mechanistic models provide scientists with insights into conditions when in vitro experiments may not accurately reflect in vivo processes and help to dissect mechanistically the overall process of drug Absorption, Distribution, Metabolism and Elimination (ADME). These models can be used to answer ‘what if’ questions across multiple species as part of a translational drug package. Simcyp Animal is aligned with the ‘three Rs (3Rs)’ guiding principles for more ethical use of animals in testing.
- Increased accuracy: When used with the Simcyp Human Simulator, the Simcyp Animal Simulators allow prediction of human PK and PD using animal PBPK/PD extrapolation without the need to entirely rely upon allometric scaling approaches.
- Save time: Compound import and batch processing capabilities allow large numbers of compounds to be screened quickly and sensitivity analysis to be performed in a high throughput setup.
- Satisfy ethical imperatives: Simcyp Animal helps fulfil the ethical obligation towards the refinement, replacement, and reduction of in vivo studies in animals.
- 省钱： The virtual genetically modified mouse simulator allows you to investigate how adding or removing specific genes controlling drug metabolizing enzymes and transporters affects the ADME properties of a drug.
- Modelling of pre-clinical species and in vitro data to confirm assumptions prior to human predictions
- Simulating exposure for different formulations and different routes of administration
- Evaluate differences in oral drug absorption due to physiological differences across species gut wall with the unique mechanistic permeability (Mech Peff) model
- Predict pharmacokinetics (PK) and pharmacodynamics (PD) of drugs using for instance the virtual humanized chimeric and genetically modified mouse models
- Evaluate inter-subject PK-PD variability arising due to physiological variability in dogs and monkeys
- Prediction of PK of therapeutic proteins and monoclonal antibodies using Simcyp Monkey
- Verify the predictions from implemented models by using peer reviewed literature data for established reference drugs to enable confident predictions for new drug molecules
- Access to transparent algorithms, methods, and visual outputs through graphical interfaces
- Translation between species, accounting for physiology and anatomy differences and differences in metabolizing enzymes and transporters and plasma protein levels between preclinical species and humans.
Our ongoing CRADA with the FDA’s Center for Veterinary Medicine, which began in 2012, aims to utilize the Simcyp Dog Simulator to streamline veterinary drug product development and evaluation to improve the safety and efficacy of veterinary drugs for canines.
This collaboration investigates the effect of extrinsic and intrinsic factors in combination or independently on drug kinetics, such as effect of drug formulation and food on drug exposure in different dog breeds.
This facilitates using pharmacokinetic principles to address questions associated with designing and interpreting animal safety studies and clinical field studies. Knowledge obtained from the model predictions can be integrated with data from safety and effectiveness studies to develop product labels.