A rapid screening service for integrating in vitro ADME data and structural information.
Predictions are delivered in an Excel workbook containing the following worksheet tabs:
Predicted PK parameters and plasma profiles can be easily read into databases or modelling software for further manipulation. Plots can be immediately copied into presentations or documents, or formatted to suit specific presentation requirements.
Two options for using the service are available:
The models underlying the PK prediction service have been carefully designed and extensively validated in order to provide flexibility of use, and reliability of output.
The primary output of the service is a predicted plasma concentration-time profile for 24 hours following the end of dosing. These predicted profiles can be used directly for driving PK/PD predictions. The reliability of the predictions has been assessed using a test set of nine compounds with diverse PK properties. The results are shown in Table 1. The average discrepancy in prediction between predicted and measured plasma concentration is approximately 3-fold, whether using hepatocytes or microsomes.
PK parameters are calculated from the predicted plasma profiles using standard methods. These predicted PK parameters can be used for prioritising compounds for progression to further screens. The reliability of the predictions has been assessed using the same test set of nine compounds as used for the plasma profiles. The results are shown in Table 2. Clearance, half-life and volume of distribution are all predicted to within 2.3- to 3.2-fold, and show high rank-ordering of values, whether using hepatocytes or microsomes.
|Steady state volume of distribution||3.2||0.95||3.2||0.92|
|Elimination phase volume of distribution||3.1||0.73||3.0||0.85|
Please provide an overview of Cyprotex's human PK prediction service.
Cyprotex’s human PK prediction service is a combined in vitro/in silico offering that has been developed to provide reliable prediction of human pharmacokinetics from a minimal set of tier 1 ADME data, making it suitable for use in lead identification (LI) and lead optimisation (LO) stages of drug discovery. At its core is a proprietary in silico model that requires several descriptors calculated from a compound’s structure, as well as measurements or predictions of hepatic metabolic intrinsic clearance (CLint) and plasma protein binding (fup), in order to make compound-specific predictions of human PK for intravenous dose regimes.
How has the model been developed?
The model used in the PK prediction service is a physiologically based pharmacokinetic (PBPK) model. It has been developed using Cyprotex proprietary methodology, and provides unique, Cyprotex-specific predictions. The model has been trained by optimisation against a set of human in vivo plasma profiles (>80 diverse pharmaceuticals for hepatocytes; >120 for microsomes). The optimisation process generates datasets from which models for key compound-specific parameters were then developed by machine learning1 and incorporated into the overall PBPK model framework. This process of optimization followed by machine learning generates robust and interpretable models that provide, in turn, reliable PK predictions.
How has the model been validated?
A set of nine compounds, distinct from the training set, were selected for testing the model. These compounds were representative of the joint distribution of in vivo clearance and steady state volume of distribution (see Figure 1, below). Clearance varies from approximately 0.4 to 40 mL/min/kg, and volume of distribution from 0.1 to 100 L/kg. Selection of a diverse set in this manner is necessary in order to obtain an unbiased estimate of the performance of the model.
What is a PBPK model?
A PBPK model is a type of mathematical model that is designed to predict the PK, in plasma, blood and/or other tissues and organs, of a compound after administration to a person or animal2. A PBPK model is a form of simulation model, so named because the goal is to simulate the processes that occur in the time following some specified triggering event. In the specific case of a PBPK model, the triggering event is usually the start of a regime of one or more drugs, and the processes that are simulated are the distribution, metabolism and elimination of the drug(s), as well as gastrointestinal absorption for an oral dose. The processes are simulated over time following start of the dosing, which enables the familiar concentration-time profiles of drugs to be replicated.
The structures of simulation models can infinitely variable and, as examples of these, structures of PBPK models that have been developed are also highly variable. The defining characteristics common to most PBPK models are:
Compounds have compound-specific rates and extents of distribution into the different tissue/organs represented in the model, and different rates of hepatic and renal clearance. The values of these compound-specific parameters are calculated from the inputs to the service (i.e. hepatic CLint, fup and structural descriptors), and thus lead to compound-specific prediction of PK.
What are the benefits of using Cyprotex’s human PK prediction service?
1Krstajic D et al., (2014) Cross-validation pitfalls when selecting and assessing regression and classification models. J Cheminform 6; 10
2 Brightman FA et al., (2006) Application of a generic physiologically based pharmacokinetic model to the estimation of xenobiotic levels in human plasma. Drug Metab Dispos 34; 94-101
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