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Predicting Drug Concentration-Time Profiles in Multiple CNS Compartments Using a Comprehensive Physiologically-Based
Yumi Yamamoto1, Pyry A Välitalo1, Dymphy R Huntjens2
1Division of Pharmacology, Cluster Systems Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands.
CPT: Pharmacometrics & Systems Pharmacology
|September 12, 2017
Summary
We created a physiologically based pharmacokinetic (PBPK) model to predict drug concentrations in the central nervous system (CNS). This tool aids in overcoming challenges in CNS drug development by improving concentration predictability.
Area of Science:
- Pharmacokinetics and Drug Metabolism
- Neuroscience and Neuropharmacology
- Computational Biology and Cheminformatics
Background:
- Central nervous system (CNS) drug development faces significant hurdles due to difficulties in predicting drug concentrations within various brain compartments.
- Accurate prediction of drug distribution is crucial for optimizing therapeutic efficacy and minimizing off-target effects in CNS treatments.
Purpose of the Study:
- To develop and validate a generic physiologically based pharmacokinetic (PBPK) model for predicting drug concentrations in key CNS compartments.
- To enhance the efficiency of CNS drug development by providing reliable temporal concentration profile predictions.
Main Methods:
- A generic PBPK model was constructed using system-specific and drug-specific parameters sourced from literature and in silico predictions.
- Model validation involved analyzing concentration-time data for 10 diverse small molecule drugs across rat plasma, brain extracellular fluid, cerebrospinal fluid, and total brain tissue.
Main Results:
- The PBPK model demonstrated adequate prediction capabilities for drug concentration-time profiles across multiple CNS compartments.
- The symmetric mean absolute percentage error for model predictions across all tested drugs and compartments was below 91%.
Conclusions:
- The developed PBPK model serves as a valuable tool for predicting drug concentrations within relevant CNS compartments.
- This predictive capability is expected to streamline and improve the efficiency of the CNS drug development process.
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