How effective are ionization state-based QSPKR models at predicting pharmacokinetic parameters in humans?

Area of Science:

• Pharmacology
• Computational Chemistry
• Drug Development

Background:

• Optimizing drug pharmacokinetics (PK) for oral dosing is crucial but challenging.
• Traditional PK estimation methods are time-consuming and expensive.
• Quantitative structure-pharmacokinetic relationship (QSPKR) models offer an in silico alternative.

Purpose of the Study:

• To develop predictive QSPKR models for human PK parameters using an ionization state-based strategy.
• To investigate the impact of molecular ionization state on PK modeling.
• To build models for plasma clearance (CL), steady-state volume of distribution (VDss), and half-life (t1/2).

Main Methods:

• Clustering a drug dataset based on ionization state at physiological pH.
• Developing global and ion subset-based QSPKR models using 'EigenValue ANalySis' and support vector machine.
• Accounting for stereospecificity in drug disposition.

Main Results:

• Categorizing compounds by ionization state did not improve QSPKR model performance.
• Narrow endpoint ranges and data redundancy negatively impacted ion subset-based models.
• The ionization state-based strategy was not superior to global modeling.

Conclusions:

• Ionization state-based QSPKR models are not effective for improving PK prediction.
• Alternative strategies like elimination route-based models for CL and chemotype-specific QSPKR for VDss are recommended.
• Further research should explore drug-transporter interactions and chemotype-specific modeling.