Leveraging Mathematical Modeling to Quantify Pharmacokinetic and Pharmacodynamic Pathways: Equivalent Dose Metric

Area of Science:

• Pharmacology
• Mathematical Biology
• Biophysics

Background:

• Traditional treatment response assays use sigmoidal functions, limiting biological insight by conflating drug pharmacokinetic (PK) and pharmacodynamic (PD) properties.
• Current methods overlook the biophysical basis of drug action, hindering a deep understanding of treatment response.
• There is a need for methods that can quantitatively decouple and analyze PK/PD pathways.

Purpose of the Study:

• To develop and validate a mathematical modeling approach to decouple and quantify PK/PD pathways.
• To introduce a biophysically-based "equivalent dose metric" for a more accurate measure of drug effect.
• To demonstrate the utility of this metric in analyzing drug perturbations and guiding combination therapies.

Main Methods:

• Utilized mathematical modeling to separate PK and PD processes in cellular response to time-varying drug treatments.
• Experimentally modulated specific cellular pathways (e.g., drug efflux, DNA repair) using small molecule inhibitors.
• Filtered experimental results through mechanistic mathematical models to derive quantitative pathway measures and the "equivalent dose metric."

Main Results:

• Successfully decoupled and quantified PK/PD pathways using mathematical modeling and experimental interventions.
• Proposed and validated the "equivalent dose metric" as a biophysically-based measure of drug effect, defined as functional nuclear drug concentration.
• Quantified intracellular effects of small molecule inhibitors and compared treatment response across cell lines with varying drug efflux pump expression.

Conclusions:

• The proposed mathematical modeling approach and "equivalent dose metric" offer a biophysically-grounded method for analyzing treatment response.
• This approach provides quantitative measures of specific cellular pathways and drug effects, advancing biological insight.
• The framework can be broadly applied to quantify the impact of various pharmaceutical and biologic perturbations on treatment response.