Integration of Clinical Pharmacokinetic Database With Compartmental Modeling and Regulatory Data Analysis

Computational pharmacokinetics increasingly relies on real-world clinical data rather than theoretical simulations. This study integrated authentic patient measurements from PK-DB clinical trials with FDA regulatory databases to characterize drug disposition patterns. Methods: Concentration-time profiles from 75 patients receiving caffeine (100-400 mg, n = 37) or paracetamol (500-1000 mg, n = 38) were analyzed using noncompartmental and one-compartment modeling approaches. FDA regulatory API queries provided contextual regulatory information. Parameters were estimated by a nonlinear least squares optimization of actual clinical measurements.

Caffeine exhibited dose-linear kinetics with apparent oral clearance of 2.96 L/h and a 4.0-hour elimination half-life. Paracetamol's elimination was faster (t½ 1.8 h) with an apparent oral clearance of 14.29 L/h. One-compartment models fit patient data excellently (R² > 0.98; AAFE 1.08-1.50 for all doses). The correlation between dose and AUC reached an impressive r = 0.9997 for caffeine, which was a confirmation of linear pharmacokinetics. The parameters derived from the models were in agreement with published population studies; thus, the approach was validated.

The combination of curated clinical databases along with empirical compartmental modeling leads to the derivation of pharmacokinetically interpretable parameters that are based on actual measurements. This framework allows for evidence-based dose optimization and is applicable to more than 150 compounds in PK-DB, thus providing a significant advantage over simulation-dependent methodologies in translational pharmacometrics.