Transitioning from Transcriptomics to Proteomics: Enhancing Mechanistic Accuracy in PBPK Modeling via Absolute Protein Abundances

ABSTRACT

Reliable physiologically based pharmacokinetic (PBPK) modeling depends on tissue‐specific expression profiles that reflect protein activities governing drug disposition. In PK‐Sim, existing expression databases rely on transcriptomics data. However, mRNA levels often exhibit limited correlation with protein abundance, frequently necessitating empirical expression modification to align bottom‐up simulations with clinical observations. To address this limitation, we developed ProteinDB as a proteomics‐based expression database for PK‐Sim, using proteomics data primarily from PaxDb v6.0. Raw proteomics data were mapped to gene identifiers and standardized into absolute concentrations (μmol/L tissue) before integration into PK‐Sim. Cross‐platform comparisons were performed for hepatic protein abundance across PBPK platforms, while cross‐omics comparisons were made of relative tissue distributions with transcriptomics‐based PK‐Sim databases. The performance of ProteinDB in PBPK modeling was evaluated using the probe substrates midazolam, digoxin, rifampicin, and tizanidine, with associated drug–drug interactions. Cross‐platform comparisons showed strong agreement for most hepatic enzymes and transporters, while revealing divergences for proteins with greater inter‐individual variability, lower abundance, or limited evidence base. Cross‐omics analyses demonstrated tissue‐dependent discrepancies between transcript‐ and protein‐based expression patterns, with higher consistency observed for kidney and small intestine, particularly with the RT‐PCR and Bgee databases. For PBPK modeling, ProteinDB showed consistently comparable or superior predictive performance for systemic exposure and other clinical endpoints compared with transcriptomics‐based baseline and empirically modified library profiles. By providing a direct physiological basis for system parameterization, ProteinDB offers a robust alternative to current transcriptomics PK‐Sim databases and reduces the reliance on empirical expression modification, thus improving the reliability of prospective PBPK modeling.