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

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.