Physiologically based pharmacokinetics modeling and transporter proteomics to predict systemic and local liver and muscle disposition of statins.
Luna Prieto GarciaAnna VildhedePär NordellChristine AhlströmAhmed B MontaserTetsuya TerasakiHans LennernäsErik SjögrenPublished in: CPT: pharmacometrics & systems pharmacology (2024)
Statins are used to reduce liver cholesterol levels but also carry a dose-related risk of skeletal muscle toxicity. Concentrations of statins in plasma are often used to assess efficacy and safety, but because statins are substrates of membrane transporters that are present in diverse tissues, local differences in intracellular tissue concentrations cannot be ruled out. Thus, plasma concentration may not be an adequate indicator of efficacy and toxicity. To bridge this gap, we used physiologically based pharmacokinetic (PBPK) modeling to predict intracellular concentrations of statins. Quantitative data on transporter clearance were scaled from in vitro to in vivo conditions by integrating targeted proteomics and transporter kinetics data. The developed PBPK models, informed by proteomics, suggested that organic anion-transporting polypeptide 2B1 (OATP2B1) and multidrug resistance-associated protein 1 (MRP1) play a pivotal role in the distribution of statins in muscle. Using these PBPK models, we were able to predict the impact of alterations in transporter function due to genotype or drug-drug interactions on statin systemic concentrations and exposure in liver and muscle. These results underscore the potential of proteomics-guided PBPK modeling to scale transporter clearance from in vitro data to real-world implications. It is important to evaluate the role of drug transporters when predicting tissue exposure associated with on- and off-target effects.
Keyphrases
- cardiovascular disease
- skeletal muscle
- mass spectrometry
- electronic health record
- label free
- big data
- oxidative stress
- insulin resistance
- type diabetes
- gene expression
- reactive oxygen species
- drug delivery
- machine learning
- cancer therapy
- ionic liquid
- adipose tissue
- adverse drug
- low density lipoprotein
- oxide nanoparticles