Utility of Quantitative Proteomics for Enhancing the Predictive Ability of Physiologically Based Pharmacokinetic Models Across Disease States

Abstract

Disease states such as liver cirrhosis and chronic kidney disease can lead to altered pharmacokinetics (PK) of drugs by influencing drug absorption, blood flow to organs, plasma protein binding, apparent volume of distribution, and drug-metabolizing enzyme and transporter (DMET) abundance. Narrow therapeutic index drugs are particularly vulnerable to undesired pharmacodynamics (PD) because of the changes in drug PK in disease states. However, systematic clinical evaluation of disease effect on drug PK and PD is not always possible because of the complexity or the cost of clinical studies. Physiologically based PK (PBPK) modeling is emerging as an alternate method to extrapolate drug PK from the healthy population to disease states. These models require information on the effect of disease condition on the activity or tissue abundance of DMET proteins. Although immunoquantification-based abundance data were available in the literature for a limited number of DMET proteins, the emergence of mass spectrometry-based quantitative proteomics as a sensitive, robust, and high-throughput tool has allowed a rapid increase in data availability on tissue DMET abundance in healthy versus disease states, especially in liver tissue. Here, we summarize these data including the available immunoquantification or mRNA levels of DMET proteins (healthy vs disease states) in extrahepatic tissue and discuss the potential applications of DMET abundance data in enhancing the capability of PBPK modeling in predicting drug disposition across disease states. Successful examples of PBPK modeling that integrate differences in DMET proteins between healthy and disease states are discussed.

Quantitative Proteomics of Hepatic Drug‐Metabolizing Enzymes and Transporters in Patients With Colorectal Cancer Metastasis

“…Quantitative proteomics provides useful abundance data for PBPK models in cancer and other disorders. 30 To our knowledge, this is the first comprehensive study on protein abundance of human liver DMEs and transporters in healthy, histologically normal adjacent to tumor and tumorous tissue, and their interindividual variation (as opposed to our previous pilot study in pooled samples). 23 The current analysis confirmed the general trends observed with pooled samples but, importantly, provided a range of protein abundance values and interindividual variability required for population-based predictions.…”

“…Quantitative proteomics provides useful abundance data for PBPK models in cancer and other disorders 30 . To our knowledge, this is the first comprehensive study on protein abundance of human liver DMEs and transporters in healthy, histologically normal adjacent to tumor and tumorous tissue, and their interindividual variation (as opposed to our previous pilot study in pooled samples) 23 .…”

Proteomic Quantification of Changes in Abundance of Drug-Metabolizing Enzymes and Drug Transporters in Human Liver Cirrhosis: Different Methods, Similar Outcomes

“…The ratios should be compatible with reverse translational modelling of drug kinetics and effects of co-morbidities as described previously (Rostami-Hodjegan, 2018). This relies on implementing the DPF into verified models in a healthy population rather than a fully bottom-up approach (Sharma, et al, 2020). Again, the Hi3 and iBAQ label-free methods were able to capture the disease perturbation in agreement with the targeted approach, with much less bias (lower AFE) and scatter in the data (lower AAFE) than the TPA.…”

Physiologically based pharmacokinetic modelling of acetaminophen in preterm neonates—The impact of metabolising enzyme ontogeny and reduced cardiac output

“…They used these data to develop PBPK models for midazolam, alfentanil, caffeine, ibuprofen, gentamicin and vancomycin in preterm neonates and successfully predicted their PK (Abduljalil et al, 2020b). Besides physiological parameters, PBPK modelling can also be used to explore the impact of pathophysiological changes, like critical illness (Paranjape et al, 2021), kidney or liver disease (Sharma et al, 2020), reduced cardiac output (CO) (Tylutki et al, 2019) of drugs. Such approaches have proven successful in adults but are only very rarely explored in (pre)term neonates with additional disease characteristics, like it was recently demonstrated in neonates undergoing therapeutic hypothermia following perinatal asphyxia (PA) (Smits et al, 2020).…”