Classification of Inhibitors of Hepatic Organic Anion Transporting Polypeptides (OATPs): Influence of Protein Expression on Drug–Drug Interactions

ABSTRACT: The hepatic organic anion transporting poly-peptides (OATPs) influence the pharmacokinetics of several drug classes and are involved in many clinical drug−drug interactions. Predicting potential interactions with OATPs is, therefore, of value. Here, we developed in vitro and in silico models for identification and prediction of specific and general inhibitors of OATP1B1, OATP1B3, and OATP2B1. The maximal transport activity (MTA) of each OATP in human liver was predicted from transport kinetics and protein quantification. We then used MTA to predict the effects of a subset of inhibitors on atorvastatin uptake in vivo. Using a data set of 225 drug-like compounds, 91 OATP inhibitors were identified. In silico models indicated that lipophilicity and polar surface area are key molecular features of OATP inhibition. MTA predictions identified OATP1B1 and OATP1B3 as major determinants of atorvastatin uptake in vivo. The relative contributions to overall hepatic uptake varied with isoform specificities of the inhibitors

In vitro and in silico Predictions of Hepatic Transporter-Mediated Drug Clearance and Drug-Drug Interactions in vivo

The liver is the major detoxifying organ, clearing the blood from drugs and other xenobiotics. The extent of hepatic clearance (CL) determines drug exposure and hence, the efficacy and toxicity associated with exposure. Drug-drug interactions (DDIs) that alter the hepatic CL may cause more or less severe outcomes, such as adverse drug reactions. Accurate predictions of drug CL and DDI risk from in vitro data are therefore crucial in drug development. Liver CL depends on several factors including the activities of transporters involved in the hepatic uptake and efflux. The work in this thesis aimed at developing new in vitro and in silico methods to predict hepatic transporter-mediated CL and DDIs in vivo. Particular emphasis was placed on interactions involving the hepatic uptake transporters OATP1B1, OATP1B3, and OATP2B1. These transporters regulate the plasma concentration-time profiles of many drugs including statins. Inhibition of OATP-mediated transport by 225 structurally diverse drugs was investigated in vitro. Several novel inhibitors were identified. The data was used to develop in silico models that could predict OATP inhibitors from molecular structure. Models were developed for static and dynamic predictions of in vivo transporter-mediated drug CL and DDIs. These models rely on a combination of in vitro studies of transport function and mass spectrometry-based quantification of protein expression in the in vitro models and liver tissue. By providing estimations of transporter contributions to the overall hepatic uptake/efflux, the method is expected to improve predictions of transporter-mediated DDIs. Furthermore, proteins of importance for hepatic CL were quantified in liver tissue and isolated hepatocytes. The isolation of hepatocytes from liver tissue was found to be associated with oxidative stress and degradation of transporters and other proteins expressed in the plasma membrane. This has implications for the use of primary hepatocytes as an in vitro model of the liver. Nevertheless, by taking the altered transporter abundance into account using the method developed herein, transport function in hepatocyte experiments can be scaled to the in vivo situation. The concept of protein expression-dependent in vitro-in vivo extrapolations was illustrated using atorvastatin and pitavastatin as model drugs

In vitro and in silico Predictions of Hepatic Transporter-Mediated Drug Clearance and Drug-Drug Interactions in vivo

The liver is the major detoxifying organ, clearing the blood from drugs and other xenobiotics. The extent of hepatic clearance (CL) determines drug exposure and hence, the efficacy and toxicity associated with exposure. Drug-drug interactions (DDIs) that alter the hepatic CL may cause more or less severe outcomes, such as adverse drug reactions. Accurate predictions of drug CL and DDI risk from in vitro data are therefore crucial in drug development. Liver CL depends on several factors including the activities of transporters involved in the hepatic uptake and efflux. The work in this thesis aimed at developing new in vitro and in silico methods to predict hepatic transporter-mediated CL and DDIs in vivo. Particular emphasis was placed on interactions involving the hepatic uptake transporters OATP1B1, OATP1B3, and OATP2B1. These transporters regulate the plasma concentration-time profiles of many drugs including statins. Inhibition of OATP-mediated transport by 225 structurally diverse drugs was investigated in vitro. Several novel inhibitors were identified. The data was used to develop in silico models that could predict OATP inhibitors from molecular structure. Models were developed for static and dynamic predictions of in vivo transporter-mediated drug CL and DDIs. These models rely on a combination of in vitro studies of transport function and mass spectrometry-based quantification of protein expression in the in vitro models and liver tissue. By providing estimations of transporter contributions to the overall hepatic uptake/efflux, the method is expected to improve predictions of transporter-mediated DDIs. Furthermore, proteins of importance for hepatic CL were quantified in liver tissue and isolated hepatocytes. The isolation of hepatocytes from liver tissue was found to be associated with oxidative stress and degradation of transporters and other proteins expressed in the plasma membrane. This has implications for the use of primary hepatocytes as an in vitro model of the liver. Nevertheless, by taking the altered transporter abundance into account using the method developed herein, transport function in hepatocyte experiments can be scaled to the in vivo situation. The concept of protein expression-dependent in vitro-in vivo extrapolations was illustrated using atorvastatin and pitavastatin as model drugs

In-depth quantitative analysis and comparison of the human hepatocyte and hepatoma cell line HepG2 proteomes

Hepatocytes play a pivotal role in human homeostasis. They are essential in regulation of glucose and lipid levels in blood and play a central role in metabolism of amino acids, lipids, drugs and xenobiotic-compounds. In addition, hepatocytes produce a major portion of proteins circulating in the blood. Hepatocytes were isolated from liver tissue obtained from surgical resections. Proteins were extracted and processed using filter aided sample preparation protocol and were analyzed by LC-MS/MS using high accuracy mass spectrometry. Proteins were quantified by the 'Total Protein Approach' and 'Proteomic Ruler'. We report a comprehensive proteomic analysis of purified human hepatocytes and the human hepatoma cell line HepG2. The complete dataset comprises 9400 proteins and provides a comprehensive and quantitative depiction of the proteomes of hepatocytes and HepG2 cells at the protein titer and copy number dimensions. We describe basic cell organization and in detail energy metabolism pathways and metabolite transport. We provide quantitative insights into protein synthesis and drug and xenobiotics catabolism. Our data delineate differences between the native human hepatocytes and HepG2 cells by providing for the first time quantitative data at protein concentrations and copy numbers.

Does the choice of applied physiologically-based pharmacokinetics platform matter? : A case study on simvastatin disposition and drug-drug interaction

Physiologically-based pharmacokinetic (PBPK) models have an important role in drug discovery/development and decision making in regulatory submissions. This is facilitated by predefined PBPK platforms with user-friendly graphical interface, such as Simcyp and PK-Sim. However, evaluations of platform differences and the potential implications for disposition-related applications are still lacking. The aim of this study was to assess how PBPK model development, input parameters, and model output are affected by the selection of PBPK platform. This is exemplified via the establishment of simvastatin PBPK models (workflow, final models, and output) in PK-Sim and Simcyp as representatives of established whole-body PBPK platforms. The major finding was that the choice of PBPK platform influenced the model development strategy and the final model input parameters, however, the predictive performance of the simvastatin models was still comparable between the platforms. The main differences between the structure and implementation of Simcyp and PK-Sim were found in the absorption and distribution models. Both platforms predicted equally well the observed simvastatin (lactone and acid) pharmacokinetics (20-80 mg), BCRP and OATP1B1 drug-gene interactions (DGIs), and drug-drug interactions (DDIs) when co-administered with CYP3A4 and OATP1B1 inhibitors/inducers. This study illustrates that in-depth knowledge of established PBPK platforms is needed to enable an assessment of the consequences of PBPK platform selection. Specifically, this work provides insights on software differences and potential implications when bridging PBPK knowledge between Simcyp and PK-Sim users. Finally, it provides a simvastatin model implemented in both platforms for risk assessment of metabolism- and transporter-mediated DGIs and DDIs

Comparative Proteomic Analysis of Human Liver Tissue and Isolated Hepatocytes with a Focus on Proteins Determining Drug Exposure

Freshly isolated human hepatocytes are considered the gold standard for in vitro studies of liver functions, including drug transport, metabolism, and toxicity. For accurate predictions of the in vivo outcome, the isolated hepatocytes should reflect the phenotype of their in vivo counterpart, i.e., hepatocytes in human liver tissue. Here, we quantified and compared the membrane proteomes of freshly isolated hepatocytes and human liver tissue using a label-free shotgun proteomics approach. A total of 5144 unique proteins were identified, spanning over 6 orders of magnitude in abundance. There was a good global correlation in protein abundance. However, the expression of many plasma membrane proteins was lower in the isolated hepatocytes than in the liver tissue. This included transport proteins that determine hepatocyte exposure to many drugs and endogenous compounds. Pathway analysis of the differentially expressed proteins confirmed that hepatocytes are exposed to oxidative stress during isolation and suggested that plasma membrane proteins were degraded via the protein ubiquitination pathway. Finally, using pitavastatin as an example, we show how protein quantifications can improve in vitro predictions of in vivo liver clearance. We tentatively conclude that our data set will be a useful resource for improved hepatocyte predictions of the in vivo outcome

Understanding Statin-Roxadustat Drug–Drug-Disease Interaction Using Physiologically-Based Pharmacokinetic Modeling

A different drug–drug interaction (DDI) scenario may exist in patients with chronic kidney disease (CKD) compared with healthy volunteers (HVs), depending on the interplay between drug–drug and disease (drug-drug-disease interaction (DDDI)). Physiologically-based pharmacokinetic (PBPK) modeling, in lieu of a clinical trial, is a promising tool for evaluating these complex DDDIs in patients. However, the prediction confidence of PBPK modeling in the severe CKD population is still low when nonrenal pathways are involved. More mechanistic virtual disease population and robust validation cases are needed. To this end, we aimed to: (i) understand the implications of severe CKD on statins (atorvastatin, simvastatin, and rosuvastatin) pharmacokinetics (PK) and DDI; and (ii) predict untested clinical scenarios of statin-roxadustat DDI risks in patients to guide suitable dose regimens. A novel virtual severe CKD population was developed incorporating the disease effect on both renal and nonrenal pathways. Drug and disease PBPK models underwent a four-way validation. The verified PBPK models successfully predicted the altered PKs in patients for substrates and inhibitors and recovered the observed statin-rifampicin DDIs in patients and the statin-roxadustat DDIs in HVs within 1.25- and 2-fold error. Further sensitivity analysis revealed that the severe CKD effect on statins PK is mainly mediated by hepatic BCRP for rosuvastatin and OATP1B1/3 for atorvastatin. The magnitude of statin-roxadustat DDI in patients with severe CKD was predicted to be similar to that in HVs. PBPK-guided suitable dose regimens were identified to minimize the risk of side effects or therapeutic failure of statins when co-administered with roxadustat

Global proteome changes in liver tissue 6 weeks after FOLFOX treatment of colorectal cancer liver metastases

(1) Oxaliplatin-based chemotherapy for colorectal cancer liver metastasis is associated with sinusoidal injury of liver parenchyma. The effects of oxaliplatin-induced liver injury on the protein level remain unknown. (2) Protein expression in liver tissue was analyzed-from eight patients treated with FOLFOX (combination of fluorouracil, leucovorin, and oxaliplatin) and seven controls-by label-free liquid chromatography mass spectrometry. Recursive feature elimination-support vector machine and Welch t-test were used to identify classifying and relevantly changed proteins, respectively. Resulting proteins were analyzed for associations with gene ontology categories and pathways. (3) A total of 5891 proteins were detected. A set of 184 (3.1%) proteins classified the groups with a 20% error rate, but relevant change was observed only in 55 (0.9%) proteins. The classifying proteins were associated with changes in DNA replication (p < 0.05) through upregulation of the minichromosome maintenance complex and with the innate immune response (p < 0.05). The importance of DNA replication changes was supported by the results of Welch t-test (p < 0.05). (4) Six weeks after FOLFOX treatment, less than 1% of identified proteins showed changes in expression associated with DNA replication, cell cycle entry, and innate immune response. We hypothesize that the changes remain after recovery from FOLFOX treatment injury

Quantification of Hepatic Organic Anion Transport Proteins OAT2 and OAT7 in Human Liver Tissue and Primary Hepatocytes

Organic anion transporter (OAT) 2 and OAT7 were recently shown to be involved in the hepatic uptake of drugs; however, there is limited understanding of the population variability in the expression of these transporters in liver. There is also a need to derive relative expression-based scaling factors (REFs) that can be used to bridge in vitro functional data to the in vivo drug disposition. To this end, we quantified OAT2 and OAT7 surrogate peptide abundance in a large number of human liver tissue samples (<i>n</i> = 52), as well as several single-donor cryopreserved human hepatocyte lots (<i>n</i> = 30) by a novel, validated liquid chromatography tandem mass spectrometry (LC–MS/MS) method. The average surrogate peptide expression of OAT2 and OAT7 in the liver samples was 1.52 ± 0.57 and 4.63 ± 1.58 fmol/μg membrane protein, respectively. While we noted statistically significant differences (<i>p</i> < 0.05) in hepatocyte and liver tissue abundances for both OAT2 and OAT7, the differences were relatively small (1.8- and 1.5-fold difference in median values, respectively). Large interindividual variability was noted in the hepatic expression of OAT2 (16-fold in liver tissue and 23-fold in hepatocytes). OAT7, on the other hand, showed less interindividual variability (4-fold) in the livers, but high variability for the hepatocyte lots (27-fold). A significant positive correlation in OAT2 and OAT7 expression was observed, but expression levels were neither associated with age nor sex. In conclusion, our data suggest marked interindividual variability in the hepatic expression of OAT2/7, which may contribute to the pharmacokinetic variability of their substrates. Because both transporters were less abundant in hepatocytes than livers, a REF-based approach is recommended when scaling in vitro hepatocyte transport data to predict hepatic drug clearance and liver exposure of OAT2/7 substrates