Cryo-EM structures of human organic anion transporting polypeptide OATP1B1

Human organic anion transporting polypeptide 1B3 (OATP1B3) is more heavily N-glycosylated than OATP1B1 in extracellular loops 2 and 5

Human organic anion transporting polypeptide 1B3 (OATP1B3) and 1B1 are two liver-specific and highly homologous uptake transporters, whose structures consist of 12 transmembrane domains. The present study showed that OATP1B3 is more heavily N-glycosylated than OATP1B1 in extracellular loop 2 (EL2) and EL5. OATP1B3 has six N-glycosylation sites, namely N134, N145, N151, N445, N503, and N516, which is twice of that of OATP1B1. Single removal of individual N-glycans seems to have minimal influence on the surface expression and function of OATP1B3. However, simultaneous removal of all N-glycans will lead to OATP1B3's large retention in the endoplasmic reticulum and cellular degradation and thus significantly disrupts its surface expression. While N-glycosylation plays a crucial role in the surface expression of OATP1B3, it also has some effect on the transport function of OATP1B3 per se, which is not due to a decrease of substrate binding affinity but due to a reduced transporter's turnover number. Taken together, N-glycosylation is essential for normal surface expression and function of OATP1B3. Its disruption by some liver diseases such as NASH might alter the pharmacokinetic/pharmacodynamic properties of OATP1B3's substrate drugs.

Impact of liver diseases and pharmacological interactions on the transportome involved in hepatic drug disposition

The liver plays a pivotal role in drug disposition owing to the expression of transporters accounting for the uptake at the sinusoidal membrane and the efflux across the basolateral and canalicular membranes of hepatocytes of many different compounds. Moreover, intracellular mechanisms of phases I and II biotransformation generate, in general, inactive compounds that are more polar and easier to eliminate into bile or refluxed back toward the blood for their elimination by the kidneys, which becomes crucial when the biliary route is hampered. The set of transporters expressed at a given time, i.e., the so-called transportome, is encoded by genes belonging to two gene superfamilies named Solute Carriers (SLC) and ATP-Binding Cassette (ABC), which account mainly, but not exclusively, for the uptake and efflux of endogenous substances and xenobiotics, which include many different drugs. Besides the existence of genetic variants, which determines a marked interindividual heterogeneity regarding liver drug disposition among patients, prevalent diseases, such as cirrhosis, non-alcoholic steatohepatitis, primary sclerosing cholangitis, primary biliary cirrhosis, viral hepatitis, hepatocellular carcinoma, cholangiocarcinoma, and several cholestatic liver diseases, can alter the transportome and hence affect the pharmacokinetics of drugs used to treat these patients. Moreover, hepatic drug transporters are involved in many drug-drug interactions (DDI) that challenge the safety of using a combination of agents handled by these proteins. Updated information on these questions has been organized in this article by superfamilies and families of members of the transportome involved in hepatic drug disposition.

Drug transporters in drug disposition - highlights from the year 2023

Drug transporter field is rapidly evolving with significant progress in in vitro and in vivo tools and, computational models to assess transporter-mediated drug disposition and drug-drug interactions (DDIs) in humans. On behalf of all coauthors, I am pleased to share the fourth annual review highlighting articles published and deemed influential in the field of drug transporters in the year 2023. Each coauthor independently selected peer-reviewed articles published or available online in the year 2023 and summarized them as shown previously (Chothe et al. 2021; Chothe et al. 2022, 2023) with unbiased perspectives. Based on selected articles, this review was categorized into four sections: (1) transporter structure and in vitro evaluation, (2) novel in vitro/ex vivo models, (3) endogenous biomarkers, and (4) PBPK modeling for evaluating transporter DDIs (Table 1). As the scope of this review is not to comprehensively review each article, readers are encouraged to consult original paper for specific details. Finally, I appreciate all the authors for their time and continued support in writing this review.

The Competitive Counterflow Assay for Identifying Drugs Transported by Solute Carriers: Principle, Applications, Challenges/Limits, and Perspectives

The identification of substrates for solute carriers (SLCs) handling drugs is an important challenge, owing to the major implication of these plasma membrane transporters in pharmacokinetics and drug-drug interactions. In this context, the competitive counterflow (CCF) assay has been proposed as a practical and less expensive approach than the reference functional uptake assays for discriminating SLC substrates and non-substrates. The present article was designed to summarize and discuss key-findings about the CCF assay, including its principle, applications, challenges and limits, and perspectives. The CCF assay is based on the decrease of the steady-state accumulation of a tracer substrate in SLC-positive cells, caused by candidate substrates. Reviewed data highlight the fact that the CCF assay has been used to identify substrates and non-substrates for organic cation transporters (OCTs), organic anion transporters (OATs), and organic anion transporting polypeptides (OATPs). The performance values of the CCF assay, calculated from available CCF study data compared with reference functional uptake assay data, are, however, rather mitigated, indicating that the predictability of the CCF method for assessing SLC-mediated transportability of drugs is currently not optimal. Further studies, notably aimed at standardizing the CCF assay and developing CCF-based high-throughput approaches, are therefore required in order to fully precise the interest and relevance of the CCF assay for identifying substrates and non-substrates of SLCs.

Systematic Evaluation of Tyrosine Kinase Inhibitors as OATP1B1 Substrates Using a Competitive Counterflow Screen

Although the primary elimination pathway for most tyrosine kinase inhibitors (TKI) involves CYP3A4-mediated metabolism, the mechanism by which these agents are brought into hepatocytes remains unclear. In this study, we optimized and validated a competitive counterflow (CCF) assay to examine TKIs as substrates of the hepatic uptake transporter OATP1B1. The CCF method was based on the stimulated efflux of radiolabeled estradiol-17β-glucuronide under steady-state conditions in HEK293 cells engineered to overexpress OATP1B1. Of the 62 approved TKIs examined, 13 agents were identified as putative substrates of OATP1B1, and pazopanib was selected as a representative hit for further validation studies. The transport of pazopanib by OATP1B1 was confirmed by decreased activity of its target VEGFR2 in OATP1B1-overexpressing cells, but not cells lacking OATP1B1, consistent with molecular docking analyses indicating an overlapping binding orientation on OATP1B1 with the known substrate estrone-3-sulfate. In addition, the liver-to-plasma ratio of pazopanib in vivo was decreased in mice with a deficiency of the orthologous transporters, and this was accompanied by diminished pazopanib-induced hepatotoxicity, as determined by changes in the levels of liver transaminases. Our study supports the utility of CCF assays to assess substrate affinity for OATP1B1 within a large set of agents in the class of TKIs and sheds light on the mechanism by which these agents are taken up into hepatocytes in advance of metabolism.

SIGNIFICANCE

Despite the established exposure-pharmacodynamic relationships for many TKIs, the mechanisms underlying the agents' unpredictable pharmacokinetic profiles remain poorly understood. We report here that the disposition of many TKIs depends on hepatic transport by OATP1B1, a process that has toxicologic ramifications for agents that are associated with hepatotoxicity.

STING-mediated IL-6 Inhibits OATP1B1 Expression via the TCF4 Signaling Pathway in Cholestasis

Background and Aims

Organic anion-transporting polypeptides (OATPs) play a crucial role in the transport of bile acids and bilirubin. In our previous study, interleukin 6 (IL-6) reduced OATP1B3 levels in cholestatic disease. However, it remains unclear whether IL-6 inhibits OATP1B1 expression in cholestatic diseases. This study aimed to investigate whether IL-6 can inhibit OATP1B1 expression and explore the underlying mechanisms.

Methods

The effect of stimulator of interferon genes (STING) signaling on inflammatory factors was investigated in a cholestatic mouse model using RT-qPCR and enzyme-linked immunosorbent assay. To assess the impact of inflammatory factors on OATP1B1 expression in hepatocellular carcinoma, we analyzed OATP1B1 expression by RT-qPCR and Western Blot after treating PLC/PRF/5 cells with TNF-α, IL-1β, and IL-6. To elucidate the mechanism by which IL-6 inhibits OATP1B1 expression, we examined the expression of the OATP1B1 regulator TCF4 in PLC/PRF/5 and HepG2 cells using RT-qPCR and Western Blot. The interaction mechanism between β-catenin/TCF4 and OATP1B1 was investigated by knocking down β-catenin/TCF4 through siRNA transfection.

Results

The STING inhibitor decreased inflammatory factor levels in the cholestatic mouse model, with IL-6 exhibiting the most potent inhibitory effect on OATP1B1. IL-6 downregulated β-catenin/TCF4, leading to decreased OATP1B1 expression. Knocking-down β-catenin/TCF4 counteracted the β-catenin/TCF4-mediated repression of OATP1B1.

Conclusions

STING-mediated IL-6 up-regulation may inhibit OATP1B1, leading to reduced transport of bile acids and bilirubin by OATP1B1. This may contribute to altered pharmacokinetics in patients with diseases associated with increased IL-6 production.

Pharmacokinetics-Pharmacodynamics Modeling for Evaluating Drug-Drug Interactions in Polypharmacy: Development and Challenges

Polypharmacy is commonly employed in clinical settings. The potential risks of drug-drug interactions (DDIs) can compromise efficacy and pose serious health hazards. Integrating pharmacokinetics (PK) and pharmacodynamics (PD) models into DDIs research provides a reliable method for evaluating and optimizing drug regimens. With advancements in our comprehension of both individual drug mechanisms and DDIs, conventional models have begun to evolve towards more detailed and precise directions, especially in terms of the simulation and analysis of physiological mechanisms. Selecting appropriate models is crucial for an accurate assessment of DDIs. This review details the theoretical frameworks and quantitative benchmarks of PK and PD modeling in DDI evaluation, highlighting the establishment of PK/PD modeling against a backdrop of complex DDIs and physiological conditions, and further showcases the potential of quantitative systems pharmacology (QSP) in this field. Furthermore, it explores the current advancements and challenges in DDI evaluation based on models, emphasizing the role of emerging in vitro detection systems, high-throughput screening technologies, and advanced computational resources in improving prediction accuracy.

Membrane transporters in drug development and as determinants of precision medicine

The effect of membrane transporters on drug disposition, efficacy and safety is now well recognized. Since the initial publication from the International Transporter Consortium, significant progress has been made in understanding the roles and functions of transporters, as well as in the development of tools and models to assess and predict transporter-mediated activity, toxicity and drug-drug interactions (DDIs). Notable advances include an increased understanding of the effects of intrinsic and extrinsic factors on transporter activity, the application of physiologically based pharmacokinetic modelling in predicting transporter-mediated drug disposition, the identification of endogenous biomarkers to assess transporter-mediated DDIs and the determination of the cryogenic electron microscopy structures of SLC and ABC transporters. This article provides an overview of these key developments, highlighting unanswered questions, regulatory considerations and future directions.

Structural insights into human organic cation transporter 1 transport and inhibition

The human organic cation transporter 1 (hOCT1), also known as SLC22A1, is integral to hepatic uptake of structurally diversified endogenous and exogenous organic cations, influencing both metabolism and drug pharmacokinetics. hOCT1 has been implicated in the therapeutic dynamics of many drugs, making interactions with hOCT1 a key consideration in novel drug development and drug-drug interactions. Notably, metformin, the frontline medication for type 2 diabetes, is a prominent hOCT1 substrate. Conversely, hOCT1 can be inhibited by agents such as spironolactone, a steroid analog inhibitor of the aldosterone receptor, necessitating a deep understanding of hOCT1-drug interactions in the development of new pharmacological treatments. Despite extensive study, specifics of hOCT1 transport and inhibition mechanisms remain elusive at the molecular level. Here, we present cryo-electron microscopy structures of the hOCT1-metformin complex in three distinct conformational states - outward open, outward occluded, and inward occluded as well as substrate-free hOCT1 in both partially and fully open states. We also present hOCT1 in complex with spironolactone in both outward and inward facing conformations. These structures provide atomic-level insights into the dynamic metformin transfer process via hOCT1 and the mechanism by which spironolactone inhibits it. Additionally, we identify a 'YER' motif critical for the conformational flexibility of hOCT1 and likely other SLC22 family transporters. Our findings significantly advance the understanding of hOCT1 molecular function and offer a foundational framework for the design of new therapeutic agents targeting this transporter.

G45 and V386 in Transmembrane Domains 1 and 8 Are Critical for the Activation of OATP1B3-Mediated E17βG Uptake by Clotrimazole

Organic anion-transporting polypeptides (OATPs) 1B1 and 1B3 are two highly homologous transport proteins. However, OATP1B1- and 1B3-mediated estradiol-17β-glucuronide (E17βG) uptake can be differentially affected by clotrimazole. In this study, by functional characterization on chimeric transporters and single mutants, we find that G45 in transmembrane domain 1 (TM1) and V386 in TM8 are critical for the activation of OATP1B3-mediated E17βG uptake by clotrimazole. However, the effect of clotrimazole on the function of OATP1B3 is substrate-dependent as clotrimazole does not stimulate OATP1B3-mediated uptake of 4',5'-dibromofluorescein (DBF) and rosuvastatin. In addition, clotrimazole is not transported by OATP1B3, but it can efficiently permeate the plasma membrane due to its lipophilic properties. Homology modeling and molecular docking indicate that E17βG binds in a substrate binding pocket of OATP1B3 through hydrogen bonding and hydrophobic interactions, among which its sterol scaffold forms hydrophobic contacts with V386. In addition, a flexible glycine residue at position 45 is essential for the activation of OATP1B3. Finally, clotrimazole is predicted to bind at an allosteric site, which mainly consists of hydrophobic residues located at the cytoplasmic halves of TMs 4, 5, 10, and 11.

Computational and artificial intelligence-based approaches for drug metabolism and transport prediction

Drug metabolism and transport, orchestrated by drug-metabolizing enzymes (DMEs) and drug transporters (DTs), are implicated in drug-drug interactions (DDIs) and adverse drug reactions (ADRs). Reliable and precise predictions of DDIs and ADRs are critical in the early stages of drug development to reduce the rate of drug candidate failure. A variety of experimental and computational technologies have been developed to predict DDIs and ADRs. Recent artificial intelligence (AI) approaches offer new opportunities for better predicting and understanding the complex processes related to drug metabolism and transport. We summarize the role of major DMEs and DTs, and provide an overview of current progress in computational approaches for the prediction of drug metabolism, transport, and DDIs, with an emphasis on AI including machine learning (ML) and deep learning (DL) modeling.