Kim L R Brouwer’s research while affiliated with University of North Carolina at Chapel Hill and other places

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Publications (304)


Correction to “Nanoparticle Drug Delivery Can Reduce the Hepatotoxicity of Therapeutic Cargo”
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January 2025

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Feifei Yang

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Liantao Li

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Andrew Z. Wang
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Baseline serum concentrations of (A) total bile acids (BAs), (B) unconjugated and conjugated BAs and (C) primary and secondary BAs in healthy volunteers and patients with ADPKD. Total BA concentrations represent the molar sum of unconjugated (Un‐BA), glycine‐ (Glyc‐BA), taurine‐ (Taur‐BA) and sulfate‐conjugated BAs. Data represent individual data points, mean and standard deviation (SD; closed circles, healthy, n = 16; open circles, ADPKD, n = 8). *, standardized difference (difference of means divided by the SD from the healthy cohort) ≥ 0.5; **, standardized difference ≥ 0.8.
(A) Principal component analysis (PCA) and (B) orthogonal partial least squares‐discriminant analysis (OPLS‐DA) score plots. Score plots of bile acid profiles in healthy volunteers (closed circles) compared to patients with ADPKD (open circles).
Baseline serum concentrations of selected individual bile acids in healthy volunteers and patients with ADPKD. Bile acids in healthy volunteers (closed circles, n = 16) and patients with ADPKD (open circles, n = 8) were measured by UPLC‐MS/MS. Data represent individual data points, mean and standard deviation (SD). Cholate (CA), glycocholate (GCA), taurocholate (TCA), chenodeoxycholate (CDCA), glycochenodeoxycholate (GCDCA), taurochenodeoxycholate (TCDCA), deoxycholate (DCA), glycodeoxycholate (GDCA), taurodeoxycholate (TDCA), lithocholate (LCA), LCA‐sulfate (LCA‐S), glycolithocholate (GLCA), taurolithocholate (TLCA). The dotted line represents the lower limit of quantification (LLOQ) of 1 nM for all bile acids except for an LLOQ of 2 nM for GDCA. *, standardized difference (difference of means divided by the SD from the healthy cohort) ≥ 0.5; **, standardized difference ≥ 0.8.
(A) Serum concentrations, (B) amount excreted in urine from 0‐2 h and (C) renal clearance of coproporphyrin‐I (CP‐I) in healthy volunteers and patients with ADPKD. CP‐I concentrations in healthy volunteers (closed circles, n = 16) and patients with ADPKD (open circles, n = 8) were measured by UPLC‐MS/MS. Data represent individual data points, mean and standard deviation (SD). Dotted line represents the lower limit of quantification (LLOQ) of 0.2 nM for CP‐I. **, standardized difference (difference of means divided by the SD from the healthy cohort) ≥ 0.8.
Standardized differences of serum bile acid (BA) and coproporphyrin‐I (CP‐I) concentrations, amount of CP‐I excreted in urine over 2 h and renal clearance (CLrenal) of CP‐I values. The standardized difference (difference of means divided by the standard deviation from the healthy cohort) and 90% confidence interval, calculated using log‐scale values, is presented for comparison of the two cohorts. Dashed lines represent the ±0.5 (medium) and ±0.8 (large) effect size.⁴² Unconjugated (Un‐BAs), glycine BAs (Glyc‐BAs), taurine BAs (Taur‐BAs), cholate (CA), glycocholate (GCA), taurocholate (TCA), chenodeoxycholate (CDCA), glycochenodeoxycholate (GCDCA), taurochenodeoxycholate (TCDCA), deoxycholate (DCA), glycodeoxycholate (GDCA), taurodeoxycholate (TDCA), ursodeoxycholate (UDCA), glycoursodeoxycholate (GUDCA), tauroursodeoxycholate (TUDCA), lithocholate (LCA), LCA‐sulfate (LCA‐S), glycolithocholate (GLCA), taurolithocholate (TLCA), dehydrolithocholate (dehydroLCA), allolithocholate (alloLCA), hyocholate (HCA), glycohyocholate (GHCA), taurohyocholate (THCA), α‐muricholate (αMCA), tauro‐α‐muricholate (TαMCA), hyodeoxycholate (HDCA), glycohyodeoxycholate (GHDCA), murocholate (MuroCA), apocholate (apoCA), 3‐dehydrocholate (3‐dehydroCA), 7‐dehydrocholate (7‐dehydroCA), 6‐ketolithocholate (6‐ketoLCA), 7‐ketolithocholate (7‐ketoLCA), 12‐ketolithocholate (12‐ketoLCA), norcholate (norCA), 23‐nordeoxycholate (norDCA), ursocholate (UCA), β‐ursodeoxycholate (β‐UDCA) and isolithocholate (isoLCA).
Altered bile acid and coproporphyrin‐I disposition in patients with autosomal dominant polycystic kidney disease

September 2024

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35 Reads

Aims Serum, liver and urinary bile acids are increased, and hepatic transport protein levels are decreased in a non‐clinical model of polycystic kidney disease. Similar changes in patients with autosomal dominant polycystic kidney disease (ADPKD) may predispose them to drug‐induced liver injury (DILI) and hepatic drug–drug interactions (DDIs). Systemic coproporphyrin‐I (CP‐I), an endogenous biomarker for hepatic OATP1B function and MRP2 substrate, is used to evaluate OATP1B‐mediated DDI risk in humans. In this clinical observational cohort‐comparison study, bile acid profiles and CP‐I concentrations in healthy volunteers and patients with ADPKD were compared. Methods Serum and urine samples from healthy volunteers (n = 16) and patients with ADPKD (n = 8) were collected. Serum bile acids, and serum and urine CP‐I concentrations, were quantified by ultra‐performance liquid chromatography tandem mass spectrometry (UPLC‐MS/MS). Results Patients with ADPKD exhibited increased serum concentrations of total (1.3‐fold) and taurine‐conjugated (2.8‐fold) bile acids compared to healthy volunteers. Specifically, serum concentrations of six bile acids known to be more hydrophobic/hepatotoxic (glycochenodeoxycholate, taurochenodeoxycholate, taurodeoxycholate, lithocholate, glycolithocholate and taurolithocholate) were increased (1.5‐, 2.9‐, 2.8‐, 1.6‐, 1.7‐ and 2.7‐fold, respectively) in patients with ADPKD. Furthermore, serum CP‐I concentrations were elevated and the renal clearance of CP‐I was reduced in patients with ADPKD compared to healthy volunteers. Conclusions Increased exposure to bile acids may increase susceptibility to DILI in some patients with ADPKD. Furthermore, the observed increase in serum CP‐I concentrations could be attributed, in part, to impaired OATP1B function in patients with ADPKD, which could increase the risk of DDIs involving OATP1B substrates compared to healthy volunteers.




(a) Regional distribution of OATP1B across the hepatic lobule from the portal vein (PV) to the central vein (CV). (b) Schematic representation of the permeability‐limited multi‐compartment liver (PerMCL) as part of a whole‐body physiologically based pharmacokinetic model within the Simcyp Simulator. (c) Workflow of drug–drug interaction (DDI) modeling framework using the matrix approach. (d) Schematic of drug–drug interaction (DDI) network with rifampicin (RIF), repaglinide (RPG), trimethoprim (TMP), cyclosporine (CsA), midazolam (MDZ), and pravastatin (PRV). CuEW, concentration of unbound drug in extracellular water; CuIW, concentration of unbound drug in intracellular water; CYP, cytochrome P450; OATP, organic anion transporting polypeptide; MRP, multidrug resistance‐associated protein. Created with BioRender.com.
Simulated plasma concentration–time profiles of repaglinide (left panel), rifampicin (middle panel), and pravastatin (right panel) in virtual patients across 10 individual trials (gray lines) and population mean or geometric mean²⁶ (black line) using a matched study design to each corresponding clinical trial (SD, single dose; MD, multiple dose). The permeability‐limited multi‐compartment liver (PerMCL) model was used for all simulations. Dotted gray lines indicate the simulated 5th and 95th percentiles. Observed data (open circles) are plotted as mean ± standard deviation or geometric mean ± standard error of the mean.²⁶
of predicted and observed maximum concentration (Cmax) ratios and area under the curve (AUC) ratios where the Cmax ratio represents substrate Cmax in the presence and absence of the perpetrator, and the AUC ratio represents substrate AUC in the presence and absence of the perpetrator. Simulations were based on the multi‐compartment liver (PerMCL) model for rifampicin (RIF), repaglinide (RPG), and pravastatin. Predicted Cmax and AUC ratios were determined based on simulated clinical trials with a matched study design. Predicted minimum and maximum values across the 10 virtual trials are indicated in parentheses. Data points are the mean,5,25,27,31,37 median,²⁴ or geometric mean4,26 of the observed clinical data and population simulations. Dosing details are provided if the reference included more than one clinical study. Error bars are the minimum and maximum values across the 10 virtual trials. The solid black line represents the line of identity, the dashed lines indicate twofold error, and the dotted lines indicate the Guest criteria. The insets represent an enlarged view of select datapoints.
Predicted versus observed (a) Cmax ratio and (b) area under the curve (AUC) ratio of repaglinide where the Cmax ratio represents repaglinide Cmax in the presence and absence of rifampicin, and the AUC ratio represents repaglinide AUC in the presence and absence of rifampicin. Predicted Cmax and AUC ratios were determined based on simulated clinical trials with a matched study design. Data points are the mean,²⁵ median,²⁴ or geometric mean²⁶ of the observed clinical data and population simulations. Simulations were conducted for three model scenarios: M1: inclusion of only OATP1B inhibition parameters (squares), M2: inclusion of only OATP1B induction parameters (triangles), and M3: inclusion of OATP1B inhibition and induction parameters (circles). CYP distribution data were incorporated into M3 in an additional simulation (M3a). The solid black line represents the line of identity, the dashed lines indicate twofold error, and the dotted lines indicate the Guest criteria.
Population simulated mean systemic rifampicin concentrations (top panel), extracellular unbound concentrations (middle panel), and intracellular unbound concentrations (bottom panel) across six hepatic zones after once daily 600 mg doses for 7 days using the rifampicin multi‐compartment liver (PerMCL) PBPK model and (a) uniformly distributed OATP1B and (b) non‐uniformly distributed OATP1B in the Simcyp Simulator. OATP, organic anion transporting polypeptide; PBPK, physiologically based pharmacokinetic.
Hepatic OATP1B zonal distribution: Implications for rifampicin‐mediated drug–drug interactions explored within a PBPK framework

June 2024

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50 Reads

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1 Citation

OATP1B facilitates the uptake of xenobiotics into hepatocytes and is a prominent target for drug–drug interactions (DDIs). Reduced systemic exposure of OATP1B substrates has been reported following multiple‐dose rifampicin; one explanation for this observation is OATP1B induction. Non‐uniform hepatic distribution of OATP1B may impact local rifampicin tissue concentrations and rifampicin‐mediated protein induction, which may affect the accuracy of transporter‐ and/or metabolizing enzyme‐mediated DDI predictions. We incorporated quantitative zonal OATP1B distribution data from immunofluorescence imaging into a PBPK modeling framework to explore rifampicin interactions with OATP1B and CYP substrates. PBPK models were developed for rifampicin, two OATP1B substrates, pravastatin and repaglinide (also metabolized by CYP2C8/CYP3A4), and the CYP3A probe, midazolam. Simulated hepatic uptake of pravastatin and repaglinide increased from the periportal to the pericentral region (approximately 2.1‐fold), consistent with OATP1B distribution data. Simulated rifampicin unbound intracellular concentrations increased in the pericentral region (1.64‐fold) compared to simulations with uniformly distributed OATP1B. The absolute average fold error of the rifampicin PBPK model for predicting substrate maximal concentration (Cmax) and area under the plasma concentration–time curve (AUC) ratios was 1.41 and 1.54, respectively (nine studies). In conclusion, hepatic OATP1B distribution has a considerable impact on simulated zonal substrate uptake clearance values and simulated intracellular perpetrator concentrations, which regulate transporter and metabolic DDIs. Additionally, accounting for rifampicin‐mediated OATP1B induction in parallel with inhibition improved model predictions. This study provides novel insight into the effect of hepatic OATP1B distribution on site‐specific DDI predictions and the impact of accounting for zonal transporter distributions within PBPK models.


Effects of PFAS on human liver transporters: implications for health outcomes

May 2024

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26 Reads

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7 Citations

Toxicological Sciences

Per- and polyfluoroalkyl substances (PFAS) have become internationally recognized over the past three decades as persistent organic pollutants used in the production of various consumer and industrial goods. Research efforts continue to gauge the risk that historically used, and newly produced, PFAS may cause to human health. Numerous studies report toxic effects of PFAS on the human liver as well as increased serum cholesterol levels in adults. A major concern with PFAS, also dubbed “forever chemicals,” is that they accumulate in the liver and kidney and persist in serum. The mechanisms responsible for their disposition and excretion in humans are poorly understood. A better understanding of the interaction of PFAS with liver transporters, as it pertains to the disposition of PFAS and other xenobiotics, could provide mechanistic insight into human health effects and guide efforts toward risk assessment of compounds in development. This review summarizes the current state of the literature on the emerging relationships (eg, substrates, inhibitors, modulators of gene expression) between PFAS and specific hepatic transporters. The adaptive and toxicological responses of hepatocytes to PFAS that reveal linkages to pathologies and epidemiological findings are highlighted. The evidence suggests that our understanding of the molecular landscape of PFAS must improve to determine their impact on the expression and function of hepatocyte transporters that play a key role in PFAS or other xenobiotic disposition. From here, we can assess what role these changes may have in documented human health outcomes.


Alterations in zonal distribution and plasma membrane localization of hepatocyte bile acid transporters in patients with NAFLD

February 2024

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24 Reads

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3 Citations

Hepatology Communications

Background NAFLD is highly prevalent with limited treatment options. Bile acids (BAs) increase in the systemic circulation and liver during NAFLD progression. Changes in plasma membrane localization and zonal distribution of BA transporters can influence transport function and BA homeostasis. However, a thorough characterization of how NAFLD influences these factors is currently lacking. This study aimed to evaluate the impact of NAFLD and the accompanying histologic features on the functional capacity of key hepatocyte BA transporters across zonal regions in human liver biopsies. Methods A novel machine learning image classification approach was used to quantify relative zonal abundance and plasma membrane localization of BA transporters (bile salt export pump [BSEP], sodium-taurocholate cotransporting polypeptide, organic anion transporting polypeptide [OATP] 1B1 and OATP1B3) in non-diseased (n = 10), NAFL (n = 9), and NASH (n = 11) liver biopsies. Based on these data, membrane-localized zonal abundance (MZA) measures were developed to estimate transporter functional capacity. Results NAFLD diagnosis and histologic scoring were associated with changes in transporter membrane localization and zonation. Increased periportal BSEP MZA (mean proportional difference compared to non-diseased liver of 0.090) and decreased pericentral BSEP MZA (−0.065) were observed with NASH and also in biopsies with higher histologic scores. Compared to Non-diseased Liver, periportal OATP1B3 MZA was increased in NAFL (0.041) and NASH (0.047). Grade 2 steatosis (mean proportional difference of 0.043 when compared to grade 0) and grade 1 lobular inflammation (0.043) were associated with increased periportal OATP1B3 MZA . Conclusions These findings provide novel mechanistic insight into specific transporter alterations that impact BA homeostasis in NAFLD. Changes in BSEP MZA likely contribute to altered BA disposition and pericentral microcholestasis previously reported in some patients with NAFLD. BSEP MZA assessment could inform future development and optimization of NASH-related pharmacotherapies.


Physiologically Based Pharmacokinetic (PBPK) Model Predictions of Disease Mediated Changes in Drug Disposition in Patients with Nonalcoholic Fatty Liver Disease (NAFLD)

February 2024

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92 Reads

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1 Citation

Pharmaceutical Research

Purpose This study was designed to verify a virtual population representing patients with nonalcoholic fatty liver disease (NAFLD) to support the implementation of a physiologically based pharmacokinetic (PBPK) modeling approach for prediction of disease-related changes in drug pharmacokinetics. Methods A virtual NAFLD patient population was developed in GastroPlus (v.9.8.2) by accounting for pathophysiological changes associated with the disease and proteomics-informed alterations in the abundance of metabolizing enzymes and transporters pertinent to drug disposition. The NAFLD population model was verified using exemplar drugs where elimination is influenced predominantly by cytochrome P450 (CYP) enzymes (chlorzoxazone, caffeine, midazolam, pioglitazone) or by transporters (rosuvastatin, ¹¹C-metformin, morphine and the glucuronide metabolite of morphine). Results PBPK model predictions of plasma concentrations of all the selected drugs and hepatic radioactivity levels of ¹¹C-metformin were consistent with the clinically-observed data. Importantly, the PBPK simulations using the virtual NAFLD population model provided reliable estimates of the extent of changes in key pharmacokinetic parameters for the exemplar drugs, with mean predicted ratios (NAFLD patients divided by healthy individuals) within 0.80- to 1.25-fold of the clinically-reported values, except for midazolam (prediction-fold difference of 0.72). Conclusion A virtual NAFLD population model within the PBPK framework was successfully developed with good predictive capability of estimating disease-related changes in drug pharmacokinetics. This supports the use of a PBPK modeling approach for prediction of the pharmacokinetics of new investigational or repurposed drugs in patients with NAFLD and may help inform dose adjustments for drugs commonly used to treat comorbidities in this patient population.


Membrane transporters in drug development and as determinants of precision medicine

January 2024

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242 Reads

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67 Citations

Nature Reviews Drug Discovery

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.


Hepatic Bile Acid Transporters and Drug-induced Hepatotoxicity

November 2023

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15 Reads

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6 Citations

Toxicologic Pathology

Drug-induced liver injury (DILI) remains a major concern in drug development from a patient safety perspective because it is the leading cause of acute liver failure. One mechanism of DILI is altered bile acid homeostasis and involves several hepatic bile acid transporters. Functional impairment of some hepatic bile acid transporters by drugs, disease, or genetic mutations may lead to toxic accumulation of bile acids within hepatocytes and increase DILI susceptibility. This review focuses on the role of hepatic bile acid transporters in DILI. Model systems, primarily in vitro and modeling tools, such as DILIsym, used in assessing transporter-mediated DILI are discussed. Due to species differences in bile acid homeostasis and drug-transporter interactions, key aspects and challenges associated with the use of preclinical animal models for DILI assessment are emphasized. Learnings are highlighted from three case studies of hepatotoxic drugs: troglitazone, tolvaptan, and tyrosine kinase inhibitors (dasatinib, pazopanib, and sorafenib). The development of advanced in vitro models and novel biomarkers that can reliably predict DILI is critical and remains an important focus of ongoing investigations to minimize patient risk for liver-related adverse reactions associated with medication use.


Citations (64)


... Although current research is insufficient, there is evidence that PFAS exposure may increase the risk of certain types of cancer [101]. It also causes cardiovascular disease, liver, and kidney damage [99,113]. There are numerous avenues through which humans can be exposed to PFASs. ...

Reference:

Bibliometric Analysis of Per- and Polyfluoroalkyl Substances (PFASs) from 2000 to 2023 Based on Web of Science Database
Effects of PFAS on human liver transporters: implications for health outcomes
  • Citing Article
  • May 2024

Toxicological Sciences

... Recently, our group generated quantitative data on the distribution of hepatocyte transporters across the liver lobule that suggest a non-uniform OATP1B abundance pattern from the periportal to the pericentral region in individuals with non-diseased livers (Figure 1a). 11 Although zonal distribution of transporters and drug-metabolizing enzymes across the hepatic lobule has been reported, 12,13 most physiologically based pharmacokinetic (PBPK) models do not account for this key feature that may affect drug disposition, specifically for high extraction compounds. CYP3A induction, both in vitro and in vivo, is dependent on the baseline abundance level of the protein. ...

Alterations in zonal distribution and plasma membrane localization of hepatocyte bile acid transporters in patients with NAFLD
  • Citing Article
  • February 2024

Hepatology Communications

... Solute carriers are known to play an important role in determining drug pharmacokinetics, safety and efficacy profiles [30]. A key goal of the International Transporter Consortium is to identify transporters involved in drug transport and highlight potential issues around adverse drug-drug interactions involving transporters during clinical trials [31]. Therefore, in parallel to the prediction of physiologically relevant substrates, we investigated whether interrogation of omics datasets could be used to identify drug molecules that are substrates for specific SLC proteins. ...

Membrane transporters in drug development and as determinants of precision medicine
  • Citing Article
  • January 2024

Nature Reviews Drug Discovery

... 173 Studies have shown that everolimus significantly inhibits NTCP. 147 Oxy185 is a semi-synthetic oxysterol. Studies have shown that Oxy185 interacts with NTCP to inhibit the oligomerization of NTCP, reducing the efficiency of HBV internalization. ...

Effect of mTOR inhibitors on sodium taurocholate cotransporting polypeptide (NTCP) function in vitro

... Bile-duct-ligated rodent models have been used as classic cholestatic in vivo models, which are replaced by genetically modified mice, and inducible models using toxicants [89]. Cell lines, such as human hepatoma HuH-7, known for high expression and localization of bile acid transporters and bile salt export pumps, are also appropriate models to investigate the underlying choleretic mechanisms [90]. However, due to feasible access and culture conditions, HepG2 cells are favorable models to study drugs that target hepatic functions [91]. ...

A novel differentiated HuH-7 cell model to examine bile acid metabolism, transport and cholestatic hepatotoxicity

... The strategic placement of P-gp across various locations such as blood-brain-barrier (BBB), gut lumen, and within the renal and hepatic systems that underscores its integral role in drug metabolism and clearance, ensuring that xenobiotics and other substances are effectively removed from the body 7 . ...

Clinical Relevance of Hepatic and Renal P‐gp/BCRP Inhibition of Drugs: An International Transporter Consortium Perspective
  • Citing Article
  • May 2022

Clinical Pharmacology & Therapeutics

... The toxicity assessment of drugs in the serum and liver is critical because these organs participate in drug metabolism and elimination [37]. In this study, the toxicity of KGS-NE was investigated via oral administrations of different concentrations (10-80 mg/kg) to C57BL/6 mice aged eight weeks for 14 days (Fig. 10A). ...

Considerations for Physiologically Based Modeling in Liver Disease: From Nonalcoholic Fatty Liver (NAFL) to Nonalcoholic Steatohepatitis (NASH)

Clinical Pharmacology & Therapeutics

... They are able to regulate target gene expression without changing its DNA sequence. DNA methylation is the attachment of methyl groups to a DNA molecule, and these predominantly occur in the promoter region of the gene, resulting in the inhibition of gene transcription [53][54][55][56]. Histone acetylation mostly happens at lysine residues at the N-terminus of histones located around the transcription start site of a gene. ...

Regulation of Drug Transport Proteins – From Mechanisms to Clinical Impact; A White Paper on Behalf of the ITC
  • Citing Article
  • April 2022

Clinical Pharmacology & Therapeutics

... In the basolateral (sinusoidal) membrane function, uptake carriers are from OATP family, i.e., organic anion transporting polypeptide 1B1 (OATP1B1 and SLCO1B1), OATP1B3 (SLCO1B3), and OATP2B1 (SLCO2B1), as well as SLC22A family, i.e., organic cation transporter 1 (OCT1 and SLC22A1) and organic anion transporter OAT2 (SLC22A7). The basolateral membrane is also endowed with efflux transporters, such as MRP3 (ABCC3), MRP4 (ABCC4), and MRP6 (ABCC6) [4]. ...

DRUG TRANSPORT IN THE LIVER
  • Citing Chapter
  • April 2022

... Culturing primary human hepatocytes (PHHs) or hepatic cell lines in various formats with fatty acidenriched media is a common approach to simulate NAFLD in vitro, but PHH data revealed that this is not sufficient to replicate the hepatocellular lipidome of patients with NASH. 84 Immortalized hepatic cell lines typically have altered metabolic functions that limit in vivo translation. Advances in hepatic spheroids and organoids using PHH or hepatic stem cells appear promising, and liver-and gut-liver-on-a-chip technologies may markedly increase the ability to mimic NAFLD in vitro. ...

Lipidomics Profiles in Hepatocytes from Nonalcoholic Steatohepatitis Patients Differ Markedly from In Vitro ‐Induced Steatotic Hepatocytes
  • Citing Article
  • February 2022