Hepatic transporter drug-drug interactions: an evaluation of approaches and methodologies

Research Landscape of Physiologically Based Pharmacokinetic Model Utilization in Different Fields: A Bibliometric Analysis (1999-2023)

PURPOSE

The physiologically based pharmacokinetic (PBPK) modeling has received increasing attention owing to its excellent predictive abilities. However, there has been no bibliometric analysis about PBPK modeling. This research aimed to summarize the research development and hot points in PBPK model utilization overall through bibliometric analysis.

METHODS

We searched for publications related to the PBPK modeling from 1999 to 2023 in the Web of Science Core Collection (WoSCC) database. The Microsoft Office Excel, CiteSpace and VOSviewers were used to perform the analyses.

RESULTS

A total of 4,649 records from 1999 to 2023 were identified, and the largest number of publications focused in the period 2018-2023. The United States was the leading country, and the Environmental Protection Agency (EPA) was the leading institution. The journal Drug Metabolism and Disposition published and co-cited the most articles. Drug-drug interactions, special populations, and new drug development are the main topics in this research field.

CONCLUSION

We first visualize the research landscape and hotspots of the PBPK modeling through bibliometric methods. Our study provides a better understanding for researchers, especially beginners about the dynamization of PBPK modeling and presents the relevant trend in the future.

Mechanistic in vitro studies indicate that the clinical drug-drug interactions between protease inhibitors and rosuvastatin are driven by inhibition of intestinal BCRP and hepatic OATP1B1 with minimal contribution from OATP1B3, NTCP and OAT3

Previous use of a mechanistic static model to accurately quantify the increased rosuvastatin exposure due to drug-drug interaction (DDI) with coadministered atazanavir underpredicted the magnitude of area under the plasma concentration-time curve ratio (AUCR) based on inhibition of breast cancer resistance protein (BCRP) and organic anion transporting polypeptide (OATP) 1B1. To reconcile the disconnect between predicted and clinical AUCR, atazanavir and other protease inhibitors (darunavir, lopinavir and ritonavir) were evaluated as inhibitors of BCRP, OATP1B1, OATP1B3, sodium taurocholate cotransporting polypeptide (NTCP) and organic anion transporter (OAT) 3. None of the drugs inhibited OAT3, nor did darunavir and ritonavir inhibit OATP1B3 or NTCP. All drugs inhibited BCRP-mediated estrone 3-sulfate transport or OATP1B1-mediated estradiol 17β-D-glucuronide transport with the same rank order of inhibitory potency (lopinavir>ritonavir>atazanavir>>darunavir) and mean IC50 values ranging from 15.5 ± 2.80 μM to 143 ± 14.7 μM or 0.220 ± 0.0655 μM to 9.53 ± 2.50 μM, respectively. Atazanavir and lopinavir also inhibited OATP1B3- or NTCP-mediated transport with a mean IC50 of 1.86 ± 0.500 μM or 65.6 ± 10.7 μM and 5.04 ± 0.0950 μM or 20.3 ± 2.13 μM, respectively. Following integration of a combined hepatic transport component into the previous mechanistic static model using the in vitro inhibitory kinetic parameters determined above for atazanavir, the newly predicted rosuvastatin AUCR reconciled with the clinically observed AUCR confirming additional minor involvement of OATP1B3 and NTCP inhibition in its DDI. The predictions for the other protease inhibitors confirmed inhibition of intestinal BCRP and hepatic OATP1B1 as the principal pathways involved in their clinical DDI with rosuvastatin.

Predicting transporter mediated drug-drug interactions via static and dynamic physiologically based pharmacokinetic modeling: A comprehensive insight on where we are now and the way forward

The greater utilization and acceptance of physiologically-based pharmacokinetic (PBPK) modeling to evaluate the potential metabolic drug-drug interactions is evident by the plethora of literature, guidance's, and regulatory dossiers available in the literature. In contrast, it is not widely used to predict transporter-mediated DDI (tDDI). This is attributed to the unavailability of accurate transporter tissue expression levels, the absence of accurate in vitro to in vivo extrapolations (IVIVE), enzyme-transporter interplay, and a lack of specific probe substrates. Additionally, poor understanding of the inhibition/induction mechanisms coupled with the inability to determine unbound concentrations at the interaction site made tDDI assessment challenging. Despite these challenges, continuous improvements in IVIVE approaches enabled accurate tDDI predictions. Furthermore, the necessity of extrapolating tDDI's to special (pediatrics, pregnant, geriatrics) and diseased (renal, hepatic impaired) populations is gaining impetus and is encouraged by regulatory authorities. This review aims to visit the current state-of-the-art and summarizes contemporary knowledge on tDDI predictions. The current understanding and ability of static and dynamic PBPK models to predict tDDI are portrayed in detail. Peer-reviewed transporter abundance data in special and diseased populations from recent publications were compiled, enabling direct input into modeling tools for accurate tDDI predictions. A compilation of regulatory guidance's for tDDI's assessment and success stories from regulatory submissions are presented. Future perspectives and challenges of predicting tDDI in terms of in vitro system considerations, endogenous biomarkers, the use of empirical scaling factors, enzyme-transporter interplay, and acceptance criteria for model validation to meet the regulatory expectations were discussed.

Studying the right transporter at the right time: an in vitro strategy for assessing drug-drug interaction risk during drug discovery and development

INTRODUCTION

Transporters are significant in dictating drug pharmacokinetics, thus inhibition of transporter function can alter drug concentrations resulting in drug-drug interactions (DDIs). Because they can impact drug toxicity, transporter DDIs are a regulatory concern for which prediction of clinical effect from in vitro data is critical to understanding risk.

AREA COVERED

The authors propose in vitro strategies to assist mitigating/removing transporter DDI risk during development by frontloading specific studies, or managing patient risk in the clinic. An overview of clinically relevant drug transporters and observed DDIs are provided, alongside presentation of key considerations/recommendations for in vitro study design evaluating drugs as inhibitors or substrates. Guidance on identifying critical co-medications, clinically relevant disposition pathways and using mechanistic static equations for quantitative prediction of DDI is compiled.

EXPERT OPINION

The strategies provided will facilitate project teams to study the right transporter at the right time to minimise development risks associated with DDIs. To truly alleviate or manage clinical risk, the industry will benefit from moving away from current qualitative basic static equation approaches to transporter DDI hazard assessment towards adopting the use of mechanistic models to enable quantitative DDI prediction, thereby contextualising risk to ascertain whether a transporter DDI is simply pharmacokinetic or clinically significant requiring intervention.

UPLC-MS/MS method for the simultaneous quantification of pravastatin, fexofenadine, rosuvastatin, and methotrexate in a hepatic uptake model and its application to the possible drug-drug interaction study of triptolide

A rapid and specific UPLC-MS/MS method with a total run time of 3.5 min was developed for the determination of pravastatin, fexofenadine, rosuvastatin, and methotrexate in rat primary hepatocytes. After protein precipitation with 70% acetonitrile (containing 30% H2 O), these four analytes were separated under gradient conditions with a mobile phase consisting of 0.03% acetic acid (v/v) and methanol at a flow rate of 0.50 mL/min. The linearity, recovery, matrix effect, accuracy, precision, and stability of the method were well validated. We evaluated drug-drug interactions based on these four compounds in freshly suspended hepatocytes. The hepatic uptake of pravastatin, fexofenadine, rosuvastatin, and methotrexate at 4°C was significantly lower than that at 37°C, and the hepatocytes were saturable with increased substrate concentration and culture time, suggesting that the rat primary hepatocyte model was successfully established. Triptolide showed a significant inhibitory effect on the hepatic uptake of these four compounds. In conclusion, this method was successfully employed for the quantification of pravastatin, fexofenadine, rosuvastatin, and methotrexate and was used to verify the rat primary hepatocyte model for Oatp1, Oatp2, Oatp4, and Oat2 transporter studies. Then, we applied this model to explore the effect of triptolide on these four transporters.

The evolution of strategies to minimise the risk of human drug-induced liver injury (DILI) in drug discovery and development

Early identification of toxicity associated with new chemical entities (NCEs) is critical in preventing late-stage drug development attrition. Liver injury remains a leading cause of drug failures in clinical trials and post-approval withdrawals reflecting the poor translation between traditional preclinical animal models and human clinical outcomes. For this reason, preclinical strategies have evolved over recent years to incorporate more sophisticated human in vitro cell-based models with multi-parametric endpoints. This review aims to highlight the evolution of the strategies adopted to improve human hepatotoxicity prediction in drug discovery and compares/contrasts these with recent activities in our lab. The key role of human exposure and hepatic drug uptake transporters (e.g. OATPs, OAT2) is also elaborated.