Minimal contribution of the hepatic uptake transporter OATP1B1 to the inter-individual variability in SN-38 pharmacokinetics in cancer patients without severe renal failure

ICH M12 Drug Interaction Studies: Summary of the Efforts to Achieve Global Convergence

The ICH M12 Guideline on Drug Interaction Studies is the result of a harmonization process led by global regulatory and industry experts with experience in drug-drug interaction (DDI) assessments and interpretation. The Expert Working Group (EWG) built on areas of regional consensus and identified solutions to topics lacking initial consensus. This article describes the topics addressed in the guideline, with emphasis on areas that required extensive discussion. It mentions topics that were the subject of comments during the public consultation period. The scope of the guideline is pharmacokinetic DDIs mediated by metabolic enzymes and drug transporters. It describes in vitro and clinical DDI studies and predictive modeling evaluations conducted during drug development. The understanding of DDI liability, in the context of the intended patient population, guides the development of risk management strategies. In the in vitro area, this article describes the considerations that support the use of experimentally measured fraction unbound for drugs with > 99% protein binding, modification of several in vitro criteria used to recommend a clinical DDI study and modification of DDI assessment for metabolites. Areas of close attention by the EWG for clinical evaluation included the use of endogenous biomarker studies, the use of nested DDI studies, and the establishment of no-effect boundaries. The article indicates the value of describing a general process for evaluating UGT-mediated DDIs, although specific criteria are not available. The guideline describes the current understanding of the role of predictive modeling in DDI evaluation. The topics described in this article can stimulate further growth in the science of DDI assessments.

Impact of Cancer Cachexia Progression on OATP1B1 Transport Activity: Quantitative Analysis Using Coproporphyrin-I as an Endogenous Biomarker

Genetic factors, inflammatory cytokines such as interleukin (IL)-6 and tumor necrosis factor-α (TNF-α), and uremic substances such as 3-carboxy-4-methyl-5-propyl-2-furanpropanoic acid (CMPF) have been reported to affect organic anion transporting polypeptide (OATP)1B1 transport activity. However, the relationship between OATP1B1 transport activity and these factors in patients with cancer cachexia has not been reported. This study aimed to identify the factors contributing to individual differences in OATP1B1 transport activity in patients with cancer cachexia, using coproporphyrin-I (CP-I) as an endogenous biomarker of OATP1B1 transport activity. The study recruited 114 patients with cancer cachexia who satisfied the selection criteria. The subjects were classified into pre-cachexia, cachexia, and refractory cachexia. Median [interquartile range] plasma CP-I level was higher in patients with pre-cachexia (0.91 [0.67-1.12] ng/mL) compared with the data in the general population reported previously and tended to be higher in patients with refractory cachexia (1.06 [0.78-1.64] ng/mL) than in those with cachexia (0.87 [0.62-1.07] ng/mL), suggesting that OATP1B1 transport activity may decrease with the progression of cancer cachexia. Plasma CP-I correlated positively with IL-6 and TNF-α concentrations but did not correlate with OATP1B1 polymorphisms or CMPF concentration, which have been reported to reduce transport activity. Multiple regression analysis using the forced entry method identified refractory cachexia as a significant factor independently affecting plasma CP-I concentration. These findings suggest that the reduction in OATP1B1 transport activity in patients with cancer cachexia may be attributed to inflammatory cytokines or some other factors that are elevated by cancer cachexia progression, rather than OATP1B1 polymorphisms and CMPF.

Utility of Biomarker-Informed Drug Interaction Evaluation in Drug Development and Regulatory Decision Making

The measurement of endogenous biomarkers in plasma and urine before and after administration of an investigational drug in a clinical study may provide an early indication of its drug-drug interaction (DDI) potential via a specific pathway. In the first international harmonized guideline on drug interaction studies, the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) M12, endogenous biomarkers have been recognized as an emerging approach in the transporter- and enzyme-based DDI risk assessment. Clinical Pharmacology Roundtable Conference 2024 held at Pharmaceuticals and Medical Devices Agency (PMDA) brought together experts from regulatory agencies, academia, and industries to discuss potential advantages and challenges of the biomarkers approach in drug development and regulatory decision making. This meeting report facilitates stakeholders involved in drug development in better understanding the utility of biomarker approaches and promotes early implementation of biomarker-informed DDI evaluation in regulatory use.

Characterization and Prediction of Organic Anion Transporting Polypeptide 1B Activity in Prostate Cancer Patients on Abiraterone Acetate Using Endogenous Biomarker Coproporphyrin I

Organic anion transporting polypeptide (OATP) 1B1 and OATP1B3 are important hepatic transporters. We previously identified OATP1B3 being critically implicated in the disposition of abiraterone. We aimed to further investigate the effects of abiraterone on the activities of OATP1B1 and OATP1B3 utilizing a validated endogenous biomarker coproporphyrin I (CP-I). We used OATP1B-transfected cells to characterize the inhibitory potential of abiraterone against OATP1B-mediated uptake of CP-I. Inhibition constant (K i) was incorporated into our physiologically based pharmacokinetic (PBPK) modeling to simulate the systemic exposures of CP-I among cancer populations receiving either our model-informed 500 mg or clinically approved 1000 mg abiraterone acetate (AA) dosage. Simulated data were compared with clinical CP-I concentrations determined among our nine metastatic prostate cancer patients receiving 500 mg AA treatment. Abiraterone inhibited OATP1B3-mediated, but not OATP1B1-mediated, uptake of CP-I in vitro, with an estimated K i of 3.93 μM. Baseline CP-I concentrations were simulated to be 0.81 ± 0.26 ng/ml and determined to be 0.72 ± 0.16 ng/ml among metastatic prostate cancer patients, both of which were higher than those observed for healthy subjects. PBPK simulations revealed an absence of OATP1B3-mediated interaction between abiraterone and CP-I. Our clinical observations confirmed that CP-I concentrations remained comparable to baseline levels up to 12 weeks post 500 mg AA treatment. Using CP-I as an endogenous biomarker, we identified the inhibition of abiraterone on OATP1B3 but not OATP1B1 in vitro, which was predicted and observed to be clinically insignificant. We concluded that the interaction risk between AA and substrates of OATP1Bs is low. SIGNIFICANCE STATEMENT: The authors used the endogenous biomarker coproporphyrin I (CP-I) and identified abiraterone as a moderate inhibitor of organic anion transporting polypeptide (OATP) 1B3 in vitro. Subsequent physiologically based pharmacokinetic (PBPK) simulations and clinical observations suggested an absence of OATP1B-mediated interaction between abiraterone and CP-I among prostate cancer patients. This multipronged study concluded that the interaction risk between abiraterone acetate and substrates of OATP1Bs is low, demonstrating the application of PBPK-CP-I modeling in predicting OATP1B-mediated interaction implicating abiraterone.

Cluster Gauss-Newton method analyses of PBPK model parameter combinations of coproporphyrin-I based on OATP1B-mediated rifampicin interaction studies

Coproporphyrin I (CP-I) is an endogenous biomarker supporting the prediction of drug-drug interactions (DDIs) involving hepatic organic anion transporting polypeptide 1B (OATP1B). We previously constructed a physiologically-based pharmacokinetic (PBPK) model for CP-I using clinical DDI data with an OATP1B inhibitor, rifampicin (RIF). In this study, PBPK model parameters for CP-I were estimated using the cluster Gauss-Newton method (CGNM), an algorithm used to find multiple approximate solutions for nonlinear least-squares problems. Eight unknown parameters including the hepatic overall intrinsic clearance (CL_int,all ), the rate of biosynthesis (v_syn ), and the OATP1B inhibition constant of RIF(K_i,u,OATP ) were estimated by fitting to the observed CP-I blood concentrations in two different clinical studies involving changing the RIF dose. Multiple parameter combinations were obtained by CGNM that could well capture the clinical data. Among those, CL_int,all , K_i,u,OATP , and v_syn were sensitive parameters. The obtained K_i,u,OATP for CP-I was 5.0- and 2.8-fold lower than that obtained for statins, confirming our previous findings describing substrate-dependent K_i,u,OATP values. In conclusion, CGNM analyses of PBPK model parameter combinations enables estimation of the three essential parameters for CP-I to capture the DDI profiles, even if the other parameters remain unidentified. The CGNM also clarified the importance of appropriate combinations of other unidentified parameters to enable capture of the CP-I concentration time course under the influence of RIF. The described CGNM approach may also support the construction of robust PBPK models for additional transporter biomarkers beyond CP-I.

Effect of Cyclosporin A and Impact of Dose Staggering on OATP1B1/1B3 Endogenous Substrates and Drug Probes for Assessing Clinical Drug Interactions

This study was designed to assess the quantitative performance of endogenous biomarkers for organic anion transporting polypeptide (OATP) 1B1/1B3‐mediated drug‐drug interactions (DDIs). Ten healthy volunteers orally received OATP1B1/1B3 probe cocktail (0.2 mg pitavastatin, 1 mg rosuvastatin, and 2 mg valsartan) and an oral dose of cyclosporin A (CysA, 20 mg and 75 mg) separated by a 1‐hour interval (20 mg (−1 hour), and 75 mg (−1 hour)). CysA 75 mg was also given with a 3‐hour interval (75 mg (−3 hours)) to examine the persistence of OATP1B1/1B3 inhibition. The area under the plasma concentration‐time curve ratios (AUCRs) were 1.63, 3.46, and 2.38 (pitavastatin), 1.39, 2.16, and 1.81 (rosuvastatin), and 1.42, 1.77, and 1.85 (valsartan), at 20 mg, 75 mg (−1 hour) and 75 mg (−3 hours) of CysA, respectively. CysA effect on OATP1B1/1B3 was unlikely to persist at the dose examined. Among 26 putative OATP1B1/1B3 biomarkers evaluated, AUCR and maximum concentration ratio (C_maxR) of CP‐I showed the highest Pearson’s correlation coefficient with CysA AUC (0.94 and 0.93, respectively). Correlation between AUCR of pitavastatin, and C_maxR or AUCR of CP‐I were consistent between this study and our previous study using rifampicin as an OATP1B1/1B3 inhibitor. Nonlinear regression analysis of AUCR⁻¹ of pitavastatin and CP‐I against CysA C_max yielded K_{i,OATP1B1/1B3,app} (109 ± 35 and 176 ± 42 nM, respectively), similar to the K_{i,OATP1B1/1B3} estimated by our physiologically‐based pharmacokinetic model analysis described previously (107 nM). The endogenous OATP1B1/1B3 biomarkers, particularly C_maxR and AUCR of CP‐I, corroborates OATP1B1/1B3 inhibition and yields valuable information that improve accurate DDI predictions in drug development, and enhance our understanding of interindividual variability in the magnitude of DDIs.

Clinical Implications of Altered Drug Transporter Abundance/Function and PBPK Modeling in Specific Populations: An ITC Perspective

The role of membrane transporters on pharmacokinetics (PKs), drug-drug interactions (DDIs), pharmacodynamics (PDs), and toxicity of drugs has been broadly recognized. However, our knowledge of modulation of transporter expression and/or function in the diseased patient population or specific populations, such as pediatrics or pregnancy, is still emerging. This white paper highlights recent advances in studying the changes in transporter expression and activity in various diseases (i.e., renal and hepatic impairment and cancer) and some specific populations (i.e., pediatrics and pregnancy) with the focus on clinical implications. Proposed alterations in transporter abundance and/or activity in diseased and specific populations are based on (i) quantitative transporter proteomic data and relative abundance in specific populations vs. healthy adults, (ii) clinical PKs, and emerging transporter biomarker and/or pharmacogenomic data, and (iii) physiologically-based pharmacokinetic modeling and simulation. The potential for altered PK, PD, and toxicity in these populations needs to be considered for drugs and their active metabolites in which transporter-mediated uptake/efflux is a major contributor to their absorption, distribution, and elimination pathways and/or associated DDI risk. In addition to best practices, this white paper discusses current challenges and knowledge gaps to study and quantitatively predict the effects of modulation in transporter activity in these populations, together with the perspectives from the International Transporter Consortium (ITC) on future directions.

Physiologically-based pharmacokinetic model-based translation of OATP1B-mediated drug-drug interactions from coproporphyrin I to probe drugs

The accurate prediction of OATP1B-mediated drug-drug interactions (DDIs) is challenging for drug development. Here, we report physiologically-based pharmacokinetic (PBPK) model analysis for clinical DDI data generated in heathy subjects who received oral doses of cyclosporin A (CysA; 20 and 75 mg) as an OATP1B inhibitor, and the probe drugs (pitavastatin, rosuvastatin and valsartan). PBPK models of CysA and probe compounds were combined assuming inhibition of hepatic uptake of endogenous coproporphyrin I (CP-I) by CysA. In vivo K_i of unbound CysA for OATP1B (K_i,OATP1B ), and the overall intrinsic hepatic clearance per body weight of CP-I (CL_int,all,unit ) were optimized to account for the CP-I data (K_i,OATP1B , 0.657 ± 0.048 nM; CL_int,all,unit , 57.0 ± 6.3 L/h/kg). DDI simulation using K_i,OATP1B reproduced the dose-dependent effect of CysA (20 and 75 mg) and the dosing interval (1 h and 3 h) on the time profiles of blood concentrations of pitavastatin and rosuvastatin, but DDI simulation using in vitro K_i,OATP1B failed. The Cluster Gauss-Newton method was used to conduct parameter optimization using 1,000 initial parameter sets for the seven pharmacokinetic parameters of CP-I (β, CL_int,all , F_a F_g , R_dif , f_bile , f_syn , and v_syn ), and K_i,OATP1B , and K_i,MRP2 of CysA. Based on the accepted 498 parameter sets, the range of CL_int,all and K_i,OATP1B was narrowed, with coefficients of variation (CVs) of 9.3% and 11.1%, respectively, indicating that these parameters were practically identifiable. These results suggest that PBPK model analysis of CP-I is a promising translational approach to predict OATP1B-mediated DDIs in drug development.

Integration of DNA sequencing with population pharmacokinetics to improve the prediction of irinotecan exposure in cancer patients

BACKGROUND

Irinotecan (CPT-11) is an anticancer agent widely used to treat adult solid tumours. Large interindividual variability in the clearance of irinotecan and SN-38, its active and toxic metabolite, results in highly unpredictable toxicity.

METHODS

In 217 cancer patients treated with intravenous irinotecan single agent or in combination, germline DNA was used to interrogate the variation in 84 genes by next-generation sequencing. A stepwise analytical framework including a population pharmacokinetic model with SNP- and gene-based testing was used to identify demographic/clinical/genetic factors that influence the clearance of irinotecan and SN-38.

RESULTS

Irinotecan clearance was influenced by rs4149057 in SLCO1B1, body surface area, and co-administration of 5-fluorouracil/leucovorin/bevacizumab. SN-38 clearance was influenced by rs887829 in UGT1A1, pre-treatment total bilirubin, and EGFR rare variant burden. Within each UGT1A1 genotype group, elevated pre-treatment total bilirubin and/or presence of at least one rare variant in EGFR resulted in significantly lower SN-38 clearance. The model reduced the interindividual variability in irinotecan clearance from 38 to 34% and SN-38 clearance from 49 to 32%.

CONCLUSIONS

This new model significantly reduced the interindividual variability in the clearance of irinotecan and SN-38. New genetic factors of variability in clearance have been identified.