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

Abstract

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.

BACKGROUND

In 2024, the first guidelines on drug–drug interaction (DDI) evaluation were published by the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH). ¹ The ICH M12 guideline titled “Drug Interaction Studies” indicates use of endogenous biomarkers as an emerging topic; and the biomarker‐based approaches are expected to expand for clinical evaluation of transporter‐mediated inhibition. The Clinical Pharmacology Roundtable Conference was started in 2020 as an opportunity for clinical pharmacology experts from regulatory agencies, academia, and industries in Japan to discuss novel methodologies to be implemented in drug development or regulatory aspects related to clinical pharmacology. On January 31, 2024, the third conference was held jointly by the Pharmaceuticals and Medical Devices Agency (PMDA), the University of Tokyo, and Japan Pharmaceutical Manufacturers Association with the theme of using endogenous biomarkers for DDI evaluation. The conference held at PMDA consisted of keynote lectures in the morning, followed by eight‐group discussions including presentation of results and a general discussion in the afternoon, with approximately 80 experts participating. This meeting report provides a scientific overview of the conference as well as future perspectives obtained from the day‐long discussions such as benefits, limitations, or implementation challenges to utilize the biomarker approach in drug development or regulatory decision making.

MEETING REPORT

Morning session: Keynote lectures and introduction to the ICH M12

Two experts, Hiroyuki Kusuhara and Emi Kimoto, provided an overview of endogenous biomarkers for drug transporters and their utility to support DDI risk assessment from the perspectives of academia and industry, respectively.

Endogenous substrates with good selectivity and minimal day‐to‐day variation in the pharmacokinetic (PK) parameters can serve as reliable surrogate probes for major drug transporters in the liver and kidney, ² that is, endogenous biomarkers, to assess transporter activity changes from baseline. The current guidance is that sponsors should determine the need for clinical DDI studies when in vitro inhibition data indicate DDI potential. ³ , ⁴ , ⁵ Measuring validated transporter biomarkers early in phase I may provide an effective approach to addressing the conservative thresholds used for predicting DDI risks in preclinical phase, and to offer better understanding of transporter‐mediated interactions (Figure 1 ).

Figure 1.

The Four W's and One H for Biomarker Strategy. *Due to the characteristics of endogenous biomarkers for DDI risk assessment, pharmacokinetic parameters of the endogenous biomarker such as C_max, AUC, and CL_r, with or without treatment of precipitants, can indicate transporter inhibition. The parameters highly sensitive to transporter inhibition vary depending on the transporter inhibited. For hepatic transporters, C_max and/or AUC are used, while CL_r is generally selected for renal transporters. Additionally, to determine changes in parameters caused by precipitants, criteria such as statistical tests or bioequivalence are considered. The thresholds for parameter changes that trigger further investigation are not clearly defined in current regulatory guidelines and remain subjects for future discussion. DDI, drug–drug interaction; SAD, single ascending dose; MAD, multiple ascending dose; C_max, maximal total plasma concentration; AUC, area under the plasma concentration vs. time curve; CL_r, renal clearance; PBPK: physiologically based pharmacokinetic.

For instance, coproporphyrin I (CP‐I), a byproduct during heme synthesis, is considered as a validated biomarker for multispecific organic anion transporting polypeptide (OATP) 1B1/1B3 in the liver due to good selectivity and sensitivity with sufficient clinical reports by precipitants ( S1–S3 ). ⁶ The utility of CP‐I is that its readout can distinguish rosuvastatin DDI mechanisms by inhibiting either or both OATP1B1/1B3 and breast cancer resistance protein (BCRP) ( S4 ). ⁷ Another potential advantage of use of CP‐I is to aid in understanding OATP1B activity in disease populations, such as cancer and organ impaired patients ( S5, S6 ). ⁸ , ⁹ , ¹⁰

Endogenous biomarkers to track in vivo activity of other drug transporters are also proposed – creatinine, N¹‐methylnicotinamide (NMN) and N¹‐methyladenosine for organic cation transporter (OCT) 2‐ multidrug and toxin extrusion protein (MATE)1/2K ( S7, S8 ), ¹¹ pyridoxic acid (PDA) for organic anion transporter (OAT) 1/3 ( S7–S12 ), ¹¹ 6β‐hydroxycortisol for OAT3, ¹² and isobutyryl‐L‐carnitine (IBC) for OCT1 ( S13–S16 ). ¹³ These biomarkers are not fully validated yet. ² However, ongoing data collection using the biomarkers can support the assessment of DDI risk and inform decision making in the future.

To acquire convincing evidence, there are several important considerations in the design of clinical study. Ratios of maximal total plasma concentration (C_max), area under the plasma concentration vs. time curve (AUC) or renal clearance (CL_r) in the precipitant treatment period compared to the baseline level are used as quantitative indicators. Along with a decrease in precipitant concentrations, plasma concentrations and CL_r of endogenous biomarkers return to the baseline levels. For endogenous biomarkers that are synthesized in the body during the study, long time integration causes AUC and CL_r ratios to approach unity, resulting in underestimation of DDI liability. Adequate frequency of specimen collection is required according to the duration of the inhibitory effect.

Biomarker‐informed physiologically based pharmacokinetic (PBPK) approach can translate the endogenous biomarker data to the DDI risks. Model simulations for biomarkers using clinical observed data can establish confidence in the in vivo inhibition constant (K_i) of an inhibitor; while such verified PBPK models can subsequently be used to predict various DDIs of transporter substrate drugs ( S17–S24 ). ¹⁴ In the future, it may be possible that the modeling and simulation data could be acceptable for biowaiver of some DDI studies in addition to aid drug development decisions.

Industry case studies demonstrating the utility of transporter biomarkers in support of development compounds and internal decision making at Pfizer were shared. For example, in vitro half maximal (50%) inhibitory concentration data of PF‐0083523, which is an active moiety of lufotrelvir – severe acute respiratory syndrome coronavirus 2 3C‐like protease inhibitor for intravenous treatment, predicted a low‐risk DDIs for OATP1B1/3 and MATE1/2K at the projected clinical exposure. ¹⁵ Inhibition potential in vivo was assessed by measuring CP‐I and NMN in phase I study as a follow‐up. As a result, no change in plasma CP‐I and renal NMN in single ascending‐dose study could rule out OATP1B and MATEs liabilities. ¹⁶ Nevertheless, more clinical data are warranted for confidence in quantitative DDI predictions by endogenous biomarkers such as NMN.

An update of the ICH M12 guideline development was reported by Akihiro Ishiguro, Regulatory Chair of the M12 Expert Working Group. Findings demonstrating the utility of biomarker approaches have continued to emerge even after publishing the draft guideline of the ICH M12 in May 2022. The research progress accelerated expert discussions to clarify the role of endogenous biomarkers in clinical DDI evaluation during guideline finalization. A new section on biomarker approaches will be added providing a description of considerations for biomarker approaches, including an example of CP‐I evaluation for hepatic OTAP1B inhibition.

Afternoon session: Roundtable discussions

In the roundtable discussions, topics shown in Table S1 were discussed by experts, who pre‐read backgrounds for each topic. Eighty‐one experts (29 delegates from PMDA, two researchers from academia, 50 delegates from pharmaceutical industry including 12 non‐clinical researchers and 34 researchers of clinical development) were assigned to one of eight groups to discuss Topic 1 and Topic 2 in parallel. The followings are major opinions from participants.

In Topic 1‐a, initially, optimal development strategy for CP‐I evaluation, such as timing and study design, was discussed. Many opinions were expressed that it is efficient to obtain CP‐I data along with PK data of an investigational drug in the phase I study typically in healthy volunteers, especially within the framework of existing single and multiple ascending dose studies. Data from a single dose study may be sufficient for this purpose. CP‐I sampling points are basically assumed to be selected the same as those of the PK sampling. However, when indicating the possibility of time to reach C_max (T_max) differences between CP‐I and an investigational drug, it is important to consider additional sampling to accurately capture C_max of CP‐I. It is better to measure the genetic polymorphisms of OATP1B. If CP‐I data can be evaluated at an earlier timing to mitigate the clinical impact of OATP1B inhibition of an investigational drug, further investigational studies such as clinical DDI studies are not required, and useful information can be provided for clinical studies in patients (e.g., mitigation of concomitant medication restrictions). CP‐I data should be obtained at the recommended clinical dose of an investigational drug. Since phase I studies are usually initiated at a small dose, the timing of obtaining CP‐I data should carefully consider potential dose‐range to be tested in the early clinical studies.

Next, cutoff values for CP‐I evaluation and decision‐tree were discussed as shown in Topic 1‐b. There is no consensus at this time whether it is sufficient to evaluate DDI via OATP1B for an investigational drug using only one parameter (either C_max or AUC), or using both parameters. There were many opinions that it was too early to establish criteria. As a conservative approach at this point, it would be feasible to obtain sufficient sample data which can draw AUC for CP‐I appropriately considering diurnal variation and then to evaluate with both parameters (C_max and AUC). As for the cutoff values of CP‐I C_maxR and AUCR, which represent the ratio of C_max and AUC in the presence and absence of investigational drugs, respectively, “1.25” has been presented in several literatures, and is considered reasonable at this time. However, further knowledge is needed to eliminate false negatives of DDI liabilities. Although the PBPK modeling of CP‐I may be constructed with certain reliability based on the kinetic parameters of CP‐I obtained from the previous clinical studies with other drugs, better understanding of the biosynthesis and disposition of CP‐I, including inter‐subject variation, will lead to much more precise quantitative evaluation of DDI using this endogenous substrate.

Topic 2‐a focused on the CP‐I evaluation in non‐healthy volunteers, especially in cancer patients. CP‐I baseline concentrations increase in certain types of cancer patients, although the number of cases is limited. ⁹ , ¹⁰ However, the underlying mechanism is unclear. Decreasing hepatic uptake clearance, decreasing unbound fraction in plasma, and increasing a CP‐I biosynthesis can be considered as possible mechanisms of an increase in the CP‐I baseline in cancer patients. When the OATP1B contribution decreases, the dynamic ranges for CP‐I C_maxR and/or AUCR may become narrow, followed by an increase in false negatives using cutoff criteria set for healthy volunteers. However, other factors including an increase in CP‐I biosynthesis may not cause false negatives. Other considerations in patients include the burden of blood collection volume, PK differences in drugs themselves, and the absence of CP‐I PBPK models. In conclusion, the CP‐I evaluation still has limitations in cancer patients due to the limited data; however, the measurement of CP‐I may help us to comprehensively judge the DDI risk.

In topic 2‐b, endogenous biomarkers beyond CP‐I were discussed. Compared to CP‐I, several issues should be considered: multiple blood sampling for the pre‐dose may be required to minimize the effect of intra‐day variation on the baseline C_max and AUC; concomitant drugs may affect biosynthesis of the biomarkers (e.g. NMN), necessitating urinary measurement in addition to AUC calculation. Also, sufficient DDI data also should be required to predict adequate cutoff values for the DDI risk evaluation. A multiple measurement for endogenous substrates of multiple transporters in a clinical study may be advantageous when a drug inhibits multiple transporters in vitro, but the validated bioanalysis is required. A multiple biomarkers approach for a specified transporter may supplement each other issue such as low selectivity, but the evaluation and interpretation may become complicated. Regarding endogenous biomarkers other than CP‐I, further accumulation of clinical data and analyses are expected.

PERSPECTIVES

The research progress by academia and industries in recent years regarding transporter biomarkers has been remarkable. Regulatory authorities have also recognized the contribution of endogenous biomarkers to facilitate assessment of transporter‐mediated DDI. ¹⁷ Therefore, biomarkers with reliable metrics are being implemented in drug development to refine or replace dedicated clinical DDI studies. At the clinical pharmacology roundtable conference 2024, there was a consensus that biomarker‐informed drug interaction evaluation is an emerging approach that will become widespread in both drug developments and regulatory use.

The cutoff value of CP‐I change has been reported as 1.25 for both C_maxR and AUCR. Theoretically, AUCR of endogenous biomarker does not exceed its C_maxR when capturing T_max accurately. This makes C_maxR more conservative metrics. Discussion remains on which metrics would be used for DDI assessment, whereas the IQ Consortium recommends the use of CP‐I C_maxR. ⁶ At the moment, it would be appropriate to collect both C_maxR and AUCR based on a time course of CP‐I in order to evaluate further relationship between clinical DDIs and CP‐I changes during the investigational drugs' dosing interval. As more such data emerge, reevaluation of cutoff criterion can be made overtime. If the C_maxR and/or AUCR is beyond the cutoff value, in vivo K_i for the OATP1B of a target investigational drug can be estimated from verified precipitant and CP‐I PBPK models, which can then be utilized to predict the degree of DDI and facilitate the design of efficient dedicated DDI studies.

Availability of biomarkers also contribute to the advancement of Model‐Informed Drug Development with adequate implementation of PBPK modeling approach. In cases where conducting dedicated DDI studies in healthy adults with an investigational drug is challenging, measuring endogenous biomarkers in clinical studies in patients can become a useful means for evaluating DDI potential. However, for example, it is necessary to evaluate the cutoff value of 1.25 for CP‐I to be applied to patient populations and to accumulate knowledge regarding the PK profile of CP‐I based on characteristics of each population, such as disease mechanisms and severity. Research is also underway for the utilization of endogenous biomarkers other than CP‐I, as shown in Table  S1, 2‐b . Because the characteristics of biomarkers vary, the accumulation of knowledge for each biomarker regarding sensitivity, selectivity, dynamic range, correlation with PK parameters of probe drugs, and variability (factors such as diet, age, exercise, intra‐day variability, and pathology) is necessary. Multiplex assays may facilitate data collection in pharmaceutical development for endogenous biomarkers other than CP‐I. In cases where endogenous biomarkers are used as an alternative evaluation method for dedicated DDI studies, continuous scientific communication with regulatory authorities to validate the evaluation system and accumulate mutual knowledge and experience is also important. ¹⁸

By accumulating successful DDI evaluations using an endogenous biomarker with a new investigational drug approved, it is expected that biomarker‐informed approach can be a standard approach to assess transporter‐mediated interactions. As CP‐I is considered as the most validated biomarker for OATP1B‐mediated DDI risk assessment, no significant change in plasma CP‐I in phase I study can serve as supporting information for drug labeling to rule out OATP1B liabilities. On the other hand, the significant changes guide subsequent in vivo DDI evaluation strategies through quantitative DDI predictions by using validated PBPK models of an investigational drug, CP‐I and OATP1B1 substrates. Similar approach to CP‐I could be warranted for other endogenous biomarkers. Accumulation of scientific knowledge that warrants for confidence in quantitative DDI predictions by endogenous biomarkers will accelerate to use the results of biomarker‐informed drug interaction evaluation in drug labels. The missing piece of five Ws in Figure 1 , will be “Where can we utilize a biomarker strategy?” that is expected to be utilized in clinical settings by healthcare providers in addition to internal and regulatory decision makings.

Untargeted or targeted metabolomics is a promising technology. The untargeted approach is useful for exploratory studies to identify new endogenous biomarkers for drug transporters as previously reported, while the targeted approach is beneficial for multiplexed analysis to investigate the inhibition potency of a drug against one transporter or across multiple transporters in one clinical study. ¹⁹ In the case of DDI study with cyclosporin A, even when combining the variations of multiple biomarkers, their predictive accuracy of OATP1B‐mediated interactions did not surpass that of CP‐I. ²⁰ Considering multispecific transporters may be involved in plasma and urine levels of other endogenous biomarkers, it would be beneficial to confirm the trends of other biomarkers through targeted metabolomics analysis to enhance the reliability of the DDI risk assessment even if a single biomarker is the most validated. Besides, the impact of hepatic and renal impairment severity on the variations in plasma concentrations of endogenous biomarkers is also being collected. These biomarkers are expected to be used for personalized medicine, aiding in the selection of drugs and optimization of dosing regimens for drugs that share common clearance pathways, independent of conventional laboratory tests used to assess organ function. On top of this, the targeted approach can be used as a multiplexed quantitation of several biomarkers to support clinical DDI risk across various transporters if a drug inhibits multiple transporters. Capturing comprehensive metabolic pathways using metabolomics might be useful in elucidating unknown disease/metabolome interactions, such as baseline variations in CP‐I.

Pharmaceuticals and Medical Devices Agency started an initiative called “Early Consideration” during the fifth mid‐term plan from April 2024 to March 2029. ²¹ The initiative aims to disseminate the PMDA's opinion as soon as possible, which will guide as reference information for the practical application of innovative technology. The conference held in anticipation of the ICH M12 guideline completion has become an opportunity to deepen common understanding among stakeholders by focusing on emerging approaches that have been decided to be included in the new guideline. This report can serve as the basis for disseminating future “Early Considerations” regarding biomarker‐informed drug interaction evaluation.

Besides solute carrier transporters, plasma riboflavin has recently been recognized as an endogenous biomarker for BCRP, which is an ATP‐binding cassette efflux transporter ( S25 ). ²² Accumulation of such findings on potential biomarkers for drug metabolizing enzymes and transporters indicates the need to continue discussions on the regulatory utilization of robust endogenous biomarker candidates. As implementation of the ICH M12 guidelines steadily progresses, ²³ continuous scientific discussions among academia, industries, and regulatory agency are needed to establish best practice for biomarker‐informed drug interaction evaluation. The discussions will guide future topics that require international harmonization of DDI evaluation beyond the ICH M12.

FUNDING

No funding was received for this work.

CONFLICT OF INTEREST

The authors declare no competing interests for this work.

DISCLAIMER

The views expressed herein are the result of independent work and do not necessarily represent the views of Pharmaceuticals and Medical Devices Agency.

Supporting information

Table S1.