Year: 2023
Type: article
Abstract: Published in Clinical Pharmacology and Therapeutics in 2001, the National Institutes of Health Biomarkers Definitions Working Group provided an early definition of a biomarker as “a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention.”1 Since then, several organizations have expanded on the definition, including the World Health Organization (WHO); however, the essential features of a biomarker remain the same: it must be measurable and reflect the interaction between a biological system and a potential perturbation, which may be chemical, physical, or biological.2 Currently, the US Food and Drug Administration (FDA) defines seven biomarker categories: susceptibility/risk, diagnostic, monitoring, prognostic, predictive, pharmacodynamic/response, and safety.3 In this issue of Clinical Pharmacology and Therapeutics, several papers focus on biomarkers for pharmacokinetic transporters, which may be categorized as safety biomarkers. Rodrigues describes the current state of the art methods for assessing transporter-mediated drug–drug interaction (DDI) risk in a paper entitled “Reimagining the Framework Supporting the Static Analysis of Transporter Drug Interaction Risk; Integrated Use of Biomarkers to Generate Pan-Transporter Inhibition Signatures.”4 The author makes the point that current methods, which rely on in vitro studies in cells expressing transporters along with criteria from regulatory bodies, are “static” and result in many false positive predictions of DDI risk. In contrast, they describe the use of biomarkers for solute carrier (SLC) transporters in the liver and kidneys. Beginning with a referenced list of biomarkers for liver transporters, including OATP1B1 (SLCO1B1), OATP1B3 (SLCO1B3), and OCT1 (SLC22A1), as well as renal transporters OCT2 (SLC22A2), MATE1 (SLC47A1), MATE2K (SLC47A2), OAT3 (SLC22A6), and OAT3 (SLC22A8), Rodrigues demonstrates that quantitative changes in these biomarkers predict quantitative changes in drug exposure as indicated by an area under the curve (AUC) ratio (AUCR = AUC of the drug in the presence of a transporter inhibitor divided by control AUC of the drug).3 Clearly preferable to the current “static” methods, transporter biomarkers coupled with physiologically-based pharmacokinetic (PBPK) models, provide a quantitative prediction of transporter-mediated DDIs. Expanding on the use of biomarkers for predicting DDIs, Lin et al.5 in their paper entitled “Effect of Hepatic Impairment on OATP1B1 Activity: Quantitative Pharmacokinetic Analysis of Endogenous Biomarker and Substrate Drugs,” describe the use of biomarkers in predicting the effect of hepatic impairment on the activity of OATP1B. Notably, in a group of patients with varying degrees of hepatic impairment, the authors establish that mean levels of the OATP1B1 biomarker, coproporphyrin-I (CPI) significantly increase as hepatic function decreases (severe > moderate > mild). Similarly, mean levels of drugs that are OATP1B1 substrates, indicated as an AUCR (AUC of the drug in the presence of hepatic impairment divided by AUC of the drug in healthy controls) also significantly increase as hepatic function deteriorates. A PBPK model is developed to predict AUC change of CPI in moderate and severe hepatic impairment. Their elegant study combining biomarkers, hepatic disease, and PBPK modeling underscores how biomarkers can be used to assess transporter activity, which may be modulated by both DDIs and hepatic impairment. Whereas Rodrigues4 and Lin et al.5 focus on the use of circulating biomarkers of transporters to predict transporter activity changes either through DDIs or human disease, transporter activity may also be modified by genetic polymorphisms. For example, OATP1B1 activity is modulated by several reduced function genetic polymorphisms. Last month's paper from the Pharmacogene Variation Consortium (PharmVar) for the first time extends the star allele nomenclature, commonly used to identify distinct haplotypes (i.e., the combination of variants on the same chromosome within a defined region) of drug metabolizing enzymes, to a transporter (i.e., OATP1B1).6 Standardized SLCO1B1 allele definitions, which are compliant with PharmVar standards and allele definition criteria, are presented in this paper. The new allele definitions should replace legacy definitions, which are complicated and confusing. Many of the alleles are associated with changes in OATP1B1 activity. As such, the relationship between alleles of OATP1B1 and levels of circulating biomarkers have been the subject of several early studies published in Clinical Pharmacology and Therapeutics and in Clinical and Translational Science. In particular, Yee et al.7 exploited publicly available genomewide association studies to discover and validate several plasma biomarkers of OATP1B1. This team previously showed highly significant associations between the OATP1B1 polymorphism p.Val171Ala (rs4149056) and levels of a number of endogenous biomarkers, including tetradecanedioic acid, glycochenodeoxycholate glucuronide, and glycodeoxycholate sulfate.8 In addition, isobutyryl-L-carnitine and N1-methyladenosine have been shown by other groups to be biomarkers for drug interaction studies involving hepatic OCT1 transporter and renal OCT2 transporter, respectively.9 Polymorphisms in these transporters have been shown to have significant associations with the levels of these biomarkers.10 These studies demonstrate that circulating biomarkers can be used to predict polymorphisms of transporters and vice versa, that is, polymorphisms (which are genetic biomarkers) predict levels of circulating biomarkers as well as levels of drugs that are substrates of the transporter. The fourth manuscript by Muller et al.11 goes beyond measurements of circulating levels of biomarkers to predict DDIs; instead, the paper entitled “N1-Methylnicotinamide as Biomarker for MATE-Mediated Renal Drug–Drug Interactions: Impact of Cimetidine, Rifampin, Verapamil, and Probenecid,” focuses on the use of renal clearance (CLR) of a biomarker, which can be used to predict DDIs caused by altered activity of transporters in the kidneys.11 The paper concerns N1-methylnicotinamide (NMN) and DDIs mediated by the multidrug and toxin extrusion proteins (MATEs). The study demonstrates that rather than circulating levels of NMN, CLR of NMN is the biomarker for DDIs involving MATEs. In their elegant studies, they show that plasma levels of NMN increase in the presence of four drugs, which are not inhibitors of MATEs: digoxin, metformin, furosemide, and rosuvastatin, as well as a cocktail of the four drugs. However, CLR (0–12 hours) does not change significantly in the presence of any of the four drugs or the cocktail. In contrast, with the addition of the established MATE inhibitor, cimetidine, CLR of NMN was significantly reduced, consistent with reduced MATE-mediated renal secretion of this endogenous biomarker. The investigators conclude that NMN CLR, but not circulating levels, is a specific and sensitive marker for MATE-mediated DDIs in the kidneys. One final paper reminds us that in addition to refining and expanding the use of biomarkers for clinical DDIs, in vitro predictions also need to be continuously refined. In their paper entitled “Beyond the Michaelis–Menten: Accurate Prediction of Drug Interactions through Cytochrome P450 3A4 Induction,” Vu et al.12 point out that the FDA has established guidelines for DDIs mediated by induction of cytochrome P450 enzymes (CYPs). Challenging the assumptions of the FDA guidelines and refining equations, Vu et al. provide new equations that more accurately predict the effects of CYP inducers especially for drugs that have high affinities for CYPs. These studies highlight the need for improving the analysis of in vitro and computational methods in parallel with other methods, such as measurements and use of circulating biomarkers, to continuously increase the accuracy of predictions of DDIs. Collectively, the papers in this issue of Clinical Pharmacology and Therapeutics advance and refine our current methods for predicting DDIs mediated by drug transporters through the use of measured biomarkers or refinement of analysis of data obtained from in vitro methodologies. The papers go beyond DDIs, highlighting new advances in the methodologies used to predict perturbations in the activity of transporters and enzymes on pharmacokinetics, which are caused by human disease, genetic polymorphisms, and concomitant medications. No funding was received for this work. The author declared no competing interests for this work.
DOI: 10.1002/cpt.2880
Author: Kathleen M. Giacomini
Institution: University of California, San Francisco
Language: English
Subfield: Pharmacology
Domain: Life Sciences
Open Access status: bronze
OpenAlex ID: W4366240407