Utilising Endogenous Biomarkers in Drug Development to Streamline the Assessment of Drug-Drug Interactions Mediated by Renal Transporters: A Pharmaceutical Industry Perspective

Abstract

The renal secretion of many drugs is facilitated by membrane transporters, including organic cation transporter 2, multidrug and toxin extrusion protein 1/2-K and organic anion transporters 1 and 3. Inhibition of these transporters can reduce renal excretion of drugs and thereby pose a safety risk. Assessing the risk of inhibition of these membrane transporters by investigational drugs remains a key focus in the evaluation of drug-drug interactions (DDIs). Current methods to predict DDI risk are based on generating in vitro data followed by a clinical assessment using a recommended exogenous probe substrate for the individual drug transporter. More recently, monitoring plasma-based and urine-based endogenous biomarkers to predict transporter-mediated DDIs in early phase I studies represents a promising approach to facilitate, improve and potentially avoid conventional clinical DDI studies. This perspective reviews the evidence for use of these endogenous biomarkers in the assessment of renal transporter-mediated DDI, evaluates how endogenous biomarkers may help to expand the DDI assessment toolkit and offers some potential knowledge gaps. A conceptual framework for assessment that may complement the current paradigm of predicting the potential for renal transporter-mediated DDIs is outlined.

Fig. 1

Schematic of the kidney (A), nephron with major blood vessels (B) and renal proximal tubule cells (C). In (B), the basic physiological mechanisms of handling fluid and electrolytes by the nephron, filtration, secretion, reabsorption and excretion are labelled. In (C), the major renal membrane transporters expressed on renal proximal tubule cells and their potential endogenous biomarkers are shown. The transporters located in the basolateral plasma membrane include organic anion transporter (OAT) 1/3 and organic cation transporter (OCT) 2. Transporters located in the apical membrane include multidrug and toxin efflux protein 1 (MATE) 1/2-K. GCDCA-s glycochenodeoxycholate-3-sulphate, HVA homovanillic acid, NMN N1-methylnicotinamide, m¹A N1-methyladenosine, PDA pyridoxic acid

Fig. 2

Current approach (A) [4, 26, 27] and proposed biomarker-informed approach (B) decision process to streamline the renal transporter (organic cation transporter [OCT] 2, multidrug and toxin extrusion protein [MATE] 1, MATE2-K) drug–drug interaction (DDI) risk assessment. AUC area under the plasma concentration–time curve, CL_R renal clearance, EMA European Medicines Agency, FDA US Food and Drug Administration, NME new molecular entity, OAT organic anion transporter, PoCP proof-of-clinical principle, SRD single rising dose, MRD multiple rising dose, ↑ increased, ↓ decreased, *significant increase in AUC is greater than 1.25-fold; *baseline (predose) urine or plasma biomarker concentrations are required for an accurate assessment of biomarker exposure (AUC, CL_R) changes; ***significant decrease in CL_R is less than 0.80-fold. A PoCP study shows that a candidate drug results in a biological and/or clinical change associated with the disease and the mechanism of action. A PoCP study is most critical when developing novel innovative compounds, and less relevant for less innovative compounds developed in a pre-determined linear manner where there are fewer uncertainties and risks

Fig. 3

Correlation (R² values) between renal clearance (CL_R) ratio (adjusted gMean) of biomarkers (N¹-methyladenosine [m¹A], N¹-methylnicotinamide [NMN] and creatinine) versus metformin when administered with or without various potential renal transport inhibitors (trimethoprim [37], pyrimethamine [21], aboricitinib [41], cimetidine [42] and bevurogant [43]) in healthy volunteers. *Metformin 10 mg was given as part of transporter cocktail comprising digoxin 0.25 mg, furosemide 1 mg, metformin 10 mg and rosuvastatin 10 mg [56]. Bid twice daily, MD multiple doses, qd, once daily, qid four times daily, SD single dose

Fig. 4

Interplay of factors influencing the measurement of endogenous biomarkers for assessing renal transporter-mediated drug–drug interactions (DDIs) in clinical drug development. The dotted lines (and the respective arrows) indicate the connection between each of the components shown in the different boxes on the figure. Exogenous and endogenous factors and various diseases (several examples shown) on the left of the figure can all influence the assessment of endogenous biomarkers levels within the context of the relevant renal transporter. The exogenous, endogenous and disease impact factors, along with endogenous biomarkers both influence the drug interaction assessment and thereby influence the clinical development process shown on the right of the figure. Examples of diseases that can influence endogenous biomarker levels are shown by the solid green line. ADHD attention-deficit hyperactivity disorder, RA rheumatoid arthritis. ^aRefers to phase I clinical trials (in healthy volunteers or in oncology patients) conducted prior to a phase I DDI study. ^bOnce the drug label (package insert) is approved and available, pharmacists and healthcare providers use it to manage medication therapy (pharmacotherapy). The drug label serves as the written rule, with medication therapy being the actual implementation of that rule. Together, these two parts ensure safe and effective treatment