Estimation of Clinical Changes in SCr and CrCL

Renal tubular secretion of creatinine is mediated by drug transporters, such as organic anion transporter (OAT) 2, organic cation transporter (OCT) 2, OCT3, multidrug and toxin extrusion protein 1 (MATE1), and MATE2-K, and it was suggested that SCr is increased by the administration of drugs that inhibit especially MATE1 and MATE2-K at clinical concentrations (Chu et al., 2016). Mathialagan et al. assessed inhibition potencies against OAT2, OCT2, MATE1, and MATE2-K for 15 drugs and compared their clinical changes in SCr, CrCL, and area under the curve of metformin although the mechanistic approach to estimate SCr changes was still challenging (Mathialagan et al., 2017). We have examined whether an estimation of SCr elevation considering competitive inhibition of these renal transporters can reproduce actual changes observed in healthy adults (Nakada et al., 2018; Nakada et al., 2019).

Trimethoprim, which is used for the treatment of various infectious diseases, such as urinary tract infection and pneumocystis pneumonia, was used as a model drug (Brogden et al., 1982). During the treatment period of trimethoprim, an increase in SCr from baseline and a decrease in CrCL were reported in several studies despite no changes in renal function (Table 1). Trimethoprim has been shown to inhibit the transporters OCT2, OCT3, MATE1, and MATE2-K in in vitro studies using cell lines expressing these transporters (Table 2) and to decrease the renal clearance of metformin (typical substrates of OCT2, MATE1, and MATE2-K) in healthy adult subjects (Muller et al., 2015). However, it is unknown to what extent the inhibition of these transporters during trimethoprim treatment contributes to the SCr elevation. A creatinine pharmacokinetic model was used based on a previous model (Imamura et al., 2011) with a modification of relative contributions of these transporters (Nakada et al., 2018). The tubular secretion of creatinine and the inhibition of the renal transporters by trimethoprim are described as follows.

In eq. 1, FR (0.332), f_u,cr (1.0), CL_rs, CL_rs,int (53.0 ml/min), GFR (90 or 125 ml/min), and RPF (502 ml/min) are the fraction reabsorbed from the urinary tract, the protein unbound fraction of creatinine in serum, the renal secretion clearance, the intrinsic clearance for tubular secretion, glomerular filtration rate, and renal plasma flow, respectively. In eqs. 2–6, f_OAT2 (0.887), f_OCT2 (0.093), and f_OCT3 (0.020) are the relative contributions of OAT2, OCT2, and OCT3 on the basolateral side, respectively; f_MATE1 (0.186) and f_MATE2-K (0.814) are the relative contributions of MATE1 and MATE2-K on the apical side, respectively, and C_u,TMP is the unbound concentration of trimethoprim. The geometric mean of the multiple reported IC₅₀ values was used as the inhibitory effect of trimethoprim on each kidney transporter (Table 2). The relative contributions of OAT2, OCT2, and OCT3 to the renal uptake of creatinine (f_OAT2, f_OCT2, and f_OCT3) were estimated based on the values of V_max/K_m in HEK293 cells transfected with OAT2, OCT2, or OCT3 (Lepist et al., 2014; Shen et al., 2015) and protein expression data of these transporters in human kidney (Nakamura et al., 2016), except for OCT3, of which the mRNA expression level relative to OCT2 in human kidney (Hilgendorf et al., 2007) was used due to lack of information on its protein expression level. The relative contributions of MATE1 and MATE2-K to renal secretion of creatinine (f_MATE1 and f_MATE2-K) were estimated based on the relative values of V_max/K_m in transporter-expressing HEK293 cells (Shen et al., 2015) and protein expression levels in human kidney (Nakamura et al., 2016). R_OAT2, R_OCT2, R_OCT3, R_MATE1, and R_MATE2-K represent the contribution of these transporters to the renal uptake (i.e., R_OAT2, R_OCT2, and R_OCT3) or the renal secretion of creatinine (i.e., R_MATE1 and R_MATE2-K) in the presence or absence of an inhibitor.

The blood concentration−time profile of trimethoprim was described with a physiologically-based pharmacokinetic (PBPK) model. Simulated time-courses of SCr and CrCL after administrations of trimethoprim under actual conditions well reproduced observed increases in SCr in healthy subjects (Nakada et al., 2018). We further investigated whether this method for estimating the SCr changes can be applied to 14 renal transporter inhibitors, including trimethoprim, namely, cimetidine, cobicistat, dolutegravir, dronedarone, DX-619, famotidine, itacitinib (INCB039110), nizatidine, ondansetron, pyrimethamine, rabeprazole, ranolazine, and vandetanib (Nakada et al., 2019). Drugs were selected based on (i) available data regarding in vitro inhibition of each transporter and creatinine measurements in clinical trials; (ii) no significant reduction in GFR markers (except for rabeprazole and vandetanib, for which no information on GFR changes was available); and (iii) results from healthy adults, but excluding the elderly, to distinguish from an effect of age-related decline in renal function. In this study, the unbound maximum plasma concentrations at steady state (C_max,ss,u) were used as the drug concentration in eqs. 2–6. The estimated percentage of SCr increase from baseline (%ΔSCr) and the estimated percentage of CrCL decrease (%ΔCrCL) were calculated from SCr and CrCL at baseline (SCr_eq, CrCL_eq) and in the presence of inhibitors (SCr_I, CrCL_I) according to the following equations.

In most cases (including multiple dose regimens per drug), the estimated SCr increases and CrCL decreases from baseline were within 2- or 3-fold of the observed values, suggesting that SCr elevations seen in most drugs can be explained by competitive inhibition of transporters (Fig. 2A). By contrast, estimated SCr increases for some drugs, such as cobicistat, dolutegravir, and dronedarone were greatly underestimated. The possibilities listed as follows were inferred in relation to this underestimation: (i) the renal concentrations of the unbound form of these drugs were significantly higher than the plasma concentration due to active transport to the kidney; (ii) their metabolites inhibited the renal transporters; and (iii) in vitro inhibitory potencies were underestimated. With regard to the first possibility, a recent study using a PBPK model taking into account the detailed distribution process in renal tubular cells has been reported. However, predicted increases in SCr for drugs such as cobicistat and dolutegravir remained to be underestimated (Scotcher et al., 2020b). Regarding the second possibility, among the 14 drugs, dronedarone is metabolized to the active N-debutyl form, whose exposure is comparable with that of the parent compound in humans (Iram et al., 2016). However, even though the N-debutyl form was assumed to exhibit the same potency as unchanged dronedarone for the transporter inhibitions, the underestimation would not be resolved. A recent finding regarding the third possibility demonstrated that preincubation could alter the outcome of in vitro drug interaction risk assessment. For example, preincubation caused a shift toward 11-fold lower IC₅₀ values for dolutegravir in OCT2-expressing cells (Tátrai et al., 2019). It also investigated the effect of the presence or absence of apparatus suppressing nonspecific adsorption. Nonetheless, effective concentrations may be falsely evaluated due to the highly nonspecific binding of drugs onto the apparatus. Thus, validation will be needed to examine whether the underestimation could be overcome by carefully conducting in vitro assessment. In fact, drugs with high protein binding are generally lipophilic, and, as shown in Fig. 2B, increases in SCr and decreases in CrCL were underestimated for drugs with the unbound fraction in plasma (f_p) of <0.1. By contrast, when f_p was ≥0.1, almost all the estimated values were within three times the measured values, supporting the third possibility as a reason for the underestimation. Krishnan et al. also raised the possibility that protein-free buffer may cause nonspecific binding to cells and labware especially for drugs with high protein binding. Including this point, they highlighted a significant impact of in vitro assay designs on clinical prediction of drug interactions via OCT2 and MATE1/2-K inhibitions in terms of the impact of substrate, cell systems (e.g., HEK293 versus MDCK), assay buffers (e.g., protein-free buffer versus buffer with serum), and time-dependent inhibition (Krishnan et al., 2022). In vitro–in vivo dissociation was reported upon prediction of clinical drug interactions of metformin with in vitro data of OCT2 and MATE1/2-K inhibitions, being raised as the current challenge for risk assessment of clinical drug interactions (Mathialagan et al., 2021).

Clinical Changes in SCr and CrCL for TKIs

In addition to the 14 drugs listed above, we investigated the association between elevated SCr and inhibition of renal transporters for recently marketed drugs, and the results of clinical studies were obtained in which nine TKIs with different mechanisms of action (abemaciclib, alectinib, capmatinib, crizotinib, fedratinib, olaparib, selpercatinib, tepotinib, and tucatinib) were administered in healthy adults and patients with cancer (Tables 1 and 2). For alectinib, capmatinib, and crizotinib (500 mg, qd), GFR or estimated glomerular filtration rate (eGFR) at baseline was lower than the normal range, and the increase in SCr after the administration of these drugs was significantly higher than that after the administration of other drugs (Table 1). For example, a percent increase in SCr for alectinib was over 300% compared with the pre-dose. In this subject, markers of renal function, such as N-acetyl-β-D-glucosaminidase and β₂-microglobulin, also increased, and drug-related kidney lesions in tubules and glomeruli were confirmed by a renal biopsy (Nagai et al., 2018). This result indicated that such drastic SCr elevation was not due to the inhibition of renal transporters but due to drug-induced renal impairment. The elevated SCr and kidney-related adverse events during treatments with TKIs including these three drugs have been reported previously (Izzedine et al., 2016; Mohan and Herrmann, 2022).

In this paper, we quantitatively considered the effects of the renal transporter inhibition on the SCr elevation in terms of in vitro inhibition potencies and clinical concentrations of the TKIs (Tables 2 and 3, and Fig. 1). As a result of estimation by our method (Nakada et al., 2019), changes in SCr and CrCL for the TKIs were underestimated except for fedratinib and vandetanib (Fig. 2A, Table 3). As aforementioned and shown in Fig. 2B, abemaciclib, selpercatinib, tepotinib, and tucatinib exhibited low fp values of < 0.1, suggesting that in vitro inhibition potencies of these TKIs were undervalued. Sprowl et al. revealed that TKIs such as dasatinib inhibit the Src family kinase Yes1, which was found to be essential for OCT2 phosphorylation and function, and Yes1 diminished OCT2 activity (Sprowl et al., 2016). Arakawa et al. showed that the IC₅₀ value of crizotinib with pretreatment and co-treatment (0.347 μM) was lower than that with only co-treatment (1.58 μM) in OCT2-expressing HEK293 cells (Arakawa et al., 2017). In a subsequent study of MATE1 inhibition, crizotinib with pretreatment also resulted in a nearly 10-fold lower K_i value (0.342 μM) than that with only co-treatment (2.34 μM) (Omote et al., 2018). Among the TKIs investigated in this study, pretreatment of abemaciclib was conducted in the evaluation of IC₅₀ in the OCT2 study, whereas it was not considered in the MATE1/2-K study (Chappell et al., 2019). In this study, IC₅₀ values of its metabolites M2 and M20 were also evaluated, which were incorporated in our estimation of SCr elevation. Nevertheless, the SCr elevation for abemaciclib remained nearly 10-fold underestimated (Fig. 2A, Table 3). Taking these findings together, the estimation method for the SCr elevation will be validated with in vitro inhibition data considering effective concentrations and the pretreatment of the transporter inhibitors.

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Fig. 1.

Unbound maximum plasma concentration ratios to inhibition potencies of the renal transporters. Each value is calculated based on Tables 2 and 3. Dotted and dotted-dashed lines represent the cut-off values of drugs as transporter inhibitors according to regulatory guidance of drug–drug interactions (OCT2: C_max,u/K_i < 0.1 (European Medicines Agency, 2012; Food and Drug Administration, 2020), MATE1/2-K: C_max,u/K_i < 0.02 (European Medicines Agency, 2012) or 0.1 (Food and Drug Administration, 2020).

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Fig. 2.

Comparison of the estimated changes in SCr and CrCL from baselines with observed values in subjects with normal kidney function. A, Percent increase in SCr and percent decrease in CrCL from baseline are shown. Solid, dotted-dashed, and dotted lines represent unity, 2-fold boundaries, and 3-fold boundaries, respectively. B, Relationship between the f_p and a ratio of the estimated changes in SCr and CrCL to the observed values is shown. Dotted-dashed and dotted lines represent 2-fold and 3-fold boundaries of the ratio of estimates to observed values. The red area represents both >3-fold error of the estimates and f_p of <0.1. The blue area represents both ≤3-fold error of the estimates and f_p of ≥ 0.1. *, Inhibitory effects of both drug and its metabolite(s) on renal transporters were considered for the estimation.

Estimated SCr Elevation by Renal Transporter Inhibitions and Comparison with AKI Criteria

According to the Kidney Disease Improving Global Outcomes (KDIGO) practice guideline, AKI is diagnosed when there is a ≥0.3 mg/dL increase in SCr within 48 hours or a ≥50% increase from the known or expected baseline within seven days prior to the increase (The Kidney Disease Improving Global Outcomes Working Group, 2012). As mentioned above, some renal transporter inhibitors have been found to increase SCr without causing renal impairment. For example, some patients have experienced Grade 2 or 3 adverse events related to increased SCr based on CTCAE grades during treatment periods for abemaciclib, fedratinib, olaparib, selpercatinib, tepotinib, and tucatinib. However, there were no clear changes in renal function markers, and the possibility of renal transporter inhibition cannot be denied. Therefore, we examined whether there were cases of renal transporter inhibition that could fulfill KDIGO’s criteria for AKI. Since the contribution of MATE1/2-K inhibition is significant among renal transporters as an effect on SCr elevation (Chu et al., 2016), two-dimensional heatmaps regarding C_max,u/K_i values for MATE1 and MATE2-K against SCr elevation were depicted (Fig. 3). Based on the abovementioned two AKI criteria, we examined whether the percent increase in SCr was ≥50% or SCr increment was ≥0.3 mg/dL. The latter case is illustrated assuming that the SCr baseline value was 1.0 mg/dL, which was within the range of the normal value, a case exemplified in the KDIGO guideline (Fig. 3). We investigated whether estimated SCr elevations for some typical inhibitors can correspond to the AKI criteria. As the typical inhibitors, cimetidine, DX-619, pyrimethamine, and trimethoprim were selected because these inhibitors exhibited obvious increases in SCr, and such SCr and CrCL changes were well estimated by our method. While there were a few cases in which the SCr elevation exceeded 50%, increments from baseline could surpass the criteria in subjects with a high baseline value (1.0 mg/dL), although these inhibitors did not affect renal function. Trilaciclib, a recently approved TKI, exhibits significant inhibitory potentials against OCT2, MATE1, and MATE2-K to a similar extent as trimethoprim (Fig. 1) (Li et al., 2022). Although OCT2 was estimated to have an insignificant impact for SCr elevation in this method (Supplemental Fig. 1), trilaciclib might elevate SCr to such an extent as to lead to a false interpretation on AKI due to its significant inhibitory potentials against MATE1/2-K as the trimethoprim case (Fig. 3). To the best of our knowledge, there is no publicly available information on clinical changes in SCr and/or CrCL for trilaciclib, and this needs to be investigated based on further information.

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Fig. 3.

Two-dimensional heatmap for estimated SCr increases from baseline involving inhibitory potentials of MATE1 and MATE2-K. SCr increase percentages (A) and increments in SCr (B) were estimated based on our previous method (Nakada et al., 2019). According to the KDIGO guideline, acute SCr increases are used for defining acute kidney injury by fulfilling the criteria of increases in SCr by either ≥ 50% or at least 0.3 mg/dL from the presumed baseline (The Kidney Disease Improving Global Outcomes Working Group, 2012). Assuming healthy subjects or patients with normal range of renal function, GFR values of 125 (Davies and Morris, 1993) and 90 ml/min (The Kidney Disease Improving Global Outcomes Working Group, 2012) were used, and baseline SCr of 1.0 mg/dL was used (B). 1: cimetidine (400 mg, bid/qid, 400–400–400–800 mg, qid). 2: DX-619 (800 mg, qd). 3: pyrimethamine (25–100 mg, sd). 4: trimethoprim (200 mg, bid, 5 mg/kg, bid/qid).

As shown in Fig. 3, the SCr elevation caused by transporter inhibitions in the subjects with the GFR value of 90 ml/min was estimated more significantly than that with the GFR value of 125 ml/min. In this study, the quantitative analysis was limited within the range of normal renal function (≥90 ml/min) because not only the reduction in the filtration rate but various physiologic functions of the kidney should be considered in subjects with insufficient renal function with GFR <90 ml/min. Takita et al. reported on mechanistic analysis using the PBPK model incorporating the detailed distribution process in renal tubular cells. They considered the physiologic decline of kidney functions, such as the secretory and reabsorptive capacities, which were not proportional to GFR reduction (Takita et al., 2020). Furthermore, in addition to the general tendency of renal function to decline with age, it was suggested that the rate of creatinine biosynthesis declines in the elderly (Kampmann et al., 1974).

Creatinine is a breakdown product of muscle creatine through a reversible reaction catalyzed by creatine kinase (CK) (Alves et al., 2020). The metabolic capacity of creatine is essential for the maintenance of skeletal muscle function and has been reported to be involved in pathophysiological conditions, such as spinal and bulbar muscular atrophy and spinal muscular atrophy (Hijikata et al., 2018; Alves et al., 2020). The lower number of survival motor neuron 2 copies, the causative gene in patients with spinal muscular atrophy, is involved in the lower SCr, suggesting that SCr is a prognostic marker because it reflects the severity of the disease (Hijikata et al., 2018; Alves et al., 2020). For risdiplam, a recently approved survival motor neuron 2 mRNA splicing modifier, C_max,u/K_i values for OCT2, MATE1, and MATE2-K were 0.011, 0.65 (0.0015 for metabolite M1), and 1.08, respectively, which clearly exceeded the threshold values of 0.02 (European Medicines Agency, 2012) and 0.1 (Food and Drug Administration, 2020) defined by the guidelines for drug interaction studies for MATE1/2-K inhibition (Fig. 1) (Fowler et al., 2022). However, it will be challenging to determine whether SCr changes can be explained only by the transporters’ inhibition because the rate of creatinine biosynthesis as well as tubular secretion of creatinine is likely to be affected by risdiplam. Moreover, no publicly available data on SCr changes were found for risdiplam. With its data for SCr and creatine available, a novel framework, such as a PBPK model reflecting these mechanisms, will be worth investigating such combined effects on SCr changes.

Progress on the Modeling and Simulation Approach of Endogenous Creatinine Disposition

To characterize the creatinine–drug interactions, mechanistic approaches have been addressed. Imamura et al. first reported their investigation of whether DX-619 elevated SCr in healthy subjects by its inhibitions against OCT2 and MATE1/2-K (Imamura et al., 2011). Creatinine was described by creatinine biosynthesis, its distribution volume, and CrCL without relative contributions of renal transporters. An individual SCr profile was well reproduced with the model. Based on this model, we previously incorporated the relative contributions of the transporters (basolateral side: OAT2, OCT2, OCT3; apical side: MATE1 and MATE2-K) to suit distinct profiles of transporter activities. Our model quantitatively explained SCr increases after trimethoprim administrations in different dose regimens (Nakada et al., 2018). As aforementioned, such a mechanistic static approach, which is based on a constant drug concentration as C_max,ss,u, for SCr estimation was applied to 14 drugs (cimetidine, cobicistat, dolutegravir, dronedarone, DX-619, famotidine, INCB039110, nizatidine, ondansetron, pyrimethamine, rabeprazole, ranolazine, trimethoprim, and vandetanib) (Nakada et al., 2019). The majority of cases were estimated with good precision, but some drugs, such as cobicistat, dolutegravir, and dronedarone, remained greatly underpredicted. Scotcher et al. introduced a novel PBPK model for creatinine with detailed distribution processes in the proximal tubule via assuming either unidirectional or bidirectional OCT2 transport system driven by electrochemical gradient (Scotcher et al., 2020a; Scotcher et al., 2020b). This mechanistic-dynamic approach, which considers drug concentration–time profiles, well predicted SCr increases for 11 drugs (cimetidine, cobicistat, dolutegravir, DX-619, famotidine, indomethacin, pyrimethamine, ranitidine, ranolazine, rilpivirine, and trimethoprim). This study represented not only overall comparative performance for SCr increases between the mechanistic static and dynamic approaches, but also the similar underpredictions for drugs including cobicistat and dolutegravir as our study (Scotcher et al., 2020a). It was recently mentioned that C_max,u values for cobicistat and dolutegravir were >40 times lower than the lowest in vitro IC₅₀ values among studies, resulting in the underprediction regardless of the model structure (Krishnan et al., 2022). Therefore, future investigation and refinement of the model are to be addressed with well-designed in vitro data, as earlier described. Nonetheless, such a mechanistic-dynamic framework could be useful to fulfill the knowledge gap and address a complicated issue. As discussed in the previous section, Takita et al. applied the PBPK model to creatinine–drug interactions in 17 patients with chronic kidney disease (CKD, eGFR 15–59 ml/min/1.73 m²) by using cimetidine, famotidine, and trimethoprim (Takita et al., 2020). This creatinine–CKD model incorporated age- and sex-related differences in creatinine biosynthesis, CKD-related functional changes in GFR, and tubular secretion changes in either proportion or disproportion of GFR deterioration. These approaches with the PBPK model will provide a mechanistic framework to investigate physiologic changes in depth by the progression of CKD. Most recently, Turk et al. presented a whole-body PBPK model for creatinine considering both creatinine production via creatine metabolism and diurnal variation for GFR and renal blood flow and OCT2 activity (Türk et al., 2022). It should be noted that the whole-body PBPK model incorporates only OCT2 and MATE1 without considering the relative contribution of the transporters. Nonetheless, this model could enable us to address complicated cases, such as risdiplam, as the creatinine biosynthesis process as well as its tubular secretion can be considered.