LITMIR.BIZ

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Indication	Druga	Dose
Asthma	Zafirlukast	20 mg
Cancer	Imatinib	400 mg
Cardiovascular	Ranolazine	1,000 mg
Emesis	Ondansetron	8 mg
Infections	Pyrimethamine	50 mg
	Trimethoprim	200 mg
	Dolutegravir	50 mg
	Isavuconazole	200 mg
	Levofloxacin	750 mg
	Ciprofloxacin	500 mg
	Cetirizine	10 mg
	Cephalexin	500 mg
	Cephradine	500 mg
	Cobicistat	150 mg
	Indinavir	800 mg
	Ritonavir	100 mg
Reflux disease	Cimetidine	400 mg
	Ranitidine	300 mg
	Famotidine	200 mg
	Nizatidine	600 mg

      ^a Studies identifying these chemicals vary in design and include single and multidose regimens.

      The ability of pyrimethamine to cause a drug interaction with metformin was assessed in healthy volunteers (n = 8) using a micro‐dose (100 μg) or low therapeutic dose (250 mg) of metformin [95]. The K _i values of pyrimethamine for hMATE1, hMATE2‐K, and hOCT2 were reported to be 93, 59, and 10 μmol/l, respectively. The K _i values for MATE1 and MATE2‐K were lower in this study than previous reports [96]. Pyrimethamine administration reduced the renal clearance of metformin 23% with the micro‐dose and 35% with a low therapeutic dose of metformin. At the low therapeutic dosing, Cmax and exposure to metformin were elevated. At both the micro‐dose and therapeutic dose, pyrimethamine significantly reduced creatinine clearance, fraction excreted, and renal clearance of metformin, demonstrating the utility of using micro‐dosing of metformin to predict drug–drug interactions. However, the magnitude of changes was more pronounced at the therapeutic dosing scheme.

      Given the well‐established interactions between triazole antifungals and drug transporters (and metabolism enzymes), a phase 1 clinical trial for isavuconazole examined the potential for drug–drug interactions with several transporters using probe substrates [97]. It was also anticipated that the new antifungal would be co‐administered with several immunosuppressants in the clinical environment, which might predispose patients to potential drug–drug interactions. The potential interactions with OCT1/2 and MATE1 were conducted using metformin as the substrate after 6 days of oral isavuconazole in healthy controls (n = 21). Previous in vitro assessments of isavuconazole in MATE1‐HEK293 cells demonstrated an IC₅₀ of 6.31 μmol/l with ¹⁴C‐metformin as a substrate. Expected Cmax for isavuconazole was reported as <7 μg/ml, and it is >99% protein bound (0.07 μg/ml or 0.16 μmol/l unbound Cmax). The calculated Cmax/IC₅₀ quotient would be 0.024, which is less than the threshold for predicting a clinical interaction. Nonetheless, metformin exposures and Cmax were ~50% and 23% higher in the presence versus absence of isavuconazole, respectively, suggesting a drug–drug interaction.

       3.6.2.2 Physiologically Based Pharmacokinetic Methods to Assess Interactions

      A recent physiologically based pharmacokinetic (PBPK) modeling study investigated drug–drug interactions using trimethoprim as an inhibitor of MATEs and cytochrome P450 2C8 [98]. The model included intestinal efflux by P‐glycoprotein, CYP3A4 metabolism, hepatic clearance, and renal clearance via filtration and secretion. The model predicted drug–drug and drug–drug–gene interactions between trimethoprim and several exogenous probe substrates (metformin, repaglinide, pioglitazone, and rifampicin). The predicted exposure (AUC) and Cmax ratios for substrates were within 1.5‐fold of the observed values in clinical studies. The study supported the enhanced usage of PBPK modeling to predict drug–drug interactions with transporters in lieu of conducting studies in healthy volunteers.

       3.6.2.3 Serum Creatinine and Kidney Function

Excretion of creatinine is accomplished by filtration at the glomerulus (~85%) and secretion by the renal tubules (~15%). Numerous compounds cause elevations in serum creatinine. Clinical increases in serum creatinine result from renal cell toxicity or inhibition of creatinine transport by renal proximal tubules. Cellular toxicity can be verified by histology from a kidney biopsy or assessed by concomitant changes to urinary kidney injury protein biomarkers [99]. A mathematical method to predict inhibition of creatinine transport as a mechanism for elevations in serum creatinine has been published [100]. Expected changes to serum creatinine and creatinine clearance in healthy subjects based on available in vitro studies of several non‐nephrotoxic compounds that were MATE1 inhibitors (and reported IC₅₀ values) were incorporated. Given the localization of organic anion transporters (OAT) and OCT uptake transporters at the basolateral membrane of proximal tubules, IC₅₀ values for each compound were reported for these transporters as well. Increases in serum creatinine were found to be sensitive to MATE transporters for cimetidine, famotidine, ondansetron, ranolazine, trimethoprim, and vandetanib.

      PBPK modeling was used to investigate serum creatinine increases that are routinely observed in clinical studies and treatments [101]. The model included inhibition of tubular secretion of creatinine by trimethoprim through OCT2, OCT3, MATE1, and MATE2‐K. Relative contribution of the transporters was calculated from published data. Transport activity of creatinine at the basolateral and apical membranes of proximal tubules and available protein expression in pooled human kidney microsomes was included. Inhibition constant (K _i ) values of 86.8 mmol/l (OCT2), 3.42 mmol/l (MATE1), and 2.16 mmol/l (MATE2‐K) were used. The model was validated with clinical data sets from single and multiple doses of trimethoprim. The pharmacokinetic model of creatinine included tubular secretion from each of the transporters. The model successfully predicted serum creatinine increases at three trimethoprim dosage regimens: 5 mg/kg intravenous twice daily (29%), 5 mg/kg intravenous four times daily (40%), 200 mg oral twice daily (26%). Development and validation of models, such as these two published ones, may better inform about expected changes to creatinine to obviate the concern of toxicity raised with increases in serum creatinine. Robust models may also reduce the need for some preclinical assessments.

       3.6.2.4 Other Endogenous Probe SubstratesNMN has

      low protein binding and is filtered and secreted by the kidneys. A study evaluated the ability

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