Clinical relevance of hepatic and renal P-gp/BCRP inhibition of drugs: An International Transporter Consortium perspective

Abstract

The role of P-glycoprotein (P-gp) and breast cancer resistance protein (BCRP) in drug-drug interactions (DDIs) and limiting drug absorption as well as restricting the brain penetration of drugs with certain physicochemical properties is well known. P-gp/BCRP inhibition by drugs in the gut has been reported to increase the systemic exposure to substrate drugs. A previous International Transporter Consortium (ITC) perspective discussed the feasibility of P-gp/BCRP inhibition at the blood-brain barrier and its implications. This ITC perspective elaborates and discusses specifically the hepatic and renal P-gp/BCRP (referred as systemic) inhibition of drugs and whether there is any consequence for substrate drug disposition. This perspective summarizes the clinical evidence-based recommendations regarding systemic P-gp and BCRP inhibition of drugs with a focus on biliary and active renal excretion pathways. Approaches to assess the clinical relevance of systemic P-gp and BCRP inhibition in liver and kidney included (1) curation of DDIs involving intravenously administered substrates or inhibitors; (2) in vitro-to-in vivo extrapolation of P-gp- mediated DDIs at the systemic level; and (3) curation of drugs with information available about the contribution of biliary excretion and related DDIs. Based on the totality of evidence reported to date, this perspective supports limited clinical DDI risk upon P-gp or BCRP inhibition in liver or kidney.

Introduction

P-glycoprotein (P-gp, ABCB1, MDR1) and breast cancer resistance protein (BCRP, ABCG2) are efflux transporters located in many organs relevant to drug disposition, including the intestine, brain, liver, and kidney. These generally apically located transporters can limit intestinal absorption of substrate drugs, presenting potential drug-drug interactions (DDIs) when intestinal P-gp and/or BCRP is inhibited by co-administered drugs. P-gp and BCRP also play a crucial role in limiting brain penetration of substrate drugs, and CNS drug discovery programs often aim to prevent or limit P-gp/BCRP efflux to facilitate drug distribution into the brain. In contrast, many drugs are P-gp/BCRP-inhibitors with potencies that are more relevant to the gastrointestinal tract. Use of P-gp-inhibitors in particular to enhance systemic or tissue exposure has been the focus of investigations for many years with some success. For instance, enhanced absorption of P-gp substrates from the gastrointestinal tract is achievable because sufficient concentrations of P-gp-inhibitors, which allow relevant concentrations at the binding site of intestinal P-gp for inhibition, can be attained following oral administration. Besides the use of such P-gp-inhibitors in fixed formulations such as D-α-Tocopherol polyethylene glycol succinate (TPGS),¹ a recent clinical example is the use of encequidar to boost oral paclitaxel systemic exposure in cancer patients, allowing treatment from home instead of in an outpatient/hospital setting.² Except for the intestine, drugs that inhibit P-gp/BCRP typically may not achieve sufficient unbound systemic concentrations at tolerated clinical doses to inhibit efflux at tissue barriers such as the blood-brain barrier capillary endothelium. Extensive investigations have revealed that P-gp inhibition by a drug is not a clinically viable method to enhance CNS drug penetration.³ Because of this lack of brain P-gp inhibition by almost all approved/marketed drugs to date that can inhibit P-gp, luminal efflux can be optimized to effectively limit unwanted CNS drug exposure in humans as a strategy during drug development. Examples of P-gp drug substrates with limited brain exposure include loperamide, ivermectin, and second generation non-sedating antihistamines and antimuscarinics; if not for efflux at the blood-brain barrier, the success of these therapeutic agents likely would be more limited. Similar logic may be applicable to BCRP inhibitors, but prototypical BCRP specific inhibitors at the systemic level have not been thoroughly tested nor identified in the clinic as compared to P-gp-inhibitors.

P-gp/BCRP may also play a role in the biliary (e.g., rosuvastatin for BCRP and digoxin for P-gp) and renal (e.g., digoxin for P-gp) clearance of drugs. Examples of extensive clearance by P-gp-mediated biliary excretion or active tubular secretion are limited because P-gp substrates (generally large molecular weight, lipophilic cations) usually are cleared by metabolism instead of renal excretion (predominant clearance route for more hydrophilic drugs), and direct biliary excretion of parent drug as a major elimination pathway is not common in humans (<5% of marketed drugs).⁴ Similarly, examples of BCRP-mediated renal clearance of drugs are scarce; furthermore, BCRP protein expression in the human kidney cortex is reported to be low.⁵ However, there are cases where biliary excretion and renal efflux are (or appear to be) a major elimination pathway of a drug that is not driven by P-gp/BCRP. Thus, estimating when biliary or renal active secretion via P-gp/BCRP is involved and dictate overall drug disposition remains a challenge. Furthermore, there is potential overlap between P-gp/BCRP substrates and inhibitors. These compounds also may be substrates or inhibitors of other transporters and enzymes, which may further complicate evaluation of the hepatic/renal P-gp/BCRP contribution to elimination. The potential for inhibition of both P-gp and CYP3A by substrates and inhibitor drugs must be considered.⁶

Understanding the potential clinical relevance of systemic P-gp/BCRP inhibition may be important for other scenarios. For example, hepatic and renal P-gp/BCRP inhibition may be relevant in the context of understanding the disposition of antibody-drug conjugate payloads, which often are small molecules delivered by intravenous infusions. The payload is generally cleaved inside cells and may be a substrate for P-gp/BCRP, which may be relevant to understand the altered disposition of these small molecule payloads when P-gp/BCRP are inhibited.⁷

This perspective summarizes the clinical evidence-based recommendations regarding systemic P-gp and BCRP inhibition of drugs with a focus on biliary and active renal excretion pathways. Throughout this perspective, systemic inhibition of P-gp or BCRP refers to inhibition of either canalicular P-gp or BCRP in the liver or luminal P-gp or BCRP in the kidney, not inhibition of P-gp or BCRP in the intestine, brain or any other organ that expresses these transporters. Diverse approaches were used to assess the clinical relevance of systemic P-gp and BCRP inhibition in liver and kidney, including (1) curation of DDIs involving intravenously administered substrates or inhibitors; (2) in vitro-to-in vivo extrapolation (IVIVE) of P-gp -mediated DDIs at the systemic level; and (3) curation of drugs with information available about the contribution of biliary excretion and related DDIs.

Evaluation of hepatic and renal clinical DDIs that potentially involve systemic P-gp inhibition

P-gp knockout mice or rats provide extreme examples of the impact of P-gp loss-of-function on drug disposition, but results must be interpreted carefully. ^{3, 8} Although extensive intestinal P-gp inhibition is clinically tenable with an oral inhibitor, P-gp inhibitor drugs at their highest tolerated clinical doses at most achieve maximum unbound systemic concentrations approximating their P-gp inhibitory (IC₅₀) potencies.³ At the systemic disposition level, 100% genetic ablation of mdr1 in rodent knockout models can elicit more drastic changes in substrate disposition than the worst-case scenario of 50% inhibition of systemic P-gp in humans. For example, a 65-fold higher loperamide CNS distribution in knockout mice vs at most around two-fold increase with intravenous infusion of P-gp inhibitors at the highest level tolerated in humans has been reported.^{9, 10}

A review of the University of Washington Drug Interaction Database (UW DIDB, https://didb.druginteractionsolutions.org/) was conducted to evaluate reported systemic P-gp-mediated DDIs (searched in February of 2022). The search criteria were clinical DDIs involving P-gp inhibitors and intravenously administered substrates (victim drugs), which showed a ≥25% increase in plasma area under the concentration-time curve (AUC) of substrates (Table 1). Intravenous (IV) administration of P-gp substrates was selected to avoid DDIs driven by intestinal P-gp inhibition and to specifically focus on the implications of systemic P-gp inhibition. As shown by the data, 83% of the AUC changes (44 of 53 DDI records) for the identified drugs were within <2-fold (i.e., <100% increase) after co-administration with small molecules or drugs known to inhibit P-gp. The 15 P-gp inhibitors identified were amiodarone, biricodar (VX-710), cyclosporine, dofequidar (MS209), elacridar (GF120918), OC144-093 (ONT-093), paclitaxel, propafenone, quinidine, quinine, ritonavir, spironolactone, verapamil, valspodar (PSC833), and zosuquidar (LY335979). Some P-gp-inhibitors were excluded, such as itraconazole and ketoconazole, because the studies involved substrates that are also metabolized by cytochrome P450 (CYP) 3A, rendering difficulty in determining whether and how much P-gp inhibition contributes to the observed DDI.

Table 1:

Clinical examples of systemic P-gp inhibition with search criteria involving intravenous (IV) administration of the substrate drugs that showed AUC increase ≥25%.

Object	Object dosing route	Perpetrator	Perpetrator dosing route	% Change of Object AUC	% Change of CL	% Change of CLrenal/ CLnon- renal	Precipitant Dose	Precipitant Interval	Reference
Digoxin	IV	amiodarone	Oral	33a	−25	−16 (CLr) −31 (CLnr)	400 mg (21-25 days)	not provided	58
Digoxin	IV	amiodarone	Oral	47a	−32	−30 (CLr) −34 (CLnr)	1600 mg (14 days)	qd on Days 5-18	59
Digoxin	IV	propafenone	Oral	28	−22	−9 (CLr) −44 (CLnr)	300 mg (12 days)	tid	35
Digoxin	IV	quinidine	Oral	53a	−35	−27 (CLr) −46 (CLnr)	Not Provided	(chronic therapy)	46
Digoxin	IV	quinidine	Oral	57a	−36	−32 (CLr)	200 mg (4 days)	qid	60
Digoxin	IV	quinidine	Oral	57a	−36	−54 (CLr) −22 (CLnr)	400 mg (4 days)	qid starting 3 days before digoxin administration	61
				43a	−30	−32 (CLr) −29 (CLnr)	200 mg (4 days)
				65a	−39		200 mg (11 days)	qid	62
Digoxin	IV	quinidine	Oral	79a	−44		202 mg (26 days)	bid	63
Digoxin	IV	quinidine	Oral	83a	−45	−33 (CLr) −60 (CLnr)	200 mg (8 days)	qid	64
Digoxin	IV	quinidine	Oral	178a	−64	−61 (CLr)	200 mg (6 days)	qid	65
Digoxin	IV	quinidine	Oral	128a	−56	−52 (CLr)	0.6 g (11 days)	bid (after a loading dose of 0.1 g/10 kg)	66
Digoxin	IV	quinine	Oral	38a	−27	−15 (CLr) −69 (CLnr)	200 mg (9 days)	tid, 4 days before and after the second digoxin administration	67
Digoxin	IV	ritonavir	Oral	86	−42	−35 (CLr) −48 (CLnr)	300 mg (11 days)	bid on Days 1-11	68
Digoxin	IV	Spironolactone	Oral	47	−32	−26 (CLr) −42 (CLnr)	100 mg (5 days)	bid	69
Digoxin	IV	verapamil	Oral	57a	−36	−23 (CLr) −67 (CLnr)	80 mg (10 days)	tid	70
Digoxin	IV	verapamil	Oral	41a	−29	--	120 mg (12 days)	tid	71
Docetaxel	IV	MS209	Oral	27, 33	--	--	300 mg (1 cycle)	administered 30 min before docetaxel infusion at cycle 2	23
				38.5	--	--	600 mg (1 cycle)
				96	--	--	900 mg (1 cycle)
				54	--	--	1200 mg (1 cycle)
Doxorubicin	IV	Verapamil	Oral	71	−40	--	240-480 mg/day [7 days	80 mg tid for 3 days followed by 120 mg qid for 4 days; one hour before doxorubicin on study day.	72
Doxorubicin	IV	cyclosporine	IV	55	−47	A subset of patients −35(CLtotal) −32 (CLr) −35 (CLnr)	18 mg/Kg	Co-administered with doxorubicin as a 48-hour continuous infusion after a loading dose of 6 mg/Kg over 2 h	73
Doxorubicin	IV	cyclosporine	IV	48	−36.6	--	16 mg/kg/24h (2 days)	continuous infusion, preceded by a 6 mg/kg bolus infused over 2 hours	74
Doxorubicin	IV	Elacridar (GF120918)	Oral	47		--	400 mg (5d)	bid	20
Doxorubicin	IV	paclitaxel	IV	30.4	−11.8	--	200 mg/m2	infusion over 3 hours	75
Doxorubicin (polyethylene glycol-coated liposomal formulation	IV	paclitaxel	IV	97.3	−38.9	--	175 mg/m2	Single, 3-hr infusion	76
				58.6	−22.8	--	70 mg/m2	1-hr infusion, weekly
				65.5	−39.5	--
Doxorubicin	IV	Valspodar (PSC833)	IV	119.4	−55.7	--	1-10 mg/kg	single dose as an infusion over 24 hours after a loading dose of 1-2 mg/kg or 2 mg/kg administered as an infusion over 2 hours	77
				169.5	−61.5	--
				49.3	−36.5	--
Doxorubicin	IV	Valspodar (PSC833)	Oral	54.3	−30.3	--	1.25-12.5 mg/kg (5 days)	bid (7 am)	78
Doxorubicin	IV	Valspodar (PSC833)	Oral	84.6	−45.2	--	5 mg/kg (3 days)	qid	79
Doxorubicin	IV	Valspodar (PSC833)	Oral	93.8	−41.6	--	2.5 mg/kg (7 days)	bid	80
Doxorubicin	IV	verapamil	Oral	71	−40	--	240-480 mg/day (7 days)	80 mg tid for 3 days followed by 120 mg qid for 4 days	72
Doxorubicin	IV	Zosuquidar (LY335979)	IV	25	−17	--	80 - 640 mg/m2/day (2 days/cycle)	continuous i.v. infusion over 48 hours starting on day 1 of cycle 2	21
Edoxaban	IV	quinidine	Oral	34.8	−25.5	−25 (CLr)	300 mg (4 days)	three times daily	81
Etoposide	IV	valspodar (PSC833)	IV	59a	−37	--	1.5 mg/kg then 10 mg/kg (5 days)	2-hour infusion as 1.5 mg/kg loading dose on Day 2, followed by 10 mg/kg/day continuous infusion on Days 2-6	82
Etoposide	IV	valspodar (PSC833)	IV	133a	−57	--	10 mg/kg/day (5 days)	continuous 120 h infusion with concomitant loading dose of 2 mg/kg over 4 h	83
Etoposide	IV	valspodar (PSC833)	IV	76	−41	--	1-15 mg/kg (5 days)	continuous infusion increasing from 1 or 6.6 mg/kg to 15 mg/kg from day 2 to day 6, after a loading dose of 1 to 2 mg/kg over 2 hours	52
				90	−46	−28 (CLr) −55 (CLnr)	6.6-15 mg/kg (5 days)
Etoposide	IV	valspodar (PSC833)	Oral	113a	−53	--	4 mg/kg (n = 5), 5 mg/kg (n = 11) or 6 mg/kg (n = 7) (one 28-day cycle)	qid on days 0-4, 7-11 and 14-18	84
Idarubicin	IV	cyclosporine	IV	66.5	-	--	16 mg/kg/day (6 days)	continuous infusion on days 1-6 after a loading dose of 6 mg/kg over 2 hrs on day 1	85
Idarubicin	IV	cyclosporine	IV	78	−40.2		10 mg/kg/day (3.5 days)	injection starting 12 h before the 1st idarubicin administration and stopping 24 h after the last idarubicin dose	86
Paclitaxel	IV	biricodar (VX-710)	IV	100a	−50		120 mg/m2/hr (24 hours)	24-hr continuous infusion	18
Paclitaxel	IV	OC144–093 (ONT-093)	Oral	44.5	−29		500 mg (1-3 cycles)	14h before, 2 h before and 10 h after paclitaxel administration	87
				25	−23		300 mg (1-3 cycles)
Paclitaxel	IV	valspodar (PSC833)	Oral	108a	−52		5 mg/kg (7 days)	qid; starting 72 hours before the initiation of paclitaxel infusion	88
Paclitaxel	IV	valspodar (PSC833)	Oral	150a	−60.1		5 mg/Kg (7 days)	qid; starting 72 hours before paclitaxel infusion	88
Vinorelbine	IV	Zosuquidar (LY335979)	Oral	30	−24		200 - 300 mg/m2 (9 days/28 day-cycle)	TID for 7 doses on days 7-9 and 14-16 of the first 28-day cycle	22

^a.The AUC change was derived from the change of CL of substrate, since AUC ratio is the reciprocal of CL ratio.

Similarly, we examined the potential involvement of other transporters (e.g., BCRP, OATP1B1, OATP1B3, and the efflux transporter multidrug resistance-associated protein (MRP) 2) for studies involving cyclosporine, which inhibits all of these transporters. Only studies conducted with doxorubicin and idarubicin are included in the dataset (Table 1). Doxorubicin is not a substrate for OATP1B1 or OATP1B3.^{11, 12} Although there are conflicting data regarding whether doxorubicin is transported by BCRP, studies suggested that doxorubicin was transported by a genetic variant of BCRP but not wild-type BCRP.^{12, 13, 14} Studies demonstrated that BCRP did not confer drug resistance to idarubicin.^{15, 16} There were no reports about idarubicin being a substrate for OATP1B. As such, we assumed that idarubicin is not an OATP1B substrate. Although the interference of CYP3A or OATP1B inhibition was minimized for our analysis purpose, CYP3A and hepatic OATPs are often rate-determining in systemic drug clearance and should be considered as important parallel mechanisms of DDIs when evaluating the DDI potential between a P-gp substrate and an inhibitor.¹⁷

The largest AUC increase in the compiled dataset was 178% for digoxin in the presence of quinidine. However, numerous DDI studies involved coadministration of digoxin and quinidine (n=9). On average, the AUC of digoxin was increased by 83% (Table 1). The remaining DDIs with a substrate AUC that increased >2-fold commonly involved the inhibitor valspodar (PSC833) (a P-gp inhibitor used only as a clinical research tool). When all the DDIs were compiled, valspodar on average increased the AUC of doxorubicin by 95% (n=4), etoposide by 94% (n=4), and paclitaxel by 129% (n=2). Another study involved biricodar (VX-710), which doubled the AUC of paclitaxel.¹⁸ Valspodar (PSC833) and biricodar (VX-710) are designer P-gp-inhibitors, as are elacridar (GF120918), dofequidar, and zosuquidar (LY335979), which were developed to overcome multiple drug resistance of tumor cells to some chemotherapeutic agents that are P-gp-substrates. However, elacridar (GF120918) had only a minor effect on doxorubicin exposure, except at the highest combined doses (i.e., 75 mg/m² doxorubicin and 400 mg twice daily elacridar (GF120918)).^{19, 20} Zosuquidar (LY335979) modestly increased the AUC of doxorubicin and vinorelbine, by up to 25% and 30%, respectively.^{21, 22} Dofequidar increased docetaxel AUC by approximately 50% or less except for one group of treatments showing a 96% increase.²³ A study evaluating blood-brain barrier P-gp inhibition using PET imaging showed that tariquidar inhibited P-gp in a dose-dependent manner by up to 75% (N-des-loperamide as substrate) or 58% (verapamil as substrate); however, at the dose (2 mg/kg) evaluated in efficacy trials, tariquidar inhibited P-gp by 17%-23%.³ In addition, some P-gp-inhibitors known to increase the AUC of orally administered digoxin by ≥25% (e.g., carvedilol, clarithromycin, conivaptan, diltiazem, disopyramide, erythromycin) did not or at best modestly increased the AUC (<20%) of IV administered digoxin, suggesting that these drugs primarily inhibit intestinal P-gp at the doses studied. Collectively, based on the totality of evidence, although a few marketed drugs, such as cyclosporine, quinidine and verapamil elicit systemic P-gp inhibition at therapeutic doses, the DDI risk for canalicular and renal efflux based on systemic exposure to substrates is generally limited in magnitude (<100% increase in substrate AUC), which includes in vitro potent investigational P-gp-inhibitors.

Besides reporting alterations in substrate AUC or systemic clearance, some studies measured urine concentrations of substrates (most for digoxin, with some studies for doxorubicin, edoxaban, and etoposide) and reported renal clearance (CL_r) and non-renal clearance (CL_nr) of those drugs. In general, the reductions in substrate CL_r (range of 9-61% with an average of 32%) in the presence of inhibitors were comparable to the reductions in systemic clearance (range of 22-64% with an average of 37%) but were less than the reductions in CL_nr (range of 22-69% with an average of 45%) as shown in Figure 1. This observation may be due to CL_r consisting of both P-gp-mediated active renal secretion and glomerular filtration, the latter of which does not involve transporters and thus is not affected by P-gp- inhibitors. CL_nr may consist of P-gp-mediated biliary excretion and potentially intestinal secretion, as well as limited metabolism.

Figure 1:

Correlation between reduction in systemic CL and renal or non-renal CL for DDIs involving intravenous administration of P-gp substrate drugs. Each symbol represents one clinical DDI case (Table 1). Numbers in the symbol represent inhibitors in the DDI study (1: amiodarone, 2: cyclosporine, 3: quinidine, 4: quinine, 5: ritonavir, 6: spironolactone, 7: valspodar (PSC833), 8: verapamil). CL, clearance; DDI, drug-drug interaction.

In vitro-in vivo extrapolation of systemic P-gp inhibition

The criteria used to predict in vivo DDIs mediated by P-gp inhibition from in vitro data have been evaluated extensively for orally administered substrates.^{24, 25, 26, 27, 28, 29} However, such analyses have not been conducted for non-orally administered drugs. While the magnitude of DDIs mediated by inhibition of systemic P-gp tends to be limited as described above, the effect can still be clinically significant considering some P-gp-substrates have a narrow therapeutic window (e.g., digoxin, doxorubicin). Thus, DDI risk cannot be ruled out for an investigational drug that inhibits P-gp in vitro and is administered intravenously or via other non-oral routes with systemic drug delivery. Another applicable scenario is for a drug that is administered orally where the metabolite formed post-absorption (i.e., after the parent drug reaches the systemic circulation) inhibits P-gp. Accordingly, we compiled a dataset of IV administered substrates and/or inhibitors by searching the UW DIDB for clinical DDIs involving well-known P-gp-substrates that are minimally metabolized, i.e., dabigatran etexilate (it is a pro-drug of dabigatran which is the measured moiety), digoxin, edoxaban, fexofenadine, and talinolol to avoid possible confounding by inhibition of enzymes (e.g., CYP3A). In the case of doxorubicin which undergoes metabolism by CYP3A, majority of the studies were conducted with designer P-gp inhibitors (i.e., elacridar (GF120918), valspodar (PSC833), zosuquidar (LY335979)) which inhibited CYP3A in vitro at concentrations much higher than their C_max,u (C_max,u/K_i <0.02) and thus were determined not able to inhibit hepatic CYP3A in vivo. Two studies were conducted with cyclosporine which had limited inhibitory effects on CYP3A. Only one study was performed with verapamil that is a moderate inhibitor of CYP3A. Thereafter, we evaluated the DDI predictability of two criteria: (1) ratio of total plasma C_max of a P-gp inhibitor to in vitro P-gp inhibition potency (C_max/IC₅₀ or K_i ≥0.1) as recommended in the current FDA in vitro DDI guidance,³⁰ and (2) ratio of unbound C_max to in vitro P-gp inhibition potency (C_max,u/IC₅₀ or K_i ≥0.02) as recommended in the EMA DDI guideline.³¹ If the ratio is less than the corresponding threshold, then the risk of an inhibitor causing an in vivo DDI of importance can be considered low, which is usually defined as an increase of 25% or more in the AUC of a substrate in the presence of a P-gp-inhibitor (i.e, ratio of the AUC of the substrate in the presence to absence of inhibitor is ≥1.25), a threshold that was used in some earlier IVIVE analyses.^{24, 25, 26, 27, 28, 29}

The in vitro inhibition potency of a P-gp-inhibitor (most values were reported as IC₅₀) varies depending on experimental systems and among different laboratories.³² To minimize potential bias from a single IC₅₀ or K_i value, we searched the literature reported values for a P-gp-inhibitor via the UW DIDB and calculated the geometric mean of all the available IC₅₀ or K_i values determined with the same substrate as that was used in the clinical DDI studies. For the few cases where no IC₅₀ or K_i was determined using the same substrate as that studied in vivo (e.g., doxorubicin, talinolol), the geometric mean was calculated from all the IC₅₀ or K_i values reported for a perpetrator (e.g., digoxin, elacridar (GF120918), fluvoxamine, simvastatin, sorafenib, verapamil, zosuquidar (LY335979)) with various substrates. As details for the apparent K_i or IC₅₀ determination were not always available, a systematic determination of intrinsic K_i values for the inhibitors was not possible, hence this pragmatic approach was used. Although IC₅₀ values depend on assay conditions and comparing these values among different labs can be challenging, combining all available IC₅₀ values from public databases for meta-analyses is common practice.³³ This practice may reduce bias from individual values, with the average value more likely reflecting the true inhibition potency.

The C_max of an inhibitor was obtained from each clinical DDI study conducted with the P-gp substrate when reported. If C_max values were not available, the average concentration (C_avg) or trough concentration (C_trough) of the inhibitor was used because in most cases, the ratio of C_avg or C_trough to IC₅₀ or K_i exceeded the corresponding threshold; thus, the predictions remained the same as those predicted using C_max. For studies where inhibitor concentrations were not reported, we searched the primary literature for other pharmacokinetic studies where the same or a similar dosing regimen of the P-gp inhibitor was used and a C_max was reported. Human plasma protein binding (PPB) information for an inhibitor was obtained from the drug product labelling approved by the US FDA. If this information was not available or the P-gp inhibitor was not approved, PPB values were curated from the literature. Because some drugs are >99% bound to human plasma proteins, PPB in the current analysis was capped at 99%; that is, the lower limit of the unbound fraction in plasma (f_u,p) was set at 0.01 to be conservative and consistent with the recommendation in DDI guidance documents from regulatory agencies (FDA, EMA, and PMDA).^{30, 31, 34}

As shown in Table 2 and Figure 2, 57 DDI records (some studies have multiple DDI evaluations) with 26 inhibitors were identified. The majority (n=32) of the studies involved digoxin as the substrate, followed by 22 DDIs involving doxorubicin, 2 involving talinolol, and one involving edoxaban. Twenty-five of these records were considered positive in vivo DDIs in which the substrate AUC increased from 25% to 178%, with a median increase of 55%, in the presence of P-gp inhibitors. When total C_max/IC₅₀ or K_i ≥0.1 was applied, 23 true positive (TP), 15 true negative (TN), 17 false positive (FP), and 2 false negative (FN) predictions resulted, leading to a high sensitivity (92%) with moderate specificity (47%) (Table 3). The positive predictive error (PPE) and negative predictive error (NPE) were 43% and 12%, respectively. When unbound C_max,u/IC₅₀ or K_i ≥0.02 was applied, FPs reduced to 7 at the cost of increased FNs to 6 (Table 3). This analysis generated a more balanced sensitivity (76%) and specificity (78%). Accordingly, the PPE was 27% (i.e., a proportion of studies predicted a DDI risk that actually showed no risk), and NPE was 19% (i.e., a proportion of studies predicted as no risk actually demonstrated a DDI risk). Among the 6 FNs, the AUC increase (28%) of the substrate (digoxin) in one study marginally exceeded the threshold to be considered a positive DDI (i.e., 25%) in the presence of propafenone.³⁵ In addition, in that study, the CL_r of digoxin decreased by only 9%, indicating that propafenone has a small effect on systemic P-gp. Other FNs involved the inhibitors amiodarone and spironolactone, increasing digoxin AUC by 33% and 47%, respectively. The remaining three FNs involved the inhibitor verapamil, increasing digoxin AUC by 41% or 57%, and increasing doxorubicin AUC by 71%. In addition, we also explored applying a cut-off of 0.1 to C_max,u/IC₅₀ or K_i, this led to 9 more FNs, i.e., valspodar (PSC833), sorafenib, elacridar (GF120918), and cyclosporine on doxorubicin (AUC increased by 41%-94%, 47%, 47%, and 48%-55%, respectively), and ritonavir and amiodarone on digoxin (AUC increased by 86% and 47%, respectively). The sensitivity and specificity of this higher cut-off were 44% and 90%, respectively, with PPE of 20% and NPE of 35%. Thus, it is considered that the prediction performance becomes worse with this cut-off.

Table 2:

In vitro potency (IC₅₀ or K_i), human plasma protein binding, and clinical C_max of P-gp inhibitors and prediction of in vivo DDI based on total or unbound C_max divided by geometric mean of IC₅₀ and K_i values compared to a corresponding threshold of 0.1 or 0.02, respectively.

Inhibitor	Plasma protein binding	Inhibitor concentration (μM)		IC50 or Ki (μM) for P-gp			Cmax/IC50 or Ki	Prediction category	Cmax,u/IC50 or Ki	Prediction category	AUC ratio of P-gp substrate	Reference for inhibitor concentration
		Dosing regimen	concentration	geomean	range	substrate
Amiodarone	96%	200 mg x 15 d	1.8 (Cmax)a	10.6	4 - 45.6	digoxin	0.17	FP	0.007	TN	1.13 (digoxin)	89
		400 mg x 21-25 d	2.6 (Cavg)a				> 0.25	TP	< 0.02	FN	1.33 (digoxin)	90
		1600 mg qd x14 d	5.6 (Ctrough)				> 0.53	TP	> 0.021	TP	1.47 (digoxin)	59
Carvedilol	> 98%	25 mg, single	0.054 (Cmax)a	2.53	0.15-32	digoxin	0.021	TN	0.0004	TN	0.99 (digoxin)	91
Cimetidine	18-26% b	1000 mg/d qid x 4d	3.96 (C)	> 1000		digoxin	0.004	TN	0.003	TN	1.07 (digoxin)	92
Clarithromycin	72% c	250 mg bid	1.54 (Cmax)	21	4 - 66	digoxin	0.07	TN	0.020	FP	1.2 (digoxin)	93
		200 mg bid x 5 d	1.34 (Cmax)a				0.06	TN	0.018	TN	1 (digoxin)	94
		200 mg bid x 8 d	1.47 (Cmax)a				0.07	TN	0.019	TN	1.09 (digoxin)	94
Conivaptan	99%	20 mg, single	0.13 (Cmax)	25.3	16.6-39	digoxin	0.005	TN	0.00005	TN	1.08 (digoxin)	95
Diltiazem	70-80%	30 mg qid x 2 wk	0.17 (Cmax)a	28.6	4.6 - 88.1	digoxin	0.006	TN	0.0015	TN	0.91 (digoxin)	96
		30 mg qid x 8 d	0.17 (Cmax)a				0.006	TN	0.0015	TN	1.06 (digoxin)
Disopyramide	50-65%	200 mg tid x 21 d	8.54 (C)	40		digoxin	≥ 0.21	FP	≥ 0.09	FP	0.87 (digoxin)	97
Erythromycin	65-90% d	200 mg qid x 5 d	1.64 (Cmax)a	23.2	10 - 54.8	digoxin	0.07	TN	0.016	TN	0.96 (digoxin)	98, 99, 100, 101
Fluvoxamine	80%	50 mg bid x 17 d	0.17 (C)	146.7		talinolol	0.001	TN	0.0002	TN	0.8 (digoxin)	102
Isradipine	95%	2.5 mg bid x 2d, 5 mg bid x 2d, then 5 mg tid x 10 d	0.014 (Cmax)a	16.1	6 - 36	digoxin	0.0008	TN	0.00004	TN	0.96 (digoxin)	103
Losartan	98.7%	50 mg qd x 15 d	0.53 (Cmax)	96.7	65, 144	digoxin	0.005	TN	0.00007	TN	1.03 (digoxin)	104
Nifedipine	92-98%	10 mg tid x 10 d	0.48 (Cmax)	71.2	22 - 472	digoxin	0.007	TN	0.0003	TN	1.02 (digoxin)	105
Propafenone	>95%	150 mg tid x 12 d	1.27 (Cavg)	11.7	6.8, 20.3	digoxin	> 0.11	FP	> 0.0054	TN	1.14 (digoxin)	35
		300 mg tid x 12 d	3.51 (Cmax)a				0.30	TP	0.015	FN	1.28 (digoxin)	106
Quinidine	80-89%	200 mg qid x 4 d	7.71 (Cavg)	4.45 (2.29 Ki values)	0.2 - 56	digoxin	> 1.73	TP	> 0.27	TP	1.57 (digoxin)	60
		200 mg qid x 6 d	7.4 (Cavg)				>1.66	TP	> 0.26	TP	2.78 (digoxin)	65
		200 mg qid x 8 d	6.16 (Ctrough)				> 1.39	TP	> 0.21	TP	1.83 (digoxin)	64
		200 mg qid x11 d	9.86 (Ctrough)				> 2.22	TP	> 0.34	TP	1.65 (digoxin)	62
		200 mg bid x26 d	4.87 (Ctrough)				> 1.09	TP	> 0.17	TP	1.79 (digoxin)	63
		600 mg bid x11 d	10.3 (Cpre-dose)				> 2.31	TP	> 0.36	TP	2.28 (digoxin)	66
Quinine	80.5%	200 mg tid x 9d	7.55 (Cavg)	7.83	3.3, 18.6	digoxin	> 0.96	TP	> 0.19	TP	1.38 (digoxin)	67
Ritonavir	98.5%	300 mg bid x11 d	19.6 (Cmax)	7.65	5 - 28	digoxin	2.56	TP	0.038	TP	1.86 (digoxin)	68
Rolapitant (IV)	99.8%	185 mg,single dose	3.97 (Cmax)	7.36		digoxin	0.54	FP	0.005 g	TN	1.07 (digoxin, PO)	107
Spironolactone	> 90%	100 mg bid x 5 d	0.18 (Cmax)a	7.68	6.7, 8.8	digoxin	0.024	FN	< 0.0024	FN	1.47 (digoxin)	108
Verapamil	90%	80 mg bid x 4d, then tid x 10d	0.51 (Cmax)a	4.53 (4.76, Ki values)	0.06 - 224	digoxin	0.11	FP	0.011	TN	1.24 (digoxin)	36
		80 mg tid x 10 d	0.51 (Cmax)a				0.11	TP	0.011	FN	1.57 (digoxin)	36
		120 mg tid x12 d	0.63 (Cmax)a				0.14	TP	0.014	FN	1.41 (digoxin)	109
Cyclosporine (IV)	90%	18 mg/kg/d	3.15 (Cavg)	3.66		doxorubicin	> 0.86	TP	> 0.086	TP	1.55 (doxorubicin)	73
		38.9 mg/kg over 50 hr	1.42 (C24hr)				> 0.39	TP	> 0.039	TP	1.48 (doxorubicin)	74
Elacridar (GF120918)	>99.9% e	50 mg daily	0.06 (Cmax)	0.077 (0.307, Ki values)	0.0156 - 0.8	various substrates	0.79	FP	0.008 g	TN	0.86 (doxorubicin)	20
		50 mg bid	0.10 (Cmax)				1.24	FP	0.012 g	TN	1.03 (doxorubicin)
		100 mg bid	0.07 (Cmax)				0.91	FP	0.009 g	TN	0.88 (doxorubicin)
		200 mg bid	0.14 (Cmax)				1.83	FP	0.018 g	TN	1.23 (doxorubicin)
		400 mg bid	0.24 (Cmax)				3.16	FP	0.032g	FP	1.20 (doxorubicin)
		400 mg bid	0.35 (Cmax)				4.5	FP	0.045 g	FP	1.04 (doxorubicin)
		400 mg bid	0.31 (Cmax)				4.1	TP	0.041g	TP	1.47 (doxorubicin)
Sorafenib	99.5%	100 mg bid x19 d	3.14 (Cmax)	4.56	0.84 - 25	various substrates	0.69	FP	0.007 g	TN	1.1 (doxorubicin)	110
		200 mg bid x19 d	8.95 (Cmax)				1.96	FP	0.0196 g	TN	0.85 (doxorubicin)
		400 mg bid x19 d	7.94 (Cmax)				1.74	FP	0.017 g	TN	1.01 (doxorubicin)
		400 mg bid x19 d	16.97 (Cmax)				3.72	TP	0.037 g	TP	1.47 (doxorubicin)
Valspodar (PSC833) (IV or PO)	97.8% f	1-10 mg/kg, single dose	1.42 - 2.05 (Cmax)	0.291		doxorubicin	4.88 - 7.06	TP	0.107 - 0.155	TP	1.49 - 2.69 (doxorubicin)	77
		1.25 - 12.5 mg/kg bid for 5d	≥0.65 (Cmax)				≥ 2.24	TP	≥ 0.049	TP	1.54 (doxorubicin)	80
		2.5 mg/kg bid for 7d	0.93 (Cmax)				3.21	TP	0.07	TP	1.94 (doxorubicin)	78
		0.42 mg/kg/hr x 3 d	0.82 (Css)				> 2.82	TP	> 0.062	TP	1.41 (doxorubicin)	111
Verapamil	90%	80 mg tid x 3d then 120 mg qid x 4d	0.35 (Cmax)	8.27 (6.38, Ki values)	0.06 - 446.5	various substrates	0.043	FN	0.0043	FN	1.71 (doxorubicin)	72
Zosuquidar (LY335979) (IV or PO)	99.95% d	80 - 640 mg/m2/d x2d	0.49 (Cmax)	0.025	0.0002 - 0.38	various substrates	19.5	TP	0.19 g	TP	1.25 (doxorubicin)	21
		480-640 mg/m2/d x2d	0.96 (Cmax)				38.7	FP	0.39 g	FP	1.15 (doxorubicin)
		160-320 mg/m2 bid	0.33 (Cmax)				13.4	FP	0.134 g	FP	1 (doxorubicin)	112
		300-400 mg/m2 tid	0.41 (Cmax)				16.5	FP	0.165 g	FP	0.97 (doxorubicin)
Quinidine	84.5%	300 mg tid x 4 d	3.54 (Ctrough)	0.898		edoxaban	> 3.95	TP	> 0.61	TP	1.35 (edoxaban)	113
Digoxin	25%	0.5 mg, single dose	0.003 (Cmax)	94	7 - 393	various substrates	0.00003	TN	0.00002	TN	0.95 (talinolol)	114
Simvastatin	95%	40 mg qd x 21 d	0.013 (Cmax)	25.4	4.6 - 209	various substrates	0.0005	TN	0.00003	TN	1 (talinolol)	115

Substrates were administered intravenously, and inhibitors were given orally, unless otherwise indicated. Plasma protein binding is obtained from drug product prescriber information (U.S. labeling), unless otherwise indicated.

^a.Inhibitor concentration was not reported in the DDI study conducted with P-gp-substrates. Thus, inhibitor concentration was obtained from other PK studies with same or similar dosing regimen(s) as the that used in the DDI study with P-gp-substrates. The sources of PK studies are provided in references.

^b.Plasma protein binding of cimetidine. ¹¹⁶

^c.Plasma protein binding of clarithromycin. ^{117, 118}, ¹¹⁹, ¹²⁰

^d.Plasma protein binding of erythromycin. ^{121, 122, 123}

^e.Plasma protein binding of elacridar (GF120918) and zosuquidar (LY335979).³

^f.Plasma protein binding of valspodar (PSC833).¹²⁴

^g.Free fraction of drug in plasma (fu,p) was capped as 0.01 to calculate C_max,u.

Figure 2:

Relationships between observed AUCR and the ratio of total (left) or unbound (right) plasma concentrations of inhibitors to IC₅₀ values. Each symbol represents one clinical DDI case (Table 2). Horizontal lines represent cut-off values proposed in the FDA DDI guidance or EMA DDI guideline. Vertical lines represent AUCR of 1.25. AUCR, area under the concentration-time curve ratio; DDI, drug-drug interaction; IC₅₀, half-maximal inhibitory concentration; C_max, maximum systemic plasma concentration.

Table 3:

Summary of the DDI predictions of P-gp inhibitors using total or unbound C_max divided by geometric mean of IC₅₀ or K_i values towards P-gp.

	Total records	TN	TP	FN	FP	Sensitivity	Specificity	PPE	NPE
total Cmax/IC50 (0.1 as cut-off)	57	15	23	2	17	92%	47%	43%	12%
unbound Cmax/IC50 (0.02 as cut-off)	57	25	19	6	7	76%	78%	27%	19%

TN: true negative; TP: true positive; FN: false negative; FP: false positive; Sensitivity: TP/(TP + FN), proportion of actual positive DDIs that were predicted as such; Specificity: TN/(TN+ FP), proportion of actual negative or no DDI risks that were predicted as such; PPE: positive predictive error, FP/(FP + TP), proportion of studies that were conducted that actually did not need to be (no DDI risk); NPE: negative predictive error, FN/(FN+ TN), proportion of studies that were predicted as no risk but actually demonstrated DDI risk.

This analysis only considered P-gp inhibition by parent drugs. However, norverapamil, a metabolite with comparable in vivo plasma concentrations as verapamil,³⁶ also inhibits P-gp and appears to be more potent than the parent drug. One study examining the effect of verapamil and its metabolites on digoxin transport reported an IC₅₀ for norverapamil and verapamil of 0.3 μM and 1.1 μM, respectively.³⁷ The IC₅₀ of verapamil is lower than the geometric mean value (4.5 μM) used in the current analysis. Thus, when using 1.1 μM alone, C_max,u/IC₅₀ exceeded 0.02 and corrected the two FNs with digoxin to TPs. However, adding norverapamil can resolve the FNs if using 4.5 μM as the inhibition potency for verapamil. Norverapamil was also incorporated in a physiologically based pharmacokinetic (PBPK) model used to predict the effect of verapamil on several substrates including digoxin. ³⁸

There is large variability in inhibition potency of P-gp inhibitors. Attempts to reduce this variability by deriving an experimental system-independent K_i for P-gp-mediated digoxin transport from system-dependent IC₅₀ data have been reported. The derived K_i values represent the dissociation constant of an inhibitor relative to the aqueous concentration of the inhibitor in the cytosol of cell monolayers. The derivation required fitting a kinetic model with in vitro transport data to estimate kinetic rate constants of digoxin, which were further used to derive K_i. Using this approach, more consistent K_i values (0.3 or 0.6 μM) were estimated for verapamil from digoxin transport data from several laboratories.³⁹ In contrast, the IC₅₀ values of verapamil calculated using the same experimental data were 1.8, 8.6, 11.8, and 32.3 μM. In that study, system-independent K_i values (0.2-0.3 μM) were also estimated for quinidine and carvedilol. As a comparison, the corresponding IC₅₀ values for quinidine were 2.3, 8.4, and 15.7 μM, and those for carvedilol were 0.7, 1.4, 6.8, and 8.1 μM.³⁹ When this system-independent K_i for verapamil (0.5 μM on average) was used to calculate C_max,u/K_i, the ratios previously resulting in FN predictions were increased from 0.011, 0.014, 0.0043 to 0.1, 0.126, 0.07, respectively, converting the FN to TP predictions. Using the system-independent K_i, the predictions remained the same for quinidine (TPs) and for carvedilol (TN). Although using the model-derived K_i appeared to mitigate some FNs associated with the criterion of C_max,u/K_i ≥0.02, this observation is based on limited data of only one inhibitor (verapamil). It should be noted that derivation of system-independent K_i values requires more research and model fitting. During drug discovery and development, such analyses rarely have been conducted. However, if derivations of such K_i values for other inhibitors result in fewer FN predictions with similar FP predictions, then the expenditure of resources to update K_i values to make predictions may prove to be a cost-effective way to minimize risk to patients and avoid unnecessary clinical studies.

A related question is whether such an evaluation is also needed for orally administered drugs. Currently, the regulatory guidances from EMA, FDA, and PMDA recommend calculating [I]/IC₅₀ or K_i, where [I] is dose/250 mL as a rough estimation of intestinal drug concentration and compare this ratio with a cut-off of 10. If the ratio is 10 or above, an inhibitor may have the potential to inhibit P-gp in vivo and a further clinical DDI study may be needed. Previously, two criteria were recommended, i.e., [I₁]/IC₅₀ ≥0.1, where [I₁] is total C_max of an inhibitor at its therapeutic dose, and [I₂]/IC₅₀ ≥10, where [I₂] is dose/250 mL. The former reflects systemic inhibition of P-gp, while the latter represents inhibition of intestinal P-gp. A recent analysis of 53 orally administered drugs (as perpetrators) demonstrated that the predictions (TNs, TPs, FNs, FPs) are identical between the criterion only considering [I₂]/IC₅₀ ≥10 and the combined criteria [I₁]/IC₅₀ ≥0.1 or [I₂]/IC₅₀ ≥10, and also are identical using the criterion [I_1,u]/IC₅₀ ≥0.02 or [I₂]/IC₅₀ ≥10, where [I_1,u] is unbound C_max of an inhibitor at its therapeutic dose.²⁹ This indicates that P-gp inhibition-mediated DDIs for orally administered drugs may be driven mainly by intestinal P-gp inhibition, which is not surprising because orally administered drugs tend to have high intracellular concentrations in intestinal cells that are more likely to reach the level sufficient to inhibit P-gp than systemic drug concentrations. Thus, the current regulatory guidances recommend only [I₂]/IC₅₀ ≥10 for orally administered drugs. We also examined the datasets from other publications of IVIVE analyses for orally administered P-gp inhibitors.^{25, 26, 27} There are rare cases where an inhibitor has an [I₂]/IC₅₀ <10 but [I_1,u]/IC₅₀ ≥ 0.02, which are conivaptan, montelukast, omeprazole, and pantoprazole only when the higher IC₅₀ values of these drugs towards P-gp are used for calculations. Among these, only conivaptan increased AUC of digoxin more than 25% (by 43%).²⁶ The prediction changes depending on the IC₅₀. If a geometric mean of IC₅₀ values is used, the [I₂]/IC₅₀ of conivaptan exceeds 10. Overall, the vast majority of clinical P-gp inhibitors orally administered will be captured with the criterion just considering [I₂] (dose/250 mL).

In summary, the analysis based on the dataset we compiled suggests that for inhibition of systemic P-gp, using total C_max/IC₅₀ or K_i ≥0.1 has reasonable control on FN prediction but also leads to substantial FP prediction. In contrast, using unbound C_max,u/IC₅₀ or K_i ≥0.02 reduces FPs but has a higher risk of FN predictions. Considering the overall limited magnitude of DDIs mediated by inhibition of systemic P-gp, using C_max,u may provide an improved balance between capturing significant DDIs and reducing the cost of conducting unnecessary clinical studies. The decision whether to conduct a clinical study with a P-gp substrate and an investigational drug that is administered by a non-oral route with systemic drug exposure should take into consideration the likelihood of concomitant use of the drug with a P-gp substrate and the safety margin of that substrate. In addition, when there is a major metabolite (i.e., its radioactivity accounts for >10% of total drug-related material) with exposure comparable to parent drug, the potential of the metabolite inhibiting P-gp should be considered; if the metabolite is indeed a P-gp-inhibitor, the metabolite should be considered when predicting DDI risk. Lastly, further research is warranted to explore approaches to reduce the variability of inhibitory potency determinations from in vitro experiments. One potential approach is to derive a system-independent Ki for an inhibitor and assess prediction performance.

Evaluation of hepatic and renal clinical DDIs that potentially involve systemic BCRP inhibition

To collect clinical data for hepatic/renal BCRP inhibitors, we evaluated clinical DDI reports from the UW DIDB that met the following criteria: (1) BCRP inhibition is listed as a potential mechanism of a DDI, (2) either victim or perpetrator (or both) were administered intravenously, (3) prospectively designed DDI study, and (4) >25% increase in victim systemic exposure in the presence of the inhibitor (Table 4). Furthermore, DDI studies involving the victim drug rosuvastatin and perpetrators rifampicin or cyclosporine A (CsA) were excluded, as these DDIs were likely due primarily to inhibition of hepatic OATP1B and intestinal BCRP. As a result, only two studies showed potential involvement of hepatic BCRP inhibition between a victim and perpetrator, respectively: 1) copanlisib and itraconazole ⁴⁰ and 2) irinotecan and regorafenib.⁴¹ However, both DDIs were relatively small in magnitude (AUC increase of 58% and 28%, respectively), and the involvement of other elimination pathways cannot be excluded, e.g., copanlisib is metabolized by CYP3A. Similarly, drugs that are known BCRP inhibitors based on in vitro studies were reported to increase methotrexate exposure by >20%, although the actual increase in methotrexate AUC was not reported (Table 4). Methotrexate is a substrate of additional transporters, including MRP2, OAT1 and OAT3. In vitro inhibitors of BCRP such as vindesine also inhibit MRP2, OAT1 and OAT3.⁴² Therefore, based on currently available data, there do not appear to be any drugs in clinical use with evidence of DDIs involving systemic inhibition of hepatic/renal BCRP. There is lack of clinical evidence for the involvement of BCRP in the renal clearance of substrate drugs, consistent with the low protein expression of BCRP in the human kidney cortex.⁵ It is important to highlight that the lack of clinical evidence does not prove that there is not a DDI, particularly considering that changes in hepatic or renal cell concentrations may not be reflected in systemic concentrations. Furthermore, there may be simultaneous inhibition of uptake transporters, as discussed with the PET imaging study involving an intravenous infusion of CsA⁴³ and oral administration of rifampicin.⁴⁴

Table 4:

Four in vivo drug interaction studies discussing the involvement of BCRP (ABCG2) with the victim drug (BCRP substrate) administered intravenously.

Overall Effect	Object	Object dosing route	Precipitant	Precipitant dosing route	Percent Change AUC	Percent Change Plasma Concentration	Concentration Type	Comments	Reference
In Vivo Inhibition > 20% Effect	Copanlisiba	IV	itraconazole	Oral	57.7	3.9	Cmax	In vitro studies suggest that copanlisib is a substrate of P-gp and BCRP. According to the sponsor, the contribution of P-gp and BCRP inhibition to the increase of copanlisib exposure observed when co-administered with itraconazole is likely to be small as copanlisib was administered by IV infusion.	40
In Vivo Inhibition > 20% Effect	irinotecan	IV	regorafenib	Oral	28	22	Cmax	BCRP likely contributes to the transport of irinotecan. Regorafenib inhibits BCRP in vitro.	41
In Vivo Inhibition > 20% Effect	methotrexate	IV	proton pump inhibitors	Oral	--	103.2	Cavg	The proposed mechanisms for the interaction may involve inhibition of breast cancer resistance protein, which is also involved in mediating MTX transport.	125
In Vivo Inhibition > 20% Effect	methotrexate	IV	vindesine	IV	--	133.3	--	Membrane transport of methotrexate involves multiple transporters including the renal uptake transporters OAT1 and OAT3 in tubular cells, and a series of efflux transporters including MRP2 and BCRP. Vindesine is an inhibitor of BCRP, MRP2, OAT1, and OAT3.	42

IV – Intravenous

^a.Copanlisib is metabolized by CYP3A and transported by P-gp. Itraconazole inhibits CYP3A and P-gp.

P-gp- and BCRP-mediated inhibition of biliary clearance

Biliary excretion of drugs and their metabolites is a critical, yet often overlooked, route of hepatic elimination. Drugs or metabolites excreted from bile into the intestine may undergo further metabolism, exhibit pharmacologic or toxicologic effects, undergo fecal excretion, and/or be absorbed/reabsorbed into the systemic circulation. Canalicular (apical) transport proteins, including P-gp, BCRP, MRP2, BSEP, and MATE1 are primarily responsible for the biliary excretion of drugs/metabolites. Impaired biliary clearance of endogenous substrates or drugs due to alterations in canalicular transporters by genetic polymorphisms,⁴⁵ DDIs,^{46, 47} or disease⁴⁸ may increase hepatic and/or systemic exposure to substrates. Increased hepatocellular concentrations may impact drug efficacy and/or toxicity if the target site resides within hepatocytes. Importantly, systemic concentrations may not accurately reflect hepatocyte concentrations if hepatic transporters govern substrate disposition. Multiple canalicular transporters can excrete a substrate into bile (e.g., glucuronide conjugates). Loss of function of one transporter may result in biliary excretion or basolateral efflux of the substrate by an alternate transporter, which can shift the route of hepatic excretion from bile (e.g., via MRP2) to blood (e.g., via MRP3), or result in unexpected DDIs due to interactions with the alternate transporter(s). Evaluation of this interplay in humans would be technically difficult and requires extensive sampling from sites where catheters are not routinely placed (i.e., hepatic vein and bile duct). Such complex interactions could be simulated using mechanistic or victim PBPK models to evaluate the potential impact. However, fundamental information is required for accurate predictions of drug disposition, including a reasonable estimate of biliary clearance (CL_bile). Direct measurement of CL_bile in humans is challenging due to the anatomical location of the gallbladder and common bile duct. Numerous methods have been used to evaluate human CL_bile, including quantitative and semi-quantitative in vivo and in vitro approaches.⁴⁹ Qualitative characterization of biliary disposition using the Entero-Test,⁵⁰ or fecal recovery after IV administration, also could be used to estimate CL_bile assuming that the substrate does not undergo enterohepatic recycling or intestinal secretion into the lumen.

An extensive literature search was conducted to identify drugs that are P-gp and/or BCRP substrates that undergo biliary excretion in humans. Data were reported (Table 5) if the drug was administered IV and the biliary excretion of drug/metabolite(s) was measured directly (via a bile duct catheter), estimated from intestinal sampling via an oroenteric tube, or based on fecal recovery. Data based on fecal recovery were reported only if there was no evidence for intestinal secretion from the systemic circulation into the gut lumen via enterocytes and no significant enterocyte or luminal metabolism, which could reduce the fecal recovery of parent drug. Data obtained for drugs administered orally were excluded due to potential confounding of intestinal P-gp and/or BCRP on overall drug disposition and biliary excretion. When total systemic clearance after IV administration (CL_iv) was reported, CL_bile was estimated as the product of CL_iv and the fraction of the dose recovered in bile (f_bile). Drugs with a reported CL_bile after IV administration were evaluated further using the UW DIDB for P-gp- and/or BCRP-mediated DDIs.

Table 5:

Reported case studies for evaluation of P-gp- and BCRP-mediated inhibition of biliary clearance.

Transporter	Compound	CLiv data	CLiv [L/h]	Human CLbile	CLr [L/h]	Other				Comments	Reference
		[Yes/No]		[Yes/No]		CLbile [L/h]	% of dose excreted in bile or feces	CLmetabolic [L/h]	CLnon-renal [L/h]
BCRP	Azlocillin	Yes	8.4-22.5	Yes	5.03-9.01	0.08-1.55	1.1-6.9 (unchanged parent in bile)	1.00-18.9	--	Duodenal perfusion (n=5 healthy volunteers)	126
BCRP, P-gp, MRP2	Ceftriaxone	Yes	0.54-1.2	Yes	0.24-0.78	0.06-0.78	11-65 total	--	--	Duodenal perfusion for 6-8 hr (n=5 healthy volunteers)	127
BCRP, P-gp	Copanlisib	Yes	14.2-40.8	No	3.72 (active renal secretion)		30 (unchanged parent in feces)	--	--	41% of dose metabolized with 15% excreted unchanged in urine	128
P-gp	Digoxin	Yes	10.5-24.3	Yes	6.12-11.9	3.48-20.6		--	--	Intestinal perfusion (n=6 healthy volunteers)	129, 130
	Elsamitrucin	Yes	17.0-55.0	Yes		3.74-12.1	~22 (unchanged parent in bile)	--	--	Indwelling biliary catheter (n=1 cancer patient)	131
P-gp, MRP2	Etoposide	Yes	2.05-2.96	Yes	0.70-0.95	0.019	<2 (unchanged parent in bile)	--	1.30-1.52 L/h	Biliary drainage (n=2 pediatric cancer patients); 16 or 35 cancer patients	51, 132
BCRP, P-gp, MRP2	Fluvastatin	Yes	67.9	No			1.8 (unchanged parent in feces)	--	--	--	133
BCRP, P-gp, MRP2	Irinotecan	Yes	24.8	Yes	6.18	5.25	18.5 (unchanged parent in bile)	--	--	Bile duct exteriorized (n=1 patient)	134
	Mezlocillin	Yes	11.2-28.6	Yes	5.58-17.1	2.75-7.37	23.9-50.5 (unchanged parent in bile)	0.36-8.58	--	Duodenal perfusion (n=5 healthy volunteers)	126
BCRP, P-gp	Nemiralisib	Yes	10.8	Yes	0.93	ND	52.4 (total radioactivity in bile as unchanged parent)	--	--	Entero-Test string device (n=6 healthy men)	135
	Piperacillin	Yes	12.2-16.6	Yes	5.79-8.78	0.10-0.13	0.7-1.1 (unchanged parent in bile)	--	--	T-tube (n=5 cholecystectomized patients); duodenal aspiration (n=3 healthy volunteers)	136, 137
BSEP, MRP2, (BCRP)	Pravastatin	Yes	56.7	No	26.5		23 (unchanged parent in feces)	--	30.2	Clrenal is much higher than GFR, indicating active tubular secretion	138
	Quinupristin/Dalfopristin	Yes	~60/~60	No			<15 (unchanged in feces)	--	--	--	139
P-gp, BCRP	Revefenacin	Yes	?	No			54 (radiolabeled dose in feces)	--	--	27% of total radioactivity recovered in urine; 19% of radioactive dose recovered in feces as active metabolite	140
BCRP, P-gp	Tacrolimus	Yes	2.25	No	<0.001		0.29 (unchanged parent in feces)	--	--	--	141, 142
MPR2	Tc-99m Mebrofenin	Yes	72.7	Yes	<1.5	67.6	84.2 (unchanged parent in bile)	--	--	Duodenal aspiration (n=4 healthy volunteers)	143
P-gp	Tc-99m Sestamibi	Yes	108	Yes		25.2	21.5 (unchanged parent in bile)	--	--	Duodenal aspiration (n=7 healthy volunteers)	144
	Temocillin	Yes	ND	Yes	ND	ND	2.2 (unchanged parent in bile)	--	--	Duodenal perfusion (n=6 healthy volunteers); 38% of dose excreted as parent in urine	145
MRP2	Valsartan	Yes	2.18	Yes	0.48	1.93	~89 (unchanged parent in bile)	--	--	Biliary-enteric diversion (n=1 patient)	146
P-gp	Vincristine	Yes	19.8	Yes	ND	ND	28.4 (total radioactivity in bile)	--	--	T-tube (n=1 patient with jejunostomy); 26.2% of radioactivity excreted in urine	147, 148

ND, not determined

A total of 86 drugs identified in the initial literature search were reported to undergo a measurable extent of biliary excretion in humans, but in most cases, oral administration confounded data interpretation. Several reports attributed fecal recovery of drug to biliary excretion even when the drug was administered orally and fecal excretion due to unabsorbed drug could not be excluded. Fecal recovery of radioactivity often was attributed to biliary excretion without consideration of metabolic stability of the parent drug. CL_IV data were reported for only 20 of the 86 drugs. Of these 20 drugs, CL_bile was reported, or could be determined, for 11 (Table 5). CL_nr was calculated for some drugs (e.g., etoposide), but insufficient data were available to estimate the fraction of CL_nr that could be attributed to CL_bile. For example, CsA ⁵¹ and valspodar (PSC833) ⁵² decreased CL_nr of IV administered etoposide by 52% and 48%, respectively; however, the contribution of metabolism- vs. transporter-mediated inhibition to the overall decrease in CL_nr could not be ascertained from the data provided. Among the nine drugs with a CL_bile to CL_iv ratio of at least 0.20, relevant DDIs attributed to P-gp- and/or BCRP-mediated inhibition of biliary excretion were identified for only digoxin and etoposide. A CL_bile to CL_iv ratio of at least 0.2 was selected as a more conservative, arbitrary cut-off based on the 2020 FDA Guidance that defines an elimination pathway (e.g., renal clearance, biliary secretion) as significant when that clearance pathway contributes ≥25% to total drug clearance.

A major knowledge gap exists in understanding the contribution of P-gp- and/or BCRP-mediated CL_bile to the overall (systemic) clearance of drugs. Thus, the ability to accurately predict the clinical impact of co-administered P-gp and/or BCRP inhibitors on the systemic disposition of victim drugs is limited. Drugs with a CL_bile to CL_iv ratio of at least 0.2 may be susceptible to canalicular transporter mediated DDIs; such an observation may be assessed in clinical studies if feasible. Alternatively, the magnitude of the potential impact of impaired CL_bile on the hepatic and systemic exposure to the victim drug during co-administration of P-gp and/or BCRP inhibitors could be simulated using PBPK modelling. Innovative approaches to estimate CL_bile of drugs after IV administration in clinical studies can be implemented.^{49, 53} When possible, metabolite(s) should be differentiated from parent drug when fecal recovery of radioactivity is reported. BCRP-mediated transport of glucuronide conjugates has been documented in the literature.⁵⁴ In the case of asciminib,⁵⁵ the human mass balance study estimated that 28-58% of total drug clearance can be attributed to the glucuronidation pathway. In vitro studies with hepatocyte co-cultures have provided some evidence to support the biliary excretion of both asciminib and its glucuronide conjugate. However, analysis of human fecal samples did not show the presence of glucuronide metabolites likely due to hydrolysis by gut flora. The inability to directly attribute a specific transporter as the contributor to the active biliary secretion in humans is challenging as is the interpretation for the inhibition of such pathways by co-medications in vivo. Finally, fecal recovery of drug/metabolite(s) after oral administration should not be attributed necessarily to biliary excretion unless complete absorption of the drug has been confirmed.

Conclusion and Future Perspective

This perspective supports limited clinical DDI risk from P-gp or BCRP inhibition in liver or kidneys based on systemic exposure changes for P-gp substrates and limited examples of BCRP substrate drugs. These observations are consistent with the ITC’s previous review of PET- and/or CNS activity-focused clinical studies of blood-brain barrier P-gp inhibition that consistently showed no to negligible enhancement in CNS exposure to P-gp substrates.³ As shown by the dataset we compiled, the increases in systemic concentrations of P-gp substrates due to inhibition of liver and kidney P-gp are usually less than 2-fold. However, because some P-gp substrates have narrow therapeutic windows (e.g., digoxin, doxorubicin), the DDI risk of a P-gp-inhibitor that is administered by a non-oral route with systemic drug delivery cannot be ruled out. Such a potential DDI can be evaluated by comparing the ratio of unbound C_max divided by IC₅₀ or K_i to a cut-off of 0.02. If the ratio is less than the cut-off, then the risk can be considered as low. If not, further evaluation is warranted and the decision on whether to follow up with a clinical DDI study will depend on the likelihood of concomitant use of the inhibitor with P-gp-substrates, whether there are other interacting mechanisms besides P-gp inhibition, and the safety margin of P-gp-substrates. Such an evaluation seems unnecessary for orally administered drugs because intestinal P-gp inhibition is the main driving force in those situations. This is supported by our previous analysis showing that considering only estimated intestinal concentrations of an orally administered P-gp-inhibitor is sufficient to provide a reasonable prediction of an in vivo DDI. It should be noted that inhibition of P-gp or BCRP in liver and/or kidney may not always be reflected by the systemic exposure to substrates. Modelling and simulation approaches involving IVIVE-linked PBPK models may be employed to predict the hepatic/renal cell concentration of the P-gp/BCRP substrate/inhibitor drugs and establish correlations with their systemic, urinary, and/or pharmacodynamic effects if suitable.^{56, 57}

Abbreviations

AUC

area under the concentration vs. time curve

AUCR

ratio of the AUC of the substrate in the presence to absence of perpetrator

BCRP

breast cancer resistance protein

DDI

drug-drug interaction

FN

false negative

FP

false positive

IVIVE

in vitro-to-in vivo extrapolation

MDR

multidrug resistance

MRP

multidrug resistance-associated protein

NPE

negative predictive error

OATP

organic anion transporting polypeptide

P-gp

P-glycoprotein

PBPK

physiologically based pharmacokinetic

PPB

plasma protein binding

PPE

positive predictive error

TN

true negative

TP

true positive

UW DIDB

University of Washington Drug Interaction Database