Evaluation of the Effect of Abrocitinib on Drug Transporters by Integrated Use of Probe Drugs and Endogenous Biomarkers

Abstract

Abrocitinib is an oral Janus kinase 1 (JAK1) inhibitor currently approved in the United Kingdom for the treatment of moderate-to-severe atopic dermatitis (AD). As patients with AD may use medications to manage comorbidities, abrocitinib could be used concomitantly with hepatic and/or renal transporter substrates. Therefore, we assessed the potential effect of abrocitinib on probe drugs and endogenous biomarker substrates for the drug transporters of interest. In vitro studies indicated that, among the transporters tested, abrocitinib has the potential to inhibit the activities of P-glycoprotein (P-gp), breast cancer resistance protein (BCRP), organic anion transporter 3 (OAT3), organic cation transporter 1 (OCT1), and multidrug and toxin extrusion protein 1 and 2K (MATE1/2K). Therefore, subsequent phase I, two-way crossover, open-label studies in healthy participants were performed to assess the impact of abrocitinib on the pharmacokinetics of the transporter probe substrates dabigatran etexilate (P-gp), rosuvastatin (BCRP and OAT3), and metformin (OCT2 and MATE1/2K), as well as endogenous biomarkers for MATE1/2K (N 1 -methylnicotinamide (NMN)) and OCT1 (isobutyryl-L -carnitine (IBC)). Co-administration with abrocitinib was shown to increase the plasma exposure of dabigatran by ~ 50%. In comparison, the plasma exposure and renal clearance of rosuvastatin and metformin were not altered with abrocitinib co-administration. Similarly, abrocitinib did not affect the exposure of NMN or IBC. An increase in dabigatran exposure suggests that abrocitinib inhibits P-gp activity. By contrast, a lack of impact on plasma exposure and/or renal clearance of rosuvastatin, metformin, NMN, or IBC suggests that BCRP, OAT3, OCT1, and MATE1/2K activity are unaffected by abrocitinib.

Reimagining the Framework Supporting the Static Analysis of Transporter Drug Interaction Risk; Integrated Use of Biomarkers to Generate Pan‐Transporter Inhibition Signatures

“…Compared with formal (probe‐based) DDI studies, deployment of SLC biomarkers in most SAD‐MAD phase I studies will greatly facilitate calibration of an institution's in vitro data, especially if the growing list of published biomarker‐qualified perpetrator drugs is leveraged also. For example, during the writing of this manuscript, three NCEs (letermovir, PF‐06835919, and ipatasertib) were added to the list of biomarker (CPI)‐qualified perpetrators ( Table 1 ), clinical NMN data were reported for abrocitinib, which did not impact the plasma AUC (AUCR = 0.93) and CL renal (ΔCL renal <5%) of metformin ( Table , Figure ), and a static model was described for bridging CPI to substrates such as statins 41,101–104 …”

“…For example, during the writing of this manuscript, three NCEs (letermovir, PF-06835919, and ipatasertib) were added to the list of biomarker (CPI)-qualified perpetrators (Table 1), clinical NMN data were reported for abrocitinib, which did not impact the plasma AUC (AUCR = 0.93) and CL renal (ΔCL renal <5%) of metformin (Table S2, Figure 3b, Figure 6b), and a static model was described for bridging CPI to substrates such as statins. 41,[101][102][103][104] Currently, the International Council for Harmonization (ICH) Executive Working Group is working on the development of a new guidelines (document ICH M12) with a view to providing a consistent approach to the design, conduct, and interpretation of DDI studies (https://www.ich.org/page/multi disci plina ry-guide lines). Within the draft ICH M12 documentation, SLC biomarkers are briefly described.…”

Using Endogenous Biomarkers to Derisk Assessment of Transporter‐Mediated Drug‐Drug Interactions: A Scientific Perspective

“…The importance of OATP1B transporters in the hepatic uptake of CPI and CPIII was also demonstrated by Shen et al, 17 who showed that itraconazole and diltiazem, strong and moderate inhibitors, respectively, of cytochrome P450 (CYP) 3A4, did not affect CPI exposures, consistent with the expected lack of significant effect of these inhibitors on OATP1B1 transporters. It is important to note that, although an extensive discussion of other endogenous biomarkers for OATP1B and endogenous biomarkers for transporters other than OATP1B is outside the scope of this brief report, there are ongoing efforts to characterize endogenous biomarkers for assessing other uptake transporters such as OAT1, OAT3, and OCT2 and efflux transporters such as MATE‐1/2K 7,18–20 …”

“…It is important to note that, although an extensive discussion of other endogenous biomarkers for OATP1B and endogenous biomarkers for transporters other than OATP1B is outside the scope of this brief report, there are ongoing efforts to characterize endogenous biomarkers for assessing other uptake transporters such as OAT1, OAT3, and OCT2 and efflux transporters such as MATE-1/2K. 7,[18][19][20] Integrating Endogenous Biomarkers Early in Drug Development to Streamline DDI Assessments and Facilitate Mechanistic Understanding of Complex DDIs Assessing transporter biomarkers in single-and multiple-ascending-dose ranging trials, where a wide range of doses are generally evaluated may help to streamline and prioritize subsequent DDI trials. 21 The magnitude of change in the concentrations of CPI and CPIII has not only been demonstrated to be dose dependent for a given inhibitor but also sensitive to the potency of the OATP1B1 inhibitor, underscoring the potential utility of assessing CPI and CPIII early in development.…”

Development of 4‐Pyridoxic Acid PBPK Model to Support Biomarker‐Informed Evaluation of OAT1/3 Inhibition and Effect of Chronic Kidney Disease

“…[3][4][5][6] Alternatively, monitoring the concentrations of endogenous biomarkers for relevant transporters during phase I clinical trials has been increasingly used as a surrogate measure of transporter DDIs. [7][8][9] Such biomarker data, coupled with physiologically-based pharmacokinetic (PBPK) modeling, could enable an accurate assessment of the in vivo DDI risk of an NCE and guide the decision on necessity for a dedicated DDI study. 10 Several PBPK models for endogenous biomarkers have already been developed, verified, and applied to estimate the in vivo transporter inhibitory potential of selected inhibitors, 9,11 understand the impact of transporter genotype on endogenous biomarkerinformed DDI assessment, 12 and disease-related changes in biomarker levels.…”