Clin Pharmacokinet DOI 10.1007/s40262-014-0182-x

ORIGINAL RESEARCH ARTICLE

Physiologically Based Pharmacokinetic Modeling as a Tool to Predict Drug Interactions for Antibody-Drug Conjugates Yuan Chen • Divya Samineni • Sophie Mukadam • Harvey Wong • Ben-Quan Shen • Dan Lu • Sandhya Girish Cornelis Hop • Jin Yan Jin • Chunze Li

•

Ó Springer International Publishing Switzerland 2014

Abstract Background and Objectives Monomethyl auristatin E (MMAE, a cytotoxic agent), upon releasing from valine-citrulline-MMAE (vc-MMAE) antibody-drug conjugates (ADCs), is expected to behave like small molecules. Therefore, evaluating the drug–drug interaction (DDI) potential associated with MMAE is important in the clinical development of ADCs. The objective of this work was to build a physiologically based pharmacokinetic (PBPK) model to assess MMAE–drug interactions for vc-MMAE ADCs. Methods A PBPK model linking antibody-conjugated MMAE (acMMAE) to its catabolite unconjugated MMAE associated with vc-MMAE ADCs was developed using a mixed ‘bottom-up’ and ‘top-down’ approach. The model was developed using in silico and in vitro data and in vivo pharmacokinetic data from anti-CD22-vc-MMAE ADC. Subsequently, the model was validated using clinical pharmacokinetic data from another vc-MMAE ADC, brentuximab vedotin. Finally, the verified model was used to simulate the results of clinical DDI studies between brentuximab vedotin and midazolam, ketoconazole, and rifampicin. Y. Chen (&)  S. Mukadam  H. Wong  C. Hop Drug Metabolism and Pharmacokinetics, Genentech Inc, 1 DNA Way, South San Francisco, CA 94080, USA e-mail: [email protected] D. Samineni  D. Lu  S. Girish  J. Y. Jin  C. Li (&) Clinical Pharmacology, Genentech Inc, 1 DNA Way, South San Francisco, CA 94080, USA e-mail: [email protected] B.-Q. Shen Preclinical and Translational Pharmacokinetics and Pharmacodynamics, Genentech Inc, South San Francisco, CA, USA

Results The pharmacokinetic profile of acMMAE and unconjugated MMAE following administration of antiCD22-vc-MMAE was well described by simulations using the developed PBPK model. The model’s performance in predicting unconjugated MMAE pharmacokinetics was verified by successful simulation of the pharmacokinetic profile following brentuximab vedotin administration. The model simulated DDIs, expressed as area under the concentration-time curve (AUC) and maximum concentration (Cmax) ratios, were well within the two-fold of the observed data from clinical DDI studies. Conclusions This work is the first demonstration of the use of PBPK modelling to predict MMAE-based DDI potential. The described model can be extended to assess the DDI potential of other vc-MMAE ADCs.

Key Points Monomethyl auristatin E (MMAE), upon releasing from valine-citrulline-MMAE (vc-MMAE) antibody-drug conjugates (ADCs), is expected to behave like small molecules that could be metabolized and excreted. In order to evaluate the risk of drug–drug interactions associated with MMAE, a physiologically based pharmacokinetic (PBPK) model linking antibody-conjugated MMAE (acMMAE) to its catabolite unconjugated MMAE associated with vc-MMAE ADCs was developed using a mixed ‘bottom-up’ and ‘top-down’ approach. Drug interaction potential simulated by the PBPK model was in agreement with the observed data from clinical DDI studies.

Y. Chen et al.

1 Introduction Antibody-drug conjugates (ADCs) are a new class of therapeutic agents consisting of a monoclonal antibody (mAb) covalently bound with a cytotoxic agent through a chemical linker. ADCs are designed to selectively deliver a potent cytotoxic agent to tumor cells via tumor-specific or over-expressed cell surface antigens. After binding to the cell surface antigen, the ADC is internalized by tumor cells and then undergoes lysosomal degradation, leading to the release of the cytotoxic agent. Targeted delivery of cytotoxic drugs to tumors means that ADCs have the potential to harness and improve their antitumor effect while minimizing their impact on normal tissues. There are currently two marketed ADCs—brentuximab vedotin (ADCETRISTM), an anti-CD30 mAb conjugated to a microtubule-disrupting agent, monomethyl auristatin E (MMAE), to treat relapsed anaplastic large cell lymphoma and Hodgkin’s lymphoma; and trastuzumab emtansine (TDM1, KADCYLATM), trastuzumab linked to a maytansine derivative (DM1) to treat human epidermal growth factor receptor 2 (HER2)-positive metastatic breast cancer. More than 20 ADCs are in clinical development for cancer therapy, and approximately 50 % of them use auristatins (e.g. MMAE) as the cytotoxic agent [1]. Most auristatin ADCs use a dipeptide (valine-citrulline [vc]) linker conjugated to the MMAE (a specific auristatin) via solventaccessible thiols present in mAb cysteines (vc-MMAE ADC). An example of vc-MMAE ADC is shown in Fig. 1 [2]. Conjugation through reduced inter-chain disulfide cysteine residues results in a heterogeneous mixture of conjugated antibodies, with a drug antibody ratio (DAR) ranging from 0 to 8, and with an average DAR of 3–4 for most vc-MMAE ADCs. In addition, biotransformations in vivo can lead to additional changes in DARs, resulting in dynamically changing mixtures. In humans, multiple clinically relevant analytes can be detected and quantified for an ADC, with each analyte having a distinct in vivo behavior. Integrating information from multiple analytes is critical to understand the absorption, distribution, metabolism, and excretion (ADME) of an ADC. For most vc-MMAE ADCs in clinical development, the analytes measured in systemic circulation include antibody-conjugated MMAE (acMMAE), total antibody, and unconjugated MMAE [3, 4]. The antibody component of an ADC undergoes catabolism via nonspecific proteolytic degradation and target-mediated drug disposition with no significant involvement of cytochrome P450 isoenzymes (CYPs). Drug–drug interactions (DDIs) involving the antibody components of an ADC are thus typically limited. However, unconjugated cytotoxic agentcontaining catabolites (e.g. MMAE) that are formed via

proteolytic degradation and/or deconjugation from ADC are expected to behave like small molecules that could be metabolized and excreted by CYPs and transporters. It was reported that MMAE is a substrate of CYP3A and P-glycoprotein (P-gp) and also an inhibitor of CYP3A [5]. Although circulating unconjugated MMAE levels are relatively low, with mean maximum concentration (Cmax) of less than 10 ng/mL following administration of vc-MMAE ADCs at their therapeutic doses, DDIs may still occur through modulation of important elimination pathways [6]. A clinical DDI study has been conducted for brentuximab vedotin, a vc-MMAE ADC [7]. Brentuximab vedotin did not affect the pharmacokinetics of midazolam, a sensitive CYP3A substrate. Concomitant administration of rifampicin (a strong CYP3A inducer) and ketoconazole (a strong CYP3A inhibitor) did not alter the pharmacokinetics of the ADC measured as conjugated antibody. However, exposure of unconjugated MMAE was reduced *46 % by rifampicin and increased *34 % by ketoconazole coadministration [7]. Because most vc-MMAE ADCs contain the same vc linker conjugated to MMAE, it is conceivable that we could leverage the information learned from the brentuximab vedotin DDI study to inform risk assessment for other vc-MMAE ADCs. The use of physiologically based pharmacokinetic (PBPK) modeling to predict the pharmacokinetics and DDI has significantly increased in recent years. This is attributed to advances in predicting hepatic metabolism using the in vitro-in vivo extrapolation (IVIVE) [8, 9], tissue distribution from in silico and in vitro data [10–12], as well as commercially available software tools (e.g. GastroPlusTM, Simulations Plus, Lancaster, CA, USA; PK SimÒ, Bayer Technology Services, Leverkusen, Germany; and the SimcypÒ simulator, Simcyp, Sheffield, UK). The strength of PBPK modeling is that it allows the incorporation and understanding of the mechanistic basis of the DDI of interest. Unlike the empirical compartmental approaches, mechanistic PBPK models allow ‘what-if’ scenario analyses that are particularly useful when there is a knowledge gap about the underlying biological process and limited experimental data. The main challenge in assessing DDI risk caused by MMAE is predicting the pharmacokinetic profile of unconjugated MMAE formed through linker cleavage of vc-MMAE ADCs because the mechanisms and kinetics of this process are not completely understood [6, 13]. PBPK modeling utilizes a combined ‘bottom-up’ and ‘top-down’ approach which maintains a mechanistic PBPK structure, using parameters determined from in vitro experiments and in silico predictions (‘bottom-up’), and leveraging in vivo clinical data when required (‘top-down’) can help bridge the gaps in our understanding mentioned above. In this study, we built a PBPK model, linking unconjugated

PBPK Modeling to Predict DDIs for ADCs Fig. 1 Representative diagram of a vc-MMAE ADC. MMAE monomethyl auristatin E, vc the valine-citrulline linker, MC maleimidocaproyl-valine, PABC paminobenzoyloxycarbonyl, ADC antibody-drug conjugate

MMAE to acMMAE as metabolite-to-parent drug, utilizing properties of unconjugated MMAE derived from in silico and in vitro studies and clinical data from an in-house ADC (anti-CD22-vc-MMAE ADC, an ADC that consists of a humanized anti-CD22 monoclonal IgG1 antibody conjugated to MMAE via a protease-labile vc linker with an average DAR of *3.5 (see Fig. 1 for a representative diagram [14]). The model was validated using brentuximab vedotin pharmacokinetic data that was not included during model development and, subsequently, this verified model was used to simulate MMAE-drug interaction. Since the major pathways involved in the release of unconjugated MMAE to systemic circulation are similar for most vcMMAE ADCs once target-mediated clearance (CL) is saturated [6], a model developed to simulate the pharmacokinetics of unconjugated MMAE released from acMMAE following ADC administration can be used to assess MMAE–drug interactions for other vc-MMAE ADCs.

2 Methods 2.1 Physiologically Based Pharmacokinetic Model Development The SimcypÒ population-based ADME simulator (Simcyp version 12) was used to perform PBPK modeling. Model development included two parts: the acMMAE PBPK submodel (Sect. 2.1.1) and the MMAE PBPK submodel (Sect. 2.1.2). The final assembled PBPK model treats acMMAE as a parent drug that leads to the formation of unconjugated MMAE (modeled as a metabolite formed

from acMMAE) associated with potential DDIs (Scheme 1). An MMAE equivalent dose was calculated using the ADC dose and the average MMAE to antibody ratio (DAR). The developed model was validated using the pharmacokinetic data from brentuximab vedotin clinical pharmacokinetic studies. The verified model was used to assess DDI potential for brentuximab vedotin, and the simulated results were compared against observed data. 2.1.1 Antibody-Conjugated MMAE (acMMAE) Pharmacokinetic Submodel A minimal PBPK model was built for acMMAE using a ‘top-down’ approach, in which the clearance (CL) and volume of distribution at steady state (Vss) data came from the clinical pharmacokinetic study obtained from intravenous infusion of anti-CD22-vc-MMAE ADC (Table 1). Details of the clinical pharmacokinetic study for antiCD22-vc-MMAE ADC are presented in Sect. 2.4.1. The minimal PBPK distribution model is a lumped PBPK model that has only three compartments (systemic and liver for intravenous administration, plus portal vein for oral administration) [15]. To best characterize the multi-compartmental plasma concentration-time profile observed for acMMAE, the minimal PBPK model with the addition of a single adjusting compartment (SAC) is used. The minimal PBPK model with the addition of an SAC implemented in the Simcyp, and the relationship between volume of the SAC (Vsac), volume of the systemic compartment (Vsys), Vss, and volume of the liver (Vh) are shown in Scheme 2. For the acMMAE model, while CL and Vss from the clinical study are used as fixed parameters, the values of

Y. Chen et al.

Scheme 1 Elucidation of the acMMAE–MMAE-linked PBPK model development process. acMMAE antibody-conjugated MMAE, MMAE monomethyl auristatin E, PBPK physiologically based pharmacokinetic,

vc-MMAE MMAE conjugated via the valine-citrulline linker, ADC antibody-drug conjugate, ADME absorption, distribution, metabolism, excretion

Table 1 Pharmacokinetic parameters for acMMAE in the Simcyp PBPK model Source

MMAE ADC [5]. As such, the CL of acMMAE served as the formation clearance of unconjugated MMAE (i.e. MMAE is implemented as a metabolite) in the model. 2.1.2 Unconjugated MMAE Pharmacokinetic Submodel

Parameter

Value

CL

21.8 mL/day/kg

NCA of clinical data

Vss

0.09 L/kg

NCA of clinical data

Vsac

0.04 L/kg

kin/kout

0.00727/0.00249 h

Simcyp best fita -1

Simcyp best fit

The values of Vh (1.59 L) and QH (arterial—6.5, and portal—20 mL/ min/kg) used are the defaults from Simcyp acMMAE antibody-conjugated monomethyl auristatin E, PBPK physiologically based pharmacokinetic, CL clearance, Vss volume of distribution at steady state, Vsac apparent volume of distribution of SAC, kin/kout rate constant from the systemic compartment to the SAC/rate constant from the SAC to the systemic compartment, NCA non-compartmental analysis, SAC single adjusting compartment, Vh volume of liver, QH hepatic blood flow a

Output value from best-fit model simulation

Vsac, rate constant from the systemic compartment to the SAC (kin), and rate constant from the SAC to the systemic compartment (kout) were obtained from simulation that provided the best fit to the observed acMMAE pharmacokinetic data. The final pharmacokinetic model was able to capture the multi-phase decline with a long terminal halflife (t‘ ) characteristic of the observed acMMAE concentration-time profile in patients. Values for pharmacokinetic parameters from the acMMAE model simulation are shown in Table 1. In the absence of complete understanding of the elimination mechanism for anti-CD22-vc-MMAE ADC, the model assumed all acMMAEs are eventually converted to unconjugated MMAE before being eliminated from the body. This assumption is consistent with a previous model reported for brentuximab vedotin which is another vc-

The unconjugated MMAE PBPK model was built using a combined ‘bottom-up’ and ‘top-down’ approach (Scheme 3). In silico calculated physiochemical properties, in vitro ADME, and in vivo data were used to predict CL and Vss for MMAE and are presented in Table 2. The formation of unconjugated MMAE (implemented as a metabolite of acMMAE) was rate-limited by the CL of acMMAE based on observed data described in Sect. 2.4.1. The CL of MMAE was mainly through the CYP3Amediated metabolic pathway and biliary/urine excretion, based on in vitro and preclinical and clinical in vivo data [5, 16]. The in vivo metabolic CL of MMAE was extrapolated from in vitro intrinsic CL (CLint *1.9 lL/min/million cells) estimated from a metabolism study in human hepatocytes. The contribution of non-metabolic CL was estimated based on existing brentuximab vedotin clinical data and MMAE preclinical data. A human mass balance study following a single dose of brentuximab vedotin showed that the primary route of excretion of unconjugated MMAE was via feces [5]. Consistent with human data, an in-house bile-duct cannulated rat study (data not shown) with radiolabeled unconjugated MMAE dosing, showed that approximately 60 % of the total dose was excreted unchanged in bile. Collectively, these data suggest that at least 50 % of MMAE is likely excreted unchanged via biliary CL. Based on all the above information, the total CL of MMAE in the PBPK model was determined as follows. The in vivo metabolic CL was predicted to be *4 L/h based on hepatocyte CLint and using an IVIVE

PBPK Modeling to Predict DDIs for ADCs

Scheme 2 General concept of minimal PBPK ? SAC distribution model (adapted from a guide for IVIVE and PBPK/PD modeling using the Simcyp Population-Based Simulator, Version 12; Published by Simcyp Limited [now Certara]). PBPK physiologically based pharmacokinetic, SAC single adjusting compartment, IVIVE in vitro-

in vivo extrapolation, PD pharmacodynamic, QH hepatic blood flow, QHA hepatic artery blood flow, Qpv portal vein blood flow, kin rate constant from the systemic compartment to the SAC, kout rate constant from the SAC to the systemic compartment, IV intravenous

Scheme 3 PBPK model for unconjugated MMAE. PBPK physiologically based pharmacokinetic, acMMAE antibody-conjugated MMAE, MMAE monomethyl auristatin E, QH hepatic blood flow,

kin rate constant from the systemic compartment to the SAC, kout rate constant from the SAC to the systemic compartment, CL clearance, CYP cytochrome P450

approach with the well-stirred liver model built into Simcyp. The non-metabolic pathway (mainly biliary) was assumed to account for at least *50 % of the total MMAE CL based on in vivo studies mentioned above. Thereafter, an estimate of total CL value of *8 L/h for MMAE was used in the initial model development. The Vss of MMAE was predicted to be 8.4 L/kg using the mechanistic tissue composition equation after Rogers and Rowland [12]. Similar to acMMAE, a minimal PBPK distribution model with the addition of an SAC provided the best description of the shape of the plasma concentration-time profile of MMAE obtained from an intravenous infusion study of anti-CD22-vc-MMAE ADC. The best-fit parameters (Vsac, kin, kout) from simulation are listed in Table 2.

data obtained from an intravenous infusion study of antiCD22-vc-MMAE ADC. The pharmacokinetic model simulations used randomly selected individuals aged 20– 50 years and a sex ratio of 1:1 from the built-in healthyvolunteer profile in Simcyp. The dose regimen and trial size used in the simulations were matched to the clinical studies detailed in Sect. 2.4. For the acMMAE and unconjugated MMAE pharmacokinetics, a total of 10 trials of 11 subjects administered anti-CD22-vc-MMAE at 2.4 mg/kg were simulated. The MMAE equivalent dose of 2.4 mg/kg anti-CD22-vc-MMAE was 3.0 mg, converted using the average DAR (Eq. 1):

2.1.3 Pharmacokinetic Simulations To assess model performance, the simulated acMMAE and unconjugated MMAE concentrations were compared with

acMMAE equivalent dose ¼   dose  3:5  MW(MMAE) MW(ADC)

ð1Þ

where MW(ADC) represents the molecular weight of antiCD22-vc-MMAE ADC, i.e. 146,455 Da. The MW (MMAE) represents the molecular weight of the unconjugated MMAE,

Y. Chen et al. Table 2 Input parameters for MMAE in the Simcyp PBPK model Parameter

Value

Reference

MW

717.98 g/mol

[5]

clogP

2.6

In silico calculateda

Compound type

Monoprotic base

cpKa

8.08

In silico calculateda

B/P ratio

1.45

In-house data

fu,plasma

0.178

[18]

Vss

8.4 L/kg

Simcyp predicted

Vsac

2.0 L/kg

Simcyp best fit

kin/kout

8 9 10-4/1 9 10-8 h-1

Simcyp best fit

CLint-CYP3A4

0.04486 lL/min/pmol

Simcyp IVIVE based on CLint from hepatocyte and entered as enzyme kinetics

CLbiliary

2.15 lL/min/106

Assume 50 % of total CL (in-house data and [5]), and back-calculated by Simcyp Assumed based on clinical information [5]

CLrenal

0

CYP3A4 reversible inhibition

IC50 = 10 lM

[16]

Time-dependent CYP3A4 inhibition

kinact = 0.10 min-1

[16]

KI = 1.12 lM MW molecular weight, clogP (o:w) calculated octanol-water partition co-efficient, cpKa calculated acid dissociation constant, B/P ratio blood/ plasma partition ratio, fu, plasma free fraction in plasma, Vss volume of distribution at steady state, Vsac volume of the SAC, kin rate constant from the systemic compartment to the SAC, kout rate constant from the SAC to the systemic compartment, CYP cytochrome P450, CLint intrinsic clearance, IVIVE in vitro-in vivo extrapolation, CLbiliary biliary clearance, IC50 half maximal inhibitory concentration, kinact maximal inactivation rate constant, KI inhibitor concentration at 50 % of kinact, MMAE monomethyl auristatin E, PBPK physiologically based pharmacokinetic, SAC single adjusting compartment a

Calculated in MoKa V 1.1.0 (Molecular Discovery, Perugia, Italy)

i.e. 718 g/mol. The dose of ADC was converted to an MMAE equivalent dose using an average DAR of 3.5; average body weight was assumed to be 70 kg. 2.2 Validation of the MMAE Pharmacokinetic Model Using Brentuximab Vedotin To validate our PBPK approach of predicting the unconjugated MMAE pharmacokinetics of vc-MMAE ADCs in general, the model was applied to simulate the pharmacokinetics of unconjugated MMAE from an intravenous infusion study of brentuximab vedotin whose data was not used during model development. Simulations were performed with dose regimens and trial sizes similar to those described in the clinical report (Sect. 2.4), in which brentuximab vedotin was administered at levels of 1.2, 1.8, and 2.7 mg/kg with trial sizes (trials 9 subjects) of 10 9 4, 10 9 12, and 10 9 12, respectively. The MMAE equivalent dose of 1.2, 1.8, and 2.7 mg/kg brentuximab vedotin was 1.5, 2.3, and 3.5 mg, respectively. The predicted pharmacokinetic parameters and concentration-time profiles were compared with observed data (Sect. 2.4.2). Unconjugated MMAE pharmacokinetic data obtained from dosing brentuximab vedotin in the DDI study (SGN35-008A) was also used for additional cross-

validation. The model-simulated pharmacokinetics of unconjugated MMAE were compared with clinical data obtained from dosing brentuximab vedotin in the absence of ketoconazole and rifampicin. As conjugated antibody, instead of acMMAE, was measured for brentuximab vedotin, the acMMAE pharmacokinetic data was not available from studies mentioned above; therefore, no comparison to the observed data was presented. 2.3 Prediction of MMAE–Drug Interaction Following verification of the unconjugated MMAE pharmacokinetic model with brentuximab vedotin clinical data, the PBPK model was used to simulate the DDI between brentuximab vedotin and ketoconazole, rifampicin, and midazolam. The pharmacokinetic profile of MMAE after an intravenous infusion of brentuximab vedotin was simulated using the pharmacokinetic model described earlier. The in vitro CYP3A inhibition data for MMAE was entered into the model to simulate drug interaction in which MMAE served as a CYP3A inhibitor (Table 2). For DDI involving MMAE as a CYP3A victim drug, the contribution of CYP3A to total MMAE CL (fmCYP3A4) was entered as enzyme kinetic data derived from a retrograde calculation of total CL (Table 2). Models for ketoconazole,

PBPK Modeling to Predict DDIs for ADCs

rifampicin, and midazolam were available in the Simcyp compound library, and were directly used in the simulations. The simulations of DDI were conducted using the dosing regimens and trial sizes described in the clinical study [7]. Briefly, the interaction between ketoconazole (400 mg, orally once daily for 24 days) and MMAE (intravenous infusion of brentuximab vedotin at 1.2 mg/kg on day 3) was simulated with a trial size of 10 9 16. The simulated Cmax and AUC ratio of MMAE in the presence and absence of ketoconazole was compared with observed data. The fmCYP3A4 was determined based on simulations that provided the best fit to the observed DDI data. The model with the above-determined fmCYP3A4 value was then used to simulate the interaction between rifampicin (600 mg, orally once daily for 29 days) and MMAE (intravenous infusion of brentuximab vedotin at 1.8 mg/kg) with a trial size of 10 9 14. The inhibitory effect of MMAE (brentuximab vedotin at 1.8 mg/kg) on midazolam (1 mg, intravenously over a period of at least 2 min) was simulated with a trial size of 10 9 15. To simplify trial design and shorten simulation time for the simulations with rifampicin and ketoconazole, a single cycle of brentuximab vedotin was simulated (rather than two cycles). 2.4 Clinical Pharmacokinetics and DDI Study Data 2.4.1 Clinical Pharmacokinetic Data for Anti-CD22-vcMMAE ADC The pharmacokinetic data of acMMAE and unconjugated MMAE from an expansion cohort of a phase I study in patients with relapsed/refractory diffuse large B-cell lymphoma, after a 30-min intravenous infusion of anti-CD22vc-MMAE ADC at the maximum tolerated dose of 2.4 mg/ kg (every 3 weeks), was used for the non-compartment model analysis (NCA). Blood samples were obtained at pre-dose and at 30 min, 4, 24 and 72 h, and 7, 10, 14 and 21 days after the first dose in cycle 1 (21-day period) and at later cycles. The acMMAE and unconjugated MMAE plasma concentrations were determined using validated liquid chromatography tandem mass spectrometry (LCMS/MS) methods. Pharmacokinetic parameters such as CL and Vss of acMMAE after the first-dose were estimated using NCA methods (Table 1). Preliminary analysis showed that the pharmacokinetics of anti-CD22-vc-MMAE ADC were linear at the dose of 2.4 mg/kg [17]. 2.4.2 Clinical Pharmacokinetics and DDI Study Data for Brentuximab Vedotin The pharmacokinetic data obtained after a single dose of brentuximab vedotin (study SG035-0001 [5]) were used for

model validation. Briefly, this is an open-label, single arm, dose escalation study with a dose range of 0.1–3.8 mg/kg, administered as an intravenous infusion, every 3 weeks (one cycle) in 45 patients with CD30-positive hematologic cancers. The pharmacokinetics of conjugated antibody and unconjugated MMAE were characterized at doses of 1.2, 1.8, and 2.7 mg/kg (delivered as a 30-min intravenous infusion of brentuximab vedotin). Pharmacokinetic parameters such as AUC and Cmax were reported [5]. It was shown that the pharmacokinetics of brentuximab vedotin were linear at doses of 1.2, 1.8, and 2.7 mg/kg [5]. Han et al. [7] evaluated the CYP3A-mediated DDI potential of brentuximab vedotin when co-administered with midazolam (a sensitive CYP3A substrate), rifampicin (strong CYP3A inducer), and ketoconazole (strong CYP3A inhibitor) in patients with CD30-positive hematologic cancers. Briefly, brentuximab vedotin was given as an intravenous infusion on day 1 of each of two 21-day cycles at a dose of 1.2 mg/kg in the ketoconazole arm, and 1.8 mg/kg in the midazolam and rifampicin arms. Based on their assigned treatment arm, 15 patients received concomitant administration of intravenous midazolam (1 mg) at 3 days pre- and post- brentuximab vedotin dose in cycle 1; 14 patients received a rifampicin (600 mg) oral dose once daily from cycle 1, day 14, through cycle 2, day 21; and 16 patients received a ketoconazole (400 mg) oral dose once daily from cycle 1, day 19, through cycle 2, day 21. In the rifampicin and ketoconazole arms, pharmacokinetic samples for the brentuximab vedotin analytes (conjugated antibody and unconjugated MMAE) were taken frequently until the end of cycle 2, day 21. The pharmacokinetic parameters (Cmax and AUC) for assessing brentuximab vedotin DDI potential were evaluated and reported [7].

3 Results 3.1 Simulation of acMMAE Pharmacokinetics The PBPK model built for acMMAE was able to simulate the pharmacokinetic profile of acMMAE observed in the clinic following an intravenous infusion of anti-CD22-vcMMAE. Using the acMMAE equivalent dose (3 mg) as well as CL (21.8 mL/day/kg) and Vss (0.09 L/kg) from non-compartmental analysis of the pharmacokinetic data from 11 subjects, the simulated Cmax (0.90 lg/mL) and AUC (2.13 lg  day/mL) were in good agreement with the observed mean values of 0.89 lg/mL and 2.34 lg  day/ mL, respectively. The optimized distribution model with the addition of an SAC captured the shape of the observed concentration-time curve which exhibited a multi-exponential decline with a long terminal t‘ (Fig. 2).

Y. Chen et al. Fig. 2 Simulated and observed plasma concentration-time profiles of acMMAE following administration of a 2.4 mg/kg dose of anti-CD-22-vc-MMAE ADC (3.0 mg MMAE dose equivalent). The black line represents the mean concentration for the simulated population (n = 11). The thin grey lines represent simulated individual trials (10 trials of 11 subjects). The circles and error bars denote the mean and SD values from the clinical trials for anti-CD-22-vc-MMAE ADC, respectively. acMMAE antibody-conjugated MMAE, ADC antibody-drug conjugate, MMAE monomethyl auristatin E

3.2 Simulation of Unconjugated MMAE Pharmacokinetics

3.3 Validation of MMAE Pharmacokinetics Prediction Using Brentuximab Vedotin

The pharmacokinetic profile of unconjugated MMAE treated as a metabolite of acMMAE in the model was simulated using the final acMMAE–MMAE-linked PBPK model developed using in silico, in vitro, and in vivo data. The predicted Vss and CL for MMAE used in the final model were 8.4 L/kg and 8.09 L/h (CYP3A metabolic and biliary CL), respectively. The model successfully predicted both the formation and elimination of MMAE as the observed and simulated unconjugated MMAE pharmacokinetic profile match reasonably well (Fig. 3). At a 3 mg MMAE equivalent dose of anti-CD22-vc-MMAE (2.4 mg/kg), the simulated AUC of MMAE was 0.0504 lg  day/mL compared with the observed value of 0.0544 lg  day/mL. The simulated median value of time to reach Cmax (tmax) as determined by the rate of acMMAE elimination (responsible for unconjugated MMAE formation) and MMAE clearance was 52 h (28–91 h in the 5th–95th percentile), which is in the range of 24–119 h observed from anti-CD22-vc-MMAE and 24–72 h observed from brentuximab vedotin clinical studies [5]. The simulated Cmax was 0.0075 lg/mL at *52 h, at which no mean value of observed data was reported from the anti-CD22-vc-MMAE study for direct comparison (Fig. 3). While the shape of the plasma-concentration curve for MMAE was predicted reasonably well with regard to tmax and Cmax, the Clast (at day 21) was predicted to be approximately two-fold higher than the observed value (0.46 vs. 0.19 ng/mL).

The validation of the acMMAE–MMAE-linked PBPK model in predicting the pharmacokinetics of unconjugated MMAE was performed using brentuximab vedotin clinical data [5]. The simulated unconjugated MMAE pharmacokinetic profile after an intravenous infusion of brentuximab vedotin at 1.2, 1.8, and 2.7 mg/kg (MMAE equivalent at 1.5, 2.3, and 3.5 mg) are presented in Fig. 4. The shape of the concentration-time profiles were captured by the model. For all three dose levels, the predicted Cmax and AUC of unconjugated MMAE are comparable to the observed data (Table 3). For the 1.2 mg/kg dose, although the predicted pharmacokinetic profile did not match at Cmax from the digitized plasma-concentration time profile from the report, the predicted values of Cmax and AUC were comparable to the reported geometric mean [5]. This discrepancy may be a result of the plasma-concentration time profile being from the mean of individual patients’ pharmacokinetic profiles versus the reported pharmacokinetic data which represents the geometric mean of all patients. A further cross-validation was conducted by comparing the simulated unconjugated MMAE pharmacokinetics with those obtained from the brentuximab vedotin DDI study. In the DDI study, brentuximab vedotin was administered at 1.2 mg/kg in DDI study with ketoconazole, and at 1.8 mg/ kg with rifampicin. The unconjugated MMAE pharmacokinetic data obtained in the absence of interaction drugs (ketoconazole and rifampicin) were compared with those predicted by the model. The model-predicted Cmax and

PBPK Modeling to Predict DDIs for ADCs Fig. 3 Simulated and observed plasma concentration-time profiles of unconjugated MMAE formed from acMMAE following the administration of a 2.4 mg/kg dose of anti-CD22-vc-MMAE ADC (3.0 mg MMAE dose equivalent). The black line represents the mean concentration for the simulated population (n = 11); the thin grey lines represent simulated individual trials (10 trials of 11 subjects); and the circles and error bars denote the mean and SD values from the clinical trials for anti-CD-22-vc-MMAE ADC, respectively. acMMAE antibody-conjugated MMAE, ADC antibody–drug conjugate, MMAE monomethyl auristatin E

AUC values (Table 3) are in a close agreement with the observed data.

comparable to the observed value of 1.25 and 1.34, respectively.

3.4 Prediction of DDI for Brentuximab Vedotin

3.4.3 Effect of Rifampicin on Unconjugated MMAE Pharmacokinetics

3.4.1 Effect of Brentuximab Vedotin on Midazolam Pharmacokinetics The effect of brentuximab vedotin on the pharmacokinetics of midazolam was simulated using the dose regimen described by Han et al. [7]. Predictions showed that there was no change in midazolam AUC following a single 1.8 mg/kg intravenous dose for brentuximab vedotin. The predicted pharmacokinetic parameters for midazolam (AUC and Cmax), with and without brentuximab vedotin co-administration, match well with observed data (Table 4). 3.4.2 Effect of Ketoconazole on Unconjugated MMAE Pharmacokinetics The effect of ketoconazole on the pharmacokinetics of unconjugated MMAE was simulated, and the changes of AUC and Cmax of unconjugated MMAE following coadministration of brentuximab vedotin with ketoconazole were compared with the observed data (Table 4). To obtain the closest estimation of fmCYP3A4, simulations with variable fmCYP3A4 values of 0.3–0.5 were conducted. Based on the best fit to the observed data (both AUC and Cmax changes), at fmCYP3A4 0.4, the model simulated a 1.21-fold increase in MMAE Cmax and 1.47-fold increase in MMAE AUC in the presence of ketoconazole, which is most

Following co-administration of brentuximab vedotin with rifampicin (7 days treatment at 600 mg), the model predicted a 2.0-fold decrease (geometric mean ratio [GMR] 0.51) in unconjugated MMAE AUC, and a 1.5-fold decrease (GMR 0.69) in unconjugated MMAE Cmax. Predicted DDI potential was comparable to the observed clinical data (Table 4).

4 Discussion PBPK models are frequently used in the assessment and prospective prediction of enzyme-based DDI for traditional small molecule therapeutics. Assessing DDI risk associated with antibody-based therapy is relatively rare since DDIs involving antibodies are typically limited. ADCs represent a new class of therapeutic agents with both mAb and smallmolecule characteristics. The cytotoxic agent component of an ADC functions as a small molecule upon release from the ADC and is of concern for enzyme-based DDIs. PBPK model-based predictions of DDI caused by unconjugated cytotoxic agents (e.g. MMAE) from ADCs is a new area that should be explored further. Here, we describe the first application using PBPK modeling to predict DDI involving vc-MMAE ADCs. An acMMAE–MMAE-linked PBPK

Y. Chen et al. Fig. 4 Simulated versus observed plasma concentrationtime profiles of unconjugated MMAE following an intravenous infusion of brentuximab vedotin at (a) 1.2 mg/kg (MMAE equivalent 1.5 mg), (b) 1.8 mg/ kg (MMAE equivalent 2.3 mg), and (c) 2.7 mg/kg (MMAE equivalent 3.5 mg). Black lines represent the simulated mean of the healthy volunteer population (10 trials 9 4 subjects, 10 9 12, 10 9 12, respectively). The open circles denote mean values from clinical studies for brentuximab vedotin [5, 7]. MMAE monomethyl auristatin E

PBPK Modeling to Predict DDIs for ADCs Table 3 Validation of the PBPK model for unconjugated MMAE pharmacokinetic prediction AUC? (ng  day/mL)

Cmax (ng/mL) Brentuximab vedotin dose (mg/kg)

1.2

1.8

2.7

1.2

1.8

2.7

Observed (SGN035-0001 [5])b

2.72

4.97

7.00

20.3

37.0

53.2

Observed ([7])c

4.11

4.98

NA

26.7

40.1

NA

Model predicted

3.39

5.23

7.96

23.4a

36.6a

55.6a

Values are expressed as geometric mean MMAE monomethyl auristatin E, PBPK physiologically based pharmacokinetic, Cmax maximum concentration, AUC? area under the concentration-time curve at time infinity, NA not available a

AUC area under the concentration-time curve

b

Brentuximab vedotin pharmacokinetic study (Sect. 2.4.2)

c

Brentuximab vedotin drug interaction study, observed AUC and Cmax of unconjugated MMAE when brentuximab vedotin was administered alone

Table 4 Comparison of the observed and predicted DDI data (Cmax and AUC ratio) Analyte pharmacokinetic parameter

Simulated GMR (90 % CI)

Observed GMR (90 % CI)

Brentuximab vedotin with or without ketoconazole MMAE AUC?

1.47 (1.43–1.51)a

1.34 (0.98–1.84)

Cmax

1.21 (1.19–1.22)

1.25 (0.90–1.72)

Brentuximab vedotin with or without rifampicin MMAE AUC?

0.51 (0.49–0.53)a

0.54 (0.43–0.68)

Cmax

0.69 (0.67–0.71)

0.56 (0.42–0.76)

Midazolam with or without brentuximab vedotin Midazolam AUC?

1.00a

0.94 (0.81–1.10)

Cmax

1.00

1.15 (0.76–1.74)

AUC area under the concentration-time curve, GMR geometric mean ratio, CI confidence interval, AUC? area under the concentrationtime curve at time infinity, Cmax maximum concentration, DDI drug– drug interaction, MMAE monomethyl auristatin E a

Ratio of AUC

model was developed using in vitro MMAE data and in vivo clinical data from anti-CD22-vc-MMAE ADC, and successfully applied to simulate the pharmacokinetics and DDI potential for brentuximab vedotin, a similar vcMMAE ADC and the only ADC that has reported a dedicated clinical DDI study to date. The predicted low DDIs suggest that vc-MMAE ADCs have limited potential for causing significant DDIs. One of the challenges in PBPK modeling for the pharmacokinetic/DDI assessments is the requirement for a large amount of information to enable standard ‘bottom-up’ model development. Our understanding of the formation mechanism and kinetics of MMAE from vc-MMAE ADCs is incomplete and the experimental data related to MMAE disposition in humans is limited. Thus, we utilized a hybrid

‘bottom-up’ and ‘top-down’ approach that leverages the use of existing preclinical and clinical data and a minimal PBPK distribution model to account for the ‘gap’ in our understanding. The assembled PBPK model resulting from our hybrid approach well-described the observed clinical acMMAE and unconjugated MMAE pharmacokinetic data following administration of anti-CD22-vc-MMAE. More importantly, the model was able to successfully simulate the unconjugated MMAE pharmacokinetic data from brentuximab vedotin clinical studies that were not included in the original model development, which served as the first step of validation of the model’s applicability for other vc-MMAE ADCs. The model’s performance in simulating brentuximab vedotin clinical DDI data suggests that the present PBPK model could be applied to other vc-MMAE ADCs with the same linker and similar DAR ratio, and to assess the DDI potential of unconjugated MMAE released from the corresponding ADC conjugate. The development of additional vc-MMAE ADCs will prove useful in verifying this hypothesis. Predicting MMAE CL in humans is another critical component in PBPK modeling of ADCs. Initial model development relied on mechanistic information on metabolic CL estimates from MMAE hepatocyte incubations, as well as information from a mass balance study of MMAE in rats and brentuximab vedotin in humans where MMAE excretion was characterized to likely account for approximately 50–60 % of total CL. Utilization of both the in vitro and in vivo data resulted in an initial estimated total CL value of *8 L/h, which was comprised of a metabolic CL (*4 L/h) component scaled using IVIVE and a non-metabolic CL (mainly biliary) component accounting for the other 50 % of the MMAE CL. As the resulting model was able to describe the pharmacokinetics of unconjugated MMAE from dosing anti-CD-22-vc-MMAE as well as brentuximab vedotin, the estimate of *8 L/h is likely a reasonable estimate. It must be noted that a higher estimate of metabolic CL ([13 L/h) was extrapolated from CLint

Y. Chen et al.

obtained from human liver microsome incubations. An additional simulation with higher CL was also conducted but the simulated unconjugated MMAE AUC and Cmax were significantly lower than the observed data. Considering the poor permeability of MMAE, it is highly possible that in vitro CLint estimates from human liver microsomes are an overestimation of MMAE metabolic CL in vivo. While our PBPK model was built using a mechanistic approach with careful consideration of the current knowledge of ADCs, the model does hold some assumptions, such that all acMMAE are eventually catabolized to unconjugated MMAE before being metabolized and/or excreted. Although we do not have human data to directly support this assumption, a rat ADME study with antiCD22-vc-MMAE ADCs showed that the majority of acMMAE was catabolized to MMAE prior to subsequent elimination. In addition, this assumption was also used in the reported pharmacokinetic model describing brentuximab vedotin [5]. As the linker used to link MMAE to the mAb for brentuximab vedotin and anti-CD22-vc-MMAE is the same, it is not unreasonable to conclude that the same assumption can apply. Finally, we would like to highlight that the PBPK model not only enables us to simulate DDI scenarios for brentuximab vedotin, but also provides a valuable tool to evaluate the DDI risk for other vc-MMAE ADCs during clinical development. For ADCs with the same vc linker, site of conjugation, and cytotoxic agent (MMAE) as brentuximab vedotin, the formation mechanisms and kinetics of unconjugated MMAE from a vc-MMAE ADC is expected to be similar. Our in-house data showed that the pharmacokinetics of unconjugated MMAE were similar across vc-MMAE ADCs regardless of the mAb component when the pharmacokinetics of the ADC reaches its linear range. Considering that the verified PBPK model has successfully predicted the low clinical CYP3A-based DDI potential for brentuximab vedotin, it is conceivable that DDIs with other vc-MMAE ADCs can be predicted reasonably well by our developed PBPK model. As a result, a dedicated clinical CYP3A-based DDI study might not be necessary for vc-MMAE ADCs. Instead, evaluating DDI potential in clinical combination studies and/or concomitant medication analysis using the population pharmacokinetics approach, together with the PBPK model-based assessment, might be sufficient to assess the DDI of vcMMAE ADCs.

5 Conclusions This work demonstrates the value of utilizing PBPK modeling to predict ADC drug interactions. It demonstrates the power of using a mixed ‘bottom-up’ and ‘top-down’

approach to leverage existing in vitro and clinical data to bridge knowledge gaps in our understanding of MMAE disposition. More importantly, our work supports that vcMMAE ADCs, as a class, have a limited potential for causing significant DDIs. Finally, the approach described in this manuscript can be applied across all classes of ADCs. Acknowledgments This study was funded by Genentech (a member of the Roche group). All authors were employees of Genentech when this work was carried out. They have no other conflicts of interest to declare. We would like to thank the DMPK ADME group for generating the in vitro data, Priya Agarwal for processing the CD22 pharmacokinetic data, Amita Joshi, John Prescott, and Yu-Waye Chu for reviewing the manuscript, and Anshin BioSolutions for editorial support. Author contributions Yuan Chen, Divya Samineni, Sophie Mukadam, Jin Yan Jin, and Chunze Li participated in the model design; Yuan Chen, Divya Samineni, Sophie Mukadam, Ben-Quan Shen, Dan Lu, and Chunze Li collected data and ran simulations; Yuan Chen, Divya Samineni, Sophie Mukadam, Harvey Wong, and Chunze Li performed the data analysis and wrote the manuscript; and Yuan Chen, Divya Samineni, Sophie Mukadam, Ben-Quan Shen, Harvey Wong, Jin Yan Jin, Dan Lu, Sandhya Girish, Cornelis Hop, and Chunze Li reviewed the manuscript and approved it for submission.

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