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\begin{document}

\title{Model-Based Meta-analysis for the Population Pharmacokinetics of
Iberdomide and Its Major Active Metabolite in Healthy Subjects and
Subjects with Relapse and Refractory Multiple Myeloma}



\author[1]{Yiming Cheng}%
\author[2]{Yongjun Xue}%
\author[2]{Lu Chen}%
\author[3]{Mark Masin}%
\author[1]{Paulo Maciag}%
\author[3]{Teresa Peluso}%
\author[1]{Simon Zhou}%
\author[1]{Yan Li}%
\affil[1]{Bristol-Myers Squibb Co Summit}%
\affil[2]{Bristol Myers Squibb Co}%
\affil[3]{Bristol-Myers Squibb Europe Moyen-Orient Afrique SARL}%


\vspace{-1em}



  
  \date{May 27, 2024}


\begingroup
\let\center\flushleft
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\selectlanguage{english}
\begin{abstract}
Aim A parent-metabolite population pharmacokinetic (popPK) model of
iberdomide was developed and the influence of demographic and
disease-related covariates on popPK parameters was assessed based on
data from three clinical studies of iberdomide (dose range, 0.1 to 6 mg)
in healthy subjects (N=81) and subjects with relapsed and refractory
multiple myeloma (N=245). Methods Nonlinear mixed effects modeling was
used to develop the popPK model based on data from 326 subjects across 3
clinical studies. Results The pharmacokinetics (PK) of iberdomide were
adequately described with a two-compartment model with first-order
absorption and elimination. A first order conversion rate was used to
link the one-compartment linear elimination metabolite model with the
parent model. Subject type (multiple myeloma subjects vs. healthy
subject) was a statistically significant covariate on apparent clearance
(CL/F) and apparent volume of distribution for the central compartment
(V1/F), suggesting different PK between subjects with multiple myeloma
and healthy subjects. Aspartate aminotransferase (AST), alkaline
phosphatase (ALP) and sex were statistically but not clinically relevant
covariates on CL/F. Metabolite (M12) PK tracked the PK of iberdomide.
The metabolite to parent ratio was consistent across doses and
combinations. Conclusion In conclusion, the parent-metabolite population
pharmacokinetic model adequately described the time course PK data of
iberdomide and M12. Iberdomide and M12 show robust PK exposure, not
complicated by demographic factors, combination, hepatic or (mild and
moderate) renal impairments. The model can be used to guide the dosing
strategy for special patient population and inform future iberdomide
study design.%
\end{abstract}%



\sloppy


\textbf{Title} : Model-Based Meta-analysis for the Population
Pharmacokinetics of Iberdomide and Its Major Active Metabolite in
Healthy Subjects and Subjects with Relapse and Refractory Multiple
Myeloma

Yiming Cheng\textsuperscript{1}; Yongjun Xue\textsuperscript{2}; Lu
Chen\textsuperscript{1}; Mark Masin\textsuperscript{3}; Paulo
Maciag\textsuperscript{4}; Teresa Peluso\textsuperscript{3}; Simon
Zhou\textsuperscript{1}; Yan Li\textsuperscript{1}

\textsuperscript{1}Clinical Pharmacology \& Pharmacometrics, Bristol
Myers Squibb, Princeton, NJ, USA

\textsuperscript{2}Non-Clinical Research \& Development, Bristol Myers
Squibb, Princeton, NJ, USA

\textsuperscript{3}Global Drug Development, Celgene International S\selectlanguage{ngerman}àrl,
a Bristol-Myers Squibb Company, Boudry, Switzerland

\textsuperscript{4}Global Drug Development, Bristol Myers Squibb,
Princeton, NJ, USA

\textbf{Correspondence}

Yan Li, PhD

Clinical Pharmacology \& Pharmacometrics,

Bristol Myers Squibb

556 Morris Ave, Summit, NJ 07901

Phone: 908-481-6203

E-mail: yan.li@bms.com

\textbf{Running Title} : Iberdomide Parent-Metabolite Population
Pharmacokinetics Modeling and Simulation

\textbf{Word Count} : 3682; \textbf{Table Count} : 5; \textbf{Figure
Count} : 9

\textbf{What is already known about this subject}

Iberdomide is a potent and orally available cereblon E3 ligase
modulator, which is being investigated for the treatment of inflammatory
and autoimmune-mediated diseases and hematological malignancies
including multiple myeloma.

\textbf{What this study adds}

\begin{itemize}
\tightlist
\item
  Iberdomide and major metabolite (M12) show robust PK exposure, not
  complicated by demographic factors, combination, hepatic or (mild and
  moderate) renal impairments
\item
  M12 PK tracked the PK of iberdomide. The metabolite to parent ratio
  was consistent across doses and combinations
\item
  Results should inform iberdomide dosing strategy and study design
\end{itemize}

\textbf{Aim}

A parent-metabolite population pharmacokinetic (popPK) model of
iberdomide was developed and the influence of demographic and
disease-related covariates on popPK parameters was assessed based on
data from three clinical studies of iberdomide (dose range, 0.1 to 6 mg)
in healthy subjects (N=81) and subjects with relapsed and refractory
multiple myeloma (N=245).

\textbf{Methods}

Nonlinear mixed effects modeling was used to develop the popPK model
based on data from 326 subjects across 3 clinical studies.

\textbf{Results}

The pharmacokinetics (PK) of iberdomide were adequately described with a
two-compartment model with first-order absorption and elimination. A
first order conversion rate was used to link the one-compartment linear
elimination metabolite model with the parent model. Subject type
(multiple myeloma subjects vs. healthy subject) was a statistically
significant covariate on apparent clearance (CL/F) and apparent volume
of distribution for the central compartment (V1/F), suggesting different
PK between subjects with multiple myeloma and healthy subjects.
Aspartate aminotransferase (AST), alkaline phosphatase (ALP) and sex
were statistically but not clinically relevant covariates on CL/F.
Metabolite (M12) PK tracked the PK of iberdomide. The metabolite to
parent ratio was consistent across doses and combinations.

\textbf{Conclusion}

In conclusion, the parent-metabolite population pharmacokinetic model
adequately described the time course PK data of iberdomide and M12.
Iberdomide and M12 show robust PK exposure, not complicated by
demographic factors, combination, hepatic or (mild and moderate) renal
impairments. The model can be used to guide the dosing strategy for
special patient population and inform future iberdomide study design.

\textbf{Keyword}

iberdomide, metabolite, multiple myeloma, population pharmacokinetics

\textbf{1. Introduction}

Iberdomide (CC-220) is an orally available agent which binds to the
cereblon E3 ubiquitin ligase complex, resulting in proteasomal
degradation of Ikaros and Aiolos, which are key transcriptional
regulators in hematopoietic cell differentiation of the immune system,
including B cells, T cells, monocytes, and plasmacytoid dendritic cells.
Iberdomide is a potent antiproliferative agent in B cell-derived tumors,
including multiple myeloma (MM) and lymphoma tumor cell
lines\textsuperscript{1-3}. Based on these pharmacological activities,
iberdomide is being investigated for the treatment of inflammatory and
autoimmune-mediated diseases and hematological malignancies including
multiple myeloma \textsuperscript{4-7}.

The pharmacokinetics (PK) of iberdomide have been characterized in
several clinical studies. Reports from single-ascending-dose (SAD) and
multiple-ascending dose (MAD) studies conducted in healthy subjects
showed that the iberdomide PK exposure (e.g., maximum (peak) plasma
concentration (Cmax) and area under the plasma concentration time-curve
(AUC)) increased in a dose-proportional manner over the tested dose
range (0.1 to 6 mg). Following multiple daily oral doses, iberdomide
steady state was attained after approximately 7 days, with an
accumulation ratio of 2-fold. The median time of maximum observed
concentration (Tmax) was observed between 2.5-4 hours postdose. The
half-life of iberdomide was estimated to be approximately 9 to 13 hours
after single oral dose. Coadministration with food did not affect the
oral bioavailability of iberdomide. The systemic exposure of the
R-enantiomer of iberdomide (CC-17195) was \textless{}9\% of iberdomide
in the SAD study across all doses and \textless{}8\% in the MAD study (1
mg) based on AUC \textsuperscript{8}. Additionally, Gaudy et al.
evaluated the iberdomide drug-drug interaction potential with cytochrome
P450 (CYP) 3A4/5 enzymes in a clinical study. Results showed that
co-administration with itraconazole (a strong CYP3A inhibitor) increased
the AUC for plasma iberdomide by approximately 136\%; and
co-administration with rifampin (CYP3A inducer) substantially decreased
the overall exposure (AUC) and Cmax by approximately 82\% and 70\%,
respectively \textsuperscript{9}.

Based on results from a radiolabeled human mass balance study (data on
file, Bristol Myers Squibb), from which 1 mg \textsuperscript{14}{[}C{]}
labelled iberdomide was administered in healthy subjects, intact parent
molecule in urine and feces constitute 16\% and 11\% respectively,
indicating the absorbed drug is extensively metabolized and excreted
mostly as metabolites, with nearly equal contribution from urinary and
fecal elimination route. In systemic circulation, iberdomide was the
predominant component in human plasma, with M12 being the most prominent
circulating metabolite. Based on exposure (AUC), iberdomide and M12
accounted for approximately 59\% and 14\% of circulating total
radioactivity (TRA) exposure, respectively.

CC-220-MM-001 study (herein as MM-001) is an on-going phase 1b/2a,
multicenter, open-label, dose-escalation study to determine the maximum
tolerated dose, assess the safety, tolerability, pharmacokinetics and
efficacy of CC-220 as monotherapy and in combination with other
treatments in subjects with multiple myeloma (MM). Iberdomide in
combination with dexamethasone (DEX), in combination with DEX and
daratumumab (DARA) or bortezomib (BORT), are being investigated in
CC-220-MM-001 as treatment regimens and showed encouraging clinical
outcome in subjects with heavily pre-treated relapsed and refractory
(RR) MM \textsuperscript{6,10}.

To support the clinical development of iberdomide in RRMM, a
parent-metabolite population pharmacokinetic (popPK) model was developed
to: 1) characterize the PK of iberdomide and M12 in subjects with RRMM
given both molecules were prominent in systemic circulation and
pharmacologically active; and 2) assess the influence of covariates on
the PK of iberdomide and M12 to inform the dosing strategy in special
populations.

\textbf{2. Method}

\textbf{2.1 Study Description}

Three clinical studies were pooled together for popPK analysis,
including CC-220-CP-001(single ascending dose study in healthy subjects;
herein as CP-001; NCT01733875), CC-220-CP-002 (multiple ascending dose
study in healthy subjects; herein as CP-002; NCT02034773) and
CC-220-MM-001 studies (subjects with RRMM; NCT02773030). Study
description was presented in Table 1.

The studies were conducted in accordance with the ethical principles of
Good Clinical Practice. All subjects gave written informed consent prior
to enrollment. The studies were approved by the institutional review
boards of the participating center and were conducted according to the
Declaration of Helsinki and the ICH Guidelines for Good Clinical
Practice.

\textbf{2.2 Pharmacokinetic Schedule}

Iberdomide PK data was collected for all three studies. All available PK
data from CP-001, Part 1(multiple ascending dose; Table 1) and 3
(relative bioavailability; Table 1) from CP-002 and cohorts A
(iberdomide monotherapy dose escalation; Table 1), B (iberdomide in
combination with DEX dose escalation; Table 1), D (iberdomide in
combination with DEX dose expansion; Table 1), E (iberdomide in
combination with DARA and DEX dose escalation; Table 1),) and I
(iberdomide in combination with DEX dose expansion; Table 1) from MM-001
studies were included in the popPK analysis, based upon the relevance to
this analysis. M12 PK data was only collected in CC-220-MM-001 Cohorts
A, B and E. The sampling schedule of M12 is similar as iberdomide.

The PK sampling schedule was detailed in Table 1.

\textbf{2.3 Bioanalytical Method}

Human whole blood was collected and centrifuged to obtain plasma. The
harvested plasma was transferred to precharged citric acid tubes to
obtain acidified plasma (40 mM) for measurement of iberdomide and M12,
with two validated human plasma liquid chromatography-tandem mass
spectrometry assays (LC-MS/MS): 1) solid phase extraction (SPE, Waters
Oasis Elution HLB cartridges) for iberdomide or M12 sample cleanup; 2)
chiral LC (Chiral Tech, CHIRALPAK\selectlanguage{english} CBH, 100 x 3 mm, 5 \selectlanguage{greek}μ\selectlanguage{english}m column) for
iberdomide separation (from its R-enantiomer), or achiral LC (Phenomenex
Synergi Polar-RP, 2.0 x 50 mm, 4 \selectlanguage{greek}μ\selectlanguage{english}m column) for M12 separation; and 3)
Turbo IonSpray\selectlanguage{ngerman}® with MS/MS detection for iberdomide (SCIEX API 4000,
Framingham, MA) or M12 (SCIEX API 5500). The calibration curves for
iberdomide and M12 ranged from 0.1 to 100 ng/mL and 0.1 to 50 ng/mL in
acidified human plasma respectively, with the lower limit of
quantification (LLOQ) being 0.1 ng/mL for both assays. In both method
validation and study sample analysis, the precision and accuracy data
for standard/quality control samples were well within acceptance
criteria for both iberdomide and M12.

\textbf{2.4 Software}

The software applications used in the data analyses and data
presentation include:

\begin{itemize}
\tightlist
\item
  NONMEM (version 7.4, ICON Development Solution, MD, US)
\item
  R (version 3.5.3 or higher, The R Foundation for Statistical
  Computing, US)
\item
  SAS (version 9.4, SAS Institute, Inc., Cary, NC, US)
\item
  Monolix (Version 2020R1, Lixoft, Antony, France)
\end{itemize}

The analyses were performed on Intel Xeon-based multi-core Central
Processing Unit servers running Ubuntu 18.04 in Amazon Web Services
(AWS).

\textbf{2.5 Population Pharmacokinetic Model Building}

Based on previous experience and visual inspection, a 2-compartment
structure PK model was selected based on the objective function value
(OFV) using the log-likelihood ratio test, Bayesian information
criterion (BIC), the goodness-of-fit criteria and visual predictive
check.

Unexplained interindividual variability (IIV) in a PK parameter was
assumed to be log-normally distributed. The relationship was shown in
equation 1.

Equation 1:\(\text{Log\ }\left(\text{Pi}\right)=Log\ \left(P_{\text{pop}}\right)+\ \eta_{i}\)

where Pi is the individual parameter; Ppop represents the typical
parameter (fixed effect); \(\eta_{i}\) denotes the random effect.

Intra-individual or residual variability (RV) was modeled according to
the Monolix library. Proportional error model was chosen based on the
model fitting (Equation 2).

\textbf{2.6 Covariates Model}

The following variables were explored as covariates for their potential
to influence iberdomide PK parameters:

\begin{itemize}
\tightlist
\item
  Demographic factors: age, body weight, body surface area (BSA), body
  mass index (BMI), sex, and race;
\item
  Baseline laboratory test: total bilirubin (BILI), aspartate
  aminotransferase (AST), alanine aminotransferase (ALT), Alkaline
  Phosphatase (ALP) and Albumin (ALB);
\item
  Renal function marker: creatinine clearance estimated by
  Cockcroft-Gault formula, Estimated Glomerular Filtration Rate (eGFR)
  by CKD EPI equation;
\item
  Disease status: healthy subjects versus MM patients;
\item
  Baseline disease: ECOG, ISS stage at study entry;
\item
  Iberdomide combinations: monotherapy versus Iberdomide in combination
  with DEX versus Iberdomide in combination with DEX and DARA
\end{itemize}

Data visualization was used to examine the relationship between
intrinsic or extrinsic factors and subject-level PK parameters. Initial
selection of covariates was guided by graphic inspection and biological
plausibility. Potential covariates were tested further in Monolix.

COSSAC (conditional sampling use for stepwise approach based on
correlation tests) was used for covariate search based on a previous
publication \textsuperscript{11}. The iterations of COSSAC alternated
between a forward selection and a backward selection, depending on the
results of the correlation tests. In brief, during the forward selection
step, covariate with the smallest correlation p-value (among the
remaining parameter-covariate relationships) was added to the model, or
the next smallest if the smallest has already been tried, and so until
no correlation p-values above a threshold remain. Acceptance/rejection
of the relationship was based BIC: The covariate was not retained if the
criteria (0.3) did not improve (with a threshold for the likelihood
ratio test). During the backward selection, among the covariates
presented in the model, covariate with the highest (less significant)
correlation p-value, or the next highest if the highest has already been
tried, was removed, until no correlation p-values below a threshold
remain. Acceptance/rejection of the relationship removal was based on
BIC: The covariate was not retained if the criteria (0.01) did not
improve (with a threshold for the likelihood ratio test). The algorithm
continued until no forward or backward selection was possible.

\textbf{2.7 Model Performance}

Model evaluation was performed using visual predictive check (VPC) that
provided an evaluation of model assumptions and population parameter
estimates by comparing model predictions with observations. The ability
of the final popPK model to describe the observed concentration data was
evaluated by simulating 500 datasets having the same doses, dosing
schedules and sampling times as the original dataset and by performing
VPCs. Binning of observations around logical post dose time points of
interest was done to ensure sufficient density of observations.

\textbf{3. Results}

\textbf{3.1 Demographic}

The iberdomide popPK analysis dataset included 326 subjects. Table 2
summarized the demographic and baseline disease characteristics of the
subjects. There were more male subjects (63.2\%) than female (36.8\%).
Most of the subject were white with a median age of 61 years and median
body weight of 76.8 kg. Of note, based on creatinine clearance (CLcr),
45.1\% subjects were with normal renal function (CLcr \selectlanguage{english}[?] 90 mL/min),
37.1\% subjects were with mild (CLcr 60 to 89 mL/min) and 17.8\%
moderate (CLcr 30 to 59 mL/min) renal impairment, respectively. Most
subjects were with normal hepatic function (93.9\%) as per National
Cancer Institute organ dysfunction working group criteria.

\textbf{3.2 Population Pharmacokinetic Base Model}

All iberdomide and M12 concentrations were converted to molar
concentrations for modeling purpose. The starting structural model of
iberdomide was a two-compartment model with first order absorption and
elimination based on the observed concentration-time profile (Figure 1).
Incorporating a lag time improved the model fitting by significantly
decreasing OFV (-576). A first order conversion rate (Kpm) was used to
account for the transformation of iberdomide to M12. M12 PK was
described by a one-compartment model with first order elimination. Of
note, to rule out the occurrence of parameter identifiability issues
existing in typical parent-metabolite popPK models which has been
discussed in previous publication \textsuperscript{12}, the volume of
distribution of M12 was assumed to be similar as that of iberdomide
(V1/F). The model structure was shown in Figure 2. During the modelling
process, iberdomide and M12 PK data were fitted simultaneously into the
model.

In addition, the inter-individual variability (IIV) associated with the
PK parameters in the model were estimated as log-normally distributed
with a non-zero covariance, as described in the method section. Based on
modeling fitting, proportional error model was selected for both
iberdomide and M12 PK. Equation 2 described the error model, where y1
and y2 denoted observed concentration of iberdomide and M12,
respectively; Cp and Cm represented the model predicted concentration of
iberdomide and M12; b1 and b2 were proportional parameters; e was the
base of the natural logarithm.

Equation 2: \(y1=Cp+b1*Cp*e\)

\(y2=Cm+b2*Cm*e\)

In conclusion, a two-compartment model with first order absorption rate
constant incorporating a lag time for iberdomide and a first order
conversion rate linking the one-compartment metabolite model was
selected as the base model.

\textbf{3.3 Covariate Analysis}

Covariates of interest which were presented in Table 1 were included in
the covariate model development using the COSSAC algorithm. The
covariate search results showed that inclusion of ALP, AST, Sex, Subject
Type (MM vs. Healthy Subjects) on CL/F and Subject Type (MM vs. Healthy
Subjects) on V1/F significantly improved the model fitting. The output
of the final model was summarized in Table 3.

Most of the PK parameters for the final model were estimated with good
precision (i.e., small \% relative standard error (RSE)), suggesting
adequate reliability. However, the covariates (ALP, AST and sex) on CL/F
were estimated with relatively low confidence (\%RSE \textgreater{} 30).
This was likely attributed to the marginal effects of such covariates on
PK parameters. To validate this point, a forest plot (Figure 3) was
generated which showed that the influence of ALP (1st tertile and 3rd
tertile vs. 2nd tertile {[}reference{]}), AST (1st tertile and 3rd
tertile vs. 2nd tertile {[}reference{]}) and sex (female vs. male
{[}reference{]}) was in small magnitude (\selectlanguage{english}[?] 80\% and \selectlanguage{english}[?] 125\%), and
therefore, was unlikely to be clinically meaningful.

Subject type was a significant covariate on CL/F and V1/F. Based on the
forest plot, MM patients showed 238\% higher CL/F and 84\% lower V1/F as
compared to healthy subjects (Figure 3). It should be noted that the
CL/F only constituted part of total iberdomide clearance. The metabolism
clearance, which can be calculated as Kpm*V1, was believed to largely
contribute to the iberdomide clearance. In fact, the total iberdomide
clearance for typical MM subjects (9.48 L/hr) was even lower than that
of healthy subjects (20.41 L/hr) (Table 4). Notably, the model estimated
clearance for healthy subjects were in line with the published results
based on non-compartmental analysis\textsuperscript{8}, indicating good
model performance.

Renal function was examined on the PK of iberdomide and M12. The impact
of CLcr, eGFR and renal function category on PK parameters, including
CL/F, V1/F, CLm and Kpm were assessed. Figure 4 showed that no
correlation was observed between CLcr and eGFR versus PK parameters and
the PK parameters were comparable among renal function groups. These
results suggested that moderate and mild renal impairment (CLcr \selectlanguage{english}[?] 30
mL/min) was unlike to change the PK exposure of iberdomide and M12.

Combinations (DEX and/or DARA) were assessed as a covariate. Figure 5
showed comparable PK among different combos, suggesting that combination
had no impact on the PK of iberdomide and M12.

Other covariates, including age, body weight, BMI, BSA and baseline
disease factors (e.g., ECOG) were not significant in the model and
therefore, were not retained in the final model.

\textbf{3.4 Model Evaluation}

The model was validated by visual predictive check (VPC). The results of
the VPC evaluation were presented in Figure 6. The 5th, 50th and 95th
percentiles of the observed concentration data at each time point were
generally contained within the respective 90\% CI (shaded area, pink for
the 50th percentile and blue for 5th and 95th percentiles) of the
simulated data (Figure 5). There was a good agreement in the time course
and central tendency between distributions of observed and simulated
data, with no obvious bias. Overall, the estimated IIV adequately
described the observed variability in iberdomide and M12 concentrations.

Taken together, iberdomide and M12 concentrations were well
characterized by the final popPK model. This enabled the proper use of
the model for subsequent simulations.

\textbf{3.5 Metabolite (M12) PK Exposure Relative to Parent
(iberdomide)}

Based on the model, AUC and Cmax for each individual patient who had
both iberdomide and M12 PK data (Cohorts A, B and E from MM-001 study)
were simulated. Metabolite to parent ratio was calculated and stratified
by dose and cohort (Figure 7 and Table 5). The goal was to assess the
influence of dose and combination on the extent of metabolite exposure.
Results showed that the metabolite to parent ratio (both in terms of
Cmax and AUC) was consistent, irrespective of dose and cohorts. In
addition, iberdomide and M12 PK profile were simulated for a typical MM
patient on 1.6 mg 21/28 schedule and were overlaid in Figure 8. The plot
showed that M12 PK profile generally tracked the PK of iberdomide, as
demonstrated by the two parallel curves.

In conclusion, the totality of data suggested that iberdomide PK was
adequate to represent the pharmacological active exposures in systemic
circulation, regardless of time, dose and combinations.

\textbf{4. Discussions and Conclusions}

Herein, we reported the first population pharmacokinetic analysis for
iberdomide and its major active metabolite, M12 in both healthy and RRMM
subjects and assessed the effect of covariates of interest on iberdomide
and M12 PK. Over the tested dose range (0.1 mg to 6 mg), iberdomide and
M12 (where applicable) showed linear, time-independent PK. Subject type
(MM vs. healthy subject) was a statistically significant covariate on
CL/F and V1/F. Sex, AST, ALP was statistically but not clinically
relevant on CL/F. Metabolite (M12) PK was consistent across doses and
combinations, as demonstrated by the comparable metabolite to parent
ratio. Overall, the model was robust and adequate to describe the
central tendency and variabilities of the data.

Given the lack of actual input information (e.g., amount) of metabolite,
mathematically the metabolite model parameters were unidentifiable.
Evans et al. discussed this and suggested taking prior knowledge of
metabolite volume of distribution in the model to avoid the
issue\textsuperscript{12}. In line with this, Bertrand et al. assumed
similar volume of distribution for parent and metabolite, which enabled
all the parameters to be identifiable \textsuperscript{13}.
Alternatively, a more mechanistical solution was to fix the metabolite
conversion ratio (e.g., fm) based on the understanding of the drug's PK
properties\textsuperscript{14}. Herein, we took the former approach to
develop the model for the following reasons: 1). the parsimonious
structural model can adequately fit the purpose of the analysis; 2).
biological evidence showed that the plasma protein binding of iberdomide
and M12 was roughly comparable (78.4\% vs. 67.4\%; data on file, Bristol
Myers Squibb), with no saturation of plasma protein binding for both
molecules.

In the covariate analysis, the most influential covariates on the
disposition of iberdomide were subject type (MM vs. healthy subjects),
AST, ALP and sex on CL/F and subject type (MM vs. healthy subjects) on
V1/F. However, the forest plot suggested that except for subject type on
CL/F and V1/F, the effects of AST, ALP and sex on CL/F were marginal and
unlikely to be clinically relevant.

The iberdomide PK difference between healthy subjects and those with MM
was noticeable. The model estimated total clearance of iberdomide and
V1/F were lower in subjects with MM as compared to healthy subjects.
Based on the model, subjects with MM were expected to have higher
exposure than healthy subjects for the same dose (Figure 9). However,
this difference was not taken into consideration on iberdomide dosing,
given the iberdomide therapeutic doses were selected solely based on
data from clinical trials in RRMM. Of note, similar observation was seen
with lenalidomide \textsuperscript{15}, in which higher exposure was
observed in MM patients than healthy subjects. Other compounds,
including pomalidomide, enasidenibCC-122 also exhibited disease specific
PK \textsuperscript{16-18}.

Due to the lack of M12 data in healthy subjects in this analysis, the
influence of subject type on metabolite PK (Kpm and CLm) was not
assessed.

Based on the mass balance study, only 16\% of the iberdomide dose was
excreted as unchanged in urine, suggesting limited contribution of renal
route on drug elimination. In line with this, the popPK analysis showed
that renal functions (CLcr, eGFR and renal function category) had no
impact on the PK parameters. It should be noted that in the current
dataset, there were no subjects with severe or end-stage renal
deficiency (CLcr \textless{} 30 mL/min). Therefore, the interpretation
should be only focused on moderate and mild renal impairment population
only.

DEX in combination with iberdomide was one of the dosing regimens in the
MM-001 study. DEX is known as a CYP3A4 inducer \textsuperscript{19,20}.
The popPK analysis assessed the influence of iberdomide in combination
with DEX given iberdomide was a substrate for CYP3A. Post hoc analysis
showed similar PK between DEX and/or DARA combo vs. iberdomide
monotherapy, suggesting that the presence of DEX and/or DARA was
unlikely to change iberdomide and M12 PK.

Although the hepatic function covariates were not considered as
clinically relevant, it should be noted that in the current dataset,
most of the subjects (93.9\%) had liver tests within the limits of
normal. Therefore, the dedicated hepatic impairment study (data on file,
Bristol Myers Squibb) was more informative to guide dose adjustment for
patients with hepatic deficiency.

Body weight (range: 41 to 172 kg), BSA (range: 1.4 to 2.7 m2), BMI (16.4
to 59.3 kg/m2) and age (19 to 82 years) were statistically
non-significant. Race was found not to have any effects on iberdomide
and M12 PK. The popPK dataset contained majority of Whites (73.3\%), and
only 2 Asians, 1 American Indian/Alaska Native and 44 Black/African
Americans. Thus, the effects of race should be interpreted with
cautions. Baseline disease characteristics, including ISS and ECOG had
no statistically significant effects and therefore was not retained in
the final model.

Given the level of exposure and the pharmacological activities, M12 PK
was characterized during the clinical development of iberdomide. In the
popPK analysis, M12 PK data from MM-001 cohorts A, B and E were
incorporated. The objective was to assess the M12 exposure relative to
the parent drug across doses and combinations. Results suggested
consistent metabolite to parent ratio among the tested doses and
cohorts. Additionally, the parallel PK profiles indicated that M12 PK
tracked iberdomide, irrespective of time. Moreover, based on the human
mass balance study in healthy subject, the metabolite to parent ratio
was 0.24, which was in general comparable with that in subjects with MM
(0.25 and 0.28 for Cmax and AUC, respectively) taking into account the
observed variability (\%CV: 29.8 to 33.1). Taken together, given the
stable and time-insensitive M12 PK in relative to parent drug, the PK of
iberdomide was adequate to represent the major pharmacological active
exposure in the systemic circulation in the context of extent and time
course.

In conclusion, the iberdomide parent-metabolite popPK model described
herein was adequate to describe the PK of iberdomide and M12. Covariate
analysis suggested that MM patients had lower clearance (iberdomide) and
volume of distribution than healthy volunteers. Other covariates,
including demographic, combination, renal and hepatic function tests had
no clinically relevant impact. M12 PK in relative to iberdomide was
consistent across doses and combinations.

\textbf{Consent for publication}

All the authors have reviewed and concurred with the manuscript.

\textbf{Data Sharing Statement}

The datasets are available from the corresponding author upon request.

\textbf{Funding}

This work was sponsored and funded by Bristol Myers Squibb.

\textbf{Disclosures}

Y.C., Y.X., L.C, M.M, P.M., T.P., S.Z. and Y.L. are employees and hold
equity ownership in Bristol Myers Squibb.

\textbf{Authors' contributions}

Y.C., Y.X., M.M., P.M. and T.P. contributed to conception and design;
Y.C., Y.X. and M.M. contributed to acquisition of data; Y.C., L.C., S.Z.
and Y.L. contributed to analysis; P.M. and T.P. conducted the clinical
trial; Y.C., S.Z. and Y.L. drafted and revised the article. All authors
made substantial contributions to conception and design, acquisition of
data, or analysis and interpretation of data; took part in drafting the
article or revising it critically for important intellectual content;
agreed to submit to the current journal; gave final approval of the
version to be published; and agreed to be accountable for all aspects of
the work.

\textbf{Acknowledgments}

The authors thank all study participants, their families and clinical
study team members.

\textbf{Figure Legend:}

\textbf{Figure 1.} Concentration - time profiles of iberdomide and M12
by study. {[}A{]}. Iberdomide; {[}B{]}. M12. Solid circles represent
observed concentrations, which were grouped by dose level.

\textbf{Figure 2.} The joint population pharmacokinetic model of
iberdomide and M12. The model comprises two parts: parent (iberdomide)
and metabolite (M12). The parent model is a two-compartment with
first-order absorption incorporating a lag time and first-order
elimination. The metabolite model is a one compartment model with first
order elimination. A first order conversion rate described the
transformation from parent to metabolite.

\textbf{Figure 3.} Forest plot of significant covariates on apparent
clearance (CL/F, A) and apparent volume of distribution of the central
compartment (V1/F, B). Data are shown as median (90\% confidence
interval). (A) References are 2nd tertile of AST (17 to 23 U/L), healthy
subject, 2nd tertile of ALP (55.0 to 72.5 U/L) and male subjects,
respectively. (B). Reference is healthy subject.

\textbf{Figure 4.} Correlation between renal function and population PK
parameters. Solid circles are individual PK parameters generated from
the model. Blue line represented the locally weighted smoothing line.
CLcr = creatinine clearance; EGFR = estimate glomerular filtration rate.

\textbf{Figure 5.} Correlation between combinations and population PK
parameters. Solid circles are individual PK parameters generated from
the model. dex = dexamethasone; dara = daratumumab.

\textbf{Figure 6.} Visual predictive check plots of iberdomide and M12.
{[}A{]}. iberdomide concentration; unit: umol/L; {[}B{]}. M12
concentration; unit: umol/L. The blue dots show the observed
concentration data. The solid blue lines show the empirical 10th, 50th,
and 90th percentiles calculated directly from the population data. The
blue-shaded areas denote the confidence intervals of the 10th and 90th
percentiles from the model; and the pink shaded area shows the 50th
percentile.

\textbf{Figure 7.} Metabolite to parent ratio by cohort and dose from
CC-220-MM-001 study. {[}A and B{]}: Cmax ratio; {[}C and D{]}: AUC
ratio. Cmax = maximum concentration; AUC = area under the concentration
- time profile over one dosing interval (24 hours).

\textbf{Figure 8.} Simulated concentration - time profile of iberdomide
(red) and M12 (blue) for a typical subject from CC-220-MM-001 study on
1.6 mg, 21/28 day schedule.

\textbf{Figure 9.} Simulated iberdomide concentration - time profile for
a typical healthy subject (red) and multiple myeloma patients (blue) on
1.6 mg, 21/28 day schedule.

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