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Anti-TNF therapy in inflammatory bowel diseaseTowards personalized medicineBerends, S.E.

Link to publication

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Citation for published version (APA):Berends, S. E. (2020). Anti-TNF therapy in inflammatory bowel disease: Towards personalized medicine.

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Download date: 15 Jul 2020

3Explainig interpatient

variability in adalimumab pharmacokinetics in patients

with Crohn’s disease

S.E. Berends, A.S. Strik, J. van Selm, M. Löwenberg, C.Y. Ponsioen, G.R.A.M. D’Haens, R.A.A. Mathôt

Therapeutic Drug Monitoring 2018

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ABSTRACT

Background A significant proportion of patients with Crohn’s disease (CD) require dose escalation or fail adalimumab (ADL) therapy over time. ADL, a monoclonal antibody directed against tumor necrosis factor, is approved for treatment of CD. Understanding pharmacokinetics (PK) of ADL is essential to optimize individual dosing in daily practice. The aim of this study was to evaluate PK of ADL in patients with CD and to identify factors that influence PK of ADL.

Methods In a retrospective cohort study, the authors reviewed the charts of 96 patients with CD receiving ADL induction and maintenance treatment. This patient cohort was used for external validation of population pharmacokinetic models of ADL available from literature. In addition, a novel population PK model was developed using nonlinear mixed-effects modeling.

ResultsNone of the literature models properly described the PK of ADL in our cohort. Therefore, a novel population pharmacokinetic model was developed. Clearance of ADL increased 4-fold in the presence of anti-ADL antibodies. Patients who received ADL every week had a 40% higher clearance compared with patients receiving ADL every other week.

ConclusionsClearance of ADL increased in the presence of anti-ADL antibodies and was associated with weekly ADL administrations. In clinical practice, the decision to intensify ADL treatment to weekly administrations is primarily based on disease activity. Increased disease activity may be the result of lower drug concentrations due to higher clearance. However, increased disease activity may also increase clearance due to increased target engagement. The causal relationship between these factors remains to be elucidated.

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INTRODUCTIONCrohn’s disease (CD) is a relapsing inflammatory disease mainly affecting the gastrointestinal tract with a prevalence of 1.5-213 cases per 100,000 persons in Europe.1 Patients with active CD frequently suffer from abdominal pain, fever, and diarrhea.2 Because there is not yet a cure for CD, the primary therapeutic goal is to induce and maintain remission with medical treatment.3

Adalimumab (ADL) is a fully human, subcutaneously administered antibody that is approved for the treatment of moderate to severe CD since 2007.4,5 Treatment with a monoclonal antibody (mAb) against tumor necrosis factor (TNF) is associated with a success rate in up to 70% of patients with CD, and approximately 60% of these responders will achieve a long-term remission.6

Several factors can interfere with the clearance of therapeutic mAbs and, as a consequence, can affect their serum concentrations. For infliximab, another anti-TNF mAb, it has been shown that undetectable serum concentrations at trough influence clinical outcomes in patients with CD, including reduced rates of clinical and endoscopic remission.7 The presence of antibodies against infliximab and, as a consequence, low serum concentrations of the drug at trough have been implicated as a predisposing factor for treatment failure with infliximab.7,8

Early and long-term treatment discontinuation of ADL has been related to low serum concentrations at trough, mainly due to the presence of anti-ADL antibodies (AAAs).8 In patients with inflammatory bowel disease who are in clinical remission, higher median trough levels of ADL have been found compared with patients who suffer from active disease. Trough levels of ADL were also higher in patients with mucosal healing compared with patients without mucosal healing.9

The term pharmacokinetics (PK) describes what the body does to the drug, by describing the absorption of drugs into the body, their distribution into various tissues, and their elimination from the body. Population PK modeling is a powerful approach where sources of PK variability can be identified in a target patient population receiving a pharmacological agent.10 Understanding PK of a pharmacological agent and the associated variability is essential to optimize individual dosing. Before implementing population PK models in clinical practice, external validation using an independent data set is needed to systematically evaluate the model and the results relied on.10

Ternant et al were the first to report a quantitative description of the PK of ADL in patients with CD.11 They showed that patients on ADL exhibited a 5.5-fold increased clearance in the presence of AAAs. The presence of AAAs has also been associated with low ADL serum trough levels and has a negative impact on response to ADL treatment in patients with CD.12,13 This underscores the importance to understand the PK of ADL to optimize treatment of an individual patient with CD.

The aim of this retrospective analysis was to evaluate the PK of ADL in patients with CD during induction and maintenance treatment. Five population PK models obtained from

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literature describing the PK of ADL in patients with inflammatory diseases were externally validated.11,14–16 Concentration-time data were predicted based on the selected population PK models to evaluate the predictive performance. In addition, a novel population PK model was developed.

METHODS

PatientsIn this retrospective cohort study, we reviewed the charts of 96 patients with CD on ADL induction and maintenance treatment at the Academic Medical Center in Amsterdam, the Netherlands, between 2006 and 2015. The Human Research Ethics Committee of the Academic Medical Center has taken notice of the study protocol and has decided that no ethical approval is required, given that anonymous data from routine diagnostic databases are used. Patients were treated with ADL according to standard guidelines and at least one ADL serum concentration was available as part of routine monitoring. Data that were collected included age, sex, body weight, AAA status/serum concentration, anti-TNF naïve/exposed status, serum albumin levels, serum C-reactive protein (CRP) levels, use of concomitant immunomodulators (thiopurine/methotrexate), clinical disease activity, and dosing regimen (every week/every other week) available at the time of the measured ADL serum concentration.

Sample AnalysisADL serum concentrations were measured using an enzyme-linked immunosorbent assay based on the principle that ADL is captured using its ability to bind TNF.12,17 ADL binding was assessed by incubation with biotinylated rabbit IgG directed to the ADL idiotype. The lower limit of quantification (LLOQ) of this assay was 0.01 mg/L.17 AAA serum concentrations were measured using an antigen-binding test. Interference in antigen-binding test for ADL has been assessed in a model system using polyclonal rabbit anti-idiotype antibodies against ADL.18 The addition of 6 ng ADL (corresponding to a serum concentration of 6 mg/L) interfered with measurement of anti-idiotype antibodies. From that, it was concluded that AAA concentrations are not detectable when ADL concentrations are >5 mg/L. Patients were defined as positive for clinically relevant AAAs if titers were above 12 AU/mL on at least one occasion combined with ADL serum concentrations below 5.0 mg/L, also previously described by Bartelds et al.12 Both assays were developed by Sanquin Laboratories, Amsterdam, the Netherlands.12,17,18

Population PK Models From LiteraturePubMed was searched in August 2016 for the indexed terms “adalimumab” or “Humira” and “PK.” Five population PK models were selected based on search results and availability of model specifications.

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All selected population PK models best described PK of ADL using a one-compartment model and included patients with both induction and maintenance treatment. The model developed by Sharma et al (CD-1 model) included 189 pediatric CD patients treated with ADL in a phase-3 randomized, double-blind, 52-week study.15 Higher body weight and the presence of AAAs (yes/no) were associated with higher clearance of ADL. In a post hoc analysis of a prospective cohort, Ternant et al (CD-2 model) included 65 patients with at least 4 ADL concentrations available.11 This study demonstrated that clearance of ADL was influenced by the presence of AAAs (yes/no). A second post hoc analysis by Ternant et al (rheumatoid arthritis (RA) model) studied PK of ADL in patients with RA.16 In this pharmacokinetic/pharmacodynamic population model, the PK of ADL was influenced by both sex and body weight. The model by Mostafa et al (PP-1 and PP-2 model) was based on data of 2 previously performed phase-2 and phase-3 studies in patients with the chronic immune-mediated inflammatory disease plaque psoriasis.14 Data from all patients who received at least one dose of ADL and had at least one measurable ADL serum concentration during active treatment in both studies were combined. A total of 827 patients were included, and body weight was found to be a significant covariate influencing clearance of ADL. In addition, clearance of ADL was estimated to be 60% lower in the phase-2 study compared with the phase-3 study, possibly due to different study designs or inclusion criteria. Because this covariate could not be incorporated for validation, both estimates for clearance were validated in 2 separate models. Table 1 summarizes patient characteristics and model specifications of the selected models. More information on the pharmacokinetic (PK) parameter equations can be found in Supplemental Digital Content 1 (see Table 1).

Evaluation of Predictive PerformanceData of our own cohort were simulated based on 5 models using nonlinear mixed-effects modeling (NONMEM; ICON Development Solutions, Dublin, Ireland, version 7.3). The predictive performance of the models was evaluated by visual inspection of the goodness-of-fit plots including visual predictive checks (VPCs) using the VPC package (version 0.9.1), in R (version 3.2.2, Vienna, Austria).19,20 A VPC can be used to assess the appropriateness of a model. The principle of a VPC is to assess graphically whether simulations from a model of interest are able to reproduce the observed data, both the average observed concentrations and their variability. Therefore, the model of interest is used to generate multiple (>=1000) simulations of the observed data. Percentiles of the simulated data are then compared with the corresponding percentiles of the observed data and plotted against an independent variable, for example, time after dose.21,22 The observed concentrations were binned in predefined time intervals. Bias and precision were calculated for trough levels at days 7 and 14 where most data were present. Mean prediction error (MPE, Equation 1) and root mean square error (RMSE, Equation 2) were calculated for bias and precision, respectively.

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𝑀𝑀𝑀𝑀𝑀𝑀 = ∑𝑌𝑌( − 𝑌𝑌

𝑛𝑛 Equation 1

𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 = '∑(𝑌𝑌+ − 𝑌𝑌).

𝑛𝑛 Equation 2

in which Ŷ represents the population-predicted ADL concentration, Y represents the ob-served ADL concentration, and n is the number of observations.23

Population PharmacokineticsIn addition to the evaluation of the predictive performance of the literature models, a novel population PK model was developed. Population PK parameters of ADL were estimated by NONMEM using first-order conditional estimation with INTERACTION method (FOCE+I). Tools such as PsN (version 4.4.8, Uppsala, Sweden) and R were used to visualize and evaluate the model. Goodness-of-fit was assessed by evaluation of the objective function value (OFV), precision of the parameter estimates, as well as visual inspection of diagnostic plots including VPCs.24 Concentration-time data below LLOQ were discarded when this concerned less than 10% of the available ADL concentrations.25 A one-compartment model was used as starting model. A 2-compartment model was evaluated based on decrease in OFV and parameter estimates. Allometric scaling on clearance using total body weight was evaluated by evaluating both a coefficient of 0.75 and estimation of this coefficient.26 To differentiate between patients for induction and maintenance treatment, a patient was considered in a steady state >=100 days after the first ADL administration or >=100 days after dose adjustment.

In the covariate selection, covariates were investigated using a stepwise forward addition (p<0.05) and backward elimination procedure (p<0.01). Continuous covariates were centered respective to their median values. For missing time-varying covariates, the last observation was carried forward if available within the treatment period; otherwise, median value was imputed. For missing time-invariant covariates, the median value was imputed. Covariates evaluated included age, sex, body weight, AAA status, anti-TNF naïve/exposed status, albumin serum levels, CRP serum levels, use of concomitant immunomodulators, clinical activity, dosing regimen (every week/every other week), and treatment phase (induction/maintenance).

A VPC was used for final model evaluation. Robustness of parameter estimates was evaluated using a bootstrap with 2,000 samples. Bootstrap is a tool for calculating bias, standard errors, and confidence intervals of parameter estimates. It does so by generating a set of new data sets by sampling individuals with replacement from the original data set and fitting the model to each new data set.27

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Table 1: Patient characteristics and model specifications literature models

CD-1 model15* CD-2 model11† RA-model20† PP-1/2 model14†

Disease CD CD RA PP‡

Population Pediatric Adult Adult Adult

No. of patients 189 65 30 827

Weight, kg 45.2 (18.0-119.0) 68 [43-109] 67 [45-115] 89.0 [40.0-204]

Age, yr 13.6 (6.0-17.0) 37 [17-61] 55 [24-77] 44.0 [18.0-86.0]

Male, N (%) 105 (55.6%) 17 (25%) 7 (23%) 557 (67.3%)

Albumin (g/L) 40 (24-53) NA NA NA

CRP (mg/L) 2.4 (0.0-16.8) NA 22 [5-139] NA

Prior infliximab (%)

83 (43.9%) NA NA NA

AAAs (%) 3.3% 13.8% 0% 8.8%

Concomitant immunomodula-tor (%)

Thiopurines 94 (49.7%) NA 0% NA

Methotrexate 26 (13.8%) NA 30 (100%) NA

AAA assay bridging ELISA double-antigen ELISA

double-antigen ELISA

double-antigen ELISA

Estimate (%RSE§) Estimate (%RSE§) Estimate (%RSE§) Estimate (%RSE§)

Proportional error 0.071 0.15 (16%) 0.24 (9%) 0.56 (17.6%)

Additive error (mg/L)

1.9 1.8 (8%) - -

CL/F (L/day) 0.28 (4.06%) 0.42 (9%) 0.32 (5%) 0.59/0.24¶ (3.8%)

V/F(L) 4.75 (4.11%) 13.5 (10%) 10.8 (20%) 11.4 (11.4%)

Ka (/day) 0.2 (6.7%) 0.15 0.28 (4%) 0.625 (28.8%)

AAA-CL/F 1.08 (12.04%) 4.5 - -

Body weight (kg) - CL/F

0.48 (23.96%) - 0.81 (28%) 0.0065 (12.8%)

Body weight (kg) - V/F

0.904 (9.55%) - - 0.0563 (47.2%)

IIV - CL/F (%) 45.9% 64.8% (10%) 17% 43.6% (8.6%)

IIV - V/F (%) - 48% (19%) 92% 62% (31.5%)

*Mean (range) †Median [range], unless otherwise stated ‡Plaque psoriasis

§if available ¶CL/F for 2 different study populations AAA, anti-adalimumab antibodies; CL/F, apparent clearance; IIV, interindividual variability; Ka, absorption rate constant; NA, not available; RA, rheumatoid arthritis; RSE, residual standard error; V/F, apparent volume of distribution

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RESULTS

PatientsThe charts of 96 patients with CD were reviewed. Patient characteristics are listed in Table 2. All patients had at least one ADL concentration available, and each dose of ADL combined with at least one ADL concentration was considered a treatment course. Table 2: Patient characteristics retrospective cohort

No.

Patients 96

Treatment courses 178

ADL serum samples 181

ADL serum concentrations (mg/L)* 9 [5-13]

AAA serum samples 142

AAA serum concentrations (AU/ml)* 34 [18-210]

Age (years)* 38 [32-44]

Age at diagnosis (years)* 23 [19-30]

Body weight (kg)* 65 [58-76]

Albumin (g/L)* 43 [40-45]

CRP (mg/L)* 3.4 [1.3-7.4]

Male (%) 35 (36%)

Anti-TNF naïve 64 (67%)

Clinical disease activity 65 (68%)

Concomitant immunomodulator

Thiopurines 21 (22%)

Methotrexate 8 (8%)

AAA positive 17 (18%)

Treatment

Induction 9%

Maintenance 91%

Dosing regimen

Every week 25%

Every other week 75%

*Median [interquartile range] AAAs, anti-adalimumab antibodies; ADL, adalimumab; CRP, C-reactive protein; TNF, tumor necrosis factor

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ADL and AAA Serum ConcentrationsA total of 181 ADL serum concentrations were available. Serum concentrations were mostly taken at trough at day 7 or 14, related to a dosing regimen of every week or every other week, respectively. At day 7, 33 ADL serum concentrations were available with 94% of the samples being trough ADL serum concentrations. At day 14, 83 samples were available and 99% of the samples were trough ADL serum concentrations. All ADL serum concentrations are represented in Figure 1. A total of 6 concentrations below LLOQ (3%) were discarded with detection of AAAs in 5 out of 6 concentrations below LLOQ. An additional ADL concentration was discarded with a time after dose of 50 days. A total of 142 AAA serum concentrations were available, of which 23 concentrations (16%) were above 12 AU/ml. A low ADL serum concentration (<5 mg/L) combined with a positive AAA serum status resulted in 17 patients (18%) who developed clinically relevant AAAs.

Figure 1: Observed ADL serum concentrations (mg/L) versus time after dose (days)

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Figure 2: VPCs of the literature models. A, CD-1 model; B, CD-2 model; C, RA model; D, PP-1 model; and E, PP-2 model. Individual observations of ADL are represented by the open circles. The black solid line depicts the median of the observed ADL concentrations and the dashed black lines depict the observed fifth and 95th percentiles. The blue shaded area represents the 95% confidence interval of the median of the simulated data and the red shaded areas represent the 95% confidence intervals of the 5th and 95th percentiles of the simulated data.

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Predictive Performance of the Literature ModelsTo create a VPC, 2,000 simulations of the observed data were used to assess model appropriateness of the selected literature models (Figure 2). VPCs of the CD-1 model (Figure 2A), CD-2 model (Figure 2B), and RA model (Figure 2C) show reasonable agreement between simulated and observed ADL concentrations. Percentiles of the observed data, represented by the dashed (5th and 95th percentile) and solid black lines (50th percentile), are mostly situated within the associated confidence intervals, represented by the red and blue shaded areas, respectively, of the simulated data. However, at day 7, one of the 2 time points at which most information was available, VPC of the CD-1 model shows a large deviation in terms of overestimation of the observed data. The VPC of the PP-1 model (Figure 2D) shows underestimation of ADL concentrations observed throughout the whole timeline. With 60% decrease of the value for clearance (PP-2 model, Figure 2E), percentiles of the observed data are more closely situated to the confidence intervals of the simulated data, but at day 7, overestimation of the observed data is shown. At day 14, the other time point with most information available, underestimation of the observed ADL concentration is seen in the CD-1, CD-2, and PP-1 model. After day 14, all models show overestimation of the variability due to very limited availability of observations after 14 days.

Table 3 shows the performance of the literature models in terms of bias and precision at trough level at days 7 and 14 separately. As indicated by the VPCs, significant bias of the predictive performance of all models is seen at both days 7 and 14. All models show structural bias, except for the CD-2 model where zero is included in the confidence interval for predictions at day 7.

Table 3: Bias and Precision (mg/L)

Day 7 Day 14

Model Bias (95% CI) Precision (95% CI) Bias (95% CI) Precision (95% CI)

CD-1 3.52 (1.26-5.79) 7.43 (6.04-8.61) -6.18 (-8.21- -4.15) 9.88 (8.69-11.0)

CD-2 0.41 (-1.90-2.73) 6.71 (3.56-8.79) -3.52 (-4.83- -2.21) 6.83 (5.40-8.00)

RA 4.65 (2.49-6.8) 10.15 (8.64-11.5) -2.71 (-4.80- -0.62) 9.52 (8.56-10.4)

PP-1 -2.88 (-5.45--0.31) 7.96 (3.61-10.7) -5.70 (-7.11- -4.29) 8.50 (6.92-9.84)

PP-2 10.4 (7.7 – 13.1) 13 (11.3-14.6) -1.19 (-0.2 – 2.6) 6.4 (5.53-7.16)

CI, confidence interval

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Population PK AnalysisBecause the predictive performance by the available literate models for our cohort was poor, we performed a new population PK analysis. PK of ADL in patients with CD was best described using a one-compartment model. The data did not support parameter estimations for a 2-compartment model. Allometric scaling on clearance evaluating both a fixed coefficient (0.75) and estimation of this coefficient did not improve the OFV or goodness-of-fit and was therefore discarded.

Apparent clearance (CL/F) and apparent volume of distribution (V/F) reflect clearance and volume of distribution, respectively, and do not take bioavailability (F) of a drug into account. Interindividual variability on CL/F was applied, but could not be estimated on V/F, probably due to very sparse sampling at the first day after administration of ADL.

In the forward inclusion step of the covariate modeling, the presence of AAAs (yes/no), dosing regimen, clinical disease activity, and CRP exhibited significant influence on CL/F (P < 0.05). Age, sex, albumin serum concentration, anti-TNF naïve status, AAA serum concentration, the use of concomitant immunomodulators, and treatment phase (induction/maintenance) did not significantly improve the model. After the backward elimination step, detection of AAAs (yes/no) (Figure 3A) and dosing regimen (Figure 3B) were found to be significant (P < 0.001) covariates on CL/F and were incorporated in the final model: 𝐶𝐶𝐶𝐶𝐹𝐹 = 0.324 × (1 + 3.14 × 𝐴𝐴𝐴𝐴𝐴𝐴) × (1 + 0.40 × 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷)

in which AAA is a binary covariate with 0 and 1 representing the absence and presence of AAAs, respectively. Dosing regimen defines 2 groups who administered ADL every week (1) or every other week (0).

Figure 3: A, Post hoc clearance versus dosing regimen. B, Post hoc clearance versus presence of anti-ADL antibodies.

The VPC of the final model is represented in Figure 4. The figure shows that median and 5th and 95th percentiles are situated within the associated confidence intervals. Table 4

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shows predictive performance of the final model in terms of bias and prediction. Out of 2000 bootstrap runs for model evaluation, only 2 runs minimized unsuccessfully and were skipped. Stratification was applied for the presence of AAAs. Bootstrap summary statistics were derived from all successful runs. Parameter estimates of the final model and results of the bootstrap are shown in Table 5. No differences were observed between bootstrapping results and NONMEM estimates of the PK parameters and associated standard errors.

Table 4: Bias and Precision final model (mg/L)

Day 7 Day 14

Bias (95% CI) Precision (95% CI) Bias (95% CI) Precision (95% CI)

-0.88 (-3.2-1.43) 6.75 (2.47-9.23) -2.88 (-4.21- -1.56) 6.59 (5.18-7.75)

CI, confidence interval

0

10

20

30

40

0 5 10 15 20

Time after dose (days)

ADL

Con

cent

ratio

n (m

g/L)

Figure 4: VPC of the final model for ADL concentrations in patients with CD. Individual observations of ADL are represented by the open circles. The black solid line depicts the median of the observed ADL concentrations and the dashed black lines depict the observed 5th and 95th percentiles. The blue shaded area represents the 95% confidence interval of the median of the simulated data and the red shaded areas represent the 95% confidence intervals of the 5th and 95th percentiles of the simulated data.

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Table 5: Final pharmacokinetic parameters

Final model Bootstrap results (n=2000)

Estimate (%RSE) 95% CIMean value (%RSE) 95% CI

Proportional error 0.30 (9%) 0.25 - 0.36 0.296 (12%) 0.23 - 0.37

Additive error (mg/L) 1.02 (23%) 0.56 - 1.48 1.04 (33%) 0.35 - 1.7

CL/F (L/day) 0.32 (8%) 0.27 - 0.37 0.32 (8%) 0.27 - 0.38

V/F (L) 4.07 (27%) 1.91 - 6.23 4.7 (36%) 0.76 - 7.38

Ka (L/day) 0.2 (fix) - 0.2 (fix) -

AAA-CL/F 3.14 (24%) 1.65 - 4.63 3.47 (37%) 0.61 - 5.67

Dosing-CL/F 0.40 (23%) 0.23 - 0.58 0.40 (25%) 0.21 - 0.60

IIV-CL/F (%) 49.1% (12%) 39.1 - 57.4% 49.0% (26%) 34.3-60.4%

AAA, anti-adalimumab antibodies; CI, confidence interval; CL/F, apparent clearance;IIV, interindividual variability; Ka, absorption rate constant; %RSE, relative standard error (=SE/median x 100); V/F, apparent volume of distribution; fix, fixed parameter

DISCUSSIONThis study externally validated 5 literature models describing PK of ADL in inflammatory diseases. Because predictive performance of available literature models was poor, a novel population PK model was developed based on our CD cohort. A one-compartment model best described our data with the presence of AAAs and a weekly ADL dosing regimen as significant covariates influencing CL/F.

Both the CD-1 and CD-2 models were developed to describe the population PK of ADL in patients with CD. The CD-1 model included pediatric data and allometric scaling of the PK parameters. The VPCs of these models showed that underestimation and overestimation of the observed data were present at time points (days 7 and 14), where most data were present. The RA model described PK of ADL in patients with RA, an autoimmune inflammatory disease. The corresponding VPC showed overestimation of the observed data at day 7. The PP models were developed using ADL data from patients with plaque psoriasis, a chronic inflammatory disease characterized by inflammation and thickening of the epidermis, leading to thick scaly plaques on the skin.14 Data consisted of 2 performed phase-2/phase-3 studies where CL/F was estimated to be 60% lower in one study compared with the other study possibly due to different study designs or eligibility criteria. This specific significant covariate could not be incorporated in the model evaluation, and therefore, 2 models with different CL/F were evaluated.

CL/F in the PP-1 model was 0.59 L/d, which was higher than observed in all other models. The VPC of this model showed underestimation of the observed data at all time points, indicating an overestimation of CL/F. Results of the predictive performance of the PP-2 model with a 60% lower CL/F showed overestimation of the observed data, indicating

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an underestimation of CL/F. Also, variability of the observed data was overestimated in the PP-2 model. Bias of both the PP-1 and PP-2 models is most likely related to the underlying disease affecting PK of ADL differently compared with CD.28

In the newly developed population PK model, CL/F for adult patients with CD was estimated to be 0.32 L/d, which is approximately 25% lower than estimated by the CD-2 model (0.42 L/d) that described a similar patient population. V/F was, however, estimated to be 4.07 L, which deviated largely from the estimated V/F by the CD-2 model (13.5 L). Fixing V/F to a value of 13.5 L in the present population PK model resulted in a worse fit of the model to the data as indicated by a significant increase in OFV. A different estimation for V/F could be related to different types of data available for both studies because for each patient included in the CD-2 model, at least 4 ADL concentrations were available compared with only one ADL concentration available for most patients in our cohort.

Allometric scaling was applied in the pediatric model (CD-1), which is commonly seen.26,29,30 Allometric scaling did not improve the population PK model in our cohort. This is likely to be caused by the narrow range of body weights in our cohort. Immunogenicity has been related to an increased clearance of ADL.8,17,31 In line with that notion, our population PK analysis demonstrated that clearance of ADL in patients with CD was highly influenced by the presence of AAAs. The CD-1 and CD-2 models showed a significant influence on the clearance of ADL due to immunogenicity. None of the patients in the RA model had a positive AAA status. For 3 patients in this RA study, relatively low ADL serum concentrations and increased clearance were found, but the amount of ADL probably interfered with the drug-sensitive assay used to detect AAAs. The absence of AAAs in this cohort might be further explained by the relatively small patient population but more likely by the high percentage of methotrexate use (100%), which has been related to lower immunogenicity rates.32,33 In the PP models, CL/F was also 2-fold higher in the presence of AAAs, but this covariate was not significant probably due to large interindividual variability in ADL serum concentrations among patients without detectable AAAs. Our cohort showed a relatively higher amount of AAA-positive patients (18%) compared with the evaluated literature models. All these models, including our own model, made use of a drug-sensitive AAA assay not able to detect AAAs in the presence of ADL. Because the technique of the assays used is based on the same principle, the assumption is made that the different assays exhibit similar properties. For a patient losing response, an ADL concentration is more likely to be measured than for a patient in remission; therefore, our retrospective cohort might represent a higher amount of AAA-positive patients. In clinical practice, the decision to intensify ADL treatment to weekly administrations is primarily based on clinical disease activity. In our cohort, patients receiving ADL treatment every week exhibited higher clearance compared with patients receiving ADL treatment every other week. In the forward inclusion step, both clinical disease activity and dosing regimen were significant covariates associated with an altered clearance. In the backward elimination step, clinical disease activity was excluded as a significant covariate. Because dosing regimen and disease activity are considered

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as correlated covariates, inclusion of both covariates will give the problem of masking; correlated covariates can overlap in explaining a source of variability. For our population, dosing regimen could be a surrogate for disease activity. Increased disease activity may be the result of lower drug concentrations due to higher clearance of the drug. However, increased disease activity may also increase clearance due to increased target engagement (target-mediated drug disposition).34 The causal relationship between these factors remains to be elucidated. Target-mediated drug disposition has been previously reported for mAbs meaning that PK of mAbs are affected because of their high target affinity.34,35 For infliximab in patients with RA, this phenomenon has been suggested.36

This study has several limitations. First of all, reliability of the data is limited by its retrospective character. As a result, registration of the data of administration of ADL may be dependent on correct documentation by the clinician of information given by the patient at that time. Also, patients’ compliance was not evaluated, and this can potentially influence the reliability of the data. Furthermore, mainly trough levels were available resulting in difficulties in estimating the absorption constant (Ka). Consequently, this parameter was, therefore, fixed to a value of 0.2/day. Fixing Ka at value ranging from 0.1 to 0.2/day did not influence the estimation of other PK parameters. There was a relative high number of missing data for several covariates, such as CRP (32%) and albumin (39%). In addition, available CRP and albumin levels were within a narrow range that might explain the exclusion of these parameters as significant covariates.

CONCLUSIONThis retrospective study characterized the PK of ADL in patients with CD. The presence of AAAs (yes/no) and a dosing regimen of every week were associated with an increased CL/F. To further optimize treatment for individual patients with CD who receive ADL treatment, a prospective study should be performed to further explore relationships between disease activity (indicated by biomarkers, such as CRP or fecal calprotectin) and clearance of ADL.

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REFERENCES1. Burisch J, Jess T, Martinato M, Lakatos

PL. The burden of inflammatory bowel disease in Europe. J Crohn’s Colitis. 2013;7:322–37.

2. Baumgart DC, Sandborn WJ. Crohn’s disease. Lancet. 2012;380(380):1590–605.

3. Baumgart DC, Sandborn WJ. Inflammatory bowel disease: clinical aspects and established and evolving therapies. Vol. 369, Lancet. 2007. p. 1641–57.

4. FDA. Adalimumab Product Approval Information [Internet]. Center for Drug Evaluation and Research; [cited 2016 Jul 19]. Available from: http://www.fda.gov/Drugs/DevelopmentApprovalProcess/ugsareDeve lopedandApproved/A p p r o v a l A p p l i c a t i o n s /herapeut icBio logicAppl icat ions/ucm080610.htm

5. European Medicines Agency - Find medicine - Humira [Internet]. [cited 2018 Jan 29]. Available from: http://www.ema.europa.eu/ema/index.jsp?curl=pages/medicines/human/medicines/000481/h u m a n _ m e d _ 0 0 0 8 2 2 .sp&mid=WC0b01ac058001d124

6. Rogler G. Top-down or step-up treatment in Crohn’s disease? Dig Dis. 2013;31(1):83–90.

7. Maser E, Villela R, Silverberg MS, Greenberg GR. Association of trough serum infliximab to clinical outcome after scheduled maintenance treatment for Crohn’s disease. Clin Gastroenterol Hepatol. 2006;4(10):1248–54.

8. Karmiris K, Paintaud G, Noman M, et al. Influence of trough serum levels

and immunogenicity on long-term outcome of adalimumab therapy in Crohn’s disease. Gastroenterology. 2009;137(5):1628–40.

9. Roblin X, Marotte H, Rinaudo M, et al. Association between pharmacokinetics of adalimumab and mucosal healing in patients with inflammatory bowel diseases. Clin Gastroenterol Hepatol. 2014;12(1):80-84.e2.

10. Sherwin CMT, Kiang TKL, Spigarelli MG, Ensom MHH. Fundamentals of population pharmacokinetic modelling. Clin Pharmacokinet. 2012 Sep 13;51(9):573–90.

11. Ternant D, Karmiris K, Vermeire S, et al. Pharmacokinetics of adalimumab in Crohn’s disease. Eur J Clin Pharmacol. 2015;71(9):1155–7.

12. Bartelds GM, Wijbrandts CA, Nurmohamed MT, et al. Clinical response to adalimumab: relationship to anti-adalimumab antibodies and serum adalimumab concentrations in rheumatoid arthritis. Ann Rheum Dis. 2007 Jul;66(7):921–6.

13. West RL, Zelinkova Z, Wolbink GJ, et al. Immunogenicity negatively influences the outcome of adalimumab treatment in Crohn’s disease. Aliment Pharmacol Ther. 2008 Nov 1;28(9):1122–6.

14. Mostafa NM, Nader AM, Noertersheuser P, Okun M, Awni WM. Impact of immunogenicity on pharmacokinetics, efficacy and safety of adalimumab in adult patients with moderate to severe chronic plaque psoriasis. J Eur Acad Dermatology Venereol. 2017;31(3):490–7.

78

PK a

dalim

umab

3

15. Sharma S, Eckert D, Hyams JS, et al. Pharmacokinetics and exposure-efficacy relationship of adalimumab in pediatric patients with moderate to severe Crohn’s disease: results from a randomized, multicenter, phase-3 study. Inflamm Bowel Dis. 2015;21(4):783–92.

16. Ternant D, Ducourau E, Fuzibet P, et al. Pharmacokinetics and concentration-effect relationship of adalimumab in rheumatoid arthritis. Br J Clin Pharmacol. 2015 Feb;79(2):286–97.

17. Kneepkens EL, Wei JC-C, Nurmohamed MT, et al. Immunogenicity, adalimumab levels and clinical response in ankylosing spondylitis patients during 24 weeks of follow-up. Ann Rheum Dis. 2015 Feb;74(2):396–401.

18. Van Schouwenburg PA, Bartelds GM, Hart MH, et al. A novel method for the detection of antibodies to adalimumab in the presence of drug reveals hidden immunogenicity in rheumatoid arthritis patients. J Immunol Methods. 2010;362:82–8.

19. R Core Team. A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.

20. Ron Keizer. Vpc: Create visual predictive checks in R. R package version 0.1.1. [Internet]. Available from: http://vpc.ronkeizer.com/

21. Karlsson MO, Holford N. A Tutorial on Visual Predictive Checks. PAGE 17 Abstr 1434. 2008;

22. Bergstrand M, Hooker AC, Wallin JE, Karlsson MO. Prediction-corrected visual predictive checks for diagnosing nonlinear mixed-effects models. AAPS J.

2011;13(2):143–51.23. Sheiner LB, Beal SL. Some suggestions

for measuring predictive performance. J Pharmacokinet Biopharm. 1981;9(4):503–12.

24. Committee for Medicinal Products for Human Use (2007) Guideline on reporting the results of population pharmacokinetic analyses. 2007;

25. Byon W, Smith MK, Chan P, et al. Establishing best practices and guidance in population modeling: An experience with an internal population pharmacokinetic analysis guidance. CPT Pharmacometrics Syst Pharmacol. 2013;2(7):e51.

26. Anderson BJ, Holford NHG. Mechanism-based concepts of size and maturity in pharmacokinetics. Annu Rev Pharmacol Toxicol. 2008;48:303–32.

27. Efron B. An Introduction to the Bootstrap. London, Chapmann & Hall. 1993.

28. Passot C, Mulleman D, Bejan-Angoulvant T, et al. The underlying inflammatory chronic disease influences infliximab pharmacokinetics. MAbs. 2016 Aug 9;8(7):1407–16.

29. West G, Brown J, Enquist B. General model for the origin of allometric scaling laws in biology. Science (80- ). 1997;276(1997):122–126.

30. Fasanmade AA, Adedokun OJ, Blank M, Zhou H, Davis HM. Pharmacokinetic Properties of Infliximab in Children and Adults with Crohn’s Disease: A Retrospective Analysis of Data from 2 Phase III Clinical Trials. Clin Ther. 2011;33(7):946–64.

31. Bartelds GM, Krieckaert CLM, Nurmohamed MT, et al. Development

79

PK adalimum

ab

3

of Antidrug Antibodies Against Adalimumab and Association With Disease Activity and Treatment Failure During Long-term Follow-up. JAMA. 2011 Apr 13;305(14):1460–8.

32. Krieckaert CLM, Nurmohamed MT, Wolbink GJ. Methotrexate reduces immunogenicity in adalimumab treated rheumatoid arthritis patients in a dose dependent manner. Ann Rheum Dis. 2012;71(11):1914–5.

33. Krieckaert CLM, Bartelds GM, Lems WF, Wolbink GJ. The effect of immunomodulators on the immunogenicity of TNF-blocking therapeutic monoclonal antibodies: a review. Arthritis Res Ther. 2010;12(5):217.

34. Dua P, Hawkins E, van der Graaf P. A tutorial on target-mediated drug disposition (TMDD) models. CPT Pharmacometrics Syst Pharmacol. 2015;4(6):324–37.

35. Gibiansky L, Gibiansky E, Kakkar T, et al. Approximations of the target-mediated drug disposition model and identifiability of model parameters. J Pharmacokinet Pharmacodyn. 2008;35:573–91.

36. Meno-Tetang GML, Lowe PJ. On the prediction of the human response: a recycled mechanistic pharmacokinetic/pharmacodynamic approach. Basic Clin Pharmacol Toxicol. 2005 Mar;96(3):182–92.

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SUPPLEMENTARYSupplement Table 1: Population pharmacokinetic equations

Model EquationCD-1

𝐶𝐶𝐶𝐶/𝐹𝐹 = 0.281 × (1 + 1.08 × 𝐴𝐴𝐴𝐴𝐴𝐴) × 0𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑤𝑤𝐵𝐵𝐵𝐵𝑤𝑤ℎ𝑡𝑡

𝑀𝑀𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵(𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑤𝑤𝐵𝐵𝐵𝐵𝑤𝑤ℎ𝑡𝑡)AB.CD

× exp(𝜂𝜂I)

𝑉𝑉/𝐹𝐹 = 4.75 × *𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑤𝑤𝐵𝐵𝐵𝐵𝑤𝑤ℎ𝑡𝑡

𝑀𝑀𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵(𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑤𝑤𝐵𝐵𝐵𝐵𝑤𝑤ℎ𝑡𝑡)=>.?>@

CD-2 𝐶𝐶𝐶𝐶/𝐹𝐹 = 0.42 × (1 + 4.5 × 𝐴𝐴𝐴𝐴𝐴𝐴) × exp(𝜂𝜂6)

𝑉𝑉/𝐹𝐹 = 13.5 × exp(𝜂𝜂0) RA

𝐶𝐶𝐶𝐶/𝐹𝐹 = 0.32 × (1 + 0.32 ∗ 𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺 ∗ 3𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑤𝑤𝐵𝐵𝐵𝐵𝑤𝑤ℎ𝑡𝑡

𝑀𝑀𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵(𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑤𝑤𝐵𝐵𝐵𝐵𝑤𝑤ℎ𝑡𝑡)EF.GH

× exp(𝜂𝜂H)

𝑉𝑉/𝐹𝐹 = 10.8 × exp(𝜂𝜂0) PP-1

𝐶𝐶𝐶𝐶/𝐹𝐹 = 0.586 × ,𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑤𝑤𝐵𝐵𝐵𝐵𝑤𝑤ℎ𝑡𝑡

𝑀𝑀𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵(𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑤𝑤𝐵𝐵𝐵𝐵𝑤𝑤ℎ𝑡𝑡)?@.@@AB

× exp(𝜂𝜂G)

𝑉𝑉/𝐹𝐹 = 11.4 × )𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑤𝑤𝐵𝐵𝐵𝐵𝑤𝑤ℎ𝑡𝑡

𝑀𝑀𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵(𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑤𝑤𝐵𝐵𝐵𝐵𝑤𝑤ℎ𝑡𝑡)<=.=>?@

× exp(𝜂𝜂E)

PP-2𝐶𝐶𝐶𝐶/𝐹𝐹 = 0.24 × +

𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑤𝑤𝐵𝐵𝐵𝐵𝑤𝑤ℎ𝑡𝑡𝑀𝑀𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵(𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑤𝑤𝐵𝐵𝐵𝐵𝑤𝑤ℎ𝑡𝑡)>

?.??@A

× exp(𝜂𝜂F)

𝑉𝑉/𝐹𝐹 = 11.4 × )𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑤𝑤𝐵𝐵𝐵𝐵𝑤𝑤ℎ𝑡𝑡

𝑀𝑀𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵(𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑤𝑤𝐵𝐵𝐵𝐵𝑤𝑤ℎ𝑡𝑡)<=.=>?@

× exp(𝜂𝜂E)

AAA, anti-adalimumab antibodies; CD, Crohn’s Disease; CL/F, apparent clearance; GEND, gender; PP, Plaque Psoriasis; V/F, apparent volume of distribution