1. Background and Objectives
Population PK analysis of Sym004 and the influence of variations in base model structure on covariate model building Janet R Wade1, Rik Schoemaker1 and Lene Alifrangis2 1Occams, The Netherlands; 2Symphogen A/S, Denmark
BLOCK(3)
Diagonal
Background and Objectives
Sym004 is a mixture of two synergistic full-length anti-EGFR antibodies (futuximab & modotuximab) that bind to 2 separate non-overlapping epitopes and inhibit the sustained growth of cancer cells.
The objectives of this work were;
1. Develop a population (Pop) pharmacokinetic (PK) model for Sym004 and evaluate the potential for covariates to explain the inter-individual variability (IIV) in the model.
2. Evaluate if the Sym004 covariate model depended on the presence/absence of correlations between the IIV parameters.
3. Evaluate if the Sym004 Pop PK model could also describe the PK of the two constituent antibodies.
Data
PK data were available from 136 patients from 2 trials in advanced solid tumours, metastatic colorectal cancer and squamous-cell carcinomas of the head and neck (Sym and -02), and who contributed concentration measurements (Figure 1).
Sym004 ( mg/kg) was dosed by IV infusion weekly or every 2nd week or as a 9 mg/kg loading dose followed by 6 mg/kg weekly.
Figure 2 Forest plots showing single screening covariate effects for Sym004 using diagonal and BLOCK(3) omega structures.
Block(3)
Diagonal
Vmax (mcg/h)
1371
1340
Km (mcg/mL)
2.91
2.86
V1 (mL)
3564
3570
Q (mL/hr)
36.6
36.8
V2 (mL)
2856
2860
CL (mL/hr)
13.7
14.5
Prop error (fraction)
0.181
Add error (mcg/mL)
1.79
Weight on Vmax
0.592
0.645
Weight on CL
0.525
0.309
Weight on V1
0.634
0.639
Weight on V2
log Albumin on CL
-0.344
-0.368
ECOG 3 or greater on CL
-0.261
-0.373
log dose on VMAX
0.186
0.190
ECOG 3 or greater on VMAX
-0.182
-0.090
log tumour type on Vmax (Other solid tumours)
-0.139
-0.197
log Male Sex on V2
-0.283
-0.282
log Male Sex on CL -
log age on Vmax
-0.183
log tumour size at baseline on Vmax
0.264
Table 1 Parameter estimates for the Sym004,covariate influences for the final models with diagonal and BLOCK(3) omega structures.
Sym004
Futuximab (mAb992)
Modotuximab (mAb1024)
Vmax (mcg/h)
1371
612
724
Km (mcg/mL)
2.91
1.19
1.96
V1 (mL)
3564
3893
3247
Q (mL/hr)
36.6
33.4
36.2
V2 (mL)
2856
3051
2641
CL (mL/hr)
13.7
17.0
11.7
Prop error (fraction)
0.181
0.207
0.206
Add error (mcg/mL)
1.79
0.68
1.11
Weight on Vmax
0.592
0.574
0.745
Weight on CL
0.525
0.547
0.373
Weight on V1
0.634
0.629
0.647
Weight on V2
log Albumin on CL
-0.344
-0.296
-0.382
ECOG 3 or greater on CL
-0.261
-0.231
-0.860
log dose on VMAX
0.186
0.130
0.160
ECOG 3 or greater on VMAX
-0.182
0.112
0.162
log tumour type on Vmax (Other solid tumours)
-0.139
-0.132
-0.129
log Male Sex on V2
-0.283
-0.228
-0.304
Figure 1 Observed Sym004 concentrations versus time, by dose level. Red points and lines are the observed data. Blue lines are smoothes.
Methods
Modelling was done in NONMEM v7.3 (FOCEI).
A two compartment model with target mediated drug disposition (TMDD) as the Michaelis- Menten implementation and including an a priori influence of weight was fit to the data. Inter- individual variability was included on clearance (CL), volume of the central compartment (V1) and maximum velocity (Vmax). A proportional + additive error model was used.
Covariate model building was performed by evaluating each covariate one by one (on preselected parameters) and then building a full final model with all covariates whose point estimates were outside the arbitrary range of 0.8 to 1.25 and whose 90% confidence intervals did not overlap the null value [1].
Covariate model building for Sym004 was done with both diagonal and a BLOCK(3) omega structures.
The model structure of the base and final Sym004 Pop PK BLOCK(3) models were applied separately to each Sym004 constituent antibody.
Table 2 Parameter estimates for Sym004, futuximab & modotuximab final models, using the BLOCK(3) omega structure.
Conclusions
The final Sym004 Pop PK covariate model structure depended upon the underlying statistical model structure, despite low correlations between the IIV parameters [2]. The extra covariates present in the final model with the diagonal omega structure were scientifically plausible.
The time gained by using the more simple omega structure (more stable model, faster run times) could be lost by having to explain/explore additional covariates that may be present in final models with a more simple statistical structure.
Effort should be made to consider the magnitude of included covariate effects in relation to their clinical importance when building Pop PK models. If the Pop PK analysis is supporting submission documents and labelling then effort should be made to a priori define when a covariate effect is clinically meaningless (no effect), clinically irrelevant (small effect) or clinically important (larger effect) during the planning of analyses.
The minor differences in the parameter estimates for the two Sym004 constituent antibodies for both base and final Pop PK models supports analysing the combination, Sym004.
Results
The two compartment model with TMDD fit the Sym004 data well. Correlations between the 3 IIV parameters were , and
Forest plots showing the one by one covariate screening results for Sym004, using both the diagonal and BLOCK(3) omega structures are shown in Figure 2. Differences can be seen in the magnitude and precision of the covariates effects for the diagonal and BLOCK(3) omega structures.
The final models for Sym004 BLOCK(3) and diagonal models included 6 and 9 covariates, respectively, above the influence of weight.
The parameter estimates for the covariate influences for Sym004, for the diagonal and BLOCK(3) omega structures are presented in Table 1.
The parameter estimates for the base structural model for Sym004, futuximab & modotuximab were very similar (results not shown).
The parameter estimates for the Sym004, futuximab & modotuximab final models, using the BLOCK(3) omega structure are presented in Table 2.
References
1. Gastonguay MR. Full Covariate Models as an Alternative to Methods Relying on Statistical Significance for Inferences about Covariate Effects: A Review of Methodology and 42 Case Studies. PAGE 20 (2011) Abstr 2229 [
2. JR Wade, SL Beal and NC Sambol (1994). Interaction between the choice of structural, statistical and covariate models in population pharmacokinetic analysis. J. Pharmacokin Biopharm, 22,