Microsoft powerpoint - dartois_leiden2006.ppt [mode de compatibilité]
C. Dartois1, G. Freyer1, A. LemenueI2, I. Darlavoix3, C. Vermot Desroches3, M. Michallet4, B.Tranchand1,5, P. Girard1,6
1EA3738, University Lyon I, Lyon, France; 2IRIS, Courbevoie, France 3OPi, Limonest, France; 4 Hôpital Edouard Herriot, Lyon, France ;
5 Centre Anti-Cancéreux L. Bérard, Lyon, France ; 6INSERM, Lyon, France.
To identify a population pharmacokinetic–pharmacodynamic (PK-PD) model
- A 2-compartments model was the most appropriate to describe the data.
of inolimomab, a monoclonal antibody directed against the alpha chain of the IL-2
Clearance was estimated at 0.077 l/hr (19 %), volume of compartment 1 at 2.76 l
receptor (CD25) administered in first-line treatment of patients with acute graft
(26 %), volume of compartment 2 at 2.25 l (18 %) and inter-compartmental
versus host disease (aGvHD) on ordinal efficacy categorical scores (IBMTR and
clearance at 2.22 l/hr (52 %). No covariate was found significant and a visual
predictive check (n= 1000) on concentrations allowed us to qualify our model.
- PKPD exploratory analysis revealed that composite scores (IBMTR,
Glucksberg and Karnofsky) were not linked to exposure whatever its calculation.
On the opposite, organ scores showed apparent and increasing relationship
Table I. Patient characteristics (n = 21)
between PK exposure and probabilities of lowest grade. Figure 1 show the most
apparent relationship: with cumulated AUC.
- A dose-escalating phase I-II Males (n = 12)
mu ated probabilities of IBMTR grades Cumu
grade II to IV aGvHD after haematopoietic stem cell transplantation (HSCT).
- Patients were assigned to 1 of 4 cohorts ( 0.10, 0.20, 0.30 or 0.40 mg/kg)
of inolimomab by i.v. infusion over a 30-minutes period, in combination with 2
mg/kg of methylprednisolone. Treatment was divided on 2 periods: an induction
phase (daily adm during 1 or 2 weeks, depending on aGvHD status at day 9) and
a maintenance phase (3 adm / week). Total duration of treatment was 4 weeks.
- Blood samples were col ected prior to and after administration and were
quantified by a validated Enzyme-Linked ImmunoSorbent Assay (ELISA).
- This relationship with organ scores was model ed for skin by an Emax
evaluated from Day 1 to Day 28, as wel as at
model, and for gut and liver by a linear model, with IIV on the logit for all models.
Moreover, we identified in the skin model 2 subpopulations of patients (sensitive /
Karnofsky scores (defined as composite scores).
less sensitive) based on a mixture model for EA50 parameter. Most important
IBMTR determines aGvHD severity (grades 0 to
parameters are presented in Table III.
4) based on the scores of different organs (skin
rash, diarrhoea volume, and total bilirubin
concentrations). Glucksberg’s score is a
Table III. Final parameter estimates of each organ
combination of the different organ scores and
clinical performance. And finally , Karnofsky
defines overal performance status of a patient (from 0 to 100 %). Independent
organs as wel as composite scores were considered for pharmacodynamics
- Qualification of our PKPD models was based on predictive checks (see
Figure 2) and different statistical criteria. For
instance, we choose time to the first transition to the
- PK analysis was carried out using mixed-effect modeling in NONMEM
version V (FOCE) by testing 1 to 3 compartment models with linear or non linear
elimination. Inter (IIV) and Intra individual variability were assumed to follow a log-
normal distribution. Adequacy of the different developed models was evaluated
with OF, GOF plots, and precision of parameter estimates. Potential covariates
were tested and selected through plots, univariate tests in NONMEM and finally a
backward approach using the LRT (∆=10.83, p=0.001). After final model selection,
correlation between al PK parameters were estimated, and parameter estimates
used to simulate individual PK exposure.
- Exposure was defined as Cmax, Area Under the Curve (AUC), cumulated
AUC, or AUC intensity (cumulated AUC/duration). PKPD relationship was
explored through sophisticated graphics which plotted cumulative probabilities of
the score grade versus PK exposure. Then we quantified potential PKPD
relationship by a proportional odds model. For that, cumulative probabilities of
observed score j were logit transformed and linked to PK exposure:
Figure 3 : PD profiles of patient ID5, ID10, and ID 6.
- Nature of this link (f) was tested with different PD models, like Emax, log-
linear or linear models. Inter individual variability on the logit followed a normal
distribution and a log-normal distribution if it was on EA50. Estimation was
performed using the LAPLACIAN estimation method. Adequacy of the different
Inolimomab exposure–effect relationship has been identified and model ed
developed models and covariate testing was performed as for PK model.
for aGvHD targeted organ scores (skin, gut and liver). This model links cumulative
- Qualification of our PK and PKPD analyses was based on visual predictive
AUC to grade score probabilities and correctly predicts majority of PD individual
checks and predictive checks depending of the model purposes.
profiles (see Figure 3) and time necessary to response in a patient (see Figure 2).
But conceptually, it can not predict appearance of aGvHD relapse or resistance to
treatment (1). Nevertheless, this first modelling allowed to confirm effect of this
(1) Karlsson et al. 1998. A general model for time-dissociated PK-PD relationship
first line treatment and more data should overcome this limitation.
exemplified by paclitaxel myelosuppression.
Pharmaceutical & Medical Terminology for Pharmacy Technicians Acknowledgements Winnipeg Technical College and the Department of Labour and Immigration of Manitoba wish to express sincere appreciation to all contributors. Special acknowledgments are The Citizenship and Multicultural Division, Funding for this project has been provided by the Manitoba Labour an
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