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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.

Source: http://www.pols-phase1.eu/pdf/DARTOIS_LEIDEN2006.pdf