Joint modelling and simulation of M-protein dynamics and progression-free survival for alternative isatuximab dosing with pomalidomide/dexamethasone.

AIMS: Addition of isatuximab (Isa) to pomalidomide/dexamethasone (Pd) significantly improved progression-free survival (PFS) in patients with relapsed/refractory multiple myeloma (RRMM). We aimed to characterize the relationship between serum M-protein kinetics and PFS in the phase 3 ICARIA-MM trial (NCT02990338), and to evaluate an alternative dosing regimen of Isa by simulation. METHODS: Data from the ICARIA-MM trial comparing Isa 10 mg/kg weekly for 4 weeks then every 2 weeks (QW-Q2W) in combination with Pd versus Pd in 256 evaluable RRMM patients were used. A joint model of serum M-protein dynamics and PFS was developed. Trial simulations were then performed to evaluate whether efficacy is maintained after switching to a monthly dosing regimen. RESULTS: The model identified instantaneous changes (slope) in serum M-protein as the best on-treatment predictor for PFS and baseline patient characteristics impacting serum M-protein kinetics (albumin and ß2-microglobulin on baseline levels, non-IgG type on growth rate) and PFS (presence of plasmacytomas). Trial simulations demonstrated that switching to a monthly Isa regimen at 6 months would shorten median PFS by 2.3 weeks and induce 42.3% patients to progress earlier. CONCLUSIONS: Trial simulations supported selection of the approved Isa 10 mg/kg QW-Q2W regimen and showed that switching to a monthly regimen after 6 months may reduce clinical benefit in the overall population. However, patients with good prognostic characteristics and with a stable, very good partial response may switch to a monthly regimen after 6 months without compromising the risk of disease progression. This hypothesis will be tested in a prospective clinical trial.

A novel method based on unbiased correlations tests for covariate selection in nonlinear mixed effects models: The COSSAC approach.

Building a covariate model is a crucial task in population pharmacokinetics and pharmacodynamics in order to understand the determinants of the interindividual variability. Identifying a good covariate model usually requires many runs. Several procedures have been proposed in the past to automatize this task. The most commonly used is Stepwise Covariate Modeling (SCM). Here, we present a novel stepwise method based on statistical tests between individual parameters sampled from their conditional distribution and the covariates. This strategy, called the COnditional Sampling use for Stepwise Approach based on Correlation tests (COSSAC), makes use of the information contained in the current model to choose which parameter-covariate relationship to try next. This strategy greatly reduces the number of covariate models tested, while retaining on its search path the models improving the log-likelihood (LL). In this article, we detail the COSSAC method and its implementation in Monolix, and evaluate its performance. The performance was assessed by comparing COSSAC to the traditional SCM method on 17 representative data sets. For the large majority of cases (15 out of 17), the final covariate model is identical (11 cases) or very similar (4 cases with LL differences less than 3.84) with both procedures. Yet, COSSAC requires between 2 to 20 times fewer runs than SCM. This represents a decisive speed up, especially for models that take long to run and would not be tractable using the SCM method.

Efficient Pharmacokinetic Modeling Workflow With the MonolixSuite: A Case Study of Remifentanil.

MonolixSuite is a software widely used for model-based drug development. It contains interconnected applications for data visualization, noncompartmental analysis, nonlinear mixed effect modeling, and clinical trial simulations. Its main assets are ease of use via an interactive graphical interface, computation speed, and efficient parameter estimation even for complex models. This tutorial presents a step-by-step pharmacokinetic (PK) modeling workflow using MonolixSuite, including how to visualize the data, set up a population PK model, estimate parameters, and diagnose and improve the model incrementally.