Relevance of primary lesion location, tumour heterogeneity and genetic mutation demonstrated through tumour growth inhibition and overall survival modelling in metastatic colorectal cancer

Parametric time‐to‐event analysis is an important pharmacometric method to predict the probability of an event up until a certain time as a function of covariates and/or drug exposure. Modeling is performed at the level of the hazard function describing the instantaneous rate of an event occurring at that timepoint. We give an overview of the parametric time‐to‐event analysis starting with graphical exploration by Kaplan–Meier plotting for the event data including censoring and nonparametric hazard estimators such as the kernel‐based visual hazard comparison for the underlying hazard. The most common hazard functions including the exponential, Gompertz, Weibull, log‐normal, log‐logistic, and circadian functions are described in detail. A Shiny application was developed to graphically guide the modeler which of the most common hazard functions presents a similar shape compared to the data in order to guide which hazard functions to test in the parametric time‐to‐event analysis. For the chosen hazard function(s), the Shiny application can additionally be used to explore corresponding parameter values to inform on suitable initial estimates for parametric modeling as well as on possible covariate or treatment relationships to certain parameters. Moreover, it can be used for the dissemination of results as well as communication, training, and workshops on time‐to‐event analysis. By guiding the modeler on which functions and what parameter values to test and compare as well as to assist in dissemination, the Shiny application developed here greatly supports the modeler in complicated parametric time‐to‐event modeling.

Section: Hazard Functions For Parametric Time‐to‐event Analysismentioning

confidence: 99%

Finding the right hazard function for time‐to‐event modeling: A tutorial and Shiny application

Wijk

Simonsson

2022

“…Part of the unexplained variability could also be attributable to drug pharmacokinetics, 5 molecular characteristics of the tumor (e.g., PD‐L1 expression 4 ), or histological features, such as lymphocyte infiltration status, 39,40 that were not collected in our study. Additionally, the number of lesions, as well as metrics related to their location and heterogeneity could improve TGI models 41–43 …”

Section: Discussionmentioning

confidence: 99%

“…Additionally, the number of lesions, as well as metrics related to their location and heterogeneity could improve TGI models. [41][42][43] Our proof of concept for selecting radiomic features as model covariates provides an example of "mechanistic learning", 8 where ML approaches are incorporated in a traditional pharmacometric workflow. There is a growing interest in ML as a powerful tool for screening of highdimensional covariates.…”

Section: F I G U R Ementioning

confidence: 99%

Modeling tumor size dynamics based on real‐world electronic health records and image data in advanced melanoma patients receiving immunotherapy

Courlet

Abler

Guidi

et al. 2023

Self Cite

The development of immune checkpoint inhibitors (ICIs) has revolutionized cancer therapy but only a fraction of patients benefits from this therapy. Model‐informed drug development can be used to assess prognostic and predictive clinical factors or biomarkers associated with treatment response. Most pharmacometric models have thus far been developed using data from randomized clinical trials, and further studies are needed to translate their findings into the real‐world setting. We developed a tumor growth inhibition model based on real‐world clinical and imaging data in a population of 91 advanced melanoma patients receiving ICIs (i.e., ipilimumab, nivolumab, and pembrolizumab). Drug effect was modeled as an ON/OFF treatment effect, with a tumor killing rate constant identical for the three drugs. Significant and clinically relevant covariate effects of albumin, neutrophil to lymphocyte ratio, and Eastern Cooperative Oncology Group (ECOG) performance status were identified on the baseline tumor volume parameter, as well as NRAS mutation on tumor growth rate constant using standard pharmacometric approaches. In a population subgroup (n = 38), we had the opportunity to conduct an exploratory analysis of image‐based covariates (i.e., radiomics features), by combining machine learning and conventional pharmacometric covariate selection approaches. Overall, we demonstrated an innovative pipeline for longitudinal analyses of clinical and imaging RWD with a high‐dimensional covariate selection method that enabled the identification of factors associated with tumor dynamics. This study also provides a proof of concept for using radiomics features as model covariates.

“…Because panitumumab is an EGFR inhibitor, we model it as reducing the tumor growth rate 35 . The tumor dynamics for patients receiving FOLFOX and panitumumab simultaneously are given by:

\frac{dSLD}{d normalt} goodbreak= ()(, k_{g} goodbreak- k_{s, Pani}) SLD goodbreak- k_{s, FOLFOX} SLD,

SLD ()(, 0) goodbreak= SL D_{0} .

where

k_{s, Pani} goodbreak= a_{Pani} D_{Pani} e^{- γ_{Pani} t} .

…”

Section: Methodsmentioning

confidence: 99%

“…Because panitumumab is an EGFR inhibitor, we model it as reducing the tumor growth rate. 35 The tumor dynamics for patients receiving FOLFOX and panitumumab simultaneously are given by: where We also tried using a I max function for panitumumab but because we only have one dose level and use the average dose this led to similar but slightly worse results. Moreover, the potency function we use can be seen as a linear approximation of an I max function valid for small (non-saturating) exposures.…”

Section: Tumor Growth Inhibition Modelmentioning

confidence: 99%

Model‐based prediction of progression‐free survival for combination therapies in oncology

Baaz

Cardilin

Jirstrand

2023

Progression‐free survival (PFS) is an important clinical metric for comparing and evaluating similar treatments for the same disease within oncology. After the completion of a clinical trial, a descriptive analysis of the patients' PFS is often performed post hoc using the Kaplan–Meier estimator. However, to perform predictions, more sophisticated quantitative methods are needed. Tumor growth inhibition models are commonly used to describe and predict the dynamics of preclinical and clinical tumor size data. Moreover, frameworks also exist for describing the probability of different types of events, such as tumor metastasis or patient dropout. Combining these two types of models into a so‐called joint model enables model‐based prediction of PFS. In this paper, we have constructed a joint model from clinical data comparing the efficacy of FOLFOX against FOLFOX + panitumumab in patients with metastatic colorectal cancer. The nonlinear mixed effects framework was used to quantify interindividual variability (IIV). The model describes tumor size and PFS data well, and showed good predictive capabilities using truncated as well as external data. A machine‐learning guided analysis was performed to reduce unexplained IIV by incorporating patient covariates. The model‐based approach illustrated in this paper could be useful to help design clinical trials or to determine new promising drug candidates for combination therapy trials.