A Review of Mixed‐Effects Models of Tumor Growth and Effects of Anticancer Drug Treatment Used in Population Analysis

Ribba, Benjamin; Holford, Nicholas H. G.; Magni, Paolo; Trocóniz, Iñaki F.; Gueorguieva, Ivelina; Girard, Pascal; Sarr, Céline; Elishmereni, Moran; Kloft, Charlotte; Friberg, Lena E.

doi:10.1038/psp.2014.12

Cited by 150 publications

(165 citation statements)

References 64 publications

(144 reference statements)

Supporting

Mentioning

165

Contrasting

Order By: Relevance

“…Different data-driven tumor models have been suggested (8,9). One of the most commonly applied experimental models is the tumor growth inhibition (TGI) model (10)(11)(12)(13).…”

Section: Introductionmentioning

confidence: 99%

Tumor Static Concentration Curves in Combination Therapy

Cardilin

Almquist

Jirstrand

et al. 2016

AAPS J

View full text Add to dashboard Cite

Abstract.Combination therapies are widely accepted as a cornerstone for treatment of different cancer types. A tumor growth inhibition (TGI) model is developed for combinations of cetuximab and cisplatin obtained from xenograft mice. Unlike traditional TGI models, both natural cell growth and cell death are considered explicitly. The growth rate was estimated to 0.006 h −1 and the natural cell death to 0.0039 h −1 resulting in a tumor doubling time of 14 days. The tumor static concentrations (TSC) are predicted for each individual compound. When the compounds are given as single-agents, the required concentrations were computed to be 506 μg · mL −1 and 56 ng · mL −1 for cetuximab and cisplatin, respectively. A TSC curve is constructed for different combinations of the two drugs, which separates concentration combinations into regions of tumor shrinkage and tumor growth. The more concave the TSC curve is, the lower is the total exposure to test compounds necessary to achieve tumor regression. The TSC curve for cetuximab and cisplatin showed weak concavity. TSC values and TSC curves were estimated that predict tumor regression for 95% of the population by taking between-subject variability into account. The TSC concept is further discussed for different concentration-effect relationships and for combinations of three or more compounds.

show abstract

“…Different data-driven tumor models have been suggested (8,9). One of the most commonly applied experimental models is the tumor growth inhibition (TGI) model (10)(11)(12)(13).…”

Section: Introductionmentioning

confidence: 99%

Tumor Static Concentration Curves in Combination Therapy

Cardilin

Almquist

Jirstrand

et al. 2016

AAPS J

View full text Add to dashboard Cite

show abstract

“…The term representing drug‐induced decay can be constant or exponential, driven by drug exposure, and it can incorporate a resistance term or a delay term to accommodate a wide range of tumor response shapes (see refs. 8, 9, 19, 20, 21, 22, 23, 24 for reviews).…”

Section: Methodsmentioning

confidence: 99%

Preclinical Modeling of Tumor Growth and Angiogenesis Inhibition to Describe Pazopanib Clinical Effects in Renal Cell Carcinoma

Ouerdani

Struemper

Suttle

et al. 2015

CPT Pharmacometrics Syst. Pharmacol.

Self Cite

View full text Add to dashboard Cite

The objective was to leverage tumor size data from preclinical experiments to propose a model of tumor growth and angiogenesis inhibition for the analysis of pazopanib efficacy in renal cell carcinoma (RCC) patients. We analyzed tumor data in mice with RCC CAKI‐2 cell line treated with pazopanib. Clinical tumor size data obtained in a subset of patients with RCC were also analyzed. A model accounting for the processes of tumor growth, angiogenesis, and drug effect was developed. The final tumor model was composed of two variables: the tumor and its vasculature. Our results show that, both in mice and in humans, pazopanib exhibits a dual mechanism of action, and parameter estimation values highlight the inherent difference between mice and humans on the time scale of tumor size response. We developed a semimechanistic tumor growth inhibition model that takes into account tumor angiogenesis in order to describe the effects of pazopanib in mice. Analyzing rich preclinical data with a semimechanistic model may be a relevant approach to facilitate the description of sparse clinical longitudinal tumor size data and to provide insights for the understanding of the drug mechanisms of action in patients.

show abstract

“…Added benefits have been gained from mathematical models of tumor size (TS) dynamics4, 5, 6 and tumor growth inhibition,7, 8, 9 which can characterize anticancer drug effects over time and provide improved predictors of long‐term clinical outcomes 10, 11…”

mentioning

confidence: 99%

Assessing Similarity Among Individual Tumor Size Lesion Dynamics: The CICIL Methodology

Terranova

Girard

Ιωάννου

et al. 2018

CPT Pharmacom & Syst Pharma

View full text Add to dashboard Cite

Mathematical models of tumor dynamics generally omit information on individual target lesions (iTLs), and consider the most important variable to be the sum of tumor sizes (TS). However, differences in lesion dynamics might be predictive of tumor progression. To exploit this information, we have developed a novel and flexible approach for the non‐parametric analysis of iTLs, which integrates knowledge from signal processing and machine learning. We called this new methodology ClassIfication Clustering of Individual Lesions (CICIL). We used CICIL to assess similarities among the TS dynamics of 3,223 iTLs measured in 1,056 patients with metastatic colorectal cancer treated with cetuximab combined with irinotecan, in two phase II studies. We mainly observed similar dynamics among lesions within the same tumor site classification. In contrast, lesions in anatomic locations with different features showed different dynamics in about 35% of patients. The CICIL methodology has also been implemented in a user‐friendly and efficient Java‐based framework.

show abstract

A Review of Mixed‐Effects Models of Tumor Growth and Effects of Anticancer Drug Treatment Used in Population Analysis

Cited by 150 publications

References 64 publications

Tumor Static Concentration Curves in Combination Therapy

Tumor Static Concentration Curves in Combination Therapy

Preclinical Modeling of Tumor Growth and Angiogenesis Inhibition to Describe Pazopanib Clinical Effects in Renal Cell Carcinoma

Assessing Similarity Among Individual Tumor Size Lesion Dynamics: The CICIL Methodology

Contact Info

Product

Resources

About