Predictive pharmacokinetic–pharmacodynamic modeling of tumor growth after administration of an anti-angiogenic agent, bevacizumab, as single-agent and combination therapy in tumor xenografts

Rocchetti, Maurizio; Germani, Massimiliano; Bene, Francesca Del; Poggesi, Italo; Magni, Paolo; Pesenti, Enrico; Nicolao, Giuseppe De

doi:10.1007/s00280-013-2107-z

Cited by 29 publications

(33 citation statements)

References 26 publications

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“…The TGI model can be generalized to combination therapy with two or more cytostatic and/or cytotoxic compounds. There have been several reported successes of this (16)(17)(18)(19)(20).…”

Section: Introductionmentioning

confidence: 99%

Tumor Static Concentration Curves in Combination Therapy

Cardilin

Almquist

Jirstrand

et al. 2016

AAPS J

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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.

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“…The TGI model can be generalized to combination therapy with two or more cytostatic and/or cytotoxic compounds. There have been several reported successes of this (16)(17)(18)(19)(20).…”

Section: Introductionmentioning

confidence: 99%

Tumor Static Concentration Curves in Combination Therapy

Cardilin

Almquist

Jirstrand

et al. 2016

AAPS J

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“…Previous attempts to model combination therapies between antiangiogenics and cytotoxics had first been merely theoretical, i.e. with no quantitative comparison to experimental data [15, 18, 34], apart from two recent studies [20, 21]. Here, our mathematical model was able to fully reproduce experimental data generated in xenografted mice, thus illustrating its robustness and the fact that although not mechanistic, it could mimic the different pharmacodynamic effects of the combination, depending on dosing and scheduling.…”

Section: Discussionmentioning

confidence: 99%

“…Despite these encouraging findings, the need for identifying proper biomarkers to forecast bevacizumab impact on neovessels remains critical [13] and until they are made available, in silico tools could be helpful to optimize alternate schedules. In contrast to the many pharmacodynamic models describing the action of cytotoxics on tumor growth [14], and despite substantial theoretical efforts in the field of cancer modeling to simulate angiogenesis and tumor-vasculature interactions [15–18], relatively few mathematical models of anti-angiogenic therapy have been actually confronted to experimental data [19, 20], and even less have investigated combined effects of cytotoxics with antiangiogenics [20, 21]. To address this issue, our group has developed a phenomenological model describing the effect of antiangiogenics on vasculature quality throughout time, thus potentially forecasting when the normalization phase occurs [22].…”

Section: Introductionmentioning

confidence: 99%

Model driven optimization of antiangiogenics + cytotoxics combination: application to breast cancer mice treated with bevacizumab + paclitaxel doublet leads to reduced tumor growth and fewer metastasis

et al. 2017

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Bevacizumab is the first-in-class antiangiogenic drug and is almost always administrated in combination with cytotoxics. Reports have shown that bevacizumab could induce a transient phase of vascular normalization, thus ensuring a better drug delivery when cytotoxics administration is adjuvant. However, determining the best sequence remains challenging. We have developed a mathematical model describing the impact of antiangiogenics on tumor vasculature. A 3.4 days gap between bevacizumab and paclitaxel was first proposed by our model. To test its relevance, 84 mice were orthotopically xenografted with human MDA-231Luc+ refractory breast cancer cells. Two sets of experiments were performed, based upon different bevacizumab dosing (10 or 20 mg/kg) and inter-cycle intervals (7 or 10 days), comprising several combinations with paclitaxel. Results showed that scheduling bevacizumab 3 days before paclitaxel improved antitumor efficacy (48% reduction in tumor size compared with concomitant dosing, p < 0.05) and reduced metastatic spreading. Additionally, bevacizumab alone could lead to more aggressive metastatic disease with shorter survival in animals. Our model was able to fit the experimental data and provided insights on the underlying dynamics of the vasculature's ability to deliver the cytotoxic agent. Final simulations suggested a new, data-informed optimal gap of 2.2 days. Our experimental data suggest that current concomitant dosing between bevacizumab and paclitaxel could be a sub-optimal strategy at bedside. In addition, this proof of concept study suggests that mathematical modelling could help to identify the optimal interval among a variety of possible alternate treatment modalities, thus refining the way experimental or clinical studies are conducted.

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“…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

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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.

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Predictive pharmacokinetic–pharmacodynamic modeling of tumor growth after administration of an anti-angiogenic agent, bevacizumab, as single-agent and combination therapy in tumor xenografts

Cited by 29 publications

References 26 publications

Tumor Static Concentration Curves in Combination Therapy

Tumor Static Concentration Curves in Combination Therapy

Model driven optimization of antiangiogenics + cytotoxics combination: application to breast cancer mice treated with bevacizumab + paclitaxel doublet leads to reduced tumor growth and fewer metastasis

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

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