Modeling of human tumor xenografts and dose rationale in oncology

Simeoni, Monica; Nicolao, Giuseppe De; Magni, Paolo; Rocchetti, Maurizio; Poggesi, Italo

doi:10.1016/j.ddtec.2012.07.004

Cited by 39 publications

(37 citation statements)

References 54 publications

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“…To overcome some of the limitations of these approaches, mathematical models have been proposed as a tool to derive meaningful parameters independent of the experimental settings, that can be later used to optimise upcoming experiments or guide dose rationale in humans (Simeoni et al, 2013).…”

Section: Discussionmentioning

confidence: 99%

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Systematic Modeling and Design Evaluation of Unperturbed Tumor Dynamics in Xenografts

Parra-Guillén

Mangas‐Sanjuán

Garcia‐Cremades

et al. 2018

J Pharmacol Exp Ther

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Section: Discussionmentioning

confidence: 99%

“…However, pharmacokinetic, pharmacodynamic and disease progression properties of the systems are rarely taken into account when designing xenograft experiments (Simeoni et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Systematic Modeling and Design Evaluation of Unperturbed Tumor Dynamics in Xenografts

Parra-Guillén

Mangas‐Sanjuán

Garcia‐Cremades

et al. 2018

J Pharmacol Exp Ther

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“…By now, several tumor growth pharmacokinetic-pharmacodynamic (PK-PD) inhibition models are being used in clinical and preclinical drug-development from Jusko models [2,3] onwards. For a review of their features, limits and potentialities the reader is referred to three recent survey papers [4][5][6]. A relatively popular model in this area is the Simeoni TGI one [7,8] describing the dynamics of proliferating tumor cells that, once hit by the drug, enter a progressive damage process, eventually leading to their death.…”

Section: Introductionmentioning

confidence: 99%

Biomarker- versus drug-driven tumor growth inhibition models: an equivalence analysis

Sardu

Poggesi

Nicolao

2015

J Pharmacokinet Pharmacodyn

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The mathematical modeling of tumor xenograft experiments following the dosing of antitumor drugs has received much attention in the last decade. Biomarker data can further provide useful insights on the pathological processes and be used for translational purposes in the early clinical development. Therefore, it is of particular interest the development of integrated pharmacokinetic-pharmacodynamic (PK-PD) models encompassing drug, biomarker and tumor-size data. This paper investigates the reciprocal consistency of three types of models: drug-to-tumor, such as established drug-driven tumor growth inhibition (TGI) models, drug-to-biomarker, e.g. indirect response models, and biomarker-to-tumor, e.g. the more recent biomarker-driven TGI models. In particular, this paper derives a mathematical relationship that guarantees the steady-state equivalence of the cascade of drug-to-biomarker and biomarker-to-tumor models with a drug-to-tumor TGI model. Using the Simeoni TGI model as a reference, conditions for steady-state equivalence are worked out and used to derive a new biomarker-driven model. Simulated and real data are used to show that in realistic cases the steady-state equivalence extends also to transient responses. The possibility of predicting the drug-to-tumor potency of a new candidate drug based only on biomarker response is discussed.

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“…Models capable of discriminating between compoundspecific, system-specific, and experiment parameters can be used to improve the design of preclinical experiments and thus reduce the costs and number of animals used (56,57). They have also been recently advocated for translational drug research and for quantitatively predicting the pharmacokinetics and pharmacodynamics of drugs in humans (58).…”

Section: Translational Usesmentioning

confidence: 99%

“…Their limited predictive capability could be attributed to their inability to represent the true environment in which a tumor grows in humans, and the inability of implanted tumors to metastasize or to develop drug resistance. Genetically engineered mice mimicking the pathophysiological and molecular features of human malignancies have been introduced recently to overcome these limitations (57,61,62), but they still need further validation. Until then, xenograft models remain to be widely used due to the experience with them and the relatively limited resources required.…”

Section: Translational Usesmentioning

confidence: 99%

Modeling NSCLC Progression: Recent Advances and Opportunities Available

Suleiman

Nogová

Fuhr

2013

AAPS J

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Abstract. Non-small cell lung cancer (NSCLC) is one of the leading causes of death around the world with an estimated 5-year relative survival rate of 16% at diagnosis. Development of drugs treating NSCLC is not easy, and the success rate for an anticancer treatment to pass through the whole clinical development process is as low as 5%. Modeling and simulation lend themselves as tools which can potentially streamline drug development. A critical component of the models developed is a description of how the disease progresses over time and how a treatment would affect its trajectory. Our aim was to review the literature to present the models and growth functions which have been used for describing NSCLC dynamics, and how anticancer treatments can affect such dynamics, both in animals and in humans. Only a limited set of models were identified for such a purpose. Most of the models which have been used were descriptive of tumor growth, yet there were attempts to account for the underlying processes, especially in animals where it is more feasible to collect data needed for developing such models. Moreover, we discuss how modeling and simulation can aid in decision making across the different stages of drug development. Based on some encouraging results from trials of other cancer types where modeling tumor dynamics has played an important role, we propose further exploration of NSCLC using model-based techniques and further use of these techniques in designing and evaluating NSCLC trials.

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Modeling of human tumor xenografts and dose rationale in oncology

Cited by 39 publications

References 54 publications

Systematic Modeling and Design Evaluation of Unperturbed Tumor Dynamics in Xenografts

Systematic Modeling and Design Evaluation of Unperturbed Tumor Dynamics in Xenografts

Biomarker- versus drug-driven tumor growth inhibition models: an equivalence analysis

Modeling NSCLC Progression: Recent Advances and Opportunities Available

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