Literature DB >> 28655786

Pharmacokinetics and Pharmacodynamics-Based Mathematical Modeling Identifies an Optimal Protocol for Metronomic Chemotherapy.

Joseph Ciccolini1, Dominique Barbolosi1, Christophe Meille2, Aurélie Lombard1, Cindy Serdjebi1, Sarah Giacometti1, Laetitia Padovani3,4, Eddy Pasquier5,6, Nicolas André7,4,8,9.   

Abstract

Metronomic chemotherapy is usually associated with better tolerance than conventional chemotherapy, and encouraging response rates have been reported in various settings. However, clinical development of metronomic chemotherapy has been hampered by a number of limitations, including the vagueness of its definition and the resulting empiricism in protocol design. In this study, we developed a pharmacokinetic/pharmacodynamic mathematical model that identifies in silico the most effective administration schedule for gemcitabine monotherapy. This model is based upon four biological assumptions regarding the mechanisms of action of metronomic chemotherapy, resulting in a set of 6 minimally parameterized differential equations. Simulations identified daily 0.5-1 mg/kg gemcitabine as an optimal protocol to maximize antitumor efficacy. Both metronomic protocols (0.5 and 1 mg/kg/day for 28 days) were evaluated in chemoresistant neuroblastoma-bearing mice and compared with the standard MTD protocol (100 mg/kg once a week for 4 weeks). Systemic exposure to gemcitabine was 14 times lower in the metronomic groups compared with the standard group. Despite this, metronomic gemcitabine significantly inhibited tumor angiogenesis and reduced tumor perfusion and inflammation in vivo, while standard gemcitabine did not. Furthermore, metronomic gemcitabine yielded a 40%-50% decrease in tumor mass at the end of treatment as compared with control mice (P = 0.002; ANOVA on ranks with Dunn test), while standard gemcitabine failed to significantly reduce tumor growth. Stable disease was maintained in the metronomic groups for up to 2 months after treatment completion (67%-72% reduction in tumor growth at study conclusion, P < 0.001; ANOVA on ranks with Dunn test). Collectively, our results confirmed the superiority of metronomic protocols in chemoresistant tumors in vivoCancer Res; 77(17); 4723-33. ©2017 AACR. ©2017 American Association for Cancer Research.

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Year:  2017        PMID: 28655786     DOI: 10.1158/0008-5472.CAN-16-3130

Source DB:  PubMed          Journal:  Cancer Res        ISSN: 0008-5472            Impact factor:   12.701


  13 in total

1.  Lung Cancer Radiotherapy: Simulation and Analysis Based on a Multicomponent Mathematical Model.

Authors:  Wen-Song Hong; Shun-Guan Wang; Gang-Qing Zhang
Journal:  Comput Math Methods Med       Date:  2021-04-29       Impact factor: 2.238

Review 2.  Optimizing the future: how mathematical models inform treatment schedules for cancer.

Authors:  Deepti Mathur; Ethan Barnett; Howard I Scher; Joao B Xavier
Journal:  Trends Cancer       Date:  2022-03-09

Review 3.  Understanding intratumor heterogeneity by combining genome analysis and mathematical modeling.

Authors:  Atsushi Niida; Satoshi Nagayama; Satoru Miyano; Koshi Mimori
Journal:  Cancer Sci       Date:  2018-02-28       Impact factor: 6.716

Review 4.  Clinical Efficacy of a Novel Therapeutic Principle, Anakoinosis.

Authors:  Daniel Heudobler; Michael Rechenmacher; Florian Lüke; Martin Vogelhuber; Sebastian Klobuch; Simone Thomas; Tobias Pukrop; Christina Hackl; Wolfgang Herr; Lina Ghibelli; Christopher Gerner; Albrecht Reichle
Journal:  Front Pharmacol       Date:  2018-11-28       Impact factor: 5.810

Review 5.  Trends and Challenges in Tumor Anti-Angiogenic Therapies.

Authors:  József Jászai; Mirko H H Schmidt
Journal:  Cells       Date:  2019-09-18       Impact factor: 6.600

6.  A Mathematical Model to Estimate Chemotherapy Concentration at the Tumor-Site and Predict Therapy Response in Colorectal Cancer Patients with Liver Metastases.

Authors:  Daniel A Anaya; Prashant Dogra; Zhihui Wang; Mintallah Haider; Jasmina Ehab; Daniel K Jeong; Masoumeh Ghayouri; Gregory Y Lauwers; Kerry Thomas; Richard Kim; Joseph D Butner; Sara Nizzero; Javier Ruiz Ramírez; Marija Plodinec; Richard L Sidman; Webster K Cavenee; Renata Pasqualini; Wadih Arap; Jason B Fleming; Vittorio Cristini
Journal:  Cancers (Basel)       Date:  2021-01-25       Impact factor: 6.639

7.  Emerging roles for clinical pharmacometrics in cancer precision medicine.

Authors:  Sujit Nair; Ah-Ng Tony Kong
Journal:  Curr Pharmacol Rep       Date:  2018-04-20

8.  Dose- and time-dependence of the host-mediated response to paclitaxel therapy: a mathematical modeling approach.

Authors:  Madeleine Benguigui; Dror Alishekevitz; Michael Timaner; Dvir Shechter; Ziv Raviv; Sebastien Benzekry; Yuval Shaked
Journal:  Oncotarget       Date:  2017-12-20

9.  Delicate Balances in Cancer Chemotherapy: Modeling Immune Recruitment and Emergence of Systemic Drug Resistance.

Authors:  Anh Phong Tran; M Ali Al-Radhawi; Irina Kareva; Junjie Wu; David J Waxman; Eduardo D Sontag
Journal:  Front Immunol       Date:  2020-06-30       Impact factor: 7.561

10.  Applying dynamic contrast enhanced MSOT imaging to intratumoral pharmacokinetic modeling.

Authors:  Ted G Xiao; Jared A Weis; F Scott Gayzik; Alexandra Thomas; Akiko Chiba; Metin N Gurcan; Umit Topaloglu; Abhilash Samykutty; Lacey R McNally
Journal:  Photoacoustics       Date:  2018-07-27
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