Preclinical pharmacokinetic/pharmacodynamic models to predict synergistic effects of co-administered anti-cancer agents
Purpose Pharmacokinetic/pharmacodynamic (PK/PD) models have been shown to be useful in predicting tumor growth rates in mouse xenografts. We applied novel PK/PD models to the published anticancer combination therapies of tumor growth inhibition to simulate synergistic changes in tumor growth rates. The parameters from the PK/PD model were further used to estimate clinical doses of the combination. Methods A PK/PD model was built that linked the dosing regimen of a compound to the inhibition of tumor growth in mouse xenograft models. Two subsequent PK/PD models were developed to simulate the published tumor growth profiles of combination treatments. Model I predicts the tumor growth curve assuming that the effect of two anticancer drugs, AZD7762 and irinotecan, is synergistic when given in combination. Model II predicts the tumor growth curve assuming that the effect of co-administering flavopiridol and irinotecan is maximally synergistic when dosed at an optimal interval. Results Model I was able to account for the synergistic effects of AZD7762 following the administration of irinotecan. When Model II was applied to the antitumor activity of irinotecan and flavopiridol combination therapy, the modeling was able to reproduce the optimal dosing interval between administrations of the compounds. Furthermore, Model II was able to estimate the biologically active dose of flavopiridol recommended for phase II studies. Conclusions The timing of clinical combination therapy doses is often selected empirically. PK/PD models provide a theoretical structure useful in the design of the optimal clinical dose, frequency of administration and the optimal timing of administration between anticancer agents to maximize tumor suppression..
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2009 |
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| Erschienen: |
2009 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:66 |
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| Enthalten in: |
Cancer chemotherapy and pharmacology - 66(2009), 2 vom: 16. Okt., Seite 245-254 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Goteti, Kosalaram [VerfasserIn] |
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| Links: |
Volltext [lizenzpflichtig] |
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| Themen: |
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| Anmerkungen: |
© Springer-Verlag 2009 |
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| doi: |
10.1007/s00280-009-1153-z |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a Purpose Pharmacokinetic/pharmacodynamic (PK/PD) models have been shown to be useful in predicting tumor growth rates in mouse xenografts. We applied novel PK/PD models to the published anticancer combination therapies of tumor growth inhibition to simulate synergistic changes in tumor growth rates. The parameters from the PK/PD model were further used to estimate clinical doses of the combination. Methods A PK/PD model was built that linked the dosing regimen of a compound to the inhibition of tumor growth in mouse xenograft models. Two subsequent PK/PD models were developed to simulate the published tumor growth profiles of combination treatments. Model I predicts the tumor growth curve assuming that the effect of two anticancer drugs, AZD7762 and irinotecan, is synergistic when given in combination. Model II predicts the tumor growth curve assuming that the effect of co-administering flavopiridol and irinotecan is maximally synergistic when dosed at an optimal interval. Results Model I was able to account for the synergistic effects of AZD7762 following the administration of irinotecan. When Model II was applied to the antitumor activity of irinotecan and flavopiridol combination therapy, the modeling was able to reproduce the optimal dosing interval between administrations of the compounds. Furthermore, Model II was able to estimate the biologically active dose of flavopiridol recommended for phase II studies. Conclusions The timing of clinical combination therapy doses is often selected empirically. PK/PD models provide a theoretical structure useful in the design of the optimal clinical dose, frequency of administration and the optimal timing of administration between anticancer agents to maximize tumor suppression. | ||
| 650 | 4 | |a Xenograft mice |7 (dpeaa)DE-He213 | |
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| 650 | 4 | |a Tumor growth |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Edwin Garner, C. |4 aut | |
| 700 | 1 | |a Utley, Lucas |4 aut | |
| 700 | 1 | |a Dai, Jing |4 aut | |
| 700 | 1 | |a Ashwell, Susan |4 aut | |
| 700 | 1 | |a Moustakas, Demetri T. |4 aut | |
| 700 | 1 | |a Gönen, Mithat |4 aut | |
| 700 | 1 | |a Schwartz, Gary K. |4 aut | |
| 700 | 1 | |a Kern, Steven E. |4 aut | |
| 700 | 1 | |a Zabludoff, Sonya |4 aut | |
| 700 | 1 | |a Brassil, Patrick J. |4 aut | |
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