Screening of well-established drugs targeting cancer metabolism: reproducibility of the efficacy of a highly effective drug combination in mice.
Summary Alterations in metabolic pathways are known to characterize cancer. In order to suppress cancer growth, however, multiple proteins involved in these pathways have to be targeted simultaneously. We have developed a screening method to assess the best drug combination for cancer treatment base...
Ausführliche Beschreibung
Autor*in: |
Abolhassani, Mohammad [verfasserIn] Guais, Adeline [verfasserIn] Sanders, Edward [verfasserIn] Campion, Frédéric [verfasserIn] Fichtner, Iduna [verfasserIn] Bonte, Jacques [verfasserIn] Baronzio, Gianfranco [verfasserIn] Fiorentini, Giammaria [verfasserIn] Israël, Maurice [verfasserIn] Schwartz, Laurent [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2011 |
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Schlagwörter: |
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Übergeordnetes Werk: |
Enthalten in: Investigational new drugs - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1983, 30(2011), 4 vom: 09. Juni, Seite 1331-1342 |
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Übergeordnetes Werk: |
volume:30 ; year:2011 ; number:4 ; day:09 ; month:06 ; pages:1331-1342 |
Links: |
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DOI / URN: |
10.1007/s10637-011-9692-7 |
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Katalog-ID: |
SPR012019518 |
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520 | |a Summary Alterations in metabolic pathways are known to characterize cancer. In order to suppress cancer growth, however, multiple proteins involved in these pathways have to be targeted simultaneously. We have developed a screening method to assess the best drug combination for cancer treatment based on targeting several factors implicated in tumor specific metabolism. Following a review of the literature, we identified those enzymes known to be deregulated in cancer and established a list of sixty-two drugs targeting them. These molecules are used routinely in clinical settings for diseases other than cancer. We screened a first library in vitro against four cell lines and then evaluated the most promising binary combinations in vivo against three murine syngeneic cancer models, (LL/2, Lewis lung carcinoma; B16-F10, melanoma; and MBT-2, bladder cancer). The optimum result was obtained using a combination of α-lipoic acid and hydroxycitrate ($ METABLOC^{TM} $). In this study, a third agent was added by in vivo evaluation of a large number of combinations. The addition of octreotide strongly reduced tumor development (T/C% value of 30.2 to 34.5%; P < 0.001) in the same models and prolonged animal survival (P < 0.001) as compared to cisplatin. These results were confirmed in a different laboratory setting using a human xenograft model (NCI-H69, small cell lung cancer). None of these three molecules are known to target DNA. The effectiveness of this combination in several animal models, as well as the low toxicity of these inexpensive drugs, emphasizes the necessity of rapidly setting up a clinical trial. | ||
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650 | 4 | |a Hydroxycitrate |7 (dpeaa)DE-He213 | |
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650 | 4 | |a Screening |7 (dpeaa)DE-He213 | |
700 | 1 | |a Guais, Adeline |e verfasserin |4 aut | |
700 | 1 | |a Sanders, Edward |e verfasserin |4 aut | |
700 | 1 | |a Campion, Frédéric |e verfasserin |4 aut | |
700 | 1 | |a Fichtner, Iduna |e verfasserin |4 aut | |
700 | 1 | |a Bonte, Jacques |e verfasserin |4 aut | |
700 | 1 | |a Baronzio, Gianfranco |e verfasserin |4 aut | |
700 | 1 | |a Fiorentini, Giammaria |e verfasserin |4 aut | |
700 | 1 | |a Israël, Maurice |e verfasserin |4 aut | |
700 | 1 | |a Schwartz, Laurent |e verfasserin |4 aut | |
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10.1007/s10637-011-9692-7 doi (DE-627)SPR012019518 (SPR)s10637-011-9692-7-e DE-627 ger DE-627 rakwb eng 610 ASE 44.40 bkl Abolhassani, Mohammad verfasserin aut Screening of well-established drugs targeting cancer metabolism: reproducibility of the efficacy of a highly effective drug combination in mice. 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Summary Alterations in metabolic pathways are known to characterize cancer. In order to suppress cancer growth, however, multiple proteins involved in these pathways have to be targeted simultaneously. We have developed a screening method to assess the best drug combination for cancer treatment based on targeting several factors implicated in tumor specific metabolism. Following a review of the literature, we identified those enzymes known to be deregulated in cancer and established a list of sixty-two drugs targeting them. These molecules are used routinely in clinical settings for diseases other than cancer. We screened a first library in vitro against four cell lines and then evaluated the most promising binary combinations in vivo against three murine syngeneic cancer models, (LL/2, Lewis lung carcinoma; B16-F10, melanoma; and MBT-2, bladder cancer). The optimum result was obtained using a combination of α-lipoic acid and hydroxycitrate ($ METABLOC^{TM} $). In this study, a third agent was added by in vivo evaluation of a large number of combinations. The addition of octreotide strongly reduced tumor development (T/C% value of 30.2 to 34.5%; P < 0.001) in the same models and prolonged animal survival (P < 0.001) as compared to cisplatin. These results were confirmed in a different laboratory setting using a human xenograft model (NCI-H69, small cell lung cancer). None of these three molecules are known to target DNA. The effectiveness of this combination in several animal models, as well as the low toxicity of these inexpensive drugs, emphasizes the necessity of rapidly setting up a clinical trial. Lipoic acid (dpeaa)DE-He213 Hydroxycitrate (dpeaa)DE-He213 Octreotide (dpeaa)DE-He213 Metabolic enzymes (dpeaa)DE-He213 Screening (dpeaa)DE-He213 Guais, Adeline verfasserin aut Sanders, Edward verfasserin aut Campion, Frédéric verfasserin aut Fichtner, Iduna verfasserin aut Bonte, Jacques verfasserin aut Baronzio, Gianfranco verfasserin aut Fiorentini, Giammaria verfasserin aut Israël, Maurice verfasserin aut Schwartz, Laurent verfasserin aut Enthalten in Investigational new drugs Dordrecht [u.a.] : Springer Science + Business Media B.V, 1983 30(2011), 4 vom: 09. Juni, Seite 1331-1342 (DE-627)320481492 (DE-600)2009846-7 1573-0646 nnns volume:30 year:2011 number:4 day:09 month:06 pages:1331-1342 https://dx.doi.org/10.1007/s10637-011-9692-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA SSG-OPC-PHA SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.40 ASE AR 30 2011 4 09 06 1331-1342 |
spelling |
10.1007/s10637-011-9692-7 doi (DE-627)SPR012019518 (SPR)s10637-011-9692-7-e DE-627 ger DE-627 rakwb eng 610 ASE 44.40 bkl Abolhassani, Mohammad verfasserin aut Screening of well-established drugs targeting cancer metabolism: reproducibility of the efficacy of a highly effective drug combination in mice. 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Summary Alterations in metabolic pathways are known to characterize cancer. In order to suppress cancer growth, however, multiple proteins involved in these pathways have to be targeted simultaneously. We have developed a screening method to assess the best drug combination for cancer treatment based on targeting several factors implicated in tumor specific metabolism. Following a review of the literature, we identified those enzymes known to be deregulated in cancer and established a list of sixty-two drugs targeting them. These molecules are used routinely in clinical settings for diseases other than cancer. We screened a first library in vitro against four cell lines and then evaluated the most promising binary combinations in vivo against three murine syngeneic cancer models, (LL/2, Lewis lung carcinoma; B16-F10, melanoma; and MBT-2, bladder cancer). The optimum result was obtained using a combination of α-lipoic acid and hydroxycitrate ($ METABLOC^{TM} $). In this study, a third agent was added by in vivo evaluation of a large number of combinations. The addition of octreotide strongly reduced tumor development (T/C% value of 30.2 to 34.5%; P < 0.001) in the same models and prolonged animal survival (P < 0.001) as compared to cisplatin. These results were confirmed in a different laboratory setting using a human xenograft model (NCI-H69, small cell lung cancer). None of these three molecules are known to target DNA. The effectiveness of this combination in several animal models, as well as the low toxicity of these inexpensive drugs, emphasizes the necessity of rapidly setting up a clinical trial. Lipoic acid (dpeaa)DE-He213 Hydroxycitrate (dpeaa)DE-He213 Octreotide (dpeaa)DE-He213 Metabolic enzymes (dpeaa)DE-He213 Screening (dpeaa)DE-He213 Guais, Adeline verfasserin aut Sanders, Edward verfasserin aut Campion, Frédéric verfasserin aut Fichtner, Iduna verfasserin aut Bonte, Jacques verfasserin aut Baronzio, Gianfranco verfasserin aut Fiorentini, Giammaria verfasserin aut Israël, Maurice verfasserin aut Schwartz, Laurent verfasserin aut Enthalten in Investigational new drugs Dordrecht [u.a.] : Springer Science + Business Media B.V, 1983 30(2011), 4 vom: 09. Juni, Seite 1331-1342 (DE-627)320481492 (DE-600)2009846-7 1573-0646 nnns volume:30 year:2011 number:4 day:09 month:06 pages:1331-1342 https://dx.doi.org/10.1007/s10637-011-9692-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA SSG-OPC-PHA SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.40 ASE AR 30 2011 4 09 06 1331-1342 |
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10.1007/s10637-011-9692-7 doi (DE-627)SPR012019518 (SPR)s10637-011-9692-7-e DE-627 ger DE-627 rakwb eng 610 ASE 44.40 bkl Abolhassani, Mohammad verfasserin aut Screening of well-established drugs targeting cancer metabolism: reproducibility of the efficacy of a highly effective drug combination in mice. 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Summary Alterations in metabolic pathways are known to characterize cancer. In order to suppress cancer growth, however, multiple proteins involved in these pathways have to be targeted simultaneously. We have developed a screening method to assess the best drug combination for cancer treatment based on targeting several factors implicated in tumor specific metabolism. Following a review of the literature, we identified those enzymes known to be deregulated in cancer and established a list of sixty-two drugs targeting them. These molecules are used routinely in clinical settings for diseases other than cancer. We screened a first library in vitro against four cell lines and then evaluated the most promising binary combinations in vivo against three murine syngeneic cancer models, (LL/2, Lewis lung carcinoma; B16-F10, melanoma; and MBT-2, bladder cancer). The optimum result was obtained using a combination of α-lipoic acid and hydroxycitrate ($ METABLOC^{TM} $). In this study, a third agent was added by in vivo evaluation of a large number of combinations. The addition of octreotide strongly reduced tumor development (T/C% value of 30.2 to 34.5%; P < 0.001) in the same models and prolonged animal survival (P < 0.001) as compared to cisplatin. These results were confirmed in a different laboratory setting using a human xenograft model (NCI-H69, small cell lung cancer). None of these three molecules are known to target DNA. The effectiveness of this combination in several animal models, as well as the low toxicity of these inexpensive drugs, emphasizes the necessity of rapidly setting up a clinical trial. Lipoic acid (dpeaa)DE-He213 Hydroxycitrate (dpeaa)DE-He213 Octreotide (dpeaa)DE-He213 Metabolic enzymes (dpeaa)DE-He213 Screening (dpeaa)DE-He213 Guais, Adeline verfasserin aut Sanders, Edward verfasserin aut Campion, Frédéric verfasserin aut Fichtner, Iduna verfasserin aut Bonte, Jacques verfasserin aut Baronzio, Gianfranco verfasserin aut Fiorentini, Giammaria verfasserin aut Israël, Maurice verfasserin aut Schwartz, Laurent verfasserin aut Enthalten in Investigational new drugs Dordrecht [u.a.] : Springer Science + Business Media B.V, 1983 30(2011), 4 vom: 09. Juni, Seite 1331-1342 (DE-627)320481492 (DE-600)2009846-7 1573-0646 nnns volume:30 year:2011 number:4 day:09 month:06 pages:1331-1342 https://dx.doi.org/10.1007/s10637-011-9692-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA SSG-OPC-PHA SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.40 ASE AR 30 2011 4 09 06 1331-1342 |
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10.1007/s10637-011-9692-7 doi (DE-627)SPR012019518 (SPR)s10637-011-9692-7-e DE-627 ger DE-627 rakwb eng 610 ASE 44.40 bkl Abolhassani, Mohammad verfasserin aut Screening of well-established drugs targeting cancer metabolism: reproducibility of the efficacy of a highly effective drug combination in mice. 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Summary Alterations in metabolic pathways are known to characterize cancer. In order to suppress cancer growth, however, multiple proteins involved in these pathways have to be targeted simultaneously. We have developed a screening method to assess the best drug combination for cancer treatment based on targeting several factors implicated in tumor specific metabolism. Following a review of the literature, we identified those enzymes known to be deregulated in cancer and established a list of sixty-two drugs targeting them. These molecules are used routinely in clinical settings for diseases other than cancer. We screened a first library in vitro against four cell lines and then evaluated the most promising binary combinations in vivo against three murine syngeneic cancer models, (LL/2, Lewis lung carcinoma; B16-F10, melanoma; and MBT-2, bladder cancer). The optimum result was obtained using a combination of α-lipoic acid and hydroxycitrate ($ METABLOC^{TM} $). In this study, a third agent was added by in vivo evaluation of a large number of combinations. The addition of octreotide strongly reduced tumor development (T/C% value of 30.2 to 34.5%; P < 0.001) in the same models and prolonged animal survival (P < 0.001) as compared to cisplatin. These results were confirmed in a different laboratory setting using a human xenograft model (NCI-H69, small cell lung cancer). None of these three molecules are known to target DNA. The effectiveness of this combination in several animal models, as well as the low toxicity of these inexpensive drugs, emphasizes the necessity of rapidly setting up a clinical trial. Lipoic acid (dpeaa)DE-He213 Hydroxycitrate (dpeaa)DE-He213 Octreotide (dpeaa)DE-He213 Metabolic enzymes (dpeaa)DE-He213 Screening (dpeaa)DE-He213 Guais, Adeline verfasserin aut Sanders, Edward verfasserin aut Campion, Frédéric verfasserin aut Fichtner, Iduna verfasserin aut Bonte, Jacques verfasserin aut Baronzio, Gianfranco verfasserin aut Fiorentini, Giammaria verfasserin aut Israël, Maurice verfasserin aut Schwartz, Laurent verfasserin aut Enthalten in Investigational new drugs Dordrecht [u.a.] : Springer Science + Business Media B.V, 1983 30(2011), 4 vom: 09. Juni, Seite 1331-1342 (DE-627)320481492 (DE-600)2009846-7 1573-0646 nnns volume:30 year:2011 number:4 day:09 month:06 pages:1331-1342 https://dx.doi.org/10.1007/s10637-011-9692-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA SSG-OPC-PHA SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.40 ASE AR 30 2011 4 09 06 1331-1342 |
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10.1007/s10637-011-9692-7 doi (DE-627)SPR012019518 (SPR)s10637-011-9692-7-e DE-627 ger DE-627 rakwb eng 610 ASE 44.40 bkl Abolhassani, Mohammad verfasserin aut Screening of well-established drugs targeting cancer metabolism: reproducibility of the efficacy of a highly effective drug combination in mice. 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Summary Alterations in metabolic pathways are known to characterize cancer. In order to suppress cancer growth, however, multiple proteins involved in these pathways have to be targeted simultaneously. We have developed a screening method to assess the best drug combination for cancer treatment based on targeting several factors implicated in tumor specific metabolism. Following a review of the literature, we identified those enzymes known to be deregulated in cancer and established a list of sixty-two drugs targeting them. These molecules are used routinely in clinical settings for diseases other than cancer. We screened a first library in vitro against four cell lines and then evaluated the most promising binary combinations in vivo against three murine syngeneic cancer models, (LL/2, Lewis lung carcinoma; B16-F10, melanoma; and MBT-2, bladder cancer). The optimum result was obtained using a combination of α-lipoic acid and hydroxycitrate ($ METABLOC^{TM} $). In this study, a third agent was added by in vivo evaluation of a large number of combinations. The addition of octreotide strongly reduced tumor development (T/C% value of 30.2 to 34.5%; P < 0.001) in the same models and prolonged animal survival (P < 0.001) as compared to cisplatin. These results were confirmed in a different laboratory setting using a human xenograft model (NCI-H69, small cell lung cancer). None of these three molecules are known to target DNA. The effectiveness of this combination in several animal models, as well as the low toxicity of these inexpensive drugs, emphasizes the necessity of rapidly setting up a clinical trial. Lipoic acid (dpeaa)DE-He213 Hydroxycitrate (dpeaa)DE-He213 Octreotide (dpeaa)DE-He213 Metabolic enzymes (dpeaa)DE-He213 Screening (dpeaa)DE-He213 Guais, Adeline verfasserin aut Sanders, Edward verfasserin aut Campion, Frédéric verfasserin aut Fichtner, Iduna verfasserin aut Bonte, Jacques verfasserin aut Baronzio, Gianfranco verfasserin aut Fiorentini, Giammaria verfasserin aut Israël, Maurice verfasserin aut Schwartz, Laurent verfasserin aut Enthalten in Investigational new drugs Dordrecht [u.a.] : Springer Science + Business Media B.V, 1983 30(2011), 4 vom: 09. Juni, Seite 1331-1342 (DE-627)320481492 (DE-600)2009846-7 1573-0646 nnns volume:30 year:2011 number:4 day:09 month:06 pages:1331-1342 https://dx.doi.org/10.1007/s10637-011-9692-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA SSG-OPC-PHA SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.40 ASE AR 30 2011 4 09 06 1331-1342 |
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Enthalten in Investigational new drugs 30(2011), 4 vom: 09. Juni, Seite 1331-1342 volume:30 year:2011 number:4 day:09 month:06 pages:1331-1342 |
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Lipoic acid Hydroxycitrate Octreotide Metabolic enzymes Screening |
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Abolhassani, Mohammad @@aut@@ Guais, Adeline @@aut@@ Sanders, Edward @@aut@@ Campion, Frédéric @@aut@@ Fichtner, Iduna @@aut@@ Bonte, Jacques @@aut@@ Baronzio, Gianfranco @@aut@@ Fiorentini, Giammaria @@aut@@ Israël, Maurice @@aut@@ Schwartz, Laurent @@aut@@ |
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Abolhassani, Mohammad |
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Abolhassani, Mohammad ddc 610 bkl 44.40 misc Lipoic acid misc Hydroxycitrate misc Octreotide misc Metabolic enzymes misc Screening Screening of well-established drugs targeting cancer metabolism: reproducibility of the efficacy of a highly effective drug combination in mice. |
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610 ASE 44.40 bkl Screening of well-established drugs targeting cancer metabolism: reproducibility of the efficacy of a highly effective drug combination in mice Lipoic acid (dpeaa)DE-He213 Hydroxycitrate (dpeaa)DE-He213 Octreotide (dpeaa)DE-He213 Metabolic enzymes (dpeaa)DE-He213 Screening (dpeaa)DE-He213 |
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ddc 610 bkl 44.40 misc Lipoic acid misc Hydroxycitrate misc Octreotide misc Metabolic enzymes misc Screening |
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ddc 610 bkl 44.40 misc Lipoic acid misc Hydroxycitrate misc Octreotide misc Metabolic enzymes misc Screening |
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ddc 610 bkl 44.40 misc Lipoic acid misc Hydroxycitrate misc Octreotide misc Metabolic enzymes misc Screening |
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Screening of well-established drugs targeting cancer metabolism: reproducibility of the efficacy of a highly effective drug combination in mice. |
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Screening of well-established drugs targeting cancer metabolism: reproducibility of the efficacy of a highly effective drug combination in mice |
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Abolhassani, Mohammad |
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Abolhassani, Mohammad Guais, Adeline Sanders, Edward Campion, Frédéric Fichtner, Iduna Bonte, Jacques Baronzio, Gianfranco Fiorentini, Giammaria Israël, Maurice Schwartz, Laurent |
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Abolhassani, Mohammad |
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10.1007/s10637-011-9692-7 |
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title_sort |
screening of well-established drugs targeting cancer metabolism: reproducibility of the efficacy of a highly effective drug combination in mice |
title_auth |
Screening of well-established drugs targeting cancer metabolism: reproducibility of the efficacy of a highly effective drug combination in mice. |
abstract |
Summary Alterations in metabolic pathways are known to characterize cancer. In order to suppress cancer growth, however, multiple proteins involved in these pathways have to be targeted simultaneously. We have developed a screening method to assess the best drug combination for cancer treatment based on targeting several factors implicated in tumor specific metabolism. Following a review of the literature, we identified those enzymes known to be deregulated in cancer and established a list of sixty-two drugs targeting them. These molecules are used routinely in clinical settings for diseases other than cancer. We screened a first library in vitro against four cell lines and then evaluated the most promising binary combinations in vivo against three murine syngeneic cancer models, (LL/2, Lewis lung carcinoma; B16-F10, melanoma; and MBT-2, bladder cancer). The optimum result was obtained using a combination of α-lipoic acid and hydroxycitrate ($ METABLOC^{TM} $). In this study, a third agent was added by in vivo evaluation of a large number of combinations. The addition of octreotide strongly reduced tumor development (T/C% value of 30.2 to 34.5%; P < 0.001) in the same models and prolonged animal survival (P < 0.001) as compared to cisplatin. These results were confirmed in a different laboratory setting using a human xenograft model (NCI-H69, small cell lung cancer). None of these three molecules are known to target DNA. The effectiveness of this combination in several animal models, as well as the low toxicity of these inexpensive drugs, emphasizes the necessity of rapidly setting up a clinical trial. |
abstractGer |
Summary Alterations in metabolic pathways are known to characterize cancer. In order to suppress cancer growth, however, multiple proteins involved in these pathways have to be targeted simultaneously. We have developed a screening method to assess the best drug combination for cancer treatment based on targeting several factors implicated in tumor specific metabolism. Following a review of the literature, we identified those enzymes known to be deregulated in cancer and established a list of sixty-two drugs targeting them. These molecules are used routinely in clinical settings for diseases other than cancer. We screened a first library in vitro against four cell lines and then evaluated the most promising binary combinations in vivo against three murine syngeneic cancer models, (LL/2, Lewis lung carcinoma; B16-F10, melanoma; and MBT-2, bladder cancer). The optimum result was obtained using a combination of α-lipoic acid and hydroxycitrate ($ METABLOC^{TM} $). In this study, a third agent was added by in vivo evaluation of a large number of combinations. The addition of octreotide strongly reduced tumor development (T/C% value of 30.2 to 34.5%; P < 0.001) in the same models and prolonged animal survival (P < 0.001) as compared to cisplatin. These results were confirmed in a different laboratory setting using a human xenograft model (NCI-H69, small cell lung cancer). None of these three molecules are known to target DNA. The effectiveness of this combination in several animal models, as well as the low toxicity of these inexpensive drugs, emphasizes the necessity of rapidly setting up a clinical trial. |
abstract_unstemmed |
Summary Alterations in metabolic pathways are known to characterize cancer. In order to suppress cancer growth, however, multiple proteins involved in these pathways have to be targeted simultaneously. We have developed a screening method to assess the best drug combination for cancer treatment based on targeting several factors implicated in tumor specific metabolism. Following a review of the literature, we identified those enzymes known to be deregulated in cancer and established a list of sixty-two drugs targeting them. These molecules are used routinely in clinical settings for diseases other than cancer. We screened a first library in vitro against four cell lines and then evaluated the most promising binary combinations in vivo against three murine syngeneic cancer models, (LL/2, Lewis lung carcinoma; B16-F10, melanoma; and MBT-2, bladder cancer). The optimum result was obtained using a combination of α-lipoic acid and hydroxycitrate ($ METABLOC^{TM} $). In this study, a third agent was added by in vivo evaluation of a large number of combinations. The addition of octreotide strongly reduced tumor development (T/C% value of 30.2 to 34.5%; P < 0.001) in the same models and prolonged animal survival (P < 0.001) as compared to cisplatin. These results were confirmed in a different laboratory setting using a human xenograft model (NCI-H69, small cell lung cancer). None of these three molecules are known to target DNA. The effectiveness of this combination in several animal models, as well as the low toxicity of these inexpensive drugs, emphasizes the necessity of rapidly setting up a clinical trial. |
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container_issue |
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title_short |
Screening of well-established drugs targeting cancer metabolism: reproducibility of the efficacy of a highly effective drug combination in mice. |
url |
https://dx.doi.org/10.1007/s10637-011-9692-7 |
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Guais, Adeline Sanders, Edward Campion, Frédéric Fichtner, Iduna Bonte, Jacques Baronzio, Gianfranco Fiorentini, Giammaria Israël, Maurice Schwartz, Laurent |
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score |
7.3994884 |