Back

Hypercholesterolemia-induced impairment in sorafenib functionality is overcome by avasimibe co-treatment

Athavale, D.; Yaduvanshi, H.; Bhati, F. K.; Mayengbam, S. S.; More, T.; Rapole, S.; Bhat, M. K.

2024-03-28 cancer biology
10.1101/2024.03.27.586757 bioRxiv
Show abstract

Avasimibe; a cholesterol-lowering drug with a proven safety in clinical trials, has recently been repositioned as an anticancer agent in various preclinical investigations. A study from our group reported that hypercholesterolemia promotes hepatocellular carcinoma (HCC) cell survival and hampers the anticancer effect of sorafenib, a kinase inhibitor. In the present study, we demonstrate that in HCC under hypercholesterolemic conditions the anticancer property of sorafenib is potentiated by avasimibe (AVA) co-treatment. Further, to elucidate the role of hypercholesterolemia on sorafenib efficacy, in vitro and in vivo models of HCC were used. In vitro, co-treatment of both drugs synergistically inhibited HCC cell viability and induced cell death under normal and hypercholesterolemic conditions. At the molecular level, downregulation of ERK signalling and induction of endoplasmic reticulum stress are likely to contribute to the combinatorial cytotoxic effect of sorafenib and avasimibe in vitro. In mice, fed on a high-cholesterol diet (HCD), the efficacy of sorafenib was restored by co-administration of AVA. Collectively, these findings suggest that impairment in the efficacy of sorafenib because of hypercholesterolemic phenotype could be restored by AVA co-treatment, which may have implications towards treatment strategy. HighlightsO_LICholesterol impedes sorafenib efficacy in Hepatocellular carcinoma cells. C_LIO_LIAvasimibe restores the functionality of sorafenib under hypercholesterolemic environment. C_LIO_LICombine treatment of sorafenib and avasimibe synergistically enhances cytotoxicity in hepatocellular carcinoma. C_LIO_LISorafenib and avasimibe treatment in the presence of LDLc.is associated with diminished ERK activation and increased ER stress. C_LI

Matching journals

The top 14 journals account for 50% of the predicted probability mass.

1
Biomedicine & Pharmacotherapy
43 papers in training set
Top 0.1%
10.8%
2
eLife
5422 papers in training set
Top 12%
6.6%
3
International Journal of Molecular Sciences
453 papers in training set
Top 1%
5.0%
4
Cancers
200 papers in training set
Top 1%
3.8%
5
Frontiers in Oncology
95 papers in training set
Top 1%
3.7%
6
Pharmaceuticals
33 papers in training set
Top 0.2%
3.2%
7
Frontiers in Pharmacology
100 papers in training set
Top 1%
3.2%
8
ACS Pharmacology & Translational Science
40 papers in training set
Top 0.2%
2.7%
9
British Journal of Cancer
42 papers in training set
Top 0.6%
2.5%
10
Molecules
37 papers in training set
Top 0.5%
2.2%
11
PLOS ONE
4510 papers in training set
Top 47%
2.1%
12
Life Sciences
25 papers in training set
Top 0.4%
1.9%
13
Biochemical Pharmacology
18 papers in training set
Top 0.1%
1.7%
14
Scientific Reports
3102 papers in training set
Top 63%
1.4%
50% of probability mass above
15
Cell Communication and Signaling
35 papers in training set
Top 0.5%
1.4%
16
EMBO Molecular Medicine
85 papers in training set
Top 2%
1.4%
17
Theranostics
33 papers in training set
Top 0.7%
1.4%
18
Frontiers in Molecular Biosciences
100 papers in training set
Top 3%
1.3%
19
Frontiers in Cell and Developmental Biology
218 papers in training set
Top 6%
1.1%
20
Biochimica et Biophysica Acta (BBA) - Molecular Cell Research
28 papers in training set
Top 0.3%
1.0%
21
The Journal of Pharmacology and Experimental Therapeutics
15 papers in training set
Top 0.2%
1.0%
22
Journal of Translational Medicine
46 papers in training set
Top 2%
0.9%
23
Cell Death & Disease
126 papers in training set
Top 2%
0.9%
24
Free Radical Biology and Medicine
33 papers in training set
Top 0.3%
0.8%
25
Frontiers in Chemistry
14 papers in training set
Top 0.3%
0.8%
26
Cells
232 papers in training set
Top 5%
0.8%
27
Journal of Cellular Biochemistry
10 papers in training set
Top 0.1%
0.8%
28
PLOS Computational Biology
1633 papers in training set
Top 23%
0.8%
29
Nature Communications
4913 papers in training set
Top 62%
0.8%
30
Cell Death & Differentiation
48 papers in training set
Top 0.7%
0.8%