Excipient of paclitaxel induces metabolic dysregulation and unfolded protein response

Taxane-based reagents, such as Taxol, Taxotere, and Abraxane, are popular anti-cancer drugs that can differ in their clinical efficacy. This difference is generally attributed to their active pharmaceutical ingredients. Here, we report a serendipitous discovery that Taxol induces metabolic dysregulation and unfolded protein response. Surprisingly, these effects of Taxol are entirely dependent on its excipient, Cremophor EL (CrEL). We show that CrEL promotes aerobic glycolysis and in turn results in drastic upregulation of angiopoietin like 4 (ANGPTL4), a major regulator of human blood lipid profile. Notably, premedication with dexamethasone further enhances the expression of ANGPTL4. Consistently, we find that the amplitude and frequency of increase in triglycerides is more prominent in Taxol-treated patients with breast cancer. In addition, we find that CrEL activates the unfolded protein response pathway to trigger proinflammatory gene expression and caspase/gasdermin E-dependent pyroptosis. Finally, we discuss the implications of these results in anti-cancer therapies.

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Cremophor EL, the excipient of chemotherapy drug Taxol, is biologically active

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Cremophor EL promotes aerobic glycolysis in cancer and primary human immune cells

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Dexamethasone and Cremophor EL may cause dyslipidemia via ANGPTL4 upregulation

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Cremophor EL promotes the unfolded protein response

Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review

Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only give a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The "central hit strategy" selectively targets central nodes/edges of the flexible networks of infectious agents or cancer cells to kill them. The "network influence strategy" works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved by targeting the neighbors of central nodes/edges. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.

Response to Comment on “A Dynamic Network Model of mTOR Signaling Reveals TSC-Independent mTORC2 Regulation”: Building a Model of the mTOR Signaling Network with a Potentially Faulty Tool

Dalle Pezze et al . respond to criticisms regarding the specificity of phosphorylation of Ser 2481 of mTOR as a specific readout of mTORC2 activity.