J Cancer 2014; 5(6):406-416. doi:10.7150/jca.7680 This issue

Research Paper

Predictive Simulation Approach for Designing Cancer Therapeutic Regimens with Novel Biological Mechanisms

Nicole A. Doudican1✉, Amitabha Mazumder2, Shweta Kapoor3, Zeba Sultana3, Ansu Kumar3, Anay Talawdekar3, Kabya Basu3, Ashish Agrawal3, Aditi Aggarwal3, Krithika Shetty3, Neeraj K Singh3, Chandan Kumar3, Anuj Tyagi3, Neeraj Kumar Singh3, Janitha C Darlybai3, Taher Abbasi4, Shireen Vali3,4

1. The Ronald O. Perelman Department of Dermatology, New York University School of Medicine, New York, New York, USA;
2. New York University Cancer Institute, New York, New York, USA;
3. Cellworks Research India Limited - R&D Center, Bangalore, India;
4. Cellworks Group Inc., San Jose, CA, USA.

This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY-NC) License. See http://ivyspring.com/terms for full terms and conditions.
Doudican NA, Mazumder A, Kapoor S, Sultana Z, Kumar A, Talawdekar A, Basu K, Agrawal A, Aggarwal A, Shetty K, Singh NK, Kumar C, Tyagi A, Singh NK, Darlybai JC, Abbasi T, Vali S. Predictive Simulation Approach for Designing Cancer Therapeutic Regimens with Novel Biological Mechanisms. J Cancer 2014; 5(6):406-416. doi:10.7150/jca.7680. Available from https://www.jcancer.org/v05p0406.htm

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Introduction Ursolic acid (UA) is a pentacyclic triterpene acid present in many plants, including apples, basil, cranberries, and rosemary. UA suppresses proliferation and induces apoptosis in a variety of tumor cells via inhibition of nuclear factor kappa-light-chain-enhancer of activated B cells (NFκB). Given that single agent therapy is a major clinical obstacle to overcome in the treatment of cancer, we sought to enhance the anti-cancer efficacy of UA through rational design of combinatorial therapeutic regimens that target multiple signaling pathways critical to carcinogenesis.

Methodology Using a predictive simulation-based approach that models cancer disease physiology by integrating signaling and metabolic networks, we tested the effect of UA alone and in combination with 100 other agents across cell lines from colorectal cancer, non-small cell lung cancer and multiple myeloma. Our predictive results were validated in vitro using standard molecular assays. The MTT assay and flow cytometry were used to assess cellular proliferation. Western blotting was used to monitor the combinatorial effects on apoptotic and cellular signaling pathways. Synergy was analyzed using isobologram plots.

Results We predictively identified c-Jun N-terminal kinase (JNK) as a pathway that may synergistically inhibit cancer growth when targeted in combination with NFκB. UA in combination with the pan-JNK inhibitor SP600125 showed maximal reduction in viability across a panel of cancer cell lines, thereby corroborating our predictive simulation assays. In HCT116 colon carcinoma cells, the combination caused a 52% reduction in viability compared with 18% and 27% for UA and SP600125 alone, respectively. In addition, isobologram plot analysis reveals synergy with lowered doses of the drugs in combination. The combination synergistically inhibited proliferation and induced apoptosis as evidenced by an increase in the percentage sub-G1 phase cells and cleavage of caspase 3 and poly ADP ribose polymerase (PARP). Combination treatment resulted in a significant reduction in the expression of cyclin D1 and c-Myc as compared with single agent treatment.

Conclusions Our findings underscore the importance of targeting NFκB and JNK signaling in combination in cancer cells. These results also highlight and validate the use of predictive simulation technology to design therapeutics for targeting novel biological mechanisms using existing or novel chemistry.

Keywords: ursolic acid, c-Jun N-terminal kinase, NFκB, computer modeling, carcinogenesis