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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.

Background: Given the genomic variability observed across multiple myeloma patients, treatment personalization becomes imperative. Personalization (N = 1) driven by actionable mutations alone is a step forward but has limitations. Holistically incorporating genomic profiling information to create a patient simulation avatar provides a personalization paradigm beyond the one-drug one-mutation method of individualization. Methods: A bone marrow sample from the patient was analyzed for chromosome alterations and molecular aberrations by NYU and Gen Path Diagnostics. Using this genomic profiling information, a predictive simulation avatar of the patient was created that comprehensively models signal transduction, epigenetic regulation, protein homeostasis, autophagy, and metabolic pathways. The simulation highlights the patient's dominant driver characteristics and enables personalized therapy design. Here, a library of FDA-approved, molecularly targeted drugs from across indications were simulated individually and in combination on the patient simulation avatar. Results: The patient had high risk IgA multiple myeloma and refractory to following treatment regimens: Thalidomide/Dexamethasone (DEX), Velcade/DEX, Revlimid (REV)/DEX, REV/DEX/Elotuzumab, stem cell collection with high dose Cytoxan, stem cell transplant with high dose Melphalan, Velcade/Bendamustine, Pomalidomide, Carfilzomib (CAR), Doxil, CAR/Cytoxan, CAR/Pomalidomide. The patient's cytogenetic information revealed tetrasomy of chromosomes (Ch) 9 and 15; trisomy of Ch 2, 3, 5, 7, 11, 14, 19, 20, and 22; monosomy of Ch 16. Also, additional material was attached to the short (p) arms of Ch 1 and 21 and the long (q) arms of Ch 6, 14 (involving the IGH locus), and 16. The amplified and deleted genes were used to create the patient simulation avatar definition. The resulting patient avatar demonstrated increased expression of the following key growth factor receptors and signaling intermediates: EGFR, HGF, MET, IGF1R, SHC, and SRC. Additionally, increased PI3K and AKT copy number was noted, resulting in a dominant AKT/PI3K signaling axis. IL6, IL6R, and JAK2 amplification was observed, resulting in increased JAK/STAT signaling. Nelfinavir and Ruxolitinib was administered to impact the PI3K/AKT and JAK/STAT signaling axis predicted by simulation. This treatment exhibited a clinical response in the highly refractory patient indicated by a significant drop in the IgA from 3610 to 2650 mg/dl, the M spike from 3.9 to 2.7 g/dl and the total protein from 10.6 to 8.2 gm/dl. The response lasted 2.5 months with minimal toxicity. Conclusions: This study demonstrates and provides validation of technology to holistically incorporate big data from genomics for actionable insights for treatment personalization. Citation Format: Nicole A. Doudican, Amitabha Mazumder, Shireen Vali, Kabya Basu, Ansu Kumar, Neeraj Kumar Singh, Zeba Sultana, Taher Abbasi. Predictive simulation-driven personalization methodology for refractory multiple myeloma. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 5443. doi:10.1158/1538-7445.AM2015-5443

Background: The unique signature of a patient's tumor mandates the need to rationally design personalized therapies employing N=1 segmentation conceptually. By focusing on rationally designed personalized treatments, our strategy targets key pathways to address the clinical problem of therapy resistance. To overcome bortezomib resistance, we have (1) employed predictive simulation modeling using patient genomic profiling to design patient specific combinatorial therapeutic regimens and (2) validated designed therapy ex-vivo in patient-derived cell lines. Methods: Clinical patient samples were analyzed for chromosomal alterations using array Comparative Genomic Hybridization (aCGH) by GenPath Diagnostics and cytogenetic chromosome analysis by NYU. Using this information, we created a patient simulation avatar. To identify effective personalized therapeutics, we focused on molecularly targeted agents with clinical data. The predictive simulation based approach from Cellworks provides a comprehensive representation of plasma cell myeloma (PCM) disease physiology incorporating signaling and metabolic networks with an integrated phenotype view. Therapeutic hits were shortlisted from over 1000 pharmacodynamic dose-response simulation studies using efficacy and synergy criteria. Simulation modeling identified therapeutic mechanisms that impact proliferation and agent combinations that overcome bortezomib resistance. These predictive findings are in the process of being assessed ex vivo using patient primary cells and prospectively validated. Results: A loss of the CUL1 due to deletion of chromosome 7q36.1 region and CCND1 as a result of gain of chromosome 11q13 region was detected. Simulation of this patient's avatar showed high proliferation phenotype as a consequence of stated aberrations. CUL1 deletion caused increase in NFKB and CTNNB1 and a synergistic increase in CCND1. CUL1 deletion excludes target proteins from proteasomal degradation, thereby making cells resistant bortezomib effect. Simulation screening identified adjuvant NVP-BEZ235 (pan PI3K/mTORC1 and mTORC2 inhibitor) with current background of bortezomib to show enhanced efficacy in this patient. NFKB is reduced by this mechanism and along with reduction in translation of pro-proliferative genes mediated by mTOR inhibition. IC20 concentrations with respect to proliferation of the single agents in combination showed 38% inhibition of proliferation in simulation. These findings are currently being prospectively validated ex-vivo in patient primary cells. Conclusions: This study demonstrates and validates simulation for developing novel therapeutics and technologies to truly leverage “big data”. This level of individualization, beyond linking point mutations to associated drugs targeting the same mutations, truly incorporates the complete patient tumor signature with a clinical translation pathway. Citation Format: Nicole A. Doudican, Shireen Vali, Annette Leon, Ansu Kumar, Neeraj Kumar Singh, Anuj Tyagi, Shweta Kapoor, Zeba Sultana, Taher Abbasi, Amitabha Mazumder. Individualized therapy identified using simulation for bortezomib resistant patient with ex-vivo validation. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 1706. doi:10.1158/1538-7445.AM2014-1706

2041 Background: The unique signature of a patient’s tumor implies that a one-size-fits-all treatment approach will likely fail. This necessitates a rationally designed personalized therapeutic app...

e19582 Background: The unique signature of a patient’s tumor implies that a one-size-fits-all treatment approach is suboptimal, thereby underscoring the need to rationally design therapies employin...

Background: Drug repositioning - the application of marketed drugs to new diseases - is a time and cost effective alternative to de novo drug development. Thalidomide is a hallmark example of drug repositioning success. Withdrawn from the market in 1961 after causing thousands of severe birth defects, its newly discovered anti-angiogenic and immunomodulatory properties cleared the way for Food and Drug Administration (FDA) approval in 2006 for multiple myeloma (MM). Although thalidomide has demonstrated remarkable success in the treatment of MM, responses are typically short-lived with the emergence of resistance. For complex diseases like cancer, multi-target therapeutics are well suited to address efficacy and drug resistance challenges. Here we have designed a novel therapeutic combination of drugs for MM that impacts all disease phenotypes. Methods: Using a computer modeling system from Cellworks that mimics and simulates cancer disease physiology to predict clinical outcomes, we identified AT101 (Bcl2 antagonoist) and tesaglitazar (PPAR α/γ agonist) as a potentially effective drug combination for MM. This combination was shortlisted from over two hundred pharmacodynamic dose-response simulation studies using criteria of efficacy and synergy. Computer modeling predicted that this drug combination mechanistically targets apoptotic pathways and the combination of the agents provides greater than additive activity. These predictive findings were assessed in vitro using OPM2 and U266 human MM cell lines. AT101 and tesaglitazar growth inhibition was evaluated using the MTT assay and analysis of synergy was determined using the Bliss Independence model. Apoptotic induction was analyzed by immunoblotting for cleaved forms of caspases and PARP. Results: 10 μM tesaglitazar and 2 μM AT101 display minimal and moderate growth inhibition respectively as single agents in OPM2 and U266 MM cell lines. Growth inhibition in these cell lines is dramatically enhanced when the drugs are used in combination, reducing cellular viability by 88% and 77% in OPM2 and U266 cells, respectively. Based upon the Bliss Independence model, the relationship between tesaglitazar and AT-101 is synergistic in both cell lines. Combination treatment in both cell lines results in increased apoptosis as indicated by enhanced cleavage of PARP and caspase 3 and 9. Conclusions: Not only do these experiments identify AT101 and tesaglitazar as a potentially effective drug combination for the treatment of MM, these results also validate the use of rationally based, computer modeling to design effective therapeutics and predict clinical outcomes. Future studies will evaluate the mechanism of action for the synergistic interaction between tesaglitazar and AT101 in MM. Citation Format: Nicole A. Doudican, Shireen Vali, Shweta Kapoor, Anay Talawdekar, Zeba Sultana, Taher Abbasi, Gautam Sethi, Seth J. Orlow, Amitabha Mazumder. In vitro validation of rationally designed therapeutic based on drug repositioning and combinations. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 2104. doi:10.1158/1538-7445.AM2013-2104