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Predictive Simulation Based Design and Validation Of Repurposed Novel Therapeutics With Multi-Target Mechanisms For Multiple Myeloma

Nicole A Doudican, Shireen Vali, Shweta Kapoor, Anay Talawdekar, Zeba Sultana, Aftab Alam, Taher Abbasi and Amitabha Mazumder

Abstract

Introduction Development of resistance to single agent therapy is a significant clinical obstacle in the treatment of multiple myeloma (MM). Genetic mutations and the bone marrow micro-environment are major determinants of MM resistance mechanisms. Given the complexity of MM, the need for combinatorial therapeutic regimens targeting multiple biological mechanisms of action is pressing. Repurposing has the advantage of using drugs with known clinical history.

Methodology We used a predictive simulation-based approach that models MM disease physiology in plasma cells by integrating and aggregating signaling and metabolic networks across all disease phenotypes. We tested the efficacy of over 50 repurposed molecularly targeted agents both individually and in combination across simulation avatars of the MM cell lines OPM2 and U266. OPM2 harbors mutations in KRAS, CDKN2A/2C, PTEN, RASSF1A and P53, whereas U266’s mutational components include BRAF, CDKN2A, P53, P73, RASSF1A and RB1. These cell lines were used as models because they possess mutations in genes classically known to be involved in myeloma. The predicted activity of novel combinations of existing drug agents was validated in vitro using standard molecular assays. MTT 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 and the Bliss independence model.

Results Through simulation modeling, we identified two novel therapeutic regimens for MM using repurposed drugs: (1) AT101 (Bcl2 antagonist) and tesaglitazar (PPAR α/γ agonist) and (2) Ursolic acid (UA, inhibitor of NFκβ) and SP600125 (pan-JNK inhibitor). Simulation predictions showed that combining the IC30 concentrations with respect to viability of AT101 and tesaglitazar reduced proliferation by 40% and viability by 50%. Similarly simulation predictions showed that the combination of the IC30 concentrations of UA and SP600125 reduced proliferation by 50% and viability by 40%.

Corroborating our predictive simulation assays, 10 µM tesaglitazar and 2 µM AT101 caused minimal growth inhibition as single agents in OPM2 and U266 MM cell lines. Growth inhibition in these cell lines is synergistically enhanced when the drugs are used in combination, reducing cellular viability by 88% and 77% in OPM2 and U266 cells, respectively. Similarly, proliferation was reduced by 34% with 7.5 μM UA and 25% with 10 μM SP600125 in OPM2 cells. When used in combination, cellular proliferation was synergistically reduced by 64%. In addition, isobologram analysis predicted synergy of lowered doses of the drugs in combination. Both combinations 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).

Conclusions These results highlight and validate the use of our predictive simulation approach to design therapeutic regimens with novel biological mechanisms using drugs with known chemistries. This allows for design of personalized treatments for patients using their tumor genomic signature beyond the “one-gene, one-drug” paradigm. The reuse of existing drugs with clinical data facilitates a rapid translational path into clinic and avoids the uncertainties associated with new chemistry. The corroboration of these results with patient derived cell lines will be pursued and discussed.

Disclosures: No relevant conflicts of interest to declare.

  • * Asterisk with author names denotes non-ASH members.