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The phosphatidylinositide 3-kinase (PI3K) pathway is activated and correlated with drug resistance in multiple myeloma (MM). In the present study we investigated the role of PI3KCA (PI3K-α) in the progression and drug resistance in MM. We showed that the gene expression of PI3KCA isoform was higher in MM compared to normal subjects. BYL719, a novel and specific PI3KCA inhibitor inhibited the survival of primary MM cells and cell lines but not normal peripheral blood mononuclear cells. BYL719 induced the apoptosis of MM cells and inhibited their cell cycle by causing G1 arrest. BYL719 inhibited PI3K signalling, decreased proliferation and cells cycle signalling, and induced apoptosis signalling in MM cells. Finally, BYL719 synergized with bortezomib and carfilzomib, and overcame drug resistance induced by bone marrow stroma. These results were confirmed using in silico simulation of MM cell lines, BYL719 and bortezomib, and showed similar trends in survival, proliferation, apoptosis, cell signalling and synergy with drugs. In conclusion, PI3KCA plays a major role in proliferation and drug resistance of MM cells, the effects of which were inhibited with BYL719. These results provide a preclinical basis for a future clinical trial of BYL719 in MM as a single agent or in combination with other drugs.

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.

Dopaminergic neurodegeneration during Parkinson disease (PD) involves several pathways including proteasome inhibition, alpha-synuclein (alpha-syn) aggregation, mitochondrial dysfunction, and glutathione (GSH) depletion. We have utilized a systems biology approach and built a dynamic model to understand and link the various events related to PD pathophysiology. We have corroborated the modeling data by examining the effects of alpha-syn expression in the absence and presence of proteasome inhibition on GSH metabolism in dopaminergic neuronal cultures. We report here that the expression of the mutant A53T form of alpha-syn is neurotoxic and causes GSH depletion in cells after proteasome inhibition, compared to wild-type alpha-syn-expressing cells and vector control. Modeling data predicted that GSH depletion in these cells was due to ATP loss associated with mitochondrial dysfunction. ATP depletion elicited by combined A53T expression and proteasome inhibition results in decreased de novo synthesis of GSH via the rate-limiting enzyme gamma-glutamyl cysteine ligase. Based on these data and other recent reports, we propose a novel dynamic model to explain how the presence of mutated alpha-syn protein or proteasome inhibition may individually impact on mitochondrial function and in combination result in alterations in GSH metabolism via enhanced mitochondrial dysfunction.

Background The personalization of cancer treatments implies the reconsideration of a one-size-fits-all paradigm. This move has spawned increased use of next generation sequencing to understand mutations and copy number aberrations in cancer cells. Initial personalization successes have been primarily driven by drugs targeting one patient-specific oncogene (e.g., Gleevec, Xalkori, Herceptin). Unfortunately, most cancers include a multitude of aberrations, and the overall impact on cancer signaling and metabolic networks cannot be easily nullified by a single drug. Methods We used a novel predictive simulation approach to create an avatar of patient cancer cells using point mutations and copy number aberration data. Simulation avatars of myeloma patients were functionally screened using various molecularly targeted drugs both individually and in combination to identify drugs that are efficacious and synergistic. Repurposing of drugs that are FDA-approved or under clinical study with validated clinical safety and pharmacokinetic data can provide a rapid translational path to the clinic. High-risk multiple myeloma patients were modeled, and the simulation predictions were assessed ex vivo using patient cells. Results Here, we present an approach to address the key challenge of interpreting patient profiling genomic signatures into actionable clinical insights to make the personalization of cancer therapy a practical reality. Through the rational design of personalized treatments, our approach also targets multiple patient-relevant pathways to address the emergence of single therapy resistance. Our predictive platform identified drug regimens for four high-risk multiple myeloma patients. The predicted regimes were found to be effective in ex vivo analyses using patient cells. Conclusions These multiple validations confirm this approach and methodology for the use of big data to create personalized therapeutics using predictive simulation approaches. Electronic supplementary material The online version of this article (doi:10.1186/s12967-015-0399-y) contains supplementary material, which is available to authorized users.

Protein aggregation is the major pathological hallmark seen in neurodegenerative disorders such as Parkinson's disease (PD). Alpha-synuclein (αS) is the main component of protein aggregates that form Lewy bodies (LBs) in PD and dementia with LBs. There have been several attempts to intervene in the process of expression, modification, clearance, and aggregation of αS as a therapeutic strategy toward neuroprotection. In this study, we have employed a novel, predictive, system level approach in silico to study four different strategies of anti-aggregation therapies: (a) reduction in αS modifications such as phosphorylation, nitration, or truncation in an approach called “seed clearance;” (b) “anti-oligomerization” approach through blocking the early oligomers formation; (c) “oligomers clearance” process by increasing its lysosomal degradation; and (d) “anti-aggregation” that involves prevention of aggregate formation at a later stage. These strategies were tested in a virtual dopaminergic neuronal system triggered by overexpression (OE) of mutant αS-A53T with or without rotenone (Rot)-induced oxidative stress. The results were compared by analyzing markers related to various end points such as oxidative stress, dopamine (DA) metabolism, proteasome function, survival and apoptosis. The experimental system and anti-oligomerization strategies were recapitulated in vitro in M17 dopaminergic cells overexpressing mutant αS-A53T triggered with Cu(II)-mediated oxidative stress, and the experimental data prospectively corroborated with the predictive results. Through this analysis, we found that intervention in the early part of the aggregation pathway by prevention of oligomer formation and increased clearance is indeed a good neuroprotective strategy, whereas anti-aggregation efforts to break up the aggregate at later stages has negative effects on the system.

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 epitome of cancer treatment personalization is N=1 segmentation where a custom therapy is designed for every patient. Because most cancer aberrations are not actionable mutations and tumors can have more than one actionable mutation, this one biomarker/one drug approach to cancer personalization has inherent limitations due to its over simplification. Personalization 2.0 methodology creates a patient simulation avatar incorporating a patient’s genomic profile information holistically. Methods Bone marrow samples from two myeloma patients (P1 and P2) refractory to most recent treatment was collected, and P1’s sample was sorted into CD138+ and CD138- cells. The patient cells were analyzed for chromosomal alterations using Comparative Genomic Hybridization (aCGH) arrays by GenPath Diagnostics and cytogenetic chromosome analysis by Washington University School of Medicine and New York University (NYU), respectively. Using this information, a predictive simulation avatar model of each patient was created by Cellworks based on genomic profile of patients. A digital functional library of over 80 FDA-approved drugs and agents currently in clinical trials were simulated individually and in combination using the two patient avatars to create a personalized treatment for each patient. The findings were prospectively validated using patient cells ex vivo as assessed by MTT assay at New York University. Results P1 aberrations included trisomy of CCND1 and deletion of TP53 along with single copy losses in different arms of chromosomes 1, 6, 8, 12, 13, 14, 16, 17 and 22 and gains in different arms and regions of chromosomes X, 1, 4, 7, 9, 17, 3, 5, 11, 15 and 19, indicating the presence of hyperdiploid clones. Using this information, 897 gene perturbations were included to model this patient simulation avatar. Simulation predicted high beta-catenin (CTNNB1) activity with increased hedgehog and NOTCH pathways that were inherent causes of Bortezomib resistance. Significant activation of STAT3 and STAT5 due to amplification of IL6 pathway, JAK2 and JAK3 was noted. Amplifications of MET, IGFR and FGFR converged at ERK and AKT signaling loops. Along with deletion of TP53, this profile had amplification of many anti-apoptotic genes including survivin, MCL1 and XIAP. Modeling predicted sensitivity to the JAK inhibitor Tofacitinib, a drug approved for rheumatoid arthritis. This was prospectively validated ex vivo, and the experimental data correlated with the prediction showing a reduction in viability. P2 aberrations include losses in chromosomes X and 9 and a chromosome 11:14 translocation that is a common occurrence in MM. This translocation results in an amplification of CCND1 expression. The genomic aberrations reported include knockdown of tumor suppressors RXRA, TGFBR1, TJP2 and TSC1. TSC1 regulates the mTOR pathway, and its deletion causes an aberrant activation of mTOR and its downstream targets. Reduced expression of RXRA and TJP2 both in different manners leads to increase in AP1 activation. NFkB is also activated due to RXRA reduction. TGFBR1 reduction decreases the expression of cell cycle inhibitors via SMAD2/3 down-regulation. In this patient avatar, modeling predicted sensitivity to a combination of Sirolimus and Trametinib. Ex vivo validation confirmed this prediction of additive synergy of these two drug agents in the context of this patient. Conclusions This study demonstrates and validates the personalization of treatment through two patient cases based on creating predictive simulation avatar models using genomic profile information. This modeling holistically incorporates all genomic aberration information and is not limited to associating drugs to actionable mutations. Disclosures Doudican: Cellworks: Research Funding. Vali:Cellworks: Employment. Basu:Cellworks: Employment. Kumar:cellworks: Employment. Singh:Cellworks: Employment. Sultana:Cellworks: Employment. Abbasi:Cellworks: Employment, Equity Ownership.

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 The unique signature of a patient’s tumor mandates the need to rationally design personalized therapies employing N=1 segmentation conceptually. Repurposing of existing drug agents with validated clinical safety and pharmacokinetics data provides a rapid translational path to clinic which otherwise would require years of development time and associated new chemical risks. By focusing on rationally designed personalized treatment mechanisms, our strategy targets multiple key pathways to address the clinical problem of emergence of single therapy resistance. In order to overcome MM resistance, we have (1) employed predictive simulation modeling based upon patient genetic and environment profiling to design patient context specific combinatorial therapeutic regimens using library of drug agents from across indications with prior clinical data and (2) validating designed therapy ex-vivo in patient derived cell lines. Methods Clinical patient samples were analyzed for chromosome evaluation and molecular cytogenetic analysis by NYU. Using this information, an in silico simulation avatar of the patient was created. To identify effective personalized therapeutics, we focused our study on compounds from the National Center for Advanced Translational Science (NCATS) and other molecularly targeted agents. The predictive simulation based approach from Cellworks provides a comprehensive representation of MM disease physiology incorporating signaling and metabolic networks with an integrated phenotype view. This extensively validated simulation model predicts clinical outcomes with phenotype and bio-marker assays. Hits were shortlisted from over thousand pharmacodynamic dose-response simulation studies using criteria of efficacy and synergy. Computer modeling predicted that therapeutic combination mechanistically targets apoptotic pathways and the combination of the agents provides greater than additive activity. These predictive findings are in the process of being assessed ex vivo and retrospectively validated. Results The analysis detected loss of chromosome 13 signal consistent with monosomy 13 and loss of TP53 signal consistent with deletion of TP53; all other probes contained normal signal patterns. The shortlisted therapeutic combination identified from predictive simulation-based screening was BEZ235 (PI3K/mTOR inhibitor) and ABT-199 (BCL2 inhibitor). IC30 concentrations of the single agents resulted in a 56% inhibition of proliferation and 49% inhibition of viability in predictive simulations. The apoptotic markers CASP3, CASP9, Cleaved-PARP1 and BAK1 increased by 74% (1.75 fold), 132% (2.32 fold), 81% (1.8 fold), 217% (3.17 fold), respectively. The proposed mechanism of action using simulation model identified the p53 deletion as responsible for increased BCL2 activity and levels of activated AKT. Deletion of p53 increased levels of activated AKT via decreases in PTEN and IGFBP3. Hence, a mechanism that targets the PI3K/AKT/mTOR and BCL2 family showed efficacy in the simulation avatar of the patient and are currently being validated ex-vivo in patient cells. Conclusions This study demonstrates and validates simulation approaches and technologies to leverage big data from patient genomic analysis to create a simulation avatar for rational design of personalized therapeutics. This level of personalization, beyond linking point mutations to associated drugs targeting the same mutations, truly incorporates the broad patient tumor signature in translational path forward. Disclosures: No relevant conflicts of interest to declare.

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.

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

Background: Glioblastoma (GBM) is an aggressive disease associated with poor survival. It is essential to account for the complexity of GBM biology to improve diagnostic and therapeutic strategies. This complexity is best represented by the increasing amounts of profiling (“omics”) data available due to advances in biotechnology. The challenge of integrating these vast genomic and proteomic data can be addressed by a comprehensive systems modeling approach. Methods: Here, we present an in silico model, where we simulate GBM tumor cells using genomic profiling data. We use this in silico tumor model to predict responses of cancer cells to targeted drugs. Initially, we probed the results from a recent hypothesis-independent, empirical study by Garnett and co-workers that analyzed the sensitivity of hundreds of profiled cancer cell lines to 130 different anticancer agents. We then used the tumor model to predict sensitivity of patient-derived GBM cell lines to different targeted therapeutic agents. Results: Among the drug-mutation associations reported in the Garnett study, our in silico model accurately predicted ~85% of the associations. While testing the model in a prospective manner using simulations of patient-derived GBM cell lines, we compared our simulation predictions with experimental data using the same cells in vitro. This analysis yielded a ~75% agreement of in silico drug sensitivity with in vitro experimental findings. Conclusions: These results demonstrate a strong predictability of our simulation approach using the in silico tumor model presented here. Our ultimate goal is to use this model to stratify patients for clinical trials. By accurately predicting responses of cancer cells to targeted agents ap riori ,t hisin silico tumor model provides an innovative approach to personalizing therapy and promises to improve clinical management of cancer.