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.


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.


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.


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.


No relevant conflicts of interest to declare.

Author notes


Asterisk with author names denotes non-ASH members.

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