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Optimizing drug combinations against multiple myeloma using a quadratic phenotypic optimization platform (QPOP). (Sci Trans Med, Aug 2018)

Rashid MBMA1,2, Toh TB1, Hooi L1, Silva A3, Zhang Y1, Tan PF4, Teh AL4, Karnani N4,5, Jha S1,5, Ho CM3,6,7, Chng WJ1,8, Ho D2,6,7,9,10,11, Chow EK12,2.

Author information
1 Cancer Science Institute of Singapore, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117599, Singapore.
2 Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore.
3 Department of Mechanical Engineering, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, CA 90095, USA.
4 Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research, Brenner Centre for Molecular Medicine, Singapore 117609, Singapore.
5 Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117596, Singapore.
6 Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, CA 90095, USA.
7 Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, CA 90095, USA.
8 Department of Haematology-Oncology, National University Cancer Institute, Singapore, National University Health System, Singapore 119228, Singapore.
9 Department of Biomedical Engineering, National University of Singapore, Singapore 117583, Singapore.
10 Bioengineering Institute for Global Health Research and Technology, National University of Singapore, Singapore 117599, Singapore.
11 Division of Oral Biology and Medicine and the Jane and Jerry Weintraub Center for Reconstructive Biotechnology, School of Dentistry, University of California, Los Angeles, Los Angeles, CA 90095, USA.
12 Cancer Science Institute of Singapore, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117599, Singapore.

Abstract
Multiple myeloma is an incurable hematological malignancy that relies on drug combinations for first and secondary lines of treatment. The inclusion of proteasome inhibitors, such as bortezomib, into these combination regimens has improved median survival. Resistance to bortezomib, however, is a common occurrence that ultimately contributes to treatment failure, and there remains a need to identify improved drug combinations. We developed the quadratic phenotypic optimization platform (QPOP) to optimize treatment combinations selected from a candidate pool of 114 approved drugs. QPOP uses quadratic surfaces to model the biological effects of drug combinations to identify effective drug combinations without reference to molecular mechanisms or predetermined drug synergy data. Applying QPOP to bortezomib-resistant multiple myeloma cell lines determined the drug combinations that collectively optimized treatment efficacy. We found that these combinations acted by reversing the DNA methylation and tumor suppressor silencing that often occur after acquired bortezomib resistance in multiple myeloma. Successive application of QPOP on a xenograft mouse model further optimized the dosages of each drug within a given combination while minimizing overall toxicity in vivo, and application of QPOP to ex vivo multiple myeloma patient samples optimized drug combinations in patient-specific contexts.

PMID: 30089632