4.8 Article

Optimizing drug combinations against multiple myeloma using a quadratic phenotypic optimization platform (QPOP)

Journal

SCIENCE TRANSLATIONAL MEDICINE
Volume 10, Issue 453, Pages -

Publisher

AMER ASSOC ADVANCEMENT SCIENCE
DOI: 10.1126/scitranslmed.aan0941

Keywords

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Funding

  1. National Research Foundation Singapore
  2. Singapore Ministry of Education under its Research Centers of Excellence initiative
  3. RNA Biology Center at the Cancer Science Institute of Singapore
  4. National University of Singapore, Singapore Ministry of Education's Tier 3 grants [MOE2014-T3-1-006]
  5. Singapore Ministry of Education Academic Research Fund [MOE AcRF Tier 2 MOE2015-T2-2-126, MOE AcRF Tier 1 T1-2012 Oct -04]
  6. NCIS Yong Siew Yoon Research Grant from the Yong Loo Lin Trust
  7. National Medical Research Council (NMRC)
  8. NMRC CBRG-NIG [BNIG11nov001]
  9. NMRC Singapore Translational Research Investigatorship
  10. Ben Rich-Lockheed Martin Professor endowment fund
  11. NSF CAREER Award [CMMI-1350197]
  12. Center for Scalable and Integrated Nanomanufacturing [DMI-0327077, CMMI-0856492, DMR-1343991]
  13. V Foundation for Cancer Research Scholars Award
  14. Wallace H. Coulter Foundation Translational Research Award
  15. National Cancer Institute [U54CA151880]
  16. Society for Laboratory Automation and Screening Endowed Fellowship
  17. Beckman Coulter Life Sciences

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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.

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