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Algorithmic guided screening of drug combinations of arbitrary size for activity against cancer cells
Ralph G Zinner1, Brittany L Barrett, Elmira Popova
1Department of Thoracic/Head and Neck Medical Oncology, Unit 432, The University of Texas M. D. Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, USA. rzinner@mdanderson.org
Abstract:
The standard treatment for most advanced cancers is multidrug therapy. Unfortunately, combinations in the clinic often do not perform as predicted. Therefore, to complement identifying rational drug combinations based on biological assumptions, we hypothesized that a functional screen of drug combinations, without limits on combination sizes, will aid the identification of effective drug cocktails. Given the myriad possible cocktails and inspired by examples of search algorithms in diverse fields outside of medicine, we developed a novel, efficient search strategy called Medicinal Algorithmic Combinatorial Screen (MACS). Such algorithms work by enriching for the fitness of cocktails, as defined by specific attributes through successive generations. Because assessment of synergy was not feasible, we developed a novel alternative fitness function based on the level of inhibition and the number of drugs. Using a WST-1 assay on the A549 cell line, through MACS, we screened 72 combinations of arbitrary size formed from a 19-drug pool across four generations. Fenretinide, suberoylanilide hydroxamic acid, and bortezomib (FSB) was the fittest. FSB performed up to 4.18 SD above the mean of a random set of cocktails or "too well" to have been found by chance, supporting the utility of the MACS strategy. Validation studies showed FSB was inhibitory in all 7 other NSCLC cell lines tested. It was also synergistic in A549, the one cell line in which this was evaluated. These results suggest that when guided by MACS, screening larger drug combinations may be feasible as a first step in combination drug discovery in a relatively small number of experiments.
Insights
A new search strategy, Medicinal Algorithmic Combinatorial Screen (MACS), efficiently identifies effective cancer drug combinations. MACS discovered a potent three-drug cocktail (FSB) that shows promise for treating non-small cell lung cancer.
Area of Science:
- Oncology
- Pharmacology
- Computational Biology
Background:
- Multidrug therapy is standard for advanced cancers, but clinical outcomes often differ from predictions.
- Identifying effective drug combinations is challenging due to the vast number of possibilities.
- Biological assumptions alone are insufficient for predicting synergistic drug combinations.
Purpose of the Study:
- To develop and validate an efficient search strategy for discovering effective drug combinations, irrespective of combination size.
- To introduce the Medicinal Algorithmic Combinatorial Screen (MACS) as a novel approach to drug combination screening.
- To identify potent drug cocktails for cancer treatment through functional screening.
Main Methods:
- Developed Medicinal Algorithmic Combinatorial Screen (MACS), an efficient search strategy inspired by algorithms.
- Created a novel fitness function based on drug inhibition level and quantity, as direct synergy assessment was not feasible.
- Screened 72 drug combinations of arbitrary size from a 19-drug pool across four generations using a WST-1 assay on A549 cells.
Main Results:
- The MACS strategy identified fenretinide, suberoylanilide hydroxamic acid, and bortezomib (FSB) as the fittest drug combination.
- FSB demonstrated significantly superior performance compared to random drug cocktails, indicating its effectiveness.
- FSB showed inhibitory effects across 7 non-small cell lung cancer (NSCLC) cell lines and synergistic activity in A549 cells.
Conclusions:
- The MACS strategy is a feasible and effective approach for initial drug combination discovery, enabling the screening of larger combinations.
- The identified FSB cocktail holds potential for treating non-small cell lung cancer.
- Functional screening guided by MACS can overcome limitations of predicting drug synergy based solely on biological assumptions.
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