A novel network based linear model for prioritization of synergistic drug combinations
Jiaqi Li, HongyanXu, Richard A. McIndoe
Abstract
Drug combination therapies can improve drug efficacy, reduce drug dosage, and overcome drug resistance in cancer treatments. Current research strategies to determine which drug combinations have a synergistic effect rely mainly on clinical or empirical experience and screening predefined pools of drugs. Given the number of possible drug combinations, the speed, and scope to find new drug combinations are very limited using these methods. Due to the exponential growth in the number of drug combinations, it is difficult to test all possible combinations in the lab. There are several large-scale public genomic and phenotypic resources that provide data from single drug-treated cells as well as data from small molecule treated cells. These databases provide a wealth of information regarding cellular responses to drugs and offer an opportunity to overcome the limitations of the current methods. Developing a new advanced data processing and analysis strategy is imperative and a computational prediction algorithm is highly desirable. In this paper, we developed a computational algorithm for the enrichment of synergistic drug combinations using gene regulatory network knowledge and an operational module unit (OMU) system which we generate from single drug genomic and phenotypic data. As a proof of principle, we applied the pipeline to a group of anticancer drugs and demonstrate how the algorithm could help researchers efficiently find possible synergistic drug combinations using single drug data to evaluate all possible drug pairs.
Introduction
Drug combination therapy is becoming an important tool in cancer treatment. With the emergence of cancer cell resistance to the front line of anticancer drugs and the slow process of new drug discoveries via traditional drug developing methods, synergistic drug combinations offer a potential treatment optimization against drug resistance and side effects. As a result, there is an increasing interest in screening the expansive combination of both approved drugs and other potential therapeutic agents. A synergistic drug combination means the overall therapeutic effect of the combination is greater than the simple sum of effects caused by individual drugs [1].
Materials and methods
Cell line and tissue culture
Frozen MCF7 cells were obtained from the ATCC and grown according to ATCC recommendations. The cell lines were maintained in DMEM media (Gibco) with 10% serum (Gibco) at 37°C with 5% CO2 in a humidified incubator. Before the experiment, the cell line was passed twice after thawing.
Measure drug response in MCF7
MCF7 cells were seeded in 96 well plates at 3000 cells per well and grown overnight allowing them to adhere and recovered from trypsin treatment. Cells were treated with a dilution series of the indicated drugs. After 72 hours of incubation, cell viability was measured using the CCK8 assay [20]. Briefly, after removing the medium from the 96 well plate, fresh medium with 5% CCK8 was added to the wells and incubated for 4 hours. Absorbance at 450 nm was measured using a BioTek Synergy H1 microplate reader. The drug growth rate inhibition was calculated following the GR metrics algorithms [20]. The formula to assess growth rate metrics (1) calculates the ratio between growth rates under treated and untreated conditions normalized to a single cell division.
Results
The GRmax of anticancer drugs show heterogeneity in their cellular response
We selected the GRmax from the growth rate (GR) inhibition algorithm as an indication of the cellular response to anticancer drugs (20). GRmax captures the maximal drug effect on growth rate and has a defined range of -1 to 1. It can easily be integrated into our algorithm in order to calculate the mean GRmax of the OMUs and has an interpretable meaning on cell growth. A total of 57 randomly selected anticancer drugs were applied to the breast cancer cell line MCF7. Fig 4 presents the GRmax of each drug used in the study. The distribution of the calculated GRmax on MCF7 cells indicates that most drugs either inhibit cellular growth (0 <GRmax <1) or kill the cells (-1<GRmax < 0). A number of drugs had GRmax close to 1, suggesting they do not substantially affect cellular growth of MCF7. Including these drugs in the analysis could potentially expand the number of possible candidate drug pairs. Our data showed no bias in the range of GRmax.
Discussion
Cancer is a complex disease where treatment using anticancer drugs often leads to drug resistance. As a result, there is a rising demand for more effective therapies. Using synergistic drug combinations of two or more drugs can overcome toxicity and side effects which can be associated with high doses of single drug therapies. In recent years, several drug combination therapies show promising synergistic effects and have been approval by the FDA [31,32]. Here, we developed a gene network-based algorithm, using both lab data and public data, to successfully prioritize and enrich for synergistic two-drug combinations from a group of candidate drugs and presented opportunities for further exploring multiple drug combinations.
Acknowledgments
We thank Dr. Ashok Sharma for giving valuable suggestions. We thank the Center for Biotechnology and Genomic Medicine at Augusta University, Dr. Jin-Xiong She and his lab member Dr.Haitao Liu for providing lab resources and support.
Citation: Li J, Xu H, McIndoe RA (2022) A novel network based linear model for prioritization of synergistic drug combinations. PLoS ONE 17(4): e0266382. https://doi.org/10.1371/journal.pone.0266382
Editor: Jishnu Das, University of Pittsburgh, UNITED STATES
Received: June 22, 2021; Accepted: March 18, 2022; Published: April 5, 2022
Copyright: © 2022 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting Information files.
Funding: This work is supported by Center for Biotechnology & Genomic Medicine in August University. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0266382#ack
