Algorithmic guided screening of drug combinations of arbitrary size for activity against cancer cells

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. [Mol Cancer Ther 2009;8(3):521 -32]

In-Silico Methodologies for Cancer Multidrug Optimization

“…AstraZeneca-Sanger Drug Combination DREAM Challenge was launched using 85 cancer cell lines and 11,759 drug combination screening for 118 drugs [8]. The predictive models were designed to differentiate synergistic, additive and antagonistic combinations and predict new synergistic combinations in silico [9][10][11][12][13][14][15][16][17].…”

“…This facilitates dealing with highly complex, nonlinear systems. One of the restrictions in this approach is the presence of measurement errors and variability in the noisy biological system that may affect the execution of the algorithm [11]. Moreover, there is always a probability that the algorithm will converge to a local minimum or maximum.…”

“…Zinner et al [11] attempted to find the most effective drugs combinations from a set of 19 drugs based on hill climbing search. The first generation was composed of 18 random combinations.…”

Delivering an Automated and Integrated Approach to Combination Screening Using Acoustic-Droplet Technology

“…For example, a study of 50 different drugs, each tested pairwise with three possible doses, requires a minimum of 11,175 experiments. Researchers have aimed to address this challenge using in silico approaches, [10][11][12] closed-loop optimization studies, [13][14][15] and medium-throughput screening. [16][17][18][19][20][21] Challenges exist in the efficient implementation of such a capability within early drug discovery, where there is a need to translate complex drug-combination study designs into the required contents for assay plates and in some cases for medium-throughput screens.…”

Combination therapy design for maximizing sensitivity and minimizing toxicity

“…Note that currently available combination therapy design techniques are model free and require multiple experimental iterations to arrive at the optimal strategy [8][9][10][11][12][13][14][15]. This article considers model based combination therapy design over multiple models of tumor and normal cell lines.…”