Modeling the Impact of Drug Interactions on Therapeutic Selectivity
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ARTICLE DOI: 10.1038/s41467-018-05954-3 OPEN Modeling the impact of drug interactions on therapeutic selectivity Zohar B. Weinstein 1, Nurdan Kuru2, Szilvia Kiriakov 3,4, Adam C. Palmer 5, Ahmad S. Khalil 4,6,7, Paul A. Clemons8, Muhammad H. Zaman7,9, Frederick P. Roth10,11,12 & Murat Cokol2,5 Combination therapies that produce synergistic growth inhibition are widely sought after for the pharmacotherapy of many pathological conditions. Therapeutic selectivity, however, 1234567890():,; depends on the difference between potency on disease-causing cells and potency on non- target cell types that cause toxic side effects. Here, we examine a model system of anti- microbial compound combinations applied to two highly diverged yeast species. We find that even though the drug interactions correlate between the two species, cell-type-specific differences in drug interactions are common and can dramatically alter the selectivity of compounds when applied in combination vs. single-drug activity—enhancing, diminishing, or inverting therapeutic windows. This study identifies drug combinations with enhanced cell-type-selectivity with a range of interaction types, which we experimentally validate using multiplexed drug-interaction assays for heterogeneous cell cultures. This analysis presents a model framework for evaluating drug combinations with increased efficacy and selectivity against pathogens or tumors. 1 Department of Pharmacology and Experimental Therapeutics, Boston University School of Medicine, Boston, MA 02115, USA. 2 Faculty of Engineering and Natural Sciences, Sabanci University, 34956 Istanbul, Turkey. 3 Program in Molecular Biology, Cell Biology & Biochemistry, Boston University, Boston, MA 02215, USA. 4 Biological Design Center, Boston University, Boston, MA 02215, USA. 5 Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA 02115, USA. 6 Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA. 7 Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA. 8 Chemical Biology & Therapeutics Science Program, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA. 9 Howard Hughes Medical Institute, Boston University, Boston, MA 02215, USA. 10 Donnelly Centre and Departments of Molecular Genetics and Computer Science, University of Toronto, Toronto, ON M5S 3E1, Canada. 11 Lunenfeld-Tanenbaum Research Institute, Toronto, ON M5S 3E1, Canada. 12 Canadian Institute for Advanced Research, Toronto, ON M5S 3E1, Canada. Correspondence and requests for materials should be addressed to M.C. (email: [email protected]) NATURE COMMUNICATIONS | (2018) 9:3452 | DOI: 10.1038/s41467-018-05954-3 | www.nature.com/naturecommunications 1 ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/s41467-018-05954-3 ombination therapies are common in the treatment of Table 1 Antifungal drugs used in the study Ccancer and infectious diseases1,2. The use of drug combi- nations is motivated by evidence that they can achieve Drug Abbreviation PubChem alb cer cure rates superior to monotherapies3–5. Drug combinations may fi ID IC50 IC50 be classi ed as synergistic or antagonistic when the observed μg/ml μg/ml effect of the combination is greater or lesser, respectively, than is ’ 6,7 Benomyl BEN 28780 130 9.6 expected based on the components effects as single agents . Bromopyruvate BRO 70684 367 1086 Consequently, much effort has been applied to the task of iden- Calyculin A CAL 5311365 2.1 2.2 tifying synergistic combinations. Drug-interaction screens iden- Dyclonine DYC 3180 5.4 8.9 tify combinations with increased efficacy against specific cell lines Fenpropimorph FEN 93365 0.1 0.3 or phenotypes8, and computational methods aim to predict drug Haloperidol HAL 3559 150 39 synergies using chemogenomics9, genetic interactions10, and Methyl MMS 4156 128 65 physicochemical properties11,12. methanesulfonate However, consider a drug combination that is synergistic Pentamidine PEN 4735 27 73 against pathogenic or cancerous cells, but also has synergistic Rapamycin RAP 5284616 0.15 0.04 fi Staurosporine STA 44259 0.2 0.6 toxicity to healthy host cells. In this case, there will be no bene t Terbinafine TER 1549008 1.3 2.1 to the therapeutic window, being the difference between the Tunicamycin TUN 6433557 1.7 0.3 dose required for the desired effect and the dose-limiting toxicity. It is apparent that the efficacy of a synergistic combination is All drug names are provided as well as abbreviations used in figures, PubChem ID and IC50 (µg/mL) for each drug tested in C. albicans (alb) and S. cerevisiae (cer). IC50 levels were entirely dependent on avoiding synergistic toxicity to unintended determined with yeast cells grown overnight and diluted in liquid culture to OD600 = 0.1. S. cell types, as shown by Lehar et al.13 and reviewed in Bulusu cerevisae and C. albicans concentration-response experiments were conducted in parallel to allow for direct comparison of IC50 levels et al.14. Extending this idea, it has been debated that it is not synergy itself that is pharmacologically useful, but differential drug interactions between cell types, with the essential goal being identified from these confidence intervals as α < −0.8 or α > + a more favorable interaction on the target cell type than on non- 15–17 0.8, respectively (Fig. 1a). Among the 66 drug combinations target cell types . tested in C. albicans, 20 synergistic and 27 antagonistic drug pairs To test this idea, we implemented an experimentally tractable fi fi were identi ed (Fig. 1b). system to systematically characterize how cell-type-speci c drug Drug interactions were substantially, but not perfectly interactions affect the selectivity of combination therapies, by fi conserved between C. albicans and S. cerevisiae (Spearman pro ling combinations of antifungal drugs applied to the yeast correlation test r = 0.42, p-value = 1.8 × 10−4) (Fig. 2). Synergis- species Saccharomyces cerevisiae and Candida albicans. Individual tic, but not antagonistic, interactions significantly overlapped differences in single-drug sensitivity constitute therapeutic win- in these related species (Fisher’s exact test, shared synergy: dows that select for single cell-type; however combinations of p <3×10−5, shared antagonism: p = 0.44). Notably, nine combi- drugs may have selectivity that varies from individual agents. We nations had highly divergent interactions, being synergistic in used a sensitive screen to assess all 66 pairwise interactions of “ ” one species and antagonistic in the other, suggesting that drug 12 antifungal small molecules (henceforth drugs )inC. albicans, combinations may be used to selectively inhibit a particular selected for direct comparison to a recent drug-interaction data cell type. set in S. cerevisiae10 to determine differences in selectivity due to single agents vs. combinations. Our model framework and sub- sequent mixed culture assays show that therapeutic windows may Cell-type selectivity of individual drugs. In order to understand be enhanced or diminished by differential drug interactions. the relationship between drug interactions and cell-selective inhibition, we first considered the selectivity of individual drugs. The concentrations required for 50% growth inhibition of Results C. albicans (IC50alb) and S. cerevisiae (IC50cer) were correlated = −13 Precise assessment of drug interactions in two model yeasts. between species (r 0.91, p <10 ; Supplementary Fig. 1), but We used a sensitive 8 × 8 checkerboard assay to assess all 66 most drugs had a therapeutic window for cell-selective inhibition pairwise interactions of 12 antifungal drugs (Table 1), in C. albi- due to a two-fold or greater difference in IC50 between cell types. fi “ ” cans and S. cerevisiae10. This screen included drugs that target We de ned the selectivity score of a single-drug A (selectivityA) DNA, cell wall, and metabolism as well as microtubule, phos- as log2(IC50A,alb / IC50A,cer), such that a score of 0 indicates no − phatase, and kinase inhibitors. All C. albicans experiments were selectivity, and a score of 1 (or 1) denotes that twice (or half) conducted in this study. Eight S. cerevisiae experiments were as much drug is required to inhibit C. albicans compared to newly conducted for this study: methyl methanesulfonate tested S. cerevisiae. Neither species was on average more drug-sensitive fi against itself, bromopyruvate, calyculin A, dyclonine, fenpropi- or resistant than the other (no signi cant bias in selectivity scores = morph, haloperidol, rapamycin, and tunicamycin. Other experi- by sign test, p 0.77). Benomyl and tunicamycin had the greatest mental data involving S. cerevisiae were obtained from Cokol single-agent selectivity for C. albicans (3.8 and 2.4), while staur- et al., 2011. Drug interactions were quantified by isobologram osporine and bromopyruvate were most selective for the growth − − analysis; briefly, each interaction score (α) for a drug pair was of S. cerevisiae ( 1.7 and 1.6). calculated from the concavity of the isophenotypic contours that map regions of similar growth inhibition across the drug- Drug interactions alter the selectivity of drug combinations. concentration matrix. We explored the impact of drug interactions on selectivity by To produce a reference that is by definition non-interacting, superimposing the isophenotypic contours from drug-interaction we measured “self–self” interactions (drugs combined with experiments for each cell type, for the greatest level of inhibition themselves) for ten drugs in both yeast species.