title: A pragmatic definition of therapeutic synergy suitable for clinically relevant in vitro multicompound analyses.
abstract: For decades, the standard procedure when screening for candidate anticancer drug combinations has been to search for synergy, defined as any positive deviation from trivial cases like when the drugs are regarded as diluted versions of each other (Loewe additivity), independent actions (Bliss independence), or no interaction terms in a response surface model (no interaction). Here, we show that this kind of conventional synergy analysis may be completely misleading when the goal is to detect if there is a promising in vitro therapeutic window. Motivated by this result, and the fact that a drug combination offering a promising therapeutic window seldom is interesting if one of its constituent drugs can provide the same window alone, the largely overlooked concept of therapeutic synergy (TS) is reintroduced. In vitro TS is said to occur when the largest therapeutic window obtained by the best drug combination cannot be achieved by any single drug within the concentration range studied. Using this definition of TS, we introduce a procedure that enables its use in modern massively parallel experiments supported by a statistical omnibus test for TS designed to avoid the multiple testing problem. Finally, we suggest how one may perform TS analysis, via computational predictions of the reference cell responses, when only the target cell responses are available. In conclusion, the conventional error-prone search for promising drug combinations may be improved by replacing conventional (toxicology-rooted) synergy analysis with an analysis focused on (clinically motivated) TS.
authors.affiliation: Authors' Affiliations: Department of Medical Sciences, Cancer Pharmacology, and Computational Medicine, Uppsala University Academic Hospital;
authors.forename: Muhammad
authors.initials: M
authors.lastname: Kashif
authors.affiliation: Authors' Affiliations: Department of Medical Sciences, Cancer Pharmacology, and Computational Medicine, Uppsala University Academic Hospital;
authors.forename: Claes
authors.initials: C
authors.lastname: Andersson
authors.affiliation: Authors' Affiliations: Department of Medical Sciences, Cancer Pharmacology, and Computational Medicine, Uppsala University Academic Hospital; Department of Engineering Sciences, Solid State Physics;
authors.forename: Magnus
authors.initials: M
authors.lastname: Åberg
authors.affiliation: Department of Radiology, Oncology, and Radiation Sciences; and.
authors.forename: Peter
authors.initials: P
authors.lastname: Nygren
authors.affiliation: Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden.
authors.forename: Tobias
authors.initials: T
authors.lastname: Sjöblom
authors.affiliation: Authors' Affiliations: Department of Medical Sciences, Cancer Pharmacology, and Computational Medicine, Uppsala University Academic Hospital;
authors.forename: Ulf
authors.initials: U
authors.lastname: Hammerling
authors.affiliation: Authors' Affiliations: Department of Medical Sciences, Cancer Pharmacology, and Computational Medicine, Uppsala University Academic Hospital;
authors.forename: Rolf
authors.initials: R
authors.lastname: Larsson
authors.affiliation: Authors' Affiliations: Department of Medical Sciences, Cancer Pharmacology, and Computational Medicine, Uppsala University Academic Hospital; Mats.Gustafsson@medsci.uu.se.
authors.forename: Mats G
authors.initials: MG
authors.lastname: Gustafsson
mesh_terms: D000971:Antineoplastic Combined Chemotherapy Protocols / Q000494:pharmacology*; D045744:Cell Line, Tumor; D004357:Drug Synergism; D004359:Drug Therapy, Combination; D045325:HCT116 Cells; D006801:Humans; D008954:Models, Biological