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A Pragmatic Definition of Therapeutic Synergy Suitable for Clinically Relevant In Vitro Multicompound Analyses
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Solid State Physics.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Radiology, Oncology and Radiation Science, Oncology.
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2014 (English)In: Molecular Cancer Therapeutics, ISSN 1535-7163, E-ISSN 1538-8514, Vol. 13, no 7, 1964-1976 p.Article in journal (Refereed) Published
Abstract [en]

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. 

Place, publisher, year, edition, pages
2014. Vol. 13, no 7, 1964-1976 p.
National Category
Cancer and Oncology Engineering and Technology
Research subject
Engineering Science with specialization in Solid State Physics
Identifiers
URN: urn:nbn:se:uu:diva-229737DOI: 10.1158/1535-7163.MCT-13-0430ISI: 000338710100026PubMedID: 24755197OAI: oai:DiVA.org:uu-229737DiVA: diva2:738405
Available from: 2014-08-18 Created: 2014-08-12 Last updated: 2017-12-05Bibliographically approved
In thesis
1. Integrated Computational and Experimental Approaches for Accelerated Drug Combination Discovery and Development: Applications in Cancer Pharmacology
Open this publication in new window or tab >>Integrated Computational and Experimental Approaches for Accelerated Drug Combination Discovery and Development: Applications in Cancer Pharmacology
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Today the norm in modern cancer treatment is to use different forms of drug combinations. Recently anti-cancer treatment using drug combinations has gained increased attention due to the outstanding pharmacotherapeutic opportunities provided by combination therapies. However, the potential of this field is largely unexplored, partly due to the complexities associated with the astronomical number of possible combinations and partly due to the lack of means for quantifying clinically relevant adverse side effects in the early stages of the combination discovery and development process. This has resulted in relatively limited progress in this area. Motivated by this unfortunate state-of-affairs, the research reported in this thesis was aimed at developing and implementing computational and experimental methods to facilitate and accelerate the discovery and development of anti-cancer therapies. In paper I, the largely overlooked concept of therapeutic synergy is re-introduced and demonstrated to be useful already at the level of combination discovery by taking both curative and adverse effects into account. In paper II, a semiautomatic combination discovery platform was developed based on a tailored programming of a pipetting robot system and application of a new in-house developed combination search algorithm, the therapeutic algorithmic combinatorial screen (TACS) algorithm. TACS seems to be the first algorithm of its kind that takes experimental variability into account during the iterative search process. The semiautomatic hardware platform along with TACS can perform de novo or knowledge based combination drug discovery and development without brute force comprehensive search efforts. One promising discovery made using this platform is a combination of the drugs 17-AAG, afungin and trichostatin a for treatment of colorectal cancer carcinoma (CRC). In paper III, an algorithm is developed and applied in order to use single drug induced systemic gene expression profiles for rational drug combination design by assuming additive combination effects. The resulting algorithm, combo-CMap, is applied and validated using a slightly extended version of the freely available Connectivity Map (CMap) database which is currently containing 6190 chemically induced mRNA gene expression signatures. In paper IV, a software (R package) was developed and applied to perform improved synergy/antagonism analysis, in particular joint Loewe and Bliss analyses while taking associated experimental variability into account using non-parametric statistics including bootstrap intervals. Applying this software to the synergy analysis of interaction effects among clinically used and/or relevant drugs in CRC cell lines revealed complex patterns of synergy and antagonism. In conclusion, the work presented here offers important contributions and findings that may accelerate and/or improve different parts of the field of drug combination discovery and development.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2015. 58 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Medicine, ISSN 1651-6206 ; 1073
National Category
Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:uu:diva-245573 (URN)978-91-554-9177-2 (ISBN)
Public defence
2015-04-16, Rosénsalen, Academic Hospital, Ing 95/96 nbv, Uppsala, 09:00 (English)
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Supervisors
Available from: 2015-03-25 Created: 2015-02-27 Last updated: 2015-04-17

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Kashif, MuhammadAndersson, ClaesÅberg, MagnusNygren, PeterSjöblom, TobiasHammerling, UlfLarsson, RolfGustafsson, Mats G.

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