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In vitro discovery of promising anti-cancer drug combinations using iterative maximisation of a therapeutic index
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 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.
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2015 (English)In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 5, 14118Article in journal (Refereed) Published
Abstract [en]

In vitro-based search for promising anti-cancer drug combinations may provide important leads to improved cancer therapies. Currently there are no integrated computational-experimental methods specifically designed to search for combinations, maximizing a predefined therapeutic index (TI) defined in terms of appropriate model systems. Here, such a pipeline is presented allowing the search for optimal combinations among an arbitrary number of drugs while also taking experimental variability into account. The TI optimized is the cytotoxicity difference (in vitro) between a target model and an adverse side effect model. Focusing on colorectal carcinoma (CRC), the pipeline provided several combinations that are effective in six different CRC models with limited cytotoxicity in normal cell models. Herein we describe the identification of the combination (Trichostatin A, Afungin, 17-AAG) and present results from subsequent characterisations, including efficacy in primary cultures of tumour cells from CRC patients. We hypothesize that its effect derives from potentiation of the proteotoxic action of 17-AAG by Trichostatin A and Afungin. The discovered drug combinations against CRC are significant findings themselves and also indicate that the proposed strategy has great potential for suggesting drug combination treatments suitable for other cancer types as well as for other complex diseases.

Place, publisher, year, edition, pages
2015. Vol. 5, 14118
National Category
Cancer and Oncology Pharmaceutical Sciences
Identifiers
URN: urn:nbn:se:uu:diva-246221DOI: 10.1038/srep14118ISI: 000361515800001PubMedID: 26392291OAI: oai:DiVA.org:uu-246221DiVA: diva2:792383
Funder
Swedish Foundation for Strategic Research
Available from: 2015-03-03 Created: 2015-03-03 Last updated: 2017-12-04Bibliographically 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)
Opponent
Supervisors
Available from: 2015-03-25 Created: 2015-02-27 Last updated: 2015-04-17

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Kashif, MuhammadAndersson, ClaesHassan, SadiaKarlsson, HenningSenkowski, WojciechFryknäs, MårtenNygren, PeterLarsson, RolfGustafsson, Mats

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Kashif, MuhammadAndersson, ClaesHassan, SadiaKarlsson, HenningSenkowski, WojciechFryknäs, MårtenNygren, PeterLarsson, RolfGustafsson, Mats
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