uu.seUppsala University Publications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Integrated Computational and Experimental Approaches for Accelerated Drug Combination Discovery and Development: Applications in Cancer Pharmacology
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
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: urn:nbn:se:uu:diva-245573ISBN: 978-91-554-9177-2 (print)OAI: oai:DiVA.org:uu-245573DiVA: diva2:791212
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
List of papers
1. A Pragmatic Definition of Therapeutic Synergy Suitable for Clinically Relevant In Vitro Multicompound Analyses
Open this publication in new window or tab >>A Pragmatic Definition of Therapeutic Synergy Suitable for Clinically Relevant In Vitro Multicompound Analyses
Show others...
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. 

National Category
Cancer and Oncology Engineering and Technology
Research subject
Engineering Science with specialization in Solid State Physics
Identifiers
urn:nbn:se:uu:diva-229737 (URN)10.1158/1535-7163.MCT-13-0430 (DOI)000338710100026 ()24755197 (PubMedID)
Available from: 2014-08-18 Created: 2014-08-12 Last updated: 2017-12-05Bibliographically approved
2. In vitro discovery of promising anti-cancer drug combinations using iterative maximisation of a therapeutic index
Open this publication in new window or tab >>In vitro discovery of promising anti-cancer drug combinations using iterative maximisation of a therapeutic index
Show others...
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.

National Category
Cancer and Oncology Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-246221 (URN)10.1038/srep14118 (DOI)000361515800001 ()26392291 (PubMedID)
Funder
Swedish Foundation for Strategic Research
Available from: 2015-03-03 Created: 2015-03-03 Last updated: 2017-12-04Bibliographically approved
3. Using single drug induced systemic gene expression profiles for rational drug combination design by assuming additive combination effects
Open this publication in new window or tab >>Using single drug induced systemic gene expression profiles for rational drug combination design by assuming additive combination effects
(English)Manuscript (preprint) (Other academic)
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:uu:diva-246226 (URN)
Available from: 2015-03-03 Created: 2015-03-03 Last updated: 2015-04-17
4. Integrated Bliss and Loewe synergy analyses of clinically relevant drug combinations in human colon cancer cell lines reveal that regions of synergy often come together with regions of antagonism
Open this publication in new window or tab >>Integrated Bliss and Loewe synergy analyses of clinically relevant drug combinations in human colon cancer cell lines reveal that regions of synergy often come together with regions of antagonism
Show others...
(English)Manuscript (preprint) (Other academic)
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:uu:diva-246230 (URN)
Available from: 2015-03-03 Created: 2015-03-03 Last updated: 2015-04-17

Open Access in DiVA

fulltext(1009 kB)300 downloads
File information
File name FULLTEXT01.pdfFile size 1009 kBChecksum SHA-512
856aaae7bba6f9712124fc81a174a8ac4297f70a0c5fbf788081f47ccf751bc48013f632102ea5207518b6f3e90d13489ad9e169389567eb6cddb2897a1e8ad0
Type fulltextMimetype application/pdf
Buy this publication >>

By organisation
Cancer Pharmacology and Computational Medicine
Bioinformatics (Computational Biology)

Search outside of DiVA

GoogleGoogle Scholar
Total: 300 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 1360 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf