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Article: A new classifier-based strategy for in-silico ion-channel cardiac drug safety assessment.

Mistry, Hitesh B / Davies, Mark R / Di Veroli, Giovanni Y

2015 Volume 6, Page(s) 59

Abstract: There is currently a strong interest in using high-throughput in-vitro ion-channel screening data to make predictions regarding the cardiac toxicity potential of a new compound in both animal and human studies. A recent FDA think tank encourages the use ... ...

Abstract	There is currently a strong interest in using high-throughput in-vitro ion-channel screening data to make predictions regarding the cardiac toxicity potential of a new compound in both animal and human studies. A recent FDA think tank encourages the use of biophysical mathematical models of cardiac myocytes for this prediction task. However, it remains unclear whether this approach is the most appropriate. Here we examine five literature data-sets that have been used to support the use of four different biophysical models and one statistical model for predicting cardiac toxicity in numerous species using various endpoints. We propose a simple model that represents the balance between repolarisation and depolarisation forces and compare the predictive power of the model against the original results (leave-one-out cross-validation). Our model showed equivalent performance when compared to the four biophysical models and one statistical model. We therefore conclude that this approach should be further investigated in the context of early cardiac safety screening when in-vitro potency data is generated.
Language	English
Publishing date	2015-03-24
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2587355-6
ISSN	1663-9812
ISSN	1663-9812
DOI	10.3389/fphar.2015.00059
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Article ; Online: Modelling of the cancer cell cycle as a tool for rational drug development: A systems pharmacology approach to cyclotherapy.

Jackson, Robert C / Di Veroli, Giovanni Y / Koh, Siang-Boon / Goldlust, Ian / Richards, Frances M / Jodrell, Duncan I

PLoS computational biology

2017 Volume 13, Issue 5, Page(s) e1005529

Abstract: The dynamic of cancer is intimately linked to a dysregulation of the cell cycle and signalling pathways. It has been argued that selectivity of treatments could exploit loss of checkpoint function in cancer cells, a concept termed "cyclotherapy". ... ...

Abstract	The dynamic of cancer is intimately linked to a dysregulation of the cell cycle and signalling pathways. It has been argued that selectivity of treatments could exploit loss of checkpoint function in cancer cells, a concept termed "cyclotherapy". Quantitative approaches that describe these dysregulations can provide guidance in the design of novel or existing cancer therapies. We describe and illustrate this strategy via a mathematical model of the cell cycle that includes descriptions of the G1-S checkpoint and the spindle assembly checkpoint (SAC), the EGF signalling pathway and apoptosis. We incorporated sites of action of four drugs (palbociclib, gemcitabine, paclitaxel and actinomycin D) to illustrate potential applications of this approach. We show how drug effects on multiple cell populations can be simulated, facilitating simultaneous prediction of effects on normal and transformed cells. The consequences of aberrant signalling pathways or of altered expression of pro- or anti-apoptotic proteins can thus be compared. We suggest that this approach, particularly if used in conjunction with pharmacokinetic modelling, could be used to predict effects of specific oncogene expression patterns on drug response. The strategy could be used to search for synthetic lethality and optimise combination protocol designs.
MeSH term(s)	Antineoplastic Agents/pharmacology ; Antineoplastic Agents/therapeutic use ; Cell Cycle Checkpoints/drug effects ; Cell Line, Tumor ; Computational Biology ; Drug Discovery/methods ; Humans ; Models, Biological ; Neoplasms/drug therapy ; Pharmacology
Chemical Substances	Antineoplastic Agents
Language	English
Publishing date	2017-05-03
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1005529
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Article ; Online: Combenefit: an interactive platform for the analysis and visualization of drug combinations.

Di Veroli, Giovanni Y / Fornari, Chiara / Wang, Dennis / Mollard, Séverine / Bramhall, Jo L / Richards, Frances M / Jodrell, Duncan I

Bioinformatics (Oxford, England)

2016 Volume 32, Issue 18, Page(s) 2866–2868

Abstract: ... sourceforge.net/projects/combenefit/).: Contact: Giovanni.DiVeroli@cruk.cam.ac.uk ...

Abstract	Motivation: Many drug combinations are routinely assessed to identify synergistic interactions in the attempt to develop novel treatment strategies. Appropriate software is required to analyze the results of these studies. Results: We present Combenefit, new free software tool that enables the visualization, analysis and quantification of drug combination effects in terms of synergy and/or antagonism. Data from combinations assays can be processed using classical Synergy models (Loewe, Bliss, HSA), as single experiments or in batch for High Throughput Screens. This user-friendly tool provides laboratory scientists with an easy and systematic way to analyze their data. The companion package provides bioinformaticians with critical implementations of routines enabling the processing of combination data. Availability and implementation: Combenefit is provided as a Matlab package but also as standalone software for Windows (http://sourceforge.net/projects/combenefit/). Contact: Giovanni.DiVeroli@cruk.cam.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.
MeSH term(s)	Computational Biology/methods ; Drug Combinations ; Drug Delivery Systems ; High-Throughput Nucleotide Sequencing ; Software ; User-Computer Interface
Chemical Substances	Drug Combinations
Language	English
Publishing date	2016-04-25
Publishing country	England
Document type	Journal Article
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btw230
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Article: Combenefit: an interactive platform for the analysis and visualization of drug combinations

Di Veroli, Giovanni Y / Fornari, Chiara / Wang, Dennis / Mollard, Séverine / Bramhall, Jo L / Richards, Frances M / Jodrell, Duncan I

Bioinformatics. 2016 Sept. 15, v. 32, no. 18

2016

Abstract: ... sourceforge.net/projects/combenefit/). Contact: Giovanni.DiVeroli@cruk.cam.ac.uk Supplementary information ...

Abstract	Motivation: Many drug combinations are routinely assessed to identify synergistic interactions in the attempt to develop novel treatment strategies. Appropriate software is required to analyze the results of these studies. Results: We present Combenefit, new free software tool that enables the visualization, analysis and quantification of drug combination effects in terms of synergy and/or antagonism. Data from combinations assays can be processed using classical Synergy models (Loewe, Bliss, HSA), as single experiments or in batch for High Throughput Screens. This user-friendly tool provides laboratory scientists with an easy and systematic way to analyze their data. The companion package provides bioinformaticians with critical implementations of routines enabling the processing of combination data. Availability and Implementation: Combenefit is provided as a Matlab package but also as standalone software for Windows (http://sourceforge.net/projects/combenefit/). Contact: Giovanni.DiVeroli@cruk.cam.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.
Keywords	bioinformatics ; combination drug therapy ; computer software ; models ; synergism
Language	English
Dates of publication	2016-0915
Size	p. 2866-2868.
Publishing place	Oxford University Press
Document type	Article
ZDB-ID	1468345-3
ISSN	1460-2059 ; 1367-4811 ; 1367-4803
ISSN (online)	1460-2059 ; 1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btw230
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Article ; Online: An automated fitting procedure and software for dose-response curves with multiphasic features.

Di Veroli, Giovanni Y / Fornari, Chiara / Goldlust, Ian / Mills, Graham / Koh, Siang Boon / Bramhall, Jo L / Richards, Frances M / Jodrell, Duncan I

Scientific reports

2015 Volume 5, Page(s) 14701

Abstract: In cancer pharmacology (and many other areas), most dose-response curves are satisfactorily described by a classical Hill equation (i.e. 4 parameters logistical). Nevertheless, there are instances where the marked presence of more than one point of ... ...

Abstract	In cancer pharmacology (and many other areas), most dose-response curves are satisfactorily described by a classical Hill equation (i.e. 4 parameters logistical). Nevertheless, there are instances where the marked presence of more than one point of inflection, or the presence of combined agonist and antagonist effects, prevents straight-forward modelling of the data via a standard Hill equation. Here we propose a modified model and automated fitting procedure to describe dose-response curves with multiphasic features. The resulting general model enables interpreting each phase of the dose-response as an independent dose-dependent process. We developed an algorithm which automatically generates and ranks dose-response models with varying degrees of multiphasic features. The algorithm was implemented in new freely available Dr Fit software (sourceforge.net/projects/drfit/). We show how our approach is successful in describing dose-response curves with multiphasic features. Additionally, we analysed a large cancer cell viability screen involving 11650 dose-response curves. Based on our algorithm, we found that 28% of cases were better described by a multiphasic model than by the Hill model. We thus provide a robust approach to fit dose-response curves with various degrees of complexity, which, together with the provided software implementation, should enable a wide audience to easily process their own data.
MeSH term(s)	Algorithms ; Dose-Response Relationship, Drug ; Humans ; Models, Theoretical ; Software
Language	English
Publishing date	2015-10-01
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/srep14701
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Article ; Online: hERG inhibitors with similar potency but different binding kinetics do not pose the same proarrhythmic risk: implications for drug safety assessment.

DI Veroli, Giovanni Y / Davies, Mark R / Zhang, Henggui / Abi-Gerges, Najah / Boyett, Mark R

Journal of cardiovascular electrophysiology

2013 Volume 25, Issue 2, Page(s) 197–207

Abstract: Introduction: Since the discovery of the link that exists between drug-induced hERG inhibition and Torsade de Pointes (TdP), extreme attention has been given to avoid new drugs inhibiting this channel. hERG inhibition is routinely screened for in new ... ...

Abstract	Introduction: Since the discovery of the link that exists between drug-induced hERG inhibition and Torsade de Pointes (TdP), extreme attention has been given to avoid new drugs inhibiting this channel. hERG inhibition is routinely screened for in new drugs and, typically, IC50 values are compared to projected plasma concentrations to define a safety margin. Methods and results: We aimed to show that drugs with similar hERG potency are not uniformly pro-arrhythmic-this depends on the drug binding kinetics and mode of action (trapped or not) rather than the IC50 value only. We used a mathematical model of hERG and its related encoded current IKr to simulate drug binding in different configurations. Expression systems mimicking the screening process were first investigated. hERG model was then incorporated into a canine action potential (AP) and tissue model to study the impact of drug binding configurations on AP and pseudo-ECG (QT interval prolongation). Our data show that: (1) trapped and not trapped configurations and different binding kinetics could be identified during hERG screening; (2) slow binding, not trapped drugs, induced less AP prolongation and minimal QT interval prolongation (4.7%) at a concentration equal to the IC50 whereas maximal pro-arrhythmic risk was observed for trapped drugs at the same concentration (QT interval prolongation, 23.1%). Conclusion: Our study demonstrates the need for screening for hERG binding configurations rather than potency alone. It also demonstrates the potential link between hERG, drug mode of action and TdP, and the need to question the current regulatory guidance.
MeSH term(s)	Animals ; Arrhythmias, Cardiac/chemically induced ; Arrhythmias, Cardiac/metabolism ; Binding Sites ; Calcium Channel Blockers/administration & dosage ; Calcium Channel Blockers/adverse effects ; Computer Simulation ; Dogs ; Dose-Response Relationship, Drug ; Drug Evaluation, Preclinical/methods ; ERG1 Potassium Channel ; Ether-A-Go-Go Potassium Channels/antagonists & inhibitors ; Ether-A-Go-Go Potassium Channels/metabolism ; Humans ; Kinetics ; Models, Cardiovascular ; Models, Chemical ; Protein Binding ; Therapeutic Equivalency
Chemical Substances	Calcium Channel Blockers ; ERG1 Potassium Channel ; Ether-A-Go-Go Potassium Channels ; KCNH2 protein, human
Language	English
Publishing date	2013-10-07
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1025989-2
ISSN	1540-8167 ; 1045-3873
ISSN (online)	1540-8167
ISSN	1045-3873
DOI	10.1111/jce.12289
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Article ; Online: Modelling of the cancer cell cycle as a tool for rational drug development

Robert C Jackson / Giovanni Y Di Veroli / Siang-Boon Koh / Ian Goldlust / Frances M Richards / Duncan I Jodrell

PLoS Computational Biology, Vol 13, Iss 5, p e

A systems pharmacology approach to cyclotherapy.

2017 Volume 1005529

Abstract	The dynamic of cancer is intimately linked to a dysregulation of the cell cycle and signalling pathways. It has been argued that selectivity of treatments could exploit loss of checkpoint function in cancer cells, a concept termed "cyclotherapy". Quantitative approaches that describe these dysregulations can provide guidance in the design of novel or existing cancer therapies. We describe and illustrate this strategy via a mathematical model of the cell cycle that includes descriptions of the G1-S checkpoint and the spindle assembly checkpoint (SAC), the EGF signalling pathway and apoptosis. We incorporated sites of action of four drugs (palbociclib, gemcitabine, paclitaxel and actinomycin D) to illustrate potential applications of this approach. We show how drug effects on multiple cell populations can be simulated, facilitating simultaneous prediction of effects on normal and transformed cells. The consequences of aberrant signalling pathways or of altered expression of pro- or anti-apoptotic proteins can thus be compared. We suggest that this approach, particularly if used in conjunction with pharmacokinetic modelling, could be used to predict effects of specific oncogene expression patterns on drug response. The strategy could be used to search for synthetic lethality and optimise combination protocol designs.
Keywords	Biology (General) ; QH301-705.5
Subject code	570
Language	English
Publishing date	2017-05-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
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Article ; Online: High-throughput screening of drug-binding dynamics to HERG improves early drug safety assessment.

Di Veroli, Giovanni Y / Davies, Mark R / Zhang, Henggui / Abi-Gerges, Najah / Boyett, Mark R

American journal of physiology. Heart and circulatory physiology

2012 Volume 304, Issue 1, Page(s) H104–17

Abstract: The use of computational models to predict drug-induced changes in the action potential (AP) is a promising approach to reduce drug safety attrition but requires a better representation of more complex drug-target interactions to improve the quantitative ...

Abstract	The use of computational models to predict drug-induced changes in the action potential (AP) is a promising approach to reduce drug safety attrition but requires a better representation of more complex drug-target interactions to improve the quantitative prediction. The blockade of the human ether-a-go-go-related gene (HERG) channel is a major concern for QT prolongation and Torsade de Pointes risk. We aim to develop quantitative in-silico AP predictions based on a new electrophysiological protocol (suitable for high-throughput HERG screening) and mathematical modeling of ionic currents. Electrophysiological recordings using the IonWorks device were made from HERG channels stably expressed in Chinese hamster ovary cells. A new protocol that delineates inhibition over time was applied to assess dofetilide, cisapride, and almokalant effects. Dynamic effects displayed distinct profiles for these drugs compared with concentration-effects curves. Binding kinetics to specific states were identified using a new HERG Markov model. The model was then modified to represent the canine rapid delayed rectifier K(+) current at 37°C and carry out AP predictions. Predictions were compared with a simpler model based on conductance reduction and were found to be much closer to experimental data. Improved sensitivity to concentration and pacing frequency variables was obtained when including binding kinetics. Our new electrophysiological protocol is suitable for high-throughput screening and is able to distinguish drug-binding kinetics. The association of this protocol with our modeling approach indicates that quantitative predictions of AP modulation can be obtained, which is a significant improvement compared with traditional conductance reduction methods.
MeSH term(s)	Action Potentials ; Animals ; CHO Cells ; Cisapride/toxicity ; Computer Simulation ; Cricetinae ; Cricetulus ; Dogs ; Dose-Response Relationship, Drug ; ERG1 Potassium Channel ; Ether-A-Go-Go Potassium Channels/antagonists & inhibitors ; Ether-A-Go-Go Potassium Channels/genetics ; Ether-A-Go-Go Potassium Channels/metabolism ; High-Throughput Screening Assays/methods ; Humans ; Kinetics ; Long QT Syndrome/chemically induced ; Long QT Syndrome/metabolism ; Markov Chains ; Models, Cardiovascular ; Patch-Clamp Techniques ; Phenethylamines/toxicity ; Potassium Channel Blockers/metabolism ; Potassium Channel Blockers/toxicity ; Propanolamines/toxicity ; Protein Binding ; Risk Assessment ; Sulfonamides/toxicity ; Torsades de Pointes/chemically induced ; Torsades de Pointes/metabolism ; Toxicity Tests ; Transfection
Chemical Substances	ERG1 Potassium Channel ; Ether-A-Go-Go Potassium Channels ; KCNH2 protein, human ; Phenethylamines ; Potassium Channel Blockers ; Propanolamines ; Sulfonamides ; almokalant (I9NG89L275) ; dofetilide (R4Z9X1N2ND) ; Cisapride (UVL329170W)
Language	English
Publishing date	2012-10-26
Publishing country	United States
Document type	Comparative Study ; Journal Article
ZDB-ID	603838-4
ISSN	1522-1539 ; 0363-6135
ISSN (online)	1522-1539
ISSN	0363-6135
DOI	10.1152/ajpheart.00511.2012
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Article ; Online: Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen.

Menden, Michael P / Wang, Dennis / Mason, Mike J / Szalai, Bence / Bulusu, Krishna C / Guan, Yuanfang / Yu, Thomas / Kang, Jaewoo / Jeon, Minji / Wolfinger, Russ / Nguyen, Tin / Zaslavskiy, Mikhail / Jang, In Sock / Ghazoui, Zara / Ahsen, Mehmet Eren / Vogel, Robert / Neto, Elias Chaibub / Norman, Thea / Tang, Eric K Y /

Garnett, Mathew J / Veroli, Giovanni Y Di / Fawell, Stephen / Stolovitzky, Gustavo / Guinney, Justin / Dry, Jonathan R / Saez-Rodriguez, Julio

Nature communications

2019 Volume 10, Issue 1, Page(s) 2674

Abstract: The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal ... ...

Abstract	The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.
MeSH term(s)	ADAM17 Protein/antagonists & inhibitors ; Antineoplastic Combined Chemotherapy Protocols/pharmacology ; Antineoplastic Combined Chemotherapy Protocols/therapeutic use ; Benchmarking ; Biomarkers, Tumor/genetics ; Cell Line, Tumor ; Computational Biology/methods ; Computational Biology/standards ; Datasets as Topic ; Drug Antagonism ; Drug Resistance, Neoplasm/drug effects ; Drug Resistance, Neoplasm/genetics ; Drug Synergism ; Genomics/methods ; Humans ; Molecular Targeted Therapy/methods ; Mutation ; Neoplasms/drug therapy ; Neoplasms/genetics ; Pharmacogenetics/methods ; Pharmacogenetics/standards ; Phosphatidylinositol 3-Kinases/genetics ; Phosphoinositide-3 Kinase Inhibitors ; Treatment Outcome
Chemical Substances	Biomarkers, Tumor ; Phosphoinositide-3 Kinase Inhibitors ; ADAM17 Protein (EC 3.4.24.86) ; ADAM17 protein, human (EC 3.4.24.86)
Language	English
Publishing date	2019-06-17
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-019-09799-2
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Article ; Online: Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

Michael P. Menden / Dennis Wang / Mike J. Mason / Bence Szalai / Krishna C. Bulusu / Yuanfang Guan / Thomas Yu / Jaewoo Kang / Minji Jeon / Russ Wolfinger / Tin Nguyen / Mikhail Zaslavskiy / AstraZeneca-Sanger Drug Combination DREAM Consortium / In Sock Jang / Zara Ghazoui / Mehmet Eren Ahsen / Robert Vogel / Elias Chaibub Neto / Thea Norman /

Eric K. Y. Tang / Mathew J. Garnett / Giovanni Y. Di Veroli / Stephen Fawell / Gustavo Stolovitzky / Justin Guinney / Jonathan R. Dry / Julio Saez-Rodriguez

Nature Communications, Vol 10, Iss 1, Pp 1-

2019 Volume 17

Abstract: Resistance to first line treatment is a major hurdle in cancer treatment, that can be overcome with drug combinations. Here, the authors provide a large drug combination screen across cancer cell lines to benchmark crowdsourced methods and to ... ...

Abstract	Resistance to first line treatment is a major hurdle in cancer treatment, that can be overcome with drug combinations. Here, the authors provide a large drug combination screen across cancer cell lines to benchmark crowdsourced methods and to computationally predict drug synergies.
Keywords	Science ; Q
Language	English
Publishing date	2019-06-01T00:00:00Z
Publisher	Nature Publishing Group
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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