Taking Open Innovation to the Molecular Level - Strengths and Limitations

Open Data for Open Innovation: An Analysis of Literature Characteristics

In this paper, we review some characteristics of the literature that studies the uses and applications of open data for open innovation. Three research questions are proposed about both topics: (1) What journals, conferences and authors have published papers about the use of open data for open innovation? (2) What knowledge areas have been analysed in research on open data for open innovation? and (3) What are the methodological characteristics of the papers on open data for open innovation? To answer the first question, we use a descriptive analysis to identify the relevant journals and authors. To address the second question, we identify the knowledge areas of the studies about open data for open innovation. Finally, we analyse the methodological characteristics of the literature (type of study, analytical techniques, sources of information and geographical area). Our results show that the applications of open data for open innovation are interesting but their multidisciplinary nature makes the context complex and diverse, opening up many future avenues for research. To develop a future research agenda, we propose a theoretical model and some research questions to analyse the open data impact process for open innovation.

3D Modeling of Tumor Necrosis Factor Receptor and Tumor Necrosis Factor-bound Receptor Systems

The interactions between the tumor necrosis factor (TNF) and its receptor molecule are responsible for various signaling networks that are central to the functioning of human immune homeostasis. The present work is a computational study of certain structural aspects of this cell-signaling protein, specifically focusing on the molecular level analyses of the TNF receptor (TNF-R), guided by its crystallographic structure. We also examine the possible binding sites of the TNF onto TNF-R, and the associated interactions. The structural and conformational variations in the TNF-R and TNF bound TNF-R systems are examined in this context using molecular dynamics (MD) simulations. The time dependent variations of the dimeric TNF-R structures are compared with, and shown to be steadier than their isolated monomers. This dimeric stability is favored under acidic conditions. The results are used to further illustrate how 3D modeling and computer simulations can aid the structure-based approach to probing a ligand-receptor system.

Crowdsourcing in pharma: a strategic framework

Conceptually, all organizations can be described as coordinated actors working together to deliver a product(s), or provide a service(s). For organizations to remain competitive, it is important to have processes that look outward for external 'innovations' that could improve how work is done, and what is delivered. We present a comprehensive review of a variety of processes that pharmaceutical companies have used to engage external actors ('the crowd') to provide innovation in the service of delivering novel therapeutic agents. This culminates in a framework that provides a consolidated view of crowdsourcing processes, which in turn enables a strategic application of a crowdsourcing methodology based on problem type.

How Far Could We Go with Open Data - A Case Study for TRPV1 Antagonists

Publicly open databases of small compounds have become an indispensable tool for chemoinformaticians for collection and preparation of datasets suitable for drug discovery questions. Since these databases comprise compounds coming from structure-activity relationship (SAR) studies performed by different research groups, they are very diverse with respect to the biological assays used. In the present study we analyzed the applicability of a thoroughly curated dataset gathered from open sources for ligand-based studies, using the transient receptor potential vanilloid type 1 (TRPV1) as use case. Thorough curation of compounds according to the biological assay type and conditions led to a dataset of comparable bioactive chemicals. Subsequent exhaustive analysis of the obtained dataset using classification algorithms demonstrated that the models obtained in most of the cases possess reliable quality. Analysis of constantly misclassified compounds showed that they belong to local SAR series, where small changes in structure lead to different class labels. These small structural differences could not be captured by the classification algorithms. However application of the 3D alignment-independent QSAR technique GRIND for local, structurally related series overcomes this problem.

Annotating Human P-Glycoprotein Bioassay Data

Huge amounts of small compound bioactivity data have been entering the public domain as a consequence of open innovation initiatives. It is now the time to carefully analyse existing bioassay data and give it a systematic structure. Our study aims to annotate prominent in vitro assays used for the determination of bioactivities of human P-glycoprotein inhibitors and substrates as they are represented in the ChEMBL and TP-search open source databases. Furthermore, the ability of data, determined in different assays, to be combined with each other is explored. As a result of this study, it is suggested that for inhibitors of human P-glycoprotein it is possible to combine data coming from the same assay type, if the cell lines used are also identical and the fluorescent or radiolabeled substrate have overlapping binding sites. In addition, it demonstrates that there is a need for larger chemical diverse datasets that have been measured in a panel of different assays. This would certainly alleviate the search for other inter-correlations between bioactivity data yielded by different assay setups.