Bryn Williams-Jones

Lowering industry firewalls: pre-competitive informatics initiatives in drug discovery

Pharmaceutical research and development is facing substantial challenges that have prompted the industry to shift funding from early- to late-stage projects. Among the effects is a major change in the attitude of many companies to their internal bioinformatics resources: the focus has moved from the vigorous pursuit of intellectual property towards exploration of pre-competitive cross-industry collaborations and engagement with the public domain. High-quality, open and accessible data are the foundation of pre-competitive research, and strong public–private partnerships have considerable potential to enhance public data resources, which would benefit everyone engaged in drug discovery. In this article, we discuss the background to these changes and propose new areas of collaboration in computational biology and chemistry between the public domain and the pharmaceutical industry.

Scientific competency questions as the basis for semantically enriched open pharmacological space development

Molecular information systems play an important part in modern data-driven drug discovery. They do not only support decision making but also enable new discoveries via association and inference. In this review, we outline the scientific requirements identified by the Innovative Medicines Initiative (IMI) Open PHACTS consortium for the design of an open pharmacological space (OPS) information system. The focus of this work is the integration of compound-target-pathway-disease/phenotype data for public and industrial drug discovery research. Typical scientific competency questions provided by the consortium members will be analyzed based on the underlying data concepts and associations needed to answer the questions. Publicly available data sources used to target these questions as well as the need for and potential of semantic web-based technology will be presented.

Precompetitive activity to address the biological data needs of drug discovery

The efficiency and effectiveness of target selection and validation could be improved with accessible, standardized and integrated biological reference data sets. Such resources should be established through precompetitive approaches.

Open Innovation in Drug Discovery.

Open innovation is considered a promising strategy by which pharmaceutical industry may overcome the current crisis caused by declining productivity and decreasing numbers of NCEs. As drug discovery is a data-driven process, the amount and diversity of drug discovery data in the omics and high-throughput driven paradigms have significantly grown to the point where standard relational data models reach their performance limits both in terms of technical and scientific capabilities. In addition to the need for integrating this vast amount of data, which is spread over hundreds of public and private data bases, it is recognized that providing capabilities for semantic inference is a key challenge and offers a wealth of opportunities. However, data organization, integration and management are far from being trivial. One of the key challenges is that current data sources are largely incompatible with massive computational approaches and the vast majority of drugdiscovery sources cannot easily interoperate. Recently developed data and text mining approaches, improved data capture standards, and leveraging semantic web technology open a first-time-opportunity to achieve interoperability through the semantic harmonization of data in key data sources. One of the pioneering semantic molecular information systems was developed by David Wild and his group at Indiana University with the Chem2Bio2RDF system. Recognizing the challenges for knowledge management and data mining in the new century, the EU (European Union) and EFPIA (European Federation of Pharmaceutical Industry and Associations) founded the IMI (Innovative Medicines Initiative) Joint Undertaking, a public private partnership devoted to target current bottlenecks in drug discovery and development, thereby focusing on precompetitive areas. One of the projects funded is Open PHACTS (Open PhArmacological Concepts Triple Store), which brings together 28 academic and pharmaceutical partners to design and implement an open source, open standards and open access innovation platform, the Open Pharmacological Space (OPS), via a semantic web approach. OPS will comprise data, vocabularies and infrastructure needed to accelerate pharmaceutical research. A key feature of the OPS is the openness for new data additions which could include data from text mining of scientific publications as well as opportunities for integration with proprietary or commercial data sources. Another key aspect is the development of novel visualization and data analysis tools that facilitate the navigation and knowledge extraction from all the integrated data. With this special issue, we want to highlight some of the major challenges and opportunities linked to open innovation concepts. In her review, Zdrazil and colleagues provide an overview on the strengths and limitations of open innovation with a special focus on small molecules. Stierand et al. , Eriksson et al. , as well as Carrascosa et al. present new, innovative tools for visualizing and mining the huge amount of data available by semantic integration of public data bases, such as provided by the Open PHACTS platform. Ekins and colleagues, as well as Clark et al. , present future approaches and tools for connecting mobile devices to large open data depositories. In her article, Pinto and colleagues show the utilization of public available data for creation of in silico classification models for substrates for an ABC-transporter. Finally, as proper usage of public data for drug discovery can only be assured by a thorough investigation of data quality and integrity, Zdrazil et al. provide an analysis of the data available in ChEMBL for inhibitors of the Multidrug Resistance Protein 1 (P-glycoprotein). All articles are available as open access at www.molinf.com, thanks to financial support from Open PHACTS and WileyVCH.

CHAPTER 13:Bioinformatics for Medicinal Chemistry

With the cost of whole genome sequencing rapidly decreasing to less than

Toward interoperable bioscience data

1000 per patient genomics will have a profound impact on health research and drug discovery. Stratified medicine projects are dissecting the mechanisms of complex diseases such as diabetes, asthma and cardiovascular diseases to identify patient populations for targeted treatment and oncology research, and increasingly care is being informed by the identification of the underlying genomic drivers. When the medicines researched in today’s drug discovery programmes reach the market many, if not most, patients will have their genome sequenced and pharmacogenetic considerations will likely inform both treatment and dosage regimen. This chapter gives an overview of bioinformatic tools and resources available for medicinal chemists in preclinical research programmes. The focus is on public, open access resources, in particular for generating overviews of function, modification and population variants of protein targets, generating 3D structural models to further analyse variants and homologs from animal models. Finally resources for pharmacogenetics and data integration are covered with an aim to provide practical starting points for drug discovery scientists.