Niklas Blomberg

The FAIR Guiding Principles for scientific data management and stewardship

There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.

Structure of the PPARα and -γ Ligand Binding Domain in Complex with AZ 242; Ligand Selectivity and Agonist Activation in the PPAR Family

Abstract Background: The peroxisome proliferator-activated receptors (PPAR) are ligand-activated transcription factors belonging to the nuclear receptor family. The roles of PPARα in fatty acid oxidation and PPARγ in adipocyte differentiation and lipid storage have been characterized extensively. PPARs are activated by fatty acids and eicosanoids and are also targets for antidyslipidemic drugs, but the molecular interactions governing ligand selectivity for specific subtypes are unclear due to the lack of a PPARα ligand binding domain structure. Results: We have solved the crystal structure of the PPARα ligand binding domain (LBD) in complex with the combined PPARα and -γ agonist AZ 242, a novel dihydro cinnamate derivative that is structurally different from thiazolidinediones. In addition, we present the crystal structure of the PPARγ_LBD/AZ 242 complex and provide a rationale for ligand selectivity toward the PPARα and -γ subtypes. Heteronuclear NMR data on PPARα in both the apo form and in complex with AZ 242 shows an overall stabilization of the LBD upon agonist binding. A comparison of the novel PPARα/AZ 242 complex with the PPARγ/AZ 242 complex and previously solved PPARγ structures reveals a conserved hydrogen bonding network between agonists and the AF2 helix. Conclusions: The complex of PPARα and PPARγ with the dual specificity agonist AZ 242 highlights the conserved interactions required for receptor activation. Together with the NMR data, this suggests a general model for ligand activation in the PPAR family. A comparison of the ligand binding sites reveals a molecular explanation for subtype selectivity and provides a basis for rational drug design.

An Integrated Approach to Fragment-Based Lead Generation:Philosophy, Strategy and Case Studies from AstraZenecas Drug Discovery Programmes

Fragment-based lead generation (FBLG) has recently emerged as an alternative to traditional high throughput screening (HTS) to identify initial chemistry starting points for drug discovery programs. In comparison to HTS screening libraries, the screening sets for FBLG tend to contain orders of magnitude fewer compounds, and the compounds themselves are less structurally complex and have lower molecular weight. This report summarises the advent of FBLG within the industry and then describes the FBLG experience at AstraZeneca. We discuss (1) optimising the design of screening libraries, (2) hit detection methodologies, (3) evaluation of hit quality and use of ligand efficiency calculations, and (4) approaches to evolve fragment-based, low complexity hits towards drug-like leads. Furthermore, we exemplify our use of FBLG with case studies in the following drug discovery areas: antibacterial enzyme targets, GPCRs (melanocortin 4 receptor modulators), prostaglandin D2 synthase inhibitors, phosphatase inhibitors (protein tyrosine phosphotase 1B), and protease inhibitors (b-secretase).

Big pharma screening collections: more of the same or unique libraries? The AstraZeneca–Bayer Pharma AG case

In this study, the screening collections of two major pharmaceutical companies (AstraZeneca and Bayer Pharma AG) have been compared using a 2D molecular fingerprint by a nearest neighborhood approach. Results revealed a low overlap between both collections in terms of compound identity and similarity. This emphasizes the value of screening multiple compound collections to expand the chemical space that can be accessed by high-throughput screening (HTS).

Strategies to improve in vivo toxicology outcomes for basic candidate drug molecules

A valid PLS-DA model to predict attrition in pre-clinical toxicology for basic oral candidate drugs was built. A combination of aromatic/aliphatic balance, flatness, charge distribution and size descriptors helped predict the successful progression of compounds through a wide range of toxicity testing. Eighty percent of an independent test set of marketed post-2000 basic drugs could be successfully classified using the model, indicating useful forward predictivity. The themes within this work provide additional guidance for medicinal design chemists and complement other literature property guidelines.

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.

Multifingerprint based similarity searches for targeted class compound selection

Molecular fingerprints are widely used for similarity-based virtual screening in drug discovery projects. In this paper we discuss the performance and the complementarity of nine two-dimensional fingerprints (Daylight, Unity, AlFi, Hologram, CATS, TRUST, Molprint 2D, ChemGPS, and ALOGP) in retrieving active molecules by similarity searching against a set of query compounds. For this purpose, we used biological data from HTS screening campaigns of four protein families (GPCRs, kinases, ion channels, and proteases). We have established threshold values for the similarity index (Tanimoto index) to be used as starting points for similarity searches. Based on the complementarities between the selections made by using different fingerprints we propose a multifingerprint approach as an efficient tool to balance the strengths and weaknesses of various fingerprints.

Physicochemical property profiles of marketed drugs, clinical candidates and bioactive compounds

We performed a comparison of several simple physicochemical properties between marketed drugs, clinical candidates and bioactive compounds using commercially available databases (GVKBIO, Hyderabad, India). In contrast to previous studies this comparison was performed at the individual target level. Confirming earlier studies this shows that marketed drugs have, on average and taken as a single set, lower physicochemical property values than the corresponding clinical candidates and bioactive compounds but that there is considerable variation between drug targets. This work complements earlier studies by using a much larger annotated dataset and confirms that there is a shift in physicochemical properties for targets with launched drugs and clinical candidates compared to bioactive compounds.

A comparative analysis of the molecular topologies for drugs, clinical candidates, natural products, human metabolites and general bioactive compounds

Natural Products (NPs) and their subset Human Metabolites (HMs) are synthesized in living organisms and, due to their biogenic nature, can be used as starting points for drug discovery projects as well as a source of inspiration for designing new chemical libraries. It is therefore of interest to characterize NPs and HMs in relation to other types of molecules relevant for drug discovery. In this study, a comparative analysis of the molecular topologies was carried out for NPs, HMs, drugs from different time periods, clinical candidates and general bioactive compounds. It is shown that the NP and HM sets have the highest percentage of compounds with only one ring system. NPs have also the highest Ring System Complexity (RSC) of the compared datasets, while general bioactive compounds have the largest number of ring systems. The difference in molecular topology between the datasets is independent of the molecular size and lipophilicity. Further analysis of topological descriptors shows that NPs and HMs have a larger proportion of side chain atoms in relation to their size and have a higher proportion of aliphatic carbons (indicating a more three-dimensional shape) than compounds from the other sets.

Identifiers for the 21st century: How to design, provision, and reuse persistent identifiers to maximize utility and impact of life science data

In many disciplines, data are highly decentralized across thousands of online databases (repositories, registries, and knowledgebases). Wringing value from such databases depends on the discipline of data science and on the humble bricks and mortar that make integration possible; identifiers are a core component of this integration infrastructure. Drawing on our experience and on work by other groups, we outline 10 lessons we have learned about the identifier qualities and best practices that facilitate large-scale data integration. Specifically, we propose actions that identifier practitioners (database providers) should take in the design, provision and reuse of identifiers. We also outline the important considerations for those referencing identifiers in various circumstances, including by authors and data generators. While the importance and relevance of each lesson will vary by context, there is a need for increased awareness about how to avoid and manage common identifier problems, especially those related to persistence and web-accessibility/resolvability. We focus strongly on web-based identifiers in the life sciences; however, the principles are broadly relevant to other disciplines.