A large-scale dataset of in vivo pharmacology assay results

The ChEMBL Database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods

ChEMBL (https://www.ebi.ac.uk/chembl/) is a manually curated, high-quality, large-scale, open, FAIR and Global Core Biodata Resource of bioactive molecules with drug-like properties, previously described in the 2012, 2014, 2017 and 2019 Nucleic Acids Research Database Issues. Since its introduction in 2009, ChEMBL's content has changed dramatically in size and diversity of data types. Through incorporation of multiple new datasets from depositors since the 2019 update, ChEMBL now contains slightly more bioactivity data from deposited data vs data extracted from literature. In collaboration with the EUbOPEN consortium, chemical probe data is now regularly deposited into ChEMBL. Release 27 made curated data available for compounds screened for potential anti-SARS-CoV-2 activity from several large-scale drug repurposing screens. In addition, new patent bioactivity data have been added to the latest ChEMBL releases, and various new features have been incorporated, including a Natural Product likeness score, updated flags for Natural Products, a new flag for Chemical Probes, and the initial annotation of the action type for ∼270 000 bioactivity measurements.

An extension of the BioAssay Ontology to include pharmacokinetic/pharmacodynamic terminology for the enrichment of scientific workflows

With the capacity to produce and record data electronically, Scientific research and the data associated with it have grown at an unprecedented rate. However, despite a decent amount of data now existing in an electronic form, it is still common for scientific research to be recorded in an unstructured text format with inconsistent context (vocabularies) which vastly reduces the potential for direct intelligent analysis. Research has demonstrated that the use of semantic technologies such as ontologies to structure and enrich scientific data can greatly improve this potential. However, whilst there are many ontologies that can be used for this purpose, there is still a vast quantity of scientific terminology that does not have adequate semantic representation. A key area for expansion identified by the authors was the pharmacokinetic/pharmacodynamic (PK/PD) domain due to its high usage across many areas of Pharma. As such we have produced a set of these terms and other bioassay related terms to be incorporated into the BioAssay Ontology (BAO), which was identified as the most relevant ontology for this work. A number of use cases developed by experts in the field were used to demonstrate how these new ontology terms can be used, and to set the scene for the continuation of this work with a look to expanding this work out into further relevant domains. The work done in this paper was part of Phase 1 of the SEED project (Semantically Enriching electronic laboratory notebook (eLN) Data).

Development of In Silico Methods for Toxicity Prediction in Collaboration Between Academia and the Pharmaceutical Industry

The pharmaceutical industry would benefit from the collaboration with academic groups in the development of predictive safety models using the newest computational technologies. However, this collaboration is sometimes hampered by the handling of confidential proprietary information and different working practices in both environments. In this manuscript, we propose a strategy for facilitating this collaboration, based on the use of modeling frameworks developed for facilitating the use of sensitive data, as well as the development, interchange, hosting, and use of predictive models in production. The strategy is illustrated with a real example in which we used Flame, an open-source modeling framework developed in our group, for the development of an in silico eye irritation model. The model was based on bibliographic data, refined during the company-academic group collaboration, and enriched with the incorporation of confidential data, yielding a useful model that was validated experimentally.

Preliminary survey of the attitudes of Brazilian scientists towards pain management and assessment in animals used in science

OBJECTIVE

To investigate the current scenario in Brazil regarding pain assessment and control in experimental animals.

STUDY DESIGN

Prospective survey.

METHODS

A confidential questionnaire was available online and sent by e-mail to Brazilian scientists working with animal experimentation in Brazil. Data collection was conducted from October 2016 to October 2017. The exclusion criteria included blank questionnaires or with <80% completed responses, researchers not performing experiments involving animals and foreign scientists.

RESULTS

A total of 96 questionnaires from 104 respondents were analyzed. The Fisher's exact test showed a disparity between the proportions of scientists who recognized the importance of analgesia and their application of analgesic techniques in painful procedures (p < 0.0003), and also for the researchers who assumed that experiments inflicted pain and their classification of the degree of invasiveness (p < 0.0001), indicating their insufficient knowledge of these topics. Overall, 77% of institutions did not offer specific training to assess pain in experimental animals, and 24% of respondents had no training to work with animal experimentation. In total, 62% of the studies inflicted pain, 48% of respondents used pain scales, and the drugs administered most frequently for pain management were morphine (44%), meloxicam (43%) and tramadol (37%); 15% of respondents did not include analgesics even though their studies inflicted pain. Commonly used animals were rats (33%), mice (29%) and rabbits (8%).

CONCLUSIONS AND CLINICAL RELEVANCE

The results of this preliminary survey indicated that in Brazil there is a gap in the knowledge and training on pain assessment and management of experimental animals. Therefore, there is a necessity for an educational program to prepare and train scientists to assess and manage pain in laboratory or experimental animals. Further studies using a psychometrically validated survey instrument are warranted.

Evolving applications of the egg: chorioallantoic membrane assay and ex vivo organotypic culture of materials for bone tissue engineering

The chick chorioallantoic membrane model has been around for over a century, applied in angiogenic, oncology, dental and xenograft research. Despite its often perceived archaic, redolent history, the chorioallantoic membrane assay offers new and exciting opportunities for material and growth factor evaluation in bone tissue engineering. Currently, superior/improved experimental methodology for the chorioallantoic membrane assay are difficult to identify, given an absence of scientific consensus in defining experimental approaches, including timing of inoculation with materials and the analysis of results. In addition, critically, regulatory and welfare issues impact upon experimental designs. Given such disparate points, this review details recent research using the ex vivo chorioallantoic membrane assay and the ex vivo organotypic culture to advance the field of bone tissue engineering, and highlights potential areas of improvement for their application based on recent developments within our group and the tissue engineering field.

Precision medicine review: rare driver mutations and their biophysical classification

How can biophysical principles help precision medicine identify rare driver mutations? A major tenet of pragmatic approaches to precision oncology and pharmacology is that driver mutations are very frequent. However, frequency is a statistical attribute, not a mechanistic one. Rare mutations can also act through the same mechanism, and as we discuss below, “latent driver” mutations may also follow the same route, with “helper” mutations. Here, we review how biophysics provides mechanistic guidelines that extend precision medicine. We outline principles and strategies, especially focusing on mutations that drive cancer. Biophysics has contributed profoundly to deciphering biological processes. However, driven by data science, precision medicine has skirted some of its major tenets. Data science embodies genomics, tissue- and cell-specific expression levels, making it capable of defining genome- and systems-wide molecular disease signatures. It classifies cancer driver genes/mutations and affected pathways, and its associated protein structural data guide drug discovery. Biophysics complements data science. It considers structures and their heterogeneous ensembles, explains how mutational variants can signal through distinct pathways, and how allo-network drugs can be harnessed. Biophysics clarifies how one mutation—frequent or rare—can affect multiple phenotypic traits by populating conformations that favor interactions with other network modules. It also suggests how to identify such mutations and their signaling consequences. Biophysics offers principles and strategies that can help precision medicine push the boundaries to transform our insight into biological processes and the practice of personalized medicine. By contrast, “phenotypic drug discovery,” which capitalizes on physiological cellular conditions and first-in-class drug discovery, may not capture the proper molecular variant. This is because variants of the same protein can express more than one phenotype, and a phenotype can be encoded by several variants.