Dataset Information

The global preclinical antibacterial pipeline.

ABSTRACT: Antibacterial resistance is a great concern and requires global action. A critical question is whether enough new antibacterial drugs are being discovered and developed. A review of the clinical antibacterial drug pipeline was recently published, but comprehensive information about the global preclinical pipeline is unavailable. This Review focuses on discovery and preclinical development projects and has found, as of 1 May 2019, 407 antibacterial projects from 314 institutions. The focus is on Gram-negative pathogens, particularly bacteria on the WHO priority bacteria list. The preclinical pipeline is characterized by high levels of diversity and interesting scientific concepts, with 135 projects on direct-acting small molecules that represent new classes, new targets or new mechanisms of action. There is also a strong trend towards non-traditional approaches, including diverse antivirulence approaches, microbiome-modifying strategies, and engineered phages and probiotics. The high number of pathogen-specific and adjunctive approaches is unprecedented in antibiotic history. Translational hurdles are not adequately addressed yet, especially development pathways to show clinical impact of non-traditional approaches. The innovative potential of the preclinical pipeline compared with the clinical pipeline is encouraging but fragile. Much more work, focus and funding are needed for the novel approaches to result in effective antibacterial therapies to sustainably combat antibacterial resistance.

SUBMITTER: Theuretzbacher U

PROVIDER: S-EPMC7223541 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Introduction: Despite an increase in the quality of clinical trials in stroke recovery, interventions have failed to markedly impact the trajectory of recovery after stroke. Failure may be due to the lack of consideration for the complexity of dose and its articulation within research trials. Prior to commencing the scoping review, we identified two research gaps to be addressed. Firstly, transparent application of a multidimensional definition of dose to clinical trial phases and secondly, the development of a quality tool to critique the articulation of dose across the pipeline. Building on this, we present the protocol for a scoping review that aims to synthesis what is known about dose articulation in stroke recovery in preclinical and clinical populations, and characterize research designs and statistical approaches used in dose articulation trials, and the associated advantages and disadvantages. Methods: The scoping review will apply Arksey and O'Malley's methodological framework. Two systematic searches that target preclinical and clinical literature will be run in Medline and Embase, which will be complimented by consultation with field experts and hand searching of included trials and relevant reviews. Search results will be imported into Covidence for transparent management. One reviewer will screen all abstracts and titles. Two reviewers will screen full text and a third reviewer included to resolve discrepancies. A standardized data charting form will be used to extract information and appraise the intervention description, risk of bias, and quality of both preclinical and clinical studies. Results will be summarized in tabular and narrative format to inform the development of recommendations for future research. Ethics approval is not required as data used will be secondary and de-identified. Conclusion: Development of a new quality tool to appraise the quality of both preclinical and clinical dose studies may serve to strengthen collaborative efforts between the fields. The findings from this review will advance the use of a discovery pipeline in stroke recovery research to ultimately inform clinical practice.

Project description:Single-particle cryo-electron microscopy (cryo-EM) and three-dimensional (3D) image processing have gained importance in the last few years to obtain atomic structures of drug targets. Obtaining atomic-resolution 3D structure better than ~2.5 Å is a standard approach in pharma companies to design and optimize therapeutic compounds against drug targets like proteins. Protein crystallography is the main technique in solving the structures of drug targets at atomic resolution. However, this technique requires protein crystals which in turn is a major bottleneck. It was not possible to obtain the structure of proteins better than 2.5 Å resolution by any other methods apart from protein crystallography until 2015. Recent advances in single-particle cryo-EM and 3D image processing have led to a resolution revolution in the field of structural biology that has led to high-resolution protein structures, thus breaking the cryo-EM resolution barriers to facilitate drug discovery. There are 24 structures solved by single-particle cryo-EM with resolution 2.5 Å or better in the EMDataBank (EMDB) till date. Among these, five cryo-EM 3D reconstructions of proteins in the EMDB have their associated coordinates deposited in Protein Data Bank (PDB), with bound inhibitor/ ligand. Thus, for the first time, single-particle cryo-EM was included in the structure-based drug design (SBDD) pipeline for solving protein structures independently or where crystallography has failed to crystallize the protein. Further, this technique can be complementary and supplementary to protein crystallography field in solving 3D structures. Thus, single-particle cryo-EM can become a standard approach in pharmaceutical industry in the design, validation, and optimization of therapeutic compounds targeting therapeutically important protein molecules during preclinical drug discovery research. The present chapter will describe briefly the history and the principles of single-particle cryo-EM and 3D image processing to obtain atomic-resolution structure of proteins and their complex with their drug targets/ligands.

Dataset Information

The global preclinical antibacterial pipeline.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets