Improving target assessment in biomedical research: the GOT-IT recommendations

Emmerich, Christoph H.; Martínez-Gamboa, Lorena; Hofmann, Martine; Bonin-Andresen, Marc; Arbach, Olga; Schendel, Pascal; Gerlach, Björn; Hempel, K; Bespalov, Anton; Dirnagl, Ulrich; Parnham, Michael J.

doi:10.1038/s41573-020-0087-3

Cited by 132 publications

(80 citation statements)

References 208 publications

(227 reference statements)

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“…Although the biochemical function is an important prerequisite for R&D of drugs, alone, without biological function, it is not sufficient for identifying the potential drug target. [78, 79] Therefore, the putative biological functions of the template-homologs were acquired to predict the role of PUFs for virulence and antibiotic resistance. During the selection of putative drug targets, we have considered the information from the large-scale screens [30, 32-36] about the relationship of target P. aeruginosa PUFs with virulence or essential-processes, conservation of PUFs in other high-priority relevant pathogens and absence of PUF homologs in humans.…”

Section: Discussionmentioning

confidence: 99%

Predicting drug targets by homology modelling of Pseudomonas aeruginosa proteins of unknown function

Babić

Kovačić

2021

Preprint

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Efficacies of antibiotics to treat bacterial infections rapidly decline due to antibiotic resistance. This stimulated the development of novel antibiotics, but most attempts failed. As a response, the idea of mining uncharacterised genes of pathogens to identify potential targets for entirely new classes of antibiotics raised. Without knowing the biochemical function of a protein it is difficult to validate its potential for drug targeting; therefore progress in the functional characterisation of bacterial proteins of an unknown function must be accelerated. Here we present a paradigm for comprehensively predicting biochemical functions of a large set of proteins encoded by hypothetical genes in human pathogens, to identify candidate drug targets. A high-throughput approach based on homology modelling with ten templates per target protein was applied on the set of 2103 P. aeruginosa proteins encoded by hypothetical genes. Obtained >21000 homology modelling results and available biological and biochemical information about several thousand templates was scrutinised to predict the function of reliably modelled proteins of unknown function. This approach resulted in assigning, one or often multiple, putative functions to hundreds of enzymes, ligand-binding proteins and transporters. New biochemical functions were predicted for 41 proteins whose essential or virulence-related roles in P. aeruginosa were already experimentally demonstrated. Eleven of them were shortlisted as promising drug targets which participate in essential pathways (maintaining genome and cell wall integrity), virulence-related processes (adhesion, cell motility, host recognition) or antibiotic resistance, which are general drug targets. These proteins are conserved among other WHO priority pathogens but not in humans, therefore they represent high-potential targets for pre-clinical studies. These and many more biochemical functions assigned to uncharacterised proteins of P. aeruginosa, available as PaPUF database may guide the design of experimental screening of inhibitors which is a crucial step toward validation of the most potential targets for the development of novel drugs against P. aeruginosa and other high-priority pathogens.

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Section: Discussionmentioning

confidence: 99%

Predicting drug targets by homology modelling of Pseudomonas aeruginosa proteins of unknown function

Babić

Kovačić

2021

Preprint

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“…Although biochemical function is an important prerequisite for drug R&D, it is not sufficient alone, without biological function, for identifying potential drug targets [ 78 , 79 ]. Therefore, the putative biological functions of the template homologues were used to predict the roles of PUFs in virulence and antibiotic resistance.…”

Section: Discussionmentioning

confidence: 99%

Predicting drug targets by homology modelling of Pseudomonas aeruginosa proteins of unknown function

Babić

Kovačić

2021

PLoS ONE

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The efficacy of antibiotics to treat bacterial infections declines rapidly due to antibiotic resistance. This problem has stimulated the development of novel antibiotics, but most attempts have failed. Consequently, the idea of mining uncharacterized genes of pathogens to identify potential targets for entirely new classes of antibiotics was proposed. Without knowing the biochemical function of a protein, it is difficult to validate its potential for drug targeting; therefore, the functional characterization of bacterial proteins of unknown function must be accelerated. Here, we present a paradigm for comprehensively predicting the biochemical functions of a large set of proteins encoded by hypothetical genes in human pathogens to identify candidate drug targets. A high-throughput approach based on homology modelling with ten templates per target protein was applied to the set of 2103 P. aeruginosa proteins encoded by hypothetical genes. The >21000 homology modelling results obtained and available biological and biochemical information about several thousand templates were scrutinized to predict the function of reliably modelled proteins of unknown function. This approach resulted in assigning one or often multiple putative functions to hundreds of enzymes, ligand-binding proteins and transporters. New biochemical functions were predicted for 41 proteins whose essential or virulence-related roles in P. aeruginosa were already experimentally demonstrated. Eleven of them were shortlisted as promising drug targets that participate in essential pathways (maintaining genome and cell wall integrity), virulence-related processes (adhesion, cell motility, host recognition) or antibiotic resistance, which are general drug targets. These proteins are conserved in other WHO priority pathogens but not in humans; therefore, they represent high-potential targets for preclinical studies. These and many more biochemical functions assigned to uncharacterized proteins of P. aeruginosa, made available as PaPUF database, may guide the design of experimental screening of inhibitors, which is a crucial step towards the validation of the highest-potential targets for the development of novel drugs against P. aeruginosa and other high-priority pathogens.

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“…Our lab has established a quality control system in order to standardize our data generation and acquisition procedures, aiming to elevate the relevance of our research for clinical application [ 22 ]. To this end, we were inspired by the Guidelines On Target Assessment for Innovative Therapeutics (GOT-IT), which incorporate target-drug concentration range validation [ 28 ]. As far as determining the right dosage when translating from in vitro to human application is concerned, allometric scaling, pharmacokinetics/pharmacodynamics, and physiologically-based pharmacokinetic models can be used [ 29 ].…”

Section: Discussionmentioning

confidence: 99%

Different Calculation Strategies Are Congruent in Determining Chemotherapy Resistance of Brain Tumors In Vitro

Fischer

Nickel

Qin

et al. 2020

Cells

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In cancer pharmacology, a drug candidate’s therapeutic potential is typically expressed as its ability to suppress cell growth. Different methods in assessing the cell phenotype and calculating the drug effect have been established. However, inconsistencies in drug response outcomes have been reported, and it is still unclear whether and to what extent the choice of data post-processing methods is responsible for that. Studies that systematically examine these questions are rare. Here, we compare three established calculation methods on a collection of nine in vitro models of glioblastoma, exposed to a library of 231 clinical drugs. The therapeutic potential of the drugs is determined on the growth curves, using growth inhibition 50% (GI50) and point-of-departure (PoD) as the criteria. An effect is detected on 36% of the drugs when relying on GI50 and on 27% when using PoD. For the area under the curve (AUC), a threshold of 9.5 or 10 could be set to discriminate between the drugs with and without an effect. GI50, PoD, and AUC are highly correlated. The ranking of substances by different criteria varies somewhat, but the group of the top 20 substances according to one criterion typically includes 17–19 top candidates according to another. In addition to generating preclinical values with high clinical potential, we present off-target appreciation of top substance predictions by interrogating the drug response data of non-cancer cells in our calculation technology.

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Improving target assessment in biomedical research: the GOT-IT recommendations

Cited by 132 publications

References 208 publications

Predicting drug targets by homology modelling of Pseudomonas aeruginosa proteins of unknown function

Predicting drug targets by homology modelling of Pseudomonas aeruginosa proteins of unknown function

Predicting drug targets by homology modelling of Pseudomonas aeruginosa proteins of unknown function

Different Calculation Strategies Are Congruent in Determining Chemotherapy Resistance of Brain Tumors In Vitro

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