Thioredoxin-1 PEGylation as an In Vitro Method for Drug Target Identification

2019 ◽  
pp. 143-149
Author(s):  
Jolanta Skalska
Keyword(s):  
Drug Target ◽  
Target Identification ◽  
In Vitro Method ◽  
Drug Target Identification ◽  
Thioredoxin 1

scholarly journals Preclinical validation of therapeutic targets predicted by tensor factorization on heterogeneous graphs

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Saee Paliwal ◽  
Alex de Giorgio ◽  
Daniel Neil ◽  
Jean-Baptiste Michel ◽  
Alix MB Lacoste
Keyword(s):  
Drug Target ◽  
Disease Gene ◽  
Historical Data ◽  
State Of The Art ◽  
Target Identification ◽  
Tensor Factorization ◽  
Prioritization Method ◽  
Drug Target Identification ◽  
Heterogeneous Knowledge

Abstract Incorrect drug target identification is a major obstacle in drug discovery. Only 15% of drugs advance from Phase II to approval, with ineffective targets accounting for over 50% of these failures1–3. Advances in data fusion and computational modeling have independently progressed towards addressing this issue. Here, we capitalize on both these approaches with Rosalind, a comprehensive gene prioritization method that combines heterogeneous knowledge graph construction with relational inference via tensor factorization to accurately predict disease-gene links. Rosalind demonstrates an increase in performance of 18%-50% over five comparable state-of-the-art algorithms. On historical data, Rosalind prospectively identifies 1 in 4 therapeutic relationships eventually proven true. Beyond efficacy, Rosalind is able to accurately predict clinical trial successes (75% recall at rank 200) and distinguish likely failures (74% recall at rank 200). Lastly, Rosalind predictions were experimentally tested in a patient-derived in-vitro assay for Rheumatoid arthritis (RA), which yielded 5 promising genes, one of which is unexplored in RA.

scholarly journals Drug Target Identification with Machine Learning: How to Choose Negative Examples

2021 ◽  
Vol 22 (10) ◽  
pp. 5118
Author(s):  
Matthieu Najm ◽  
Chloé-Agathe Azencott ◽  
Benoit Playe ◽  
Véronique Stoven
Keyword(s):  
Machine Learning ◽  
Drug Target ◽  
Target Identification ◽  
Target Prediction ◽  
False Positives ◽  
Machine Learning Algorithms ◽  
Statistical Bias ◽  
Protein Targets ◽  
Drug Target Identification ◽  
Approved Drugs

Identification of the protein targets of hit molecules is essential in the drug discovery process. Target prediction with machine learning algorithms can help accelerate this search, limiting the number of required experiments. However, Drug-Target Interactions databases used for training present high statistical bias, leading to a high number of false positives, thus increasing time and cost of experimental validation campaigns. To minimize the number of false positives among predicted targets, we propose a new scheme for choosing negative examples, so that each protein and each drug appears an equal number of times in positive and negative examples. We artificially reproduce the process of target identification for three specific drugs, and more globally for 200 approved drugs. For the detailed three drug examples, and for the larger set of 200 drugs, training with the proposed scheme for the choice of negative examples improved target prediction results: the average number of false positives among the top ranked predicted targets decreased, and overall, the rank of the true targets was improved.Our method corrects databases’ statistical bias and reduces the number of false positive predictions, and therefore the number of useless experiments potentially undertaken.

scholarly journals Some Perspectives on Network Modeling in Therapeutic Target Prediction

2013 ◽  
Vol 5 ◽  
pp. BECB.S10793 ◽  
Author(s):  
Reka Albert ◽  
Bhaskar DasGupta ◽  
Nasim Mobasheri
Keyword(s):  
Therapeutic Target ◽  
Drug Target ◽  
Biological Network ◽  
Target Identification ◽  
Target Prediction ◽  
Identification Problem ◽  
Pharmaceutical Companies ◽  
Specific Behavior ◽  
Drug Target Identification ◽  
Key Steps

Drug target identification is of significant commercial interest to pharmaceutical companies, and there is a vast amount of research done related to the topic of therapeutic target identification. Interdisciplinary research in this area involves both the biological network community and the graph algorithms community. Key steps of a typical therapeutic target identification problem include synthesizing or inferring the complex network of interactions relevant to the disease, connecting this network to the disease-specific behavior, and predicting which components are key mediators of the behavior. All of these steps involve graph theoretical or graph algorithmic aspects. In this perspective, we provide modelling and algorithmic perspectives for therapeutic target identification and highlight a number of algorithmic advances, which have gotten relatively little attention so far, with the hope of strengthening the ties between these two research communities.

Drug Target Identification in Tissues by Thermal Proteome Profiling

2021 ◽  
Vol 62 (1) ◽  
Author(s):  
André Mateus ◽  
Nils Kurzawa ◽  
Jessica Perrin ◽  
Giovanna Bergamini ◽  
Mikhail M. Savitski
Keyword(s):  
Drug Target ◽  
Target Identification ◽  
Annual Review ◽  
Publication Date ◽  
Proteome Profiling ◽  
Biological Phenomena ◽  
Target Deconvolution ◽  
Drug Target Identification ◽  
Recent Developments ◽  
Fundamental Biology

Drug target deconvolution can accelerate the drug discovery process by identifying a drug's targets (facilitating medicinal chemistry efforts) and off-targets (anticipating toxicity effects or adverse drug reactions). Multiple mass spectrometry–based approaches have been developed for this purpose, but thermal proteome profiling (TPP) remains to date the only one that does not require compound modification and can be used to identify intracellular targets in living cells. TPP is based on the principle that the thermal stability of a protein can be affected by its interactions. Recent developments of this approach have expanded its applications beyond drugs and cell cultures to studying protein-drug interactions and biological phenomena in tissues. These developments open up the possibility of studying drug treatment or mechanisms of disease in a holistic fashion, which can result in the design of better drugs and lead to a better understanding of fundamental biology. Expected final online publication date for the Annual Review of Pharmacology and Toxicology, Volume 62 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Sign in / Sign up

Export Citation Format

Share Document