An Update on the Pharmacological Usage of Curcumin: Has it Failed in the Drug Discovery Pipeline?

2020 ◽  
Vol 78 (3) ◽  
pp. 267-289 ◽  
Author(s):  
Fisayo Olotu ◽  
Clement Agoni ◽  
Opeyemi Soremekun ◽  
Mahmoud E. S. Soliman
Keyword(s):  
Drug Discovery ◽  
Discovery Pipeline

The Discovery Channel: microfluidics and microengineered systems in drug screening

2015 ◽  
Vol 7 (3) ◽  
pp. 285-288 ◽  
Author(s):  
Christopher Moraes
Keyword(s):  
Drug Discovery ◽  
Engineering Design ◽  
High Throughput ◽  
Drug Screening ◽  
High Throughput Screening ◽  
Recent Literature ◽  
Analytical Strategies ◽  
Discovery Pipeline

We highlight exciting findings and promising approaches in the recent literature in which researchers integrate advanced micro-engineering, design, and analytical strategies to improve the relevance and utility of high-throughput screening in the drug discovery pipeline.

Drug-Discovery Pipeline for Novel Inhibitors of the Androgen Receptor

2016 ◽  
pp. 31-54 ◽  
Author(s):  
Kush Dalal ◽  
Ravi Munuganti ◽  
Hélène Morin ◽  
Nada Lallous ◽  
Paul S. Rennie ◽  
...  
Keyword(s):  
Androgen Receptor ◽  
Drug Discovery ◽  
Discovery Pipeline

scholarly journals Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19

2020 ◽  
Vol 60 (12) ◽  
pp. 5832-5852 ◽  
Author(s):  
A. Acharya ◽  
R. Agarwal ◽  
M. B. Baker ◽  
J. Baudry ◽  
D. Bhowmik ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Ensemble Docking ◽  
Discovery Pipeline

scholarly journals Activity artifacts in drug discovery and different facets of compound promiscuity

F1000Research ◽  
2014 ◽  
Vol 3 ◽  
pp. 233 ◽  
Author(s):  
Jürgen Bajorath
Keyword(s):  
Drug Discovery ◽  
Specific Interactions ◽  
Multiple Targets ◽  
Target Proteins ◽  
Therapeutic Applications ◽  
Specific Assay ◽  
Substantial Problem ◽  
Compound Promiscuity ◽  
Discovery Pipeline ◽  
Assay Interference

Compounds with apparent activity in a variety of assays might disable target proteins or produce false assay signals in the absence of specific interactions. In some instances, such effects are easy to detect, in others they are not. Observed promiscuity of compounds might be due to such non-specific assay artifacts. By contrast, promiscuity might also result from specific interactions with multiple targets. In the latter case, promiscuous compounds can be attractive candidates for certain therapeutic applications. However, compounds with artificial activity readouts are often not recognized and are further progressed, which presents a substantial problem for drug discovery. In this context, the concept of PAINS (pan-assay interference compounds) should be seriously considered, which makes it possible to eliminate flawed compounds from the discovery pipeline, even if their activities appear to be sound at a first glance.

scholarly journals Application of hepatocyte-like cells to enhance hepatic safety risk assessment in drug discovery

2018 ◽  
Vol 373 (1750) ◽  
pp. 20170228 ◽  
Author(s):  
Dominic P. Williams
Keyword(s):  
Risk Assessment ◽  
Drug Discovery ◽  
High Throughput Screening ◽  
Three Dimensional ◽  
Induced Pluripotent Stem Cell ◽  
Genetic Characteristics ◽  
Short Period ◽  
Donor Variation ◽  
Discovery Pipeline

Hepatic stress and injury from drugs continues to be a major concern within the pharmaceutical industry, leading to preclinical and clinical attrition precautionary warnings and post-market withdrawal of drugs. There is a requirement for more predictive and mechanistically accurate models to aid risk assessment. Primary human hepatocytes, subject to isolation stress, cryopreservation, donor-to-donor variation and a relatively short period of functional capability in two-dimensional cultures, are not suitable for high-throughput screening procedures. There are two areas within the drug discovery pipeline that the generation of a stable, metabolically functional hepatocyte-like cell with unlimited supply would have major impact. First, in routine, cell health risk-assessment assays where hepatic cell lines are typically deployed. Second, at later stages of the drug discovery pipeline approaching candidate nomination where bespoke/investigational studies refining and understanding the risk to patients use patient-derived induced pluripotent stem cell (iPSC) hepatocytes retaining characteristics from the patient, e.g. HLA susceptibility alleles, iPSC hepatocytes with defined disease phenotypes or genetic characteristics that have the potential to make the hepatocyte more sensitive to a particular stress mechanism. Functionality of patient-centric hepatocyte-like cells is likely to be enhanced when coupled with emerging culture systems, such as three-dimensional spheroids or microphysiological systems. Ultimately, the aspiration to confidently use human-relevant in vitro models to predict human-specific hepatic toxicity depends on the integration of promising emerging technologies. This article is part of the theme issue ‘Designer human tissue: coming to a lab near you’.

scholarly journals Metabolic Clustering Analysis as a Strategy for Compound Selection in the Drug Discovery Pipeline for Leishmaniasis

2018 ◽  
Vol 13 (5) ◽  
pp. 1361-1369 ◽  
Author(s):  
Emily G. Armitage ◽  
Joanna Godzien ◽  
Imanol Peña ◽  
Ángeles López-Gonzálvez ◽  
Santiago Angulo ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Clustering Analysis ◽  
Compound Selection ◽  
Discovery Pipeline

scholarly journals Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19

2020 ◽  
Author(s):  
Atanu Acharya ◽  
Rupesh Agarwal ◽  
Matthew Baker ◽  
Jerome Baudry ◽  
Debsindhu Bhowmik ◽  
...  
Keyword(s):  
Drug Discovery ◽  
National Laboratory ◽  
Massively Parallel ◽  
Enhanced Sampling ◽  
Autodock Vina ◽  
Protein Targets ◽  
Oak Ridge ◽  
Preliminary Results ◽  
Ensemble Docking ◽  
Discovery Pipeline

We present a supercomputer-driven pipeline for <i>in-silico</i> drug discovery using enhanced sampling molecular dynamics (MD) and ensemble docking. We also describe preliminary results obtained for 23 systems involving eight protein targets of the proteome of SARS CoV-2. THe MD performed is temperature replica-exchange enhanced sampling, making use of the massively parallel supercomputing on the SUMMIT supercomputer at Oak Ridge National Laboratory, with which more than 1ms of enhanced sampling MD can be generated per day. We have ensemble docked repurposing databases to ten configurations of each of the 23 SARS CoV-2 systems using AutoDock Vina. We also demonstrate that using Autodock-GPU on SUMMIT, it is possible to perform exhaustive docking of one billion compounds in under 24 hours. Finally, we discuss preliminary results and planned improvements to the pipeline, including the use of quantum mechanical (QM), machine learning, and AI methods to cluster MD trajectories and rescore docking poses.

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