scholarly journals The Integration of High Throughput Technologies for Drug Discovery

2001 ◽  
Vol 6 (4) ◽  
pp. 213-218 ◽  
Author(s):  
Gregory P. Sabbatini ◽  
William A. Shirley ◽  
David L. Coffen

Producing quality clinical candidates less prone to late stage failure is greatly facilitated by better integration of the relevant high throughput functions and the inclusion of ADME/toxicology further upstream in the discovery process. We describe the tasks and their integration in the context of the design, make and test triad.


2011 ◽  
Vol 51 (02) ◽  
pp. 169-173 ◽  
Author(s):  
François Noël ◽  
Dayde Mendonça-Silva ◽  
Luis Quintas


2021 ◽  
pp. 247255522110006
Author(s):  
Puneet Khurana ◽  
Lisa McWilliams ◽  
Jonathan Wingfield ◽  
Derek Barratt ◽  
Bharath Srinivasan

Target engagement by small molecules is necessary for producing a physiological outcome. In the past, a lot of emphasis was placed on understanding the thermodynamics of such interactions to guide structure–activity relationships. It is becoming clearer, however, that understanding the kinetics of the interaction between a small-molecule inhibitor and the biological target [structure–kinetic relationship (SKR)] is critical for selection of the optimum candidate drug molecule for clinical trial. However, the acquisition of kinetic data in a high-throughput manner using traditional methods can be labor intensive, limiting the number of molecules that can be tested. As a result, in-depth kinetic studies are often carried out on only a small number of compounds, and usually at a later stage in the drug discovery process. Fundamentally, kinetic data should be used to drive key decisions much earlier in the drug discovery process, but the throughput limitations of traditional methods preclude this. A major limitation that hampers acquisition of high-throughput kinetic data is the technical challenge in collecting substantially confluent data points for accurate parameter estimation from time course analysis. Here, we describe the use of the fluorescent imaging plate reader (FLIPR), a charge-coupled device (CCD) camera technology, as a potential high-throughput tool for generating biochemical kinetic data with smaller time intervals. Subsequent to the design and optimization of the assay, we demonstrate the collection of highly confluent time-course data for various kinase protein targets with reasonable throughput to enable SKR-guided medicinal chemistry. We select kinase target 1 as a special case study with covalent inhibition, and demonstrate methods for rapid and detailed analysis of the resultant kinetic data for parameter estimation. In conclusion, this approach has the potential to enable rapid kinetic studies to be carried out on hundreds of compounds per week and drive project decisions with kinetic data at an early stage in drug discovery.



2004 ◽  
Vol 9 (4) ◽  
pp. 286-293 ◽  
Author(s):  
Hong Xin ◽  
Alejandro Bernal ◽  
Frank A. Amato ◽  
Albert Pinhasov ◽  
Jack Kauffman ◽  
...  

The drug discovery process pursued by major pharmaceutical companies for many years starts with target identification followed by high-throughput screening (HTS) with the goal of identifying lead compounds. To accomplish this goal, significant resources are invested into automation of the screening process or HTS. Robotic systems capable of handling thousands of data points per day are implemented across the pharmaceutical sector. Many of these systems are amenable to handling cell-based screening protocols as well. On the other hand, as companies strive to develop innovative products based on novel mechanisms of action(s), one of the current bottlenecks of the industry is the target validation process. Traditionally, bioinformatics and HTS groups operate separately at different stages of the drug discovery process. The authors describe the convergence and integration of HTS and bioinformatics to perform high-throughput target functional identification and validation. As an example of this approach, they initiated a project with a functional cell-based screen for a biological process of interest using libraries of small interfering RNA (siRNA) molecules. In this protocol, siRNAs function as potent gene-specific inhibitors. siRNA-mediated knockdown of the target genes is confirmed by TaqMan analysis, and genes with impacts on biological functions of interest are selected for further analysis. Once the genes are confirmed and further validated, they may be used for HTS to yield lead compounds.



2021 ◽  
Author(s):  
Erik Weis ◽  
Maria Johansson ◽  
Pernilla Korsgren ◽  
Belén Martín-Matute ◽  
Magnus J Johansson

Herein, we report an iridium-catalyzed directed C−H amination methodology developed using a high-throughput experimentation (HTE)-based strategy, applicable for the needs of automated modern drug discovery. The informer library approach for investigating accessible directing group chemical space for the reaction, in combination with functional group tolerance screening and substrate scope investigations, allowed for the generation of an empirical predictive model to guide future users. Applicability to late-stage functionalization of complex drugs and natural products, in combination with multiple deprotection protocols leading to the desirable aniline matched pairs, serve to demonstrate the utility of the method for drug discovery. Finally reaction miniaturization to a nano molar range highlights the opportunities for more sustainable screening with decreased material consumption.



2016 ◽  
Vol 52 (58) ◽  
pp. 9067-9070 ◽  
Author(s):  
J. Aretz ◽  
Y. Kondoh ◽  
K. Honda ◽  
U. R. Anumala ◽  
M. Nazaré ◽  
...  

Incorporation of early druggability assessment in the drug discovery process provides a means to prioritize target proteins for high-throughput screening.



2000 ◽  
Vol 22 (6) ◽  
pp. 169-170 ◽  
Author(s):  
Charles J. Manly

Drug discovery today requires the focused use of laboratory automation and other resources in combinatorial chemistry and high-throughput screening (HTS). The ultimate value of both combinatorial chemistry and HTS technologies and the lasting impact they will have on the drug discovery process is a chapter that remains to be written. Central to their success and impact is how well they are integrated with each other and with the rest of the drug discovery processes-informatics is key to this success. This presentation focuses on informatics and the integration of the disciplines of combinatorial chemistry and HTS in modern drug discovery. Examples from experiences at Neurogen from the last five years are described.



2020 ◽  
Author(s):  
Puneet Khurana ◽  
Lisa McWilliams ◽  
Jonathan Wingfield ◽  
Derek Barratt ◽  
Bharath Srinivasan

<p>Target engagement by small-molecules is necessary for producing a physiological outcome. In the past, a lot of emphasis was placed on understanding the thermodynamics of such interactions to guide structure-activity relationship. However, it is becoming clearer that understanding the kinetics of the interaction between a small molecule inhibitor and the biological target (structure kinetic relationship, SKR) is critical for selection of the optimum candidate drug molecule for clinical trial. However, the acquisition of kinetic data in high-throughput manner using traditional methods can be labor intensive, limiting the number of molecules that can be tested. As a result, in depth kinetic studies are often carried out only on a small number of compounds and, usually, at a later stage in the drug discovery process. Fundamentally, kinetic data should be used to drive key decisions much earlier in the drug discovery process but the throughput limitations of traditional methods precludes this. A major limitation that hampers acquisition of high-throughput kinetic data is the technical challenge in collecting substantially confluent datapoints for accurate parameter estimation from time-course analysis. Here we describe the use of Fluorescent Imaging Plate Reader (FLIPR), a CCD camera technology, as a potential high-throughput tool for generating biochemical kinetic data with smaller time-intervals. Subsequent to the design and optimization of the assay, we demonstrate the collection of highly confluent time-course data for various kinase protein targets with reasonable throughput to enable SKR-guided medicinal chemistry. We select kinase target 1 as a special case study with covalent inhibition and demonstrate methods for rapid and detailed analysis of the resultant kinetic data for parameter estimation . In conclusion, this approach has the potential to enable rapid kinetic studies to be carried out on 100's of compounds per week and drive project decisions with kinetic data at an early stage in drug discovery.</p>



2020 ◽  
Author(s):  
Puneet Khurana ◽  
Lisa McWilliams ◽  
Jonathan Wingfield ◽  
Derek Barratt ◽  
Bharath Srinivasan

<p>Target engagement by small-molecules is necessary for producing a physiological outcome. In the past, a lot of emphasis was placed on understanding the thermodynamics of such interactions to guide structure-activity relationship. However, it is becoming clearer that understanding the kinetics of the interaction between a small molecule inhibitor and the biological target (structure kinetic relationship, SKR) is critical for selection of the optimum candidate drug molecule for clinical trial. However, the acquisition of kinetic data in high-throughput manner using traditional methods can be labor intensive, limiting the number of molecules that can be tested. As a result, in depth kinetic studies are often carried out only on a small number of compounds and, usually, at a later stage in the drug discovery process. Fundamentally, kinetic data should be used to drive key decisions much earlier in the drug discovery process but the throughput limitations of traditional methods precludes this. A major limitation that hampers acquisition of high-throughput kinetic data is the technical challenge in collecting substantially confluent datapoints for accurate parameter estimation from time-course analysis. Here we describe the use of Fluorescent Imaging Plate Reader (FLIPR), a CCD camera technology, as a potential high-throughput tool for generating biochemical kinetic data with smaller time-intervals. Subsequent to the design and optimization of the assay, we demonstrate the collection of highly confluent time-course data for various kinase protein targets with reasonable throughput to enable SKR-guided medicinal chemistry. We select kinase target 1 as a special case study with covalent inhibition and demonstrate methods for rapid and detailed analysis of the resultant kinetic data for parameter estimation . In conclusion, this approach has the potential to enable rapid kinetic studies to be carried out on 100's of compounds per week and drive project decisions with kinetic data at an early stage in drug discovery.</p>



1997 ◽  
Vol 2 (4) ◽  
pp. 24-29
Author(s):  
Chris Shumate ◽  
Scott Beckey ◽  
Peter Coassin ◽  
Harry Stylli

Aurora Biosciences Corporation designs and develops proprietary drug discovery systems, services and technologies to accelerate and enhance the discovery of new pharmaceuticals. Aurora is developing an integrated technology platform centered around two technologies; 1) a portfolio of proprietary fluorescent assay technologies and, 2) an ultra-high throughput screening (“UHTS”) system designed to allow assay miniaturization and to overcome many of the limitations associated with the traditional drug discovery process. This approach takes advantage of the opportunities created by recent advances in genomics and combinatorial chemistry that have generated many new therapeutic targets and an abundance of new small molecule compounds. Aurora believes its integrated platform will accelerate the drug discovery process by shortening the time required to identify high quality lead compounds and to optimize those compounds into drug development candidates.



2018 ◽  
Vol 54 (50) ◽  
pp. 6759-6771 ◽  
Author(s):  
Vitaly V. Komnatnyy ◽  
Thomas E. Nielsen ◽  
Katrine Qvortrup

High-throughput screening is an important component of the drug discovery process.



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