Development of a high throughput equilibrium solubility assay using miniaturized shake‐flask method in early drug discovery

2007 ◽  
Vol 96 (11) ◽  
pp. 3052-3071 ◽  
Author(s):  
Liping Zhou ◽  
Linhong Yang ◽  
Suzanne Tilton ◽  
Jianling Wang
Keyword(s):  
Drug Discovery ◽  
High Throughput ◽  
Shake Flask ◽  
Equilibrium Solubility ◽  
Early Drug

High throughput P450 inhibition screens in early drug discovery

2005 ◽  
Vol 10 (21) ◽  
pp. 1443-1450 ◽  
Author(s):  
Gregor Zlokarnik ◽  
Peter D.J. Grootenhuis ◽  
John B. Watson
Keyword(s):  
Drug Discovery ◽  
High Throughput ◽  
Early Drug

scholarly journals Providing the ‘Best’ Lipophilicity Assessment in a Drug Discovery Environment

2021 ◽  
Author(s):  
george chang ◽  
Nathaniel Woody ◽  
Christopher Keefer
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
High Throughput ◽  
In Silico ◽  
Shake Flask ◽  
Chromatographic Method ◽  
Learning Approach ◽  
Rule Based ◽  
Machine Learning Approach ◽  
High Throughput Screens

Lipophilicity is a fundamental structural property that influences almost every aspect of drug discovery. Within Pfizer, we have two complementary high-throughput screens for measuring lipophilicity as a distribution coefficient (LogD) – a miniaturized shake-flask method (SFLogD) and a chromatographic method (ELogD). The results from these two assays are not the same (see Figure 1), with each assay being applicable or more reliable in particular chemical spaces. In addition to LogD assays, the ability to predict the LogD value for virtual compounds is equally vital. Here we present an in-silico LogD model, applicable to all chemical spaces, based on the integration of the LogD data from both assays. We developed two approaches towards a single LogD model – a Rule-based and a Machine Learning approach. Ultimately, the Machine Learning LogD model was found to be superior to both internally developed and commercial LogD models.<br>

scholarly journals Providing the ‘Best’ Lipophilicity Assessment in a Drug Discovery Environment

2021 ◽  
Author(s):  
george chang ◽  
Nathaniel Woody ◽  
Christopher Keefer
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
High Throughput ◽  
In Silico ◽  
Shake Flask ◽  
Chromatographic Method ◽  
Learning Approach ◽  
Rule Based ◽  
Machine Learning Approach ◽  
High Throughput Screens

Lipophilicity is a fundamental structural property that influences almost every aspect of drug discovery. Within Pfizer, we have two complementary high-throughput screens for measuring lipophilicity as a distribution coefficient (LogD) – a miniaturized shake-flask method (SFLogD) and a chromatographic method (ELogD). The results from these two assays are not the same (see Figure 1), with each assay being applicable or more reliable in particular chemical spaces. In addition to LogD assays, the ability to predict the LogD value for virtual compounds is equally vital. Here we present an in-silico LogD model, applicable to all chemical spaces, based on the integration of the LogD data from both assays. We developed two approaches towards a single LogD model – a Rule-based and a Machine Learning approach. Ultimately, the Machine Learning LogD model was found to be superior to both internally developed and commercial LogD models.<br>

scholarly journals Positioning High-Throughput CETSA in Early Drug Discovery through Screening against B-Raf and PARP1

2018 ◽  
Vol 24 (2) ◽  
pp. 121-132 ◽  
Author(s):  
Joseph Shaw ◽  
Ian Dale ◽  
Paul Hemsley ◽  
Lindsey Leach ◽  
Nancy Dekki ◽  
...  
Keyword(s):  
Drug Discovery ◽  
High Throughput ◽  
Target Engagement ◽  
Native State ◽  
Cellular Target ◽  
Protein Thermal Stability ◽  
Cellular Thermal Shift Assay ◽  
Hit Identification ◽  
Early Drug ◽  
Thermal Shift

Methods to measure cellular target engagement are increasingly being used in early drug discovery. The Cellular Thermal Shift Assay (CETSA) is one such method. CETSA can investigate target engagement by measuring changes in protein thermal stability upon compound binding within the intracellular environment. It can be performed in high-throughput, microplate-based formats to enable broader application to early drug discovery campaigns, though high-throughput forms of CETSA have only been reported for a limited number of targets. CETSA offers the advantage of investigating the target of interest in its physiological environment and native state, but it is not clear yet how well this technology correlates to more established and conventional cellular and biochemical approaches widely used in drug discovery. We report two novel high-throughput CETSA (CETSA HT) assays for B-Raf and PARP1, demonstrating the application of this technology to additional targets. By performing comparative analyses with other assays, we show that CETSA HT correlates well with other screening technologies and can be applied throughout various stages of hit identification and lead optimization. Our results support the use of CETSA HT as a broadly applicable and valuable methodology to help drive drug discovery campaigns to molecules that engage the intended target in cells.

scholarly journals Application of High-Throughput Flow Cytometry in Early Drug Discovery: An AstraZeneca Perspective

2018 ◽  
Vol 23 (7) ◽  
pp. 719-731
Author(s):  
Mei Ding ◽  
Roger Clark ◽  
Catherine Bardelle ◽  
Anna Backmark ◽  
Tyrrell Norris ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Drug Discovery ◽  
High Throughput ◽  
High Throughput Screening ◽  
Single Cells ◽  
Phenotypic Screening ◽  
Antibody Screening ◽  
Structure Activity ◽  
High Level ◽  
Early Drug

Flow cytometry is a powerful tool providing multiparametric analysis of single cells or particles. The introduction of faster plate-based sampling technologies on flow cytometers has transformed the technology into one that has become attractive for higher throughput drug discovery screening. This article describes AstraZeneca’s perspectives on the deployment and application of high-throughput flow cytometry (HTFC) platforms for small-molecule high-throughput screening (HTS), structure–activity relationship (SAR) and phenotypic screening, and antibody screening. We describe the overarching HTFC workflow, including the associated automation and data analysis, along with a high-level overview of our HTFC assay portfolio. We go on to discuss the practical challenges encountered and solutions adopted in the course of our deployment of HTFC, as well as future enhancements and expansion of the technology to new areas of drug discovery.

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