The importance of triaging in determining the quality of output from high-throughput screening

The impact of storage conditions on compound stability and compound solubility has been debated intensely over the past 5 years. At Novartis, the authors decided to opt for a storage concept that can be considered controversial because they are using a DMSO/water (90/10) mixture as standard solvent. To assess the effect of water in DMSO stocks on compound stability, the authors monitored the purity of a subset of 1404 compounds from ongoing medicinal chemistry projects over several months. The study demonstrated that 85% of the compounds were stable in wet DMSO over a 2-year period at 4 °C. This result validates the storage concept developed at Novartis as a pragmatic approach that takes advantage of the benefits of DMSO/water mixtures while mediating the disadvantages. In addition, the authors describe how purity data collected over the course of the chemical validation of high-throughput screening actives are used to improve the analytical quality of the Novartis screening deck. ( Journal of Biomolecular Screening 2008:999-1006)

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High Throughput Screening for Drug Discovery and Virus Detection

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207324666210811124856 ◽

2021 ◽

Vol 24 ◽

Author(s):

Adetola Okea ◽

Deniz Sahin ◽

Xin Chen ◽

Ying Shang

Keyword(s):

Drug Discovery ◽

High Throughput ◽

High Throughput Screening ◽

Virus Detection ◽

Flow Time ◽

New Drugs ◽

Mixed Integer ◽

General Process ◽

Screening Systems

Background: High throughput screening systems are automated labs for the analysis of many biochemical substances in the drug discovery and virus detection process. This paper was motivated by the problem of automating testing for viruses and new drugs using high throughput screening systems. The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) at the turn of 2019-2020 presented extradentary challenges to public health. Existing approaches to test viruses and new drugs do not use optimal schedules and are not efficient. Objective: The scheduling of activities performed by various resources in a high throughput screening system affects its efficiency, throughput, operations cost, and quality of screening. This study aims to minimize the total screening (flow) time and ensure the consistency and quality of screening. Methods: This paper develops innovative mixed integer models that efficiently compute optimal schedules for screening many microplates to identify new drugs and determine whether samples contain viruses. The methods integrate job-shop and cyclic scheduling. Experiments are conducted for a drug discovery process of screening an enzymatic assay and a general process of detecting SARS-CoV-2. Results: The method developed in this article can reduce screening time by as much as 91.67%. Conclusion: The optimal schedules for high throughput screening systems greatly reduce the total flow time and can be computed efficiently to help discover new drugs and detect viruses.

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Hit Discovery for Public Target Programs in the European Lead Factory: Experiences and Output from Assay Development and Ultra-High-Throughput Screening

SLAS DISCOVERY Advancing Life Sciences ◽

10.1177/2472555220942765 ◽

2020 ◽

pp. 247255522094276

Author(s):

Saman Honarnejad ◽

Stan van Boeckel ◽

Helma van den Hurk ◽

Steven van Helden

Keyword(s):

High Throughput ◽

High Throughput Screening ◽

Small And Medium Enterprises ◽

Development Programs ◽

Preclinical Development ◽

Compound Libraries ◽

Medium Enterprises

The European Lead Factory (ELF) consortium provides European academics and small and medium enterprises access to ~0.5 million unique compounds, a state-of-the-art ultra-high-throughput screening (u-HTS) platform, and industrial early drug discovery (DD) expertise with the aim of delivering innovative DD starting points. From 2013 to 2018, 154 proposals for eight target classes in seven therapeutic areas were submitted to the ELF consortium, 88 of which were accepted by the selection committee. During this period, 76 primary assays based on seven different readout technologies were optimized and mainly miniaturized to 1536-well plates. In total, 72 u-HTS campaigns were carried out, and follow-up work including hit triage through orthogonal, deselection, selectivity, and biophysical assays were finalized. This ambitious project showed that besides the quality of the compound library and the primary assay, the success of centralized u-HTS of large compound libraries across many target classes, various assay types, and different readout technologies is also largely dependent on the capacity and flexibility of the automation on one hand and the hit-triaging phase on the other, particularly because of undesired compound-assay interference. Thus far, the delivered hit lists from the ELF consortium have resulted in spinoffs, patents, in vivo proof of concepts, preclinical development programs, peer-reviewed publications, PhD theses, and much more, demonstrating early success indications.

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Enrichment of Extremely Noisy High-Throughput Screening Data Using a Naïve Bayes Classifier

CrossRef Listing of Deleted DOIs ◽

10.1177/1087057103260590 ◽

2004 ◽

Vol 9 (1) ◽

pp. 32-36 ◽

Cited By ~ 55

Author(s):

Meir Glick ◽

Anthony E. Klon ◽

Pierre Acklin ◽

John W. Davies

Keyword(s):

High Throughput ◽

High Throughput Screening ◽

Naive Bayes ◽

Computing Time ◽

Poor Quality ◽

Naïve Bayes ◽

Classification Model ◽

Bayes Classifier ◽

Short Computing Time

The noise level of a high-throughput screening (HTS) experiment depends on various factors such as the quality and robustness of the assay itself and the quality of the robotic platform. Screening of compound mixtures is noisier than screening single compounds per well. A classification model based on naïve Bayes (NB) may be used to enrich such data. The authors studied the ability of the NB classifier to prioritize noisy primary HTS data of compound mixtures (5 compounds/well) in 4 campaigns in which the percentage of noise presumed to be inactive compounds ranged between 81% and 91%. The top 10% of the compounds suggested by the classifier captured between 26% and 45% of the active compounds. These results are reasonable and useful, considering the poor quality of the training set and the short computing time that is needed to build and deploy the classifier. ( Journal of Biomolecular Screening 2004:32-36)

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A Simple Statistical Parameter for Use in Evaluation and Validation of High Throughput Screening Assays

CrossRef Listing of Deleted DOIs ◽

10.1177/108705719900400206 ◽

1999 ◽

Vol 4 (2) ◽

pp. 67-73 ◽

Cited By ~ 4426

Author(s):

Ji-Hu Zhang ◽

Thomas D. Y. Chung ◽

Kevin R. Oldenburg

Keyword(s):

High Throughput ◽

High Throughput Screening ◽

Dynamic Range ◽

Statistical Parameter ◽

Assay Optimization ◽

Screening Assays ◽

Signal Dynamic Range ◽

Hit Identification ◽

Data Variation

The ability to identify active compounds ("hits") from large chemical libraries accurately and rapidly has been the ultimate goal in developing high-throughput screening (HTS) assays. The ability to identify hits from a particular HTS assay depends largely on the suitability or quality of the assay used in the screening. The criteria or parameters for evaluating the "suitability" of an HTS assay for hit identification are not well defined and hence it still remains difficult to compare the quality of assays directly. In this report, a screening window coefficient, called "Z- factor," is defined. This coefficient is reflective of both the assay signal dynamic range and the data variation associated with the signal measurements, and therefore is suitable for assay quality assessment. The Z-factor is a dimensionless, simple statistical characteristic for each HTS assay. The Z-factor provides a useful tool for comparison and evaluation of the quality of assays, and can be utilized in assay optimization and validation.

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