Mining Natural-Products Screening Data for Target-Class Chemical Motifs.

In this article, we describe two complementary data-mining approaches used to characterize the GlaxoSmithKline (GSK) natural-products set (NPS) based on information from the high-throughput screening (HTS) databases. Both methods rely on the aggregation and analysis of a large set of single-shot screening data for a number of biological assays, with the goal to reveal natural-product chemical motifs. One of them is an established method based on the data-driven clustering of compounds using a wide range of descriptors,(1)whereas the other method partitions and hierarchically clusters the data to identify chemical cores.(2,3)Both methods successfully find structural scaffolds that significantly hit different groups of discrete drug targets, compared with their relative frequency of demonstrating inhibitory activity in a large number of screens. We describe how these methods can be applied to unveil hidden information in large single-shot HTS data sets. Applied prospectively, this type of information could contribute to the design of new chemical templates for drug-target classes and guide synthetic efforts for lead optimization of tractable hits that are based on natural-product chemical motifs. Relevant findings for 7TM receptors (7TMRs), ion channels, class-7 transferases (protein kinases), hydrolases, and oxidoreductases will be discussed.

Discovery and biochemical characterization of Plasmodium thioredoxin reductase inhibitors from an antimalarial set.

Plasmodium falciparum is the most prevalent and deadly species of the human malaria parasites, and thioredoxin reductase (TrxR) is an enzyme involved in the redox response to oxidative stress. Essential for P. falciparum survival, the enzyme has been highlighted as a promising target for novel antimalarial drugs. Here we report the discovery and characterization of seven molecules from an antimalarial set of 13533 compounds through single-target TrxR biochemical screens. We have produced high-purity, full-length, recombinant native enzyme from four Plasmodium species, and thioredoxin substrates from P. falciparum and Rattus norvegicus. The enzymes were screened using a unique, high-throughput, in vitro native substrate assay, and we have observed selectivity between the Plasmodium species and the mammalian form of the enzyme. This has indicated differences in their biomolecular profiles and has provided valuable insights into the biochemical mechanisms of action of compounds with proven antimalarial activity.

Statistics and decision making in high-throughput screening.

Screening is about making decisions on the modulating activity of one particular compound on a biological system. When a compound testing experiment is repeated under the same conditions or as close to the same conditions as possible, the observed results are never exactly the same, and there is an apparent random and uncontrolled source of variability in the system under study. Nevertheless, randomness is not haphazard. In this context, we can see statistics as the science of decision making under uncertainty. Thus, the usage of statistical tools in the analysis of screening experiments is the right approach to the interpretation of screening data, with the aim of making them meaningful and converting them into valuable information that supports sound decision making.In the HTS workflow, there are at least three key stages where key decisions have to be made based on experimental data: (1) assay development (i.e. how to assess whether our assay is good enough to be put into screening production for the identification of modulators of the target of interest), (2) HTS campaign process (i.e. monitoring that screening process is performing at the expected quality and assessing possible patterned signs of experimental response that may adversely bias and mislead hit identification) and (3) data analysis of primary HTS data (i.e. flagging which compounds are giving a positive response in the assay, namely hit identification).In this chapter we will focus on how some statistical tools can help to cope with these three aspects. Assessment of assay quality is reviewed in other chapters, so in Section 1 we will briefly make some further considerations. Section 2 will review statistical process control, Section 3 will cover methodologies for detecting and dealing with HTS patterns and Section 4 will describe approaches for statistically guided selection of hits in HTS.

Process validation and screen reproducibility in high-throughput screening.

The use of large-scale compound screening has become a key component of drug discovery projects in both the pharmaceutical and the biotechnological industries. More recently, these activities have also been embraced by the academic community as a major tool for chemical genomic activities. High-throughput screening (HTS) activities constitute a major step in the initial drug discovery efforts and involve the use of large quantities of biological reagents, hundreds of thousands to millions of compounds, and the utilization of expensive equipment. All these factors make it very important to evaluate in advance of the HTS campaign any potential issues related to reproducibility of the experimentation and the quality of the results obtained at the end of these very costly activities. In this article, the authors describe how GlaxoSmithKline (GSK) has addressed the need of a true validation of the HTS process before embarking in full HTS campaigns. They present 2 different aspects of the so-called validation process: (1) optimization of the HTS workflow and its validation as a quality process and (2) the statistical evaluation of the HTS, focusing on the reproducibility of results and the ability to distinguish active from nonactive compounds in a vast collection of samples. The authors describe a variety of reproducibility indexes that are either innovative or have been adapted from generic medical diagnostic screening strategies. In addition, they exemplify how these validation tools have been implemented in a number of case studies at GSK.