CHAPTER 4. Venoms-Based Drug Discovery: Bioassays, Electrophysiology, High-Throughput Screens and Target Identification

2015 ◽  
pp. 97-128
Author(s):  
Irina Vetter ◽  
Wayne C. Hodgson ◽  
David J. Adams ◽  
Peter McIntyre
2021 ◽  
Author(s):  
george chang ◽  
Nathaniel Woody ◽  
Christopher Keefer

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>


2021 ◽  
Author(s):  
george chang ◽  
Nathaniel Woody ◽  
Christopher Keefer

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>


2020 ◽  
Vol 10 (13) ◽  
pp. 4629 ◽  
Author(s):  
Aaron Goff ◽  
Daire Cantillon ◽  
Leticia Muraro Wildner ◽  
Simon J Waddell

Multi-omics strategies are indispensable tools in the search for new anti-tuberculosis drugs. Omics methodologies, where the ensemble of a class of biological molecules are measured and evaluated together, enable drug discovery programs to answer two fundamental questions. Firstly, in a discovery biology approach, to find new targets in druggable pathways for target-based investigation, advancing from target to lead compound. Secondly, in a discovery chemistry approach, to identify the mode of action of lead compounds derived from high-throughput screens, progressing from compound to target. The advantage of multi-omics methodologies in both of these settings is that omics approaches are unsupervised and unbiased to a priori hypotheses, making omics useful tools to confirm drug action, reveal new insights into compound activity, and discover new avenues for inquiry. This review summarizes the application of Mycobacterium tuberculosis omics technologies to the early stages of tuberculosis antimicrobial drug discovery.


2019 ◽  
Vol 25 (2) ◽  
pp. 137-147 ◽  
Author(s):  
Mark J. Henderson ◽  
Marc A. Holbert ◽  
Anton Simeonov ◽  
Lorena A. Kallal

Thermal shift assays (TSAs) can reveal changes in protein structure, due to a resultant change in protein thermal stability. Since proteins are often stabilized upon binding of ligand molecules, these assays can provide a readout for protein target engagement. TSA has traditionally been applied using purified proteins and more recently has been extended to study target engagement in cellular environments with the emergence of cellular thermal shift assays (CETSAs). The utility of CETSA in confirming molecular interaction with targets in a more native context, and the desire to apply this technique more broadly, has fueled the emergence of higher-throughput techniques for CETSA (HT-CETSA). Recent studies have demonstrated that HT-CETSA can be performed in standard 96-, 384-, and 1536-well microtiter plate formats using methods such as beta-galactosidase and NanoLuciferase reporters and AlphaLISA assays. HT-CETSA methods can be used to select and characterize compounds from high-throughput screens and to prioritize compounds in lead optimization by facilitating dose–response experiments. In conjunction with cellular and biochemical activity assays for targets, HT-CETSA can be a valuable addition to the suite of assays available to characterize molecules of interest. Despite the successes in implementing HT-CETSA for a diverse set of targets, caveats and challenges must also be recognized to avoid overinterpretation of results. Here, we review the current landscape of HT-CETSA and discuss the methodologies, practical considerations, challenges, and applications of this approach in research and drug discovery. Additionally, a perspective on potential future directions for the technology is presented.


2006 ◽  
Vol 361 (1467) ◽  
pp. 413-423 ◽  
Author(s):  
Tom L Blundell ◽  
Bancinyane L Sibanda ◽  
Rinaldo Wander Montalvão ◽  
Suzanne Brewerton ◽  
Vijayalakshmi Chelliah ◽  
...  

Impressive progress in genome sequencing, protein expression and high-throughput crystallography and NMR has radically transformed the opportunities to use protein three-dimensional structures to accelerate drug discovery, but the quantity and complexity of the data have ensured a central place for informatics. Structural biology and bioinformatics have assisted in lead optimization and target identification where they have well established roles; they can now contribute to lead discovery, exploiting high-throughput methods of structure determination that provide powerful approaches to screening of fragment binding.


2021 ◽  
Author(s):  
Kate Stafford ◽  
Brandon M. Anderson ◽  
Jon Sorenson ◽  
Henry van den Bedem

Structure-based, virtual High Throughput Screening (vHTS) methods for predicting ligand activity in drug discovery are important when there are no or relatively few known compounds that interact with a therapeutic target of interest. State-of-the-art computational vHTS necessarily relies on effective methods for pose sampling and docking to generate an accurate affinity score from the docked poses. However, proteins are dynamic; in vivo, ligands bind to a conformational ensemble. In silico docking to the single conformation represented by a crystal structure can adversely affect the pose quality. Here we introduce AtomNet PoseRanker, a graph convolutional network trained to identify, and re-rank crystal-like ligand poses from a sampled ensemble of protein conformations and ligand poses. In contrast to conventional vHTS methods that incorporate receptor flexibility, a deep learning approach can internalize valid cognate and non-cognate binding modes corresponding to distinct receptor conformations. AtomNet PoseRanker significantly enriched pose quality in docking to cognate and non-cognate receptors of the PDBbind v2019 dataset. Improved pose rankings that better represent experimentally observed ligand binding modes improve hit rates in vHTS campaigns, and thereby advance computational drug discovery, especially for novel therapeutic targets or novel binding sites.


2019 ◽  
Author(s):  
Michael Gerckens ◽  
Hani Alsafadi ◽  
Darcy Wagner ◽  
Katharina Heinzelmann ◽  
Kenji Schorpp ◽  
...  

2003 ◽  
Vol 9 (1) ◽  
pp. 49-58
Author(s):  
Margit Asmild ◽  
Nicholas Oswald ◽  
Karen M. Krzywkowski ◽  
Søren Friis ◽  
Rasmus B. Jacobsen ◽  
...  

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