The Distribution of Standard Deviations Applied to High Throughput Screening

ShiftScan: A tool for rapid analysis of high-throughput differential scanning fluorimetry data and compound prioritization

Differential scanning fluorimetry (DSF) can be an effective high-throughput screening assay in drug discovery for detecting protein-compound interactions that stabilize or destabilize macromolecules. Due to the magnitude and quality of the data produced by this biophysical assay, analyzing and prioritizing compounds from large-scale DSF data sets has proven challenging to the research community. Here, we present ShiftScan-a powerful, stand-alone tool designed for the rapid analysis of DSF data and compound prioritization based on thermal transition patterns. ShiftScan accurately and quickly predicts melting temperatures (Tm values) from both canonical and non-canonical transition patterns, efficiently filtering out spurious data to minimize false positives. We report on the use of this tool for data analysis of screens involving both pure compound and natural product fraction libraries and provide the software to the screening community to aid in the discovery of molecularly-targeted compounds. Instructions for installation and usage of ShiftScan can be found at our GitHub repository: https://github.com/samche42/ShiftScan.

Bayesian approach enabled objective comparison of multiple human iPSC-derived Cardiomyocytes' Proarrhythmia sensitivities

The one-size-fits-all approach has been the mainstream in medicine, and the well-defined standards support the development of safe and effective therapies for many years. Advancing technologies, however, enabled precision medicine to treat a targeted patient population (e.g., HER2+ cancer). In safety pharmacology, computational population modeling has been successfully applied in virtual clinical trials to predict drug-induced proarrhythmia risks against a wide range of pseudo cohorts. In the meantime, population modeling in safety pharmacology experiments has been challenging. Here, we used five commercially available human iPSC-derived cardiomyocytes growing in 384-well plates and analyzed the effects of ten potential proarrhythmic compounds with four concentrations on their calcium transients (CaTs). All the cell lines exhibited an expected elongation or shortening of calcium transient duration with various degrees. Depending on compounds inhibiting several ion channels, such as hERG, peak and late sodium and L-type calcium or IKs channels, some of the cell lines exhibited irregular, discontinuous beating that was not predicted by computational simulations. To analyze the shapes of CaTs and irregularities of beat patterns comprehensively, we defined six parameters to characterize compound-induced CaT waveform changes, successfully visualizing the similarities and differences in compound-induced proarrhythmic sensitivities of different cell lines. We applied Bayesian statistics to predict sample populations based on experimental data to overcome the limited number of experimental replicates in high-throughput assays. This process facilitated the principal component analysis to classify compound-induced sensitivities of cell lines objectively. Finally, the association of sensitivities in compound-induced changes between phenotypic parameters and ion channel inhibitions measured using patch clamp recording was analyzed. Successful ranking of compound-induced sensitivity of cell lines was in lined with visual inspection of raw data.

Statistical models for identifying frequent hitters in high throughput screening

High throughput screening (HTS) interrogates compound libraries to find those that are “active” in an assay. To better understand compound behavior in HTS, we assessed an existing binomial survivor function (BSF) model of “frequent hitters” using 872 publicly available HTS data sets. We found large numbers of “infrequent hitters” using this model leading us to reject the BSF for identifying “frequent hitters.” As alternatives, we investigated generalized logistic, gamma, and negative binomial distributions as models for compound behavior. The gamma model reduced the proportion of both frequent and infrequent hitters relative to the BSF. Within this data set, conclusions about individual compound behavior were limited by the number of times individual compounds were tested (1–1613 times) and disproportionate testing of some compounds. Specifically, most tests (78%) were on a 309,847-compound subset (17.6% of compounds) each tested ≥ 300 times. We concluded that the disproportionate retesting of some compounds represents compound repurposing at scale rather than drug discovery. The approach to drug discovery represented by these 872 data sets characterizes the assays well by challenging them with many compounds while each compound is characterized poorly with a single assay. Aggregating the testing information from each compound across the multiple screens yielded a continuum with no clear boundary between normal and frequent hitting compounds.

Major Improvements in Robustness and Efficiency during the Screening of Novel Enzyme Effectors by the 3-Point Kinetics Assay

The throughput level currently reached by automatic liquid handling and assay monitoring techniques is expected to facilitate the discovery of new modulators of enzyme activity. Judicious and dependable ways to interpret vast amounts of information are, however, required to effectively answer this challenge. Here, the 3-point method of kinetic analysis is proposed as a means to significantly increase the hit success rates and decrease the number of falsely identified compounds (false positives). In this post-Michaelis-Menten approach, each screened reaction is probed in three different occasions, none of which necessarily coincide with the initial period of constant velocity. Enzymology principles rather than subjective criteria are applied to identify unwanted outliers such as assay artifacts, and then to accurately distinguish true enzyme modulation effects from false positives. The exclusion and selection criteria are defined based on the 3-point reaction coordinates, whose relative positions along the time-courses may change from well to well or from plate to plate, if necessary. The robustness and efficiency of the new method is illustrated during a small drug repurposing screening of potential modulators of the deubiquinating activity of ataxin-3, a protein implicated in Machado-Joseph disease. Apparently, intractable Z factors are drastically enhanced after (1) eliminating spurious results, (2) improving the normalization method, and (3) increasing the assay resilience to systematic and random variability. Numerical simulations further demonstrate that the 3-point analysis is highly sensitive to specific, catalytic, and slow-onset modulation effects that are particularly difficult to detect by typical endpoint assays.

Solving the Competitive Binding Equilibria between Many Ligands: Application to High-Throughput Screening and Affinity Optimization

Modern small-molecule drug discovery relies on the selective targeting of biological macromolecules by low-molecular weight compounds. Therefore, the binding affinities of candidate drugs to their targets are key for pharmacological activity and clinical use. For drug discovery methods where multiple drug candidates can simultaneously bind to the same target, a competition is established, and the resulting equilibrium depends on the dissociation constants and concentration of all the species present. Such coupling between all equilibrium-governing parameters complicates analysis and development of improved mixture-based, high-throughput drug discovery techniques. In this work, we present an iterative computational algorithm to solve coupled equilibria between an arbitrary number of ligands and a biomolecular target that is efficient and robust. The algorithm does not require the estimation of initial values to rapidly converge to the solution of interest. We explored binding equilibria under ligand/receptor conditions used in mixture-based library screening by affinity selection-mass spectrometry (AS-MS). Our studies support a facile method for affinity-ranking hits. The ranking method involves varying the receptor-to-ligand concentration ratio in a pool of candidate ligands in two sequential AS-MS analyses. The ranking is based on the relative change in bound ligand concentration. The method proposed does not require a known reference ligand and produces a ranking that is insensitive to variations in the concentration of individual compounds, thereby enabling the use of unpurified compounds generated by mixture-based combinatorial synthesis techniques.

High-Throughput Screening: today's biochemical and cell-based approaches

High-throughput screening (HTS) provides starting chemical matter in the adventure of developing a new drug. In this review, we survey several HTS methods used today for hit identification, organized in two main flavors: biochemical and cell-based assays. Biochemical assays discussed include fluorescence polarization and anisotropy, FRET, TR-FRET, and fluorescence lifetime analysis. Binding-based methods are also surveyed, including NMR, SPR, mass spectrometry, and DSF. On the other hand, cell-based assays discussed include viability, reporter gene, second messenger, and high-throughput microscopy assays. We devote some emphasis to high-content screening, which is becoming very popular. An advisable stage after hit discovery using phenotypic screens is target deconvolution, and we provide an overview of current chemical proteomics, in silico, and chemical genetics tools. Emphasis is made on recent CRISPR/dCas-based screens. Lastly, we illustrate some of the considerations that inform the choice of HTS methods and point to some areas with potential interest for future research.

An influenza A hemagglutinin small-molecule fusion inhibitor identified by a new high-throughput fluorescence polarization screen

Influenza hemagglutinin (HA) glycoprotein is the primary surface antigen targeted by the host immune response and a focus for development of novel vaccines, broadly neutralizing antibodies (bnAbs), and therapeutics. HA enables viral entry into host cells via receptor binding and membrane fusion and is a validated target for drug discovery. However, to date, only a very few bona fide small molecules have been reported against the HA. To identity new antiviral lead candidates against the highly conserved fusion machinery in the HA stem, we synthesized a fluorescence-polarization probe based on a recently described neutralizing cyclic peptide P7 derived from the complementarity-determining region loops of human bnAbs FI6v3 and CR9114 against the HA stem. We then designed a robust binding assay compatible with high-throughput screening to identify molecules with low micromolar to nanomolar affinity to influenza A group 1 HAs. Our simple, low-cost, and efficient in vitro assay was used to screen H1/Puerto Rico/8/1934 (H1/PR8) HA trimer against ∼72,000 compounds. The crystal structure of H1/PR8 HA in complex with our best hit compound F0045(S) confirmed that it binds to pockets in the HA stem similar to bnAbs FI6v3 and CR9114, cyclic peptide P7, and small-molecule inhibitor JNJ4796. F0045 is enantioselective against a panel of group 1 HAs and F0045(S) exhibits in vitro neutralization activity against multiple H1N1 and H5N1 strains. Our assay, compound characterization, and small-molecule candidate should further stimulate the discovery and development of new compounds with unique chemical scaffolds and enhanced influenza antiviral capabilities.

Potent and selective inhibitors for M32 metallocarboxypeptidases identified from high-throughput screening of anti-kinetoplastid chemical boxes

Enzymes of the M32 family are Zn-dependent metallocarboxypeptidases (MCPs) widely distributed among prokaryotic organisms and just a few eukaryotes including Trypanosoma brucei and Trypanosoma cruzi, the causative agents of sleeping sickness and Chagas disease, respectively. These enzymes are absent in humans and several functions have been proposed for trypanosomatid M32 MCPs. However, no synthetic inhibitors have been reported so far for these enzymes. Here, we present the identification of a set of inhibitors for TcMCP-1 and TbMCP-1 (two trypanosomatid M32 enzymes sharing 71% protein sequence identity) from the GlaxoSmithKline HAT and CHAGAS chemical boxes; two collections grouping 404 compounds with high antiparasitic potency, drug-likeness, structural diversity and scientific novelty. For this purpose, we adapted continuous fluorescent enzymatic assays to a medium-throughput format and carried out the screening of both collections, followed by the construction of dose-response curves for the most promising hits. As a result, 30 micromolar-range inhibitors were discovered for one or both enzymes. The best hit, TCMDC-143620, showed sub-micromolar affinity for TcMCP-1, inhibited TbMCP-1 in the low micromolar range and was inactive against angiotensin I-converting enzyme (ACE), a potential mammalian off-target structurally related to M32 MCPs. This is the first inhibitor reported for this family of MCPs and considering its potency and specificity, TCMDC-143620 seems to be a promissory starting point to develop more specific and potent chemical tools targeting M32 MCPs from trypanosomatid parasites.