Hit Prioritization Research Articles

Function-first discovery of high affinity monoclonal antibodies using Nanovial-based plasma B cell screening.

Antibody discovery technologies, essential for research and therapeutic applications, have evolved significantly since the development of hybridoma technology. Various in vitro (display) and in vivo (animal immunization and B cell-sequencing) workflows have led to the discovery of antibodies against diverse antigens. Despite this success, standard display and B-cell sequencing-based technologies are limited to targets that can be produced in a soluble form. This limitation inhibits the screening of function-inducing antibodies, which require the target to be expressed in cells to monitor function or signaling, and antibodies targeting proteins that maintain their physiological structure only when expressed on cell membranes, such as G-protein coupled receptors (GPCRs). A high-throughput two-cell screening workflow, which localizes an antibody-secreting cell (ASC) and a cell expressing the target protein in a microenvironment, can overcome these challenges. To make function-first plasma cell-based antibody discovery accessible and scalable, we developed hydrogel Nanovials that can capture single plasma cells, target-expressing cells, and plasma cell secretions (antibodies). The detection and isolation of Nanovials harboring the antigen-specific plasma cells are then carried out using a flow cytometry cell sorter - an instrument that is available in most academic centers and biopharmaceutical companies. The antibody discovery workflow employing Nanovials was first validated in the context of two different cell membrane-associated antigens produced in recombinant form. We analyzed over 40,000 plasma cells over two campaigns and were able to identify a diversity of binders that i) exhibited high affinity (picomolar) binding, ii) targeted multiple non-overlapping epitopes and iii) demonstrated high developability scores. A campaign using the two-cell assay targeting the immune checkpoint membrane protein PD-1 yielded cell binders with similar EC50s to clinically used Pembrolizumab and Nivolumab. The highest selectivity for binders was observed for sorted events corresponding with the highest signal bound to target cells on Nanovials. Overall, Nanovials can provide a strong foundation for function-first antibody discovery, yielding direct cell binding information and quantitative data on prioritization of hits with flexibility for additional functional readouts in the future.

Machine Learning Assisted Hit Prioritization for High Throughput Screening in Drug Discovery.

Efficient prioritization of bioactive compounds from high throughput screening campaigns is a fundamental challenge for accelerating drug development efforts. In this study, we present the first data-driven approach to simultaneously detect assay interferents and prioritize true bioactive compounds. By analyzing the learning dynamics during training of a gradient boosting model on noisy high throughput screening data using a novel formulation of sample influence, we are able to distinguish between compounds exhibiting the desired biological response and those producing assay artifacts. Therefore, our method enables false positive and true positive detection without relying on prior screens or assay interference mechanisms, making it applicable to any high throughput screening campaign. We demonstrate that our approach consistently excludes assay interferents with different mechanisms and prioritizes biologically relevant compounds more efficiently than all tested baselines, including a retrospective case study simulating its use in a real drug discovery campaign. Finally, our tool is extremely computationally efficient, requiring less than 30 s per assay on low-resource hardware. As such, our findings show that our method is an ideal addition to existing false positive detection tools and can be used to guide further pharmacological optimization after high throughput screening campaigns.

High-throughput nephelometry methodology for qualitative determination of aqueous solubility of chemical libraries

The purpose of the protocol reported in this work is the solubility profiling of large chemical libraries using nephelometry. This technique allows the qualitative classification of compounds as highly, moderately, or poorly water-soluble. The described methodology is not intended to yield quantitative solubility values of the studied compounds but can be used as a primary solubility assessment of large chemical libraries, to guide hit prioritization after High Throughput Screening (HTS) campaigns.

Structure-free antibody paratope similarity prediction for in silico epitope binning via protein language models

Antibodies are an important group of biological molecules that are used as therapeutics and diagnostic tools. Although millions of antibody sequences are available, identifying their structural and functional similarity and their antigen binding sites remains a challenge at large scale. Here, we present a fast, sequence-based computational method for antibody paratope prediction based on protein language models. The paratope information is then used to measure similarity among antibodies via protein language models. Our computational method enables binning of antibody discovery hits into groups as the function of epitope engagement. We further demonstrate the utility of the method by identifying antibodies targeting highly similar epitopes of the same antigens from a large pool of antibody sequences, using two case studies: SARS CoV2 Receptor Binding Domain (RBD) and Epidermal Growth Factor Receptor (EGFR). Our approach highlights the potential in accelerating antibody discovery by enhancing hit prioritization and diversity selection.

Selective VPS34 Inhibitors: High Efficiency Design by Profiting from Small Structural Differences

VPS34 has attracted attention in oncology as a target to modulate autophagy. However, the generation of selective VPS34 inhibitors with suitable PK properties has been a difficult task. The discovery of compound 5 provides lessons in hit prioritization and achieving kinase selectivity while maintaining a good in vivo pharmacokinetic profile.

Synergistic FRET assays for drug discovery targeting RyR2 channels

A key therapeutic target for heart failure and arrhythmia is the deleterious leak through sarcoplasmic reticulum (SR) ryanodine receptor 2 (RyR2) calcium release channels. We have previously developed methods to detect the pathologically leaky state of RyR2 in adult cardiomyocytes by monitoring RyR2 binding to either calmodulin (CaM) or a biosensor peptide (DPc10). Here, we test whether these complementary binding measurements are effective as high-throughput screening (HTS) assays to discover small molecules that target leaky RyR2. Using FRET, we developed and validated HTS procedures under conditions that mimic a pathological state, to screen the library of 1280 pharmaceutically active compounds (LOPAC) for modulators of RyR2 in cardiac SR membrane preparations. Complementary FRET assays with acceptor-labeled CaM and DPc10 were used for Hit prioritization based on the opposing binding properties of CaM vs. DPc10. This approach narrowed the Hit list to one compound, Ro 90–7501, which altered FRET to suggest increased RyR2-CaM binding and decreased DPc10 binding. Follow-up studies revealed that Ro 90–7501 does not detrimentally affect myocyte Ca2+ transients. Moreover, Ro 90–7501 partially inhibits overall Ca2+ leak, as assessed by Ca2+ sparks in permeabilized rat cardiomyocytes. Together, these results demonstrate (1) the effectiveness of our HTS approach where two complementary assays synergize for Hit ranking and (2) a drug discovery process that combines high-throughput, high-precision in vitro structural assays with in situ myocyte assays of the pathologic RyR2 leak. These provide a drug discovery platform compatible with large-scale HTS campaigns, to identify agents that inhibit RyR2 for therapeutic development.

ITARGEX analysis of yeast deletome reveals novel regulators of transcriptional buffering in S phase and protein turnover

Integrating omics data with quantification of biological traits provides unparalleled opportunities for discovery of genetic regulators by in silico inference. However, current approaches to analyze genetic-perturbation screens are limited by their reliance on annotation libraries for prioritization of hits and subsequent targeted experimentation. Here, we present iTARGEX (identification of Trait-Associated Regulatory Genes via mixture regression using EXpectation maximization), an association framework with no requirement of a priori knowledge of gene function. After creating this tool, we used it to test associations between gene expression profiles and two biological traits in single-gene deletion budding yeast mutants, including transcription homeostasis during S phase and global protein turnover. For each trait, we discovered novel regulators without prior functional annotations. The functional effects of the novel candidates were then validated experimentally, providing solid evidence for their roles in the respective traits. Hence, we conclude that iTARGEX can reliably identify novel factors involved in given biological traits. As such, it is capable of converting genome-wide observations into causal gene function predictions. Further application of iTARGEX in other contexts is expected to facilitate the discovery of new regulators and provide observations for novel mechanistic hypotheses regarding different biological traits and phenotypes.

Machine Learning Uses Chemo-Transcriptomic Profiles to Stratify Antimalarial Compounds With Similar Mode of Action.

The rapid development of antimalarial resistance motivates the continued search for novel compounds with a mode of action (MoA) different to current antimalarials. Phenotypic screening has delivered thousands of promising hit compounds without prior knowledge of the compounds’ exact target or MoA. Whilst the latter is not initially required to progress a compound in a medicinal chemistry program, identifying the MoA early can accelerate hit prioritization, hit-to-lead optimization and preclinical combination studies in malaria research. The effects of drug treatment on a cell can be observed on systems level in changes in the transcriptome, proteome and metabolome. Machine learning (ML) algorithms are powerful tools able to deconvolute such complex chemically-induced transcriptional signatures to identify pathways on which a compound act and in this manner provide an indication of the MoA of a compound. In this study, we assessed different ML approaches for their ability to stratify antimalarial compounds based on varied chemically-induced transcriptional responses. We developed a rational gene selection approach that could identify predictive features for MoA to train and generate ML models. The best performing model could stratify compounds with similar MoA with a classification accuracy of 76.6 ± 6.4%. Moreover, only a limited set of 50 biomarkers was required to stratify compounds with similar MoA and define chemo-transcriptomic fingerprints for each compound. These fingerprints were unique for each compound and compounds with similar targets/MoA clustered together. The ML model was specific and sensitive enough to group new compounds into MoAs associated with their predicted target and was robust enough to be extended to also generate chemo-transcriptomic fingerprints for additional life cycle stages like immature gametocytes. This work therefore contributes a new strategy to rapidly, specifically and sensitively indicate the MoA of compounds based on chemo-transcriptomic fingerprints and holds promise to accelerate antimalarial drug discovery programs.

Cheminformatic Profiling and Hit Prioritization of Natural Products with Activities against Methicillin-Resistant Staphylococcus aureus (MRSA).

Several natural products (NPs) have displayed varying in vitro activities against methicillin-resistant Staphylococcus aureus (MRSA). However, few of these compounds have not been developed into potential antimicrobial drug candidates. This may be due to the high cost and tedious and time-consuming process of conducting the necessary preclinical tests on these compounds. In this study, cheminformatic profiling was performed on 111 anti-MRSA NPs (AMNPs), using a few orally administered conventional drugs for MRSA (CDs) as reference, to identify compounds with prospects to become drug candidates. This was followed by prioritizing these hits and identifying the liabilities among the AMNPs for possible optimization. Cheminformatic profiling revealed that most of the AMNPs were within the required drug-like region of the investigated properties. For example, more than 76% of the AMNPs showed compliance with the Lipinski, Veber, and Egan predictive rules for oral absorption and permeability. About 34% of the AMNPs showed the prospect to penetrate the blood–brain barrier (BBB), an advantage over the CDs, which are generally non-permeant of BBB. The analysis of toxicity revealed that 59% of the AMNPs might have negligible or no toxicity risks. Structure–activity relationship (SAR) analysis revealed chemical groups that may be determinants of the reported bioactivity of the compounds. A hit prioritization strategy using a novel “desirability scoring function” was able to identify AMNPs with the desired drug-likeness. Hit optimization strategies implemented on AMNPs with poor desirability scores led to the design of two compounds with improved desirability scores.

Generating Selective Leads for Mer Kinase Inhibitors-Example of a Comprehensive Lead-Generation Strategy.

Mer is a member of the TAM (Tyro3, Axl, Mer) kinase family that has been associated with cancer progression, metastasis, and drug resistance. Their essential function in immune homeostasis has prompted an interest in their role as modulators of antitumor immune response in the tumor microenvironment. Here we illustrate the outcomes of an extensive lead-generation campaign for identification of Mer inhibitors, focusing on the results from concurrent, orthogonal high-throughput screening approaches. Data mining, HT (high-throughput), and DECL (DNA-encoded chemical library) screens offered means to evaluate large numbers of compounds. We discuss campaign strategy and screening outcomes, and exemplify series resulting from prioritization of hits that were identified. Concurrent execution of HT and DECL screening successfully yielded a large number of potent, selective, and novel starting points, covering a range of selectivity profiles across the TAM family members and modes of kinase binding, and offered excellent start points for lead development.

Denoising DNA Encoded Library Screens with Sparse Learning.

DNA-encoded libraries (DELs) are large, pooled collections of compounds in which every library member is attached to a stretch of DNA encoding its complete synthetic history. DEL-based hit discovery involves affinity selection of the library against a protein of interest, whereby compounds retained by the target are subsequently identified by next-generation sequencing of the corresponding DNA tags. When analyzing the resulting data, one typically assumes that sequencing output (i.e., read counts) is proportional to the binding affinity of a given compound, thus enabling hit prioritization and elucidation of any underlying structure-activity relationships (SAR). This assumption, though, tends to be severely confounded by a number of factors, including variable reaction yields, presence of incomplete products masquerading as their intended counterparts, and sequencing noise. In practice, these confounders are often ignored, potentially contributing to low hit validation rates, and universally leading to loss of valuable information. To address this issue, we have developed a method for comprehensively denoising DEL selection outputs. Our method, dubbed "deldenoiser", is based on sparse learning and leverages inputs that are commonly available within a DEL generation and screening workflow. Using simulated and publicly available DEL affinity selection data, we show that "deldenoiser" is not only able to recover and rank true binders much more robustly than read count-based approaches but also that it yields scores, which accurately capture the underlying SAR. The proposed method can, thus, be of significant utility in hit prioritization following DEL screens.

High-throughput identification of post-transcriptional utrophin up-regulators for Duchenne muscle dystrophy (DMD) therapy

Upregulation of endogenous utrophin offers great promise for treating DMD, as it can functionally compensate for the lack of dystrophin caused by DMD gene mutations, without the immunogenic concerns associated with delivering dystrophin. However, post-transcriptional repression mechanisms targeting the 5′ and 3′ untranslated regions (UTRs) of utrophin mRNA significantly limit the magnitude of utrophin upregulation achievable by promoter activation. Using a utrophin 5′3′UTR reporter assay, we performed a high-throughput screen (HTS) for small molecules capable of relieving utrophin post-transcriptional repression. We identified 27 hits that were ranked using an algorithm that we designed for hit prioritization that we call Hit to Lead Prioritization Score (H2LPS). The top 10 hits were validated using an orthogonal assay for endogenous utrophin expression. Evaluation of the top scoring hit, Trichostatin A (TSA), demonstrated utrophin upregulation and functional improvement in the mdx mouse model of DMD. TSA and the other small molecules identified here represent potential starting points for DMD drug discovery efforts.

An All-Optical Electrophysiology Screening Platform to Identify Nav Channel Modulators as Pain Therapeutics

More than 100M people in the US suffer from chronic pain. Current treatments, including opioids and non-steroidal inflammatory agents, have severe limitations for chronic treatment due to tolerance and dose-limiting toxicities. To develop novel pain therapeutics, we have created an all-optical electrophysiology (Optopatch) screening platform using engineered optogenetic proteins. Blue and red light can be used to stimulate and record bioelectrical activity, respectively. For target-based screening application, we demonstrate Optopatch measurements in engineered excitable HEK cells (spiking HEK cells) with heterologous expression of Nav channels targets implicated in pain transmission, including Nav1.7, Nav1.8 and Nav1.9. The Nav channel Optopatch assays can distinguish compounds with different working mechanisms (state-dependent versus state independent block). To achieve a throughput of 10,000 compounds/day, we developed a next generation kinetic plate reader (SWARM) capable of recording 24 wells simultaneously. We have executed a 14,000-compound screen on Nav1.7 spiking HEK cells and identified novel Nav1.7 inhibitors with sub-micromolar potency. To further qualify candidate compounds, we have developed proprietary secondary assays for hit prioritization, which combined the Optopatch platform with an in vitro model of chronic pain, in which dorsal root ganglion (DRG) sensory neurons are exposed to a mixture of inflammatory mediators. This assay leverages our custom Firefly microscope to make highly-parallelized measurements of DRG excitability, where ∼100 neurons can be measured in parallel with single-cell precision and ms-temporal resolution. The newly identified hits are benchmarked against Nav1.7 inhibitors in Phase II trial (PF-05089771 and vixotrigine) and selected hits demonstrate more favorable features in our secondary assay. Interestingly, we found that highly selective Nav1.7 cannot fully reverse the inflammatory mediator-induced DRG hyperexcitability; we dissect Nav-subtype contributions to hyperexcitability using control pharmacology and protein knockdown with antisense oligonucleotides (ASOs).

Genome-Wide CRISPR Screening Identifies MAPK and Mtorc Pathways As Regulators of Sorafenib Resistance in Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is a fast progressing blood malignancy with impaired differentiation and proliferation of myeloid precursors. It is one of the most common leukemias in adults and is known for its molecular and biological heterogeneity, with a variety of genetic lesions implicated in the disease. Among these variants, internal tandem duplication (ITD) or point mutations in the tyrosine kinase domain (TKD) of FLT3 tyrosine kinase are found in around 30% of AML patients. Sorafenib, a multi-kinase inhibitor that targets FLT3, RAF, VEGFR, FGFR, KIT and RET, is approved for use in hepatocarcinoma, renal cell carcinoma, and thyroid carcinoma treatments. Addition of different FLT3 inhibitors such as sorafenib to standard-of-care chemotherapy treatment prolongs AML patient survival with or without FLT3 mutations, although relapse caused by drug resistance remains a clinical challenge. Understanding the mechanisms of resistance to FLT3-targeted drugs, therefore, is necessary to improve treatment options and patient outcomes in AML. We aimed to elucidate resistance mechanisms to sorafenib by subjecting MOLM13 AML cells to genome-wide CRISPR screening to identify genes whose loss-of-function contributes to reduced drug sensitivity. Using Mageck along with an internally developed tiering system for screen hit prioritization, we identified negative regulators of MAPK as well as mTOR pathways as main players in sorafenib resistance. We validated prioritized hit genes using individual sgRNAs to generate single gene deficient cell models for LZTR1, NF1, TSC1 or TSC2. Drug sensitivity assays confirmed an increase in sorafenib resistance in these knockout cells. LZTR1-, TSC1- or TSC2-deficient cells also exhibited reduced sensitivity to a panel of additional FLT3 inhibitors. RNA sequencing results from 271 AML patient peripheral blood or bone marrow samples revealed a correlation between sorafenib sensitivity and lower expression of LZTR1, NF1, TSC1, and TSC2. MOLM13 cell lines resistant to crenolanib, quizartinib, and sorafenib were independently generated by incremental increase in concentration of each drug in cell culture media. Similarly, western blot analysis demonstrated up-regulation of MAPK and/or mTORC1 activity in these resistant cell lines. In addition, these cells were sensitive to MEK inhibitors, and the combination of FLT3 + MEK inhibitors showed synergistic efficacy over single agents in both resistant and parental cells. Taken together, our work identifies the contribution of the MAPK and PI3K/mTOR pathways to FLT3 inhibitor resistance in AML and suggests the combination of FLT3 + MEK inhibitors may be effective for AML patients with FLT3 mutations and those with resistance to FLT3 inhibitors. Disclosures Tyner: Aptose: Research Funding; Array: Research Funding; Agios: Research Funding; Genentech: Research Funding; Janssen: Research Funding; Syros: Research Funding; Janssen: Research Funding; Incyte: Research Funding; Takeda: Research Funding; Array: Research Funding; Constellation: Research Funding; Genentech: Research Funding; Seattle Genetics: Research Funding; Gilead: Research Funding; AstraZeneca: Research Funding; Gilead: Research Funding; Incyte: Research Funding; Takeda: Research Funding; Syros: Research Funding; Aptose: Research Funding; Petra: Research Funding; Seattle Genetics: Research Funding; Petra: Research Funding; Constellation: Research Funding; AstraZeneca: Research Funding; Agios: Research Funding.

Epigenetic drug discovery: systematic assessment of chemical space.

Aim: The druggability of epigenetic targets has prompted researchers to develop small-molecule therapeutics. However, no systematic assessment has ever been done to investigate the chemical space of epigenetic modulators. Herein, we report a comprehensive chemoinformatic analysis of epigenetic ligands from EpiDBase, HEMD, ChEMBL and PubChem databases. Results: Nearly, 0.45 × 106 ligands were analyzed for assay interference compounds, target profiling, drug-like properties and hit prioritization. After eliminating approximately 96,000 problematic compounds, the remaining 0.36 × 106 compounds were studied for their physicochemical distributions, principal component analysis and hit prioritization. More than 30% of assay interference compounds were determined for many proteins. Conclusion: This systematic assessment of epigenetic ligands will help in the enrichment of screening libraries with high-quality compounds and thus, the generation of efficacious drug candidates.

Prediction and Optimization of Pharmacokinetic and Toxicity Properties of the Ligand.

A crucial factor for the approval and success of any drug is how it behaves in the body. Many drugs, however, do not reach the market due to poor efficacy or unacceptable side effects. It is therefore important to take these into consideration early in the drug development process, both in the prioritization of potential hits, and optimization of lead compounds. In silico approaches offer a cost and time-effective approach to rapidly screen and optimize pharmacokinetic and toxicity properties. Here we demonstrate the use of the comprehensive analysis system pkCSM, to allow early identification of potential problems, prioritization of hits, and optimization of leads.

Prospective Assessment of Virtual Screening Heuristics Derived Using a Novel Fusion Score

High-throughput screening (HTS) is a widespread method in early drug discovery for identifying promising chemical matter that modulates a target or phenotype of interest. Because HTS campaigns involve screening millions of compounds, it is often desirable to initiate screening with a subset of the full collection. Subsequently, virtual screening methods prioritize likely active compounds in the remaining collection in an iterative process. With this approach, orthogonal virtual screening methods are often applied, necessitating the prioritization of hits from different approaches. Here, we introduce a novel method of fusing these prioritizations and benchmark it prospectively on 17 screening campaigns using virtual screening methods in three descriptor spaces. We found that the fusion approach retrieves 15% to 65% more active chemical series than any single machine-learning method and that appropriately weighting contributions of similarity and machine-learning scoring techniques can increase enrichment by 1% to 19%. We also use fusion scoring to evaluate the tradeoff between screening more chemical matter initially in lieu of replicate samples to prevent false-positives and find that the former option leads to the retrieval of more active chemical series. These results represent guidelines that can increase the rate of identification of promising active compounds in future iterative screens.

An Ultra-High-Throughput Screen for Catalytic Inhibitors of Serine/Threonine Protein Phosphatases Types 1 and 5 (PP1C and PP5C)

Although there has been substantial success in the development of specific inhibitors for protein kinases, little progress has been made in the identification of specific inhibitors for their protein phosphatase counterparts. Inhibitors of PP1 and PP5 are desired as probes for research and to test their potential for drug development. We developed and miniaturized (1536-well plate format) nearly identical homogeneous, fluorescence intensity (FLINT) enzymatic assays to detect inhibitors of PP1 or PP5. The assays were used in an ultra-high-throughput screening (uHTS) campaign, testing >315,000 small-molecule compounds. Both assays demonstrated robust performance, with a Z′ of 0.92 ± 0.03 and 0.95 ± 0.01 for the PP1 and PP5 assays, respectively. Screening the same library with both assays aided the identification of class inhibitors and assay artifacts. Confirmation screening and hit prioritization assays used [32P/33P]-radiolabel protein substrates, revealing excellent agreement between the FLINT and radiolabel assays. This screening campaign led to the discovery of four novel unrelated small-molecule inhibitors of PP1 and ~30 related small-molecule inhibitors of PP5. The results suggest that this uHTS approach is suitable for identifying selective chemical probes that inhibit PP1 or PP5 activity, and it is likely that similar assays can be developed for other PPP-family phosphatases.

HC StratoMineR: A Web-Based Tool for the Rapid Analysis of High-Content Datasets.

High-content screening (HCS) can generate large multidimensional datasets and when aligned with the appropriate data mining tools, it can yield valuable insights into the mechanism of action of bioactive molecules. However, easy-to-use data mining tools are not widely available, with the result that these datasets are frequently underutilized. Here, we present HC StratoMineR, a web-based tool for high-content data analysis. It is a decision-supportive platform that guides even non-expert users through a high-content data analysis workflow. HC StratoMineR is built by using My Structured Query Language for storage and querying, PHP: Hypertext Preprocessor as the main programming language, and jQuery for additional user interface functionality. R is used for statistical calculations, logic and data visualizations. Furthermore, C++ and graphical processor unit power is diffusely embedded in R by using the rcpp and rpud libraries for operations that are computationally highly intensive. We show that we can use HC StratoMineR for the analysis of multivariate data from a high-content siRNA knock-down screen and a small-molecule screen. It can be used to rapidly filter out undesirable data; to select relevant data; and to perform quality control, data reduction, data exploration, morphological hit picking, and data clustering. Our results demonstrate that HC StratoMineR can be used to functionally categorize HCS hits and, thus, provide valuable information for hit prioritization.

Compound Prioritization in Single-Concentration Screening Data Using Ligand Efficiency Indexes.

The triage of compounds at the single-concentration screening phase of high-throughput screening (HTS) requires multiobjective optimization in order to achieve the best selection of hits, both in terms of potency and physicochemical properties, for a given number of compounds to test. In this regard, ligand efficiency indexes, well established as guides for hit prioritization in the dose-response phase of HTS, are less studied in the single-concentration phase. In the present work the use of ligand efficiency indexes in the prioritization of compounds in single-concentration is investigated. Formulas for deriving them from single-concentration screening data are provided. The statistical association between the single-concentration and dose-response ligand efficiency indexes is evaluated with a wide historical data set including multiple screens of different target classes and screening technologies. The results show Pearson's correlation coefficients r above 0.9 for compounds with dose-response curves, and areas under the curve (AUC) of receiver operating characteristic (ROC) curves above 0.85 after including compounds with no dose-response curves. This good statistical association contains the contribution of both the physicochemical parameter(s) in the ligand efficiency indexes, as well as the biological activity, as demonstrated by permutation tests. The "cost/benefit" of using different thresholds of single-concentration ligand efficiency indexes in rescuing different numbers of efficient compounds is systematically investigated, and cost/benefit curves are provided. Approximate thresholds are proposed for the different ligand efficiency parameters that results in large percentages of efficient compounds rescued while attempting to reduce the cost of compounds to test in dose-response mode. Finally, a practical example of implementation of these indexes that considers clustering of compounds is described, where the rescue of efficient compounds is higher and with a much lower cost than a typical response-driven selection.