High-throughput Screening Programs Research Articles

WLB-87848, a Selective σ1 Receptor Agonist, with an Unusually Positioned NH Group as Positive Ionizable Moiety and Showing Neuroprotective Activity.

The synthesis and pharmacological activity of a new series of thieno[2,3-d]pyrimidin-4(3H)-one derivatives as sigma-1 receptor (σ1R) ligands are reported. A hit from a high-throughput screening program was evolved into a highly potent and selective σ1R agonist (14qR) that contains a free NH group as positive ionizable moiety, not fulfilling the usual pharmacophoric features of the σ1R. The compound shows good physicochemical and ADMET characteristics, displays an agonist profile in the binding immunoglobulin protein/σ1R association assay, induces neuron viability in an in vitro model of β-amyloid peptide intoxication, and presents positive results against recognition memory impairment induced by hippocampal injection of Aβ peptide in rats after oral treatment, altogether making 14qR (WLB-87848) an interesting candidate for neuroprotection.

Abstract IA013: Integrating natural product samples back into drug screening programs

Abstract Natural products have a proven track record in use as anti-cancer agents. As of 2020 natural products (NPs), NP pharmacophores, and NP mimics represent a significant percentage of small molecule-based anti-cancer drugs. However, accessing the potential of NPs in high-throughput screening (HTS) campaigns has been challenging due to the complexity of the crude NP extracts, the time necessary for follow-up isolation efforts, and indefinite capabilities for resupply. This has resulted in NP-based libraries being significantly underrepresented in most recent large-scale HTS programs. The US National Cancer Institute’s Natural Product Extract Repository is one of the world’s largest, most diverse collections of natural products containing >200,000 unique extracts derived from plant, marine and microbial organisms. To address the challenges in screening NPs and encourage increased assay of NP chemical diversity, the NCI created a Program for Natural Product Discovery (NPNPD) which has developed an automated, high-throughput robotics platform that has created 500,000 pre-fractionated NP samples and released them to the public for screening. In addition, automated secondary purification and structure elucidation systems were created that significantly decrease costs and timelines. This presentation will discuss the reasons for, challenges to, and mechanisms by which the NCI is working to increase the pace and efficiency of NP-based drug discovery. Citation Format: Barry R. O'Keefe. Integrating natural product samples back into drug screening programs [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr IA013.

Glioblastoma and the search for non-hypothesis driven combination therapeutics in academia.

Glioblastoma (GBM) remains a cancer of high unmet clinical need. Current standard of care for GBM, consisting of maximal surgical resection, followed by ionisation radiation (IR) plus concomitant and adjuvant temozolomide (TMZ), provides less than 15-month survival benefit. Efforts by conventional drug discovery to improve overall survival have failed to overcome challenges presented by inherent tumor heterogeneity, therapeutic resistance attributed to GBM stem cells, and tumor niches supporting self-renewal. In this review we describe the steps academic researchers are taking to address these limitations in high throughput screening programs to identify novel GBM combinatorial targets. We detail how they are implementing more physiologically relevant phenotypic assays which better recapitulate key areas of disease biology coupled with more focussed libraries of small compounds, such as drug repurposing, target discovery, pharmacologically active and novel, more comprehensive anti-cancer target-annotated compound libraries. Herein, we discuss the rationale for current GBM combination trials and the need for more systematic and transparent strategies for identification, validation and prioritisation of combinations that lead to clinical trials. Finally, we make specific recommendations to the preclinical, small compound screening paradigm that could increase the likelihood of identifying tractable, combinatorial, small molecule inhibitors and better drug targets specific to GBM.

Development of a high-throughput screening method for exopolysaccharide-producing Streptococcus thermophilus based on Congo red

As a thickening or stabilizing agent, exopolysaccharide (EPS) can significantly enhance food texture. However, the conventional analytical and screening methods of EPS have been unable to meet the current needs of industrial production, owing to low efficiency and heavy time-consumption. A simple and rapid qualitative/quantitative method is needed to accelerate the selection of high EPS-producing strains. Here, a high-throughput screening (HTS) program for EPS production was established in S. thermophilus by combining Congo red agar method (CRA, primary screening) and microplate colorimetric assay (MPC, secondary screening). The correlation coefficient (R2) between CRA/MPC and the phenol-sulfuric acid assay (a classical EPS quantification method) were 0.779 and 0.862, respectively, suggesting the feasibility of this HTS method. A mutant library (>300 colonies) of S. thermophilus was constructed using ribosome engineering strategy, and EPS-producing mutants with the titer from 50 mg/L to 200 mg/L were rapidly obtained by our developed HTS method, confirming that the HTS method is simple and efficient for the isolation of EPS-producing S. thermophilus. Taken together, this HTS method was developed for rapidly screening of EPS-producing S. thermophilus, which could be a valuable tool not only for the rapid detection of EPS production capabilities, but also for the screening of strain libraries from genetic engineering with desirable characteristics.

Estimating provisional margins of exposure for data-poor chemicals using high-throughput computational methods.

Current computational technologies hold promise for prioritizing the testing of the thousands of chemicals in commerce. Here, a case study is presented demonstrating comparative risk-prioritization approaches based on the ratio of surrogate hazard and exposure data, called margins of exposure (MoEs). Exposures were estimated using a U.S. EPA’s ExpoCast predictive model (SEEM3) results and estimates of bioactivity were predicted using: 1) Oral equivalent doses (OEDs) derived from U.S. EPA’s ToxCast high-throughput screening program, together with in vitro to in vivo extrapolation and 2) thresholds of toxicological concern (TTCs) determined using a structure-based decision-tree using the Toxtree open source software. To ground-truth these computational approaches, we compared the MoEs based on predicted noncancer TTC and OED values to those derived using the traditional method of deriving points of departure from no-observed adverse effect levels (NOAELs) from in vivo oral exposures in rodents. TTC-based MoEs were lower than NOAEL-based MoEs for 520 out of 522 (99.6%) compounds in this smaller overlapping dataset, but were relatively well correlated with the same (r 2 = 0.59). TTC-based MoEs were also lower than OED-based MoEs for 590 (83.2%) of the 709 evaluated chemicals, indicating that TTCs may serve as a conservative surrogate in the absence of chemical-specific experimental data. The TTC-based MoE prioritization process was then applied to over 45,000 curated environmental chemical structures as a proof-of-concept for high-throughput prioritization using TTC-based MoEs. This study demonstrates the utility of exploiting existing computational methods at the pre-assessment phase of a tiered risk-based approach to quickly, and conservatively, prioritize thousands of untested chemicals for further study.

Comparison of anterior nares CT values in asymptomatic and symptomatic individuals diagnosed with SARS-CoV-2 in a university screening program.

At our university based high throughput screening program, we test all members of our community weekly using RT-qPCR. RT-qPCR cycle threshold (CT) values are inversely proportional to the amount of viral RNA in a sample and are a proxy for viral load. We hypothesized that CT values would be higher, and thus the viral loads at the time of diagnosis would be lower, in individuals who were infected with the virus but remained asymptomatic throughout the course of the infection. We collected the N1 and N2 target gene CT values from 1633 SARS-CoV-2 positive RT-qPCR tests of individuals sampled between August 7, 2020, and March 18, 2021, at the BU Clinical Testing Laboratory. We matched this data with symptom reporting data from our clinical team. We found that asymptomatic patients had CT values significantly higher than symptomatic individuals on the day of diagnosis. Symptoms were followed by the clinical team for 10 days post the first positive test. Within the entire population, 78.1% experienced at least one symptom during surveillance by the clinical team (n = 1276/1633). Of those experiencing symptoms, the most common symptoms were nasal congestion (73%, n = 932/1276), cough (60.0%, n = 761/1276), fatigue (59.0%, n = 753/1276), and sore throat (53.1%, n = 678/1276). The least common symptoms were diarrhea (12.5%, n = 160/1276), dyspnea on exertion (DOE) (6.9%, n = 88/1276), foot or skin changes (including rash) (4.2%, n = 53/1276), and vomiting (2.1%, n = 27/1276). Presymptomatic individuals, those who were not symptomatic on the day of diagnosis but became symptomatic over the following 10 days, had CT values higher for both N1 (median = 27.1, IQR 20.2-32.9) and N2 (median = 26.6, IQR 20.1-32.8) than the symptomatic group N1 (median = 21.8, IQR 17.2-29.4) and N2 (median = 21.4, IQR 17.3-28.9) but lower than the asymptomatic group N1 (median = 29.9, IQR 23.6-35.5) and N2 (median = 30.0, IQR 23.1-35.7). This study supports the hypothesis that viral load in the anterior nares on the day of diagnosis is a measure of disease intensity at that time.

On the Utility of ToxCast-Based Predictive Models to Evaluate Potential Metabolic Disruption by Environmental Chemicals.

Background:Research suggests environmental contaminants can impact metabolic health; however, high costs prohibit in vivo screening of putative metabolic disruptors. High-throughput screening programs, such as ToxCast, hold promise to reduce testing gaps and prioritize higher-order (in vivo) testing.Objectives:We sought to a) examine the concordance of in vitro testing in 3T3-L1 cells to a targeted literature review for 38 semivolatile environmental chemicals, and b) assess the predictive utility of various expert models using ToxCast data against the set of 38 reference chemicals.Methods:Using a set of 38 chemicals with previously published results in 3T3-L1 cells, we performed a metabolism-targeted literature review to determine consensus activity determinations. To assess ToxCast predictive utility, we used two published ToxPi models: a) the 8-Slice model published by Janesick et al. (2016) and b) the 5-Slice model published by Auerbach et al. (2016). We examined the performance of the two models against the Janesick in vitro results and our own 38-chemical reference set. We further evaluated the predictive performance of various modifications to these models using cytotoxicity filtering approaches and validated our best-performing model with new chemical testing in 3T3-L1 cells.Results:The literature review revealed relevant publications for 30 out of the 38 chemicals (the remaining 8 chemicals were only examined in our previous 3T3-L1 testing). We observed a balanced accuracy (average of sensitivity and specificity) of 0.86 comparing our previous in vitro results to the literature-derived calls. ToxPi models provided balanced accuracies ranging from 0.55 to 0.88, depending on the model specifications and reference set. Validation chemical testing correctly predicted 29 of 30 chemicals as per 3T3-L1 testing, suggesting good adipogenic prediction performance for our best adapted model.Discussion:Using the most recent ToxCast data and an updated ToxPi model, we found ToxCast performed similarly to that of our own 3T3-L1 testing in predicting consensus calls. Furthermore, we provide the full ranked list of largely untested chemicals with ToxPi scores that predict adipogenic activity and that require further investigation. https://doi.org/10.1289/EHP6779

Impact of High-Throughput Model Parameterization and Data Uncertainty on Thyroid-Based Toxicological Estimates for Pesticide Chemicals.

Chemical-induced alteration of maternal thyroid hormone levels may increase the risk of adverse neurodevelopmental outcomes in offspring. US federal risk assessments rely almost exclusively on apical endpoints in animal models for deriving points of departure (PODs). New approach methodologies (NAMs) such as high-throughput screening (HTS) and mechanistically informative in vitro human cell-based systems, combined with in vitro to in vivo extrapolation (IVIVE), supplement in vivo studies and provide an alternative approach to calculate/determine PODs. We examine how parameterization of IVIVE models impacts the comparison between IVIVE-derived equivalent administered doses (EADs) from thyroid-relevant in vitro assays and the POD values that serve as the basis for risk assessments. Pesticide chemicals with thyroid-based in vitro bioactivity data from the US Tox21 HTS program were included (n = 45). Depending on the model structure used for IVIVE analysis, up to 35 chemicals produced EAD values lower than the POD. A total of 10 chemicals produced EAD values higher than the POD regardless of the model structure. The relationship between IVIVE-derived EAD values and the in vivo-derived POD values is highly dependent on model parameterization. Here, we derive a range of potentially thyroid-relevant doses that incorporate uncertainty in modeling choices and in vitro assay data.

BAYESIAN JOINT MODELING OF CHEMICAL STRUCTURE AND DOSE RESPONSE CURVES.

Today there are approximately 85,000 chemicals regulated under the Toxic Substances Control Act, with around 2,000 new chemicals introduced each year. It is impossible to screen all of these chemicals for potential toxic effects, either via full organism in vivo studies or in vitro high-throughput screening (HTS) programs. Toxicologists face the challenge of choosing which chemicals to screen, and predicting the toxicity of as yet unscreened chemicals. Our goal is to describe how variation in chemical structure relates to variation in toxicological response to enable in silico toxicity characterization designed to meet both of these challenges. With our Bayesian partially Supervised Sparse and Smooth Factor Analysis (BS3FA) model, we learn a distance between chemicals targeted to toxicity, rather than one based on molecular structure alone. Our model also enables the prediction of chemical dose-response profiles based on chemical structure (i.e., without in vivo or in vitro testing) by taking advantage of a large database of chemicals that have already been tested for toxicity in HTS programs. We show superior simulation performance in distance learning and modest to large gains in predictive ability compared to existing methods. Results from the high-throughput screening data application elucidate the relationship between chemical structure and a toxicity-relevant high-throughput assay. An R package for BS3FA is available online at https://github.com/kelrenmor/bs3fa.

Histopathology is required to identify and characterize myopathies in high-throughput phenotype screening of genetically engineered mice.

The development of mouse models that replicate the genetic and pathological features of human disease is important in preclinical research because these types of models enable the completion of meaningful pharmacokinetic, safety, and efficacy studies. Numerous relevant mouse models of human disease have been discovered in high-throughput screening programs, but there are important specific phenotypes revealed by histopathology that are not reliably detected by any other physiological or behavioral screening tests. As part of comprehensive phenotypic analyses of over 4000 knockout (KO) mice, histopathology identified 12 lines of KO mice with lesions indicative of an autosomal recessive myopathy. This report includes a brief summary of histological and other findings in these 12 lines. Notably, the inverted screen test detected muscle weakness in only 4 of these 12 lines (Scyl1, Plpp7, Chkb, and Asnsd1), all 4 of which have been previously recognized and published. In contrast, 6 of 8 KO lines showing negative or inconclusive findings on the inverted screen test (Plppr2, Pnpla7, Tenm1, Srpk3, Sidt2, Yif1b, Mrs2, and Pnpla2) had not been previously identified as having myopathies. These findings support the need to include histopathology in phenotype screening protocols in order to identify novel genetic myopathies that are not clinically evident or not detected by the inverted screen test.

Optimization and biological validation of an in vitro assay using the transfected Dm28c/pLacZ Trypanosoma cruzi strain.

There is an urgent need to develop safer and more effective drugs for Chagas disease, as the current treatment relies on benznidazole (BZ) and nifurtimox (NFX). Using the Trypanosoma cruzi Dm28c strain genetically engineered to express the Escherichia coli β-galactosidase gene, lacZ, we have adapted and validated an easy, quick and reliable in vitro assay suitable for high-throughput screening for candidate compounds with anti-T. cruzi activity. In vitro studies were conducted to determine trypomastigotes sensitivity to BZ and NFX from Dm28c/pLacZ strain by comparing the conventional labour-intensive microscopy counting method with the colourimetric assay. Drug concentrations producing the lysis of 50% of trypomastigotes (lytic concentration 50%) were 41.36 and 17.99 µM for BZ and NFX, respectively, when measured by microscopy and 44.74 and 38.94 µM, for the colourimetric method, respectively. The optimal conditions for the amastigote development inhibitory assay were established considering the parasite–host relationship (i.e. multiplicity of infection) and interaction time, the time for colourimetric readout and the incubation time with the β-galactosidase substrate. The drug concentrations resulting in 50% amastigote development inhibition obtained with the colourimetric assay were 2.31 µM for BZ and 0.97 µM for NFX, similar to the reported values for the Dm28c wild strain (2.80 and 1.5 µM, respectively). In summary, a colourimetric assay using the Dm28c/pLacZ strain of T. cruzi has been set up, obtaining biologically meaningful sensibility values with the reference compounds on both trypomastigotes and amastigotes forms. This development could be applied to high-throughput screening programmes aiming to identify compounds with anti-T. cruzi in vitro activity.

Bayesian Joint Modeling of Chemical Structure and Dose Response Curves

Today there are approximately 85,000 chemicals regulated under the Toxic Substances Control Act, with around 2,000 new chemicals introduced each year. It is impossible to screen all of these chemicals for potential toxic effects either via full organism in vivo studies or in vitro high-throughput screening (HTS) programs. Toxicologists face the challenge of choosing which chemicals to screen, and predicting the toxicity of as-yet-unscreened chemicals. Our goal is to describe how variation in chemical structure relates to variation in toxicological response to enable in silico toxicity characterization designed to meet both of these challenges. With our Bayesian partially Supervised Sparse and Smooth Factor Analysis (BS3FA) model, we learn a distance between chemicals targeted to toxicity, rather than one based on molecular structure alone. Our model also enables the prediction of chemical dose-response profiles based on chemical structure (that is, without in vivo or in vitro testing) by taking advantage of a large database of chemicals that have already been tested for toxicity in HTS programs. We show superior simulation performance in distance learning and modest to large gains in predictive ability compared to existing methods. Results from the high-throughput screening data application elucidate the relationship between chemical structure and a toxicity-relevant high-throughput assay. An R package for BS3FA is available online at https://github.com/kelrenmor/bs3fa.

The Alginate Immobilization of Metabolic Enzymes Platform Retrofits an Estrogen Receptor Transactivation Assay With Metabolic Competence.

The U.S. EPA Endocrine Disruptor Screening Program utilizes data across the ToxCast/Tox21 high-throughput screening (HTS) programs to evaluate the biological effects of potential endocrine active substances. A potential limitation to the use of in vitro assay data in regulatory decision-making is the lack of coverage for xenobiotic metabolic processes. Both hepatic- and peripheral-tissue metabolism can yield metabolites that exhibit greater activity than the parent compound (bioactivation) or are inactive (bioinactivation) for a given biological target. Interpretation of biological effect data for both putative endocrine active substances, as well as other chemicals, screened in HTS assays may benefit from the addition of xenobiotic metabolic capabilities to decrease the uncertainty in predicting potential hazards to human health. The objective of this study was to develop an approach to retrofit existing HTS assays with hepatic metabolism. The Alginate Immobilization of Metabolic Enzymes (AIME) platform encapsulates hepatic S9 fractions in alginate microspheres attached to 96-well peg lids. Functional characterization across a panel of reference substrates for phase I cytochrome P450 enzymes revealed substrate depletion with expected metabolite accumulation. Performance of the AIME method in the VM7Luc estrogen receptor transactivation assay was evaluated across 15 reference chemicals and 48 test chemicals that yield metabolites previously identified as estrogen receptor active or inactive. The results demonstrate the utility of applying the AIME method for identification of false-positive and false-negative target assay effects, reprioritization of hazard based on metabolism-dependent bioactivity, and enhanced in vivo concordance with the rodent uterotrophic bioassay. Integration of the AIME metabolism method may prove useful for future biochemical and cell-based HTS applications.

Direct-RT-qPCR Detection of SARS-CoV-2 without RNA Extraction as Part of a COVID-19 Testing Strategy: From Sample to Result in One Hour

Due to the lack of protective immunity in the general population and the absence of effective antivirals and vaccines, the Coronavirus disease 2019 (COVID-19) pandemic continues in some countries, with local epicentres emerging in others. Due to the great demand for effective COVID-19 testing programmes to control the spread of the disease, we have suggested such a testing programme that includes a rapid RT-qPCR approach without RNA extraction. The Direct-One-Step-RT-qPCR (DIOS-RT-qPCR) assay detects severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in less than one hour while maintaining the high sensitivity and specificity required of diagnostic tools. This optimised protocol allows for the direct use of swab transfer media (14 μL) without the need for RNA extraction, achieving comparable sensitivity to the standard method that requires the time-consuming and costly step of RNA isolation. The limit of detection for DIOS-RT-qPCR was lower than seven copies/reaction, which translates to 550 virus copies/mL of swab. The speed, ease of use and low price of this assay make it suitable for high-throughput screening programmes. The use of fast enzymes allows RT-qPCR to be performed under standard laboratory conditions within one hour, making it a potential point-of-care solution on high-speed cycling instruments. This protocol also implements the heat inactivation of SARS-CoV-2 (75 °C for 10 min), which renders samples non-infectious, enabling testing in BSL-2 facilities. Moreover, we discuss the critical steps involved in developing tests for the rapid detection of COVID-19. Implementing rapid, easy, cost-effective methods can help control the worldwide spread of the COVID-19 infection.

Abstract 6338: High-throughput screening program to target ovarian cancer leader cells

Abstract Despite its relatively low incidence ovarian cancer is one of the most lethal gynecological malignancies, characterized by high recurrence rate following primary therapy and almost universal acquisition of chemoresistance. Recently, we identified a subpopulation of “Leader Cells” (LCs) that control invasion, progression and acquired chemoresistance. Therapies targeting leader cells are likely to be pivotal to improving prognosis, particularly in patients who are refractory to standard-of-care. To investigate the role of LCs in chemoresistance, we used CRISPR to integrate an in-frame T2A-GFP expression cassette downstream of the native LC marker gene, Keratin 14 (KRT14). Genomic integration drives the LC specific expression of GFP, facilitating visualization and quantitative tracking of the LC subset. The status of GFP as a surrogate marker of LCs in vitro was confirmed by immunostaining and expression analyses. Chemotherapeutic intervention studies and co-staining with the apoptosis marker cleaved caspase 3, demonstrated a significant enrichment of LCs that represent a non-apoptotic subset of cells resistant to standard chemotherapy. High-throughput drug screening, combining large functional drug libraries with high-throughput viability and invasion assays, was used to identify drugs that could inhibit LC function in vitro. Ovarian cancer cell lines (SKOV3 and COV362.4 expressing KRT14-T2A-GFP) were screened against 1450 FDA-approved drugs, and 218 hits identified that inhibited LC viability, invasive capacity or both. Amongst the hits that we identified, the neural signaling pathway emerged as a potential novel mediator for LC function. Further validation experiments confirmed that inhibition of the neural signaling pathway did not affect viability but synergized with chemotherapy to reduce cisplatin IC50 by 10-20-fold. Our data demonstrate that the high-throughput screen developed is a viable approach to identify opportunities for drug repurposing. Moreover, the neural signaling pathway appears to be important in LC function and inhibiting this pathway results in increased sensitivity to chemotherapy in vitro. Ongoing work will evaluate the applicability of this new tool for clinical screening programs, and further, validate existing drugs for potential repurposing. Citation Format: Nazanin Karimnia, Maree Bilandzic, Magdalena Plebanski, Andrew N. Stephens. High-throughput screening program to target ovarian cancer leader cells [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 6338.

Systematic assessment of mechanistic data for FDA-certified food colors and neurodevelopmental processes

Seven US FDA-batch certified synthetic food colors are approved for use as food additives in the United States. Perceived neurodevelopmental concerns for these colors persist. This study assessed the plausibility of such an association through the evaluation of mechanistic evidence collected from in vitro assays or other alternative models. Mechanisms and molecular targets underlying neurodevelopmental processes associated with attention deficit and hyperactivity disorder (ADHD) and other neurodevelopmental-related symptoms (e.g., cognitive function, learning and memory disorder, etc.) were identified. Publicly available data from the ToxCast/Tox21 high-throughput screening (HTS) program and peer-reviewed literature that measure activity of the colors for such molecular targets were analyzed and reviewed. Erythrosine (Red No. 3) was active in several assays mapped to neurodevelopmental processes — specifically, HTS assays that measure signals in neurotransmitter pathways. The remaining six colors do not appear to alter signaling pathways related to neurodevelopmental processes on the molecular or cellular level. This assessment provides an approach for systematically identifying and mapping mechanistic data to putative neurodevelopmental processes as a means to prioritize substances for possible further investigation. The assessment also provides insights into the lack of activity of synthetic food colors for key events in neurodevelopmental signaling pathways.

Validation of Fragile X Screening in the Newborn Population Using a Fit-for-Purpose FMR1 PCR Assay System

Newborn screening is designed for presymptomatic identification of serious conditions with effective early interventions. Clinical laboratories must perform prospective pilot studies to ensure that the analytical performance and workflow for a given screening test are appropriate. We assessed the potential to screen newborns for fragile X syndrome, a monogenic neurodevelopmental disorder, by establishing a customized, high-throughput PCR and analysis software system designed to detect fragile X mental retardation 1 gene repeat expansions from dried blood spots (DBSs). Assay precision, accuracy, sensitivity, and specificity were characterized across the categorical range of repeat expansions. The assay consistently resolved genotypes within three CGG repeats of reference values up to at least 137 repeats and within six repeats for larger expansions up to 200 repeats. Accuracy testing results were concordant with reference results. Full and premutation alleles were detected from subnanogram DNA inputs eluted from DBSs and from mixtures with down to 1% relative abundance of the respective expansion. Analysis of 963 deidentified newborn DBS samples identified 957 normal and 6 premutation specimens, consistent with previously published prevalence estimates. These studies demonstrate thatthe assay system can support high-throughput newborn screening programs.

Predicting drug activity against cancer cells by random forest models based on minimal genomic information and chemical properties.

A key goal of precision medicine is predicting the best drug therapy for a specific patient from genomic information. In oncology, cancers that appear similar pathologically can vary greatly in how they respond to the same drug. Fortunately, data from high-throughput screening programs often reveal important relationships between genomic variability of cancer cells and their response to drugs. Nevertheless, many current computational methods to predict compound activity against cancer cells require large quantities of genomic, epigenomic, and additional cellular data to develop and to apply. Here we integrate recent screening data and machine learning to train classification models that predict the activity/inactivity of compounds against cancer cells based on the mutational status of only 145 oncogenes and a set of compound structural descriptors. Using IC50 values of 1 μM as activity cutoffs, our predictive models have sensitivities of 87%, specificities of 87%, and yield an area under the receiver operating characteristic curve equal to 0.94. We also develop regression models to predict log(IC50) values of compounds for cancer cells; the models achieve a Pearson correlation coefficient of 0.86 for cross-validation and up to 0.65–0.73 against blind test sets. Predictive performance remains strong when as few as 50 oncogenes are included. Finally, even when 40% of experimental IC50 values are missing from screening data, they can be imputed with sufficient reliability that classification accuracy is not diminished. The presented models are fast to generate and may serve as easily implemented screening tools for personalized oncology medicine, drug repurposing, and drug discovery.

Comparative analysis of Rapid Equilibrium Dialysis (RED) and solid phase micro-extraction (SPME) methods for In Vitro-In Vivo extrapolation of environmental chemicals

In vitro to in vivo extrapolation (IVIVE) is a critical component of the efforts to prioritize and assess environmental chemicals using high-throughput in vitro assays. The plasma unbound fraction (Fub) is a key toxicokinetic parameter in IVIVE, and is usually measured via the Rapid Equilibrium Dialysis (RED) assay widely used for pharmaceuticals. However, pharmaceuticals have a narrower range of physicochemical properties than environmental chemicals. Motivated by the observation that high LogKOW compounds appeared to have disproportionately low Fub measurements using RED, we added a protein-free control in order to verify equilibration to 100% unbound in the absence of proteins. We found that many high LogKOW non-pharmaceuticals fail to equilibrate in RED in protein-free controls, and thus had apparent values of Fub = 0 in plasma. In these cases, Solid Phase Microextraction (SPME) as an alternative method provided an accurate, though more time-consuming, alternative to accurately determine Fub. We propose an updated IVIVE workflow that adds a protein-free control to the RED protocol, with the use of alternative approaches, such as SPME, in cases where compounds fail to adequately equilibrate. These refinements will provide additional confidence in the use of IVIVE as part of high-throughput screening programs of chemicals.

Identification of pyrrolo-pyridine derivatives as novel class of antibacterials.

A series of 5-oxo-4H-pyrrolo[3,2-b]pyridine derivatives was identified as novel class of highly potent antibacterial agents during an extensive large-scale high-throughput screening (HTS) program utilizing a unique double-reporter system-pDualrep2. The construction of the reporter system allows us to perform visual inspection of the underlying mechanism of action due to two genes-Katushka2S and RFP-which encode the proteins with different imaging signatures. Antibacterial activity of the compounds was evaluated during the initial HTS round and subsequent rescreen procedure. The most active molecule demonstrated a MIC value of 3.35µg/mL against E. coli with some signs of translation blockage (low Katushka2S signal) and no SOS response. The compound did not demonstrate cytotoxicity in standard cell viability assay. Subsequent structural morphing and follow-up synthesis may result in novel compounds with a meaningful antibacterial potency which can be reasonably regarded as an attractive starting point for further in vivo investigation and optimization.