Assay Endpoints Research Articles

Microfluidic digital focus assays for the quantification of infectious influenza virus.

Quantifying infectious virus is essential for vaccine development, clinical diagnostics, and infectious disease research, but current assays are constrained by long turnaround times, high costs, and laborious procedures. To address these limitations, we present a digital focus assay employing an array of independent nanoliter cell cultures. The microfluidic platform allows cells in each nanowell to be inoculated with virus, followed by oil discretization to prevent cross-contamination. After incubation, infected cells are visualized through immunofluorescence staining, and a binary map of wells positive for viral antigen is generated by automated image analysis, allowing infectious viral titer to be calculated by statistical analysis. The platform requires significantly smaller sample and reagent volumes than conventional focus assays while enhancing assay automation and endpoint time flexibility. The technology is applied to the quantification of infectious influenza A using both model virus and clinical specimens, demonstrating the digital platform as an accurate, rapid, cost-effective, and convenient tool for viral load quantification with broad utility in clinical, pharmaceutical, and research applications.

Cell Painting for cytotoxicity and mode-of-action analysis in primary human hepatocytes.

High-throughput, human-relevant approaches for predicting chemical toxicity are urgently needed for better decision-making in human health. Here, we apply image-based profiling (the Cell Painting assay) and two cytotoxicity assays (metabolic and membrane damage readouts) to primary human hepatocytes after exposure to eight concentrations of 1085 compounds that include pharmaceuticals, pesticides, and industrial chemicals with known liver toxicity-related outcomes. Three computational methods (CellProfiler, a Cell Painting-specific convolutional neural network, and a pretrained vision transformer) were compared to extract morphology features from single cells or entire images. We used these morphology features to predict activity in the measured cytotoxicity assays, as well as in 412 curated ToxCast assays that span cytotoxicity, cell-based, and cell-free categories. We found that the morphological profiles detect compound bioactivity at lower concentrations than standard cytotoxicity assays. In supervised analyses, they predict cytotoxicity and targeted cell-based assay readouts, but not cell-free assay readouts. We also found that the various feature extraction methods performed relatively similarly and that filtering out non-bioactive or cytotoxic concentrations did not boost supervised assay prediction performance for any assay endpoint category, although it did have a large influence on unsupervised cluster analysis. We envision that image-based profiling could serve as a key component of modern safety assessment.

Modified throughput ninhydrin method for the qualitative assessment of dietary protein absorption in pig plasma.

Protein maldigestion and malabsorption lead to malnutrition and are a feature of exocrine pancreatic insufficiency (EPI). Although it is the current standard, measurement of nitrogen in stool to assess protease activity is indirect. Up to 80% of hydrolysed proteins appear in blood in the form of peptides, so we developed a method to measure peptide-derived amino acids in plasma as a relevant measure of proteolysis, verified its accuracy, precision, and linearity, and validated it in a porcine model. We modified a ninhydrin method. Large proteins were eliminated from plasma with 10 kDa-cut-off centrifugal filters. Free and total amino acids were measured in permeate before and after its hydrolysis. Peptide-derived amino acids were quantified by subtracting free amino acids from total amino acids. We verified the method in vitro and by comparing results in healthy and EPI pigs. The accuracy of the analysis was close to 100%, with excellent precision (mean relative standard deviation for low, medium, and high amino acid levels = 0.88%) and with stringent linearity (r2 = 0.986, %RE = 5.23). The high-throughput ninhydrin method detected levels of peptide-derived amino acids in vivo with maximal changes seen approximately 2 hours postprandially in young pigs. The AUC and Cmax were significantly higher in healthy compared to EPI pigs (P = .0026 and P = .0037, respectively). The high-throughput ninhydrin method is a sensitive, reliable, and practical method for the estimation of dietary peptide-derived amino acids. This assay endpoint could serve as a direct biomarker of protein digestion and absorption.

The mode of toxic action of ionic liquids: Narrowing down possibilities using high-throughput, in vitro cell-based bioassays

Growing concerns about the environmental impact of ionic liquids (ILs) have spurred research into their (eco)toxic effects, but studies on their mode of toxic action (MOA) still remain limited. However, understanding the MOA and identifying structural features responsible for enhanced toxicity is crucial for characterising the hazard and designing safer alternatives. Therefore, 45 ILs, with systematically varied chemical structures, were tested for cytotoxicity and two specific endpoints in reporter gene assays targeting the Nrf2-ARE mediated oxidative stress response (AREc32) and aryl hydrocarbon receptor activation (AhR-CALUX). While none of the ILs activated the reporter genes, cytotoxicity was high and markedly different between cell lines. Seven and 25 ILs proved more cytotoxic than predicted by baseline toxicity model in the AREc32 and the AhR-CALUX assays, respectively. The length of the side chain and headgroup structures of ILs altered the MOA of ILs. Cellular metabolism of the ILs, investigated by LC-MS/MS, showed side-chain oxidation of the long-chain quaternary ammonium compounds in AhR-CALUX cells and, to a lower extent, in AREc32 cells, however, this transformation could not explain the high cytotoxicity. Effect data for 72 ILs for ten endpoints retrieved from the Tox21 database identified the inhibition of aromatase activity and of mitochondrial membrane potential as potential MOAs. However, in vitro fluorimetric assays for these endpoints demonstrated that effects were activated in a non-specific manner, probably through cytotoxicity. Although many of the ILs tested induced cytotoxicity at concentrations lower than baseline toxicity, the specific MOAs responsible could not be identified. Alternatively, we suggest that the descriptors currently used may fail to define the affinity of ILs for cells. Testing of the affinity of ILs for a diverse range of biomolecules is needed to accurately describe their interactions with cells.

Updated results on multiple antigens stimulating cellular therapy (MASCT-I) in metastatic urothelial carcinoma: A randomized, open-label, phase I trial.

2545 Background: The appropriate treatment for mUC remains unmet medical needs. MASCT-I, an adoptive transfer of dendritic cells (DCs) presenting 15 types of tumor-associated antigens (TAAs), showed valuable treatment in solid tumors. We previously reported the efficacy and safety of MASCT-I alone, or plus camrelizumab or chemotherapy in mUC (NCT03034304). The biomarker analysis and long-term follow-up to for this promising approach was unknown. Methods: All enrolled patients were divided into in five groups (G) as previous reported. Patients in G2 receiving maintenance therapy of MASCT-I after first-line platinum based-chemotherapy were allowed to enter enrolled in G3 and receiving MASCT-I plus camrelizumab when disease progressed. We updated the exploratory endpoint of the IFN-γ ELISPOT assay, which was used to measure activation of the cytotoxic T Lymphocyte (CTL) response in eligible patients. Long-term follow-up results for safety, progression-free survival (PFS) and overall survival (OS) were reported. Results: 39 patients were enrolled. By January 31st, 2024, median follow-up time was 31.1 months. No new safety signals were updated. As previously reported, the most common adverse events (AEs) associated with MASCT-I included grade 1 and 2 flushing, pruritus, rash, muscle cramp, fever, and arthralgia. No MASCT-I-related death occurred. Median PFS and OS for all patients were 2.3 months, and 16.9 months, respectively. Patients in G2 presented superior OS of 41.2 months and duration of response (DOR) of 6.4 months. Nine patients treated with MASCT-I and camrelizumab after progression on MASCT-I in G2 presented a 24-month PFS rate of 55.6% and a 48-month DOR rate of 71.8%. Five patients had prolonged PFS for more than 20 months while two of them had PFS of more than 43 months. To identify the potentially beneficial population, IFN-γ ELISPOT data analysis revealed that patients who had positive TAAs ELISPOT response had significant prolonged PFS and OS compared to the non-responders (median PFS: 10.1 months vs 4.8 months, HR=0.30, 95% CI: 0.14-0.61, P<0.005; median OS: not reached vs 13.6 months, HR=0.16, 95% CI: 0.05-0.57, P<0.005). The Cmax value of INF-γ-positive T cells after cell infusion was also higher in patients with better PFS. In addition, we observed a significant increase in INF-γ-positive T cell activities in patients who progressed from G2 and were subsequently treated with a combination of MASCT-I and camrelizumab (Cmax mean (SD): 72 spots per million bulk PBMCs at G2 to 186 spots per million bulk PBMCs after transfer to G3, P=0.002). Conclusions: MASCT-I alone or in combination with immunotherapy or chemotherapy are safe and well tolerated, with inspiring survival benefit. The combination of PD-1 inhibitors may enhance the antitumor activity of MASCT-I. IFN-γ ELISPOT might be able to predict potentially beneficial populations. Clinical trial information: NCT03034304 .

Abstract 7600: Synergy of ZEN3694 and VIP152 for dual transcriptional targeting of BET and CDK9 in patient derived organoid models of pancreatic cancer

Abstract Background: Pancreatic ductal adenocarcinoma (PDAC) remains a leading cause of cancer related mortality in the United States, largely due to ineffective systemic therapy. While novel KRAS target strategies are in development, few agents have been investigated to target the transcriptional machinery for global inhibition of expression. Recent small molecule inhibitors targeting transcription (i.e., CDK7) have shown preliminary evidence of disease control; however, combination therapies have lagged, likely due to uncertainty in clinical toxicity. Text mining analysis suggests combination BET and CDK9 inhibition may be promising against PDAC. Here, we present preclinical evidence of synergistic activity for the combination of bromodomain and extraterminal (BET) inhibitor (ZEN3694) and CDK9 inhibitor (VIP152) using patient derived cancer organoids (PCOs). Methods: Dose response curves were generated for ZEN3694 and VIP152 to assess their single agent activity against PCOs using a low volume format. ZEN3694 was treated continuously while VIP152 was removed after 24h to mimic pharmacokinetics with assay endpoint at 144h. Synergy assays were designed using a combination dose titration grid. Viability was determined in all assays using 3D CellTiter Glo (CTG, 50% v/v). Synergy scores were produced using SynergyFinder 3.0 including Zero Interaction Potency (ZIP), Bliss Independence (Bliss), Highest Single Agent (HSA), and Loewe Additivity (Loewe). Western blot analysis was performed against global markers of active RNA polymerase II (phosphorylation at serine 2, 5, 7) and antiapoptotic protein, BCL xL, and MCL 1. Results: ZEN3694 was observed to have a single agent IC50 in the single uM range across 3 independent PDAC PCO lines (PDAC1 3.7uM, PDAC2 1.1uM, PDAC3 2.9uM). VIP152 showed significant potency in the same 3 PDAC PCO lines (PDAC1 140.2nM, PDAC2 30.5nM, PDAC3 43.4nM). The combination of ZEN3694 and VIP152 was found to have increased therapeutic activity with double digit synergy scores across all reference models: PDAC1 (Loewe 17.3, HSA 19.8, ZIP 20.2, and Bliss 17.2) and PDAC2 (Loewe 34.7, HSA 28.3, ZIP 19.7, and Bliss 19.6). Western blots of protein collected from organoids treated for 48h showed on target activity, including decrease in the phosphorylation of Rbp1 (Ser 2, Ser 5, and Ser 7) compared to both control and single agents and decreased expression in antiapoptotic proteins (MCL 1 and BCL xL). Conclusion: Here, we show the activity of dual transcription targeting with BET and CDK9 inhibition in patient derived pancreatic cancer organoid models. The combination of ZEN3694 and VIP152 has in vitro synergy in PDAC PCO models with on target activity. Ongoing work is validating this combination in animal models to assess toxicity, in vivo activity, and RNA expression profiling to understand transcriptional pathway dependency and therapeutic resistance. Citation Format: Lucas J. Koeppel, Austin Stram, Robert J. Millikin, Ellie Riedl, Md Shahadat Hossan, Ethan Samuel Lin, Ron Stewart, Jeremy D. Kratz. Synergy of ZEN3694 and VIP152 for dual transcriptional targeting of BET and CDK9 in patient derived organoid models of pancreatic cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7600.

Abstract 6788: Comparative analysis of anti-HER2 antibody-drug conjugates in tumor spheroid models

Abstract Robust techniques are needed to screen the anti-tumor activity of antibody-drug conjugates (ADCs) in vitro. Traditional monolayer models provide a cost-effective and scalable option but lack many features of the tumor microenvironment, such as complex cell-cell and cell-extracellular matrix interactions. Tumor spheroids afford a more translational model for quantification of ADCs. Here, spheroids are used to compare the mechanism of action (MoA) of anti-HER2 ADCs. Single spheroids were formed from mono- or co-cultures of HER2 over-expressing cancer cells and HER2 negative control cells. Incucyte® Nuclight Green Lentivirus was used to label HER2 positive BT474 cells, whilst HER2 negative MDA-MB-231 cells were left unlabeled. Antibodies were added after 72 hours of spheroid formation in 96-well ultra-low attachment plates. Brightfield and fluorescence images were captured every 3 hours using the Incucyte® Live-Cell Analysis System and quantified, for metrics such as spheroid size and fluorescence intensity, to give an indication of ADC cytotoxicity. Co-culture spheroids were dissociated at assay endpoint and the remaining proportion of green and unlabeled cells was measured using the iQue® Advanced Flow Cytometry Platform. The area of the BT474 mono-culture spheroids decreased in a concentration-dependent manner in the presence of both ADCs tested (trastuzumab emtansine and trastuzumab deruxtecan), indicating increased cytotoxicity. In contrast, only the highest concentration of trastuzumab, which is the monoclonal antibody backbone on which they are based, resulted in a reduction in spheroid size. This displays the increased anti-tumor activity due to the addition of the ADC payloads. In the co-culture model, only the trastuzumab deruxtecan induced a reduction in spheroid area, with the size of the spheroids in the presence of trastuzumab emtansine and trastuzumab remaining comparable to control levels. Analysis of the spheroid composition using flow cytometry showed that for the BT474 cells, again, percentage cell death compared to the IgG control was greater for the two ADCs, at 96.4% and 97.2%, respectively. This death was greater than with trastuzumab, which induced 46.4% cell death. Unlike in the mono-culture assays, a high level of death of the HER2 low expressing cells was also seen in the co-culture in the presence of trastuzumab deruxtecan (94.1% death compared to control). This displays the unique bystander activity of trastuzumab deruxtecan, which has led to its approval as a treatment for both HER2 high and HER2 low breast cancers. These data display how the Incucyte® Live-Cell Analysis System combined with iQue® Advanced Flow Cytometry can be leveraged to provide comprehensive assessment of the in vitro function of ADCs and how it can enable differences in their MoAs to be distinguished in 3D spheroid models. Citation Format: Kirsty McBain, Kalpana Barnes, Nicola Bevan. Comparative analysis of anti-HER2 antibody-drug conjugates in tumor spheroid models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6788.

Abstract 1231: First-in-class AR-V7/AR-fl small molecule molecular glue degrader for prostate cancer treatment

Abstract Metastatic castration resistance prostate cancer (mCRPC) is a lethal disease due to the development of treatment to androgen receptor (AR) signaling inhibitors (ARSi) and taxane chemotherapy. Treatment resistance occurs partly due to the expression of AR splice variants that lack the ligand binding domain (LBD) and are constitutively active in the nucleus. AR-V7 is the most prevalent variant conferring clinical resistance to both ARSi and taxanes. Currently, there is no selective AR-V7 inhibitor leaving patients with limited therapeutic options. Thus, the development of selective AR-V7 inhibitors is a high priority, clinically unmet need. To identify AR-V7 pharmacologic inhibitors, we performed a high throughput small molecule phenotypic screen using enzymatic complementation and nuclear AR-V7 as the assay endpoint. Our primary screen of ~170K compounds (z score = 0.8), followed by a cell-toxicity counter screen and a secondary GFP screen identified hit compound 7907, as a dual AR-V7/AR-fl protein degrader, with unique chemotype compared to all known AR modulators. Hit to lead optimization by medicinal chemistry/SAR studies identified lead compound 15, with increased potency compared to 7907. Mechanistically, we showed that compound 15 shortened AR-V7 protein half-life by activating the ubiquitin-proteasome pathway and inducing proteasomal degradation of both AR-V7/AR-fl, without affecting their transcription. Importantly, compound 15 induced degradation occurred within 3hr of treatment and was blocked by the clinically approved proteasome inhibitor, bortezomib. TurboID proximity ligation assay identified distinct E3 ligases, uniquely interacting with AR-V7 or AR-fl. Using AR-V7/AR-fl deletion mutants we further showed that compound 15 activity is mediated by the N-terminal domain of AR, present in both proteins. Remarkably, compound 15 sensitized LNCaP95 cells (endogenous AR-V7/AR-fl expression) to enzalutamide suggesting potential therapeutic synergism and ability to reverse enzalutamide resistance. Ongoing studies aim to narrow down the binding site of compound 15 as well as identify the E3 ligases mediating its activity. Together, these data support a molecular glue degrader mechanism of action, consistent with published studies showing that molecular glue degraders are ideal for targeting classically “undruggable” proteins lacking an LBD or containing intrinsically disordered domains, as is the case for AR-V7. Currently, all AR-directed therapies target the LBD of AR-fl, inhibiting AR signaling. AR-V7 expression is a direct outcome of this inhibition, leading to AR-fl and AR-V7 co-expression in patient tumors. We posit that our drug candidate, by offering dual AR-V7/AR-fl inhibition in a single treatment, has the potential to not only benefit patients with mCRPC but also patients with hormone-sensitive disease, and delay AR-V7 expression. Citation Format: CheukMan C. Au, Catrina Estrella, Prerna Vatsa, Michelle Naidoo, Michael Miller, Peter T. Meinke, David M. Nanus, Paraskevi Giannakakou. First-in-class AR-V7/AR-fl small molecule molecular glue degrader for prostate cancer treatment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1231.

Abstract LB_A21: NCI60 HTS384: The next phase of the NCI60 screen

Abstract The NCI60 human tumor cell line screen, which has operated as a free service to the cancer research community for over 20 years, has proven to be an important drug discovery tool. The screen has operated using cutting-edge technology of the 1990s including a 96-well microplate format, a 2-day drug exposure period and following cell fixation, and a visible absorbance endpoint by the protein-staining dye sulforhodamine B (SRB). The SRB endpoint determines cell density based on protein content with a lower resolution limit of 1,000 – 2,000 cells. This report describes the next phase of this important cancer research tool, the NCI60 HTS384 screen format. While the cell lines remain the same, the screen is performed using 384-well microplates with a 3-day compound exposure period and a CellTiter-Glo luminescence endpoint. The CellTiter-Glo endpoint determines cell number based upon ATP content with a lower resolution limit of <10 cells. To develop a baseline comparison, data were generated from a library of 1,000 FDA-approved and investigational small molecule anticancer agents by the two assay formats. Although the mean-graph patterns and COMPARE analyses are very similar for many compounds, there is a divergence between the two screens for some molecular targets. Within subsets of approved and investigational agents directed toward the same target (e.g., EGFR, BRAF/MEK/ERK, and PI3K inhibitors), the clustering of compound data from the NCI60 HTS384 screen is qualitatively similar to the clustering of compound data from the classic NCI60 SRB screen and there is a strong COMPARE correlation between the two screens. However, for some drug targets there is very little or no clustering between data from the two screens, although the clustering within each screen is moderate-to-good. Examples include mTOR rapalogs, BET bromodomain and NAMPRTase inhibitors. Differences in data between the two screens might be partially attributed to increases in the assay endpoint sensitivity and compound exposure time of the NCI60 HTS384 assay. Compounds submitted for NCI60 screening after September 30, 2023, will be tested in the modernized NCI60 HTS384 format. This project was funded in part with federal funds from the NCI, NIH, under contract no. HHSN261201500003I and by the Intramural Research Program of the NIH, National Cancer Institute. Citation Format: Beverly A Teicher, Mark W Kunkel, Joel Morris, Ronald C Taylor, Thomas S Dexheimer, Nathan P Coussens, James H Doroshow. NCI60 HTS384: The next phase of the NCI60 screen [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 LB_A21.

WITHDRAWN: Digital microfluidics as an emerging tool for bacterial protocols

Three dimensional models of cell culture enables researchers to recreate aspects of tumour biology not replicated by traditional two dimensional techniques. Here we describe a protocol to enable automated high throughput phenotypic profiling across panels of patient derived glioma stem cell spheroid models. We demonstrate the use of both live/dead cell end-points and monitor the dynamic changes in the cell cycle using cell lines expressing the FUCCI cell cycle reporter. Together, these assays provide additional insight into the mechanism of action of compound treatments over traditional cell viability assay endpoints.

Assessment of Micronuclei Frequency in the Peripheral Blood of Adult and Pediatric Patients Receiving Fractionated Total Body Irradiation

The cytokinesis-block micronucleus (CBMN) assay is an established method for assessing chromosome damage in human peripheral blood lymphocytes resulting from exposure to genotoxic agents such as ionizing radiation. The objective of this study was to measure cytogenetic DNA damage and hematology parameters in vivo based on MN frequency in peripheral blood lymphocytes (PBLs) from adult and pediatric leukemia patients undergoing hematopoietic stem cell transplantation preceded by total body irradiation (TBI) as part of the conditioning regimen. CBMN assay cultures were prepared from fresh blood samples collected before and at 4 and 24 h after the start of TBI, corresponding to doses of 1.25 Gy and 3.75 Gy, respectively. For both age groups, there was a significant increase in MN yields with increasing dose (p < 0.05) and dose-dependent decrease in the nuclear division index (NDI; p < 0.0001). In the pre-radiotherapy samples, there was a significantly higher NDI measured in the pediatric cohort compared to the adult due to an increase in the percentage of tri- and quadri-nucleated cells scored. Complete blood counts with differential recorded before and after TBI at the 24-h time point showed a rapid increase in neutrophil (p = 0.0001) and decrease in lymphocyte (p = 0.0006) counts, resulting in a highly elevated neutrophil-to-lymphocyte ratio (NLR) of 14.45 ± 1.85 after 3.75 Gy TBI (pre-exposure = 4.62 ± 0.49), indicating a strong systemic inflammatory response. Correlation of the hematological cell subset counts with cytogenetic damage, indicated that only the lymphocyte subset survival fraction (after TBI compared with before TBI) showed a negative correlation with increasing MN frequency from 0 to 1.25 Gy (r = −0.931; p = 0.007). Further, the data presented here indicate that the combination of CBMN assay endpoints (MN frequency and NDI values) and hematology parameters could be used to assess cytogenetic damage and early hematopoietic injury in the peripheral blood of leukemia patients, 24 h after TBI exposure.

Toxicity assessment of CeO₂ and CuO nanoparticles at the air-liquid interface using bioinspired condensational particle growth

CeO2 and CuO nanoparticles (NPs) are used as additives in petrodiesel to enhance engine performance leading to reduced diesel combustion emissions. Despite their benefits, the additive application poses human health concerns by releasing inhalable NPs into the ambient air. In this study, a bioinspired lung cell exposure system, Dosimetric Aerosol in Vitro Inhalation Device (DAVID), was employed for evaluating the toxicity of aerosolized CeO2 and CuO NPs with a short duration of exposure (≤10 min vs. hours in other systems) and without exerting toxicity from non-NP factors. Human epithelial A549 lung cells were cultured and maintained within DAVID at the air-liquid interface (ALI), onto which aerosolized NPs were deposited, and experiments in submerged cells were used for comparison. Exposure of the cells to the CeO2 NPs did not result in detectable IL-8 release, nor did it produce a significant reduction in cell viability based on lactate dehydrogenase (LDH) assay, with a marginal decrease (10%) at the dose of 388 μg/cm2 (273 cm2/cm2). In contrast, exposure to CuO NPs resulted in a concentration dependent reduction in LDH release based on LDH leakage, with 38% reduction in viability at the highest dose of 52 μg/cm2 (28.3 cm2/cm2). Cells exposed to CuO NPs resulted in a dose dependent cellular membrane toxicity and expressed IL-8 secretion at a global dose five times lower than cells exposed under submerged conditions. However, when comparing the ALI results at the local cellular dose of CuO NPs to the submerged results, the IL-8 secretion was similar. In this study, we demonstrated DAVID as a new exposure tool that helps evaluate aerosol toxicity in simulated lung environment. Our results also highlight the necessity in choosing the right assay endpoints for the given exposure scenario, e.g., LDH for ALI and Deep Blue for submerged conditions for cell viability.

AI/ML Models to Predict the Severity of Drug-Induced Liver Injury for Small Molecules.

Drug-induced liver injury (DILI), believed to be a multifactorial toxicity, has been a leading cause of attrition of small molecules during discovery, clinical development, and postmarketing. Identification of DILI risk early reduces the costs and cycle times associated with drug development. In recent years, several groups have reported predictive models that use physicochemical properties or in vitro and in vivo assay endpoints; however, these approaches have not accounted for liver-expressed proteins and drug molecules. To address this gap, we have developed an integrated artificial intelligence/machine learning (AI/ML) model to predict DILI severity for small molecules using a combination of physicochemical properties and off-target interactions predicted in silico. We compiled a data set of 603 diverse compounds from public databases. Among them, 164 were categorized as Most DILI (M-DILI), 245 as Less DILI (L-DILI), and 194 as No DILI (N-DILI) by the FDA. Six machine learning methods were used to create a consensus model for predicting the DILI potential. These methods include k-nearest neighbor (k-NN), support vector machine (SVM), random forest (RF), Naïve Bayes (NB), artificial neural network (ANN), logistic regression (LR), weighted average ensemble learning (WA) and penalized logistic regression (PLR). Among the analyzed ML methods, SVM, RF, LR, WA, and PLR identified M-DILI and N-DILI compounds, achieving a receiver operating characteristic area under the curve of 0.88, sensitivity of 0.73, and specificity of 0.9. Approximately 43 off-targets, along with physicochemical properties (fsp3, log S, basicity, reactive functional groups, and predicted metabolites), were identified as significant factors in distinguishing between M-DILI and N-DILI compounds. The key off-targets that we identified include: PTGS1, PTGS2, SLC22A12, PPARγ, RXRA, CYP2C9, AKR1C3, MGLL, RET, AR, and ABCC4. The present AI/ML computational approach therefore demonstrates that the integration of physicochemical properties and predicted on- and off-target biological interactions can significantly improve DILI predictivity compared to chemical properties alone.

Spectrophotometric-Based Assay to Quantify Relative Enzyme-Mediated Degradation of Commercially Available Bioplastics.

We present a spectrophotometric-based assay to identify enzymes that degrade commercially available bioplastics. Bioplastics comprise aliphatic polyesters with hydrolysis-susceptible ester bonds and are proposed as a replacement for petroleum-based plastics that accumulate in the environment. Unfortunately, many bioplastics can also persist in environments including seawater and waste centers. Our assay involves an overnight incubation of candidate enzyme(s) with plastic, followed by A610 spectrophotometry using 96-well plates to quantify both a reduction in residual plastic and the liberation of degradation by-products. We use the assay to show that Proteinase K and PLA depolymerase, two enzymes that were previously shown to degrade pure polylactic acid plastic, promote a 20-30% breakdown of commercial bioplastic during overnight incubation. We validate our assay and confirm the degradation potential of these enzymes with commercial bioplastic using established mass-loss and scanning electron microscopy methods. We show how the assay can be used to optimize parameters (temperature, co-factors, etc.) to enhance the enzyme-mediated degradation of bioplastics. The assay endpoint products can be coupled with nuclear magnetic resonance (NMR) or other analytical methods to infer the mode of enzymatic activity. Overall, the screening capacity of the spectrophotometric-based assay was demonstrated to be an accurate method to identify bioplastic-degrading enzymes.

Artificial Intelligence in Drug Toxicity Prediction: Recent Advances, Challenges, and Future Perspectives.

Toxicity prediction is a critical step in the drug discovery process that helps identify and prioritize compounds with the greatest potential for safe and effective use in humans, while also reducing the risk of costly late-stage failures. It is estimated that over 30% of drug candidates are discarded owing to toxicity. Recently, artificial intelligence (AI) has been used to improve drug toxicity prediction as it provides more accurate and efficient methods for identifying the potentially toxic effects of new compounds before they are tested in human clinical trials, thus saving time and money. In this review, we present an overview of recent advances in AI-based drug toxicity prediction, including the use of various machine learning algorithms and deep learning architectures, of six major toxicity properties and Tox21 assay end points. Additionally, we provide a list of public data sources and useful toxicity prediction tools for the research community and highlight the challenges that must be addressed to enhance model performance. Finally, we discuss future perspectives for AI-based drug toxicity prediction. This review can aid researchers in understanding toxicity prediction and pave the way for new methods of drug discovery.

Bayesian matrix completion for hypothesis testing.

We aim to infer bioactivity of each chemical by assay endpoint combination, addressing sparsity of toxicology data. We propose a Bayesian hierarchical framework which borrows information across different chemicals and assay endpoints, facilitates out-of-sample prediction of activity for chemicals not yet assayed, quantifies uncertainty of predicted activity, and adjusts for multiplicity in hypothesis testing. Furthermore, this paper makes a novel attempt in toxicology to simultaneously model heteroscedastic errors and a nonparametric mean function, leading to a broader definition of activity whose need has been suggested by toxicologists. Real application identifies chemicals most likely active for neurodevelopmental disorders and obesity.

A mechanistic understanding of the effects of polyethylene terephthalate nanoplastics in the zebrafish (Danio rerio) embryo

Plastic pollution, especially by nanoplastics (NPs), has become an emerging topic due to the widespread existence and accumulation in the environment. The research on bioaccumulation and toxicity mechanism of NPs from polyethylene terephthalate (PET), which is widely used for packaging material, have been poorly investigated. Herein, we report the first use of high-resolution magic-angle spinning (HRMAS) NMR based metabolomics in combination with toxicity assay and behavioural end points to get systems-level understanding of toxicity mechanism of PET NPs in intact zebrafish embryos. PET NPs exhibited significant alterations on hatching and survival rate. Accumulation of PET NPs in larvae were observed in liver, intestine, and kidney, which coincide with localization of reactive oxygen species in these areas. HRMAS NMR data reveal that PET NPs cause: (1) significant alteration of metabolites related to targeting of the liver and pathways associated with detoxification and oxidative stress; (2) impairment of mitochondrial membrane integrity as reflected by elevated levels of polar head groups of phospholipids; (3) cellular bioenergetics as evidenced by changes in numerous metabolites associated with interrelated pathways of energy metabolism. Taken together, this work provides for the first time a comprehensive system level understanding of toxicity mechanism of PET NPs exposure in intact larvae.

Spermatogonial Transplantation.

Spermatogonial transplantation is the unequivocal method to detect spermatogonial stem cells (SSCs) based strictly on the functional definition of stem cells - the cells' regenerative capacity. This method further allows for SSC quantification. A weakness of spermatogonial transplantation is its time-consuming nature; it takes 2 months to confirm the production of terminally differentiated cells in spermatogenesis, spermatozoa, in mice, which gives the assay endpoint. Using the mouse as the model system, we here describe the basic techniques of spermatogonial transplantation and provide practical guidance to successfully carry out this technique and to interpret data generated.

The impact of biostatistics on hazard characterization using in vitro developmental neurotoxicity assays.

In chemical safety assessment, benchmark concentrations (BMC) and their associated uncertainty are needed for the toxicological evaluation of in vitro data sets. A BMC estimation is derived from concentration-response modelling and results from various statistical decisions, which depend on factors such as experimental design and assay endpoint features. In current data practice, the experimenter is often responsible for the data analysis and therefore relies on statistical software, often without being aware of the software default settings and how they can impact the outputs of data analysis. To provide more insight into how statistical decision-making can influence the outcomes of data analysis and interpretation, we have developed an automated platform that includes statistical methods for BMC estimation, a novel endpoint-specific hazard classification system, and routines that flag data sets that are outside the applicability domain for an automatic data evaluation. We used case studies on a large dataset produced by a developmental neurotoxicity (DNT) in vitro battery (DNT IVB). Here we focused on the BMC and its confidence interval (CI) estimation as well as on final hazard classification. We identified five crucial statistical decisions the experimenter must make during data analysis: choice of replicate averaging, response data normalization, regression modelling, BMC and CI estimation, and choice of benchmark response levels. The insights gained are intended to raise more awareness among experimenters on the importance of statistical decisions and methods but also to demonstrate how important fit-for-purpose, internationally harmonized and accepted data evaluation and analysis procedures are for objective hazard classification.

Screening Method for the Identification of Compounds That Activate Pregnane X Receptor.

The pregnane X receptor (PXR) is a nuclear receptor found mainly in the liver and intestine, whose main function is to regulate the expression of drug-metabolizing enzymes and transporters. Recently, it has been noted that PXR plays critical roles in energy homeostasis, immune response, and cancer. Therefore, identifying chemicals or compounds that can modulate PXR is of great interest, as these can result in downstream toxicity or, alternatively, may have therapeutic potential. Testing one compound at a time for PXR activity would be inefficient and take thousands of hours for large compound libraries. Here, we describe a high-throughput screening method that encompasses plating and treating HepG2-CYP3A4-hPXR cells in a 1536-well plate, as well as reading and interpreting assay (e.g., luciferase reporter gene activity) endpoints. These cells are stably transfected with a human PXR expression vector and CYP3A4-promoter-driven luciferase reporter vector, allowing the identification of compounds that activate PXR through cytochrome 450 3A4. We also describe how to analyze the data from each assay and explain follow-up steps, namely pharmacological characterization and quantitative polymerase chain reaction (qPCR) assays, which can be performed to confirm results from the original screen. These methods can be used to identify and confirm hPXR activators after completion of a compound screening. Published 2022. This article is a U.S. Government work and is in the public domain in the USA. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Establishment of a high-throughput assay to identify hPXR activators Basic Protocol 2: Quantitative high-throughput screening a compound library to classify hPXR activators Basic Protocol 3: Performing pharmacological characterization and qPCR assays to confirm hPXR activators.