Estimation of the Mechanism of Adrenal Action of Endocrine-Disrupting Compounds Using a Computational Model of Adrenal Steroidogenesis in NCI-H295R Cells

Thiourea-functionalized aminoglutethimide derivatives as anti-leishmanial agents

Aim: We aim to develop new anti-leishmanial agents against Leishmania major and Leishmania tropica. Materials & methods: A total of 23 thiourea derivatives of (±)-aminoglutethimide were synthesized and evaluated for in vitro activity against promastigotes of L. major and L. tropica. Results & conclusion: The N-benzoyl analogue 7p was found potent (IC50 = 12.7 μM) against L. major and non toxic to normal cells. The docking studies, indicates that these inhibitors may target folate and glycolytic pathways of the parasite. The N-hexyl compound 7v was found strongly active against both species, and lacked cytotoxicity against normal cells, whereas compound 7r, with a 3,5-bis-(tri-fluoro-methyl)phenyl unit, was active against Leishmania, but was cytotoxic in nature. Compound 7v was thus identified as a hit for further studies.

Insights into drug development with quantitative systems pharmacology: A prospective case study of uncovering hyperkalemia risk in diabetic nephropathy with virtual clinical trials

The quantitative systems pharmacology (QSP) approach is widely applied to address various essential questions in drug discovery and development, such as identification of the mechanism of action of a therapeutic agent, patient stratification, and the mechanistic understanding of the progression of disease. In this review article, we show the current landscape of the application of QSP modeling using a survey of QSP publications over 10 years from 2013 to 2022. We also present a use case for the risk assessment of hyperkalemia in patients with diabetic nephropathy treated with mineralocorticoid receptor antagonists (MRAs, renin-angiotensin-aldosterone system inhibitors), as a prospective simulation of late clinical development. A QSP model for generating virtual patients with diabetic nephropathy was used to quantitatively assess that the nonsteroidal MRAs, finerenone and apararenone, have a lower risk of hyperkalemia than the steroidal MRA, eplerenone. Prospective simulation studies using a QSP model are useful to prioritize pharmaceutical candidates in clinical development and validate mechanism-based pharmacological concepts related to the risk-benefit, before conducting large-scale clinical trials.

Towards a microfluidic H295R steroidogenesis assay-biocompatibility study and steroid detection on a thiol-ene-based chip

The development of cell-based microfluidic assays offers exciting new opportunities in toxicity testing, allowing for integration of new functionalities, automation, and high throughput in comparison to traditional well-plate assays. As endocrine disruption caused by environmental chemicals and pharmaceuticals represents a growing global health burden, the purpose of the current study was to contribute towards the miniaturization of the H295R steroidogenesis assay, from the well-plate to the microfluidic format. Microfluidic chip fabrication with the established well-plate material polystyrene (PS) is expensive and complicated; PDMS and thiol-ene were therefore tested as potential chip materials for microfluidic H295R cell culture, and evaluated in terms of cell attachment, cell viability, and steroid synthesis in the absence and presence of collagen surface modification. Additionally, spike-recovery experiments were performed, to investigate potential steroid adsorption to chip materials. Cell aggregation with poor steroid recoveries was observed for PDMS, while cells formed monolayer cultures on the thiol-ene chip material, with cell viability and steroid synthesis comparable to cells grown on a PS surface. As thiol-ene overall displayed more favorable properties for H295R cell culture, a microfluidic chip design and corresponding cell seeding procedure were successfully developed, achieving repeatable and uniform cell distribution in microfluidic channels. Finally, H295R perfusion culture on thiol-ene chips was investigated at different flow rates (20, 10, and 2.5 µL/min), and 13 steroids were detected in eluting cell medium over 48 h at the lowest flow rate. The presented work and results pave the way for a time-resolved microfluidic H295R steroidogenesis assay.

Evaluating structure-based activity in a high-throughput assay for steroid biosynthesis

Data from a high-throughput human adrenocortical carcinoma assay (HT-H295R) for steroid hormone biosynthesis are available for >2000 chemicals in single concentration and 654 chemicals in multi-concentration (mc). Previously, a metric describing the effect size of a chemical on the biosynthesis of 11 hormones was derived using mc data referred to as the maximum mean Mahalanobis distance (maxmMd). However, mc HT-H295R assay data remain unavailable for many chemicals. This work leverages existing HT-H295R assay data by constructing structure-activity relationships to make predictions for data-poor chemicals, including: (1) identification of individual structural descriptors, known as ToxPrint chemotypes, associated with increased odds of affecting estrogen or androgen synthesis; (2) a random forest (RF) classifier using physicochemical property descriptors to predict HT-H295R maxmMd binary (positive or negative) outcomes; and, (3) a local approach to predict maxmMd binary outcomes using nearest neighbors (NNs) based on two types of chemical fingerprints (chemotype or Morgan). Individual chemotypes demonstrated high specificity (85-98%) for modulators of estrogen and androgen synthesis but with low sensitivity. The best RF model for maxmMd classification included 13 predicted physicochemical descriptors, yielding a balanced accuracy (BA) of 71% with only modest improvement when hundreds of structural features were added. The best two NN models for binary maxmMd prediction demonstrated BAs of 85 and 81% using chemotype and Morgan fingerprints, respectively. Using an external test set of 6302 chemicals (lacking HT-H295R data), 1241 were identified as putative estrogen and androgen modulators. Combined results across the three classification models (global RF model and two local NN models) predict that 1033 of the 6302 chemicals would be more likely to affect HT-H295R bioactivity. Together, these in silico approaches can efficiently prioritize thousands of untested chemicals for screening to further evaluate their effects on steroid biosynthesis.

The influence of TNF-α on the expression profile of key enzymes of steroidogenesis in H295R cells

INTRODUCTION

Tumor necrosis factor-α (TNF-α) plays an extremely important role in the regulation of hypothalamicpituitary-adrenal axis. It is believed that chronic inflammation is the main cause of cancerogenesis and TNF-α plays a significant role in both of these processes. Unfortunately, the function of TNF-α in human adrenal steroidogenesis has not been explained enough.

AIM

To evaluate the changes in transcriptional activity of STAR, CYP11A1, CYP11B1, and CYP11B2 in H295R cell line exposed to TNF-α.

MATERIAL AND METHODS

NCI-H295R, human adrenocortical cell line was exposed to human recombinant TNF-α at the concentrations ranging from 0.001 to 10 nM for 3, 12, 24, and 48 h. Cells not exposed to TNF-α were the control of this experiment. RTqPCR assay was used to determine the changes in the expression of genes encoding STAR, CYP11A1, CYP11B1, and CYP11B2.

RESULTS

The highest differences between stimulated and non-stimulated cells were observed in the expression of STAR (FC = +2.2; 0.01 nM of TNF-α; 48 h); CYP11A1 (FC = +3.5; 0.1 nM of TNF-α; 24 h); CYP11B1 (FC = +7.0; 10 nM of TNF-α; 48 h); CYP11B2 (FC = +2.5; 10 nM of TNF-α; 48 h). Statistically significant differences (p < 0.05) in the expression were found only for CYP11A1. The interaction effect between genes was also noticed (p < 0.05).

CONCLUSIONS

The research showed the impact of TNF-α on the expression of the key genes encoding enzymes involved in adrenal steroidogenesis. Different expression patterns of was observed, depending on time and TNF-α concentration increased synthesis of this pro-inflammatory cytokine may intensify adrenal steroidogenesis.

Computational modelling of the Δ4 and Δ5 adrenal steroidogenic pathways provides insight into hypocortisolism

This study demonstrates the application of a mathematical steroidogenic model, constructed with individual in vitro enzyme characterisations, to simulate in vivo steroidogenesis in a diseased state. This modelling approach was applied to the South African Angora goat, that suffers from hypocortisolism caused by altered adrenal function. These animals are extremely vulnerable to cold stress, leading to substantial monetary loss in the mohair industry. The Angora goat has increased CYP17A1 17,20-lyase enzyme activity in comparison with hardy livestock species. Determining the effect of this altered adrenal function on adrenal steroidogenesis during a cold stress response is difficult. We developed a model describing adrenal steroidogenesis under control conditions, and under altered steroidogenic conditions where the animal suffers from hypocortisolism. The model is parameterised with experimental data from in vitro enzyme characterisations of a hardy control species. The increased 17,20-lyase activity of the Angora goat CYP17A1 enzyme was subsequently incorporated into the model and the response to physiological stress is simulated under both control and altered adrenal steroidogenic conditions.

Development of a prioritization method for chemical-mediated effects on steroidogenesis using an integrated statistical analysis of high-throughput H295R data

Synthesis of 11 steroid hormones in human adrenocortical carcinoma cells (H295R) was measured in a high-throughput steroidogenesis assay (HT-H295R) for 656 chemicals in concentration-response as part of the US Environmental Protection Agency's ToxCast program. This work extends previous analysis of the HT-H295R dataset and model by examining the utility of a novel prioritization metric based on the Mahalanobis distance that reduced these 11-dimensional data to 1-dimension via calculation of a mean Mahalanobis distance (mMd) at each chemical concentration screened for all hormone measures available. Herein, we evaluated the robustness of mMd values, and demonstrate that covariance and variance of the hormones measured appear independent of the chemicals screened and are inherent to the assay; the Type I error rate of the mMd method is less than 1%; and, absolute fold changes (up or down) of 1.5 to 2-fold have sufficient power for statistical significance. As a case study, we examined hormone responses for aromatase inhibitors in the HT-H295R assay and found high concordance with other ToxCast assays for known aromatase inhibitors. Finally, we used mMd and other ToxCast cytotoxicity data to demonstrate prioritization of the most selective and active chemicals as candidates for further in vitro or in silico screening.

High-Throughput H295R Steroidogenesis Assay: Utility as an Alternative and a Statistical Approach to Characterize Effects on Steroidogenesis

The U.S. Environmental Protection Agency Endocrine Disruptor Screening Program and the Organization for Economic Co-operation and Development (OECD) have used the human adrenocarcinoma (H295R) cell-based assay to predict chemical perturbation of androgen and estrogen production. Recently, a high-throughput H295R (HT-H295R) assay was developed as part of the ToxCast program that includes measurement of 11 hormones, including progestagens, corticosteroids, androgens, and estrogens. To date, 2012 chemicals have been screened at 1 concentration; of these, 656 chemicals have been screened in concentration-response. The objectives of this work were to: (1) develop an integrated analysis of chemical-mediated effects on steroidogenesis in the HT-H295R assay and (2) evaluate whether the HT-H295R assay predicts estrogen and androgen production specifically via comparison with the OECD-validated H295R assay. To support application of HT-H295R assay data to weight-of-evidence and prioritization tasks, a single numeric value based on Mahalanobis distances was computed for 654 chemicals to indicate the magnitude of effects on the synthesis of 11 hormones. The maximum mean Mahalanobis distance (maxmMd) values were high for strong modulators (prochloraz, mifepristone) and lower for moderate modulators (atrazine, molinate). Twenty-five of 28 reference chemicals used for OECD validation were screened in the HT-H295R assay, and produced qualitatively similar results, with accuracies of 0.90/0.75 and 0.81/0.91 for increased/decreased testosterone and estradiol production, respectively. The HT-H295R assay provides robust information regarding estrogen and androgen production, as well as additional hormones. The maxmMd from this integrated analysis may provide a data-driven approach to prioritizing lists of chemicals for putative effects on steroidogenesis.

Identification of candidate reference chemicals for in vitro steroidogenesis assays

The Endocrine Disruptor Screening Program (EDSP) is transitioning from traditional testing methods to integrating ToxCast/Tox21 in vitro high-throughput screening assays for identifying chemicals with endocrine bioactivity. The ToxCast high-throughput H295R steroidogenesis assay may potentially replace the low-throughput assays currently used in the EDSP Tier 1 battery to detect chemicals that alter the synthesis of androgens and estrogens. Herein, we describe an approach for identifying in vitro candidate reference chemicals that affect the production of androgens and estrogens in models of steroidogenesis. Candidate reference chemicals were identified from a review of H295R and gonad-derived in vitro assays used in methods validation and published in the scientific literature. A total of 29 chemicals affecting androgen and estrogen levels satisfied all criteria for positive reference chemicals, while an additional set of 21 and 15 chemicals partially fulfilled criteria for positive reference chemicals for androgens and estrogens, respectively. The identified chemicals included pesticides, pharmaceuticals, industrial and naturally-occurring chemicals with the capability to increase or decrease the levels of the sex hormones in vitro. Additionally, 14 and 15 compounds were identified as potential negative reference chemicals for effects on androgens and estrogens, respectively. These candidate reference chemicals will be informative for performance-based validation of in vitro steroidogenesis models.