Introduction: Analysis of streamlined computational models used to predict androgen disrupting chemicals revealed that assays measuring androgen receptor (AR) cofactor recruitment/dimerization were particularly indispensable to high predictivity, especially for AR antagonists. As the original dimerization assays used to develop the minimal assay models are no longer available, new assays must be established and evaluated as suitable alternatives to assess chemicals beyond the original 1,800+ supported by the current data. Here we present the AR2 assay, which is a stable, cell-based method that uses an enzyme complementation approach.Methods: Bipartite domains of the NanoLuc luciferase enzyme were fused to the human AR to quantitatively measure ligand-dependent AR homodimerization. 128 chemicals with known endocrine activity profiles including 43 AR reference chemicals were screened in agonist and antagonist modes and compared to the legacy assays. Test chemicals were rescreened in both modes using a retrofit method to incorporate robust cytochrome P450 (CYP) metabolism to assess CYP-mediated shifts in bioactivity.Results: The AR2 assay is amenable to high-throughput screening with excellent robust Z’-factors (rZ’) for both agonist (0.94) and antagonist (0.85) modes. The AR2 assay successfully classified known agonists  and antagonists  as well as or better than the legacy assays with equal or higher estimated potencies. The subsequent reevaluation of the 128 chemicals tested in the presence of individual human CYP enzymes changed the activity calls for five compounds and shifted the estimated potencies for several others.Discussion: This study shows the AR2 assay is well suited to replace the previous AR dimerization assays in a revised computational model to predict AR bioactivity for parent chemicals and their metabolites. A key aim of the ToxCast program was to implement a two-tiered testing approach to pre-screen chemicals for their potential effect on three major endocrine signaling pathways: estrogen, androgen, and thyroid. This approach relies on quantifying endocrine disruption using in vitro high-throughput screening (HTS) assays and integrating results across wide-ranging chemical and biological endpoints using computational models  are compounds that alter normal hormone signaling. Human exposure to EDCs can have profound and lasting effects, particularly during developmental life stages . In 1998l models . The usein vivo uterotrophic screening assay  ligand binding, 2) receptor dimerization and/or co-factor recruitment, 3) transactivation, and 4) cell proliferation and viability. While minimal (streamlined) models built on subsets of these HTS assays generally required at least one of each type, (A subset of the ToxCast assays was used to construct a predictive model of estrogen receptor (ER) bioactivity . The Toxng assay . A similch type, , receptoThis study describes the development of a novel cell-based HTS assay that measures ligand-dependent AR homodimerization using the split-reporter Nanoluciferase Binary Technology (NanoBiT) . The Nanin vivo xenobiotic metabolism , (17b)-17-hydroxy-17-methyl-estra-4,9,11-trien-3-one (R1881), bicalutamide (BICAL) and dichlone (DCLN) were purchased from Sigma-Aldrich . The 128 chemical test set used to evaluate the AR2 assay, and their respective sources, are listed in 4Sx2 linker (N-GGGGSGGGGS-C) was cloned into the pTRED plasmid (A double-stranded oligonucleotide encoding a G plasmid  between  plasmid .2 in a complete growth medium composed of high-glucose Dulbecco’s Modified Eagle Medium (DMEM) with L-glutamine and sodium pyruvate  supplemented with 10% qualified fetal bovine serum (Life Technologies), penicillin-streptomycin , and 5 mM HEPES (Sigma-Aldrich). HepG2 cryostocks were preserved at passage 91. Wild-type HepG2 cells were recovered from cryopreservation and sub-cultured every 48–72 h at a density of 8-12 × 104 cells/cm2 up to passage 121. For all experiments, cells were seeded into solid white 384-well microplates  pre-coated with collagen I  at 8,000 cells per well in 40 µL of assay medium composed of phenol red-free DMEM  supplemented with 4 mM glutamine and 2% charcoal-dextran stripped FBS  using a Certus Flex automated liquid dispensing system  fitted with a 0.45/0.15 mm microvalve at 0.2 bar. Cells were counted with a Scepter 3.0 Handheld Automated Cell Counter .HepG2 human hepatocellular carcinoma cells (passage 86) were purchased from American Type Culture Collection  and cultured in a humidified 37°C atmosphere containing 5% COPurified plasmids encoding AR- and SRC1-LgBit and -SmBit fusions were transiently transfected into HepG2 cells (plated in 384-well plates as described) using Lipofectamine 3000™ . Transfection mixes were prepared in 327 µL batches composed of 300 µL phenol red-free Opti-MEM™ , 3 µg each of LgBit and SmBit fusion plasmid (equimolar), 9 µL of Lipofectamine 3000™, and 12 µL of P3000 reagent per manufacturer’s protocol. Each well was transfected with 2.5 µL of DNA lipid nanoparticles dispensed using a Certus Flex fitted with a 0.45/0.15 mm microvalve at 0.06 bar. Transfected cells were incubated 56 h as previously described prior to administration of test chemicals.Lentiviral vectors for the AR-LgBitC and SmBitN-AR fusions were prepared and titered as previously described . HepG2 c2 before reading on a CLARIOstar microplate reader using a fluorescence intensity protocol, single multichromatic setting with a 530 nm excitation filter (15 nm bandpass), a 590 nM emision filter (20 nm bandpass), a 558.8 nm dichroic setting, a restricted gain setting of 860 (0-4095), top optic read with a focal height of 7.8 mm, integrating 35 flashes per well.AR2 cells were seeded in 384-well plates (as described) and incubated 24 h prior to chemical exposure. For agonist mode experiments, cells were treated with DMSO , R1881 (positive agonist control) titrated at 12 concentrations from 722 pM to 10 nM  with four replicate wells at 10 nM , or test chemicals titrated at 11 concentrations from 691 nM to 99.9 µM for 18 h. For antagonist mode experiments, all cells were treated with R1881 at a final concentration of 10 nM (to induce AR homodimerization) and co-treated with either DMSO (vehicle), bicalutamide (positive antagonist control) titrated at 12 concentrations from 722 nM to 99.8 µM  with four replicate wells at 99.8 µM (MEC), or test chemicals titrated at 11 concentrations from 691 nM to 99.9 µM for 18 h. Dichlone titrated at 12 concentrations from 722 nM to 99.8 µM  with four replicate wells at 99.8 µM (MEC) was included as a positive cytotoxicity control. Final DMSO concentrations were 0.1% for all wells. Assay plates were equilibrated to ambient temperature for 15 min before 2.5 µL of a NanoLuc substrate solution (18x) composed of 250 µM furimazine , 290 mM NaCl, 5.5 mM EDTA, 27.5 mM HEPES and 0.275% Tween-20 was added to each well using a Certus Flex fitted with a 0.45/0.15 mm microvalve at 0.2 bar. Assay plates were read immediately on a CLARIOstar microplate reader  using an endpoint luminescent protocol (top read) with an integration time of 0.08 s, unrestricted gain to maximize signal detection and a 384-well aperture spoon to minimize signal interference from neighboring wells. Following luciferase assay signal detection, 2.66 µL of an Alamar Blue solution (18x) composed of 120 µM resazurin sodium (Thermo Fisher) solubilized in 1x phosphate-buffered saline (pH 7.4) was added to each well using a Certus Flex fitted with a 0.45/0.15 mm microvalve at 0.2 bar. Assay plates were incubated for 2 hours in a humidified 37°C atmosphere containing 5% COin vitro transcription of beta-galactosidase , human cytochrome P450 enzymes CYP1A2, CYP2A6, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP2E1, CYP2J2, CYP3A4, and P450 oxidoreductase (POR) were previously described , contained a poly(A) tail, and were capped using CleanCap®. All mRNAs were processed via standard procedures including enzymatic treatment with DNase and phosphatase to remove the DNA template and the terminal 5’ triphosphate, respectively. Upon receipt, mRNA was aliquoted into single use volumes and stored at −80°C.DNA plasmids serving as templates for escribed . A singl2 humidified atmosphere and incubated 6 hours before controls or test chemicals were administered.For all mRNA transfection experiments, mRNA:lipid complexes were prepared at 18 h post-seeding using Lipofectamine MessengerMAX (ThermoFisher) per the manufacturer’s recommendations. Briefly, MessengerMAX transfection reagent was diluted 1:20 into phenol red-free Opti-MEM™ and incubated for 10 min at room temperature. Separately, mRNA was diluted into an equivalent volume of phenol red-free Opti-MEM™ and then mixed with the diluted MessengerMax transfection reagent and incubated for an additional 5 minutes at ambient temperature. The mRNA lipid nanoparticles were dispensed at 5 µL per well using a Certus Flex fitted with a 0.45/0.15 mm microvalve at 0.06 bar. The 5 µL delivery constituted 50 ng total mRNA per well with 1% (500 pg) being POR mRNA for all CYP transfection groups. The Bgal group contained no POR mRNA. Cell plates were returned to a 37°C/5% COhttps://github.com/SimmonsLabEPA/AR2-Assay-Method.git). Plate level statistics were derived for each assay plate using well-level raw relative light unit (RLU) data for all luminescent endpoints or relative fluorescence unit (RFU) data for the Alamar Blue cytotoxicity assay. For agonist mode, well-level normalized response (resp) was calculated as:Data were analyzed using R (version 4.1.0) and RStudio (version 1.4.1717). All data files and source code are made available at  was calculated as:50) value at least 3.16 times (0.5 as log 10) lower than its cytotoxicity AC50 value .These normalization methods yielded zero-centered values with positive responses suitable for concentration-response modeling using the tcpl package in R. Concentration-response curves were fit to a 3-parameter Hill model (bottom constrained to zero) using tcpl_lite R source code and plotted using the ggplot2 package. Active agonists were defined as those with a median response at any single concentration exceeding a 5*bmad threshold, where bmad is the median absolute deviation of normalized DMSO responses across the entire experiment. Active antagonists were defined as those with a median response at any single concentration exceeding a 3*bmad threshold and an antagonist half-maximal activity concentration , but also the variability around both responses . rZ’ facData for Odyssey Thera and University of Pittsburg AR co-factor recruitment (heterodimerization) assays were retrieved from the invitroDBv3.4 data base (released October 2021) on 5 May 2022 .DNA encoding full-length human AR and the SRC1 receptor interaction domain were cloned in frame with either LgBit or SmBit in each possible orientation into a lentiviral transfer plasmid to facilitate subsequent stable cell line development. A complete set of all possible AR homodimer and AR-SRC1 heterodimer LgBit/SmBit pairs . A–L werThe rZ’ factor is a unitless metric that quantifies both the difference between the median vehicle and positive control responses , but also the variability around both responses . Assays 50) of 319 pM . The lowest tested R1881 concentration (100 pM) produced a 2.41-fold response above vehicle control, suggesting lower concentrations were needed to observe a no effect level. Testosterone was not as efficacious as R1881, evoking only a 23-fold response (4.55 shown as log-2) and only at the highest tested concentration of 1 µM. Testosterone was also more than 200 times less potent than R1881 with an AC50 of 90.5 nM . R1881 was selected as the positive control for agonist mode experiments whereby test chemicals were evaluated for their ability to promote AR homodimerization. R1881 at 10 nM (blue arrow) produced a >95% maximal response and was selected as the inducer for antagonist mode experiments where test chemicals are co-administered with R1881 and evaluated for their ability to disrupt AR homodimerization.AR2 cells were treated with a titrated concentration series of two known AR agonists, the synthetic androgen R1881 and testosterone for 18 h  was nearly as potent with an AC50 of 271 nM. Two other chemicals, hydroxyflutamide (dark blue) and nilutamide (dark green) elicited full antagonist responses (>90% inhibition) albeit at higher concentrations . The antihypertensive drug spironolactone (light green) partially inhibited AR dimerization with maximum efficacy of 79% at the highest test concentrations. Amitrole (red) was used as a negative control and did not significantly inhibit AR dimerization. Together, these results show that AR2 responds predictably to both agonists and antagonists.An example of antagonist mode testing is shown in et al. to validate the Tox21 AR computational model (50 of 560 nM and a median assay plate rZ’ factor of 0.94. Thirty (23%) of the 128 test chemicals tested active  in agonist mode including all eight active AR agonist reference chemicals (100%) and all five androgenic drugs (100%) including 5α-dihydrotestosterone  threshold (3*bmad) and have an AR2 assay AC50 value that is at least 0.5 log units (3.16-fold) lower that its cytotoxicity AC50 value. This is illustrated clearly in 50 of 560 nM and a median assay plate rZ’ factor of 0.85. Of the 128 test chemicals, 72 (56%) tested active in antagonist mode. Sixteen of the 18 AR antagonist reference chemicals (88%) tested active in antagonist mode including flutamide  and University of Pittsburgh (UPitt). The OT AR_ARSRC1 assay utilizes a reporter complementation approach fusing full-length human AR and the receptor interacting domain (RID) of SRC1 to bipartite domains of yellow fluorescent protein (YFP) in human HEK293T cells . Both ag50 value from either the agonist or antagonist mode were used and the lower AC50 value was used for compounds active in both modes. Inactive chemicals were assigned an AC50 value of 2.5 for both assays. Of the 104 common chemicals, 45 were active at 8 hours and 58 at 16 h in the OT assay. Among these, 39 (87%) were active in the AR2 assay: 23 as agonists, 34 as antagonists with 18 testing active in both modes. The six chemicals missed by AR2 phenol, 4-dodecylphenol, chlorothalonil, folpet, raloxifene hydrochloride, and tetramethrin) were inactive as agonists, and all excepting tetramethrin had an active antagonist response confounded by cytotoxicity. As with AR2, the OT assay identified all eight AR reference agonists and all five androgenic drugs but with AC50 values lower than those estimated from AR2 (red circles below dashed line). The OT assay also identified 14 of the 17 (82%) AR reference antagonists while 15 (88%) were identified by AR2. Among the six anti-androgen drugs (pink circles), the OT assay correctly identified five including hydroxyflutamide and cyproterone acetate which were missed in the subsequent AR2 experiments, but misclassified finasteride. The antagonist AC50 values estimated from the OT assay were generally higher (lower potency) than those from AR2 (pink circles above dashed line).Of the 128 chemicals used to evaluate the performance of the AR2 assay, 104 had been previously tested in the OT assay using two exposure durations of eight and 16 h, whereas the AR2 assay used an 18-h exposure period. Since the OT assay does not have distinct agonist and antagonist modes, AR2 agonist and antagonist data were combined to facilitate a comparison of AR2 and OT assays at both timepoints . The AR250 values derived from AR2 were generally lower (median = 0.08 log units) than those from the UPitt assay with the notable exceptions of two estrogenic drugs (blue circles), 17α-estradiol and 17β-estradiol, which were active in the UPitt assay at concentrations more than 100-fold lower than in the AR2 assay. The UPitt assays previously tested 101 of the 128 chemicals used to evaluate AR2 . The UPitt assays utilized a 2-h exposure and tested chemicals in both agonist and antagonist modes which allowed for a direct comparison with the AR2 assay for agonists  and antaet al. for HEK293T cells was adapted to retrofit the AR2 assay with CYP metabolic activity. The optimized mRNA transfection protocol using CleanCap® mRNA in this study for HepG2 cells resulted in a 100% increase in CYP activity compared to the previously published study using ARCA-capped mRNA in HEK293T (data not shown). AR2 cells were transfected 6 hours prior to chemical treatment to promote CYP expression and activity and then exposed for 18 h prior to endpoint detection. The previous study showed that maximal CYP activity was sustained beyond 18 h. The impact of CYP metabolism on an AR-active chemical can be clearly seen in 50 of ∼9 μM; however, the clinical effectiveness of flutamide as an antiandrogenic drug depends on its metabolism in vivo to the more potent 2-hydroxyflutamide, primarily by CYP1A2  with an AC50 value (811 nM), more than 10-fold lower than flutamide. Flutamide-treated AR2 cells transfected with CYP1A2 (red) produced an AC50 value of 1.22 µM, nearly 8-fold lower (more potent) than mock-, Bgal-, and CYP3A4-transfected cells. Based on the bioactivity of the 2-hydroxyflutamide control, these results suggest that more than 80% of the flutamide parent was metabolized to 2-hydroxyflutamide by the augmented CYP1A2 activity in AR2 cells.The mRNA transfection method used in DeGroot y CYP1A2 . A distiy CYP1A2 . Flutami50 value of 5.98 nM across all biogroups. CYPs 2C8, 2C9, and 2J2 considerably reduced the potency of danazol , Bgal mRNA, and mRNA of ten human CYPs 1A2, 2A6, 2B6, 2C8, 2C9, 2C19, 2D6, 2E1, 2J2 and 3A4. The agonist mode dose-response curves for all 128 evaluation chemicals and assay controls across all ten CYP biogroups  are provided in  danazol -W, Y inc50 = 732 nM) AR antagonist in all but one biogroup, but its potency was reduced nearly 2-fold with CYP2C8 and CYP2C19  whose potency was reduced 4-fold by CYP2C9 (50 = 2.15 µM). As a final example, CYP2C19 reduced the potency of equilin  are provided in  CYP2C19 , and CYP CYP2C19  completeh CYP2C8 , 333 nM h CYP2C8 , 440 nM h CYP2C8 , and 380h CYP2C8 . 17α-eth equilin .There are thousands of commercially used chemicals for which endocrine disruption data does not exist, and the current EDSP battery of assays is too time- and resource-intensive to practicably solve this challenge. The ToxCast and Tox21 programs have implemented HTS assays and computational approaches to identify endocrine-active chemicals rapidly and inexpensively. The most successful project to date has been the ToxCast ER Model for bioactivity which integrates data from 18 ER HTS assays to predict ER agonist and antagonist activity in EDSP Tier 1 screening assays . In 2015Subsequent efforts have examined whether data from a smaller subset of the original 11 HTS assays and three additional assays can be used without sacrificing model predictivity . Those eet al. state that “not all of the AR assays are currently available through the original sources, so in order to implement subset models, one may need to find existing or develop new assays that have similar behavior to the ones selected for the optimal subsets.” They also state that one of the outstanding challenges to using in vitro data to predict in vivo outcomes is the lack of xenobiotic metabolism. Indeed, both the OT and UPitt assays are no longer available through their original sources, driving the need to develop a replacement AR PPI assay(s). Ideally, a replacement assay would: 1) have protocols for both agonist and antagonist modes, 2) provide AR bioactivity data that maintains or improves the predictivity of the computational model, 3) possess equal or superior throughput to the legacy assays, 4) be made widely accessible for interlaboratory validation, 5) not require cost-prohibitive reagents or detection equipment, and 6) have the potential to incorporate xenobiotic metabolism inexpensively and without sacrificing throughput. This study details the development of AR2, a stable cell-based AR homodimerization assay, and presents its evaluation using 128 chemicals including all but one of the 44 reference chemicals used to develop the AR computational model as well as a demonstration of its metabolic retrofit using an mRNA transfection approach.While noting the importance of the dimerization assays, Judson The AR2 assay was executed in agonist and antagonist modes and data compared to that derived from the single mode OT and bimodal UPitt assays used to build the AR computational model. Binary classifications using 43 of the reference chemicals used to train the AR model can facilitate useful comparisons of AR2 assay performance to the OT and UPitt assays. There were 24 agonist reference chemicals in the evaluation set where eight were active , and twenty inactive. All 24 were tested in the AR2 and OT assays and all but one (17β-trenbolone) were tested in the UPitt assay. The AR2 agonist assay had the highest balanced accuracy (91.7%) compared to OT at eight (75.0%) and 16 h (62.5%). The UPitt agonist assay performed poorly (BA = 43.5%) largely due to its inability to detect active agonists . The AR2 agonist assay misclassified just 2 of the 24 reference agonists (17α-estradiol and prochloraz), and both were false positives with maximal activities nearly equal to the activity threshold.While the high AR2 agonist BA is impressive, the antagonist performance is of greater interest since AR antagonist activity is more prevalent among xenobiotic chemicals. There were 24 antagonist reference chemicals in the evaluation set where 17 were active , and seven inactive. All 24 were tested in the AR2 and OT assays and all but one (hydroxyflutamide) were tested in the UPitt assay. The UPitt antagonist assay had the highest BA (95.7%), correctly classifying 15 of 16 reference antagonists and all seven inactive chemicals, while only misclassifying spironolactone as a false negative. The AR2 antagonist assay had a higher BA (79.2%) than did OT at eight (62.5%) or 16 h (75.0%). Although the AR2 antagonist assay performed well, the BA from this experiment suffered due to two factors. First, there were three reference inactive chemicals misclassified as false positives in the antagonist mode: 17-methyltestosterone, 4-androstene-3,17-dione, and testosterone propionate. This lowered the AR2 sensitivity to 57.1% (compared to 100% in UPitt antagonist assay) given there were a total of seven inactive chemicals. Secondly, there were two reference antagonists misclassified by AR2 in antagonist mode. In the larger 128-chemical evaluation, hydroxyflutamide  failed t95) of strong androgen agonist (R1881) was co-administered with test compounds or controls to stimulate assay signal. The use of a strong agonist reference in antagonist mode testing is standard practice for in vitro AR assays. The University of Pittsburgh assays used 20 mM DHT for antagonist mode testing . Perhaps more important than the selection of agonist is the concentration of agonist used to stimulate assay signal in antagonist mode. Using a higher concentration such as the EC95 produces the highest signal-to-background but introduces the possibility that weaker or less potent antagonists might go undetected. Using a lower concentration of agonist may improve assay sensitivity to better detect weaker competitive inhibitors. Formal implementation of this assay for ToxCast testing will use two parallel agonist concentrations for this reason.For antagonist mode testing, a near maximally effective concentration  and antagonist (0.84) modes suggest there is ample signal-to-background for further miniaturization to higher-throughput 1536-well format. The homogenous (add-only) protocol which also includes the mRNA transfection method to retrofit the assay with CYP activity is also preferable for HTS applications. Yet the AR2 assay does not require automated liquid dispensers and can be manually pipetted for lower-throughput, larger volume formats like 96-well applications and thus is highly transferrable. The AR2 assay requires no expensive detection reagents or equipment, only a microplate reader capable of luminescent and fluorescent detection which is relatively inexpensive compared to the imaging systems used by the legacy assays.vitro assays was recently described by Thomas et al. . Also, the methylcytosine (5 mC) substitution used previously was omitted during the CleanCap® mRNA synthesis for this study which alternatively used only a pseudouracil substitution. In the previous study, the CYP1A2 open reading frame (ORF) was found to harbor an unreported G to D mutation at amino acid 81 which was implicated in the lack of CYP1A2 activity observed using both a luminogenic substrate and phenacetin. Prior to this study, the CYP1A2 ORF was corrected to the canonical wild-type sequence by site-directed mutagenesis. The wild-type CYP1A2 used in this study performed as expected, converting >80% of the parent flutamide to 2-hydroxyflutamide over an 18-h exposure .in vitro toxicity assays.The impact of retrofitting the AR2 assay with CYP metabolism was restricted to only a few chemicals, most of which are highlighted in max (slower metabolism) as the substrate concentration falls relative to the Km.The introduction of xenobiotic metabolism into cell-based assays does raise several unforeseen issues. The first is the paradox posed by testing chemicals solubilized in DMSO, a solvent known to inhibit CYP activity . DMSO dipost hoc multiplexed endpoint such as Alamar Blue in this study. Using cytotoxicity CYP-specific controls would obviate the need to search for pathway-specific CYP controls. Conversely, the assay controls used would ideally be unaffected by CYP metabolism. The AR2 controls  were all unimpacted by CYP metabolism.Positive controls for CYP activity (independent of assay controls) are another unresolved technical challenge. One solution would be to identify chemicals known to be transformed by specific CYP enzymes to metabolites with altered bioactivity in an endpoint of interest  like flutamide. However, there are few examples of such chemicals for most toxicity endpoints. An alternative approach would be to find CYP-metabolized chemicals that elicit a response that can be measured independently  to the endpoint of interest. Cytotoxicity may be the best candidate as it is a response available to all cell-based assays that can be measured using a pre- or Interpretation of retrofitted data is another complicated issue. Criteria are needed to clearly define when bioactivation or inactivation has occurred. Once defined, there is still the question of biological significance, i.e., should the bioactivation of an otherwise inactive chemical with a single CYP biogroup  change its activity designation? It should also be made plain that an absence of CYP-shifted bioactivity in a given assay should not be interpreted as evidence that a chemical is not CYP-metabolized. CYP-shifted bioactivities are only manifested after at least two criteria have been met: 1) the chemical is metabolized by a CYP enzyme, and 2) the metabolite(s) after any subsequent Phase II metabolism or other biochemical transformation elicits a disparate response to the parent chemical. Flutamide is known to be metabolized by CYP1A2 and also CYP2C19 to hydroxyflutamide which is much more anti-androgenic, but also 4-nitro-3-(trifluoromethyl)phenylamine (Flu-1) by CYP3A4 . It is hin vitro endocrine testing.A formal re-evaluation of the AR2 assay in addition to several OECD-approved AR transactivation assays  will be


Flavivirus infection remodels the host cell to favour viral replication. In particular, the host cell lipid profile is altered, and it has been proposed that this process alters membrane fluidity to allow wrapping of the outer structural proteins around the viral nucleocapsid. We investigated whether expression of the Zika virus (ZIKV) and dengue virus (DENV) protease induced alterations in the cellular lipid profile, and subsequently whether co-expression of these proteases with VLP constructs was able to improve VLP yield.Studies have shown that Our results showed that both ZIKV and DENV proteases induced alterations in the lipid profile, but that both active and inactive proteases induced many of the same changes. Neither co-transfection of protease and VLP constructs nor bicistronic vectors allowing expression of both protease and VLP separated by a cell cleavable linker improved VLP yield, and indeed many of the constructs showed significantly reduced VLP production. Further work in developing improved VLP expression platforms is required.The online version contains supplementary material available at 10.1186/s13104-023-06572-z. Virus-like particles (VLPs) offer an alternative to genetic material based vaccines and a number of commercial VLPs are available to protect against hepatitis B virus, (e.g. Engerix-B®) human papillomavirus (e.g. Gardasil®) and hepatitis E virus (e.g. Hecolin®). We have previously shown that production of dengue vius (DENV) VLPs is markedly less efficient than the production of virions by natural infection . It is woC with 5% CO2 and maintained in DMEM .HEK293T/17 cells (ATCC CRL-11268) were cultured at 37The ZIKV VLP construct used in this study was as previously described , 9. The ZIKV VLP and NS3 protease (DENV2 NS2B-NS3pro (DWt), DENV2 NS2B-NS3S135A) (DMut), ZIKV ZNS2B-NS3pro (ZWt) and ZIKV NS2B-NS3(S135A) (ZMut) and ZIKV NS3pro (ZNS3) were amplified from plasmid templates. The two proteins were joined together with a P2A cleavage site into pcDNA3.1+  expression plasmid . Briefly35A (DMut5) into 24-well culture plates in DMEM supplemented with 10% heat-inactivated fetal bovine serum (FBS) without antibiotics at 37 oC. A total of 1.0 µg of each plasmid was incubated with 2x HEPES buffered saline solution (HBS) and 2.5 M CaCl2 at room temperature for 30 min, before addition to cells and incubation under standard conditions. For co-transfections, 0.5 μg of each plasmid were mixed before incubation with 2x HEPES/2.5 M CaCl2 as for single plasmid transfections, before adding to 1.3 × 106 HEK293T/17 cells grown in 6-well plates. For bicistronic constructs, 3.0 µg plasmid was incubated with 2x HEPES/2.5 M CaCl2 before being added to 1.3 × 106 HEK293T/17 cells grown in 6-well plates. Although the amount of plasmids was different between transfection and co-transfection, expression levels were similar as shown by comparison of EGFP + ZIKV VLP and the bicistronic EGFP-ZIKV as assesses by fluorescent microscopy  plasmids were transfected into HEK293T/17 cells by the calcium precipitation method essentially as described previously  with som35A plasmTo detect proteins in supernatants or cell lysates, 44 µl of supernatant was mixed with 11 µl of non-reducing lane marker sample marker (Thermo Fisher Scientific) while 20 µl of cell lysate was mixed with 5 µl reducing protein sample marker. Supernatants or lysates were then boiled for 5 min before loading 40 µl of supernatant or 20 µl of lysates onto 12% SDS-PAGE gels. After electrophoresis proteins were transferred to Amersham Protran 0.2 μm nitrocellulose membranes . Membranes were blocked with 5% skim milk  in 0.05% TBS-T and incubated with a specific primary antibody followed by appropriate secondary antibody conjugated HRP (Table S3). Chemiluminescence signal was developed using Immobilon Forte Western HRP substrate , and signal was collected using an AZURE c400 Gel Imaging System  or X-ray film. Protein band intensity was analyzed using the Image J program version 1.47 [Electron microscopy of VLPs produced from selected constructs was undertaken as described previously , 8.All statistical analyses of the numerical data were performed using Student T-test and one-way ANOVA on GraphPad Prism version 7.0.0 . The p values of less than 0.05 were considered statistically significant. Volcano plots analysis was undertaken using Agilent MassProfiler Professional software .We have previously described  a ZIKV NTo determine partial lipid profiles of the transfected cells, lipids were extracted, subjected to transesterification and the resultant fatty acid methyl esters (FAME) were analyzed by mass spectrometry. The data were annotated using Agilent MassProfiler Professional (MPP) software, and results were statistically analyzed. The resultant Volcano plots are shown in Figures S2 and S3, and Tables S4 to S7 show the statistically significant differently regulated FAME (as compared to mock) for DNS2B-NS3pro (Table S4), DNS2B-NS3pro(135 A) (Table S5), ZNS2B-NS3pro (Table S6) and ZNS2B-NS3pro(135 A) (Table S7). It was noted that the majority of fatty acid moieties were down-regulated, with only a few upregulated . Of particular note, both the inactive and active constructs for both DENV and ZIKV showed many of the same moieties being regulated in the same manner (bolded in the Table S4 to S7). This strongly suggests that NS2B-NS3pro can modulate lipid composition to at least some extent, irrespective of proteolytic activity. Collectively however, these results showed that both DENV and ZIKV constructs (active/inactive) were able to exert an effect on the cellular lipid profile.To determine whether expression of NS2B-NS3pro (active or inactive) could enhance VLP expression, we undertook double transfections between a ZIKV VLP , 9 and tAll these constructs were transfected or co-tansfected (as appropriate) into HEK293T/17 cells in parallel with a single ZIKV VLP transfection, as well as a co-transfection between ZIKV VLP and a construct containing EGFP. After 3 days, the expression of E protein in the supernatant and cells was determined by western blot analysis  together with its NS2B cofactor. In addition, the lipid analysis was confined to a FAME analysis, and so lipid categories other than fatty acyls were not investigated.Viruses in the genus Below is the link to the electronic supplementary material.Supplementary Material 1


Androgenic alopecia (AGA) is a dermatological disease with psychosocial consequences for those who experience hair loss. AGA is linked to an increase in androgen levels caused by an excess of dihydrotestosterone in blood capillaries produced from testosterone by 5α-reductase type II (5αR2), which is expressed in scalp hair follicles; 5αR2 activity and dihydrotestosterone levels are elevated in balding scalps. The diverse health benefits of flavonoids have been widely reported in epidemiological studies, and research interest continues to increase. In this study, a virtual screening approach was used to identify compounds that interact with active site residues of 5αR2 by screening a library containing 241 flavonoid compounds. Here, we report two potent flavonoid compounds, eriocitrin and silymarin, that interacted strongly with 5αR2, with binding energies of −12.1 and −11.7 kcal/mol, respectively, which were more significant than those of the control, finasteride . Molecular dynamic simulations (200 ns) were used to optimize the interactions between compounds and 5αR2 and revealed that the interaction of eriocitrin and silymarin with 5αR2 was stable. The study shows that eriocitrin and silymarin provide developmental bases for novel 5αR2 inhibitors for the management of AGA. Androgenic alopecia (AGA), also known as male pattern baldness, is a common type of hair loss. Hair is an essential bodily structure that protects the scalp, enhances human individuality, and serves a variety of purposes such as insulation, attractiveness, and tangibility . AGA is For many years, the treatment of AGA has been a point of contention in clinical dermatology. Many treatments for baldness are now available, including bioengineered hair transplants , hair foNew drug development is one of the most important tasks of biomedical research from scientific and economic perspectives. However, despite the advancements made in informatics and computational biology, as well as parallel increases in drug development productivity, drug development has been sluggish due to heavy dependence on synthetic small molecules as a source of innovation. Computer-Aided Drug Design (CADD) has emerged as a highly effective technique for identifying promising lead compounds and advancing prospective pharmaceutical medicines addressing a wide range of disorders ,14,15. CThe crystal structure of 5αR2 in complex with finasteride  was obtained from a protein data bank . After rhttps://www.selleckchem.com (accessed on 13 December 2022)) in .sdf format. These compounds were minimized, prepared with DS, and converted to ‘pdbqt’ format for docking analysis.The unique collection of 241 flavonoid compounds was obtained from Selleck Chemicals , entails the computational evaluation of large chemical repositories to identify molecules that exhibit complementarity to structurally characterized targets; compounds predicted to exhibit favorable binding characteristics are then subjected to experimental validation ,21. ReceThe SwissADME web server was used to assess the predicted physiochemical and pharmacokinetic properties of the top two compounds (eriocitrin and silymarin) , and Pro+ and Cl− , Cl− (10), 5αR2-silymarin; Na+ (0), Cl− (10), and 5αR2-finasteride; Na+ (0), Cl− (10)) were introduced, while preserving a salt concentration of 150 mM. The solvation of the protein was accomplished using the Simple Point Charge (spc216) water model. Using the energy-grps in the MDs parameters (mdp) file, the particle-mesh Ewald method was utilized to study interactions between 5αR2 and eriocitrin, silymarin, and finasteride. To achieve system equilibration, the NPT and NVT ensembles were employed at a temperature of 310 K and pressure of 1 bar. The GROMACS analysis module was utilized to examine trajectories by plotting graphs for Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF), Radius of Gyration (Rg), Solvent Accessible Surface Area (SASA), and H-bonding.Studying the intramolecular dynamics of proteins can reveal hidden biological functions and intricate mechanisms. GROMACS2019.6  was used5αR2 is involved in the pathophysiology of AGA, and thus, 5αR2 inhibitors are considered crucial for the development of anti-baldness therapies. Accordingly, we screened 241 flavonoids against the active pocket of 5αR2 and identified 11 compounds whose BEs were superior to those of the finasteride . These 1Silymarin had a BE of −11.7 kcal/mol  and inteFinasteride, a 5αR2 inhibitor, reduces serum and scalp DHT levels by inhibiting testosterone to DHT conversion and is often used to treat AGA . ClinicaA higher negative BE of a compound with the target enzyme indicates a stronger interaction with its amino acid residues in the catalytic pocket, and the dissociation rate of such compounds from the target enzyme will be slower ,32,33. ISilybum marianum (L.) gaernt (the milk thistle), while Citrus limon is a rich source of eriocitrin. The pharmacological effects of these compounds have been well explored, especially their hepatoprotective, antioxidant, anticancer, anti-diabetic, anti-inflammatory, and cardioprotective activities [Silymarin is derived from tivities ,35, and Further, the toxicity assessment of the selected compounds, eriocitrin and silymarin, was assessed by ProTox-II. To estimate a wide range of toxicity endpoints, the ProTox-II methodology employs a comprehensive computational strategy that integrates molecular similarity, pharmacophores, fragment propensities, and machine-learning models. This platform predicts toxicity based on chemical compounds that have been confirmed using various experiments. The web server provides confidence levels for the results and permits similarity comparisons. The predicted lethal dose 50 (pLD50) of eriocitrin was determined to be 12,000 mg/kg, placing it in Toxicity Class six. This classification suggests that eriocitrin exhibits no toxicity. The average similarity between the predicted and actual toxicity statistics for eriocitrin was determined to be 98.6%, while the prediction accuracy was found to be 72.9%. On the other hand, the pLD50 of silymarin was estimated to be 2000 mg/kg, which placed it in the ‘harmful if swallowed’ category and Toxicity Class four. The average similarity between predicted and experimental toxicity was 76.44%, and the prediction accuracy was 69.26%. Toxicity endpoints for silymarin and eriocitrin, such as acute toxicity, hepatotoxicity, cytotoxicity, carcinogenicity, mutagenicity, and immunotoxicity, were within acceptable ranges . FurtherSubsequently, MD simulations were conducted on the docked complexes of the aforementioned compounds with the 5αR2 enzyme, encompassing a duration of 200 ns. The primary objective of these simulations was to evaluate the stability of the docked complexes. MD simulations primarily aid in the understanding of conformational stability, a phenomenon that has a significant impact on the efficacy of therapeutic compounds in inhibiting target proteins. These simulations, on the other hand, demonstrate their utility by providing insights into interaction dynamics, including bonding events and stability patterns over time. RMSD is a metric utilized to evaluate protein stability, where lower RMSD deviations indicate greater stability. 5αR2-control, 5αR2-eriocitrin, and 5αR2-silymarin had RMSD average values of 0.45, 0.36, and 0.35 nm, respectively, and the RMSD plot revealed that 5αR2-eriocitrin and 5αR2-silymarin complexes had greater binding stability than the 5αR2-control complex. The bound structure of the 5αR2-control complex exhibited high deviation from its initial conformation, whereas the catalytic pocket of 5αR2 formed stable interactions with eriocitrin and silymarin. Furthermore, ligand RMSDs showed that 5αR2-eriocitrin exhibited a high deviation, whereas the 5αR2-control and 5αR2-silymarin complexes exhibited low deviations. It showed that the eriocitrin molecule did not tightly interact in the catalytic pocket of 5αR2 and therefore showed higher deviation in the pocket of the enzyme A–C.The average fluctuation of all residues throughout the simulation and the RMSD of 5αR2 during the binding of 5αR2-control, 5αR2-eriocitrin, and 5αR2-silymarin was plotted as a function of 5αR2 residue numbers. Consistent fluctuations in the catalytic pocket were observed in the backbones of 5αR2-silymarin and 5αR2-control, possibly due to orientation differences, while the 5αR2-eriocitrin complex exhibited high fluctuation in the 130–140 residue region of 5αR2 D, agreeiRg plots were used to obtain the compactness profiles of the complexes. The 5αR2-control, 5αR2-eriocitrin, and 5αR2-silymarin complexes had average Rg values of 1.79, 1.82, and 1.76 nm, respectively, and the Rg plots showed that 5αR2-eriocitrin and 5αR2-control complexes were less compact than the 5αR2-silymarin complex. These findings suggest that silymarin binding to 5αR2 increased enzyme stability, as evidenced by the reduced Rg compactness and little effect on the 5αR2 structure A, and th2, respectively  landscape and project the respective first (PC1) and second (PC2) eigenvectors (darker blue shades indicate lower energy levels). During the simulations, ligand binding to 5αR2 caused fluctuations in the global minima of 5αR2, as observed in the GFE contour maps. 5αR2-control and 5αR2-silymarin had similar projections, whereas 5αR2-eriocitrin had different global minima, indicating that the global minima of eriocitrin had changed during simulation B–D.Over the years, various potential 5αR2 inhibitors have been explored, and some have been synthesized. Furthermore, potent 5αR2 inhibitors have consistently been found to bind strongly to 5αR2 . FinasteThe involvement of 5αR2 in the pathophysiological mechanisms of AGA establishes the necessity of 5αR2 inhibitors in the advancement of therapeutic interventions for AGA. VS has been routinely employed to identify new drug leads. In the present study, in silico methodologies, viz., molecular docking-based VS, toxicity prediction, and MD simulation, were used to identify potential nontoxic 5αR2 inhibitors in a natural flavonoid library. Eriocitrin and silymarin were found to interact strongly with 5αR2 and form stable complexes and could be potential future anti-baldness drug candidates. However, further experimental research is needed to optimize them as 5αR2 inhibitors.