Processing of materialsthat originated from tailings of industrialplants without grinding has several advantages since mines are faced witha lot of pressure to minimize their environmental impacts. This articleindicates that the introduction of submicron bubbles  to conventional flotation could improve the separationefficiency of valuable minerals from their associated gangue phases.It was demonstrated that metallurgical responses  of NB flotation could improve comparedto those of conventional tests. Various hydrodynamic cavitation setupsfor NB generation may lead to different metallurgical responses. Ingeneral, the addition of surfactants (frothers and collectors) forNB generation could increase both mass and water recoveries, whichwould be key factors on selectivity. Selectivity is also markedlydependent on the PSD of feed, and the selectivity of NB flotationis improved significantly by decreasing the feed size. In general,generation of NBs in the presence of a frother leads to higher flotationmetallurgical responses than in the presence of a collector. Tailingsrepositories pose an environmental concern3 and, in somecases, present reprocessing opportunities.4 Dueto the stricter environmental regulation, mining houses are underpressure to ensure the impacts are mitigated. On the other hand, tailings’reprocessing as secondary resources often bring economic advantagesas grinding is usually minimal or not required. With the fast-paceddevelopments of more efficient machines and improved technologies,mining houses are considering reprocessing these tailings to maximizeprofit maximization, reducing tailings dam’s issues.Beneficiationof nonconventional sources and waste streams hasgained popularity due to the current demand for minerals and exhaustionof primary sources. As such, apatite ((Ca2O5 (4–8%) from the previous magnetite recovery.Efforts are being made to ensure the efficient extraction of the mineralsof interest with a minimum CO2 footprint. The possibilityof coarse flotation is being investigated given the relatively liberatedmineral particles from the mineralogy.5 Coarse flotation presents an attractive beneficiation option to eliminate the energy-intensivegrinding. This process also reduces water usage and reagent consumptionand makes the tailings dam more stable due to the deposition of coarsermaterials. However, conventional mineral separation by flotation haspoor performance on coarse particles (>150 μm).In pursuit of their continued efforts in reducing environmentalimpact and sourcing of critical raw materials, Luossavaara-KiirunavaaraAktiebolag (LKAB) has embarked on a tailings retreatment project.A large apatite-iron tailings deposit is located north of Sweden inKiruna and Malmberget, containing a substantial amount of P7 For promoting attachment,the use of nanobubbles has been reported to be effective,11 mainly due to their high stability, great longevity, and rapid attachmentto hydrophobic surfaces.12 Studies havebeen conducted to assess the influence of bubble sizes generated invarious hydrodynamic cavitation systems.14 Additionally, liquid properties have been addressed,namely, temperature, surfactant type, and dosage. Through the NB generation,an increase in the surfactant concentration has been found to reducebubble sizes and improve their stability.17 The change in surface tensionexplains this as the liquid’s surface tension decreases, andthe bubbles formed become finer.14 Many NB flotationstudies reported improving flotation recoveries, grades, and kineticsand even reduced reagent consumption.23 In a recent study, Pourkarimi et al.24 indicated that generation of NBs in the presence of selective collectorscould significantly increase the flotation efficiency of fine phosphateore samples. Surprisingly, surface analyses of flotation concentratesindicated that the amounts of flotation collectors adsorbed onto thesurface of floated particles were lower in the presence of NBs thanin their absence. However, adding nanobubbles to a conventional flotationsystem can also increase the entrainment rate. Entrainment could bethe main reason for improving the metallurgical responses of puremineral flotation, where a selective separation would not be an issue.25Foraddressing these challenges, the application of submicron bubblesin the flotation (known as “NB flotation”) of pure phosphateminerals, and some on actual ores, has been understudied for overa decade 1.The tyThis study will examine theeffect of nanobubbles generated with/withoutsurfactants on the selective flotation performance of coarse apatitefrom Malmberget tailings (Sweden). For the first time, this investigationassessed the effect of entrainment through NB flotation of coarseparticles originated from sorting plant tailings. In general, NB flotationhas been recommended for fine particles; therefore, for comparisonpurposes, two size fractions of the samples were subject for NB flotation.The overall goal is assessing the reprocessing of tailings as received(−850 μm) or sieved size fractions  in flowsheet developmentunder different conditions using NBs generated by various reagents.2O5 recovery, it resulted in the lowest P2O5 grade compared to other experiments for the as-received samples compared to theconventional bubbles (3.3%) for the −850 μm size fraction.After 7 min, NB, NBC, and NBF had the highest mass recoveries of 12.0,11.2, and 11.9%, respectively, compared to 10.6% for the conventionalbubbles for the −850 μm samples. k forthe finer size fraction was higher than that for the as-received feed.The fact that the curves for NB, NBC, and NBF were always above theconventional bubbles’ curves indicated the improved flotation k. This is consistent with the findings reported in theliterature where nanobubbles result in an increased flotation rateconstant.22 From the classical first-order model used  are high comparedto those of CB  fraction is lower compared to that for the fine (−106μm) fraction, consistent with the findings by Nazari and Hassanzadeh.8 In their investigation on the effects of differentsurfactants in the bulk generation of nanobubbles, they reported thefollowing order of the effect on the rate constant: dodecylamine >pine oil > MIBC  > A65 (polypropylene glycol). Theflotationrate, k, as expected, also decreased as the feedparticle size increased regardless of the surfactant used. The pronouncedeffect on the fine fraction, when compared to the coarser fraction,could be attributed to the relatively high proportion of the appropriateparticle size fraction for optimum flotation performance (+38–106μm).27 However, comparing both fractionsshows the following change when comparing conventional bubbles andnanobubbles: the −850 μm sample has the biggest changecompared to the −106 μm one.In other words, it could be observedthat the predicted se of CB 5. NBF ha2O5, P,Fe, and SiO2 for both size fractions under different hydrodynamicconditions. The grades of the gangue phases (Fe and SiO2) slightly increased with reagents’ introduction (NBC andNBF). This increase, to some extent, was higher for the −106μm sample than the −850 μm one. The increase ingangue materials (SiO2 and Fe) was accompanied by a slightdecrease in P2O5, which means that a higherwater recovery resulted in a concentrate grade reduction. To furtherconfirm this, analysis shows a strong linear correlation between bothSiO2 and Fe recoveries and water recovery 29 compared to conventional bubbles supporting the linear relationship.recovery 7. The ob31 Kracht et al.30 investigatedthe effect of the surfactant type and concentration on the entrainmentrate. The results show that the entrainment rate depends on the reagenttype and indicates that it is higher for MIBC  than for Atrac1563 (carboxylic acid). Previously, it was reported that alcoholstended to increase entrainment compared to polyethylene glycols.32 Of the alcohols investigated, MIBC was foundto give more entrainment compared to hexanol despite similar molecularweights, and the difference was attributed to the branched structureof MIBC.30 This could also explain theincreased entrainment for nanobubbles generated with MIBC comparedto Atrac 1563, a carboxylic acid.The degree of entrainment is morepronounced in the scenario wherenanobubbles were generated with surfactants. The hydrophilic particlesthat get entrained by the nanobubbles to the froth zone evident fromthe increased water recovery remain entrapped in the stabilized froth(due to the surfactant), thus reporting to the concentrate. Therefore,the increased entrainment  and entrapment in the froth zone due tothe stable froth resulted in a lower concentrate grade. The additionof surfactant stabilizes the bubbles but also plays a critical rolein the selective drainage of water in the froth zone.2 footprint of mineral processing by eliminating regrinding. The nanobubblesgenerated with surfactants (collectors and frothers) could cause adecrease in grade, mainly because of the increased entrainment rate.For maximum benefit of the nanobubbles, it could be proposed thatthe nanobubbles generated with different surfactants (frothers orcollectors) should be used in the different flotation stages (rougheror cleaner). Where recovery is more preferred than grade control,adding reagents could be beneficial, and for the cleaner stages, theuse of surfactants in the generation stage is not recommended. Furtherwork is required to optimize the hydrodynamic conditions and surfactanttypes to reduce the amount of water recovery associated with nanobubbles,which will reduce the rate of entrainment.Theapplication of nanobubbles through flotation of coarse apatitetailings evidently improved the process’ metallurgical responsescompared to flotation by conventional bubbles only. The results indicatedthat nanobubble flotation of the as-received apatite tailings (−850μm) was an attractive option that can minimize the CO5 The as-received material was relatively coarse with F80 = 227 μm (coarse), which was sievedto give a −106 μm fraction (fine) with F80 = 65 μm for further assessments  presented in essments 8. The tw8 The NBs were delivered to the flotation cellas a concentrated air dispersion at mild-pulp agitation before thecell air valve was opened for the flotation process. The NBs wereintroduced in varying quantities for a set time to ensure a constantsupply throughout the flotation time. The typical size range of NBsgenerated is in a range of 100–200 nm.33The nanobubbleswere produced usingan air-in-water microdispersion generator developed by Living Energies& Co. (Japan). The NB generation is carried out based on the cavitationthrough a Venturi tube under a working pressure of ∼2 MPa.The process is controlled by the feed inlet valve, pump speed, airuptake valve, and operating pressure. For the NB generation, two surfactantswere used: (1) Atrac 1563, a fatty acid collector with frothing properties,“NBC”, and (2) methyl isobutyl carbinol (MIBC), an alcohol-basedfrother, “NBF” (molecular weight: 102), at a dosageof 20 ppm based on a work reported elsewhere by Nazari and Hassanzadeh.34 The pulp was conditioned for 1 min after which500 g/t water glass was added as a depressant. Atrac 1563 (200 g/t)was added to the pulp and conditioned for 2 min. No frother was addedto the flotation cell since Atrac 1563 has frothing properties itself.Once the air was opened, the concentrates were collected at 1, 3,5, and 7 min. At the end of the flotation process (after 7 min), therespective samples and the remaining material reported to the tailingswere collected in different containers. The flotation products wereweighed and dried at 110 ° C. After drying, the concentrateswere stored and packed in plastic bags for further PSD and chemicalanalyses by XRF to determine metallurgical responses.All the flotation experiments wereconducted in the rougher stage. For both conventional bubble size(CB) flotation and different NB flotation setups (NB (without reagents),NBF, and NBC), the respective feed material  was pulped with Luleå tap waterto give 35 wt % solids. The pulp was conditioned for 5 min in an automatedOutotec GTK LabCell, a laboratory-scale mechanical cell 4. The pHk, for each scenario,was calculated using the first-order model (To further evaluate the NBs’ effect on flotationkinetics,the flotation rate constant er model 11S, which is given by eq R1 is the recoveryof P2O5 and R2 isthe recovery of SiO2/Fe. A higherselectivity index S extrapolates better selectivityfor the process.19 All experiments wererepeated twice, and their averages were reported. Accordingly, experimentalerrors were determined at a 95% confidence level.Theseparation efficiency can be expressed by the selectivity index,


Similar results were generally found for the separate consumer age cohorts (18–64 years) with lower sensory acuity in the 65+ age cohort. For the descriptive analysis, the Tender genotype scored numerically more tender, juicy and flavoursome, although the differences were only significant for one of the panels. The critical outcome from this study is that parental average genetic merit can be used to pre-select groups of animals for tenderness, which, in turn, can be detected by consumers.The objective of the present study was to determine if animals who were genetically divergent in the predicted tenderness of their meat actually produced more tender meat, as well as what the implications were for other organoleptic properties of the meat. The parental average genetic merit for meat tenderness was used to locate 20 “Tough genotype” heifers and 17 “Tender genotype” heifers; M. longissimus thoracis steaks from all heifers were subjected to sensory affective analysis (140 consumers) and sensory profiling using two trained sensory panels. All sample steaks were treated identically regarding pre- and post-mortem handling, storage, cooking and presentation . For the affective consumer study, eight steaks were sectioned from the same location of the striploin muscles from each of the heifers. In total, 108 steaks from the Tender genotype and 118 from the Tough genotype were tested in the consumer study to determine the preference or liking of these steaks for appearance, aroma, flavour, tenderness, juiciness and overall acceptability. The consumer study found that the Tender genotype scored higher ( A consumer’s decision to purchase beef is guided by the perception of healthiness along with a variety of sensory characteristics including colour, appearance, tenderness, juiciness, aroma and flavour . Of courGenetic differences in meat quality exist represented both by inter-breed and intra-breed differences . From a A is the numerator relationship matrix and II is an identity matrix. The heritability of mean tenderness score was assumed to be 0.16 as estimated by Berry et al. (2021) [A genetic evaluation was undertaken for meat tenderness in Mix99  based on. (2021)  using thn = 17) versus genetically Tough (n = 20) genotypes based on their sire’s EBV. All heifers were fed a total mixed ration ad libitium comprised of a concentrate, hay and water fresh-weight ratio of 10:3:9, as described in detail by Kelly et al. [The Irish Cattle Breeding Federation (ICBF) operate the Irish national beef breeding program on behalf of the Irish industry. Semen of young test bulls is distributed to participating beef producers as part of the Gene Ireland breeding program; hence, a strong relationship exists between these herds and the ICBF, with the latter routinely purchasing cattle for performance tests from these producers. Therefore, the identified sires divergent in genetic merits for tenderness with progeny in these herds were considered. Divergence in sire EBV was sought while remaining cognisant of minimising the over-representation of any one breed in either a high or a low genetic merit stratum; this is because breed differences exist in meat tenderness . A totaly et al. , and werAnimals were slaughtered and after 48 h post-mortem hanging, 8 steaks (2.54 cm thick) were cut from a deboned section of the longissimus thoracis (striploin) muscle on the right-hand side of the carcass. All samples were aged for 21 days at 4 °C before being transferred to a commercial freezer, individually vacuum-packed and held at −20 °C.n = 140) of varying age groups (18–65+) and genders  were recruited via email and participated in this consumer study, which took place in the School of Food and Nutritional Sciences in University College Cork, Cork, Ireland. Consumers were asked to provide demographic information prior to tasting including information on age group, nationality, gender, occupation and steak cooking preference.Consumer Demographics: Beef consumers , tenderness , juiciness , liking of flavour  and overall liking . In total, 14 sensory sessions were carried out with 10 consumers per session and 4 samples per consumer per treatment.Sample Preparation and Presentation: Striploin steaks were thawed by placing the frozen steaks in a chill (4 °C) for 24 h prior to cooking. Steaks were then cooked on an open clam shell grill (Velox CG-6 400 V 3 Phase Model) using a teaspoon of sunflower oil at 210 °C. Steaks were cooked for 1 min on each side to seal the meat and then turned every 2 min until cooked. The internal temperature was continually probed until 71 °C was reached. The steaks were then labelled, wrapped in aluminium foil and allowed to rest for 3 min. Steaks were cut into portions of 1.27 cm × 1.27 cm × 2.54 cm, ensuring that no gristle was present, the portions and two randomised portions from each sample were wrapped in aluminium foil, labelled with the corresponding 3 digit code and served to panellists.Experimental Design: Each session contained a mix of Tough and Tender genotypes that were served blindly in a randomised order. Panellists received a dummy sample at the beginning of each session to reduce first order biases, assist in panellist standardisation and enhance panellist calibration before each session.MQ4 Score: In addition to the scores provided by the consumers, a weighted eating quality score, MQ4 score, was calculated for each sample using the Australian MSA model (0.3 TE + 0.1 JU + 0.3 FL + 0.3 OL). Thus, a combined satisfaction score (MQ4 score) was also calculated by combining four sensory variables assessed by consumers, including tenderness (TE), juiciness (JU), flavour liking (FL) and overall liking (OL) . In thisSensory analyses of steak samples were undertaken by a commercial company, at Sensory Location 1, and by a sensory laboratory at Sensory Location 2, using trained sensory panels . The training of panellists for profiling meat description texture attributes was according to the American Meat Science Association (2015) . The proSample Preparation and Presentation Location 1: Frozen samples were thawed in a chill (4 °C) 24 h prior to analysis. Steaks were tempered at ambient without packaging 60 min prior to being fried on a “Mirror Griddle Model M1000E ENG” to an internal temperature of 71 °C. Steaks were rested and then cut into 1.27 cm × 1.27 cm × 2.54 cm portions (no gristle) and presented to each panellist while hot.Sensory Evaluation: Each panellist was directed to cut the steak piece in half across the cooked surface and evaluate the sample in the same fashion (duplicate) using the assessment sheet provided. The steak sample was scored for tenderness, juiciness and flavour using a structured scale .Sample Preparation and Presentation Location 2: Sensory samples were prepared identically as for location one. However, steaks were fried on a CG6 Velox grill to an internal temperature of 71 °C, wrapped in aluminium foil and rested for 2 min before cutting, similarly to Location 1.Sensory Evaluation: Two randomised portions from each steak were wrapped in aluminium foil, labelled with the corresponding three-digit code and served to the panellists. A warm-up sample was served at the beginning of every session to reduce first-order bias and assist with the calibration of the panellists. Panellists were asked to score the samples using the assessment protocol defined during panel training, and to expectorate the sample into a cup/bowl . The firt-test.The association between the genetic merit stratum  and each of the mean tenderness, juiciness, flavour and chewiness (only for Sensory Location 2) values was determined using the linear mixed model PROC MIXED of SAS  for each sensory location separately. The sire was included as a random effect and the genetic merit stratum was included as a fixed effect; neither age nor heterosis coefficients were associated with the sensory-dependent variables and, because all animals were slaughtered on the same day, and underwent sensory analysis together on the same day, there was no need to adjust for either effect as per previous models ,17. The The mean (standard deviation) EBV of the sires in the Tender and Tough genotypes was 0.21 (0.06) and −0.10 0.11), respectively; this represents an average difference of 1.86 within-breed genetic standard deviation units . Using t, respectp < 0.0001) higher for liking of tenderness, juiciness, flavour and overall acceptability compared to the Tough genotype  , who invp < 0.05) MQ4 score to the Tender genotype compared to the Tough genotype. The MQ4 scores for the Tender genotype samples  for chewiness in Sensory Location 2.The association between animal genotype and the different sensory quality measures assessed by both trained panels is presented in Previous studies have compared the phenotypic performance of cattle differing in genetic merit ,32,33,34Although the reliability of the EBVs of individual animals for slaughter can be low, the reliability of the mean of a group can be high . Hence, Another encouraging feature of the results from the present study was the favourable scoring for the other quality assessments of the meat samples other than just tenderness. Superior assessment for the other sensory qualities, however, is not unexpected given the moderate to strong genetic correlations between tenderness and both juiciness and flavour in cattle . Hence, While the core objective of the present study was to validate genetic evaluations as a means of potentially stratifying products at the point of sale, an additional outcome is the proof that genetic selection for improved tenderness should result in the mean tenderness of a population shifting over time (assuming no change in management). Breeding has a particular advantage of being sustainable, rapid, cumulative and permanent. Several breeding goals exist in the dairy , beef 3,38 and bThe critical outcome from this study is that the estimated genetic merit of individuals, even animals at birth, can be used as a screening tool for tenderness and other meat quality traits, which, in turn, can be detected by consumers. Consumers significantly liked the Tender genotype samples for the liking of tenderness, liking of juiciness, liking of flavour and overall acceptability compared to the Tough genotype samples. Additionally, the Tender genotype, besides scoring better for tenderness, also scored better for juiciness, flavour and overall acceptance. All age cohorts (18–65+) scored the Tender genotype samples higher for liking of tenderness compared to the Tough genotype samples, as well as displaying this trend for the liking of juiciness, liking of flavour and overall acceptance in general across the consumer age spectrum . MQ4 data were significantly higher for the Tender genotype samples compared to the Tough genotype samples for all age cohorts except the 65+ group. Overall, these data align with the trained panel data, particularly for Location 1, with significantly higher scores for tenderness, juiciness and flavour attributes for the Tender genotype samples.


Km,u) of gadoxetate was 106 μM (17%) (n = 4 rats), and active saturable uptake accounted for 94% of totaluptake into hepatocytes. PBPK–IVIVE of these data (bottom-upapproach) captured reasonably systemic exposure, but underestimatedthe in vivo gadoxetate DCE–MRI profiles and elimination fromthe liver. Therefore, in vivo rat DCE–MRI liver data were subsequentlyused to refine gadoxetate transporter kinetic parameters in the PBPKmodel (top-down approach). Active uptake into the hepatocytes refinedby the liver-imaging data was one order of magnitude higher than theone predicted by the IVIVE approach. Finally, the PBPK model was fittedto the gadoxetate DCE–MRI data  obtainedwith and without coadministered rifampicin. Rifampicin was estimatedto inhibit active uptake transport of gadoxetate into the liver by96%. The current analysis highlighted the importance of gadoxetateliver data for PBPK model refinement, which was not feasible whenusing the blood data alone, as is common in PBPK modeling applications.The results of our study demonstrate the utility of organ-imagingdata in evaluating and refining PBPK transporter IVIVE to supportthe subsequent model use for quantitative evaluation of hepatic tDDI.Physiologically basedpharmacokinetic (PBPK) models are increasinglyused in drug development to simulate changes in both systemic andtissue exposures that arise as a result of changes in enzyme and/ortransporter activity. Verification of these model-based simulationsof tissue exposure is challenging in the case of transporter-mediateddrug–drug interactions (tDDI), in particular as these may leadto differential effects on substrate exposure in plasma and tissues/organsof interest. Gadoxetate, a promising magnetic resonance imaging (MRI)contrast agent, is a substrate of organic-anion-transporting polypeptide1B1 (OATP1B1) and multidrug resistance-associated protein 2 (MRP2).In this study, we developed a gadoxetate PBPK model and explored theuse of liver-imaging data to achieve and refine in vitro–invivo extrapolation (IVIVE) of gadoxetate hepatic transporter kineticdata. In addition, PBPK modeling was used to investigate gadoxetatehepatic tDDI with rifampicin i.v. 10 mg/kg. In vivo dynamic contrast-enhanced(DCE) MRI data of gadoxetate in rat blood, spleen, and liver wereused in this analysis. Gadoxetate in vitro uptake kinetic data weregenerated in plated rat hepatocytes. Mean (%CV) in vitro hepatocyteuptake unbound Michaelis–Menten constant ( Confidence is lower for PBPK models that involve drugs thatare substrates or inhibitors of transporter proteins, such as hepaticorganic anion transporter polypeptides (OATP).7 These trends are in part due tothe additional complexity and uncertainty in the quantitative in vitro–invivo extrapolation (IVIVE) of transporter kinetic data used to obtaindrug-specific parameters of PBPK models.8 Moreover, for these drugs, the lack of in vivo tissueexposure data to support PBPK model development and verification oftissue simulations represents a key limitation.4The physiologicallybased pharmacokinetic (PBPK) modeling approachprovides an effective mechanistic framework for quantitative translationof pharmacokinetic (PK) data. One of the highest impact areas of PBPKmodeling is the prediction of drug–drug interactions (DDI).When performed using appropriately validated and refined models, PBPKmodeling can support drug labeling and facilitate precision dosingin the absence of suitable clinical data.4 However, an understanding of these local concentrations can aid the delineation of sources of variabilityin drug response, for which measurements of drug concentrations inplasma may be insufficient.9 For drugs predominantly eliminated by liver, perturbations of effluxtransporters relevant for their biliary excretion may lead to clinicallyrelevant changes in liver exposure, which may not be reflected inthe systemic concentrations (depending on rate-limiting processes).11 In this context, PBPK model-basedpredictions of local drug concentrations represent a useful surrogate,yet verifying key assumptions of model structure and parameter values solely from plasma clinicaldata is challenging.Direct measurement of in vivo drug concentration–timedatain specific tissues of interest is practically and ethically challenging.18 An advantage of DCE–MRI over SPECT and PET is that studysubjects are not exposed to ionizing radiation. In addition, DCE–MRIcontrast agents are commonly used in clinical practice, do not requirespecialized synthesis facilities, and are, therefore, more easilyaccessed than PET or SPECT tracers.19Application of imaging techniques suchas positron emission tomography(PET), single-photon emission computed tomography (SPECT), and dynamiccontrast-enhanced magnetic resonance imaging (DCE–MRI) enablesthe derivation of local tissue concentrations of radiolabeled compoundsor contrast agents in vivo. Such techniques have shown promising resultsin delineating the roles of uptake and efflux transporters based onmeasurement of concentrations over time in the liver.20 This contrast agent has been shown to be a substrate for human uptaketransporters OATP1B1, OATP1B3, and sodium/taurocholate co-transportingpolypeptide and an efflux transporter multidrug resistance-associatedprotein 2 (MRP2). In addition, gadoxetate has been reported to bea substrate for rat Oatp1a125 and Mrp2 based on in vivo studies on Mrp2-deficient rats.26 Trends in the literature indicate increasinginterest in using gadoxetate for evaluating the liver transporterinhibition noninvasively.29 Researchers have continued to advance multicompartmentalmodeling for deriving quantitative parameters reflecting the livertransporter activity, using gadoxetate administered either alone orin combination with perpetrators of transporters relevant for gadoxetatedisposition.29 PBPK modelinghas been previously applied to DCE–MRI data with other gadolinium-basedcontrast agents than gadoxetate.31 Recently,a minimal PBPK model of gadoxetate in humans was reported, where gadoxetatesystemic exposure and urinary data were used for model development.32Gadoxetateis a metabolically stable MRI contrast agent currentlyindicated for detection and characterization of lesions in patientswith known or suspected focal liver disease.With the aim of evaluating gadoxetateas a potential imaging biomarkerfor hepatic transporter DDIs, we developed a reduced gadoxetate PBPKmodel for characterizing the PK of this imaging agent in rat blood,spleen, and liver and its interaction with a potent OATP1B inhibitor,rifampicin. Gadoxetate in vitro uptake kinetics was characterizedover a concentration range in plated rat hepatocytes, and these datawere implemented in the reduced PBPK model with mechanistic descriptionof hepatobiliary disposition of gadoxetate. Although systemic exposurewas predicted well, initial IVIVE (bottom-up approach) significantlyunderestimated in vivo gadoxetate DCE–MRI liver elimination.Subsequently, liver-imaging data of gadoxetate administered alone(control phase) were used to refine PBPK transporters kinetic parametersin a top-down manner. Finally, simultaneous fitting of DCE–MRIdata from both the control and the rifampicin phases was performed(as per ref .)to det2 overdose followed by cervical dislocation in the morningof the study day. The kinetics experiment, as described below, wasperformed using n = 4 animals. A minimum of n = 3 was required to explore interanimal variability inkinetic parameters, with surplus hepatocytes also used from an additionalanimal that was part of a separate project. Primary rat hepatocytes,prepared as described below, were used to evaluate test compound cytotoxicity/effecton hepatocyte attachment (n = 3), preliminary assaysto determine uptake assay conditions (n = 4), andfor assays that were failed, for example, poor cell viability .Male adultSprague-Dawley rats  were housedin groups of two in individually ventilated cages with free accessto food (Chow rat and mouse diet) and fresh drinking water. The designatedrat housing facility maintained a controlled temperature (20 ±3 °C), humidity (40–70%), and 12 h light/dark cycle conditions.All animal protocols were approved by the University of Manchesterreview committee and adhered to the UK Home Office Animals (ScientificProcedures) Act (1986). Rats (250–300 g) were sacrificed usingCO36 Cell preparationswith viability <85% were not used for experiments. Hepatocyteswere seeded at 240,000 cells per well in collagen I-coated 24-wellplates, and incubated for at least 2 h at 37 °C and 95% air/5%CO2 to allow cell attachment to the plate.36Isolation of hepatocytes using exvivo collagenase perfusion was performed, followed by cell count andviability assessment, as previously described.35 Uptake of gadoxetate  was evaluated following incubation (0.5–150 min) atnominal media concentrations of 0.01–10 mM. Extended incubationtimepoints (up to 150 min) were selected based on previous publications35 and existing in-house data to enable characterizationof the steady-state intracellular concentration. Uptake of pitavastatin (0.2 μM) inthe absence and presence of a pan-inhibitor of uptake transporters,rifamycin SV  (100 μM), was also evaluatedfor 0.5–2 min as positive control for functional transporteractivity. Following sample preparation including addition of internalstandard (Table S1), gadoxetate and pitavastatinin cell samples, and gadoxetate in media samples, were quantifiedby liquid chromatography–tandem mass spectrometry (LC–MS/MS).LC–MS/MS quantification was performed using selective reactionmonitoring (SRM) against calibration standards; only standards within30% of nominal concentration were included. LC–MS/MS equipmentand conditions are listed in Table S1.The protein content of plated rat hepatocytes was measured using thePierce BCA Protein Assay Kit .Uptake experiments were performed at 37 °C withduplicateincubations per condition, as previously described., the amount of gadoxetatein cell as quantified by LC–MS/MS) (pmol) at each time pointwas normalized for cellularity using the measured protein contentand assuming that 106 hepatocytes contain 1 mg protein.34 For gadoxetate, the data from each animal wereused for simultaneous estimation of in vitro kinetic parameters usinga mechanistic hepatocyte model reported previously.35 It should be noted that CLpassive,u impliesnonsaturable clearance equal in both directions under experimentalconditions (0.01–10 mM).Km,u representsthe unbound Michaelis constant (μM); Vmax the maximum transport rate (pmol/min/106 cells);and fu,cell the fraction unbound in celland nonsaturable, bidirectional clearance . ccell and cmedium represent concentrationsin cell and medium compartments, respectively, and Vcell represents the cell volume.In vitrohepatocyte uptake  to t = 0 min. Measuredmedia concentrations were similar to nominal concentrations, indicatingnegligible nonspecific binding, and therefore a nominal medium concentrationwas used as the initial condition for modeling. The model was implementedin MATLAB R2017a37 and the parameter estimationwas performed using the lsqnonlin function.Intracellular concentrationswere calculated assuming a hepatocyte cell volume of 3.9 μL/10active,u; μL/min/106 cells) of the saturable uptake for unbound concentrations≪Km,u was calculated using Unbound intrinsic clearance  of pitavastatin, in the absence and presence of rifamycinSV, was calculated from the uptake rate  and measured medium concentration . The uptake rate was obtained from the linearregression slope between the pitavastatin uptake amount (pmol) andtime (min), normalized by the cell number.Unbound intrinsic uptake clearance using a hepatocellularity of 120 ×106 cells/g liver,34 and a liverweight (wliver) of 40 g/kg body weight,38 as in IVIVE of gadoxetateCLDCE–MRIdata generatedusing the 3D Fast Low Angle Shot RF-spoiled gradient echo sequence(FLASH) protocol in male Wistar-Han rats were used for assessing thePBPK IVIVE performances and for obtaining some of the PBPK parameterswithin the bottom-up and top-down approaches, respectively. The DCE–MRIdata were acquired in a multicenter study at two magnetic field strengths,4.7 and 7 T. Gadoxetate was administered at a dose of 25 μmol/kg,either alone or 1 h after IV administration of 10 mg/kg rifampicin.When gadoxetate was administered alone (control arm), 43 profilesof blood, spleen, and liver measured at a field strength of 4.7 Tand 52 profiles of blood, spleen, and liver measured at 7 T were available.In the case of gadoxetate administered with rifampicin, 7 blood, spleen,and liver profiles at a field strength equal to 4.7 T and 6 at 7 Twere available. All gadoxetate DCE–MRI data and study protocolsare detailed in a companion paper .R1 (s–1), the change of the water protonlongitudinal relaxation rate, a magnetic property of the tissues,due to the presence of the contrast agent. In sufficiently homogeneoustissues, the tissue concentration of the contrast agent as a functionof time, c(t), can be derived fromΔR1(t). The relationshipbetween c(t) and ΔR1(t) depends on the physicalinteractions between the contrast agent molecules and the tissue.39 For a given tissue τ, the relation betweenΔR1,τ(t)and cτ(t) is generallyconsidered to be linear, as in 40The measured quantity in DCE–MRI is Δr1,τ (in L mmol–1 s–1) is the relaxivityof the contrast agent of the tissue τ.39 The r1,τ values are typicallydifficult to measure in vivo, therefore, in this study, the ex vivovalues in r1,τ and ΔR1,τ change as a function of the magnetic field strength used by themagnetic resonance machine for image acquisition.40 Therefore, ΔR1 profilesacquired at different field strengths are not directly comparable.The proportionalityconstant 41 The gadoxetate PBPK model is composed of seven compartments andits structure is shown in Supporting Information, Sections2, 3, and 5.A reducedPBPK model was developedto describe the PK of gadoxetate in rats; a permeability-limited livermodel was implemented capturing relevant processes, as done previously.43 Gadoxetate distributes only in the extracellular space of all theorgans; liver is the exception, where gadoxetate undergoes activeuptake into the hepatocytes.44 Therefore,in the gadoxetate PBPK model, the volume of all the compartments,except the blood and the liver, corresponded to the organ extracellularspace, that in turn was considered to be composed of the blood withinthe organ and the interstitial space. For highly vascularized andperfused organs with fenestrated capillaries , the exchangebetween the plasma and the interstitial space is generally consideredto be fast.19 Therefore, for liver, spleen,and splanchnic compartments, the extracellular volumes were consideredto be the sum of the blood within the organ and the interstitial spacevolumes. However, this hypothesis does not hold true for all the organs.43 A permeability limitation between the vascular and interstitialspace was, therefore, assumed for the ROB compartment, as shown in cb, crob,v and crob,ev [μmol/L]are the gadoxetate concentration in blood and ROB vascular and extravascularcompartments; Vrob,v and Vrob,ev [L] are the ROB vascular and extravascular compartmentvolumes, respectively; and PS [L/h] is the permeability surface product.Standard kinetic models used for MRI contrast agents generallydescribe the organs by using three compartments: plasma, interstitial,and intracellular spaces.splan [μmol/h] representthe venous input from the splanchnic organs, while inputart [μmol/h] is the input from the hepatic artery; cliv,extr and cliv,cell [μmol/L]are the drug concentrations in the extracellular liver  and in the hepatocytes, respectively; Vliv,extr and Vliv,int [L] are the extracellular and hepatocytes liver volumes; Qh [L/h] is the liver blood flow; Kliv,extr-b is the extracellular liver to bloodpartition coefficient; CLactive and CLpassive [L/h] are the active and passive clearances across the hepatocytescell membrane; CLbiliary [L/h] is the clearance representingthe excretion from the hepatocytes into the bile; and fu,liv,cell is the gadoxetate fraction unbound in the hepatocytes. Recent PBPK studies of hepatic transporter substrateshave used a 5-compartment liver model45 and the use of this particular liver model was also explored inthe current PBPK modeling.To describe the gadoxetate-active uptake into the hepatocytes,a permeability-limited liver model was used, as shown in R1. Therefore,the relationship between the compartmentalconcentrations represented by the PBPK model state variables and theΔR1 measurements for the blood,liver, and spleen needed to be described. The linear relationshipin R1 of the liver, ΔR1,liv, because intracellular and extracellulartissue compartments have different relaxivities. To derive ΔR1,liv, a volume fraction-weighted mean of thecontributions of the gadoxetate concentration in all the compartmentsused to model the liver was performed.40 In the spleen, the entire distribution space was supposed to havethe same relaxivity as blood and, therefore, R1,spl. Concerningthe blood, the blood value for r1 in R1,b to be directly related to the blood concentration.The relations between ΔR1,liv, ΔR1,spl, and ΔR1,b and the concentrations of the PBPK compartments are shown in cspl,extr is theextracellular concentration in the spleen and Vspl,extr is the extracellular volume of the spleen and Vliv and Vspl arethe whole volumes of liver and spleen. r1,liv,extr and r1,spl,extr were assumed to be equalto r1,b, as described in Hines et al.The DCE–MRI data used in thisstudy were reported as ΔR1 values do not correspond uniquely to a specifictime point. In fact, each of the data are acquired during a time intervalΔt, which was 57 s in the current dataset.To account for this characteristic when performing the parameter estimation,the residuals were calculated as the difference of the observed ΔR1 corresponding to a given time interval minusthe mean of the simulated PBPK ΔR1 within the same interval, as explained in the Supporting Information, Section 4.Another difference of the DCE–MRI data relative to the drugconcentration commonly used in PBPK modeling is that ΔThe PBPK analysis was performedin three sequential steps: IVIVE of gadoxetate transporter kineticdata obtained in rat hepatocytes (bottom-up predictions), PBPK modelrefinement using DCE–MRI imaging data, and estimation of thetransporter-mediated interaction with rifampicin 1.R1 in blood, spleen, and liver after administration of gadoxetate alone(in the absence of an inhibitor). CLactive, CLpassive, and fu,liv,cell were obtained fromthe in vitro experimental values obtained in this study, as detailedin the section “r was fixed to a literaturevalue, as in biliary could not be obtained from the in vitroexperiments, and therefore the values for both parameters were assumedequal to CLpassive. All the other parameters were obtainedusing literature values, as detailed in the Supporting Information, Sections 2 and 5.All the parameterswere initially obtained using literature values and in vitro experiments.Prospective transporter clearance IVIVE was performed with the aimof predicting the gadoxetate Δ46 Briefly, in this analysis, all the uncertainor unknown parameters were considered as random variables with a givenprobability distribution function (pdf) and then a Monte Carlo simulationwas performed. In the Monte Carlo simulations, the samples were extractedfrom the parameters’ joint pdf and, for each sample, the modelwas evaluated. The uncertain parameters considered in this analysiswere: Vmax, Km,u, CLpassive, fu,liv,cell,PS, and CLbiliary. All these parameters were consideredto be independent and uniformly distributed between the ranges reportedin 48 was then performed consideringthe intracellular liver AUC calculated from 0 to 100 h after gadoxetateadministration as the PK endpoint. The number of samples in both theuncertainty analysis and GSA was set to 10,000. The confidence intervalsof the sensitivity indices were calculated by using 1,000 bootstrapsamples.49An uncertainty analysiswas performed to account for the in vitrodata uncertainty within the bottom-up transporter clearance IVIVE.R1 blood, spleen,and liver profiles of the gadoxetatecontrol group were used to refine the transporter IVIVE with the PBPKmodel. In this context, a naïve pooled approach was used forestimating CLactive, CLbiliary, and PS, whileCLpassive and fu,liv,cell werefixed to the in vitro values, and CLr was fixed to theliterature value, as reported in Concerning the top-downanalysis, the ΔR1 in the control and inhibitory phase. In this analysis, PSwas considered the same for both phases, whereas the other parameterswere separately estimated in the absence (CLactive andCLbiliary) and in the presence of rifampicin . CLbiliary,inh was consideredto account for possible inhibition of Mrp2 in rat by rifampicin, aspreviously reported.45 To understand theimpact of the inclusion of the liver-imaging data on the parameteroptimization, the simultaneous estimation was also performed consideringonly the blood data.The extent of interaction with rifampicinwas estimated by performinga simultaneous fitting of gadoxetate Δ50 the ordinary differentialequations were solvedwith the function “ode15s”, while theparameter optimization was performed with the function “lsqnonlin”. The uncertainty of the parameters estimatewas evaluated with the case-bootstrap,51 using 1000 samples.All the PBPK analyses were performed inMATLAB R2020a,Km,u mean  was 106 μM(17%) for n = 4 animals, while fu,cell was 0.65 (24%) (6 cells (24%) and addition of 100μM rifamycin SV reduced the uptake of pitavastatin by 54% (23%).The rank order of unbound active uptake clearance in the absence ofan inhibitor for gadoxetate and pitavastatin was consistent, althoughthere was lower inter animal variability in gadoxetate estimates . Translation of the in vitro uptakeclearances of gadoxetate gave a predicted in vivo active uptake andpassive clearances of 14.7 and 0.93 mL/min/kg body weight, respectively.Gadoxetate exhibited concentration-dependentuptake into plated primaryrat hepatocytes. In this analysis, concentrations in hepatocyte lysatethat were below the lower limit of quantification (0.2 μM) wereexcluded; these excluded data typically represented the lower concentrationsevaluated  and the earliest time points.Following calculation of the intracellular concentrations from lysateconcentrations, the mechanistic hepatocyte model was used to estimatethe in vitro hepatocyte uptake kinetic parameters 3. Gadoxe65 (24%) 2. SaturaR1 profiles in blood,spleen, and liver at two field strengths, 4.7 and 7 T. The GSA resultsare shown in Figure S2.The results of the prospectivebottom-up IVIVE of transporter kineticdata are shown in R1 . Forboth blood and spleen, the parameter uncertainty had a minimal impacton the respective predicted ΔR1 profiles,as CV values for all the blood and spleen AUC at both the field strengthswere lower than 11%. However, this was not the case for the liverAUC, where the CV was ∼32%.The fast disappearanceof gadoxetate from both the blood and thespleen in rat was reasonably predicted by the PBPK model, despiterelatively minor overprediction of ΔR1 4: meanp, uncertain model parameters–hepatocyteAUC) relationship is linear. In our case, the R2 of the linear regression is 0.93, therefore, the linearitycondition was considered to be satisfied. In Figure S2, the squared standardized regression coefficients (SRC2) are reported. When the model is linear, the SRC2 correspond to the portion of output variance explained by the parameters,and thus they correspond to the first order effect of the variance-basedGSA.52 The GSA showed that the most importantparameters for explaining the liver AUC were CLbiliary and fu,liv,cell, whereas the uncertainties of CLpassive, Vmax, and Km,u (and consequently of CLactive) had a minimalimpact on the gadoxetate liver AUC variation.For the GSA, the SRC methodwas used and the liver AUC was consideredas the model output. The SRC method is suitable when the input–output, parameter estimation was also performed using onlythe blood data. In this context, CLbiliary was practicallyunidentifiable (CV > 1000%). Moreover, the estimates of CLactive and PS were 1.88 and 1.92 L/h, differed from those reportedin R1 was poor . In ouranalysis, gadoxetate CLr was fixed to a literature reportedvalue.44 As a further exercise, we attemptedto simultaneously estimate this parameter, in addition to CLactive, CLpassive, CLbiliary, and PS. In this context,CLactive was practically unidentifiable, with a CV higherthan 1000% . Considering combined hepatic and renal elimination of gadoxetate,information on the urinary or biliary amounts excreted would be beneficialfor a precise parameter’s identification and optimization ofboth CLr and CLactive.To explore whether the blood data alone were sufficiently informativeto obtain the transporter kinetic parameters of the model .To estimate the effect of rifampicin on gadoxetateDCE–MRI profiles in blood and liver, a simultaneous estimationwas performed using both the control and inhibitory phases and PS,CLapproach3, but thR1 profiles.In this analysis, both CLbiliary and CLbiliary,inh are practically unidentifiable (Table S5) and the liver profiles are not well predicted in both the controland inhibitory phases . In this analysis, CLactive and CLactive,inh were equal to 1.82 and 0.29 L/h. In particular, CLactive,inh resulted to be ∼3-fold higher than the one obtained whenthe liver profile was included in parameters identification  best describe thePK data when the hepatocellular space is divided into five subcompartments.55 Therefore, the impact of different structuresof liver model on gadoxetate parameter estimates was evaluated hereby performing the simultaneous estimation with the 5-compartment livermodel . The values of gadoxetate CLactive and CLactive,inh obtained using this approach were of the same order of magnitudewith respect to the ones of the standard permeability-limited livermodel, with no appreciable differences in the in vivo data fittingand in the extent of CLactive inhibition by rifampicin.It has been reportedthat PBPK liver models for certain OATP1B1substrates  may cause different effects on drug exposure in the plasmaand tissues of interest.4 PBPK modelingprovides a mechanistic insight into the interplay of multiple processesat the tissue level and allows prospective prediction of transporter-mediatedchanges in tissue exposure. However, verification of such model-basedsimulations is challenged by the lack of appropriate tissue data inhuman or reliance on plasma clinical data that may not always be informativefor PBPK model development and qualification.54 As such, imagingbiomarkers for in vivo hepatobiliary transporter DDI are needed, enablingmore ethical and efficient sampling of tissue concentrations of thetransporter substrate than more invasive approaches . Gadoxetate hasbeen proposed as a potential imaging biomarker for evaluation of DDImediated by OATP1B1 and MRP2.57 In this work, a PBPK model for the MRI contrast agent gadoxetatewas developed to enable characterization of liver transporter DDIand to explore the use of liver-imaging data to achieve and refinehepatic transporter IVIVE.Conventional PK DDI studieson drug development evaluate changesin drug exposure based on central plasma or blood concentrations,and may, therefore, be limited when pharmacological effects are drivenby drug exposure in specific tissue or cells, as seen in the exampleof metformin DDIs.R1 blood and spleen profiles . The importance of CLbiliary and fu,liv,cell on liverAUC is completely expected from a PK point of view, as both parametersdrive the removal of gadoxetate from the hepatocytes ,6 which are predominantly eliminated by the liverand where metabolic clearance/biliary excretion drives liver AUC ratherthan active uptake clearance.4The GSA performed herehighlighted that the most important parameters in driving the gadoxetateliver AUC were CLatocytes 6. In conR1 profiles were usedto refine the PBPK transporter parameters and to quantify the magnitudeof gadoxetate DDI with rifampicin. Rifampicin single dose is clinicallyused as an OATP1B inhibitor33 for evaluationof DDI via this transporter.60 The currentstudy aimed to develop and evaluate the PBPK model for gadoxetate;prospective prediction of the gadoxetate–rifampicin interactionwas not performed due to uncertainties associated with IVIVE of invitro inhibition data and complexities of substrate-dependent inhibitionassociated with OATP1B1.58 Applicationof the PBPK modeling for quantitative and translational predictionof gadoxetate–drug interactions will be explored in futurework with an extended dataset of transporter inhibitors.In the top-down approach, observed blood, spleen,and liver Δactive that was one order of magnitude higher than the onepredicted in the bottom-up manner from the in vitro data. Moreover,in the control phase, the simulated maximum gadoxetate concentrationin the interstitial ROB was one order of magnitude lower than thepredicted value for the hepatocytes . This suggests that, according to the PBPK model, in rats, gadoxetatedistributes mainly into the hepatocytes and to a lower extent in theinterstitial space of the other organs, in agreement with recent analysisof gadoxetate PK in humans.32Theoptimized PBPK model accurately described the PK of gadoxetate inall the observed organs both in the control and in the inhibitoryphase 5 and 6. 33Useof both control and data obtained in the presence of rifampicinfor simultaneous estimation resulted in comparable gadoxetate parameterestimates for the control phase 4 to the 64 The PBPK model described very well the liver profilesof the 4.7 T group, but slightly overpredicted the 7 T group . The liver data of the inhibitoryphase were quite noisy, sampling did not capture the full terminalphase of the liver profile  of theactive uptake of gadoxetate into the hepatocytes. This result is inaccordance with literature reports of interaction between rifampicinand OATP1B1 substrates. T group S10. The R1 profiles played an essential role in the parameter identifiability.As expected, it was not possible to estimate the biliary clearancewithout including the information of gadoxetate PK in the liver4 . Moreover, when fitting the model to only blood data, the extentof the active uptake inhibition was underestimated (96% inhibitionestimated with liver data vs 84% without liver data) and the estimatesof the active clearance and permeability surface product were differentthan when considering the liver profiles as well. Imaging methodscan be a solution to characterize noninvasively organ concentrations,and thus can be particularly informative in the evaluation of DDIsvia modulation of multiple transporters and/or refinement of PBPKmodeling of tissue exposure. It is important to consider that gadoxetatehas a substantial contribution of renal excretion to the overall eliminationfrom the blood. Therefore, care should be taken in extending the resultsto drugs whose systemic exposure is mainly sensitive to modulationof liver active uptake.In the top-down analysis, the liver Δ55 In the current analysis, we explored the use of a 5-compartmentliver model for gadoxetate PBPK analysis and found a minimal impacton the description of the data and the estimation of DDI .Previous PBPK studies of hepatic transporter substrateshave useda 5-compartment liver model, based on the empirical observation thatthis approach mimics the dispersion model.Theestimation of the PBPK model parameters in the current study useda naïve pooled data analysis approach. Such an approach lacksinsights into interanimal and intersite variability, which would berequired to give context to the estimated rifampicin treatment effectson transporter activities. As such, application of the PBPK modelwithin a nonlinear mixed effect statistical framework should be consideredfor future research.R1 profiles. As briefly outlined in the section “R1 is derived from measured MRI signals usingsignal models that represent approximations of reality and dependon technical parameters that may not be known accurately—suchas the flip angle of the MRI pulse sequence. These effects are knownto cause some bias in the generated ΔR1 profiles,66 though this is continuallybeing improved by better controlled acquisitions and refined signalmodels. The reproducibility of the DCE–MRI data used in thisstudy was assessed across different sites and the technical parameterswere chosen carefully after extensive optimization .In our preliminary analyses, the suboptimal choice of some of theseparameters resulted in discrepancies between the ΔR1 values of the two field strengths, and subsequent inabilityof the PBPK model to appropriately describe all the ΔR1 profiles. Similar to DCE–MRI, quantitativePBPK analysis of other imaging-derived PK data requires a signal conversion,whose accuracy in deriving the true concentration of an underlyingtracer or contrast agent may differ depending on the maturity of thefield and characteristics of the imaging technique . Inconclusion, we recommend the PBPK analysts dealing with the DCE–MRIdata not to ignore the process of data generation and involve imagingexperts in the modeling team.The use of the DCE–MRIdata within PBPK modeling and simulations is not trivial. Perhaps,the most relevant issue that we have faced in using the DCE–MRIdata within PBPK modeling was the uncertainty in ΔThecurrent work illustrates the essential role of liver-imagingdata/PK in the evaluation of predictive performance of prospectivetransporter IVIVE of gadoxetate within the PBPK framework. Moreover,the liver data were essential in refining the gadoxetate transporterIVIVE to appropriately describe organ concentrations and to adequatelycharacterize the magnitude of hepatic transporter DDI with rifampicin.The use of the tissue exposure data for such purposes is still verylimited. The analysis performed here provides novel insights thatwould be of particular importance for drugs with combined elimination, where the effectson the OATP1B1 uptake transporter may not be solely/easily deducedfrom the changes in the systemic exposure data. The results of thiswork highlight that gadoxetate is a promising probe to quantify theeffect of perpetrator drugs on hepatic transporter  function in vivo. Work is ongoing to evaluate the performanceof this imaging biomarker against OATP1B/MRP2 inhibitors with differentdegrees of potency and to extend the work into human.


Salmonella bacteria are the causative agent of salmonellosis, which accounts for the majority of foodborne illness of bacterial etiology in humans. Here, we demonstrate the safety and efficacy of the prophylactic administration of a bacteriophage preparation termed FOP (foodborne outbreak pill), which contains lytic phages targeting Salmonella , Shiga toxin-producing Escherichia coli (STEC), and Listeria monocytogenes, for lowering Salmonella burdens in OMM12 gnotobiotic mice. Prophylactic administration of FOP significantly reduced the levels of Salmonella in feces and in intestinal sections compared to the levels in controls. Moreover, the overall symptoms of the disease were also considerably lessened. Dose-dependent administration of FOP showed that phage amplification reached similarly high levels in less than 48 h independent of dose. In addition, 16S rRNA gene analysis showed that FOP did not alter the intestinal microbiota of healthy OMM12 mice and reduced microbiota perturbations induced by Salmonella. FOP maintained its full potency against Salmonella in comparison to that of SalmoFresh, its Salmonella-targeting component phages alone. Altogether, the data support that preventive administration of FOP may offer a safe and effective approach for reducing the risk of foodborne infections caused by Salmonella and, potentially, other foodborne bacteria  targeted by the FOP preparation.Nontyphoidal IMPORTANCE Foodborne bacterial infections cause worldwide economic loss. During an epidemic, the use of antibiotics to slow down the spread of the disease is not recommended because of their side effects on the resident microbiota and the selection of antibiotic-resistant bacteria. Here, we investigated the potential for the prophylactic administration of bacteriophages (viruses infecting bacteria) to reduce the burden of Salmonella in vivo using mice colonized by a synthetic microbiota. We found that the repeated administration of bacteriophages was safe and efficient in lowering the Salmonella burden. Perturbations of the microbiota by the Salmonella infection were also reduced when mice received bacteriophages. Altogether, these data support the use of bacteriophages as a prophylactic intervention to lower the spread of foodborne epidemics. Salmonella (NTS) strains are among those most commonly associated with invasive disease  in humans typically are acquired during transmission from the consumption of contaminated food or water, often from animal sources  offer one such approach. Phages are bacterial viruses that, unlike antibiotics, are highly specific and effective at killing their targeted host bacteria. In addition, the mechanisms by which antibiotics and phages kill bacteria and, conversely, the mechanisms of bacterial resistance to antibiotics or phages are fundamentally different –17. Ther–12) to examine the tolerability and efficacy of the prophylactic administration of a phage preparation to target an intestinal foodborne pathogen. The OMM12 mice harbor a defined and stable intestinal community composed of 12 bacterial strains of mouse origin, which represent the five most prevalent and abundant phyla of the native laboratory mouse intestine  strains, including E. coli O157:H7 , that target  O157:H7 . FOP wasSalmonella in vivo, we first evaluated SalmoFresh, a product that contains six lytic phages  , while the other SalmoFresh group received PBS as a SalmoFresh control . SalmoFresh  and PBS were then administered to the respective groups by oral gavage twice a day for an additional 4 days (from day 2 to day 5). Of note, four of the six phages included in SalmoFresh infect strain ST784 with similar efficiencies in vitro . The single oral administration of ST784 in OMM12 mice receiving PBS (ST784 PBS group) led to a significant loss of weight compared to the weight loss in uninfected mice , starting 48 h post-ST784 administration and lasting two more days before animals reached the experimental endpoint  , 25Salmo weight) . Concomiby day 6 . Intestimeasured  and F. Snd colon . Phages 9 PFU/dose; n = 14) or PBS (n = 11) twice a day for 6 days. Both groups were challenged with ST784 (1 × 108 CFU) on day 2 (ST784 PBS and ST784 FOP groups). We observed in both groups a median weight loss of approximately 10% of their initial weights within 24 h after Salmonella administration , and increased up to 108 to 1010 PFU/g during the last 2 days (days 5 and 6), which is a pattern expected from continuous infection of increasing levels of Salmonella in the intestine  were altered by the presence of Salmonella (Tables S3). Interestingly, in ST784 FOP samples, the abundances of C. clostridioforme and M. intestinale were not significantly altered, while the abundances of L. reuteri and E. faecalis were. These results indicate that the latter two strains were strongly associated with the burden of Salmonella, while the other two were not, suggesting that FOP prevented their variation (Table S3). These data are consistent with those previously obtained with an in vitro human model system  on day 2. No differences in mouse behavior or weight were observed between the three groups  had detectable levels of Salmonella in feces, while only one mouse in the group receiving the middle dose (1 × 108 PFU) and none receiving the highest dose (1 × 109 PFU) had detectable ST784 counts   status by the U.S. Food and Drug Administration (FDA), which is in agreement with a large body of literature that has shown that phages are innocuous when ingested by humans –29. This–15) . Neverthreatment , 23, in Salmonella bacteria. The safety of such repetitive treatment was confirmed by the lack of any abnormal behavior or clinical symptoms, as well as the quantitation of an intestinal inflammatory marker , which remained below the threshold of inflammation. In addition, no impact on the microbiota structure was observed following 48 h of such treatment. Altogether, these data confirm that FOP is equally as safe as any of the three GRAS phage cocktails that compose it   .Salmonella infection compared to the time of onset and severity of symptoms in PBS controls. Remarkably, the nearly 2-log decrease in CFU in the feces of mice that received FOP was also observed in each gut section, showing that phages can kill Salmonella with the same efficacy throughout the intestine. When comparing three different doses of FOP, we observed that 3 h after the ST784 challenge, the highest dose was sufficient to reduce the shedding of the inoculum to below the limit of detection. This rapid efficacy is congruent with data obtained with an in vitro human gut simulation model, which showed that FOP treatment killed ≥90% of ST784 after 5 h while simultaneously inhibiting the ability of ST784 to invade human intestinal cells  would correspond to a dose of 3.5 × 1011 CFU for a 70-kg human, which is several log higher than the infective dose for nontyphoidal salmonellosis (<103 CFU)  , 34, as 103 CFU) –37. TherSalmonella burden.During all experiments, and more strikingly during the FOP dose experiments, the fecal levels of phages rose rapidly following ST784 challenge, demonstrating that orally administered FOP is actively replicating in the gut at the expense of ST784. Interestingly, during the dose experiment, within 24 h there was already no difference between the phage levels in mice that received the middle and the high doses, and within another 24 h, the phage levels resulting from the three doses were undistinguishable, while all three doses were significantly different before the challenge. These data showed that within 48 h, a 100-fold-lower dose of FOP reached a similar density in the gut that was associated with an equivalent impact on the 12 mice were altered by increased levels of ST784 Salmonella. However, the FOP treatment limited these variations to increased abundances of L. reuteri and E. faecalis, suggesting that these two strains benefit from the presence of Salmonella. These observations illustrate that the growth of a pathogen has a direct impact on microbiota structure and that a phage intervention can limit such impact. They also demonstrate that murine gnotobiotic models, despite their limitations, are suitable to study mechanisms involved in the role of the gut microbiota during infection by Salmonella  mice from 7 to 9 weeks old, bred at Institut Pasteur, were used.All animal experiments were approved by the committee on animal experimentation at the Institut Pasteur  and by the French Ministry of Research. A total of 68 OMMS. enterica subsp. enterica serovar Typhimurium strain ST784 was obtained from Intralytix, Inc. , and routinely cultured in lysogeny broth (LB), on LB agar, or on Drigalski agar  at 37°C. For oral administration of ST784, an overnight liquid culture in LB was diluted 1/10 in sucrose bicarbonate buffer . SalmoFresh , and Salmonella, respectively  and onto LB plates overlaid with ST784 for phage titration (PFU).In all experiments, mice were randomly assigned to a group, and each group included approximately the same numbers of male and female animals kept in separate cages. Every day, mice were observed and weighed and feces were collected. Fecal pellets were transferred into preweighed, sterile, 2 ml tubes, weighed, and then resuspended in 1 ml of PBS. Serial dilutions in PBS were performed and plated onto Drigalski plates for 9 PFU, or lower as specified. Strain ST784 (1 × 108 CFU) was administered by oral gavage in 200 μl on day 2, 3 h after the first phage gavage on that day. Control mice not receiving ST784 received PBS instead. At the end of all experiments, mice were sacrificed by cervical dislocation, and intestinal sections  were collected without the separation of luminal and mucosal contents. These sections were homogenized in PBS using the gentleMACS OctoDissociator (Miltenyi Biotec), serially diluted in PBS, and plated on both Drigalski plates and LB plates overlaid with strain ST784.SalmoFresh, FOP, or PBS was administered by oral gavage in 200 μl twice daily, 6 h apart. The dose of phages administered through each gavage was 1 × 10Frozen supernatants of fecal samples resuspended in PBS were thawed before lipocalin-2 quantification using a commercial enzyme-linked immunosorbent assay (ELISA) kit  according to the manufacturer’s instructions. The threshold of inflammation (5 ng/g of feces) was defined according to the method in reference g for 10 min, and the supernatant removed. Pellets were resuspended in 500 μl of lysis buffer  and incubated for 15 min at 50°C  and amplified by PCR for 25 cycles. The libraries were sequenced on an Illumina MiSeq instrument (2 × 300 bp). FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) was used for the quality control of the reads. Read filtering, operational taxonomic unit (OTU) clustering, and annotation were performed with the MASQUE pipeline (https://github.com/aghozlane/masque). All statistical analyses were performed with SHAMAN (http://shaman.c3bi.pasteur.fr) as previously described  were centrifuged at 8,000 ×  at 50°C . Then, 1escribed .10 transformed prior to analysis. Given the nonlinearity of responses, the day at which a measure was performed was considered a categorical variable. Linear mixed models were used to account for random experimental effects . Overall effects were assessed with analysis of variance (ANOVA) and post hoc Tukey’s comparisons and were performed using the lsmeans R package  and phages (PFU), as well as mouse weights, were carried out using the lme4 and lmerTest packages of R , 43. Bot package . A P val


Typical small-pot culture systems are not ideal for controlled environment phenotyping for drought tolerance, especially for root-related traits. We grew soybean plants in a greenhouse in 1-m rooting columns filled with amended field soil to test the effects of drought stress on water use, root growth, shoot growth, and yield components. There were three watering treatments, beginning at first flower: watered daily to 100% of the maximum soil water holding capacity (control), 75% (mild drought stress), or 50% (drought stress). We also tested whether applying fertilizer throughout the 1-m soil depth instead of only in the top 30 cm would modify root distribution by depth in the soil profile and thereby affect responses to drought stress. Distributing the fertilizer over the entire 1-m soil depth altered the root biomass distribution and volumetric soil water content profile at first flower, but these effects did not persist to maturity and thus did not enhance drought tolerance. Compared to the control (100%) watering treatment, the 50% watering treatment significantly reduced seed yield by 40%, pod number by 42%, seeds per pod by 3%, shoot dry matter by 48%, root dry matter by 53%, and water use by 52%. Effects of the 75% watering treatment were intermittent between the 50 and 100%. The 50% treatment significantly increased root-to-shoot dry matter ratio by 23%, harvest index by 17%, and water-use efficiency by 7%. Seed size was not affected by either fertilizer or watering treatments. More than 65% of the total root dry matter was distributed in the upper 20 cm of the profile in all watering treatments. However, the two drought stress treatments, especially the mild drought stress, had a greater proportion of root dry matter located in the deeper soil layers. The overall coefficient of variation for seed yield was low at 5.3%, suggesting good repeatability of the treatments. Drought stress imposed in this culture system affected yield components similarly to what is observed in the field, with pod number being the component most strongly affected. This system should be useful for identifying variation among soybean lines for a wide variety of traits related to drought tolerance. Glycine max (L.) Merr.] is the number one field crop grown in Ontario, Canada, with a cultivated area of more than 1.3 million ha and a value of over $1.9 billion in 2018  , whole-pDrought stress may limit soybean’s yield through a reduction of any of its yield components . In soybean, yield reduction due to drought stress can occur at any stage of the crop although the magnitude of the yield reduction depends on the stage of development, the timing of the stress, and the severity of the stress. For example, water deficits imposed during the vegetative stage may not have a significant effect on final seed yield, provided that >95% light interception is achieved by the R1 developmental stage . HoweverWhen the number of pods is reduced by drought stress, the remaining seeds are often well filled resulting in a heavier (larger) individual seed weight at maturity . When drMost controlled-environment phenotyping studies of soybean germplasm for traits related to drought tolerance are carried out in artificial media in small pots , where roots easily explore the entire pot volume very rapidly so that rooting traits such as rate of root elongation or final rooting depth have almost no effect on the plant’s ability to access soil water e.g., . In addiArachis hypogaea L.), chickpea (Cicer arietinum L.), and pigeonpea (Cajanus cajan L.). Specifically, Similar phenotyping methods (1 m long 20 cm diameter PVC tubes) for drought tolerance have also been reported by others e.g.,  in legumExtensive literature is available describing roots as potential targets for modification to improve yield and resilience under drought stress e.g., , 2019, aOAC Bayfield was used to see how it responded to the different soil water treatments in terms of the growth, yield and yield components, rooting and VSWC profiles by depth. In this system, we were looking for a reproductive phase drought stress protocol that had a realistic and large effect on yield, and where the primary yield component that would be affected was pod number, as is typically the case in the field  and so do not provide the opportunity to investigate drought stress effects on yield and yield components e.g., . Growinghe field .Additionally, as another test of the system’s ability to measure belowground (rooting) plant responses to abiotic factors, we introduced another treatment (fertilizer placement) that might affect both root dry matter and VSWC profiles by depth. Fertilizer placement (nutrient availability) may affect the rooting depth, rooting patterns, and soil water extraction in such a manner that crop response to soil water deficits is altered. The concentration of P primarily in upper soil layers (as can occur under no-till production systems) when applied as mineral fertilizers may encourage root proliferation near the soil surface, with possible deleterious effects on crop response to soil water deficits. Rooting zones exposed to high P concentrations may cause a localized increase in the initiation and subsequent extension of the primary and secondary roots, compared to those rooting zones experiencing low P concentration . PhosphaWe hypothesized that (1) drought stress treatments imposed in this culture system would significantly reduce whole-plant water use (WU), whole-plant dry matter (DM) accumulation, soybean yield, and yield components ; (2) considering yield components, pod number would be the main yield component driving the final yield and thus would be most strongly affected by the drought stress treatments, as is typically observed under field conditions; effects on seeds per pod and single-seed size would be very minor; (3) distributing the fertilizer over the whole soil profile would alter the root DM distribution, and that would also affect soil water extraction at different depths of the soil profile, plant growth, seed yield, and yield components. That is, placing the fertilizer throughout the profile would result in more roots at depth which would, in turn, give the plants the ability to extract soil water from the lower depth and therefore be more drought tolerant.The specific objectives of the present study were to (1) develop and characterize a drought stress simulation method for controlled environment phenotyping of soybean germplasm that has a more realistic belowground environment (1-m rooting columns) than can be achieved in small pot experiments; (2) evaluate the effects of different drought stress protocols on VSWC and rooting profiles by depth, whole-plant water use, whole-plant DM accumulation, soybean yield components  and final yield; and (3) test the effects of fertilizer placement on rooting and VSWC profiles by depth under control and drought stress conditions, in 1-m rooting columns.OAC Bayfield was sown on July 15, four seeds per tube at 3 cm depth, and then thinned after emergence to one per tube. OAC Bayfield is a commercial soybean variety released to the public in 1993 through SeCan 1, and has been successfully grown for over 20 years with excellent yield potential, broad adaptability in Ontario, adequate lodging resistance, and above-average oil content. In controlled-environment studies, it had the highest water use efficiency among seven soybean genotypes and the lowest dark-adapted leaf conductance, gdark (mmol m–2 s–1), among 63 soybean genotypes tested , Guelph, ON, Canada, in 2016 using a culture system developed and described in detail by s tested ; both trLondon loam” (Gray-brown Podzolic loam till), was collected from the topsoil (upper 15 cm) at the Elora Research Station  that had a prior history of soybean production. It was a silty loam  texture and contained 4.2% organic matter, 23.5 ppm P, 61.5 ppm K, 280 ppm Mg, 2,375 ppm Ca, 15 ppm Na, with 14.4 meq 100 g–1 CEC, and a pH of 7.4 according to a soil test performed by A&L Laboratories Inc., London, ON, Canada.The plants were grown in 1 m long 10 cm diameter PVC rooting columns (tubes) lined with polyethylene liners, and PVC end caps at the bottom with a drainage hole. The soil mixture was a blend of six parts by volume field soil, two parts granitic sand , and one part peat-based potting mix . The field soil used, classified as a “–1. Variation in fertilizer distribution was created by loading the fertilizer (mixed with the soil mixture) just in the top of the profile  to mimic typical fertilizer distribution in the field, or throughout the soil profile . In the top loading treatment, the bottom 70 cm of the tube was first loaded with the soil mixture that had no fertilizer added to it; instead, 70 mL of water was sprayed and mixed with the soil mixture. Then, the 0.8 g tube–1 fertilizer was dissolved in 30 mL of water and thoroughly mixed with the soil mixture before it was loaded to the top 30 cm of the tube. The fertilizer in the full loading treatment was dissolved in 100 mL water, thoroughly mixed with the soil mixture, and then loaded to the whole tube. During the process of potting, the tubes were filled in a systematic fashion of loading and packing until the soil reached approximately 1 cm below the top part of the tube. The total weight of each tube with its soil was then recorded.The soil mixture loaded into each tube contained a commercial 20-20-20 N-P-K plus micronutrients fertilizer  at the rate of 0.8 g tube–2 s–1 photosynthetic photon flux density at the top of the canopy during the photoperiod, and to provide daylength extension to achieve 16 h of light and 8 h of dark.Greenhouse target temperatures were set at 25°C during the day and 20°C during the night with an average relative humidity of 80%. The actual greenhouse daily minimum, maximum, and average temperatures are given in To determine the soil water content and mass of dry soil in each tube, soil samples were taken during the potting process and dried in a forced-air drier at 80°C until a constant weight was attained. All the tubes in all treatments were then watered until they started dripping water from the drainage holes. After 24 h, they were watered again to free drainage to ensure that the soil was completely saturated. Elastic bands were used to close the plastic liners at the tops of the tubes to prevent surface evaporation. The tubes were allowed to drain until a constant weight was achieved . The weights of the dry soil and the tubes were then subtracted from this weight to determine the soil water content at maximum SWHC. Then, the target weight for each tube was calculated as the tube + soil dry weight, plus the water weight at maximum SWHC measured for that tube multiplied by the target fraction of the maximum SWHC .The experiment was arranged as a randomized complete block design, with two fertilizer placement treatments (top loading or full loading) and three watering treatments , replicated five times (2 × 3 × 5 = 30 tubes). To test the effect of the fertilizer placement on root DM and VSWC distribution, two tubes per replication (2 × 5 = 10 tubes) were also included in the experimental design for a destructive harvest measurement made at the R1 (beginning flowering) developmental stage , prior to the initiation of the drought stress treatments  = [total amount of water added to each tube from planting to harvest + (starting weight – end weight of each tube at harvest) + whole-plant fresh biomass at harvest]. The daily cumulative WU (g plant–1) was also calculated for each tube on each day as = [(starting weight – current weight) + total amount of water added to the tube up until that point].All tubes were weighed and watered daily to their maximum SWHC until R1. At this point, watering treatments were imposed and lasted through the R8 (full maturity) developmental stage. During this period (R1 to R8 stages), tubes were returned to either 100% (control), 75% (mild drought stress), or 50% (drought stress) of the maximum SWHC by daily weighing and watering. The whole-plant water use (WU) from planting to harvest was calculated as WU  millisecond readings were recorded once a week for the duration of the study, beginning right at the planting date and ending at the R7  stage. Before every set of TDR measurements, the TDR meter calibration procedure was performed. The TDR measurements were performed at five equally spaced points  via the pre-drilled TDR access holes in the sides of the tubes. The TDR measurements were always made just before daily watering . The volumetric soil water content  was calculated from the TDR millisecond readings using a calibration curve developed for the growth medium.Time-domain reflectometry  was recorded, and the total number of seeds per plant was counted. Seeds per pod (SPP) was then calculated by dividing the total number of seeds by the total PN per plant. Individual seed weight  was calculated by dividing the SY by the total number of seeds per plant. Harvest index (HI), the fraction of SDM allocated to the seed, was calculated as the SY divided by the SDM per plant. After harvesting the aboveground plant parts, the soil and the intact root systems within each rooting column were carefully removed by pulling out the translucent plastic liner after laying the tube down on its side. The rooting profile was then divided into five equal sections  by cutting from top to bottom with a large kitchen knife. Each root section was separately washed and placed into a labeled paper bag, so that root DM distribution could be determined by depth. All the root samples were oven-dried in a forced air drier at 80°C until a constant weight was attained  and then final root DM (RDM) of each sample was recorded. Root-to-shoot DM ratio (R:S) was calculated as the ratio of RDM to SDM. Whole-plant DM-based water use efficiency  was calculated by dividing whole-plant DM  by total WU from planting to full maturity.About 5–10 days after full maturity , all plants were cut at soil level and total aboveground plant fresh biomass  was recorded. Immediately after harvest, the total number of filled pods (pods with seeds) per plant  were counted. All these aboveground samples were then placed in labeled paper bags and oven-dried in a forced air drier at 80°C until a constant weight was attained. Then, the total aboveground (shoot) dry matter (SDM) per plant was determined, pods were threshed by hand, seed yield . A Type 1 error rate of 0.05 was used for all statistical tests. Since the dependent variables SY, PN, SPP, SW, TDM, SDM, RDM, R:S, HI, WU, WUE, and VSWC were quantitative and continuous, a linear mixed model (LMM) was fitted. The variances of SY, PN, SPP, SW, TDM, SDM, RDM, R:S, HI, WU, and WUE were partitioned into the fixed effects of fertilizer placement and watering treatments, and their interactions (fertilizer × watering treatments), and the random effects of blocks. The following statistical model was used:Yijk denotes the value of the measured trait for the ith fertilizer placement treatment (top loading or full loading) of the jth watering treatment  in the kth block, μ is the grand mean, fi is the effect of the ith fertilizer placement treatment (the first factor), wj is the effect of the jth watering treatment , iwjf is the interaction effect between the ith fertilizer placement and the jth watering treatment, Bk is the effect of the kth block (treated as a random effect), and εijk is the residual.where subject × depth was included in the model where the subjects (tubes) were assumed independent (identity covariance structure) and for depth three possible types of covariance structures  were compared. In each case, the most appropriate model was selected based on AICC and BIC. F-tests and log-likelihood ratio tests were used to determine the significance of fixed and random effects, respectively. Since the RDM and VSWC measurements were equally spaced along the height of the tubes, the Kenward-Roger adjustment for bias correction for the denominator degrees of freedom was applied , and the random effects of blocks. The random interaction term  applied . Least-sThe assumptions for the LMM, in particular, random and normally distributed experimental errors and constant (homogeneous) error variance were tested (1) by plotting the studentized residuals against factor levels and predicted values, (2) by generating a Q-Q plot and scatterplots of the residuals vs. fitted values, and (3) by performing a formal test of normality using a Shapiro-Wilk. Putative outliers, if any, were detected if the values of the studentized residuals were not within the range of -3.4 to 3.4 . The relp < 0.0001) and PN by 24% (p < 0.001), as compared to the full loading (FL) fertilizer placement treatment. The fertilizer placement, however, did not affect SPP or SW (p < 0.0001), PN by 42% (p < 0.0001), and SPP by 3% (p < 0.05) as compared to the control (100% SWHC) watering treatment, but it did not affect SW. The mild drought stress (75% SWHC) treatment was intermittent between the other two watering treatments for SY and PN (p < 0.01) and PN (p < 0.05) (PP or SW . AverageY and PN . Moreove < 0.05) . When th < 0.05) .p < 0.0001) increased SDM by 60%, RDM by 40%, TDM by 55%, leaf area by 63%, and WU by 36% (data not shown). At full maturity, averaged across the three watering treatments, the TL fertilizer placement significantly (p < 0.0001) increased final SDM by 12%, TDM by 12%, and WU by 10%, as compared to the FL fertilizer placement; however, the fertilizer placement did not affect final RDM, R:S, HI, or WUE (p < 0.0001), RDM by 53% (p < 0.0001), TDM by 49% (p < 0.0001), and WU by 52% (p < 0.0001), as compared to the 100% watering treatment. The 75% watering treatment was intermittent between the other two watering treatments for these parameters. However, the 50% watering treatment significantly increased R:S by 23% (p < 0.01), HI by 17% (p < 0.0001), and WUE by 7% (p < 0.0001), as compared to the 100% watering treatment (p < 0.05) and WU (p < 0.01)  and seed yield   . Under t < 0.01) . It shou 0.0029) . The TL  0.0029) . Before p < 0.0001) fertilizer placement by depth interaction effect for VSWC. The effect of depth on VSWC differed between the fertilizer placement treatments at 21, 27, and 32 DAP, but not on the planting date , there was a significant (ing date . That ising date . Howevering date . The trep < 0.0001) fertilizer placement by depth interaction effect for RDM and percent RDM distribution, at the R1 developmental stage. Fertilizer placement treatments ranked differently at different depths of the profile. Compared to the FL, plants in the TL treatment had significantly higher RDM and percent RDM in the top 0–40 cm depth but lower RDM and percent RDM starting from the middle to the bottom part of the soil profile  (m depth) .p < 0.0001) effects of fertilizer placement, watering treatment, depth, and water × depth for VSWC but there were no significant two-way , or three-way (fertilizer × water × depth) interaction effects. In other words, the TL fertilizer placement resulted in significantly more water use, resulting in lower VSWC than the FL fertilizer placement treatment, and this difference was similar across soil depth × watering treatment combinations within each developmental stage. The overall pattern of soil water depletion was consistent for the two fertilizer placement treatments for most of the profile depths and developmental stages within each watering treatment (p < 0.0001) lower VSWC in the rooting columns than the FL at all developmental stages, after the initiation of the drought stress treatment (reatment . Averagereatment .p < 0.0001) differences for VSWC by depth among the three watering treatments. The 100% watering treatment had a significantly higher VSWC by depth (a wetter profile) followed by the 75% watering treatment. As expected, the 50% watering treatment resulted in the lowest VSWC (the driest profile) of the three watering treatments, and this effect increased as plants grew larger and daily water use increased. Compared to the other two watering treatments, the 100% watering treatment resulted in a quite uniform VSWC over most of the 1-m depth, and with the smallest change in VSWC between the bottom and top, providing more available water for plant growth , there were also clear differences in VSWC profiles among the three watering treatments throughout the remainder of the experiment . That ist growth . For thet growth .p < 0.0001) effects of watering treatment, depth, and water × depth but there were no significant effects of fertilizer placement, fertilizer × water, fertilizer × depth, or fertilizer × water × depth for RDM distribution. Additionally, there were significant (p < 0.0001) effects of depth and water × depth but there were no significant effects of fertilizer placement, watering treatment, fertilizer × water, fertilizer × depth, or fertilizer × water × depth for percent RDM distribution. At full maturity, there was no significant effect of fertilizer placement on RDM and percent RDM distribution by depth  followed by 20–40 cm (0.49 ± 0.022 g) and 40–60 cm (0.30 ± 0.013 g) depths. The two bottom parts of the profile (60–80 cm and 80–100 cm depths) had lower but statistically similar RDM, 60–80 cm (0.21 ± 0.012 g) and 80–100 cm (0.24 ± 0.019 g) . Likewisp < 0.0001) differences in RDM among the three watering treatments at different profile depths. Drought stress significantly altered RDM and percent RDM distribution by depth  treatment produced effects on DM and SY that are of an appropriate magnitude for future genotype comparisons. Moreover, the overall coefficients of variation (C.V.) were low, at 5.3 and 5.7% for SY and TDM, respectively, suggesting a very consistent yield reduction and a good repeatability of the treatments. This would offer very good statistical power for future phenotyping studies to detect genotype differences for traits related to drought stress tolerance.Zea mays L.  are located in the upper  soil layer under both control and drought stress conditions, for example, in soybean  and maiz mays L. ].As expected, both drought stress treatments significantly increased R:S and WUE, with the effects being stronger under the severe drought stress treatment. These effects are consistent with a growth strategy that prioritizes both acquisition and efficient utilization of water under drought stress. Similar observations of increased R:S under drought stress have been reported previously e.g., . Earl 2 and soybTriticum aestivum L.), possibly by preventing the condition where nutrients in upper soil layers became unavailable to plants when those layers dried . However, We predicted that the fertilizer placement would change the RDM and VSWC by depth. That is, placing the fertilizer throughout the soil profile would result in more roots at depth which would, in turn, enhance the plants’ ability to extract soil water from the lower depths and therefore be more drought tolerant. At R1, we observed this predicted effect of fertilizer placement on root biomass distribution and soil water extraction. Similar to our results, In our study, the effects of fertilizer placement on soil water extraction at depth did not persist until maturity, and we did not find an advantage of deep fertilizer placement with respect to enhancing drought tolerance. However, our protocol was not ideal for generating a soil water “reward” in response to deep rooting; deeper rooting did not bring a permanent advantage in terms of soil water access because we replaced the water used by each plant every 24 h regardless. Also, the bottom part of the soil profile became relatively dry in all cases when plants were large, because the water added to the tops of the tubes with daily watering, enough to achieve the target weight, was partially transpired before it could percolate to the bottom part of the profile. Instead, our system was well-designed to assess where the roots were and where they were actively extracting water in the soil profile. The fact that we were able to detect as statistically significant small fertilizer placement effects on root activity in terms of soil water extraction suggests that this phenotyping system could be useful for investigating how genotypes differ for small differences in root activity by depth. In other words, if there were genotype differences in soil water extraction by depth at the R1 developmental stage, we should be able to detect those effects in this system, even for a small difference ; also, the measurement is non-destructive, and so can be repeated on the same experimental units over time.The FL treatment appeared to create a nutrient deficiency relative to the TL treatment, as evidenced by significantly reduced SY, PN and TDM. The advantage of the TL treatment may have arisen from improved plant nutrient uptake when the fertilizer was concentrated in the same layer of the profile where the majority (over 65%) of the roots were distributed. However, the absolute size of the fertilizer placement effect on SY, PN, SDM, and WU was strongly dependent on the watering treatment; when water was not limiting, the advantage of TL over FL was larger than it was under drought stress. It appears that in the FL treatment, nutrient deficiency prevented the plants from making full use of the water-replete conditions; that is, soil water was less limiting to growth in the presence of a nutrient deficiency. As with the main effect of the water treatment, PN also clearly drove the fertilizer × water interaction for SY; there was no fertilizer x water interaction effect for SPP or SW, while 91% of the variation in SY could be explained by PN .Although R2 = 0.99) across all fertilizer placement and watering treatments. This suggests that WU measurement can serve as a reasonable proxy for real-time whole-plant DM accumulation (growth) without requiring a destructive harvest. The linear association between SY and WU was slightly weaker (R2 = 0.92) as would be expected if WU primarily predicts TDM, since the association between WU and SY is weakened by any variation in HI.In this experiment, whole-plant water use strongly correlated with whole-plant DM accumulation  than can be achieved in small pot experiments. It produced drought responses that are large enough to easily measure, and the type of physiological changes in yield and yield components, DM, WU, WUE, and rooting profiles that are expected for soybean encountering drought stress in the field. This system can also detect non-destructively small in-season differences between treatments  in soil water extraction by depth. This system should be ideal for comparing genotypes for their root activity; it could be useful to select for root function and yield formation traits that could decrease soybean yield losses under drought stress conditions.Our results suggest that the 50% maximum SWHC drought stress protocol was the best treatment for controlled-environment phenotyping studies. Drought stress treatments imposed in this culture system affected yield components in a manner similar to what is observed in the field, with PN being the yield component most strongly affected. The C.V. for SY was low, at 5.3%, suggesting a very consistent yield reduction and a good repeatability of the treatments, and potentially offering good statistical power for investigating genotype differences in abiotic stress responses. In addition to exploring the effects of drought stress on shoot traits, this novel phenotyping system provides an opportunity to investigate final RDM distribution in the profile, as well as soil water extraction from different profile strata at any developmental stage.All datasets generated for this study are included in the article/MG performed the experiment, data collection, statistical data analysis and presentation, and drafted the manuscript. MG and HE conceived the project and experimental design and collaborated on the data interpretation and manuscript revision. Both authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.