The object recognition system involves both selectivity to specific object category and invariance to changes in low‐level visual features. Mounting neuroimaging evidence supports that brain areas in the ventral temporal cortex, such as the FFA and PPA, respond preferentially to faces and houses, respectively. However, how regions in human ventral temporal cortex partitioned and functionally organized to selectively and invariantly respond to different object categories remains unclear. What are the changes of response properties at the intersection of adjacent but distinctively‐selective regions?Here, we conducted an fMRI study and three‐pronged analyses to compare the brain mapping relationships between the FFA to faces and the PPA to houses. Specifically, we examined: 1) the response properties of object selectivity to the preferred category; 2) the response properties of invariance to contrast and a concurrently presented non‐preferred category; 3) whether there are asymmetrical changes of response properties across the boundary from the FFA to PPA versus from the PPA to FFA.We found that the response properties of FFA are highly selective and reliably invariant, whereas the responses of PPA vary with the image contrast and concurrently presented face. Moreover, the response properties across the boundary between the FFA and PPA are asymmetrical from face‐selective to house‐selective relative to from house‐selective to face‐selective.These results convergently revealed distinct response properties between the FFA to faces and the PPA to houses, implying a combination of spatially discrete domain‐specific and relatively distributed domain‐general organization mapping in human ventral temporal cortex.  At any given moment, our visual environment normally contains multiple objects. We recognize these objects quickly and effortlessly even when they are encountered in unusual orientations, under different illumination conditions, or partially occluded by other objects. Extensive human and nonhuman primate studies have identified putative higher‐level visual areas in ventral temporal cortex that mediate object recognition. Neurons from distinct clusters in ventral temporal cortex differentially and selectively activate to specific object category  remains elusive. Measurements in the primate inferior temporal cortex have shown that the proportion of selective neurons for a particular category within a category‐selective region, which was detected by using fMRI, is significantly higher than the outside of this region. Within fMRI identified category‐selective regions in temporal cortex, a wide range of 29–97% neurons are selective for the preferred category, with the highest proportion in face‐selective patches  on a fine‐scale along a virtual line connecting the FFA and PPA on the inflated gray matter surface to investigate response patterns of two adjacent regions. Responses of defined ROIs to image contrasts of faces and houses as well as concurrently presented face‐house stimuli were examined. In addition, we also compared variations in category selectivity and invariance across the boundary between the FFA and PPA. We hypothesize that, if category‐selective regions might be modularly partitioned and functionally organized at the same level, changes in face‐selectiveness versus house‐selectiveness across the boundary between the FFA and PPA should be symmetrical. If how the FFA responds to faces might be different from how the PPA responds to houses, we should observe asymmetrical changes in face‐selectiveness versus house‐selectiveness across the boundary from the FFA to PPA and vice versa from the PPA to FFA.22.1Seventeen healthy adults participated in this study with informed consent, including seven females, on average 26 years old. All subjects had normal or corrected‐to‐normal visual acuity and color vision. This study was approved by the Dartmouth College Human Protection Committee. Data from five subjects with head movements greater than 3 mm were excluded from further data analyses. Part of the data was independently analyzed and published for a separate study  and a low‐contrast version equivalent to 0.025 RMS in normalized units. The contrast of all stimuli conditions was adjusted by using MATLAB and the SHINE toolbox . BOLD images were collected using an echo‐planar imaging (EPI) sequence . During EPI scans, visual stimuli were presented to subjects via a Panasonic PT‐D4000U projector using MATLAB with Psychtoolbox . The participant performed a one‐back repetition detection task to press a button whenever there was a repetition of an image.In the main experiment, we presented a participant with eight different types of visual stimuli  in a block design. The participant performed a color detection task to press a button whenever the whole image turned to red for 200 ms at random times during the stimulus presentation. This study was consisted of nine or ten runs, where each run was comprised of eight blocks, one for each stimulus condition. Each run started with a 16 s fixation block, and each stimulation block was interleaved with a 16 s fixation block. Within each block there were eight images from a single stimulus condition, and each image was presented for 1700 ms with a 300 ms ISI. In total, each stimulation block was 16 s, and each run was 272 s. The blocks were randomized across each run.2.52.5.13dDespike, slice‐time correction using 3dTshift, compute anatomical alignment transformation to EPI registration using align_epi_anat, head motion correction using 3dvolreg and normalization using 3dcalc .2.5.3To investigate the brain selectivity for faces and houses from the occipital lobe to the ventral temporal cortex, we specified two virtual lines on the inflated gray matter surface from the occipital pole to the FFA and from the occipital pole to the PPA. We first selected the median coordinate of the occipital lobe (ROI index 16) as one starting point of the virtual line according to the anatomical mask in FreeSurfer. The selected coordinates of the occipital pole in the right hemisphere were , and those in the left hemisphere were  in the MNI surface coordinate system. Then, 28 sets of coordinates (ROI index 17–44) were selected with equal spacing in‐between along the virtual line from the occipital pole (ROI index 16) to the FFA/PPA peak (ROI index 45). The virtual line was extended by 15 sets of coordinates (ROI index 1–15 and ROI index 46–60) with identical space to both sides. Similarly, for each set of coordinates, we grouped 5 coordinates closest to the given coordinate as one ROI (index) according to Euclidean distance. In total, there were 60 sets of ROIs along the two virtual lines connecting the occipital pole and the FFA/PPA for each hemisphere. Finally, we get the corresponding nodes index of the selected 60 sets of ROIs on the inflated surface. The cortical surface distance between the occipital pole and the FFA peak was about 72.55 mm for the left hemisphere and 75.17 mm for the right hemisphere on average. The cortical surface distance between occipital pole and the PPA peak was about 73.80 mm for the left hemisphere and 74.46 mm for the right hemisphere on average.2.5.4We examined the changes of BOLD responses Figures  and 2) aSelectivity = Preferred beta values – nonpreferred beta valuesFor face selectivity, preferred beta values were responses to conditions containing face , and nonpreferred beta values represented responses to house‐only conditions . The selectivity for face was obtained by contrasting responses of lF versus lH, hF versus hH, lFlH versus lH, lFhH versus hH, hFlH versus lH and hFhH versus hH. For house selectivity, preferred beta values were responses to conditions containing houses , and nonpreferred beta values represented responses to face‐only conditions . The selectivity for house was obtained by contrasting responses of lH versus lF, hH versus hF, lFlH versus lF, lFhH versus lF, hFlH versus hF, and hFhH versus hF.2.5.5Multivariate pattern analysis (MVPA) was performed using skit‐learn packages  and the PPA peak (ROI index 36). The significant ROIs were ROI index 16–18 in the left hemisphere and ROI index 14–19 in the right hemisphere on the FFA side, displaying a narrow distribution of face selectivity, whereas the significant ROIs were ROI index 26–43 in the left hemisphere and ROI index 33–46 in the right hemisphere on the PPA side, displaying a wide distribution of house selectivity. Furthermore, we conducted a MVPA to investigate the category representation patterns for faces versus houses across ROIs from the FFA to PPA. Figure ps < .001 corrected, marked with * in Figure Figure 3.2t‐test for BOLD responses to each condition. The significant p values are provided in Table ps < .01, corrected). Bonferroni post hoc tests revealed that only activity of the lFhH condition , which might due to the low visibility of superimposed face image in this condition. The significant p values are provided in Table http://github.com/SheffieldML/GPy) for each individual subject. Then, we averaged the smoothed beta values across subjects for each condition . Bonferroni post hoc tests revealed that the contrast of houses and overlapping presented faces could influence the activities of ROIs on the PPA side . Activities of the hFlH condition were significantly different from other conditions in several ROIs , similar to the effect of the lFhH condition. The differences between responses of ROIs on the PPA side to houses and those on the FFA side to faces were mainly reflected in the following two ways. First, there was a significant contrast effect in ROIs on the PPA side rather than on the FFA side, that is, activities of the high‐contrast house (hH) condition were significantly greater than those of the low‐contrast house (lH) condition in several ROIs around the PPA peak. Second, activities of the lH condition and lFhH condition were significantly greater than those of the lFlH condition in ROIs on the PPA side, indicating that responses of PPA to low‐contrast houses were modulated by the overlapping presented low‐contrast face. However, responses of PPA to high‐contrast houses cannot be influenced by the overlapping presented face. The significant p values are provided in Table To test the significance of house selectivity, we also performed another two‐way repeated‐measures ANOVA (two levels of house contrast (low vs. high) × three levels of face contrast (none vs. low vs. high), the dashed blue box in Figure ps < .01, corrected), and the regression lines of house‐only conditions were almost flat in the horizontal direction. Statistical significance levels were shown in Table It seemed that results shown in Figure t‐tests revealed a significant difference in slopes between the responses to face‐only conditions in the boundary side (ROI index 21–30) and the nonboundary side . However, there were no significant difference in slopes between the responses to face‐only conditions in the boundary side (ROI index 21–30) and the responses to house‐only conditions in the nonboundary side . We also found no significant differences in slops between the responses to face‐only conditions in the nonboundary side (ROI index 1–10) and the responses to house‐only conditions in the boundary side (ROI index 21–30). That is, the differences between the responses of FFA to faces and those of the PPA to house across the boundary can hardly be explained by the size differences between the face‐selective area and the house‐selective area. These results convergently suggested that distinct category tuning profiles between the FFA and PPA were mainly due to the heterogeneity of these two regions. We attached a table in supplementary materials regarding the slope coefficients for each subject  should be different from those of house‐only conditions on the boundary side (ROI index 21–30) and nonboundary side (ROI index 41–50). We found significant main effects of the boundary side and interaction effect. Further paired ct Table .To further quantify the differences of selectivity between FFA to faces and PPA to houses, we calculated the selectivity for faces relative to houses and the selectivity for houses relative to faces (see Section 2). We found that the selectivity of ROIs on the FFA side to all conditions were only in two modes, corresponding to the difference between the presence (or high visibility) and absence (or low visibility) of a face Figure . In cont3.3ps < .05, corrected) in both hemispheres. We further conducted a Bonferroni multiple comparison test to investigate the interaction effect. The face classification accuracy in the lFhH condition was significantly different from other conditions  in several ROIs, presumably due to low visibility of the superimposed face image in this condition, which is consistent with the univariate results. The significant p values are provided in Table Figure ps < .05, corrected) in both hemispheres. Multiple comparison test revealed that the house classification accuracy in the hFlH condition was significantly different from other conditions  in several ROIs, similar to the effect of the lFhH condition described above. Consistent with a previous report  × three levels of face contrast (none vs. low vs. high), the dashed blue box in Figure https://github.com/nilearn/nilearn). We first defined an adjacency matrix based on the inflated gray matter surface mesh such that nearby vertices were concatenated within the same searchlight. Then, we employed a 5 mm radius of disks to construct a searchlight structure for each vertex of the surface mesh. We used the dataset of face‐only  and house‐only  conditions in the main experiment to train and test a linear Ridge regression classifier, with all parameters set to default values as provided by the scikit‐learn packages . For each vertex, a fivefold cross‐validation classifier was performed to distinguish between faces and houses. This procedure yielded an information‐based map highlighted regions containing category information on the cortical surface. For each subject, the searchlight information‐based map showed considerable overlap with the activation‐based map from the univariate GLM analysis of the localizer data. To assess the statistical validity of the decoding accuracy map on the group level, we transformed the accuracy map of individual subject into the standard surface space (fsaverage). Then, a one sample t‐test was performed to compare the classification accuracy against the chance level (0.5) for each vertex. Figure p < .001, uncorrected). These results, together with our ROI analyses, suggested that the defined FFA and PPA were highly sensitive and selective to faces and houses, respectively.In addition, we adopted a surface‐based searchlight decoding analysis to assess local regions whose activity patterns distinguish between face and house categories throughout the whole brain. Given that the FFA and PPA were defined from the activation‐based GLM analysis by contrasting face and house conditions in the localizer scan, the searchlight analysis maximizes the possibility of finding vertices outside of the defined FFA and PPA that may be preferentially associated with the perception of faces and houses  in both hemispheres. Multiple comparisons revealed that the contrast effect was in the occipital cortex not in the FFA, except for the low visibility of overlapping presented face condition (lFhH). The significant p values are displayed in Table ps < .05, corrected). Multiple comparisons revealed that activity of the hH condition was significantly greater than that of the lH condition in several ROIs along the virtual line. Activity of the lFhH condition was significantly greater than that of the lFlH condition in ROIs around the PPA peak, which was consistent with results shown in Figure p values are displayed in Table To further confirm the differences of category selectivity were specific to the category selective regions, we investigated the changes of BOLD activities and classification accuracies across ROIs from the occipital pole to the FFA and PPA, respectively. Consistent with the results shown in Figure ps < .05, corrected). Multiple comparisons revealed that significant effects of contrast were in the occipital cortex not in the FFA or PPA, except for the low visibility of overlapping presented face condition (lFhH) and low visibility of overlapping presented house condition (hFlH). The significant p values are displayed in Table Figures 4The main finding of this study is that response properties of the FFA to faces are different from those of the PPA to houses. First, responses of the FFA were invariant to the changes in low‐level visual features , while responses of the PPA were modulated by stimulus contrast. Second, responses of the FFA were highly selective to faces and invariant to the concurrently presented house image, whereas responses of the PPA were significantly susceptible to the concurrently presented face image. Third, responses of ROIs to faces across the presumed boundary from face‐selective to house‐selective decreased sharply, suggesting a boundary line of face‐selective area. In contrast, responses of ROIs to houses across the presumed boundary from house‐selective to face‐selective only slightly decreased and surprisingly increased again on the FFA side, making it difficult to determine a borderline of house‐selective area. By conducting linear regressions and comparing the slopes of response changes, we found asymmetry changes of response properties from the FFA to PPA for faces versus from the PPA to FFA for houses. Further analyses of multivariate representation patterns demonstrated that the representation of preferred stimulus was more robust in the FFA than in the PPA, confirming that underlying mechanism of face processing in the FFA is different from house processing in the PPA.Responses of the PPA were modulated by low‐level features, such as image contrasts and concurrently‐presented images. However, distinct response properties between the FFA to faces and the PPA to houses can hardly be explained by any effects of difference in low‐level visual features for the following reasons. First, stimulus contrast was carefully controlled by using the SHINNE toolbox. Second, by analyzing multivariate representation patterns in ROIs from the occipital pole to the FFA and PPA, we did not observe any significant differences of classification accuracy between face category and house category in the occipital lobe, both in the main and localizer experiment data. Third, consistent with our results, low‐level visual features  modulated responses in the house‐selective region have been reported in previous studies  and concurrently presented multiple stimuli  on a fine‐scale by using ultra‐high field fMRI and vertex‐wise tuning models  and a concurrently presented stimuli . In addition, response properties of ROIs across the boundary between the FFA and PPA are asymmetrical from face‐selective to house‐selective relative to from house‐selective to face‐selective. These results convergently suggest distinct response properties between the FFA to faces and the PPA to houses, supporting a combination of specialized modules and relatively distributed organization of object representation in ventral temporal cortex (Bell et al., The authors declare no potential conflict of interest.https://publons.com/publon/10.1002/brb3.2706The peer review history for this article is available at Supplementary TablesClick here for additional data file.


No time‐ or metabolism‐dependent CYP450 inhibition occurred. FFA and nFFA did not induce CYP1A2; both induced CYP2B6  and CYP3A4 . Mechanistic static pharmacokinetic models predicted that neither CYP450 inhibition nor induction was likely to be clinically relevant at doses typically used for seizure reduction . Transporters OCT2 and MATE1 were inhibited by FFA  and nFFA  at concentrations higher than clinically achievable; remaining transporters were not inhibited. Results suggest that FFA and nFFA are unlikely drug–drug interaction perpetrators at clinically relevant doses of FFA (0.2−0.7 mg/kg/day).Studies support the safety and efficacy of fenfluramine (FFA) as an antiseizure medication (ASM) in Dravet syndrome, Lennox‐Gastaut syndrome, or CDKL5 deficiency disorder, all pharmacoresistant developmental and epileptic encephalopathies. However, drug–drug interactions with FFA in multi‐ASM regimens have not been fully investigated. We characterized the perpetrator potential of FFA and its active metabolite, norfenfluramine (nFFA), in vitro by assessing cytochrome P450 (CYP450) inhibition in human liver microsomes, CYP450 induction in cultured human hepatocytes, and drug transporter inhibition potential in permeability or cellular uptake assays. Mean plasma unbound fraction was ~50% for both FFA and nFFA, with no apparent concentration dependence. FFA and nFFA were direct in vitro inhibitors of CYP2D6 (IC Mechanistic static pharmacokinetic models predict that CYP450 inhibition and induction are unlikely for fenfluramine or its major metabolite norfenfluramine at doses used for seizure reduction (0.2−0.7 mg/kg/day). These results suggest minimal drug‐drug interaction perpetrator potential for either fenfluramine or norfenfluramine. ABCATP‐binding cassette transporterASMantiseizure medicationAUCarea under the concentration curveBCRPbreast cancer resistance proteinBEHethylene bridged hybridCDDCDKL5 deficiency disordermaxCmaximum drug concentrationCYP450cytochrome P450DDIdrug–drug interactionDMSOdimethyl sulfoxideDSDravet syndrome50ECconcentration of half‐maximal responseEDTAethylenediaminetetraacetic acidmaxEmaximum induction effectFDAUnited States Food and Drug AdministrationFFAfenfluraminemffraction metabolizeduffraction unboundGAPDHglyceraldehyde 3‐phosphate dehydrogenaseHEKhuman embryonic kidney cell50ICpotency of inhibition (concentration of 50% inhibition)max,uImaximum unbound systemic plasma concentration of an inhibitormKMichaelis‐Menten rate constantLC‐MS/MSliquid chromatography/tandem mass spectrometryLDHlactose dehydrogenaseLGSLennox‐Gastaut syndromeLIMSlaboratory information management systemMATEmultidrug and toxin extrusionMDCKIIMadin‐Darby canine kidney cell2MgClmagnesium chloridemRNAmessenger RNANADPHnicotinamide adenine dinucleotide phosphatenFFAnorfenfluramineOATorganic anion transporterOCTorganic cation transporterPBSphosphate buffered salinePCRpolymerase chain reactionP‐gpP‐glycoprotein multidrug transporterqRT‐PCRquantitative reverse transcription‐polymerase chain reaction1Rthe predicted ratio of FFA’s AUC in the presence or absence of an inhibitor3Rthe predicted ratio of FFA’s AUC in the presence or absence of an inducerREDrapid equilibration dialysis50Smaxsubstrate concentration at 50% VSDstandard deviationSEMstandard error of the meanSLCsolute carrier transportermaxVmaximum rate of reactionΔΔCTchange in threshold cyclesFenfluramine demonstrated efficacy for reducing convulsive seizure frequency in patients with Dravet syndrome and other developmental and epileptic encephalopathies. These patients typically take multiple concurrent antiseizure medications, emphasizing the importance of evaluating fenfluramine’s drug–drug interaction potential. We characterized the perpetrator potential of fenfluramine and its active metabolite, norfenfluramine, in vitro by assessing cytochrome P450 (CYP450) inhibition and induction, as well as drug transporter inhibition potential. Mechanistic static pharmacokinetic models confirm minimal perpetrator potential. A companion paper evaluates fenfluramine’s victim potential.1Fenfluramine (FFA), as designated by International Union of Pharmacology (IUPHAR),During the FFA development program, the potential for drug–drug interactions (DDIs) when FFA is added to existing ASM regimens was evaluated in vivo. Initial treatment for patients with LGS is usually valproate, lamotrigine, and/or topiramate, followed by adjunctive felbamate, clobazam, levetiracetam, and cannabidiol.norfenfluramine (nFFA)cytochrome P450 (CYP450) mixed‐function oxidase system. Therefore, both FFA and nFFA were evaluated for their drug interaction potential. CYP1A2, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP3A4, and CYP3A5 catalyze most drug biotransformation reactions. These enzymes are commonly implicated in DDIs.FFA undergoes de‐alkylation to Polypharmacy is typically used to manage the multiplicity of seizures characteristic of developmental and epileptic encephalopathies. FFA will be used in combination regimens, necessitating a thorough characterization of potential for DDIs. The FDA and EMA have developed comprehensive “Guidance for Industry” documents which outline the most current state of the science for predicting DDIs. These documents use “a systematic, risk‐based approach to assessing DDI potential of investigational drugs and making recommendations to mitigate DDIs.”Perpetrator (or precipitant) ASMs may affect the clearance, efficacy, and/or toxicity of victim (or object) ASMs in combination therapy, especially if the perpetrator induces or inhibits an enzyme responsible for a single‐elimination pathway.In this study, we performed in vitro DDI studies in accordance with FDA guidance to identify the CYP450 enzymes and transporter proteins that could be altered and contribute to the perpetrator potential of FFA and its major metabolite, nFFA, in the context of multi‐ASM dosing regimens.22.1S‐mephenytoin from Toronto Research Chemicals . Substrate metabolites 4′‐hydroxy‐diclofenac and 6β‐hydroxytestosterone were obtained from Sigma‐Aldrich; acetaminophen, N‐desethylamodiaquine, 4′‐hydroxymephenytoin, dextrorphan, and 1′‐hydroxymidazolam from Cerilliant ; and 8‐hydroxyefavirenz from Toronto Research Chemicals. Internal standards for all CYP450 bioanalytical assays were as follows: acetaminophen‐d4 (Cerilliant Corporation), 8‐hydroxyefavirenz‐d4 , N‐desethyl‐amodiaquine‐d5 , 4′‐hydroxydiclofenac‐d4 , 4′‐hydroxymephenytoin‐d3 , dextrorphan‐d3 , 1′‐hydroxymidazolam‐d4 (Cerilliant Corporation), and 6β‐hydroxy‐testosterone‐d3 (Cerilliant Corporation). The α‐naphthoflavone, orphenadrine, modafinil, quinidine, and ketoconazole used as positive controls for direct inhibition of CYP450s were obtained from Sigma‐Aldrich; montelukast from Sequoia Research Products ; and sulfaphenazole from Santa Cruz Biotechnology . Tienilic acid , esomeprazole , furafylline, phencyclidine, paroxetine (Sigma‐Aldrich), troleandomycin , and gemfibrozil glucuronide  were used as positive controls for metabolism‐dependent CYP450 inhibition. Components of the NADPH‐generating system  were obtained from Sigma‐Aldrich. FFA hydrochloride and nFFA hydrochloride—[1‐(3‐trifluoromethyl) phenyl]propan‐2‐amine hydrochloride—were obtained from Zogenix, Inc. . Flumazenil, omeprazole, phenobarbital, and rifampin used in CYP450 induction studies were obtained from Sigma‐Aldrich.The CYP450 substrates phenacetin, diclofenac, dextromethorphan, midazolam, and testosterone were obtained from Sigma‐Aldrich ; efavirenz and amodiaquine from the US Pharmacopeia ; and p‐aminohippuric acid, probenecid, novobiocin, estrone‐3‐sulfate, ibuprofen, and quinidine. Digoxin‐d3 and prazosin‐d8 were obtained from Toronto Research Chemicals; lopinavir from the European Pharmacopoeia ; [3H]‐p‐aminohippuric acid from American Radiolabeled Chemicals ; [3H]‐estradiol‐17β‐glucuronide and [3H]‐estrone‐3‐sulfate from PerkinElmer ; and [14C]‐metformin from Moravek, Inc. .For studies examining cell membrane transporters, the following agents were all obtained from Sigma‐Aldrich: digoxin, valspodar, verapamil, prazosin, Ko143, estradiol‐17β‐glucuronide, rifampin, cyclosporine, butyric acid, 2.250 or Km) used to select marker substrate concentrations were previously determined.S‐mephenytoin 4′‐hydroxylation; CYP2D6, dextromethorphan O‐demethylation; CYP3A4/5, midazolam 1′‐hydroxylation; and CYP3A4/5, testosterone 6β‐hydroxylation.Inhibition of CYP450s by FFA and nFFA was assessed according to methods described previously.2 (3 mM), and EDTA (1 mM). Incubations to measure CYP450 enzyme activity were conducted at approximately 37°C in 200‐µL incubation mixtures containing the buffer mixture described previously, an NADPH‐generating system , and a concentration of marker substrate based on the Km or S50 determined previously. Concentrations of FFA ranged from 0.06−60 µM for all CYP450 enzymes examined except for CYP3A4/5. CYP3A4/5 inhibition was measured using higher concentrations of FFA ranging from 0.6−600 µM with the intention of identifying the potential of this test article to cause interactions with CYP3A4/5 in the intestine after oral administration. Concentrations of nFFA ranged from 0.1−100 µM. Detailed analytical and statistical methods for determining the degree of inhibition can be found in the Supplemental Material.Aliquots of FFA or nFFA solutions were added to buffer (pH 7.4) containing potassium phosphate buffer (50 mM), MgCl2.3To examine metabolism‐dependent inhibition of the CYP450 enzymes, FFA and nFFA were preincubated in duplicate at 37 ± 1°C with human liver microsomes as described above for ~30 min in the presence of an NADPH‐generating system, which allowed for the generation of potential intermediates that could irreversibly inhibit human CYP450 enzymes. For comparison, additional duplicate samples containing FFA or nFFA were preincubated for 30 min in the absence of NADPH. These preincubations allowed assessment of the NADPH‐dependence of any time‐dependent CYP450 inhibition. Following the 30‐min preincubation, the NADPH‐generating system and/or marker substrate was added, and incubations were continued as described previously to measure residual CYP450 enzyme activity. Incubations that contained FFA and nFFA but were not preincubated served as negative controls for time‐dependent CYP450 inhibition.2.4g . FFA or nFFA (2 µM in dimethyl sulfoxide [DMSO]) or the reference assay control (chloroquine) was incubated in both whole blood and reference plasma  and was centrifuged . Plasma (30 µL) was aliquoted to multiwall plates and was quenched with 300 µL of 50% acetonitrile, 50% methanol, and 0.05% formic acid solution containing internal standards (bucetin and warfarin). Plates were vortexed and centrifuged ; supernatants were transferred to fresh plates for quantification by liquid chromatography/tandem mass spectrometry . Red blood cell‐to‐plasma ratio was calculated as follows:To determine the extent of red blood cell accumulation of FFA and nFFA, the red blood cell/plasma partition of both drugs was assessed in human blood obtained from BioIVT . Fresh reference plasma was separated from whole blood by centrifugation at 2000 × H = hematocrit ,Cp = ratio of peak areas of the analyte over internal standard in reference plasma,Cb = ratio of peak areas of the analyte over internal standard in top plasma layer separated from whole blood .2.5® Gene Expression Assays. The quantity of target cDNA relative to control cDNA (GAPDH) was determined by the ΔΔCT method (Applied Biosystems User Bulletin #2). Relative quantitation measures the change in mRNA expression in a test sample relative to that in the control sample . This method assumes that the efficiency of target amplification and the efficiency of endogenous control amplification are approximately equal.Measurement of CYP450 induction in human hepatocyte cultures was performed as previously described.The potential of FFA and nFFA to cause cytotoxicity was assessed based on the release of lactate dehydrogenase (LDH) into the culture medium (Sigma‐Aldrich Cytotoxicity Detection Kit [LDH] Cat. # 11644793001) and based on daily microscopic evaluation.2.6FFA and nFFA were each added to triplicate samples of pooled rat, dog, and human plasma . In vitro plasma protein binding was then determined by using the Rapid Equilibrium Device (RED) with a molecular weight cutoff of 8 kDa . The warfarin positive control was added to separate samples of pooled human plasma for a final concentration of 10 μM. The internal standard was niflumic acid. FFA and nFFA (1 and 10 ng/mL each) were added to plasma of each species, mixed gently, and stored in an ice bath until subsequent analysis. Protein binding was determined by adding a 300‐μL aliquot of the test sample or the control sample to the sample chamber of the RED and adding a 500‐μL aliquot of PBS (pH 7.4) to the buffer chamber. The device unit was sealed and incubated at 37°C for 4 h with gentle shaking (~750 rpm). After dialysis, three aliquots of the test samples (50 μL) were collected from both sample and buffer chambers and were mixed with 50 μL of methanol as “donor samples” and “acceptor samples,” respectively. Initial plasma samples and donor samples, acceptor samples, samples for calibration curves, and samples for positive control were spiked with the internal standard and were subjected to LC‐MS/MS quantification of FFA and nFFA. Concentrations for test samples were determined by regression from peak area values obtained from standard curves . A detailed description of the LC‐MS/MS process can be found in the Supplemental Material. Supernatant fractions were analyzed by LC‐MS/MS using an Acquity Ultra Performance Liquid Chromatography system with ethylene bridged hybrid (BEH) C18 columns  coupled to an API4000 mass spectrometer (AB Sciex). Calibration curves were determined by dissolving FFA or nFFA in methanol to prepare curves of 0, 1, 3, 10, 30, 100, and 300 ng/mL for FFA, and 0, 0.1, 0.3, 1, 3, 10, 30, and 10 ng/mL for nFFA. Samples were prepared in triplicate and were dialyzed for 4 h. Peak area values were obtained from LC‐MS/MS chromatograms corresponding to the analyte and the internal standard. Analyst software  was used to calculate a regression equation based on the calibration curve by weighted least squares and concentrations of analyte in the samples. Protein binding was calculated as follows:2.7ATP‐Binding Cassette Transporters (ABC)BCRP and P‐gp and the Solute Carrier (SLC) transporters OAT1, OAT3, OCT2, MATE1 and MATE2‐K, OATP1B1, and OATP1B3, according to published methods . Assays were performed in duplicate or triplicate according to previously published reports. Statistical analysis for replicates was not possible with n = 2 sample sizes.Summary data are expressed as percent of control and mean ± standard deviation (SD) or standard error of the mean (SEM), as appropriate. Individual values from inhibition studies were processed with the laboratory information management system (LIMS) Galileo v3.3 (Thermo Fisher Scientific), which was used to calculate IC2.9http://www.guidetopharmacology.org, the common portal for data from the IUPHAR/BPS Guide to PHARMACOLOGY,Key protein targets and ligands in this article are hyperlinked to corresponding entries in 33.1Protein binding of the positive control, 10 µM warfarin, was 99.5 ± 0.2% in human plasma, in close agreement with published data.3.250, percentage of inhibition, R1 values calculated for the basic model of reversible inhibition, and AUCR values calculated for the static mechanistic model are shown in Table 1 value is the ratio of intrinsic clearance values of a probe substrate for an enzymatic pathway in the absence and in the presence of FFA or nFFA.50 values for CYP1A2, CYP2B6, CYP2C8, CYP2C9, and CYP2C19 were above 60 µM—the highest concentration of the drug tested. FFA inhibited these enzymes by 2.4%–14%. FFA directly inhibited 90% of CYP2D6 activity with an IC50 of 4.7 ± 0.2 μM .The slight CYP3A4 and CYP2B6 induction observed in our study is unlikely to be clinically relevant per the static mechanistic model predictions. CYP2B6 does not appear to have a high fOur in vitro results suggest that FFA is unlikely to affect drug distribution and/or elimination by inhibiting the major drug transporters.max values of valproate, stiripentol, and clobazam, all highly protein‐bound drugs,Our in vitro results align with clinical reports suggesting that FFA is unlikely to have sufficient perpetrator potential when used in combination ASM regimens, although DDI have been reported. A recent clinical study investigated the impact of FFA coadministration on plasma levels of stiripentol, valproate, and clobazam—a common combination regimen prescribed for treating DS.The primary strength of this study is its rigorous, systematic approach to investigating perpetrator potential of FFA. Previous studies published when FFA was marketed as an anorectic agent were targeted investigations of specific CYP enzymes. By adhering to FDA and EMA Guidance to Industry, we report the most comprehensive, systematic report of FFA and nFFA perpetrator potential published to date.FFA is a racemic mixture of two enantiomers. Although prior publications have evaluated the enantiomers separately, the clinically used FFA is administered as a racemate and therefore the experimental studies were conducted with the racemic form.This study has some limitations. It should be noted that pharmacogenetic variants cause affected individuals to be classified as poor, extensive, or ultra‐metabolizers of CYP2D6 substrates, as well as CYP2C9, CYP2C19, and CYP3A4 substrates,In conclusion, this study provides a comprehensive examination of the impact of FFA and nFFA on clinically relevant CYP450s and transporter proteins. The in vitro DDI data suggest limited potential for FFA to have significant perpetrator activity in multidrug ASM regimens.P.M. and B.B. are employees of, and own stock in, Zogenix, Inc., S.S. is a consultant for Zogenix, Inc., B.W.O., M.C., and P.B.L. are consultants for Zogenix, Inc., and are employees of Sekisui XenoTech, LLC.This study is exempt from ethics approval.Participated in research design: BWO, Conducted experiments: Sekisui XenoTech, LLC, Contributed new reagents or analytical tools: N/A, Performed data analysis: MC, PLB, Wrote or contributed to the writing of the manuscript: PM, SS, BB, MC, PLB, BWO.Table S1‐S3Click here for additional data file.Supplementary MaterialClick here for additional data file.


Qi-Yi Xing  Central Cel Qian, . After tel Qian, Figure 2PIn the 1950s, chloramphenicol was the only antibiotic that could be chemically synthesized on a large scale. By studying the stereochemical process of PRINS reaction, Qi-Yi Xing and Gan-Huan Dai et al. designed a new method to synthesize chloramphenicol from the readily available industrial raw material styrene Hsing, , b. ThisIn the early 1950s, protein synthesis was a mysterious and tantalizing field, and many of the world’s top organic chemists were focusing on it. In 1955, British biochemist Dr. Sanger determined the amino acid sequences (primary structure) of the bioactive protein molecule, bovine insulin, by biodegradation and labeling methods for the first time. In 1958, Qi-Yi Xing studied the peptide binding method and terminal group protection of cysteine-containing fragments, also began the synthesis of cysteine-containing polypeptides which laid the foundation for the artificial synthesis of protein insulin Li,  Fig. 3)3). In 19Traditional Chinese Medicine (TCM) is one of China’s greatest treasures. Qi-Yi Xing devoted great energy to the study on the effective ingredients of TCM. He also noted that most of the TCMs are decocted in water, so repeatedly proposed to carry out the research on the water-soluble components of TCMs, which was a very difficult research project. Ginseng is one of the most famous traditional Chinese medicine in China, many research results of its chemical constituents have been reported, but these often overlook the water-soluble substances such as peptides. Xing’s Group successfully isolated and identified several water-soluble peptides from the famous Ginseng, including γ-GABA and some polypeptides containing cysteine, and further studied their chemical structures and biological activities Yang, . He openOrganic Chemistry, which was the first organic chemistry textbook over millions of words in China. Due to its heavy content, the concise course Fundamentals of Organic Chemistry was published, which was affectionately known as “Xing’s big textbook”(邢大本) and “Xing’s little textbook”(邢小本). In the 1970s, he led his team to supplement the research results of organic chemistry in the latest 20 years and re-wrote and republished Basic Organic Chemistry. In 1988, the textbook was rated as Excellent Textbook by the National Education Commission of China. Basic Organic Chemistry is both a set of textbooks for higher education and a set of large-scale reference books for all organic chemists, influencing several generations of chemical professional and technicians in China. It was the main reference book for the entrance examination of organic chemistry for graduate students in many universities and research institutions. Qi-Yi Xing’s educational ideas and teaching materials have undoubtedly showed a far-reaching and enormous influence on the discipline of “organic chemistry” in China.Qi-Yi Xing’s motto in the teaching of organic chemistry was “Hardwork stimulates ideas, Comfort leads to confusion ”. One of the most valuable aspect of his lectures was consistently inspiring students for new ideas. He was a very engaging, compelling and immensely knowledgeable lecturer. He aimed not only at improving his students’ knowledge but also at stimulating their critical and innovative thinking Fig. . He put In China, courses on total synthesis of artificial bioactive proteins and organic chemistry cannot avoid mentioning Professor Qi-Yi Xing from Peking University, so indispensable and important were his contributions. On the occasion of 110th anniversary of Qi-Yi Xing’s brith, everyone who has used “Xing’s big textbook”(邢大本) and “Xing’s little textbook”(邢小本) will deeply cherish the memory and commemorate him.


AR mutation linked to neurodegeneration. SBMA symptoms fully manifest in males due to high circulating androgen levels. Androgen deprivation therapy (ADT) has shown promise in SBMA patients.Androgen receptor (AR) is the main mediator of androgen signaling. Androgens are sex steroid hormones released into the bloodstream in response to activation of the hypothalamic–pituitary‐target endocrine gland axis. Free androgens enter the cells and bind to AR in the cytosol. In response to ligand binding, AR shuttles to the nucleus and works as a ligand‐activated transcription factor. AR is widely expressed, from reproductive organs to skeletal muscle and brain. Hundreds of AR mutations have been linked to androgen insensitivity syndrome and prostate cancer. The expansion of a microsatellite CAG tandem repeat is the only et al. reignites a long‐standing debate whether the size of the CAG microsatellite expansion in the AR gene influences development and progression of prostate cancer, the second most prevalent malignancy in men worldwide.The case report from Atsushi With only a handful of reported cases and lack of solid epidemiological data, this question remains open. Additional mechanisms that may drive cancer initiation and progression beyond AR include age and loss of DNA repair, which are both a feature of SBMA and other polyglutamine disease. Those may be responsible for the insurgence of androgen‐independent and more clinically aggressive prostate cancers in SBMA patients.The authors have no conflict of interest to report.Maria Pennuto: Conceptualization; writing – original draft. Monica Montopoli: Conceptualization; writing – original draft. Carlo Rinaldi: Conceptualization; writing – original draft.


Glycophorin A (GPA), a component of the AE1 complexes, facilitates AE1 expression and anion transport, but Glycophorin B (GPB) does not. Here, we dissected the structural components of GPA/GPB involved in glycophorin-AE1 trafficking by comparing them with three GPB variants—GPBhead (lacking the transmembrane domain [TMD]), GPBtail (mainly the TMD), and GP.Mur (glycophorin B-A-B hybrid). GPB-derived GP.Mur bears an O-glycopeptide that encompasses the R18 epitope, which is present in GPA but not GPB. By flow cytometry, AE1 expression in the control erythrocytes increased with the GPA-R18 expression; GYP.Mur+/+ erythrocytes bearing both GP.Mur and GPA expressed more R18 epitopes and more AE1 proteins. In contrast, heterologously expressed GPBtail and GPB were predominantly localized in the Golgi apparatus of HEK-293 cells, whereas GBhead was diffuse throughout the cytosol, suggesting that glycophorin transmembrane encoded an ER/Golgi retention signal. AE1 coexpression could reduce the ER/Golgi retention of GPB, but not of GPBtail or GPBhead. Thus, there are forward-trafficking and transmembrane-driven ER/Golgi retention signals encoded in the glycophorin sequences. How the balance between these opposite trafficking signals could affect glycophorin sorting into AE1 complexes and influence erythroid anion transport remains to be explored.Anion exchanger-1 (AE1) is the main erythroid Cl Cl−/HCO3− transport by AE1 also helps stabilize cellular pH [Anion exchanger-1  and Glycophorin A (GPA) are the two most abundant membrane proteins in human red blood cells (RBCs), each with ~10per cell . AE1 is lular pH . The N-tlular pH ,8,9,10. The surface expression of AE1 is greatly facilitated by GPA ,12,13. Gb) antigen on the interface between AE1 and GPA is solid proof that the two proteins physically interact on the RBC membrane [GYPA mRNA could still be found in the erythroid precursor cells of these AE1 knockout mice, indicating that the expression of erythroid GPA protein requires AE1 [GYPA knockdown by shRNA in human erythroleukemic K562 cells does not affect the surface expression of AE1 [kMk blood type), the anion transport activity of AE1 is significantly reduced, though the protein content of AE1 is not significantly affected [− flux [Numerous studies have revealed a structural–functional association between AE1 and GPA . Early fmembrane ,25,26. Tmembrane ,27,28. Iires AE1 . On the n of AE1 , indicataffected . Thus, eaffected ,29, GPA affected ,29. For  [− flux ,29,30,31 [− flux ,32.GYPB mRNA variants cloned from the reticulocytes of healthy people  RBC samples were differentiated from heterozygous ones by their lack of GPB expression on Western blot. HEK-293 cells were maintained in Dulbecco’s Modified Eagle’s Medium (DMEM) containing 10% fetal bovine serum (FBS) and penicillin-streptomycin.The Mackay Memorial Hospital Institutional Review Board approved the collection of human blood from consented non-diseased donors . The Miltenberger subtype III (Mi.III) phenotype was serologically screened with anti-MiGYPB clones were verified by DNA sequencing. An alternatively spliced variant of glycophorin B (GenBank accession EU338230) lacks GYPB exon 5 that encodes the TMD; this glycophorin B spliced variant was thus named “GPBhead”. Another GYPB spliced variant lacks exons II-IV of GYPB and encompasses only exons I, V (coding for the TMD), and VI, and was thus named “GPBtail”  and then reverse transcribed into cDNA. Glycophorin B transcripts were PCR-amplified using the following primer pair: 5′-AAGCTTTTTGCACTAACTTCAGGAACCAGC (GYPA19HindIII) and 5′-GGCATAAGCAAAGGAATAGCAGG (GYPB453r) with PfuUltra High-fidelity DNA Polymerase AD (Stratagene). The PCR products were cloned into TOPO vectors using a Zero Blunt TOPO PCR Cloning Kit for Sequencing (Invitrogen). Individual GPBtail” A.GYPB cDNA (NM002100) and its naturally occurred spliced variants—GYPBhead (EU338230) and GYPBtail (EU338238)—were each subcloned into pcNDA3.1  to generate pcGPB, pcGPBhead, and pcGPBtail, respectively. For confocal imaging experiments, GYPB, GYP.Mur, GYPBhead, and GYPBtail were each subcloned into pEGFP-N2  to generate the green fluorescence fusion protein constructs—pGPBgfp, pGPMURgfp, pGPBhead-GFP, and pGPBtail-GFP. GYPA cDNA (NM002099) was subcloned into pEYFP-N1 (Clontech) to generate a yellow fluorescence fusion construct—pGPAyfp. The other plasmids used in this study  were described previously [eviously .Heterologous gene expression in HEK-293 cells was introduced by transient transfection using Lipofectamine 2000 Transfection Reagent (Invitrogen) or T-Pro Non-liposome Transfection Reagent II . AE1 and one of the glycophorin plasmids were either singly transfected or co-transfected at equimolar ratios or at other molar ratios as specified into HEK-293 cells cultured in 6-well plates. For all experimental sets, the control or vector plasmids  were supplemented to make the total amount of DNA for transfection constant. All imaging experiments were conducted during 36–72 h post-transfection.To assess the surface expression of AE1, human red blood cells were immunolabelled with BRIC 71 and BRIC 6 , two monoclonal antibodies that target the extracellular loops of AE1 ,35. BRICTo study how the different glycophorin structural components could affect AE1-glycophorin trafficking and surface expression, HEK-293 cells were transiently transfected with AE1 alone or together with pcGPA/pGPAyfp, pGPBgfp, pGPMURgfp, pGPBtail-GFP, or pGPBhead-GFP. As the spectra of YFP and GFP were quite similar (both showed green fluorescence in our imaging system), we avoided co-transfection of GPAyfp and GPBgfp (or other GPBgfp variants). For the triple expression of AE1, GPA, and GPBgfp, on day 2 or 3 post-transfection, the triply transfected cells were fixed and permeabilized, and then immunostained with Alexa Fluor 568-conjugated anti-GPA BRIC163, and mouse anti-AE1 antibodies (BRIC 6 + BRIC 71 + BRIC 170) which were then labeled with Alexa Fluor 647-conjugated anti-mouse antibody. Confocal imaging utilized a Leica TCS SP equipped with an argon/Krypton laser. To determine subcellular localization, the ER-Tracker blue-white DPX dye (Invitrogen), BODIPY TR-ceramide (Invitrogen), and PE-Cy5 mouse anti-human CD107a antibody  were used to label the endoplasmic reticulum (ER), the Golgi apparatus, and lysosomes, respectively.GYP.Mur was derived from the insertion of GYPA in the middle of GYPB [+/+GYP.Mur subjects  exhibited more R18 epitopes and AE1 on their cell membrane, compared to the non-Miltenberger samples (+/+GYP.Mur and the control (non-Miltenberger) RBCs exhibit similar levels of GPA [+/+GYP.Mur RBCs were attributed to the R18 epitope from GP.Mur protein. The direct quantitative correlation between expressions of the R18 epitope and AE1 .To explore how GPA/GPB/GP.Mur could influence AE1 trafficking or vice versa, we first verified the previous observation that AE1 is capable of forward trafficking to the plasma membrane by itself A, left. Co-transfection of equimolar AE1 (blue) and glycophorin A (red) in HEK-293 cells resulted in nearly complete colocalization (purple) in the intracellular and surface membranes B. For coThe ratio of the protein copy number of GPA: GPB: AE1 in human erythrocytes is 1:0.2:1. We mimicked their erythroid gene expression heterologously in HEK-293 cells by cotransfecting the same molar ratio of the GPA, GPBgfp, and AE1 plasmids. We found that GPBgfp coexpression did not affect the nearly perfect colocalization between GPA and AE1 E. GPBgfpTo verify the sites where GPB aggregated , we labeWe next labeled GPBgfp-transfected cells with BODIPY TR-ceramide, a red fluorescent marker for Golgi complexes. We found that the sites of massive GPBgfp aggregation also included the Golgi apparatus . In addiGYPB—GYPBhead and GYPBtail, cloned from the reticulocytes of healthy subjects. To track their cellular expression, we also tagged GFP to their C-termini to make GPBhead-GFP and GPBtail-GFP fusion proteins. The N-terminal sequence of GPBhead is identical to that of GPB. On the other hand, GPBhead lacks exon 5 in GYPA/GYPB that codes for the transmembrane sequence and was predicted to be novel and “non-transmembrane” (either cytosolic or membrane-associated) by TMHMM 2.0, a structural prediction program for transmembrane helices [To probe into the nature of GPBgfp aggregation, we utilized two naturally occurring, exon-skipped variants of  helices . In contBy tracking their subcellular localization, we found that, unlike GPBgfp, the GPBhead-GFP fusion protein expressed almost everywhere in the cytoplasm C. On theWhile AE1 coexpression could dissolve GPB aggregation in the ER and the Golgi, AE1 coexpression with either GPBtail or GPBhead did not affect their individual subcellular localization . Thus, tIn this study, we identified that the glycophorin transmembrane domain encoded an ER/Golgi retention signal, which could be counteracted by forward-trafficking signals  or by AWe did not observe any known amino acid motifs for ER or Golgi retention in the glycophorin alignment . On the kMk RBCs, the glycosylation profile of AE1 is altered [b, En(a-), or Dantu phenotype, their expressions of AE1 and GPA/GPB were mutually affected [From FLIM-FRET measurements, AE1 and GPA proteins physically interact in the transfected cells (unpublished data). Though this study only observed AE1-GPA colocalization, the two proteins likely utilize the same trafficking route and begin oligomerization before they reach to the plasma membrane as AE1 protein complexes ,25,26,32 altered . Second,affected ,43,44. Taffected . The diraffected ,46,47,48Pang et al. found that the ER processing of endogenous GPA in the human K562 erythroleukemic cell line is very efficient and takes only 5–10 min for the glycans of GPA to be modified from high mannose forms to complex ones. Endogenous GPA in K562 cells bears complex carbohydrates, and primarily resides in the Golgi apparatus and on the plasma membrane. Heterologously expressed AE1 bears a high content of mannose and is much retained in the ER of K562 cells . A high 2 metabolism and acid-base homeostasis), RBC structural stability, as well as red cell processing of nitric oxide (NO) [AE1 is a versatile and functionally important protein that facilitates major physiologic functions (blood COide (NO) ,54,55,56


A complete and detailed mass spectral analysis of the newly obtained derivatives of amphetamine with ibuprofen, flurbiprofen, ketoprofen, naproxen, and carprofen was performed. In vitro inhibition of albumin denaturation of each new compound was assessed, and they showed significant activity. The IC50 values of the obtained amphetamine-profen derivatives ranged from 92.81 to 159.87 µg/mL. This indicates that the new hybrids inherit the anti-inflammatory properties of profens. Using in silico method, the toxicity was also calculated. The obtained results are given in LD50 values. Depending on the route of administration, the amfens are less toxic compared to the standard amphetamine. Herein, we report the obtaining of new hybrid molecules of amphetamine with different profens (amfens). The obtained amfens are characterized by their melting points, UV,  For many years, scientists have been fascinated by the synthesis of amphetamine and its derivatives . To dateAmphetamine is a compound that consists of a phenyl ring connected to an amino group by a two-carbon hydrocarbon chain and a methyl group at the α-carbon. It is a psychoactive substance that induces feelings of cheerfulness, strength, and lightness in people who are severely physically exhausted. The compound and its derivatives are used to treat depression, narcolepsy, and obesity . Fenethylline or Captagon, is a combination of amphetamine (given in red) and theophylline (given in blue)  and was It creates a feeling of immense power and euphoria, while at the same time keeping you awake for a long time. The two components, amphetamine and theophylline, act separately on the brain and enhance each other’s psychoactive properties. It is used to treat narcolepsy, fatigue, and behavioral disorders such as "minimal brain dysfunction." Unlike amphetamines, captagon does not raise blood pressure, which means it can also be used to treat people with cardiovascular disease. However, it turned out to be highly addictive, and in 1986, its use was banned in most countries around the world .Some of the most used amphetamine derivatives are 3,4-methylenedioxymethamphetamine (MDMA), 4-iodo-2,5-dimethoxyamphetamine (DOI), norfenfluramine, deprenyl, 4-methylthioamphetamine (MTA), bupropion, and phentermine, which are well-known for their entactogenic, hallucinogenic, anorectic, antiparkinsonian, flat liner, antidepressant, and anorectic properties, respectively + C22H30NO+ = 324.2322, found 324.2316 (mass error Δm = −1.85 ppm).2--N-(1-phenylpropan-2-yl)propanamide3b + C25H26NO2+ = 372.1958, found 372.1951 (mass error Δm = −1.88 ppm).2--N-(1-phenylpropan-2-yl)propanamide3d.1H NMR  δ 7.66 – 7.58 , 7.54 – 7.46 , 7.25 – 7.16 , 7.12 – 6.97 , 6.94 – 6.86 , 6.78 – 6.71 , 5.10 , 4.15 , 3.85 , 3.53 , 2.66 – 2.52 , 2.52 , 1.47 , 0.93 . 13C NMR  δ 173.61(C=O), 157.77((Ar)C-O-CH3), 137.75(Ar), 137.44(Ar), 136.62(Ar), 136.30(Ar), 133.76(Ar), 129.40(Ar), 129.02(Ar), 128.15(Ar), 127.60(Ar), 126.25(Ar), 126.10(Ar), 119.13(Ar), 105.66(Ar), 55.36(-OCH3), 47.12(CHNH), 45.71(CH2CHNH), 42.01(CHCH3), 19.88(NHCHCH3), 18.28(CHCH3). UV λmax, MeOH: 253  nm, 283 (ε = 5200) nm, 351 (ε = 1800) nm. HRMS Electrospray ionization (ESI) m/z calcd for [M+H]+ C23H26NO2+ = 348.1958, found 348.1951 (mass error Δm = −2.01 ppm).2-(6-chloro-9H-carbazol-2-yl)-N-(1-phenylpropan-2-yl)propanamide3e.1H NMR  δ 8.83 , 7.92 , 7.84 , 7.29 – 7.19 , 7.08 – 6.88 , 6.77 – 6.70 , 5.25 , 4.18 , 3.55 , 2.66 – 2.46 , 1.47 , 0.96 . 13C NMR  δ 173.73(C=O), 140.76(Ar), 139.92(Ar), 139.62(Ar), 138.29(Ar), 137.36(Ar), 129.32(Ar), 128.33(Ar), 128.16(Ar), 126.24(Ar), 125.86(Ar), 124.81(Ar), 120.79(Ar), 119.90(Ar), 119.56(Ar), 111.80(Ar), 109.63(Ar), 47.70, 45.97, 42.04(CHCH3), 20.06(NHCHCH3), 18.64(CHCH3). UV λmax, MeOH: 260  nm, 283  nm, 321  nm. HRMS Electrospray ionization (ESI) m/z calcd for [M+H]+ C24H24ClN2O+ = 391.1572, found 391.1563 (mass error Δm = −2.30 ppm).oC for 15 min and then heated at 70 oC for 15 min in water bath. After cooling, the turbidity was measured at 660 nm with a spectrophotometer . The experiment was performed three times. Percentage inhibition of albumin denaturation (IAD) was calculated against control. The control sample is albumin with the same concentration dissolved in distilled water.In vitro analysis of anti-inflammatory activity was assessed as inhibition of albumin denaturation (IAD). The analysis was performed according to Sakat method  with min3a–e are expected to be less toxic, especially via oral administration, based on the calculated values via GUSAR software. The obtained predicted toxicity results for oral administration of the newly synthesized amfens show much lower toxicity values in comparison with the amphetamine. This makes them promising and interesting for future biological evaluation.In conclusion, we have successfully obtained four new hybrid molecules containing an amphetamine skeleton and profen part. The newly obtained amfens are fully characterized. A detailed mass spectral analysis of the newly obtained amfens was performed. In vitro analysis showed that the amfens inherit the anti-inflammatory properties of profens. Compounds


Androgen receptor (AR) signaling plays important roles in breast cancer progression. We show here that Kindlin-2, a focal adhesion protein, is critically involved in the promotion of AR signaling and breast cancer progression. Kindlin-2 physically associates with AR and Src through its two neighboring domains, namely F1 and F0 domains, resulting in formation of a Kindlin-2-AR-Src supramolecular complex and consequently facilitating Src-mediated AR Tyr-534 phosphorylation and signaling. Depletion of Kindlin-2 was sufficient to suppress Src-mediated AR Tyr-534 phosphorylation and signaling, resulting in diminished breast cancer cell proliferation and migration. Re-expression of wild-type Kindlin-2, but not AR-binding-defective or Src-binding-defective mutant forms of Kindlin-2, in Kindlin-2-deficient cells restored AR Tyr-534 phosphorylation, signaling, breast cancer cell proliferation and migration. Furthermore, re-introduction of phosphor-mimic mutant AR-Y534D, but not wild-type AR reversed Kindlin-2 deficiency-induced inhibition of AR signaling and breast cancer progression. Finally, using a genetic knockout strategy, we show that ablation of Kindlin-2 from mammary tumors in mouse significantly reduced AR Tyr-534 phosphorylation, breast tumor progression and metastasis in vivo. Our results suggest a critical role of Kindlin-2 in promoting breast cancer progression and shed light on the molecular mechanism through which it functions in this process. Breast cancer is one of the most frequently diagnosed cancers worldwide with more than 2.26 million newly diagnosed cases and over 680,000 death cases in 2020 . A largeWe previously showed that Kindlin-2, a key component of cell-extracellular matrix adhesions –47, physIn this study, we have employed a photo-pTyr-scaffold strategy to investigate the role of Kindlin-2 in protein tyrosine phosphorylation and identified AR as a key target of Kindlin-2-mediated regulation of protein tyrosine phosphorylation. Furthermore, we provide evidence showing that Kindlin-2 forms a supramolecular complex with both AR and Src and thereby promotes Src-mediated Tyr-534 phosphorylation of AR, breast cancer cell migration and proliferation. Finally, using a genetic knockout strategy, we show that Kindlin-2 is crucial for AR signaling and breast cancer progression in vivo. Our studies provide a rationale for targeting the Kindlin-2-Src-AR signaling axis in therapeutic control of breast cancer progression.2 domain) photo-pTyr-scaffold to capture Kindlin-2 regulated pTyr protein complexes in response to EGF stimulation by mass spectrometry (MS). A number of pTyr proteins were captured by the Src superbinder photo-pTyr-scaffold efficiently in wild-type BT549 cells but much less efficiently in Kindlin-2 knockdown BT549 cells -regulated protein complexes are key factors for regulating signaling transduction during cancer progression –70. To ills Fig. , suggestlls Fig. . Given tlls Fig. . Howeverlls Fig. , but notlls Fig. , was remlls Fig. . Reciprolls Fig. . These rlls Fig. , suggestNext, we sought to identify the Kindlin-2 subdomain that is critical for the complex formation with AR. To do this, we prepared GST-fusion proteins containing various subdomains of Kindlin-2 and tested their abilities to associate with AR. The results showed that both the full-length Kindlin-2 and the F0F1 subdomains  in Kindlin-2 knockdown BT549 cells Fig. , co-IP eOur previous studies have shown that the F0 subdomain of Kindlin-2 mediates the interaction between Kindlin-2 and Src . To testTo explore Kindlin-2 deficiency-induced inhibition of AR signaling and breast cancer progression is due to the reduction of AR Tyr-534 phosphorylation, we used a phosphor-mimic mutant AR-Y534D (tyrosine 534 replaced by an aspartic acid) to study the function of AR Tyr-534 phosphorylation. Figure +/+;MMTV-Cre (referred to as WT hereafter) control mice were confirmed by PCR analysis of tail genomic DNA  using the Cre/LoxP system (MMTV-Cre), which targets exons 5 and 6 of the Kindlin-2 allele  as well as several other pathological processes . Thus, ifl/fl mice (C57BL/6 background) generated as described before  and one time with 50 mM ammonium bicarbonate (ABC). After being reduced by 10 mM Tris (2-carboxyethyl) phosphine hydrochloride (TCEP) and alkylated by 30 mM iodoacetamide (IAA), the beads were washed with 50 mM ABC and incubated with trypsin (Promega) overnight at 37 °C. The digested peptides were washed with 1% (v/v) formic acid, subjected to StageTip C18 desalting and MS analysis.Control or Kindlin-2 knockdown BT549 cells were cultured in serum-free DMEM media containing 100 ng/ml EGF or vehicle for 24 h. Cells were lysed by ice-cold NP-40 buffer  NP-40, pH 7.4). The Photo-pTyr-scaffold operation was performed as described before . In brieThe raw data were searched with MaxQuant software (version 1.5.5.1) against the human UniProt FASTA database . The false discovery rate evaluation was done by searching a reverse database and was set to 0.05 for proteins and peptides. All statistical and bioinformatics analyses were performed with the Perseus software (version 1.5.5.3) and Microsoft Excel.t test was used to compare two groups of samples. One-way ANOVA was used for multiple comparisons. Survival analysis was carried out using the log-rank test. p values <0.05 were considered significant. Prism 7 (GraphPad) was used for statistical analysis.All data represent as mean ± SEM. Two-tailed Student’s Supplementary Figures-clean versionSupplementary Table S1Supplementary Original Western BlotsReproducibility checklist


Dear Editor,Rehabilitation is defined by the WHO as ‘a set of interventions designed to optimize functioning and reduce disability in individuals with health conditions in interaction with their environment’Every disease has its own prognosis and associated complication. The need for rehabilitation varies according to the disease and condition of the patient. However, we are calling DSRR as the protocol of rehabilitation for all possible chronic diseases and the referral pathways for rehabilitation for each disease. For example, in the case of Parkinson’s disease the need for rehabilitation at the early stage it not so severe but properly planned exercise can deter the progression of the disease and at the more sever stages of Parkinson’s disease rehabilitation can improve the quality of lifeIn responding to the pressing need for rehabilitation services worldwide WHO launched the ‘Rehabilitation 2030’ initiative in February 2017 and raised a ‘Call for action’Parkinson’s disease, a neurodegenerative disorder primarily affects the dopaminergic neurons of substantiating region of the brain. As this region of the brain mainly control the movements, Parkinson’s disease always coupled with many forms of physical disabilitiesProperly structured health strategy is of utmost important for better healthcare services. WHO provided eight recommendations on rehabilitation in health system. In its eight points at least four points have direct relationship with the health systemTo ensure UHC, the complete sets of health requirements of every people should be addressed by the health system and service. Again, comorbidity is very common among the chronic and old patient. So it is of utmost important to strengthen the links between all the sectors of healthcare services to meet population needs efficiently and effectively, and without incorporating rehabilitation related pedagogy as a topic in all health education system it is impossible to make rehabilitation service available in the primary healthcare facilitiesNot commissioned, externally peer reviewed.This case report has been reported in line with the Case Report (CARE) guidelines.None.I.H.: conceptualization, data curation, writing – original draft preparation, writing – reviewing and editing. M.S.R., S.S.H., and M.E.H.: data curation, writing – original draft preparation, writing – reviewing and editing. T.B.E.: writing – reviewing and editing, visualization, supervision.The authors declare that they have no financial conflict of interest with regard to the content of this report.Not applicable.Talha B. Emran.


Neuropilin 1 (NRP1) represents one of the two homologous neuropilins  found in all vertebrates. It forms a transmembrane glycoprotein distributed in many human body tissues as a (co)receptor for a variety of different ligands. In addition to its physiological role, it is also associated with various pathological conditions. Recently, NRP1 has been discovered as a coreceptor for the SARS-CoV-2 viral entry, along with ACE2, and has thus become one of the COVID-19 research foci. However, in addition to COVID-19, the current review also summarises its other pathological roles and its involvement in clinical diseases like cancer and neuropathic pain. We also discuss the diversity of native NRP ligands and perform a joint analysis. Last but not least, we review the therapeutic roles of NRP1 and introduce a series of NRP1 modulators, which are typical peptidomimetics or other small molecule antagonists, to provide the medicinal chemistry community with a state-of-the-art overview of neuropilin modulator design and NRP1 druggability assessment. Neuropilins (NRPs) represent transmembrane glycoprotein receptors important for the proper functioning of diverse biological processes due to their broad tissue distribution. They are mainly involved in neuronal development and axon guidance, angiogenesis , immune Extracellular domains of NRPs have defined, although not necessarily overlapping, binding sites that can accommodate various endogenous ligands and can interact with diverse receptors. NRPs are well known for their binding of class 3 semaphorins (SEMA3) ,11 and sVascular endothelial growth factor (VEGF) represents a family of signalling proteins involved in the development of blood vessels, including pathological angiogenesis as in cancer, vascular branching, and maturation, along with cardiovascular development ,24. The NRPs have also been identified as binding partners of other growth factors , which dNRPs play an essential role in angiogenesis and lymphogenesis in endothelial cells through the binding of VEGF family members. The main pathway by which NRPs promote angiogenesis is through the formation of the NRP/VEGF/VEGFR complex, in which NRPs act as co-receptors with VEGFRs and enhance VEGF-induced activation of intracellular signaling pathways that consequently influence cell adhesion, migration, and permeability during angiogenesis under both physiological and pathological conditions ,28,29. NClass 3 of NRPs endogenous ligands semaphorins also play an important role in vascular development, mainly by inhibiting angiogenesis. The semaphorins SEMA3A, SEMA3B, SEMA3D, SEMA3E, and SEMA3F interfere with VEGF-induced angiogenesis to promote their antiangiogenic effects .Due to NRPs interacting with a broad range of endogenous ligands and triggering diverse physiological as well as pathological mechanisms, the modulation of their endogenous ligand binding exhibits a high potential for drug development. Therefore, small peptide ligands mimicking endogenous ligands have already been developed. Unfortunately, they lack metabolic stability and display a low bioavailability . MoreoveDespite NRP2 being an equivalently important target as NRP1, the latter has been more studied and better characterised. Although NRPs bind a large set of diverse endogenous ligands, little is known about the details of individual ligand interactions with its binding domain on the NRP receptor. The most studied and explored is the binding of VEGF-A165 to the b1 domain of NRP1, which serves as a basis for developing NRP small molecule antagonists . Due to The fact that the protein ligands binding to the b1 domain of NRP1 share a common C-terminal arginine motif is also evident from the recently solved crystal structure of SARS-CoV-2 CendR bound to NRP-1 . The comEndogenous VEGF family ligands have been found instrumental in the pathophysiology of chronic pain in several diseases, including cancer, neuropathic pain, osteoarthritis, rheumatoid arthritis, migraine, and diabetic complications. Although mainly involved in the development and maintenance of pain, the best-studied VEGF-A165 shows not only pro- but also anti-nociceptive effects leading to analgesia ,40. ThesNRP receptors have been found to contribute to the infectivity of many viruses by enhancing their host cell entry . This maHuman T-cell lymphotropic virus type 1 (HTLV-1) represents the main cause of T-cell lymphoma and leukaemia. Cell-to-cell contact forms the major route of HTLV-1 infection, with NRP1 being essential for the viral entry. NRP1 is highly expressed in dendritic, T-cells, and endothelial cells, which represent the main targets of the HTLV-1 infection . HTLV-1 Epstein–Barr virus (EBV) represents human herpesvirus 4 (HVV4) implicated in malignancies of lymphoid or epithelial origin. Glycoprotein B (gB) in the virus envelope forms a critical factor for the infection of B and epithelial cells, whereby NRP1 was demonstrated as the main attachment point of gB, facilitating viral entry and the infection of nasopharyngeal epithelial cells, while its connection to B cells remains unknown. Upon EBV binding to nasopharyngeal epithelial cells, furin cleaves gB, exposing a CendR motif that binds to NRP1 and promotes viral fusion and internalisation into host cells. Moreover, the binding of EBV to NRP1 also activates receptor tyrosine kinase (RTK) signalling, which consequently promotes EBV infection of nasopharyngeal epithelial cells, but the detailed mechanism remains unknown . With boCOVID-19 is primarily a disease of the respiratory system causing mild to severe respiratory symptoms, although expression in the central nervous system (CNS) has also been observed, implicating neurologic manifestations . The entSome of the recent studies found that NRP1 may serve as an entry point also for other viruses, including β-herpesvirus murine cytomegalovirus (MCMV)  and EnteNRPs are involved in angiogenesis and cardiovascular diseases. On one hand, the knockout of NRP1 from cardiomyocytes and vascular smooth muscle cells causes cardiomyopathy, aggravated ischemia-induced heart failure, and hereditary haemorrhagic telangiectasia arteriovenous malformations, thus revealing its cardioprotective role ,66. On tThe role of NRP in diabetes pathology was reviewed in 2002 by Mamluk et al. . EspeciaPatients with diabetic nephropathy represent a group at higher risk for COVID-19 disease severity. NRP1 is found in the kidney, particularly in podocyte cells, where it is important for proper podocyte function, such as adhesion to extracellular matrix proteins, cytoskeletal reorganisation, and apoptosis. Its role is therefore important in diabetic nephropathy, in which it has been demonstrated that suppression of NRP1 expression may be responsible for podocyte damage and loss, leading to deterioration of renal function. It is speculated that the high expression of NRP1 in the kidney of diabetic patients facilitates the invasion of SARS-CoV-2 into this tissue, while the interaction of both processes leads to depletion of NRP1, which then exacerbates the pathogenesis of diabetic nephropathy. However, further research is needed to refine the current understanding of the potential role of NRP1 in diabetic nephropathy, particularly in conjunction with COVID-19 ,79.It was discovered that insulin-producing pancreatic β-cells express ACE2 and TMPRSS2 at low levels, whereas NRP1 expression is high in patients with COVID-19 ,81. InfeCancer remains the second most common cause of death worldwide, responsible for almost 10 million deaths in 2020 alone . While cMoreover, the other member of the neuropilin family, NRP2, also contributes to the cancer progression. For example, it was shown that NRP2 is expressed during macrophage differentiation, promotes efferocytosis, facilitates tumour growth  and promWhile peptide antagonists and monoclonal antibodies have failed to fulfil expectations , several1 in 50 = 3 µM and was able to inhibit the VEGF-A mediated biological function partially  we we201] wDespite the long research and development of NRP1 antagonists, none of the peptidomimetic or the small-molecule antagonists has advanced to clinical studies yet. Although all discovery efforts are focused on NRP1, and no specific NRP2 antagonists are known, there are indications that antagonists targeting NRP2 exclusively are needed. Recently, some attempts were made toward the development of selective NRP2 benzamidine-based antagonists of VEGF-C that exhibited a modest NRP2 potency and provided the basis for further development of NRP2 small molecule antagonists targeting related pathological functions .In this article, we reviewed the physiological role of NRPs and their association with various pathological conditions. In addition to the discovery of NRP1 as a SARS-CoV-2 entry coreceptor along with ACE2, we have assembled the pathological roles of NRPs and their involvement in clinical diseases, particularly highlighting the participation of NRPs in cancer. NRPs, therefore, represent mature and viable therapeutic targets. Structurally, neuropilin-1 and 2 contain several conserved motifs and domains suitable for a structure-based medicinal chemistry elaboration. In addition, NRPs possess an abundance of endogenous ligands such as SEMA, VEGF, FGF, HGF, PDGF, TGF-β, and miRNAs that facilitate viable ligand-based design approaches and mode of action studies (MOA). The latter is evident from the chemical space of the most successful NRP1 modulators , which are typically peptidomimetics. However, a handful of other NRP1 small-molecule antagonists have also been reported leaving ample room for future developments. In case the inclined reader would like to learn more about specific NRP-related pathologies, we have compiled the most relevant literature reports in


Various strategies to overcome the MDR phenomenon have been developed, and one of the most attractive research directions is focused on the inhibition of MDR transporters, membrane proteins that extrude cytotoxic drugs from living cells. Here, we report the results of our studies on a series newly synthesized of 5-arylidenerhodanines and their ability to inhibit the ABCB1 efflux pump in mouse T-lymphoma cancer cells. In the series, compounds possessing a triphenylamine moiety and the carboxyl group in their structure were of particular interest. These amphiphilic compounds showed over 17-fold stronger efflux pump inhibitory effects than verapamil. The cytotoxic and antiproliferative effects of target rhodanines on T-lymphoma cells were also investigated. A putative binding mode for  Multidrug resistance (MDR) is a severe problem in the treatment of various diseases such as cancer, bacterial, fungal, and parasitic infections. One of the mechanisms of cancer drug resistance is associated with increased drug efflux from cells, mediated by the ATP-binding cassette (ABC) transporters . More thThe MDR-ABC transporter most studied in cancer is P-glycoprotein . A wide range of compounds with beneficial inhibitory activity toward P-gp have been reported, however, most clinical trials targeting P-gp inhibition have failed, mainly due to the toxicity of the compounds . Multipl3, 7, and 11 were determined by the X-ray diffraction method to compare molecular geometries and to search for differences that could be responsible for the observed biological activity/inactivity. Additionally, for the representative of the most active amphiphilic derivative, 11, the molecular docking studies into the P-gp structure have been performed. The selected physicochemical properties for the whole series of compounds have been estimated as well.Rhodanine-derived compounds have been reported to possess a wide spectrum of biological activities, e.g., antidiabetic, antibacterial, antifungal, antimalarial, antitubercular, antiviral and anticancer ,14,15,1610–12  sho sho43] sm/z, positive ionization mode. CID fragmentation was in the ion trap analyzer with the aid of helium gas. The collision energy was set to ca. 1 eV. The samples were introduced into the mass spectrometer in a CH3OH:CHCl3 1:1 solution with 0.1% HCOOH acidification. The 1H-NMR spectra and 13C-NMR spectra were recorded on a JEOL JNM-ECZR500 RS1 (ECZR version) at 500 and 126 MHz, respectively, and were reported in parts per million (ppm) using deuterated solvent for calibration (DMSO-d6). The coupling constants values (J) were reported in hertz (Hz).All reagents for syntheses were purchased from Merck KGaA  and used without further purification. Melting point (uncorrected) has been determined on the Boetius apparatus (Carl Zeiss Jena). The IR spectrum has been recorded with Jasco FT IR-670 Plus spectrophotometer  in the KBr disk (1 mg sample/400 mg KBr). The MS analyses were obtained on the AmaZon ETD mass spectrometer . Scan parameters: scan range 100–1000 Two mmol of rhodanine or the appropriate rhodanine-3-carboxyalkyl acid, 3 g of 4-Å molecular sieves, 20 mL of isopropyl alcohol, 2 mmol of the appropriate aldehyde, and 1.4 mL (5 eq) of triethylamine were placed in the round bottom flask. The reaction mixture was heated under reflux for 5 h. After heating, the hot solution was filtered. The filtrate was cooled, and 2M hydrochloric acid was added thereto until the solution was acidic. The obtained precipitate was filtered off and crystallized from glacial acetic acid.Five mmol of rhodanine, 7 g of 4Å molecular sieves, 30 mL of isopropyl alcohol, 5 mmol of the appropriate aldehyde, and 3.5 mL (5 eq) of triethylamine were placed in the round bottom flask. The reaction mixture was heated under reflux for 6 h. After heating, the hot solution was filtered. The filtrate was cooled, and 2 M hydrochloric acid was added thereto until the solution was acidic. The resulting mixture was extracted with two 25 mL portions of chloroform. The organic fractions were combined and washed with three 25 mL portions of water. The solution was dried over anhydrous magnesium sulfate, filtered, and chloroform was evaporated from the filtrate. The obtained precipitate was crystallized from isopropanol.1)5--2-thioxothiazolidin-4-one  cm−1: 1719.23 N-CO-C, 1705.73 C=O conj., 1642.09 C=C exo., 1173.47 C=S; 1H-NMR  δ 13.82 , 7.80 , 7.70 , 7.63 , 7.45 , 7.37 ; 13C-NMR  δ 196.0 (C=S), 169.9 (N-C=O), 142.6 (C4′-Ar), 139.3 (Ar-C1″), 132.5 C=CH-Ar, 131.7 (C1′), 129.6 , 128.9 (C4″), 128.0 , 127.4 , 125.8 (C=CH-Ar).Yield 85.2%, m.p. 239–241 °C, MS [M+1]2)2--4-oxo-2-thioxothiazolidin-3-yl)acetic acid  cm−1: 1735.62 N-CO-C, 1711.51 C=O conj., 1641.13 C=C exo., 1199.51 C=S; 1H-NMR  δ 13.42 , 7.89 , 7.83 , 7.73–7.70 , 7.46 , 7.39 , 4.71 ; 13C-NMR  δ 193.6 (C=S), 167.9 (HOOC-), 166.9 (N-C=O), 143.0 (C4′), 139.2 (Ar-C1″), 134.1 , 132.4 (C1′), 132.1 (C=CH-Ar), 129.7 , 129.0 (C4″), 128.1 , 127.4 , 122.0 (C=CH-Ar), 45.6 (HOOC-CH2-).Yield 64.0%, m.p. 257–258 °C, MS [M+1]3)2--4-oxo-2-thioxothiazolidin-3-yl)propionic acid  cm−1: 1737.55 N-CO-C, 1707.66 C=O conj., 1641.13 C=C exo., 1171.54 C=S; 1H-NMR  δ 12.51 , 7.81–7.79 , 7.70 , 7.66 , 7.45 , 7.38 , 4.19 , 2.60 ; 13C-NMR  δ 193.7 (C=S), 172.3 (HOOC-), 167.2 (N-C=O), 142.8 (C4′-Ar), 139.2 (Ar-C1″), 133.0 , 132.5 (C=CH-Ar), 131.9 (C1′), 129.7 , 128.9 (C4″), 128.1 , 127.4 , 122.7 (C=CH-Ar), 40.5 (HOOC-CH2-CH2-), 31.3 (HOOC-CH2-CH2-). Yield 97.2%, m.p. 253–255 °C, MS [M+1]4)2--4-oxo-2-thioxothiazolidin-3-yl)butyric acid  cm−1: 1736.58 N-CO-C, 1712.48 C=O conj., 1641.13 C=C exo., 1167.69 C=S; 1H-NMR  δ 12.11 , 7.82 , 7.78 , 7.71 , 7.67 , 7.46 , 7.38 , 4.04 , 2.27 , 1.89–1.83 ; 13C-NMR  δ 194.0 (C=S), 174.3 (HOOC-), 167.7 (N-C=O), 142.7 (C4′-Ar), 139.2 (Ar-C1″), 132.8 (C=CH-Ar), 132.6 (C1′), 131.9 , 129.7 , 128.9 (C4″), 128.1 , 127.4 , 122.8 (C=CH-Ar), 44.3 (HOOC-CH2-CH2-CH2-), 31.5 (HOOC-CH2-CH2-CH2-), 22.5 (HOOC-CH2-CH2-CH2-).Yield 57.3%, m.p. 197–198 °C, MS [M+1]5)5-(4-(benzyloxy)benzylidene)-2-thioxothiazolidin-4-one  δ 13.72 , 7.56 , 7.52 , 7.42 , 7.36 , 7.30 , 7.14 , 5.17 ; 13C-NMR  δ 196.1 (S=C-S), 170.1 (N-C=O), 160.9 (C4 Ar), 137.0 (C1′ Ar), 133.2 , 132.3 (=CH-Ar), 129.0 , 128.6 , 128.4 (C4′ Ar), 126.2 (C=CH-Ar), 123.0 (C1 Ar), 116.4 , 70.1 (O-CH2-Ar).Yield 74.8%, m.p. 232–234 °C, MS [M-1]ˉ 326, IR (1 mg/400 mg KBr) cm6) 7)2-(5-(4-(benzyloxy)benzylidene)-4-oxo-2-thioxothiazolidin-3-yl)propionic acid  cm−1: 1738.51 N-CO-C, 1711.51 C=O conj., 1642.09 C=C exo., 1260.25 O-CH2-, 1171.54 C=S; 1H-NMR  δ 12.47 , 7.72 , 7.56 , 7.44–7.42 , 7.38–7.35 , 7.32–7.29 , 7.16–7.14 , 5.16 , 4.18 , 2.61–2.57 ; 13C-NMR  δ 193.6 (S=C-S), 172.3 (HOOC-), 167.3 (N-C=O), 161.2 (C4 Ar), 136.9 (C1′ Ar), 133.7 , 133.5 (=CH-Ar), 129.0 , 128.6 (C4′ Ar), 128.4 , 126.2 (C=CH-Ar), 119.7 (C1 Ar), 116.5 , 70.1 (O-CH2-Ar), 40.5 (HOOC-CH2CH2), 31.3 (HOOC-CH2CH2).Yield 85.9%, m.p. 190–192 °C, MS [M+1]8)2-(5-(4-(benzyloxy)benzylidene)-4-oxo-2-thioxothiazolidin-3-yl)butyric acid  cm−1: 1738.50 N-CO-C, 1710.01 C=O conj., 1643.31 C=C exo., 1262.18 O-CH2-, 1173.44 C=S; 1H-NMR  δ 12.10 , 7.71 , 7.56 , 7.43 , 7.38–7.35 , 7.32–7.29 , 7.15 , 5.16 , 4.03 , 2.25 , 1.87–1.82 ; 13C-NMR  δ 194.0, 174.2 (HOOC-), 167.7 (N-C=O), 161.1 (C4 Ar), 136.9 (C1′ Ar), 133.5 , 133.4 (=CH-Ar), 129.0 , 128.6 (C4′ Ar), 128.4 , 126.3 (C=CH-Ar), 119.8 (C1 Ar), 116.4 , 70.1 (O-CH2-Ar), 44.2 (HOOC-CH2CH2CH2), 31.5 (HOOC-CH2CH2CH2), 22.5 (HOOC-CH2CH2CH2).Yield 42.3%, m.p. 165–167 °C, MS [M+1]9)5-(4-(diphenylamino)benzylidene)-2-thioxothiazolidin-4-one  cm−1: 1720.19 N-CO-C, 1682.59 C=O conj., 1643.05 C=C exo., 1174.44 C=S; 1H-NMR  δ 13.65 , 7.46 , 7.36 , 7.32 , 7.13 , 7.07 , 6.85 ; 13C-NMR  δ 195.8, 170.0, 150.2, 146.2, 132.9, 132.3, 130.4, 126.4, 125.6, 125.4, 121.8, 120.3.Yield 25.0%, m.p. 237–239 °C, MS [M+1]L5178Y mouse T-cell lymphoma cells (PAR)  were transfected with pHa MDR1/A retrovirus, as previously described by Cornwell et al. . The ABC® flow cytometer , measuring 10,000 individual cells in the given sample. The inhibition of the MDR efflux pump P-gp (ABCB1) was detected by the retention of rhodamine 123 by ABCB1 (P-glycoprotein) in MDR mouse T-lymphoma cells using flow cytometry according to the protocol published by the former study . The flu4 cells in 100 μL of medium and in the case of the antiproliferative assay, 6 × 103 cells in 100 μL medium were pipetted to each well, with the exception of the medium control wells. The plates were incubated at 37 °C for 24 h or 72 h, respectively; at the end of the incubation period, 20 μL of MTT  solution (from a 5 mg/mL stock) were added to each well as described previously , R1 = 0.0650, wR2 = 0.1074 .7: C20H17NO4S2, Mr = 399.46, triclinic, space group P-1, a = 11.1930(3) Å, b = 13.2410(4) Å, c = 13.9940(4) Å, α = 113.250(2)°, β = 93.208(2)°, γ = 101.598(2)°, V = 1845.5(1) Å3, Z = 4, T = 293(2)K, 14691 reflections collected, 8425 unique reflections (Rint = 0.0254), R1 = 0.0481, wR2 = 0.1149 [I > 2σ(I)], R1 = 0.0626, wR2 = 0.1223 .11: C22H20N2O3S2, Mr = 460.55, triclinic, space group P-1, a = 7.9420(2) Å, b = 9.5630(3) Å, c = 16.0670(4) Å, α = 77.790(2)°, β = 76.769(2)°, γ = 69.832(1)°, V = 1103.25(5) Å3, Z = 2, T = 100(2)K, 8969 reflections collected, 5028 unique reflections (Rint = 0.0279), R1 = 0.0358, wR2 = 0.0816 [I > 2σ(I)], R1 = 0.0508, wR2 = 0.0889 .www.ccdc.cam.ac.uk/data_request/cif (accessed on 22 May 2022).CCDC 2174182-2174184 contains the supplementary crystallographic data. These data can be obtained free of charge from The Cambridge Crystallographic Data Centre via The cryo-EM structure of the human P-gp bound to encequidar was retrieved from the PDB database (PDB code 7O9W). The homology model of the human P-gp was built by multiple comparative modeling using X-ray structures 4Q9H, 4XWK, and 4M1M of the murine P-gp as templates) . The seq11 and verapamil was performed in Schrödinger Suite [11, obtained in this work under crystallographic studies, was used as the initial conformation of the ligand. The protonation states of the 11 and verapamil were generated in the LigPrep module to give neutral or ionized structures that were further optimized by minimization of energy, using options of the MacroModel module  Flexible receptor docking of compound er Suite . Both pr11, the receptor grid box was defined as a large binding cavity limited by the transmembrane region of the hP-gp, reported for the P08183 sequence. For verapamil, the putative binding site was built around a centroid of P-gp residues found by Loo in the mutagenesis experiments to interact with this drug [11 and verapamil to individual conformations of hP-gp.An induced-fit docking study was performed according to the protocol implemented in Schrödinger Suite. In the case of compound his drug ,41. DuriThe graphic presentation of selected protein-ligand complexes with the highest docking scores was prepared in the PyMOL software, v. 2.5.2 .10–12 (substituted with the carboxyl group) exhibit greater potential than 9 (no carboxyl substituent) to inhibit the P-gp. The obtained results indicate derivatives of rhodanine as a new group of compounds for medicinal chemistry and drug design in the search for new inhibitors of efflux pump P-gp.The present work demonstrated the previously unknown biological activity of rhodanine derivatives and the influence of the carboxyl group in their structure on this activity. Importantly, 8, showing additive interaction. An emerging research strategy could be the application of efflux pump inhibitors (EPIs) and their use as adjuvant compounds or chemosensitizers to improve the outcome of antitumor therapy by co-administering them with chemotherapeutic agents. For this reason, the ability of the compounds to inhibit ABCB1 should be investigated; furthermore, their interaction with standard chemotherapeutic drugs should be evaluated. Based on our results, potent ABCB1 modulators have been found; however, their combination with doxorubicin was not synergistic. They cannot be applied in combination with doxorubicin except for compound