Thephysical properties of nanoparticles may affect the uptakemechanism, biodistribution, stability, and other physicochemical propertiesof drug delivery systems. This study aimed to first develop a modelexploring the factors controlling the nanogel physical propertiesusing a single drug (propranolol), followed by an evaluation of whetherthese models can be applied more generally to a range of drugs. Size,polydispersity, ζ potential, and encapsulation efficiency wereinvestigated using a design of experiment (DOE) approach to optimizeformulations by systematically identifying the effects of, and interactionsbetween, parameters associated with nanogel formulation and drug loading.Three formulation factors were selected, namely, chitosan concentration,the ratio between the chitosan and cross-linker—sodium triphosphate—andthe ratio between the chitosan and drug. The results indicate thatthe DOE approach can be used not only to model but also to predictthe size and polydispersity index (PDI). To explore the applicationof these prediction models with other drugs and to identify the relationshipbetween the drug structure and nanogel properties, nanogels loadedwith 12 structurally distinct drugs and 6 structurally similar drugswere fabricated at the optimal condition for propranolol in the model.The measured size, PDI, and ζ potential of the nanogels couldnot be modeled using distinct DOE parameters for dissimilar drugs,indicating that each drug requires a separate analysis. Nevertheless,for drugs with structural similarities, various linear and nonlineartrends were observed in the size, PDI, and ζ potential of nanogelsagainst selected molecular descriptors, indicating that there areindeed relationships between the drug molecular structure and theperformance outcomes, which may be modeled and predicted using theDOE approach. In conclusion, the study demonstrates that DOE modelscan be applied to model and predict the influence of formulation anddrug loading on key performance parameters. While distinct modelsare required for structurally unrelated drugs, it was possible toestablish correlations for the drug series investigated, which werebased on polarity, hydrophobicity, and polarizability, thereby elucidatingthe importance of the interactions between the drug and the nanogelsbased on the nanogel properties and thus deepening the understandingof the drug-loading mechanisms in nanogels. They havea three-dimensional (3D) hydrophilic structure containing voids thatallow the encapsulation of active pharmaceutical ingredients, rangingfrom small-molecule drugs to macromolecules such as peptides, proteins,and genes. They are formed by cross-linking hydrophilic natural and/orsynthetic polymers either physically or chemically and possess strongwater-holding ability without self-dissolution or self-disintegration.Beneficial uses of nanogels include protecting the drug cargo frommetabolism, targeted delivery via enhanced permeation and retention(EPR) effects, or active targeting with the ligands conjugated onthe polymer. This makes them ideal for delivering fragile therapeutics,such as proteins, enzymes, and genes. Chitosan and sodium triphosphatepentabasic (TPP) nanogels are the most extensively explored systems,which only require mild fabrication conditions to form nanogels viaionic gelation. Chitosan is a linear polycationic polysaccharide derivedfrom chitin found in the cell wall of fungi, exoskeletons of arthropods,and shells in crustaceans.5 Furthermore,Tang et al. showed that only cationic nanoparticles suspended in culturemedium were ingested by endocytosis, suggesting the importance ofnanoparticle surface charge in cellular uptake.6 Stability is another key parameter for nanoparticle formulation.In chitosan–TPP nanogels, the nanoparticles are stabilizedby electrostatic repulsion and ζ potential (ZP) is an indicationof the potential stability of the colloidal system. Particles withζ potentials more positive than 30 mV or more negative than−30 mV are considered stable, and the nanoparticles are unlikelyto flocculate due to the repulsion between nanoparticles.7 In short, the successful delivery of nanogelsrequires optimal size and charge, and thus, the fabrication processneeds to be optimized.Controlling the parameters ofnanoparticles is crucial for anynanoparticle formulation, including nanogels. Size and surface chargeof nanoparticles are especially important as these parameters impactthe solubility, biodistribution, stability, cytotoxicity, cellularuptakes, and clearance of the nanoparticles. For instance, the uptakeof nanoparticles into cells is dependent on the size, shape, surfacehydrophobicity, and charge of the nanoparticles, although the permeabilityvaries between different cells.Design of experiment (DOE) is a popularmethod for optimizing pharmaceuticalformulation development. It allows a systematic evaluation of theeffect of multiple factors in the variation of the response measuredand reduces the number of experimental runs. However, the drawbackis that the process is driven by experiments and is labor-intensive,as DOE models are constructed based on the input parameters, and anychanges in the input factors require the construction of a new model.Thus, a model is usually reported for a particular drug and deliverysystem. The prediction ability of these models with new drug payloadsis rarely explored in the literature and therefore is poorly understood.Indeed, the drugs are commonly thought to be encapsulated in the voidsbetween polymer chains and thus the drug choice is often assumed tobe of limited influence if that drug is of a size and solubility toallow incorporation into the nanogel voids. Here, we challenge thisview by exploring both the development of predictive models usinga DOE approach and the possible generalizability of both the approachand the model to a range of drugs. We suggest that a predictive understandingof the effects of composition on nanogel performance parameters willboth aid the formulator and aid in understanding the role of drugstructure on both incorporation and nanogel architecture.Themechanism of drug loading in chitosan–TPP nanogels isnot well understood. It is thought that the drugs are loaded in thevoids formed between the cross-linked chitosan matrix during the gelationprocess. If the loading mechanism is purely entrapment during thegelation process, the nanogel properties should be governed by theformulation, such as the polymer and cross-linker concentrations.Thus, different drugs could be loaded into the nanogel without alterationof the nanogel properties, as long as the drugs fit into the voidsof the nanogels and the prediction models remain valid for differentdrugs. Since these nanogels are fabricated via electrostatic interactionsbetween chitosan and TPP, it is expected that the drug molecules couldalso potentially impact the gelation process, altering the interactionbetween the drug, chitosan, and TPP. Thus, the structure of drugs,drug properties, and pH are important. We hypothesize that drugs areloaded both via physical entrapment and through interaction with thenanogel carrier during the gelation process, where the drug electrostaticallyinteracts with either oppositely charged chitosan or TPP. Other interactionssuch as weak van der Waal’s force and hydrophobic interactioncould also play a role in the interactions. Thus, it is expected thatthe properties vary when the properties of the drug change.To verify this hypothesis, we initially used propranolol as a modeldrug for nanogel fabrication. Response surface models were constructedfor the unloaded and drug-loaded systems to predict the propertiesof propranolol-loaded chitosan/TPP nanogels, namely, hydrodynamicsize, polydispersity index (PDI), ζ potential (ZP), and encapsulationefficiency (%EE). Seventeen experimental runs were performed to builda three-factor, three-level face-centered cubic (FCC) central compositemodel in which chitosan, chitosan/TPP ratio, and chitosan/propranololratio were varied. The models were then verified by an individualtest group before identifying the optimal conditions. The optimalcondition was selected and explored experimentally with 12 other drugswithout structural similarity to propranolol and 6 β-blockerdrugs with close structural relationships to propranolol, of whichthe latter are expected to similarly interact with the polymer orcross-linker. In this way, we intend to establish whether the modelsmay be applied to other drugs and if so whether structural similarityis a requirement for such extrapolation.8 which can be classifiedbased on either the dimensionality or information content. The formerclassifies descriptors from zero-dimensional (0D) to seven-dimensional(7D) descriptors, whereas the latter classifies them into constitutional,topological, geometric, and electronic descriptors.9 The classifications overlap extensively and are not mutuallyexclusive. Thus, molecular descriptors discussed in this study willbe based on the constitutional and topological properties for clarity.Constitutional descriptors are calculated from molecular formulae,such as molecular weight atom and bond count, whereas topologicaland structural descriptors, including counts of fragments and functionalgroups, are calculated from the two-dimensional (2D) structure. Geometricspatial and electronic descriptors are derived from the three-dimensional(3D) structure.9 Numerous open-source andcommercial software exist for computing these molecular descriptors,including PaDEL,10 MORDRED,11 CDK,12 Dragon,13 and RDKit.14 As aproof-of-concept study, these drugs were described in terms of 15basic molecular descriptors, which include the number of acid (nAcid),base (nBase), rings (nRings), hydrogen bond acceptors (nHBAcc), hydrogenbond donors (nHBDon), the sum of the atomic polarizability (apol),sum of the absolute value of the difference between atomic polarizabilityof all bonded atoms (bpol) in the molecule, Wiener path number (WPATH),Wiener polarity number (WPOL), topological polar surface area (TopoPSA),topological diameter (TopoDiameter), Petitjean topological shape index(Toposhape), and two logarithms of n-octanol/waterpartition coefficients ALog P and XLog P. The constituent descriptors, especially the number ofchemical groups, were selected to evaluate the chemical groups responsiblefor the interactions with the carriers and to identify the mode ofloading in the nanogels, where the polarity and polarizability werepicked to evaluate the effects of other interactions, such as vander Waal’s force and hydrophobic interaction. The topologicalshape and size descriptors were selected to evaluate whether the sizeand shape of the drugs impact drug entrapment since the drugs areloaded into the voids of the nanogels. The relationship between nanogelproperties and molecular descriptors of drugs was determined via multiplelinear regressions, logarithmic, exponential, and quadratics correlations.This facilitates our understanding of how a drug may influence entrapmentand nanogel structure, with the intention of establishing the generalizabilityof the DOE modeling approach across a wide range of drug structuresand increasing our understanding of the mechanism of drug loadingin nanogels. Notably, the development of predictive models for optimizingthe properties of drug delivery systems is a highly useful and well-establishedapproach within the field. In this particular case, we focus on twoas yet unexplored applications of performance modeling. First, theformulation of nanogels is a complex and as yet poorly predictableprocess whereby each system is explored on a largely individual basisdue to the absence of a validated methodology for performance prediction;hence, there is a clear requirement and novel application for suchapproaches for these systems. Second, the effect of drug incorporationon performance and properties is a highly important area, which hasas yet again not received systematic study; hence, our intention isto develop methodologies whereby the effects of incorporation of theactive agent may be developed at least, in the present case, for structurallyrelated molecules, with the intention of this providing a basis forstudies into a broader range of active pharmaceutical ingredients.To describe a drugmolecule, one of the common representationsis the molecular descriptor. It is defined as an algorithm-generatedmathematical representation of structural or physicochemical propertiesof molecules,22.1Low-molecular-weight(LMW)chitosan was purchased from Sigma-Aldrich  with a molecularweight of 50–190 kDa according to the manufacturer. Paracetamol,metoclopramide hydrochloride, metoprolol tartrate, lidocaine hydrochloride,theophylline, ofloxacin, metronidazole, acebutolol, pindolol, andesmolol hydrochloride were also acquired from Sigma-Aldrich . Pentabasic sodium triphosphate (TPP) and caffeine were purchasedfrom Fluka (Switzerland), while propranolol hydrochloride (PropranololHCl), quinine anhydrous, lisinopril dihydrate, verapamil hydrochloride,and betaxolol were acquired from Acros Organics . Pirfenidoneand atenolol were purchased from Tokyo Chemical Industry .Chlorpheniramine maleate was acquired from the LKT Laboratory . Glacial acetic acid was obtained from Fisher Scientific. Sodium hydroxide pellets were acquired from VWR . All chemicals were of analytic grade and used as supplied.2.215 Low-molecular-weight chitosan was first dissolvedin 1% acetic acid solution until it formed a clear solution, followedby adjustment of the pH to 4.5 with 0.1 M sodium hydroxide solution.Chitosan solution was filtered through a 0.22 μm syringe filterbefore use. Propranolol HCl was weighed and dissolved in the chitosansolution before the addition of the TPP solution. Meanwhile, variousamounts of TPP were dissolved in deionized water to prepare differentconcentrations and the TPP solutions were also filtered with a 0.22 μmsyringe filter. An equal amount of TPP solution was added to the chitosansolution under stirring at room temperature. The solution was thenstirred at 600 rpm for 1 h. A range of nanogels were prepared by varyingthese factors according to the experimental matrix shown in Propranolol-loaded chitosan nanogels were prepared by the ionic cross-linkingmethod, adapted from the method reported by Al-Kassas et al.2.3Table S1.A total of 17 experimental runs were denoted as the training set andwere performed in triplicate to construct the response surface model.The composite matrix was constructed using JMP 15 . Four properties of nanogels (Z-average(size), ZP, PDI, and %EE), which contribute to being a successfuldrug carrier, were determined as the dependent variables.Response surfacemethodology was used to determine the optimal condition for preparingpropranolol-loaded nanogels. A face-centered cubic (FCC) central compositedesign (CCD) was used in the optimization, which was formed by threefactors, namely, the chitosan concentration (CC), the chitosan–TPPmass ratio (C/T), and the chitosan–propranolol mass ratio (C/P),at three levels, as shown in p-value <0.05 is consideredstatistically significant.Astepwise least-squares regression was used to fit the polynomial modelto the data individually for each dependent variable. Fivefold cross-validationwas performed to validate the model for all dependent variables. One-wayanalysis of variation (ANOVA) test and lack-of-fit test were conductedto determine the statistical significance and goodness of fit forthe model, respectively, at a confidence interval (CI) of 95%. Responsesurfaces were plotted to visualize the relationship between independentand dependent variables. A 2.3.116 in 1965and later advocated by Derringer and Suich,17 is one of the most widely used methods in multiple response optimization.It transformed the response variables (yi) into an individual desirability function di(yi), with a number assigned between 0 and 1. di(yi) = 0 indicates a completely undesirable response,while di(yi) = 1 represents the most desirableresponse. Individual desirability functions were transformed usingJMP 15 software to minimize the particle size and PDI while maximizingthe %EE and ZP. Individual desirability functions were then combinedinto overall desirability, as shown in eq d1(y1) and d2(y2) denote theindividual desirability functionfor factors 1 and 2, respectively, i is the totalnumber of factors, and di(yi) is the individualdesirability function of factor i.Multiple response optimization was employed to determinethe optimalcondition for fabricating propranolol-loaded nanogels, as the dependentvariables might contradict each other. The desirability function approach,first proposed by HarringtonThe runningconditions with the highest overall desirability were deemed as theoptimal condition and were determined by JMP 15. Nanogels were thenfabricated under the optimal conditions in triplicate, with the dependentvariables measured experimentally and compared with the predictedvalues to validate the models. Nanogels produced were then freeze-driedand characterized.2.3.2To determine the predictive accuracy ofthe models, an independenttest set with 13 formulations was used, with the properties of nanogelsmeasured experimentally and compared with the predicted value fromthe model. The experimental conditions for the formulations in thetraining set were generated randomly, and the test set is reportedin 2.42.4.1–1 andwith a resolution of 1 cm–1.Analysis was performed with a Spectrum100 FTIR spectrometer equippedwith an attenuated total reflectance (ATR) sampling accessory  in the range of 650–4000 cm2.52.5.1The shape and morphology of the nanogelswere characterized by anFEI CM120 Bio Twin Transmission Electron Microscope (TEM) . One drop of the nanogel sample was dropped onto 200-mesh carbonlacey-coated copper grids and stained with 1% uranyl acetate solution,followed by air-drying at room temperature for a few minutes. Theexcess solution was removed using filter paper. Particle size distributionwas performed using ImageJ .2.5.2The average diameter and polydispersityof the nanogels were measured using a Zetasizer Ultra  at room temperature using a backscatter angle of 173°.A disposable polystyrene cuvette was employed in the analysis. ζpotentials were measured using U-shaped capillary cells . The results were measured intriplicate obtained from three independent experiments.2.5.315 Instead of separating the nanogels via centrifugationsolely, 0.5 mL of the propranolol-loaded nanogel solutions were loadedinto a 0.5 mL Amicon diafiltration tube (molecular weight cut-off(MWCO) 3000; Merck Millipore, Billerica, MA). The solutions were thencentrifuged at 14 000g for 30 min at 4 °Cusing a refrigerated mini centrifuge , and the filtrate was isolated and assayed by a UV–visspectrometer  at a wavelength of 280nm. A range of concentrations between 5 and 100 μg/mL were preparedto construct the calibration curve, which is shown in Figure S1. %EE was calculated using DTheoretical refersto the amount of propranolol added into the solution, while DFree refers to the amount of propranolol presentin the aliquot after centrifugation.Measurement of %EE of propranololwas adapted from the method reported by Al-Kassas et al.2.6Dissolution tests were performed in 50 mL phosphate-bufferedsaline (PBS)  solution with continuous stirring at37 °C for 72 h. Two milliliters of the nanogel solutions wereloaded into a cellulose dialysis bag  with both ends tied, followed bysubmerging into PBS. One milliliter of aliquot was withdrawn at certaintime points, and an equal volume of the fresh preheated PBS solutionwas added to maintain a constant volume. Propranolol was assayed byUV–vis spectroscopy using a UV–vis spectrometer . The wavelength was set at 280 nm, and drugconcentrations were calculated using predetermined calibration curves.The experiment was replicated independently three times, and the resultswere presented as the mean value ± standard derivation.2.7To determine the possibility of applying the validatedmodels to other drugs, nanogels were fabricated at the optimal condition,as discussed in Table S2, which are subdividedinto constitutional, topological descriptors and molecular properties.These molecular descriptors of the drugs were calculated with PaDEL.10 The correlations between molecular descriptorsand the properties of nanogels were determined with linear regressionusing JMP 15, with the correlation coefficient (R2) aimed above 0.7. For nonlinear correlations, the dataset was fitted to compute the regression coefficients (R2) and p-values.Owing to the poor predictability of the models, ζ potentialsof the nanogels were measured but not predicted. To describe eachdrug molecule, 15 basic molecular descriptors were selected and areshown in 33.13.1.1p-values obtainedin the ANOVA test for all of the models were smaller than 0.05, demonstratingthe significance of the correlations between the training set andthe models. Furthermore, the p-values in the lack-of-fittests for all models were larger than 0.05, which indicate that thesemodels were a good fit for the training set data. Thus, these modelscan predict the properties of nanogels.One-way analysisof variation (ANOVA) and lack-of-fit test were performed on the responsesurface models for each individual dependent variable to determinethe statistical significance and the goodness of fit of these modelson the training set, respectively. The null hypothesis of the ANOVAis that these models have no correlation to the training data setand thus do not have the predictive capacity. The results of the ANOVAand lack-of-fit tests are reported in 3.1.23.1.2.15Z-average is measuring the hydrodynamic size of the nanoparticles,which is a better indication of the size of the nanogels in solutionthan the size measured in TEM as the latter measures the dried state.All nanogels in the 17 formulations from the training set were foundto be within a range from 56.8 to 208.1 nm. These nanoparticles werein the range for endocytic uptakes. Chitosan concentration, the interactioneffect between the chitosan concentration and chitosan/TPP ratio,and the quadratic effect of the chitosan/TPP ratio were found to havesignificant effects on the Z-average of nanogels.Although the effect of the individual term for the chitosan/TPP ratiowas not significant, it had to be included in the model as its interactionand quadratic terms were included.Size is one of the important factors controllingthe performanceof the nanogels in cellular uptake.15 in which the “one-factor-at-a-time”(OFAT) optimization approach was used to prepare propranolol-loadedchitosan–TPP nanogels. Nanogels fabricated in their study witha 0.1% chitosan were much larger than those prepared from 0.2 and0.3% chitosan. Moreover, the size of the nanogels prepared in thisstudy was generally smaller than those reported by Al-Kassas’group. The discrepancy in nanogel sizes between the two studies isprobably due to a different grade of chitosan being used, with low-molecular-weightchitosan used in this study while medium-molecular-weight chitosanbeing used by Al-Kassas et al. As chitosan concentration increaseswith the viscosity, the cross-linking between chitosan and TPP isinefficient at high chitosan, eventually forming larger particles.18 Furthermore, an interaction between chitosanconcentration and the chitosan–TPP ratio was identified inthe response surface model, which demonstrated that the nanogels formedwere bigger at high chitosan concentration and high chitosan–TPPratio. It is likely due to more chitosan and TPP being available andthus more cross-linking was formed20 Ma et al. successfully delivered large (>400 nm) and polydisperse(PDI = 0.5) chitosan–TPP nanoparticles to small intestine Caco2cell lines.20 However, from a pharmaceuticalperspective, a successful nanoformulation should be stable, safe,and effective, and the preparation method should be robust. Thus,the population of the nanocarriers should be as homogeneous as possible.The PDI is a measure of the homogeneity of the nanoparticles in termsof size distribution,21 which is a valuebetween 0 and 1 for the Malvern Zetasizer series. Hence, the smallerthe PDI, the more uniform the size of the nanogels. A high PDI value(>0.7) denotes a very broad size distribution of the nanoparticles,which might indicate agglomeration of the nanoparticles or the presenceof other contaminants.The size of the nanogelsincreased with the chitosan concentration, as shown in 22 It is likely due to more cross-linking formed at high chitosan concentrationand high TPP concentration , forming largerparticles and agglomerating. Therefore, with larger particles beingformed, the distribution of size for the nanogels was broadened andhigher PDI was observed. However, their interaction was not significant,albeit it was estimated in the stepwise regression, which indicatedthat the nanogels are more polydisperse at high chitosan concentrationand chitosan/TPP ratio, with no synergetic effect observed betweenthese two factorsPDI of the training set formulations was between 0.217 and 0.507,of which only four among these formulations were considered polydisperse(PDI > 0.4) and the rest of the formulations were moderately dispersed.The response surface model is shown in 3.1.2.223 Nanogels with ZP valuesof 30 mV are generally stable in suspension due to the sufficientelectronic repulsion between particles.24 ζ potentials of the nanogels from the training set, as shownin The nanogelsare formed by ionic gelation between cationic chitosan and anionicTPP, where chitosan is generally used in excess compared to TPP. Therefore,nanogels are generally positively charged at acidic conditions withpH < 6, where the amine group on the chitosan is protonated. TheZP of nanogels is an important influencer on the colloidal stabilityof the nanogels, as the agglomeration of nanogels is attenuated bythe electronic repulsion.18 In contrast, TPP is an anionic molecule, of which increasesin chitosan/TPP ratio will lead to a decreasing amount of TPP forcross-linking and reduction of the negative charge on nanogels; thus,the ZP is inversely correlated to the chitosan/TPP ratio. These findingswere in good agreement with the study conducted by Al-Kassas et al.,using the OFAT approach.15 Design of experimentapproach is generally more advanced and allows interactions and quadraticeffects to be identified, compared to the OFAT approach. Therefore,several additional factors were identified influencing the ζpotential of the nanogels in the RSM model. A quadratic effect ofthe chitosan concentration is demonstrated in All selected factors were found to have significanteffects on the ZP of the nanogels. Positive coefficients were associatedwith the chitosan concentration and chitosan/propranolol ratio in 3.1.2.315 Thediscrepancy is probably related to the nanogels formed using low-molecular-weightchitosan in this study. Chitosan concentration and chitosan/TPP ratiowere found to have inverse effects on the %EE, with low %EE observedat high chitosan concentration and low chitosan/TPP ratio , as shown in 25 The %EE of lysozyme in nanogels was higher, with at least 54% ofthe drug input. One possible reason to account for the discrepancyin encapsulation efficiency is that the molecular weight of propranololand lysozyme is massively different of 259.34 and 14.3 kDa, respectively,even though both are carrying a positive charge. Propranolol is, therefore,more likely to leach out from the nanogels, as compared to lysozyme.Moreover, the study identified different important factors on %EE,which also revealed that the encapsulations of payloads are differentbetween microfluidics and stirringAnothercrucial property of nanogels is their ability to encapsulate therapeuticmolecules. The %EE of propranolol in the nanogel formulation was between10 and 40%, as shown in 3.1.3Figure S2, aiming to achieve the highest %EE and ZP and the lowest Z-averageand PDI. The optimal running condition for nanogel production is at0.10% chitosan concentration, a chitosan–TPP mass ratio of3, and a chitosan–propranolol mass ratio of 0.5, as shown in The optimal fabricating condition was determined by multiple responseoptimization (MRO), as shown in 3.1.4Q2) werecompared to those of the training set (R2) for each parameter of nanogels. Good fitting is reflected on R2 closer to 1, while similar R2 and Q2 indicate that themodel was working independently from the training data set, whichindicates the power of model prediction. Size and PDI models weregood with the relatively high Q2 value(>0.6) compared to R2, as shown in R2 and Q2 for the EE model were low, which suggestedthat the model is not a good representation of the %EE within thedesign space nor having a good prediction ability. Therefore, onlythe hydrodynamic size and PDI of the nanogels were predicted, as shownin Thirteentest set formulations were performed to determine the predictionaccuracy of the models, with the measured and predicted results shownin Figure S4, while the identified experimentalconditions are presented in Two other formulations (F2 andF3) were produced according to thepredicted experimental conditions identified in the contour plotsto produce nanogels with the smallest PDI and size of 100 and 200nm, respectively. These contour plots are displayed in 3.23.2.1–1 are associated with O–H and N–H stretching and intramolecularhydrogen bonds, while the peak at around 2870 cm–1 corresponds to asymmetric C–H stretching. Similar bands at3288 and 3414 cm–1 were observed in freeze-driednanogels, which also correspond to these intramolecular hydrogen bonds.The symmetric C–H stretching was not obvious in the spectra,as no peak was observed around 2900 cm–1. N-Acetylationof chitosan was confirmed by the bands at 1642–1650 and 1323cm–1, which are the C=O stretching and C–Nstretching of amide, respectively, as well as the peak at around 1590cm–1, which corresponds to the N–H bending.The strong bands at 1027–1068 cm–1 are associatedwith the C–O stretching. The spectra agree with the resultreported in the literature.27 In the IR spectrumof propranolol, a band at 3277 and 3221 cm–1 correspondsto the O–H and N–H stretching with intramolecular hydrogenbonds, respectively. A peak at 796 cm–1 correspondsto the naphthalene in propranolol, while the aryl alkyl ether is associatedwith the peak at 1266 cm–1.28 C=C stretching in naphthalene is observed with a sharp peakat 1578 cm–1. The spectrum obtained agrees withother studies.29 As for TPP, the band at3326 cm–1 corresponds to the O–H stretching,while the band at 1135 and 1209 cm–1 associateswith O–P=O and P=O stretching, respectively.30 A sharp peak at 1094 cm–1 correspondsto the P–O stretching. The sharp peaks at 1255 and 1269 cm–1 were present in the drug-free and propranolol-loadednanogels, respectively, which are indicative of the P=O bondin TPP within the nanogel structure, albeit shifted from 1209 cm–1 in TPP alone as a result of the interaction withchitosan.25 Drug-free nanogels exhibitedsharper peaks at 1558 and 1648 cm–1 compared tothe chitosan, which showed that the complexation of chitosan withTPP is likely to influence the chemical interaction between chitosan.Moreover, the C–O stretching of either group in chitosan wasobserved at 1087 cm–1, which shifted to 1009 and1018 cm–1 in the drug-free and propranolol-loadednanogels. The shift was similar to the reported literature.25 Moreover, several distinct peaks for propranololat 776, 795, and 1269 cm–1 were present in propranolol-loadednanogels, which were not observed in the drug-free nanogels. In conclusion,the IR spectrum confirms the presence of the individual componentsin the nanogels and structural change of the nanogels after encapsulationand loading of propranolol was not observed.3.3The in vitro drugrelease test predicts the dissolution profile and bioavailabilityof the loaded drugs. The use of PBS (pH 7.4) is to stimulate the releaseat physiological pH. A burst release of encapsulated propranolol inthe first 2 h can be observed in 3.4Nanogels were fabricated at the optimal condition identifiedin the model using a total of 19 other drugs, where the drugs aredivided into two groups based on the molecular similarity to propranolol.The measured size, PDI, and ζ potential of the nanogels fabricatedusing structurally distinct drugs with propranolol are shown in Ka of ∼pH = 9. Thus, at pH4.5, the drugs were ionized and likely interacted with anionic TPPin the nanogels. However, there are also repulsions between cationicchitosan and drugs, which could limit the encapsulation and decreasethe drug loading. As the drugs and TPP are polar and possess a hydrogenacceptor and donor, these interactions are likely to play a role indrug loading. Multiple linear regressions and nonlinear fittings wereconducted to evaluate the correlations between the selected moleculardescriptors and nanogel properties, which are shown in 31 Thus, the extra cross-linking may mask the effect of the drug itself.As the sample sizes were too small with only seven β-blockers,these drugs were not subgrouped into training and test sets.To investigate whether drugs withmolecular similarities behaveddifferently, seven β-blocker drugs with structure–activityrelationships (SARs), including propranolol, were tested. All β-blockerdrugs had similar structures, the same number of acid and base groups,and pP andsize, as shown in P and XLog P are atom-additive approaches to calculate the partitioncoefficient (Log P) theoretically, where XLog P is an enhanced modification of ALog P. The correlation coefficient (R2) betweenthe size and XLog P was 0.64, and the p-value was 0.0313, which demonstrated that the correlationswere moderately strong and statistically significant. Hence, the nanogelsare likely to be smaller in size when a β-blocker with higherXLog P is used. As Log P is a measure of hydrophobicity, higher XLog P indicates higher hydrophobicity. Therefore, the observation elucidatesthat the nanogel size reduced with the hydrophobicity of β-blockers.Another strong correlation was estimated between PDI and topologicaldiameter (TopoDiameter), indicating a potentially strong influenceof graph-theoretical sizes on the PDI of the nanogel as TopoDiameteris a measure of the maximum atom eccentricity. However, the p-values for the ANOVA and lack of fit were 0.0589 and 0.7043,respectively, which indicated that the relationship was well fittedbut was statistically insignificant. Other multiple correlations wereestimated between the sum of all atomic polarizability (apol), thesum of the absolute value of the difference between atomic polarizabilitiesof all bonded atoms in the molecule (bpol), Wiener path number (WPATH),and TopoDiameter with ζ potentials. Apol and bpol are two measuresof the polarizability of the drug, while WPATH is a topological descriptor,which is defined as the sum of the lengths of the shortest paths betweenall pairs of vertices in the chemical graphs.32 The Wiener index helps to identify the branching, cyclicity, andcentricity of the compounds. The first three correlations were statisticallysignificant, with a p-value of 0.0302, 0.0313, and0.0173, whereas the correlation between TopoDiameter and ZP was not.All regression coefficients were over 0.6, which demonstrated thatthese correlations were moderately strong. The result revealed thatthe polarizability and molecular topography of the drug could potentiallyaffect the ζ potential of the nanogels.Several strong linear correlations were estimated by the row-wisemethod between some molecular descriptors and properties of nanogels.As the number of acid and base in all tested β-blockers arethe same, the correlation coefficients were 0. Correlations with strongeffects were estimated between XLog p-value of 0.0447and an R2 of 0.5865. The result revealedthat the polarizability of the drugs impacted the PDI of the nanogels,potentially in an exponential growth fashion. Moreover, the interactionsbetween apol, bpol, WPATH, and ZP could be fitted in other relationships,as shown in R2 >0.9). However, the vertex of the quadratic fit could not be confirmedby the existing data set and thus is likely to be overfitted. Exponentialfitting between apol and ZP, as well as bpol and ZP, was also good,with p-values of 0.0078 and 0.0021, respectively.Hence, the result showed that these two factors could have negativeexponential effects on the ZP, instead of linear relationships. Contrarily,the exponential fit was invalid for WPATH and ZP, as there are invalidarguments. The quadratic fit was the best fit among all relationships,with the highest R2. Both linear and logarithmiccorrelations were comparable and slightly above 0.7. With the existingdata set, there is likely a quadratic correlation between the WPATHand ZP. Last but not least, a new correlation between ALog P and ZP was identified in the quadratic fit, with a p-value of 0.0081 and an R2 of0.9098. The result indicates that hydrophobicity also influenced theZP and the colloidal stability of the nanogels.Nonlinear correlations, such as logarithmic, exponential, and quadraticcorrelations, were also fitted between the molecular descriptors andthe nanogel properties. As with the linear regressions, the correlationcoefficients for nAcid and nBase were 0. Interestingly, the exponentialcorrelation between PDI and bpol was significant despite the moderateeffect of the correlation, with a To summarize, the results indicatethat the properties of the payloadimpacted the properties of nanogels. Interaction between the drugsand other components of nanogels is most likely to play an importantrole in determining the nanogel properties in addition to the formulations.Thus, the loading of drugs in nanogels is not purely a simple entrapmentinside the void. Drugs with various sizes and shapes could still beencapsulated in the nanogels but altered the nanogel properties. Furthermore,most of the topological size and shape descriptors selected failedto influence the nanogel properties, which also supported the finding.Interestingly, since the structural difference between drugs is smallfor drugs with structural activity relationships, hence the interactionsbetween nanogel and drugs remain similar. The constitutional descriptorsselected did not impact the nanogel properties, which demonstratedthat the hydrogen bondings and the heterocyclic and aromatic ringswere not the key interactions or groups between the drugs and thecarrier. Instead, the hydrophilicity, polarity, and polarizabilityof the drugs were more important, which were found to impact the nanogelproperties. With these correlations, the DOE models established couldpotentially apply to similar drugs to estimate the nanogel properties.In practice, for example, if the target payload is expensive or haslimited availability, cheaper drugs with an SAR could be used to optimizethe formulation and the optimum conditions could then be applied tothe target payload.As a proof-of-concept study, there are severallimitations to thisapproach. First, only a very small number of molecular descriptorswere selected compared to approximately 1800 descriptors computedby PaDEL. Therefore, the future use of machine learning could helpidentify the molecular descriptors that have stronger and more nonlinearcorrelations with the properties of nanogels, as well as from a largerpool of molecular descriptors. Despite the limitations, this studyrevealed that the established DOE models could be applied to similardrugs with the help of molecular descriptors. It also provided a deeperunderstanding of how drugs are loaded in nanogels as well as how payloadscould impact the properties of nanogels.4Z-average and PDIof the optimum nanogel formulation were 75.5 ± 2.2 nm and 0.211,respectively, which were similar to the predicted values. However,ZP and the %EE were not predicted as the predictability of these modelswas weak, indicating the importance of performing a test set. To evaluatethe application of these prediction models to different drugs, thenanogels loaded with other drugs were fabricated at the optimal conditionin the model with 12 structurally distinct and 6 structurally similardrugs, and the size, PDI, and ZP of the nanogels were measured. Theseproperties were distinct from the predicted value, which indicatedthat the DOE models must be refined when a new drug is used. Nevertheless,relationships were found between structurally related drugs and performanceparameters; hence, there is a dependence on the molecular structure,which could potentially be solved for a wider range of drugs. Theseoutcomes also indicate that encapsulation and formation processesare indeed drug-dependent and are not simply a matter of incorporationinto interchain voids. We therefore suggest that the interactionsbetween the nanogels and drugs are important mechanisms for encapsulation,which also govern the properties of nanogels.Prediction models for the properties of propranolol-loaded nanogelswere constructed using a DOE approach. Three investigated factorswere chitosan concentration, chitosan–TPP ratio, and chitosan–propranololratio, and their effects on the hydrodynamic size, PDI, ZP, and the%EE of the nanogels were determined. Following the multiple responseoptimization, an optimal condition of 0.1% chitosan concentration,a chitosan–TPP ratio of 3, and a chitosan–propranololratio of 0.5 was predicted. The


Cichorium glandulosum Boiss.et Huet and Cichorium intybus L.) has been found to have therapeutic potential for metabolic diseases in recent years. Although it is widely used as an ethnic medicine, there is still a lack of targeted quality control indicators in quality standards. Hence, this study was conducted to further develop a strategy to reveal bioactive-chemical quality markers based on the existing foundation. First, through the comparative screening of fingerprint profiles of a large amount of Cichorium glandulosum Boiss.et Huet and Cichorium intybus L., superiority components were found to be potential indicators of chemical quantitative properties for the roots and above-ground parts. The results of content determination showed that their contents differed among different species and parts. Second, the potential dominant components were further confirmed using network pharmacology and molecular docking techniques. Again, the results of RAW264.7 cells and L02 cells experiments showed that chicory acid and lactucin were the main components that could reflect the anti-inflammatory and uric acid-lowering potential of chicory. Finally, under this strategy, this study reveals that cichoric acid and lactucin have the properties of quality markers and quality control of chicory. In a word, this work contributes to the quality control, standard improvement, and rational clinical use of chicory.As a miraculous Xinjiang Uyghur customary traditional Chinese medicine (TCM), Chicory ( Artemisia annua L., berberine in Coptis chinensis Franch., baicalin in Scutellaria baicalensis Georgi, and so on  in major public health events, particularly in times of novel coronavirus epidemics, has led to its beneficial role in the treatment of diseases –3. As a nd so on –9. Howevnd so on  used a be Cortex . HoweverCichorium glandulosum Boiss. et Huet and Cichorium intybus L. of the Asteraceae family  were obtained from Fisher . Water used for the experiment was produced by a Milli-Q system . Reference standards of chlorogenic acid, esculetin, cichoric acid, and quercetin were obtained from National Institutes for Food and Drug Control , lot numbers 110753-202018, 110741-202109, 111752-201703, and 100081-201610, with a purity of 96.10, 96.00, 97.6, and 99.1%, respectively. Lactucin and 11β,13-dihydrolactucopicrin were purchased from Shanghai Yuanye Biotechnology Co., Ltd. , the lot numbers were A09GB144753 and A28GB144754, respectively, with purity ≥ 95%. Isochlorogenic acid A was purchased from Shanghai Constantine Standard Technical Service Co. Ltd. , with the lot number STC0680120 and purity ≥ 98%. Allopurinol and xanthine were purchased from Shanghai Yuanye Biotechnology Co. Ltd. .Cichorium glandulosum Boiss. et Huet and Cichorium intybus L., this study was collected from various herb markets, pharmacies, herb companies, and main production areas. The samples included above-ground parts and roots. There were 32 batches of above-ground parts of Cichorium intybus L. , 39 batches of Cichorium intybus L. roots (numbered JJG1-JJG39), and 11 batches of above-ground parts of Cichorium glandulosum Boiss. et Huet (numbered MJJ1-MJJ15), including control herbs and cultivated products. Cichorium glandulosum Boiss. et Huet root. There are 5 batches of Cichorium glandulosum Boiss. et Huet roots (Nos. MJJG1-MJJG5). The samples were identified by Professor Yaojun Yang of the Beijing University of Chinese Medicine. It is the dried above-ground part or root of Cichorium glandulosum Boiss. et Huet and Cichorium intybus L., family Asteraceae. Sample information is provided in To ensure the extensiveness and representativeness of The RAW 264.7 cells and L02 cells were purchased from the Cell Bank of the Chinese Academy of Sciences . Phosphate-buffered saline (PBS), fetal bovine serum (FBS), and DMEM medium were obtained from Gibco Co., Ltd. . Uric acid salts were obtained from Sigma Aldrich .Reference standards of chlorogenic acid, esculetin, lactucin, cichoric acid, isochlorogenic acid A, 11β,13-dihydrolactucopicrin, and quercetin were weighed precisely and dissolved in 70% methanol. The reference mix solution containing chlorogenic acid (1.90 μg), esculetin (5.43 μg), lactucin (3.57 μg), cichoric acid (1.78 μg), isochlorogenic acid A (4.40 μg), 11β,13-dihydrolactucopicrin (0.52 μg), and quercetin (4.96 μg) per 1 mL of solution was prepared.Take about 0.25 g of the sample powder (40 mesh), put it into a conical flask with stopper, and add 70% methanol to make up to 25 mL. Make it dense stopper and weigh. The sample was extracted by reflux in a water bath  for 60 min. After cooling, weigh again and make up the lost weight with 70% methanol. Shake to homogenize and filter through a 0.22-μm microporous membrane , and take the filtrate for analysis.To determine the uric acid-lowering biological activity and anti-inflammatory property of chemical components, the reference standards were first dissolved in DMSO, before diluting to the desired experimental concentration with a culture medium and using for pharmacokinetic experiments.The high-performance liquid chromatography  was used for fingerprinting analysis and content determination. A CAPCELL PAK C18  column was used at 30? with a flow rate of 0.9 mL/min. Methanol (A) and 0.2% phosphoric acid solution (B) were used as the mobile phase. A gradient elution  was used. The injection volume was 10 μL. Detection wavelengths in this experiment were 254 and 330 nm, respectively.The analytical conditions are optimized according to the characteristics of the target components. This includes detection wavelength, mobile phase, gradient elution conditions, and flow rate, and finally, chromatographic conditions are determined in terms of the performance of chromatographic behavior. Then the sample processing conditions were briefly examined. Different extraction solvents and different extraction methods (hot reflux and sonication) were explored separately based on the extraction rates. Under the conditions, the quantitative methods for seven potential components were validated in terms of sensitivity, linearity, stability, precision, reproducibility, and recovery in accordance with the guidelines for the validation of analytical methods of Chinese Pharmacopoeia [Chinese Pharmacopoeia Commission (the fourth part) ]. The de1 and the Swiss Target Prediction database.2 The species was selected as ‘‘Homo sapiens,’’ and all the component targets obtained from the two databases were combined and duplicates were removed to obtain the component targets. The keywords ‘‘hyperuricaemia, gouty arthritis’’ were used in OMIM,3 Drugbank,4 GeneCards,5 and DisGeNET.6 The component targets and disease targets were imported into the Venny 2.1 online mapping tool platform for analysis, and the intersection targets were obtained as component-disease key targets. The component-disease key targets were imported into Cytoscape software to construct a “component-disease-target” network for chicory.Based on the results of fingerprinting and content determination, seven target components were imported into the Herb Herbology database7 and the species ‘‘Homo sapiens’’ was selected for searching to obtain the target interaction network. The target interaction files were imported into Cytoscape 3.7.2 software for visualization and analysis. The key targets obtained above were entered into the DAVID 6.8 database,8 and the species was limited to “Homo sapiens.” Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed at P ≤ 0.05 and FDR ≤ 0.01 to screen for key pathways associated with the disease.The obtained component-disease key targets were imported into the STRING 11.5 database,On the Autodock Tools 1.5.6 and PyMOL 2.4.0 software platforms, key proteins were selected for docking simulation to extract each component. The higher gold fraction indicates that the compound has a larger binding surface to the protein crystal and is more active.2 incubator at 37°C and were used for experiments until the cell growth density was approximately 80%.For cell culture, RAW 264.7 cells and L02 cells were independently cultured in DMEM medium supplemented with 10% FBS, 100 U/mL penicillin, and 100 μg/mL streptomycin. Cells were cultured in a 5% COThe cell viability was analyzed using the Cell Counting Kit-8  assay. Cells were inoculated into 96-well plates at 100 μL per well, and cells were incubated for 24 h in an incubator with different concentrations of compounds (25-200 μmol/L), and the supernatant was discarded. The absorbance (OD) of each group was measured at 450 nm with an enzyme marker, and the cell viability (CV) was calculated.The ELISA test was performed for the detection of TNF-α in RAW264.7 cell supernatant. The RAW264.7 macrophages were inoculated in a 24-well plate, and different concentrations of compounds  were added for 5 h. Monosodium urate (MSU) was added at a final concentration of 150 μg/mL for 24 h. The cell supernatant was collected at 4? and centrifuged at 3,000 rpm for 10 min. The cell supernatant was collected, and the level of TNF-α in the cell supernatant was measured according to the instructions of the mouse TNF-α ELISA kit.The HPLC was performed for the detection of UA in the L02 cell supernatant. The L02 cells were inoculated in a 24-well plate. In the model group was added 500 μL of DMEM basal medium containing 100 μM xanthine, and in the drug administration group was added solutions of different concentrations of each compound based on the model group. In the normal group, DMEM solution without xanthine and drug solution was added, and three replicate wells were set up in each group and incubated for 24 h. HPLC was used to determine the UA of the cell supernatant and further optimized according to a previous study  was used for chemometric analysis.All data are expressed as the mean ± SD of at least three independent experiments. The statistical analysis was performed by GraphPad PRISM software (version 8.01). Statistical differences were analyzed by ANOVA followed by Dunnett’s Traditional quality control indicators are selected for the major components of the chemical composition, and this simple strategy ignores the manifestation of TCM efficacy. The corresponding limits are set by single chemical composition. Although more directly reflecting the composition and content, this method ignores the characteristics of TCM under the guidance of TCM theory. To address this problem, a screening strategy for biochemical activity-based Q markers of chicory was established in this study . First, In the HPLC method, the combination of methanol and acetonitrile as the organic phase and water, 0.1% formic acid, and 0.2% phosphoric acid as the aqueous phase were optimized, respectively. Based on the peak shape and separation, and with reference to literature reports , it was The traditional extraction methods of herbs include ultrasound and heating reflux methods with methanol or ethanol. In this study, ultrasonication and water bath reflux were compared separately, and methanol and 70% methanol were used as extraction solvents, respectively. After fixing the sample sampling amount, time, and solvent, the chromatograms and peak areas were compared by different methods. It was found that hot reflux extraction in a water bath was superior to the ultrasonic method. Moreover, 70% methanol extraction was superior to methanol extraction at the same sampling volume . ThroughThe results of methodological validation showed that the correlation coefficients of chlorogenic acid, esculetin, lactucin, cichoric acid, isochlorogenic acid A, 11β,13-dihydrolactucopicrin, and quercetin were ≥ 0.995, and the linearity was good in the linear range. The precision, reproducibility, stability, and recovery were as presented in All the chicory samples were first analyzed by fingerprinting according to the established method. As shown in Cichorium glandulosum Boiss. et Huet, there were slight differences between above-ground and root components. As shown in Cichorium glandulosum Boiss. et Huet is mainly cultivated and usually 1-year-old, and its resources are concentrated in Xinjiang. Unlike Cichorium glandulosum Boiss. et Huet, Cichorium intybus L. is a perennial plant. Some samples of chicory are obtained from the medicinal herb market as cultivated products, and some other samples are from the main producing area of Xinjiang. In China, Cichorium intybus L. cultivation is mainly distributed throughout the country and is used medicinally as well as in food. For the above-ground parts, Cichorium intybus L. collected in the main production areas is mainly divided into stems and roots, with fewer leaves. The fingerprint profiles of the above two samples differed significantly, which was found to be different from other herbs in this study , orthogonal partial least squares (OPLS), cluster analysis (CA), etc.In is study . The chrfference , with th similar . In summCichorium glandulosum Boiss. et Huet root as a common pattern revealed , while the similarity in lots JJ17-JJ32 was more consistent. In Cichorium intybus L. root, similarity behavior showed two phenomena: JJG1-JJG25 similarity was close and JJG26-JJG39 similarity was more consistent, while these two parts of roots were market samples and production area samples.Similarity evaluation was able to show the initial identification of differences between medicinal parts. The chromatographic results were imported into ChemPattern software, and the similarity calculation function was used for fingerprint similarity analysis. In order to carry out the differences between different species and different parts horizontally, the above-ground part of JJ was used as a reference. The distance value is the overall similarity of the samples after unification. The fingerprint chromatographic similarity results visually showed that the comparative analysis of all samples with revealed  that larCichorium intybus L., and the root and above-ground parts of Cichorium glandulosum Boiss. et Huet were all indistinguishable in PCA, and the scatter spatial distances were interleaved. Meanwhile, we tried to analyze with OPLS  roots, stems, leaves, and flowers . In the Network pharmacology is often able to correlate and predict potential dominant ingredient targets of action and diseases. In this study, seven target components of chicory  were searched in the Herb Herbology database and Swiss Target Prediction database, and 377 component targets were obtained after integrating and removing duplicates. The keywords “hyperuricemia, gout arthritis” were searched in OMIM, Drugbank, GeneCards, and DisGeNET databases, and 867 targets for hyperuricemia and 1,638 targets for gout were obtained after integrating and removing duplicates. The 377 component targets, 867 hyperuricemia disease targets, and 1,638 gout disease targets were imported into the Venny 2.1 online tool for Venn diagramming, and 51 component-disease common targets were obtained. As shown in The 51 key targets were imported into the STRING 11.5 database, and 51 nodes with 535 edges were obtained. The proteins with strong interactions were VEGF-KDR, TP53-BCL2L1, RELA-NFKBIA, etc. Among them, SLC37A4 had no interaction with other proteins . TopologFifty-one key targets were imported into the DAVID 6.8 database for GO and KEGG enrichment analysis. The GO analysis yielded a total of 90 GO entries, including 77 entries for biological processes, mainly involving regulation of RNA polymerase II promoter transcription, regulation of apoptosis and proliferation process, response to inflammation and drugs, immune response, protein phosphorylation, lipopolysaccharide-mediated signaling pathways, etc. The cellular component had three entries. The molecular function had 10 entries, mainly including protein binding, protein homodimer and heterodimer activity, enzyme binding, transcription factor binding, phosphatase binding, protein tyrosine kinase activity, etc. The top 10 entries of CC composition were selected with BP and MF entries .The KEGG enrichment analysis yielded a total of 82 entries, mainly enriched in pathways such as inflammatory response, signaling, and human diseases, involving TNF signaling pathway, NF-κB signaling pathway, NOD-like signaling pathway, TOLL-like signaling pathway, etc. The top 20 results were visualized and analyzed .Molecular docking studies were performed to validate the biological activity of the seven key components. The 3D structures of IL-6 (PDB ID: 1ALU), TNF (PDB ID: 2E7A), TP53 (PDB ID: 6MXX), XDH (PDB ID: 1N5X), and ABCG2 (PDB ID: 6HBU) were downloaded from the PDB database, de-watered, de-liganded, and hydrogenated, and then docked with the Pubchem database obtained from the 3D structures of the seven indicator components. Each protein was processed with the component with the highest binding potential by visualization (the lowest binding energy ≤ −5.0 kJ/mol), and the results showed that quercetin and lactucin had a good binding activity with all five proteins .To further validate and identify the active ingredients in chicory based on chemical characterization and network pharmacological results, two cellular models were used in this study. Briefly, MSU-stimulated RAW264.7 cells to produce an inflammatory response as a brief model of gout to simulate the physiological pathogenesis of the disease. This was used as a gout model to evaluate the potential active compounds. On the other hand, L02 cells, using the liver as the target organ, were used to evaluate compounds with potential uric acid-lowering ability by administering xanthines to simulate the physiological process of uric acid production. Thus, the above two cellular models were applied for further screening and identification of chemically superior components in chicory.p<0.01, 5 μM, 10 μM). Allopurinol is a commonly used XOD inhibitor in clinical practice. It can inhibit the production of uric acid, thus achieving the therapeutic purpose of lowering uric acid. Here, we found that the superior components in chicory, represented by chicoric acid and lactucin, have good activity. Their mechanism of action may be related to the inhibition of XOD activity. This situation is consistent with the results of the network pharmacology evaluation. In this way, better XOD inhibitors contribute to discovering new drugs for the treatment of HUA and gout.In humans, the pathogenesis of HUA and gout is dominated by the whole process from high uric acid to urate deposition to inflammatory outbreak. Thus, after confirming the appropriate concentration by CCK-8, chlorogenic acid, esculetin, lactucin, cichoric acid, isochlorogenic acid A, 11β,13-dihydrolactucopicrin, and quercetin in chicory were first validated for their potential inhibitory effect on uric acid production. As shown in Tumor necrosis factor-α, a representative of inflammatory factors, has been reported in recent years to be closely associated with MSU-stimulated inflammation in gout disease. In this study, we found that TNF-α was associated with multiple pathways based on network pharmacology analysis. Therefore, we tried to stimulate the inflammatory response of RAW264.7 cells by MSU. Fortunately, we screened the dominant components of chicory and found that some of them have the potential ability to reduce the release of TNF-α by MSU-induced inflammation in macrophages . AlthougBased on the scientific connotation of Q markers in TCM, the quality control indexes in chicory should meet the requirements of Q markers in terms of potency, specificity, quality transferability, measurability, and compatibility . The resFor quality standards, when setting standard limits for chicory content, chicoric acid and lactucin are recommended as quality control indicators according to the results of this study. According to the current research on the chemical composition of chicory, chicoric acid and lactucin are the specific components of the genus Chicory in the family Asteraceae, so they can reflect the specificity and different characteristics of chicory. At present, chicoric acid has a legal reference, and the standard substance is available, and its stability and purity can meet the requirements of standard analysis.For the limit indicators of different medicinal parts, in the root study, it was found that the chicory acid content in the roots originating from the main production area and the roots in the market differed greatly. In this study, the external standard method was used for the calculation of the content of all sample components. The content of the markers in the samples was calculated from the concentration of the corresponding reference standards. The content in roots from the main production area was able to be applied for limit setting, but the content in roots from the source market was generally low (0.02–0.05 mg/g), making it difficult to set consistent limits with the above-ground part. In contrast, lactucin was more stable in the above-ground part with the content in the roots. Thus, it is recommended to choose lactucin as the limit indicator for roots, and it can meet the mass transferability requirements. According to previous research , 34, chiFurthermore, at the cellular level, pharmacodynamic experiments in this study showed that chicory acid and lactucin not only reduce uric acid production but also have the potential to inhibit the release of TNF-α inflammatory factors. They have therapeutic potential to control high uric acid and gout. Meanwhile, it is possible to correspond to the formula-evidence correspondence of chicory compounding, so that the quality research can return to the theory of TCM and reflect the expression of chicory effectiveness and the objective essence of its substances for diseases.Cichorium glandulosum Boiss.et Huet and Cichorium intybus L., superiority components were validated to be potential indicators of chemical quantitative properties for the roots and above-ground parts. Based on the content, bioinformatic analysis, and biological activity, chicory acid and lactucin were the main components that could reflect the anti-inflammatory and uric acid-lowering potential of chicory. The strategy contributes to the quality control, standard improvement, and rational clinical use of chicory.In this study, a strategy to reveal bioactive-chemical quality markers was established. Through the comparative screening of fingerprint profiles of a large amount of The original contributions presented in this study are included in the article/BZ conceived and designed the study. YL and SJ performed the experiments and analyzed the data. ZL, YW, HW, HJ, and SM supported the planning and interpretation of experiments. YL drafted the manuscript. All authors revised and approved the final manuscript.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Pseudomonas, Sphingomonas and Burkholderia had higher abundances in the fields with less incidence. This study will provide a theoretical basis for microbial control of Cotton Verticillium wilt.Soil microorganisms could affect the growth of plants and play an important role in indicating the change of soil environment. Cotton Verticillium wilt is a serious soil borne disease. This study aimed to analyze the community characteristics of soil microorganisms in cotton fields with different incidences of Verticillium wilt, so as to provide theoretical guidance for the prevention and control of soil borne diseases of cotton. Through the analysis of soil microbial communities in six fields, the results showed that there was no difference in fungal and bacterial alpha-diversity index before cotton planting, while there were differences in rhizosphere of diseased plants. For fungal beta diversity indexes, there were significant differences in these six fields. There was no significant difference for bacterial beta diversity indexes before cotton planting, while there was a certain difference in the rhizosphere of diseased cotton plants. The composition of fungi and bacteria in different fields was roughly the same at the genus level, but the abundances of the same genus varied greatly between different fields. Before cotton planting, there were 61 fungi (genera) and 126 bacteria (genera) with different abundances in the six fields.  Beneficial soil microbes contribute to promoting plant growth,10 and pathogen resistance.11 In return, plants secrete fixed carbon and nitrogen to the rhizosphere, thereby supporting the microbial community.12 Some studies have shown that regulating the ecological balance of soil microorganisms is helpful to inhibit crop diseases.Plant microbiome is composed of a variety of microorganisms, which is called the second genome of plants. Plant root microorganisms are diverse, with a number of about tens of thousands of species.13 Many studies have shown that soil microbial structure could affect the occurrence of plant diseases. In susceptible and resistant cotton varieties, the relative abundance of many rhizosphere and endophytic microorganisms was different, and the relative abundance of beneficial microbes were higher in resistant cultivars, such as Bacillales, Pseudomonadales, Rhizobiales, and Trichoderma;14 the bacterial alpha-diversity in rhizosphere of diseased cotton plants was lower than that in healthy cotton plants; many rhizospheric microorganism differed in their relative abundance between diseased and healthy cotton plants;7 the number and abundance of operational taxonomic units (OTUs) in the soil of naturally seriously diseased cotton fields were higher than those in lightly diseased or disease-free fields, while the diversity of fungi was low in Xinjiang.15 The effects of plants on soil microbiome varied greatly, and plant growth and resistance to pathogens were related to complex soil microbial communities.13 At present, several studies have investigated on soil fungal and bacterial microbial communities with different incidences of Cotton Verticillium wilt.15 The difference of soil microorganisms before planting is not clear. Therefore, the purpose of this study was mainly to report the differences of soil microorganisms before planting and rhizosphere microorganisms of diseased plants in fields with different disease degrees.The microbial community structure is one of the indicators of soil condition. Interactions between plants and soil microbes are important for plant growth and resistance.2.2.1Six monoculture cotton fields (100 m apart) with the incidence of Verticillium wilt were used for experiment in 2018 in Xiajin(XJ), Linqing(LQ), Juye(JY), Lijin(LJ), Jinxiang(JX), Huimin(HM), Shandong Province, China. The cotton cultivar was Lumian 1141.2.214 level 0: no symptoms, level 1: incidence area ≤ 33%, level 2: 33% < incidence area ≤ 66%, level 3: 66% < incidence area ≤ 99%, level 4: incidence area is 100%, and the disease index (DI) of each plot was calculated, DI = (0·n0 + 1·n1 + 2·n2 + 3·n3 + 4·n4)/(4·n) × 100%. n0-n4 is the number of plants with corresponding disease grade (0–4), and n is the total number of plants investigated.At the peak of Cotton Verticillium wilt, August 26, 2018, Cotton Verticillium wilt was investigated in 6 monoculture cotton fields, HM, LJ, JY, JX, XJ and LQ, respectively. The diagonal 5-point survey method was adopted, with an interval of 10 m between the two points, and 50 plants were continuously investigated at each point. The disease index was investigated according to the 5-level classification standard, and the classification standard was referred to the method Wei et al.,2.32 to mix the soil. A total of 18 samples were taken as the control . In the period of Cotton Verticillium wilt, the rhizosphere soil of diseased cotton plants was taken. 2 mm rhizosphere soil attached to the root system of cotton plants with Verticillium wilt was collected by shaking off method.7 Three samples were taken from each field with an interval of 30 m. Five plants were taken from each sampling point, and roots were first shaken to remove loosely adhering soil particles, then the roots were cut into pieces of 2-cm length. Rhizosphere samples were collected in aliquots of 30-g roots in 1:50 TE buffer by shaking, filtering, and centrifuging. A total of 18 samples were taken .About April 20, 2018, before cotton planting, soil samples 10–20 cm away from the soil surface were taken from the six fields where Cotton Verticillium wilt was investigated above. Three samples were taken from each filed at an interval of 30 m. Each sample was taken five times in 1 m2.4TM SPIN Kit for Soil extraction specification, and agarose gel electrophoresis was used to detect DNA quality. DNA concentration detector One drop 2000  was used to determine the concentration of the DNA. The bacterial 16S rRNA sequencing fragment was V3-V4, and the regional universal primer was 338 F:5’-ACTCCTACGGGAGGCAGCAG-3’, 806 R: 5’-GGACTACHVGGGTWTCTAAT-3’,16 the fungal ITS sequencing fragment was ITS2, and the regional universal primer was fITS7: 5’-GTGARTCATCGAATCTTTG-3’, ITS4: 5’-TCCTCCGCTTATTGATATGC-3’,17 PCR reaction system and program were performed formed following a previous study.18 PCR products were purifed with Gel Extraction Kit  and pooled in equimolar concentrations. Paired-end sequencing (PE300) of bacterial and fungal amplicons were carried out on an Illumina MiSeq platform at Hangzhou Lianchuan Biotechnology Co., Ltd .The soil samples were collected to extract DNA according to the FastDNA2.5p ≤ .05). For species annotation, the feature-classifier was used for sequence alignment. Alpha-diversity and beta diversity were normalized by flattening, and species annotation was normalized by relative abundance. Alpha-diversity and beta diversity were analyzed with QIIME 2.19 Alpha-diversity analyses included observed species (Sobs), Shannon, Simpson and Chao1. Beta diversity was calculated by weighted UniFrac distance and analyzed by Principal coordinate analyses (PCoA).20 The diferences of disease index and alpha-diversity index were determined by one-way analysis of variance (ANOVA), and Tukey’s HSD method was used for test.Fqtrim and Vsearch (v2.3.4) were used to filter the low-quality sequences and the chimera. After noise reduction with DADA2, the feature table and feature sequence are obtained. According to 97% sequence similarity, the sequences obtained above were clustered into OTUs. After OTU clustering, the OTU abundance (number of sequences) in each sample was counted, and the OTU whose abundance value was lower than 0.001% of the total sequencing amount of all samples was removed to ensure the accuracy of the analysis data. Classify the OTU sequences in the fungal ITS database SILVA and unite, and then difference analysis were conducted based on the OTU abundance , LJ (568) was the least; the common bacterial OTUs in the six fields were 3768, the number of OTUs in XJ (7653) was the largest, and the least was in LJ (6634). In the period of Cotton Verticillium wilt, the common fungal OTUs in the rhizosphere of diseased plants in six fields were 119, the number of OTUs in XJ (931) was the largest, and the least was in LJ (468); the common bacterial OTUs in the rhizosphere of diseased plants in six fields was 2203, the number of OTUs in LQ (7440) was the largest, and the least was in LJ (5248) .Figure 3.3Before cotton planting in these six fields, the fungal alpha-diversity indexes Sobs, Shannon and Simpson in LQ and JY fields were relatively high, but there was no significant difference among the six fields. The alpha-diversity index Sobs of rhizosphere fungi of diseased plants in XJ and JX fields were significantly higher than that in LJ field, and there was no significant difference in Shannon and Simpson indexes . Before 3.4In contrast to the alpha-diversity indexes, PCoA analysis based on OTU level showed that beta diversity indexes of fungi showed differences in these six different fields, while the difference in beta diversity indexes of bacteria was not very obvious; Adonis analysis showed that there were significant differences among different samples in the same field. The beta diversity index also showed some differences in rhizosphere bacteria of diseased plants in different fields .Figure 3.5Alternaria, Hypocreales, Typhula, Plectosphaerella, Tetraclaudium, Davidella and Plectosporium to Sordariales, Cercophora, Myrothecium, Heydenia, Agaricomycetes, Chaetomiaceae and Sordariomycetes, the other 13 genera remained unchanged , including Gibellulopsis, Podospora, Preussia, Plectosporium, Haematonectria, Plectosphaerella, Stachybotrys, Acremonium, Wardomyces, Heydenia, Fusarium, etc. Fusarium fungi are pathogens of many diseases. During the onset of Cotton Verticillium wilt, there were 65 fungi with significant differences in abundances in rhizosphere(p < .05), including Podospora, Haematonectria, Cercophora, Pseudonymnoascus, Fusarium, Metacordyceps, Tetraclaudium, Acromonium, Dokmaia, Emericellopsis, etc , mainly including Sphingomonas, Pseudomonas, Rhizorhapis, Geminicoccus, Beggiatoa, Caldilinea, Desulfoglaeba, Arthrobacter, Albidovulum, Aciditerrimonas, Dehalococcoides, etc (Pseudomonas from high to low was XJ (3.32), LQ (3.19), JX (2.87), JY (2.62), LJ (1.27) and HM (1.10) , XJ (0.02), JX (0.01), LQ (0.01), HM (0.01), and JY (0.00) from high to low (p < .05), such as Pontibacter, Gaiella, Gp10, Burkholderia, Sphaerobacter, Arthrobacter, Solidubrobacter, Blastococcus, Aciditerrimonas, Desulfoglaeba, etc . The abundances of Burkholderia were JX (1.54), LQ (1.44), XJ (1.35), HM (1.00), JY (0.92) and LJ (0.15) from high to low, and the abundance of the highest field was 10.27 times that of the lowest field  ; the abuh to low . During st field .Table 8.4.13 and soil microbes were thought to be key drivers of plant-soil feedbacks through affecting plant growth or acting as antagonists of plant pathogens.22 Microorganisms are an indicator of soil health, especially the inhibition of diseases.23–25 High microbial diversity can improve the stability of the community. Higher diversity in soil bacteria is often associated with greater resistance to pathogens.27 In these six fields, there was no difference in the alpha-diversity index of fungi and bacteria before cotton planting, but there were differences in rhizosphere of Cotton Verticillium wilt diseased plants in some fields, so they showed different degrees of incidence. Previous studies have shown that fungal and bacterial communities in different soils had some difference in alpha-diversity, relative abundance, structure and taxonomic composition, but microbial groups showed similarity in the same habitat, despite different sampling sites; 3128 the higher the diversity of soil bacteria, the healthier the cotton plants were,3229 but the opposite was true in tomato; 3330 the abundance of almost all bacterial OTUs with different abundance in healthy cotton plants was higher than that in diseased cotton, while the abundance of fungal OTUs was the opposite.7 In this study, the fungal alpha-diversity index before cotton planting was high in 2 of the 3 fields with severe disease, and the bacterial alpha-diversity index before cotton planting was relatively high in 2 of the 3 fields with mild disease, which was consistent with Wei et al.7 Therefore, soil microbial diversity is very important for plant growth.The interaction between plants and soil microorganisms is of great significance for plant growth and resistance,Bacilli, Gemmatimonadetes, Pseudomonas, Stenotrophomonas,31Trichoderma,32 etc. The diversity and community members of Gammaproteobacterial have been identified as potential health indicators;31Sphingomonas was reported to increase Chinese medicinal plant biomass;33Actinoplanes could promote cucumber growth;34Caulobacter produced phytohor mones in lavender.35 In this study, before cotton planting, the abundances of Pseudomonas and Sphingomonas in the soil microbial community in the JX field with light disease were significantly higher than that in other fields, which was consistent with that of Köberl et al.31 and Ali et al.,33 while the abundance of Rhizorhapis in the LJ field with heavy disease was significantly higher than that in other fields, which could promote plant nitrogen fixation and may have little correlation with Verticillium wilt resistance. For fungi, before planting, the abundance of Chaetomium in XJ field was the highest, and the incidence of Verticillium wilt in XJ field was relatively mild. Chaetomium globosum can inhibit a variety of pathogens, inhibit the growth of V. dahliae, promote the growth of cotton plants and induce cotton to produce defense response to resist the infection of V. dahliae.36–38 This study suggested that the application of Sphingomonas during cotton planting maybe reduce the occurrence of Verticillium wilt. The next step is to isolate the Sphingomonas sp. strains and detect their resistance to Cotton Verticillium wilt.In the process of plant growth, some soil microorganisms promote plant growth and help plants resist various biological and abiotic stresses, that is, beneficial microorganisms. At present, the recognized beneficial microorganisms included 5.Pseudomonas, Sphingomonas and Burkholderia had higher abundances in the fields with less incidence. In conclusion, we speculate that the lower the fungal diversity index in soil, the higher the bacterial diversity index, and the lighter the incidence of Cotton Verticillium wilt. In addition, Pseudomonas, Sphingomonas and Burkholderia may improve the resistance of cotton to Verticillium wilt.Through the analysis of soil microbial communities in six fields with different incidences of Verticillium wilt, there was no difference in fungal and bacterial alpha-diversity indexes before cotton planting, while there were differences in rhizosphere of diseased plants. For fungal beta diversity indexes, there were significant differences in these six fields. The composition of fungi and bacteria in different fields was roughly the same at the genus level, but the abundances of the same genus varied greatly between different fields.


GLA gene on the X chromosome. The enzyme deficiency leads to a continuous deposition of neutral glycosphingolipids (globotriaosylceramide) in the lysosomes of numerous tissues and organs, including endothelial cells, smooth muscle cells, corneal epithelial cells, renal glomeruli and tubules, cardiac muscle and ganglion cells of the nervous system. This condition leads to progressive organ failure and premature death. The increasing understanding of FD, and LSD in general, has led in recent years to the introduction of enzyme replacement therapy (ERT), which aims to slow, if not halt, the progression of the metabolic disorder. In this review, we provide an overview of the main features of FD, focusing on its molecular mechanism and the role of biomarkers.Fabry disease (FD) (OMIM #301500) is a rare genetic lysosomal storage disorder (LSD). LSDs are characterized by inappropriate lipid accumulation in lysosomes due to specific enzyme deficiencies. In FD, the defective enzyme is α-galactosidase A , which is due to a mutation in the  GLA gene present in the short arm of chromosome X (Xq22.1) [Fabry disease (FD) OMIM #301500) is a rare disease with a prevalence of 1/80,000 live births 500 is a ,3,4,5. TXq22.1) . Progres2.1 . Pro(Xq22.1)  and 20 y(Xq22.1) . The dia(Xq22.1) ,14,15,16The clinical manifestations of Fabry disease are slowly progressive, with variable onset, severity, age and course. The disease affects both females and males, but because it is an X-linked disease, males are more severely affected. There are two forms of FD in male patients: one with early onset and one with late onset. In the first case, a typical cluster of symptoms manifest in early childhood or early adolescence. In these patients, little or no enzymatic activity of α-galactosidase A is observed. Early symptoms include acute and chronic neuropathic pain, hypohidrosis, cutaneous angiokeratomas, gastrointestinal symptoms, lenticular and corneal opacities, microalbuminuria or proteinuria. With advancing age, these symptoms progress to renal failure, cardiomyopathy, cerebrovascular involvement and early death . AcroparPenetrance in heterozygous females is 70% . In contGLA gene located on the long arm of the X chromosome (Xq22.1) [FD is a lysosomal enzyme disease caused by a functional defect of the enzyme α-Gal A. The defect is due to mutations in the (Xq22.1) . The cod(Xq22.1) ,33,34. T(Xq22.1) ,36,37.GLA mutations on the same allele, such as p.Glu66Gln and p.Arg112Cys or p.Leu89Arg and a 1 bp deletion in codon 303 [GLA mutation and the non-disease-associated variant p.Asp313Tyr. Mutations of the GLA gene include point mutations , “short-length” rearrangements (with about 60 nucleotides: 26.8%) and large rearrangements (involving one or more exons: 1.9%). The nonsense, missense and premature stop codons that lead to the absence or reduced activity of the enzyme α-Gal A are usually associated with the classic early-onset FD, characterized in males by the onset of symptoms in childhood, by multiorgan involvement and by the rapid progression of the disease, with clinical manifestations that often involve the heart, kidneys and central nervous system [http://fabry-database.org/, accessed on 11 October 2022). The study of the mutations has shown that there are no ‘hot spots’ of mutations, but that a high percentage of point mutations in exon 5 (3.15/10 bp) have been observed. This can be explained by the large size of the exonic portion (162 bp). Point mutations are also detectable in exon 6 (2.83/10 bp) and in exon 3 (2.53/10 bp) [GLA missense mutations into three groups [Therefore, it is possible to observe different phenotypes even in one and the same family. In rare cases, male patients have been known to carry two different odon 303 ,39. In cs system ,41,42. Es system . Therefos system . Many of3/10 bp) . The enz3/10 bp) . It is a3/10 bp)  and exhi3/10 bp)   . In anotThe cardiovascular involvement of FD is extremely important as it usually manifests as left ventricular hypertrophy (LVH), hypertrophic cardiomyopathy (HCM), myocardial fibrosis, heart failure and arrhythmias, which shorten the patient’s life span and lead to death if left untreated ,40,41,50However, studies have shown that in addition to Gb3 accumulation, dysfunction also contributes to the development of Fabry cardiomyopathy. The results of several recent studies suggest that glycosphingolipids may play a role in the development of Fabry disease. These lipids are known to impair endocytosis and autophagy, triggering apoptosis  and inteGLA gene only from their mother (as it is located on the X chromosome) and are therefore always hemizygous for the Fabry gene. Consequently, males only pass on the mutated gene to their own daughters. Females are usually heterozygous for the mutations in GLA gene. While it is possible for a woman to have the mutated gene on both X chromosomes, the homozygous form is extremely rare [GLA gene has a 50% chance of passing the disease to her offspring, regardless of sex. Due to lyonization (the random inactivation of one of the two X chromosomes), the activity of α-Gal A in affected women can range from low to normal [GLA gene [In the past, FD was classified as an X-linked recessive hereditary disease. However, women who carry the mutated gene may also show signs and symptoms of the disease. For this reason, FD is now described as an “X-linked transmission” disease, and the use of the term “carrier” for women carrying a disease-causing mutation may no longer be appropriate . In factely rare . A womano normal . Affecteo normal , who havGLA gene ,65. OverGLA gene , carrierGLA gene  and prenGLA gene .GLA gene is required (as in the diagnosis of women). Often the diagnosis of FD is made late, when the damage caused by the disease is already irreversible . NewbornGLA gene are capable of abolishing or significantly reducing α-galactosidase A activity. Today, targeted sequencing of the seven coding exons (including the promoter and adjacent and flanking intronic regions) of the GLA gene is the gold standard for molecular diagnosis of FD [GLA gene for screening high-risk patients, including people with HCM. This has resulted in the identification of many GLA variants of unknown significance (VUS) [GLA gene is rich in Alu repeat elements (approximately 1 Alu repeat per kb) [Currently, gene sequencing is the method of choice for screening all female patients and for confirming the diagnosis in males with low α-galactosidase A activity for FD. Similar to other inherited diseases, genetic testing is mandatory in Fabry disease ,90,91,92is of FD ,93. Advace (VUS) ,95,96. Hce (VUS) ,98. It s per kb)  and that per kb) , chemica per kb) . Once th per kb) .The major diagnostic challenge of cardiovascular involvement in FD is that there is no single pathological phenotype. The significant overlap with other cardiovascular pathologies in terms of structural and functional alterations such as HCM and other storage pathologies involving the myocardium represents a complex of correct and early diagnosis. Some of the biomarkers normally used in clinical practice may be useful in the cardiovascular diagnosis process of Fabry patients. The dosage of cardiac troponins seems to be related to the presence of myocardial areas affected by necrosis and fibrosis . FurtherThe deacylated derivative of Gb3, globotriaosylsphingosine (Lyso-Gb3), was identified as a hallmark of FD . Nowak eIn addition, recent studies consider the Lyso-Gb3 level as a possible diagnostic marker. Determination of plasma lyso-Gb3 levels should be considered for assessing disease severity in FD patients or in the diagnostic algorithm for patients with genetic GLA variants of unknown significance . TherefoThus, in patients with FD, there is reduced or undetectable α-Gal-A enzyme activity and progressive accumulation of glycosphingolipids, especially globotriaosylceramide (Gb3) and its deacylated form globotriaosylsphingosine (Lyso-Gb3), and gradual damage to cells throughout the body, including vascular endothelial cells, smooth muscle cells and cardiomyocytes .For this reason, early diagnosis of FD represents a promising strategy to reduce organ damage, morbidity and premature mortality, which represents a clinical challenge, especially in patients with the nonclassic phenotype or who are asymptomatic. In this case, it is very important to have noninvasive biomarkers that allow for early detection and prediction of disease progression.GLA gene [Lyso-Gb3 levels have been observed to be elevated in patients with genetic mutations that determine the classic phenotype, while they tend to be less elevated or normal in the “late-onset” variants or in benign mutations . FurtherGLA gene . The potGLA gene .Recently, several alternatives have been proposed for monitoring FD patients. Several recent studies recommend miRNAs as a new class of biomarkers in FD, in particular mitochondrial miRNAs (mitomiRs). J. Gambardella et al. have demonstrated significant dysregulation of mitomiRs in FD patients. This dysregulation is probably an intimate mechanism of FD, independent of GB3. The concentration of mitomiRs could therefore be useful to monitor the progression of FD and highlight potential new therapeutic targets .Recent advances in molecular biology and genetic engineering have enabled the development of therapies for FD . Enzyme GLA gene and structural analysis of the enzyme, which allowed for the production of recombinant human α-galactosidase A  cannot be reliably diagnosed by enzyme testing, so genetic analysis is required. In some individuals, α-Gal A activity is below the normal reference range due to the presence of a pseudodeficiency allele. This condition can be confirmed by mutational analysis. Recent advances in our understanding of the complexity of cardiac FD have significantly improved diagnostic and therapeutic approaches. Although ERT has significantly changed the natural history of FD, cardiac involvement remains a key prognostic determinant. Cardiac manifestations benefit from early ERT, but clinical effects are limited in more advanced cases. New studies provide important insights into the limitations of enzyme replacement therapy (ERT) as a treatment for Fabry disease. This highlights the need for further research into the efficacy of ERT in the treatment of Fabry disease to ensure that patients receive the best possible care. A deeper understanding of secondary pathogenic pathways, particularly myocardial inflammation, may influence future therapeutic strategies. Although new disease-specific therapies appear promising, diagnostic delay and timely initiation of current treatments remain critical concerns for many patients with FD. Therefore, collaboration between FD specialists and cardiologists remains essential to identify patients before the onset of cardiac involvement, to enable them to gain maximum benefit from current and future therapeutic approaches.FD is a rare X-linked disorder that can occur before adolescence or later in life. In this disease, the α-Gal A enzyme may have decreased or absent activity. Affected males are always hemizygous and can be diagnosed by testing α-Gal A activity in plasma and/or leukocytes. The absence or reduced activity of the enzyme must be confirmed by the presence of mutations in the