Characterization and Application of Nanomaterials (2022) Volume 5 Issue 2 doi:10.24294/can.v5i2.1768 99 Original Research Article Development, optimization, and evaluation of Cisplatin-loaded PLGA nanoparticles Purushothaman Bhuvaneshwaran, Ramaiyan Velmurugan* Faculty of Pharmaceutical Sciences, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu 602105, India. E-mail: ramaiyan.dr@gmail.com ABSTRACT Nanoparticle drug delivery systems are engineered technologies that use nanoparticles for the targeted delivery and controlled release of therapeutic agents. Cisplatin-loaded nanoparticle formulations were optimized utilizing response surface methods and the central composite rotating design model. This study employed a central composite rotatable design with a three-factored factorial design with three tiers. Three independent variables namely drug polymer ratio, aqueous organic phase ration, and stabilizer concentration were used to examine the particle size, entrapment efficiency, and drug loading of cisplatin PLGA nanoparticles as responses. The results revealed that this response surface approach might be able to be used to find the best formulation for the cisplatin PLGA nanoparticles. A polymer ratio of 1:8.27, organic phase ratio of 1:6, and stabilizer concentration of 0.15 were found to be optimum for cisplatin PLGA nanoparticles. Nanoparticles made under the optimal conditions found yielded a 112 nm particle size and a 95.4 percent entrapment efficiency, as well as a drug loading of 9 percent. The cisplatin PLGA nanoparticles tailored for scanning electon micros- copy displayed a spherical form. A series of in vitro tests showed that the nanoparticle delivered cisplatin progressively over time. According to this work, the Response Surface Methodology (RSM) employing the central composite rotatable design may be successfully used to simulate cisplatin-PLGA nanoparticles. Keywords: Cisplatin; PLGA; Nanoparticles; Response Surface Methodology; Central Composite Rotatable Design ARTICLE INFO Received: 2 August 2022 Accepted: 28 September 2022 Available online: 12 October 2022 COPYRIGHT Copyright © 2022 by Purushothaman Bhu- vaneshwaran, et al. EnPress Publisher LLC. This work is li- censed under the Creative Commons Attrib- ution-NonCommercial 4.0 International Li- cense (CC BY-NC 4.0). https://creativecommons.org/licenses/by- nc/4.0/ 1. Introduction Tumors form when cells grow and divide improperly and uncon- trollably, which is the hallmark of the cancerous condition. New technol- ogies that can distinguish between healthy and cancerous cells fol- lowed by the targeting of the tumor with precision are attracting a lot of attention. With (Transdermal drug delivery) TDD, the medicine is en- capsulated inside of a nanocarrier like liposomes or liposomal particles to transport it directly to the patient. Both the effectiveness and toxicity of the medicine may be improved by TDD, and it can overcome a broad variety of difficulties, such as drug solubility and instability, and the ease of delivery to the target cells. Passive and active medication targeting methods are available[1]. On the basis of the enhanced permeability and retention (EPR) effect, which occurs in most solid tumors, passive tar- geting relies on molecules of specific sizes being preferentially taken up and retained by the tumors[2]. However, the reticuloendothelial system (RES) rapidly removes intravenously delivered nanocarriers containing anticancer medicines from circulation. These nanocarriers have a hydro- philic polymer, for example, polyethylene glycol (PEG) coating applied on top of them to increase their circulation duration and consequently 100 their targeting of tumor tissue[3]. The adsorption of plasma proteins (opsonin), which is critical for phag- ocytosis, would be prevented, resulting in a longer period for blood to circulate[4,5]. Nanoparticles (NPs) are regarded as drug delivery mechanism that allows for novel approaches to cancer therapy, and one of the most important methods used in nanomedicine. There are several NP delivery techniques, in which the medication is dissolved, encapsulated, and en- trapped inside the matrix[6]. The potential of NPs coupled with biodegradable polymers such as PLGA to actively and passively target tumors has drawn considerable interest[7]. The exterior diameters of NPs can range from a few nanometers to over 1,000 nanometers in length. Due to the EPR effect, NPs coated with PEG can accumulate in a variety of solid tumors, making them ideal carriers for hydrophobic medicines, which can provide effective tumor target- ing with the fewest adverse responses[8,9]. Nanopre- cipitation[10], solvent evaporation[11], dialysis[12], and salting out[13] have all been used for the formation of NPs. For the treatment of a wide range of solid ma- lignancies, including cervical cancer, cisplatin is a powerful anticancer drug[14]. In order to eliminate cancer cells, cisplatin causes cross-linking of DNA, which leads to cell death. Although cisplatin has a powerful anticancer impact, its severe side effects such as nephrological and neurological toxicities[15] limit its effectiveness. Chronic and acute kidney damage are common side effects of cisplatin, while neurotoxicity is cumula- tive-dose dependent. Cisplatin’s immediate inactiva- tion in the systemic circulation is one of the greatest concerns[16,17]. As a result, cis-dichlorodiam- mineplatinum (II) (cis-[PtCl2(NH3)2], cisplatin (CDDP’s) pharmacological effect must be protected and its systemic circulation must be prolonged. Drug must be delivered over an extended period of time in order to maximise its anticancer properties and min- imise its negative effects. For passive targeting following intravenous de- livery, researchers are trying to integrate cisplatin into poly (lactic-coglycolic acid) (PLGA) NPs. Re- sponse Surface Methodology - Central Composite Rotatable Design will be used to optimise the nanoparticles created. After optimizing cisplatin loading, we will conduct in vitro drug release and physicochemical evaluations of the PLGA NPs. 2. Materials and method Dichloromethane and sodium cholate were pro- vided by Madras pharmaceuticals, India. Cisplatin and PLGA was purchased from Sigma-Aldrich, India. All other chemicals were of analytical grade and used as such. 2.1 Preparation of cisplatin nanoparticles In order to create nanoparticles, a solvent evap- oration approach was used[18]. Sonication was used for 5 min to create an emulsion between an organic polymer solution (o) and an aqueous solution (w) containing the medication (5 mg of cisplatin in 2 mL distilled water). It was then mixed with 50 mL of wa- ter and sonicated to create the double-emulsion, which was then dissolved in an equal amount of wa- ter. A mild magnetic stirring at room temperature was used to evaporate the solvent. Recovered nanoparti- cles were rinsed with distilled water, dried, and kept in cold temperatures (2–8 °C) for future use. 2.2 Experimental design According to preliminary investigations, the variables including drug polymer ratio, water to or- ganic phase ratio, and stabilizer concentration during synthesis of the cisplatin nanoparticles, had the greatest impact on particle size, distribution, entrap- ment, and drug loading efficiency. These responses were considered for optimization as they accounts very much for rapid drug absorption and drug avail- ability. In order to study the impact of these three es- sential formulation factors on particle size, entrap- ment efficiency, and drug loading efficiency, a central composite rotatable design–response surface methodology (CCRD–RSM) was adopted[19]. Table 1 lists the design specifications. Preliminary tests and the possibility of making nanoparticles at extreme levels were used to select the experimental ranges for each component. For the drug polymer ratio (X1), the range was 1:1–1:7; for the aqueous-to-organic phase ratio (X2), it was 1:1–1:5, and for the stabilizer concentration (X3), it was 0.1–0.5%. There were a 101 total of 20 tests carried out. In these tests, every for- mulation was made in two separate batches. Since it may investigate many variables at multiple levels with a small number of tests, the central composite rotating design–response surface methodology (CCRD–RSM) is an excellent alternative strategy. After conducting exploratory trials, we came up with the factors in Table 1. Particle size distribution, drug loading, and entrapment efficiency were all exam- ined in Table 2 of the experiments. 94–104 nm, 75– 94 %, and 4–13% were the three dependent variables. Design-Expert® 7.0 software was used to perform response surface regression analysis on variables and parameters. Table 1. Independent variables and their corresponding levels of Nanoparticle preparation for CCRD Independent variables Levels −1 0 +1 Drug/polymer ratio 1:1 1:5 1:9 Aqueous to organic phase ratio 1:1 1:3.5 1:6 Stabilizer concentration 0.1 0.55 1.0 Table 2. Central composite design consisting of experiments for the study of three experimental factors in coded and actual levels with experimental results S. NO Trial Drug poly- mer ratio Aqueous organic phase ratio Conc. of stabilizer Particle size Entrapment efficiency Drug load- ing 1 1 1 1 0.1 100.335 84.076 7.231 2 2 9 1 0.1 99.3895 83.012 8.254 3 3 1 6 0.1 96.8928 81.123 9.014 4 4 9 6 0.1 96.3544 79.089 8.543 5 5 1 1 1 97.9656 87.12 11.239 6 6 9 1 1 95.3729 75.13 13.231 7 7 1 6 1 103.267 85.065 10.123 8 8 9 6 1 102.642 89.87 11.435 9 9 −1.72717 3.5 0.55 102.176 92.1009 5.098 10 10 11.7272 3.5 0.55 104.079 79.34 11.675 11 11 5 −0.704482 0.55 102.726 84.012 9.233 12 12 5 7.70448 0.55 94.2578 90.012 5.987 13 13 5 3.5 −0.206807 96.3522 89.122 9.234 14 14 5 3.5 1.30681 104.532 93.9741 5.123 15 15 5 3.5 0.55 98.5098 91.012 4.098 2.2.1 Particle size analysis A Malvern Zetasizer 3000 HSA was used to measure particle size using dynamic light scat- tering (DLS) (Malvern Instruments, UK). The polydispersity index (PI), a measure of the breadth of the size distribution, and the mean diameter are both obtained using DLS. Temper- atures of 25 °C were used to measure the mean diameter and the proportional index (PI). An ac- ceptable scattering intensity was achieved by di- luting all samples with double-distilled water prior to testing. 2.2.2 Zeta potential Zeta potential, which reflects the electric charge on a particle’s surface and indicates its physical stability, was determined by measuring electrophoretic mobility using the Malvern Zetasizer 3000 HSA (Figure 9) (Malvern Instru- ments, UK). Sodium chloride solution (0.9% w/v) was used to modify the conductivity of the sample to 50 IS/cm in double distilled water. The applied field strength was 20 V/cm and the pH ranged between 5.5 and 7.5. 2.2.3 Scanning Electron Microscopy (SEM) measurement Using a Hitachi S4800 Field Emission Scanning Electron Microscope (FESEM), the surface and surface morphology of the particles were analyzed in detail (Hitachi, Gaithersburg, 102 MD, USA). Analysis settings comprised a vac- uum pressure of 40 Pascals, an accelerating volt- age of 10 keV, and a working distance of 13.5 mm 2.2.4 Differential Scanning Calorimetry (DSC) analysis Pure cisplatin, PLGA, physical mixtures, and cisplatin nanoparticles were all examined using a differential scanning calorimeter (DSC) (Shimadzu DSC-60, Columbia, MD, USA). It was crimped non-hermetically in an aluminum pan and heated at a rate of 10 °C/min from 23 °C to 300 °C under a nitrogen purge for DSC anal- ysis (3–5 mg). 2.2.5 Fourier Transform Infrared spectros- copy (FTIR) analysis FTIR analyses of cisplatin, PLGA, physical mixture and cisplatin nanoparticles were carried out using IR Prestige-21 (Shimadzu, Columbia, MD, USA). The sample was placed in direct contact with ATR crystal ensuring good contact. All the spectra were recorded as a mean of 20 scans, with a resolution of 4 cm−1 and in the range of 800 to 4,000 cm−1. 2.2.6 Chromatographic conditions Chromolith RP-18e (E-Merk, 4.6 × 50 mm) column was used to measure the cisplatin con- centration in the HPLC system[20]. An acetoni- trile-water mixture containing 0.1% formic acid (40:60) was utilized as the mobile phase. A flow rate of 0.5 mL/min was measured. 10 µL of in- jection was used with a 490 nm laser and a wave- length of 10 nm. 2.2.7 Determination of drug entrapment effi- ciency (EE) and drug loading (DL) Centrifuged nanoformulations were evalu- ated by HPLC[21], and the supernatant containing the free drug, which was recovered, was further studied. Unentrapped nanoparticles of drug may be determined using this method. In order to determine the amount of drug encapsulated in nanoparticles, a subtraction was made from the total amount of drug added to the formulation. For the evaluation of the formulations, the following formula was used: 𝐸𝐸𝐸𝐸 = 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 𝐴𝐴𝑜𝑜 𝑑𝑑𝑑𝑑𝐴𝐴𝑑𝑑 𝑖𝑖𝐴𝐴 𝐴𝐴𝑛𝑛𝐴𝐴𝐴𝐴𝑛𝑛𝑛𝑛𝑑𝑑𝐴𝐴𝑖𝑖𝑛𝑛𝑛𝑛𝑛𝑛 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 𝐴𝐴𝑜𝑜 𝑑𝑑𝑑𝑑𝐴𝐴𝑑𝑑 − 𝐿𝐿𝐴𝐴𝑛𝑛𝑑𝑑𝑛𝑛𝑑𝑑 𝐴𝐴𝑛𝑛𝐴𝐴𝐴𝐴𝑛𝑛𝑛𝑛𝑑𝑑𝐴𝐴𝑖𝑖𝑛𝑛𝑛𝑛𝑛𝑛 × 100 𝐷𝐷𝐿𝐿 = 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 𝐴𝐴𝑜𝑜 𝑑𝑑𝑑𝑑𝐴𝐴𝑑𝑑 𝑖𝑖𝐴𝐴 𝐴𝐴𝑛𝑛𝐴𝐴𝐴𝐴𝑛𝑛𝑛𝑛𝑑𝑑𝐴𝐴𝑖𝑖𝑛𝑛𝑛𝑛𝑛𝑛 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 𝐴𝐴𝑜𝑜 𝑑𝑑𝑑𝑑𝐴𝐴𝑑𝑑 𝑜𝑜𝐴𝐴𝑑𝑑 𝑛𝑛𝐴𝐴𝑛𝑛𝑑𝑑𝑖𝑖𝐴𝐴𝑑𝑑 × 100 2.2.8 In vitro release study Dialysis bags (cellulose membrane, 12400MW, Sigma) were used to contain nano- particle samples, which were incubated in 30 mL of PBS (pH 7.4) at 37 °C under gentle agitation in a water bath at 37 °C (20). Samples were taken from the incubation mixture at predefined intervals and tested for cisplatin using the HPLC technique as described above. Every time a sam- ple was taken, the incubation media was changed with new PBS. In addition, a control experiment was conducted to assess the free drug’s release behavior. Dialysis bags were filled with PBS, PBS at 37 °C, and dissolved cis- platin in 1 mL of this solution, which was depos- ited in 30 mL of PBS. It was determined that cis- platin was released in the manner stated above. 2.3 Data analysis Design-Expert® software was used to ana- lyze the connections between model responses and their corresponding formulation factors. Stepwise linear regression and response surface analysis were used in the statistical study. In the final equations, only significant terms (p < 0.05) were used. Linear, quadratic, and special cubic models are all suitable for three-component models. On the basis of statistical comparisons of a number of statistical parameters, including the coefficient of variation (CV), the multiple correlation coefficient (R2), and an adjusted mul- tiple correlation coefficient (adjusted R2) proved by Design-Expert software, the best fit- ting mathematical model was selected. Student’s t-test and one-way ANOVA were used to deter- mine the significance of differences at a 0.05 significance level. 103 3. Results and discussion 3.1 Optimization of formulas According to the most statistically signifi- cant factors on the examined parameters, 3D re- sponse surface graphs are provided in Figures 1–3. The experiment yielded a desirability of 0.542 (Figure 4). Particle size and entrapment efficiency improve with a rise in polymer con- tent and the aqueous to organic phase ratio. Pol- ymer concentration and aqueous to organic phase ratio both reduce drug loading. The corre- lation coefficients (r) of the optimized variables were 0.9365, 0.9289, and 0.9698, respectively, for the second-order polynomial equation. The r value reduced significantly to 0.9112, 0.2089, and 0.9312 after model simplification with back- ward stepwise solution. At a 95% confidence level, there was a substantial lack of fit. At p < 0.05, all of the remaining variables were signif- icant. The best-fitting model was the quadratic model, and the comparative values of R, SD, and percent CV along with the regression equation developed for the selected answers are shown in Table 3. The following polynomial equations were derived from the statistical analysis of the results: PS = +98.57 – 0.1099A – 0.5967B + 1.47C + 0.2968AB – 0.2167AC + 2.38BC + 1.25A2 – 0.3845B2 + 0.3050C2 EE = +91.17 – 2.32A + 1.16B + 1.32C + 1.98AB – 0.5109AC + 2.45BC – 2.90A2 – 2,44B2 – 0.8352C2 DL = +4.04 +1.09A – 0.4612B + 0.4446C – 0.2717AB + 0.3440AC – 0.6230BC + 1.93A2 + 1.65B2 + 1.50C2 Figure 1. Three-dimensional (3D) response surface plots showing the effect of drug/polymer ratio and aqueous to organic phase ratio on particle size. 104 Figure 2. Three-dimensional (3D) response surface plots showing the effect of drug/polymer ratio and aqueous to organic phase ratio on entrapment efficiency. Figure 3. Three-dimensional (3D) response surface plots showing the effect of drug/polymer ratio and aqueous to organic phase ratio on drug loading. 105 Figure 4. Contour plot showing the desirability with a value of 0.542. Table 3. Reduced response models and statistical parameters obtained from ANOVA Responses Regression model Adjusted R2 Model P value % CV Adequate preci- sion Particle size PS = +98.57 – 0.1099A – 0.5967B + 1.47C + 0.2968AB – 0.2167AC + 2.38BC + 1.25A2 – 0.3845B2 + 0.3050C2 0.9365 0.0001 2.86 6.47 Entrapment efficiency EE = +91.17 – 2.32A + 1.16B + 1.32C + 1.98AB – 0.5109AC + 2.45BC – 2.90A2 – 2,44B2 – 0.8352C2 0.9289 0.0001 3.12 9.10 Drug loading DL = +4.04 +1.09A – 0.4612B + 0.4446C – 0.2717 AB + 0.3440AC – 0.6230BC + 1.93A2 + 1.65B2 + 1.50C2 0.9698 0.0001 3.98 11.28 Acceptance criteria 1 <0.05 <4 >4 A drug polymer to aqueous/organic phase ratio of 1:6 and a stabilizer concentration of 0.1% produced nanoparticles with high EE, high DL, and a small mean diameter, according to the fit- ting findings. Data from the two batches that were created in optimal ranges were extremely near to the projected values, with a minimal per- centage bias. This indicates that the optimized formulation was trustworthy and reasonable. Figures 5–7 show the effect of an independent factor on a specific response, with all other char- acteristics maintained constant at a reference factor. A high inclination or curve indicates that the reaction to a given element is very sensitive. The aqueous-to-organic phase ratio, drug poly- mer ratio, and stabilizer concentration are all shown to have significant effects on particle size in Figure 5. Following stabilizer concentration and drug polymer ratio, the aqueous to organic phase ratio had the most significant influence on entrapment efficiency, as shown in Figure 6. Drug polymer ratio, stabilizer concentration, and aqueous-to-organic phase ratio are shown in Figure 7 to have the most significant effect on drug loading. Response surface methodology (RSM) using the central composite rotatable 106 design model was used to optimize formulations of dihydroartemisinin nanostructured lipid car- rier. The experimental values of the nanoparti- cles prepared under the optimum conditions were mostly close to the predicted values (Table 4)[22]. The ansamycin-loaded polymeric nanoparticles were optimized using the central composite rotatable design–response surface methodology by fitting a second-order model to the response data and the experimental values of the nanoparticles shows that it deliver the encap- sulated drug well to the target site[23]. Figure 5. Perturbation plot showing the effect of independent variables on Particle size where A, B and C are Drug/polymer ratio, aqueous to organic phase ratio and stabilizer concentration respectively. Figure 6. Perturbation plot showing the effect of independent variables on entrapment efficiency where A, B and C are Drug/polymer ratio, aqueous to organic phase ratio and stabilizer concentration respectively. 107 Figure 7. Perturbation plot showing the effect of independent variables on drug loading where A, B and C are Drug/polymer ratio, aqueous to organic phase ratio and stabilizer concentration respectively. Table 4. Predicted and experimental values under predicted optimal conditions Drug/polymer ratio Aqueous to or- ganic phase ratio Stabilizer con- centration (%) Particle size (nm) Entrapment ef- ficiency (%) Drug loading (%) 1:8.27 1:6 0.1 Predicted 114 83.5 8.6 Experimental 112 85.4 9.0 Bias % 1.75% 2.27% 4.6% Acceptance criteria 6% Bias was calculated as (predicted value - experimental value)/predicted value × 100 3.2 Particle size, zeta potential and SEM measurement It was discovered that the average cisplatin nanoparticle particle size was 112 nm (Figure 8). As shown in Figure 9, the zeta potential of this compound is high enough to allow for the crea- tion of a stable pharmaceutical formulation. Fig- ure 10 shows the SEM images taken of the im- proved cisplatin nanoparticles to offer information on their shape. These nanoparticles have been fine-tuned to be spherical. The nano- particles had the higher absolute values of zeta potential, indicating a better stability of this col- loid system[24]. Zeta potential under −30 mV showed good physical stability[25]. Figure 8. Size distribution of Cisplatin PLGA NP. 108 Figure 9. Zeta potential of Cisplatin PLGA NP. Figure 10. Scanning electron microscopy of Cisplatin PLGA NP. 3.3 Differential Scanning Calorimetry (DSC) analysis After the preparation, DSC was used to ex- amine the cisplatin’s physical condition within the PLGA particles. Drug-loaded nanoparticles did not exhibit the glass transition peak seen in the DSC thermogram of PLGA (Figure 11). The cisplatin thermogram revealed an exothermic peak at 280–285 °C. It is possible that the drug is scattered in an amorphous form due to its lack of this characteristic peak. 3.4 Fourier Transform Infrared spectros- copy (FTIR) analysis FTIR analysis is used to study the interac- tions between cisplatin and PLGA during the en- trapment procedure and the FTIR spectrum ob- tained for cisplatin, PLGA, physical mixture and the drug loaded nanoparticle is presented in Fig- ure 12. Figure 11. DSC thermogram of PLGA (polymer), Cisplatin (drug), PLGA Cisplatin mixture, Cisplatin PLGA NP. Figure 12. FTIR spectra of PLGA (polymer), Cisplatin (drug), PLGA Cisplatin mixture, Cisplatin PLGA NP. The pure cisplatin obtained the characteris- tic peaks that includes amine stretching (3,208 cm−1), symmetric amine bending (1,302 cm−1) and chloride stretching (766 cm−1). PLGA nano- particles obtained its characteristic peaks that in- clude C = O stretching (1,728 cm−1) and C-O stretching (1,020–1,280 cm−1). The FTIR spec- tra of the cisplatin loaded nanoparticles obtained a peak for amine stretching (3,279 cm−1), indi- cating the presence of cisplatin in the formula- tion. The FTIR data obtained indicates that there were no chemical interactions between PLGA and the study drug cisplatin. 3.5 In vitro drug release study In vitro cisplatin release from PLGA nano- particles is presented in Figure 13. Biphasic 109 release pattern with an initial fast release for the first 48 hrs, followed by a steady release for six days is observed. The drug may have accumu- lated on the nanoparticle surface during manu- facturing, resulting in a fast release. Comparison of cisplatin release profiles with those of cispla- tin solution demonstrates that nanoparticle en- trapment greatly slowed cisplatin’s release from the solution. According to the data (Figure 13), roughly 90% of cisplatin in phosphate buffer so- lution was released in 24 hours. For the next six days, the cisplatin nanoparticles released at a consistent and modest rate. In vitro, the cisplatin nanoparticles showed a clear sustained-release impact as compared to cisplatin. The decreased percentage of cumulative drug release may be due to the enhanced particle size and also hence smaller sized surface area at greater polymer concentration. An additional description for re- duced cumulative drug release at greater poly- mer concentration might be the enhanced con- centration of the polymer existing which impedes the drug release by diffusion[26]. Figure 13. In-vitro drug release study of pure cisplatin and cis- platin PLGA NPs in PBS (pH 7.4). 4. Conclusion The cisplatin-loaded PLGA nanoparticles were made using the double emulsion solvent evaporation process. A second-order model was fitted to the response data of the cisplatin PLGA nanoparticles using the central composite rotat- able design–responsive surface approach. 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