Preparation and Evaluation of Poloxamer/Carbopol In-Situ Gel Loaded with Quercetin: In-Vitro Drug Release and Cell Viability Study

Abstract

BACKGROUND:

Periodontitis is a severe chronic inflammatory disease, whose traditional systemic antimicrobial therapy faces great limitations. In-situ gels provide an effective solution as an emerging local drug delivery system.

METHODS:

In this study, the novel thermosensitive poloxamer/carbopol in-situ gels loaded with 20 μmol/L quercetin for the treatment of periodontitis were prepared by cold method. Thirteen batches of in-situ gels based on two independent factors (X₁: poloxamer 407 and X₂: carbopol 934P) were designed and optimized by the statistical method of central composite design (CCD). The transparency, pH, injectability, viscosity, gelation temperature, gelation time, elasticity modulus, degradation rate and in-vitro drug release studies of the batches were evaluated, and the percentage of drug release in the first hour, the time required for 90% drug release, gelation temperature, and gelation time were selected as dependent variables.

RESULTS:

These two independent factors significantly affected the four dependent variables (p < 0.05). The optimization result displayed that the optimized concentration of poloxamer 407 was 20.84% (w/v), and carbopol 934P was 0.5% (w/v). The optimized formulation showed a clear appearance (++), acceptable injectability (Pass), viscosity(151,798 mPa s), gelation temperature (36 °C), gelation time (213 s), preferable cell viability and cell proliferation, conformed to first-order release kinetics, and had a significant antibacterial effect.

CONCLUSIONS:

The article demonstrates the great potential of the quercetin in-situ gel as an effective treatment for periodontitis.

Introduction

Periodontitis is a common disease, and according to the Global Burden of Disease Study 2019, the prevalence of severe periodontitis has reached 50% worldwide [1]. Periodontitis is one of the many factors that cause infectious inflammatory diseases, primarily caused by microorganisms and their products that colonize, grow, and survive within the periodontal pockets and may reach connective tissues, root-end dentin, exposed dentin, and alveolar bone [2, 3]. Periodontitis is characterized by an inflammatory response in the periodontal tissue caused by bacteria on the tooth surface, and by the progressive breakdown of the supporting tissues around the teeth under the control of the host, which leads to tooth loss [4, 5]. Periodontitis causes connective tissue trauma and loss of bone support. Advanced lesions are characterized by periodontal pocket formation, surface suppuration, surface ulceration, loss of alveolar bone and periodontium, tooth movement, and eventual tooth loss [3]. This may increase the overall inflammatory burden on the body and aggravate diseases such as diabetes and atherosclerosis [6].

The treatment of periodontitis usually involves a variety of approaches, with the traditional treatment being mechanical therapy including curettage and a root canal program to remove all plaque, stones, and plaque-chelating factors [7]. Surgical treatment, topical medication, and systemic medication, especially systemic antimicrobial and anti-inflammatory agents, are also available. However, long-term use of systemic antimicrobials is fraught with potential dangers, resulting in low drug concentrations in the gingival crevicular fluid, which can lead to ecological disorders, poor biological distribution of systemic bacteria, and other strong side effects [8]. The increasing resistance of many common pathogenic bacteria to therapeutic drugs now in use has generated interest in the discovery of new natural anti-inflammatory analogs [9]. Quercetin is a flavonoid monomer compound extracted from Chinese herbs, which exists mainly in the form of glycosides in the leaves, stems, and fruits of a variety of plants and is also found in many Chinese herbal medicines, such as locust, jujube, and buckwheat. It has a variety of bioactivities, including antioxidant, anti-aging, anti-inflammatory, anti-tumor, anti-cancer, anti-platelet aggregation, and immunomodulatory activities [10–13]. In addition to relieving pain and healing wounds, it is comparatively safe and nontoxic. Owing to these properties of quercetin, it has been shown that it is recommended for dental applications.

Quercetin plays an anti-inflammatory role by down-regulating the activity of nuclear transcription factor-κB (NF-κB), reducing inflammatory factors, and inhibiting macrophage infiltration [13]. Moreover, quercetin can promote the differentiation of M2-type macrophages to repair damaged kidney tissues by blocking the NF-κB signaling pathway as a treatment option for acute kidney injury. In the oral cavity, M2-type macrophage polarization reduces gingival inflammation, inhibits alveolar bone defects, and promotes periodontal tissue regeneration [12]. Quercetin can prevent and treat periodontal diseases by affecting pathogenic bacterial adhesion, the structure of biofilms and bacteria, and related enzymes and virulence factors. Quercetin promotes bone cell formation and inhibits bone cell resorption. Quercetin can be used to treat periodontitis and maintain alveolar bone. However, low chemical stability and bioavailability, poor water solubility, and short biological half-life of quercetin hinder its clinical application and reduce its efficacy [14].

Drug delivery systems for periodontitis include systemic and local means as an adjunct to treatment; however, because of obvious disadvantages, the use of local drug delivery systems is essential to improve the prevention and treatment of periodontitis. Local drug delivery systems placed directly in the periodontal pockets can deliver sufficiently high concentrations of active drugs for a sufficiently long period. Local delivery systems offer additional advantages over systemic administration, including avoiding gastrointestinal problems and first-pass metabolism by administering drugs directly to specific sites, improving efficacy, reducing side effects by controlling drug release, and improving patient compliance by reducing the frequency of administration [15, 16]. Quercetin localization-targeted drug delivery can significantly prolong the residence time of the active substance and has good slow and controlled release properties. Therefore, the local introduction of quercetin in a system that maintains its chemical stability and improves its solubility and bioavailability can be effective in the treatment of periodontitis.

In recent years, in-situ gels have become a new type of gel drug delivery system based on traditional hydrogel agents [9]. An in-situ gel is a type of sol–gel formed from polymers, which are initially in liquid form when administered to specific parts of the body. Once administered, the liquid transforms into a gel in-situ, facilitating easy delivery of drugs to the local area. This prolonged residence time of the active substance enhances its effectiveness [8]. Thermosensitive in-situ gels containing poloxamers and pH-triggered in-situ gels containing carbopol have been studied as convenient dosage forms for use in periodontal pockets. Once the aqueous solution is injected into the periodontal pockets, it covers the pocket and becomes a gel because of changes in the temperature and pH of the environment [7, 17].

Poloxamers are thermosensitive polymers with mucosal adhesion properties that respond to temperature changes. They undergo a temperature-dependent sol–gel transition in aqueous solution, meaning, they are liquid at room temperature (25 °C) and gelatinize due to temperature changes upon contact with body fluids (35–37°C). Owing to its thermoreversible nature, it can easily deliver the exact dose of an antimicrobial drug in an injectable form with minimum resistance, and the drug is gradually converted into a gel at a predetermined body temperature [16]. Carbopol belongs to a class of adhesive polymers with good bio-adhesive properties and can be used as gel thickeners [18, 19]. They improve the contact closeness and increase the duration of the formulation in the periodontal pockets. The addition of carbopol improves the weaker mechanical properties and transition temperature of poloxamers, further improving the bio-adhesive properties of poloxamers, and enhancing the residence time. Because the formulation gels at body temperature, it maintains a longer residence time in the periodontal pockets thereby sustaining drug delivery.

This study aimed to design and evaluate quercetin in-situ gel formulations by combining the thermosensitive polymer “Poloxamer 407” and the mucosal adhesion polymer “Carbopol 934P” as localized intra-pocket drug delivery. The dosage was designed to ensure the residence time and activity of quercetin. It has the advantage of being easy to administer, providing sustained drug release to increase clinical efficacy, reducing the frequency and dose of administration, and improving patient compliance. Therefore, the gel prepared in this study has the potential for the treatment of periodontitis.

Materials and methods

Materials

Quercetin (purity 95%) poloxamer 407, and Carbopol 934P were bought from Shanghaiyuanye Biotechnology Co., Ltd. The mouse osteoblast cell line MC3T3-E1, macrophages RAW264.7 were obtained from the American Type Culture Collection (ATCC). Fetal bovine serum (FBS), penicillin/streptomycin (PS), trypsin-EDTA, Dulbecco’s Modified Eagle’s medium (DMEM) and phosphate buffer saline (PBS) were purchased from Gibco (Waltham, MA USA). Cell Counting Kit-8 (CCK-8) was purchased from Enzo Life Sciences, Inc. (New York, NY, USA). LPS was purchased from Solarbio (Beijing, China).

Preparation of quercetin in-situ gels

An in-situ gel loaded with quercetin was prepared using the cold method. The quercetin powder was dissolved in anhydrous ethanol by adding it to the ethanol in advance to make a 20 μmol/L quercetin solution. In a beaker containing 100 ml of deionized water, a predetermined amount of Carbopol 934P (0.2–0.5% w/v) was slowly added and stirred until fully dissolved. The solution was then cooled in an ice bath at 4 °C. Subsequently, poloxamer 407 (17.5–22.5% w/v) was slowly added with continuous stirring until poloxamer 407 was completely dissolved. The prepared Carbopol-Poloxamer solution was placed in a 4 °C ice bath and 20 μmol/L of quercetin solution was added slowly with constant stirring separately, protected from light until quercetin was completely dissolved. It was left in a refrigerator at 4 °C overnight to allow the complete evaporation of ethanol. The final formulation was transferred to an amber bottle and stored in the refrigerator at 4 °C for future use.

Optimization of formulations using central composite design (CCD)

To achieve the desired gelation time, gelation temperature, and in-vitro drug release, prescriptions were created using different concentrations of poloxamer 407 and carbopol 934 using CCD and Design-Expert version 11 software (Stat-Ease Inc., Minneapolis, MN, USA). These parameters included the percentage of drug released in the first h (R₁), 90% drug release time (t_90%) (R₂), gelation time (R₃), and gelation temperature (R₄). Poloxamer 407 concentrations of 17.5–22.5% (w/v) and Carbopol 934 concentrations of 0.2–0.5% (w/v) were determined by preliminary tests. The design uses two components at three levels to determine the primary impacts and their interactions. The description of the nine design groups is as follows: one center point, four star points, and four factorial points, along with four replicates of the center point totaling 13 tests (Table 1). The replication runs aimed to enhance accuracy and reduce experimental errors.

Table 1.

The percentage of drug release at first hour (R₁), time required for 90% drug release (t_90%) (R₂), gelation time (R₃) and gelation temperature (R₄) of in-situ gel formulations B1–B13

Batch	Poloxamer 407 (X1) (%, w/v)	Carbopol 934P (X2) (%, w/v)	The percentage of drug release at first hour (R1) (%)	Time required for 90% drug release (R2) (h)	Gelation time (R3) (s)	Gelation temperature (R4) (°C)
B1	17.5	0.2	101.94	45.57	390	38
B2	22.5	0.2	96.47	47.34	210	28
B3	17.5	0.5	98.77	46.3	–	–
B4	22.5	0.5	95.98	57.11	290	29
B5	16.4645	0.35	100.09	48.78	–	–
B6	23.5355	0.35	97.06	50.95	190	28
B7	20	0.13787	100.54	45.8	280	31
B8	20	0.56213	96.47	53.93	310	35
B9	20	0.35	98.95	48.56	370	32
B10	20	0.35	108.74	45.82	330	32
B11	20	0.35	99.94	43.77	300	33
B12	20	0.35	97.47	53.04	330	33
B13	20	0.35	97.41	50.16	360	32

Evaluation parameters

Transparency

To ensure the absence of any foreign particles, the formulation was alternately illuminated under a black and white background before and after formulation, visually checking the homogeneity of the formulation and surveying the aspect and presence of any particles in the formula to record the transparency of the gel as +, ++, +++ [20].

(+) The solution was opaque white with small white flocs.

(++) The solution was relatively clear.

(+++) The solution was colorless and clear.

Determination of pH before gelation

A pH meter (PHS-3C, Shanghai Yueping Scientific Instrument Manufacturing Co., LTD.) was used to measure the pH of in-situ gel at 25 °C. (n = 3) [20].

Determination of pH after gelation

A pH meter (PHS-3C, Shanghai Yueping Scientific Instrument Manufacturing Co., LTD.) was used to measure the pH of 1 g of in-situ gel in 5 ml of normal saline at 37 °C (n = 6).

Gelation temperature

The gelation temperature was determined by the test-tube inversion method. Initially, a 2 ml sample of the gel was placed in a sealed test tube. Subsequently, the test tube was immersed in a water bath at 15 °C. The bath temperature was then gradually added in 1 °C augmenters, the tube was inverted every 2 min to determine gel formation. The temperature was recorded when the tube was inverted 90° and the gel stopped flowing. Three tests were performed [21].

Gelation time

The gelation time was measured by keeping the formulation at its gelation temperature and stirring continuously at 100 rpm using a magnetic stirrer, and the time at which the magnetic beads stopped moving was recorded as the gelation time. Three tests were performed [21].

Injectability

A 5 ml syringe with a 21-gauge needle was filled with 1 ml of the prepared solution and manually pushed. It was subjectively judged that the preparation could easily pass through the needle as “Pass”, and could not easily pass as “Fail” [5].

Determination of viscosity

The ND-8S digital rotary viscometer was used to measure the viscosity of the hydrogel. An appropriate amount of in-situ gel was placed in a 200 ml beaker and placed in a 37 °C constant temperature water bath. The rotor of the viscometer was then placed in the in-situ gel solution, and the data was measured and recorded after 30 min (n = 6).

Determination of modulus of elasticity

A cylindrical in-situ gel sample (d = 10 mm, h = 2 mm) was prepared ahead of time and squeezed using a universal testing machine (WD1000N, JIMTEC, China) at a compressive strain rate of 1 mm/min until the sample failed. The in-situ gel’s elastic modulus was recorded (n = 6).

Degradation experiment

Take 500 µL of in-situ gel solution, cross-link it at 37 °C and place it in a 10 mL centrifuge tube, and record the original weight (W_O) of in-situ gel. Add PBS (pH = 7.4) 10 mL into the centrifuge tube as the degradation solution, and then incubate the centrifuge tube on a shaker in a 37 °C incubator at a rate of 50 r/min. Take out the centrifuge tube at 4, 8, 12, 16, 20, and 24 h respectively, wipe off the water droplets outside the centrifuge tube and pour out the degradation solution, and then weigh the weight of the in-situ gel (W_L). Slowly add 2 mL of degradation solution at the same temperature, then repeat until all of the solution in the centrifuge tube has been drained out. The formula to calculate the deterioration rate is:

$$\text{Degradation}=\frac{w_0-w_L}{w_0}\ast 100\%$$

In-vitro release studies and release kinetics

In-vitro drug release studies

In-vitro release of the in-situ gels was based on diffusion. In this study, we used red ink instead of drugs for in vitro drug release studies to test the sustained release performance of in situ gels more intuitively. A 300 μL sample gel was added with red ink was immersed in a beaker having 3 ml of phosphate buffer with pH 6.8. The research was made at a temperature of 37 ± 0.5 °C. The beakers were then placed in a thermostat. All 3 ml solutions were removed at set intervals and substituted with the same amount of unsalted phosphate buffer solution at pH 6.8, and the concentration of the red ink in the samples was surveyed using a 375 nm UV–visible spectrophotometer (Nano-MD UV–Vis, Scinco, Seoul, Korea). The time required for 90% of drug release and the percentage of the drug released in the first hour was calculated [9].

Release kinetics

In-vitro release curves have been used in various kinetic models to determine drug release mechanisms. Zero-order, one-order, Higuchi, Korsmeyer–Peppas, and Hixon–Crowell models were used to describe the release kinetics (Table 2).

Table 2.

Relevant mathematical models and corresponding equations

Mathematical model	Diagrams	Equations
Zero-order	Cumulative percentage of drug release versus time	${\text{Q}}_1={\text{Q}}_0+{\text{k}}_0\text{t}$
First-order	The logarithm of cumulative drug residual percentage versus time	$log{\text{Q}}_{\text{t}}=log{\text{Q}}_0-{\text{K}}_{\text{t}}/2.303$
Higuchi	The square root of cumulative percent drug release versus time	${\text{Q}}_{\text{t}}={\text{k}}_{\text{H}}{\text{t}}^{1\left./\phantom{12}\right)2}$
Hixson–Crowell	Cubic root ratio time for a percentage of drugs remaining	${\text{W}}_0^{1\left./\phantom{13}\right)3}-{\text{W}}_{\text{t}}^{1\left./\phantom{13}\right)3}={\text{K}}_{\text{s}}\text{t}$
Korsemeyer–Peppas	The logarithm of cumulative percent drug release versus cumulative time	${\text{Q}}_{\text{t}}\left./\phantom{{\text{Q}}_{\text{t}}{\text{Q}}_{\infty }}\right){\text{Q}}_{\infty }={\text{K}}_{\text{k}}{\text{t}}^{\text{n}}$

Q_t = amount of drug released in time “t”, Q₀ = initial dose by dosage form, ${\text{Q}}_{\text{t}}\left./\phantom{{\text{Q}}_{\text{t}}{\text{Q}}_{\infty }}\right){\text{Q}}_{\infty }$ = fraction of drug released in time “t”; K₀, K_t, K_H, K_s, and K_k = release rate constants, and n = release index (“release mechanism indication”)

Assay of cell viability

In this experiment, the CCK-8 assay was used to evaluate the effect of the gel system on cell viability. Mouse embryonic osteoblast (MC3T3-E1) were cultured in Dulbecco’s Modified Eagle’s medium (DMEM) supplemented with 10% fetal bovine serum (FBS) and 1% penicillin and streptomycin (pH 7.2–7.4, 37 °C and 5% CO₂) [22–24]. The supernatant was obtained after placing 0.2 g/ml blank in-situ gel (CP) and in-situ gel loaded with quercetin (CPQ) into the cell culture. Each prepared extract was continuously diluted in DEME medium containing 1% streptomycin and penicillin to obtain concentrations with gradients: of 100%, 50%, 25%, 12.5%, and 6.25% [24]. First, 100 μL of the medium was added into the peripheral wells of the 96-well plates with a pipette as a blank control group. In the other wells, 100 ul of cell suspension was dispensed per well (1 × 10⁴ cells/well), and incubated for 24 h (5% CO₂, 37 °C) [22–25]. On the second day, cells were rinsed with phosphate-buffered saline (PBS) at pH 7.4, 100 μL of gel extracts of different concentrations were added to each well, and a negative control group was set up in which only cell culture medium was added. Each group comprised five samples. After incubation for 72 h (37 °C, 5% CO₂), the cells were washed with PBS and incubated with CCK-8 solution (100 μL) added to all wells. After 24 h and 72 h of incubation, the absorbance was measured at 450 nm using an ELISA reader (Molecular Devices, EMax, San Jose, CA, USA), and the OD values of each well were recorded [25, 26].

Anti-inflammatory effect in vitro

RAW264.7 cells were seeded in 6-well plates at a density of 1 × 10⁵ RAW264.7 cells were induced to produce inflammatory factors by 500 ng/mL LPS for 24 h and then replaced with fresh DMEM medium supplemented with 10 μmol/L and 20 μmol/L quercetin (Q10 and Q20 groups). After 24 h of therapy, cellular proteins were extracted and the expression of inflammatory factors interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α) was measured using Western blot.

Results and discussion

Transparency, injectability, and pH

Solutions B3-5 as well as B8-13 exhibited a white and cloudy appearance with large amounts of white precipitates and a transparency of (+). In contrast, B6 was transparent and clear with a transparency of (++). Solutions B1, B2, and B7 were colorless and clear, with no signs of particles and a transparency of (+++). Figure 1A, B shows in situ gels of three kinds of transparency on a black and white background.

Fig. 1.

A, B are in-situ gels of three kinds of transparency under black and white background, from left to right, they are +++, ++, +. C The injectability of in-situ gel is “pass”. D–G The sol–gel transformation process of a typical gel group

Injectability was assessed by testing whether the prepared formulations could easily flow through the required gauge needle. Formulations of group B1-13 passed easily through the needle with hand pressure and were considered to have “Passed”, as shown in Fig. 1C.

The mean pH values before and after gelation of the preparations in Table 3 were found to be in the range of 3.80–4.68; this low pH maintains the sol–gel form of carbopol 934P before administration, and during the inflammatory phase after administration, the in-situ gel keeps the wound in a low pH micro-environment that inhibits bacterial infection [27]. Furthermore, it is necessary to maintain the drug’s chemical stability. Notably, when deposited in periodontal pockets, a gel with adhesive qualities can form at the physiological pH and temperature of 37 °C.

Table 3.

Transparency, injectability, and pH before and after gelation of in-situ gel formulations B1–B13

Batch	Transparency	Injectability	pH before gelation	pH after gelation	Viscosity (mPa s)
B1	+++	Pass	4.67 ± 0.006	4.13 ± 0.006	146,111
B2	+++	Pass	4.59 ± 0.006	4.10 ± 0.006	165,169
B3	+	Pass	3.84	3.84	140,131
B4	+	Pass	4.04	3.97	155,573
B5	+	Pass	3.92 ± 0.006	3.99	108,166
B6	++	Pass	4.27 ± 0.006	4.01 ± 0.006	168,946
B7	+++	Pass	4.61	4.04	165,394
B8	+	Pass	3.96	3.8	152,045
B9	+	Pass	4.19	3.96	149,403
B10	+	Pass	4.23	4.05	146,947
B11	+	Pass	4.30 ± 0.006	4.05	145,892
B12	+	Pass	4.34 ± 0.006	4.02	146,533
B13	+	Pass	4.42 ± 0.006	4.02	149,651

Gelation temperature and gelation time

The phase transition of polymers plays a crucial role in the temperature-dependent conversion of liquid formulations into gels because of their thermosensitivity after entering periodontal pockets [5], as shown in Fig. 1D–G. The gel developed in the periodontal pocket has the potential to sustain the slow release of drugs over an extended period. Figure 3 depicts a typical sol–gel transition of a group during the gelation temperature measurement. As for in-situ gel formulations, the desired gelation temperature should range from 25 to 37 °C. Temperatures below 25 °C can lead to gel formation at room temperature, making manufacturing, handling, and administration challenging. Conversely, temperatures above 37 °C can keep the gel in a liquid state, resulting in transoral clearance at the early stages of administration. Table 1 shows that the gelation temperature ranged from 28 to 38 °C, which is considered optimal. The gelation time, on the other hand, ranged from 195 to 1180 s. Formulations B2 (22.5 + 0.2%, w/v) and B6 (23.5% + 0.35%, w/v) were reconstituted to gel at the temperature of 28 °C. Additionally, all formulations except for B1 exhibited a gelation temperature ranging from 25 to 37 °C, which suggests that the majority of the formulations exhibited excellent thermosensitive behavior demonstrated by the in-situ gels.

Fig. 3.

A The degradation rate of B1–B13 were determined at 37 °C. B In-vitro drug release profile of B1–B13 formulations

Viscosity

The viscosity of injectable quercetin in-situ gel can improve the combination of gel and periodontal diseased tissues, as well as the drug’s continuous release, which is significant in the treatment of periodontitis. Table 3 shows that the in-situ gels from each formula had a high viscosity and great adhesive strength after half an hour at 37 °C. Composite gels have good adhesion due to a variety of physical interactions. Gel adhesion variables can interact with the substrate’s surface, such as hydrophobicity, hydrogen bonding, metal complexation, and so on, to generate strong adherence.

Determination of modulus of elasticity

The compression test was performed using a universal testing machine, and the elastic modulus of different groups could be measured based on stress and strain. Figure 4 illustrates the elastic modulus for different formulations. As shown in Fig. 2, B5 has the smallest elastic modulus and B4 has the largest elastic modulus, and the difference between the two formulations is significant, which could be due to the high concentration of B4 poloxamer 407, poloxamer 407 will form micelles, which aggregate and intertwine with each other to form a three-dimensional network structure [28], resulting in its large compression modulus. While B5 poloxamer 407 had the lowest concentration, resulting in a lower compression modulus, B7–B13 formulations had nearly identical poloxamer 407 concentrations and showed no significant difference in elastic modulus. In general, the compression modulus of each preparation gel was minimal, indicating that the gel has adequate fluidity and can be employed as a carrier for drug delivery systems, with precise drug delivery achievable by modifying its compression modulus.

Fig. 4.

The normal plot of residuals for responses. The percentage of drug release at first hour (R₁), time required for 90% drug release (t_90%) (R₂), gelation time (R₃), and gelation temperature (R₄)

Fig. 2.

The elastic modulus of B1–B13 and the optimized formulation (1–13: B1–B13, 14: the optimized formulation), different letters represent p < 0.05

According to Fig. 2, the elastic modulus of the optimized formula B14 differs greatly from that of B1–B6, while B7–B13 has no significant variation from B14. The optimized formulation contains 20.84% (w/v) poloxamer 407, has a mild elastic modulus, and has better fluidity and injectability.

Degradation rate

Figure 3A depicts the gel’s mass time loss curve in PBS (pH = 7.4, 37 °C). After 12 h of degradation, the mass loss of B1, B2, B4, B6, B13, and the ideal ratio were 100.00%, 77.44%, 76.31%, 61.68%, 77.30%, 57.85%, 59.16%, 90.78%, 56.59%, 79.04%, 77.76%, and 62.23%. B1 has been fully damaged, and B2, B4, B7, B10, B12, and B13 have lost the majority of their mass. After 28 h of degradation, all 14 groups were totally deteriorated. The solid content of a gel is calculated by dividing the total solid mass of poloxamer and carbomer by the total mass of all gel components. The results show that the degradation rate of the gel is affected by its solid content. As the gel’s solid content increases, so do the cross-linking sites of the three-dimensional network structure, and the fluidity of the molecular chains in the gel decreases. There will also be more chemical linkages in the hydrogel’s interior structure, which will slow down the degradation process. B1 with the lowest solid content totally degrades in 8 h, whereas B4, B6, and B8 with more solid content deteriorate at the slowest rate, taking 28 h to achieve 100% mass loss. In conclusion, the gel is prolonged with the increase of solid content, and the complete degradation time can be controlled within 8–28 h.

In-vitro release study

All formulations B1–B13 were subjected to in-vitro release studies at pH 6.8 with phosphate as the dissolving medium. In addition, the time required for 90% drug release and the amount of drug released in 1 h were also calculated, and the drug release of formulations B1–B13 is illustrated in Fig. 3B. It was observed that there was a decreasing trend in the release of quercetin as the polymer concentration increased, with a higher initial release of the drug. Formulation B4 showed the slowest release at all time points, which may be due to its higher polymer concentration, especially the higher concentration of Carbopol 934P. The prolonged post-release time may be attributed to the relatively slow dispersion of drug molecules during gelatinization. The results showed that formulation B4 had a good, sustained-release, while formulations B5 and B12 had a higher initial release, followed by a sluggish continuous release at later stages. The DD-Solver Excel Sheet software was used to implement in-vitro release figures into a variety of kinetic models to ascertain the release mechanism of the drug. The results demonstrated that first-order kinetics proved to be the best-fitting model in most of the formulations, indicating that the drug release pattern may follow a first-order kinetic dispersion mechanism, which is presented in Table 4.

Table 4.

Release kinetics data for selected model

Batches	Zero-order	First-order	Hixson–Crowell	Higuchi	Korsemeyer–Peppas		The best fit
	R2	R2	R2	R2	R2	n
B1	0.57117	0.98103	0.93769	0.83762	0.95795		First-order
B2	0.7873	0.27962	− 0.06134	0.89712	0.5237		Higuchi
B3	0.67274	0.9808	0.96715	0.90125	0.9538		First-order
B4	0.84969	0.94408	0.95204	0.93796	0.93804		Hixson–Crowell
B5	0.47561	0.97139	0.85226	0.75979	0.97958	0.17368 ± 0.03156	Korsemeyer–Peppas
B6	0.68073	0.99168	0.98285	0.90393	0.25867		First-order
B7	0.51663	0.99292	0.96028	0.7907	0.92194		First-order
B8	0.75136	0.93626	0.92442	0.93232	0.95864	0.35966 ± 0.06237	Korsemeyer–Peppas
B9	0.57288	0.96897	0.91572	0.83433	0.95518		First-order
B10	0.70858	0.98002	0.96964	0.91779	0.95253		First-order
B11	0.62406	0.99706	0.98856	0.87055	0.93694		First-order
B12	0.63461	0.94334	0.90683	0.87717	0.96557	0.2707 ± 0.04844	Korsemeyer–Peppas
B13	0.57282	0.9828	0.95351	0.83303	0.93154		First-order

The design of experiments: CCD

The concentrations of Poloxamer 407 (X₁) and Carbopol 934P (X₂) were found to exert a significant impact on R_1, R_2, R₃ and R₄. In the statistic-based analysis, each of the four responses was separately fitted to a polynomial model, with the validity of each model assessed using an analysis of variance (ANOVA) by the Design-Expert, as illustrated in Table 5. The coefficient and p values mentioned in the ANOVA models demonstrate the quantitative relationship between the factors and their interactions (Tables 5, 6). Regarding the Design-Expert, another critical element that affects the validation of the model is the lack of fit, which is contingent on the F-value and p-value. It is worth noting that the fitting model equations were more effective, as the F-value was comparatively low with a relatively non-significant p-value, as shown in Table 5, R1, R2, R3 and R4 exhibited a better model fit.

Table 5.

The lack of fit

Response	2F1	Linear	Quadratic	Suggested model	Lack of fit
					F-value	p-value	Lack of fit
Response 1	0.8176	0.7725	0.8183	Linear	0.65	0.6965	Good
Response 2	0.6151	0.5025	0.6397	Linear	0.48	0.8016	Good
Response 3	0.8284	0.8127	0.9001	Linear	1.36	0.3853	Good
Response 4	0.9125	0.9089	0.9855	Quadratic	0.30	0.6122	Good

Table 6.

The coefficient value for independent factors and interactions

	Intercept	A	B	AB	A2	B2
R1	98.45	− 1.57	− 1.18	–	–	–
p values		0.0009	0.0057	–	–	–
R2	49.01	1.96	2.75	–	–	–
p values		0.0953	0.0270	–	–	–
R3	326.18	− 85.88	20.64	–	–	–
p values		0.0004	0.1174	–	–	–
R4	32.40	− 5.9	1.44	− 0.96	1.89	0.26
p values		< 0.0001	0.0022	0.0761	0.0046	0.2542

The high reliability of the model can be observed based on the Normal Plot of Residuals, which shows the degree of mutual deviation between the model residuals and random errors. A lower degree of deviation indicated higher model reliability. The residual data exhibited a linear trend, indicating adherence to normal distribution conditions and a reasonable range of experimental errors (Fig. 4). The predicted and actual values of responses R₁, R₂, R₃, and R₄ are in reasonable agreement with each other (Figs. 5, 6).

Fig. 5.

The residuals versus predicted values of the percentage of drug release at first hour (R₁), time required for 90% drug release (t_90%) (R₂), gelation time (R₃), and gelation temperature (R₄)

Fig. 6.

The predicted versus actual values of the percentage of drug release at the first hour (R₁), the time required for 90% drug release (t_90%) (R₂), gelation time (R₃), and gelation temperature (R₄)

Statistical analysis of R₁

Regression equations were acquired from the Design-Expert and polynomial equations were developed accordingly.

$${\text{Y}}_1=98.45-1.57{\text{X}}_1-1.18{\text{X}}_2$$ 1

The values of the coefficient of X₁ were higher than those of X₂, indicating that the effect of the concentration of X₁ on R₁ was greater than the effect of the concentration of X₂. The coefficients of X₁ and X₂ were negatively signed, indicating that an increase in the concentration of X₁ and X₂ exerted a negative impact on R₁. X₁ and X₂ were significant modeling terms that affected R₁ (p < 0.05).

Statistical analysis of R₂

The Design-Expert provided a linear regression equation, and a polynomial equation was created as follows:

$${\text{Y}}_2=49.01+1.96{\text{X}}_1+2.75{\text{X}}_2$$ 2

The values of the coefficient of X₂ were higher than those of X₁ revealing that the effect of the concentration of X₂ on R₂ was greater than the effect of X₁ on it. The coefficient of both X₁ and X₂ were positively signed indicating that the increase in the concentration of both X₁ and X₂ positively impacted R₂. X₂ was a significant modeling term affecting R₂ (p < 0.05).

Statistical analysis of R₃

The gelation time ranged between 195 and 1180 s. It can be observed from the equations and the response surface plots that the gelation time correspondingly changed with variations in the concentration of polymers. The regression equations were obtained from Design-Expert software, which generated the polynomial equation given below:

$${\text{Y}}_3=326.18-85.88{\text{X}}_1+20.64{\text{X}}_2$$ 3

The high values of the coefficient of X₁ over X₂ indicate that X₁ contributes more to gelation time when compared to X₂. Increasing the concentration of X₁ had a negative effect on the gelation time, as demonstrated by the negative sign of its coefficient. Furthermore, increasing the concentration of X₂ had a positive effect on the gelation time, as indicated by the positive sign of its coefficient. X₁ was a significant model term that affected gelation time (p < 0.05), as shown in Table 6.

Statistical analysis of R₄

The regression equation was derived from the Design-Expert, and polynomial equations were developed as follows:

$${\text{Y}}_4=32.40-5.90{\text{X}}_1+1.44{\text{X}}_2-0.96{\text{X}}_1{\text{X}}_2+1.89{\text{X}}_1^2+0.26{\text{X}}_2^2$$ 4

It can be deduced from the equation and plots of the response surface that the gelation temperature varies according to the changing concentrations of poloxamer 407 and carbopol 934P. The coefficient values of X₁ were higher than those of X₂, indicating that the concentration of X₁ exerts a greater impact on the gelation temperature compared to that of X₂. The negative signs of the coefficients for X₁ and X₂ indicate that R₄ declines as the concentrations of X₁ and X₂ increase. Additionally, X₁ and X₂ were both significant model terms (p < 0.05) that affected R₄. However, the interaction term was not significant (p > 0.05), as shown in Table 6.

From the polynomial equation and the response surface plot, it is evident that the values of the coefficient of X₁ are higher than the coefficient of the independent variable X₂, with the former having a relatively small p-value. Therefore, it can be concluded that poloxamer 407 has a predominant impact on R₁, R_3, and R₄.

The contour and response surface plots presented in Figs. 7 and 8 further demonstrate the relationships between the dependent and independent variables. The top two responses selected from the drug release profile were R₁ and R₂, which were significantly related to the concentrations of the two polymers.

Fig. 7.

The contour plots for the percentage of drug release at the first hour (R₁), the time required for 90% drug release (t_90%) (R₂), gelation time (R₃), and gelation temperature (R₄)

Fig. 8.

The response surface plots for the percentage of drug release at the first hour (R₁), the time required for 90% drug release (t_90%) (R₂), gelation time (R₃), and gelation temperature (R₄)

The data revealed that as the concentrations of poloxamer 407 and carbopol 934P increased, R₁ and R₂ decreased. The results indicated that the percent drug release in the first hour decreased as the concentration of the polymers increased. In addition, the figures also indicate that the time required for 90% drug release (t_90%) increased as the concentration of polymers increased. At lower temperatures, there was an increase in the number of micelles formed, ultimately leading to a tight lattice-like arrangement of micelles. Therefore, the gelation temperature varies with the concentration of poloxamer 407 and carbopol 934P. Notably, the gelation temperature decreased with increasing poloxamer concentration and decreasing carbopol 934P concentration. Moreover, the gelation time decreased with an increase in the concentration of poloxamer 407 and increased with an increase in the concentration of carbopol 934P. These observations can be ascribed to the properties of the Polypropylene Oxide (PPO) and Polyethylene Oxide (PEO) structural domains in poloxamer 407. The PEO and PPO structural domains in poloxamer 407 undergo hydration reactions at temperatures below the critical micelle temperature. However, when the temperature reaches the crucial micelle temperature, the solubility of the PPO strands decreases, and the PPO structural domains are dehydrated. As a result, the PPO structural domains form spherical micelles through hydrophobic interactions, with the dehydrated PPO structural domains located at the core of the micelles and the hydrated PEO structural domains located at the outer shell of the micelles. These micelles entangle with each other to create a three-dimensional network structure [28]. Consequently, when the critical micelle temperature is reached, formulations with a high concentration of poloxamer 407 would have more micelles and therefore exhibit lower sol–gel transition temperatures and shorter gelation times than formulations with lower poloxamer 407 concentrations. The phenomenon of increased gelation temperature with increasing concentrations of carbopol 934P can be explained by polymer interactions. One hypothesis is that a 3D framework is formed between the carboxy group of carbopol 934P and the poloxamer ether group due to hydrogen bonding, and the other is that the binding of carbopol molecules to the cavities between multimolecular poloxamer micelles blocks the interactions between the poloxamer chains, which also induces a higher gelation temperature and longer gelation time.

The perturbation plots (Fig. 9) show the effect of the independent variables A (poloxamer 407) and B (carbopol 934P) on four responses: R₁, R₂, R₃, and R₄. The steep line obtained for the independent variable A (poloxamer 407) indicates that it has a significant effect on all four responses. Conversely, variable B (carbopol 934P) significantly affected R₁ and R₂.

Fig. 9.

The perturbation plots for the percentage of drug released at first hour (R₁), time required for 90% drug release (t_90%) (R₂), gelation time (R₃), and gelation temperature (R₄)

Optimization of design of experiment and control study

The optimized formulations for the four responses in the in-situ gel were obtained using Design-Expert. Constraints (Table 7) like minimum R₁, the maximum R₂, the minimum R₃ (190–390 s), and R₄ (27–32 °C) were to identify the optimal setup of the independent variables in the new formulation. A concentration of 20.8412% (w/v) of poloxamer 407 and 0.5% (w/v) of carbopol 934P were found to be the optimal formulations (Fig. 10).

Table 7.

Standards for the alternative of the optimized formulation

Response	Standard	The lower limit	The upper limit
The percentage of drugs released at first hour (R1)	Minimize	95.98	101.94
Time required for 90% drug release (t90%) (R2)	Maximize	43.77	57.11
Gelation time (R3)	Minimize	190	390
Gelation temperature (R4)	Is in range	32	37

Fig. 10.

Overlay plot for prediction of the optimized batch

The global desirability function (D) was used to optimize a sequence of models drawn from the analysis of the experimental statistics. Two independent variables were included in the optimization of the designed volume. The desirability plot of the responses revealed that the maximum value of D obtained at the optimum concentrations of the independent variables was 0.588. Based on the desirability methodology, the prerequisites for the optimal formulation could be accomplished using a formulation consisting of 20.8412% (w/v) poloxamer 407 and 0.5% (w/v) carbopol 934P (Fig. 11).

Fig. 11.

Optimized desirability ramps and bar graphs. A Desirability slopes showing the levels of the independent variables and the predicted values of the optimal formulation; B bar chart showing the combined desirability values of R₁–R₄

Gel behavior studies were undertaken on the optimized formulations to assess the transparency, injectability, pH, viscosity, R₁, R2, R3, and R4, as shown in Table 8. The elastic modulus of the optimized formulation was shown in Fig. 2. The degradation rate of the optimized formulation was shown in Fig. 12A. In-vitro release curves were matched with various kinetic models to understand the drug release mechanism, as illustrated in Fig. 12B. The results showed that the first-order model was the best-fitting model, as shown in Table 9, which is consistent with the general process of sustained-release systems releasing drugs via a first-order mechanism to achieve long-lasting effects.

Table 8.

Evaluation parameters of the optimized gel

Evaluation parameter	Optimized batch
Transparency	++
Injectability	Pass
pH before gelation	4.28
pH after gelation	3.95
Viscosity (mPa s)	151,798
R1	97.34%
R2	51.06 min
R3	213 s
R4	36 °C

Fig. 12.

A The degradation rate of the optimized formulation was determined at 37 °C. B In-vitro drug release profile of the optimized formulation

Table 9.

Release kinetics of optimized batch

Optimized batch	Zero-order	First-order	Hixson–Crowell	Higuchi	Korsemeyer–Peppas	Best fit
	R2	R2	R2	R2	R2
	0.70354	0.98864	0.9786	0.90803	0.91545	First-order

Assay of cell viability

Analyzing the results by CCK-8 assay (Fig. 13A), except for the 100% CPQ group (CPQ100-1), which had low cell viability, the cell viability was more than 70% in all groups, indicating that almost all groups showed no cytotoxicity. Additionally, the OD values of CP CPQ groups at concentrations of 6.25%, 12.5%, and 25% were higher than those of the control groups at both 24 h and 72 h, suggesting that these groups have certain effects on promoting cell proliferation. It is worth noting that, although there was no significant difference in cell viability between the CPQ and CP groups (p > 0.05), the mean values of the CPQ group were generally higher than those of the CP group, as shown in the graph, which also implies that quercetin may play a role in promoting cell proliferation. Over time, the OD values of CPQ-25 and CPQ-100 increased to some extent (p < 0.05), implying that time may promote cell proliferation. Cell viability experiments with CP and CPQ provided a basis for the dosage selection in subsequent experiments. Furthermore, we intend to introduce additional cellular experiments, including the determination of signaling pathways and the detection of cytokines, to confirm the role of quercetin in promoting cell proliferation.

Fig. 13.

A The graph shows the effect of different concentrations of blank in-situ gel (CP) and in-situ gel loaded with quercetin (CPQ) on MC3T3-E1 cell proliferation at 24 h and 72 h, respectively. Two-way ANOVA with Tukey’s multiple comparisons was used to analyze. B TNF-α expression levels for different quercetin contents. C IL-6 expression levels for different quercetin contents. * represents p < 0.05, ** represents p < 0.01, *** represents p < 0.001, and **** represents p < 0.0001 in comparison with control

Anti-inflammatory effect in vitro

To verify the anti-inflammatory effect of CPQ, LPS was used as an inflammatory inducer, and 10 μmol/L and 20 μmol/L quercetin were used to stimulate macrophages for 24 h, respectively (Q10 and Q20 groups). Then the secretion levels of inflammatory cytokines (IL-6 and TNF-α) were detected. Compared with the LPS stimulation group (control group), the expression of IL-6 and TNF-α in the Q10 and Q20 groups decreased (Fig. 13B, C). The expression of TNF-α in the Q20 group was significantly lower than that in the control group, and the overall reduction in the Q20 group was greater than that in the Q10 group. These results indicate that both Q10 and Q20 groups can inhibit the production of inflammatory-related factors. Quercetin at the concentration of 20 μmol/L had the best anti-inflammatory effect. It was confirmed that in situ gels containing quercetin exhibited potent anti-inflammatory effects on LPS-stimulated macrophages [29]. Based on these results, we hypothesized that CPQ could target macrophages, thereby changing the phenotype of macrophages and blocking the production of proinflammatory cytokines such as IL-6 and TNF-α to improve the inflammatory environment. The in-situ gel can inhibit the expression of inflammatory factors within 24 h, which is a high requirement for its use as an injectable gel in the periodontal pocket.

Periodontitis is the leading cause of tooth loss in adults. The existing treatment methods for periodontitis are mainly mechanical therapy and systemic medication, which have invasive side effects and strong systemic toxicity [8]. In this study, the natural compound quercetin, a key component of the in-situ gel, was not only safe and non-toxic but also had significant antibacterial and anti-inflammatory effects. The in-situ poloxamer/carbopol gel loaded with quercetin is temperature-sensitive. The prepolymer before cross-linking is a solution and can be injected with a syringe, which reduces the difficulty and invasiveness of the operation. In-situ solution-gel transformation was formed in the periodontal pocket to prolong the retention time and achieve sustained release of quercetin. At the same time, this gel can be filled to cover the gingival soft tissues, providing them with a protective barrier. Therefore, the quercetin-loaded poloxamer/carbopol in-situ gel may be effective in treating periodontitis.

Conclusions

We successfully prepared and evaluated a temperature-sensitive poloxamer 407/carbopol 934P in-situ gel loaded with quercetin. The designed formulations were deemed acceptable. The formulation remained liquid under non-physiological conditions and converted to the gel phase at physiological temperatures. The statistical analysis showed that the concentrations of poloxamer 407 and carbopol 934P significantly affected the gelation temperature, gelation time, and drug release studies the percentage of drug release at first hour and the time required for 90% drug release (p < 0.05). When the optimized dosage of poloxamer 407 was 20.8412% (w/v) and the dosage of carbopol 934P was 0.5% (w/v), the optimized gelation temperature was 36 °C, gelation time was 213 s, and the percentage of drug release at 1 h was 97.34%. The desirability of the optimization results is reasonable. Cell viability tests showed that pure gel and gel + quercetin formulations exhibited low cytotoxicity and promoted cell proliferation. In vitro anti-inflammatory experiments showed that 20 μmol/L quercetin in situ gel could significantly inhibit the expression of inflammatory factors IL-6 and TNF-α induced by macrophages and eliminate inflammation. Additional animal experiments are required to confirm the anti-inflammatory role of quercetin in promoting cell proliferation and osteogenic differentiation. Therefore, we anticipate that this novel and safe thermosensitive in-situ gel drug carrier system could serve as an effective candidate in clinical treatment for periodontitis when delivered to the periodontal system.

Author contributions

Conceptualization, P.Z. and Q.C.; methodology, P.Z and X.L.; software, X.M. and Z.Z; validation, E.-S.L. and Q.C.; formal analysis, P.Z and X.L. and Y.J.; investigation, X.M.; resources, Q.J.; data curation, P.Z.; writing—original draft preparation, P.Z, X.L., Y.J., X.M., Z.Z. and Q.J.; writing—review and editing, H.-B.J., E.-S.L. and Q.C.; funding acquisition, X.L. project administration, X.L. and Q.C.

Data availability

The data used to support the findings of this study are included in the article.

Declarations

Conflict of interest

There is no conflict of interests in this article.

Ethical statement

There are no animal experiments carried out for this article.