Fabrication and characterization of agar-based hydrogel incorporating gelatin-coated silver nanoparticles with mupirocin for diabetic wound therapy

Abstract

Diabetic foot ulcers complicated by infection pose significant challenges in wound care, often resulting in prolonged healing times, increased risk of amputation, and substantial healthcare costs. Innovative treatment strategies involving the integration of bio-polymers and nanoparticles into hydrogel formulations offer promising solutions for managing diabetic wounds. This study focuses on synthesizing agar-based hydrogel containing gelatin-coated silver nanoparticles with mupirocin (GAgNPM). Gelatin was isolated from fish skin through acid (GAC) and alkaline pretreatment (GAL) techniques. In comparison to acid pretreatment extraction, the percentage yield of gelatin was higher with alkaline pretreatment at 16.43 ± 0.5%. The isolated GAL and GAC gelatin was characterized based on pH, viscosity, and gel strength. The GAL exhibited a higher pH and viscosity of 6.51 ± 0.1, and 306 ± 1.0 cps, respectively. The isolated GAL gelatin demonstrated superior characteristics and suitability for requiring optimized formulations. Using Design of Expert software, the optimal concentration for GAgNPM formulation was determined through factorial design and desirability approach. Atomic force microscopy and scanning electron microscopy demonstrated that the GAgNPM exhibited a spherical structure. Further, the in vitro antibacterial assay against Staphylococcus aureus and scratch assay were conducted utilizing a 3T3 fibroblasts cell line. Furthermore, the in vitro antibacterial and scratch assays demonstrated significant (p < 0.05) antibacterial and wound healing properties of the nanocomposite hydrogel. These findings strongly support the potential of agar–hydrogel-incorporated gelatin, and silver nanoparticles and mupirocin as an advanced carrier wound dressing material for diabetic wound treatment.

Introduction

Diabetes is a widespread epidemic of metabolic disorders that imposes substantial physical, psychological, and economic burdens on society. Currently 451 million adults are globally affected by diabetes as of 2017. Hence, projections indicate a potential raise to 693 million by the year 2045 in the absence of successful prevention measures. The majority of instances happen in nations with low and moderate income, contributing to 1.5 million diabetes-related deaths annually. It still remains a major global public health problem (Cho et al. 2018). Wound care is a significant concern in the medical field. Chronic wounds exhibit impaired healing via the conventional wound repair mechanisms over an extended time frame. This chronic wound is posed by the increasing prevalence of diabetic ulcers, traumatic injury, surgical wounds, and burns (Ndlovu et al. 2021). However, diabetes condition produces severe physiological consequences such as diabetic foot ulcers (DFUs). Further complications and consequences of DFU lead to amputation of lower extremity. The main factor contributing to persistent DFU is bacterial infection. When DFUs become infected with external agents, primarily bacteria, their condition worsens and frequently requires hospitalization. Chronic DFUs are mainly due to bacterial infections or their biofilms with Streptococci, Staphylococcus aureus, and Escherichia coli species (Kale et al. 2023). Bacteria within biofilms are inherently more resistant to antibiotics due to the protective matrix that prevents drug penetration. In addition, bacteria within biofilms may enter a dormant state, reducing their metabolic activity and rendering them less susceptible to antibiotics targeting active cellular functions (Lim et al. 2017).

A multidisciplinary approach is essential for the prevention and management of DFUs. The control of several risk variables, such as blood pressure, cholesterol, glucose regulation and smoking cessation, plays a crucial role in reducing the risk of cardiovascular disease. The local management of DFUs involves debridement, offloading, wound dressing, topical antimicrobials revascularization, negative pressure wound therapy, growth factor, skin substitutes, etc. However, current wound dressings exhibit limited antimicrobial effectiveness and can contribute to antibiotic resistance (Cheah et al. 2023).

Treatment strategies involving integration of bio-polymers, such as gelatin, chitin, cellulose, chitosan, collagen, silver nanoparticles, and antibiotics into hydrogel offer promising features including excellent biocompatibility, biodegradability, and properties to activate wound healing.

Biomaterials act as surrogates for native biological tissues in the human body, and also can interact harmoniously with physiological systems. Gelatin is a biopolymer derived from collagen, and demonstrates characteristics such as biocompatibility, biodegradability, low antigenic, and flexibility. It functions as a scaffold resembling the extracellular matrix (ECM) and is cost-effective and easily processed (Naomi et al. 2021). The glycine amino acid significantly contributes to cell adhesion. The gelatin promotes cell migration, particularly fibroblasts to the injury site. Moreover, it has facilitates the process of vascularization in the newly regenerated tissues at the wound site (Xiao et al. 2024).

Nanoparticles derived from noble metals such as gold and silver are being explored for drug delivery due to their distinctive physical and chemical characteristics. Particular attention is paid to silver nanoparticles (AgNP) for their intrinsic antimicrobial characteristics and shows promise for aiding wound healing in diabetes patients (Abdel-Mageed et al. 2022). Research demonstrated that metal nanoparticles along with antibiotics can lower the minimum inhibitory concentrations of antibiotics, potentially by increasing the efficacy while reducing the toxicity of drugs (Abdel-Aty et al. 2024).

Mupirocin, also known as pseudomonic acid A, is a derivative of secondary metabolism derived from the Gram-negative Pseudomonas fluorescens bacterium. It serves as a topical antibacterial agent and its mode of action is to bind to isoleucyl-tRNA synthetase enzyme in bacteria, thereby impeding the synthesis of protein. The therapeutic range of mupirocin includes the infections produced by pathogens like methicillin-sensitive and resistant streptococci and staphylococci organism. Furthermore, mupirocin facilitates wound healing by promoting the proliferation of human keratinocytes and amplifying the production of several growth factors (Abdel-Mageed et al. 2024). Commercially available mupirocin formulations are utilized for the treatment of skin wound infections occurring in operative wounds, burn wounds, and diabetic wounds. Administering mupirocin topically enhances its effectiveness and reduces the frequency of application to once daily. According to Golmohammadi R et al., study demonstrated that the use of selenium nanoparticles and mupirocin, in chitosan-cetyltrimethylammonium bromide-based hydrogel, significantly accelerates wound healing (Burdușel et al. 2018).

Hydrogels are three-dimensional polymeric matrices that possess physical and chemical properties similar to that of ECM, which provide more ideal treatment for diabetic wound healing. Hydrogels possess the capability to encapsulate a variety of medications or cytokines tailored to address key challenges in diabetic wounds, including anti-diabetic drugs and antibiotics that promote cell proliferation and tissue regeneration (Gkartziou et al. 2021). Its distinct porosity allows them to support the removal of wound exudates and appropriate cellular interactions, and deliver bioactive agents that encourage re-epithelialization, reduce inflammation, and facilitate healing (Twilley et al. 2022). Polymer, that provides a moist environment, absorbs the wound exudates and maintains temperature (Golmohammadi et al. 2020). This hydrated milieu is crucial for wound healing, as it supports cell migration, enhances angiogenesis, and helps modulate inflammation. By promoting these processes, hydrogel dressings contribute to the accelerated repair of chronic wounds, making them an increasingly valuable tool in the treatment of DFUs (Abdel-Mageed et al. 2022).

Ming Liu et al. emphasize that ensuring an adequate level of moisture in the wound environment can help reduce pain, decrease scarring, stimulate collagen production, and support the migration of keratinocytes, thereby aiding in the wound healing process (Zhang et al. 2023). Heidi Mohamed Abdel-Magee et al., describe alginate as an anionic biopolymer commonly utilized in hydrogel production. It also supports the creation of a moist environment, which promotes reepithelization and accelerates granulation tissue formation. Hydrogels composed of gelatin and alginate have been identified as optimal physicochemical mimetic biological scaffolds for wound healing applications. These materials serve as effective delivery vehicles by maintaining a moist environment, absorbing wound exudates, and exhibiting antibacterial properties (Güiza-Argüello et al. 2022). Hydrogels possessing significant swelling capacity can effectively absorb blood and bodily fluids, facilitating tissue regeneration and serving as efficient carriers for drug delivery (Basha et al. 2020).

In this study, formulation is optimized using the Quality-by-Design (QBD) approach for optimizing the concentration of reducing agents. It is a more reliable approach for processing a product (Fukuda et al. 2018). Design of Experiments (DoE) is a systematic and structured approach used to determine the relationships between factors influencing one or more responses utilizing statistical methods. Two-level full factorial designs are used to estimate the main effects of factors and their interactions with main responses (Abdel-Mageed et al. 2022). The concentration of reducing agents is considered as an independent factor in formulation design as it plays a critical role in influencing the synergistic or antagonistic interactions among ingredients, which impacts the performance of the final product.

To date, no research has explored this specific combination and its potential efficacy in diabetic foot ulcer treatment. The combination of silver nanoparticles, gelatin, mupirocin, and agar was chosen owing to its antibacterial wound healing properties, exudate absorption, and capacity to form a scaffold. These materials are intended for use in treating diabetic wounds. The present research was undertaken to develop an agar-based hydrogel-incorporated silver nanoparticles and mupirocin (GAgNPM).

Materials and methods

Materials

The 3T3 (cell line mouse embryonic fibroblast) cell line was purchased from NCCS, Pune. Phosphate buffered saline, gelatin standard, sodium chloride (SRL), sodium hydroxide, silver nitrate (Himedia), hydrochloric acid (HCl), mupirocin (Fourrts India), methanol, poly vinyl pyrrolidine, Dulbecco modified Eagle’s medium (DMEM), resazurin (Himedia), agar (Otto Chemi), Mueller–Hinton broth (Himedia), and all other chemicals were purchased from certified supplier and were of analytical grade. The fresh fish sea bass skin (Lates calcarifer) was obtained from the fish market.

Collection and processing of fish skin

The collected fish were first subjected to skin removal, wherein the skin was carefully separated from the flesh to avoid any damage. Following skin removal, the skins were washed thoroughly with tap water to eliminate any adhered flesh and scales. This washing step is essential to ensure the skin is thoroughly cleaned and free from contaminants that could interfere with the gelatin extraction process. Once cleaned, the skins were placed in suitable containers and kept at − 20 °C. Freezing the skins at this temperature helps to preserve them by preventing microbial growth and enzymatic degradation until they are ready for further processing.

Acid treatment

For the acid treatment, the frozen skins were first allowed to thaw at room temperature, which makes them easier to handle and cut. The thawed skins were then cut into small pieces to increase the surface area for the subsequent treatment. These skin pieces were immersed in a 3% HCl (hydrochloric acid) solution at a ratio of 1:7 w/v (weight to volume) and left to soak for a day. The acid treatment aims to eliminate non-collagenous substances such as fats, pigments, and other impurities that might be present in the skin. The acidic environment helps to break down these substances, making it easier to extract pure gelatin in the later stages (Yahdiana et al 2018).

Alkaline treatment

For the alkaline treatment process, the skins were immersed in a 0.1 M NaOH (sodium hydroxide) solution for 3 h at room temperature. The alkaline treatment serves to further remove non-collagenous elements such as remaining fats and proteins that were not eliminated during the acid treatment. The basic conditions created by the NaOH solution help to break down these substances. After the 3-h treatment, the skins were thoroughly washed with tap water to neutralize the pH. This step is important to ensure that there is no residual alkalinity, which could interfere with the gelatin extraction process. Achieving a neutral pH is crucial for maintaining the structural integrity of the collagen fibers, which will be converted into gelatin (Venupriya et al. 2022).

Gelatin extraction and purification

For gelatin extraction, the pretreated skins were immersed in distilled water at 1:3 w/v ratio and heated at 70 °C for 2 h. The resulting solution was subsequently filtered using Whatman No.4 filter paper and centrifuged at 10,000 rpm for 15 min to remove insoluble material. Finally, the supernatant was subjected to dialysis to purify the gelatin (Roy et al. 2017).

Characterization

Yield of gelatin

As per the methodology outlined in the cited Ref. (Chua et al. 2023), the gelatin yield was evaluated on a wet weight basis utilizing Eq. 1.

$$\text{Yield}\text{of}\text{gelatin}\left(\%\right)=\left(\text{Weight}\text{of}\text{the}\text{gelatin}\text{powder}/\text{weight}\text{of}\text{the}\text{fish}\text{skin}\right)\times 100$$ 1

Analysis of pH and viscosity

The gelatin’s pH was determined utilizing a calibrated digital pH meter (µ pH system 361, Systronics, New Delhi) following the 1975 British Standard Institution protocol with slight modifications. 100 ml of distilled water was used to dissolve 1 g of gelatin for measurement. Viscosity of samples GAC and GAL, along with standard gelatin (G), was measured using a Brookfield viscometer (D-VE model) with spindle 64 at 50 rpm for 1 min. Color comparisons of the gelatin samples were also made (Hidayati et al. 2021).

Determination of functional group

The functional groups and structural characteristics of the isolated gelatin samples (GAC, GAL, and G) were analyzed using Fourier transform infrared spectroscopy (FTIR). The dried gelatin samples were mixed with potassium bromide (KBr) and pressed into pellets using a hydraulic press under 3–5 kg pressure. Spectroscopic analysis was conducted within the wavenumber range of 4000–400 cm⁻¹ (Khanh et al. 2019).

Texture analysis

A texture analyzer (TA-XT plus, Stable micro system) was utilized to assess the gel strength (bloom strength or rupture of characteristics of gels). The 1% of GAC and GAL was prepared and heated at 50 °C for 15 min for complete dissolution and cooled. The 0.5-inch cylindrical probe was lowered in the samples. The strength of the gel is assessed as the peak force (the force to penetrate to a smaller region of chosen depth) (Ismail and Abdullah 2019) (Table 1).

Table 1.

pH, viscosity, and color of gelatin: G, GAC, and GAL

Sample name	pH	Viscosity (Cps)	Color
G (standard)	5.21 ± 0.2	315 ± 1.5	Pale yellow color
GAC	4.92 ± 0.3	256 ± 1.2	Light white color
GAL	6.51 ± 0.1	306 ± 1	Pale yellow color

Design of experiments

Experimental design

Factorial designs with a 2² matrix were utilized alongside Design Expert software (Version 13, Stat-Ease) to establish mathematical correlations between independent and dependent variables. The independent variables included concentrations of trisodium citrate and tannic acid (reducing agent). Each independent variable was set at two levels, as detailed in Tables 2 and 3. The impact of these variables on dependent variables such as particle size (PS, Y1), entrapment efficiency (EE, Y2), and in vitro drug release (DR, Y3) was systematically investigated. Statistical analysis and validation of each result were carried out using Analysis of Variance (ANOVA) (Zhang et al. 2023).

Table 2.

Independent variables and dependent variables

Factors (independent variables)	Levels
	Low level	High level
X1: Trisodium citrate	1 mM	7 mM
X2: Tannic acid	3 mM	15 mM
Response (dependent variables)	Desirability characteristics
Y1: Particle size	Minimize
Y2: Entrapment efficiency	Maximize
Y3: In vitro drug release	Maximize

Table 3.

Experimental design for the optimization of formulation

Runs	X1:TSC(mM)	X2:TA(mM)	Y1: particle size	Y2:entrapment efficiency	Y3: in vitro drug release
1	1	15	168	97	95
2	7	3	144	92	93
3	7	3	144	92	93
4	7	15	250	86	85
5	1	3	102	98	81
6	1	3	102	98	81
7	7	15	250	86	85
8	1	15	168	97	95

Preparation of gelatin-coated silver nanoparticles with mupirocin (GAgNPM)

According to the desirability data, we prepared the optimized formulation. First, 10 ml of 1 mM silver nitrate solution was prepared and heated to 60 °C with continuous stirring. Then 1 mM of trisodium citrate (TSC) and 12.17 mM of tannic acid (TA) were slowly added for reduction. The solution gradually changed from colorless to yellow, indicating the reduction of silver nitrate. Then 40 mg of the drug was added and allowed to stir for 1 h. After this, 3% gelatin was gradually added. Finally, the GAgNPM nanoformulation was subjected to characterization and stored. The optimized formulation was further characterized (Patarroyo et al. 2020).

Determination of morphology, particle size, and zeta potential

The formulation’s particle size and zeta potential were assessed using a Zeta sizer (Malvern Zeta Sizer, ZS 90, UK). Fourier transform infrared (FTIR, Shimadzu) spectroscopy was used to identify functional groups, with measurements taken at a resolution of 4 cm⁻¹within the range of 4000–500 cm⁻¹. Surface smoothness, roughness, and shape were studied using atomic force microscopy (AFM). In addition, the sample’s structure was analyzed using scanning electron microscopy (SEM, JEOL Model, Zeiss) at an acceleration voltage of 20 kV (Ismail and Abdullah 2019).

Determination of drug loading and entrapment efficiency

Drug loading and entrapment efficiency were investigated by determining the percentage of drug encapsulated within the gelatin matrix relative to the total amount of drug added using spectrophotometry. The formulation was centrifuged at 15,000 rpm for 20 min at 4 °C to separate the supernatant liquid. The recovered supernatant was filtered for the measurement of unentrapped drug concentration following appropriate dilution using UV–visible spectroscopy at 222 nm. All experiments were performed in triplicates according to the method. The drug loading and entrapment efficiency percentages for the formulation were calculated using Eqs. (2) and (3), respectively (Patarroyo et al. 2022).

$$\begin{array}{cc}\hfill \text{DL}\left(\%\right)& =\text{Total}\text{amount}\text{of}\text{drug}\hfill \\ \hfill & -\text{amount}\text{of}\text{unentrapped}\text{drug}/\text{total}\text{weight}\text{of}\text{GAgNM}\hfill \\ \hfill & \times 100\hfill \\ \hfill \end{array}$$ 2

$$\begin{array}{cc}\hfill \text{EE}\left(\%\right)& =\left(\text{Total}\text{amount}\text{of}\text{drug}-\text{amount}\text{of}\text{unentrapped}\text{drug}\right)\hfill \\ \hfill & /\text{total}\text{amount}\text{of}\text{drug}\times 100\hfill \\ \hfill \end{array}$$ 3

Preparation of GAgNPM-loaded hydrogel using a crosslinker

At low concentrations, agar creates a translucent gel with excellent consistency. A 1% w/v agar solution was dispersed in 100 ml distilled water to prepare the gel base. Subsequently, 0.3 g of methylparaben was dissolved in propylene glycol and added to the base for thickening. Triethanolamine was then added to neutralize the mixture and maintain a pH of 7. The prepared nanocomposite GAgNPM formulation was loaded into the agar base and mixed using a mechanical stirrer for 5 min. The pH of the formulation and its viscosity (measured using a S63 Spindle at 10 rpm) were determined at room temperature.

Swelling studies

The pre-weighed hydrogel sample was placed in a beaker containing an excess amount of distilled water as the swelling medium. Ensuring that the samples are fully immersed in the medium, they were incubated for 15, 30, 60, and 120 min. After incubation, the swollen hydrogel was carefully removed from the beakers. Excess water was gently blotted using filter paper (Hidayati et al. 2021). The swelling percentage was calculated using Eq. 4.

$$\text{Swelling}\left(\%\right)=W_{\text{s}}-W_0/W_0\times 100$$ 4

where W_s is weight of the swollen hydrogel and W₀ is weight of the initial hydrogel.

Texture profile analysis

The prepared hydrogel was characterized using texture analysis. Briefly, gel strength was measured using a 0.5-mm radius probe. The force required to penetrate the gel to a specific distance was recorded. Spreadability was assessed using Perspex cones. Firmness and shear work were recorded at a speed of 3 mm and a distance of 23 mm with an automated trigger force. Extrudability was determined using a forward extrusion rig, where a piston disc extrudes the sample through an orifice. The force required to extrude the hydrogel was recorded (Srivatsan et al. 2015).

In vitro drug release kinetics

In brief, the in vitro release data profile for the optimized formulation was performed at different pH conditions (5.5, 6.8, 7.4) at 37 °C, using a dialysis membrane with slight modifications (Khanh et al. 2019). One gram of the sample was transferred into the dialysis membrane with a molecular weight cutoff of 12–14 kDa, and placed in a 200 ml donor compartment with 200 rpm rotation. The percentage of drug release at different time intervals was calculated using the drug’s lambda max with a UV–visible spectrophotometer (Suhail et al. 2022).

In vitro evaluation

Minimum inhibitory concentration (MIC)

The antibacterial susceptibility testing was conducted to determine the minimum inhibitory concentration (MIC) against Gram-positive bacteria (Staphylococcus aureus) and Gram-negative bacteria (Escherichia coli) following the Clinical and Laboratory Standards Institute (CLSI) guidelines, third edition. The broth dilution method was utilized in microtiter plates for this purpose. The bacterial inoculum was adjusted to the 0.5 McFarland standard. A 100 µl aliquot of the formulation (60, 120, 180, 240, 300 µg/ml concentration) was added and diluted with 100 µl of media. The microtiter plate contained a range of concentrations from highest to lowest. Negative controls (only medium) and positive controls (medium and microorganism) were included. Five microliters of resazurin solution were added, and the plates were incubated at 37 °C for 3 h, after which color changes were observed (Loo et al. 2018).

Zone of inhibition (ZOI)

In brief, 20 ml of Mueller–Hinton agar was sterilized and cooled to 45–50 °C. To this, 500 µl of microorganism was added, mixed, and immediately poured into sterile plates, ensuring the media was distributed uniformly. About 20 µl of the (240 µg/ml) sample was injected into sterile discs, which were then placed evenly spaced on the surface of the inoculated agar plates. The plates were inverted and incubated. After the incubation period, antimicrobial activity was evaluated by measuring the diameter of each zone of inhibition. Discs of amoxicillin (10 µg/disc) were used as a positive control (Diniz et al. 2020).

In vitro cell line wound healing study

The in vitro scratch assay is primarily performed to study cell migration and wound closure in cultured 3T3cells. The cells were Seeded into a 24-well tissue culture plate at a concentration of 1 × 10⁵ cells/ml and cultured at 37 °C in a medium containing 10% FBS until they reached 70–80% confluency. Subsequently, a linear wound was created in the cell monolayer using a sterile 100 µL plastic pipette tip across the center of each well. Debris was removed using phosphate buffer. The formulations were then added, and the plates were incubated for 24 h at 37 °C with 5% CO2. The percentage of wound closure was then calculated (Bolla et al. 2019).

Stability study

The stability studies of the optimized formulation were performed according to the International Conference on Harmonization (ICH, Q1A) guidelines at three different storage temperatures: 04 ± 0.5 °C/no relative humidity (RH), 25 ± 0.2 °C/60 ± 5% RH, and 30 ± 2 °C/65 ± 5% RH, for 6 months (Qindeel et al. 2019).

Statistical analysis

All the experiments were performed using Design Expert 13, Graph Pad Prism8, Origin Pro. The data represent the average of three independent experiments ± SD (standard deviation). The factors within the groups were compared using one-way analysis of variance (ANOVA).

Results and discussion

Yield

The efficiency of gelatin is highly important in medical applications, given its broad uses in pharmaceuticals and wound care. Figure 1a illustrates the percentage yield of gelatin obtained through acid and alkaline pretreatment, calculated on a wet weight basis. Various factors such as age, species of raw materials, as well as extraction parameters (temperature, time, and pH) and collagen content, affect gelatin yield (Shyni et al. 2014).

Fig. 1.

a Percentage yield of GAC and GAL; b gel strength of GAC and GAL

The presented data show that alkaline pretreatment of fish skin yields a higher amount of gelatin (GAL—16.43 ± 0.5%) compared to acid pretreatment (GAC—12.33 ± 0.14%). Previous research findings reported lower gelatin yield (7.2 ± 1.07%) from Indian major carps (Arnesen and Gildberg 2007). Interestingly, the yield from acid pretreatments was lower than that of gelatin extracted from Nile tilapia (21.93%). Acid and alkaline pretreatments are employed to solubilize non-collagenous material and disrupt the triple-helix structure of collagen in fish skins. The varying yields between acid and alkaline treatments may vary from the loss of collagen during the pretreatment process, potentially due to differences in intermolecular cross-linking within the collagen (Shyni et al. 2014; Arnesen and Gildberg 2007). Notably, there was a significant difference (p < 0.05) in the extracted gelatin % yield. Maximizing gelatin yield from fish skin waste is crucial for cost-effectiveness across applications. Efficient extraction minimizes waste, optimizes resource use, and reduces production costs. Effective methods like acid or alkaline pretreatments dissolve non-collagenous materials and disrupt collagen’s triple-helix structure. Optimizing processing conditions enhances yield, enhancing overall cost-effectiveness.

Determination of pH and viscosity

In formulation development, maintaining optimal pH and viscosity of gelatin is crucial to ensure product quality, consistency, and stability. Gelatin typically exhibits an acidic pH range between 4.0 and 6.5. Table 1 presents the color, pH, and viscosity of the extracted gelatin. The pH values of GAC and GAL are 4.92 ± 0.3 and 6.51 ± 0.1, respectively. Previous literature has reported that gelatin extracted from catfish and Pangasius catfish displayed lower pH values of 5.22 ± 0.02 and 5.27 ± 0.03, respectively, compared to GAL (Jakhar et al. 2016).

The viscosity of GAC and GAL was measured at 256 ± 1.2 and 306 ± 1.6 cps, which was less than the standard gelatin. The existence of a low molecular weight fraction of peptides may be responsible for this. Gel strength, viscosity, and yield are influenced by gelatin pH with neutral pH showing maximum gel strength and viscosity due to a higher abundance of low-molecular-weight peptides (Zilhadia et al. 2018; Jakhar et al. 2016).

FTIR analysis

FTIR spectra were employed to analyze the functional groups, peak intensities, and positions in the spectra, providing insights into the secondary structure of the protein. All gelatins exhibited similar spectra, with absorption areas matching Amide A, Amide B, Amide I, Amide II, and Amide III. Amide A signifies the stretching vibration of the N–H bond in the range of 3100–3400 cm⁻¹. Previous studies indicate that this peak reflects molecule degradation during the gelatin extraction process (Venupriya et al. 2022). Amide B denotes the CH2 asymmetrical stretching in the peak area of 2956.32 and the interaction of –NH3 on the peptide chain in the peak region of 2900–3100 cm⁻¹. The presence of Amide I is observed in the peak region between 1600–1700 cm⁻¹, primarily reflecting the stretching vibration of the C=O bond with the peptide backbone. In this study, absorption peaks at 1642, 1623, and 1635 cm⁻¹ were observed for samples G, GAC, and GAL, respectively. This region also suggests the presence of the OH group paired with the COO⁻ group. Amide I also offers insights into the α-helices and random coil conformations. Typically, the Amide II absorption area showed around 1500–1600 cm⁻¹. This suggests that the peptide framework contains C–N stretching and N–H bending vibrations. Amide III exhibits a peak area extending from 1200 to 1300 cm⁻¹. This region encompasses various vibrations, including CN stretching and NH bending vibration. The absorption peaks at 1237, 1198, and 1236 cm⁻¹ correspond to G, GAC, and GAL respectively.

Texture analysis

The firmness or rigidity for a gel is formed by gelatin and is measured by its bloom strength. It is an essential parameter to assess the quality and functionality of an extracted gelatin. The gel strength is attributed to the three-dimensional hydrogen bonds formed between water molecules and the unbound hydroxyl groups of amino acids (Basha et al. 2020). The gel strength of G, GAC, and GAL showed the gel strength of 15.16 ± 0.25, 27.2 ± 0.3, 23.3 ± 0.2 g, respectively, graphically, as shown in Fig. 1b. In addition, the quantity of free hydroxyl groups in amino acids, the ratio of α-to-β-chains, and interactions between imino groups are other important elements that affect gel strength and play significant roles in determining the gel strength. Previous research found that jellyfish (Lobonema smithii) gelatin gel strength was higher compared to this study. This may be due to the interaction of hydrogen bonds with carbonyl oxygen of an adjacent peptide group (Abdel-Mageed et al. 2023).

Design of experiments

Statistical optimization and evaluation of dependent variables

To forecast process behavior within the design space and maximize the impact of various elements on response, Design of Experiment was employed. In 2² factorial designs, the levels of the factors may be arbitrarily low and high.

In this research, the impact of the concentration of reducing agents TSC (X1) and TA (X2) on the conversion of yield was studied. The TSC and TA play an important role in the synthesis of silver nanoparticles by reducing silver ions to form nanoparticles, stabilizing the nanoparticles to prevent agglomeration, and potentially influencing the size, shape, and antibacterial properties of the final product. The concentration of theses reducing agents can be tailored to optimize the formulation for specific applications. Table 4 depicts the actual design factors and responses such as particle size (Y1), entrapment efficiency (Y2), and in vitro drug release (Y3) studied for the four runs with replications. By applying mathematical equations and statistical measurement through experiment design, it offers the best formula by analyzing the effect of the independent variables (factors) on the dependent variables (responses). ANOVA data help in identifying statistically significant factors through variance analysis. A high (coefficient of determination) R² indicates a better model fit for the specific response, while a probability value (p < 0.05) signifies model significance depicted in Table 5. The Adjusted R² values in regression models account for insignificant independent variables. The difference between adjusted R² and predicted R² is less than 0.2, indicating statistical validity and model suitability.

Table 4.

Model statistics data and ANOVA results for the optimal design of formulations

Responses	PS (nm)	EE (%)	In vitro drug release (%)
Model	Factorial model	Factorial model	Factorial model
R2	0.9656	0.9311	0.9924
Adjusted R2	0.9519	0.9036	0.9893
Predicted R2	0.9120	0.8237	0.9805
Adequate precision	19.106	12.393	36.147
Sum of square	22,480.00	169.00	260.00
p value	< 0.0002	< 0.0012	< 0.0001
Status	Significant	Significant	Significant

Table 5.

Optimal formulation with its predicted and observed responses

Variables	Optimized values	Responses	Predicted values	Observed values
X1 (mM)	1	Y1 (PS) nm	157.31	190.2
X2 (mM)	12.1	Y2 (EE) %	96.59	95.21
		Y3 (in vitro drug release) %	92.13	86.36

Influence of input variables on particle size

Table 4 shows the PS range between 102 and 250 nm. The analysis was done on the effects of factors X1 and X2 on the formulation PS. According to the coded polynomial Eq. 5, increase in TSC and TA concentration significantly increases the particle size. Both the factors show a positive effect on particle size. Increased particle size may be due to particle aggregation and precipitation of the nanoparticle. This may be explained due to the larger surface area. The higher concentrations of tannic acid can lead to excessive cross-linking or bridging between nanoparticles, which may cause aggregation. The model shows the R² value of 0.9656 with a significant p value (< 0.0002) effect on formulation.

$$Y1=+166.00+31.00A+43.00B$$ 5

Influence of input variables on entrapment efficiency

In Table 4, the EE of the formulation ranges from 86 to 97%. An increase in factor (X1) leads to a decrease in the entrapment efficiency. A similar negative effect was observed due to factor (X2) on formulation. A decrease in the concentration of X2 shows decreased EE. The factorial model indicates an EE, regression R² value of 0.9311, signifying the significant (p < 0.0001) impact on the formulation, possibly due to the presence of a reducing agent.

$$Y2=+93.25-4.25A-1.75B$$ 6

Influence of input variables on in vitro drug release

The in vitro drug release studies provide valuable insights about the rate and extent of drug release from topical formulations. Efficiently the release of drug is essential for assessing its potential efficacy in treating diabetic wounds. According to Eq. 7, the increase in the X2 factor increases the rate of drug release. Further, the interaction between X1 and X2 factor decreases the rate of drug release with the R² value of 0.9924.

$$Y3=+88.50+1.50B-5.50AB$$ 7

Optimization and validation of results

Point prediction involves selecting specific values for these factors to predict the outcome of an experiment. The interval estimations and projections are generated by the linear model, according to Eqs. (5), (6), (7). Confirmation measures the model’s prediction interval against the average of a subsequent sample. The model is verified if the sample average falls within the expected range. Usually, confirmation is carried out at or close to the factor settings that numerical optimization suggests. A point that maximizes the desirability function is studied by numerical optimization. Numerical optimization studies a point that maximizes the desirability to value 1. The predicted values for PS, %EE, and DR of the formulation are shown in Table 6. The predicted values are 157.31 nm, 96.59%, and 92.13%. The experimentally optimized value is 190.2 nm, 95.21, and 96.36%, respectively. The graphical optimization represents the 3D responses plot in Fig. 2.

Table 6.

In vitro drug release kinetic mechanism

Kinetic model	Zero order	First order	Higuchi	Korsmeyer–Peppas	Hixon–Crowell
Regression coefficient nanoparticles	0.7562	0.8653	0.9563	0.9465	0.9185
Regression coefficient of hydrogel	0.8918	0.9021	0.9771	0.8932	0.9652

Fig. 2.

3D contour plot for responses

The interval estimations and projections are generated by the models. Confirmation measures the model’s prediction interval against the average of a subsequent sample. The model is verified if the sample average falls within the expected range. Usually, confirmation is carried out at or close to the factor settings that numerical optimization suggests. A point that maximizes the desirability function is studied by numerical optimization. If the goals were easily attained, better outcomes were expected, through the attractiveness desirability of 1. The predicted values for PS, %EE, and DR of the formulation are shown in Table 6. The predicted values are 157.31 nm, 96.59%, and 92.13%. The experimentally optimized values are 190.2 nm, 95.21, and 96.36%, respectively. The graphical optimization represents the 3D responses plot in Fig. 2. The smaller particle size increases the surface area for faster absorption and improves the therapeutic effect. High entrapment efficiency ensures the delivery of sufficient amount of drug at the site of action and the controlled release of the drug from the formulation which is essential for maintaining therapeutic levels over an extended period.

Characterization of the optimized formulation

Particle size, zeta potential, and PDI

Nanoparticles size and size distribution influence their interactions with biological targets and its stability (Abdel-Mageed et al. 2023). Smaller nanoparticles typically exhibit improved tissue penetration and enhanced drug delivery efficiency. The optimized formulation was observed to have a size of 190.2 nm, as represented in Fig. 3a. The PDI quantifies the distribution of particle sizes within a nanoparticle formulation (Mahmoodani et al. 2014; See et al. 2015). The formulation shows a PDI of 0.223, indicating it is highly mono-dispersed in nature. A low PDI value indicates a narrow size distribution, which indicates uniformity among particles. The zeta potential measures the electrical charge on the surface of nanoparticles, indicating the stability of colloidal dispersions by assessing the repulsion between particles. Figure 3b shows a zeta potential value of + 38 mV. Nanoparticles with higher positive or negative zeta potentials are less likely to aggregate due to electrostatic repulsion (Islam et al. 2017).

Fig. 3.

a Particle size, b zeta potential, c FTIR, d 2D AFM, e 3D topography, f SEM

FTIR spectroscopy

FTIR spectroscopy reveals changes in the functional groups of nanoformulations during the optimization processes within the nanoparticles. For mupirocin, O–H stretching and medium sharpness are indicated by peaks in the FTIR spectra located at 3515 cm⁻¹ and 3201 cm⁻¹, respectively. The peak at 1537 cm⁻¹ is due to conjugated C=O stretching, while the peak at 1743 cm⁻¹ is attributed to C=O stretching. In addition, the ester acetate C–O stretching is responsible for the peak at 1229 cm⁻¹.

Gelatin displays absorption peaks indicative of its molecular structure as shown in Fig. 3c: 1635 cm⁻¹ for amide I with C=O stretching, 1658 and 1546 cm⁻¹ for amide II (N–H bend-linked stretching), and 1236 cm⁻¹ for amide III (CH₂ group stretching). Amide A shows a peak at 3165 cm⁻¹, representing the N–H stretch involved in hydrogen bonding, while Amide B exhibits a peak of 3118 cm⁻¹, indicating CH2 asymmetric stretching vibrations. AgNP, on the other hand, demonstrates peaks at 3122 cm⁻¹ for O–H stretching, 1264 and 1247 cm⁻¹ for O–H bending, and 798 cm⁻¹ for the N–H rocking bond between nitrogen and hydrogen. AgNP-MP-coated gelatin (GAgNP-MUP) demonstrates prominent peaks corresponding to mupirocin, collagen, and AgNP suggesting minimal interactions between them.

Morphological study

Atomic force microscopy (AFM) is used for imaging surfaces at the nanometer scale by scanning a sharp probe over the surface and detecting the interactions between the probe and the sample. In the context of gelatin-based nanoparticles, AFM can provide valuable information about their morphology, size distribution, and surface characteristics. Figure 3d shows the 2D and 3D images of Gel-AgNP-M, revealing a smooth surface with a spherical shape owing to the existence of gelatin-stabilized silver nanoparticles with mupirocin. The average particle size is around 185.6 nm and the average surface roughness is 11.11 nm. These findings are consistent with the spherical structure of the particles due to the presence of TSC and TA.

Drug loading and entrapment efficiency

For optimum therapeutic efficacy and minimal cytotoxicity, drug loading and entrapment efficiency are important. EE and DL of gelatin-based formulation were 86.36% ± 0.15% and 2.5 ± 0.2% respectively. To be precise, the formulations encapsulated more than 85% drug. Efficient drug loading into the gelatin-based silver nanoparticles can enhance their penetration into the skin layers.

Swelling index

The prepared hydrogel showed a light brown color and a pH of 7.53 ± 0.3. The viscosity results showed that the viscosity of the formulation was 94,760 ± 2.6 cps. The prepared hydrogels are highly hydrated with agar (hydrophilic polymer), which allows them to hold water in their structure. Hydrogels for wound healing applications are mostly characterized by swelling index investigations. It assesses the capability of hydrogels to absorb fluids, such as wound exudate, blood, or physiological fluids. In the present research, swelling index (%) results were 20.3 ± 0.25% at 60 min. The results demonstrated the impact of hydrogel on increasing swelling index (%) with an increase in the reaction time from 15 to 120 min, where it attains the maximum value. Inadequate moisture can lead to dryness and delayed wound healing.

Texture profile analysis

The texture profile is used to quantitatively evaluate the textural properties of hydrogels including gel strength, firmness, and adhesiveness. To use hydrogel as a wound dressing, these parameters offer information on the hydrogel and its mechanical behavior and consistency. Bloom strength is a measure of gel strength, particularly those derived from gelatin. The force required for the penetration of the optimized formulation loaded agar hydrogel was shown to be 14.56 ± 0.47 g. This gel strength is generally desirable as it ensures that the hydrogel maintains its shape and provides effective coverage of the wound area.

The spreadability study evaluates a hydrogel’s ability to spread evenly and smoothly over the skin surface during application. The firmness of the hydrogel is 71.86 ± 0.50 g. Previous research supports that the firmness value ensures even coverage of the wound area and facilitates easier application without excessive friction (Németh et al. 2022). Extrudability measures the ease of hydrogel extrusion from its packaging, which is essential for wound healing applications. The force required for extrusion is 3498.33 ± 2.51 g. The data are represented in Fig. 4a–c. The obtained data reflect the flow properties and rheological behavior of the hydrogel under pressure.

Fig. 4.

Texture analysis profile: a bloom strength, b spreadability, c extrudability, d in vitro drug release

In vitro drug release and its release mechanism

The in vitro drug release investigations of the formulations aimed to examine the impact of formulation parameters on drug release and their implications for diabetic wound healing. Figure 4d illustrates the cumulative release profiles of mupirocin from both nanoformulation and the hydrogel-loaded formulation at two distinct pH levels. The nanoformulation exhibited a rapid release compared to the hydrogel-loaded formulation at pH 7.4, over a 48-h period. Conversely the hydrogel-loaded formulation demonstrated maximal drug release at pH 7.4 compared to pH 5.5, indicative of prolonged release characteristics compared to the nanoformulation. This may be due to the cross-linking of formulation within the hydrogel.

The application of mathematical models (e.g., zero-order, first-order, etc.) is used to analyze drug kinetics and elucidate the pattern of drug release. Table 7 shows that the hydrogel-loaded formulation follows the Higuchi model. This model describes drug release from matrix-based systems, allowing the diffusion of drugs through the matrix. Such controlled release kinetics are advantageous for diabetic wound healing, as they prolong exposure of the therapeutic substances at the wound site, promoting enhanced efficacy and potentially reducing dosing frequency.

Table 7.

Stability studies of agar hydrogel

Time in months	Physical appearance			pH	Drug content in percentage
	Phase separation	Color change	Grittiness
0	No	No color change	None	7.4	98.5
1	No	No color change	None	7.8	98
3	No	No color change	None	7.1	95
6	No	No color change	None	6.8	95

In vitro evaluation studies

Antibacterial studies

The minimum inhibitory concentration (MIC) values provide quantitative data on the potency of formulations against Staphylococcus aureus (Gram-positive) organisms. MIC tests were performed for formulations. Silver nanoparticles with mupirocin and gelatin combinations showed a MIC value of 120 µg/ml. Hence, the mupirocin MIC value was reduced to twofold and showed antibacterial activity also. Gelatin polymer is mainly a carrier that provides controlled release and enhances the stability of the encapsulated antibacterial agents. These results agreed with the previous research study (Oliveira et al. 2023).

This reference supports that determining the MIC values of formulations incorporating antimicrobial agents effectively inhibits bacterial growth, consequently mitigating the chance of wound infections along and fostering wound healing. The disc diffusion method provides a rapid and reliable means of screening formulations for their ability to inhibit bacterial growth. The antibacterial activity is indicated by a zone of inhibition (ZOI). Figure 5a shows that the formulation exhibits an increased ZOI of 21 mm, which is significantly larger than that of silver nanoparticles with gelatin which has a ZOI of 11 mm. Due to their potent bactericidal properties, silver nanoparticles have been employed in biomedical applications to treat infectious diseases. The formulation produced a synergistic action against Staphylococcus aureus. As shown in Fig. 6, methyl paraben was used as a control, and it exhibited no activity. This indicates that methyl paraben alone did not produce any significant effect or zone of inhibition in the tested conditions.

Fig. 5.

Zone of inhibition for formulation

Fig. 6.

Zone of inhibition for control group (methyl paraben)

Scratch assay

The scratch assay is a commonly used in vitro technique for studying cell migration and wound closure in response to various treatments. After 24 h of treatment, the formulation showed 25% closure, which increased to 56% after 48 h of treatment. This may be attributed to gelatin, which promotes cell migration. Its structural and characteristic similarity to the extracellular matrix (ECM) plays a major role in healing. Agar also supports cell growth and migration in wound healing assays due to its non-toxic nature to cells. Fibroblast cell migrate into the wound area and proliferate to close the gap, showcasing the valuable potential of this therapy in wound healing (FitzSimons et al. 2022). Our findings were consistent with previous research. A nanocomposite scaffold was fabricated using chitosan and gelatin, incorporating chitosan nanoparticles loaded with basic fibroblast growth factor (bFGH) and bovine serum albumin (BSA). The results demonstrated a sustained release of the growth factor, leading to a significant enhancement in fibroblast proliferation (Abdel-Mageed et al. 2021).

Stability studies

The stability study evaluates the physical and chemical stability of agar hydrogel formulations over a 6-month period, important for their application in wound healing. Through systematic monitoring of consistency, color, pH, and drug content significant findings emerge, revealing a progressive decrease in pH from 3 months onwards alongside a notable alteration in drug content, highlighting potential challenges to long-term stability outlined in Table 7. Despite consistent physical appearance, the observed pH shift suggests underlying chemical changes within the hydrogel matrix, prompting further investigation into potential hydrolysis or environmental interactions (Németh et al. 2022; Oliveira et al. 2023). Moreover, the significant difference in drug content underscores the necessity of stringent quality control to ensure consistent therapeutic efficacy. These findings emphasize the importance of ongoing stability assessment to safeguard the effectiveness of agar hydrogel formulations in promoting optimal wound healing conditions.

Conclusion

This study involved the isolation of gelatin from fish skin through alkaline treatment, resulting in higher yield, which was subsequently characterized. Utilizing Quality by Design and DoE for optimization ensures the reliability and effectiveness of the formulated hydrogel. The concentration of reducing agent TSC and TA was optimized based on this approach, allowing for the synthesis of GAgNPM nanocomposite. These nanocomposites were successfully integrated into agar hydrogel for the treatment of DFU. Incorporating biopolymers like gelatin, along with silver nanoparticles and mupirocin, into hydrogel formulations shows promise in addressing the complexities of diabetic wound healing. Moreover, the texture properties of the hydrogel were compared with the standard, revealing synergistic antibacterial activity. In vitro cell line studies conducted against 3T3 fibroblast demonstrated improved cell proliferation and wound closure. In future perspectives, animal studies will be conducted to evaluate the efficacy and safety of the developed formulation. Overall, developing gelatin-based hydrogels incorporated with silver nanoparticles and mupirocin represents a promising avenue for improving the treatment outcomes of diabetic foot ulcers.

Author contributions

Conceptualization: V.S and E.A. Methodology: V.S and E.A. Data collection and curing: V.S, B.R, and E.A. Writing—original draft preparation: V.S, B.R, P.R, and E.A.; Writing—review and editing: V.S, A.J, and B.R. Supervision: V.S. All authors reviewed the manuscript and agreed to the published version of the manuscript.

Funding

The authors did not receive support from any organization for the submitted work.

Data availability

The data presented in this study are available from the corresponding author upon request.

Declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

Not applicable.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Research involving humans and animals

The article does not include humans and animals used for the study.