*Article* **Harnessing Folate-Functionalized Nasal Delivery of Dox–Erlo-Loaded Biopolymeric Nanoparticles in Cancer Treatment: Development, Optimization, Characterization, and Biodistribution Analysis**

**Ms Farheen <sup>1</sup> , Md Habban Akhter 1,\* , Havagiray Chitme <sup>1</sup> , Md Sayeed Akhter <sup>2</sup> , Fauzia Tabassum <sup>3</sup> , Mariusz Jaremko <sup>4</sup> and Abdul-Hamid Emwas <sup>5</sup>**


**Abstract:** The aim of the present study is to develop Doxorubicin–Erlotinib nanoparticles (Dox–Erlo NPs) and folate-armored Dox–Erlo-NP conjugates for targeting glioma cancer. Glioma is one of the most common progressive cancerous growths originating from brain glial cells. However, the blood–brain barrier (BBB) is only semi-permeable and is highly selective as to which compounds are let through; designing compounds that overcome this constraint is therefore a major challenge in the development of pharmaceutical agents. We demonstrate that the NP conjugates studied in this paper may ameliorate the BBB penetration and enrich the drug concentration in the target bypassing the BBB. NPs were prepared using a biopolymer with a double-emulsion solvent evaporation technique and functionalized with folic acid for site-specific targeting. Dox–Erlo NPs and Dox–Erlo-NP conjugates were extensively characterized in vitro for various parameters. Dox–Erlo NPs and Dox–Erlo-NP conjugates incurred a z-average of 95.35 ± 10.25 nm and 110.12 ± 9.2 nm, respectively. The zeta potentials of the Dox–Erlo NPs and Dox–Erlo-NP conjugates were observed at −18.1 mV and −25.1 mV, respectively. A TEM image has shown that the NPs were well-dispersed, uniform, deaggregated, and consistent. A hemolytic assay confirmed hemocompatibility with the developed formulation and that it can be safely administered. Dox–Erlo-NP conjugates significantly reduced the number of viable cells to 24.66 ± 2.08% and 32.33 ± 2.51% in U87 and C6 cells, respectively, and IC50 values of 3.064 µM and 3.350 µM in U87 and C6 cells were reported after 24 h, respectively. A biodistribution study revealed that a significant concentration of Dox and Erlo were estimated in the brain relative to drug suspension. Dox–Erlo-NP conjugates were also stable for three months. The findings suggest that the developed Dox–Erlo-NP conjugates may be a promising agent for administration in glioma therapy.

**Keywords:** brain targeting; nanoparticles; folate receptor; glioma cancer; doxorubicin; erlotinib; blood–brain barrier

#### **1. Introduction**

Glioma results from the growth of malignant tissue in the brain or spinal cord and is extremely difficult to treat. Any drug targeting glioma must overcome the blood–brain barrier and effectively target any of a variety of cells proliferating at different rates while

**Citation:** Farheen, M.; Akhter, M.H.; Chitme, H.; Akhter, M.S.; Tabassum, F.; Jaremko, M.; Emwas, A.-H. Harnessing Folate-Functionalized Nasal Delivery of Dox–Erlo-Loaded Biopolymeric Nanoparticles in Cancer Treatment: Development, Optimization, Characterization, and Biodistribution Analysis. *Pharmaceuticals* **2023**, *16*, 207. https://doi.org/10.3390/ ph16020207

Academic Editor: Huijie Zhang

Received: 19 December 2022 Revised: 5 January 2023 Accepted: 13 January 2023 Published: 30 January 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

also providing a satisfactory safety profile. Gliomas destroy surrounding tissue and are associated with a devastating loss of functionality and a poor prognosis, with the mean survival rate from the time of diagnosis being <2 years [1]. The tumors show a limited response to conventional chemotherapy, and the development of therapies to specifically target the malignant cerebral or spinal tissue is extremely challenging due to the nature of the blood bran barrier (BBB) and the wide variety of malignant cells, different locations of tumors, and high rates of cell proliferation. Current therapeutic approaches are based on neurosurgical procedures, advanced radiotherapy, and a variety of emerging chemotherapies. Among novel drug therapies, nano-particle compounds are the most promising as these formulations penetrate the blood–brain barrier (BBB) and the blood–brain tumor barrier (BBTB) more effectively than conventional drugs. They allow for more selective tumor targeting as well as a reduction in the size and frequency of dosage, thus improving the options for the development of tailored formulations and less invasive therapies for this patient population. When successful, targeted drug therapies that are tailored to the specific malignancies of individual patients will provide hope to this patient population as a whole, promising to meaningfully extend their individual lifetimes following a glioma diagnosis [1].

Despite advances in nanotechnology and the development of multimodal therapies, disease prognosis remains a main challenge for therapeutic, drug-based interventions. Gliomas in the brain represent 57% of all gliomas, while 48% are malignancies associated with the central nervous system [2].A range of tumors may develop within each category, each requiring tailored intervention. The current standard of therapy for gliomas includes chemotherapy and radiotherapy with temozolomide, a combination of radiation and chemotherapy followed by surgical resection [3]. The surgery is carried out to excise the tumor; however, successful excision is no guarantee against re-growth or metastasis. The invasive nature of surgical procedures is also associated with risk to surrounding tissue, and the development of efficient, effective, and safe drug-based therapies tailored to target individual tumors is therefore highly desirable and may lead to a real improvement in survival rates and quality of life for this patient population [4].

Conventional chemotherapy is inefficient in treating gliomas because of the two barriers in the brain: the blood–brain barrier (BBB), and the blood–brain tumor barrier (BBTB). These barriers limit the transportation of dissolved, active therapeutic agents to the brain while also inhibiting drug excretion, i.e., the removal of metabolites and drug residues via the blood stream, thus limiting the effects on the tumor while also increasing the risk of damage to other, healthy tissue. Chemotherapeutic agents are toxic formulations known to cause multiple adverse responses due to their lack of tissue specificity, the high doses required to successfully target malignant tissue, and the required frequency of dosage. In addition, the limited excretion of metabolites and drug residues from the treated tissues leads to drug deposition and the accumulation of damaged tissues in the normal and neighboring cells/tissues, adding to the toxic burden and exacerbating the harmful effects of already toxic drugs [5,6].

In accordance with estimates for 2021, 83,570 people in the U.S.A. alone were expected to be diagnosed with a brain tumor. Of this number, 24,530 were expected to be malignant tumors and 59,040 were expected to be non-malignant tumors, with brain tumors being established as the likely cause of death in 18,600 of these cases [7]. More alarmingly, the most recent evidence suggests that there was a massive growth in the global occurrence of glioblastoma between 1995 and 2015, with more than double the rate of 2.4 to 5.0 per 100,000 individuals in the U.K. Glioblastoma occurrence is predicted to also increase dramatically in the U.S.A. over the next 30 years [8].

Erlotinib [N-(3-ethynylphenyl)-6,7-bis(2-methoxyethoxy)-4-quinazolinamine] is a quinazoline compound with antineoplastic activity that functions as an epidermal growth factor receptor (EGFR) antagonist and protease inhibitor. The main action mechanism of this drug is the inhibition of the phosphorylation of tyrosine kinase associated with tumor growth. However, this compound has a limited ability to overcome the constraints of the BBB and BBTB; it may therefore not be a suitable alternative for glioma patients [9].

Doxorubicin (Dox) is one of the most commonly recommended agents for the treatment of benign and malignant tumors, including solid and liquid tumors. Its action mechanism involves the inhibition of the topoisomerase II inhibitor, leading to a transient arrest of the cell cycle in the G2 to M phases. However, as is the case with quinazoline, this drug has limited BBB and BBTB penetrability [10].

In recent decades, nano-scale technology has emerged as a useful tool in a range of sciences and industrial applications, including pharmacology and the production of pharmaceuticals [11]. The size of nanoparticles (NPs) generally varies in the range of 1–100 nm, thus meeting one criterion for successful drug penetration of the BBB and the BBTB. As drug carriers, nanoparticles protect therapeutic agents from degradation in the biological fluid, provide bio-stability, prevent early release, and enable the transport of drug compounds to the intended tissues/cells [12]. The surface area and mass ratio of NPs are higher than macro-scale particles, resulting in the unique features of very small size and high drug-loading/encapsulation capacities, making nanoparticles important candidates as carriers of both diagnostic and therapeutic agents [13,14]. NP technologies applied to biomedical sciences enable the tailoring of drugs to specific tissues in a controlled manner, thus opening up the development of drug tailoring for individual patients and novel diagnostic tools [15]. So far, NPs have demonstrated advantages in the targeting of glioma tumors by enabling BBB and BBTB penetration for site-specific delivery, enabling precise drug-to-tissue tailoring, minimizing off-target effects, and reducing drug dose and frequency, and reducing the duration of drug administration, which also helps to improve patient compliance [16,17].

The BBB was first identified by Ehrlich in 1885 through a dye test. It comprises endothelial cells such as astrocytes, pericytes, and neuronal cells. Endothelial cells primarily restrict the passive transport of substances from blood to the brain. The permeability of brain blood vessels can be increased only when the BBB is ruined; however, some blood vessels nurturing a tumor form the BBTB. The BBTB causes less hindrance to the transport of substances in a brain tumor. The blood vessels bear over-expressed receptors, which could facilitate the ligand-gated active targeting of substances in the tumor microenvironment. To overcome the BBB and achieve a successful therapeutic delivery in the brain, ligandtargeted NPs have an overwhelming response in the diagnosis and treatment of glioma [18]. Luque-Michel et al. injected mice with polymeric NPs loaded with both superparamagnetic iron oxide NPs and Dox and, using an MRI, observed the high accumulation of nanocarriers in a glioma tumor region, leading to the successful suppression of tumor growth [19].

It is well established that the folate receptors (FR) are over-expressed in a range of solid tumor cells, viz., non-small cell lung cancers, colorectal, pancreatic, ovarian, breast, kidney, gastric, and prostate cancers, including glial tumors of the brain and central nervous system [20]. Folate receptors are glycoprotein-based receptors with a molecular weight in the range of 38–45 kDa, and folic conjugate has been proven to have higher rates of uptake than conventional therapies through folate-receptor-mediated endocytosis in tumor cells [21]. A high level of expression of folate receptors has been observed in solid tumors of the body. These sites may therefore represent an ideal target domain for nanocarriers [22]. In the development of drugs, folic acids (FA) are biotechnological ligands as they retain a high affinity for folate receptors and enable the targeted delivery of drugs to a tumor. Like antibodies, FAs are superior targeting ligands due to their relatively smaller size and lack of immunogenicity. They are readily available and have relatively simple conjugation properties. Folic acids have been used for a while as targeting ligands for nanoparticle uptake in cancer cell lines and are extensively explored as a targeting ligand for cell lines and tumors that over-express folate [23].

A biopolymer is a macromolecule composed of repeating structural units of monomers with covalent bonds that form a chain-like structure. Recently, biopolymers have gained wider attention with a view to develop pharmaceutical nanocomposites meeting the essential requirements of having antimicrobial properties: having stability and flexibility and being biocompatible, biodegradable, and bioresorbable [24]. Biopolymers have the major advantage of being easily broken down in the biological system by naturally occurring microorganisms and enzymes. Additionally, as their by-products are organic and without detrimental impact on the biological system, they are highly promising carriers for therapeutic drugs. As a result, a significant body of work has emerged on polysaccharidebased biopolymers for biomedical applications, replacing synthetic nanomaterials to hopefully improve efficacy and safety profiles and reduce the harmful side effects of anti-cancer drugs.

In the present study, biopolymers obtained from a natural source—the bark of *Cinnamomum zeylanicum*—were used to achieve drug encapsulation and the delivery of the active compound to the targeted site, facilitating its entry into the brain tumor [25]. This is the first time we have prepared a combined formulation of Erlo and Dox in functional biopolymer. We expect this formulation to have the dual advantage of diminishing resistance development in cancer cells and eliciting ameliorating, anti-cancer effects via the inhibition of epidermal growth factor receptor (EGFR) and damage to DNA. The functionalized nanoparticles were delivered via a naturally obtained *Cinnamomum zeylanicum* biopolymer, while the formulation was evaluated in vitro for physicochemical characteristics such as particle size, surface charge, % drug release. The biocompatibility of NPs was assessed using a hemolysis assay. We also examined the formula's biodistribution and cell viability against glioma cell lines and performed stability studies in vitro.

#### **2. Results**

#### *2.1. Formulation Optimization*

The experimental design applied a novel tool to achieve statistical optimization and enable the minimization of method-induced variability while yielding a high-quality product with uniform and homogeneous particle size distribution, as well as methodical stability for other parameters under study. As in many other experimental designs, the Box–Behnken design was used to optimize and investigate the principal effects, interactions, and quadratic effects of the independent variables on responses, viz., particle size, PDI, and %drug release. This design is effective for exploring quadratic response surfaces and constructing second-order polynomial models [26]. The Dox–Erlo NPs were optimized using the Box– Behnken design and a preliminary formulation was developed based on the trial-and-error method to identify the desirable components and select the appropriate concentration for the independent variables. According to this examination, a surfactant concentration lower than 1.00% *w*/*v* yielded larger NP sizes owing to a minimum size reduction attributed to poor emulsification, resulting in low drug encapsulation and impaired drug release. Furthermore, NPs in the >3.00% *w*/*v* size range demonstrated a diminished overlay and an extremely poor drug profile. To obtain the required particle sizes for the formulation, sonication below 3.00 min of sonication time is required. Size reductions of the nanoparticles were observed to be low. Above 12.00 min small particles were in line to accumulate, leading to instability issues, possibly due to an excess reduction of particle size. Thus, the selection of low and high levels of excipient concentrations was based solely on primary investigations. In this context, the surfactant levels were low (−1), medium (0), and high, (+1); at 0.50, 1.50, and 2.50% (*w*/*v*), respectively. Polymer concentrations were 1.00, 2.00, and 3.00% (*w*/*v*); and the sonication time levels were low, (−1), medium (0), and high (+1); corresponding to 3.00, 7.50, and 12.00 min, respectively, as is depicted in Table 1. The obtained independent variable data in the Box–Behnken design and their corresponding responses according to experimental runs are shown in Table 2.


**Table 1.** Box–Behnken design variables for formulation of Dox–Erlo NPs.



(A) Polymer concentration, % *w*/*v*; (B) surfactant concentration, % *w*/*v*; and (C) sonication time, min. R1—Particle size; R2—drug release %; and R3—PDI.

The linear correlation plots (A, C, E) and their residual plots (B, D, F) between actual vs. predicted values of particle size, PDI, and % drug release, are indicated in Figure 1. Fitting data to the various models—viz., cubic, 2FI, linear, and quadratic—in the Box–Behnken design indicated the quadratic model for each response. The best-fitted model for each response was selected using ANOVA by regression analysis the calculation of F values. The response surface morphology of the BBD expresses the individual, combined, and quadratic impacts on the dependent variables, viz., particle size (nm), PDI, and % drug release (Figure 2). The outcome of the regression analysis for particle size (R1), % drug release (R2), and the PDI (R3) of formulation are provided in Table 3. The analysis of variance of the calculated models for responses are shown in Table 4.

**Figure 1.** The actual vs. predicted values represented as linear correlation plots (**A**,**C**,**E**), and associated residual plots (**B**,**D**,**F**) providing responses according to particle size, PDI, and %drug release. **Figure 1.** The actual vs. predicted values represented as linear correlation plots (**A**,**C**,**E**), and associated residual plots (**B**,**D**,**F**) providing responses according to particle size, PDI, and %drug release.

**Figure 2.** Three-dimensional (3D) surface response plot (**A**–**F**) indicating comparative effects of polymer, surfactant, and sonication time on responses, particle size (**A**–**C**), % drug release (**D**–**F**), and PDI (**G**–**I**). **Figure 2.** Three-dimensional (3D) surface response plot (**A**–**F**) indicating comparative effects of polymer, surfactant, and sonication time on responses, particle size (**A**–**C**), % drug release (**D**–**F**), and PDI (**G**–**I**).

*2.2. Response 1: Effect on Particle Size*  The effects on particle size of various excipients used in the formulation are explained **Table 3.** Summary results of regression analysis for response fitting of quadratic models for R1, R2, and R3.


formulation number 7. Formulation number 6 and formulation number 1 demonstrated a <sup>−</sup> 0.0290 <sup>×</sup> <sup>B</sup> <sup>×</sup> C + 0.0414 <sup>×</sup> <sup>A</sup><sup>2</sup> + 0.489 <sup>×</sup> <sup>B</sup> <sup>2</sup> + 0.0144 <sup>×</sup> <sup>C</sup> 2


**Table 4.** Analysis of variance (sum of square, degree of freedom, mean square, F-value, and *p*-value) for response, particle size, drug release, and PDI.

## *2.2. Response 1: Effect on Particle Size*

The effects on particle size of various excipients used in the formulation are explained by the quadratic equation (Table 2).In the above equation of particle size (Table 2), the terms A, B, C, AB, AC, BC, A<sup>2</sup> , B<sup>2</sup> , and C<sup>2</sup> are significant. The model's F-value, 32.68, suggested a significant model. The statistical *p*-values < 0.05 indicate significant model terms, while *p* > 0.05 indicates insignificant model terms. The lack-of-fit F-value of 0.85 expressed remained insignificant for this quadratic model.

The size of particles in the formulations were reported from 100 to 240 nm in formulations number 6 to 3 (Table 2). The surfactant concentration revealed positive and negative effects on particle size. For example, formulations number 2 and 4 exhibited particle sizes of 230 nm and 240 nm, respectively, at a 0.5% concentration of surfactant. On the other hand, a particle size of 227 nm at a 2.5% concentration of surfactant was found for formulation number 7. Formulation number 6 and formulation number 1 demonstrated a particle size of 100 nm and 121 nm, respectively, at a 1.5% surfactant concentration (Table 2).

The concentration of the polymer provided a positive effect on particle size. Raising the polymer concentration to 3% *w*/*v* led to an increased size of particle. For example, formulations number 6 and 13, having a polymer concentration of 1% *w/v,* had particle sizes of 100 nm and 136 nm, respectively. On the other hand, formulations number 11 and 7 demonstrated particle sizes of 200 nm and 227 nm, respectively, with a 2% *w*/*v* polymer concentration. Again, increasing the polymer concentration (3% *w*/*v*) led to larger particle size: 240 nm.

On the other hand, sonication time demonstrated a negative impact on particle size. The formulations number 6 and 1 achieved particle sizes of 121 nm and 100 nm, respectively, with 12 min of sonication. With the same sonication time, formulation 16 exhibited a 232 nm particle size, probably due to a combined effect. Formulations 2 and 10 achieved a particle size of 230 nm and 170 nm, respectively, by sonication for 3 min.

#### *2.3. Response 2: Effect on % Drug Release*

The impact on % drug release of various excipients used in the formulation is explained by quadratic equation (Table 3).

In the above equation, the terms A, B, C, AB, AC, BC, A<sup>2</sup> , and B<sup>2</sup> are significant. The Model F-value, 29.09, indicated a significant model for the % drug release. The

lack-of-fit F-value of 0.53 entails insignificant for the model to fit. The polymer concentration positively impacted the % drug release. Formulation number 14 demonstrated that a 45% drug release had a 1% *w*/*v* polymer concentration. The increase of polymer concentration to 2% *w*/*v* led to an increased drug release: 71% and 75%, as seen in formulations number 3 and 17, respectively. Furthermore, after an increase in the polymer concentration of the formulation to 3% *w*/*v*, an increase in drug release was observed, such as cases of 89% and 83% in formulations number 1 and 10, respectively (Table 2).

#### *2.4. Response 3: Effect on the PDI*

The effect on the PDI of various excipients used in the formulation is explained by the quadratic equation (Table 3).

Surfactant concentration provided both positive and negative effects on the PDI. The observed PDI values were achieved: 0.123 and 0.231 at 1.5% and 2.5% of *w*/*v* surfactant concentration, corresponding to formulations number 1 and 5, respectively. Similarly, the polymer concentration provided both a positive and a negative effect on the PDI. When increasing the concentration of the polymer from 1 to 3% *w*/*v*, the PDI was initially increased and later decreased. For example, the polymer concentration of 3% *w/v,* as seen in formulations 4 and 5, indicated PDI values of 0.221 and 0.231, respectively. On the other hand, sonication time had a less negative impact on the PDI of formulations (Table 2).

In view of the above obtained outcomes, the optimized formulation was generated using a point-prediction technique with a Box–Behnken design. The optimized formula for preparation included a polymer concentration (2.94% *w*/*v*), surfactant concentration (2.20% *w*/*v*), and sonication time (11.39 min). The experimental design predicted a particle size of 92.76 nm, a % drug release of 89.31%, and a PDI of 0.102. The experimental or observed values of particle size, % drug release, and PDI were 95.35 ± 10.25 nm, 70.42 ± 7.25%, and 0.109, respectively.

#### *2.5. Characterization of Dox–ErloNPs*

## 2.5.1. Particle Size and Zeta Potential

The particle size and zeta potential of Dox–Erlo NPs and Dox–Erlo-NP conjugates are shown in Figure 3A,B. Dox–Erlo NPs demonstrated a particle size of 95.35 ± 10.23 nm. On the other hand, Dox–Erlo-NP conjugates appeared at a particle size of 110.12 ± 9.2 nm. The zeta potential of Dox–Erlo NPs and the Dox–Erlo-NP conjugates were −18.1 mV and −25.1 mV, respectively, as is shown in Figure 3C,D. The size range of the Dox–Erlo NPs and the Dox–Erlo-NP conjugates was between 50 and 150 nm (Figure 4A,B). The entrapment efficiency % of Erlo and Dox was 80 ± 2.3% and 78 ± 4.8%, respectively, and the polydispersity index (PDI) of the Dox–Erlo NP formulation was reported be 0.1027. The predicted NP size of the optimized formulation was 92.7661 nm, vs. an experimental particle size of 95.35 ± 10.25, reporting a percentage error of 2.79%. On the other hand, the % drug release of the optimized NPs was 89.91%, vs. the experimental value of 79.203 ± 0.24%, demonstrating a percentage error of 11.90%. The PDI of the predicted formulation was 0.102, compared to a PDI of 0.10, for the experimental value, demonstrating a percentage error of 6.8% (shown in Table 5).

**Table 5.** The optimized composition using experimental design for the development of Dox–Erlo NPs with experimental and predicted responses.


centage error of 6.8% (shown in Table 5).

**Figure 3.** Particle size and size distribution analysis of Dox–Erlo NPs (**A**), particle size and size distribution analysis of Dox–Erlo-NP conjugates (**B**), zeta potential of Dox–Erlo NPs (**C**) and Dox–Erlo-NP conjugates (**D**). **Figure 3.** Particle size and size distribution analysis of Dox–Erlo NPs (**A**), particle size and size distribution analysis of Dox–Erlo-NP conjugates (**B**), zeta potential of Dox–Erlo NPs (**C**) and Dox– Erlo-NP conjugates (**D**). *Pharmaceuticals* **2023**, *16*, x FOR PEER REVIEW 11 of 30

–25.1 mV, respectively, as is shown in Figure 3C,D. The size range of the Dox–Erlo NPs and the Dox–Erlo-NP conjugates was between 50 and 150 nm (Figure 4A,B). The entrapment efficiency % of Erlo and Dox was 80 ± 2.3% and 78 ± 4.8%, respectively, and the polydispersity index (PDI) of the Dox–Erlo NP formulation was reported be 0.1027. The predicted NP size of the optimized formulation was 92.7661 nm, vs. an experimental particle size of 95.35 ± 10.25, reporting a percentage error of 2.79%. On the other hand, the %drug release of the optimized NPs was 89.91%, vs. the experimental value of 79.203 ± 0.24%, demonstrating a percentage error of 11.90 %. The PDI of the predicted formulation was 0.102, compared to a PDI of 0.10, for the experimental value, demonstrating a per-

**Figure 4.** Transmission electron microscopic image of Dox–Erlo NPs (**A**), Dox–Erlo NPs conjugate (**B**).

#### **Figure 4.** Transmission electron microscopic image of Dox–Erlo NPs (**A**), Dox–Erlo NPs conjugate 2.5.2. DSC of Dox–Erlo NPs

(**B**). 2.5.2. DSC of Dox–Erlo NPs DSC is used for the physicochemical characterization of the nature of substance. The DSC peaks of Erlo, cinnamon biopolymer, polyvinyl alcohol, Dox–Erlo NPs, and Dox– DSC is used for the physicochemical characterization of the nature of substance. The DSC peaks of Erlo, cinnamon biopolymer, polyvinyl alcohol, Dox–Erlo NPs, and Dox–Erlo-NP conjugates are shown in Figure 5. The pure Erlo has a characteristic peak at 234.544 ◦C. Polyvinyl alcohol has shown a peak at 316.97 ◦C. Further, the endothermic peak, obtained at 168.136 ◦C, corresponds to the mannitol that was detected in the Dox–Erlo-NP conjugate in Figure 5.

#### Erlo-NP conjugates are shown in Figure 5. The pure Erlo has a characteristic peak at 2.5.3. FT-IR Spectral Analysis

234.544 °C. Polyvinyl alcohol has shown a peak at 316.97 °C. Further, the endothermic peak, obtained at 168.136°C, corresponds to the mannitol that was detected in the Dox– Erlo-NP conjugate in Figure 5. FT-IR spectroscopy characterized the chemical stability of NPs encapsulated in the core of the biopolymer. The FT-IR spectra of Erlo, biopolymer, polyvinyl alcohol, Dox–Erlo NPs, and Dox–Erlo-NP conjugates are indicated in Figure 6. The structure of Erlo shows a 2-methoxy ethoxy group (C-O stretching) and amino-group (N-H stretching) of quinazoline ring. The biopolymer demonstrated a peak around 2743.12 cm−<sup>1</sup> , and 2918.33 cm−<sup>1</sup> belongs to the carboxylic acid group. The Erlo drug demonstrated absorption bands at

**Figure 5.** DSC thermogram of Erlo (A), cinnamon biopolymer (B), polyvinyl alcohol (C), Dox–Erlo

core of the biopolymer. The FT-IR spectra of Erlo, biopolymer, polyvinyl alcohol, Dox– Erlo NPs, and Dox–Erlo-NP conjugates are indicated in Figure 6. The structure of Erlo shows a 2-methoxy ethoxy group (C-O stretching) and amino-group (N-H stretching) of quinazoline ring. The biopolymer demonstrated a peak around 2743.12 cm−1, and 2918.33 cm−1 belongs to the carboxylic acid group. The Erlo drug demonstrated absorption bands at 3267.14 cm−1, corresponding to N-H stretching, and at 1081.18 cm−1, attributed to C-O

FT-IR spectroscopy characterized the chemical stability of NPs encapsulated in the

NPs (D), and Dox–Erlo-NP conjugates (E).

2.5.3. FT-IR Spectral Analysis

stretching (Figure 6).

(**B**).

2.5.2. DSC of Dox–Erlo NPs

3267.14 cm−<sup>1</sup> , corresponding to N-H stretching, and at 1081.18 cm−<sup>1</sup> , attributed to C-O stretching (Figure 6). peak, obtained at 168.136°C, corresponds to the mannitol that was detected in the Dox– Erlo-NP conjugate in Figure 5.

**Figure 4.** Transmission electron microscopic image of Dox–Erlo NPs (**A**), Dox–Erlo NPs conjugate

DSC peaks of Erlo, cinnamon biopolymer, polyvinyl alcohol, Dox–Erlo NPs, and Dox– Erlo-NP conjugates are shown in Figure 5. The pure Erlo has a characteristic peak at 234.544 °C. Polyvinyl alcohol has shown a peak at 316.97 °C. Further, the endothermic

DSC is used for the physicochemical characterization of the nature of substance. The

*Pharmaceuticals* **2023**, *16*, x FOR PEER REVIEW 11 of 30

**Figure 5.** DSC thermogram of Erlo (A), cinnamon biopolymer (B), polyvinyl alcohol (C), Dox–Erlo NPs (D), and Dox–Erlo-NP conjugates (E). **Figure 5.** DSC thermogram of Erlo (A), cinnamon biopolymer (B), polyvinyl alcohol (C), Dox–Erlo NPs (D), and Dox–Erlo-NP conjugates (E).
