Paclitaxel loaded Capmul MCM and tristearin based nanostructured lipid carriers (NLCs) for glioblastoma treatment: screening of formulation components by quality by design (QbD) approach

Abstract

Paclitaxel (PTX), a naturally occurring diterpenoid isolated from Taxus brevifolia, is a first-line drug for the treatment of glioblastoma; however, it suffers from the disadvantages of poor water solubility and nonspecific biodistribution, which cause serious side effects in the human body. The marketed formulation suffers from serious side effects, such as allergic reactions, neutropenia, and neuropathy, which require safe and effective formulations of PTX. In the present study, PTX was entrapped in a solid–liquid lipid mixture with the aid of a surfactant using a modified solvent evaporation technique. Higher entrapment of the impressive stability of the formulation was achieved by employing quality design-based strategies. Optimized levels by employing a numerical optimization technique for each factor, that is, surfactant concentration (X1), lipid concentration (X2), and amount of organic solvent (X3) were 0.3%, 0.76% & 8.3 ml respectively. The resultant formulation exhibited a particle size of 121.44 nm, entrapment efficiency of 94.27%, and zeta potential of −20.21 mV with unimodal size distribution. A reduction in the % crystalline index from 48 to 3.4% ensured the amorphous form of the entrapped drug inside the formulation, which precludes the fear of leakage and instability of the formulation. Cell line studies conducted on U87MG Cell lines also suggested that the NLC of paclitaxel are more effective than those of pure PTX. In summary, PTXNLC seem to be a superior alternative carrier system for the formulation industry to obtain higher entrapment with excellent stability.

Introduction

Gliomas are the predominant primary brain tumors in adults, comprising more than 80% of all malignant cerebral neoplasms [1]. Glioblastoma (GBM) is the most prevalent primary cerebral tumor, accounting for over 47% of all gliomas. It has an extremely poor prognosis and is classified as a WHO grade IV disease. This particular type of glioma is characterized by its aggressive behavior and widespread occurrence [2]. It is a diverse type of cancer that affects the brain and is often characterized by an intricate network of aberrant blood vessels, distinguished by the existence of glomeruloid structures and the excessive proliferation of endothelial cells [3]. In 2019, brain tumors resulted in 246,253 deaths worldwide, irrespective of age or gender [4]. These tumors may be categorized into two types: the IDH wild-type, which accounts for approximately 90% of GBM cases, and the IDH mutant, which corresponds to approximately 10% of cases and develops gradually from low-grade astrocytoma. The key molecular abnormalities often seen in GBM include mutation of EGFR, LOH of chromosome 10q at the PTEN locus, and mutation of the TERT gene promoter [5].

Treatment of the majority of progressive neurological tumors remains difficult. This is because the blood–brain barrier (BBB) exists, which reduces the possibility that the treatment will be successful, and there is a considerable risk of cancer recurrence even after tumor removal [6]. Numerous synthetic drugs have inadequate physicochemical properties, such as low hydrophilicity and bioavailability, as well as a high degree of individual pharmacokinetic variability, along with some serious long-term side effects [7]. The WHO has endorsed the intake of herbal remedies as a primary means of healthcare because of their widespread accessibility, affordability, cultural acceptance, and the trust people place in them [8]. Paclitaxel (PTX), a naturally occurring diterpenoid isolated from Taxus Brevifolia. This chemotherapeutic drug has a substantially higher potency, approximately 1,400 times greater than temozolomide, which is the first-line chemotherapy agent used for GBM [8, 9]. However, it should be noted that PTX, like TMZ, is unable to pass through the blood–brain barrier. It interferes with the normal disintegration of microtubules, causing cessation of cell division and ultimately resulting in cell death. Moreover, it improves the process by which glucose is broken down and used by the body while also bolstering the body's immune system. Research has demonstrated that PTX imitates LPS in triggering the NF-κB pathway and provoking immunomodulatory effects by promoting MΦ M1 polarization. PTX has low solubility in water and other solvents routinely used in the pharmaceutical sector [10, 11]. Consequently, it is important to emulsify it using surfactants to render it appropriate for delivery. The formulation used was a mixture of cremophor EL and ethanol in equal proportions, usually known as "Taxol.” Unfortunately, individuals implementing this therapy often encounter significant adverse effects including allergic responses, neutropenia, and neuropathy. Hence, the pharmaceutical industry is aggressively pursuing alternative formulations of PTX that are devoid of cremophor EL [12]. Multiple studies have shown that the behavior of medications with limited water solubility may be controlled by encapsulating them in nanocolloidal systems, enabling the profiling of cells and tissues. Various parenteral PTX formulation approaches have been investigated, but unfavorably, these trials did not provide the desired outcomes owing to the formulation's poor ability to dissolve and its unstable characteristics [13]. To avoid complications associated with the naturally occurring bioactive phytoconstituent PTX, lipid-based nanoformulation systems can be employed to form nanoformulations, which enhance the efficacy and safety of phytotherapies by ensuring targeted delivery, improved bioavailability, controlled release, and increased stability. The technology developed by NLCs is ideal for our specific needs because of its outstanding lipophilicity. Lipids are essential in NLCs and have a significant impact on parameters such as drug-loading capacity, duration of action, and formulation stability [14]. These substances exhibit greater permeability through the cell membranes, allowing for more efficient drug administration. In essence, the use of NLCs obviates the need for detrimental surfactants and other substances owing to the employment of GRAS-approved lipids, thus yielding delivery methods that are both secure and effective [15]. Fabricating NLCs may be complex because several parameters associated with the formulation and manufacturing process might influence the characteristics of the end product. Developing an optimal item that fulfills all consumer demands requires careful optimization of both the product and process factors [16]. By incorporating the Quality by Design (QbD) methodology into the development of lipid-based nanosystems, the final pharmaceutical product may have superior quality, safety, and efficacy [17]. This approach leads to the design of a highly efficient optimized formulation. Furthermore, any variation in production was identified at an early stage. In addition, it improves the release and targeting of medications, leading to enhanced pharmacokinetic and pharmacodynamic features. Within this particular framework, the use of Design of Experiments (DoE) improves the overall quality of the final product by significantly reducing the number of required trials [18]. However, this still requires a substantial number of runs, which may incur expenses in terms of materials and personnel. These enhancements have been verified using diverse mathematical models. The Placket-Burman (PB) factorial design was chosen as the ideal experimental design for first-factor screening owing to its effectiveness in finding the most significant factors with a reduced number of repetitions. Furthermore, the most effective formulation was enhanced via the use of response surface methodology (RSM) using Box–Behnken design (BBD), and further refined using a computational optimization strategy based on the desirability approach [19, 20].From this perspective, the current study aimed to utilize the potential advantages of NLC systems along with the concepts and principles of QbD, which can accommodate a higher amount of drug with a lower production cost. The drug can be delivered in a controlled manner for a longer period without hypersensitivity or allergic reactions.

Materials

Paclitaxel (PTX) was received as a kind gift from Emcure Pharmaceuticals Ltd., Pune (India). Tristearin and D-α-Tocopherol polyethylene glycol 1000 succinate (TPGS) were purchased from Sigma Aldrich, India. Glycerol tristearate was purchased from TCI Chemicals, India. Antares Pharma, USA, provided TPGS as a gift sample. Captex 355, Captex 300P, Capmul, and Capmul MCM were supplied as a gift sample by Abitac, USA. Chika Pvt. Ltd., Mumbai, generously gifted Miglyol. Compritol 888ATo was received as a generous gift from Gattefosse International, Mumbai. Cremer oleo GmbH, Germany, provided a gift sample of Dynasan 114, 118 & Imwitor 900 K. Poloxamer 188, pluronic 407, and Solutol HS15 were generously donated by BASF, Mumbai. Fischer Scientific, Mumbai, provided all other chemicals for the experiment. Ultrapure-Milli-Q water was used throughout the experiment. Throughout the experiment, all of the solvents used for the study were of HPLC quality.

Methods

Solubility in liquid lipids

Five liquid lipids were studied for the solubility of phytoconstituents in oils. Each vial containing 1.0 ml of a different oil received an excess amount of drug. After sealing, the mixture was sonicated for 10 min to facilitate proper mixing of drug with the vehicles. We then shook the mixtures for 48 h at room temperature in a water bath shaker (Remi, Mumbai, India). We centrifuged the mixtures at 5000 rpm for 15 min. Aliquots of supernatant were filtered through a membrane filter (0.45 μm) and diluted with mobile phase (methanol). Drug content was quantified directly by using a UV–VIS spectrophotometer at λ max of 261.5 nm [21–23].

Solubility in solid lipids

We studied the solubility of the drug in different lipids. Briefly, 1 g of lipid is taken into a test tube and heated on a water bath at a temperature 5–10 °C above the melting point of the lipid. Gradually, drug was added to melted lipid with continuous stirring and examined visually for solubility [22, 24].

Quantitative solubility determination

We determined the quantitative solubility of the drug in the selected solid lipids. We took a known amount of the drug in the test tube, added the weighed amount of solid lipid gradually, and stirred the mixture continuously at a temperature above the solid lipid's melting point. We visualized the amount of lipid required to form a clear, transparent solution [22, 25].

Physical compatibility of solid & liquid lipids

We mixed the selected liquid lipid in different glass vials at a 1:1 ratio with solid lipids that had the highest affinity for the dug. We melted the lipid mixture, shook it, and allowed it to congeal at room temperature. We visually analyzed the glass tubes to check for the absence of separate layers in the congealed lipid mass. Furthermore, we smeared the congealed solid–liquid lipid over a glass slide and examined it microscopically [22].

Selection of solid lipid to liquid lipid ratios

We first evaluated the ratios between the weights of solid lipid and liquid lipid by taking optical microscopic pictures of the SLBs with different ratios, and then further screened them out by determining the melting point of the SLBs. Selected solid lipids and liquid lipids were mixed in a ratio ranging from 95:05 (SL:LL) to 05:95, melted above the melting point of solid lipids until both lipids got completely mixed and congealed at room temperature. We used optical microscopy to verify the proper mixing of the congealed mixtures. Furthermore, we determined the melting points of the selected mixtures using a capillary method, and further confirmed these results with DSC. The solid samples (2 mg) of binary mixtures as well as pure solid lipid were scanned on DSC 6000 (Pyrix 6, Serial Number: 002082704; Software Version: 11.0.0.0449) differential scanning calorimeter at a scanning rate of 10 °C/min over the temperature range of 10–400 °C [25, 26].

Selection of surfactant

For the preparation of NLCs, surfactants were selected by their ability to emulsify solid–liquid lipid binary mixture. 100 mg of a solid–liquid lipid binary mixture was dissolved in 3 mL of dichloromethane (DCM) and added to 10 mL of 5% surfactant solutions under magnetic stirring. The organic phase was removed at 40 °C, and the resultant dilutions were diluted with milli-Q water. Percentage transmittance of the resultant samples was observed using a UV spectrophotometer at λ max of 638.2 nm [20, 21].

Method of preparation

The NLC system was prepared by an emulsification solvent evaporation method [20, 21, 27]. The formulation components that were screened out from the previous experiments were further optimized by the experimental statistical designs for their respective compositions for the preparation of NLCs. Briefly, the modified solvent evaporation technique consists of the following steps: 187.5 mg (0.75% for 25 ml total formulation) of SLB (in 60:40; SL:LL; 112.5 mg SL + 75 mg LL) was dissolved in 7.5 ml of dichloromethane (DCM) along with 10 mg of PTX. The surfactant mixture consists of 0.3% (75 mg for 25 ml formulation) TPGS in Milli Q water. We kept both the aqueous phase and organic phase at 60 °C and 900 RPM for 4 min. We then added the organic phase to the aqueous phase using a high-shear IKA T25 digital Ultraturrax homogenizer, operating at 12,500 RPM for 15 min. We maintained the same temperature (60 °C) throughout the addition process. Then the formed nanoformulation was sonicated for 4 min using the Ningbo Haishu Sklon probe ultrasonicator, which was already set at 40% amplitude on–off cycles. It results in the formation of an aqueous colloidal NLC suspension. Figure 1 illustrates the process. The formulation was kept for a whole day at room temperature to check any instability and was further characterized by particle size, polydispersity index, zeta potential, surface morphology, and entrapment efficiency using suitable techniques.

Fig. 1.

Method of preparation of PTXNLCs

Optimization of process and product parameters for NLCs

Risk assessment studies

Optimization of different product and process parameters is required for the improvement of the quality of the drug product which requires in-depth knowledge of risk assessment control strategy. The elements of the QbD are.

• Quality target product profile (QTPP) as menbtioned in Table 1, identifies critical quality attributes (CQAs) of the drug products. CQAs are 2 types: Critical material attributes (CMAs) & critical process parameters (CPPs). Tables 2 and 3 shows the CMAs and CPPs of the product.hmmsta

• Product designing and identification of critical material attributes (CMAs).

• Process designing and identification of critical process parameters (CPPs).

• Process capability and continual improvement [25, 28, 29].

Table 1.

Quality Target Product Profile (QTPP) [28, 29]

QTPP Elements	Target	Justification
Dosage form	Nanostructured lipid carriers (NLCs)	Lipid based systems that help in enhancing the bioavailability of the poorly water soluble drug and nano systems helps in targeting the drug to the particular area in case of cancer
Dosage design	Delayed release	Decreases dosage frequency as well as toxicity caused by drugs
Administration route	IV	Required to target the drug to the cancerous area
Finished product	Lyophilized powder	It will be stable and easy for packaging
Stability	Minimum 08 months	To maintain the therapeutic potential of the drug

Table 2.

Control Impact Matrix [28, 29]

Control	Impact
Critical quality attributes (CQAs)	High	Medium	Low
In our Control	Type of raw materials (oils, solid lipids, surfactants etc Concentration of lipids, surfactants Type of water/organic solvent used Amount of Water phase/ organic phase Speed of magnetic stirrer/homogenizer/sonicator Time of homogenization Method of preparation Injection speed Needle size used Temperature of the system	Efficiency of formulator	–
Out of Control	Purity of raw materials Partition coefficient of the drug Solubility profile of drug Efficiency of measurement system Environmental conditions (Room temperature, humidity, pressure etc.)	Efficiency of Analyst, Chemist	Contamination

Table 3.

Critical Process parameters and material attributes [28, 29]

Sr. No	CPPs	CMAs
1	Method of preparation used	Type of Liquid lipid, solid lipids & surfactants
2	Speed of magnetic stirrer/homogenizer/sonicator	Concentration of SL, LL & surfactant used
3	Time of homogenization/ sonication	Type of water/ organic solvent used
4	Injection Speed	Ration of Aqueous phase/ organic phase
5	Temperature of the system	Needle size
6	Efficiency of measurement system

We will further use the data gathered from the aforementioned studies to develop a validated formulation method that will be consistent over time.

Risk assessment studies help in the identification of CMAs and CPPs, which significantly affect the product CQAs (Fig. 1). We further employed failure mode effect analysis (FMEA) to rank CTQs based on relative effectiveness (Table 4). FMEA aids in prioritizing the independent variables before implementing DoE strategies. All the parameters impacting the optimization of a drug formulation are mentioned in Fig. 2.

Table 4.

Risk Estimation Matrix [28, 29]

CTQ (CMAS + CPPs)	Particle size	Polydispersity index	Entrapment efficiency
Type of lipids used	High	Low	High
Amount of lipids	High	Medium	High
Type of surfactant	High	Low	Low
Surfactant conc	High	High	Medium
Solvent type	High	Low	Medium
Humidity	Low	Low	Low
Solvent ratio	High	Low	High
Temperature of the system	High	Low	High
Speed of homogenizer	High	Medium	Medium
Homogenization time	High	Low	High
Sonication time	High	Medium	Low
Stirring speed	Medium	Low	Medium
Type of Analyst	Low	Low	Low
Stirring time	High	Low	Low
Injection Speed	High	Medium	Low
Needle Size	Medium	Low	Low
Method of preparation	High	Low	Medium
Room Temperature	Low	Low	Low

Fig. 2.

Ishikawa Fish Bone diagram

Screening of factors by placket Burman design

After performing the above-mentioned studies, some of the unimportant factors were rejected from the design, but still, a large number of factors are in the picture, which, if taken during the experimental design based on RSM, will result in a huge number of experimental runs. It will ruin the optimization phenomenon's basic purpose. We used the factorial Placket-Burman design to further sort the significant parameters. Using Minitab 17, we can apply the PB design to investigate "n" variables through "n + 1" experiments, utilizing a factorial technique for more than three factors. The main advantage of this design is that it results in a lesser number of experimental runs even if the factors are much higher. It was employed to study the important factors that significantly affect the dependent variables [21, 30]. The factors were studied at two levels, i.e., low (−1) and high level (+ 1), respectively. All factors and responses, along with their lower and higher levels, are described in Table 5.

Table 5.

Details of factors used for Placket Burman design [28, 29]

Codes	Independent variables	Low level (−1)	High level (+ 1)	Unit	Type of factor
A	Brand of the same lipid	TCI Chemicals	Sigma aldrich	–	Category
B	Brand of the same surfactant	Antares	Sigma aldrich	–	Category
C	Injection speed	5	10	ml/min	Numeric
D	Height of the syringe	2	5	cm	Numeric
E	Speed of magnetic stirrer	900	1200	Rpm	Numeric
F	Time of magnetic stirring	4	8	Min	Numeric
G	Homogenization time	10	15	Min	Numeric
H	Homogenization Speed	12,500	15,000	Rpm	Numeric
I	Ultrasonication Time	4	8	Min	Numeric
J	Lipid Concentration	0.75	1.0	% w/v	Numeric
K	Surfactant Concentration	0.1	0.3	% w/v	Numeric
L	Amount of Organic solvent	5	10	ml	Numeric
Dependent variables
Particle Size (nm)
Poly dispersity index
Entrapment efficiency (%)

Optimization by Box-Behnken methodology

A QbD approach based on RSM was employed to construct second order polynomial models. RSM is a collaboration of mathematical and statistical principles useful for problems whose dependable factors are influenced by several independent factors, and our objective is to optimize the response [26, 31, 32]. A 3-factor, 3-level (3³) BBD with 16 no. of runs was utilized to investigate the effect of independent variables on responses. Lipid concentration (X1), surfactant concentration (X2), and amount of organic solvent (X3) were chosen as independent variables based on the preliminary screening studies performed earlier, and particle size (Y1), polydispersity index (Y2), and entrapment efficiency (Y3) were selected as dependable variables based on the requirements of the NLC systems. The variables were varied at 3 different levels, i.e., −1 (lower level), 0 (medium level), and + 1 (higher level). All independent and dependent variables, along with their coded variables, are represented in Table 6. The experimental design was executed using Design-Expert® software (7.0, Stat Ease Inc., Minneapolis), which comprised 16 no. of runs. We performed the experiments in a randomized order to prevent potential biases between runs and to improve the predictability of the design. The relationship between independent and dependent variables was predicted by the polynomial equations generated for each response, which was further explained by the 3D response surface plots. Data were analyzed by using the principles of analysis of variance (ANOVA), in which the regression coefficient, the coefficient of determination, and the lack of fit were determined to check the adequacy of the data.

Table 6.

Statistical ANOVA based results of quadratic model & the quadratic equations generated by Design Expert®

	Quadratic model
Response	F- Value	P- Value*	R- Square	R-Sq (adj)	CV%	Lack of fit	Remark
P.Size(nm)	228.26	< 0.0001	0.997	0.9927	2.58	3.06	Significant
EE (%)	66.79	< 0.0001	0.9901	0.9753	1.48	1.34	Significant
PDI	26.78	< 0.0004	0.957	0.9393	7.67	4.8	Significant
R-Sq (adj) = R Square adjusted; CV = Coefficient of variation
*p-value < 0.05 is considered as statistically significant

*Y	Particle size (Y1)	Entrapment efficiency (Y2)	Polydispersity index (Y3)
X0	+ 272.58	+ 85.85	+ 0.14
A	−104.44	+ 9.41	−0.052
B	+ 12.66	+ 2.63	+ 04.25E-003
C	−4.5	0.99	−0.023
A*B	−1.52	−4.53	+ 4.75E-003
A*C	−21.90	−0.35	+ 0.014
B*C	−0.75	+ 0.025	+ 7.75E-003
A2	−19.87	−1.87	+ 0.052
B2	−15.87	−3.10	−5.75E-003
C2	+ 14.75	−0.73	+ 8.00E-003

^*Y = response; X0 = intercept; A-C = Factors

We further optimized the system by employing the desirability approach based on numerical optimization. The concept of design space was utilized well by keeping the responses under constraints, and percentage biases between experimental and practical values of the optimum formulation were calculated, which is elaborated in Tables 16 and 17, Fig. 3.

Table 16.

Desirability approach-based numerical optimization of various factors [28, 29]

Independent variables	Predicted levels
Surf conc (X1)	0.3%w/v
Lipid conc (X2)	0.76%w/v
Amt of org solvent (X3)	8.31 ml

Table 17.

Results of the numerical optimization approach [28, 29]

Responses	Predicted value	Experimental value	% biasa
Particle size	118.205 nm	121.44 nm	−2.7%
Entrapment efficiency	92.036%	94.27%	−2.42%
Polydispersity index	0.120	0.114	5%
Overall desirability	0.969
Drug loading	4.32 ± 0.43%
Total drug content (TDC)	4.7 mg in 25 ml formulation

All results were expressed as mean ± SD, n = 3. ^aBias is calculated as {(predicted value- experimental value) / predicted value} × 100

Fig. 3.

Predicted levels of various responses based on numerical optimization of desirability approach

Characterization tests

HPLC method development

Reverse phase high-performance liquid chromatography technique was utilized for the quantification of PTX in the formulation. HPLC system was consisted of waters 1525 binary HPLC pump (Waters, USA), rheodyne 7725i manual injector (Waters, USA), C18 reverse-phase (4.6 × 75 mm; 3.5 µm) Symmetry® C18 column and waters 2998 photodiode array detector (Waters, USA) [30, 33, 34]. The mobile phase consisted of acetonitrile: 2 mM phosphoric acid buffer in Milli-Q water (50:50). The flow rate of the mobile phase was kept at 1.0 mL min⁻¹. The temperature of the column was maintained at 30 ± 1 °C using column heater. And the peak was detected at 227 nm. HPLC peak area and retention time were calculated by using the Breeze2 software. Standard calibration curves in different media were plotted from 500 to 3000 ng mL⁻¹ of PTX [35].

Evaluation of quality problems of NLC systems

Fourier transformed infrared studies (FTIR)

The interactions between the drug and the excipients were recognized by taking FTIR spectra of the same. Fourier Transformed Infrared (FTIR-8400S, Shimadzu) was utilized throughout the study. FTIR spectra of PTX, Lipids, surfactant, Physical mixture (1:1) and PTX NLC was obtained using traditional KBr pellet technique. The samples were prepared by grinding with anhydrous KBr and compressed into pellets by using a hydraulic press. The spectra were measured over the range of 4000–400 cm⁻¹ with a resolution of 4 cm⁻¹ for 50 scans.

Measurement of crystallinity, lipid modification and assessment of supercooled melts by differential scanning calorimetry (DSC)

NLC systems were made to circumvent the drawbacks of the existing SLN systems like leakage of the drug during temperature changes and storage and low entrapment efficiency which are due to the crystalline changes in the lipids during storage. So, a keen attention needs to be paid to the degree of lipid crystallinity and the modifications of the lipid apart from particle size and surface morphology considerations [36, 37]. Both stability of NLCs and lipid pack density increases while drug entrapment rates decrease in the following order: Supercooled melts > α modifications > β^’ modifications > β modifications. Small size and presence of emulsifiers lead to retardation of modifications, and the formulations can be stable for several months. DSC can be broadly used to generate the status of the lipid [38]. It utilizes the concept that different modifications possess different melting points as well as different melting enthalpies [39].

The existence of supercooled melts can be acknowledged by the presence of low melting temperatures of the nanoparticles. Differential scanning calorimetric analysis was performed using DSC 6000 (Pyrix 6, Serial Number: 002082704; Software Version: 11.0.0.0449). Pure solid lipid, solid and liquid lipid binary mixture, a ternary mixture of lipids and drug (physical mixture) & freeze dried PTX NLC formulation were subjected to heat flow to study the temperature associated changes. A small amount of samples (1–5 g) were weighed and placed in the aluminum pan and crimped. The samples were heated between from 0.00 to 400.00 °C at 10.00 °C/min. Nitrogen gas was introduced immediately at a flow rate of 20 ml/min [22, 40].

Gelation phenomenon

Transformation of low viscosity NLC dispersion into a viscous gel is known as a gelation phenomenon whose existence in the nanoformulation system is not tolerable. Mostly gel production is an irreversible process which indicates the loss of nano range particle size of the formulation [36]. It is influenced by the strong contact of the NLC dispersion with other surfaces and forced shear forces, for example, a syringing needle. During the production process, contact of the formulation with a syringe or during the administration of the formulation to the subject through intravenous or intramuscular route, this phenomenon can occur which can prove to be very dangerous. So, investigation of gelation is a crucial step for the formulation of NLCs. The changes during the gelation process are attributed to the aggregation of nanoparticles in response to external surfaces. To identify this, the prepared nanoemulsion was taken into a syringe with a needle and pumped into the beaker. The process was repeated 20 times, and particle size was determined before and after this process. Viscosity changes in the nanosuspension before and after application of stress were also recorded using Brookfield viscometer. The artifacts of zeta potential were also used for the evaluation of gelation which is supported by the fact that high lipid, as well as ionic concentration, promotes the gelation. The zeta potential of the formulation was determined as described in Sect. 4.6 of this paper.

Stability studies and calculation of shelf life

Stability study was performed on the final optimized batch to get an idea about the stability and performance of the product on storage under harsh conditions of temperature and humidity. The samples were packed in high-density plastic bottles and were kept at 5 ± 3 °C (refrigeration condition) as well as at 40 ± 2 °C; 75 ± 5% relative humidity (RH) (accelerated studies) for six months. At the same time, samples were kept at room temperature (25˚C/70% RH) for six months and were evaluated at specified time periods for changes in the formulation regarding particle size, zeta potential, PDI and % entrapment efficiency. The whole procedure followed was according to ICH guideline Q1 A(R²) [30, 41]. The shelf life was calculated using Minitab® ver.17.

TEM (Transmission electron microscopy)

The surface morphology of the formulation was investigated using transmission electron microscopy (TEM, FEI TECNAI G²20 TWIN MODEL 943205022121). Samples were prepared by placing a drop of nanoparticle suspension which was diluted previously with water, onto a copper grid and kept fortnight for air drying. The air-dried samples were then directly examined under the TEM.

AFM (Atomic force microscopy)

The surface morphology was further studied by AFM technique (AFM NT-MDT, NTEGRA Prima). Samples were prepared on previously treated glass slides by Electro spinner technique. The washed slides were placed in the spin rotor, and diluted formulation was added to the slides with the help of micropipette. The samples were rotated at 4000 RPM for 2 min, and then kept for air drying. The dried samples were directly examined under AFM.

Particle size and polydispersity index (PDI)

The mean Particle size and Particle size distribution were determined by Particle size analyzer (Delsa Nano C Beckman Culter).

Zeta potential

Zeta potential is a measure of the magnitude of the electrostatic or charge repulsion/attraction between particles, and is one of the fundamental parameters known to affect stability. Its measurement brings detailed insight into the causes of dispersion, aggregation or flocculation, and can be applied to improve the formulation of dispersions, emulsions, and suspensions. It was determined by using Particle size analyzer (Delsa Nano C Beckman Cutler).

Encapsulation efficiency (EE), total drug content (TDC) & loading efficiency (LE)

Indirect method was utilized to calculate the EE, TDC & LE. The free drug in the supernatant was calculated by this method. The Drug encapsulation efficiency was determined by using HPLC (WATERS; Breeze 2 software). Briefly the 1.5 ml of formulation was centrifuged (Eltek Cooling Centrifuge) at 14,000 RPM for 15 min at temperature 10 °C using Nanosep (Pall Corporation, 100 K Omega). The clear liquid was collected from the lower chamber of the tube and was further diluted 375 times with HPLC grade methanol. The samples were filtered through syringe filters (Axiva, PES 0.45 micron) and the peak area was measured against the standard. The encapsulation efficiency was expressed as a percentage of the amount of drug encapsulated in the nanoparticles to the total drug content.

$$\text{EE}\left(\%\right)=\left(\text{Total Drug Content}-\text{Free Drug Content}/\text{Total Drug Content}\right)\text{x 1}00$$

$$\text{LE}\left(\%\right)=\left(\text{Total Drug Content}-\text{Free Drug Content}/\text{Total Lipid Content}\right)\text{x 1}00$$

Total drug content (TDC) is a determination of total drug (entrapped + free drug) present in the formulation. The 1 ml of formulation was taken and fully dissolved the solvent. Further, it was diluted with methanol and peak area was calculated by using HPLC against the standard. The total amount of drug in ‘mg’ was calculated by using a calibration curve.

Percentage drug release

In vitro release of the formulation was performed by the dialysis bag diffusion method. Nanosuspension (10 ml) was added to the dialysis membrane with 8–12 kDa (Himedia labs, India) molecular weight cut off which was tied from both ends. The bag was incubated in 50 ml of release medium (PBS 7.4) maintained at 37.5 ± 0.5 °C at 150 rpm. At predetermined time points, 1 ml of sample was withdrawn, and whole media was replaced by the fresh buffer every time to maintain the sink conditions. The samples were filtered and analyzed by using the HPLC method described above. Cumulative percentage drug release was calculated. Data was fitted to various kinetic models (zero order, first order, Higuchi kinetics & Korsmeyer Peppas model) to get the release kinetics. Sink conditions were maintained throughout the release period (Table 7).

Table 7.

Correlation coefficients & release exponent values for various release kinetics models during in vitro release kineticc from PTXNLC [32]

Release kinetics models	Correlation coefficient (R2)	Release exponent (n)
Zero order	0.825	–
First order	0.9173	–
Higuchi model	0.9893	–
Korsemeyer-Peppas model	0.9826	0.464

In vitro cell line studies

The human Glioblastoma cancer cell line U87MG was grown in Dulbecco‟s modified Eagle Medium (DMEM, Himedia) supplemented with 10% fetal bovine serum (FBS) and antibiotics (100 U mL⁻¹ penicillin and 100 U mL⁻¹ streptomycin) in a CO₂ incubator at 37 °C and 5% CO₂ atmosphere. MTT assay (3-(4, 5-dimethylthiazol-2-yl)-2,5 diphenyltetrazolium bromide) was employed to study the in vitro cytotoxicity of pure drugs, formulation and placebo formulations in U87MG (Human Glioblastoma cell line). 0.9% normal saline solution equivalent to nanoparticle dispersion was kept as control. Briefly, U87MG cells were seeded onto 96 well microtitre plates at 1 × 10⁴ cells per well in complete DMEM medium and incubated at 37 °C in humidified CO₂ (5%) incubator for 24 h after that they were exposed to fresh DMEM culture medium containing different concentrations of test samples for 72 h under same conditions. After incubation, the medium was replaced with 20 ml of MTT (5 mg/mL in PBS) and the cells were incubated for 4 h under same conditions. The culture medium and MTT were washed completely after 4 h and the formed insoluble formazan crystals which were proportional to the number of viable cells were dissolved in 100 ml of dimethyl sulfoxide (DMSO). The plates were agitated for 10 min and absorption was measured at 570 nm using a multimode reader (Synergy H1 hybrid, Biotek, USA). The absorbance of the control cells was used to calculate the % cell viability of the test formulations. The percentage cell viability was calculated by the following equation:

$$\text{Cell}\text{Viability}(\%)=\left(\frac{\text{Absorbance}\text{of}\text{Test}}{\text{Absorbance}\text{of}\text{Control}}\right)\times 100$$

Data are expressed as mean ± S.D. (n = 3).

Results and discussion

Selection of solid lipid and liquid lipids

We selected liquid lipids (LL) and solid lipids (SL) based on their solubility with the drug. For NLCs, we selected lipids with maximum solubility with the drug as LL and SL, respectively. A good SL and LL affinity for the drug may warrant higher entrapment efficiency and a more stable system [21, 22]. Capmul MCM, Captex 355, and Capmul MCM C8 exhibited good solubility. However, Capmul MCM C8 showed the highest solubility of 31.1 mg/ml as mentioned in Tables 8 and 9 [42]. The studies with solid lipids showed that tristearin, Dynasan 114, Imwitor 900 K, compritol 888 ATO, and glyceryl monostearate showed good affinity to carry the drug, but among these, tristearin and imwitor 900 K showed maximum solubility of 22.58 and 14.2 mg/g, respectively, as shown in Tables 10 and 11. We further evaluated the selected SLs for their physical compatibility with the chosen LL. The results revealed that Imwitor 900 K was unable to congeal with the selected LL (Capmul MCM C8) at room temperature, leading to its rejection. Table 12 [43] represented all the results. A lowering of the combined melting temperature for the lipid mix may be the cause of the failure to congeal. Optical microscopy, as shown in Fig. 4, confirmed the congealing of tristearin with the LLs [44]. The presence of black particles confirms the amorphous nature of the molecules. This is because amorphous substances possess isotropicity, a property that prevents them from transmitting light through cross-polarizing filters due to their single refractive index, resulting in their appearance as black. The pictures of the black particles confirmed the presence of amorphous mixtures of both lipids, demonstrating complete melting of SL and LL. Therefore, based on the previous studies, we selected Tristearin and Capmul MCMC8 as the lipid phases for the preparation of NLC [34].

Table 8.

Comparative solubility of drug in different oils [22, 24]

Sr. No	Oil	Solubility
1	Miglyol	–
2	Captex 355	+  +
3	Capmul MCM EP	+  +  +
4	Captex 300P	–
5	Capmul MCM C8	+  +  +

Table 9.

Quantitative solubility of drug in Liquid lipids [22, 24]

Sr. No	Oil	Solubility (mg/mL)
1	Miglyol	0.87 ± 0.13
2	Captex 355	5.10 ± 0.32
3	Capmul MCM EP	7.51 ± 0.35
4	Captex 300P	3.92 ± 0.16
5	Capmul MCM C8	31.1 ± 0.34

Data expressed as mean ± S.D; n = 3

Table 10.

Solubility of drug in Solid lipid [22, 24]

Sr. No	Solid lipid	Solubility
1	Imwitor 900 K	+  +  +
2	Compritol 888ATO	+  +
3	Tristearin	+  +  +
4	Dynasan 118	+
5	Dynasan 114	+  +
6	Glyceryl monostearate	+  +
7	Glyceryl monooleate	+

Table 11.

Quantitative Solubility of drug in selected solid lipids (SL) [22, 24]

Sr. No	Solid lipid	Solubility(mg/g)
1	Imwitor 900 K	14.2 ± 0.34
2	Tristearin	22.54 ± 0.45

Table 12.

Compatibility study of selected SL with LL [22, 24, 42]

Sr. No	Solid liquid lipid binary mixture	Congealing
1	Imwitor 90 K + Capmul MCMC8	–
3	Tristearin + Capmul MCMC8	+

Fig. 4.

Optical microscopic picture of solid lipid liquid lipid binary mixture (SLB) (Tristearin + Capmul MCMC8)

Selection of solid lipid to liquid lipid ratio

The major difference between the SLN and NLC is the presence of liquid lipids along with the solid lipids in the case of NLCs, whereas only solid lipids are present as the lipid phase in the case of SLNs [45]. Liquid lipids are known to carry the maximum drug as drugs possess higher solubility in LL than in SL, and hence the entrapment efficiency (EE) of NLC is higher than the SLNs [46, 47]. Increasing the LL content in the formulation could improve its EE, but it's important to also study their melting point range. This is because increasing the LL content can lower the melting points of the solid lipid binary mixtures (SLBs), which could compromise the formulation's consistency. We made different combinations of SL and LL, ranging from 90 to 10% SL and 10–90% LL. The combinations are mentioned in Table 13. We further evaluated the combinations that showed proper congealing at room temperature for their melting points using the capillary method, as shown in Fig. 5. We selected SLB ratios with melting points between 50 and 60 °C for the formulation aspect, as a higher liquid lipid promotes a higher drug solubilization. However, the consistency at room temperature compromises the formulation, as the nanoparticles cannot keep up with the solid or semi-solid form [22]. In this study, SL:LL from 10:90 to 30:70 were considered to be below 30% of SL content; the mixtures were not able to congeal at room temperature. Optical microscopy, as mentioned in Fig. 6 confirmed the further congealing of the combinations with higher SL content. We selected only three combinations of SL:LL, namely 65:35, 60:40, and 70:30, and found that SL:LL 60:40 was the best for PTX-NLC formulation. This decision was based not only on its optical microscopic image, but also on its ability to entrap a sufficient amount of drug and its sufficiently high melting point to maintain consistency with the formulation at room temperature. DSC further confirmed the melting point of the selected mixture, as shown in Fig. 7 [42].

Table 13.

Selection of solid lipid to liquid lipid ratio (SL:LL) [22, 24]

Sr. No	Ratio (SL:LL)	Congealing	Melting point (°C) (By capillary method)	Microscopy
SL: Tristearin, LL: Capmul MCMC8
1	10:90	-	-	-
2	15:85	-	-	-
3	20:80	-	-	-
4	25:75	-	-	-
5	30:70	+	44.3	-
6	35:65	+	45.6	-
7	40:60	+	48.2	-
8	45:55	+	50.7	–
9	50:50	+	54.2	–
10	55:45	+	57.2
11	60:40	+	59.3	Good
12	65:35	+	59.8	Good
13	70:30	+	60.7	Good
14	75:25	+	61.6	–
15	80:20	+	64.2	-
16	85:15	+	64.6	-
17	90:10	+	68.5	-

–Indicates separation; + Idicates formation of congealed mixtures; – Indicates improper mixing of SL with LL

Fig. 5.

Melting point ranges of Solid lipid liquid lipid binary mixtures (LL:SLs)

Fig. 6.

Optical microscopic pictures of selected SLBs Optical microscopic pictures of selected SLBs

Fig. 7.

DSC curve of selected solid lipid liquid lipid mix in ratio of 60:40

Selection of surfactant

We selected the surfactants for the preparation of NLCs based on their emulsification capability, measured in terms of percentage transmittance. This observation is based on the fact that smaller particles exhibit higher percentage transmittance; hence, the higher the percentage transmittance, the smaller the particles formed, and the higher the surfactant's emulsification capability [20, 21]. Table 14 [48–50] revealed that TPGS vitamin E produced an emulsion with the highest percentage transmittance compared to others. However, tests revealed that other surfactants such as Tween 80, Poloxamer 188, and Pluronic 407 possessed sufficient emulsification ability. We chose TPGS as a surfactant because it has other benefits as well, such as its ability to fight cancer, which will work with the other ingredients to make them more effective, and its safety, biodegradability, and antioxidant properties [34, 51–55].

Table 14.

Percentage transmittance values of different surfactants [48–50]

Sr. No	Surfactant	Transmittance (%)
1	Solutol HS15	86 ± 2.74
2	Tween 80	90.6 ± 4.22
3	Poloxamer 188	92.4 ± 3.12
4	TPGS Vitamin E	97.9 ± 2.54
5	Pluronic 407	90.56 ± 3.15
6	Brij 78	82.5 ± 2.87

Method of preparation

The emulsification solvent evaporation technique with slight modifications was utilized for the preparation of PTXNLCs. The detailed method of preparation is explained in Fig. 1. To carry out our work efficiently, we used quality by design principles, with the aid of which we had separated 12 factors that were important to consider. Since the number of factors was very large to handle during preparation, we had conceptualized a combination methodology for optimization. The particle size, % entrapment efficiency (EE), and polydispersity index (PDI) of the optimized formulation were found to be 121.44 nm, 94.27% and 0.114, respectively.

The prepared formulation was freeze dried to carry out the further evaluations. The conditions of freeze drying were:

Pre-freeze conditions

NLCs were dispersed in an aqueous medium containing mannitol (5% w/v) to act as a cryoprotectant and stabilizer. Freezing temperature was −40 to −80 °C. Freezing time was 2 h.

Primary drying (sublimation)

Chamber pressure was maintained at 50–100 mTorr (vacuum applied) while the Shelf temperature: −40 °C to −20 °C. the sample was dried for 24 h.

Secondary drying (desorption)

The shelf temperature was maintained at 20 °C and the drying time was 6 h.

The placket Burman design (PBD)

The PBD aids in the initial screening and segregation of numerous variables according to the formulation characteristics they display. We generated a total of 20 experimental runs using 12 factors at two PB design levels, each with three responses. Table 5 provides details of the independent variables and their values at lower and higher levels, while Table 15 summarizes the PB design. We applied the statistical principles of ANOVA to analyze each response, and constructed Pareto charts for each response separately, highlighting the most significant factors for that specific response. We found that the brand of lipid, the brand of surfactant, and the lipid concentration primarily influenced particle size. We found that the brand of lipid, the brand of surfactant, and the surfactant concentration significantly influenced the entrapment efficiency, while the brand of lipid, the brand of surfactant, and the organic solvent concentration significantly influenced the polydispersity index as shown in Fig. 8, p 0.05). Based on our data, we determined that different brands of the same lipid and different brands of the same surfactant significantly influence all three responses. We manually solved our problem by utilizing all four combinations of surfactant and lipid brands. We then characterized the formulation for all three of the aforementioned responses and determined the brands for each. Based on our observations, we found that using Sigma Aldrich-based tristearin as a lipid and Sigma Aldrich-based TPGS vitamin E as a surfactant yielded the best results across all three responses. We further selected the three remaining parameters, lipid concentration (which affects particle size), surfactant concentration (which affects entrapment efficiency), and organic solvent concentration (which affects polydispersity index), as variables for further response surface methodology.

Table 15.

Summary of Plackett–Burman design [28, 29]

Plackett–Burman design
Factors:	12	Replicates:	1
Base runs:	20	Total runs:	20
Base blocks:	1	Total blocks:	1

Fig. 8.

Pareto chart showing the influence of variables (A) Influence of process variables on polydispersity index (B) Influence of process variables on particle size (C) influence of process variables on entrapment efficiency

The Box-Behnken design (BBD): the response surface methodology

A significant effect of independent variables [i.e., surfactant concentration (X1), lipid concentration (X2), and amount of organic solvent (X3)] on dependent variables [i.e., particle size (Y1), entrapment efficiency (Y2), and polydispersity index (Y3)] was evaluated by employing 3 factors and 3 levels (3³) of response surface-based BBD methodology (Table 6).The design consisted of total 16 runs.. We statistically analyzed the results by applying the principles of ANOVA using Design Expert® software, ensuring a 95% confidence interval. For each response, we generated quadratic equations, where the positive and negative signs in front of the factor indicate the direct and inverse effects of that specific variable on the given response. We generated quadratic equations for each response. We determined the best fitting regression model from their F values. We further constructed contour plots and 3D surface plots to demarcate the interactive effects of 2 independent variables on dependent variables, as shown in Fig. 9. The quadratic model analysis results showed a lack of fit value and a p-value [32, 41].

Fig. 9.

Graphical representation of effect of independent variables (Surfactant concentration (X1), lipid concentration (X2) & amount of organic solvent (X3)) on dependant variables (particles size (Y1), entrapment efficiency (Y2) & polydispersity index (Y3), A–F represents the 3D plots & contour plots for particle size, while G–L represents the 3D and contour plots related to entrapment efficiency & M–R represents the 3D and contour plots of polydispersity index

Influence of variables on particle size

The particle size of PTXNLCs varied from 123.3 to 389.3 nm for various level combinations of all factors in the design matrix. The F value of 228.26 indicates that the model fits the data satisfactorily, with a nonsignificant lack of fit of 3.06. Moreover, a p < 0.0001 at a 95% confidence interval suggests that this is the best-fitted model for this particular response. The low value of the coefficient of variation (2.58) also reveals some important facts that the model possesses a high degree of precision and reliability. The "Pred R-Squared" value of 0.9636 is in reasonable agreement with the "Adj R-Squared" value of 0.9927. Therefore, we can use this response to navigate the design space. Table 16 represented the results of the statistical analysis. The results of the statistical analysis (Table 6 and Fig. 9A–F) revealed that both the surfactant concentration (X1) and the amount of organic solvent (X3) have a negative effect on the particle size, while the lipid concentration has a positive effect. This means that if we continue to decrease the surfactant concentration and solvent amount, the nanoformulation's particle size will increase, while it will also increase with an increase in lipid content. In this case, the concentration of surfactant (X1) seems to have a big effect on particle size because it changes how well the formulation emulsifies, which in turn changes the particle size. It has a negative relationship with particle size. Conversely, we observed a notable rise in particle size as the lipid concentration increased. The increase in the viscosity of the contents leads to a reduction in the stirrer's shearing efficiency and the surfactant's emulsification ability. In a similar manner, the amount of organic solvent exhibits an inverse relationship with particle size. This could be attributed to a decrease in the viscosity of the lipid contents with a higher amount of organic solvent, leading to a high shear stress that would break the emulsion droplets without any coalescence [30, 32, 41].

Influence of variables on entrapment efficiency (EE)

The entrapment efficiency of PTXNLCs varied from 63.4 to 93.4% for different formulation variable combinations. Table 6 presents the second-order polynomial equation that the Design Expert software generated, equating entrapment efficiency with various factors. The modal value of F = 66.79 (p 0.0001) implied that the chosen model is the right choice for relating % EE with independent factors. A nonsignificant lack of fit (1.34) also certified the model's suitability with excellent data fitting. The correlation coefficient (R²) was sufficiently high (0.9901), indicating a good correlation between factors and responses. The "Pred R-Squared" of 0.9047 is in reasonable agreement with the "Adj R-Squared" of 0.9753. "Adeq Precision" measures the signal-to-noise ratio. A ratio greater than 4 is desirable. Our ratio of 30.543 indicates an adequate signal. You can use this model to navigate the design space. Table 6 and Fig. 9G–L reveal that surfactant concentration (X1), lipid concentration (X2), and the amount of organic solvent (X3) all have a positive impact on the percentage of entrapment efficiency (EE). Therefore, increasing any of these factors will positively enhance the entrapment efficiency. Increasing the lipid concentration creates a thick layer that inhibits the drug’s further diffusion into the surrounding area. Additionally, a higher lipid content dissolves more of the drug, leading to an increase in EE [30, 41]. Only a higher surfactant and organic solvent amount will be effective, as merely raising the lipid concentration without boosting the organic solvent concentration could result in a drop in EE. This is because a high concentration of organic solvent is required to dissolve the lipid, and a low concentration of organic solvent will result in the formation of thick foam in the medium, leaving the drug outside. The viscosity of the emulsion changes when the organic phase content decreases, providing another explanation. A rise in viscosity leads to heightened resistance to shear force, impeding the creation of nanodroplets. Additionally, a smaller quantity of drug will dissolve into the viscous lipid matrix, ultimately leading to a reduction in entrapment efficiency [56]. Similarly, forming a uniformly sized particle with good entrapment efficiency requires a sufficiently high surfactant concentration, as lower surfactant concentrations prevent sufficient drug from dissolving in the lipid medium, further reducing entrapment efficiency [57].

Influence of process variables on polydispersity index (PDI)

The PDI value varies from 0.112 to 0.289 for various combinations of process parameters at their minimum and maximum levels. Table 6 presents the second-order polynomial equation that multiple linear regressions generate. We found the chosen model to be statistically significant, displaying a good F value of 26.78 at p 0.0001, with a 95% confidence interval. Moreover, the nonsignificant lack of fit value (4.8) reflected the suitability and excellent reliability of the PDI on the chosen responses. The sufficiently high R-square value (0.957) justified a satisfactory correlation between the dependent and independent factors. Less than 0.0500 values for "Prob > F" indicate that the model terms were significant. In this case, A, C, and A2 were significant model terms (Table 6. We discovered that the amount of surfactant (X1) and organic solvent (X3) had a negative effect on the response, while the concentration of lipids (X2) had a positive effect. An increase in lipid concentration will increase the PDI value, as it directly impacts the thickness of the formulation contents (Fig. 9M–R). The high viscosity of the lipid matrix will either suppress the segregation of the nanoparticles or promote their aggregation by suppressing their negative charge. This will result in an irregular distribution of the particles, leading to a higher PDI [30,Despite this, the PDI went down significantly when the amount of surfactant (X1) and organic solvent (X3) went up. This is because the interfacial tension between the water and organic phases went down a lot, making the particles more uniform and lowering the PDI [25, 29, 30, 32].

Optimization of PTXNLCs

We used a numerical optimization technique based on the desirability function to optimize the final formulation of PTXNLC, following the Box-Behnken methodology. It is challenging to finalize a single formulation solely based on the results and 3-D contour plots of the box Behnken design, as the final formulation must be reliable for all responses. We can observe that some responses require the maximum value, while others require the minimum value. In these conditions, finalizing the formulation solely based on observations presents a challenge. We fixed the constraints for each variable using the desirability approach, resulting in maximum entrapment efficiency values as well as minimum particle size and PDI values, as listed in Table 16 and Fig. 3. Consequently, we developed an optimized formulation to verify the software's reliability. We calculated the percentage bias between the experimental and predicted values, as illustrated in Table 17. The response surface design for PTX-NLC formulation optimization was proven to be reliable when the predicted values and experimental values were very close to each other.

HPLC analytical method development

The calibration curve of PTX in methanol was found to be linear from 500 to 3000 ng/ml with a correlation coefficient value (R²) of 0.976 for in vitro samples prepared in methanol (Figs. 10 and 11). The method followed in our study was already reported [35], so we had mentioned only the R² value here.

Fig. 10.

Callibration curve of PTX in methanol

Fig. 11.

HPLC chromatogram of PTX in Methanol

Evaluation of quality problems of NLCs

Fourier transformed infrared studies (FTIR)

Our study employed the FTIR technique to examine the drug excipient compatibility. Figure 12 displays the FTIR spectra of PTX, PTX-NLC, and all the excipients. The FTIR spectra of PTX exhibited characteristic peaks near 1100 cm¹, which correspond to C–O stretch, and another peak near 1350 cm¹, which corresponds to C = C stretch. Two more peaks near 1680 and 1750 cm¹ correspond to the C = O stretch of amide and ester, respectively. We observed further peaks near 2970 cm⁻¹, 3100 cm⁻¹, 3250 cm⁻¹, and 3400 cm⁻¹, which could be the result of an aliphatic –CH– chain stretch and an aromatic stretch of –CH–, –NH–, and –OH–, respectively. The major peaks mentioned above confirm the PTX structure, which confirms the drug's identity. Furthermore, the FTIR data of the optimized formulation (A2) revealed the absence of major PTX peaks, confirming the encapsulation of the maximum drug inside the NLC molecules, while retaining all the major peaks of the lipid mixture and the used surfactant. Furthermore, the presence of all major peaks in the components confirmed the compatibility of the excipients and the drug.

Fig. 12.

Overlay FTIR spectra of PTXNLC, Physical mixture, PTX and all the excipients

Measurement of crystallinity

We performed DSC to analyze the presence of various lipid modifications, supercooled melts, and to calculate the percentage crystallinity in the formulation. The melting peaks near 62 °C and 390 °C, as well as one crystallization peak near 220 °C, were found in pure lipid (SL) (Fig. 13). The peak 1 and peak 3 (black arrow, SL) correspond to the actual melting of the SL, while peak 2 (blue arrow) showed crystallization of the solid lipid. The SL + LL binary mix (peak with orange arrow) displayed a shift in the melting peak, indicating the liquid lipid's lowering of the solid lipid's melting point. We calculated the area under the curves to determine the percentage of crystallinity in both the solid lipid + liquid lipid (SL + LL) mixtures and PTXNLC. We then used the area under the curve to calculate the percentage of crystallinity in both samples, following the method outlined in (28, 44–49). Our observations concluded that the percentage crystalline content in the SL + LL mix was 48.32%, and for the formulation (PTXNLC), it was 15.3% in total. The DSC thermogram confirmed the crystalline content in the SL, observing a low dip crystallization peak near 220 °C (Table 18). The DSC curves in Fig. 13 reveal that the presence of mannitol in the final formulation is responsible for the major peaks (green arrows) in A2. Subtracting the crystallinity content due to mannitol, we find that the formulation PTXNLC contains only 3.4% of the crystallinity content, indicating the formulation's amorphous nature. Figure 13 also reveals that A2 (PTXNLC) lacks sharp melting peaks at various temperatures, confirming the absence of lipid modifications [36, 46, 47]. This conclusion is further supported by the small particle size of the nanoformulation, as the tendency to form lipid modifications decreases with a decrease in droplet size. The absence of separate peaks in the SL + LL mix suggests that the two phases are homogeneously mixed during macrogelation [22].In thermogram of Pure drug, the endothermic peak near 220 °C shows the sharp melting point of the drug indicating its crystalline nature. In PTXNLC (A2), we observed a shift in the melting point (represented by a black arrow) of the solid lipid, potentially due to the incorporation of the drug [22]. We also observed two additional peaks (represented by a green arrow) at approximately 185 °C and 365 °C, indicating the addition of mannitol to facilitate the lyophilization of the nanoformulation. Furthermore, the absence of the crystallization peak in the PTXNLC (A2) thermogram indicates the formulation's amorphous nature, which is further supported by the nanoformulation's smaller particle size. This is because crystallization requires a critical number of nuclei to initiate, which is less likely to occur in small droplets. Additionally, the absence of the PTX peak in the PTXNLC thermogram confirms that solubilization encapsulated most of the drug in the NLC, preventing its incorporation into lipid imperfections [22], thereby reducing the risk of drug expulsion during storage. Therefore, we can conclude that our formulation produced type III (amorphous type) of NLC [37, 42, 58].

Fig. 13.

DSC thermograms of PTX-NLC, physical mixture, PTX and all excipients. The individual images show DSC thermograms of solid liquid lipid binary mix, PTX-NLC formulation and solid lipid respectively

Table 18.

Percentage crystalline index [36]

Component	Crystalline index (%)
Solid liquid lipid binary mixture	48.32
Optimized formulation with mannitol	15.30
Mannitol	11.9
Optimized formulation	3.4

Gelation phenomenon

Stress-induced gelation is one of the reasons for apprehension, the case of lipid nanoparticles, as lipid nanoparticles, if, under stress induced by the needle during syringing, transformed to a gel form, may lead to serious problems like blocking of veins and may prove to be fatal. This may also lead to formulation instability. We measured the particle size both before and after the syringing. The mean particle size was found to be 123.6 ± 2.34 nm after syringing, which was not significantly different from the particle size observed before syringing (121.44 nm; p < 0.05). Hence, no significant change in particle size was observed when they were exposed to stress by multiple syringing. Furthermore, the viscosity of the nanoformulation before and after multiple syringing was found to be 47.66 ± 3.2 cps and 48.75 ± 4.1 cps, respectively. The nonsignificant difference (p < 0.05) in the viscosity also confirms the stability and non-gelation property of the nanoformulation [21, 22, 46, 47].

Stability studies

We conducted stability studies to determine the shelf life of the optimized batch, calculating it by % EE under various chosen conditions (Fig. 14). We also determined particle size, Zeta potential, and PDI in comparison to fresh samples, and found no significant difference (p < 0.05) in these traits under these conditions. EE observed a shelf life of 11.9, 12.5, and 12.9 months for accelerated, refrigerated, and room temperature conditions, respectively, with an average shelf life of 11.9 months across all conditions. Thus, all conditions found the PTXNLCs to have sufficient shelf life [41].

Fig. 14.

Shelf life estimation of optimized batch of PTX-NLC formulation at various conditions

Transmission electron microscopy (TEM)

We observed the prepared formulation under a TEM microscope to study the size of the formed nanoparticles. Figure 15 shows that the nanoparticles ranged in size from 110 to 120 nm throughout the image area. The size observed under the microscope was also acquiescent with the particle size observed under the particle size analyzer. Also, the uniformity in size excludes the presence of different colloidal species, as only single types of colloidal particles were present.

Fig. 15.

Transmission electron microscopy image of optimized PTX-NLC formulation

Atomic force microscopy (AFM)

The three-dimensional AFM images (Fig. 16) were explored to study the surface morphology of PTX-loaded NLCs, which were generated by the atomic force interaction between a sharp tip and the surface of nanoformulation. The particle size and morphology determined by the AFM were determined by the particle size analysis.

Fig. 16.

Atomic force microscopic images of optimized formulation (A) 3-D images (B) 2-D image

Particle size (PS), Polydispersity index (PDI), Entrapment efficiency (EE %) & Zeta potential (ZP) determination

A particle size analyzer (Delsa Nano C) was used to calculate the three parameters (PS, PDI, and ZP). Determination of particle size is based on the phenomenon of Brownian motion and light scattering principles, whereas the value and charge of zeta potential are determined by the chemical nature of the polymer, oil, and most importantly, the nature of the surfactant used. The zeta potential of the optimized formulation was found to be −20.21 mV, which is large enough to keep the particles apart and provide stability to the colloidal system [30, 32, 51, 53]. Also, the negative charge of the nanoparticles will delay their protein binding and thereby result in a longer circulation half-life of the nanoparticles. The average particle size of the optimized formulation was found to be 121.44 ± 2.34 nm, and the polydispersity index was found to be 0.114, which states that particles of excellent nano range were fabricated and the size was consistent through the formulation [31, 59]. The entrapment efficiency of the optimized formulation was found to be 94.27% which shows that the optimized NLC formulation can entrap sufficient amounts of drug inside to release the appropriate amount of drug for an extended period.

Cumulative percentage drug release (%CDR)

We conducted an in vitro drug release to evaluate the potential of NLCs in regulating the release of PTX from the formulation. The in vitro release profile of the optimized PTX-NLC formulation is shown in Fig. 17. The release of PTX from the formulation did not show any burst release, which can be due to the lack of free drugs present in the formulation [34]. Further, the formulation exhibited sustained release due to the presence of the lipid matrix system. Sustained release of the drug can be explained on the basis of the strong affinity of the drug for the lipidic system, which also provides the lipid barrier to curtail the immobilization of the drug from the matrix system. We had analyzed the release mechanism by substituting the release profile data to various kinetics models, and their correlation coefficients and release exponent values are mentioned in Table 7. From the values of R², it can be concluded that the release kinetics of PTXNLC followed the Higuchi model of release kinetics, and the fickian diffusion-based release mechanism was explained by the release exponent value of the Korsmeyer Peppas model, which was found to be 0.464 (n < 0.5 for the fickian diffusion-controlled mechanism) [32, 61, 62].

Fig. 17.

In vitro drug release profile of optimized PTXNLC in phosphate buffer saline pH 7.4. Data is represented as mean ± S.D, n = 3

In vitro cell line studies

We conducted in vitro cell line studies on U87MG cell lines. 100 μM solutions of PTX and the prepared formulations were prepared in distilled water as stock solutions. From the stock, dilutions were made in the range 10 to 50 µg mL⁻¹. Cells in conc. 1×104 cells per well were incubated in 96 well plates for 48 h and then treated with the prepared dilutions. During the studies, we used a placebo of the formulation as the positive control and normal saline as the negative control. Figure 18 demonstrates the results. At higher concentrations, the PTX NLC formulation was more effective at controlling cell growth than PTX alone. We also observed a minor cytotoxic potential for the placebo formulation, which we can explain by the presence of TPGS in the formulation [60]. The enhanced cytotoxic potential of TPGS will provide adjuvant therapy to treat GBM.

Fig. 18.

% cell viability of pure drug PTX, formulation PTX NLC & placebo at different concentrations. Results were analyzed by two way ANOVA followed by bonferroni posthoc test; @: when compared with UA at p < 0.05. Values are expressed as mean ± SEM, n = 3

Statistical analysis

All the experiments were performed in triplicate (n = 3), and the results were expressed as mean ± SD (standard deviation). ANOVA followed by a post-Benferroni test (using GraphPad Prism 5®) was performed for the statistical comparisons of results with control by taking p < 0.05 as a statistically significant level.

Conclusion

The present study provides a deep insight into the charismatic features of NLCs for intravenous delivery of PTX. PTX-loaded NLCs were successfully formulated after optimization by employing quality by design-based principles. State-of-the-art facilities were used to optimize formulation. We made significant efforts to increase the drug's entrapment through quality-driven design strategies.The solvent evaporation technique with modifications was used for the formulation, and the resultant formulation with 121.5 nm particle size, narrow PDI, and high entrapment efficiency was obtained by virtue of the desirability approach-based numerical optimization technique. The optimized formulation was able to provide sustained release up to 4 days. Various techniques authenticated the potential of PTXNLCs for intravenous administration. Throughout the study, the prepared formulation demonstrated compatibility with blood cells, indicating its suitability for intravenous use. In this context, NLCs pave a new path in the formulation field by enhancing the stability of highly lipophilic drugs for intravenous delivery.

Author contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by [Dr. Pooja Mittal], [Smriti] and [Madhav Singla]. The first draft of the manuscript was written by [Dr. Ramit Kapoor and Dr. Dileep Kumar] and all authors commented on previous versions of the manuscript. Formal analysis and investigation was performed by [Dr Tanima Bhattacharya], [Dr. Saurav Gupta] and [Dr. Gaurav Gupta]. All authors read and approved the final manuscript.

Data availability

All data supporting the findings of this study are available within the paper.

Declarations

Competing  interests

The authors declare no competing interests.