Multichannel 3D-printed bionanoparticles-loaded tablet (M3DPBT): designing, development, and in vitro functionality assessment

Abstract

Background

The intersubject variability which was related to the genetic makeup was the major cause of change in pharmacological and pharmacokinetic behavior of same dosage form in varied human being. 3D printing technology will help therapy evolve and eliminate the limitations of conventional technologies. Nebivolol's (NBL)-limited oral bioavailability is mainly due to its poor aqueous solubility. The research aims to combine advanced 3D printing technology and nanotechnology to design customized therapy and enhance the functionality of NBL using a statistical approach.

Results and discussion

The results of the phase solubility indicated that NBL was a poorly aqueous soluble drug. Its solubility was increased by employing nanoparticle drug delivery, which is a promising solubility enhancement technique. The 3² full factorial design was employed to develop and optimize bionanoparticles (BNPs) by solvent evaporation technique using poly (lactic-co-glycolic acid 50:50) (PLGA 50:50) and poloxamer-407 as a surfactant. The BNPs were characterized by % encapsulation efficiency (% EE), Fourier transform infrared spectroscopy (FTIR) and differential scanning calorimeter (DSC), transmission electron microscope (TEM), zeta potential, polydispersity index (PDI), particle size, in vitro drug release, etc. The BNPs loaded of NBL were further incorporated into the multichannel 3D-controlled release tablets made by PVA filaments employing fused deposition modeling (FDM) technology optimized by central composite design (CCD). Multichannel 3D-printed bionanoparticles-loaded tablet (M3DPBT) was optimized using CCD. All designed M3DPBTs were evaluated for post-fabrication parameters. The optimized M3DPBT could release more than 85% NBL within 10 h.

Conclusions

The newly fabricated M3DPBT was found stable. The amount of PLGA 50:50 and Polaxomer was significant for developing BNPs. % infill and layer height were observed as critical for the designing M3DPBT. The combined novel 3D printing and nanotechnology technology will open a new direction for patient compliance and better therapeutic effects.

Graphical abstract

Designing and developing of M3DPBT is substantially improve the patient compliance and therapeutic effectiveness of Nebivolol.

Background

Hypertension is a global health concern. It is common and, if left unchecked, can lead to severe problems. High blood pressure affects nearly a million people worldwide, two-thirds in developing countries. Hypertension affects approximately 8 million people worldwide each year. According to projections, 1.56 billion people will have hypertension by 2025, indicating a growing epidemic. The primary symptoms of the condition are chronic, resembling cardiovascular disease and stroke in developed countries. One of the global noncommunicable disease goals is to reduce the prevalence of hypertension by 33% between 2010 and 2030 [1, 2].

A β1-selective beta-blocker or high cardioselective NBL has been recommended for the curative action against heart failure and hypertension [3]. By increasing the release of nitric oxide, NBL induces endothelium-dependent lessening and quickly lowers blood pressure. Clinical trials have shown that reducing oxidative stress and vascular inflammation increases vasodilation [4]. It was also shown to have antihypertensive efficacy and to lower blood pressure dose-dependently, similar to older beta-blockers, calcium channel blockers, and ACE inhibitors [4,5,6]. As a BCS class II antihypertensive drug, NBL is selected as the study's model drug. It has three major drawbacks: First, it has poor aqueous solubility; second, it exhibits high dose variability, which causes intersubject variability; and third, it has low bioavailability [7,8,9].

BNPs have attracted particular attention among the reported diverse nanoformulations due to their controlled release action and better biocompatibility. The drug solution was covered by a polymeric membrane, which was stabilized using a surfactant in BNPs. Although some work has been done to develop solid forms that integrate BNP stability and storage, it is a significant concern. However, it can be overcome by loading into another dosage form [10, 11]. Intersubject variability relates to how each person's genetic makeup and pharmacological and pharmacokinetic behavior changes and is the leading cause for concern. Take a typical oral solid dosage form tablet with minimal restrictions when produced using the conventional method. It is impossible to tailor a tablet to each person's demand with current technologies. Other people need additional tablet dosages that cannot be produced on demand using the conventional procedure. The evolution of therapy will be aided by 3D printing technology, which will eliminate the limitations of conventional technologies [12].

No efforts have been made in the casing to create specialized NBL delivery using simulated 3D printing and nanotechnology. The existing investigation aims to explore the revolutionary 3D technology to address this issue by creating a tailored dose form for NBL with less toxicity. We explore methods for creating M3DPBT that can be used as drug-delivery devices. FDM designs unique tailor-made devices incorporating variable polymeric filaments. BNPs were effectively loaded. The drug loading and release patterns from the printlets were significantly impacted by the layer height of the polymer and infill percentages. This work put forth a unique platform for creating customized oral dosage forms in terms of dose and dissolution characteristics.

Methods

Materials

NBL HCl was acquired as a gratis sample from Intas Pharma Pvt. Ltd., India. Biodegradable PLA and PLGA 50:50 were acquired from Merck Chemical Pvt. Ltd., India. Non-ionic surfactant Poloxamer-407 was purchased from Chemdys Corporation, India. PVA filaments for printing 3D tablets were purchased from Makeerboat 3D Pvt. Ltd., India. All the analytical-scale organic solvents were procured from SD Fine Chemicals Pvt. Ltd., India.

Phase solubility study

Excess NBL was added to 3 ml of varied solvents like methanol, 0.1N HCl, and water, then shifted to a 10 ml volumetric flask. The test suspensions were kept for 72 h in an orbital shaker at 37 °C and 100 rpm. The suspension was centrifuged at 4000 rpm after 72 h of vigorous and continuous mixing, and the supernatant was collected. UV absorbance at 281 nm wavelength was used to assess the solubility of NBL in mg/ml using UV spectrophotometers (UV 1900, Shimadzu, Japan) [13].

Modified solvent evaporation method

The solvent evaporation process (168A, Jain Scientific Glassware, India), high-speed homogenizer (HSH) (T 25 digital ULTRA-TURRAX®, IKA India Private Limited), ultracentrifuge (ROTA 4R V/FM Plasto Crafts Industries Pvt. Ltd, India), and lyophilization (SP VirTis AdVantage Pro, Spinco Biotech Pvt. Ltd., India) were all used in this preparation method. This procedure entails dissolving the 10 mg of NBL HCl and PLGA 50:50 in 10 Dichloromethane (DCM) and adding the required amount of surfactant (Poloxamer-407) to the resultant solution. Allowing the mixture to evaporate using a rotary evaporator at a temperature above their boiling point (50 °C) and 80 rpm until a thin coating at the bottom of the flask was obtained. To form a suspension of BNPs, the thin film was reconstituted with deionized water. The BNPs are homogenized in HSH for 5 min at a speed of 10,000 rpm. The BNPs were separated employing the high-speed centrifuge. The surplus solvent was removed, and the concentrated formed BNPs were frozen for 24 h at − 22 °C in a deep freezer. The samples were freeze-dried for 72 h at 80 °C and 1.0 mbar under absolute vacuum pressure in a lyophilizer [14,15,16].

Experimental design (3² full factorial design)

As part of quality by design (QbD), the design of experiment (DoE) was used to find the empirical relationship between the critical materials attributes (CMAs) and critical quality attributes (CQAs) systematically and scientifically. An orthogonal polynomial is conceivable if the points of the factors are consistently set apart. A full factorial design with two variables and three levels (3²) was created and used to examine all potential combinations of all factors at all levels. A total of nine experiments comprise the experimental design using Design Expert v11 software (Stat-Ease, Inc., Minneapolis). The amount of PLGA 50:50 (X₁–0.2 to 0.7%) and Polaxomer 407 (X₂–1 to 3%) was chosen as independent factors. Throughout the investigation, all other material attributes and process parameters remained invariant. The nine experimental runs that were investigated are summarized in Table 1, along with the design matrix in actual values. The response variables tested were the % CDR (Y₁ – NLT 85%) and % EE (Y₂ – NLT 85%). The mathematical models were created using Design Expert (DoE) software v11 [17, 18].

Table 1 Design matrix and responses and characterization of BNPs

Evaluation of drug-loaded BNPs

Encapsulation efficiency

A solvent extraction approach was used to evaluate encapsulation efficiency. NBL was extracted from dried BNPs (equal to 2 mg of medication) using dichloromethane: methanol (1:9) and centrifugation at 5000 rpm for 15 min (R-4C, Remi Instruments Pvt. Ltd., India). The supernatant was collected and diluted suitably. The amount of NBL was calculated using a standard curve of NBL inculcating the UV–visible spectrophotometer (UV 1900, Shimadzu, Japan) at 281 nm [19, 20]. By using the formula below, EE (%) was obtained:

$$\boldsymbol{\%}{\varvec{E}}{\varvec{E}}= \frac{Practical NBL content}{Theoretical NBL content } X 100$$

(1)

Particle size analysis

Malvern zetasizer (Nano ZS90, Malvern Instruments Limited) was used to calculate the mean particle size. Prepare a 15-ml solution with 5 mg of BNPs. Prepare a solution containing 5 mg BNPs in 15-ml solvent. The aqueous BNP dispersion was kept in a sample holder with a probe. Coated-window optical probes were linked with the opposite electrodes in an insulating sample cell. An electric field is applied between the optical probes and the associated electrodes. Under the field's influence, particle motion is investigated. The particle size distribution was derived based on the velocity of the BNPs using the perceptions of dynamic light scattering. Software called Flex was used to obtain a graph showing particle size distribution. The below equation was used to determine the PDI for studying particle size distribution [21,22,23].

$${\varvec{P}}{\varvec{D}}{\varvec{I}}=\frac{\left(D 0.9\right)-(D 0.1)}{(D 0.5)}$$

(2)

Zeta potential

The zeta potential is the direct measurement of the stability of the suspended particles. The surface charge was measured using this technique. The zeta potential was measured by Malvern zeta sizer (Nano ZS90, Malvern). The ideal zeta potential value should not be  − 10 and +10 mV for a stable formulation. The zeta potential of the formulation was identified and reported [10, 22].

Structural analysis by FTIR

The structural analysis of NBL and BNPs was observed using FTIR analysis (JASCO FT/IT6100, Japan). The BNPs were characterized by identifying new functional groups that confirmed the structural changes that led to improvements in solubility and stability. The NBL or BNPs were mixed with potassium bromide for a few minutes. The pellets were pressed and placed in a sample holder in the path of light for the structural analysis. The NBL or BNP pellet was scanned in the 400 to 4000 cm⁻¹ range [24, 25].

Characterization of BNPs by DSC

Solid physical state alteration coupling with thermal events during heating can be observed with the help of DSC (DSC 7020 HITACHI, Japan). The DSC spectra confirmed the conversion of crystalline to amorphous. Approximately 2mg of NBL or BNPs were kept in an aluminum pan under the N₂ environment. The temperature was gradually increased at a rate of 10 °C/min in the range of 25 and 300 °C. Thermograms of NBL and BNPs were recorded and compared for any solid-state alteration [26].

Morphology determination by TEM

Morphology, especially BNP shape, size, and uniformity, was observed employing a transmission electron microscope (Tecnai 20 S-twin, Philips, Netherlands). A dilute aqueous dispersion of BNPs (1%) was formed to identify shape and structure morphology properly. A small, thin dispersion layer was applied on a carbon-coated copper-stained grid surface of 300 mesh. Negative TEM staining was utilized for better resolution and identification. Phosphotungstic acid was used as a dye. The layer was air dried at 27 °C, observed under the TEM, and the images were taken at an accelerated voltage of 80 kV and recorded.

XPRD of NBL BNPs

PXRD was performed to characterize the solid state of NBL and BNPs. The XRD spectra was recorded using PW 17291 powder X-Ray diffractometer using Ni-filtered, Cu kV radiation with a voltage of 40 kV and a 25 mA current. The scanning rate employed was 1° min⁻¹ over 10–40° 2θ range [27, 28].

In-vitro diffusion study

BNPs containing NBL were observed for diffusion patterns and performed through Franz diffusion cells. About 1.5 ml BNP dispersion was reserved in the donor section. During the diffusion study, the receptor section was occupied with phosphate buffer pH 7.4 solution (25 ml). The sink condition and 37 ± 0.5 °C were maintained to simulate in vivo conditions. The test solution was sampled at a pre-fixed time duration and exchanged with a pH 7.4 phosphate buffer solution to maintain constant volume. The test solution was diluted, and the amount of NBL was estimated using a UV–visible spectrophotometer at 281 nm. One formulation was analyzed six times, and an average was taken and reported with standard deviation [29].

Preparation of multichannel 3D-printed tablet (M3DPT) by FDM technology

An excellent performance and efficient cost ratio are characteristics of FDM. It is mainly utilized for recreation and in industry. Once put into the nozzle, a heating tool was used to melt the PVA filament. A heated nozzle was used to extrude the melted polymer. Starting at the bottom of the intended tablet, the printing was done layer by layer (Zorthrax Ultra 200 plus, India). PVA was selected as a polymer because it is hydrophilic, has a low melting point, and is an FDA-approved material [30,31,32,33].

Preliminary design of 3D-printed tablet

Round shape tablet

Tinker CAD (https://www.tinkercad.com/) was used to design the round-shaped tablet with a 10 mm diameter and 2 mm height. The design file was downloaded into (.stl) format and transferred to Zorthrax Ultra 200 plus. The design file was printed into a 3-D round tablet using a 3D printer with an MK8 single-head extruder, as shown in Fig. 1. Different process parameters like printing temperature (270 °C), %infill (100%), layer of height (0.2 mm), and build plate temperature (60 °C) were adjusted to form a round tablet [32].

Fig. 1

Design of 3D tablet: a round shape tablet and b M3DPT

Multichannel 3-D-printed tablet (M3DPT)

Tinker CAD (https://www.tinkercad.com/) was used to design the M3DPT with an 8 mm width and 16 mm length. The design file was downloaded into (.stl) format and transferred to Zorthrax Ultra 200 plus. The design file was printed into M3DPT using a 3D printer with an MK8 single-head extruder, as shown in Fig. 1. Different process parameters like printing temperature (270 °C), %Infill (10–15%), layer of height (0.7 mm), and build plate temperature (60 °C) were adjusted to form M3DPT.

Development of M3DPBT

The BNPs were loaded into the 3D-printed tablet to form M3DPBT by soaking method. The multichannel tablet was soaked in 2 ml BNPs containing dispersion for 24 h under closed conditions at 37 ± 0.5 °C. Two ml suspension contains BNPs equivalent to 10 mg of NBL. The 3D-printed tablets were removed, dried at room temperature, and stored in a closed container till further evaluation. The researcher was also allowed to vary the dose of NBL as per the need of patient.

Statistical design—CCD

A CCD was employed to optimize formulation using DoE v11.0.0. According to the design space of the CCD model, as described in Table 2, 13 experimental runs were designed and assessed for desired responses (Table 2). The design consisted of one dependent variable, % cumulative drug release at 10 h (Y₁ - NLT 85%), and critical process parameters X₁-% infill (5–15%) and X₂- layer height (0.2–0.7 mm). Analysis of variances (ANOVA) and multiple linear regression analysis (MLRA) were performed to signify the correlation between the chosen process parameter and critical quality attribute. Two-dimensional contour plot and Three-dimensional response surface plot were generated to understand the correlation. Numerical and graphical optimization was performed to optimize the M3DPBT by setting R₁ that should not be less than 85%. The validation and prediction capability of the developed model was ascertained by grid search analysis [30, 34, 35].

Table 2 Design matrix, measured responses, and characterization of M3DPBT

Characterization of M3DPBT

The basic post-manufacturing parameters of the tablet characterized M3DPBT. M3DPBT was characterized by the weight of the formulation, time to manufacture M3DPBT, hardness, and friability.

In-vitro drug release of M3DPBT

Employing the dissolution type-II equipment, the dissolution study of the M3DPBT was carried out (TDT 08L, Electrolab, India). In vitro drug release was assessed in 0.1 N HCL (900ml) at 37±0.5 °C. The tablets were placed, and the paddle was rotated at 50 rpm as per USP. In accordance with USP, the M3DPBT was deposited, and the paddle was rotated at 50 rpm. The sample (5 ml) was collected at a pre-fixed duration and exchanged with the same amount of 0.1N HCl to maintain the constant volume. The samples were filtered and diluted with 0.1N HCL solutions to the proper concentration. Under a UV-visible double beam spectrophotometer, the absorbance of the solutions was measured at 281 nm wavelength. An equation derived from the standard curve was then used to estimate the cumulative percentage of drug release [36, 37].

Stability study

M3DPBT was tested for short-term stability over 1 month in closed amber vials under controlled environmental conditions at 40 °C/75% RH for 6 months (Stability Chamber, Nihaar Equipment Pvt. Ltd., India). During a month, the % EE and % CDR measurements were conducted every 1-month interval [38].

Results

Phase solubility study

The analytical composition of the solute determines a saturated solution's solubility about a specific amount of solvent. With various solvents, solubility levels vary greatly. To conduct the phase solubility analysis, 10 mg of the drug was taken into a 2 ml solvent containing Eppendorf. Here, the solubility of NBL in several solvents, including water, 0.1N HCL, and methanol, was evaluated. NBL has poor water solubility, 0.0403 mg/ml, and is therefore considered as poorly aqueous soluble drug. Hence, it was necessary to enhance the NBL solubility before inculcating into the M3DPT, which results in improved NBL release and therapeutic effectiveness [7, 39].

BNPs optimization using 3² full factorial designs

Using DoE v11.0.0, a total of nine experimental trials containing two independent variables and the two response variables % EE and % CDR were constructed. The amount of the surfactant (X₂) and polymer (X₁) was chosen as the formulation’s independent variables. Throughout the investigation, all other formulation and process variables remained constant [40]. Table 1 shows the % EE and % CDR results of the design batches performed to optimize and validate the model.

The DoE software was used to analyze variance (ANOVA) to scrutinize the significance of co-relationships among chosen variables. Table 3 summarizes the ANOVA statistics for % EE and % CDR regarding Fischer's ratio (F-value) and p-value.

Table 3 ANOVA analysis and validation of model for BNPs

Influence on % EE of BNPs

A very low p-value (0.013) and high F-value (111.5) indicated that model terms were crucial. In this case, X₁ is the amount of PLGA 50:50, and X₂ is the amount of Polaxomer 407. “X₁X₂” is interaction effects. The positive effect of X₁ clearly shows that increasing polymer concentration will directly increase the % EE, and negative values will show a decrease in effect on % EE. The developed model was highly predictive for the chosen response and could find the variability as high R² (0.99). Contour plots and 3D-RSM of % EE illustrated in Fig. 2a and b, respectively, show that when polymer concentration increased, the entrapment of the drug inside the BNPs was observed to be higher and in the desired range. However, keeping the polymer concentration constant and surfactant concentration at a high level, the entrapment increased to a definite level and then reduced. While increasing both amounts of PLGA 50:50 and Polaxomer, the entrapment of the drug was found to be irrespectively decreased. Hence, it concluded that polymer concentration only influences the entrapment of the drug inside the BNPs.[41, 42] The polynomial equation of % EE is expressed:

$${\text{Y}}_{{1}} = {95}.{8}0 + 0.{\text{7167X}}_{{1}} {-}{5}.{\text{35X}}_{{2}} {-}{3}.0{\text{8X}}_{{1}} {\text{X}}_{{2}} {-}{4}.{\text{95X}}_{{1}}^{{2}} {-}{4}.{\text{35X}}_{{2}}^{{2}}$$

(3)

Fig. 2

a Contour plot of Y₁, b 3D-RSM of % EE of Y₁, c Contour plot of Y₂, d 3D-RSM of % EE of Y₂, and e overlay plot

Influence on % CDR

A very low p-value (0.0048) and a high F-value (46.69) specify that the model terms were significant. The developed model was highly predictive for the chosen response and could find the variability as high R² (0.98). Figure 2c and d shows the contour plot and 3D-RSM of % CDR when the polymer and surfactant concentrations were too optimum. Similar to the drug entrapment, the influence on % CDR was observed in that drug release gradually increased with the polymer concentration. In contrast, surfactant concentration did not produce significant effects on drug release in individuals. However, the combined influence of both variables significantly increases the drug release to a certain extent, then decreases gradually. Only the desired surfactant concentration reduces the surface tension and facilitates the particle partition during homogenization. [43, 44] The polynomial equation of % CDR is expressed:

$${\text{Y}}_{{2}} = {95}.{6667} + 0.{\text{666667 X}}_{{1}} - {5}.{\text{5 X}}_{{2}} - {\text{3 X}}_{{1}} {\text{X}}_{{2}} - {\text{5 X}}_{{1}}^{{2}} - {4}.{\text{5 X}}_{{2}}^{{2}}$$

(4)

Validation of design model

The overlay plot has been constructed by overlaying the contour plot of each response. Numerical and graphical optimization was performed by adding the desired range of each response and independent variables. The yellow-colored region was identified in the overlay plot and considered optimal. One optimal implant and two checkpoint BNPs were chosen, formulated, and evaluated for selected CQAs related to the anticipated values, and the % prediction error was calculated. The % prediction error for all responses was less than 10% (Fig. 2e and Table 3), which ascertains the high predictive ability of the developed quadratic model [42, 45].

Particle size and PDI measurement of optimal BNPs by DLS

The particle size is critical since it significantly influences drug release, cellular uptake, stability, and biodistribution [46]. NBL-loaded BNPs had a narrower size distribution histogram and a mean particle diameter of roughly 277 nm, as shown in Fig. 3a. The PDI value was discovered to be 0.014, within the acceptance range. The particle size was observed in the nano range as shown in Table 1. The low PDI value indicates that the uniform dispersion of particle was observed.

Fig. 3

a Particle size and b zeta potential of optimized batch

Zeta potential

The zeta potential is another imperative variable for BNPs as it shows the intensity of repulsion between the neighboring equal-charged particles and provides the probability of physical stability and particle aggregation in the colloids [47]. Figure 3b shows that the zeta potential of the optimal NBL BNPs was observed at − 49 mV, indicating that the BNPs have incipient stability. The zeta potential was measured, reported in Table 1. The formulated BNPs was observed stable from the reported data.

Structural analysis by FTIR

The functional groups between the drug and the BNP matrix forming the polymer blends were identified using FTIR. To analyze the chemical interactions between the NBL and other excipients, FTIR spectra of NBL were performed with PLGA 50:50 polymer and Poloxamer- 407. Figure 4 shows that NBL's FTIR spectra showed the considerable changes. The major peak was observed in the range of 3650–3350 cm⁻¹ which was indicated that there is the formation of newer bonds of hydroxyl. The conversion was mainly due to change in crystalline to amorphous form, which enhances the solubility and leads to better drug release [13].

Fig. 4

Structure elucidation by FTIR

Thermal analysis

The NBL melting point was at 229.31 °C, as shown in Fig. 5, showing that the drug is pure and crystalline. The glass transition temperature of the PLGA 50:50 polymer caused a sharp endothermic peak to be detected in BNPs at 53.2 °C. However, with BNPs containing NBL, there was no clear endothermic peak of the drug. This indicates that amorphous NBL is embedded inside the polymeric matrix [48].

Fig. 5

Thermal behavior analysis

Morphology by TEM

The TEM images of optimized NBL-loaded BNPs are shown in Fig. 6a. Results ascertained that the spherical shape and nano-sized (174 nm) BNPs were formed, which was desired. The difference in diameter measured by TEM and DLS was due to the hydrodynamic effect of the BNPs in the fluid medium. Because DLS measures the size of particles based on intensity distribution while TEM measures the number of distributions [49].

Fig. 6

a TEM images of optimized BNPs, b % CDR of BNPs, and c PXRD diffractogram

XPRD of NBL BNPs

The X-ray diffractogram of NBL and BNPs are depicted in Fig. 6c. The sharp and intense peaks were observed with the NBL. The number of the peaks, intensity, and sharpness were reduced after the formulation of BNPs of NBL. The results were indicated that the conversion of crystalline to amorphous form up to some level or NBL may be entrapped within the BNPs.[50]

In vitro NBL release of BNPs

The profile of in vitro NBL release is illustrated in Fig. 6b, and after 10 h about NLT, 85% was drug released in batches P-15, 16, and 19. The drug release kinetics in vitro are influenced by the PLGA and Polaxomer combination. Nevertheless, it is feasible for a multifaceted phenomenon between the NBL and PLGA, such as drug entrapment in the polymer and drug adsorption on the surface of the polymer matrix due to electrostatic adhesion. The surface-adsorbed drug is expected to be the one that causes the initial burst release from the BNPs. After 10 h, a burst drug release was seen. This drug release loaded near the BNPs' surface may have been driven by polymer diffusion in the BNPs' surface. The implication is that a mechanism for drug release from BNPs might combine diffusion and dissolution [16, 51].

Optimization of M3DPBT by DoE

For the RSM involving CCD, M3DPBT was designed as shown in the design matrix, which incorporated star and axial batches. The design matrix of CCD with measured CQAs and other post-fabrication parameters is shown in Table 2. MLRA and ANOVA were carried out by employing the DoE to establish the relationship among the two chosen critical process variables (% infill - A and Layer height in mm - B) and the one quality attribute (% CDR at 10h - R₁) in CCD. The results of MLRA (coefficient value) and ANOVA (Fisher’s ratio and p-values) are depicted in Table 4 for the chosen CQA [33, 52].

Table 4 ANOVA analysis and model validation of M3DPBT

Influence on % CDR

A very low p-value (0.0005) and high F (19.59) indicate that model terms were significant. Results showed that the squared correlation coefficient R² and the adjusted R² were 0.93 and 0.88, respectively, which indicated that the model could explain above 85% variability in the response and showed the adequacy of the model to predict the response. The lack of fit was found to be 98.06%, implying that this value is significant due to noise and was non-significant. Figure 7a and b shows the contour plot and 3D-RSM of % CDR when the % infill and height of the layer were too optimum. The figures show that by keeping the infill percentage at a high level and the layer’s height increasing from low to high grade, the % CDR was increased. Whereas the infill percentage at a low level and height of the layer increased from low to a high level, the % CDR was decreased. When both independent variables were increased, the % CDR gradually increased to a certain level [53]. Table 4 briefs the ANOVA statistics for the % CDR of the multichannel tablet. The polynomial equation of % CDR is expressed:

$${\text{R}}_{{1}} = {82}.{33}{-}{5}.{\text{48A}}{-}0.{\text{51B}}{-}{4}.{2}0{\text{AB}}{-}0.{\text{23A}}^{{2}} + 0.{\text{56B}}^{{2}}$$

(5)

Fig. 7

a Contour plot, b 3D response surface plot, c overlay plot of M3DPBT, and d % CDR of M3DPBT

Overlay plot of design

An overlay plot was drawn in the DoE v.11.0.0. The design space is shown in Fig. 7c. The formulator is free to choose any point within the design space. It is worth noting that the FDA requires that the design space be clearly defined in ANDA. Optimization was achieved by computing overall desirability.

Validation of model

A checkpoint analysis confirmed the role of a polynomial equation and a contour plot in predicting responses. In the design space, as shown in Fig. 7c, any point selected among the region falls well within the constraints set for the response variables. From an extensive grid search, three checkpoint batches were selected after taking trials of obtained formulations as they had a significantly lower amount of error than as shown in Table 4. The % CDR of checkpoint batches was experimentally determined. They were compared with the predicted values found in the equation. The % relative error obtained from the checkpoint batch was in range. Hence, a close resemblance was obtained between an observed value and a predicted response assessed for the robustness of prediction. This value indicates the validity of the generated model [54].

Evaluation parameters of M3DPBT

The therapeutic effectiveness and bioavailability of tablets depend upon parameters like weight uniformity, friability, hardness, and content uniformity. These parameters were performed after the tablets were fabricated [55, 56]. Designed batches and optimized batches showed acceptable weight results, as shown in Table 2. The time taken for all the batches is within the range measured using the Cura software. The friability ranged from 0.01 to 0.06, less than 1%. So, all batches have shown acceptable pharmacopoeial limits.

In vitro dissolution of M3DPBT

From Fig. 7d, in the % CDR of M3DPBT, all optimized batches showed more than 80% CDR except TB10, TB12, and TB13 due to their higher infill density. Thus, the higher surface area to volume ratio of the low-density tablets likely accounts for the faster dissolution rate compared to high-density tablets [57, 58].

Stability study of M3DPBT

The stability of an optimized M3DPBT was performed at 40 ± 2 °C/75 ± 5% RH for 6 months. % EE and % CDR were measured at frequent time intervals, indicating no significant variance observed after 6 -6 months of storage. The % EE was observed at 90.78 ± 1.34, and the % CDR at 10 h was 85.67 ± 1.35.

Discussion

More emphasis was placed in this study on the techniques used to prepare poorly soluble NBL-loaded BNPs that were then incorporated into a 3D-printed tablet to form M3DPBT. It was discovered that NBL pharmacokinetic variability may occur during absorption, distribution, or excretion processes. As a result, customized doses of NBL are required because different individuals have different genetic variations and pharmacological and pharmacokinetic behavior. Furthermore, conventional/traditional manufacturing techniques produce tablets with some drawbacks. Tablets cannot be tailored to individual patient needs (dose, medication types, etc.) and, when prepared using traditional manufacturing techniques, do not allow for on-demand manufacturing. To address this issue, 3D-printed tablets provide personalized treatment. To address the shortcomings of the conventional formulation, we created a 3D-printed formulation loaded with drug-loaded BNPs.

This study chose PLGA 50:50 as a polymer and Poloxamer-407 as a surfactant. BNPs were prepared using a modified solvent evaporation technique and optimized using 3² factorial designs. Different evaluation parameters, such as zeta potential, PDI, particle size, % EE, and % CDR, were used to evaluate the optimized batches. Furthermore, the multichannel tablet was prepared using PVA filaments via FDM technology, with different % infill and layer heights were chosen to analyze the best surface-to-volume ratio for better dissolution, and it was discovered that the shape with the lowest infill% gives the promising drug release NLT 85% in 10 h. The experiment design was used, and CCD was chosen to study the FDM technique. Two factors were used to optimize the 3D printing batch: (1) % Infill in 3D CURA software and (2) layer thickness. The evaluation of all batches was based on in vitro dissolution. It was discovered that the % infill and layer thickness in 3D tablets played an essential role in modified drug release. The FDM 3D technique is feasible for producing tablets with good compatibility.

Conclusion

In the present investigation, more prominence was specified to design M3DPBT. The novel technology explored the conjugations of two progressive nanotechnology and 3D printing to produce innovative personalized nanomedicine-based drug delivery. NBL-loaded BNPs were successfully loaded in FDM-printed devices, which are successfully prepared to design novel M3DPBT. The height of the layer and the infill percentage significantly influenced the drug loading and drug release profiles from the tablets. This newer strategy yields dual advantages of both the technologies. As a proof of concept, this study proposed a new platform for developing oral dosage forms, with tailored dose and drug release profiles as personalized medicines.