Formulation Development and Optimisation of a Novel Quality-by-Design Bedaquline-Loaded Nanoemulsion for the Potential Treatment of Multi-Drug Resistant Tuberculosis in Paediatrics

doi:10.21203/rs.3.rs-4956076/v1

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Formulation Development and Optimisation of a Novel Quality-by-Design Bedaquline-Loaded Nanoemulsion for the Potential Treatment of Multi-Drug Resistant Tuberculosis in Paediatrics

https://doi.org/10.21203/rs.3.rs-4956076/v1

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Bedaquiline is a drug used for the treatment of multidrug-resistant TB in adults and children that is currently only commercially available in tablet form. The present study was aimed at preparing nanoemulsion (NE) of BDQ using natural vegetable oils to deliver BDQ. The optimisation of surfactant mixtures was undertaken using Design of Experiments (DoE), specifically an optimal mixture design. The NEs were optimised while monitoring droplet size (DS), zeta potential (ZP), polydispersity index (PDI) and drug content (DC). The optimised NEs were further characterised using transmission electron microscopy, electrical conductivity, viscosity, pH and in vitro release studies. The optimised NE showed values of 191.6 nm ± 2.38 nm, 0.1176 ± 1.69, -25.9 mV ± 3.00 mV and 3.14 ± 0.82 mg/ml for DS, PDI, ZP and DC respectively. Furthermore, the TEM studies demonstrated the spherical shape of the optimised globules. The nanoemulsion was characterised by measuring its electrical conductivity, viscosity and pH which were determined as 53.1 µS/cm, 327 ± 3.05 cP and 5.63 ± 1.78, respectively. In conclusion, these NEs have great potential for improving solubility, drug delivery, and administration of BDQ. However, further studies are required to maximise the drug content and to demonstrate to what extent these NE have effect against MDR-TB.

Health sciences/Molecular medicine

Physical sciences/Nanoscience and technology

Physical sciences/Chemistry/Surface chemistry

Nanoemulsions

Bedaquiline

Drug Release

Droplet size

Zeta potential

Quality by Design

Multi-Drug Resistant Tuberculosis

Globally, it was estimated that 410,000 people developed Multidrug-resistant tuberculosis (MDR-TB) in 2022 ¹. According to the most recent report on MDR-TB in children, an estimated 25,000–32,000 children were diagnosed with MDR-TB in 2021 ¹. MDR-TB refers to tuberculosis (TB) caused by microorganisms that exhibit significant resistance to both isoniazid and rifampicin, either independently or in combination with resistance to other anti-TB medications ². In March 2022, the WHO released updated guidelines for the management of TB in children and adolescents. These guidelines recommended the use of bedaquiline (BDQ) and delamanid (DMD) in all age groups, along with revised dosing instructions for all tuberculosis medications ³.

The recommended regimen includes seven drugs, with an initial phase lasting 4–6 months (depending on smear conversion within four months of treatment) consisting of BDQ, moxifloxacin (or levofloxacin), clofazimine, ethambutol, ethionamide, high-dose isoniazid, and pyrazinamide. The continuation phase that lasts 5–6 months comprises of the use of clofazimine, moxifloxacin/levofloxacin, ethambutol, and pyrazinamide are continued ⁴.

BDQ is an oral diarylquinoline medication that was approved by the Food and Drug Administration (FDA) for treating MDR-TB ⁵. BDQ exerts its effects by inhibiting adenosine triphosphate (ATP), thereby impeding the energy production and metabolism of mycobacterium cells. The recommended dosage is an initial 400 mg daily for 2 weeks, followed by 200 mg three times a week for 22 weeks, totalling a 24-week treatment duration ⁶. However, for paediatric patients, the dose is administered based on weight, necessitating the breaking or crushing of tablets to achieve a safe dosage, which poses challenges for administration ⁷. Improper tablet manipulation could result in either sub-therapeutic or toxic blood concentrations ⁸. Moreover, many TB medications have unpleasant tastes and may induce vomiting, potentially affecting drug absorption and bioavailability ⁹.

In the past few decades, there has been significant interest in lipid-based nanocarriers such as liposomes, nanoemulsions, and self-emulsifying drug delivery systems for oral drug delivery ^10–12. These systems offer a potential alternative to traditional administration methods and have garnered considerable attention. The oral route stands out as the most commonly favoured and practical method for administering drug formulations, especially for paediatrics ¹³. Many recently discovered drugs exhibit low water solubility or are lipophilic compounds, resulting in their limited oral bioavailability ¹⁴. BDQ is a highly lipophilic molecules with an experimental logP of 7.25 ¹⁵ making it an ideal candidate for formation into a lipid-based drug delivery system.

Nanoemulsions (NE) are an appealing lipid-based nanocarrier for the incorporation of lipophilic drugs. NEs are colloidally dispersed systems comprising an oil phase and an aqueous phase, stabilised by a combination of surfactant and co-surfactant ¹⁶. Nanoemulsions are generally characterised as oil-in-water (o/w) or water-in-oil (w/o) emulsions, where the average diameter of droplets falls within the nanometric range with more ideal mean droplet sizes falling between 100 and 500 nm ¹⁷. Nanoparticles of BDQ in lipid drug delivery systems for enhanced oral bioavailability have been reported, Ullah et al formulated a BDQ-loaded solid lipid nanoparticle which aimed to effectively carry the drug into tumour tissue to possibly reverse MDR in tumours ¹⁸. Menon et al. developed a stable nano-emulsion system incorporating first-line anti-tuberculosis drugs—rifampicin, isoniazid, pyrazinamide, and ethambutol—using various eugenol-based essential oils. The formulation was optimized through central composite design to enhance the inhibition of MDR strains of Mycobacterium tuberculosis while demonstrating excellent biocompatibility and non-toxicity¹⁹. To the best of our knowledge, the use of NEs as a novel vehicle to deliver BDQ with high loading efficiency as a liquid dosage form has not been reported.

Previously, we have performed preliminary preformulation studies to select compatible and appropriate excipients for the formulation of BDQ-loaded nanoemulsions ²⁰. Following the investigations into potential surfactants to be included, it was established that Span® 20 and Tween® 80 and ethanol as cosolvent were suitable for the formulation of nanoemulsions ²⁰.

Herein, we report the use of a suitable optimisation design technique to produce an ideal formulation that has ideal critical quality attributes viz., droplet size (DS) < 500 nm, zeta potential (ZP) > |20| mV, polydispersity index (PDI) < 0.500 and to maximise drug content (DC).

Materials

BDQ was purchased from Iffect Chemphar Co. Ltd (Hong Kong, China). Tween® 80 and Span® 20 were donated by BASF (Johannesburg, Gauteng, South Africa). Ethanol was purchased from Fisher Scientific (Massachusetts, United States). Olive oil was purchased from Escentia Products (Johannesburg, Gauteng, South Africa).

Methods

Experimental Design

A three-factor optimal mixture design was used to assess the effects of 3 critical materials attributes (CMA) on the CQA of the NE. In these experiments we monitored Tween®80 (A); Span® 20 (B); and Ethanol (C), on the response variables, namely DS and PDI, ZP and DC on the formulated NE. The design set the levels of Span® 20 (A) and Tween® 80 (B) at a minimum of 37.5 mg and maximum of 675 mg with ethanol (C) content set to a maximum of 150 mg. The amounts of olive oil and water were kept constant at 100 mg and 150 mg, respectively to make a total of approximately 1g of NE. Each design was assessed independently to observe the influence of the composition of each variable on the four responses. The constraints of the independent variables are presented in Table 1.

Table 1

Independent variable proportion constraints.
Independent variables	Lower limit (mg)	Upper limit (mg)
Tween® 80	37.5	675
Span® 20	37.5	675
Ethanol	37.5	150
Olive oil	100	100
Water	150	150

The optimisation function was used to estimate the levels for each component of the surfactant mixture, taking into account the criteria and specific constraints outlined in Table 2. These criteria and constraints subsequently produced composition solutions according to the desirability function.

Table 2

Constraints used to estimate optimised composition.
Name	Goal	Lower Limit	Upper Limit	Lower Weight	Upper Weight	Importance
A: Tween® 80	is in range	37.5	675	1	1	3
B: Span® 20	is in range	37.5	675	1	1	3
C: Ethanol	is in range	37.5	150	1	1	3
DS	minimise	155.0	398.77	1	1	3
PDI	minimise	0.125	0.5673	1	1	3
ZP	minimise	-20.2	33.7	1	1	3
DC	maximise	1.33	3.2	1	1	3

Statistical Analysis

The ideal conditions for the independent variables were determined using an optimal mixture design, facilitating the effects of how altering surfactant compositions impacts particle size, PDI, zeta potential and DC. Optimal compositions for nanoemulsion preparation were selected based on achieving the lowest DS, PDI < 0.5 and a zeta potential > |20| mv. Analysis of variance (ANOVA) and R² were used to explore notable differences among the independent variables. To establish a reliable final model, adjusted R², R² and PRESS were used to identify the best-fit model.

Preparation of Nanoemulsion

NEs of BDQ were prepared by the spontaneous emulsification method as described previously ²⁰. Briefly, 30 mg of BDQ was weighed using a Radwag AS 220.R2 Plus Analytical balance (Toruήska, Poland) and added to olive oil and stirred using a magnetic stirring hot plate (Lasec®, Cape Town, South Africa) at 200 rpm for 10 minutes. Appropriate amounts of the surfactant mix (S_mix) according to the runs suggested by optimal design were subsequently added. Distilled water was then added dropwise with continuous stirring using a magnetic stirring hot plate (Lasec®, Cape Town, South Africa) at 200 rpm. The nano-emulsions were centrifuged at 3000 rpm for 15 minutes using a Rotafix 32A centrifuge (Hettich, Tuttlingen, Germany) to separate excess BDQ from the nanoemulsion. A summary of the experiments conducted is provide in Table 3.

Table 3

Surfactant mixture composition generated by experimental design.
Run	Tween®80 (% w/w)	Span®20 (%w/w)	Ethanol (% w/w)
1	252.339	402.037	95.6246
2	108.6	491.4	150
3	562.727	137.023	50.25
4	446.302	153.698	150
5	643.125	37.5	69.375
6	252.339	402.037	95.6246
7	147.35	565.15	37.5
8	252.339	402.037	95.6246
9	252.339	402.037	95.6246
10	108.6	491.4	150
11	37.5	665.661	46.8393
12	346.791	253.209	150
13	643.125	37.5	69.375
14	477.824	234.676	87.5
15	382.606	329.894	37.5
16	537.968	62.0321	150

Characterisation of NEs

Droplet Size (DS) and Polydispersity Index (PDI)

The PDI and DS of the NE were determined using a Nanotrac wave II zetasizer (Microtrac, Osaka, Japan). The sample was prepared by diluting the nanoemulsion in distilled water (1:100) and placed into the sample holder that was then transferred to the instrument. The sample was analysed at a scattering angle of 180 ° at 25°C and light scattering data was analysed by backscattered laser-amplified scattering reference method. The experiments were conducted in triplicates (n = 3).

Zeta Potential (ZP)

The ZP of each dispersion was determined using a Nanotrac wave II zetasizer (Microtrac, Osaka, Japan). The sample was prepared by diluting the NE in distilled water (1:100) and placed into the detector. The ZP of the sample was determined by modulated power spectrum analysis of the combined Brownian motion and electric field driven motion. The experiments were conducted in triplicates.

Drug Content

The DC of the BDQ-NE produced was established in mg/ml of NE. Approximately 500 mg of the formulation was weighed placed in a 50 ml volumetric flask and made up to volume with 10:90 ammonium acetate buffer and methanol. The sample was sonicated using a Scientech ultrasonic bath (Roodepoort, South Africa). The solution was filtered with a Nylon Syringe Filter 25 mm, 0.45 µm (Scharlab®, Barcelona, Spain). The amount of BDQ in each formulation was determined using an in-house validated reverse phase ultra-fast liquid chromatography (RP-UFLC) method.

Transmission Electron Microscopy (TEM)

Transmission Electron Microscopy (TEM) was used to visualise the shape and surface morphology of the NE droplets in aqueous dispersions. Briefly, the sample was prepared by diluting the o/w NE in distilled water (1:100), the diluted sample was placed on a copper grid, dried for 48 hours and then analysed by TEM.

Fourier Transform Infrared Spectroscopy (FTIR)

The investigation of potential chemical interactions in the optimised NE was investigated using FTIR to ensure that no degradation or interaction had occurred during the formulation process. The infrared (IR) absorption spectrum of the NE was generated using a Cary 630 FTIR spectrometer (Agilent Technologies® Inc, Santa Clara, United States of America) over the wave number range 500–4000 cm^− 1 at a spectral resolution of 2 cm^− 1. The NE was mounted onto a diamond crystal and the spectral data was collected and subsequently processed using MicroLab FTIR Software Cary 630 (Agilent Technologies® Inc, Santa Clara, United States of America). Experiments were performed in triplicate (n = 3).

Viscosity, pH, Conductivity

The electrical conductivity of a nanoemulsion can indicate whether the system is oil-in-water (O/W) or water-in-oil (W/O), with conductivity values exceeding 2 µS/m suggesting an O/W emulsion ^21,22. The viscosity of an oral nanoemulsion plays a crucial role in determining its texture and palatability, which are key factors in patient acceptability. Excessive viscosity may hinder swallowing, while low viscosity could compromise the stability of the formulation. Viscosity also affects the drug release rate, with higher viscosity potentially slowing the release ²³. Additionally, the pH of the nanoemulsion must fall within an appropriate range for oral administration, as extreme pH values can irritate the mucosal lining of the mouth and gastrointestinal tract. A pH range of 2–9 is generally considered acceptable ²⁴.

The viscosity and conductivity of the optimised formulations were determined using an IKA Rotavisc lo-vi complete viscometer (Staufen, Germany) and a Cond 70 vio portable conductivity meter (Lasec, Cape Town, South Africa), respectively. The pH was measured using a Violab benchtop pH meter (Lasec, Cape Town, South Africa). Each test was conducted in triplicates.

In-vitro BDQ Release

The drug release of BDQ from the optimised NE was investigated using the dialysis method. Prior to dissolution studies, the membrane was soaked overnight in distilled water. The dissolution studies utilised a volume of OPT-BDQ-NE (15 ml) containing approximately 46.5 mg of BDQ. This amount was placed into a 7 cm dialysis bag which was then sealed from both ends with a string. Metal clips were placed on both ends of the dialysis membrane to act as sinkers.

The in vitro drug release test was performed in 900 ml of 0.1 N HCl solutions (pH 1.2) using Erweka® USP dissolution apparatus type II (Langen, German) at 50 rpm and 37 ± 0.5°C (n = 6). Samples (10 ml) were withdrawn at regular time intervals (15, 30, 60, 120, 240, 360, 480, 600, 720, 1440 min). At each time interval, an equivalent amount of fresh dissolution medium was added as replacement to maintain sink conditions. The release of BDQ from NE formulation was compared with an equivalent amount of pure drug suspended in distilled water. The collected samples were filtered and RP-UFLC analysis was performed on the collected samples to determine the drug concentration

Determination of Best Fit Drug Release Model

To investigate the kinetics and mechanism of BDQ release from optimised NE formulation, release data from in vitro drug release studies were modelled using DDSolver™, a Microsoft Excel add-in ²⁵. DDSolver™ offers many options for establishing the goodness of fit of a model including the correlation coefficient, coefficient determination or adjusted coefficient of determination and Akaike’s Information Criterion (AIC) amongst others ²⁵.

The release data obtained were fitted into several mathematical models viz zero-order, first-order, Higuchi, Korsmeyers-Peppas, Hixson-Crowell, Hopfenburg, Baker-Lonsdale, Makoid-Banakar and Weibull model. To select the optimal model for drug release kinetics, the R², Adjusted R², Mean Squared Error (MSE), and AIC are crucial ²⁶. A higher R² value, closer to 1, suggests that the model accounts for a significant portion of the variance in the data, reflecting a strong fit. However, adjusted R² is preferred over R² due to its ability to account for multiple parameters and prevent overfitting. Lower MSE values indicate a better fit as they indicate fewer differences between observed and predicted values. A lower AIC value indicates a more optimal model, which balances goodness of fit with model simplicity ²⁷.

Experimental Design

The design used a single simplex-lattice approach, where the surfactant mixture, comprising components A + B + C = 1, was utilised to produce NEs containing 10% by mass of olive oil. The design matrix generated a total of 16 runs and was statistically analysed using the Design-Expert® 13 (Stat-Ease®, Minneapolis, United states of America) as shown in Table 4.

Table 4

Surfactant compositions generated by experimental design and responses.
Run	Tween®80 (w/w)	Span®20 (w/w)	Ethanol (w/w)	DS (nm)	ZP (mv)	PDI	DC (mg/ml)
1	252.339	402.037	95.6246	370.23	-4.9	0.2024	3.09
2	108.6	491.4	150	277.96	4.3	0.125	2.66
3	562.727	137.023	50.25	263.67	-20.2	0.459	2.50
4	446.302	153.698	150	170.37	-19	0.5173	2.80
5	643.125	37.5	69.375	227.67	27.3	0.5267	1.33
6	252.339	402.037	95.6246	266.33	-7.4	0.3104	3.1
7	147.35	565.15	37.5	229.17	6.6	0.3131	2.64
8	252.339	402.037	95.6246	248.47	6.8	0.5163	1.88
9	252.339	402.037	95.6246	204.33	8	0.3202	1.83
10	108.6	491.4	150	398.77	10.6	0.175	2.19
11	37.5	665.661	46.8393	285	-7.3	0.2088	2.92
12	346.791	253.209	150	181.67	10	0.3463	3.0
13	643.125	37.5	69.375	242.8	31.2	0.2933	3.2
14	477.824	234.676	87.5	155	-7.7	0.492	2.81
15	382.606	329.894	37.5	225	9.3	0.5673	2.63
16	537.968	62.0321	150	193	33.7	0.1562	1.64

The most suitable mathematical model was determined by comparing statistical parameters such as R², adjusted R², and Predictive Residual Error Sum of Squares (PRESS) and are summarised in Table 5.

Table 5

Summary of statistical parameters of responses.
Response	Predicted model	R²	Adjusted R²	Predicted R²	Adequate Precision	PRESS
DS	Quadratic model	0.5610	0.3414	-0.0764	5.0628	70829.84
PDI	Special quartic model	0.7606	0.4870	0.3777	5.2695	0.2075
ZP	Special quartic model	0.955	0.8404	0.4611	11.889	200
DC	Mean model	0.0000	0.0000	-0.1378	NA	5.57

The PRESS value evaluates the predictive capability of the model, with the model with the lowest PRESS being considered the best predictor for the data set. As a result, the recommended models were a special quartic model for the PDI and ZP, a quadratic model for DS and a mean model for DC.

The mean model for DC implies that variations in the independent variables tested in the study have no meaningful influence on these response variables. Any observed differences are more likely due to random variation than to the systematic effects of the predictors ²⁸.

The negative predicted R² of -0.0760 for droplet size suggests that the overall mean may be a better predictor of the response variable than the current model. Furthermore, in some cases, a higher-order model may produce more accurate predictions ²⁸. However, the adjusted precision of 5.0628 indicates a good signal, as it exceeds the desirable threshold of 4. This model is therefore suitable for navigating the design space.

The comparison between the predicted R² and adjusted R², along with adequate precision, is crucial in assessing the reliability and robustness of a statistical model. The predicted R² of 0.3777 for PDI is reasonably close to the adjusted R² of 0.4870, as the difference is less than 0.2. Adequate precision measures the signal-to-noise ratio, with a desirable value being > 4. The PDI ratio of 5.269 indicates an adequate signal, meaning this model is suitable for exploring the design space.

The predicted R² of 0.4611 for ZP is not as close to the adjusted R² of 0.8404 as one might normally expect i.e. the difference is more than 0.2. The adjusted result indicates that the model fits the experimental data well, accounting for a significant portion of the variability in the response variable. A high adjusted R² indicates a good fit as it takes into account the number of predictors. A low Predicted R² value indicates the inability of the model to accurately predict new data. Predicted R², derived from cross-validation, assesses how well the model generalises to new data points ²⁹.

A composition residual analysis was performed to ensure that the ANOVA assumptions were met. To accomplish this, diagnostic plots such as Box-Cox residual plots were generated for each of the four monitored responses. Data transformation was not required for DS, ZP, PDI, or DC and the Box-Cox plots are shown in Fig. 1.

Quadratic Model Droplet Size

The resultant DS of the dispersed phase ranged between 155.00 nm and 398.77 nm. The plot shows that increasing Tween® 80 concentration generally resulted in smaller droplet sizes however larger droplet sizes are seen with increasing Span® 20 concentration (Fig. 2).

The unsaturation of the Tween® 80 non-polar tails resulted in molecular packing, which facilitated the formation of ultrafine droplets at the oil-water interface of nanoemulsions. This phenomenon was also observed in a previous study involving Tween® 80 ³⁰.

The ratio of effective cross-sectional areas of the tail and head groups of a surfactant influences its unique molecular geometry. When surfactant molecules are present in adequate quantities, they can produce an ideal monolayer curvature during spontaneous emulsification. The combination of Span® 20 and Tween® 80 can create a synergistic effect, where the balance between hydrophilic and lipophilic properties leads to the formation of smaller, more stable droplets. Higher concentrations of Tween® 80 with an HLB value of 15 combined with Span® 20 with an HLB value of 8.6 results in high overall HLB-mixture value which is suitable for the formulation of an oil in water NE, promoting smaller droplets ^31,32.

The ANOVA results for the quadratic model are listed in Table 6. The Lack of Fit F-values for the model shows that the lack of fit is not significant in comparison to the pure error, which is a ideal because it indicates that the models fit well. The F-value for droplet size is 2.56, with a 9.70% chance the outcome occurring due to noise.

Table 6

ANOVA for quadratic model for DS.
Source	Sum of Squares	df	Mean Square	F-value	p-value
Model	0.0000	6	1.988 x 10^− 6	2.26	0.1306
Linear Mixture	4.223 x 10^− 6	2	2.111 x 10^− 6	2.40	0.1460
AB	1.179 x 10^− 7	1	1.179 x 10^− 7	0.1341	0.7226
AC	2.682 x 10^− 6	1	2.682 x 10^− 6	3.05	0.1147
BC	3.503 x 10^− 6	1	3.503 x 10^− 6	3.98	0.0770
ABC	5.766 x 10^− 7	1	5.766 x 10^− 7	0.6558	0.4389
Residual	7.913 x 10^− 6	9	8.792 x 10^− 7
Lack of Fit	3.192 x 10^− 6	4	7.981 x 10^− 7	0.8453	0.5521
Pure Error	4.721x 10^− 6	5	9.441x 10^− 7
Cor Total	0.0000	15

The coded equation (Eq. 1) allows for predictions about the response based on specified levels of each factor. The first two terms of the Equation, A and B suggest that the individual excipients Span® 20 and Tween® 80 significantly influence droplet size, with B having the greatest positive impact. The correlation between independent factors and DS is provided in Eq. 1.

$$\:Droplet\:size=244.69A+270.44B-3555.48C-251.31AB+4049.48AC+5409.57BC$$

Equation 1

PDI

The contour plot for the PDI reveals the NEs in the design space ranged between 0.125 and 0.5673. The red regions, which represent higher PDI values, are observed with increased concentration of Tween® 80. Conversely, there appears to be a direct effect of lowering PDI values with increasing amounts Span® 20 and lower Tween® 80 concentrations. This could be due to particle aggregation, which results in the formation of micelles or other varying-sized structures. This can lead to a broader size distribution, increasing the PDI ³³. Increasing ethanol concentration reduced the PDI in nanoemulsions due to its impact on the solubility, interfacial tension, and stabilisation mechanisms of the system ³⁴. The rest of the region showed no particular pattern as indicated in the 3D surface and contour plot in Fig. 3.

The ANOVA results for the special quartic model are listed in Table 7.

Table 7

ANOVA data for special quartic model for PDI.
Source	Sum of Squares	df	Mean Square	F-value	p-value
Model	0.2536	8	0.0317	2.78	0.0978
Linear Mixture	0.1259	2	0.0629	5.52	0.0364
AB	0.0360	1	0.0360	3.16	0.1189
AC	0.0241	1	0.0241	2.11	0.1894
BC	0.0268	1	0.0268	2.35	0.1692
A²BC	0.0006	1	0.0006	0.0525	0.8253
AB²C	0.0347	1	0.0347	3.05	0.1245
ABC²	0.0131	1	0.0131	1.15	0.3188
Residual	0.0798	7	0.0114
Lack of Fit	0.0001	2	0.0000	0.0022	0.9978
Pure Error	0.0797	5	0.0159

The significance of the linear mixture term implies that the primary effects of the factors are important in influencing the response. The other interaction and quadratic terms (AB, AC, BC, A²BC, AB²C, ABC²) are not significant, as indicated by their high p-values. This suggests that the inclusion of these terms does not substantially improve the model’s ability to explain the variation in the response, and the response is primarily driven by the linear effects of the factors. The Lack of Fit F-values for the model show that the lack of fit is not significant in comparison to the pure error, which is good because it indicates that the models fit well. The F-value for PDI is 2.78 indicating that there is only a 9.78% chance of the data obtained being due to noise. Eq. 2 reveals that an increase in Tween® 80, Span® 20 slightly decreased PDI, however an increase in ethanol significantly decreased PDI as indicated by the larger value of ethanol.

$$\:PDI=-0.0299A-0.0422B-58.81C+2.45AB+71.12AC+77.21BC-10.01{A}^{2}BC-118.31A{B}^{2}C+186.62AB{C}^{2}$$

Equation 2

ZP

The 3D surface and contour plot (Fig. 4) reveals the presence of a significant region in blue indicating that as the concentration of Tween® 80 in the surfactant mixture increases with increase in concentration of ethanol, the ZP becomes more negative. Furthermore, the contour plot shows the upper positive point occurring when ethanol and Span® 20 are used at the upper limit. This suggests that higher concentrations of Span® 20, with lower concentrations of Tween® 80 and Ethanol, result in higher ZP values. Design points located in the central region show a mix of Tween® 80, Span® 20, and Ethanol. The ZP in these areas varies significantly, with the more negative values seen around the Tween 80®-rich region, and the more positive values in the span® 20-rich areas. BDQ is a weakly basic compound, implying that it can exist in both ionised and non-ionised states depending on the pH of the environment.

As the pH rises, BDQ becomes more likely to ionise, producing positively charged molecules ³⁵. The positively charged molecules interact with the Tween® 80, resulting in a more negative overall zeta potential because the interactions alter the surface charge distribution ³⁶.

The overall model is statistically significant, indicating that the model's factors and interactions explain the variation in the response variable. The linear combination of Tween® 80, Span® 20, and ethanol has a significant effect on the response variable, indicating that the factors' primary or direct effects play an important role in determining the response. The terms A²BC and ABC² were significant, indicating that complex interaction has a substantial impact on the response variable. The terms AB, AC, BC, AB²C were not significant and suggest that these interactions do not significantly affect the response.

The ANOVA results for the special quartic model are listed in Table 8. The Lack of Fit F-values for the model shows that the lack of fit is not significant in comparison to the pure error, which is a good result because it indicates that the models fit well. The F-value for ZP is 10.87 which is significant, with a 0.25% chance of occurring due to noise.

Table 8

ANOVA for special quartic model for ZP.
Source	Sum of Squares	df	Mean Square	F-value	p-value
Model	3584.42	8	448.05	10.87	0.0025
⁽¹⁾Linear Mixture	520.79	2	260.40	6.32	0.0270
AB	61.95	1	61.95	1.50	0.2598
AC	125.28	1	125.28	3.04	0.1247
BC	61.84	1	61.84	1.50	0.2602
A²BC	1661.01	1	1661.01	40.31	0.0004
AB²C	1.82	1	1.82	0.0443	0.8394
ABC²	746.64	1	746.64	18.12	0.0038
Residual	288.43	7	41.20
Lack of Fit	73.53	2	36.77	0.8554	0.4792
Pure Error	214.90	5	42.98
Cor Total	3872.84	15

According to Eq. 3, an increase in Tween® 80 concentration results in a more negative ZP, an increase in Span® 20 concentration also results in the ZP also becoming slightly negative, but the effect is much smaller than that of Tween® 80 or ethanol. An increasing concentration of ethanol also resulted in a negative ZP and had the largest effect compared to the other factors. Ethanol can have a significant impact on the zeta potential by changing the medium's dielectric constant, influencing ion distribution, and potentially dehydrating the particle surface layer. As the concentration of ethanol increases, it may reduce electrostatic repulsion by compressing the double layer, resulting in a more negative ZP ³⁷.

$$\:ZP=-30.79A-5.34B-3701.50C+101.49AB+5128.59AC+3710.40BC-16669.47{A}^{2}BC+857.36A{B}^{2}C+44496.93AB{C}^{2}$$

Equation 3

DC

The resultant DC of the dispersed phase ranged between 1.33 mg/ml and 3.20 mg/ml. Given that this is a mean model as illustrated in Fig. 5, which represents average responses rather than specific experimental values, we can interpret the contour plot for DC based on trends and overall behaviour across the composition space. The surfactant compositions do not have any significant effects on the DC therefore the model was not significant.

The mean model indicates that the factors being tested do not significantly impact the response variable. The ANOVA results for the mean model are listed in Table 9. The Lack of Fit F-values for the model shows that the lack of fit is not significant in comparison to the pure error, which is a good result because it indicates that the models fit well.

Table 9

ANOVA for mean model for DC.
Source	Sum of Squares	df	Mean Square	F-value	p-value
Model	0.0000	0
Residual	4.90	15	0.3266
Lack of Fit	1.50	10	0.1502	0.2210	0.9796
Pure Error	3.40	5	0.6796
Cor Total	4.90	15

Optimisation of the BDQ-NE

The optimised BDQ-NE (OPT-BDQ-NE) was produced based on the desirability function and the formulation composition for the solutions is reported in Table 10.

Table 10

Optimised surfactant solutions based on the desirability function.
No	Tween® 80	Span® 20	Ethanol	DS	PDI	ZP	DC	Desirability
1	631.425	81.075	37.500	230.446	0.125	-22.583	2.513	0.813
2	675.000	37.500	37.500	244.689	-0.030	-30.785	2.513	0.795
3	59.419	653.081	37.500	261.214	0.039	-2.848	2.513	0.701
4	281.889	376.538	91.573	260.515	0.392	-11.131	2.513	0.587
5	359.359	240.641	150.000	206.834	0.387	3.215	2.513	0.582

Based on the optimal design, the optimum levels of the studied factors were estimated for BDQ-NE preparation. The goal was to minimise DS, PDI and ZP and maximise DC to ensure optimal drug release after oral administration. Thus, simultaneous optimisation of all four responses was performed, and the desirability value was used to select the optimal formulation.

The highest desirability value represented the best ideal formulation. The optimal BDQ-NE selected by the optimal design was prepared and characterized for their DS, PDI, ZP and DC. The optimal selected formulation produced an observed DS, ZP, PDI and DC of 191.6 ± 2.38 nm, and PDI 0.1176 ± 1.69, -25.9 ± 3.00 mV, and 3.14 ± 0.82 mg/ml, respectively. As a result of these findings, the optimal BDQ-NE formulation, proposed by the design expert, offered a promising formulation of small particle size and uniformity, good stability, and maximum DC; subsequently, it was selected for in vitro drug release studies.

Zeta potential values greater than + 30 mV or less than − 30 mV are generally regarded as good stability due to electrostatic repulsion between particles, which prevents aggregation. A zeta potential of -25.9 mV ± 3.00 mV indicates moderate stability suggesting that the optimised nanoemulsion is likely to be stable ³⁸.

Characterisation of OPT-BDQ-NEs

Transmission Electron Microscopy

The smaller DS observed in TEM when compared to DLS were due to the deterioration of liquid droplets under vacuum while DLS measures samples in their hydrated forms, in the presence of water or solvents. DLS determines the hydrodynamic diameter of droplets in emulsions based on intensity, typically resulting in larger values than those measured by TEM. Since TEM measures droplet sizes in a dried state, the samples often shrink, explaining the lower droplet sizes observed for optimised samples measured using TEM ³⁹.

FTIR

The FTIR spectra in Fig. 7 demonstrate no detectable interactions between the BDQ and the excipients used following the optimisation process.

The spectra show the presence of the same functional groups of the payload in the optimised NE and those that have been previously reported in BDQ NE preformulation studies ²⁰. The spectrum reveals the presence of -OH group observed at peak 3083 cm^− 1. Similarly, chemical shifts associated with the aromatic region are visible at 2960 cm^− 1 and 1631 cm^− 1 and as well as C = C stretch in aromatic ring at wavenumber 1546 cm^− 1. Importantly, there are no new peaks or unexpected functional groups present. This lack of spectral alterations indicates the absence of any chemical interactions.

Viscosity, pH, Conductivity

The viscosity of the optimised formulation was found to be 327 ± 3.05 cP. The optimised BDQ-NE exhibited low viscosity. Low viscosity values ensure easy handling, smoother administration, and swallowing, particularly for paediatrics ^23,40.

The pH of the formulation was found to be 5.63 ± 1.78. Formulations for paediatric use should have a pH close to neutral to prevent irritation to the gastrointestinal tract. A pH of 5.63 is generally satisfactory ⁴¹.

The conductivity of the formulation was 53.1 µScm^− 1. The conductivity indicates that it is an o/w NE ⁴². This demonstrates that the API was dissolved into the oil phase and uniformly dispersed in the aqueous phase.

In vitro release studies

NEs are designed to increase the solubility and bioavailability of lipophilic drugs such as BDQ. In vitro drug release studies aid in determining the release kinetics of BDQ from the nanoemulsion and provide insight on how the NE improves BDQ solubility and bioavailability, both of which are critical for its therapeutic effectiveness ⁴³. These studies also contribute to a better understanding of the mechanisms by which BDQ is released from the NE, such as diffusion through the oil phase, micellar solubilisation, or erosion of nanoemulsion droplets using kinetic modelling ^44,45.

OPT-BDQ-NE exhibited over 97.5% drug release. However, no detectable drug release was observed from the pure BDQ drug suspension over the given time points. The drug release profile from OPT-BDQ-NE as shown in Fig. 8.

There was no detectable drug release from the drug suspension over the test period which is attributable to the low aqueous solubility of BDQ. The suspended BDQ would require to first go into solution prior to diffusion into the release medium. This initial dissolution step is the rate limiting step and is likely responsible for the undetectable BDQ levels from the pure drug suspension.

Surfactants and co-surfactants can improve the solubility of poorly water-soluble drugs such as BDQ. The increased solubility in the nanoemulsion results in a higher concentration gradient, resulting in faster drug release. The small particle size and presence of surfactants may facilitate better drug absorption. In contrast, the pure drug suspension has a larger particle size, poor solubility, and a small surface area, resulting in minimal drug release ⁴⁶.

The drug release from the BDQ-NE was delayed as drug release was only observed from 2 hours. This could have resulted from the API partitioning between the oil and water phases which influences its release rate. BDQ has greater affinity for the oil phase and, subsequently, requiring time for adequate partitioning into the aqueous phase for detectable release. There is also a possibility that within the first 2 hours the API released was undetectable. There may be an initial period when the NE distributes and equilibrates in the dissolution medium. This can cause a lag period before significant drug release occurs ^47,48.

Determination of Best Fit Drug Release Model

BDQ release data from the BDQ-NEs was fitted to different kinetic models with consideration given to the time lag for drug release (t_lag) and then the best fit was selected based on the calculated adjusted R², MSE and AIC value for each model as depicted in Table 11.

Table 11

R² values of different drug release models studied.
Release Models	R²	Adjusted R²	MSE	AIC
Zero-order	0.7916	0.7655	393.2183	84.5381
First-order	0.9264	0.9172	138.8004	74.1248
Higuchi	0.9126	0.9017	164.85	75.85
Korsmeyers-Peppas	0.9098	0.8840	194.57	78.17
Weibull	0.9919	0.9896	17.403	54.03
Hixson-Crowell	0.9478	0.9413	98.40	70.68
Hopfenberg	0.9877	0.9842	26.480	58.22
Baker-Lonsdale	0.9649	0.9605	66.28	66.73
Makoid-Banakar	0.9925	0.9888	18.83	55.27

From the data obtained, the Makoid-Banakar model had the highest R² value of 0.9925 for the BDQ-NE followed by the Weibull model. The Weibull and Makoid-Banakar models are the best fits for the drug release dataset. They both have high R² and adjusted R² values, low MSE, and low AIC, suggesting they accurately describe the drug release kinetics with minimal complexity. However, the Weibull model has a slightly better performance overall, with the lowest AIC and MSE values, making it the best choice for this dataset. The best-fit values for all the drug release models are illustrated in Table 12.

Table 12

Best fit values for kinetic model parameters.
Model	Parameter	Data value
Zero-order	k₀	0.083
	T_lag	-84.328
First-order	k₁	0.002
	T_lag	51.237
Higuchi	k_H	3.383
	T_lag	198.052
Korsmeyer-Peppas	k_KP	3.487
	N	0.481
	T_lag	112.678
Hixson-Crowell	K_HC	0.001
	T_lag	53.580
Hopfenberg	k_HB	0.002
	N	0.718
	T_lag	40.665
Baker-Lonsdale	K_BL	0.000
	T_lag	231.666
Makoid-Banakar	k_MB	0.000
	N	2.536
	K	0.002
	T_lag	-3.810
Weibull	α	1336648035.667
	β	3.266
	T_i	-142.258

The empirical Weibull model characterises drug release with a sigmoidal curve, with the aim of establishing a linear relationship between the logarithms of drug release and time ⁴⁹. Q_∞ represents the total amount of the drug released. The scale parameter T indicates the lag time before the dissolution or release process begins, which is typically zero. The scale parameter α refers to the process time, while β influences the shape of the dissolution curve. Specifically, when β = 1, the dissolution curve corresponds to an exponential profile with a consistent rate. If β > 1, the curve takes on a sigmoidal shape with a distinct turning point. Conversely, when β < 1, the curve exhibits a parabolic shape ⁴⁹. The mathematical equation is shown in Eq. 5.

$\:{\varvec{Q}}_{\varvec{t}}\mathbf{}={\varvec{Q}}_{\mathbf{\infty\:}}\mathbf{}\left(1-{\varvec{e}}^{-\left(\frac{\varvec{t}}{\varvec{\tau\:}}\right)\varvec{\beta\:}}\right)\:$ Eq. 5

At 120 minutes, detectable drug release begins with approximately 12%, and by 240 minutes, 14% of the drug had been released. This indicates the onset of the drug release process, possibly due to the activation of diffusion mechanisms. During this phase, the release rate is relatively slow, which aligns with the characteristics expected of a NE formulation designed for sustained drug release.

The Weibull value of β = 3.266 indicates a significant sigmoidal release profile, beginning slowly, then accelerating and slowing down again. There is a significant increase in release between 240 and 720 minutes. This phase is characterised by accelerated release, which signals a shift from initial barrier overcoming to a more dominant diffusion-controlled release mechanism. The accelerating release implies that the main mechanism during this phase is the diffusion process becoming more efficient as the drug concentration gradient increases. The plateau at 1440 minutes, where nearly all of the drug has been released, indicates that the release process is approaching completion, with any remaining drug being released slowly, seemingly from less accessible regions of the formulation or due to medium saturation.

The large α value indicates that the time required for significant release is long. This is typical for controlled-release formulations where the drug is designed to be released gradually ⁵⁰. The mechanism of drug release from the BDQ-NE is best described as biphasic, as it effectively releases the drug in two separate phases: an initial, burst release, and a subsequent, slower release that ensures sustained drug release over a longer period of time. Through rapid achievement of the targeted drug levels and subsequent maintenance of those levels to provide prolonged treatment benefits, this dual-phase release profile improves therapeutic efficacy ⁵¹.

The self-emulsification method was successfully used for the manufacture of BDQ-NE formulated using Span®20 and Tween®80 and ethanol. Design of Experiment was effectively utilised to identify the optimal surfactant combinations for formulating a nanoemulsion with specific properties. Smaller droplet sizes were influenced by high Tween® 80 concentration in the S_mix. However, the surfactant concentrations did not have a significant effect on DC. The in vitro studies demonstrated that the BDQ-NE achieved 97.5% drug release within 24 hours, whereas the pure drug exhibited no detectable release under the same in vitro conditions. This suggests that the OPT-BDQ-NE may offer a more convenient and user-friendly alternative to administering the 100 mg BDQ tablet, especially for children who may struggle with tablet ingestion. The findings from the studies suggest that the NE could serve as a promising carrier system for BDQ, potentially influencing its delivery and solubility. The manufacturing process for the nanoemulsion is straightforward and adaptable for industrial-scale production. However, further research involving maximising drug content and assessment of in-vivo performance is necessary to advance this work to the next stage.

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Competing Interests

The authors have no relevant financial or non-financial interests to disclose.

Please refer to the “Competing Interests” section below for more information on how to complete these sections.

Author Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Taiwo Oreoluwa Ajay. The first draft of the manuscript was written by Taiwo Oreoluwa Ajya, Madan Sai Poka and Bwalya Angel Witika and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Data Availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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No competing interests reported.

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Formulation Development and Optimisation of a Novel Quality-by-Design Bedaquline-Loaded Nanoemulsion for the Potential Treatment of Multi-Drug Resistant Tuberculosis in Paediatrics

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Abstract

Figures

Introduction

Materials and Methods

Materials

Methods

Experimental Design

Statistical Analysis

Preparation of Nanoemulsion

Characterisation of NEs

Droplet Size (DS) and Polydispersity Index (PDI)

Zeta Potential (ZP)

Drug Content

Transmission Electron Microscopy (TEM)

Fourier Transform Infrared Spectroscopy (FTIR)

Viscosity, pH, Conductivity

In-vitro BDQ Release

Determination of Best Fit Drug Release Model

Results and Discussion

Experimental Design

Quadratic Model Droplet Size

Equation 1

PDI

Equation 2

ZP

Equation 3

DC

Optimisation of the BDQ-NE

Characterisation of OPT-BDQ-NEs

Transmission Electron Microscopy

FTIR

Viscosity, pH, Conductivity

In vitro release studies

Determination of Best Fit Drug Release Model

Conclusion

Declarations

References

Additional Declarations

Status:

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