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 Table of Contents  
Year : 2020  |  Volume : 10  |  Issue : 6  |  Page : 272-278

Quality by design-enabled development and characterisation of nanocarrier of azithromycin

1 School of Pharmacy, S.R.T.M. University, Nanded (MH), Vishnupuri, India
2 University Institute of Pharmaceutical Sciences, UGC Centre of Advanced Studies, Panjab University, Chandigarh, India
3 NMIMS, School of Pharmacy and Technology Management, Shirpur, Maharashtra, India

Date of Submission13-Oct-2020
Date of Decision04-Nov-2020
Date of Acceptance09-Nov-2020
Date of Web Publication24-Dec-2020

Correspondence Address:
Mr. Swapnil Patil
School of Pharmacy, S.R.T.M. University, Vishnupuri, Nanded - 431 606, Maharashtra
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/cmrp.cmrp_43_20

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Background: Azithromycin is an antibiotic, which is preferentially used in the treatment of Mycobacterium Avium Complex (MAC). However, it suffers from drawbacks such as poor solubility, poor bioavailability and adverse effects such as gastrointestinal intolerance, resulting into poor patient compliance.
Aim: To develop and optimize lipid based nanocarriers of Azithromycin using QbD approach. Methods: Nanostructured lipid carrier (NLC) were developed by using the solvent diffusion evaporation method with view to release drug in a sustained release manner. The initial factors screening and risk assessment done through Taguchi design and afterwards the optimization of independent factors along with final outcome i.e. critical quality attributes (CQAs) were done by Box Behnken Design (BBD).
Results: The results of Physicochemical analysis revealed that a size of optimized Azithromycin Nanocarrier is 346.18 ± 12.90 nm and Polydispersity Index (PDI) of 0.21 ± 0.08; the entrapment efficiency (% EE) of 68.45 ± 4.42% w/w and drug loading of 47.16 ± 0.80 % w/w. The in vitro release study of the Azithromycin Nanocarrier showed a biphasic drug release pattern in simulated intestinal fluid (SIF) nanocarrier shown sustained release kinetics up to 02 days and similar pattern shown in Phosphate buffer saline (PBS) pH 7.4, release kinetics shows initial burst release upto 12 hr followed by sustained release upto 04 days in PBS, pH7.4. The sustained release pattern of the formulation is beneficial to improve the oral bioavailability of the drug.
Conclusion: Thus, present research leads to development of formulations which may reduce dose of drug, dosing frequency and enhanced efficaciously of drug. Ultimately it reducing the patient avoidance in treatment.

Keywords: Azithromycin, Box–Behnken design, Mycobacterium avium complex, nanocarrier, quality by design, Taguchi design

How to cite this article:
Patil S, Gattani S G, Nirbhavane P, Katare O P, Patil K. Quality by design-enabled development and characterisation of nanocarrier of azithromycin. Curr Med Res Pract 2020;10:272-8

How to cite this URL:
Patil S, Gattani S G, Nirbhavane P, Katare O P, Patil K. Quality by design-enabled development and characterisation of nanocarrier of azithromycin. Curr Med Res Pract [serial online] 2020 [cited 2021 Jan 19];10:272-8. Available from: http://www.cmrp.org/text.asp?2020/10/6/272/304821

  Introduction Top

Tuberculosis (TB) is one of the leading causes of death all over the world which is caused by a single infectious agent bacillus Mycobacterium tuberculosis, non-specifically phagocytosed by alveolar macrophages. It has bypassed the annual death toll of both malaria and HIV combined.[1],[2],[3],[4] Complete removal of the bacterial infection is the primary objective and the prime focus in the treatment of TB, however after the treatment, almost half of the patients were found with significant and permanent damage to lungs, i.e., respiratory system. To enhance the efficacy of standard drug treatment regimen in TB, adjunctive therapies are being given, to modulate the host immune response in order to reduce the excessive inflammation, to prevent alveolar region tissue damage and to preserve the functioning of lung. In the treatment of Multidrug-resistant TB, macrolide antibiotics have been used because of they recognized to induce anti-inflammatory and immunomodulatory effects in other lung diseases along with their antibiotic effects.[5] Therefore, it is the need of the hour to develop new approaches in antitubercular therapy to prevent the development of drug resistance, to ensure treatment without relapse and to alter the pattern of spread with a high safety level along with selective targetability.[6],[7],[8],[9],[10]

Azithromycin (AZT), a macrolide-containing nitrogen (azalide derived from erythromycin), is used for the treatment of respiratory tract, skin and genital infections. AZT attacks by interfering with the protein synthesis of bacteria and ultimately inhibits the growth of bacteria. It binds to the 50S subunit of the bacterial ribosome, thus inhibiting the translation of mRNA. It has bactericidal activity against the strains of Mycobacterium avium complex infections and a variety of enteric bacterial pathogens. Its ability to concentrate inside human cells, particularly neutrophils and phagocytes (200 times higher than extracellular concentration), makes it particularly useful in the treatment of infections caused by pathogens that invade host tissues.[11] However, the therapeutic utility of this potent antibiotic is limited due to its low bioavailability (36%) and high dose and dose-related adverse effects (diarrhoea, abdominal pain, chest pain, vomiting and dizziness), which results in poor patient compliance.[12],[13]

Quality by design (QbD) is a scientific technique for the step-by-step development of any product or any process based on the initial risk assessment of the impactful factors.[14],[15],[16] QbD plays an important role in obtaining the final product with all the desired aspects and optimised independent as well as dependent variables. Initial risk assessment is necessary to find out the most-impacted independent and dependent variables on the quality attributes of the formulation done through Taguchi design.

The present study aims, the formulation development of Azithromycin by using QbD approach. It can lead to reduction of dose of drug, dosing frequency and enhancement of efficaciously of drug. Ultimately it reducing the patient avoidance in treatment.

  Materials and Methods Top


Azithromycin as a gift sample was obtained from Anuh Pharma Limited, Mumbai, India. The Gelucire 50/13 and Captex 200P was obtained as a gift sample from Abitec corp. USA. Transcutol P and Tween 80 were purchased from Fisher Scientific, USA. All other solvents, chemicals and reagents were of analytical grade, while mobile phase solvents were of HPLC grade and obtained from standard companies.


Risk assessment and screening of factors

In the QbD-based optimisation technique, it is necessary to assess the effect of critical process parameters (CPPs) or the critical material attributes (CMAs) on the formulation development, which has been represented in the Ishikawa fishbone diagram [Figure 1] and also in the initial risk assessment matrix which represents the ranking in the selection of high-risk factors [Supplementary Table 2]. Taguchi design was utilised at an introductory level, for better understanding the relationship between independent and dependent variables with their proposed risk [Supplementary Table 3]. The most impacted factors were considered at different levels [Supplementary Table 4] which leads to get the experimental trials and same represented in the Pareto charts and half-normal plots showing the most impacted CMAs/CPPs [Figure 2].

Figure 1: Ishikawa fishbone diagram

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Figure 2: (a) Response surface design plots (three dimensional) and contour plots (two dimensional) describing the effect of independent variables on dependent variable, i.e., particle size. (b) Response surface design plots (three dimensional) and contour plots (two dimensional) representing the effect of independent variables on dependent variable, i.e., Polydispersity index (PDI). (c) Response surface design plots (three dimensional) and contour plots (two dimensional) representing the effect of independent variables on dependent variable, i.e., %EE

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Defining the quality target product profile and critical quality attributes

Quality target product profile (QTPP) is the initial and most important step in QbD technique, which evaluates the potential quality features of the optimised formulation that needs to be attained in the final dosage form. Various critical quality attributes (CQAs) such as particle size, particle size distribution and % drug entrapment need to be fixed in order to meet the desired QTPP, same were defined and summarized [Supplementary Table 1].

Formulation of azithromycin nanocarrier

The nanocarrier was formulated by using the solvent diffusion evaporation method[17] with minor modifications. AZT, Gelucire 50/13 and Captex 200P were dissolved in ethanol (as drug: lipid: oil ratio at 1:2:0.5 w/w/w) by heating at 60°C–70°C. Separately, the solution mixture (Smix, 0.5% v/v), i.e., tween 80 and Transcutol P (in the ratio of 1: 1 w/w) prepared at 60°C–70°C, was added drop wise to the drug containing ethanolic mixture under constant mechanical stirring. The resultant secondary emulsion was kept at overnight to evaporate ethanol under continuous stirring. The nanoparticles were recovered by centrifugation at 18,000 rpm for 60 min, washed with distilled water and there afterwards lyophilised.

Quality by design-enabled optimisation of azithromycin nanocarrier by the design of experiment method

The most impactful CMAs and CPPs factors, i.e., drug: lipid: oil concentration (X1), Smix (X2) and concentration of ethanol (X3), were identified by Taguchi design and from the initial risk assessment studies.While the nanosized of particle size (R1), its polydispersity index (PDI) (R2) and % Entrapment efficiency (R3) were selected as the Critical Quality attributes (CQA's) i.e. responses/desired variables. 33 Box–Behnken Design (BBD) was applied as a model for the optimisation and selection of the variables in formulation development, which resulted in a total of 18 experimental trials [Supplementary Table 5]. The data obtained from the design assessed through software by Design-Expert®12 were further evaluated for outcome of trial experiments. The generated quadratic polynomial model as has been illustrated in Equation 1 was further used for the optimisation of data.

Y = β0 + β1X1 + β2X2 + β3X3 + β12X1X2 + β13X1X3 + β23X2X3 + β11X12 + β22X22 + β33X32 (Equation 1)

where Y is the CQA parameter

X1, X2 and X3 are the independent variables (i.e. either CMAs or CPPs)

β0 is the intercept and β1β33 are the coefficients of regression.

The quadratic polynomial model as mentioned above establishes the mathematical relationship amongst the independent variables and CQA, i.e., desired variables for individual level of inputs through surface response plots [Supplementary Table 6].

Characterisation of nanocarrier formulation

Surface morphology

Field emission scanning electron microscopy (FESEM) technique was used for the evaluation of surface morphology of AZT nanocarrier. Suspension of the nanocarrier sample was placed as a drop on an aluminium stub which adhered to a carbon tape. The imaging of the samples done through FE-SEM instrument (HITACHI, SU8010, Japan).

Particle size, polydispersity index and zeta potential

Dynamic light-scattering method (Zetasizer Nano ZS90, Malvern Instruments, UK) was used for the estimation of the particle size, PDI and charge on the surface of AZT nanocarrier. A small volume of nanocarrier suspension was diluted with double-distilled water for about ten times. Thereafter, the samples were kept in the cuvette of the aforesaid instrument and evaluated for their particle size, while undiluted nanocarrier suspension was used to determine the zeta potential using the same instrument.

Encapsulation efficiency

Entrapment efficiency (% EE) and drug loading (DL) of AZT in nanocarrier were determined by using HPLC method. The HPLC analysis was performed on C18 column 250 mm × 4.6 mm, 5 μ, at a mobile phase composition of a combination of 10 volumes of 3.484 per cent w/v solution of dipotassium hydrogen phosphate which is adjusted to pH 6.5 previously with orthophosphoric acid, 35 volume & 55 volume of water with flow rate of 1 ml/min and detected at wavelength 215 nm.

The percentage drug % EE and DL (mg/g nanocarrier) were calculated using the following formula:

X-ray diffractometric studies

X-ray diffractometry (XRD) studies evaluate the crystalline nature of the drug before and after nanocarrier formulation.[18] A known amount (5–10 mg) of free-AZT, blank nanocarrier and AZT nanocarrier was loaded in a poly-methyl methacrylate 25-mm holder, and X-ray diffraction patterns of the nanoparticles were recorded on Bruker's D8 Advance XRD.

Differential scanning calorimetry studies

Melting temperature and transition behaviour of a drug and its nanocarrier give the mechanism behind the interaction between the drug and excipients in nanocarrier formulation development. For this purpose, differential scanning calorimetry (DSC) analysis was conducted using DSC instrument (DSC Q200 V24.11 Build, TA Instruments, USA). Weighed samples heated in a suitable aluminum pans sits upon a constantan disc in the DSC cell at a rate of 20°C/min, under a nitrogen atmosphere, from 20 to 300°C temperature range, against a reference empty aluminum pan.

In vitro drug release analysis

The in vitro drug release was evaluated by using the conventional dialysis bag method.[19] Dispersion of a known amount of nanocarrier was taken in the dialysis bag, which was further immersed in a beaker containing different dissolution media separately, i.e., in simulated gastric fluid (SGF, pH = 2.0), simulated intestinal fluid (SIF, pH = 6.8) and Phosphate Buffered Saline (PBS, pH = 7.4). All the release media were supplemented with 0.5% tween 80, to maintain sink condition. Further, all dissolution media were kept in a magnetic stirrer at 100 rpm at 37°C ± 0.5°C. 1.0 ml of the sample aliquot was removed from the aforesaid dialysis bag, at a pre-defined time interval. Equal volume of fresh dissolution media was added. From the sample aliquots, amount of drug release was estimated by using HPLC method.

Stability studies

The stability studies were carried out to estimate the effect of environmental factors on the physicochemical properties of the formulation.[20] The amount of interaction between drug-excipient, changes in nature of intrinsic properties of the formulation, alteration of safety and efficacy can be estimated by stability studies by keeping the formulation at different temperature or environmental conditions (40 ± 2°C/75 ± 5% relative humidity [RH]) over the time period, i.e., for 3 months.

  Results and Discussion Top

Risk assessment and screening of factors

Taguchi orthogonal array design was used for identification and screening of the variables (CPPs and CMAs) involved in the product development.

According to the Taguchi design, a total of eight experimental batches of the formulations (with seven independent factors at two different levels) were prepared and evaluated to obtain the most impactful formulation variables. The most significant impactful variables identified included drug: lipid: oil concentration, Smix concentration and ethanol concentration on the CQAs. Therefore, these independent variables/CPPs were selected for optimisation experiments, while the homogenising speed, homogenising time and type of mixing were kept constant.

Optimisation by design of experiments

BBD was selected as a statistical tool to optimise the variables.[15],[21] Polynomial models that resulted from the data generated by BBD were utilised to find the correlation/interaction of the independent variables (CMAs/CPPs) with the desired response/CQAs. For all the selected CQAs such as particle size, PDI and % EE, the best-fitted model was the quadratic polynomial model.

The quadratic polynomial model (P < 0.0001) as a best-fitted model was represented through the data obtained, i.e., highest R2 values for the selected CQAs/desired responses, namely particle size, PDI and per cent entrapment efficiency (%EE).

In second-order quadratic polynomial model, the coefficients in Equation 1 generated using the intercept β0 show the interaction terms between all the three CQAs, namely particle size (a), PDI (b) and per cent %EE (c). Two-dimensional (2D) contour plot and three-dimensional (3D) response surface plots as shown in [Figure 2]a, [Figure 2]b, [Figure 2]c represent the simultaneous correlation between all the variables, i.e., CQAs, namely particle size, PDI and % EE, wherein the interaction between the independent variables in a positive (+) or negative (−) coefficient represents an increase or decrease in the effects of the responses.

[Figure 2]a (a–c) represents the response surface designs (3D) and contour plots (2D) for particle size. It was observed from the curvilinear shape response surface design plot [Figure 2]a (a) that a decrease in the particle size was due to the increase in the concentration of drug: lipid: oil mixture (%) and Smix (%), and the decrease was more prominent with the increasing values of Smix (%). In [Figure 2]a (b), it has been described that the size of the particle gets decreased with increasing drug: lipid: oil mixture concentration, however increasing the level of ethanol causes increase in the particle size. In [Figure 2]a (c), a consistent effect on particle size of Smix and ethanol concentration can be observed. Smix in increasing concentration decreases particle size, whereas increasing the concentration of ethanol increases the particle size.

[Figure 2]b (a–c) describes the response surface plots (3D) and contour plots (2D) for PDI. [Figure 2]b (a) interestingly represents that the PDI was found to be lowest for higher concentration of drug: lipid: oil mixture and Smix. Similar kind of observation can be seen in [Figure 2]b (b), in which again increasing the concentration of drug: lipid: oil mixture concentration and ethanol concentration decreases PDI. In [Figure 2]b (c), which portrays the effect of ethanol concentration and Smix on PDI, an unusual pattern was observed. The PDI was found to be increased slowly with increasing Smix concentration, however the effect of ethanol was found to be non-consistent.

[Figure 2]c portrays the effect of independent factors on % EE. In [Figure 2]c (a), with the increasing concentration of drug: lipid: oil mixture, the % EE was found to be decreased. The Smix. concentration at middle showed the highest entrapment, however the effect of Smix. was uneven. [Figure 2]c (b) shows that the effect of drug: lipid: oil mixture was similar to that of [Figure 2]c (a), whereas % EE was found to be increased with increasing concentration of ethanol up to a certain extent, however after that % EE was found to be increased. The effect of % ethanol seemed to be consistent, as shown in [Figure 2]c (c), while the increasing concentration of Smix increased % EE, up to a certain extent, and then it was found to be decreased.

Characterisation of nanoparticles

Surface morphology

FESEM image confirms the spherical shape of AZT nanocarrier [Figure 3]. The image represents the nanocarrier loaded with AZT like a vesicular arrangement with distinct outer boundary and drug loaded inside at different magnification scales.
Figure 3: Field emission scanning electron microscopy images of azithromycin nanocarrier at different magnification scales

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Particle size, polydispersity index and zeta potential

The AZT nanocarrier accounted for particle size 346.18 ± 12.90 nm and PDI of 0.21 ± 0.08; lower value of PDI reflects the narrow size distribution of nanocarrier, which results in the non-aggregation of nanoparticles and finally results in good stability of nanocarrier and higher homogeneity. The AZT nanocarrier showed positive zeta potential at 26.0 ± 2.1. Surface charge on the particles could control the stability of nanocarrier formulation through strong electrostatic repulsion of particles with each other.

Percentage entrapment efficiency

The AZT nanocarrier was found to have shown % EE of 68.45% ± 4.42% w/w, and the DL (mg/g) was found to be 47.16% ± 0.80% w/w.

X-ray diffractometric studies

The change in the state of AZT before and after encapsulation in lipid carrier was evaluated by using XRD studies. The drug AZT is crystalline in nature, while the lipid mixture used in blank nanocarrier is semi-crystalline in nature. On encapsulation, the AZT nanocarrier (AZT-nanostructured lipid carrier [NLC]) showed decrease in the intensity of crystallinity and a co-amorphous behaviour of drug interspersed with characteristic crystalline peaks of lipid [Figure 4], indicating that though the majority of AZT is entrapped inside the nanoparticles, some drug is adsorbed on the surface of the nanoparticles.
Figure 4: X-ray diffractometry diffractogram of azithromycin, nanostructured lipid carriers and blank nanostructured lipid carriers

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Differential scanning calorimetry studies

DSC thermogram of AZT showed a sharp endothermic peak at near 121.51°C–133.39°C, which confirms the melting point of AZT [Figure 5]. However, the thermogram of AZT-NLC showed endothermic peak breaks at point near 93.19°C–107.84°C, which signifies with the change in its transition behaviour. Melting point of azithromycin was decreases which is due to the encapsulation into the lipid matrix of nanocarrier, along with shifting of drugs melting behaviour towards decrease in crystallinity. The broadening in peak due to mixture of amorphous nature of powder of drug.
Figure 5: Differential scanning calorimetry thermogram of azithromycin drug, azithromycin nanostructured lipid carriers

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In vitro drug release analysis

The release mechanism of AZT from its nanocarrier was studied in different dissolution medium systems. In SGF, nanocarrier showed stability and was not affected by the medium containing SGF, whereas in SIF, the nanocarrier showed sustained release kinetics up to 2 days and similar pattern was observed in physiological buffer system (PBS) for up to 4 days. The nanocarrier initially showed burst release up to 12 h with nearly 40% of the drug release; afterwards, the release sustained for longer duration up to 96 h with a cumulative drug release of about 63.46%, i.e., dual release pattern (Figure 6) which is due to the release of the drug from the surface of nanocarrier results in initial burst followed by release of captured drug through the membrane-controlled mechanism.
Figure 6: Drug release studies of azithromycin nanocarrier formulation in simulated gastric fluid (pH – 2); simulated intestinal fluid (pH – 6.8) and in PBS (pH – 7.4)

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Stability studies

AZT-loaded nanocarrier on different storage conditions, i.e., at 5°C ± 2°C and at room temperature (25°C ± 2°C), showed no significant changes in its physiochemical characteristics, whereas when stored at elevated conditions of temperature and RH (40°C ± 2°C/75% ± 5% RH), it led to increment in particle size and PDI and decrement in drug content due to the binding and aggregation nature of nanoparticles. Further, the stability study data represent that refrigerated conditions do not significantly affect the particle size of optimised formulation, so those might be the stable conditions for them.

  Conclusion Top

The drug AZT was incorporated into lipidic nanocarrier with high efficiency. Administration of a single oral dose of drug-loaded nanocarrier showed a sustained release pattern along with an increase in bioavailability as compared to the free drug. Thus, Nanocarrier prepared could be proposed as a system for administration of poorly soluble drugs and targeting the site of infection. The present research work gives the outlook on the systematic development of nanocarrier by using the novel QbD-based approach. The nanocarrier was formulated by using BBD optimisation technique, which establishes and analyses the correlation between most impactful factors, i.e., CMAs/CPPs and the desired responses, i.e., CQAs by the graphical contour (2D) plots and response surface design plots (3D).

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Conflicts of interest

There are no conflicts of interest.

  References Top

Esmail H, Barry CE, Young DB, Wilkinson RJ. The ongoing challenge of latent tuberculosis. Philos Trans R Soc B Biol Sci 2014;369:20130437.  Back to cited text no. 1
Dutta NK, Karakousis PC. Latent tuberculosis infection: Myths, models, and molecular mechanisms. Microbiol Mol Biol Rev 2014;78:343-71.  Back to cited text no. 2
Cohen A, Mathiasen VD, Schön T, Wejse C. The global prevalence of latent tuberculosis: A systematic review and meta-analysis. Eur Respir J 2019;54:1900655.  Back to cited text no. 3
Patil K, Bagade S, Bonde S, Sharma S, Saraogi G. Recent therapeutic approaches for the management of tuberculosis: Challenges and opportunities. Biomed Pharmacother 2018;99:735-45.  Back to cited text no. 4
Kanoh S, Rubin BK. Mechanisms of action and clinical application of macrolides as immunomodulatory medications. Clin Microbiol Rev 2010;23:590-615.  Back to cited text no. 5
Maitra A, Bates S, Kolvekar T, Devarajan PV, Guzman JD, Bhakta S. Repurposing A ray of hope in tackling extensively drug resistance in tuberculosis. Int J Infect Dis 2015;32:50-5.  Back to cited text no. 6
Akinpelu OI, Lawal MM, Kumalo HM, Mhlongo NN. Drug repurposing: Fusidic acid as a potential inhibitor of M. tuberculosis FtsZ polymerization – Insight from DFT calculations, molecular docking and molecular dynamics simulations. Tuberculosis 2020;121:101920.  Back to cited text no. 7
An Q, Li C, Chen Y, Deng Y, Yang T, Luo Y. Repurposed drug candidates for antituberculosis therapy. Eur J Med Chem 2020;192:112175.  Back to cited text no. 8
Rani J, Silla Y, Borah K, Ramachandran S, Bajpai U. Repurposing of FDA-approved drugs to target MurB and MurE enzymes in Mycobacterium tuberculosis. J Biomol Struct Dyn 2020;38:2521-32.  Back to cited text no. 9
Rodrigues L, Cravo P, Viveiros M. Efflux pump inhibitors as a promising adjunct therapy against drug resistant tuberculosis: A new strategy to revisit mycobacterial targets and repurpose old drugs. Expert Rev Anti Infect Ther 2020;18:1-17.  Back to cited text no. 10
Stepanić V, Koŝstrun S, Malnar I, Hlevnjak M, Butković K, Ćaleta I, et al. Modeling cellular pharmacokinetics of 14- and 15-membered macrolides with physicochemical properties. J Med Chem 2011;54:719-33.  Back to cited text no. 11
Pouretedal H.R. Preparation and characterization of azithromycin nanodrug using solvent/antisolvent method. Int Nano Lett 2014;4:103.  Back to cited text no. 12
Bertram G. Katzung, Basic & Clinical Pharmacology; Antimycobacterial Drugs. 14th ed.. USA: McGraw-Hill Education; 2018.  Back to cited text no. 13
ICH, Q8 R2, Pharmaceutical Development, 8; 2009.  Back to cited text no. 14
Patil KD, Bagade SB, Bonde SC. In-vitro and ex-vivo characterization of novel mannosylated gelatin nanoparticles of linezolid by quality-by-design approach. J Drug Deliv Sci Technol 2020;60:101976.  Back to cited text no. 15
Patil K, Bagade S, Bonde S. QbD-enabled stability-indicating assay method for the estimation of linezolid in newly developed gelatin nanoparticles for anti-tubercular therapy. Chromatographia 2020;83:963-73.  Back to cited text no. 16
Pandey R, Sharma S, Khuller GK. Oral solid lipid nanoparticle-based antitubercular chemotherapy. Tuberculosis 2005;85:415-20.  Back to cited text no. 17
Berbel Manaia E, Paiva Abuçafy M, Chiari-Andréo BG, Lallo Silva B, Oshiro-Júnior JA, Chiavacci L. Physicochemical characterization of drug nanocarriers. Int J Nanomedicine 2017;12:4991-5011.  Back to cited text no. 18
D'Souza S. A review of in vitro drug release test methods for nano-sized dosage forms. Adv Pharm 2014;2014:1-12.  Back to cited text no. 19
Selvamani V. Stability studies on nanomaterials used in drugs. Charact Biol Nanomater Drug Deliv 2019;425-44.  Back to cited text no. 20
Hao J, Fang X, Zhou Y, Wang J, Guo F, Peng X, et al. Development and optimization of solid lipid nanoparticle formulation for ophthalmic delivery of chloramphenicol using a Box-Behnken design. Int J Nanomedicine 2011;6:683-92.  Back to cited text no. 21


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