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AAPS PharmSciTech

, 20:216 | Cite as

Quality by Design Enabled Development of Oral Self-Nanoemulsifying Drug Delivery System of a Novel Calcimimetic Cinacalcet HCl Using a Porous Carrier: In Vitro and In Vivo Characterisation

  • Kahnu Charan PanigrahiEmail author
  • Ch. Niranjan Patra
  • M. E. Bhanoji Rao
Research Article
  • 34 Downloads

Abstract

In this present research, work quality by design-enabled development of cinacalcet HCl (CH)-loaded solid self-nanoemulsifying drug delivery system (S-SNEDDS) was conducted using a porous carrier in order to achieve immediate drug release and better oral bioavailability. Capmul MCM (CAP), Tween 20 (TW 20) and Transcutol P (TRP) were selected as excipients. Cumulative % drug release at 30 min (Q30), emulsification times (ET), mean globule size (GS) and polydispersity index (PDI) were identified as critical quality attributes (CQAs). Factor mode effect analysis (FMEA) and Taguchi screening design were applied for screening of factors. The optimised single dose of S-SNEDDS obtained using Box-Behnken design (BBD) consisted of 30 mg of CH, 50 mg of CAP, 149.75 mg of TW 20, 55 mg of TRP and 260.75 mg of Neusilin US2. It showed an average Q30 of 97.6%, ET of 23.3 min, GS of 89.5 nm and PDI of 0.211. DSC, XRD and SEM predict the amorphous form of S-SNEDDS. In vivo pharmacokinetic study revealed better pharmacokinetic parameters of S-SNEDDS. The above study concluded that the optimised S-SNEDDS is effective to achieve the desired objective.

Graphical Abstract

KEY WORDS

factor mode effect analysis Taguchi screening design Box-Behnken design pharmacokinetic study bioavailability enhancement 

INTRODUCTION

Cinacalcet hydrochloride (CH) is recently considered a novel therapeutic for hyperparathyroidism and parathyroid carcinoma. It is also approved by the Food and Drug Administration (FDA) and European Medicines Agency (EMA) for treatment of hypercalcemia in patient whom parathyroidectomy is clinically not applicable (1, 2, 3). CH acts as calcimimetics which attach to the calcium-sensing receptors of the parathyroid glands and lower parathyroid hormone release (4). It has a half-life of 30–40 h and reaches steady-state plasma concentration in 7 days which make it a drug candidate for immediate release formulation. It has optimal dose ranges from 30 to 180 mg once daily (5). Lipinski’s rule of five predicts that poor permeation is more likely when log P is greater than 5 (6,7). Screening of drug suitability for a lipid-based formulation depends on its aqueous solubility, bioavailability and lipophilicity (log P) (8). CH has absolute bioavailability of 20–25% and log P value of 6.8 (9). Based on the above literature, CH qualifies as drug of choice for the development of self-nanoemulsifying drug delivery system (SNEDDS).

In recent time, SNEDDS is gaining popularity for improving dissolution rate and oral absorption of lipophilic drugs. It consists of a surfactant, co-surfactant/co-solvent and oil containing dissolved drug (10). Co-surfactant/co-solvent are used in order to solubilise the hydrophobic drug until absorption takes place (11). The concentration of oil in SNEDDS is less than 20% and usually prepared using surfactants of HLB > 12 (12, 13, 14, 15). Mohd et al. (16) formulated Aerosol 200-based solid SNEDDS of glimepride which showed improved dissolution rat. The solid-state characterisation (scanning electron microscopy (SEM), X-ray diffraction (XRD) and differential scanning calorimetry (DSC)) revealed the amorphous form of the optimised S-SNEDDS (16). Ramasahayam et al. (17) formulated S-SNEDDS of isradipine using vehicles with highest drug solubility. Neusilin US2 was used as adsorbent inert carrier. The solid-state characterisation of S-SNEDDS confirmed the amorphous form transformation (17). These literatures signify the applicability of porous carriers for S-SNEDDS formulation with improvement of dissolution characteristics (18).

SEDDS preparation by altering component ration using ternary phase diagram are applied before by many researchers (19,20). Today, researchers are applying QbD approaches in order to optimise the SNEDDS formulation with allowable range of critical component to achieve desired performance (21). Important steps are identification of quality target product profile (QTTP) by considering dosage form requirement with respect to all stakeholders (i.e. industry, patient and regulatory), collecting critical quality attributes (CQAs), risk assessment, screening of factor and design of experiment (DOE) for design space (DS) development (22,23). Risk assessment is a process in quality risk management which identifies various critical factors that affect the CQAs (24,25). DOE is a critical component of QbD which uses statistical concept in order to optimise the formulation considering all significant factors and build a relationship between the input and output of a process (26).

It is the purpose of the present study to design the immediate-release S-SNEDDS granule of CH with better bioavailability and immediate drug release. We used QBD approach to optimise S-SNEDDS formulation of CH and plot the design space depicting the optimised formulation. Risk assessment was carried out using FMEA as tool. Taguchi screening design and Box-Behnken design were applied for screening and optimisation purpose. The optimised formulation was subjected to in vitro dissolution study, solid-state characterisation, pharmacokinetic study and stability study.

Material and method

Material

CH was a gift sample from Matrix Pharma (Hyderabad. India). Peceol (glycerylmonoleate), Labrasol (caprylocaproylmacrogolglycerides), Labrafac WL1349 (medium chain triglycerides), Lauroglycol 90 (propylene glycol monolaurate), Labrafac PG (propylene glycol dicaprylocaprate), Pleurol olique CC 497CG (polyglyceryl-6 dioleate) and Transcutol P (diethylene glycol monoethyl ether) were kind gifts from Gattefosse India Pvt. Ltd. (Mumbai, India). Kolliphor RH40 (macrogolglycerol hydroxystearate) and Triacetin (1,2,3-triacetoxypropane) were provided as gift samples from M/s BASF GmbH (Minden, Germany). Captex 200 (propylene glycol dicaprylate), Capmul MCM (propylene glycol monocaprylate) and Captex 355 (glycerol tricaprylate) were kindly provided as gift samples from M/s Abitec Pvt. Ltd. (USA). Neusilin US2 (Magnesium aluminometasilicate) was kindly provided as ex gratis by Gangwal Chemicals Pvt. Ltd. (Mumbai, India). All other chemicals and solvent were of analytical grade or highest quality and were used as such as obtained. All animal experiments complied with the ARRIVE guidelines and carried out in accordance with the UK Animals (Scientific Procedures) Act, 1986 and associated guidelines, EU Directive 2010/63/EU for animal experiments.

Selection of Excipients: Oil, Surfactant and Co-surfactant/Co-solvent

Selection of excipients such as oil, surfactant (HLB ≥ 12) and co-surfactant/co-solvent was performed by means of saturation solubility study. Excess amount of CH was added separately in different oil (Peceol, Capmul MCM, Labrafac WL1349, Triacetin, Labrafac PG, Captex 200P and Captex 355), surfactant (Labrasol, Kolliphor RH40, Tween 80 and Tween20) and co-solvent (Lauroglycol 90, Pleurol olique CC 497CG, PEG 300, PEG 400 and Transcutol P) in a glass vial placed on a rotary shaker (Remi, RS-12R, Mumbai, India) for mixing at 25 rpm for 48 h at room temperature. It was then kept undisturbed at room temperature for 24 h. Then it was subjected to mixing by using cyclomixer (Remi Motors CM 101, Mumbai, India) for 10 min followed by centrifugation at 2800 rpm for 5 min. The supernatant was collected and analysed in a UV spectrophotometer (Shimadzu, UV-1800, Japan) at 280 nm (27, 28, 29).

Identifying the Optimal Composition Range of Smix

Pseudoternary-phase diagram was constructed using ProSim Ternary software. Identification of optimal composition range of surfactant and co-surfactant mixture (Smix) was done on the basis of micro- or nanoemulsifying region. The different groups (1:1, 1:2, 2:1, 2:3 and 3:2) of Smix were prepared, and oil was mixed to it in different ratios (9:1, 8:2, 7:3, 6:4, 5:5, 4:6, 3:7, 2:8, 1:9). To the above mixture, water was added with an increment of 5% (w/w). The compositions of each component, i.e. oil (A), Smix (B) and water(C) were derived at a point when there was a transition from transparency to turbidity and the phase diagram was constructed by using the software (30,31).

Identification of QTPPs and CQAs in Product Development

The QTPP of the S-SNEDDS was identified as an inevitable abstract of desired quality characteristics for the product element of a QbD approach. The QTPP elements were set-up considering the need of immediate drug release, low emulsification time, better dispersity and globule size in nanorange. CQAs were identified with proper justification to achieve the formulation objective (32).

Risk Assessment

Initially, risk factors were identified which could influence the CQAs of S-SNEDDS using the Ishikawa fish-bone diagram. Then risk analysis was carried out using failure mode effect analysis (FMEA) to screen the influential factor. FMEA method was used as a tool in order to conduct the risk analysis. Each factor was assigned a score in terms of severity (S), detectability (D) and occurrence (O). S, D and O scores were multiplied together which gave the ‘risk priority number’ (RPN) for each of the risk factor. We assigned S, D and O of 10 for high risk case, 1 for least risk case and 5 for moderate risk case. A value of RPN above 250 was considered high-risk factors against low-risk factors (33,34).

Screening of Factors Using Taguchi Design

Screening of factors was carried out by employing a 7-factor 2-level Taguchi design in order to identify the influential factor affecting the CQAs. According to the design matrix, a series of formulations were prepared and characterised for the CQAs as described below (‘Preparation of S-SNEDDS of CH’ and ‘S-SNEDDS Characterisation’). Pareto charts and half normal were plotted to illustrate the influence of each factor on the CQAs. Significance of each factor was analysed using ANOVA as tool. On the basis of P value, the significant variables were identified (35,36).

Preparation of S-SNEDDS of CH

At ambient temperature, CH was added to the oil with constant stirring. The blend of surfactant and co-solvent in the pre-determined weight was added to the lipid drug solution and stirred for 15 min at 25 rpm using a magnetic stirrer (Perfit, Ambala, India) which results in liquid (L) SNEDDS (37,38). L-SNEDDS was converted to S-SNEDDS by adsorption onto the surface of NEU which was used as an inert solid adsorption carrier. A fixed weight of formed L-SNEDDS equivalent to 30 mg of CH was transferred to a China dish, and to this, a pre-determined weight amount of NEU was added with vigorous stirring. Then the dose equivalent of S-SNEDDS was made to a uniform weight of 600 mg by adding remaining amount of microcrystalline cellulose (MCC) and filled in a hard gelatin capsules of ‘0’ size (16,39).

S-SNEDDS Characterisation

USP apparatus II (paddle type) was employed for performing the in vitro dissolution studies of S-SNEDDS formulations. The dissolution studies were performed employing 0.1 N HCl as dissolution medium. At different time intervals (5, 10, 15, 30, 45 and 60 min) an aliquot of 5 mL sample was withdrawn. The samples were assayed after suitable dilution on UV spectrophotometer at 280 nm (40,41). Cumulative % drug release at 30 min (Q30) and emulsification time (ET) were measured for each of the formulation run during the in vitro drug dissolution study. Mean globule size (GS) and polydispersity index (PDI) of each formulation runs were measured using Malvern Zetasizer. Aliquots (1 mL) of each sample were serially diluted with 1000 folds of 0.1 N HCl and were analysed for GS and PDI (17,42).

Experimental Design and Analysis

Systematic optimization of S-SNEDDS of CH was accomplished employing Box-Behnken design (BBD) with the help of design expert ver. 11.1.01 software (Stat-Ease, Minneapolis, MN). The highly influential factor finalised after screening and risk assessment studies were correlated with the critical quality attributes (CQAs). According to the design matrix a series of formulations were prepared as described in previous ‘Preparation of S-SNEDDS of CH’ and characterised for the CQAs as described in ‘S-SNEDDS Characterisation’. For each CQA regression equation were analysed. Contour plot and 3D plot were plotted using response surface methodology. ANOVA study was conducted to identify the significant model term. Optimisation of S-SNEDDS of CH were carried out by setting up the upper and lower limit of different CQAs. The overlay plot was constructed to identify the design space (43, 44, 45). The proposed optimised formulation was prepared and validated by evaluating the different CQAs.

Comparison of In Vitro Drug Release Kinetic Between Optimised S-SNEDDS and Pure Drug of CH

The drug release pattern of the optimised S-SNEDDS and pure drug of CH were compared. The in vitro dissolution study was conducted as prescribed above (‘S-SNEDDS Characterisation’) using a USP dissolution apparatus-II (46,47). In vitro drug release data of pure-drug CH and optimised S-SNEDDS were fitted to different drug release kinetic like zero-order, first-order and Higuchi model. Further, Korsmeyer-Peppas model was applied in order to determine the release exponent (n).

Solid-State Characterisation

The thermogram of pure drug and S-SNEDDS of CH were analysed using a differential scanning calorimeter (DSC-60, Shimadzu, Kyoto, Japan). About 3–4 mg of pure-drug CH and optimised S-SNEDDS sample were examined at a heating rate of 10°C/min in a scanning temperature range of 25–300°C (48,49). X-ray powder diffraction was performed using X-ray diffractometer (Rigaku, Japan, Smart Lab 9 kW) in order to determine the polymorphic state of pure-drug CH and optimised S-SNEDDS. The results were then recorded as peak height (intensity) versus time (h) (50,51). The morphological properties of pure-drug CH and optimised S-SNEDDS were studied using scanning electron microscope (Jeol, Japan, JSM-6390LV). Samples were held on an aluminium stubs with double-sided tape, then gold coated and examined using a 15 kV accelerating voltage, at a working distance of 8 mm (13,52).

Measurement of Surface Charge

Malvern Zetasizer was used for measurement of zeta potential with electrical field strength of 23 V/cm. Single dose of optimised S-SNEDDS sample was diluted with 1000 mL of 0.1 N HCl and analysed for zeta potential measurement.

In Vivo Pharmacokinetic Study

The dose of CH was calculated as 2.8 mg for each rabbit weighing approximately 2 kg. The optimised S-SNEDDS and pure-drug CH was administered in the form of dispersion to three male rabbits each in two groups using Ryle’s tube. At various time points (1, 2, 4, 6, 12, 18 and 24 h) blood sample of 0.5 mL was withdrawn from marginal ear vein of rabbit. Each blood samples was centrifuged for 10 min at 3000 rpm and the supernatant layer, i.e. serum was collected using micropipette. All the samples were analysed using reverse-phase ultrafast liquid chromatographic (UFLC) method (53, 54, 55, 56). Chromatography was performed on a C18 column using 50:50 (v/v) acetonitrile:tetrabutyl ammonium hydrogen sulphate (TBHS, 10 mM) as mobile phase at a flow rate of 1 mL/min. The various pharmacokinetic parameters like elimination rate constant (K), half-life (t1/2), peak plasma concentration (Cmax), time to attain the peak plasma concentration (Tmax), area under the curve (AUC) and area under the first moment curve (AUMC) were calculated. The pharmacokinetic study was approved by the Animal Care Committee, Institutional Animals Ethics (926/PO/Re/5/06/CPCSEA, Approval No. 84). All animal experiments complied with the ARRIVE guidelines and carried out in accordance with the U.K. Animals (Scientific Procedures) Act, 1986 and associated guidelines, EU Directive 2010/63/EU for animal experiments.

Accelerated Stability Study

Accelerated stability study of the optimised S-SNEDDS of CH was conducted for a period of 6 months as prescribed in the ICH guidelines. The optimised S-SNEDDS formulations (n = 6) were examined at the prescribed time points of 0, 3 and 6 months for the different CQAs. ANOVA study was conducted in order to find any significant differences within the response obtained at different time points (57).

RESULT AND DISCUSSION

Selection of Excipients: Oil, Surfactant and Co-surfactant/Co-solvent

Figure 1 illustrates comparative saturation solubility of CH in different oil, surfactant and co-solvent. Surfactant with high HLB value (> 12) promotes good self-emulsifying and reduces globule size. Saturation solubility study among different surfactants with HLB value more than 12 (such as Labrasol, Kolliphor RH40, Tween 80 and Tween20) suggests selection of Tween 20. Co-surfactants or co-solvent (such as Lauroglycol 90, Pleurol olique CC 497CG, PEG 300, PEG 400 and Transcutol P) with low HLB value (< 12) is responsible for the formation of flexible interfacial film with different curvatures which is required to form micro/nanoemulsion. On the basis of saturation solubility, we have selected Transcutol P as co-surfactant. Similarly, Capmul MCM was selected as oil among different oil (such as Peceol, Capmul MCM, Labrafac WL1349, Triacetin, Labrafac PG, Captex 200P and Captex 355). CH showed mean saturation solubility of 38,750, 46,321 and 22,545 μg/mL in Capmul MCM, Tween 20 and Transcutol P, respectively.
Fig. 1

Bar diagram illustrating mean saturation solubility of cinacalcet hydrochloride (CH) in different oil, surfactant, co-solvent

Identifying the Optimal Composition Range of Smix

Figure 2 depicts the nanoemulsion region for 2:1 Smix combination. Among all the combinations of Tween 20 and Transcutol P, 2:1 combination found to have a maximum nanoemulsion region. This combination of surfactant and co-surfactant (Smix) gives an ideal HLB value for better self-emulsification.
Fig. 2

Representative pseudoternary-phase diagrams depicting the nanoemulsion regions between a Capmul MCM and b Smix (surfactant and co-surfactant mixture in the ratio of 2:1) and c water

Identification of Various QTPPs and CQAs

Table I represents the QTTP elements and CQAs with target and justifications for the development of the S-SNEDDS of CH. On the basis of the objective to prepare S-SNEDDS, various QTPP elements such as dosage type, dosage form, drug absorption and dispersity were set-up. Various critical quality attributes (CQAs) such as cumulative % drug release at 30 min (Q30), emulsification times (ET), mean globule size (GS) and PDI were identified. According to the USFDA, the acceptable limit of in vitro dissolution test is not less than 85% of labelled amount of drug substance dissolved within 30 min for immediate release in solid dosage form. ET of less than 1 min is considered a rapidly forming microemulsion which is clear or slightly bluish in appearance (58). The globule size of 10–100 nm is the acceptable range for the nanoemulsions to be thermodynamically stable (59). PDI of 0.3 and below is considered to be acceptable and indicates a homogenous population of phospholipid vesicles for lipid-based carriers (60).
Table I

Quality Target Product Profile (QTPP) and Critical Quality Attributes (CQAs) for the Development of S-SNEDDS of CH

QTPP

Target

CQA

Target

Justification

Dosage type

Immediate release dosage form

Cumulative % drug release at 30 min (Q30)

85 to 100%

Immediate drug release is the objective of the study and also important for fast absorption of drug, hence was regarded as highly critical.

Dosage form

S-SNEDDS

Emulsification time (ET)

0 to 60 s

Lower values of emulsification time helps in ease of formation of nanoemulsion, hence was regarded as highly critical.

Drug absorption

High Cmax, low Tmax, more AUC as compared with pure drug

Mean globule size (GS)

10 to 100 nm

Smaller globule size allows easy penetration through GI epithelial lining and lymphatic system, hence was regarded as highly critical.

Dispersity

High dispersity of the globule after emulsification

Polydispersity index (PDI)

0 to 0.3

Uniform size distribution is important for achieving the therapeutic effectiveness hence considered as highly critical

Risk Assessment

Figure 3a portrays the resultant fish-bone diagram depicting the effect of man, material, measurement, process, equipment and environment on CQAs for the development of S-SNEDDS of CH. Based on the risk identification, the risk analysis was carried out using FMEA tool. Figure 3b illustrates the RPN scores obtained for potential risk factors (failure modes). The calculated RPN scores range from 18 to 448. High RPN scores (i.e. above 250) were observed in the amount of Tween 20 (TW 20), amount of Transcutol P (TRP), amount of Neusilin US2 (NEU), Capmul MCM (CAP), stirring speed (SS), stirring time (ST) and temperature (T) (TW 20 (448), TRP (432), NEU (392), CAP (336), SS (336), T (288) and ST (280)), respectively. These factors were further screened using Taguchi design, and other factors with a lower RPN were eliminated from further study
Fig. 3

a Risk factor identification in terms of fish-bone diagram for manpower, environment, equipment, material, measurement and process variables. b Bar diagram illustrating risk analysis in terms of RPN scores

Screening of Factors Using Taguchi Design

Taguchi design was employed to screen factors short-listed using FMEA analysis. After several trials for each factor, two levels were selected (1 and 2). Table II represents the different formulation with coded value and actual value of each factor which was formulated and characterised for the various CQAs. The effect of various factor such as A-CAP, B-TW 20, C-TRP, D-NEU, E-SS, F-ST and G-T were studied. Figure 4a–d depicts the Pareto charts of various CQAs Q30, ET, GS and PDI, respectively. Factor B-TW 20 was found to be highly influential, since the t value of effect is above the t value limit and/or Bonferroni’s limits for all the CQAs. In the case of factor C-TRP, the t value of effect is above the t value limit and Bonferroni’s limits for CQAs such as GS and ET due to its co-solvency behaviour. Hence, it is a significant factor. Factor D-NEU is a highly critical factor as compared with A-CAP for CQAs such as Q30 and ET due to its porous behaviour. Table III represents the summary of ANOVA for the factor screening and its significance as per Taguchi design. The P values of the regression coefficients were determined to evaluate the significance of each factor on each of the response. The model factor B-TW 20, C-TRP and D-NEU are significant since the P value is less than the standard α value (i.e. 0.05) and other factors having P values greater than 0.1000 indicate the model terms are not significant. Thus, from the factor screening study, the factors such as B-TW 20, C-TRP and D-NEU were finally selected for further optimization.
Table II

Design Matrix for Factor Screening as per Taguchi Design Along with the Experimental Results of Various CQAs

Run

A

B

C

D

E

F

G

Q30 (%)

ET (s)

GS (nm)

PDI

1

2

1

2

1

2

1

2

61.3

37

85.6

0.35

2

1

1

1

1

1

1

1

62.6

33

185.8

0.62

3

1

2

2

2

2

1

1

98.8

27

58.7

0.13

4

2

2

1

1

2

2

1

75.7

24

142.9

0.42

5

2

1

2

2

1

2

1

68.7

39

95.2

0.28

6

1

1

1

2

2

2

2

69.3

35

229.8

0.55

7

1

2

2

1

1

2

2

87.2

22

56.3

0.15

8

2

2

1

2

1

1

2

86.5

28

138.6

0.51

Factor

Code

Low level (1)

High level(2)

CAP (mg)

A

50

100

TW 20 (mg)

B

75

150

TRP (mg)

C

25

75

NEU (mg)

D

200

300

SS (rpm)

E

25

50

ST (min)

F

15

30

T (K)

G

298

313

TW 20, amount of Tween 20; TRP, amount of Transcutol P, NEU, amount of Neusilin US2; MCM, CAP amount of Capmul; SS, stirring speed; ST, stirring time; T, temperature

Fig. 4

Pareto charts illustrating the screening of influential factors as per Taguchi design a Q30, b ET, c GS and d PDI

Table III

Summary of ANOVA for Factor Screening and Its Significance as per Taguchi Design

Source

Q30

ET

GS

PDI

P values

CAP

0.0133*

0.0351*

> 0.1000

> 0.1000

TW 20

0.0004*

0.0007*

0.0169*

0.0093*

TRP

0.0205*

> 0.1000

0.0023

0.0012

NEU

0.0049*

0.0227*

> 0.1000

> 0.1000

SS

> 0.1000

> 0.1000

> 0.1000

> 0.1000

ST

> 0.1000

> 0.1000

> 0.1000

> 0.1000

T

> 0.1000

>0.1000

>0.1000

>0.1000

Q30, cumulative % drug release at 30 min; ET, emulsification time; GS, mean globule size; PDI, polydispersity index; TW 20, amount of Tween 20;, TRP, amount of Transcutol P; ,NEU, amount of Neusilin US2, CAP, amount of Capmul MCM; SS, stirring speed; ST, stirring time; T, temperature

*Significant values, i.e. less than α value (0.05)

Experimental Design, Optimisation and Analysis

Regression Equation Analysis

Keeping the other factors like CAP, SS, ST and T constant at low level, the concentrations of TW 20, TRP and NEU were varied. On the basis of the preliminary study and the data from pseudoternary-phase diagram, three levels were selected (i.e. − 1, 0 and 1) for each of the factor. Table IV depicts a set of 15 experimental runs which are prepared as explained earlier (‘Preparation of S-SNEDDS of CH’) using a three-factor at three-level BBD. Each formulation were further characterised as explained above (‘S-SNEDDS Characterisation’) to study the effect of various factor such as A-TW 20, B-TRP and C-NEU on each of the CQAs. The equation in terms of coded factors for each of the responses was determined. The relative impact of each factor can be predicted by comparing the factor coefficients. Following are the polynomial equations obtained after a regression analysis for each CQA.
$$ {\displaystyle \begin{array}{l}\mathrm{Q}30=73.95+17.33\mathrm{A}+3.15\mathrm{B}+4.08\mathrm{C}+1.95\mathrm{AB}+2.41\mathrm{AC}-1.72\mathrm{B}\mathrm{C}-0.1758{\mathrm{A}}^2+0.3792{\mathrm{B}}^2-4.13{\mathrm{C}}^2\\ {}\mathrm{ET}=56.67-12.88A-8.63\mathrm{B}+5.00\mathrm{C}-5.00\mathrm{AB}+2.75\mathrm{AC}-1.25\mathrm{B}\mathrm{C}-16.83{\mathrm{A}}^2+5.67{\mathrm{B}}^2-1.58{\mathrm{C}}^2\\ {}\mathrm{PDI}=0.3767-0.0712A-0.2162\mathrm{B}+0.0500\mathrm{C}+0.0100\mathrm{AB}+0.0125\mathrm{AC}-0.0275\mathrm{B}\mathrm{C}-0.0308{\mathrm{A}}^2+0.0492{\mathrm{B}}^2-0.0283{\mathrm{C}}^2\end{array}} $$
Table IV

Composition of Various SNEDDS Formulation as per BBD Along with the Experimental Results of Various CQAs

Run

TW 20

TRP

NEU

Q30 (%)

ET (s)

GS (nm)

PDI

1

0

− 1

− 1

60.22

62

237.4

0.55

2

0

− 1

1

73.48

75

264.2

0.69

3

0

0

0

74.54

58

136.2

0.37

4

1

0

1

92.67

35

118.7

0.32

5

0

0

0

72.78

57

138.4

0.38

6

1

− 1

0

86.54

45

194.8

0.52

7

0

1

1

76.73

57

88.6

0.19

8

−1

1

0

57.87

56

95.7

0.25

9

0

0

0

74.52

55

135.8

0.38

10

1

1

0

96.35

16

58.3

0.12

11

0

1

− 1

70.34

49

76.8

0.16

12

− 1

− 1

0

55.84

65

278.75

0.69

13

− 1

0

− 1

51.43

47

154.8

0.34

14

1

0

− 1

81.34

20

98.4

0.18

15

−1

0

1

53.11

51

183.7

0.43

Factor

− 1

0

1

TW 20 (mg)

50

100

150

TRP (mg)

25

50

75

NEU (mg)

200

250

300

Q30, cumulative % drug release at 30 min; ET, emulsification time; GS, mean globule size; PDI, polydispersity index; TW 20, amount of Tween 20; TRP, amount of Transcutol P; NEU, amount of Neusilin US2

Response Surface Analysis of Contour Plot and 3D Plot

Effect of Factor on CQA Q30

Figure 5a represents the contour plot and 3D plot of the CQA Q30. By observing the plot, it can be predicted that at high level of TW 20 and at more than 0 level of NEU, the prevalence of red region, i.e. more than 85% of drug release in 30 min results. A comparative study among these formulations showed run No. 13 have a minimum value of Q30, i.e. 51.43% while run No. 10 have a maximum Q30 value, i.e. 96.35%. The result suggests an optimum concentration of surfactant is required for better dissolution of drug. It can also be observed that NEU has a prominence effect on improving the dissolution of drug.
Fig. 5

Contour plots and 3D-response surface plot showing the influence of significant factor on various CQAs as a Q30, b ET, c GS and d PDI

Effect of Factor on CQA ET

Figure 5b represents the contour plot and 3D plot of the CQA ET. In the case of ET, it ranges from 16 s for run 10 to 75 s for run 2. It was also observed that at high level of both A-TW 20 and B-TRP, the prevalence of blue region, i.e. low value of ET is achieved. It can be predicted that at higher level of surfactant mixture, the spontaneity of emulsification is greater. It may be due to high diffusion of aqueous phase into oil phase due to decrease in interfacial tension (61).

Effect of Factor on CQA GS

Figure 5c represents the contour plot and 3D plot of the CQA GS. In the case of GS, it ranges from 58.3 nm for run 10 to 278 nm for run 12. Contour plot and 3D plot indicating that the value of GS remain in blue range, i.e. below 100 nm for the entire range of TW 20 but decreases with increase amount of TW 20. GS value is more affected by TRP than TW 20, and it only remains in the blue region when TRP is above 0.5 level values. Co-surfactants or co-solvent reduce the interfacial tension to a negative value and form a flexible interfacial film which is helpful to acquire different curvatures over a wide range of composition (62). It is also observed by some researcher that the droplet size may also increase with increase in concentration of surfactant after attaining a critical concentration. Hence, an optimised ratio of surfactant and co-surfactant is required to achieve nanoemulsion effectively.

Effect of Factor on CQA PDI

Figure 5d represents the contour plot and 3D plot of the CQA PDI. Both A-TW 20 and B-TRP seem to equally influence the CQA PDI. It ranges from 0.12 for run 10 to 0.69 for run 12. The results showed that the value of PDI remains below 0.2 only when both TW 20 and TRP have a value above the 0.5 level. Uniform-size distribution is a vital requirement for getting drug absorbed at GI membrane. The surfactant system is responsible for maintaining the uniform-size distribution.

ANOVA of the Experimental Design

Table V represents the summary of ANOVA for different factor and its significance with respect to quadratic model. After conducting the design matrix, the resultant model F value for Q30, ET, GS and PDI is calculated as 193.12, 40.85, 686.47 and 268.69, respectively. P values of the model for various CQAs are less than 0.05 (α = 0.05) which justify that the quadratic model is significant. The lack-of-fit P values for Q30, ET, GS and PDI were calculated as 0.363, 0.1439, 0.109 and 0.104. It is not significant relative to pure error (i.e. P value > α) which is desirable for a model to be fit. For the CQA Q30, the model terms such as A, B, C, AB, BC, AC and C2 are significant. For the CQA ET, the model terms such as A, B, C, AB, A2 and B2 are significant. A, B, C, AB, B2 and C2 are significant model terms in the case of GS. In the case of PDI as CQA, the model terms such as A, B, C, BC, A2, B2 and C2 are significant. P values less than 0.05 indicate the model terms are significant.
Table V

Summary of ANOVA for Different Factors and Its Significance with Respect to Quadratic Model

Source

Q30

ET

GS

PDI

F value

P value

F value

P value

F value

P value

F value

P value

Model

193.12

< 0.0001*

40.85

0.0004*

686.47

< 0.0001*

268.69

< 0.0001*

A-TW 20

1529.35

< 0.0001*

139.84

< 0.0001*

699.94

< 0.0001*

215.64

< 0.0001*

B-TRP

50.56

0.0009*

62.75

0.0005*

5107.65

< 0.0001*

1986.44

< 0.0001*

C-NEU

84.86

0.0003*

21.09

0.0059*

91.57

0.0002*

106.19

0.0001*

AB

9.63

0.0268*

10.54

0.0228*

51.48

0.0008*

2.12

0.2048

AC

14.82

0.0120*

3.19

0.1342

1.76

0.2423

3.32

0.1281

BC

7.51

0.0408*

0.6591

0.4538

5.35

0.0687

16.06

0.0102*

A2

0.0727

0.7983

110.33

0.0001*

5.29

0.0699

18.64

0.0076*

B2

0.3378

0.5863

12.50

0.0166*

201.57

< 0.0001*

47.39

0.0010*

C2

40.15

0.0014*

0.9761

0.3685

12.55

0.0165*

15.74

0.0107*

Lack of fit

1.90

0.3633

6.11

0.1439

8.28

0.1096

8.75

0.1043

Q30, cumulative % drug release at 30 min; ET, emulsification time; GS, mean globule size; PDI, polydispersity index; TW 20, amount of Tween 20; TRP, amount of Transcutol P; NEU, amount of Neusilin US2

*Significant levels, i.e. less than α value (0.05)

Summary of BBD Quadratic Model

It can be predicted that there is a high level of correlation between actual and predicted value. Table VI represents the summary of the BBD quadratic model in the process of optimization of the S-SNEDDS. For the CQA Q30, the predicted correlation coefficient (R2) value of 0.9644 is very close to the adjusted R2 value of 0.9920. A precision ratio of 44.88 measures a good signal-to-noise ratio. Similarly, in the case of ET, the predicted R2 of 0.8035 is reasonably closer with the value of adjusted R2 of 0.9624. A high value of precision ratio of 23.06 indicates an adequate signal. For GS, the predicted R2 of 0.9879 is in narrow gape with the adjusted R2 of 0.9977. The precision ratio of 88.80 indicates an adequate signal. For PDI, the predicted R2 of 0.9690 is in reasonable agreement with the adjusted R2 of 0.9942 and also a high value of precision ratio of 51.31 indicates an adequate signal.
Table VI

Summary of Design of Experiment with Various Parameters Fitting to Quadratic Model

Responses

Q30

ET

GS

PDI

R 2

0.9971

0.9866

0.9992

0.9979

Adj. R2

0.9920

0.9624

0.9977

0.9942

Pred. R2

0.9644

0.8035

0.9879

0.9690

Adequate precision

44.8877

23.0671

84.8049

51.3157

SD

1.25

3.08

3.24

0.0137

Q30, cumulative % drug release at 30 min; ET, emulsification time; GS, mean globule size; PDI, polydispersity index; R2, correlation coefficient; SD, standard deviation

Optimisation of S-SNEDDS of CH and Construction of Overlay Plot to Identify the Design Space

For optimisation, the desirable goal was fixed for variour responses Q30, ET, GS and PDI as per the target identified in ‘Identification of Various QTPPs and CQAs’. On the basis of the QTPP requirement, the range of various CQAs was fixed which were then processed for optimisation. Figure 6 portrays the overlay plot with design space and also depicts the selected optimised S-SNEDDS composition. The optimised single dose of S-SNEDDS obtained using BBD consisted of 30 mg of CH, 50 mg of CAP, 149.75 mg of TW 20, 55 mg of TRP and 260.75 mg of NEU. The summary of the optimisation process along with predicted and experimental value of responses of the optimised formulation are expressed in Table VII. The optimised single dose of S-SNEDDS obtained using BBD consisted of 30 mg of CH, 50 mg of CAP, 149.75 mg of TW 20, 55 mg of TRP and 260.75 mg of NEU. The evaluation of the proposed optimised formulations (n = 6) showed an average Q30 of 97.6%, ET of 23.3 min, GS of 89.5 nm and PDI of 0.211. The comparison among the predicted responses and actual response shows a high degree of similarity with an F value of 0.962 which also validates the experimental design. The optimised S-SNEDDS exhibited to achieve the QTTP in an optimum composition of TW 20, TRP and NEU.
Fig. 6

Overlay contour plot depicting the design space and delineate the optimised formulation of solid self-nanoemulsifying drug delivery system (S-SNEDDS) of cinacalcet hydrochloride

Table VII

Constraints for the Process of Optimisation of S-SNEDDS of CH Using Design of Experiment

Name of factor

Lower limit

Upper limit

Coded value

Actual value

  A. TW 20 (mg)

− 1

1

0.995

149.75

  B. TRP (mg)

− 1

1

0.204

55

  C. NEU (mg)

− 1

1

0.215

260.75

Responses OR CQAs

Desirable lower limit

Desirable upper limit

Predicted responses

Experimental responses

  Q30 (%)

85

100

93.19

97.6 ± 1.18

  ET (s)

0

60

26.18

23.3 ± 1.1

  GS (nm)

10

100

91.40

89.5 ± 3.0

  PDI

0

0.3

0.246

0.211 ± 0.005

Response data are the mean values ± SD, n = 6

TW 20, amount of Tween 20; TRP, amount of Transcutol P; NEU, amount of Neusilin US2; Q30, cumulative % drug release at 30 min; ET, emulsification time; GS, mean globule size; PDI, polydispersity index

Comparison of Drug Release Kinetics

The drug release pattern of the optimised S-SNEDDS and pure drug of CH is illustrated in Fig. 7. The optimised S-SNEDDS showed better dissolution profile with nearly 2.5 times cumulative % drug release than the pure drug in 30 min. Hence, an optimum combination of CAP, TW 20, TRP and NEU provides a better dissolution profile than the pure drug. After applying different kinetic models, the R2 values for the pure-drug CH were calculated as 0.931, 0.954 and 0.980 for zero-order, first-order and Higuchi model, respectively. Similarly for optimised S-SNEDDS formulation, the R2 were found to be 0.845, 0.888 and 0.955 for zero-order, first-order and Higuchi model, respectively. The R2 obtained for different kinetic models suggest highest fitness toward the Higuchi model for both pure-drug CH and optimised S-SNEDDS formulation. The value of release exponent (n) for the pure-drug CH and optimised S-SNEDDS was found to be 0.692 and 0.440, respectively. Hence, it may be predicted that the drug release from pure drug follows Fickian diffusion kinetic whereas for optimised S-SNEDDS formulation fallows non-Fickian diffusion kinetic.
Fig. 7

Plot depicting the cumulative % drug release profile of optimised S-SNEDDS and pure drug of cinacalcet hydrochloride

Solid-State Characterisation

Differential Scanning Calorimetry

Figure 8a, b depicts the DSC pattern of the optimum S-SNEDDS and pure-drug CH, respectively. The DSC thermogram of CH exhibited a sharp endothermic peak at 181.90°C (Tfus), with onset at 178.33°C and conforms the reported range of 178–184°C (63). The DSC thermogram of optimised S-SNEDDS shows a broad endothermic peak at 138.23°C, indicating that CH maintained less crystallinity. It also indicates that the drug is present in an amorphous form or in a molecularly dissolved state (50). The addition of Neusilin US2 attracts CH crystal aggregates and lowers the melting point of CH. The enthalpy of CH and optimised S-SNEDDS were 28.86 and 11.29 mJ, respectively. This can be attributed to reduction in crystallinity and adherence of CH onto the surface of Neusilin US2.
Fig. 8

DSC of S-SNEDDS (a) and pure drug (b) of cinacalcet hydrochloride

X-ray Diffraction Studies

The XRD patterns of optimised S-SNEDDS and pure-drug CH are shown in Fig. 9a, b, respectively. Pure-drug CH showed sharp peaks at the diffraction angles such as 12.3°, 14.1°, 17.2° 18.4°, 22°, 24.6° and 26.2°, indicating a typical crystalline pattern (Fig. 9b). Optimised S-SNEDDS showed no peaks at those angles, indicating amorphisation and confinement of the drug at the molecular level in the pores of Neusilin US2 (Fig. 9a).
Fig. 9

XRD of optimised S-SNEDDS (a) and pure drug (b) of cinacalcet hydrochloride

Scanning Electron Microscopy

Figure 10a, b illustrates the scanning electron microscopic pictures of pure-drug CH and optimised S-SNEDDS, respectively. The SEM of pure-drug CH (Fig. 10a) appears to be a rough surface with crystalline structures. However, the SEM of the optimised S-SNEDDS (Fig. 10b) predicts the amorphous structure with smooth-surfaced particles. This may be due to complete adsorption of liquid SNEDDS containing CH inside the pores of Neusilin US2.
Fig. 10

SEM image of pure drug (a) and S-SNEDDS (b) of cinacalcet hydrochloride

Measurement of Surface Charge

Zeta potential of all tested optimised SNEDDS were found to be negative without any significant difference (− 13.5mv ± 0.32). This may be due to the increase in numbers of hydroxyl group of fatty acid by Tween 20. The medium chain fatty acid of Capmul MCM may also impart a negative charge on globule surface.

In Vivo Pharmacokinetic Study

Figure 11a illustrates the mean plasma concentration of CH vs. time graph of the optimised SNEDDS formulation and pure drug after conducting the pharmacokinetic study. Figure 11b represents the chromatogram of the optimised SNEDDS formulation after a time period of 2 h. A sharp peak in the chromatogram represents the retention time of 4.18 min. Table VIII illustrates the calculated values of different pharmacokinetic parameters. Tmax for the optimised formulation was found to be only 2 h as compared with 4 h for pure-drug CH which indicate less time requirement for absorption of the drug. Cmax of optimised S-SNEDDS was found to be 2.186 μg/mL; whereas, in the case of the pure drug of CH, it is 0.664 μg/mL. AUC of optimised S-SNEDDS (35.9 (μg/h)/mL) was found to be more than threefold as compared with AUC of the pure drug (10.79 (μg/h)/mL). Similarly, AUMC value for optimised S-SNEDDS (389 (μg/h2)/mL) is also nearly three times than the pure-drug CH (129 (μg/h)/mL). Other pharmacokinetic parameters such as mean residence time (MRT), volume of distribution (Vd) and clearance (Cl) were calculated and expressed in Table VIII. The reason for improvement in bioavailability is due to improvement in drug-dissolution profile and absorption of drug through the gastro-intestinal membrane. In vivo studies proved a significant augmentation in the permeability and absorption potential of the drug CH with optimised S-SNEDDS which is evident from distinctly superior pharmacokinetic parameter as compared with the pure drug.
Fig. 11

a Plot depicting the mean serum concentration vs. time of optimised S-SNEDDS and pure drug of cinacalcet hydrochloride. b Chromatogram of blood sample at 2 h after administration of optimised S-SNEDDS

Table VIII

Pharmacokinetic Parameter

Biopharmaceutical characterisation

Pure drug

Optimised formulation

K (h−1)

0.021 ± 0.0007

0.023 ± 0.0006

t 1/2 (h)

31.90 ± 1.8

29.59 ± 1.6

Cmax (μg/mL)

0.664 ± 0.032

2.186 ± 0.08

Tmax (h)

4

2

{AUC}\( \genfrac{}{}{0pt}{}{\infty }{0} \) ((μg/h)/mL)

11.79 ± 0.32

35.9 ± 2.31

{AUMC}\( \genfrac{}{}{0pt}{}{\infty }{0} \) ((μg/h2)/mL)

129 ± 3.45

389 ± 5.36

MRT (h)

10.94

10.83

Vd (L)

1. 32

3.39

Cl (mL/min)

0.027

0.077

Data are the mean values ± SD, n = 3

K, elimination rate constant; t1/2, half-life; Cmax, peak plasma concentration; Tmax, time to attain the peak plasma concentration; AUC, area under the curve; AUMC, area under the first moment curve; MRT, mean residence time; Vd, volume of distribution; Cl, clearance

Accelerated Stability Studies

The P and F values of the ANOVA design during accelerated stability study are shown in Table IX. The P value is more than 0.05, and the calculated F value is less than the critical F value for all the CQAs which indicates there is no significant change. Hence, it can be concluded that the optimised S-SNEDDS was found to satisfy the stability criteria as there is no significant change in the CQAs during the study period.
Table IX

Similarity Study of Optimised S-SNEDDS of CH During Accelerated Stability Study Using ANOVA

Responses

F value

P value

Interpretation

Inference

Q30

1.988

0.171

P value > α; F value < F crit

No significant difference

ET

0.789

0.472

P value > α; F value < F crit

No significant difference

GS

0.325

0.727

P value > α; F value < F crit

No significant difference

PDI

0.22

0.80

P value > α; F value < F crit

No significant difference

α value = 0.05 and F crit = 3.68

Q30, cumulative % drug release at 30 min; ET, emulsification time; GS, mean globule size; PDI, polydispersity index

CONCLUSION

In this present research work, a systematic development of S-SNEDDS of a novel therapeutic for hyperparathyroidism cinacalcet HCl was conducted using a quality-by-design approach in order to achieve better bioavailability and immediate drug release. The excipients such as Capmul MCM as oil, Tween 20 as surfactant, and Transcutol P as co-solvent were selected on the basis of saturation solubility study. Pseudoternary-phase diagram showed a maximum nanoemulsion region for the combination of Tween 20 and Transcutol P in the ratio of 2:1. In the process of QbD, first QTTPPs and CQAs were identified with proper justification. Risk identification and risk analysis were carried out using Ishikawa fish-bone diagram and FMEA as tools, respectively. On the basis of RPN scores, TW 20 (448), TRP (432), NEU (392), CAP (336), SS (336), T (288) and ST (280) were found as significant factors. Taguchi design confirms that the model factors such as the amount of Tween 20, Transcutol P and Neusilin US2 are significant. According to the BBD design matrix, a series of formulations were prepared and characterised for the CQAs. Regression equation and response surface were analysed. ANOVA study was conducted to identify the significant model term. Optimisation of S-SNEDDS of CH were carried out by setting up the upper and lower limits of different CQAs, and the overlay plot was constructed to identify the design space. The optimised single dose of S-SNEDDS obtained using BBD consisted of 30 mg of CH, 50 mg of CAP, 149.75 mg of TW 20, 55 mg of TRP and 260.75 mg of NEU. It shows an average Q30 of 97.6%, ET of 23.3 min, GS of 89.5 nm and PDI of 0.211. The optimised S-SNEDDS showed better dissolution profile with nearly 2.5 times cumulative % drug release than the pure drug in 30 min. DSC, XRD, and SEM studies justify that the S-SNEDDS has smooth-surfaced particles without any crystalline shape which indicates the complete adsorption of liquid SNEDDS containing CH inside the pores of NEU. Zeta potential study of optimised S-SNEDDS suggests a negative charge development after emulsification on globule surface. In vivo studies showed threefold augmentations in oral bioavailability with increased Cmax and decreased Tmax for optimised formulation in comparison with aqueous suspension of pure drug. Accelerated stability study of the optimised S-SNEDDS CH confirms insignificant changes in the CQAs during storage which was evident by conducting ANOVA. The present research concluded that an optimum combination of Capmul MCM, Tween 20, Transcutol P and Neusilin US2 in the formulation of S-SNEDDS of CH is effective in achieving the desired objective of immediate drug release and augmentation in bioavailability. The solid SNEED in the form of granules can be filled into ‘0’ size capsule and administered to patients.

Notes

Acknowledgements

The authors are thankful to Matrix Pharma India for providing the gift sample of Cinacalcet hydrochloride. M/s Gattefosse Pvt. Ltd. India, M/s BASF GmbH, Minden, Germany, Abitec Pvt. Ltd. USA and Gangwal Chemicals Pvt. Ltd. India are gratefully acknowledged for providing the essential excipients for the research work. The authors are grateful to Stat-Ease, Minneapolis, MN for the design expert ver. 11.1.01 software. The authors would also like to acknowledge BIT, Mesra, India for providing the facility of XRD and SEM. The authors are thankful to the Institute of Life Science, Bhubaneswar, India for the facility of Zetasizer.

Compliance with Ethical Standards

Declaration of Interest

The authors declare that they have no conflict of interest.

Supplementary material

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ESM 1 (JPG 287 kb)
12249_2019_1411_MOESM2_ESM.jpg (1 mb)
ESM 2 (JPG 1034 kb)

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Copyright information

© American Association of Pharmaceutical Scientists 2019

Authors and Affiliations

  1. 1.Roland Institute of Pharmaceutical Sciences (Affiliated to Biju Patnaik University of Technology)BerhampurIndia

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