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Proton Pump Inhibitors (PPIs) with Paclitaxel to Reverse PTX Resistance

Paclitaxel (PTX) is one of the major chemotherapeutic drug, effective against a wide variety of cancers especially breast, ovarian and lung cancer. It disrupts microtubule dynamics that arrests a diverse array of cell types (G2/M phase), leading to altered mitosis and cell death. However the major issues of drug resistance, low therapeutic index and poor water solubility limit the clinical effectiveness of the drug. For a weakly basic chemotherapeutic drug like paclitaxel, the development of tumor acidic microenvironment (Warburg effect) has a remarkable impact on therapeutic resistance. While crossing cellular membrane, the H­+ ions (protons) present in the extracellular acidic milieu inactivates the drug, leading to the development of drug resistance. The present approach takes the advantage of tumor acidic microenvironment, by incorporating proton pump inhibitors (PPIs) with paclitaxel, as a potent therapeutic combination, capable of reversing PTX resistance. As a viable and safe option, a dual drug loaded poly (lactic-co-glycolic acid) nanoparticles was fabricated to overcome the constraints like poor water solubility, stability and toxicity. Additionally Box behnken design was employed to analyze the impact of formulation and process variables, on the basis of critical quality aspects that makes the nanoformulation optimized and more precise. The optimum formulation represents particle size of 241.861 nm, zeta potential of 9.141 mv and encapsulation efficiency of 88.908 % and 80.353% for paclitaxel and lansoprazole respectively. The formulation exhibits in vitro sustain release profile for both the encapsulated drugs that may effectively neutralize acidic microenvironment with the potential treatment of cancer as long as for 16 days. The result indicates that the combined nanoformulation can potentially be used as a treatment modality of cancer.

Paclitaxel (PTX) is a well known member of anti neoplastic family popular for its broad spectrum activity with a unique mechanism of action. It acts as a microtubule-stabilizing agent that selectively disrupts microtubule dynamics which leads to mitotic arrest and finally resulted into cell death. It has been found effective for treating various forms of advanced and refractory cancers, including colon, bladder, kaposi’s sarcoma, lung cancer and considered best for ovarian and breast cancer (1-3) Researchers of Johns Hopkins Oncology Center reported 30% and 56% drug response rate in patients with advanced ovarian cancer and metastatic breast cancer(4). National Cancer Institute (NCI) considered this drug for delivering most significant advancement in chemotherapy in past 15–20 years(3).

In spite of having a remarkable anti tumor profile, resistance remains a major issue that limits the therapeutic efficacy of paclitaxel. Among the contributing factors, tumor acidic microenvironment is well known for developing chemoresistance that weakens and overrules antitumor efficacy. The “Warburg Effect” consisting hypoxia and altered glycolysis, creates a hostile acidic environment where the cells upregulate several classes of proton exchangers, that extrudes H+ ions outside the intracellular environment for the sake of survival. The process makes the cells able to proliferate with aggravating malignant features(5, 6).

Various studies showed that among proton pumps, V-ATPases were associated with multidrug resistance(7)which can be reversed simply by using proton pump inhibitors(5).As a prodrug they utilizes acidic pH and protons for their activation(8, 9), therefore can selectively target cancer cells in the acidic micro environment and act as an irreversible blocker of the hydrogen/potassium adenosine triphosphatase (ATPase)(10). The auxiliary effect of PPI against different cancers had already proved in both pre-clinical and clinical studies, with very few side effects. It can fight against major malignant properties of a cancer cell, such as invasiveness, migration, proliferation and drug resistance etc by reducing tumor acidity.(5, 11, 12). These drugs can also act as chemosensitizers, or provide a direct effect as an antitumor agent.(7, 13). Among all other members of its family Lansoprazole (LAN) exhibits the greatest effectivity against tumor cells even at lower doses (12, 14). Hence, at this point the combination of PTX-LAN is advantageous owing to the synergistic effects of drugs and suppression of drug resistance.

However, selection of an effective drug delivery system (DDS) that deliver optimal amounts of drugs to the targeted neoplastic cells, enhance drug efficacy and reduce adverse effects leaving healthy tissues untouched is highly difficult. While considering patient complaint treatment regiments, development of a proper drug delivery system (DDS) that deliver optimal amounts of drugs to the targeted neoplastic cells, enhance drug efficacy and reduce adverse effects leaving healthy tissues untouched is highly demanded(15, 16). Recent developments in the field of nanotechnology open several arenas of controlled and targeted delivery for combination chemotherapeutics. The nanocarrier-mediated combination chemotherapy offers advantages of low toxicity profile for combination chemotherapeutics, passive targeting by the enhanced permeability and retention (EPR) effect and the potential inhibition of drug resistance by influencing intracellular endocytic uptake(17).

Hence, the main goal of this work is to develop a polymeric nano drug delivery system for paclitaxel with lansoprazole, aimed to provide improved anti-tumoral efficacy with an avoidance of drug resistance. The nanocarrier system resolves the major physicochemical hindrances like poor water solubility of paclitaxel as well as instability of lansoprazole in heat, light, and acidic media.  The determinant factors of dual drug (PTX-LAN) loaded nanocarrier system was optimized using Box-Behnken Design to achieve a formulation with most desirable physicochemical properties appropriate for long lasting therapeutic response. PLGA was used in this study since the demonstration of biocompatibility and biodegradation makes them suitable candidates for our intended response(18). The formulation variables were statistically optimized using 33factorial design. The optimized formulation was characterized for particle size, shape, zeta potential, % drug entrapment efficiency and in-vitro drug release.

Paclitaxel (M.W. 853.906 g/mol) and lansoprazole (M.W. 369.363 g/mol) were received as a gift samples from Fresenius Kabi Oncology Ltd., Kolkata, West Bengal and Aurobindo Pharma, Hyderabad respectively. Acid terminated Poly D, L-lactide-co-glycolide (PLGA, 50:50, M.W.38, 000-54,000, Resomer ® RG 504H) and polyvinyl alcohol (PVA, M.W. 31,000-50,000 and 98-99% hydrolyzed) were purchased from Sigma-Aldrich (Germany). Reagents like dichloromethane (DCM), ammonia, sodium bi carbonate were of analytical grade and obtained from Merck Life Science Private Limited, Mumbai, India. Ethanol was purchased from Merck KGaA, Germany. Dialysis bag (i.d. 14.3mm, molecular weight cut off 12000 − 14000 Da) was obtained from Hi Media, Mumbai, India. Deionized water was acquired from the Milli – Q-system (Merck Millipore) of Bioequivalence Study Centre, Jadavpur University, Kolkata-32, West Bengal

A dual drug loaded nanoformulation was prepared by double emulsion solvent evaporation method taking paclitaxel and lansoprazole in combination. The process involved a series of nanoformulation, designed to limit the major pitfalls associated with the delivery of the concerned drugs. Major process parameters such as concentration of PVA, amount of PLGA and stirring speed of homogenizer were varied at 3 levels (Table 1), keeping drug: polymer ratio constant (1:5).The oil phase constitutes drugs and polymer, dissolved in a solvent mixture of DCM and Ethanol (7:3,2mL).On the other hand aqueous phase contains PVA solution blended with sodium bi carbonate in order to produce pH 8. The primary emulsion was formed by the drop wise addition of aqueous phase to the organic solution under homogenization (IKA T10 basic Turrax Ultra homogenizer) at 25,000 rpm for 5 minutes. Further the prepared w/o emulsion was added gently to PVA solution (60 mL, 0.5 %) and re-emulsified by homogenization for 10 minutes. The final w/o/w emulsion was stirred (at 240 rpm) overnight using a magnetic stirrer to facilate evaporation of organic solvent. Then the solidified nanoparticles were centrifuged (3K30,Sigma) at a speed of 15000 rpm for 30 min to make them free from excess surfactant (PVA) and unloaded drugs followed by washing thrice with the deionized Milli- Q water. The obtained nanoparticles were properly freeze dried in a lyophilizer at – 60 ͦ C and used for further analysis.

According to the results obtained in primary trials, PVA concentration for primary emulsion (% w/v) (X1), PLGA concentration (mg) (X2) and homogenizing speed (rpm) (X3) were found to be the major dominant independent variables influencing strongly the dependent ones including particle size (nm) (Y1), zeta potential (mv) (Y2) and encapsulation efficiency (%) (Y3 and Y4) for ‘paclitaxel and lansoprazole dual drug loaded PLGA nanoparticles’. In the present study Box-Behnken design was selected because of its suitability to explore different response surfaces together with the construction of rotatable or nearly rotatable second-order polynomial designs(19, 20) with slightly better efficacy in comparison with others (central composite, Doehlert matrix and three-level full factorial design) (21) (22).

In the experimental section, a run design comprising of 17 combinations with their coded levels were developed by the Design Expert Trial version 7 Software (State-Ease Inc, Minneapolis, MN, USA) (Table 2). In accordance with the combinations, the experiments performed for three factors at three levels to study the effect of each independent variable on the dependent ones (response variables). The combinations indicate coordinates at mid-point of each edge (eight), center point of each surface of cube (six) and the three replicated center point of the cube.

In the multi criteria problem, one response may yield optimized condition while the other may influence the opposite effect. This multi criteria problem can be converted into a single one with the help of desirability function which is a geometric mean of all transformed responses(21should be placed here). The concerned study aimed to create the best fitted values of operating variables to obtain desirable response in compliance with the selected criteria (23).The software utilized desirability function, to suggest a combination, which was later followed to prepare optimized batch of nanoformulation.

The responses were analyzed by ANOVA in Design Expert software, to select the model fitted best to the data. In the present work software suggested combinations of independent variables utilized in each of 17formulations that forwarded further with the feeding of response data in the concerned response surface quadratic design. The study utilized ANOVA test along with the multiple factorial regression analysis, performed in each trial where the response (Yi) is measured by the formula given below.

Yi = b0 + b1X1 + b2X2 + b3X3 + b4X1X2 + b5X1X3 + b6X2X3 + b7X12 + b8X22 + b9X32………………. Eq 1.


Yi = measured response;

b0 = intercept of the polynomial equation.

b1–b9 = regression coefficients regarding respective independent variables, including main effects (X1, X2, and X3); interacting effects (X1X2, X1X3and X2X3) and quadratic effects (X12, X22, and X32) (24, 25).

The acceptance of the model was considered on the basis of p value <0.05 and a reasonable agreement between predicted and adjusted correlation coefficients. A significant model was established on the basis of suitable values of b-coefficients, p-values, F-values with all other statistical parameters including multiple correlation coefficient (R2), adjusted multiple correlation coefficient (adjusted R2), predicted multiple correlation coefficient (predicted R2) and lack of fit.

In the optimization part, few suggestions were obtained by the software, based on the desired output of particle size (minimum), zeta potential (maximum) and encapsulation efficiency (maximum) of the nanoformulation. Among the solutions three were randomly chosen as check point combinations to perform experiments. The error percentages were calculated by comparing values obtained from predicted and experimental formulations. The validation of the concerned statistical experimental design was associated with the performance of these random check point formulations (21, 26, 27). Moreover, the optimized formulation was selected from the check point list in favor of maximum value of desirability and minimum percentage of errors that further utilized for nanoformulation batch preparation and characterization

The FTIR (Nicolet iS10, Thermo Fisher Scientific, USA) studies were performed to analyze the integrity of the components used in formulation. The FTIR spectra of the Paclitaxel (PTX), Lansoprazole (LAN), PLGA, PTX-LAN-PLGA physical mixture and PTX-LAN loaded PLGA nanoparticles were recorded in the solid state over the scanning range of 400–4000 cm−1. The bond vibrations of functional groups were compared between free dug, formulation and physical mixture.

Differential scanning calorimetry technique was performed to identify the final state of encapsulated drug along with the investigation of possible interactions among drugs and polymer. The typical phase transitions like glass transitions and endothermic transitions were compared among raw drug (Paclitaxel, Lansoprazole),polymer (PLGA)?, physical mixture (PTX-LAN-PLGA), blank PLGA nanoparticles and PTX-LAN loaded PLGA nanoparticles using DSC (Perkin Elmer Pyris Diamond DSC, Central research facility, IIT, Kharagpur) instrument. The test samples were sealed in an aluminum pan and analyzed from 5°C to 350 °C at a heating rate of 10°C/min under the atmosphere of nitrogen(40 ml/min).An empty aluminium pan with similar conditions was taken as a reference.

X-ray diffractometry (XRD) analysis was performed to diagnose the physical state (crystalline or amorphous) of entrapped drugs embedded within the nanoparticles. The XRD patterns of paclitaxel, lansoprazole,PLGA,Physical mixture, blank PLGA nanoparticles and PTX-LAN-PLGA loaded nanoformulation were taken by UltimaIII (Rigaku; Met. & Mat. Engg. Dept., Jadavpur University), in which the samples were exposed to X-radiation emitted from Cu source with Kᵝ filter. The system was operated in a continuous mode at a voltage of 40 kV and a current of 30 mA. In the solid state all the samples were analyzed directly in a 2θ scanning range of 5◦ to 90 at a rate of 2/min with a stepwise increment of 0.05.

Surface morphology of formulated NPs was studied by Scanning Electron Microscopy (EVO 18, Special edition, ZEISS). A tiny amount of sample was mounted on receiver plates fitted with carbon adhesive tapes and excess sample was removed by hand blower. Then the receiver plates were exposed for gold coating (Quorum Q150T ES) and analyzed directly by Field emission scanning electron microscope for the morphological study of the optimized nanoformulations. In scanning electron microscope, the data was captured at two different magnification levels (10000X and 20000X).

Particle size measurement involved suspending pre-weighed (0.1%) nanoparticles suitably diluted in Milli-Q water and sonicated for 5 min to form a uniform dispersion before placing the sample in quartz cuvette(19). Finally the resulting suspension was considered for the determination of average particle size and zeta potential of the prepared NPs by Zetasizer NanoZS90 (Malvern Instruments, United Kingdom) instrument.

The encapsulation efficiency was analyzed by quantifying entrapped drugs by LC-MS/MS system (API2000, Absciex). Nanoparticles (10 mg) were dissolved in dichloromethane to extract the PTX and LAN from polymer matrix. Then the dissolved samples were precipitated with methanol (1.3 mL) and mixed in a cyclomixer for 5mins. After that the samples were centrifuged at 12000 rpm, at 4°C for 10 minutes to collect the supernatant. The clear solution (supernatant) kept under constant nitrogen flow at a temperature of 25°C for complete evaporation of solvents. The dried sample was further reconstituted with methanol and water combination (1:1, 100 microlitre) and assayed directly by injecting into LC-MS/MS system. The instrumental run carried out with validated LC-MS/MS method taking C18 column and mobile phase combination of ACN and 2mM ammonium acetate (70:30).Later the encapsulation efficiency (%) and drug loading (%) was calculated using the equations below

The in vitro release study was carried out in a medium containing phosphate buffer saline at a pH of 7.4 under sink conditions (28, 29).A pre-weighted (10 mg) sample of PTX-LAN-PLGA nanoparticles was suspended on 5 ml of buffer solution containing 1 % Tween 80 and packed in an end sealed dialysis membrane bags. The packed bags were kept in a water bath shaker, containing 100 ml of buffer solution containing tween 80. The study was carried out at 37 ± 0.5 °C under the influence of continuous shaking at 100 rpm(28). Total amount of nanoparticles were taken in such a way that the total amount of drugs inside the particles is less than 10% of its solubility limit in PBS buffer which will ensure the perfect in-vitro release conditions of a hydrophobic drug. The samples were withdrawn at 1st, 2nd, 4th, 8th, and 24th hours followed by daily withdrawal upto 16 days (29, 30). In each time point samples (500 μL) were collected and same volume PBS buffer was replaced for maintaining the equilibrium. The samples obtained at different time points were centrifuged at 12,000 rpm for 5 min and the supernatant was collected. The amount of dichloromethane present in the supernatant solution was allowed to evaporate completely and the residue was reconstituted with methanol and water combination (1:1, 100 microlitre). The drug content in each withdrawn sample was measured by the previously-validated lc-ms/ms method.

The preparation of dual drug loaded nanoparticles happens to be the major challenge of this work. Therefore the formulation was carried out in a controlled environment based on the typical demands of the drugs encapsulated into polymeric matrix. Paclitaxel exhibits a common delivery problem as it tends to aggregate or crystallize very easily in a formulation. On the other hand inclusion of lansoprazole may create another critical hurdle owing to its instability to heat, light, and acidic environment if adequate measure is not taken. An effective method has successfully developed by controlling these parameters carefully with the help of statistical optimization. One of the prime features of the dual drug loaded nanoformulation (paclitaxel and lansoprazole) is the drug solubilization based on co-solvent approach. The structural complexity of diterpenoid ring system, substituted with hydrophobic units makes paclitaxel a highly lipophilic drug(31) with high log P value (~4) and low aqueous solubility (<0.01mg/mL)(4). Owing to the absence of any ionizable functional group in the structure the common ways to increase water solubility including alteration of pH, salt formation or addition of charged complexing agents does not work for it (3, 31). Increase of water solubility can be a contributing factor that decrease the dosage and toxicity of hydrophobic anticancer agents (32). Additionally the drug loading in nanocarriers is influenced by the relative distribution of the drug between the polymeric phase and the aqueous phase that depends largely on drug’s solid-state solubility in the nanocarrier matrix. A mixed solvent system of DCM: Ethanol (7:3) is found extremely effective to dissolve both the drugs and polymer that offers substantial stability to the drugs in nanoemulsion system with high encapsulation efficiency. On the other hand the instability of LAN in organic solvent due to the development of acidic pH, probably because of the carboxylic group of polymer(PLGA 50:50)(28) which was reversed by thorough maintainance of pH 8. Being a lipophilic weak base with pKa 4, lansoprazole seems to be especially sensitive compared to the other members of PPI family (33). The pH of the nanoemulsion was maintained by mixing sodium bi carbonate in aqueous phase during the preparation of primary and secondary emulsion. Further the sensitivity associated with heat was minimized by controlling the temperature at 20°C during the process of emulsification. The solvent evaporation process was carried out for 12hr in a dark room to limit the light catalyzed degradation reaction.

In this work, double emulsion solvent evaporation method was utilized to prepare PTX-LAN-PLGA nanoparticles according to desired particle size (small), zeta potential (high) and encapsulation efficiency (high). For an anticancer nanoformulation size plays a major role to invade leaky vascalatures of a cancer cell for the execution of therapy(34).Now to make the nanoformulation stable for longer period zeta potential is the ultimate determinant factor(R). Also it contributes to in vivo plasma components interactions depending upon its positive or negative magnitude. On the other hand encapsulation of drugs directly correlated with the therapeutic potential hence possesses utmost importance in terms of effective chemotherapy(R). Here the Box–Behnken design (BBD) was applied to reduce the variation in process and optimize the nanoformulation according to desirable properties. It was specifically selected because it contains fewer design points, and is less expensive compared to central composite design with the same number of factors (35, 36). In the selected range of independent variables, response data were shown to vary at its maximum extends. The parameters levels are expressed in codes (-1,0,+1), therefore a positive sign represents a synergistic effect; while a negative sign indicates an antagonistic effect. (19) Data were analyzed using Stat-Ease Design Expert software to obtain analysis of variance (ANOVA), regression coefficients and regression equation. Mathematical relationship generated using multiple linear regression analysis and these equations represent the quantitative effect of the independent parameters on the response variables. Coefficients with more than one factor term and those with higher order terms represent interaction terms and quadratic relationship respectively(20). Box behnken design was used to optimize and evaluate main effects, interaction effects and quadratic effects of the independent variables on the particle size, zeta potential and entrapment efficiency. The actual measured responses of the optimal batch of nanoformulation were close to the predicted responses generated by design. Each of the dependent variables was discussed below with respect to independent variables.

The shape and size of nanoparticles influence cells in the body to consider acceptance, thereby dictate their distribution, toxicity, and targeting ability. Particle size and size distribution (PDI) are two major characteristics of nanoparticles that have a significant impact for its application perspective. In 17 consecutive runs, the present quadratic model represented a wide variation of particle size (240.57 – 428.92 nm) .The F-value of 183.97made the model significant with only 0.01% chance that this value could occur due to noise. Further the “Lack of Fit F-value” of 0.66 implied that this value was not significant relative to the pure error. There was 61.89% chance that this value could occur due to noise. Considering p value <0.05, the model terms including A, B, C, AC, BC, A2, B2, C2 were found significant. The predicted coefficient value of 0.9733 showed a good correlation with the adjusted coefficient value of 0.9904. Additionally, the “Adeq Precision” value of 46.11represented sufficient signal that this model could be used to navigate the design space. The below mentioned polynomial equation represented best description of particle size,after eliminating insignificant terms.(p>0.05).

During the process of primary emulsification, a high concentration of PVA emulsified aqueous and oil phase by lowering interfacial tension. Later, for the process of secondary emulsion, it was utilized in a larger volume (aqueous phase) at lower concentration to stabilize particles of final W/O/W double emulsion. The equation and contour plots showed the reduction in particle size (Y1) with the increasing value of PVA concentration (%X1) (Table2). The higher concentration of PVA easily connected biphasic layers that restrict the formed particles to coalescence by developing a protective layer around the droplets. Additionally a high speed homogenization (X3) was applied to generate high shear stress that break the droplets into smaller pieces (37). The small droplets caused a stable emulsion with lower concentration PVA in secondary emulsion (formulation no 6). As a result a stable nanoformulation with small, discrete particles and low PDI was formed (2, 38). Y1 gradually decreases with the increase of levels. On the other hand inadequate concentration of PVA might end up with agglomeration and increased particle size(19, 39) (formulation no.16) (figure no.2A). The plots (figure no.2C) highlighted about the inverse relationship of homogenizing speed with Y1. Further it was evident from the previous studies and above mentioned equation that X2 (polymer conc.) shared a direct relationship with Y1 (Figure no.2B).The higher concentration of PLGA (50:50) could increase organic phase viscosity, resisting breakdown of droplets thereby develop larger size, fused, semiformed particles during the process of emulsification(37) (formulation no.17). The concentration ??might favor polymer-polymer interactions causing formation of viscous oil phase which in turn reduced drug diffusion rate towards the aqueous phase thereby influenced the formation of larger nanoparticles. On the other hand, higher PLGA concentration favors polymer-polymer interactions, thus more polymer chains remain associated during the solvent’s diffusion into the aqueous medium(40). Interaction terms X1X3 and X2X3 caused decrease of Y1 at both lower and higher levels X1, X2 and X3 , whereas it increased at centre level.

The Zeta (ξ) potential is the electrostatic potential that exists at the shear plane of a particle. It is a key factor that denotes the stability of a nanoformulation(41). In a colloidal dispersion the magnitude indicates the degree of electrostatic repulsion among similarly charged particles(R).In general a nanoformulation is considered stable with a high potential (> +30 mV or <30 mV)(42, 43) but for the particles intended to be in the systemic circulation a near neutral zeta potential is preferred to avoid interactions of in vivo plasma components. Further a positive potential causes toxicity owing to excessive binding affinity of the particles for cellular components(19) In the present study the model represented zeta potential values in a range of -1.7 to -9.39 mv for 17 suggested runs. The obtained ‘F-value’ of 98.45 signified the model with only a 0.01% chance that this value could occur due to noise. Further the Prob>F value of 0.05 fitted well with the criteria and made the model significant. The “Adjusted R-Square” value of 0.9821 showed reasonable agreement with the “Predicted R-Square” value of 0.9661. Additionally the lack of fit F-value” of 0.32 was not significant with only 81.42% possibility that this value could be due to noise. “Adeq Precision” ratio of 31.735 indicated an adequate signal which is sufficient for a model to navigate design space. The polynomial equation for zeta potential was shown below with significant (p <0.05) model terms (A, B, C, AC, BC, A2, B2, and C2):

The above mentioned polynomial equation is a quadratic model which showed a direct relationship between polymer content (X2) and zeta potential (Y2). It showed the higher concentration of PLGA(X2) can partially enhance the value of zeta potential (Y2) (figure no.3B). The terminal carboxylic acid group of PLGA might influence the development of such a potential difference between two surfaces of the dispersion medium. On behalf of X2, the obtained positive coefficient further supports the phenomenon (eqn: 5). On the other hand the equation shared a picture of reciprocal relation of Y2 with PVA% (X1) that exhibited the escalation of negative potential with the decreased value of X1.[39].Different studies suggest that the surface charge of PLGA nanoparticles without any PVA is approximately -45mV. As mentioned above, this is attributed to the carboxylic end groups of the polymer. According to literature, PVA has a tendency to coexist on the surface of nanoparticles even after repetitive washing. Hence it could be possible that the increased concentration of X1 tends to decrease the electro-negativity of the zeta potential(40). The contour plots (Fig. 3A.) and the negative coefficient represented by polynomial equation coincide with the mentioned fact. Additionally, PVA as a non-ionic surfactant tends to make a film around the nanoparticles. The residual content could be responsible for shielding surface charge thereby caused the reduction of zeta potential (19, 40). Further from the equation, it was observed that higher values of Y2 were attributed to the increased speed of homogenization (X3) (figure no.3C). The application of high speed homogenization produced smaller size particles with larger total surface area that caused the generation of high zeta potential. In the contour plots (Figure no.) the effects of two variables on Y2 were evident visually when third parameter was fixed at center level. Both the parameters X2 and X3 increased zeta potential when level was increased from -1 to 1 (Formulation no.2.) whereas X1 caused the reverse effect on Y2(Formulation no. 1). Interaction terms X1X2 and X2X3 increase value of zeta potential with the increase of levels. Quadratic terms X12, X22 and X32 are expressed with negative coefficients in the model, and these caused decrease of Y2 at low and high value of levels and increase of Y2 at center level.

The encapsulation efficiency for 17 suggestive runs were found in a range of 21% to 85% and 15% to 77% for paclitaxel and lansoprazole respectively, where the lower values were attributed to the loss of drug and polymer during the secondary emulsification process. Determination of the responses ‘Encapsulation efficiency’ (Y3 and Y4) is important as it has significance with therapeutic efficacy of a drug delivery system. The statistical model showed significant F value of 50.69 and 32.03 for encapsulation efficiency of paclitaxel and lansoprazole respectively. The “Lack of Fit F-value” of 0.51 (PTX) and 1.3 (LAN) indicated that it was not significant relative to the pure error. The “Pred R-Square” (00.0) and “Adj R-Square” (00.0) were in a good correlation with each other for both the drugs. Further the “Adeq Precision” ratio of 23.923 (PTX) and 19.456 (LAN) expressed an adequate signal useful for the model to navigate the design space. The significant model terms (p<0.05)were considered for paclitaxel (Eq. 6) and lansoprazole (Eq. 7) to construct polynomial equations, mentioned below.

The above mentioned polynomial equations are quadratic models and these clearly indicated a direct relationship of encapsulation efficiency with all three independent variables viz. PVA%, polymer concentration and homogenizing speed (figure no.?). According to response surface plots (Fig.4B, 5B) PLGA was found to have maximum effect on encapsulation efficiency. Sathyamoorthy N et al also supported the phenomenon of hydrophobic drugs such as PTX and LAN, exhibited greater miscibility and interactions with polymeric organic solution. A higher concentration of PVA (1-4%) was used during primary emulsion (w/o) to obtain uniform nanoparticles. The surfactant concentration in primary emulsion caused adequate lowering of surface tension that made organic and aqueous layer miscible and facilitated drug diffusion from organic to aqueous phase. As a result more amount of drug became entrapped (Fig.4A, 5A) in the dispersed phase of primary emulsion [R]. In case of the secondary emulsification process the minimum concentration (1%) of PVA was used to stabilize the primary emulsion. In this situation diffusion of entrapped drugs was inhibited towards outermost aqueous phase that make the drugs trapped in the core of W/O/W double emulsion [R]. Additionally PVA with 87-89% hydroxylation caused a significant increment in encapsulation efficiency because the hydroxyl groups of PVA might be adsorbed on the nanoparticle’s surface forming a thick film via the stronger intra- or inter- molecular interaction(44) that resisted drug diffusion. On the other hand the  higher content of polymer concentration resulted into more encapsulation efficiency (formulation no.2) probably depending on miscibility of drugs in the organic solution and drug-polymer interaction(18). Further higher amounts of polymer produced more viscous organic solutions that could hinder drug diffusion from the organic phase into the aqueous which might cause greater drug entrapment efficiency (40, 44). The increase in homogenizing speed(X3) was found responsible for slight increase of encapsulation efficiency as well (formulation no.2). The reason might be that increased homogenization speed had resulted into smaller sized particles which in turn generate larger surface area to the polymer to facilitate accommodation to greater number of drug molecules, and this fact might lead to higher values of encapsulation efficiency(40). (Fig. 4C, 5C).The contour plots of Y3 and Y4 (Encapsulation Efficiency for PTX and LAN) exhibited the effects of two factors (??) on Y3 and Y4 separately while keeping the third factor constant at center level. These figures illustrated the effects visually almost similar for both the drugs. With the increasing levels of individual factors (X1, X2 and X3) encapsulation efficiency was found to be increased (Fig.4,5 ).Interaction terms X1X2 and X2X3 increase value of Y3 with the increase of levels same as their effects on zeta potential. Quadratic terms X12 and X32 are expressed with negative coefficients in the model, and these caused decrease of Y3 at low and high value of levels and increase of Y2 at center level. Similar quadratic effects were observed in case of Y4.

The requisites of an optimum formulation were restricted to ‘minimum’ for particle size (nm), ‘maximum’ for zeta potential (mv) and EE (%). The Design expert software and the ANOVA analysis utilized successfully to draw statistical significance for each effect associated with dependent parameters, by comparing the mean square against an estimated experimental error. Using the desirability function approach a numerical optimization technique applied to generate the optimum settings and suitable levels of constraints to achieve desired responses of the nanoformulation. Among the solutions predicted by the software, randomly three checkpoint formulations (T1 and T2; Table 1) were performed with the calculation of error (%) between observed and predicted values. The final optimal PTX-LAN-NPs were obtained with the combination of coded value 0.45 for PVA, 1 for PLGA, and 1 for homogenizing speed that exhibited particle size of 240.57nm (fig no.10), Zeta Potential 9.14 mv (fig no.10) and EE of 88.908 % for paclitaxel and 80.353% for lansoprazole respectively with a desirability function value of 0.99 (Table..7).

The FTIR spectra (Figure no.6) of the Paclitaxel (PTX), Lansoprazole (LAN), PLGA, PTX-LAN-PLGA physical mixture and PTX-LAN loaded PLGA nanoparticles were depicted in figure no.6.The characteristic absorption peaks of paclitaxel regarding C–H stretching vibrations, ester C=O stretching vibrations, amide stretching vibrations, C=O stretching vibrations from ketone group, C-O stretching of C-ONH,C-O-C asymmetric stretching vibration of ester and C-O-H stretching vibration of secondary alcohol observed at 2944.98 cm1, 1732.88 cm1, 1645.13 cm1, 1704.05 cm1,3394.77 cm1,1176.17 cm1and1107.39 cm1 respectively. For lansoprazole sulfinyl (S=O), C-N on the pyridyl ring, ether bond, amine-NH,–CH2–, the aromatic ring vibrations were appeared at 1035.85 cm 1, 1281.41 cm 1, 1116.36 cm 1, 3228.24 cm 1, 1476.78 cm 1 and 1456.23 cm 1 respectively. The characteristic peaks for PLGA were found at 3358.18 cm-1for O-H stretching, 2946.06 cm-1 for C- H stretching, 1747.21 cm-1for stretching of the carbonyl group and at 1267.09 cm 1 for stretching  of C- O group. Absorption spectral peaks of paclitaxel, lansoprazole and PLGA were also observed in the physical mixture of PTX-LAN-PLGA-PVA with no significant shifts in wave numbers. The spectra of dual drug loaded nanoparticles depicted typical peaks of PLGA, in which absence of some characteristic peaks of paclitaxel and lansoprazole were noticed which may be due to higher fraction of polymer in comparison to that of drugs. This does not indicate any chemical instability of drugs within nanoparticles. The result reveals that there was no interaction among drugs and excipients used in the formulation

Differential scanning calorimetry studies were performed to observe physicochemical interactions of encapsulated drugs and polymer.DSC thermograms of pure drugs (paclitaxel, lansoprazole), physical mixture of paclitaxel-lansoprazole–PLGA ,blank PLGA nanoparticles and dual drug loaded nanoparticles are shown in Figure no.7.In the thermogram of pure paclitaxel, it represents an endothermic peak at 220˚C corresponding to its characteristic melting point. Pure LSP also represented its melting point with a sharp endothermic peak at 181.6°C. The sharp endothermic peaks for pure paclitaxel and lansoprazole at 220˚C and 181.6°C indicate their crystalline nature. The nanoformulation depicted no distinctive peak of the drugs in the DSC profiles owing to the decreased crystallinity in the formulations and/or drug solvation in the amorphous carrier as well as solid state interaction induced by heating. The DSC study confirmed the compatibility among paclitaxel, lansoprazole and PLGA

The diffractograms(Fig. No. 8) exhibit several sharp and high intensity diffraction peaks for pure paclitaxel and lansoprazole respectively. In all those respective points both the drugs showed sharp peaks specifying typical nature of crystalline form. There was not any remarkable difference between XRD patterns of blank and PTX-LAN-PLGA nanoparticles. Absence of characteristics peaks of drugs in nanoparticles may be attributed to dominance of amorphous nature of polymer over the drugs as amount of drugs was comparatively lesser, and drug molecules were embedded well under the polymer layer. Moreover, drug molecules were molecularly dispersed within the matrix; possibly drugs had been transformed to amorphous state from crystalline state after encapsulation into the nanoparticles .This amorphous property of drugs may enhance its aqueous solubility owing to high molecular motion and greater internal energy(45).

The dual drug loaded PLGA nanoparticles exhibited smooth, spherical, discrete, homogeneous particles having <250 nm in size (fig.No.9). The optimized final formulation showed particle size of 241.861 nm and poly dispersity index of 0.315. The polydispersity index of optimized nanoparticles was depicted in figure no.10. 

The study revealed initial rapid release for both the drugs PTX and LAN. The reason can be the diffusion of dissolved drugs initially deposited inside the pores of the nanoparticles (46) On the other hand the steady and controlled release of drugs happened due to diffusion of the drugs from the polymeric core of the nanoparticles to the bulk region via the thin layer of liquid surrounding the particle. A sustained release of both the drugs was obtained up to 384hrs with the initial rapid release as presented in Fig. 11. The cumulative % drug release in 16 days was 94% and 81% for paclitaxel and lansoprazole respectively. Both the drugs represent low water solubility, thus diffusion is not the only primary release mechanism for these two drugs. Hence, the release kinetics of the drugs from nanoparticles might not be explained by diffusion but can be linked to self-erosion of the polymeric matrix. The large surface to volume ratio of the nanoparticle geometry is also responsible for the erupted release.According to the literature PLGA (50:50) has a large degradation period of 102 days(47). However acid terminated PLGA (PLGA 50:50) has free carboxyl groups, therefore can provide more swelling owing to water uptake compared to more hydrophobic ester-terminated PLGA.(R)

Despite being the major cause of death worldwide, cancer remains difficult to treat due to the development of drug resistance and severe adverse effects associated with conventional chemotherapy. The novelty of this study is the development of dual drug loaded nanoformulation utilizing a common PPI that selectively target tumor microenvironment for the establishment of potential anticancer therapy with the reversal of PTX resistance. The developed dosage form, loaded in a biodegradable PLGA carrier exhibited sustained release of both the drugs simultaneously for a prolonged period that may aid to overcome the obstacles of paclitaxel chemotherapy in an acidic environment of a tumor cell. The tunable properties of double emulsion solvent evaporation method were sincerely explored to prepare nanoformulation with high encapsulation efficiency, low average particle size and high zeta potential. Nanotechnology was chosen as a drug delivery system with a belief of correcting formulation difficulties with additional features of biocompatibility and low cytotoxicity. Box-Behnken design was utilized to optimize the results statistically and develop a precise polymeric drug delivery system with minimum number of experiments. The validation of the model, performing additional check point experiments ensured integrity of results in the field of pharmaceutical drug delivery system. PTX-LAN-PLGA nanoformulation is a new formulation adopting optimization technique with Box-Behnken design. The dual drug loaded nanoformulation may serve as a potential candidate fighting against cancer essentially with reversal of drug resistance.

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