Artificial Neural Network based Particle Size ...

Artificial Neural Network based Particle Size Prediction of Polymeric Nanoparticles John Youshia1,2, Mohamed Ehab Ali1,3 and Alf Lamprecht1,4 1

Department of Pharmaceutics, Institute of Pharmacy, University of Bonn, Bonn, Germany. Department of Pharmaceutics and Industrial Pharmacy, Faculty of Pharmacy, Ain Shams University, Cairo, Egypt. 3 Department of Industrial Pharmacy, Faculty of Pharmacy, Assiut University, Assiut, Egypt. 4 FDE (EA4267), University of Burgundy/Franche-Comté, Besançon, France. 2

1

Abstract Particle size of nanoparticles and the respective polydispersity are key factors influencing their biopharmaceutical behavior in a large variety of therapeutic applications. Predicting these attributes would skip many preliminary studies usually required to optimize formulations. The aim was to build a mathematical model capable of predicting the particle size of polymeric nanoparticles produced by a pharmaceutical polymer of choice. Polymer properties controlling the particle size were identified as molecular weight, hydrophobicity and surface activity, and were quantified by measuring polymer viscosity, contact angle and interfacial tension, respectively. A model was built using artificial neural network including these properties as input with particle size and polydispersity index as output. The established model successfully predicted particle size of nanoparticles covering a range of 70-400 nm prepared from other polymers. The percentage bias for particle prediction was 2%, 4% and 6%, for the training, validation and testing data, respectively. Polymer surface activity was found to have the highest impact on the particle size followed by viscosity and finally hydrophobicity. Results of this study successfully highlighted polymer properties affecting particle size and confirmed the usefulness of artificial neural networks in predicting the particle size and polydispersity of polymeric nanoparticles.

Keywords: Polymeric nanoparticles; particle size; prediction; artificial neural network; contact angle; interfacial tension; viscosity; in-silico.

List of Abbreviations ANN EC IFT MW NPs PCL PDI

Artificial neural network Ethyl cellulose Interfacial tension Molecular weight Nanoparticles Poly(ε-caprolactone) Polydispersity index

PLA PLGA PVA PVAc RMSE RSM θc

Poly(lactic acid) Poly(lactic-co-glycolic acid) Poly(vinyl alcohol) Poly(vinyl acetate) Root mean square error Response surface methodology Contact Angle

1. Introduction Nanoparticles (NPs) have proven their efficient use as drug delivery carriers in a wide range of therapeutic applications [1,2]. This is based on unique properties allowing for the enhancement of drug penetration across biological barriers [3,4] and drug targeting towards malignant [5,6] and inflamed tissues [7]. Approaches such as passive targeting with modified surface properties by using different surfactants or stealth NPs by polyethylene glycol decoration involve significant changes to surface properties. Similarly, active targeting can be accomplished by decorating the surface of nanoparticles with targeting moieties and ligands [8]. Most of these phenomena have been found to be size-dependent, for example; enhanced oral drug absorption [9], selective targeting towards tumors [10] or inflamed tissues [11]. Therefore, controlling the particle size of 2

NPs and its distribution is of crucial importance. Size distribution is usually defined by the polydispersity index (PDI), which specifies the uniformity and stability of NPs and should be within 0.01 to 0.5 [12]. Until now, when a certain particle size with a narrow size distribution is aimed for, this has been done by empirical approaches and trial and error. Accordingly, developing a mathematical model that can predict the particle size and PDI of polymeric NPs obtained from various types of polymers would be very beneficial, as it will save time and money by preserving polymers, chemicals and materials normally consumed during the optimization phase. The main statistical and modelling tools used for optimizing and predicting characteristics of NPs are response surface methodology (RSM) [13,14] and artificial neural network (ANN) [15– 18]. Both approaches were already compared to each other with the results demonstrating the superiority of ANN to RSM in data fitting and prediction capabilities [19–22]. This was attributed to the limitation of RSM to quadratic functions only unlike ANN, which can handle a broader range of functions and find relationships between independent and dependent variables with no prior specific mathematical equation or function [19]. ANN learns by example, where a data set is used for building the model termed training data and then the efficiency of established model is checked against new data termed testing data. Previous studies using ANN focused mainly on investigating the process parameters affecting particle size and examined a limited number of polymers without relating polymer properties to the obtained particle size [16–18]. In these cases, the developed models were used to characterize and optimize the factors affecting the NPs preparation process. However, thorough evaluation of the prediction power of these models was not the primary focus in these studies, as the test data were relatively small and confined to the training range of the model. Here, ANN was utilized to develop a mathematical model capable of predicting the particle size and PDI of polymeric nanoparticles manufactured from a larger choice of pharmaceutical polymers with various properties. In order to achieve this goal, polymer properties affecting particle size and PDI were precisely identified, quantified and then used as an input for the model. Afterwards, the model was tested comprehensively against data located inside and outside the borders used to train it. Furthermore, the evaluation of the established model involved completely new polymers, which were not included in the training data.

2. Materials and Methods 2.1. Materials Ethyl cellulose (EC) with an ethoxy content of 48-49.5% but different molecular weights (MWs) and consequently viscosity grades (Ethocel® standard 4, 7, 10 and 45 premium) was a kind donation from Colorcon (Dartford, England). Acid terminated poly(lactic acid) (PLA) (Purasorb PDL 02A) was a gift from Purac Biomaterials (Gorinchem, The Netherlands). Poly(vinyl acetate) (PVAc) (Vinnapas B17 special) was kindly granted by Wacker Chemie AG (Burghausen, Germany). Ammonio Methacrylate Copolymer, Type B (Eudragit® RS PO) was a kind sample from Evonik (Darmstadt, Germany). Poly(vinyl alcohol) (PVA) (Poval® 40–88) was a gift from Kuraray (Frankfurt, Germany). Acid terminated poly(DL-lactide-co-glycolide) (PLGA) 50:50 of different MWs and viscosities (Resomer® RG 502 H MW 7,000-17,000, Resomer® RG 503 H MW 24,000-38,000 and Resomer® RG 504 H MW 38,000-54,000) and ester terminated PLGA 50:50 (Resomer® RG 505 MW 54,000-69,000) were purchased from Evonik (Darmstadt, Germany). Poly(ε-caprolactone) (PCL) (Mn 10000 and Mn 45000) were purchased from Sigma-Aldrich (Steinheim, Germany). All other chemicals were of analytical grade or equivalent purity (For further details on the characteristics of EC and PLGA polymer types see supplementary materials Tables 1 and 2). 3

2.2. Preparation of polymeric nanoparticles Polymeric nanoparticles were prepared using the emulsification solvent evaporation method [23] replacing dichloromethane with ethyl acetate. Briefly, 100 mg of the respective polymer was dissolved in ethyl acetate to form the organic phase, while the aqueous phase was composed of PVA in different concentrations (0.05-1.5%). The aqueous phase was added to the organic phase and the mixture was ultrasonicated using a probe ultrasonicator (Sonoplus HD 2200, Bandelin, Berlin, Germany) at an amplitude of 50% for 3 minutes. Preliminary experiments revealed that varying the emulsification energy input did not significantly affect neither the particle size nor PDI (Data not shown). After emulsification, the organic solvent was evaporated using a rotary evaporator (Rotavapor RE 120, Büchi, Flawil Switzerland) under reduced pressure to form the polymeric NPs. The volume of the aqueous phase was kept constant at 10 ml while the volume of ethyl acetate, in which the polymer was dissolved, was changed (2-9 ml) altering the solvent to water ratio (S:W) to elucidate its effect on the particle size.

2.3. Particle size and PDI of polymeric nanoparticles The particle size and PDI of the nanoparticles were determined by dynamic light scattering technique (Nanopartica SZ-100, Horiba, Kyoto, Japan) at a fixed angle of 90° at 25 °C using 1.5 ml polymethyl methacrylate cuvettes. The samples were diluted with distilled water before measuring to avoid multiple scattering.

2.4. Determination of polymer solutions viscosity Viscosity of polymer solutions in ethyl acetate was measured using a rotation viscometer (Haake Rheostress 1, Thermo Fisher Scientific, Karlsruhe, Germany). 100 mg of the respective polymer was dissolved in 5.5 ml of ethyl acetate (1.81% w/v) and its viscosity was measured at shear rate of 100 s-1 at 20 °C.

2.5. Determination of polymer contact angle The static contact angle (θc) of different polymers was measured by the drop shape analysis technique (DSA100, Krüss, Germany). Firstly, polymer films were fabricated using the solvent casting method [24]. The polymer was dissolved in ethyl acetate to simulate the NPs preparation procedure and then the solution was cast on a glass slide and placed in a hood overnight at room temperature to evaporate the organic solvent. To ensure complete drying, the glass slides were further dried under vacuum at 25 °C overnight (VDL23, Binder, Germany). Secondly, the θ c between the obtained dried polymer films and water was determined using sessile drop method at room temperature. A constant volume of water (8 μl) was deposited on the surface of the polymer film and the contact angle value was calculated from the recorded droplet image using sessile drop fitting method.

2.6. Interfacial tension measurements Interfacial tension (IFT) between water and either pure or polymer-containing ethyl acetate was also determined using drop shape analysis technique (DSA100, Krüss, Germany) applying pendant drop method. A water drop was formed through a needle of diameter 1.8 mm in the organic phase, which was placed in an optical glass cuvette (40 × 40 × 30 mm). IFT was then calculated from the recorded droplet image of the suspended water drop in ethyl acetate.

4

2.7. Artificial neural network model A typical ANN consists of three connected layers; input layer composed of the independent factors, output layer represented by the responses and a hidden layer(s) in-between made of a certain number of nodes connecting the input layer to the output one. The input layer had 5 factors; viscosity of polymer solutions in ethyl acetate, θc between the polymer film and water, IFT between ethyl acetate polymer solutions and water, concentration of PVA and finally S:W. The output layer had 2 responses; particle size and PDI of the polymeric NPs. There was one hidden layer with 10 nodes (Figure 1). ANN was built using Visual Gene Developer 1.7 build 763 [25]. A set composed of 24 experimental data was used for building it (Supplementary Material Table 3). It was selected to cover all of the polymer properties affecting the particle size and PDI of NPs. Ammonio methacrylate copolymer and EC represented polymers with surface-active properties, while PLA, different MWs of EC and PLGA were utilized to represent diverse viscosities and contact angles. The set was randomly split into training (85%) and validation (15%) data. The testing data were divided into three major groups; the first included polymers used in the training phase, the second was represented by polymers new to the network but closely related to those used in first group and finally the third group contained polymers with which the network was completely unfamiliar. The model was trained using a feed forward neural network with a standard back propagation learning algorithm [26] using hyperbolic tangent as the transfer function. The sum of error was calculated using the following equation: 𝑁t 𝑁o

𝑆𝑢𝑚 𝑜𝑓 𝑒𝑟𝑟𝑜𝑟 = ∑ ∑(𝑌𝑎 − 𝑌p)2

(1)

where Nt is the total number of training data sets, No is the total number of output variables, Ya is the actual value of the output variable and Yp is the predicted value of the output variable. To avoid overtraining of the network which may lower its prediction capabilities, the target sum of error was adjusted to 0.01. To test the network efficiency, the coefficient of determination (R2), root mean square error (RMSE) and percentage bias [27] were calculated using the equations mentioned in supplementary data. The contribution of each factor to the particle size and PDI was calculated using the connection weights method [28] using the following equation [29]: (2)

𝐶ik = |∑ 𝑎ij × 𝑏jk|

where aij and bjk represents the weights of the connections between ith input node and jth hidden node and between jth hidden node and kth output node, respectively. Then the relative impact of each input was determined by dividing the contribution of each factor by the sum of the contribution of all factors.

2.8. Statistical analysis Data were compared using a one-way analysis of variance (ANOVA) followed by Tukey– Kramer multiple comparisons test using GraphPad Prism 5 (GraphPad Software, CA, USA). Surface plots have been generated using a demo version of TableCurve 3D version 4 (Systat Software, CA, USA).

5

Figure 1: A diagram of the artificial neural network design representing the components of the input, hidden and output layers.

3. Results 3.1. Nanoparticle formulation parameters The viscosities of all polymers dissolved in ethyl acetate was measured at a fixed concentration of 1.81% (w/v) to obtain comparable values (Figure 2). Results revealed that there was a simultaneous increase of viscosity, as a consequence of higher EC and PLGA molecular weights. This was accompanied by a significant increase (P S:W (Figure 8).

Figure 8: Pie chart showing the relative impact of each factor on the particle size and polydispersity index of nanoparticles. PVA: Poly(vinyl alcohol), S:W: Solvent to water ratio, IFT: Interfacial tension, θc: Contact angle and η: Viscosity. Table 2: Statistical evaluation of the artificial neural network model for the particle size and polydispersity index Particle Size

Polydispersity Index

Training Validation Testing Data Data Data

All Data

Training Validation Testing Data Data Data

All Data

R2

0.994

0.995

0.966

0.981

0.992

0.996

0.625

0.854

RMSE

6.770

15.964

11.939

10.282

0.012

0.025

0.058

0.042

% Bias

2

4

6

4

8

17

41

24

11

4. Discussion Particle size is a critical parameter for the biological performance of NPs, where small differences can have a significant impact. Moreover, high PDI values indicate a broad size distribution presenting difficulty in concluding a relationship between biological effects and certain particle size [30]. Therefore, developing a mathematical model capable of predicting particle size and PDI from a pharmaceutical polymer of choice based on the respective properties, would reduce the formulation efforts enormously during the design of NPs with a specific particle size. A large majority of polymeric NPs are prepared by using water-insoluble hydrophobic polymers. Accordingly, the commonly-used emulsification solvent evaporation technique was chosen for NPs preparation because of its simplicity and low purification requirement compared to other methods [31]. This limited the solvent choice to water-immiscible and partially miscible solvents. Chlorinated solvents like dichloromethane and chloroform are among the most frequently used solvents. However, according to the ICH guideline for residual solvents Q3C(R6), both solvents belong to class 2 and should be limited in the pharmaceutical industry because of their toxicological profile and health hazards. This shifted the focus in the past years towards ethyl acetate as a better alternative. It is a class 3 solvent with lower toxicity and still is able to dissolve many pharmaceutical polymers. Nonetheless, although this model was based on ethyl acetate, it can be also extended to combine other solvents, where a similar behavior is expected, however, this was beyond the scope of this study since the polymer properties was the main focus. Viscosity of the organic phase directly affects the produced particle size of polymeric NPs, where higher viscosity results in larger particles. This is attributed to droplet breakdown resistance during the emulsification step, lowering the efficiency of particle size reduction by ultra-sonication [32]. Factors controlling organic phase viscosity include solvent-polymer interactions, polymer concentration and molecular weight. Good solvents for a certain polymer usually display higher viscosity than poor ones. This is based on the fact that better solvent-polymer interaction unfolds polymer chains and entangles solvent molecules [33]. Among the investigated polymers, EC solutions in ethyl acetate were the most viscous due to its better solvation leading to chain expansion more than the other polymers, which were comparatively coiled. Polymer chain expansion creates gaps within the polymer structure, entrapping the solvent, hindering its flow and subsequently increasing the viscosity [34]. Similar observations were noticed for PCL in dichloromethane against PLGA and Eudragit [35]. Moreover, increasing either polymer concentration [36,37] or molecular weight [34,38,39] produced larger NPs as a consequence of higher viscosity [34,36] resulting from the increased number [38] and length of polymer chains, respectively. This is in line with the results obtained here, as the MW of EC or PLGA increased, solutions viscosity became higher and led to larger NPs with lower PDI. Polymer hydrophobicity depends on its molecular structure. The ability of the polymer film to interact with water, during contact angle measurements, depends on the presence of hydrophilic groups on the polymer surface and their flexibility to orient themselves towards water molecules. Polyester polymers commonly used for drug delivery include PCL, PLA and PLGA. From their chemical structure, it can be postulated that PCL is more hydrophobic (as confirmed by measuring θc) due to the lower oxygen:carbon ratio. For PLGA, hydrophobicity depends on two factors; whether it is acid or ester terminated and ratio of the lactide moiety to the glycolide one. Ester terminated PLGA 505 had a higher θc than acid terminated PLGA 502 H, 503 H and 504 H. Thus, the carboxylic group enhances polymer-water interactions, lowering the contact angle and reducing polymer hydrophobicity. On the other hand, polymer MW did not affect hydrophobicity, as all acid terminated PLGA, although of different MW, had the same θc. This suggests that the terminal group and not the molecular weight of PLGA is the dominant factor affecting polymer hydrophobicity. 12

Since lactic acid is more hydrophobic than glycolic acid due to its extra methyl group, therefore as the lactide ratio increases PLGA hydrophobicity rises [40]. Applying the same principle PLA should be more hydrophobic than PLGA, where acid terminated PLA had a larger θ c than acid terminated PLGA. PVAc and methacrylate copolymers are characterized by the presence of the ester group as a side chain and not direclty integrated in the backbone of the polymer like polyesters. This provides the ester group more flexibility and potentially allows for more intense interaction with water molecules. This was reflected in the θc, where both had lower values than polyester polymers. PVAc was even less hydrophobic because of its higher oxygen: carbon ratio. EC exhibited a θc lower than polyesters which may be attributed to the ethoxy content of 48 - 49.5% explaining its lower hydrophobicity. Moreover, more hydrophobic polymers usually produce larger NPs. In accordance to our findings, previous studies showed that PCL produced larger NPs than PLGA [35,41]. The same was also observed for the more hydrophobic ester-terminated PLGA against its acid-terminated counterpart [42]. This is attributed to the polymer affinity to water, where the more hydrophobic polymers have lower affinity to the aqueous medium and therefore tend to lower the surface area in contact with water opting for larger particles. Therefore, higher surfactant concentration will be needed to stabilize the surface and get the same particle size obtained by the less hydrophobic ones. Whether the polymer of choice possesses surface-active properties or not, is another important character affecting the size of particles produced by different polymers. Smaller particles were produced by EC due to its surface-active properties, where it acts as a polymeric emulsifier, adsorbs at the organic/aqueous interface lowering the IFT and stabilizing the particle surface. Moreover, EC was reported earlier to lower the IFT and stabilize ethyl acetate/water emulsions [43]. Similarly, ammonio methacrylate copolymers are also surface-active and were successfully used in preparing surfactant-free NPs [44]. Owing to their positively charged quaternary groups, they were adsorbed at the interface and formed an electrical double layer stabilizing the interface [44]. The higher the emulsifying properties of the polymer, as detected by the IFT reduction, the smaller the obtained NPs. This is evident, where Eudragit RS PO produced smaller NPs than EC. Increasing the PVA concentration decreased the particle size because of the availability of more surfactant molecules, which stabilized the expanded polymer/water interface of smaller emulsion droplets. Furthermore, increasing surfactant concentration lowers PDI to a certain limit, after which PDI will eventually increase due to the formation of small and large NPs populations [43]. On the other hand, increasing the S:W was accompanied by larger particles and wider distribution in accordance with previous findings [37] and in contrast to others [45]. This may be referred to the influence of the larger volume of organic solvent involved in the production of the initial emulsion causing; net shear stress reduction [46], PVA dilution and larger organic/aqueous interface formation. The ANN mathematical model was very efficient as displayed from the R 2, RMSE and % bias values. The established model demonstrated very good predicting abilities for the seen data and test data. The polymers used to test the predictive capabilities of the network were carefully selected to examine the network thoroughly. The first group of formulations represented polymers (EC and PLGA) to which the network was already exposed in the training phase. For each polymer; there were preparations within the PVA% range (0.1 to 1% for EC 4 and 0.3 to 1% for PLGA 502 H) introduced to the network and preparations outside it. The second group represented new polymers but closely related to the ones used to train the network. These included different molecular weight of EC (EC 7) and PLGA (PLGA 503 H, ester terminated PLGA 505). The third group represented polymers with which the network was completely unfamiliar. These included; PVAc and PCL as examples of less and more hydrophobic polymers respectively with contact 13

angle values located outside the range used for training the network. Against all testing formulations, the model successfully predicted the particle size with minor error demonstrating its efficiency. However, it was less successful for PDI where the error was relatively higher than particle size. This is attributed to the smaller numeral values of PDI data compared to particle size. Accordingly, minor deviations from the practical PDI values appear as high error values. Moreover, PDI measurements usually suffer from less reproducibility than particle size making its predictions much more difficult. Furthermore, the connection weights method was used to calculate the contribution of each input to the output [47] and arrange them according to their relative impact. Surface activity, represented by both PVA% and surface-active properties of the polymer, was found to be the main contributor to the particle size as stabilizing the interface between the hydrophobic polymer chains and the hydrophilic water molecules, is the major driving force for NPs formation. S:W and polymer viscosity had a comparable impact, while the influence of the contact angle was limited which means that altering polymer hydrophobicity will cause only a minor shift in the particle size. For PDI, polymer viscosity was the second most influential factor after surfactant concentration. This indicates that increasing viscosity of polymer solutions will probably produce NPs with narrower size distribution due to reduction of particle collisions avoiding aggregation and coalescence. Generally, the results described in this study indicate the extensive power of ANN in finding relationships between various factors and building a well-performing predictive model. This opens the door for further models capable of predicting the influence of other parameters on the characteristics of NPs. Employing such machine-learning techniques for predicting the zeta potential of NPs, the influence of different preparation methods on NPs physico-chemical properties or the proper type and concentration of surfactant needed to achieve a certain particle size will be definitely more complicated, but worth the investigations.

Conclusion Through artificial neural network, a mathematical model was successfully built capable of predicting the particle size and polydispersity index of polymeric nanoparticles manufactured from a large choice of pharmaceutical polymers. This was accomplished by identifying polymer properties affecting the particle size namely; surface activity, viscosity and hydrophobicity. This study opens the door for predicting the properties of nanoparticles in silico saving time and effort.

Acknowledgments John Youshia would like to disclose financial support from the Egyptian Ministry of Higher Education and the German Academic Exchange Service (Deutsche Akademische Austauschdienst, DAAD) (91527629).

Declaration of Interest The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

14

[1] [2]

[3] [4] [5] [6]

[7] [8]

[9] [10] [11] [12] [13]

[14] [15] [16]

[17]

[18]

[19]

W. Park, K. Na, Advances in the synthesis and application of nanoparticles for drug delivery, Wiley.Interdiscip.Rev.Nanomed.Nanobiotechnol. 7 (2015) 494–508. C. Saraiva, C. Praça, R. Ferreira, T. Santos, L. Ferreira, L. Bernardino, Nanoparticle-mediated brain drug delivery: Overcoming blood-brain barrier to treat neurodegenerative diseases, J. Control. Release Off. J. Control. Release Soc. 235 (2016) 34–47. doi:10.1016/j.jconrel.2016.05.044. M.M. Abdel-Mottaleb, B. Moulari, A. Beduneau, Y. Pellequer, A. Lamprecht, Nanoparticles enhance therapeutic outcome in inflamed skin therapy, Eur.J.Pharm.Biopharm. 82 (2012) 151–157. J. Youshia, A.O. Kamel, S.A. El, S. Mansour, Design of cationic nanostructured heterolipid matrices for ocular delivery of methazolamide, Int.J.Nanomedicine. 7 (2012) 2483–2496. H. Maeda, The enhanced permeability and retention (EPR) effect in tumor vasculature: the key role of tumor-selective macromolecular drug targeting, AdvEnzyme Regul. 41 (2001) 189–207. Y. Matsumura, H. Maeda, A new concept for macromolecular therapeutics in cancer chemotherapy: mechanism of tumoritropic accumulation of proteins and the antitumor agent smancs, Cancer Res. 46 (1986) 6387–6392. A. Lamprecht, IBD: selective nanoparticle adhesion can enhance colitis therapy, Nat.Rev.Gastroenterol.Hepatol. 7 (2010) 311–312. P.I. Siafaka, N. Üstündağ Okur, E. Karavas, D.N. Bikiaris, Surface Modified Multifunctional and Stimuli Responsive Nanoparticles for Drug Targeting: Current Status and Uses, Int. J. Mol. Sci. 17 (2016) 1440. doi:10.3390/ijms17091440. C. He, L. Yin, C. Tang, C. Yin, Size-dependent absorption mechanism of polymeric nanoparticles for oral delivery of protein drugs, Biomaterials. 33 (2012) 8569–8578. D. Peer, J.M. Karp, S. Hong, O.C. Farokhzad, R. Margalit, R. Langer, Nanocarriers as an emerging platform for cancer therapy, Nat.Nanotechnol. 2 (2007) 751–760. J. Youshia, A. Lamprecht, Size-dependent nanoparticulate drug delivery in inflammatory bowel diseases, Expert. Deliv. 13 (2016) 281–294. L. Wu, J. Zhang, W. Watanabe, Physical and chemical stability of drug nanoparticles, Adv. Drug Deliv. Rev. 63 (2011) 456–469. doi:10.1016/j.addr.2011.02.001. S. Honary, P. Ebrahimi, R. Hadianamrei, Optimization of particle size and encapsulation efficiency of vancomycin nanoparticles by response surface methodology, Pharm. Dev. Technol. 19 (2014) 987– 998. doi:10.3109/10837450.2013.846375. D. Neumann, C. Merkwirth, A. Lamprecht, Nanoparticle design characterized by in silico preparation parameter prediction using ensemble models, J.Pharm.Sci. 99 (2010) 1982–1996. A.A. Metwally, R.M. Hathout, Computer-Assisted Drug Formulation Design: Novel Approach in Drug Delivery, Mol.Pharm. 12 (2015) 2800–2810. E. Esmaeilzadeh-Gharedaghi, M.A. Faramarzi, M.A. Amini, N.A. Rouholamini, S.M. Rezayat, A. Amani, Effects of processing parameters on particle size of ultrasound prepared chitosan nanoparticles: an Artificial Neural Networks Study, Pharm.Dev.Technol. 17 (2012) 638–647. R.A. Hashad, R.A.H. Ishak, S. Fahmy, S. Mansour, A.S. Geneidi, Chitosan-tripolyphosphate nanoparticles: Optimization of formulation parameters for improving process yield at a novel pH using artificial neural networks, Int. J. Biol. Macromol. 86 (2016) 50–58. doi:10.1016/j.ijbiomac.2016.01.042. K.S. Shalaby, M.E. Soliman, L. Casettari, G. Bonacucina, M. Cespi, G.F. Palmieri, O.A. Sammour, A.A. El Shamy, Determination of factors controlling the particle size and entrapment efficiency of noscapine in PEG/PLA nanoparticles using artificial neural networks, Int. J. Nanomedicine. 9 (2014) 4953–4964. doi:10.2147/IJN.S68737. D. Bas, I.H. Boyaci, Modeling and optimization II: Comparison of estimation capabilities of response surface methodology with artificial neural networks in a biochemical reaction, J. Food Eng. 78 (2007) 846–854. 15

[20] K.M. Desai, S.A. Survase, P.S. Saudagar, S.S. Lele, R.S. Singhal, Comparison of artificial neural network (ANN) and response surface methodology (RSM) in fermentation media optimization: Case study of fermentative production of scleroglucan, Biochem. Eng. J. 41 (2008) 266–273. doi:10.1016/j.bej.2008.05.009. [21] Y. Li, M.R. Abbaspour, P.V. Grootendorst, A.M. Rauth, X.Y. Wu, Optimization of controlled release nanoparticle formulation of verapamil hydrochloride using artificial neural networks with genetic algorithm and response surface methodology, Eur. J. Pharm. Biopharm. 94 (2015) 170–179. doi:10.1016/j.ejpb.2015.04.028. [22] M.R. Zaki, J. Varshosaz, M. Fathi, Preparation of agar nanospheres: Comparison of response surface and artificial neural network modeling by a genetic algorithm approach, Carbohydr. Polym. 122 (2015) 314–320. doi:10.1016/j.carbpol.2014.12.031. [23] A. Lamprecht, Y. Bouligand, J.P. Benoit, New lipid nanocapsules exhibit sustained release properties for amiodarone, JControl Release. 84 (2002) 59–68. [24] Z.G. Tang, R.A. Black, J.M. Curran, J.A. Hunt, N.P. Rhodes, D.F. Williams, Surface properties and biocompatibility of solvent-cast poly[ε-caprolactone] films, Biomaterials. 25 (2004) 4741–4748. doi:10.1016/j.biomaterials.2003.12.003. [25] S.-K. Jung, K. McDonald, Visual gene developer: a fully programmable bioinformatics software for synthetic gene optimization, BMC Bioinformatics. 12 (2011) 340. doi:10.1186/1471-2105-12-340. [26] D.E. Rumelhart, G.E. Hinton, R.J. Williams, Learning representations by back-propagating errors, Nature. 323 (1986) 533–536. doi:10.1038/323533a0. [27] S.M. Abdel-Hafez, R.M. Hathout, O.A. Sammour, Towards better modeling of chitosan nanoparticles production: screening different factors and comparing two experimental designs, Int. J. Biol. Macromol. 64 (2014) 334–340. doi:10.1016/j.ijbiomac.2013.11.041. [28] J.D. Olden, D.A. Jackson, Illuminating the black box: a randomization approach for understanding variable contributions in artificial neural networks, Ecol. Model. 154 (2002) 135–150. doi:10.1016/S0304-3800(02)00064-9. [29] T. Hattori, S. Kito, Analysis of factors controlling catalytic activity by neural network, Adv. Catal. Catal. Mater. Energy Environ. Prot. 10th Jpn.-Korea Symp. Catal. Held Shimane Prefect. Assem. Hall Matsue Jpn. 10--12 May 2005. 111 (2006) 328–332. doi:10.1016/j.cattod.2005.10.044. [30] M. Gaumet, A. Vargas, R. Gurny, F. Delie, Nanoparticles for drug delivery: The need for precision in reporting particle size parameters, Eur. J. Pharm. Biopharm. 69 (2008) 1–9. doi:10.1016/j.ejpb.2007.08.001. [31] C. Pinto Reis, R.J. Neufeld, Ribeiro António J., F. Veiga, Nanoencapsulation I. Methods for preparation of drug-loaded polymeric nanoparticles, Nanomedicine Nanotechnol. Biol. Med. 2 (2006) 8–21. doi:10.1016/j.nano.2005.12.003. [32] S. Galindo-Rodriguez, E. Allémann, H. Fessi, E. Doelker, Physicochemical parameters associated with nanoparticle formation in the salting-out, emulsification-diffusion, and nanoprecipitation methods, Pharm. Res. 21 (2004) 1428–1439. [33] K.S. Gandhi, M.C. Williams, Solvent effects on the viscosity of moderately concentrated polymer solutions, J. Polym. Sci. Part C Polym. Symp. 35 (1971) 211–234. doi:10.1002/polc.5070350117. [34] S. Desgouilles, C. Vauthier, D. Bazile, J. Vacus, J.-L. Grossiord, M. Veillard, P. Couvreur, The Design of Nanoparticles Obtained by Solvent Evaporation:  A Comprehensive Study, Langmuir. 19 (2003) 9504–9510. doi:10.1021/la034999q. [35] Y.V. Chernysheva, V.G. Babak, N.R. Kildeeva, F. Boury, J.P. Benoit, N. Ubrich, P. Maincent, Effect of the type of hydrophobic polymers on the size of nanoparticles obtained by emulsification–solvent evaporation, Mendeleev Commun. 13 (2003) 65–67. doi:10.1070/MC2003v013n02ABEH001690. [36] S. Bohrey, V. Chourasiya, A. Pandey, Polymeric nanoparticles containing diazepam: preparation, optimization, characterization, in-vitro drug release and release kinetic study, Nano Converg. 3 (2016) 1–7. 16

[37] N. Sharma, P. Madan, S. Lin, Effect of process and formulation variables on the preparation of parenteral paclitaxel-loaded biodegradable polymeric nanoparticles: A co-surfactant study, Asian J. Pharm. Sci. 11 (2016) 404–416. [38] X. Song, Y. Zhao, S. Hou, F. Xu, R. Zhao, J. He, Z. Cai, Y. Li, Q. Chen, Dual agents loaded PLGA nanoparticles: Systematic study of particle size and drug entrapment efficiency, Eur. J. Pharm. Biopharm. 69 (2008) 445–453. doi:10.1016/j.ejpb.2008.01.013. [39] G. Mittal, D.K. Sahana, V. Bhardwaj, M.N.V. Ravi Kumar, Estradiol loaded PLGA nanoparticles for oral administration: Effect of polymer molecular weight and copolymer composition on release behavior in vitro and in vivo, J. Controlled Release. 119 (2007) 77–85. doi:10.1016/j.jconrel.2007.01.016. [40] H.K. Makadia, S.J. Siegel, Poly Lactic-co-Glycolic Acid (PLGA) as Biodegradable Controlled Drug Delivery Carrier, Polymers. 3 (2011) 1377–1397. doi:10.3390/polym3031377. [41] M. Snehalatha, K. Venugopal, R.N. Saha, Etoposide-Loaded PLGA and PCL Nanoparticles I: Preparation and Effect of Formulation Variables, Drug Deliv. 15 (2008) 267–275. doi:10.1080/10717540802174662. [42] Y.N. Konan, R. Gurny, E. Allémann, Preparation and characterization of sterile and freeze-dried sub200 nm nanoparticles, Int. J. Pharm. 233 (2002) 239–252. doi:10.1016/S0378-5173(01)00944-9. [43] P. Wachsmann, A. Lamprecht, Ethylcellulose nanoparticles with bimodal size distribution as precursors for the production of very small nanoparticles, Drug Dev. Ind. Pharm. 41 (2015) 1165– 1171. doi:10.3109/03639045.2014.935393. [44] C.A. Nguyen, Y.N. Konan-Kouakou, E. Allemann, E. Doelker, D. Quintanar-Guerrero, H. Fessi, R. Gurny, Preparation of surfactant-free nanoparticles of methacrylic acid copolymers used for film coating, AAPS.PharmSciTech. 7 (2006) 63. [45] R.M. Mainardes, R.C. Evangelista, PLGA nanoparticles containing praziquantel: effect of formulation variables on size distribution, Int. J. Pharm. 290 (2005) 137–144. doi:10.1016/j.ijpharm.2004.11.027. [46] X. Song, Y. Zhao, W. Wu, Y. Bi, Z. Cai, Q. Chen, Y. Li, S. Hou, PLGA nanoparticles simultaneously loaded with vincristine sulfate and verapamil hydrochloride: Systematic study of particle size and drug entrapment efficiency, Int. J. Pharm. 350 (2008) 320–329. doi:10.1016/j.ijpharm.2007.08.034. [47] J.D. Olden, M.K. Joy, R.G. Death, An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data, Ecol. Model. 178 (2004) 389–397.

17