http://informahealthcare.com/phd ISSN: 1083-7450 (print), 1097-9867 (electronic) Pharm Dev Technol, Early Online: 1–6 ! 2014 Informa UK Ltd. DOI: 10.3109/10837450.2014.910809
RESEARCH ARTICLE
Pharmaceutical Development and Technology Downloaded from informahealthcare.com by University of Otago on 07/16/15 For personal use only.
Comparison of novel granulated pellet-containing tablets and traditional pellet-containing tablets by artificial neural networks Ying Huang1, Qinghe Yao2, Chune Zhu1, Xuan Zhang1, Lingzhen Qin1, Qinruo Wang3, Xin Pan1,4, and Chuanbin Wu1,4 1
School of Pharmaceutical Sciences, Sun Yat-Sen University, Guangzhou, P.R. China, 2School of Engineering, Sun Yat-Sen University, Guangzhou, P.R. China, 3School of Automation, Guangdong University of Technology, Guangzhou, P.R. China, and 4Research and Development Center of Pharmaceutical Engineering, Sun Yat-Sen University, Guangzhou, P.R. China Abstract
Keywords
Novel granulated pellets technique was adopted to prepare granulated pellet-containing tablets (GPCT). GPCT and traditional pellet-containing tablets (PCT) were prepared according to 29 formulations devised by the Design Expert 7.0, with doxycycline hydrochloride as model drug, blends of Eudragit FS 30D and Eudragit L 30D-55 as coating materials, for the comparison study to confirm the superiority of GPCT during compaction. Eudragit FS 30D content, coating weight gain, tablet hardness and pellet size were chosen as influential factors to investigate the properties and drug release behavior of tablets. The correlation coefficients between the experimental values and the predicted values by artificial neural networks (ANNs) for PCT and GPCT were 0.9474 and 0.9843, respectively, indicating the excellent prediction of ANNs. The similarity factors (f2) for release profiles of GPCT and the corresponding original pellets were higher than those of PCT, suggesting that the excipient layer of granulated pellets absorbed the compressing force and protected the integrity of coating films during compaction.
Artificial neural networks, granulated pellet-containing tablets, pellet-containing tablets, pellet compaction, similarity factor (f2)
Introduction As a sustained/controlled release dosage form, pellet-containing tablet (PCT) was first proposed in the 1980s, combining the advantages of both single-unit dosage form and multiple-unit dosage form1. It can rapidly disintegrate into pellets after oral administration, and prevent high local drug concentration in gastrointestinal tract. Compared with the widely used hard gelatin capsules, PCT offer advantages such as smaller volume, easier to swallow, greater patients’ compliance and unique dividable property2,3. Though PCT holds many advantages, it is a challenge to produce qualified tablets4, especially to form a flexible polymeric coating film which is able to withstand the compaction force and maintain integrity during tableting. On the other hand, to achieve a non-segregated mixture of coated pellets and excipients to ensure a constant tablet weight and drug content uniformity is quite essential and difficult. Certainly, these problems have constrained the development of PCT. Besides, many factors may influence the quality of PCT5,6. For example, small size pellets usually lead to minor deformation during compaction, but smaller coating thickness due to their larger specific surface area at a certain coating weight gain. The former is in favor of the integrity of coating film during compaction but the latter is not5. In the meantime, the coating thickness can affect
Address for correspondence: Xin Pan, School of Pharmaceutical Sciences, Sun Yat-Sen University, Guangzhou 510006, P.R. China. Tel: +862039943427. Fax: +862039943115. E-mail: pxin_1385@ 163.com
History Received 27 January 2014 Revised 21 March 2014 Accepted 23 March 2014 Published online 24 April 2014
the drug release rate directly. The composition of coating materials is also important. Eudragit FS 30D, one of the commonly used film-forming materials due to its good ductility, dissolves in medium with pH47.0, and thus is a colon-specific coating material6,7. Eudragit L 30D-55 dissolves in medium with pH45.5, but is not suitable for preparing PCT due to its friability6. However, the combinations of Eudragit FS 30D and Eudragit L 30D-55 are frequently used for coating owing to their more appropriate mechanical properties and adjustable drug release rate8. Additionally, compaction force has significant impact on the quality of PCT, as excessive compaction force could lead to an undesirable disintegration time and rupture of pellets, resulting in change of drug release9. In previous study2, a novel granulated pellets technique was invented by our group, and the tablets prepared by this method were correspondingly named as granulated pellet-containing tablets (GPCT). The difference between GPCT and the traditional PCT is containing granulated pellets instead of coated pellets in tablets, while the granulated pellets were produced from coated pellets loading with excipients. The excipients layer can disintegrate quickly on contacting with water, without changing the release properties of drug in original coated pellets. In addition, the excipient layer can increase the roughness of the pellets surface to much closer to that of cushioning granules, and effectively solve the segregation problem of pellets and granules2. Theoretically, the excipient layer has a porous structure which could protect the coating film during compaction, and this hypothesis could be demonstrated in this study. The above-mentioned factors which could affect the preparation of PCT may also affect the preparation of GPCT. With the
Pharmaceutical Development and Technology Downloaded from informahealthcare.com by University of Otago on 07/16/15 For personal use only.
2
Y. Huang et al.
possible interactions between these factors, it is quite difficult to describe the impactions using a simple model. In recent years, Artificial Neural Networks (ANNs), the information processing systems based on imitating the structure and functions of human brain10, have been successfully introduced in pharmaceutics11–13 for the prediction of solid-water distribution coefficients, optimization of formulation and process parameters, etc. This is because not only ANNs have high adaptability, generalization capabilities, precision and fitting degree in dealing with multiple factors and multiple responses14, but also the predictive ability of these open systems can be improved by selecting different training functions, algorithms and training samples. Since it is difficult to retain the drug release profiles of the pellets after compaction, the present work investigated the use of ANNs by comparing the integrity of the pellets in PCT and GPCT to estimate the protection of the excipient layer brought by the novel granulated pellets technique. The similarity factor (f2) proposed by Moore and Flanner15 for the comparison of dissolution profiles which has been adopted in SUPAC IR guidelines was introduced as output in ANNs to visualize the dissolution profiles of coated pellets and PCT/GPCT.
Materials and methods Materials Doxycycline hydrochloride was used as model drug (DOXY, 99.2%, Suhai Co., Ltd., China). Eudragit FS 30D and Eudragit L 30D-55 were donated by EvonikRo¨hm GmbH (Darmstadt, Germany). Triethyl citrate (TEC, Jingqiu Co., Ltd., China) and glycerylmonostearate (GMS, Xilong Co., Ltd., China) were used as plasticizer and anti-sticking agent, respectively. Microcrystalline cellulose (MCC, Hopetop Co., Ltd., China) and Polyplasdone XL (PVPP, ISP, Hayne, N), were used to prepare pellet-containing granules. Polyvinylpyrrolidone K-30 and K-90 (PVP K-30, PVP K-90, ISP, Hayne, NJ) were used as adherent during granulation, and Methocel E3 (HPMC-E3, Colorcon, North Wales, PA) was used for subcoating. Besides, hydrochloric acid (AR, Guangzhou Co., Ltd., China), potassium acid phthalate (AR, Tianjin Damao Co., Ltd., China) and sodium dihydrogen phosphate (CR, Guangzhou Co., Ltd., China) were used for dissolution test. Preparation of coated pellets, PCT and GPCT Preparation of coated pellets Microcrystalline cellulose pellets of 0.12–0.18, 0.18–0.25, 0.25– 0.30 mm, respectively, were prepared using a centrifugal granulation machine (BZJ-360MII, Beijing Long March Tianmin Hi-Tech Co., Ltd., China) and collected after mechanical sieving (Haver EML digital plus, Germany). Then they were layered in a fluidized bed coater (Glatt GPCG-1.1, Germany, bottom spraying) and sprayed with drug solution containing 32% (w/v) doxycycline hydrochloride and 7.5% (w/v) PVP K-30. Then the drug-layered pellets were sub-coated with 6% (w/v) HPMC-E3 to prevent the potential drug/enteric polymer interaction and/or immigration of drug into the enteric coating. Subsequently, the sub-coated pellets were coated with a combination of Eudragit FS 30D and Eudragit L 30D-55 at different ratios, 10 30% coating weight gain. TEC (5% based on dry polymer weight, w/w) and GMS (5% based on dry polymer weight, w/w) were added in the coating suspension as plasticizer and anti-sticking agent, respectively, and the solid concentration of the suspension was adjusted to 20% (w/w) with distilled water. Finally, the coated pellets were sieved mechanically (0.18–0.25, 0.25–0.30, 0.30–0.42 mm, respectively), collected and stored in a desiccator at room temperature.
Pharm Dev Technol, Early Online: 1–6
Preparation of cushioning granules Microcrystalline cellulose (400 g) and PVPP were mixed at a ratio of 19:1 and blended in a fluidized bed granulator (Glatt GPCG-1.1, top spraying) for 5 min under the air flow of 15 m3/h. Then, 240 g of PVP K-30 binder solution (10%, w/w) was added at the rate of 10 g/min. Subsequently, the wet mass was dried at 60 C in an oven for 12 h and sieved to collect different size granules (0.18–0.25, 0.25–0.30, 0.30–0.42 mm, respectively) for preparing tablets. Preparation of PCT The above-obtained cushioning granules and coated pellets in the same size range were blended at a ratio of 1:1, and then compressed to 10 mm diameter tablets using an essential single punch tablet press (ZYD-8, Shanghai Fareast Pharmaceutical Machinery General Factory, China) at a compression speed of 20 ± 2 rpm. The compression force was adjusted to achieve the certain tablet hardness according to the formulations in Table 1. Preparation of GPCT Coated pellets (150 g) were granulated with the same amount of MCC and PVPP combination (weight ratio 9:1) using a centrifugal granulation machine (BZJ-360MII, Beijing Long March Tianmin Hi-Tech Co., Ltd., China). The adherent agent PVP K-90 dissolved (5% w/w) in pH 1.0 hydrochloric acid solution to prevent the coating polymer dissolving. Subsequently, the wet mass was dried at 60 C in an oven for 12 h and sieved to collect different size granules (0.18–0.25, 0.25–0.30, 0.30–0.42 mm, respectively). The same size range of the obtained granulated pellets and cushioning granules were blended at a ratio of 1:1 and compressed to 10 mm diameter tablets. Table 1. Training formulations and prediction formulations for ANNs.
Formulation 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
Pellet size (mm)
Eudragit FS 30D content (%)
Coating weight gain (%)
Tablet hardness (N)
0.21 0.21 0.21 0.21 0.21 0.21 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.35 0.35 0.35 0.35 0.35 0.35
50.0 50.0 50.0 50.0 33.3 66.7 33.3 66.7 33.3 66.7 33.3 33.3 66.7 66.7 50.0 50.0 50.0 50.0 50.0 50.0 50.0 50.0 50.0 50.0 50.0 50.0 50.0 33.3 66.7
30 30 15 45 30 30 15 15 45 45 30 30 30 30 15 15 45 45 30 30 30 30 30 30 30 15 45 30 30
20 40 30 30 30 30 30 30 30 30 20 40 20 40 20 40 20 40 30 30 30 30 30 20 40 30 30 30 30
Comparison of GPCT and PCT
DOI: 10.3109/10837450.2014.910809
Pharmaceutical Development and Technology Downloaded from informahealthcare.com by University of Otago on 07/16/15 For personal use only.
Drug release Dissolution profiles were generated by testing PCT, GPCT or coated pellets containing equal amount of doxycycline hydrochloride prepared from a same formulation for comparison following USP 35 Method II (paddle). The dissolution medium (900 ml) was hydrochloric acid (pH 1.0) for the first 2 h, and then replaced by potassium acid phthalate buffer (pH 5.5) for 4 h at the agitation of 50 rpm. Samples were withdrawn at predetermined time points and analyzed by UV spectrophotometer (TU-1901, Beijing Purkinje General Instrument Co., Ltd. China) at l ¼ 267 nm (acidic medium) and l ¼ 345 nm (potassium acid phthalate buffer medium), respectively. The similarity factor f2 was induced to compare the release profiles of the tablets and the corresponding original pellets for evaluating changes in drug release behaviors. The f2 value 50 represents the similarity of the two profiles, and the more f2 approaching to 100, the greater equivalence between the two profiles. The f2 value can be calculated as: 8" 9 #0:5 < = n 1X 2 ðRt Tt Þ 100 ð1Þ f2 ¼ 50lg 1 þ : ; n t1 where n is the number of sampling points, Rt and Tt are the dissolved percentage of the reference and the test product at each time point. Design of formulations Design Expert 7.0. (StatEase Inc., Minneapolis, MN) Statistical Software was used for formulation design. Based on the previous study, four independent variables, pellet size, content of Eudragit FS 30D in coating polymer, coating weight gain and tablet hardness were selected as key factors. The 29 formulations were devised according to a three-level response surface design with five center points per block. A three-layer feed-forward back-propagation (BP) network consisting of 10 nodes was constructed through net ¼ newff (minmax(p), [10,1], f‘logsig’, ‘purelin’, ‘trainlm’), incorporating the four independent variables in the input layer, 10 nodes in the hidden layer and the one response variable the similarity factor f2 in the output layer to fit to the data set. The f2 values could indicate the integrity of coating films in tablets by comparing the drug releases profiles of tablets and the original pellets. MATLAB version 7.9.0 (Mathworks Inc., Natick, MA) was employed in the development and training of ANNs. The pellet size was the median diameter measured by laser scattering, using scirocco dry dispersion unit at a feed pressure of 3.0 bars (Mastersizer 2000, Malvern Instruments, Malvern, UK).
Results Prediction ability of ANNs for GPCT and PCT Dissolution profiles of 29 formulations (Table 1) were obtained for PCT, GPCT and their corresponding original coated pellets, and the f2 values for the tablets to the coated pellets and PCT to GPCT were calculated for each formulation (Table 2). Formulations 1–23 were inputted as training ones for developing a suitable ANNs model, and the rest were validating ones used to estimate the prediction ability of ANNs. In Table 2, the experimental values were calculated from the drug release profiles and the predicted values were calculated by the constructed ANNs. The correlation of experimental and predicted f2 values for PCT and GPCT in comparison with the coated pellets was shown
3
Table 2. f2 Values of drug release profiles for PCT and GPCT in comparison with the coated pellets. f2 for PCT Formulation 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
f2 for GPCT
Experimental values
Predicted values
Experimental values
Predicted values
50.66 33.96 40.02 58.61 37.02 62.48 32.28 40.83 40.26 67.25 41.96 30.17 65.25 50.05 48.64 33.63 68.91 39.47 45.75 45.75 45.75 45.75 45.75 52.20 41.11 32.45 46.64 31.81 50.04
52.66 31.05 40.12 52.64 35.69 60.55 31.55 41.25 41.59 66.58 43.55 32.24 64.55 48.26 45.58 32.55 67.56 40.69 43.56 43.15 43.18 43.15 43.55 49.55 42.48 30.71 48.91 32.79 48.44
66.86 50.97 45.32 70.83 37.04 74.88 38.73 53.10 52.31 89.20 45.31 30.06 84.78 73.62 42.47 38.87 68.69 58.03 70.64 70.67 70.93 70.66 70.68 60.62 51.03 48.61 58.61 42.24 73.72
65.10 49.16 41.45 68.40 37.58 76.89 35.45 54.78 53.65 88.02 43.56 31.54 82.89 73.65 43.52 37.12 67.45 59.52 70.66 70.64 70.65 70.66 70.66 58.45 51.02 48.51 58.56 38.54 72.65
Figure 1. Release properties of original pellets, GPCT and PCT (particle size: 0.28 mm, Eudragit FS 30D content: 66.7%, coating weight gain: 30%, tablet hardness: 20 N) in pH 1.0 hydrochloric acid for the first 2 h, and then pH 5.5 potassium acid phthalate buffer for following 2 h (paddle, 50 rpm).
in Figure 1, and all the results showed a good linear relationship between the experimental and predicted values (Figure 2). The correlation coefficients for GPCT were 0.9901 and 0.9843 for the training and validating formulations, respectively, better than the PCT formulations, which showed correlation coefficients of 0.9705 and 0.9474 for the training and validating formulations.
Pharmaceutical Development and Technology Downloaded from informahealthcare.com by University of Otago on 07/16/15 For personal use only.
4
Y. Huang et al.
Pharm Dev Technol, Early Online: 1–6
Figure 2. Correlation between the experimental and predicted f2 values for tablets and coated pellets. (A) PCT training formulations, (B) PCT validating formulations, (C) GPCT training formulations and (D) GPCT validating formulations.
Figure 3. Influence of (A) Eudragit FS 30D content and pellet size, (B) Eudragit FS 30D content and coating weight gain and (C) Eudragit FS 30D content and tablet hardness on f2 values for PCT (the underlying layer) and GPCT (the upper layer).
Influence of each variable on drug release for PCT and GPCT Figure 3 represented the experimental f2 values of PCT and GPCT in a three-dimensional diagram plotted by the 29 tablet formulations under different circumstances.
For both PCT and GPCT, the f2 values increased with the increase of Eudragit FS 30D content and coating weight gain, and decreased with the increase of tablet hardness. Besides, the f2 values increased with the increasing of pellet size, and reached the maximum when the pellets size was 0.28 mm, followed by
Comparison of GPCT and PCT
DOI: 10.3109/10837450.2014.910809
decrease of f2 value with the further increase of pellet size. Always, the f2 values of GPCT were higher than those of the corresponding PCT formulations, and the differences between their f2 values could be influenced by the variables.
Pharmaceutical Development and Technology Downloaded from informahealthcare.com by University of Otago on 07/16/15 For personal use only.
Discussions Generally, the dissolved drug concentration at each time point was chosen as output values in ANNs to obtain the corresponding drug dissolution curve16–18. While in this article, the f2 values of dosage forms in comparison with the original coated pellets were selected as output values, which made the results more simple and intuitive. The two drug release profiles could be considered similar as the f2 value is 50, therefore, the formulations with f2 values not less than 50 were discussed. Artificial neural networks models were constructed incorporating four independent variables in the input layer and the one response variable in the output layer and 10 nodes in the hidden layer, with similar method used in our previous research19. Due to their high adaptability and generalization capability in dealing with multiple factors and responses14,20, the ANNs showed excellent predictability for both GPCT and PCT dosage forms. The minimum correlation value between the predicted and the experimental f2 values for the tablets was 0.9474, indicating the predicted values were very close to the experimental values. For both GPCT and PCT dosage forms, the correlation values for training formulations were better than those for validating ones, that is because the ANNs model was constructed by the training ones, and consequently the linear dependency for training formulations seemed better than that for the validating ones. Theoretically, the prediction accuracy for GPCT would be lower than that for PCT, since the pellet-containing granulation technique was added in preparation, and the additional process might increase the vagueness of prediction. Interestingly, the prediction linear dependency for GPCT was actually much better than that for PCT. The constructed ANNs model showed more accurate predictability for GPCT due to the well dispersion of pellets in tablets brought by the pellet-containing granulation technique2 to export more precise result. Eudragit FS 30D and Eudragit L 30D-55 could be dissolved at pH over 5.5 and 7.0, respectively. Due to the excellent elongation of Eudragit FS 30D, it is commonly used to modify the mechanical properties of Eudragit L 30D-55 films21. Meanwhile, the addition of Eudragit FS 30D would not change the enteric property of the film, but only adjust the release rate of drug for its insolubility at pH 5.5. The combination coating films formed in this study seemed flexible enough to keep integrity during compaction. The f2 values for PCT and GPCT formulations were 50 when Eudragit FS 30D content attained a certain ratio. At a certain coating level, the larger size pellets with smaller specific surface area would gain thicker coating film, which is benefit for the coating film maintaining integrity. However, the larger size pellets had to withstand greater deformation and higher pressure during compaction, which could result in film rupture22. Therefore, for both PCT and GPCT systems, the f2 values fluctuated with the pellet size and reached the maximum when the pellet size was 0.28 mm. Coating level has a direct impact on the coating thickness, undoubtedly the f2 values increased with the increasing of coating level. Compaction force is also a very influential factor, as excessive force could lead to rupture of coating film and inadequate force could result in unqualified tablet hardness and friability. Hence, the minimum tablet hardness was set as 20 N to ensure the tablet quality. The f2 values increased with the decrease of tablet hardness, indicating the abrasion and rupture of coating films could be significantly diminished by at a lower compress force.
5
Three-dimensional diagram showed the f2 values of GPCT were higher than the corresponding formulations of PCT, suggesting the drug release profiles of GPCT were more similar to those of the corresponding original pellets. This could be attributed to the excipient layer, which was laid onto the coated pellets and provided protection during tablet compaction. It was indicated that by using this new GPCT technique, qualified pellet tablets could be obtained much easier.
Conclusions Artificial neural networks have been used to effectively predict the similarity factor values of drug release profiles for PCT and GPCT in comparison with the corresponding original pellets, and ANNs model was better suited to GPCT. The excipient layer of pellets in GPCT would benefit the distribution of pellets and protect the integrity of coating film during compaction, and therefore, the applicability of pellet tablets could be extended by the GPCT technique.
Acknowledgements The authors acknowledge EvonikRo¨hm GmbH and ISP for material donation.
Declaration of interest The work was supported by National Natural Science Foundation of China (81202476) and National Key Technology R&D Program (2012BAI35B02).
References 1. Abdul S, Chandewar AV, Jaiswal SB. A flexible technology for modified-release drugs: multiple-unit pellet system (MUPS). J Control Rel 2010;147:2–16. 2. Pan X, Chen M, Han K, et al. Novel compaction techniques with pellet-containing granules. Eur J Pharm Biopharm 2010;75: 436–442. 3. Ozarde Y, Kuchekar B. Multiple-unit-pellet system (MUPS): a novel approch for drug delivery. Drug Invent Today 2012;4:603–609. 4. Dreu R, Ilic I, Srcic S. Development of a multiple-unit tablet containing enteric-coated pellets. Pharm Dev Technol 2011;16: 118–126. 5. Haaser M, Karrout Y, Velghe C, et al. Application of terahertz pulsed imaging to analyse film coating characteristics of sustainedrelease coated pellets. Int J Pharm 2013;457:521–526. 6. Hosseini A, Ko¨rber M, Bodmeier R. Direct compression of cushionlayered ethyl cellulose-coated extended release pellets into rapidly disintegrating tablets without changes in the release profile. Int J Pharm 2013;457:503–509. 7. Tripathi JK, Reddy PA, Reddy GT, et al. Multi unit pelletzation system (MUPS) an account. Global J Pharm Res 2012;1:112–128. 8. Thakral S, Thakral NK, Majumdar DK. EudragitÕ : a technology evaluation. Expert Opin Drug Deliv 2012;10:1–19. 9. Patel MM, Amin AF. Formulation and development of release modulated colon targeted system of meloxicam for potential application in the prophylaxis of colorectal cancer. Drug Deliv 2011;18:281–293. 10. Varshosaz J, Emami J, Tavakoli N, Minaiyan M, Rahmani N, Dorkoosh F. Development and evaluation of a novel pellet-based tablet system for potential colon delivery of budesonide. J Drug Deliv 2012;2012:Article ID 905191, 7 pages. 11. Debunne A, Vervaet C, Mangelings D, Remon JP. Compaction of enteric-coated pellets: influence of formulation and process parameters on tablet properties and in vivo evaluation. Eur J Pharm Sci 2004;22:305–314. 12. Ko¨ppen M, Kasabov N, Coghill G. Advances in neuro-information processing: 15th International Conference, ICONIP 2008, Auckland, New Zealand, November 25–28, 2008, Revised Selected Papers: Springer; 2009. 13. Barron L, Havel J, Purcell M, et al. Predicting sorption of pharmaceuticals and personal care products onto soil and digested sludge using artificial neural networks. Analyst 2009;134:663–670.
6
Y. Huang et al.
Pharmaceutical Development and Technology Downloaded from informahealthcare.com by University of Otago on 07/16/15 For personal use only.
14. Asadi H, Rostamizadeh K, Salari D, Hamidi M. Preparation of biodegradable nanoparticles of tri-block PLA-PEG-PLA copolymer and determination of factors controlling the particle size using artificial neural network. J Microencapsul 2011;28: 406–416. 15. Chansanroj K, Petrovic´ J, Ibric´ S, Betz G. Drug release control and system understanding of sucrose esters matrix tablets by artificial neural networks. Eur J Pharm Sci 2011;44:321–331. 16. Huang Y. Advances in artificial neural networks–methodological development and application. Algorithms 2009;2: 973–1007. 17. Costa P, Sousa Lobo JM. Modeling and comparison of dissolution profiles. Eur J Pharm Sci 2001;13:123–133. 18. Barmpalexis P, Kanaze FI, Kachrimanis K, Georgarakis E. Artificial neural networks in the optimization of a nimodipine
Pharm Dev Technol, Early Online: 1–6
19. 20. 21.
22.
controlled release tablet formulation. Eur J Pharm Biopharm 2010; 74:316–323. Wang Z, He Z, Zhang L, et al. Optimization of a doxycycline hydroxypropyl-b-cyclodextrin inclusion complex based on computational modeling. Acta Pharm Sin B 2013;3:130–139. Chaibva F, Burton M, Walker RB. Optimization of salbutamol sulfate dissolution from sustained release matrix formulations using an artificial neural network. Pharmaceutics 2010;2:182–198. Ibric´ S, Jovanovic´ M, Djuric´ Z, et al. Artificial neural networks in the modeling and optimization of aspirin extended release tablets with Eudragit L 100 as matrix substance. AAPS PharmSciTech 2003;4:62–70. Pan X, Liu Y, Xiao Y, et al. Preparation and quality evaluation of a novel multi-unit dosage form by compaction of enteric pellets. Chin J New Drugs 2010;19:313–318.
RESEARCH ARTICLE
Pharmaceutical Development and Technology Downloaded from informahealthcare.com by University of Otago on 07/16/15 For personal use only.
Comparison of novel granulated pellet-containing tablets and traditional pellet-containing tablets by artificial neural networks Ying Huang1, Qinghe Yao2, Chune Zhu1, Xuan Zhang1, Lingzhen Qin1, Qinruo Wang3, Xin Pan1,4, and Chuanbin Wu1,4 1
School of Pharmaceutical Sciences, Sun Yat-Sen University, Guangzhou, P.R. China, 2School of Engineering, Sun Yat-Sen University, Guangzhou, P.R. China, 3School of Automation, Guangdong University of Technology, Guangzhou, P.R. China, and 4Research and Development Center of Pharmaceutical Engineering, Sun Yat-Sen University, Guangzhou, P.R. China Abstract
Keywords
Novel granulated pellets technique was adopted to prepare granulated pellet-containing tablets (GPCT). GPCT and traditional pellet-containing tablets (PCT) were prepared according to 29 formulations devised by the Design Expert 7.0, with doxycycline hydrochloride as model drug, blends of Eudragit FS 30D and Eudragit L 30D-55 as coating materials, for the comparison study to confirm the superiority of GPCT during compaction. Eudragit FS 30D content, coating weight gain, tablet hardness and pellet size were chosen as influential factors to investigate the properties and drug release behavior of tablets. The correlation coefficients between the experimental values and the predicted values by artificial neural networks (ANNs) for PCT and GPCT were 0.9474 and 0.9843, respectively, indicating the excellent prediction of ANNs. The similarity factors (f2) for release profiles of GPCT and the corresponding original pellets were higher than those of PCT, suggesting that the excipient layer of granulated pellets absorbed the compressing force and protected the integrity of coating films during compaction.
Artificial neural networks, granulated pellet-containing tablets, pellet-containing tablets, pellet compaction, similarity factor (f2)
Introduction As a sustained/controlled release dosage form, pellet-containing tablet (PCT) was first proposed in the 1980s, combining the advantages of both single-unit dosage form and multiple-unit dosage form1. It can rapidly disintegrate into pellets after oral administration, and prevent high local drug concentration in gastrointestinal tract. Compared with the widely used hard gelatin capsules, PCT offer advantages such as smaller volume, easier to swallow, greater patients’ compliance and unique dividable property2,3. Though PCT holds many advantages, it is a challenge to produce qualified tablets4, especially to form a flexible polymeric coating film which is able to withstand the compaction force and maintain integrity during tableting. On the other hand, to achieve a non-segregated mixture of coated pellets and excipients to ensure a constant tablet weight and drug content uniformity is quite essential and difficult. Certainly, these problems have constrained the development of PCT. Besides, many factors may influence the quality of PCT5,6. For example, small size pellets usually lead to minor deformation during compaction, but smaller coating thickness due to their larger specific surface area at a certain coating weight gain. The former is in favor of the integrity of coating film during compaction but the latter is not5. In the meantime, the coating thickness can affect
Address for correspondence: Xin Pan, School of Pharmaceutical Sciences, Sun Yat-Sen University, Guangzhou 510006, P.R. China. Tel: +862039943427. Fax: +862039943115. E-mail: pxin_1385@ 163.com
History Received 27 January 2014 Revised 21 March 2014 Accepted 23 March 2014 Published online 24 April 2014
the drug release rate directly. The composition of coating materials is also important. Eudragit FS 30D, one of the commonly used film-forming materials due to its good ductility, dissolves in medium with pH47.0, and thus is a colon-specific coating material6,7. Eudragit L 30D-55 dissolves in medium with pH45.5, but is not suitable for preparing PCT due to its friability6. However, the combinations of Eudragit FS 30D and Eudragit L 30D-55 are frequently used for coating owing to their more appropriate mechanical properties and adjustable drug release rate8. Additionally, compaction force has significant impact on the quality of PCT, as excessive compaction force could lead to an undesirable disintegration time and rupture of pellets, resulting in change of drug release9. In previous study2, a novel granulated pellets technique was invented by our group, and the tablets prepared by this method were correspondingly named as granulated pellet-containing tablets (GPCT). The difference between GPCT and the traditional PCT is containing granulated pellets instead of coated pellets in tablets, while the granulated pellets were produced from coated pellets loading with excipients. The excipients layer can disintegrate quickly on contacting with water, without changing the release properties of drug in original coated pellets. In addition, the excipient layer can increase the roughness of the pellets surface to much closer to that of cushioning granules, and effectively solve the segregation problem of pellets and granules2. Theoretically, the excipient layer has a porous structure which could protect the coating film during compaction, and this hypothesis could be demonstrated in this study. The above-mentioned factors which could affect the preparation of PCT may also affect the preparation of GPCT. With the
Pharmaceutical Development and Technology Downloaded from informahealthcare.com by University of Otago on 07/16/15 For personal use only.
2
Y. Huang et al.
possible interactions between these factors, it is quite difficult to describe the impactions using a simple model. In recent years, Artificial Neural Networks (ANNs), the information processing systems based on imitating the structure and functions of human brain10, have been successfully introduced in pharmaceutics11–13 for the prediction of solid-water distribution coefficients, optimization of formulation and process parameters, etc. This is because not only ANNs have high adaptability, generalization capabilities, precision and fitting degree in dealing with multiple factors and multiple responses14, but also the predictive ability of these open systems can be improved by selecting different training functions, algorithms and training samples. Since it is difficult to retain the drug release profiles of the pellets after compaction, the present work investigated the use of ANNs by comparing the integrity of the pellets in PCT and GPCT to estimate the protection of the excipient layer brought by the novel granulated pellets technique. The similarity factor (f2) proposed by Moore and Flanner15 for the comparison of dissolution profiles which has been adopted in SUPAC IR guidelines was introduced as output in ANNs to visualize the dissolution profiles of coated pellets and PCT/GPCT.
Materials and methods Materials Doxycycline hydrochloride was used as model drug (DOXY, 99.2%, Suhai Co., Ltd., China). Eudragit FS 30D and Eudragit L 30D-55 were donated by EvonikRo¨hm GmbH (Darmstadt, Germany). Triethyl citrate (TEC, Jingqiu Co., Ltd., China) and glycerylmonostearate (GMS, Xilong Co., Ltd., China) were used as plasticizer and anti-sticking agent, respectively. Microcrystalline cellulose (MCC, Hopetop Co., Ltd., China) and Polyplasdone XL (PVPP, ISP, Hayne, N), were used to prepare pellet-containing granules. Polyvinylpyrrolidone K-30 and K-90 (PVP K-30, PVP K-90, ISP, Hayne, NJ) were used as adherent during granulation, and Methocel E3 (HPMC-E3, Colorcon, North Wales, PA) was used for subcoating. Besides, hydrochloric acid (AR, Guangzhou Co., Ltd., China), potassium acid phthalate (AR, Tianjin Damao Co., Ltd., China) and sodium dihydrogen phosphate (CR, Guangzhou Co., Ltd., China) were used for dissolution test. Preparation of coated pellets, PCT and GPCT Preparation of coated pellets Microcrystalline cellulose pellets of 0.12–0.18, 0.18–0.25, 0.25– 0.30 mm, respectively, were prepared using a centrifugal granulation machine (BZJ-360MII, Beijing Long March Tianmin Hi-Tech Co., Ltd., China) and collected after mechanical sieving (Haver EML digital plus, Germany). Then they were layered in a fluidized bed coater (Glatt GPCG-1.1, Germany, bottom spraying) and sprayed with drug solution containing 32% (w/v) doxycycline hydrochloride and 7.5% (w/v) PVP K-30. Then the drug-layered pellets were sub-coated with 6% (w/v) HPMC-E3 to prevent the potential drug/enteric polymer interaction and/or immigration of drug into the enteric coating. Subsequently, the sub-coated pellets were coated with a combination of Eudragit FS 30D and Eudragit L 30D-55 at different ratios, 10 30% coating weight gain. TEC (5% based on dry polymer weight, w/w) and GMS (5% based on dry polymer weight, w/w) were added in the coating suspension as plasticizer and anti-sticking agent, respectively, and the solid concentration of the suspension was adjusted to 20% (w/w) with distilled water. Finally, the coated pellets were sieved mechanically (0.18–0.25, 0.25–0.30, 0.30–0.42 mm, respectively), collected and stored in a desiccator at room temperature.
Pharm Dev Technol, Early Online: 1–6
Preparation of cushioning granules Microcrystalline cellulose (400 g) and PVPP were mixed at a ratio of 19:1 and blended in a fluidized bed granulator (Glatt GPCG-1.1, top spraying) for 5 min under the air flow of 15 m3/h. Then, 240 g of PVP K-30 binder solution (10%, w/w) was added at the rate of 10 g/min. Subsequently, the wet mass was dried at 60 C in an oven for 12 h and sieved to collect different size granules (0.18–0.25, 0.25–0.30, 0.30–0.42 mm, respectively) for preparing tablets. Preparation of PCT The above-obtained cushioning granules and coated pellets in the same size range were blended at a ratio of 1:1, and then compressed to 10 mm diameter tablets using an essential single punch tablet press (ZYD-8, Shanghai Fareast Pharmaceutical Machinery General Factory, China) at a compression speed of 20 ± 2 rpm. The compression force was adjusted to achieve the certain tablet hardness according to the formulations in Table 1. Preparation of GPCT Coated pellets (150 g) were granulated with the same amount of MCC and PVPP combination (weight ratio 9:1) using a centrifugal granulation machine (BZJ-360MII, Beijing Long March Tianmin Hi-Tech Co., Ltd., China). The adherent agent PVP K-90 dissolved (5% w/w) in pH 1.0 hydrochloric acid solution to prevent the coating polymer dissolving. Subsequently, the wet mass was dried at 60 C in an oven for 12 h and sieved to collect different size granules (0.18–0.25, 0.25–0.30, 0.30–0.42 mm, respectively). The same size range of the obtained granulated pellets and cushioning granules were blended at a ratio of 1:1 and compressed to 10 mm diameter tablets. Table 1. Training formulations and prediction formulations for ANNs.
Formulation 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
Pellet size (mm)
Eudragit FS 30D content (%)
Coating weight gain (%)
Tablet hardness (N)
0.21 0.21 0.21 0.21 0.21 0.21 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.35 0.35 0.35 0.35 0.35 0.35
50.0 50.0 50.0 50.0 33.3 66.7 33.3 66.7 33.3 66.7 33.3 33.3 66.7 66.7 50.0 50.0 50.0 50.0 50.0 50.0 50.0 50.0 50.0 50.0 50.0 50.0 50.0 33.3 66.7
30 30 15 45 30 30 15 15 45 45 30 30 30 30 15 15 45 45 30 30 30 30 30 30 30 15 45 30 30
20 40 30 30 30 30 30 30 30 30 20 40 20 40 20 40 20 40 30 30 30 30 30 20 40 30 30 30 30
Comparison of GPCT and PCT
DOI: 10.3109/10837450.2014.910809
Pharmaceutical Development and Technology Downloaded from informahealthcare.com by University of Otago on 07/16/15 For personal use only.
Drug release Dissolution profiles were generated by testing PCT, GPCT or coated pellets containing equal amount of doxycycline hydrochloride prepared from a same formulation for comparison following USP 35 Method II (paddle). The dissolution medium (900 ml) was hydrochloric acid (pH 1.0) for the first 2 h, and then replaced by potassium acid phthalate buffer (pH 5.5) for 4 h at the agitation of 50 rpm. Samples were withdrawn at predetermined time points and analyzed by UV spectrophotometer (TU-1901, Beijing Purkinje General Instrument Co., Ltd. China) at l ¼ 267 nm (acidic medium) and l ¼ 345 nm (potassium acid phthalate buffer medium), respectively. The similarity factor f2 was induced to compare the release profiles of the tablets and the corresponding original pellets for evaluating changes in drug release behaviors. The f2 value 50 represents the similarity of the two profiles, and the more f2 approaching to 100, the greater equivalence between the two profiles. The f2 value can be calculated as: 8" 9 #0:5 < = n 1X 2 ðRt Tt Þ 100 ð1Þ f2 ¼ 50lg 1 þ : ; n t1 where n is the number of sampling points, Rt and Tt are the dissolved percentage of the reference and the test product at each time point. Design of formulations Design Expert 7.0. (StatEase Inc., Minneapolis, MN) Statistical Software was used for formulation design. Based on the previous study, four independent variables, pellet size, content of Eudragit FS 30D in coating polymer, coating weight gain and tablet hardness were selected as key factors. The 29 formulations were devised according to a three-level response surface design with five center points per block. A three-layer feed-forward back-propagation (BP) network consisting of 10 nodes was constructed through net ¼ newff (minmax(p), [10,1], f‘logsig’, ‘purelin’, ‘trainlm’), incorporating the four independent variables in the input layer, 10 nodes in the hidden layer and the one response variable the similarity factor f2 in the output layer to fit to the data set. The f2 values could indicate the integrity of coating films in tablets by comparing the drug releases profiles of tablets and the original pellets. MATLAB version 7.9.0 (Mathworks Inc., Natick, MA) was employed in the development and training of ANNs. The pellet size was the median diameter measured by laser scattering, using scirocco dry dispersion unit at a feed pressure of 3.0 bars (Mastersizer 2000, Malvern Instruments, Malvern, UK).
Results Prediction ability of ANNs for GPCT and PCT Dissolution profiles of 29 formulations (Table 1) were obtained for PCT, GPCT and their corresponding original coated pellets, and the f2 values for the tablets to the coated pellets and PCT to GPCT were calculated for each formulation (Table 2). Formulations 1–23 were inputted as training ones for developing a suitable ANNs model, and the rest were validating ones used to estimate the prediction ability of ANNs. In Table 2, the experimental values were calculated from the drug release profiles and the predicted values were calculated by the constructed ANNs. The correlation of experimental and predicted f2 values for PCT and GPCT in comparison with the coated pellets was shown
3
Table 2. f2 Values of drug release profiles for PCT and GPCT in comparison with the coated pellets. f2 for PCT Formulation 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
f2 for GPCT
Experimental values
Predicted values
Experimental values
Predicted values
50.66 33.96 40.02 58.61 37.02 62.48 32.28 40.83 40.26 67.25 41.96 30.17 65.25 50.05 48.64 33.63 68.91 39.47 45.75 45.75 45.75 45.75 45.75 52.20 41.11 32.45 46.64 31.81 50.04
52.66 31.05 40.12 52.64 35.69 60.55 31.55 41.25 41.59 66.58 43.55 32.24 64.55 48.26 45.58 32.55 67.56 40.69 43.56 43.15 43.18 43.15 43.55 49.55 42.48 30.71 48.91 32.79 48.44
66.86 50.97 45.32 70.83 37.04 74.88 38.73 53.10 52.31 89.20 45.31 30.06 84.78 73.62 42.47 38.87 68.69 58.03 70.64 70.67 70.93 70.66 70.68 60.62 51.03 48.61 58.61 42.24 73.72
65.10 49.16 41.45 68.40 37.58 76.89 35.45 54.78 53.65 88.02 43.56 31.54 82.89 73.65 43.52 37.12 67.45 59.52 70.66 70.64 70.65 70.66 70.66 58.45 51.02 48.51 58.56 38.54 72.65
Figure 1. Release properties of original pellets, GPCT and PCT (particle size: 0.28 mm, Eudragit FS 30D content: 66.7%, coating weight gain: 30%, tablet hardness: 20 N) in pH 1.0 hydrochloric acid for the first 2 h, and then pH 5.5 potassium acid phthalate buffer for following 2 h (paddle, 50 rpm).
in Figure 1, and all the results showed a good linear relationship between the experimental and predicted values (Figure 2). The correlation coefficients for GPCT were 0.9901 and 0.9843 for the training and validating formulations, respectively, better than the PCT formulations, which showed correlation coefficients of 0.9705 and 0.9474 for the training and validating formulations.
Pharmaceutical Development and Technology Downloaded from informahealthcare.com by University of Otago on 07/16/15 For personal use only.
4
Y. Huang et al.
Pharm Dev Technol, Early Online: 1–6
Figure 2. Correlation between the experimental and predicted f2 values for tablets and coated pellets. (A) PCT training formulations, (B) PCT validating formulations, (C) GPCT training formulations and (D) GPCT validating formulations.
Figure 3. Influence of (A) Eudragit FS 30D content and pellet size, (B) Eudragit FS 30D content and coating weight gain and (C) Eudragit FS 30D content and tablet hardness on f2 values for PCT (the underlying layer) and GPCT (the upper layer).
Influence of each variable on drug release for PCT and GPCT Figure 3 represented the experimental f2 values of PCT and GPCT in a three-dimensional diagram plotted by the 29 tablet formulations under different circumstances.
For both PCT and GPCT, the f2 values increased with the increase of Eudragit FS 30D content and coating weight gain, and decreased with the increase of tablet hardness. Besides, the f2 values increased with the increasing of pellet size, and reached the maximum when the pellets size was 0.28 mm, followed by
Comparison of GPCT and PCT
DOI: 10.3109/10837450.2014.910809
decrease of f2 value with the further increase of pellet size. Always, the f2 values of GPCT were higher than those of the corresponding PCT formulations, and the differences between their f2 values could be influenced by the variables.
Pharmaceutical Development and Technology Downloaded from informahealthcare.com by University of Otago on 07/16/15 For personal use only.
Discussions Generally, the dissolved drug concentration at each time point was chosen as output values in ANNs to obtain the corresponding drug dissolution curve16–18. While in this article, the f2 values of dosage forms in comparison with the original coated pellets were selected as output values, which made the results more simple and intuitive. The two drug release profiles could be considered similar as the f2 value is 50, therefore, the formulations with f2 values not less than 50 were discussed. Artificial neural networks models were constructed incorporating four independent variables in the input layer and the one response variable in the output layer and 10 nodes in the hidden layer, with similar method used in our previous research19. Due to their high adaptability and generalization capability in dealing with multiple factors and responses14,20, the ANNs showed excellent predictability for both GPCT and PCT dosage forms. The minimum correlation value between the predicted and the experimental f2 values for the tablets was 0.9474, indicating the predicted values were very close to the experimental values. For both GPCT and PCT dosage forms, the correlation values for training formulations were better than those for validating ones, that is because the ANNs model was constructed by the training ones, and consequently the linear dependency for training formulations seemed better than that for the validating ones. Theoretically, the prediction accuracy for GPCT would be lower than that for PCT, since the pellet-containing granulation technique was added in preparation, and the additional process might increase the vagueness of prediction. Interestingly, the prediction linear dependency for GPCT was actually much better than that for PCT. The constructed ANNs model showed more accurate predictability for GPCT due to the well dispersion of pellets in tablets brought by the pellet-containing granulation technique2 to export more precise result. Eudragit FS 30D and Eudragit L 30D-55 could be dissolved at pH over 5.5 and 7.0, respectively. Due to the excellent elongation of Eudragit FS 30D, it is commonly used to modify the mechanical properties of Eudragit L 30D-55 films21. Meanwhile, the addition of Eudragit FS 30D would not change the enteric property of the film, but only adjust the release rate of drug for its insolubility at pH 5.5. The combination coating films formed in this study seemed flexible enough to keep integrity during compaction. The f2 values for PCT and GPCT formulations were 50 when Eudragit FS 30D content attained a certain ratio. At a certain coating level, the larger size pellets with smaller specific surface area would gain thicker coating film, which is benefit for the coating film maintaining integrity. However, the larger size pellets had to withstand greater deformation and higher pressure during compaction, which could result in film rupture22. Therefore, for both PCT and GPCT systems, the f2 values fluctuated with the pellet size and reached the maximum when the pellet size was 0.28 mm. Coating level has a direct impact on the coating thickness, undoubtedly the f2 values increased with the increasing of coating level. Compaction force is also a very influential factor, as excessive force could lead to rupture of coating film and inadequate force could result in unqualified tablet hardness and friability. Hence, the minimum tablet hardness was set as 20 N to ensure the tablet quality. The f2 values increased with the decrease of tablet hardness, indicating the abrasion and rupture of coating films could be significantly diminished by at a lower compress force.
5
Three-dimensional diagram showed the f2 values of GPCT were higher than the corresponding formulations of PCT, suggesting the drug release profiles of GPCT were more similar to those of the corresponding original pellets. This could be attributed to the excipient layer, which was laid onto the coated pellets and provided protection during tablet compaction. It was indicated that by using this new GPCT technique, qualified pellet tablets could be obtained much easier.
Conclusions Artificial neural networks have been used to effectively predict the similarity factor values of drug release profiles for PCT and GPCT in comparison with the corresponding original pellets, and ANNs model was better suited to GPCT. The excipient layer of pellets in GPCT would benefit the distribution of pellets and protect the integrity of coating film during compaction, and therefore, the applicability of pellet tablets could be extended by the GPCT technique.
Acknowledgements The authors acknowledge EvonikRo¨hm GmbH and ISP for material donation.
Declaration of interest The work was supported by National Natural Science Foundation of China (81202476) and National Key Technology R&D Program (2012BAI35B02).
References 1. Abdul S, Chandewar AV, Jaiswal SB. A flexible technology for modified-release drugs: multiple-unit pellet system (MUPS). J Control Rel 2010;147:2–16. 2. Pan X, Chen M, Han K, et al. Novel compaction techniques with pellet-containing granules. Eur J Pharm Biopharm 2010;75: 436–442. 3. Ozarde Y, Kuchekar B. Multiple-unit-pellet system (MUPS): a novel approch for drug delivery. Drug Invent Today 2012;4:603–609. 4. Dreu R, Ilic I, Srcic S. Development of a multiple-unit tablet containing enteric-coated pellets. Pharm Dev Technol 2011;16: 118–126. 5. Haaser M, Karrout Y, Velghe C, et al. Application of terahertz pulsed imaging to analyse film coating characteristics of sustainedrelease coated pellets. Int J Pharm 2013;457:521–526. 6. Hosseini A, Ko¨rber M, Bodmeier R. Direct compression of cushionlayered ethyl cellulose-coated extended release pellets into rapidly disintegrating tablets without changes in the release profile. Int J Pharm 2013;457:503–509. 7. Tripathi JK, Reddy PA, Reddy GT, et al. Multi unit pelletzation system (MUPS) an account. Global J Pharm Res 2012;1:112–128. 8. Thakral S, Thakral NK, Majumdar DK. EudragitÕ : a technology evaluation. Expert Opin Drug Deliv 2012;10:1–19. 9. Patel MM, Amin AF. Formulation and development of release modulated colon targeted system of meloxicam for potential application in the prophylaxis of colorectal cancer. Drug Deliv 2011;18:281–293. 10. Varshosaz J, Emami J, Tavakoli N, Minaiyan M, Rahmani N, Dorkoosh F. Development and evaluation of a novel pellet-based tablet system for potential colon delivery of budesonide. J Drug Deliv 2012;2012:Article ID 905191, 7 pages. 11. Debunne A, Vervaet C, Mangelings D, Remon JP. Compaction of enteric-coated pellets: influence of formulation and process parameters on tablet properties and in vivo evaluation. Eur J Pharm Sci 2004;22:305–314. 12. Ko¨ppen M, Kasabov N, Coghill G. Advances in neuro-information processing: 15th International Conference, ICONIP 2008, Auckland, New Zealand, November 25–28, 2008, Revised Selected Papers: Springer; 2009. 13. Barron L, Havel J, Purcell M, et al. Predicting sorption of pharmaceuticals and personal care products onto soil and digested sludge using artificial neural networks. Analyst 2009;134:663–670.
6
Y. Huang et al.
Pharmaceutical Development and Technology Downloaded from informahealthcare.com by University of Otago on 07/16/15 For personal use only.
14. Asadi H, Rostamizadeh K, Salari D, Hamidi M. Preparation of biodegradable nanoparticles of tri-block PLA-PEG-PLA copolymer and determination of factors controlling the particle size using artificial neural network. J Microencapsul 2011;28: 406–416. 15. Chansanroj K, Petrovic´ J, Ibric´ S, Betz G. Drug release control and system understanding of sucrose esters matrix tablets by artificial neural networks. Eur J Pharm Sci 2011;44:321–331. 16. Huang Y. Advances in artificial neural networks–methodological development and application. Algorithms 2009;2: 973–1007. 17. Costa P, Sousa Lobo JM. Modeling and comparison of dissolution profiles. Eur J Pharm Sci 2001;13:123–133. 18. Barmpalexis P, Kanaze FI, Kachrimanis K, Georgarakis E. Artificial neural networks in the optimization of a nimodipine
Pharm Dev Technol, Early Online: 1–6
19. 20. 21.
22.
controlled release tablet formulation. Eur J Pharm Biopharm 2010; 74:316–323. Wang Z, He Z, Zhang L, et al. Optimization of a doxycycline hydroxypropyl-b-cyclodextrin inclusion complex based on computational modeling. Acta Pharm Sin B 2013;3:130–139. Chaibva F, Burton M, Walker RB. Optimization of salbutamol sulfate dissolution from sustained release matrix formulations using an artificial neural network. Pharmaceutics 2010;2:182–198. Ibric´ S, Jovanovic´ M, Djuric´ Z, et al. Artificial neural networks in the modeling and optimization of aspirin extended release tablets with Eudragit L 100 as matrix substance. AAPS PharmSciTech 2003;4:62–70. Pan X, Liu Y, Xiao Y, et al. Preparation and quality evaluation of a novel multi-unit dosage form by compaction of enteric pellets. Chin J New Drugs 2010;19:313–318.
