Quality-by-design in pharmaceutical development: From current perspectives to practical applications

1. R. A. Lionberger, S. L. Lee, L. M. Lee, A. Raw and L. X. Yu, Quality by design: Concepts for ANDAs, AAPS J.10 (2008) 268–276; https://doi.org/10.1208/s12248-008-9026-710.1208/s12248-008-9026-7275137618465252Search in Google Scholar

2. K. Pramod, M. Tahir, N. Charoo, S. Ansari and J. Ali, Pharmaceutical product development: A quality by design approach, Int. J. Pharm. Investig.6 (2016) 129–138; https://doi.org/10.4103/2230-973x.18735010.4103/2230-973X.187350499112127606256Search in Google Scholar

3. International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use, ICH Harmonised Tripartite Guideline, Pharmaceutical Development, Q8(R2), Current Step 5 version, August 2009; https://www.ema.europa.eu/en/documents/scientific-guideline/international-conference-harmonisation-technical-requirements-registration-pharmaceuticals-human-use_en-11.pdf; access date: October 26, 2020Search in Google Scholar

4. International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use, ICH Harmonised Tripartite Guideline, Quality Risk Management, Q9, Current Step 5 version, August 2009; https://www.ema.europa.eu/en/documents/scientific-guideline/international-conference-harmonisation-technical-requirements-registration-pharmaceuticals-human-use_en-11.pdf; access date: October 26, 2020Search in Google Scholar

5. Y. Bouwman-Boer and L. Møller Andersen, Pharmaceutical Quality Systems, in Practical Pharmaceutics (Ed. Y. Bouwman-Boer, L. Møller Andersen and P. Le Brun), Springer Nature, Cham (Switzerland) 2015, pp. 769–796.10.1007/978-3-319-15814-3_35Search in Google Scholar

6. International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use, Development and manufacture of drug substances (chemical entities and biotechnological/biological entities), Q11, Current Step 3 version, September 2011; https://www.ema.europa.eu/en/documents/scientific-guideline/draft-ich-guideline-q11-development-manufacture-drug-substances-chemical-entities-biotechnological/biological-entities_en.pdf; access date: October 26, 2020Search in Google Scholar

7. International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use, Technical and regulatory considerations for pharmaceutical product lifecycle management, Q12, Current Step 5 version, January 2020; https://www.ema.europa.eu/en/documents/scientific-guideline/ich-guideline-q12-technical-regulatory-considerations-pharmaceutical-product-lifecycle-management_en.pdf; access date: October 26, 2020Search in Google Scholar

8. L. X. Yu, G. Amidon, M. A. Khan, S. W. Hoag, J. Polli, G. K. Raju and J. Woodcock, Understanding pharmaceutical quality by design, AAPS J.16 (2014) 771–783; https://doi.org/10.1208/s12248-014-9598-310.1208/s12248-014-9598-3407026224854893Search in Google Scholar

9. International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use, Continuous Manufacturing of Drug Substances and Drug Products, Q13, Final Concept Paper, November 2018; https://database.ich.org/sites/default/files/Q13_EWG_Concept_Paper.pdf; access date: October 26, 2020Search in Google Scholar

10. International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use, Analytical Procedure Development and Revision of Q2(R1) Analytical Validation Q14, Final Concept Paper, November 2018; https://database.ich.org/sites/default/files/Q2R2-Q14_EWG_Concept_Paper.pdf; access date: October 26, 2020Search in Google Scholar

11. J. Cook, M. T. Cruañes, M. Gupta, S. Riley and J. Crison, Quality-by-design: Are we there yet?, AAPS PharmSciTech15 (2014) 140–148; https://doi.org/10.1208/s12249-013-0043-110.1208/s12249-013-0043-1390915324218058Search in Google Scholar

12. H. B. Grangeia, C. Silva, S. P. Simões and M. S. Reis, Quality by design in pharmaceutical manufacturing: A systematic review of current status, challenges and future perspectives, Eur. J. Pharm. Biopharm.147 (2020) 19–37; https://doi.org/10.1016/j.ejpb.2019.12.00710.1016/j.ejpb.2019.12.00731862299Search in Google Scholar

13. A. Aloqaily, Identification of Hazards Associated with Pipelines, in Cross-Country Pipeline Risk Assessments and Mitigation Strategies (Ed. A. Aloqaily), Elsevier, Amsterdam 2018, pp. 13–40.10.1016/B978-0-12-816007-7.00002-0Search in Google Scholar

14. J. W. Vincoli, Preliminary Hazard Analysis, in Basic Guideline to System. Safety (Ed. J. W. Voncoli), 3rd Edition, Wiley, Hoboken (NJ) 2014, pp. 71–91.10.1002/9781118904589.ch6Search in Google Scholar

15. A. F. Molland, Marine Safety, in The Maritime Engineering Reference Book (Ed. A. F. Molland), Elsevier, Amsterdam 2008, pp. 784–875.10.1016/B978-0-7506-8987-8.00011-1Search in Google Scholar

16. G. Ilie and C. Ciocoiu, Application of fishbone diagram to determine the risk of an event with multiple causes, Manag. Res. Pract.2 (2010) 1–20.Search in Google Scholar

17. M. A. Barsalou, The Quality Improvement Field Guide: Achieving and Maintaining Value in Your Organization, Taylor & Francis, Oxfordshire 2016.10.1201/b19522Search in Google Scholar

18. T. Aven, Risk assessment and risk management: Review of recent advances on their foundation, Eur. J. Oper. Res.253 (2016) 1–13; https://doi.org/10.1016/j.ejor.2015.12.02310.1016/j.ejor.2015.12.023Search in Google Scholar

19. J. F. van Leeuwen, M. J. Nauta, D. de Kaste, Y. M. C. F. Odekerken-Rombouts, M. T. Oldenhof, M. J. Vredenbregt and D. M. Barends, Risk analysis by FMEA as an element of analytical validation, J. Pharm. Biomed. Anal.50 (2009) 1085–1087; https://doi.org/10.1016/j.jpba.2009.06.04910.1016/j.jpba.2009.06.04919640668Search in Google Scholar

20. M. T. Oldenhof, J. F. van Leeuwen, M. J. Nauta, D. de Kaste, Y. M. C. F. Odekerken-Rombouts, M. J. Vredenbregt, M. Weda and D. M. Barends, Consistency of FMEA used in the validation of analytical procedures, J. Pharm. Biomed. Anal.54 (2011) 592–595; https://doi.org/10.1016/j.jpba.2010.09.02410.1016/j.jpba.2010.09.02420970277Search in Google Scholar

21. R. Fahmy, R. Kona, R. Dandu, W. Xie, G. Claycamp and S. W. Hoag, Quality by design I: Application of failure mode effect analysis (FMEA) and Plackett-Burman design of experiments in the identification of “main factors” in the formulation and process design space for roller-compacted ciprofloxacin hydrochloride immediate, AAPS PharmSciTech13 (2012) 1243–1254; https://doi.org/10.1208/s12249-012-9844-x10.1208/s12249-012-9844-x351347522993122Search in Google Scholar

22. N. A. Wessiani and F. Yoshio, Failure mode effect analysis and fault tree analysis as a combined methodology in risk management, IOP Conference Series: Materials Science and Engeneering.337 (2018) Article ID 012033 (11 pages); https://doi.org/10.1088/1757-899X/337/1/01203310.1088/1757-899X/337/1/012033Search in Google Scholar

23. M. W. Averett, Fault Tree Analysis, Risk Analysis8 (1988) 463–464; https://doi.org/10.1111/j.1539-6924.1988.tb00510.x10.1111/j.1539-6924.1988.tb00510.xSearch in Google Scholar

24. S. Iurian, L. Turdean and I. Tomuta, Risk assessment and experimental design in the development of a prolonged release drug delivery system with paliperidone, Drug Des. Devel. Ther.11 (2017) 733–746; https://doi.org/10.2147/DDDT.S12532310.2147/DDDT.S125323535707328331293Search in Google Scholar

25. S. M. Mishra and B. D. Rohera, An integrated, quality by design (QbD) approach for design, development and optimization of orally disintegrating tablet formulation of carbamazepine, Pharm. Dev. Technol.22 (2017) 889–903; https://doi.org/10.1080/10837450.2016.119956610.1080/10837450.2016.119956627346282Search in Google Scholar

26. X. Zhang and C. Hu, Application of quality by design concept to develop a dual gradient elution stability-indicating method for cloxacillin forced degradation studies using combined mixture-process variable models, J. Chromatogr. A1514 (2017) 44–53; https://doi.org/10.1016/j.chroma.2017.07.06210.1016/j.chroma.2017.07.06228760604Search in Google Scholar

27. G. L. Reid, G. Cheng, D. T. Fortin, J. W. Harwood, J. E. Morgado, J. Wang and G. Xue, Reversed-phase liquid chromatographic method development in an analytical quality by design framework, J. Liq. Chromatogr. Relat. Technol.36 (2013) 2612–2638; https://doi.org/10.1080/10826076.2013.76545710.1080/10826076.2013.765457Search in Google Scholar

28. P. Borman, M. Chatfield, P. Nethercote, D. Thompson and K. Truman, The application of quality by design to analytical methods, Pharm. Technol.31 (2007) 142–152.Search in Google Scholar

29. B. Pasquini, S. Orlandini, M. Villar-Navarro, C. Caprini, M. Del Bubba, M. Douša, A. Giuffrida, R. Gotti and S. Furlanetto, Chiral capillary zone electrophoresis in enantioseparation and analysis of cinacalcet impurities: Use of Quality by Design principles in method development, J. Chromatogr. A1568 (2018) 205–213; https://doi.org/10.1016/j.chroma.2018.07.02110.1016/j.chroma.2018.07.02130005942Search in Google Scholar

30. S. Krait and G. K. E. Scriba, Quality by design-assisted development of a capillary electrophoresis method for the chiral purity determination of dexmedetomidine, Electrophoresis39 (2018) 2575–2580; https://doi.org/10.1002/elps.20180010010.1002/elps.20180010029600596Search in Google Scholar

31. L. Zhou, J. M. Socha, F. G. Vogt, S. Chen and A. S. Kord, A systematic method development strategy for water determinations in drug substance using Karl Fischer titrations, Am. Pharm. Rev.13 (2010) 74–84.Search in Google Scholar

32. L. Zhou, F. G. Vogt, P. A. Overstreet, J. T. Dougherty, J. S. Clawson and A. S. Kord, A systematic method development strategy for quantitative color measurement in drug substances, starting materials, and synthetic intermediates, J. Pharm. Innov.6 (2011) 217–231; https://doi.org/10.1007/s12247-011-9115-510.1007/s12247-011-9115-5Search in Google Scholar

33. F. G. Vogt and A. S. Kord, Development of quality-by-design analytical methods, J. Pharm. Sci.100 (2011) 797–812; https://doi.org/10.1002/jps.2232510.1002/jps.2232521280050Search in Google Scholar

34. R. Peraman, K. Bhadraya and Y. P. Reddy, Analytical quality by design: A tool for regulatory flexibility and robust analytics, Int. J. Anal. Chem.2015 (2015) Article ID 868727 (9 pages); https://doi.org/10.1155/2015/86872710.1155/2015/868727433298625722723Search in Google Scholar

35. P. Jackson, P. Borman, C. Campa, M. Chatfield, M. Godfrey, P. Hamilton, W. Hoyer, F. Norelli, R. Orr and T. Schofield, Using the analytical target profile to drive the analytical method lifecycle, Anal. Chem.91 (2019) 2577–2585; https://doi.org/10.1021/acs.analchem.8b0459610.1021/acs.analchem.8b0459630624912Search in Google Scholar

36. M. A. Gad, S. M. Amer, H. E. Zaazaa and S. A. Hassan, Strategies for stabilizing formulation and QbD assisted development of robust stability indicating method of azilsartan medoxomil/chlorthalidone, J. Pharm. Biomed. Anal.178 (2020) Article ID 112910; https://doi.org/10.1016/j.jpba.2019.11291010.1016/j.jpba.2019.11291031618701Search in Google Scholar

37. B. Kovács, L. K. Kántor, M. D. Croitoru, É. K. Kelemen, M. Obreja, E. E. Nagy, B. Székely-Szentmiklósi and Á. Gyéresi, Reversed phase HPLC for strontium ranelate: Method development and validation applying experimental design, Acta Pharm. 68 (2018) 171–183; https://doi.org/10.2478/acph-2018-001910.2478/acph-2018-001929702478Search in Google Scholar

38. A. Dispas, H. T. Avohou, P. Lebrun, P. Hubert and C. Hubert, ‘Quality by Design’ approach for the analysis of impurities in pharmaceutical drug products and drug substances, TrAC - Trends Anal. Chem.101 (2018) 24–33; https://doi.org/10.1016/j.trac.2017.10.02810.1016/j.trac.2017.10.028Search in Google Scholar

39. P. Ramalingam and B. Jahnavi, QbD Considerations for Analytical Development, in Pharmaceutical Quality by Design – Principles and Applications (Ed. S. Beg and S. Hasnain), Elsevier, Amsterdam 2019, pp. 77–108.10.1016/B978-0-12-815799-2.00005-8Search in Google Scholar

40. R. M. Ahmed, A. Ibrahim, A. E. El-Gendy and G. M. Hadad, Implementing a Quality by Design approach in chromatographic determination of some antidiabetic drugs, SF J. Pharm. Anal. Chem. 1 (2018) Article ID 1001 (9 pages).Search in Google Scholar

41. T. Tome, N. Žigart, Z. Časar and A. Obreza, Development and optimization of liquid chromatography analytical methods by using AQbD principles: Overview and recent advances, Org. Process Res. Dev.23 (2019) 1784–1802; https://doi.org/10.1021/acs.oprd.9b0023810.1021/acs.oprd.9b00238Search in Google Scholar

42. C. Saha, N. V. Gupta and R. S. Chandan, Development and validation of a UPLC-MS method for determination of atazanavir sulfate by the “analytical quality by design” approach, Acta Pharm.70 (2020) 17–33; https://doi.org/10.2478/acph-2020-000810.2478/acph-2020-000831677371Search in Google Scholar

43. R. Deidda, H. T. Avohou, R. Baronti, P. L. Davolio, B. Pasquini, M. Del Bubba, C. Hubert, P. Hubert, S. Orlandini and S. Furlanetto, Analytical quality by design: Development and control strategy for a LC method to evaluate the cannabinoids content in cannabis olive oil extracts, J. Pharm. Biomed. Anal.166 (2019) 326–335; https://doi.org/10.1016/j.jpba.2019.01.03210.1016/j.jpba.2019.01.03230685656Search in Google Scholar

44. J. Shao, W. Cao, H. Qu, J. Pan and X. Gong, A novel quality by design approach for developing an HPLC method to analyze herbal extracts: A case study of sugar content analysis, PLoS One13 (2018); e0198515; https://doi.org/10.1371/journal.pone.019851510.1371/journal.pone.0198515599323729883452Search in Google Scholar

45. A. S. K. Sankar, P. Shanmugasundaram and R. Velayudham, Quality by design-applied liquid chromatography-tandem mass spectrometry determination of enzalutamide anti-prostate cancer therapy drug in spiked plasma samples, Anal. Chem. Insights12 (2017) 1–11; https://doi.org/10.1177/117739011772677610.1177/1177390117726776557654028874884Search in Google Scholar

46. M. Deepa, K. R. Reddy and S. V. Satyanarayana, A review on quality by design approach for analytical method development, J. Pharm. Res.11 (2017) 272–277.Search in Google Scholar

47. V. Das, B. Bhairav and R. B. Saudagar, Quality by design approaches to analytical method development, Res. J. Pharm. Technol.10 (2017) 3188–3194; https://doi.org/10.5958/0974-360X.2017.00567.410.5958/0974-360X.2017.00567.4Search in Google Scholar

48. J. R. Wagner, Jr., E. M. Mount III and H. F. Giles, Jr., Extrusion: The Definitive Processing Guide and Handbook, 2^nd Edition, Elsevier, Amsterdam 2013.Search in Google Scholar

49. I. M. Fukuda, C. F. F. Pinto, C. dos Santos Moreira, A. M. Saviano and F. R. Lourenço, Design of experiments (DoE) applied to pharmaceutical and analytical quality by design (QbD), Braz. J. Pharm. Sci.54 (2018) e01006 (16 pages); https://doi.org/10.1590/s2175-9790201800000100610.1590/s2175-97902018000001006Search in Google Scholar

50. C. Croarkin and P. Tobias, NIST/SEMATECH e-handbook of Statistical Methods, http://www.itl.nist.gov/div898/handbook; last access date January 31, 2020Search in Google Scholar

51. P. K. Sahu, N. R. Ramisetti, T. Cecchi, S. Swain, C. S. Patro and J. Panda, An overview of experimental designs in HPLC method development and validation, J. Pharm. Biomed. Anal.147 (2018) 590–611; https://doi.org/10.1016/j.jpba.2017.05.00610.1016/j.jpba.2017.05.00628579052Search in Google Scholar

52. B. Sylvester, L. Tefas, L. Vlase, I. Tomuţă and A. Porfire, A Quality by Design (QbD) approach to the development of a gradient high-performance liquid chromatography for the simultaneous assay of curcuminoids and doxorubicin from long-circulating liposomes, J. Pharm. Biomed. Anal.158 (2018) 395–404; https://doi.org/10.1016/j.jpba.2018.06.01810.1016/j.jpba.2018.06.01829966945Search in Google Scholar

53. A. Tumpa, S. Mišković, Z. Stanimirović, B. Jančić-Stojanović and M. Medenica, Modeling of HILIC retention behavior with theoretical models and new spline interpolation technique, J. Chemom.31 (2017) e2910; https://doi.org/10.1002/cem.291010.1002/cem.2910Search in Google Scholar

54. E. Ferencz, B. Kovács, F. Boda, M. Foroughbakhshfasaei, É. K. Kelemen, G. Tóth and Z. I. Szabó, Simultaneous determination of chiral and achiral impurities of ivabradine on a cellulose tris(3-chloro-4-methylphenylcarbamate) chiral column using polar organic mode, J. Pharm. Biomed. Anal.177 (2020) Article ID 112851; https://doi.org/10.1016/j.jpba.2019.11285110.1016/j.jpba.2019.11285131499427Search in Google Scholar

55. B. Kovács, F. Boda, I. Fülöp, I. Székely-Szentmiklósi, É. K. Kelemen, B. Kovács-Deák and B. Székely-Szentmiklósi, HPLC method development for fampridine using Analytical Quality by Design approach, Acta Pharm.70 (2020) 465–482; https://doi.org/10.2478/acph-2020-003610.2478/acph-2020-003632412430Search in Google Scholar

56. L. Kumar, M. S. Reddy, R. S. Managuli and G. Pai K., Full factorial design for optimization, development and validation of HPLC method to determine valsartan in nanoparticles, Saudi Pharm. J.23 (2015) 549–555; https://doi.org/10.1016/j.jsps.2015.02.00110.1016/j.jsps.2015.02.001460590326594122Search in Google Scholar

57. M. Mašković, B. Jančić-Stojanović, A. Malenović, D. Ivanović and M. Medenica, Assessment of liquid chromatographic method robustness by use of Plackett-Burman design, Acta Chromatogr.22 (2010) 281–296; https://doi.org/10.1556/AChrom.22.2010.2.1010.1556/AChrom.22.2010.2.10Search in Google Scholar

58. A. Gundala, K. Prasad and B. Koganti, Application of quality by design approach in RP-HPLC method development for simultaneous estimation of saxagliptin and dapagliflozin in tablet dosage form, Braz. J. Pharm. Sci.55 (2019) e18129; https://doi.org/10.1590/s2175-9790201900021812910.1590/s2175-97902019000218129Search in Google Scholar

59. K. Gupta, Analytical Quality by Design: A mini review, Biomed. J. Sci. Tech. Res.1 (2017) 1555–1559; https://doi.org/10.26717/bjstr.2017.01.00048410.26717/BJSTR.2017.01.000484Search in Google Scholar

60. K. E. Monks, H. J. Rieger and I. Molnár, Expanding the term “Design Space” in high performance liquid chromatography (I), J. Pharm. Biomed. Anal.56 (2011) 874–879; https://doi.org/10.1016/j.jpba.2011.04.01510.1016/j.jpba.2011.04.01521893394Search in Google Scholar

61. P. Bhatt, M. Saquib Hasnain, A. K. Nayak, B. Hassan and S. Beg, Development and validation of QbD-driven bioanalytical LC-MS/MS method for the quantification of paracetamol and diclofenac in human plasma, Anal. Chem. Lett.8 (2018) 677–691; https://doi.org/10.1080/22297928.2018.142930510.1080/22297928.2018.1429305Search in Google Scholar

62. N. V. V. S. S. Raman, U. R. Mallu and H. R. Bapatu, Analytical Quality by Design approach to test method development and validation in drug substance manufacturing, J. Chem.2015 (2015) Article ID 435129 (8 pages); https://doi.org/10.1155/2015/43512910.1155/2015/435129Search in Google Scholar

63. G. L. Reid, J. Morgado, K. Barnett, B. Harrington, J. Wang, J. Harwood and D. Fortin, Analytical Quality by Design (AQbD) in pharmaceutical development, Am. Pharm. Rev., August 27, 2013; https://www.americanpharmaceuticalreview.com/Featured-Articles/144191-Analytical-Quality-by-Design-AQbD-in-Pharmaceutical-Development/Search in Google Scholar

64. X. Yu, L. X. Yu, Y. Teng, D. K. Gaglani, B. D. Rege and S. Rosencrance, Implementation of Pharmaceutical Quality by Design in Wet Granulation, in Handbook of Pharmaceutical Wet Granulation – Theory and Practice in a Quality by Design Paradigm (Eds. A. S. Narang and S. I. F. Badawy), Elsevier, Amsterdam 2019, pp. 703–733.10.1016/B978-0-12-810460-6.00024-5Search in Google Scholar

65. L. Eriksson, E. Johansson, N. Kettaneh-Wold, C. Wikström and S. Wold, Design of Experiments, Principles and Applications, 3^rd Edition, Umetrics, Umeå 2000.Search in Google Scholar

66. Z. I. Szabó, B. Székely-Szentmiklósi, B. Deák, I. Székely-Szentmiklósi, B. Kovács, K. Zöldi and E. Sipos, Study of the effect of formulation variables on the characteristics of combination tablets containing enalapril maleate and indapamide as active substances using experimental design, Acta Pharm.66 (2016) 191–206; https://doi.org/10.1515/acph-2016-001910.1515/acph-2016-0019Search in Google Scholar

67. B. Rambali, L. Baert and D. L. Massart, Using experimental design to optimize the process parameters in fluidized bed granulation on a semi-full scale, Int. J. Pharm.220 (2001) 149–160; https://doi.org/10.1016/S0378-5173(01)00658-510.1016/S0378-5173(01)00658-5Search in Google Scholar

68. J. Djuris, D. Medarevic, M. Krstic, Z. Djuric and S. Ibric, Application of quality by design concepts in the development of fluidized bed granulation and tableting processes, J. Pharm. Sci.102 (2013) 1869–1882; https://doi.org/10.1002/jps.2353010.1002/jps.2353023568772Search in Google Scholar

69. I. Aleksić, J. Đuriš, I. Ilić, S. Ibrić, J. Parojčić and S. Srčič, In silico modeling of in situ fluidized bed melt granulation, Int. J. Pharm.466 (2014) 21–30; https://doi.org/10.1016/j.ijpharm.2014.02.04510.1016/j.ijpharm.2014.02.04524607215Search in Google Scholar

70. A. S. Zidan, M. Ebeed, H. Elghamry and A. Badawy, Nicotinamide pelletization by fluidized hot melt granulation: L18 Hunter design to screen high risk variables, Int. J. Pharm.466 (2014) 83–95; https://doi.org/10.1016/j.ijpharm.2014.03.00810.1016/j.ijpharm.2014.03.00824614582Search in Google Scholar

71. H. F. Santos Souza, D. Real, D. Leonardi, S. C. Rocha, V. Alonso, E. Serra, A. M. Silber and C. J. Salomon, Development and in vitro/in vivo evaluation of a novel benznidazole liquid dosage form using a quality-by-design approach, Trop. Med. Int. Heal.22 (2017) 1514–1522; https://doi.org/10.1111/tmi.1298010.1111/tmi.1298028944986Search in Google Scholar

72. M. Cirri, F. Maestrelli, P. Mura, C. Ghelardini and L. Di Cesare Mannelli, Combined approach of cyclodextrin complexationand nanostructured lipid carriers for the development of a pediatric liquid oral dosage form of hydrochlorothiazide, Pharmaceutics10 (2018) Article ID 287 (17 pages); https://doi.org/10.3390/pharmaceutics1004028710.3390/pharmaceutics10040287632140830572649Search in Google Scholar

73. J. Joseph, B. N. V. Hari and D. R. Devi, Experimental optimization of lornoxicam liposomes for sustained topical delivery, Eur. J. Pharm. Sci.112 (2018) 38–51; https://doi.org/10.1016/j.ejps.2017.10.03210.1016/j.ejps.2017.10.03229111151Search in Google Scholar

74. B. Sylvester, A. Porfire, D. M. Muntean, L. Vlase, L. Lupuţ, E. Licarete, A. Sesarman, M. C. Alupei, M. Banciu, M. Achim and I. Tomuţă, Optimization of prednisolone-loaded long-circulating liposomes via application of Quality by Design (QbD) approach, J. Liposome Res.28 (2018) 49–61; https://doi.org/10.1080/08982104.2016.125424210.1080/08982104.2016.125424227788618Search in Google Scholar

75. S. Alam, M. Aslam, A. Khan, S. S. Imam, M. Aqil, Y. Sultana and A. Ali, Nanostructured lipid carriers of pioglitazone for transdermal application: From experimental design to bioactivity detail, Drug Deliv.23 (2016) 601–609; https://doi.org/10.3109/10717544.2014.92395810.3109/10717544.2014.92395824937378Search in Google Scholar

76. V. Sutariya, A. Groshev, P. Sadana, D. Bhatia and Y. Pathak, Artificial neural network in drug delivery and pharmaceutical research, Open Bioinform. J.7 (2014) 49–62; https://doi.org/10.2174/187503620130701004910.2174/1875036201307010049Search in Google Scholar

77. M. Puri, A. Solanki, T. Padawer, S. M. Tipparaju, W. A. Moreno and Y. Pathak, Introduction to Artificial Neural Network (Ann) as a Predictive Tool for Drug Design, Discovery, Delivery, and Disposition: Basic Concepts and Modeling, in Artificial Neural Network for Drug Design, Delivery and Disposition (Eds. M. Puri, Y. Pathak, V. K. Sutariya, S. Tipparaju and W. Moreno), Elsevier Amsterdam 2016, pp. 3–13.10.1016/B978-0-12-801559-9.00001-6Search in Google Scholar

78. F. Amato, A. López, E. M. Peña-Méndez, P. Vaňhara, A. Hampl and J. Havel, Artificial neural networks in medical diagnosis, J. Appl. Biomed.11 (2013) 47–58; https://doi.org/10.2478/v10136-012-0031-x10.2478/v10136-012-0031-xSearch in Google Scholar

79. V. Mandlik, P. R. Bejugam and S. Singh, Application of artificial neural networks in modern drug discovery, in: Artificial. Neural Network for Drug Design, Delivery and Disposition (Eds. M. Puri, Y. Pathak, V. K. Sutariya, S. Tipparaju and W. Moreno), Elsevier, Amsterdam 2016, pp. 123–139.10.1016/B978-0-12-801559-9.00006-5Search in Google Scholar

80. P. S. Rajpal, K. S. Shishodia and G. S. Sekhon, An artificial neural network for modeling reliability, availability and maintainability of a repairable system, Reliab. Eng. System. Saf.91 (2006) 809–819; https://doi.org/10.1016/j.ress.2005.08.00410.1016/j.ress.2005.08.004Search in Google Scholar

81. M. Bianchini and F. Scarselli, On the complexity of neural network classifiers: A comparison between shallow and deep architectures, IEEE Trans. Neural Networks Learn. Syst.25 (2014) 1553–1565; https://doi.org/10.1109/TNNLS.2013.229363710.1109/TNNLS.2013.229363725050951Search in Google Scholar

82. D. A. Winkler and T. C. Le, Performance of deep and shallow neural networks, the universal approximation theorem, activity cliffs, and QSAR, Mol. Inform.36 (2017) Article ID 1600118; https://doi.org/10.1002/minf.20160011810.1002/minf.20160011827783464Search in Google Scholar

83. M. Z. Alom, T. M. Taha, C. Yakopcic, S. Westberg, P. Sidike, M. S. Nasrin, M. Hasan, B. C. Van Essen, A. A. S. Awwal and V. K. Asari, A state-of-the-art survey on deep learning theory and architectures, Electronics8 (2019) Article ID 292; https://doi.org/10.3390/electronics803029210.3390/electronics8030292Search in Google Scholar

84. N. K. Chauhan and K. Singh, A review on conventional machine learning vs deep learning, in: 2018 International Conference on Computing, Power and Communication Technologies (GUCON), Greater Noida (UP, India), Sept. 28-29, 2018; IEEE, pp. 347–352; https://doi.org/10.1109/GUCON.2018.867509710.1109/GUCON.2018.8675097Search in Google Scholar

85. T. I. Poznyak, I. Chairez Oria and A. S. Poznyak, Background on Dynamic Neural Networks, in Ozonation and Biodegradation in Environmental Engineering, Dynamic Neural Network Approach (Eds. T. I. Poznyak, I. Chairez and A. S. Poznyak), Elsevier, Amsterdam 2019, pp. 57–74.10.1016/B978-0-12-812847-3.00012-3Search in Google Scholar

86. N. Lanzetti, Y. Z. Lian, A. Cortinovis, L. Dominguez, M. Mercangöz and C. Jones, Recurrent neural network based MPC for process industries, 2019 18th European Control Conference (ECC), Naples (Italy), June 25–28, 2019, IEEE, pp. 1005-1010, https://doi.org/10.23919/ECC.2019.879580910.23919/ECC.2019.8795809Search in Google Scholar

87. B. Zhang, X. Sun, S. Liu and X. Deng, Recurrent neural network-based model predictive control for multiple unmanned quadrotor formation flight, Int. J. Aerosp. Eng.2019 (2019) (18 pages); https://doi.org/10.1155/2019/727238710.1155/2019/7272387Search in Google Scholar

88. M. Ankith, S. S. Teja and N. Demodharan, Artifical Neural networks: functioning and applications in pharmaceutical industry, Int. J. Appl. Pharm.10 (2018) 28–33; https://doi.org/10.22159/ijap.2018v10i5.2830010.22159/ijap.2018v10i5.28300Search in Google Scholar

89. M. Pishnamazi, H. Y. Ismail, S. Shirazian, J. Iqbal, G. M. Walker and M. N. Collins, Application of lignin in controlled release: development of predictive model based on artificial neural network for API release, Cellulose26 (2019) 6165–6178; https://doi.org/10.1007/s10570-019-02522-w10.1007/s10570-019-02522-wSearch in Google Scholar

90. C. Prithviraj, P. Versha, C. D. Debarupa and G. Amitava, Application of artificial neural network model in predicting physicochemical characteristics of pharmaceutically developed wafers of loratadine, Asian J. Pharm.9 (2015) 44–48; https://doi.org/10.4103/0973-8398.15003610.4103/0973-8398.150036Search in Google Scholar

91. B. Aksu, G. Yegen, S. Purisa, E. Cevher and Y. Ozsoy, Optimisation of ondansetron orally disinte-grating tablets using artificial neural networks, Trop. J. Pharm. Res.13 (2014) 1374–1383; https://doi.org/10.4314/tjpr.v13i9.110.4314/tjpr.v13i9.1Search in Google Scholar

92. R. Han, Y. Yang, X. Li and D. Ouyang, Predicting oral disintegrating tablet formulations by neural network techniques, Asian J. Pharm. Sci.13 (2018) 336–342; https://doi.org/10.1016/j.ajps.2018.01.00310.1016/j.ajps.2018.01.003703215332104407Search in Google Scholar

93. M. Ilić, J. Đuriš, I. Kovačević, S. Ibrić and J. Parojčić, In vitro - in silico - in vivo drug absorption model development based on mechanistic gastrointestinal simulation and artificial neural networks: Nifedipine osmotic release tablets case study, Eur. J. Pharm. Sci.62 (2014) 212–218; https://doi.org/10.1016/j.ejps.2014.05.03010.1016/j.ejps.2014.05.03024911992Search in Google Scholar

94. D. L. Galata, A. Farkas, Z. Könyves, L. A. Mészáros, E. Szabó, I. Csontos, A. Pálos, G. Marosi, Z. K. Nagy and B. Nagy, Fast, spectroscopy-based prediction of in vitro dissolution profile of extended release tablets using artificial neural networks, Pharmaceutics11 (2019) Article ID 400 (18 pages); https://doi.org/10.3390/pharmaceutics1108040010.3390/pharmaceutics11080400672389731405029Search in Google Scholar

95. B. Nagy, D. Petra, D. L. Galata, B. Démuth, E. Borbás, G. Marosi, Z. K. Nagy and A. Farkas, Application of artificial neural networks for process analytical technology-based dissolution testing, Int. J. Pharm.567 (2019) Article ID 118464; https://doi.org/10.1016/j.ijpharm.2019.11846410.1016/j.ijpharm.2019.11846431252145Search in Google Scholar

96. M. Zandkarimi, M. Shafiei, F. Hadizadeh, M. Ali Darbandi and K. Tabrizian, Prediction of pharmacokinetic parameters using a genetic algorithm combined with an artificial neural network for a series of alkaloid drugs, Sci. Pharm.82 (2014) 53–70; https://doi.org/10.3797/scipharm.1306-1010.3797/scipharm.1306-10395123324634842Search in Google Scholar

97. W. C. Wong, E. Chee, J. Li and X. Wang, Recurrent neural network-based model predictive control for continuous pharmaceutical manufacturing, Mathematics6 (2018) Article ID 242; https://doi.org/10.3390/math611024210.3390/math6110242Search in Google Scholar

98. P. Oliveri and M. Forina, Data Analysis and Chemometrics, in Chemical Analysis of Food: Techniques and Applications (Ed. Y. Pico), Elsevier, Amsterdam 2012, pp. 25–57.10.1016/B978-0-12-384862-8.00002-9Search in Google Scholar

99. S. Grimnes and O. G. Martinsen, Data and Models, in Bioimpedance and Bioelectricity Basics (Eds. S. Grimnes and O. G. Martinsen), 3^rd ed, Elsevier, Amsterdam 2015, pp. 329–404.10.1016/B978-0-12-411470-8.00009-XSearch in Google Scholar

100. J. S. Markowitz, Multivariate Analysis, in Mortality and Its Risk Factors Among Profession Athletes (Ed. J. S. Markowitz), Springer Nature, Cham (Switzerland) 2018, pp. 71–81.10.1007/978-3-319-77203-5_8Search in Google Scholar

101. A. P. Ferreira and M. Tobyn, Multivariate analysis in the pharmaceutical industry: enabling process understanding and improvement in the PAT and QbD era, Pharm. Dev. Technol.20 (2015) 513–527; https://doi.org/10.3109/10837450.2014.89865610.3109/10837450.2014.89865624641280Search in Google Scholar

102. I. Singh, P. Juneja, B. Kaur and P. Kumar, Pharmaceutical applications of chemometric techniques, ISRN Anal. Chem.2013 (2013) Article ID 795178 (13 pages); https://doi.org/10.1155/2013/79517810.1155/2013/795178Search in Google Scholar

103. I. T. Jolliffe and J. Cadima, Principal component analysis: A review and recent developments, Philos. Trans. R. Soc. A374 (2016) Article ID 20150202 (16 pages); https://doi.org/10.1098/rsta.2015.020210.1098/rsta.2015.0202479240926953178Search in Google Scholar

104. S. Maitra and J. Yan, Principle Component Analysis and Partial Least Squares: Two dimension Reduction Techniques for Regression, in Applying Multivariate Statistical Models, 2008 Discussion Paper Program, June 15–18, 2008, Casualty Actuarial Society, Québec City, pp.79–90.Search in Google Scholar

105. R. Roopwani, Z. Shi and I. S. Buckner, Application of principal component analysis (PCA) to evaluating the deformation behaviors of pharmaceutical powders, J. Pharm. Innov.8 (2013) 121–130; https://doi.org/10.1007/s12247-013-9153-210.1007/s12247-013-9153-2Search in Google Scholar

106. S. Bhattacharya, S. Mishra and B. G. Prajapati, Design and development of docetaxel solid self-microemulsifying drug delivery system using principal component analysis and D-optimal design, Asian J. Pharm.12 (2018) S122–S144.Search in Google Scholar

107. N. L. Calvo, T. S. Kaufman and R. M. Maggio, Mebendazole crystal forms in tablet formulations. An ATR-FTIR/chemometrics approach to polymorph assignment, J. Pharm. Biomed. Anal.122 (2016) 157–165; https://doi.org/10.1016/j.jpba.2016.01.03510.1016/j.jpba.2016.01.03526874854Search in Google Scholar

108. S. Glavanović, M. Glavanović and V. Tomišić, Simultaneous quantitative determination of paracetamol and tramadol in tablet formulation using UV spectrophotometry and chemometric methods, Spectrochim. Acta A157 (2016) 258–264; https://doi.org/10.1016/j.saa.2015.12.02010.1016/j.saa.2015.12.02026774813Search in Google Scholar

109. A. Mostafa, A. El Gindy and S. Emara, Simultaneous spectrophotometric estimation of bisoprolol fumarate and hydrochlorothiazide in tablet formulation using partial least-squares, principal component regression multivariate calibrations and RP-HPLC methods, J. Anal. Pharm. Res.4 (2017) Article ID 00124 (9 pages); https://doi.org/10.15406/japlr.2017.04.0012410.15406/japlr.2017.04.00124Search in Google Scholar

110. B. M. Marson, R. de Oliveira Vilhena, C. R. de Souza Madeira, F. L. D. Pontes, M. S. Piantavini and R. Pontarolo, Simultaneous quantification of artesunate and mefloquine in fixed-dose combination tablets by multivariate calibration with middle infrared spectroscopy and partial least squares regression, Malaria J.15 (2016) Article ID 109; https://doi.org/10.1186/s12936-016-1157-110.1186/s12936-016-1157-1476507226911371Search in Google Scholar

111. J. Silva, M. Mendes, T. Cova, J. Sousa, A. Pais and C. Vitorino, Unstructured formulation data analysis for the optimization of lipid nanoparticle drug delivery vehicles, AAPS PharmSciTech19 (2018) 2383–2394; https://doi.org/10.1208/s12249-018-1078-010.1208/s12249-018-1078-029869314Search in Google Scholar

112. S. F. B. Ali, Z. Rahman, S. Dharani, H. Afrooz and M. A. Khan, Chemometric models for quantification of carbamazepine anhydrous and dihydrate forms in the formulation, J. Pharm. Sci.108 (2019) 1211–1219; https://doi.org/10.1016/j.xphs.2018.10.02310.1016/j.xphs.2018.10.02330773201Search in Google Scholar

113. V. Arabzadeh, M. R. Sohrabi, N. Goudarzi and M. Davallo, Using artificial neural network and multivariate calibration methods for simultaneous spectrophotometric analysis of emtricitabine and tenofovir alafenamide fumarate in pharmaceutical formulation of HIV drug, Spectrochim. Acta A215 (2019) 266–275; https://doi.org/10.1016/j.saa.2019.02.07710.1016/j.saa.2019.02.07730831397Search in Google Scholar

114. G. Ioele, M. de Luca, E. Dinç, F. Oliverio and G. Ragno, Artificial neural network combined with principal component analysis for resolution of complex pharmaceutical formulations, Chem. Pharm. Bull.59 (2011) 35–40; https://doi.org/10.1248/cpb.59.3510.1248/cpb.59.3521212544Search in Google Scholar

115. R. W. Bondi and J. K. Drennen, Quality by Design and the Importance of PAT in QbD, in Separation Science and Technology (Eds. S. Ahuja and S. Scypinski), Elsevier, Amsterdam 2020, Vol. 10, pp. 195–224.10.1016/B978-0-12-375680-0.00005-XSearch in Google Scholar

116. S. Laske, A. Paudel, O. Scheibelhofer, S. Sacher, T. Hoermann, J. Khinast, A. Kelly, J. Rantannen, O. Korhonen, F. Stauffer, F. De Leersnyder, T. De Beer, J. Mantanus, P. F. Chavez, B. Thoorens, P. Ghiotti, M. Schubert, P. Tajarobi, G. Haeffler, S. Lakio, M. Fransson, A. Sparen, S. Abrahmsen-Alami, S. Folestad, A. Funke, I. Backx, B. Kavsek, F. Kjell, M. Michaelis, T. Page, J. Palmer, A. Schaepman, S. Sekulic, S. Hammond, B. Braun and B. Colegrove, A review of PAT strategies in secondary solid oral dosage manufacturing of small molecules, J. Pharm. Sci.106 (2017) 667–672; https://doi.org/10.1016/j.xphs.2016.11.01110.1016/j.xphs.2016.11.01128017464Search in Google Scholar

117. T. De Beer, A. Burggraeve, M. Fonteyne, L. Saerens, J. P. Remon and C. Vervaet, Near infrared and Raman spectroscopy for the in-process monitoring of pharmaceutical production processes, Int. J. Pharm.417 (2011) 32–47; https://doi.org/10.1016/j.ijpharm.2010.12.01210.1016/j.ijpharm.2010.12.01221167266Search in Google Scholar

118. T. Helešicová, T. Pekárek and P. Matějka, The influence of different acquisition settings and the focus adjustment on Raman spectral maps of pharmaceutical tablets, J. Drug Delivery Sci. Tehnol.47 (2018) 386–394; https://doi.org/10.1016/j.jddst.2018.08.00210.1016/j.jddst.2018.08.002Search in Google Scholar

119. H. Mitsutake, S. R. Castro, E. de Paula, R. J. Poppi, D. N. Rutledge and M. C. Breitkreitz, Comparison of different chemometric methods to extract chemical and physical information from Raman images of homogeneous and heterogeneous semi-solid pharmaceutical formulations, Int. J. Pharm.552 (2018) 119–129; https://doi.org/10.1016/j.ijpharm.2018.09.05810.1016/j.ijpharm.2018.09.05830266516Search in Google Scholar

120. K. Nomura, V. Titapiwatanakun, H. Hisada, T. Koide and T. Fukami, In situ monitoring of the crystalline state of active pharmaceutical ingredients during high-shear wet granulation using a low-frequency Raman probe, Eur. J. Pharm. Biopharm.147 (2020) 1–9; https://doi.org/10.1016/j.ejpb.2019.12.00410.1016/j.ejpb.2019.12.00431841690Search in Google Scholar

121. A. Gavan, S. Iurian, T. Casian, A. Porfire, S. Porav, I. Voina, A. Oprea and I. Tomuta, Fluidised bed granulation of two APIs: QbD approach and development of a NIR in-line monitoring method, Asian J. Pharm. Sci.15 (2020) 506–517; https://doi.org/10.1016/j.ajps.2019.03.00310.1016/j.ajps.2019.03.003748651132952673Search in Google Scholar

122. T. Casian, A. Reznek, A. L. Vonica-Gligor, J. Van Renterghem, T. De Beer and I. Tomuță, Development, validation and comparison of near infrared and Raman spectroscopic methods for fast characterization of tablets with amlodipine and valsartan, Talanta167 (2017) 333–343; https://doi.org/10.1016/j.talanta.2017.01.09210.1016/j.talanta.2017.01.09228340729Search in Google Scholar

123. H. Wu and M. Khan, THz spectroscopy: An emerging technology for pharmaceutical development and pharmaceutical Process analytical technology (PAT) applications, J. Mol. Struct.1020 (2012) 112–120; https://doi.org/10.1016/j.molstruc.2012.04.01910.1016/j.molstruc.2012.04.019Search in Google Scholar

124. P. F. Taday, D. Van Der Weide, K. Wood, M. Chamberlain, H. Roskos, C. Phillips, D. Newnham, M. Towrie and I. Appelquist, Applications of terahertz spectroscopy to pharmaceutical sciences, Philos. Trans. R. Soc. A362 (2004) 351–364; https://doi.org/10.1098/rsta.2003.132110.1098/rsta.2003.132115306525Search in Google Scholar

125. Y. B. Monakhova, U. Holzgrabe and B. W. K. Diehl, Current role and future perspectives of multivariate (chemometric) methods in NMR spectroscopic analysis of pharmaceutical products, J. Pharm. Biomed. Anal.147 (2018) 580–589; https://doi.org/10.1016/j.jpba.2017.05.03410.1016/j.jpba.2017.05.03428583765Search in Google Scholar

126. K. Korasa and F. Vrečer, Overview of PAT process analysers applicable in monitoring of film coating unit operations for manufacturing of solid oral dosage forms, Eur. J. Pharm. Sci.111 (2018) 278–292; https://doi.org/10.1016/j.ejps.2017.10.01010.1016/j.ejps.2017.10.01029020609Search in Google Scholar

127. E. M. Hansuld and L. Briens, A review of monitoring methods for pharmaceutical wet granulation, Int. J. Pharm.472 (2014) 192–201; https://doi.org/10.1016/j.ijpharm.2014.06.02710.1016/j.ijpharm.2014.06.02724950366Search in Google Scholar

128. X. Hu, J. C. Cunningham and D. Winstead, Study growth kinetics in fluidized bed granulation with at-line FBRM, Int. J. Pharm.347 (2008) 54–61; https://doi.org/10.1016/j.ijpharm.2007.06.04310.1016/j.ijpharm.2007.06.04317689213Search in Google Scholar

129. F. Alshihabi, T. Vandamme and G. Betz, Focused beam reflectance method as an innovative (PAT) tool to monitor in-line granulation process in fluidized bed, Pharm. Dev. Technol.18 (2013) 73–84; https://doi.org/10.3109/10837450.2011.62786810.3109/10837450.2011.62786822035287Search in Google Scholar

130. A. S. Narang, T. Stevens, K. Macias, S. Paruchuri, Z. Gao and S. Badawy, Application of in-line focused beam reflectance measurement to brivanib alaninate wet granulation process to enable scale-up and attribute-based monitoring and control strategies, J. Pharm. Sci.106 (2017) 224–233; https://doi.org/10.1016/j.xphs.2016.08.02510.1016/j.xphs.2016.08.02527771049Search in Google Scholar

131. U. Verma, J. B. Naik, J. S. Patil and S. K. Yadava, Screening of process variables to enhance the solubility of famotidine with 2-hydroxypropyl-β-cyclodextrin & PVP K-30 by using Plackett-Burman design approach, Mater. Sci. Eng. C77 (2017) 282–292; https://doi.org/10.1016/j.msec.2017.03.23810.1016/j.msec.2017.03.23828532031Search in Google Scholar

132. A. Anand, G. Singh and S. A. Saraf, Plackett–Burman design as a tool for screening and process optimization of rivastigmine-loaded lipid nanocarriers, Asian J. Pharm. Clin. Res.11 (2018) 155–158; https://doi.org/10.22159/ajpcr.2018.v11i12.2806610.22159/ajpcr.2018.v11i12.28066Search in Google Scholar

133. K. M. Hosny, O. A. A. Ahmed, U. A. Fahmy and H. M. Alkhalidi, Nanovesicular systems loaded with a recently approved second generation type-5 phospodiesterase inhibitor (avanafil): I. Plackett-Burman screening and characterization, J. Drug Deliv. Sci. Technol.43 (2018) 154–159; https://doi.org/10.1016/j.jddst.2017.10.00910.1016/j.jddst.2017.10.009Search in Google Scholar

134. T. Alam, S. Khan, B. Gaba, M. F. Haider, S. Baboota and J. Ali, Adaptation of Quality by Design-based development of isradipine nanostructured-lipid carrier and its evaluation for in vitro gut permeation and in vivo solubilization fate, J. Pharm. Sci.107 (2018) 2914–2926; https://doi.org/10.1016/j.xphs.2018.07.02110.1016/j.xphs.2018.07.02130076853Search in Google Scholar

135. S. S. Chudiwal and M. H. G. Dehghan, Quality by design (QbD) approach for design and development of drug-device combination products: a case study on flunisolide nasal spray, Pharm. Dev. Technol.23 (2018) 1077–1087; https://doi.org/10.1080/10837450.2016.123613010.1080/10837450.2016.123613027616074Search in Google Scholar

136. N. Jaipakdee, E. Limpongsa and T. Pongjanyakul, Optimization of minoxidil microemulsions using fractional factorial design approach, Pharm. Dev. Technol.21 (2016) 86–97; https://doi.org/10.3109/10837450.2014.97137510.3109/10837450.2014.97137525318551Search in Google Scholar

137. T. Adebileje, S. Adebileje and P. O. Aye, Ciprofloxacin hydrochloride encapsulated into PLGA nanoparticles for drug delivery application: Fractional factorial design, Open Access Library J.5 (2018) e4294; https://doi.org/10.4236/oalib.110429410.4236/oalib.1104294Search in Google Scholar

138. A. Jain, T. Sharma, G. Sharma, R. K. Khurana, O. P. Katare and B. Singh, QbD-driven analytical method development and validation for raloxifene hydrochloride in pure drug and solid oral dosage form, Anal. Chem. Lett.9 (2019) 463–477; https://doi.org/10.1080/22297928.2019.162419310.1080/22297928.2019.1624193Search in Google Scholar

139. M. J. Ramalho, J. A. Loureiro, M. A. N. Coelho and M. C. Pereira, Factorial design as a tool for the optimization of PLGA nanoparticles for the co-delivery of temozolomide and O6-benzylguanine, Pharmaceutics11 (2019) Article ID 401; https://doi.org/10.3390/pharmaceutics1108040110.3390/pharmaceutics11080401672298031405159Search in Google Scholar

140. A. Kumar, A. Nayak and S. Ghatuary, Design, optimization and characterization of a transferosomal gel of acyclovir for effective treatment of Herpes zoster, J. Drug Deliv. Ther.9 (2019) 712–721; https://doi.org/10.22270/jddt.v9i4-A.3556Search in Google Scholar

141. C. Roy and J. Chakrabarty, Quality by design-based development of a stability-indicating RPHPLC method for the simultaneous determination of methylparaben, propylparaben, diethyl-amino hydroxybenzoyl hexyl benzoate, and octinoxate in topical pharmaceutical formulation, Sci. Pharm.82 (2014) 519–539; https://doi.org/10.3797/scipharm.1312-2010.3797/scipharm.1312-20431816625853065Search in Google Scholar

142. A. R. Fernandes, N. R. Ferreira, J. F. Fangueiro, A. C. Santos, F. J. Veiga, C. Cabral, A. M. Silva and E. B. Souto, Ibuprofen nanocrystals developed by 22 factorial design experiment: A new approach for poorly water-soluble drugs, Saudi Pharm. J.25 (2017) 1117–1124; https://doi.org/10.1016/j.jsps.2017.07.00410.1016/j.jsps.2017.07.004611111230166898Search in Google Scholar

143. H. Patel, H. Patel, M. Gohel and S. Tiwari, Dissolution rate improvement of telmisartan through modified MCC pellets using 32 full factorial design, Saudi Pharm. J.24 (2016) 579–587; https://doi.org/10.1016/j.jsps.2015.03.00710.1016/j.jsps.2015.03.007505983527752231Search in Google Scholar

144. U. Verma, R. Rajput and J. B. Naik, Development and characterization of fast dissolving film of chitosan embedded famotidine using 3² full factorial design approach, Mater. Today Proc. 5 (2018) 408–414; https://doi.org/10.1016/j.matpr.2017.11.09910.1016/j.matpr.2017.11.099Search in Google Scholar

145. S. M. Soliman, N. S. Abdelmalak, O. N. El-Gazayerly and N. Abdelaziz, Novel non-ionic surfactant proniosomes for transdermal delivery of lacidipine: optimization using 2³ factorial design and in vivo evaluation in rabbits, Drug Deliv.23 (2016) 1608–1622; https://doi.org/10.3109/10717544.2015.113279710.3109/10717544.2015.113279726758033Search in Google Scholar

146. R. R. Pereira, M. Testi, F. Rossi, J. O. C. Silva Junior, R. M. Ribeiro-Costa, R. Bettini, P. Santi, C. Padula and F. Sonvico, Ucuùba (Virola surinamensis) fat-based nanostructured lipid carriers for nail drug delivery of ketoconazole: Development and optimization using Box-Behnken design, Pharmaceutics11 (2019) Article ID 284; https://doi.org/10.3390/pharmaceutics1106028410.3390/pharmaceutics11060284663098131212993Search in Google Scholar

147. P. Kraisit and N. Sarisuta, Development of triamcinolone acetonide-loaded nanostructured lipid carriers (NLCs) for buccal drug delivery using the Box-Behnken design, Molecules23 (2018) Article ID 982; https://doi.org/10.3390/molecules2304098210.3390/molecules23040982601733829690622Search in Google Scholar

148. V. Londhe and R. Shirsat, Formulation and characterization of fast-dissolving sublingual film of iloperidone using Box–Behnken design for enhancement of oral bioavailability, AAPS PharmSci-Tech19 (2018) 1392–1400; https://doi.org/10.1208/s12249-018-0954-y10.1208/s12249-018-0954-y29396734Search in Google Scholar

149. K. Ghosal, D. Ghosh and S. K. Das, Preparation and evaluation of naringin-loaded polycaprolac-tone microspheres based oral suspension using Box-Behnken design, J. Mol. Liq.256 (2018) 49–57; https://doi.org/10.1016/j.molliq.2018.02.02410.1016/j.molliq.2018.02.024Search in Google Scholar

150. M. Gad, H. E Zaazaa, S. M. Amer and M. A. Korany, Quality by Design approach for establishment of stability indicating method for determination of cefditoren pivoxil, J. Pharm. Anal. Insights2 (2017) (7 pages); https://doi.org/10.16966/2471-8122.11210.16966/2471-8122.112Search in Google Scholar

151. T. E. Yalcin, S. Ilbasmis-Tamer and S. Takka, Development and characterization of gemcitabine hydrochloride loaded lipid polymer hybrid nanoparticles (LPHNs) using central composite design, Int. J. Pharm.548 (2018) 255–262; https://doi.org/10.1016/j.ijpharm.2018.06.06310.1016/j.ijpharm.2018.06.06329969712Search in Google Scholar

152. U. C. Oz, B. Küçüktürkmen, B. Devrim, O. M. Saka and A. Bozkir, Development and optimization of alendronate sodium loaded PLGA nanoparticles by central composite design, Macromol. Res.27 (2019) 857–866; https://doi.org/10.1007/s13233-019-7119-z10.1007/s13233-019-7119-zSearch in Google Scholar

153. N. Sharma and S. Singh, Central composite designed ezetimibe solid dispersion for dissolution enhancement: Synthesis and in vitro evaluation, Ther. Deliv.10 (2019) 643–658; https://doi.org/10.4155/tde-2019-006310.4155/tde-2019-006331702450Search in Google Scholar

154. A. R. Fares, A. N. Elmeshad and M. A. A. Kassem, Enhancement of dissolution and oral bioavail-ability of lacidipine via pluronic P123/F127 mixed polymeric micelles: Formulation, optimization using central composite design and in vivo bioavailability study, Drug Deliv.25 (2018) 132–142; https://doi.org/10.1080/10717544.2017.141951210.1080/10717544.2017.1419512605870629275642Search in Google Scholar

155. S. Jebali, C. Belgacem, M. R. Louhaichi, S. Bahri and L. L. El Atarche, Application of factorial and Doehlert designs for the optimization of the simultaneous separation and determination of anti-migraine drugs in pharmaceutical formulations by RP-HPLC-UV, Int. J. Anal. Chem.2019 (2019) Article ID 9685750 (11 pages); https://doi.org/10.1155/2019/968575010.1155/2019/9685750671432431511775Search in Google Scholar