Design of Experiments, Design Space and Control Strategy

The DoE is a useful and effective tool which allows the determination of how the variables being studied (CMAs and CPPs) interact and how their interaction influences the responses (CQAs). Therefore, the DoE is the preferred approach to define the design space

during product development. Several advantages are offered over the traditional approach of changing one factor at a time such as: maximising knowledge with less resources, definition of the relative significance of each factor, construction of prediction models and optimisation of multiple CQAs [5,36,37].

The choice of a suitable experimental design is critical for the success of the DoE approach and it depends on the study goals (screening, optimisation and/or robustness testing), the investigator's experience, previous knowledge, the available resources and number and level of variables [5, 36, 37]. Screening designs such as response surface design, factorial designs, Placket- Burman and Taguchi are useful to determine the most influential factors and require few experiments in relation to the number of factors being studied. Optimisation designs (e.g. Box-Behnken, central composite design, optimal designs) can be employed following the application of screening designs and aim to find optimal operating conditions and to predict the response values.

The robustness testing allows to determine the sensitivity of CQAs to small changes in the factors levels. It is important to note, that in most of the studies related to the development of nanocarriers for brain delivery the only QbD element used is DoE [16-18,24,38-42].

This finding is transversal to many other research papers and should be emphasised that this is not the most appropriate approach. A successful DoE study must be preceded by the definition of solid objectives through QTPP and CQAs definition and the identification and selection of factors (CPPs and CMAs) based on risk assessment tools [4,5, 9, 43].

The ICH Q8 defines the design space as 'the multidimensional combination and interaction of input variables (e. g. material attributes) and process parameters that have been demonstrated to provide assurance of quality' [9]. The data obtained in the DoE is used to estimate each CQA (dimension) through statistical modelling.

The prediction profiles obtained for each CQA (dimension) can be combined to find the region where the product meets the QTPP.

The ranges/levels studied (characterisation range) in the DoE experiments represent the knowledge space while with the design space is possible to find the acceptable working range and the operating range (Fig. 11.5) [4,5, 9, 43].

Figure 11.5 Schematic representation of different regions which can be defined during QbD implementation. The characterisation range is studied during design of experiments (DoE) and constitutes the knowledge space. Within the design space it is possible to find the acceptable range, which is the output of the DoE, and the operating range, which constitutes the ranges defined for the manufacturing procedures. Adapted from Refs. [4,43].

Mendes et al. [13] and Xu et al. [35] could be a good starting point for those interested in following the QbD approach for their product development. Although not exhaustively, the QTPP was defined, the CQAs were selected, the risks were identified and analysed and DoE studies were conducted for formulation/process understanding and design space definition. In Xu et al. [35] a Placket-Burman design was used for the initial screening of eight formulation and process variables (lipid concentration, drug concentration, cholesterol concentration, buffer concentration, hydration time, sonication time, freeze-thaw cycles and extrusion pressure) which could impact on the encapsulation efficiency of liposomes. A composite design was selected for product optimisation and design space definition using the two statistically significant variables (lipid concentration and drug concentration) identified in the screening DoE. In the end, it was demonstrated that the higher the lipid concentration the higher the encapsulation efficiency, probably due to the increase in the number of vesicles and, consequently, in the internal volume available for encapsulation. On the other hand, the increase in drug concentration resulted in a decrease of the encapsulation efficiency, which may be related with a decrease in the bound between the drug and lipid.

The control strategy is established based on the knowledge and understanding gained during the development and the DoE application. It can include the control of input MAs, the control of the unit operations (CPPs) and in-process and real-time testing of the CQAs [9,12,44]. In-process controls of nanocarriers and liposomes, in particular, may include visual inspection, particle size and particle size distribution, residual solvents, extrusion membrane integrity, lipid content, percentage of free drug, pH, filter integrity and so on [44, 45]. The end-product testing is the bottom line of the control strategy and can be reduced to a minimum provided that the sources of variability were well characterised and are broadly understood.

Continuous Monitoring and Improvement

The continuous monitoring throughout the product life cycle allows to obtain additional understanding of the product and process performance and to manage the impact of any changes [9, 11, 12].

This new information can trigger the need to perform adjustments and, in this way, continuously improve the product and the process. When a design space is defined, the adjustments performed within the design space are not considered a change to the approved dossier. Therefore, no new review or approval by the regulatory authorities are required. The expansion, reduction or redefinition of the design space is subjected to post-approval submission. Control charts and product quality review reports are tools which can be used for the continuous monitoring of the product quality and the process performance.

Conclusion

The ultimate goal of the QbD implementation is to develop products and processes which have a higher quality and robustness through a deeper understanding about the science behind it. In this way go/no-go decisions are more based on sound scientific knowledge. The application of this approach from early stages will facilitate the scale-up stages, which is even more critical when working with complex products such as nanocarriers. Altogether, the project costs can be reduced because there is less waste and fewer failures; the manufacturing efficiency can be increased, and the product quality is increased when the QbD approach is followed. Additionally, in nanotechnology products where there is still uncertainty and lack of knowledge regarding their quality and safety, an integrative approach, such as the QbD, could facilitate their acceptance by the regulatory authorities. Despite that, many challenges and questions still remain to be answered regarding these complex drug products and manufacturing processes. Therefore, multidisciplinary working groups (academia, industry and regulators) must continue to work together to ensure a smooth introduction of the nanocarriers into the market.

References

• 1. Tam, V. H., Sosa, C., Liu, R., Yao, N., Priestley, R. D. (2016). Nanomedicine as a non-invasive strategy for drug delivery across the blood brain barrier. Int.J. Pharm., 515(1-2), 331-342.
• 2. Masserini, M. (2013). Nanoparticles for brain drug delivery. ISRN Biochem., 2013,1-18.
• 3. Khan, A. R., Yang, X., Fu, M., Zhai, G. (2018). Recent progress of drug nanoformulations targeting to brain./. Control. Release, 291, 37-64.
• 4. Rathore, A. S., Winkle, H. (2009). Quality by design for biopharmaceuticals. Nat.Biotechnol, 27(1), 26-34.
• 5. Schlindwein, W. S., Gibson, M. (eds.) (2018). Pharmaceutical Quality by Design, John Wiley & Sons, Chichester, UK.
• 6. Food and Drug Administration (2018b). Nanotechnology fact sheet. Retrieved January 31, 2019, from https://www.fda.gov/ ScienceResearch/SpecialTopics/Nanotechnology/ucm402230.htm.
• 7. Pepic, I., Hafner, A., Lovric, J., PerinaLakos, G. (2014). Nanotherapeutics in the EU: an overview on current state and future directions. Int. J.Nanomed., 9,1005-1023.
• 8. Bastogne, T. (2017). Quality-by-design of nanopharmaceuticals: a state of the art. Nanomed.Nanotechnol. Biol. Med., 13(7), 2151-2157.

• 9. International Conference of Harmonization (2009). ICH Harmonized Tripartite Guideline: Q8 (R2) Pharmaceutical Development. Retrieved from http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/ Guidelines/Quality/Q8_Rl/Step4/Q8_R2_Guideline.pdf.
• 10. Yu, L. X., Amidon, G„ Khan, M. A., Hoag, S. W„ Polli, J„ Raju, G. K„ Woodcock, J. (2014). Understanding pharmaceutical quality by design. AAPS]., 16(4), 771-783.
• 11. International Conference of Harmonization (2008). ICH Harmonized Tripartite Guideline: Q10 Pharmaceutical Quality System. Retrieved from http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/ Guidelines/Quality/Q10/Step4/Q10_Guideline.pdf.
• 12. International Conference of Harmonization (2005). ICH Harmonized Tripartite Guideline: Q9 Quality Risk Management. Retrieved from http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/ Guidelines/Quality/Q9/Step4/Q9_Guideline.pdf.
• 13. Mendes, M., Miranda, A., Cova, T., Gonsalves, L., Almeida, A.)., Sousa, ). )., do Vale, M. L. C., Marques, E. F., Pais, A.,Vitorino, C. (2018). Modeling of ultra-small lipid nanoparticle surface charge for targeting glioblastoma. Eur.J. Pharm. Sci., 117(2), 255-269.
• 14. Shah, B., Khunt, D., Bhatt, H., Misra, M., Padh, H. (2015). Application of quality by design approach for intranasal delivery of rivastigmine loaded solid lipid nanoparticles: effect on formulation and characterization parameters. Eur.J. Pharm. Sci., 78, 54-66.
• 15. Sanchez-Lopez, E., Ettcheto, M., Egea, M. A., Espina, M., Cano, A., Calpena, A. C., Camins, A., Carmona, N., Silva, A. M., Souto, E. B.,Garcia, M. L. (2018). Memantine loaded PLGA PEGylated nanoparticles for Alzheimer’s disease: in vitro and in vivo characterization. J.Nanobiotechnol., 16(1), 32.
• 16. Curie, A., Keller, B. L., Reul, R., Moschwitzer, J., Fricker, G. (2015). Development and lyophilization of itraconazole loaded poly(butyl cyanoacrylate) nanospheres as a drug delivery system. Eur. J. Pharm. Sci., 78,121-131.
• 17. Kumar, S., Ali, )., Baboota, S. (2016). Design Expert® supported optimization and predictive analysis of selegilinenanoemulsion via the olfactory region with enhanced behavioural performance in Parkinson's disease. Nanotechnology, 27(43), 1-24.
• 18. Wang, Q„ Wong, C. H„ Chan, H. Y. E„ Lee, W. Y„ Zuo, Z. (2018). Statistical design of experiment (DoE) based development and optimization of DB213 in situ thermosensitive gel for intranasal delivery. Int.J. Pharm., 539(1-2), 50-57.

• 19. International Conference of Harmonization (2003). ICH Harmonized Tripartite Guideline: Q1AJR2) Stability Testing of New Drug Substances and Products.
• 20. Smith, M. K., French, ). L., Kowalski, K. G., Hutmacher, M. M., Ewy, W. (2015). Quality by Design for Biopharmaceutical Drug Product Development, Vol. 18, Jameel, F., Hershenson, S., Khan, M. A., Martin- Moe, S., eds., Springer New York, New York.
• 21. Poovaiah, N., Davoudi, Z., Peng, H., Schlichtmann, B., Mallapragada, S., Narasimhan, B., Wang, Q. (2018). Treatment of neurodegenerative disorders through the blood-brain barrier using nanocarriers. Nanoscale, 10(36), 16962-16983.
• 22. Lundy, D. J., Chen, K.-H.,Toh, E. K.-W., Hsieh, P. C.-H. (2016). Distribution of systemically administered nanoparticles reveals a size-dependent effect immediately following cardiac ischaemia-reperfusion injury. Sci. Rep., 6(1), 25613.
• 23. Smith, B. R., Kempen, P, Bouley, D., Xu, A., Liu, Z., Melosh, N., Dai, H.,Sinclair, R.,Gambhir,S.S. (2012).Shape matters: intravital microscopy reveals surprising geometrical dependence for nanoparticles in tumor models of extravasation. Nano Lett., 12(7), 3369-3377.
• 24. Joseph, E., Reddi, S., Rinwa, V., Balwani, G., Saha, R. (2018). DoE based Olanzapine loaded poly-caprolactone nanoparticles decreases extrapyramidal effects in rodent model. Int.J. Pharm., 541(1-2), 198- 205.
• 25. Dordevic, S. M., Cekic, N. D., Savic, M. M., Isailovic, T. M., Randelovic, D. V., Markovic, B. D., Savic, S. R., Stamenic, T. T., Daniels, R.,Savic, S. D. (2015). Parenteral nanoemulsions as promising carriers for brain delivery of risperidone: design, characterization and in vivo pharmacokinetic evaluation. Int.J. Pharm., 493(1-2), 40-54.
• 26. European Commission (2015). Production, in EudraLex- Volume 4- Good Manufacturing Practice Guidelines.
• 27. European Commission (2013). Pharmaceutical quality system, in EudraLex- Volume 4- Good Manufacturing Practice Guidelines. Retrieved from https://ec.europa.eu/health/sites/health/files/files/eudralex/ vol-4/vol4-chapl_2013-0 l_en.pdf.
• 28. International Conference of Harmonization (1999b). ICH Harmonized Tripartite Guideline: Q6B Specifications: Test Procedures and Acceptance Criteria for Biotechnological/Biological Products.
• 29. International Conference of Harmonization (1999a). ICH Harmonized Tripartite Guideline: Q6A Specifications: Test Procedures and Acceptance Criteria for New Drug Substances and New Drug Products: Chemical Substances.

• 30. International Conference of Harmonization (2006). 1CH Harmonized Tripartite Guideline: Q3B(R2) Impurities in New Drug Products.
• 31. International Conference of Harmonization (2018). ICH Harmonized Tripartite Guideline: Q3CJR8) Impurities: Guideline for Residual Solvents.
• 32. Food and Drug Administration (2018a). Liposome Drug Products.
• 33. Food and Drug Administration (2012). Quality by Design for ANDAs: An Example for Immediate-Release Dosage Forms.
• 34. Lohan, S_v Raza, K., Mehta, S. K., Bhatti, G. K., Saini, S., Singh, B. (2017). Anti-Alzheimer’s potential of berberine using surface decorated multi- walled carbon nanotubes: a preclinical evidence. Int.J. Pharm., 530(1- 2), 263-278.
• 35. Xu, X., Mian, M. A., Burgess, D. J. (2011). A quality by design (QbD) case study on liposomes containing hydrophilic API: I. Formulation, processing design and risk assessment. Int.J. Pharm., 419(1-2), 52- 59.
• 3 6. Eriksson, L. (20 00). Design of Experiments: Principles and Applications, 3rd ed., Umetrics Academy.
• 37. Politis, S.N., Colombo, P., Colombo, G., Rekkas, D. M. (2017). Design of experiments (DoE) in pharmaceutical development. Drug Dev. Ind. Pharm., 43(6), 889-901.
• 38. Bragagni, M., Mura, P., Mennini, N., Ghelardini, C., Orlandini, S., Furlanetto, S. (2014). Development and characterization of functionalized niosomes for brain targeting of dynorphin-B. Eur. J. Pharm.Biopharm., 87(1), 73-79.
• 39. Shah, V., Sharma, M., Pandya, R., Parikh, R. K., Bharatiya, B., Shukla, A., Tsai, H. C. (2017). Quality by design approach for an in situ gelling microemulsion of Lorazepam via intranasal route. Mater. Sci. Eng. C, 75,1231-1241.
• 40. Toyota, H., Asai, T, Oku, N. (2017). Process optimization by use of design of experiments: application forliposomalization of FK506. Eur. J. Pharm. Sci., 102,196-202.
• 41. Dordevic, S. M., Radulovic, T. S., Cekic, N. D., Randelovic, D. V., Savic, M. M., Krajisnik, D. R., Milic,). R.,Savic, S. D. (2013). Experimental design in formulation of diazepam nanoemulsions: physicochemical and pharmacokinetic performances./. Pharm. Sci., 102(11), 4159-4172.
• 42. Kamal, N., Cutie, A. J., Habib, M. )., Zidan, A. S. (2015). QbD approach to investigate product and process variabilities for brain targeting liposomes./. Liposome Res., 25(3), 175-190.

• 43. Boukouvala, E, Muzzio, F. J., Ierapetritou, M. G. (2010). Design space of pharmaceutical processes using data-driven-based methods./. Pharm. Innovation, 5(3), 119-137.
• 44. American Pharmaceutical Review (2018). Process control and monitoringfor continuous production of biopharmaceuticals. Retrieved March 16, 2019, from https://www.americanpharmaceuticalreview. com/Featured-Articles/348877-Process-Control-and-Monitoring-for- Continuous-Production-of-Biopharmaceuticals/.
• 45. Kapoor, M., Lee, S. L., Tyner, К. M. (2017). Liposomal drug product development and quality: current US experience and perspective. AAPSJ., 19(3), 632-641.