The application of Quality by Design framework in the pharmaceutical development of dry powder inhalers

European Journal of Pharmaceutical Sciences 113 (2018) 64–76

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European Journal of Pharmaceutical Sciences journal homepage: www.elsevier.com/locate/ejps

The application of Quality by Design framework in the pharmaceutical development of dry powder inhalers

T

Francesca Buttinia, Stavroula Rozoub, Alessandra Rossia, Varvara Zoumplioub, Dimitrios M. Rekkasc,⁎ a

Food and Drug Department, University of Parma, Parco Area delle Scienze 27/A, 43124 Parma, Italy ELPEN Pharmaceutical Co. Inc., 95 Marathonos Ave., 19009 Pikermi, Attica, Greece c Department of Pharmacy, National and Kapodistrian University of Athens, 15784 Athens, Greece b

A R T I C L E I N F O

A B S T R A C T

Keywords: Quality by Design Dry powder inhalers Orally inhaled drug products Design of Experiments Risk analysis System thinking

This review aims in discussing the application of the Quality by Design (QbD) approach on the development of the Dry Powders Inhalers (DPIs). It starts with a thorough presentation of the Quality's concept evolution within the pharmaceutical sector and how this slowly adopted set of quality guidelines is now a major scientific and regulatory requirement. DPIs represent a type of delivery system where the system's thinking approach integrating the device, the formulation and the patient represent a major challenge to be met. Within this context this review points out the critical gaps in this optimization exercise and proposes a series of remedies in overcoming the obstacles when the system's parts are viewed alone and not as a whole. Statistical thinking and the corresponding tools are the means for successfully carrying out this purpose. QbD is not just another guideline to simply comply with. It is the ultimate scope of any pharmaceutical development effort, which is to fully understand and then consistently meet the patient's needs during the entire lifecycle of the product. QbD is focusing on delivering quality value to the patients without compromises in product's safety and effectiveness profile.

1. Analysis of the QbD framework and the roadmap towards process and product knowledge in Pharmaceutics A search in Scopus and the similar databases during the last decade reveals a sharp increase in the use of the Quality by Design (QbD) concept in the titles of pharmaceutical technology and engineering papers, which is in contrast with the recent past where this approach was completely unknown within the said sector. A number of interrelated reasons contributed to the late realization on behalf of the pharmaceutical industry of a well-established scientific truth easy to be proved (Politis and Rekkas, 2011): Quality cannot be tested into the final product, but it should be built in by design (web ref. 1–3). The awareness for the new mind-set for Quality in the pharmaceutical business environment officially started with the FDA's loud noise calling for a serious change upon the rise of the 21st century, linking directly patient's safety with the level of science integrated in development and manufacturing activities. The introduction of a new regulatory series of the so called “Quality related guidelines” combined

with updated editions of previous relevant publications injected with terms such as the “Systems and Risk based approaches to cGMPs” (web ref. 1,2), revealed new key words for industry introducing the concepts of QbD, Quality Risk Management (QRM), Pharmaceutical Quality Systems (PQS) and the similar (web ref. 4,5). The above facts taken together with newspaper columns (web ref. 6) full of negative comments regarding the old manufacturing paradigms still adopted by the pharmaceutical industry, created initially a turmoil which in the following years turned into an unavoidable big wave of change in the way quality assurance and design were perceived and realized (Politis and Rekkas, 2011). QbD is not a new concept. It is dated several decades back starting with W.A. Shewhart's work (Shewhart, 1931) who by introducing the famous control charts in the early '30s practically shared for free with the scientific community a fundamental principal: the only way to assure the quality of a product is by controlling its manufacturing process. This rational claim shifted for the first time the focus from the end product towards its process as the later shapes the quality

Abbreviations: APSD, aerodynamic particle size distribution; BDP, beclomethasone dipropionate; CCD, Central Composite Design; CMA, Critical Material Attribute; CPP, Critical Process Parameter; CQA, Critical Quality Attribute; DD, delivered dose; DoE, Design of Experiments; FF, formoterol fumarate; FPD, fine particle dose; FPF, fine particle fraction; MMAD, mass median aerodynamic diameter; PAT, Process Analytical Technology; PCA, principal component analysis; QbD, Quality by Design; QTPP, Quality Target Product Profile; QRM, Quality Risk Management; RA, Risk Assessment ⁎ Corresponding author at: Department of Pharmacy, National and Kapodistrian University of Athens, Athens 15784, Greece. E-mail address: [email protected] (D.M. Rekkas). https://doi.org/10.1016/j.ejps.2017.10.042 Received 29 June 2017; Received in revised form 31 October 2017; Accepted 31 October 2017 Available online 23 November 2017 0928-0987/ © 2017 Elsevier B.V. All rights reserved.

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Is it really possible for drug product to be safe but not effective and at the same time to comply with its predefined quality objectives? Definitely not. Last but not least, the persistence of using the term “efficacy” instead of “effectiveness” (web ref. 5,6) is out-dated given the well-recognized need to close this gap between what is perceived in the ideal environment of the well-controlled clinical trials compared to what is realized by the patient in the real conditions when the drug is approved and marketed (Singal et al., 2014). Quality should objectively reflect the indisputable capability of the drug product to meet its predefined safety and effectiveness requirements as perceived by the patient during its entire lifecycle. In other words, quality should be evaluated only from the patients' perspective and this is the absolute rational condition to be fully met and conceptually understood before starting any pharmaceutical development project. There are however official guidelines (web ref. 2) where quality of a drug product is correctly linked to its safety and effectiveness profile and thus these major terms are presented as they should. It must be also emphasized that a key aspect for achieving the desired state in a “quality system approach to cGMPs” is the “supportive management both financially and philosophically”. The latter while a very critical aspect for successful implementation of Quality clearly defined in Deming's work, remains a very rare, if not unique, expression for regulatory type of documents and is warmly welcomed. QbD is officially defined as a systematic approach to development that begins with predefined objectives and emphasizes product and process understanding and process control, based on sound science and Quality Risk Management (web ref. 9). From a process engineering perspective, this is achieved by identifying the Critical Material Attributes (CMAs) which together with the Critical Process Parameters (CPPs), both defined as inputs, will deliver through their proper selection and interaction the output. This is the drug product with the desired quality substantiated by the simultaneous optimization of its Critical Quality Attributes (CQAs) to finally and hopefully meet the predefined QTPP. The caution here is to always recall that the beneficiaries of the entire process are the patients and more in particular the ultimate scope to consistently fulfil their expectations regarding the specific medical condition. In other words, QTPP definition should start and finish with the patients and meeting their unresolved needs. The mathematical approach at process level by which the inputs or independent variables, i.e. the CMAs and the CPPs, could consistently produce the desired output i.e. the final product with CQAs always within the set specifications, has been proposed by Box and Wilson through their landmark paper published in 1951 (Box and Wilson, 1951). Many papers then followed and the famous "Design Space" concept (web ref. 9), which practically reflects and visualizes the above mathematical relationship, is now efficiently understood as during the last decade many applications in the pharmaceutical field reached the scientific literature. The guidelines ICH Q8, Q9 and Q10 should be always considered and implemented linked to each other, as correctly recommended by the regulatory authorities. The relation between the first two i.e. the new framework for Pharmaceutical Development (ICHQ8) and Risk Management (ICHQ9) has been nowadays well justified, starting with the publication of two detailed QbD application examples by the FDA for an immediate and a modified release dosage form (web ref. 10, 11). However, the third ICH Q10 guideline on the Pharmaceutical Quality System, while adequately presented the need for a systems approach, it fails to highlight the most important aspect in systems thinking theory. This relates to the concept of interactions between the parts of a system both at the organizational and process level, as the interactions practically control how the system functions and explain its behaviour. L. Von Bertalanffy with his revolutionary work on the "General System Theory" provided the foundations, which then quickly formed a

characteristics of the former. In other words, Shewhart made very clear that Quality could be described by one or more dependent variables and as such it could be only assured and realized through the optimization of the process inputs, which represent the independent variables. This fundamental principle that quality is controlled through the process has, since then, affected other senior scientists in the field such as W.E. Deming and J. Juran who both pushed much further this new concept through their many lectures and books. This took place in an era where minimizing manufacturing cost, without compromising product's quality, was considered very important and the only way forward. Deming's work has been widely recognized for keeping, among others, the right balance between organizational issues and statistics. His 14 points to the management (web ref. 7) are still known among the most wise, substantial and laconic set of advices ever given in this field proving that when a critical subject is deeply understood it can be communicated briefly, efficiently and solidly through the time. Another important contribution was his “System of Profound Knowledge” which again, through a short and insightful way, suggested four interrelated aspects i.e. appreciation for a system, knowledge of variation, theory of knowledge and psychology, as the pillars for integrating quality either at the organizational or process level (web ref. 8). These critical areas for thought and action are now mirrored as the cornerstones of the recent quality guidelines. These include the theories of knowledge, system and variability the latter defined as inversely proportional to Quality at the technical level, as proposed by Montgomery (2009). The strong link between knowledge and quality, as highlighted now in many regulatory publications, has been adequately presented in the past revealing not only how the knowledge philosopher C.I. Lewis has influenced Deming's work on this subject, but the epistemological origins of Quality as well (Maul on and Bergman, 2009; Strickland, 1995). Following Shewhart's steps, Deming emphasized process engineering and systems thinking approaches indicating that failure to describe a phenomenon as a process reveals dangerous ignorance. The introduction of QbD into the pharmaceutical world is relatively new but all its theoretical and practical basis have been published long ago just waiting patiently to be integrated into the development and industrial process adopted by the Pharma industry. The term QbD, while conceptually introduced during the early '30s and fully developed after the second war, was coined by Juran (1992) several years before the evolution of the corresponding guidelines. It is interesting to note that Juran (1992) defined a series of rational steps in QbD as follows: identify the customer, determine his/her needs, translate these needs into product features, develop the process to realize this specific product and transfer to operations. A comparison with the QbD presentation in the ICHQ8R2 pharmaceutical development guideline (web ref. 9) reveals similarities regarding both its process oriented framework and the “new steps” introduced in this so called “enhanced” regulatory approach (Juran, 1992). However, the quality guidelines fail to adequately emphasize on Juran's first two steps of QbD, or in other words to underline the importance of identifying, in a scientifically solid way, the patients unmet needs. Within the same context the definition adopted for the so called “basis of the design” i.e. the Quality Target Product Profile (QTPP) as described in the relevant publication (web ref. 15), does not include directly the “user”, who is of course the patient, the one and only reasoning behind the whole development process of any drug product. Moreover, there are two other issues in the Quality Guidelines (web ref. 4,5) which might need to be revisited as well. The first is the adopted definition for quality: “The suitability of either a drug substance or a drug product for its intended use". This term includes such attributes as the identity, strength, and purity (ICH Q6A)” (web ref. 9), which is vague and bypasses again the beneficiary who is the patient. Secondly, the terms “quality”, “safety” and “efficacy” are wrongly adopted as separate aspects (web ref. 9) as quality should “equal” or in other words embrace safety and efficacy. 65

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unique scientific interdisciplinary field ranging from mathematics and engineering to management (Mele et al., 2010). The key aspect of this theory is that a system is an entity of interrelated parts which interact to accomplish its purpose (Mele et al., 2010). Therefore, a process should be viewed as a system of causes producing the output with CQAs realized and explained mainly through the interactions of the CMAs and CPPs. Within this context when considering a dry powder inhaler (DPI), its overall clinical performance as a delivery system is affected from its three main players: the patient, the formulation and the device. Is it enough when these "players" are considered separately? No, not at all! In order to define the DPIs clinical performance, the interactions of the said systems' elements should be first fully understood and then accordingly optimized. At the wider picture systems approach also requires the best possible interaction between the formulators and the engineers designing the device, while both need to comply with the idea that the absolute judge of the device's clinical performance is the patient. Statistical thinking and knowledge management are the pillars of QbD within the system's context (Korakianiti and Rekkas, 2011). Successful implementation of the QbD requires beyond the above, the statistical toolbox to assist its implementation. Design of Experiments (DoE) as originally introduced by Sir R. Fisher almost a century ago, provides a rational and relatively simple approach for meeting the requirements in pharmaceutical development within the QbD framework (Politis et al., 2017).

blend and/or during powder dosing either in the unit dose or device reservoir. Furthermore, the weight variability during capsule or blister filling is also a potential risk factor due to the rheological characteristics of the fines and the low dose requirements of the powder mixture. The "Statistical Quality Control" tools such as the control charts invented by W. E. Shewhart for process monitoring and the DoE as proposed by R.A. Fischer for understanding and reducing variability, both within the QbD concept and representing the “statistical thinking hat”, should be routinely employed to effectively increase DPI process capability. Thirdly, QbD tools could be particularly helpful in DPIs development because environmental humidity is crucial factor that needed to be considered as well during the development and production stages since it negatively affects both the chemical stability of the API and the aerosolization of the powder bed. The adsorption of water on the surface of microparticles has a significant impact on the capillary forces, solid bridge formation and electrostatic forces. High relative humidity may increase inter-particulate forces due to increased capillary interactions resulting in the formation of larger agglomerates which are less breakable: lactose monohydrate may dissolve and then recrystallize resulting in solid bridges among crystals producing stronger agglomerates that do not disperse in an air flow (Hoppentocht et al., 2014; Le et al., 2012). Lastly, it is well established that the compendia's in vitro analytical techniques applied to DPIs result sometimes in data with variability and thus are far from being robust; QbD tools could improve the robustness of the data. Potential risk factors contributing to the variability of the obtained in vitro data can be the coating of the collection plates or cups, the flow rate and actuation time tolerances, the temperature and relative humidity or even the equipment technical characteristics. Moreover, for pulmonary products there is a lack between the in vitro-in vivo correlation: the impactors, important quality control equipment used for the in vitro mapping of the drug distribution, are not lung models and therefore cannot represent in a realistic way the human respiratory geometry and physiology. Lately, oropharyngeal throat casts have been introduced as more close to the throat anatomy. Nevertheless, impactors remain important to the in vitro characterization of DPIs and DoE can be a powerful tool for the identification and understanding of the effect of multiple parameters that are involved in this methodology. Therefore, adopting and routinely working within this QbD mind-set could facilitate managing and reducing variability of the aerodynamic particle assessment methods which could eventually lead to stronger IV/IVCs (Adams, 2010). Obviously, such a multi parameter DPI development exercise could be managed efficiently only through statistically designed experiments within the QbD framework (Politis et al., 2017). The QbD roadmap has to be built keeping in mind the ultimate condition that the product should be developed and produced to meet consistently the patient's needs during its entire lifecycle. Failure to address this requires a thoughtful reset and then starting over after a thorough study of the quality related literature and regulatory guidelines. In other words, the patient requirements and relevant aspects facilitating drug administration and therapy compliance should govern product development. DPI pharmaceutical technology development within the QbD framework should include the steps illustrated in Fig. 1. Defining the DPI's QTPP comes first as it represents the scope of the said development. This practically corresponds to the definition of the therapeutic need to be met, the scientific proof that this can be achieved through the selected API, dose, delivery device, route of administration, PK-PD profile and patient's usability/acceptability/satisfaction data. The realization of this QTPP will be achieved through the production of a DPI with a particular quality profile determined through CQAs, which should satisfy predefined specifications after applying the official QC testing methodology. Besides the general CQAs applicable to all pharmaceutical products such as the content uniformity, stability profile and the similar, there is a well-defined number of DPI's specific prerequisites regarding the product aerodynamic profile reflecting in

2. QbD: bridging the gaps in the development of DPIs A key aspect of the QbD paradigm is the detailed understanding of the process design space, mapping the influence and interaction of the various formulations and operating parameters on process performance. As pharmaceutical development moves away from the traditional Quality by Testing (QbT) towards the QbD approach, DPIs in particular cannot not be excluded from this change. QbD is particularly important for inhaled products for several reasons. Firstly, a DPI is a typical formulation-device combination delivery system as “the product is comprised of two or more regulated components that are physically, chemically, or otherwise combined or mixed and produced as a single entity” (Donawa, 2008a; Donawa, 2008b). DPIs are thus more complex compared to the conventional dosage forms. Device and formulation interconnecting features should work together to aerosolize the drug and deliver it to the site of action. Within this context, patient behaviour plays a significant role in the product delivery, deposition in the lungs and hence bioavailability: the breathing pattern and the disease pathology could significantly affect the dose assumption especially when a DPI with flow rate dependent performance is used. Furthermore, the product quality in terms of respirable dose may be affected by the proper or not product handling by the patient. It has been recently reported that handling errors of inhaler devices are underestimated in real life and are associated with an increased rate of severe COPD exacerbation (Lavorini et al., 2016), thus training in inhaler use is an integral part of COPD management (Molimard et al., 2017; Price et al., 2017). In this perspective, QbD tools help in understanding of the interaction of the two product components and in monitoring their contribution on the product performance enabling the minimization of the patient errors during the device preparation and inhalation. Secondly, the DPI manufacturing process exhibit often low process capability that is a statistical measure of the process variability for a given characteristic (Adams, 2010). Low capability means that variability is quite large compared to the product's specifications and the process should be redesigned to attenuate the significant root causes of variance (Yu, 2008). Many DPI formulations are physical mixtures of a coarse carrier, usually α-lactose monohydrate and a micronized low dose of API with an aerodynamic particle size ranging between 1 and 5 μm. This limitation favours lack of API homogeneity in the original 66

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Fig. 1. Quality design roadmap which lead to define Quality Target Product Profile, QTPP, Critical Process Parameters (CPPs), Critical Quality Attributes (CQAs) and Critical Material Attributes (CMAs) for a dry powder inhaler product.

quality from a patient perspective, is usually based on assessing the Risk Priority number (RPN) i.e. the probability, severity and detectability of a risk, accomplished with the use of appropriate well established techniques, such as Failure Mode and Effects Analysis (FMEA), Failure Mode, Effects and Criticality Analysis (FMECA), Fault Tree Analysis (FTA), Cause and Effect diagrams or other similar statistical tools indicated in the ICH Q9 guideline. As more information on the process is collected with the aid of all these systematic activities in combination with prior knowledge and statistically designed experiments, the list of potential risks could be refined and adequately addressed through welldefined actions with the overall aim to be substantially mitigated for assuring quality. The Ishikawa diagram is a graphical tool capable for highlighting all possible variables which could pose a potential risk for the CQAs of a drug product. Fig. 2 is an example of this cause and effect diagram applied to a spray drying process of a high dose powder for inhalation. As previously stated, DPIs are pharmaceutical products where the formulation and the device are integral parts of this delivery system. In this respect a formal device risk analysis is a critical requirement under the medical device directive for devices of all classes. EN ISO 14971:2012 specifies risk management requirements and describes the steps to follow in identifying potential hazards and their associated risks, together with evaluation and mitigation of those risks. The risk management process is initially applied at the design stage, but also throughout the lifecycle of the device especially if modifications are made, production processes change, complaints or adverse events occur. In addition, the risk management should address the patient use errors (Donawa, 2017). Design of Experiments (DoE) is a rational experimental planning, as it provides a structured statistical system that links mathematically the input factors with the selected measured responses describing their relationship. DoE is an excellent tool that allows pharmaceutical scientists to systematically vary input factors such as the material and process parameters according to a predefined way and study their effect on the CQAs. DoE can help to identify the CMAs, CPPs and ultimately the design space leading to process and product optimization. The multidimensional space that depicts the mathematical relationship between the process inputs such as the CMAs and the CPPs with the CQAs known as “Design Space” provide the real quality assurance. DoE and similar mathematical tools assist in defining the critical factors, their interactions and the proven acceptable ranges (PARs) of the process parameters, thus assuring that product's specifications are met consistently through the control of the operating conditions. Verification

practice its clinical performance. These inhaler specific CQAs include the following: delivered dose, aerodynamic particle size distribution and delivered dose uniformity (Fig. 1). All the above CQAs, when within the specs and taken together, assure that the DPI exhibits its desired clinical performance consistently, in real life conditions and always from the patient perspective. As an example, a DPI product for COPD is clinically relevant only when the patients state that they can breathe much better, apart from improvement of breathing indices in statistical terms. The obvious question is how the selected CQAs representing the process outputs or depended variables could take the desired values to meet the specs. The only way to achieve this is by finding the values or ranges of the independent or input variables within which the selected CQAs could be optimized as originally shown by the Box and Wilson (1951). These parameters fall into two categories as the general definition of the process (Montgomery, 2009; Politis et al., 2017) implied. The Critical Material Attributes (CMAs) refer to properties of the raw materials important for the DPI product such as the API crystal form, purity, stability and particle size distribution which overall affect among others its pharmacological activity. The Critical Process Parameters (CPPs) are the process variables such the mixing time/rate, the spray drying operating conditions etc., which have a major impact to the selected CQAs. The latter are practically shaped through the CMAs and the CPPs taking into consideration their interactions and how they are affected by the design of the delivery device and the inhalation capability of the patient. In the formulation and manufacturing process of DPI products, the parameters that could have an impact on the quality of the finished product are numerous, interconnected and thus very difficult to be controlled and managed when working outside the QbD framework. 3. Statistical thinking and tools for implementing QbD One of the key components consisting the various phases of the QbD initiative is the Risk Assessment (RA), which is part of the Quality Risk Management (QRM), as described in the relevant regulatory guideline ICH Q9. RA is a systematic process of organizing information to support a risk decision and is a key activity of the QbD based methodology (Pallagi et al., 2016). It is a very valuable tool that contributes to gaining process knowledge by identifying and ranking the criticality of the parameters affecting this process through DoE and other mathematical tools. Ranking the variables in terms of their importance on products 67

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Fig. 2. Ishikawa diagram illustrating the parameters and process influencing the quality of a high dose DPI product. The fishbone diagram hypothesises the production of drug microparticles by spray drying and their agglomeration in soft pellets.

runs together with the ANOVA results prove the validity of the findings. DoE is a relatively simple tool suitable for pharmaceutical development and fits well into the above mentioned regulatory requirements for quality (Politis et al., 2017). Generally there are three times product's lifecycle when DoE may be used: early in process development (screening DoE); for determining the values of the variables which maximize process performance (optimization DoE); and when exploring robustness to assist in defining a design space in support of a QbD filing with regulatory agencies (robustness DoE) (Weissman and Anderson, 2015; Mills, 2009). A key goal of process understanding with DoE is to establish the functional relationship between the process parameters and quality attributes, including their interactions. The knowledge acquired during product development constructs the “knowledge space” which includes information about critical and non-critical process parameters and CQAs. The design space, which is an output of the process understanding studies, determines the critical process input variables and their ranges to ensure consistent quality for large-scale commercial manufacture (Garcia et al., 2008). The construction of the "knowledge space" which embraces the design space begins with product conceptualisation and development, evolves gradually with product commercialization, and continuous throughout the product lifecycle as shown in Fig. 3. Commercial manufacturing processes are operated within the “control space”, which is a subarea of the design space far from the edge of failure. A control strategy is then applied to ensure that the process stays within the designated operating limits and implemented for a further risk reduction. The QdD roadmap does not end with the launch of the product on the market since the knowledge derived from the data trending helps in improving product understanding and process optimization. ICH Q8 (R2) promotes the use of Process Analytical Technology (PAT) to ensure that the process factors remain within the established operating ranges assuring products quality. PAT can provide real time continuous monitoring of the critical process inputs to accelerate valid go/no go decisions and demonstrate that the process is maintained within its design space (Yu et al., 2014). Some of the potential benefits of PAT based pharmaceutical manufacturing include: enhancing process understanding and reducing process failures; ensuring quality

through optimal design, continuous monitoring and feedback control; reducing cycle time to improve manufacturing efficiency; identifying the root causes of process deviations; basing regulatory scrutiny on process knowledge and scientifically based RA (Yu et al., 2004). Some PAT methods have been explored in the spray drying process which is often used for the production of orally inhaled particles. An example is the application of an in- and at-line laser diffraction to measure the particle size of spray-dried maltodextrin microspheres as presented by Chan et al. (2008). In the in-line system, laser diffraction system was positioned between the drying chamber and the cyclone, while in the at-line system sampling and sizing were performed after the product had left the process stream. Although PAT was found to be an efficient and convenient tool, careful data interpretation was needed taking into account the cohesiveness of the material measured: particle size results were higher for in-line laser diffraction analysis whereas atline laser diffraction gave smaller size values more closely correlated with individual actual microsphere size due to agglomerate breakdown during the measurement process. In the same field of investigation, it has been shown a correlation between the droplet size from the spray drying atomizer and the MMAD value of insulin particles: this finding supports the assumption that one droplet dries to one particle and the nozzle gas flow rate is an important process parameter (Maltesen et al., 2012). In this case, a real time droplet size monitoring would allow for immediate interruption of the process in case of a non-constant droplet size drying. Furthermore, for the spray drying of amorphous solids, near infrared spectroscopy (NIR) and Raman probes could be useful to analyse the polymorphic changes in the product (Singh and Van den Mooter, 2016). Similarly, NIR was demonstrated to be a suitable tool for control of moisture content and particle size of smoothed and spherical insulin spray-dried particles (Maltesen et al., 2008). These techniques, combined with temperature, airflow and feed flow spray-drying monitoring could be used to build up fast control loops enabling feedback on deviations from the constructed design space and avoid an out-of- specification product. Yu et al. (2004) reviewed the role of PAT in crystallization of active pharmaceutical ingredients and several analytical techniques for assessing polymorphic form in situ are reported. Powder blending is another typical process employed in the 68

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Fig. 3. Product life-cycle based on QbD process approach which starts with the product design and definition followed by the achievement of the process knowledge, setting of process control strategy and continuous improvement of process according to the knowledge derived from the data trending. The interaction among the “knowledge”-“design”-“control” space is represented where the knowledge space is a summary of all process knowledge obtained during product development and encompasses the design space, control space and areas where it is known that unacceptable product is obtained (modified from web ref. 12).

distribution and other physicochemical properties are some of the most critical parameters that should be considered for assuring delivery of the active to the target site of a specific disease. The same features are equally important for the excipients when used. Carriers have an active role in DPIs clinical performance and their particle size distribution, shape and surface characteristics are engineered to meet specific requirements. It is thus becoming obvious how many variables are affecting the performance of a DPI and consequently the absolute need to manage the whole process with the “statistical thinking hat” while looking only towards the patient. To further underline this concept, Table 1 summarizes selected QbD applications in DPIs development. Lakio et al., applied an experimental design for the formulation and manufacturing method during the optimization study of high dose L-arginine powder for pulmonary delivery, proposed to be used for adjunctive treatment of cystic fibrosis and/or tuberculosis (Lakio et al., 2015). The effect of processing methods, such as jet milling, mechanofusion, ball milling and spray-drying, and type of excipient, i.e. leucine, isomalt and magnesium stearate was investigated. By application of principal component analysis (PCA), another well-established statistical tool, parameters like moisture uptake, particle size, densities, Hausner ratio, Carr's index, cohesion, fine particle dose (FPD) and fine particle fraction (FPF) were analysed and the results were classified by putting in the same area of the score scatter plot the batches with similar behaviour. The PCA approach allowed the discrimination among the batches manufactured with different process methods and added excipient. In particular, considering FPD and FPF as the most important parameters for the characterization of the in vitro powder aerosolisation, the jet milling combined with mechanofusion, in presence of leucine or magnesium stearate, produced coated arginine particles with better performance. These combined methods allowed to obtain high dose arginine powder, differently from the spray-drying powder which, despite having excellent aerosolisation, showed a reduced drug load due to higher amount of the excipients required to optimize the aerodynamic pattern. Milling is a common process used to micronize APIs before blending with excipients. This multivariate process can be individually studied using DoE, as it drastically affects the quality of the powders and their behaviour. A crystalline material is preferred as starting material and

manufacturing of a DPI product. The use of Raman spectroscopy in pharmaceutical product design has been recently reviewed (Paudel et al., 2015). Despite the fact that few applications of this technology in DPI are reported, Raman spectroscopy can be used as PAT tool for the in-line and real-time endpoint monitoring and understanding of a powder blending process. Furthermore, the accuracy of the Raman endpoint conclusions was demonstrated by using a second independent endpoint monitoring tool such as the NIR spectroscopy (De Beer et al., 2008). 4. Case studies: applications of QbD within the DPIs research field The most common problems during the development of a DPI product are related to the formulation and its production process, the modulation of the device for orally inhaling the drug and the powder filling process. Application of the QbD principles mathematically simplifies the complexity of the development and leads to better understanding of the link between the CPPs and CMAs with the CQAs of the finished product. Product/process knowledge and understanding is facilitated through the simultaneous implementation of the three interrelated ICH guidelines which should be used in a holistic manner to ensure consistent product quality and thus its clinical performance. The most important sources of variability in DPIs development and production can be categorised as follows:

• Quality characteristics of drug substance and excipients • Microparticle production process (milling, mechanofusion, spraydrying, etc.) • Powder manufacturing process (API-carrier blending, soft pellets production, etc.) • Device filling process • Environmental conditions • Design of the delivery device • Primary packaging materials The physicochemical properties of the drug substance(s) play a significant role in powder handling, its aerosolisation after inhalation and its fate in vivo. Different polymorphs and crystallinities affect solubility and absorption of the API which together with particle size 69

70

CCD (optimization DoE) CCF (5 parameters at 3 levels) two-level fractional factorial design (25–1design)

– –

Amikacin

Insulin

Meloxicam

Ciprofloxacin

Rifapentine proliposomes

Solely on the liposome adjuvant system

Coarse lactose carriers/ fine particle lactose

Lactose free flowing/ lactose cohesive Lactose free flowing/ lactose cohesive BDP:FF 100 + 6 lactose blend NextHaler and RS01 device

Microparticles by spray drying

Microparticles by spray drying

Microparticles by spray drying

Microparticles by spray drying

Pro-liposomes for inhalation by spray drying

Liposome-based cationic adjuvant formulation

Physical and bulk properties of lactose preblends

Capsule filling process

Effect of two inhalers and inspiratory profiles on in-vitro drug performances

Device reservoir filling process

Capsule filling process

CCD (optimization DoE) Half fractional factorial (screening DoE)

– –

Ibuprofen Amikacin

Full factorial, CCF and model validation

Yes –

Full factorial (72 exp)

Full factorial

Multi-objective simultaneous optimization approach incorporating artificial neural networks (39 carrier blended with budesonide) Full factorial

–

–

CCF (29 experimental runs)

Factorial design 32

–

Yes

Severity scores for the CQAs and CPPs were calculated and ranked

Yes

Yes

Full factorial

–

L-Arginine

Process investigation among microparticles jet milling, mechanofusion, ball milling and spray-drying Microparticles by jet milling Microparticles by spray drying

Statistical tools

RA

API

Purpose

Table 1 Examples of QbD applied in DPIs development.

PCA; surface plots; coefficient plots

Model validation and DS establishment Pareto chart; factorial plot

Coefficient plots

OOS modeling based on overlaying contour plots for all CQAs Artificial neural networks analysis; coefficient plots

Contour plots; multiple regression analysis

Pareto chart

– Perturbation graph; Pareto chart; interaction plot. Contour plot; surface plot; perturbation plot. Partial least squares projections to latent structures Pareto chart

PCA

Characteristics of the bulk powder; FPF; DD; APIs assays; filling weight DD, MMAD, FPD, extra FPD

Target fill weight; target RSD

Target fill weight; target RSD

In vitro APSD (FPF, MMAD and pre-separator deposition)

Yield, Dv0.5, Dv0.9 Yield, residual water, Dv0.5, bulk density, emitted dose, FPD Residual water, Dv0.5, emitted dose, FPD, ethanol role in particle formation Geometric particle size, density, morphology, outlet temperature, moisture content, and physical and chemical degradation Excipients, particle size, dissolution profile, toxicity, wettability, solid state excipients (quality profile); particle size/specific surface area; appearance, dissolution wettability solid state; solubility Geometric particle size >5 μm; liposomal vesicle size 200–600 nm; encapsulation efficiency > 50%; MMAD <5 μm FPF >50% MMAD; liposome size during processing; moisture content; yield

Moisture uptake, particle size, densities, Hausner ratio, Carr's index, cohesion, FPD and FPF

Output

Buttini et al., 2016a; Buttini et al., 2016b

Cantarelli et al., 2010

Faulhammer et al., 2016

Faulhammer et al., 2014

Kinnunen et al., 2014

Ingvarsson et al., 2013

Patil-Gadhe and Pokharkar, 2014

Karimi et al., 2016

Pallagi et al., 2016

Maltesen et al., 2008

Belotti et al., 2015

Yazdi and Smyth, 2017 Belotti et al., 2014

Lakio et al., 2015

Ref.

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solvent on the morphology of the produced particles. Peclet number and drug solubility in the spraying solution helped to understand the formation mechanism of these amikacin spray-dried particles: amikacin is poorly soluble in water ethanol mixture therefore accumulating and precipitating at the surface, resulting in shell particle formation with voids and low density. A similar finding was published when mixtures of acetone and methanol at different molar ratios were applied to dissolve celecoxib and PLGA: the drug and polymer molecules exhibited different diffusion rates during the process of particle formation, resulting in a non-homogenous drug distribution in the resulting particles (Wan et al., 2013). Within the same context a central composite face centred design (CCF) with five factors at three levels was built to investigate the spray drying parameters and insulin concentration on the product characteristics (Maltesen et al., 2008). DoE and multivariate data analysis (MVDA) were employed to study the effect of process parameters on the characteristics of spray dried insulin particles. The work illustrates the use of the principal component analysis (PCA) to visualize differences and correlations in the spray-dried samples. PCA is a projection method used to reduce complexity and to visualize patterns in complex data sets. It was observed that the first three principal components described 74% of the total variation in the data set, which originated from insulin concentration (PC1), inlet drying air temperature (PC2) and nozzle gas flow rate (PC3). A loading plot of PC1 and PC2 described the relationship between observations and variables: MMAD, mass median diameter, yield, tap density and droplet size all increased with increasing insulin concentration, whereas the degradation product “high molecular weight protein” content decreased with increasing insulin concentration. Recently, Pallagi and co-authors selected an anti-inflammatory microcomposite formula for pulmonary use with local and immediate effect as the target product for modeling the procedure of a QbD based early drug development (Pallagi et al., 2016). According to the QbD methodology and RA, a mannitol based co-spray dried formula was produced with different excipients and meloxicam as the model active agent. The RA performed and the interdependence between QTPPs and CQAs, as well as between CQAs and CPPs was structured, evaluated one by one and then the interaction was rated on a three-level scale. This scale reflected the impact of the parameters' interaction on the product as high (H), medium (M) or low (L). Pareto diagrams showed the ranked parameters according to their potential impact on product quality. Particle size (or the specific surface area) of the API had the highest impact on the desired quality of the final product. It was followed by pulmonary irritation or toxicity properties, wettability and solubility. Microcomposite meloxicam particles were prepared by high pressure homogenization and co-spray drying (Pomázi et al., 2013).

the most common CPPs to vary and investigate are milling (or grinding nozzle) pressure, feed (or pushing nozzle) pressure and feed rate (Vatsaraj et al., 2003). The optimization of air-jet milling process of ibuprofen using DoE methodology was recently performed applying a CCD where the grinding and pushing nozzle pressures were varied from 20 to 110 psi (Yazdi and Smyth, 2017). Output variables included yield and particle diameters at the 50th and 90th percentile. The DoE approach elucidated the optimal milling conditions, which were used to micronize another non-steroidal anti-inflammatory drug using the optimized milling conditions. The milled ibuprofen powders showed a high in vitro respirability (FPF of 67–85%). However, the importance of powder conditioning following milling was pointed out as the storage duration and temperature significantly affected performance (Yazdi and Smyth, 2016). In line with the concept of QbD, numerous examples are reported in the literature aiming to define, understand and control formulation and spray-drying variables to assure the quality attributes of the final product. Several particle engineering approaches are focused on characterization of the final product, followed by adjustment of the composition of the formulation and/or optimizing the spray drying process parameters to meet the specifications. It should be taken in consideration that it is of crucial importance to understand the mechanisms of the particle formation process when spray-drying technology is used. In this regards, Belotti et al. have applied a screening DoE as a statistical tool for the exploration of amikacin spray drying process, through the establishment of mathematical relationships between six CQAs of the finished product and five CPPs (Belotti et al., 2014). From this work it was concluded that the surface-active excipient, studied for particle engineering, in general did not benefit the CQAs of the spray dried powders for inhalation. The spray drying feed solution required the inclusion of 10% (v/v) ethanol in order to produce powders with the desired aerodynamic performance. The Pareto chart (Fig. 4a) illustrated the effect on FPD of each factor and their interactions. The presence of the surface-active excipient (factor D) had a large negative effect on powder's respirability. In contrast, interaction between excipient presence and ethanol content (CD) had a positive effect and it was identified as the only statistically positive interaction. Finally, the drying temperature (factor A) increased the amount of deposited powder smaller than 5 μm (Fig. 4a). Then, an optimization work followed and a CCD was applied in order to identify positive combinations of the production parameters of amikacin spray-dried powders with the intent to expand the experimental space defined in the previous halffractional factorial design (Belotti et al., 2015). It was observed that amikacin respirability was maximized by the addition of ethanol (Fig. 4b). However, expanding the design space towards smaller ethanol levels, including its complete absence, revealed the crucial role of this

Fig. 4. (a) Pareto chart illustrating the rank of the effect the factor values and their interactions on the amikacin FPD (empty bars: significant; full bars: non- significant). Blue bars = negative effects; orange bars = positive effects. A: Drying temp.; B: feed rate; C: ethanol content; D: excipient presence; E: AMK conc. (b) 3D plots for and FPD as a function of feed rate and ethanol proportion in the spray-dried solution. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) Adapted with permission from ref. Belotti et al., 2014 and Belotti et al., 2015.

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are the measured responses. CMAs of lactose monohydrate in carrier based DPI were explored quite recently (Kinnunen et al., 2014). For this analysis, a multi-objective simultaneous optimization approach incorporating artificial neural networks (ANN), together with linear regression analysis, was used to understand the relationship between particle size, bulk property and powder rheological properties of lactose blends, made of different types of coarse and fine lactose fractions, on the in vitro performance of budesonide DPI formulations. Lactose pre-blend carriers were prepared by adding different fractions of fine lactose samples to the coarse carriers and then micronized budesonide was added and the powders mixed. ANN is structured in several hidden and output layers, in which each layer incorporates many units named “neurons”, interconnected with synapses. The strength of connections between two units is called “weights”. ANN and linear regression analyses were then performed to determine how the CMAs of lactose influenced budesonide DPI formulation performance. According to the ANN model, the de-agglomeration efficiency of the drug-carrier blends, measured as FPF, was mainly governed by the percentage of fine lactose particles with size < 4.5 μm whereby a decrease in the drug deposition in the pre-separator was observed. On the contrary, the increase of MMAD was related to the proportion of fine lactose particles with size <15 μm. Filling of powder for inhalation is not an easy task. Respirable drug particles are micronized (1–5 μm) hence are rather cohesive and show poor flow properties and dosing uniformity. When the API dose is high (e.g. up to 200 mg as in the administration of antibiotics) the formulation strategy should limit the inclusion of excipient and the powder flowability has to be improved with manufacturing strategies as particle engineering or microparticle agglomeration (Balducci et al., 2015). On the other hand, several common drugs to treat lung diseases such as β-agonist, corticosteroids and muscarinic receptor antagonists, are active at very low dose (2–600 μg) and they need a bulk excipient, usually lactose, in order to increase the amount of product and to allow manufacturing operations (De Boer et al., 2016). Carrier particles are used to improve drug particle flowability, thus improving dosing accuracy, minimizing dose variability compared with drug alone and make handling easier during manufacturing operations. Filling automatically DPI capsules, reservoirs or blisters, requires equipment capable to dose powders without compaction, as it is essential that powder particles freely de-aggregate when the DPI is activated. Different factors have to be considered (e.g. equipment type, speed, scale) when developing either a clinical or a commercial DPI product. Filling systems are also required to facilitate accurate dosing at low weights, while also ensuring that the amount of powder in every dosage unit filled is precise and reproducible. Filling of these mixtures in single-dose units or reservoirs is a process prone to great variability because of the numerous factors acting either alone or through their interaction. All these processes generate a lot of electrostatic charges that impede the flowability and the uniform distribution of the powders and, if they are not suppressed, they are transferred to the finished product creating aerosolisation problems (Kaialy, 2016). Environmental conditions are closely related to triboelectricity and their effect should be studied. All these parameters, if added to a typical manufacturing process, result in a multicomponent system that, per se, is very difficult to be interpreted and understood without statistical approaches. DPIs are known for their great variability, even after they are launched in the market, creating a lot of concern to the manufacturers, the authorities and the patient. Working within the QbD framework could contribute to more robust manufacturing processes and thus more consistent products. Weight and weight variability of capsules filled with a powder blend for inhalation has been recently investigated (Faulhammer et al., 2014). The influence of two types of powder and four process parameters on the filling of low weight capsules was investigated with a D-Optimal design. The capsule filling machine parameters examined were the dosator diameter, dosing chamber length, powder layer depth and capsule filling speed. The

The optimization of the coating composition with mannitol, PVA and leucine resulted in decreased toxicity and irritation of meloxicam and also increased the wettability. A QbD risk based approach was also employed in the construction of a ciprofloxacin DPI (Karimi et al., 2016). The interdependent rating of the QTPPs and CQAs was calculated using a specific RA software and ranked as "H", "M" or "L" (above detailed). Pareto charts also give a graphical overview of the hierarchy of CQAs and CPPs based on their calculated numerical difference of their influence on the aimed quality of the finished product. This systematic approach led to the conclusion that the particle size of the API was the factor with the highest impact on the quality of the final product. This factor was followed by wettability and dissolution properties. Furthermore, among the CPPs for the product construction by spray drying, the powder composition and in particular the type of adjuvant, was found to have the highest impact on the desired product's quality. The principles of QbD, using DoE were also applied to prepare and optimize proliposomal DPI (Patil-Gadhe and Pokharkar, 2014). The rifapentine loaded proliposomes for the treatment of tuberculosis were prepared in a single step by spray drying method and the independent variables were optimized using a factorial design approach. The work investigated the effect of drug: hydrogenated soya phosphatidylcholine ratio and type of charged lipid on the CQAs, namely mass median diameter, liposomal vesicle size, encapsulation efficiency, MMAD and FPF. Contour plots and multiple regression analysis were used to explain the effect of selected independent variables on dependent variables. Contour plot is a graphical technique for representing a 3-dimensional surface by plotting constant z slices, called contours, on a 2dimensional format. That is, given a value for z, lines are drawn for connecting the (x, y) coordinates where that z value occurs. The contour plot is an alternative to a 3-D surface plot. The results showed that both the independent variables were found influencing positively MMAD and negatively the FPF values. Within the same field, a QbD approach was adopted to the production process of liposome-based cationic adjuvant formulation (Ingvarsson et al., 2013). These types of structures aim to be carriers of vaccines capable of eliciting both humoral and cell-mediated immune responses against co-administered antigen. The applied DoE allowed for the identification of the optimal operating space (OOS) suitable for the production of a final product with the desired CQAs. The operating space is the best set of parameters inside the control space, determined statistically, which enable to accommodate and minimize any natural variability in CPPs and CQAs. In this work the OOS was given by a feed flow rate (1.5 mL/min), a low outlet temperature (75 °C), a medium aspirator rate (90%) and in the area of low feedstock concentration and high atomizing air-flow. Finally, Lebrun et al. demonstrated the applicability of a Bayesian statistical methodology to identify the design space of a spray-drying process (Lebrun et al., 2012). The work started by a predictive riskbased approach quantifying the guarantees and the risks to observe whether the process shall run according to specifications. These specifications describe satisfactory quality outputs and were defined a priori given safety, efficiency, and economical reasons. Within the identified design space, validation of the optimal condition was effectuated. As presented above there are several examples in the literature on QbD approach in microparticle formulation and innovative process manufacturing. On the contrary only a few systematic data have been published up to date on the mixing process and lactose characteristics for carrier based formulations. The progress in this field will remain limited as the thorough investigation of the correlation between mixing parameters, formulation composition, interfacial forces and the aerosolisation performance is not advancing (De Boer et al., 2012). During the formulation development, the type of the carrier and its ratio with the API together with the mixing parameters (e.g. type of equipment, rotation speed, mixing time) are factors that construct a typical DoE whereas content uniformity and flowability of the generated mixtures 72

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same design was applied to the capsule filling feasibility study of different powders: one for powders with a particle size larger than 10 μm and a bulk density > 0.45 g/mL (coarse carriers) and the other for more challenging powders (fine carriers and API) with a mean particle size under 10 μm and a bulk density < 0.45 g/mL. A MVDA (partial least square method) was used to analyse the regression between factors (X) and responses (Y) and a linear model for capsule filling of low- fillweight inhalation products was developed for the two groups of powders. The fill weight of both powder groups was affected by the same process parameters but by different material attributes. The RSD of both powder groups was influenced by the powder density as material attribute but the significant process parameters affecting the filling were different. While for bigger particles with high density, dosator diameter and powder layer depth influenced the capsule filling performance, smaller particles with lower density were also affected by the capsule filling speed. In a later work, the development and refinement of a QbD-based model for capsule filling operations of low fill weight inhalation products is described. The model and the associated design space were used to predict the capsule filling performance of powders that were not part of the development set using D-optimal designs (Faulhammer et al., 2016). In this study a DoE was constructed with experimental runs for free-flowing powders and a second one for cohesive powders. Then, a linear predictive model for each powder group was developed using partial least square statistical method. Furthermore, a model refinement was performed by CCF design developed within the space of the screening DoE that produced the most promising results. Finally, the model for each powder group was validated using a lactose type (InhaLac 250) that was not included in the design matrix. Based on the developed model Fig. 5 illustrates the knowledge space for free-flowing lactose for a target weight of 19.5–20.5 mg (common fill- weight in capsules for DPI). In the light grey area, either target fill weight or target RSD were satisfied and in the white area none respectively while the dark grey area represents the “process window” where target weight and RSD will be achieved. The predicted process window was validated using InhaLac 250 with a combination of CPP indicated by the star point. The results were in perfect agreement with the prediction of the model. This indicates that InhaLac 250 could be successfully filled giving the desired quality attributes in the narrow process window predicted by the model. The filling process to dose bulk powder in a DPI reservoir device was investigated as well using a QbD approach (Cantarelli et al., 2010). A full factorial study was constructed to analyse the effect of three main

factors (Auger speed [RPM], Auger number of revolutions [n] and hopper loading frequency [valve]) on the filling of NEXTHaler® device. It was determined that the API assay and delivered dose were not influenced by any of these factors or their interactions. The average filling weight was influenced by all the three parameters considered in the DoE, although the filling weight precision (standard deviation) was independent of them. Since the only non-continuous CPP was the valve, and flowability data showed valve at −1 level as the preferred condition, a mathematical model was devised in order to predict the filling weight only with different Auger speed and Auger number of revolutions. The mathematical model predicting the filling weight was the following: Average filling weight [g] = a + b (Auger speed) + c (Auger number of revolutions). The predominant quality aspect in the formulation of a DPI is the respirability of the product. Aerosolisation properties, including emission, dispersion and deposition profile, are governed besides the formulation properties by the device characteristics and patient inhalation profile (Azouz and Chrystyn, 2012; De Boer et al., 2016; Hoppentocht et al., 2014). The de-aggregation of the powder formulation is driven by a turbulent energy, measured as pressure change, created inside the inhaler by the interaction between the patient's inhalation pattern and the resistance of the DPI (Buttini et al., 2016a; Pavkov et al., 2010). One major trend of the ongoing research on the devices for inhalation is the development of flow rate independent inhalers (Corradi et al., 2014; Chrystyn and Niederlaender, 2012; Hamilton et al., 2015), i.e. those capable of providing consistent emitted and respirable dose that are minimally affected by changes in the patient's breathing pattern as in the case of asthma attacks or exacerbation (Islam and Cleary, 2012). On this respect, the effect of two different types of inhaler and of the actual inspiratory profiles of asthmatic patients on in vitro aerodynamic performances of a formoterol fumarate and beclomethasone dipropionate powder formulation was evaluated by means of a multivariate approach (Buttini et al., 2016b). The two devices considered were a multidose inhaler and a single dose capsule inhaler having a similar resistance. A full factorial design was performed and multiple linear regression allowed estimating the coefficients of the model for the response PC1 (results related to the aerodynamic particles distribution) and PC2 (delivered dose) (Fig. 6). Inspiratory profiles had an influence on inhalation performance in the case of the capsule inhaler: delivered and respirable dose were significantly lower when the device was actuated using the profile with the minimum peak inspiratory flow. Regarding the inhaler design several aspects need to be (re)considered and evaluated to assure a desired and consistent performance. Fig. 5. Process window of a free-flowing lactose powder capsule filling process. In the light grey area, either target fill weight or target RSD, and in the white area none of them are satisfied. The dark grey area represents the process window where target weight and RSD will be achieved. The star point (*) represents the operating conditions used for process validation. Adapted with permission from ref. Faulhammer et al., 2016.

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whether QbD is even nowadays a standard widely used approach for product development across the Industrial sector. Based on a “better late than never” attitude the next steps could be to assist in identifying the gaps in the QbD implementation and construct the corresponding bridges to conclude the development cycle successfully. The first pitfall is the non conscious and occasional use of QbD for just conforming to the guidelines. Adoption without understanding the fundamental aspect of the Quality theory calling for the absolute respect of the patient needs does not improve the outcome. This focal point is the cornerstone of the QbD roadmap as originally defined by J. Juran. However, this is not stressed enough in the regulatory guidelines starting for example with the vague, dictionary type, of definition for Quality where the major beneficiary i.e. the patient is missing. This is also reflected in the description of the QTPP, which while is correctly considered as the starting development point fails in twofold as it is both not patient centric and not referred as the end point as well, to underline the need of comparison between its theoretical aim and the practical result when the development loop is closed. The quality movement in the pharmaceutical industry was practically led by the FDA scientists publishing and lecturing extensively upon the rise of the 21st century. A series of guidelines focusing on Quality, process engineering theory and tools were proposed for immediate implementation, which in practice was not the case for reasons related to non-philosophically supportive management. Moreover, these guidelines alone do not embrace all the necessary knowledge either for working within the QbD mind-set or indicating which type of statistical tool should be used, how and when. For example while promoting the pharmaceutical quality system at the organizational level, the guideline fails to show both the process as a system of input variables interacting to each other to achieve its aim and the basics of the system thinking theory. These general pitfalls in QbD implementation across the Pharma sector are evident in the DPIs development as well. The major missing link here is lack of understanding the interaction between the formulation, the device and the patient in order to optimize inhalers' clinical performance and assure its consistency through product's lifecycle. This DPI specific pitfall might lead to non-clinically relevant developments while reflecting at the same time that device engineers usually work in different silos than the formulators. This finally reveals that we all work without the system thinking hat on and with our eyes not entirely focused on the patient.

Fig. 6. Multiple linear regression on the two main responses described within the PC1 and PC2 (i.e. Fine Particle Mass and Delivered Dose) was performed and the coefficients and the equation of the model for the formoterol fumarate delivered dose are represented. The statistically significant coefficients of the model were the type of inhaler, the type throat, the interaction between the type of throat and the type of inhaler, the quadratic term of the inspiration volume. Adapted from ref. Buttini et al. (2016b).

The geometry and the tolerances of the active parts of the mouthpiece contribute to the dispersion energy, the velocity of the airflow and the created turbulence. These are significant elements for the efficient disaggregation of the drug particles and their momentum. The capacity of the device to deliver consistently the same dose is a very critical feature, especially in cases of lifesaving medications. Even the selection of the construction materials could be important as they may create electrostatic effect to the powders or be source of extractables and leachables. Inhalers have to comply with the Center for Devices and Radiological Health program of FDA and with the new European medical device regulation (Regulation (EU) 2017/745) which was adopted in May 2017 and will entry into force after a transitional period of three years (Donawa, 2017; web ref 13). A RA plan on the device should be developed at the beginning of inhaler development and then in combination with the API's formulation as the performance aspects of the devices interrelated with the formulation characteristics. Through a well-designed series of experiments the various sources of variability could be identified, understood and subsequently controlled and thus the specifications for the delivering device could be consistently met. Another crucial factor that is part of a RA plan is the packaging of the product. For DPIs, any absorption of moisture could have catastrophic results, as micronized powders tend to agglomerate and increase their size to bigger than 5 μm. The type of material and its compatibility with the enclosed ingredients can be considered as high risk variables. Packaging materials are therefore very important to the chemical and physical stability of the product and should be included in an experimental design within a formal QbD development.

Acknowledgments The authors would like to acknowledge the financial support provided by COST-European Cooperation in Science and Technology, to the COST Action MP1404: Simulation and pharmaceutical technologies for advanced patient-tailored inhaled medicines (SimInhale). Authors wish also to acknowledge Dr. Stavros Politis and Paolo Mazzoni for their advice and support during the in the internal reviewing of this article. Disclaimer The content of this article is the authors' responsibility and neither COST nor any person acting on its behalf is responsible for the use, which might be made of the information contained in it.

5. Conclusions References QbD theory and tools, while published and analysed long ago, were frozen for decades before to be sporadically adopted by the Pharmaceutical Industry. This was an inevitable “compliance” after the explosion of the numerous quality related guidelines by the regulatory authorities during the last ten years or so. While many researchers are rushing and competing for publishing within this field, it is not sure

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