A compression behavior classification system of pharmaceutical powders for accelerating direct compression tablet formulation design

Journal Pre-proofs A compression behavior classification system of pharmaceutical powders for accelerating direct compression tablet formulation design Shengyun Dai, Bing Xu, Zhiqiang Zhang, Jiaqi Yu, Fen Wang, Xinyuan Shi, Yanjiang Qiao PII: DOI: Reference:

S0378-5173(19)30787-2 https://doi.org/10.1016/j.ijpharm.2019.118742 IJP 118742

To appear in:

International Journal of Pharmaceutics

Received Date: Revised Date: Accepted Date:

19 March 2019 26 August 2019 26 September 2019

Please cite this article as: S. Dai, B. Xu, Z. Zhang, J. Yu, F. Wang, X. Shi, Y. Qiao, A compression behavior classification system of pharmaceutical powders for accelerating direct compression tablet formulation design, International Journal of Pharmaceutics (2019), doi: https://doi.org/10.1016/j.ijpharm.2019.118742

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A compression behavior classification system of pharmaceutical powders for accelerating direct compression tablet formulation design

Shengyun Dai1,2, Bing Xu1,3*, Zhiqiang Zhang4, Jiaqi Yu1, Fen Wang1, Xinyuan Shi1,3, Yanjiang Qiao1,3*

1

Department of Chinese Medicine Information Science, Beijing University of

Chinese Medicine, Beijing 100029, P. R. China 2

National Institutes for Food and Drug Control, Beijing 100050, P. R. China

3

Beijing Key Laboratory of Chinese Medicine Manufacturing Process Control

and Quality Evaluation, Beijing 100029, P. R. China 4

Beijing Tcmages Pharmceutical Co. LTD, Beijing 101301, P. R. China

Correspondences: Bing Xu and Yanjiang Qiao. Address: School of Chinese Materia Medica, Beijing University of Chinese Medicine, No.11, North Third Ring East Road, Beijing City, 100029, P. R. China. E-mail: [email protected] (Bing Xu), [email protected] (Yanjiang Qiao)

1

Abstract In this paper, a compression behavior classification system (CBCS) for direct compression (DC) pharmaceutical powders is presented. Seven descriptors from a series of compression models for powder compressibility, compactibility and tabletability analysis were included in the CBCS. A new tabletability index d was proposed to differentiate three categories of tensile strength (TS) vs. pressure relationships, and its physical meaning was explained thoroughly. 130 materials containing diverse pharmaceutical excipients and natural product powders (NPPs) were fully characterized and were compiled into an in-house developed material library, in which 70 materials with potential DC applications were used to justify the effectiveness of the CBCS. The principle component analysis (PCA) was used to uncover the latent structure of compression variables. Moreover, the partial least squares (PLS2) regression models are established in prediction of both tablet TS and solid fraction (SF) based on the raw materials’ physical characteristics, the compression behavior indices and the compression force. The obtained scores and loadings are used to group the materials and the compression variables, respectively. Different categories of tabletability for DC powders were clearly clustered along two orthogonal directions pointing to the index d and the compression force. Finally, a multi-objective design space was identified under the latent variable space, 2

summarizing the operationally possible region for both material properties and compression pressure required in DC tablet formulation design.

Key

Words:

Compression

behavior

classification

system

(CBCS),

Compressibility, Compactibility, Tabletability, Design space, Formulation design

1. Introduction As one of the most important dosage forms, the tablet has increased in popularity as both old and young patients find it convenient and easy to use. According to the U.S. FDA’s Center for Drug Evaluation and Research (CDER) report about novel drug approvals, tablets dominated 31.8%, 37.0% and 39.0% of new pharmaceutical products for the three consecutive years from 2016 to 2018, respectively, demonstrating that tablets were the first choice when developing drugs into medicines (CDER Report, 2016; CDER Report, 2017; CDER Report, 2018). A recent survey of 354 European Public Assessment Reports (EPAR) for tablet formulations from the manufacturing classification system (MCS) working group of Academy of Pharmaceutical Sciences (APS) summarized that tablet products were mainly produced by direct compression (DC), dry granulation (DG), wet granulation (WG) or other technologies (Leane et al., 2018). Among the four tablet manufacturing processes, DC has many advantages, such as simplicity, cost effectiveness and inherently continuous 3

nature (Van Snick et al., 2017). The resulting tablets must possess a certain mechanical strength to withstand the subsequent processing, transport and handling by the patient (Pitt et al., 2013). Understanding the ability of a powder mixture to form a compact under a specific mechanical stress is of great significance to achieve the science-based tablet formulation development and manufacturing process control within the framework of quality by design (QbD) (ICH Q8, 2009; Tho and Bauer-Brandl, 2011). During the densification process of a powder bed after die filling, a number of mechanisms including particle rearrangement, fragmentation, plastic and elastic deformation, are active (Nordström et al., 2009; Klevan et al., 2009; Roopwani and Buckner, 2011). These mechanisms play a major role in the inter-particulate bonding force and the bonding area that dominate the tensile strength (TS) of tablets (Osei-Yeboah et al., 2016; Lamešić et al., 2018; Sun, 2011; Eriksson and Alderborn, 1995). For instance, brittle materials like lactose will more easily fragment into smaller parts which potentially increase the bonding area (Nyström et al, 1993). Several well established functional relationships between porosity vs. pressure (compressibility) (Busignies et al., 2006), TS vs. porosity (compactibility) (Wu et al., 2005; Michrafy et al., 2007) and TS vs. pressure (tabletability) (Joiris et al., 1998; Sun and Grant, 2001) were proposed and widely used to describe the compression behavior of powders, since natural strain is proportional to the changes in the powder-bed height or volume under applied pressure. The widely used mathematical 4

functions including those of the Heckel Equation (Heckel, 1961), the Kawakita Equation (Kawakita, 1971), the Adams Equation (Adams et al., 1994; Adams and McKeown, 1996), the Cooper-Eaton Equation (Cooper and Eaton, 1962; Paronen and Ilkka, 1996) and the Gurnham Equation (Gurnham and Masson, 1946; Zhao et al., 2006), etc., were used to correlate the pressure and porosity. The Ryshkewitch–Duckworth (Ryshkewitch, 1953; Duckworth, 1953) Equation was used to correlate tensile strength and porosity. Attempts to mechanistically model the relationship between tensile strength and pressure in terms of theoretical or semi-empirical expressions have also been presented, for example by Leuenberger (Leuenberger, 1982) and Alderborn and coworkers (Eriksson and Alderborn, 1995; Alderborn, 2003). The compression parameters such as Kawakita a, b-1, and Heckel Py, that reflect the physical properties of the powder can then be derived from the above equations to differentiate powders showing different compression behaviors. It is generally accepted that a single compression characterization method isn’t necessary applicable for every type of material (Paul and Sun, 2017; Michrafy et al., 2002; Roopwani and Buckner, 2011). A reliable understanding of the deformation behavior should depend on simultaneous analysis of diverse compression models. For instance, Nordström et al (Nordström et al., 2012) proposed a protocol for classification of powder compression characteristics by setting critical values for a set of compression descriptors. The classification steps were run sequentially, and powders could be classified into four groups 5

with different particle rearrangement and fragmentation propensity. Multivariate analysis (MVA) tools are also useful to uncover the intrinsic structures or to group a variety of materials. Roopwani et al (Roopwani et al., 2013; Roopwani and Buckner, 2011) applied the principle component analysis (PCA) to extract variance information from both the force-displacement profile data and the deformation parameters derived. It was found that the first principal component related with the plastic deformation parameters. In another case, Klevan et al (Klevan et al., 2010) revealed that the variance of the first PC was mainly explained by the particle rearrangement indices. In additional to unsupervised pattern recognition via PCA, it is practical to quantitatively predict the tablet performance. Haware et al (Haware et al., 2009) developed a partial least square (PLS) model to quantify the relationships between the material properties, the process parameters and the tablet TS, and it was found that the Kawakita parameter (b-1) and apparent work of compression could aid the prediction of TS of plain lactose tablets. On the basis of the volumetric additive rule, Reynolds et al (Reynolds et al., 2017) developed a model by combining the Gurnham Equation and the Ryshkewitch-Duckworth Equation. Within the scope of investigated pharmaceutical materials, i.e. microcrystalline cellulose (MCC), mannitol and anhydrous dicalcium phosphate, they found this model could predict TS for Type I(a), I (b), I (d) and I (g) binary mixtures out of the 15 sub-classes summarized by Sun (Sun, 2016). Furthermore, a large dataset of pharmaceutical materials covering as much 6

as variation is another key for successful formulation design and accurate prediction of tablet forming ability (Paul and Sun, 2017; Haware et al., 2009; Peeters et al., 2018). Thoorens et al (Thoorens et al., 2015) described a database of 84 MCC materials with 10 physicochemical properties. The particle size and the moisture content were identified as the critical material attributes (CMAs) that were well correlated with TS of neat MCC tablets. In a recent research, Hayashi et al (Hayashi et al., 2018) built a dataset containing active pharmaceutical ingredient (API) characteristics of 81 kinds of model APIs. Tablets with 50% API, 49% MCC and 1% lubricant were compressed. By defining 12 physical characteristics of API and 3 levels of compression force as independent variables and the TS at every compression force as the dependent variable, a nonlinear regression model fitted by the boosted-tree algorithm was obtained with satisfied calibration and prediction performance. The Center for Structured Organic Particulate Systems (C-SOPS) reported a material library containing 20 pharmaceutical materials with 32 property measurements (Escotet-Espinoza et al., 2018a) and the same group aimed at developing correlations between raw material properties and unit operation models which can aid process development, especially in design space characterization and robustness analysis (Escotet-Espinoza et al., 2018b). The current study is intended to develop the compression behavior classification system (CBCS) for pharmaceutical powders for direct compression, based on the state-of-the-art knowledge from the compression 7

mechanics. Compared with the Nordström’s classification system that relied solely on compressibility descriptors and ignored the last stage of a compression profile (i.e. the formation of a compact of nearly zero porosity), the CBCS includes not only the compressibility descriptors but also the compactibility and tabletability descriptors. A new tabletability index d is proposed from a power model describing the TS vs. pressure relationship, and its physical meaning is well explained in detail. A large material database containing pharmaceutical excipients and natural product powders (NPPs) is established to test the effectiveness of CBCS. PCA analysis is used to transform the original dataset into a low dimensional space. In addition, PLS models are established in prediction of both tablet TS and solid fraction (SF) based on the raw materials’ physical characteristics, the compression behavior indices and the compression force. The obtained scores and loadings are used to group the materials and their compression properties, respectively. Finally, under the latent variable space, a multi-objective design space is developed to summarize the feasible region and knowledge for DC tablet formulation design.

2. Materials and methods 2.1 Materials A total of 130 powdered materials including 77 pharmaceutical excipients and 53 NPPs were obtained. A variety of diluents, binders, disintegrants and lubricants were purchased commercially to expand the variation coverage of 8

material properties. NPPs were manufactured by a series of unit operations, such as pretreatment of medicinal herbs, extraction, filtration, concentration and spray drying, at GMP certified facility of Beijing Tcmages Pharmaceutical Co., Ltd. The spray dried NPP materials were multi-component in nature, and they were expected to exhibit different properties compared to the commonly used excipients (Li et al., 2018). Different batches or types of the same material were also considered and included in the material library. For instance, thirteen types of MCC powders such as PH101, Heweten 101, PH102, Oricel PH-112, PH200, vivapur type200, PH301, PH302, KG802, etc., were contained. The detailed information of all excipients and NPPs including the names, abbreviations, the batch numbers and/or types, and the vendors are described in Appendix A. 2.2 Characterization of powders The pharmaceutical excipients were sifted through an 850 μm aperture size sieve to remove any lumps present, spread over a paper-lined tray and conditioned in a hot air oven at 60 °C for 2 days (Tay et al., 2017). This heat conditioning operation was to reduce possible variability due to moisture contents and powder history. The conditioned powders were then equilibrated for at least 3 days in an environment of relative humidity (RH) maintained at 50% and temperature of 25±2°C. Before testing, the powders were sifted again as before. Because NPPs are sensitive to heat and RH, they were stored in an environment with room temperature and RH lower than 35%. The NPPs were only sifted through an 850 μm aperture size sieve to remove any lumps before 9

use. A comprehensive physical characterization of powders was performed according to the SeDeM expert system (Cui et al., 2017; Hamman et al., 2018; Hamman et al., 2019). 12 direct compression related parameters, such as bulk density (Da, g·cm-3), tapped density (Dc, g·cm-3), inter-particle porosity (Ie), Carr’s index (IC), Hausner ratio (IH), angle of repose (AOR), flow time (t’’, min), cohesion index (Icd, N), loss on drying (%HR), hygroscopicity (%H), particle size less than 50 μm (%Pf) and homogeneity index (Iθ) were obtained by methods already thoroughly described by other authors (Dai et al., 2019). All the characterization methods and the derived parameters from the references are listed in Table 1. Besides, some other information of materials were added, such as the true density (Dt, g·cm-3), the solid fraction for powder (SFp), the scanning electron microscope (SEM) graphs and the particle size distribution (PSD) determination. The true density was determined with a specific surface & pore size analysis instrument (3H-2000PS1, Beishide Instrument, China). The testing process of true density included three steps. The first step was to measure the volume of the sample tube (V1) when the sample was not added. In the second step, the powdered sample which did not exceed 2/3 of the sample tube volume was added, and the weight of the sample (m) was accurately weighed before. The volume of sample tube after adding sample was recorded as V2. The third step was to calculate the true density according to the Eq. 1. Reported results 10

were the mean value of three repeated experiments. ௠

‫ ݐܦ‬ൌ ௏ ି௏ భ

మ

(Eq. 1)

The solid fraction for powder was calculated by Da and Dt using the following equation: ܵ‫ܨ‬௉ ൌ

஽ೌ ஽೟

(Eq. 2)

The particle morphology testing process included two steps. The first step was sample processing. The conductive adhesive was adhered to the sample base and the powder sample was sprinkled on it. Then, the unbonded powder was blown away by an ear washing ball. After that, the sample was coated with a conductive film. The second step was sample testing. The SEM images were collected at different magnifications with the accelerating voltage of 15.0 kV and the spot size of 3.0 nm under the Everhart-Thornley Detector (ETD) mode of a scanning electron microscope (Quanta 250, FEI Company, Czech). The particle size distribution of materials was measured by means of laser diffraction on a particle size analyzer (BT 2001, Dandong Bettersize Instrument Ltd., China). About 3 grams of samples were continuously delivered with dry vibrating powder dispersion. Inlet air pressure was kept at about 0.2 MPa to make the dispersion of powders effective. The D10, D50 and D90 are the diameters for which 10%, 50% and 90% of the population is below each value, respectively. The width of distribution (Span) is expressed in terms of Eq. 3. Each sample was tested three times. 11

ܵ‫ ݊ܽ݌‬ൌ

஽వబ ି஽భబ ஽ఱబ

(Eq. 3)

The material characterization results were stored at in-house developed intelligent Traditional Chinese Medicine (iTCM) database (V1.0) which was run on the basis of SQL server 2000 management system and programmed by Visual Basic. The physical characterization results of all pharmaceutical excipients and some representative NPPs are accessible from the website (Xu et al., 2018).

2.3 Compression of powders Tablets were manufactured using a single punch tablet press machine (C&C600A, Beijing C&C CAMBCAVI Co., Ltd, China), which was mechanically tooled with flat faced punch and die with 10 mm in diameter. The compacts were produced by unidirectional compression. The applied mean velocity of the upper punch was 28 mm/s. The lower punch was attached to the compression force transducer. Magnesium stearate (Alfa Aesar, England) suspended in acetonitrile was used to lubricate the punch surfaces and the die walls. Once applied, the acetonitrile needs to evaporate before use. The powder was filled manually. A nominal 350 mg fill weight was used for materials with bulk densities above 0.35 g·cm-3. For materials with low bulk densities, i.e. lower than 0.35 g·cm-3 but higher than 0.30 g·cm-3, a smaller fill weight (300 mg) was used to ensure that the compacts produced were thick enough. 12

Materials with bulk densities less than 0.30 g·cm-3 were considered to be unsuitable for direct compression in this situation. Through adjusting the drop distance of the upper punch, each powder was compressed to at least 20 different compression heights. At every compression height, the compression force (in KN) was recorded and displayed on the system control panel. The corresponding compression pressure (in MPa) was calculated from the obtained compression force divided by the punch tip area (1 KN = 12.74 MPa). The compression pressures were between a lower limit giving coherent tablets and an upper limit of 200 MPa. Tablets were distributed in the range of compression pressure as uniform as possible. For each tablet successfully ejected, for consistency, the digital calliper (547-401 Digimatic Caliper, Mitutoyo, Japan) was used to measure its height and diameter, followed by measuring its diametrical breaking force with a tablet hardness tester (YPD-500, Shanghai Huanghai medicine inspection instrument Co., Ltd., China). The tablet tensile strength was calculated from the tablet hardness and geometry using the following equation: ܶܵ ൌ

ଶி

(Eq. 4)

గ஽ு

where TS (MPa) is the tablet tensile strength, F (N) is the breaking force, D (mm) is the tablet diameter and H (mm) is the tablet thickness (Fell and Newton, 1970). The tablet solid fraction (SF) was calculated using the following equation: 13

ܵ‫ ܨ‬ൌ ͳ െ ߝ

ߝൌ

(Eq. 5)

ఘೌ೛೛ ఘ౪

ߩ௔௣௣ ൌ

(Eq. 6)

௠

(Eq. 7)

ವమ గ ு ర

where ¦ is the tablet porosity, ²app is the apparent tablet porosity, ρt is the true tablet density, m (g) is the tablet weight. Since only single powder was compressed into tablets, ρt is equal to the true density of the powder (i.e. Dt). Values of ε, TS and SF were determined for every tablet produced.

2.4 Compression models for powders Once the porosity vs. pressure, TS vs. porosity and TS vs. pressure curves were obtained according to the procedures in Section 2.3, different compression equations could be used to respectively interpret the compressibility, compactibility and tabletability properties of powdered materials. 2.4.1 The Kawakita model The Kawakita Equation (Kawakita, 1971) was developed to study powder densification using the degree of reduction in volume, C, and was expressed as Eq. 8: ‫ܥ‬ൌ

௏బ ି௏ು ௏బ

ൌ

Eq.8 can be rearranged to give: ௉ ஼

௉

ൌ ൅ ௔

௔௕௉

ଵା௕௉

ଵ

௔௕

(Eq. 8)

(Eq. 9) 14

where V0 is the initial volume of the powder bed and VP is the powder volume after the application of pressure P. V0 is set from the bulk density transformed into a corresponding height in die (Odeku, 2007). a and b are constants which are obtained from the slope and intercept of the P/C versus P plots in the pressure range of 10 - 200 MPa, respectively. The constant a is equal to the minimum porosity of the powder system prior to compression, while b-1 (MPa), also known as the coefficient of compression, is related to the plasticity of the material. 2.4.2 The Shapiro model As a model of the powder compression process, the Shapiro compression parameter f is derived from the Shapiro general compaction equation (GCE) (Klevan et al., 2009): Žሺߝሻ ൌ Žߝ଴ െ ݇ܲ െ ݂ܲ଴Ǥହ

(Eq. 10)

where ε is the porosity of the powder bed, ε0 is the initial porosity of the powder bed, P is the applied compression pressure and k and f are constants. Curve fitting of the experimental data was done in the pressure range up to an applied pressure of 50 MPa. 2.4.3 The Heckel model The Heckel Equation (Heckel, 1961; Persson, 2016) is among the most popular methods used in pharmaceutical research to determine the volume reduction mechanism during compression. The degree of compact densification with increasing compression pressure is directly proportional to the porosity: 15

ଵ

Ž ൌ ݇ܲ ൅ ‫ܣ‬

(Eq. 11)

ఌ

where ε is the porosity of the powder bed, P is the applied compression pressure, and k and A are constants. The yield pressure Py (MPa) is the reciprocal of the slope k, which is calculated using linear regression in the specified pressure range of 50-200 MPa. 2.4.4 Gurnham model Considering several limitations of the Heckel equation, the Gurnham equation was proposed to characterize the deformation behavior of pharmaceutical material (Zhao, 2006). The Gurnham Equation was used to model porosity as a function of pressure: ଵ

௉

ߝ ൌ െ Žሺ ሻ ௄

(Eq. 12)

௉బ

where ε is the porosity of the tablet, P is the applied pressure, P0 is the pressure required to produce a zero porosity compact and K is related to the compressibility resistance of the powder. 2.4.5 Ryshkewitch-Duckworth model Ryshkewitch (Ryshkewitch, 1953) and Duckworth (Duckworth, 1953) developed an equation that correlated the tensile strength and the porosity, which was called the Ryshkewitch-Duckworth Equation: ܶܵ ൌ ܶ଴ ‡š’ሺെ݇௕ ߝሻ

(Eq. 13)

where T0 is the tensile strength of the compact with zero porosity and kb is a constant representing the bonding capacity. This relationship has previously 16

been shown to provide a good model for the variation in tensile strength with porosity for a wide range of materials (Wu et al., 2006). 2.4.6 Power model After the study of more than one hundred materials, it was found that a simple power equation (Eq. 14) could be used to describe the relationship between tensile strength and compression pressure: ܶܵ ൌ ݀ܲ ௚

(Eq. 14)

where TS is the tensile strength of the tablet and P is the compression pressure. The parameters d and g are constants, the physical meaning of which can be explained by combining Eq. 12 and Eq. 13 as follows: ܶܵ ൌ ்బ

ೖ್ ௉బ಼

்బ

ೖ್

಼ ೖ್ ܲ

௉బ಼

corresponds to the constant d and

(Eq. 15) ௞್ ௄

corresponds to the constant g. d

is a constant representing the tabletability capacity. Higher T0 and lower P0 means that a powder bed is easier to be compressed into the zero porosity compact. g is a pressure sensitivity index. Higher g value means that a powder bed is easier to be compressed into a tablet at higher pressure. The pressure ranges and mathematical procedures used to calculate different compression parameters are summarized in Table 2. The linear and non-linear regressions are run offline by commercial software. The goal for non-linear fitting is to use an iterative series of linear approximations until the 17

residual becomes less than the tolerance.

2.5 Suggested compression behavior classification system The variables of Kawakita a, b-1 and ab, Shapiro f, Heckel Py, Ryshkewitch-Duckworth kb, and d from the power equation are used as criteria for the compression behavior classification system. To perform the classification of DC powders, a sequential data treatment should be done, which is illustrated in Fig. 1 and the specific steps are as follows. Step 1 to step 3 are identical to the Nordström’s classification system (Nordström et al., 2012) which describes the powder compression process from the aspect of compressibility. Step 4 and Step 5 aim at powder compression classification from the aspect of compactibility and tabletability, respectively. Step 1: The compression profile is firstly adapted to the Kawakita Equation. Powders whose particle rearrangement will have a significant influence on the overall compression process are associated with low values of the parameter b-1 and high values of the parameter a. A combination of ab > 0.1, a > 0.6 and b-1 < 7 are thus typical features for Class I powders. Class II powders are obtained at ab < 0.1. Step 2: The compression profile is thereafter adapted to the Shapiro general compression Equation. For a Class II powder, the f parameter is used as an indication of the degree of particle fragmentation that occurs during compression, i.e. the particles can be classified as brittle (f > 0.1) or ductile (f < 18

0.1). With this parameter, a Class II powder is classified as Type IIA (the ductile) or IIB (the brittle), which means the particle fragments to a low or high degree during compression. Step 3: The compression profile is described in terms of another porosity-pressure relationship, i.e. the Heckel Equation. The apparent mean yield pressure Py gives an indication of the plastic deformation ability of the material and thus can be used to categorize materials from very soft to hard. Step 4: The powder compactibility behavior is described with the Ryshkewitch-Duckworth Equation. The parameter kb is used to reflect the bonding capability between particles. The materials can be classified into the easily compacted (Type E, kb<10) and the difficultly compacted (Type D, kb>10). Step 5: This step aims to explain the categories of tabletability for the powder compression process on the basis of the parameter d (see Section 3.2.3 for details). Category 1 (d ≥ 0.5) and Category 3 (d < 2×10-3) correspond to the excellent and unacceptable tabletability for powdered materials, respectively. Category 2 (2×10-3 ≤ d ˘0.5) powders can be further distinguished into three subcategories (see Section 3.2.3) .

2.6 Multivariate analysis Principal component analysis (PCA) was performed to reveal latent 19

structures in the data set and to identify potential groups of materials. Partial least square (PLS) was a method for relating two data blocks, i.e. the independent variables matrix and the dependent variables matrix. Before modeling, data can be preprocessed using several techniques, such as mean removal, scaling, normalization, standardization, etc. In this paper, the independent data matrix was centered on the variables’ mean vector and scaled by the variance vector. The PCA and PLS algorithms were performed on Matlab 2009a software (Mathworks, USA) with the PLS Toolbox 2.1 (Eigenvector Research Inc., USA). The goodness of fit of the model (R2X) is related to the amount of variability that was captured by the principal components (PCs) in PCA analysis or by the latent variables (LVs) in PLS analysis. The goodness of prediction (Q2X) is calculated by k-fold cross validation. The rule of thumb for building a multivariate model is to select the optimal number of PCs or LVs that result in both good and robust prediction.

3. Results and discussion 3.1 Powder physical properties In terms of general appearance and particle structure, the SEM images show that the particles are of two types, i.e. the primary particles and the complex particles. The primary particles consist of a single solid phase while the complex particles consist of two or more phases, i.e. agglomerated or porous particles (Klevan, 2011). 10 excipients and 38 NPPs are categorized as primary 20

particles. While the remaining 15 NPPs and 67 excipients are categorized as complex particles. It is obvious that NPPs are more likely to be classified into the primary particles and the excipients are more likely to be classified into the complex particles. The morphology of 2 typical primary particles and 2 complex particles are visualized in Fig. 2. The irregular surfaces and shapes can be found in most of the materials. The SEM micrograph of croscarmellose sodium, calcium carboxymethylcellulose (Fig. 2a), and HPMC reveal small, sharp needle-like or strip-like particle. Besides, some strips of particles interweave to form a large particle, such as sodium bicarbonate (Fig. 2b). Most of NPPs (e.g. Radix Glycyrrhizae extract as shown in Fig. 2c) show the spherical shape which is a common feature of spray drying (Gallo et al, 2015), and their surfaces vary from smooth to moderate roughness. For PVPP (Fig. 2d), an interactive mixture is formed, and in other words very small particles are mixed with larger particles, and these smaller particles tend to form a “coat” on the surface of the larger particles. The measured 18 physical properties for 130 materials in the material library are given in Appendix B. To name just a few, the histograms of the median particle size (D50), the span, the bulk density, the tapped density, the Carr’s index and the Hausner ratio are shown in Fig. 3. In terms of particle size, a wide variation is observed among powders. Magnesium stearate (no. 75) has the smallest particle size (D50 = 2.82 μm), while D-Sorbitol (no. 64) has the largest particle size (D50 = 395 μm). 62 kinds of powders, such as corn starch, 21

PVPP XL-10, aluminum hydroxide, etc., can be classified into the Geldart’s group C powders with particle size lower that 30 μm (Geldart, 1973). 44 batches of powders, such as pregelatinized starch, HPMC, L-HPC, lactose Flowlac 100, etc., have D50 values in the range of 30 μm to 100 μm. Compared with frequently used pharmaceutical excipients, the D10, D50 and D90 values of NPP powders are relatively small. The D50 values of all NPPs are no more than 50 μm. The particle size distribution, expressed in span are in a range of 0.85 to 3.90, except for the aluminum hydroxide whose span value is 8.52. A higher span value indicates a wider particle size distribution. Due to the large variation in size and shape, the powders differ considerably in packing properties, having bulk densities ranging from the very lightly packed Radix Dipsaci powder (no. 95, Da = 0.152 g·cm-3) to the densely packed calcium phosphate dibasic powder (no. 68, Da = 0.912 g·cm-3). There is a strong linear relationship between Da and Dc (R2 = 0.89), as shown in Fig. 4. The inter-particle porosity indices are spread from 0.152 (no.62, sucrose) to 3.15 (no. 95, Radix Dipsaci), meaning that different void space occupy the bulk powder bed. The IH values for different powders vary between 1.11 and 1.91, and the Carr’s index changed from 9.80 to 47.8. According to the scale value of flowability specified in the Eur. Ph. 9.0 (European Pharmacopeia, 2017), the IH value smaller than 1.34 or the IC value smaller than 25 is considered an indication of the passable flowability of powder. The powders vary in 22

flowability from the very poor (e.g. no. 70, dicalcium phosphate dehydrate with IH = 1.89) to the excellent (e.g. no. 66, malic acid with IH = 1.14). The angle of repose (AOR) and the flow time (t") are direct flow descriptors for powdered material. Angle of repose values vary between 26.9º and 59.1º. Some powders could not flow freely through the specified test orifice of hopper, and the flow time values tended to infinity. According to the Eur. Ph. 9.0, if the angle of repose is less than 40º, the powder flow property is fair and can flow freely from the funnel without aid. About 32 kinds of powders in our database belong to this group. Good flow property of a pharmaceutical powder is essential to ensure proper die fill during direct compression. However, no single index could quantify power flowability. For example, the IC and IH values of MCC PH102 (no. 8) are 26.3 and 1.36, respectively, revealing the flow character of this material is poor. Whereas, its angle of repose was 38.3º, indicating fair flowability. There are notable differences between excipients and NPPs for the value of hygroscopicity. The average hygroscopicity for the NPPs is 22.8% and the lowest and highest values are 15.7% and 38.7%, respectively. However, as for the excipients, the hygroscopicity is relative low with the average value of 5.70%. The high hygroscopicity value for NPPs may own to their complex compositions containing both APIs and associated components like glycosides, starches, proteins, gums or pectins, etc.. The average loss on drying (i.e. %HR) for NPPs is 5.56% which satisfied the requirements for the botanical extracts in 23

the Chinese pharmacopeia (National Pharmacopoeia Committee, 2015). The %HR of excipients has wide range which begins from 0 to 27.6%. Some excipients, such as CMS-Na (no. 24, 25, 26), PVPP (no. 41, 43, 44, 45), PVP (no. 48), CMC-Ca (no. 50), pregelatinized starch (no. 60), calcium phosphate dehydrate (no. 70), talc (no. 72) and magnesium stearate (no. 76), hold relatively high %HR values which are more than 10%. The proper moisture is conducive to powder compression, but too high values will lead to powder caking and influence the powder compression behavior and the tablet tensile strength (Ahlneck and Alderborn, 1989a). Therefore, different powder pretreatment methods for pharmaceutical excipients and NPPs in Section 2.2 are used to reduce the influence of water changes in powders on the following compression experiments as much as possible. And different powders are stored at low relative humidity that had weak influence on the tablet strength (Ahlneck and Alderborn, 1989b).

3.2 Classification of powders based on compression descriptors The CBCS is intended to provide standardized approaches for powder compression analysis and to categorize pharmaceutical powders with respect to their compression behavior. A total of 70 materials including 37 excipients and 33 NPPs, whose bulk density are higher than 0.30 g·cm-3, are selected to complete the direct compression experiment. Seven compression descriptors included in the CBCS are calculated and presented in Appendix B. According to 24

the CBCS’s step 1 to step 5, powdered materials can be classified into different categories as shown in Appendix B.

3.2.1 Compressibility parameters The Kawakita parameter a represents the maximal engineering strain C∞. The C∞ values of excipients powder bed range from 0.04 (Croscarmellose sodium, no. 27) to 0.71 (MCC vivapur type 102, no. 4). Mathematically the parameter b-1 is equal to the pressure when the value of C reaches one-half of the limiting value (C = C∞/2) (Kawakita, 1966), and for tested materials the b-1 parameter ranges from 0.09 MPa (Croscarmellose sodium, no. 27) to 24.49 MPa (Corn starch, no. 59). It was earlier suggested (Odeku, 2007) that the high ab value corresponded to the high degree of particle rearrangement, i.e. a powder characterized by the high value of parameter a combined with the low b-1. Radix Puerariae extracts (no. 124) and low-substituted hydroxypropyl cellulose LH-21 (no. 49) show high values for the ab index (1.86 and 1.35, respectively), whereas calcium phosphate dibasic (no. 68) and corn starch (no. 59) have low values (both 0.02), meaning that the latter two materials show nearly no initial particle rearrangement and vice versa. The overall compression profile is often divided into two regions, I and II, where region I was non-linear followed by a nearly linear region II. The compression parameter referred to as the f parameter described the bending of the compression profile in region I, here being defined as a range of 25

compression pressures up to 50 MPa. It was earlier suggested (Klevan, 2009) that this parameter indicated particle fragmentation if the incidence of particle rearrangement was low or in other cases indicated a combined effect of particle rearrangement and particle fragmentation. The f parameters vary substantially for the powders used, from 0.51 (Malic acid, no. 66) to 0.01 (Rhizoma Alismatis extract, no. 128), indicating a large variation in compression behavior between the powders in region I of the compression profile. The Heckel yield pressure Py is obtained by nonlinear regression in the pressure range of 50 - 200 MPa. Py represents the compressibility of the powder and varies from 5.96 MPa to 828.5 MPa. Since Py is often used as an indication of the plasticity of particles, the powders could be classified into four different groups that are very soft (e.g. D-Sorbitol, no. 64, Py = 11.9; Herba Ecliptae extract, no. 81, Py = 5.96), soft (e.g. MCC PH102, no. 9, Py = 73.37; Flos Farfarae extract, no. 98, Py = 59.4), moderately hard (e.g. Flowlac 100, no. 18, Py = 197.8; Radix Scrophulariae extract, no. 89, Py = 161.4) and hard (HPMC E5LV ,no. 55, Py = 848.5; Radix Scutellariae extract, no. 80, Py = 207.2), as categorized by Roberts and Rowe (Roberts and Rowe, 1987). The parameter K in Gurnham equation is related to the compressibility resistance of the powder. The Fructus Mume powder (no. 96) has the lowest compressibility resistance (K=6.57) and malic acid (no. 66) has the highest (K=25.8). However, there is slight deviation at very low pressures for malic acid and at higher pressures for Fructus Mume powder, where the elastic recovery 26

dominated. Currently, there are no established criteria for the parameter K to discriminate the compressibility of powdered materials. Hence, K is not included in the proposed CBCS.

3.2.2 Compactibility parameters The parameter kb in the Ryshkewitch-Duckworth equation represents the bonding capacity of powder. Due to extrapolation, the 95% confidence interval for the tensile strength at zero porosity is relatively large, but this is of limited importance when looking over the fitted range of porosities. The bonding capacity, kb, showing that D-sorbitol (no. 64) has the lowest value (kb=3.93) and sodium bicarbonate (no. 71) has the highest (kb=15.59). Generally, different types of lactose had relatively high values of kb, ranging from 9.67 (no. 16, Cellactose 80) to 15.33 (no. 20, Spherolac 100). This implies that lactose powders possess inferior bonding capability. Some frequently used fillers such as calcium phosphate dehydrate (no. 70), calcium phosphate dibasic (no.69) corn starch (no.59) and pregelatinized starch (no.60) also have high kb values. By contrast, the cellulosic materials like different types of MCC, EC (no. 31) L-HPC (no.49), CMC-Na (no. 27) and HPMC (no. 53) bore relatively low kb values. At the same porosity, MCC powders have higher tensile strength than other materials. For MCC PH 102, Wu et al. (Wu et al., 2006), Tye et al. (Tye et al., 2005) and Reynolds et al. (Reynolds et al., 2017) presented values of T0= 29.96 MPa, kb = 7.6; T0 = 24.53 MPa, kb = 6.9 and T0 = 19.4 MPa, kb = 6.1, 27

respectively. Our estimated values range from 13.56 MPa to 18.04 MPa for T0 and from 4.82 to 6.31 for kb, slightly lower than the published data. This may be due to materials’ batch to batch variability or differences in measurement methodology. Besides, it is observed that the correlation coefficient between kb and the tapped density Dc is 0.65, which shows that the correlation strength is moderate (Asuero et al, 2006). This is similar to findings by Thoorens et al that there is an inverse correlation between tapped density and tablet tensile strength (Thoorens et al., 2015). Higher kb or Dc indicates that the inter-particulate interactions are weak.

3.2.3 Tabletability parameters The tabletability could be defined as the ability of a powder to form a coherent tablet with preferred mechanical strength and under the condition of given compression force (Joiris et al., 1998; Sun and Grant, 2001). In Section 2.3.6, Eq. 14 is the simple style of Eq.15 and this equation also illustrates that the tabletability is bound of the compressibility and compactibility. Tablet TS versus pressure relationships for 70 pharmaceutical materials are fitted by Eq. 14. The regression correlation coefficients for all materials are larger than 0.94. Values of the tabletability descriptor d vary from 1.01h10-3 to 2.35, and values of the pressure sensitivity descriptor g vary from 0.202 to 1.32. As shown in Fig. 5, the high correlation strength can be obtained between 28

௞್ ௄

and g (R2=0.7036),

as well as between

between

௞್ ௄

்బ

ೖ್ ௉బ಼

and d (R2=0.9749). The p value for the linear regression

and g is no more than 0.0001, indicating that the linear model is

statistically significant. The parameter g is not included in CBCS, but it shows the changing trend of the TS vs. pressure relationship curve (Fig. 6). The graphic representation has convex function shape when the value of g is lower than 0.95, and 50 materials belong to this group (e.g. the red lines in Fig. 6). When the value of g ranges from 0.95 to 1.05, the graphic representation approximates a straight line, and 12 materials (e.g. some blue and green lines in Fig. 6) fall within this group. 8 materials (e.g. some pink lines) have the concave function shape, showing that the value of g is larger than 1.05. Depending on the magnitude of parameter d, three categories (Table 3) can be used to generally describe the tabletability of different powders. The first category is characterized by dı0.5. Twelve materials falling in this category are all pharmaceutical excipients, such as MCC, Sorbitol, HPMC, EC and L-HPC. At extremely low compression forces of 20~30 MPa, these powders could be easily compressed into tablets with TS higher than 2.0 MPa. The third category is characterized by d < 2h10-3. Five materials such as corn starch, CMS-Na (no. 28), two batches of CMC-Na (no. 25 and 26) and Rhizoma Ligusticie powder belong to this group. Even at high pressure range (150~200 MPa), Category 3 powders could not be compressed into tablet with TS higher 29

than 2.0 MPa. The second category is featured by 2h10-3İd˘0.5. The tabletability behavior of this category could be further divided into three subcategories, i.e. Category 2A, 2B and 2C. Category 2A powders had acceptable tabletability (TS ˚2.0 MPa) at low to middle pressure range (50~100 MPa), while having good tabletability (TS˚3.0 MPa) at middle to high pressure range (100~150 MPa). A total of 30 materials including 4 excipients and 26 NPPs are classified as Category 2A powders. It is found that most NPPs belonged to this subcategory, indicating that these NPPs powders not only serve the purpose of tablet formulations from the aspect of tabletability but also enrich the materials’ diversity of this group. This result is different from Li. et al (Li et al., 2018) who investigated 27 NPP powders and concluded that 85% of NPPs exhibited poor compactibility (i.e. TS<2MPa under 6 KN). The reasons may be that different kinds of NPP powders with diverse preparation technology are employed. Category 2B powders have acceptable tabletability only at middle to high pressure range (100~200 MPa). Fifteen materials including 12 excipients and 3 NPPs are within this group. It could be seen that most of lactose (i.e. 8 kinds out of a total 10), malic acid, one batch of calcium phosphate dibasic (no. 70), and one batch of CMC-Na (no. 29) belong to Category 2B powders. Category 2C powders contain 8 materials including 5 excipients and 3 NPPs. Within this group, five powders such as anhydrous lactose (no. 22), dextrin (no. 61), 30

sodium bicarbonate (no. 71) Radix Glycyrrhizae powder (no. 86) and Radix Scutellariae powder (no. 80) could be compressed into tablet with TS up to 1.67~1.88 MPa, and increasing the compression force is not conducive to improvement of TS. While pregelatinized starch (no. 60), one batch of calcium phosphate dibasic (no. 69) and Radix Puerariae powder (no. 124) could only be compressed into tablet with TS up to 0.73 MPa, 1.34 MPa and 0.89 MPa, respectively. When Category 2C and Category 3 powders are involved in DC tablet development, attentions should be paid to their percolation thresholds in formulation. A significant disruption in the compact’s properties could be observed when approaching the percolation threshold (Gentis and Betz, 2012; Worku et al, 2017). For Category 2A and 2B powders, the compression force needs to be fine-tuned in applications.

3.3 Principal component analysis of compression parameters PCA is performed on nine compression parameters that are a, ab, b-1, f, Py, K, kb, g and d. In general, the PCA transforms a data matrix in such a way that the first principal component (PC) lies along the direction with the largest variation in the data set. PC2 is orthogonal to PC1 along the direction of the second largest variation, etcetera. In our case, the first two PCs explain (R2X) 58.2% and predicted (Q2X) 17.7% of the variability of 70 samples. Adding an extra PC does not improve the predictive ability of the model. The PCA biplot is drawn to display together the correlation scaled loading vector and score vector. 31

As shown in Fig. 7, 70 powders are distributed in all quartiles of the biplot, where each variable’s contribution to the first two PCs, and how each observation is represented in terms of PCs are visualized. The loading information revealed that ab, d and b-1 are mainly associated with PC1. Parameters d and ab are on the right side of PC1 while b-1 is on the left side. Parameters Py and K are mainly associated with PC2, and they are on the upper side of PC2. Other four parameters a, f, kb and g, are associated to both PC1 and PC2. The parameters f is located in the upper right quartile, kb is located in the upper left quartile, a is located in the lower right quartile and g is located in the lower left quartile. Variables on the same side of a PC are positively correlated, opposite ones are negatively correlated. The Nordström’s classification protocol used five compression descriptors, i.e. a, ab, b-1, Py, and f (Nordström et al., 2012). Except for the parameter f, the locations of other four parameters in both Nordström’s and our PCA biplots are almost the same, indicating that the two datasets of pharmaceutical materials shared the similar hidden structure. The materials are clustered in Fig. 7 if they possess similar compression features. By contrast, materials with distinctly different parameters are located on opposite sides of the score plot. Although there existed some overlapping regions between NPPs and the excipients, their compression behaviors are different. It is clearly seen that the NPPs are relatively concentrated a bit below the center point, and are inversely correlated with Py and K. While, the 32

excipients are roughly divided into two categories, in which one category is close to b-1, kb, K and Py and the other approaches f, d and ab.

3.4 Latent variable modeling in prediction of tablet properties The consistent use of CBCS is expected to facilitate the development of DC tablet formulation based on understanding the multidimensional combination and interaction among raw material properties, the process parameters and the tablet performance. Such relationships are further investigated by latent variable regression modeling. In this paper, a detailed single material database consisting of 70 powders with DC potentials is built. Each material is characterized by 27 quality attributes (i.e. 18 physical material properties, 9 compression parameters), providing a material property space full of information and diversity. Considering the compression force as the critical process parameter (CPP), 5 compression force points for every material are selected respectively from five compression force ranges (i.e. 3f0.5, 5f0.5, 7 f0.5, 9f0.5 and 11f0.5 KN). And the number of observations (i.e. tablets) is theoretically 350 (i.e. 70h5=350). Since the compression curves for some materials do not cover the full pressure range, the actual observations number is 332. The measured TS and SF values for all 332 tablets are given in Appendix B. The effects of input parameters on tablet properties (i.e. tensile strength and solid fraction) are investigated. Table 4 shows the input and output variables 33

employed in the PLS model. Different combinations of input variables are evaluated to test their predictability. The material properties and the compression force are selected as the predictors for Model 1. The inputs for Model 2 are the compression parameters and the compression force. In Model 3, the effects of the material properties, the compression parameters and the compression force on tablet properties are studied. The sizes of calibration data matrices for PLS Model 1, Model 2 and Model 3 are 332h19, 332h10 and 332h28, respectively. The PLS2 algorithm is used since there are two dependent variables. The diagnostics of the three PLS models are displayed in Table 5. For each PLS model, four latent factors are enough to explain both the X variance and the Y variance. Adding one latent factor does not improve the model performance. The cumulative values of R2Y (R2Ycum) and seven fold cross validation Q2Y (Q2Ycum) for Model 3 are 73.7% and 72.4%, respectively, which are the highest among the three established models. The results reveal that the predictive performance of Model 3 is the best, as well as indicating that an integration of all predictors is of benefit in prediction of tablet properties. Weight vectors that combine the independent variables to form the scores of the PLS model are found by the NIPALS algorithm. Fig. 8 shows the bar plots of the loading weights that express the dominating correlation structure of the X matrix. X variables with large weights are highly correlated with Y scores. 34

The first LV mainly describes the contribution of kb, d and Icd. The second LV is mainly affected by Py, H and Dt. The third LV shows SFp is the main factors, while P is critical for the last LV. The variable importance in the projection (VIP) index is then employed to summarize the importance of the X variables, as shown in Fig. 9. The VIP value larger than 1 indicated the critical variable. Accordingly, the material properties including Da, Dc, Dt, SFp, Icd and H, compression parameters including Py, d, kb and g, and the compression pressure P are recognized as critical parameters. It could be found that these identified important variables reflected the critical aspects of materials’ density, compressibility, compactibility and tabletability. The VIP value of %HR index is low, implying that the impact of powders’ moisture content on tablet tensile strength and solid fraction is weak by controlling the powder pretreatment and storage conditions. The latent space projected from the X data space could then be visualized. The PLS biplot is used to co-chart scores and loadings simultaneously, where both of them are scaled into the -1 to 1 range. Taken the first and the second latent variables as example, the biplot is shown in Fig. 10. Variables near each other are positively correlated, while variables opposite to each other are negatively correlated. The parameter ab is situated near the Iθ, D10, D50 and D90, indicating that particle rearrangement during the powder compression process relied on the particle size distribution of the powder (Nordström et al., 2009). The bonding parameter kb shows strong affinities to Da and Dc, which 35

demonstrates that the density of the powder would affect the bonding capability (Šantl et al., 2011). The parameter g is close to %Pf, implying that powder bed with more fine particles is more sensitive to the pressure changes (Yohannes et al., 2015). Besides, variables close to the center point is not well explained by the model. For example, the parameter f is located near the originate point, and the reason may be that samples with fragmentation properties (e.g. lactose) accounted for a small proportion of the database. It should be noted that the compression force P is also close to center in Fig. 10, since the variability information of P are captured by the fourth LV as shown in Fig. 8. And it is clearly seen that all samples with varying pressure levels are arranged along the direction of the variable P, demonstrating that compression force is the dominating parameter in the tableting process (Patel et al., 2006). What’s more, TS and SF are influenced by different independent variables. TS is mostly affected by Icd, which is the cohesion index in the SeDeM expert system and is determined as the mean hardness of the tablets. The tabletability index d is positively correlated with TS, while the flowability descriptors (i.e. IH, IC and AOR) and the compactibility index kb are negatively correlated with TS. This explains that the interactions between particles played the important role in determining TS (Panda et al., 2015; Paul et al., 2019). SF is highly correlated with the variable a, while the compressibility indexes Py and K are situated on its negative side, confirming that the plasticity indicators of powder made large contributions to the tablet SF. Besides, variables situate at 90 36

degrees from each other are almost uncorrelated in these 2 components. As depicted in Fig. 10, tow arrows across the originate point are drawn to represent two orthogonal directions. One direction is from the point g to the point ab and further to the point d, reflecting the changes in tabletability and particle rearrangement. The other direction is from the point Py to the point P and then to the point SF, corresponding to the changes in powder plasticity as well as the changes of SF under different compression force. Along the two directions, it is found that the powdered samples with different categories of tabletability illustrated in 3.2.3 are well clustered. Observations in Category 1 are situated near the variables d and TS, meaning that their measurements were high in these variables. Observations in Category 2A are situated near the variable SF and are opposite to the variables Py and K, reflecting that they are highly compressible powders. Most of Category 2B observations are close to the plot origin and are close the variable f, demonstrating that they had average properties. Category 3 powders are situated on the right side of the zero horizontal line, and they are characterized by low interparticle bonding capacity and relatively high tapped density. Category 2C powders are not clearly discriminated from other Category 2 observations, coinciding with the previous results as revealed in Fig. 6. All in all, the clustering results within the latent variable space verify the proposed tabletability classification system in Table 3.

3.5 Development of tabletability design space 37

By setting target values on each Y variable and other constraints required, the tabletability design space could be explored and built. One advantage of PLS2 modeling technique is that one set of latent components could generate good linear models for prediction of al1 dependent variables. Such advantage allows the visualization of the multi-objective design space on the same latent variables’ space. In this paper, the target mechanical requirements for DC tablet are defined as TS˚2.0 MPa (Shi and Sun, 2010; Osamura, et al., 2016; Osamura, et al., 2018) at SF between 0.8 and 0.9 (Leane, 2015). When TS is above 2.0 MPa, tablets are often robust to further processing such as coating, packaging, and shipping. As for SF, when its value is greater than 0.9, the risk of coverage and delamination increased due to the presence of locally high density regions. Besides, the 95% confidence limit for all observations’ Euclidean distances from the origin in the score space is set, giving a high level of assurance of the reliability of the developed design space. Samples above the critical limit meant they are far away from others and would be outliers pulling the model in a detrimental way. Fig. 11 shows the latent variable design space based on the first two components of the PLS2 Model 3 developed in Section 3.4. The qualified predicted tablet TS (TS>2.0 MPa) is on the left part of the latent variable space, while the qualified predicted tablet SF (0.8
performance constraints and one model constraint corresponds to the multi-objective design space, as shown by the red region in Fig. 11. In the scores space, there are seven samples outside the 95% confidence ellipse that are MCC type200 (no. 1), MCC PH200 (no. 2), MCC PH102, SCG (no. 3), D-Sorbitol (no. 64), two batches of calcium phosphate dibasic (no. 69 and no. 68) and dicalcium phosphate dehydrate (no. 70), respectively. The three outliers, i.e. materials no. 68, 69 and 70 employ both low values of tensile strength and solid fraction, so their compression behaviors are not satisfactory. Other four outliers obtain very high values of tensile strength, and the three kinds of MCC are with ideal solid fraction while the D-Sorbitol is not. It is also noticed that some powders are located on the boundary of the design space, implying that the corresponding observations would move outside the design space if the compression force is not well manipulated. For instance, the solid fraction of MCC PH102 (no. 9) would be larger than 0.9 when the compression force exceeds 7 KN. The situation for Oricel PH302NF (no. 6) is the same to MCC PH102 (no. 9) but the limit compression force is 9 KN. HPMC Methocel E5LV (no. 55) is more sensitive to the compression force, and only the compression force exceeds 7 KN could it be in the design space. Given proper compression forces, it could be found that most of the Category 1 and Category 2B materials are included in the design space. On the contrary, most of the Category 2A, Category 2C and Category 3 powders are situated outside. These results proves that the feasible region for DC tablet formulation is narrow, and 39

the successful DC formulation design relies on a thorough understanding of the interactions among the critical material attributes, the critical process parameters and the critical quality attributes. A new single material or a powder mixture having similar material properties like Category 1 or Category 2B materials and appropriate compression conditions may be projected into the design space, which provides the practical guides for DC tablet formulation.

4. Conclusion In this paper, the compression behavior classification system for pharmaceutical powders is successfully built. A series of standardized approaches for powder compression analysis are established, and seven compression descriptors representing the powder compressibility, compactibility and tabletability, respectively, are derived. The compression behavior of powders in the iTCM database could be well explained and classified by using critical values of these compression descriptors. The newly proposed tabletability index d integrated four parameters from the Gurnham equation and the Ryshkewitch-Duckworth equation, generating three major categories of tensile strength vs. pressure relationships as well three subcategories for Category 2. It is further concluded that multivariate approaches could be valuable tools to group powders with respect to their compression behavior. Given the same compressibility parameters, two datasets from this paper and the reference exhibited almost the same latent structure, uncovering the possibility 40

for fusing multiple data sources to produce more consistent, reliable and accurate information in the future. Moreover, the CBCS could be a valuable asset in DC tablet formulation design and development. With the help of PLS2 modeling technique, an enhanced knowledge of DC tablet performance over a range of material properties and compression forces is demonstrated. A multi-objective DC tablet formulation design space within the latent variable space is identified. Impacts of materials’ compression behavior and compression force on both tablet tensile strength and solid fraction are clearly visualized. Powders with Category 1 and Category 2B tabletability are found to be prone to form DC tablets. Such knowledge could be implemented to make the development phase more effective and less time consuming. It is important to note that the design space built were based on a library of single materials, and there are still some holes or blank space within the knowledge space. Works of this paper are part of a big project, and future studies would focus on expanding the scope of materials by introducing APIs, premixed excipients and granules, as well as improving the empirical correlations between material properties and manufacturing conditions to provide greater detail and confidence in this system.

Acknowledgement The authors are thankful to Scientific Research Program of Beijing University of Chinese Medicine (No.2019-JYB-JS-015) and research funding 41

supports from the National Science and Technology Major Projects (No. 2018ZX09201011-006, China). The authors would also like to acknowledge the samples provided by Beijing Tcmages Pharmaceutical Co., Ltd.

Disclosure of potential conflicts of interest All authors declare that they have no conflict of interest.

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Conflicts of Interest All authors declare that they have no conflict of interest.

55

Fig. 1 An overview of the suggested compression behavior classification system (CBCS) of pharmaceutical powders. Fig. 2 The SEM graphs of four representative materials. Fig. 2a represents calcium carboxymethylcellulose. Fig. 2b represents sodium bicarbonate. Fig. 2c represents Radix Glycyrrhizae extract. Fig. 2d represents polyvinylpolypyrrolidone (PVPP). Fig. 3 Distributions of D50 (a), Span (b), Da (c), Dc (d), IC(e) and IH (f). Fig. 4 The linear relationship between Da and Dc. ௞ ் Fig. 5 The linear relationship between ೖబ್ (or ್ ) and d (or g). ௉బ಼

௄

Fig. 6 The tensile strength vs. pressure relationships for 70 materials. Fig. 7 The biplot for PC1 and PC2 of the PCA model. Fig. 8 The bar plots of the weights W* of the PLS Model 3. Fig. 7a, 7b, 7c and 7d represent the four latent variables, respectively. Fig. 9 The VIP indexes of independent variables for PLS Model 3. Fig. 10 The biplot for the first two latent variables of Model 3. The red squares represent the Category 1 samples. The Blue triangles represent the Category 2A samples. The green triangles represent the Category 2B samples. The yellow triangles represent the Category 2C samples. The pink diamonds represent the Category 3 samples. The yellow circles represent the material properties. The dark green circles represent the compression parameters. The dark red circle represents the compression force. The dark blue squares represent the tablet properties. Fig. 11 The multi-objective design space developed for the direct compression process. The red squares represent the Category 1 samples. The Blue triangles represent the Category 2A samples. The green triangles represent the Category 2B samples. The yellow triangles represent the Category 2C samples. The pink 56

diamonds represent the Category 3 samples. The light blue areas represent the TS>2.0 MPa. The light yellow areas represent the SF is from 0.8 to 0.9. The light red areas are the design space. The black line is the 95% confidence limit.

57

Table 1 The derived parameters from the SeDeM expert system and the characterization methods.

Incidence

Parameter

Symbol

Equation

characterization methods

Dimension

Bulk density

Da

Da=m/Va

Ph. Eur. (Section 2.9.34) provided detailed descriptions for Da. The bulk density was determined by

pouring powder (m) into a 100 mL graduated cylinder (Va) readable to 1 mL.

Compressibility

Tapped density

Dc

Da=m/Vc

A settling apparatus with a graduated cylinder was used to obtain the volume value (Vc) after 2500 strokes.

Inter-particle porosity

Ie

Ie=(Dc-Da)/Dc×Da

This parameter was calculated by Da and Dc.

Carr’s index

IC

IC=100×(Dc-Da)/ Dc

This parameter was calculated by Da and Dc.

Cohesion index

Icd

Experimental

This parameter was obtained by the hardness (N) of the tablets.

Hausner ratio

IH

IH=Dc/Da

This parameter was calculated by Da and Dc.

Angle of response

AOR

Experimental

This parameter was tested according to Ph. Eur. in Section 2.9.36 using standard apparatuses. Basic

Flowability methods were used to determine the static angle of repose.

Powder flow

t''

Experimental

The test method of this parameter was described in Ph. Eur. (Section 2.9.36). It represented the time

required for the 100 grams of samples to flow from the hole, usually in seconds and 1/10 seconds.

Lubricity/Stability

Loss on drying

Experimental

%HR

The test method for this parameter was described in Ph. Eur. (Section 2.2.32). The sample was dried in the

temperature of 105°C ± 2°C until a constant weight was reached.

Hygroscopicity

Experimental

%H

Determine the increased percentage of the weight of the sample after placing the sample in the

environment with 76% (± 2%) of the relative humidity and 22°C (± 2°C) of the temperature for 24 hours.

Lubricity/Dosage

Particle <50 μm

%Pf

Experimental

The two parameters were determined by the sieve test following the Ph. Eur. The sieve sizes included 355

Homogeneity index

Iθ

Iθ = Fm / (100 +

μm, 212 μm, 100 μm and 50 μm. The percentage of powder retained in each sieve was calculated. The

ΔFmn )

percentage of the powder passing through 50 μm sieve was calculated as %Pf. The homogeneity index was calculated by the equation1.

1

୊

೘ : ‫ ߠܫ‬ൌ ଵ଴଴ାሺୢ೘ ିୢ೘షభ ሻ୊೘షభ ାሺୢ೘శభ ିୢ೘ሻ୊೘శభ ାሺୢ೘ ିୢ೘షమ ሻ୊೘షమ ାሺୢ೘శమ ିୢ೘ ሻ୊೘శమ

where:

ା ‫ڮ‬ାሺୢ೘ ିୢ೘ష೙ ሻ୊೘ష೙ ାሺୢ೘శ೙ ିୢ೘ ሻ୊೘శ೙

Iθ, homogeneity index. Fm, percentage of powder in the majority range;

Fm-1 and Fm+1, percentage of powder in the range immediately below/above the majority range; n,

the number of the fraction studied under a series, with respect to the major fraction;

dm, mean diameter of the powder in the major fraction; dm-1 and dm+1, mean diameter of the powder in the fraction of the range immediately below /above the majority range.

Table 2 Specifications of procedures used to calculate compression parameters. Compression

Equation

Pressure

Curve

Fitting

Compression

Classification

range (MPa)

fitting

requirement

parameter

symbol

Kawakita

25-200

Linear regression

R2 > 0.999

ab, a, b-1

Classĉ

Shapiro

0-50

Non-linear regression

tolerance=1×10-8, iterations=200

f

Class ĊA, ClassĊB

Heckel

50-200

Non-linear regression

tolerance=1×10-8, iterations=200

Py

Very soft, Soft,

behavior Compressibility

Moderately hard, Hard

Compactibility

Gurnham

0-200

Non-linear regression

tolerance=1×10-8, iterations=200

K

None

Ryshkewitch-

0-200

Non-linear regression

tolerance=1×10-8, iterations=200

kb

Type E, Type D

0-200

Non-linear regression

g>0, tolerance=1×10-8,

d, g

Category 1, Category

Duckworth Tabletability

Power

iterations=200

2(A,B,C), Category 3

Table 3 The criteria for tabletability descriptor d and classification results of 70 materials Category

Criteria

Characters description

No. of excipients

No. of NPPs

1

d ≥ 0.5

Excellent tabletability at extremely low pressure range (20~30 MPa)

12

0

2

2h10-3 ≤ d <0.5

A: Acceptable tabletability at low to middle pressure range (50~100

4

26

12

3

C: Unacceptable tabletability over the full pressure range (˘200 MPa)

5

3

Unacceptable tabletability even at high pressure range (150~200 MPa)

4

1

MPa) & Good tabletability at middle pressure range (100~150 MPa) B: Acceptable tabletability at middle to high pressure range (100~200 MPa)

3

d < 2h10-3

Table 4 Description of input and output variables for the PLS model Variable type

Input

Output

Variables

Material property

D10, D50, D90, Span, Da, Dc, t”, %HR, %H, IH, IC, Icd, AOR, Ie, Dt, SFp, %Pf , Iθ

Compression descriptor

a, b-1, ab, Py, f, K, kb, d, g

Compression force

P

Tablet property

TS, SF

Table 5 Diagnostics of three PLS models with different input variables Model 1

Model 2

Model 3

LVs R2Xcum R2Ycum Q2Ycum R2Xcum R2Ycum Q2Ycum R2Xcum R2Ycum Q2Ycum 1

30.9%

31.2%

30.7%

29.3%

40.8%

40.2%

27.2%

35.9%

35.4%

2

45.4%

55.3%

54.4%

50.0%

60.3%

59.4%

41.9%

59.9%

59.1%

3

66.2%

62.6%

61.6%

61.0%

66.2%

65.1%

60.9%

63.7%

62.8%

4

72.6%

69.0%

67.8%

75.4%

69.3%

68.1%

65.4%

73.7%

72.4%

5

77.1%

70.7%

69.0%

78.4%

71.9%

69.3%

72.5%

75.8%

74.3%

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

Figure 9

Figure 10

Figure 11

*Graphical Abstract (for review)