SeDeM expert system for directly compressed tablet formulation: A review and new perspectives

Accepted Manuscript SeDeM expert system for directly compressed tablet formulation: A review and new perspectives

Shengyun Dai, Bing Xu, Guolin Shi, Junwen Liu, Zhiqiang Zhang, Xinyuan Shi, Yanjiang Qiao PII: DOI: Reference:

S0032-5910(18)30871-4 doi:10.1016/j.powtec.2018.10.027 PTEC 13801

To appear in:

Powder Technology

Received date: Revised date: Accepted date:

2 August 2018 29 September 2018 13 October 2018

Please cite this article as: Shengyun Dai, Bing Xu, Guolin Shi, Junwen Liu, Zhiqiang Zhang, Xinyuan Shi, Yanjiang Qiao , SeDeM expert system for directly compressed tablet formulation: A review and new perspectives. Ptec (2018), doi:10.1016/ j.powtec.2018.10.027

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ACCEPTED MANUSCRIPT

SeDeM expert system for directly compressed tablet formulation: A review and new perspectives Shengyun Dai1 , Bing Xu1,2 *, Guolin Shi1 , Junwen Liu1 , Zhiqiang Zhang3 , Xinyuan

Department of Chinese Medicine Information Science, Beijing University of

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Shi1,2 , Yanjiang Qiao1,2 *

Chinese Medicine, Beijing 100029, P. R. China

Beijing Key Laboratory of Chinese Medicine Manufacturing Process Control and

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2

Quality Evaluation, Beijing 100029, P. R. China

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Beijing Tcmages Pharmceutical Co. LTD, Beijing 101301, China

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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)

ACCEPTED MANUSCRIPT Abstract The pharmaceutical tablet formulation design is a risky and challenging process since it largely depends on experience. The fundamental reasons lie in the lack of understanding of powder properties and the interactions between the active

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pharmaceutical ingredients and excipients. To compensate these shortness, the use of

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expert system (ES) in the formulation development has gradually drawn attentions

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during the last two decades. The SeDeM expert system is one such intelligent tool aiming at designing direct compression (DC) tablet. It gathers almost all the

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frequently used physical parameters to fully characterize the compressibility of

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powdered substances. The mathematical equations for selection of excipients reflect the state of art knowledge of DC tablet formulation. In this paper, the detailed history,

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principles, applications and derived forms of the SeDeM expert system were reviewed.

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Contributions of the SeDeM expert system to the manufacturing classification system (MCS) were illustrated. A SeDeM database named iTCM was innovatively proposed. All in all, the functions and application scopes of the originally developed SeDeM

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expert system have been continuously extended and more improvement could be achieved in the future.

Key words: SeDeM expert system, tablet formulation, direct compression, quality by design, database

ACCEPTED MANUSCRIPT 1

Introduction Nowadays, tablets are still the most widely used pharmaceutical dosage forms

of the drugs market. The report presented by the Center for Drug Evaluation and Research (CDER) about the number of novel new drug approvals in 2017 was 46 [1]

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and 23 (50%) were for oral delivery as either capsules or tablets. Oral solid dosage

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(OSD) has been the most important form of administration for a long time and it is

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possible to continue to play a dominant role in the future for their flexible administration, convenient manufacturing processes, good drug compliance, good

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stability and low costs. Direct compression (DC) is the most attractive way to develop

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tablets due to the short manufacturing time, low requirements for equipment, solvents and even residues. The influence of physical properties of excipients on the DC

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method is more significant than other tablet manufacturing methods [2]. It is of great

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importance to select the right excipients to design the tablet formulation when comes to the direct compression.

Innovation of pharmaceutical formulations or improvement of existing

and

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pharmaceutical formulations involves adjusting many raw materials, process variables interactions among them. Thus,

formulation design

is based

on a

multi-dimensional space that is difficult to conceptualize for scientists working in this field [3-4]. For decades, the successful pharmaceutical formulation development has largely depended on researchers’ prior experience, knowledge or expertise. As to the limitations of empirical methods, the development of a formulation not only consumes a considerable amount of materials but also prolongs the time and still

ACCEPTED MANUSCRIPT cannot achieve a flexible formulation design [5]. In the empirical paradigm, formulation development will bring great challenges because there are lots of excipients in each functional category and it is hard to choose the most suitable one [6].

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However, regulatory health authorities have focused on new formulation

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strategies. The international conference on harmonization of technical requirements

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for registration of pharmaceuticals for human use (ICH) Q8 on Pharmaceutical Development outlined the Quality by Design (QbD), which was one of the most

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important fields for this purpose. Pharmaceutical QbD is a systematic approach to

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develop new product which is on the basis of quality risk management and sound science [7-8]. The critical point of QbD based formulation design no longer relied on

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trial and error method but on scientific experimental design and knowledge-based

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methods. The FDA also proposed to establish an expert system to assist the quick selection of excipients and efficient optimization of formulation in its own official report in 2007 [6].

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In ICH Q8, QbD demands to understand how formulation and process variables affect product quality and establish the design space within the knowledge space [9]. In the knowledge space, it could combine as much domain knowledge as possible rather than just the measured for their identification which reduces the need for large quantities of data [10]. The SeDeM expert system developed by Carreras et al [11], could provide such knowledge space to propose assertive solutions during the tablet formulation optimization. It is an improvement of traditional formulation design

ACCEPTED MANUSCRIPT method because it includes not only the physical characterization of powdered drugs and excipients, but also the information about the suitability of the powdered substance for direct compression. Begin with the first reference published in 2005, the attention of the SeDeM has

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grown rapidly. Table 1 shows all the related references published in the past 13 years

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[11-42]. As can be seen from the Figure 1, before 2012, the SeDeM did not receive

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much attention but researchers who proposed it constantly improved it. However, after 2012, more researchers began to discover the advantages of it and the

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applications increased rapidly. The most remarkable development was that the SeDeM

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had been used not only in the direct compression tablets but also in the wet granulation tablets and multiple- unit pellet system. That’s to say, the SeDeM could be

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used to evaluate the physical characteristics of both powders and granules. Besides, in

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order to perform the SeDeM in the formulation design of orally disintegrating tablets (ODT), the SeDeM-ODT was born at the right moment. In recent years, with the increasing focus on the concepts of big data and intelligent manufacturing in the

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pharmaceutical process, the comprehensive evaluation of the powder properties by the SeDeM has laid the foundation for building a direct compression database which facilitates the pharmaceutical manufacturing classification and excipient classification systems. In this work, the detailed history, principles, applications and derived forms of the SeDeM expert system were reviewed. Until now, the SeDeM expert system has become one of the most successful pre- formulation methods, since it gathers almost

ACCEPTED MANUSCRIPT all the frequently used physical parameters to fully characterize the properties of pharmaceutical powders. Besides, contributions of the SeDeM expert system to the manufacturing classification system (MCS) were also illustrated. Moreover, the SeDeM expert system provides a structured and standard form of data collection, so a

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SeDeM database named iTCM was innovatively proposed. All in all, the functions

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and application scopes of the originally developed SeDeM expert system have been

Fundamental aspects of the SeDeM

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continuously extended and more improvement could be achieved in the future.

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In this section, a general introduction of the methodology of SeDeM was presented. The detailed information can be found in the references and books (Table 1)

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if wanted.

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2.1 The 12 basic parameters

As established in the earlier paper [11], twelve basic parameters were used in the SeDeM to quantify the physical properties of powder to be compressed, and these

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parameters could be classified into five groups as follows (Table 2): Dimension Incidence. This incidence included the bulk density (Da) and the tapped density (Dc). The two parameters represented the ability of the powder to pile up and can be used for the calculation of other parameters. Compressibility Incidence. This incidence included the inter-particle porosity (Ie), the Carr index (IC) and the cohesion index (Icd) which were crucial to the compressibility of the powder.

ACCEPTED MANUSCRIPT Flowability Incidence. This incidence included the Hausner ratio (IH), the angle of repose (α) and the flow time (tʺ). These three parameters determined the flowability of the powder Lubricity/Stability Incidence. This incidence included the loss on drying (%HR)

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and the hygroscopicity (%H) which were of great importance in the stability of the

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tablets.

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Lubricity/Dosage Incidence. This incidence included %Particles < 50 μm (% Pf) and the homogeneity index (Iθ). They indicated the uniformity of the powder particle

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size distribution, the lubricity of the powder and the dosage of the finished

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formulation.

Besides, a simple version of SeDeM according to the characteristic of the sample

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was performed by Ofori-Kwakye et al [31]. The dimensional parameters (bulk density

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and tapped density), compressibility parameters (inter-particle porosity and Carr’s index), flowability parameters (Hausner ratio and angle of repose), stability parameter

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(loss on drying) and dosage parameter (% Pf < 75μm) were included.

2.2 Test methods of 12 parameters Whenever possible, the test methods of the 12 parameters indicated in pharmacopoeias were applied. If not available, the experimental procedures specifically for the SeDeM were accepted [11]. The European Pharmacopoeia (Ph. Eur.) [41] was selected to perform the test of Da, Dc, t”, %HR, %Pf and Iθ when the SeDeM was first proposed and some research groups chose the same way. Besides,

ACCEPTED MANUSCRIPT the United States Pharmacopoeia [44] was referenced to perform the test of Da, Dc, α, t”, %HR, %Pf and Iθ by other researchers [37-38]. Bulk density (Da): Ph. Eur. (Section 2.9.34) provided a detailed descriptions for Da [43]. The bulk density was determined by pouring powder (m) into a 100 mL

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graduated cylinder (Va) readable to 1 mL. The bulk density was calculated according

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to Eq. 1: 𝑚

Da = 𝑉

(Eq. 1)

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𝑎

Tapped density (Dc): A settling apparatus with a graduated cylinder was used to

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obtain the volume value after 2500 strokes (Vc). This parameter was calculated using

𝑚

Dc = 𝑉

𝑐

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Eq. 2:

(Eq. 2)

and Dc from the Eq. 3:

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Inter-particle porosity (Ie) (Font, 1962): This parameter was calculated by Da

𝐷𝑐 −𝐷𝑎

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Ie =

(Eq. 3)

𝐷𝑐 ×𝐷𝑎

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Carr index (IC%): This parameter was calculated as follows: IC =

𝐷𝑐 −𝐷𝑎 𝐷𝑐

× 100

(Eq. 4)

Hausner ratio (IH): Hausner ratio was calculated by Dc and Da using Eq. 5: 𝐷

IH = 𝐷𝑐

𝑎

(Eq. 5)

Cohesion index (Icd): This parameter was obtained by the hardness (N) of the tablets. If the powder cannot be compressed, some other powder with the percentage of 3.5% were need to add. Those powder were as follows: talcum powder 2.36%, silicon dioxide 0.14% and magnesium stearate 1.00%.

ACCEPTED MANUSCRIPT Angle of repose (α): This parameter was tested according to Ph. Eur. [43] in Section 2.9.36 using standard apparatuses. Basic methods were used to determine the static angle of repose. This method measured the height (h) and the base (d) of the powder cone, and α was calculated using Eq. 6. 2ℎ 𝑑

(Eq. 6)

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tan(𝛼) =

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Flow time (t’’): The test method of this parameter was described in the Ph. Eur.

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(Section 2.9.36) [43]. It represented the time required for the 100 grams of samples to flow from the holes, usually in seconds and 1/10 seconds, and the measured results

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were the average of the three measured values.

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Loss on drying (%HR): The test method for this parameter was described in the Ph. Eur. (Section 2.2.32) [43]. The sample was dried in the temperature of 105°C ±

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2°C until a constant weight was reached.

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Hygroscopicity (%H): Determine the increased percentage of the weight of the sample after placing the sample in the environment with 76% (± 2%) of the re lative humidity and 22°C (± 2°C) of the temperature for 24 hours.

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Homogeneity index (Iθ) and Percentage of particles measuring < 50 μm (%Pf): The two parameters were determined by the sieve test following the Ph. Eur. [43]. The sieve sizes included 355 μm, 212 μm, 100 μm and 50 μm. The percentage of powder retained in each sieve was calculated. The percentage of the powder passing through 50 μm sieve was calculated as %Pf. The homogeneity index was calculated by Eq.7. Iθ =

F𝑚 100+ ( d𝑚−d𝑚−1 ) F 𝑚−1 +( d𝑚+1 −d𝑚 ) F 𝑚+1+ ( d𝑚−d𝑚 −2 ) F 𝑚−2 +( d𝑚+2 −d𝑚 ) F 𝑚+2 + ⋯+(d𝑚 −d𝑚−𝑛 )F 𝑚−𝑛 +(d𝑚 +𝑛 −d𝑚 )F 𝑚+𝑛

where:

(Eq. 7)

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

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n,

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fraction;

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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

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immediately below /above the majority range.

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It was noteworthy that different authors had different opinions on the selection of sieve sizes to obtain the parameter of homogeneity index Iθ. Gülbağ S et al selected

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for sieve sizes that were 1000 μm, 450 μm, 250 μm and 125 μm to determine the Iθ

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[36]. The sieve sizes of 710 μm, 500 μm, 355 μm, 180 μm, 90 μm and 45 μm were selected by Galdón et al [34]. A more detailed sieve sizes used by Hamman et al [38] included 2800 μm, 2360 μm, 2000 μm, 1700 μm, 1200 μm, 1000 μm, 850 μm, 710

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μm, 500 μm, 355 μm, 212 μm, 106 μm and 45 μm. The sieve sizes used by Luo et al were 180 μm, 250 μm, 355 μm, and 850 μm [32]. As a result, the percentage of powder that passed through the 45 μm or 75 μm sieves was also measured as the parameter of % Pf under different circumstances. Compared with the sieve analysis talked above, powder particle size determination using laser diffraction was an easier, quicker and more accurate method associated with better reproducibility [33,37,46]. The data generated could be split

ACCEPTED MANUSCRIPT into the following different size fractions: the percentage particles between 0 μm and 50 μm, 50 μm to 100 μm, 100 μm to 212μm, 212 μm to 355μm and larger than 355 μm. This data was then used to determine both the homogeneity index, as well as the

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particle size smaller than 50 μm.

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2.3 Transformation equation for each parameter

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After the values were obtained as described above, certain limits of the 12 parameters were set according to the Handbook of Pharmaceutical Excipients [47]

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(Table 2) or alternatively based on experiments. The following step was to convert the

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numeric limits to radius values r for each parameter. All the convert formulas are described in Table 2. The converted data eliminated the influence of the unit, making

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the data of different materials more comparable. The exceptional values that appear

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below 0 were considered to be 0 and those above 10 were considered to be 10.

2.4 Index of good compression

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The numerical values of the 12 parameters of the powder were converted on the same scale from 0 to 10. The circumscribed regular polygon is drawn by connecting all the radius values of the parameters with linear segments (Figure 2). 5 was the minimum acceptable value (MAV) for one parameter that was considered being suitable for direct compression [19]. Three comprehensive indexes, i.e. the index parameter (IP) (Eq. 8), the index of profile parameter (IPP) (Eq. 9), and the index of good compressibility (IGC) (Eq. 10), were calculated to determine whether or not a

ACCEPTED MANUSCRIPT material was suitable for direct compression [19]. The acceptability limit for IP would be equal or higher than 0.5. IP =

𝑁0 P ≥ 5

(Eq. 8)

𝑁0 Pt

No P ≥ 5: the number of parameters when the value of parameters equal or higher

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than 5.

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No Pt: the total number of parameters tested.

(Eq. 9)

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IPP = mean radius value of all parameters

The acceptability limit for IPP would be equal or higher than 5. (Eq. 10)

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IGC = IPP × 𝑓

𝑓 =

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f: the reliability factor and can be calculated as Eq. 11: Polygon area Circle area

(Eq. 11)

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When the tested parameters are 12, f = 0.952.

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If the three comprehensive indexes satisfied the limitation that is IP>0.5, IPP>5 and IGC>5, the powder was suitable for direct compression. If not, some correction

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should be performed for the unsuitable powders.

2.5 Correction for unsuitable materials The SeDeM was beneficial for characterization of materials when considering their suitability for direct compression. Assuming that the physical properties of powdered substance followed the ideal mixing rule, or in other words, the physical parameters could be linearly added, a mathematical equation [13] was established to choose the best direct compression excipient and its optimum amount in correcting

ACCEPTED MANUSCRIPT the unsuitable API. CP = 100 − (

𝑅𝐸−𝑅 𝑅𝐸−𝑅𝑃

× 100)

(Eq. 12)

CP: the percentage of a corrective excipient. RE: the mean radius value of the corrective excipient.

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RP: the mean radius value of the API to be corrected.

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R: the mean radius value to be obtained of the mixture of excipient and API.

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The smallest percentage of the corrective excipient is determined once the unknown values in Eq. 12 have been replaced with the calculated values required for

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each material so as to obtain an R value being equal to 5. If the API had some

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deficient parameters whose radius values were lower than 5, such as Da, %Pf, Iθ, α, t”, Ie, %HR and %H in Figure 3, this API could be mixed with excipients having radius

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values of corresponding parameters higher than 5. In this way, physical properties of

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the API could be corrected in the final mixture. The dashed line in Figure 3 shows the compression characteristics of the final mixture after the addition of the correction material theoretically. The information provided by SeDeM enables the formula tion

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designer to quickly choose the potential excipient, thereby shortening the development time of the formulation.

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Improvements of the SeDeM expert system With the deepening of the research on the SeDeM and the diversification of

research objects, some modifications and improvements of the SeDeM parameters have appeared during its applications. The nonlinear behavior of the powdered system

ACCEPTED MANUSCRIPT was observed, complementing the SeDeM method to identify the critical point of the formulation design space. 3.1 Optimization of the transformation equation of relative humidity (%HR) The moisture of materials was of great importance in compress process. Initially,

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three intervals (Table 3) were used to calculate the relative humidity and each interval

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had a convention equation [15]. Nevertheless, experience using the SeDeM has shown

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that the results of three relative humidity intervals do not change significantly. Powders with the percentage of humidity below 1% was too dry to be compressed. By

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contrast, powder agglomeration and adhesion to punches or dies easily appeared when

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the moisture was more than 3%. Therefore, the range from 1% to 3% was the best

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interval for %HR [48] and a unified conversion formula, (10-v), was built [15].

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3.2 Optimization of the transformation equation of Hausner index (IH) At the initial establishment stage of SeDeM, the limit values used to calculate the Hausner index were based on 22 excipients and the limit values of IH were between

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1.1 and 2.46 [11]. For the purpose of expanding the scope of application and simplicity of calculation, modified limit values were deduced to calculate the Hausner index.

For example, Suñé-Negre et al [15] proposed the limit values between 1 and 3. And the equation 10-(10v/3) was used to calculated the radius of Hausner index. The materials that would have a radius of zero when the values less than 1 and those materials was considered as nonflowing or almost nonflowing.

ACCEPTED MANUSCRIPT 3.3 The SeDeM-ODT expert system A new expert system named “SeDeM-ODT” which helped to identify the characteristics of excipients to be employed to produce orally dispersible tablet (ODT) by direct compression was proposed by Aguilar-Díaz et al [17]. It was the

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combination of the previous SeDeM and three new parameters that were the

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effervescence, the disintegration with disc and the disintegration without disc [17].

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The three new parameters were grouped to form the disintegrability parameter of the excipient. Table 4 demonstrates the disintegrability parameter in the SeDeM-ODT and

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the relative abbreviations, units and transformation formulas are also shown. The test

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methods for the three parameters were presented in the Monograph 701 USP–NF [44] or the Ph Eur <0478>. [43] and Ph Eur <2.9.1> [43]. And the specification for the

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disintegration with disc and the disintegration without disc was lower than 3 min

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which was a specific requirement to evaluate ODT. The SeDeM-ODT included 15 parameters which were used to draw an irregular 15 sided polygon (Figure 4). The index of good compressibility and bucodispersibility

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(IGCB) was provided by SeDeM-ODT to indicate whether the materials were capable to be compressed by direct compression and even demonstrate whether these tablets were satisfying the demands of ODT. The calculation method and the acceptance limit for IGCB were the same as IGC, but the reliability factor equaled 0.971. The difference between IGC and IGCB will be noticed when the powder has good compressibility but poor disintegration. And in this case, the materials will have an IGC value higher than 5, but the IGCB value will be lower than 5. On the other

ACCEPTED MANUSCRIPT hand, if the materials have a good aptitude to be compressed and a good disintegrability properties, the IGCB will be increased with respect to the IGC. Thus, the SeDeM-ODT will select accurately the excipients that can be used to make compressed tablets orodispersible.

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3.4 Nonlinear characterization of parameters

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The SeDeM has taken a step forward in the formulation development,

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standardized the rheological parameters of the powder, and determined the shortcomings and advantages of the powder or powder mixture [12]. Further, the

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basic assumption of this method was that the properties of different materials in the

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formulation were linearly summed [13]. But the percolation theory showed that the powder blend was not a linear system, but a nonlinear system. And a sudden change

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in the properties of the geometric phase transition was possible [34].

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For this purpose, it was proposed to observe the non- linear relationship in the SeDeM based on the percolation theory. For instance [34], different powder mixtures including lactose and theophylline with varied concentrations were prepared and then

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the SeDeM analysis had been applied to each mixture in order to explore the changes of their properties. The percolation thresholds for the powder mixture were found. As was demonstrated in the reference [34], the percentages of lactose proposed by the SeDeM was 74% (w/w) and the maximum percentages of API must be 26%. However, in view of percolation theory, it was necessary to have the API below its percolation threshold in order to reduce the influence of the properties of the drug in the flowability of the mixture. Therefore, the percentages of API needed was below 33%

ACCEPTED MANUSCRIPT (w/w). Although the results of the two theories all show the same trend, about 7% of the excipients will be added in excess according to SeDeM. Therefore, it can be concluded form the comparison of percolation theory and SeDeM that percolation theory can be a complement for SeDeM to achieve more accurate estimation of the

Applications of the SeDeM expert system

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4

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design space for the formulation.

There are two main application fields of the SeDeM expert system that were

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evaluation of the DC suitability of a material and determination of the amount of an

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excipient in DC formulation design. Figure 5 shows a summary on how to apply the SeDeM in the formulation design [19]. According to Figure 5, all related materials

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including the API and the excipients must be characterized by the SeDeM diagram

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beforehand. The characteristics of excipients was beneficial to select a suitable one for direct compression and then the correction equation is used to determine the percentage of the selected excipient. At last, the final formulation of tablets can be

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obtained after the lubricants are added. 4.1 Evaluation of the suitability of a material for direct compression Suñé Negre et al employed API SX-325 as an example to characterize the physical property by the SeDeM and further determined whether it satisfied the requirements for direct compression [16]. The values for the dimension, compressibility, flowability, lubricity/stability and lubricity/dosage were all above 5 and the value for IGC was also above 5, implying that API SX-325 was suitable for

ACCEPTED MANUSCRIPT direct compression. Similarly, the SeDeM was used to evaluate the capability of direct compression for other active pharmaceutical ingredients, such as Captopril [24], memantine orally [36], Polythiourethane - D,L - 1,4 – dithiothreitol - hexamethylene diisocyanate [PTU(DTT-HMDI)] [26], Natural gum [31], Theophylline anhydrous

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[40], Domperidone [22], Cefuroxime axetil (CfA) [18], Paracetamol (PCM) [18] and

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Panax notoginseng saponins [33].

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Excipients can also be physically characterized as the API. For instance, Suñé-Negre et al studied 43 excipients with the disintegrant properties from eight

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chemical families, including microcrystalline cellulose, starch derivates, sodium

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starch glycolate, sodium carboxymethylcellulose, alginic acid, crospovidone, copovidone, magnesium aluminium silicate and calcium silicate [14]. The results

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demonstrated that only 9 excipients had IGC higher than 5 and 5 excipients had IGC

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near to 5. Those 14 excipients can be used for direct compression. 4.2 Determination of the amount of excipient in formulation development The correction equation could be used to calculate the minimum amount of the

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excipient selected to make up the deficiency of the API. Suñé Negre et al described an example including one API (IBUSDM0001) and five excipients (Avicel PH102, Avicel 200, Vivapur 12, Microcel MC250 and Pharmaburst C1) for direct compression to select the optimum excipient for the API. The SeDeM diagram of the API revealed that it had deficiency in the parameters of dimension (r = 3.39), flowability (r = 1.90) and compressibility (r = 4.46). By applying the Equation 12, two kinds of Avicel with the lowest amount were selected in the formulation [19], but the author did not tell us

ACCEPTED MANUSCRIPT the performance information of the tablet based on the formulation. However, there were some failure formulations when using the SeDeM. Scholtz et al studied the ability of the SeDeM to predict concentration combinations between APIs and excipients on account of Equation 12 [37]. The results illustrated that some

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APIs, such as paracetamol and furosemide, could not acquire a success DC

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formulation considering the friability of tablet. Currently, the SeDeM has been mainly

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tested on binary powder systems. The feasibility of SeDeM methodology on a formulation containing two or more excipients can be investigated in future. The

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possibility of using SeDeM methodology for optimization of final tablet product

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quality attributes also need to be further verified.

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4.3 Classification of directly compressible excipients

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The classification of different excipients based on the SeDeM is beneficial for quickly arranging the characteristics of the excipients and choosing the best suitable one for the formulation design. Suñé-Negre et al presented 51 excipients in a periodic

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table with rectangular coordinates [23]. The main characteristics of excipients, such as type (processed or simple), solubility and physical structure could be obtained as soon as possible. Some significant information could be found in the periodic table. Firstly, the best excipient for direct compression had the IGC value of 8.832. Then, it was extremely lack of excipients which were suitable for direct compression because no excipient was distributed in the upper right corner. And such kind of excipients must be obtained through future research. Lastly, of course, more researches were needed

ACCEPTED MANUSCRIPT to validate and perfect the periodic table and the classification accuracy.

4.4 Understanding the physical properties of granules The SeDeM method was extended to evaluate the characteristics of 15 batches

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MCC granules to determine whether the MCC could be employed for direct

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compression [32]. Cui et al presented modalities of the SeDeM method with 16

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parameters [41]. Except for the original 12 parameters, four parameters, i.e. the span, the width, the relative homogeneity index and the aspect ratio were introduced. The

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modified SeDeM diagram were used to understand the physical properties of the

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ginkgo leaf granules which were the intermediate products of the ginkgo leaf tablets. Critical material attributes (CMAs) of granules including Dc、Da、t’’、Ie、IC、width、

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IH、α and Iθ were identified by the orthogonal partial least squares model.

4.5 Development of new forms of tablets A more targeted SeDeM-ODT was used to design the bucodispersibility

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formulation [17]. As the put forward of the SeDeM-ODT, several researchers have used it in the design and optimization of some formulation such as the Itopride HCl ODT tablets [30] and pediatric ibuprofen ODT tablets [35]. The SeDeM also could be applied to investigate the formulation design of multiple- unit pellet system (MUPS) tablets consisting of pellets (produced by means of extrusion spheronisation) with different sizes [38-39].

ACCEPTED MANUSCRIPT 4.6 Other applications The main objective in some studies is not the SeDeM but to employ it to make the studies more perfect and convincing. The SeDeM and QbD were combined to understand the physicochemical phenomena involved in controlled release of

PT

captopril SR matrix tablets [25]. The SeDeM can also be employed as a quality

RI

control and assurance tool for Plant Viagra and Panax notoginseng saponins [27, 42].

SC

The physical characteristics of pharmaceutical excipients by spray drying technique could be modified with the help of SeDeM [28]. In addition, it was valid in defining

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the most appropriate manufacturing technology which is beneficial to the design of

New perspectives

ED

5

MA

the process route for production.

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The SeDeM is not only a pre- formulation design method for DC tablet, but also is helpful to supply a representation of the knowledge that is acquired. The SeDeM provides a structured and standard form of data collection, laying a solid foundation

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for development of a data warehouse. 5.1 Contribution of the SeDeM to the manufacturing classification system Manufacturing classification system (MCS) was proposed by the Academy of Pharmaceutical Sciences (APS) focus groups and the MCS working party in a dedicated APS conference and subsequent discussion in the year of 2014. The MCS was based on the properties of the API and the needs of the formulation to rank the practicability of different processing routes for the manufacture of OSD [50]. Four

ACCEPTED MANUSCRIPT routes, i.e. direct compression (І), dry granulation (Ⅱ), wet granulation (Ⅲ) and other technologies (Ⅳ), were included. A common understanding of risk by defining what the ‘‘right particles’’ for the selection of the best manufacturing process for OSD was proposed by MCS. The capability of every manufacturing route to adapt unsatisfied

PT

physical properties of APIs increases as to choose from Class І to Ⅳ. Figure 6 is a

RI

summary of what material attributes and process parameters constitute the basis class,

SC

together with tentative ranges [51-53]. Generally speaking, finished tablets having a tensile strength greater than 1.7 MPa when the solid fraction is in the range of 0.85 ±

NU

0.05 will be acceptable for subsequent processing such as coating, packaging and

MA

circulations in the market [54]. The desired D50 for tabletting was in the range from 50 μm to 500 μm. At lower sizes, electrostatic, flow [55] and adhesion issues can be seen.

ED

Data in the SeDeM is beneficial for MCS to make a better classification between

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Class (І) and other processing routes. 12 sided radar charts applied by the SeDeM could provide a quick overview of risk levels and help identify flawed physical characteristics and possible failure modes of the compression process.

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5.2 The iTCM database

In the published articles about the SeDeM, many authors proposed to build a database to accelerate the formulation design [22, 37]. Such database laid the foundation of the knowledge space under the framework of pharmaceutical QbD, and could ideally continue to be perfected. Based on the SeDeM parameters, a database named iTCM (http://info.pharm.bucm.edu.cn/xsgz/sjgxpt/48350.htm) is established (Figure 7). It was currently consisted of 91 pharmaceutical excipients which are

ACCEPTED MANUSCRIPT commonly used in pharmaceutical processes, as well as 73 botanical extract powders. As far as we know, the iTCM database is the largest and the solely one at present based on the SeDeM expert system. Except for the 12 basic parameters, other information of the material were added to the database, such as solid fraction, the

PT

SEM graphs, laser particle size test report and true density test report.

RI

In order to acquire high quality, comparable and unified data for the database, the

SC

standard operating procedures (SOPs) for test methods of all parameters in iTCM database were proposed. All the excipients underwent the pretreatment process to

NU

eliminate the influence of the powder history before being tested. The pretreatment

MA

methods were as follows: the powder were sifted through an 850 μm aperture size sieve to remove any clumps present, spread over a paper-lined tray and conditioned in

ED

a hot air oven at 60 °C for 2 days. The conditioned powders were then equilibrated for

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at least 3 days in an environment of relative humidity (RH) maintained at 50% and temperature of 25 °C. Before testing, the powders were sifted again as before. The test method for the 12 parameters was based on the Ph. Eur. except for %Pf and Iθ which

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were obtained by the laser particle size distribution analyzer. The data generated were split into the following different size fractions: the percentage particles between 0 μm and 50 μm, 50 μm to 100 μm, 100 μm to 212 μm, 212 μm to 355 μm and larger than 355 μm. The optimized equations of the %RH and IH were chosen to calculate the radius values. Some other information were collected or recorded, such as batches numbers, merchants, brands, SEM graphs, specific surface, Tg (if any) and production conditions (if any). That was to say, all available information can be easily found in

ACCEPTED MANUSCRIPT the iTCM database. It could be seen that there were obvious differences in parameters values between excipients and natural extract powders. As for the parameters of lubricity/dosage, the particles size of nearly all the extracts were smaller than 50 μm,

PT

resulting in a radius value of zero for the Pf % in the SeDeM. The Iθ parameter had

RI

two extremes that were neither too low nor too high. As for the parameters of

SC

lubricity/Stability, all the extracts showed high hygroscopicity, the values of which were between 15.68 % and 39.57 %, and were higher than that of excipients. If these

NU

extracts were used to manufacture tablet products, special packaging techniques were

MA

required to avoid moisture absorption problems. Furthermore, the limits for the data in the iTCM database for those 12 parameters are shown in Table 5. It can be seen from

ED

the table that the parameters of Dc, Ie, Icd, α, t”, %HR, %H and %Pf was out of the

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acceptable ranges in the SeDeM. Although the value of Iθ located in the acceptable ranges, the value was almost 200 times smaller. The botanical drug powders broke through the generally accepted threshold limits in SeDeM transformation equations

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for chemical drugs and excipients, providing the possibility to expand the application scope of the SeDeM expert system. 6

Conclusion

In this paper, a thorough introduction about the theory, progression and applications of the SeDeM methodology was presented. Until now, the SeDeM expert system has become one of the most successful pre- formulation methods, since it gathers almost all the frequently used physical parameters to fully characterize the

ACCEPTED MANUSCRIPT properties of pharmaceutical powders. The limits of transformation equation of each parameter and the correction equation reflect an integration of the state of art knowledge for tablet formability by direct compression. Moreover, the SeDeM expert system provides a structured and standard form of data collection. If the same test

PT

methods were used, different research groups could compare their SeDeM diagrams

RI

on the same scale. Such way of knowledge sharing is extremely important for the

SC

pharmaceutical development and innovation.

NU

Acknowledgments

MA

The authors are thankful to the Project of National Standardization of Traditional Chinese Medicine (No. ZYBZH-C-QIN-45) and the National Natural Science

Disclosure

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ED

Foundation of China (No. 81403112) for the generous financial supports.

The authors declare there is no conflict of interests regarding the publication of

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this paper.

References

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2017

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ED

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formulations containing novel excipients and different types of active ingredients,

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of multiple-unit pellet system tablets by employing the SeDeM Expert Diagram

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System I: Pellets with different sizes, Pharm. Dev. Technol. (2017) 1. 39. H. Hamman, J. Hamman, A. Wessels, J. Scholtz, J.H. Steenekamp. Development of multiple- unit pellet system tablets by employing the SeDeM expert diagram

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53. J.I. Wells, Determination of the tensile strength of elongated tablets, E. Horwood

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ED

Powder Technol. 238 (2013) 169.

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55. S. Tan, J. Newton, Powder flowability as an indication of capsule filling

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performance, Int. J. Pharm. 61 (1990) 145.

ACCEPTED MANUSCRIPT Table 1 SeDeM related references published Company/institut

Domain

para

ion

Objectives

Develope

meters

d/first reported

Refe rence

year University

of

Barcelona

API

SB-50002,

12

powder-tablet

University

of

Barcelona

2005

[11]

Control

2006

[12]

time

API (Lotes 4/0008, 4/0009

Build the SeDeM for the first

and

4/0011),

glucosamine

salt

12

of

lot-to-lot

reproducibility

of

API

glucosamine

12

salt F0357

Establish

a

six DC diluents

API

University

of

43 excipients

12

powder-tablet

SC

powder-tablet

Barcelona

mathematical

2008

[13]

2009

[14]

2011

[15]

2011

[16]

2012

[17]

equation to correct the defici ent of

RI

University Barcelona

PT

F0130, powder-tablet

Determine the suitability of diluents–disintegrants

for

University

of

Barcelona

22 excipients and

12

10 APIs

12

powder-tablet

University

of

Barcelona

Microencapsulated Ibuprophen

(Model

drug),

several

disintegrants

of Pharmacy

Cefuroxime axetil

EP T

Chitkara College

A summary of the application

of SeDeM

15

ED

Barcelona

API SX-325

MA

of

Optimizing the parameters of

IH and %HR

powder-tablet University

NU

direct compression

An application of the new

SeDeM-ODT

12

An application of SeDeM

2012

[18]

12

An introduction of SeDeM

2013

[19]

15

An

of

2013

[20]

Design formulas for ibuprofen

2014

[21]

(CfA) and paracetamol (PCM)

Barcelona

of

API

CPSMD0001

AC C

University

API

IBUSDM0001 et al

University Barcelona

University

of

of

of

of

12

Domperidone,

tablets. 12

An application of SeDeM

2014

[22]

51 DC

12

Establish a periodic table of DC

2014

[23]

SR

2014

[24]

the

2014

[25]

excipients of

Barcelona University

21

excipients

Barcelona University

Ibuprophen,

introduction

SeDeM-ODT

disintegrants

Peshawar University

and

disintegrant

Barcelona University

API

Captopril,

excipients based on SeDeM 12

excipients of

Captopril,

Optimize the Captopril formula

12

understand

ACCEPTED MANUSCRIPT Barcelona

excipients

physicochemical phenomena

involved

in

captopril SR tablets University

of

Seville

Theophylline

12

anhydrous

SeDeM

2015

[26]

2015

[27]

used to investigate the API suitability

through

a

direct

compression Alexandria

Sildenafil,

University

12

Vardenafil, Tadalafil,

SeDeM system was used for quality

University

of

10 excipients

12

[28]

HPMC

K15M,

12

2015

[29]

ethylcellulose

University

of

Peshawar

Itopride

of

2015

[30]

The SeDeM used to know

2016

[31]

2016

[32]

2016

[33]

2016

[34]

pediatric

2017

[35]

orally

2017

[36]

Determine i f the SeDeM able to

2017

[37]

2017

[38]

2018

[39]

2017

[40]

physical characteristics of excipients HCl,

15

excipients

University

of

Science

and

diclofenac sodium,

12

metformin

15

12

University of Chinese

microcrystalline

Medicine

cellulose 5

University of Chinese

bathes

of

Seville

University

Anhydrous

Gazi University

North-West

15

Memantine, super

12

DC

85™, excipients

the

SeDeM

3 API, 7 excipients

Optimize

the

Develop

memantine

disintegrating tablets 12

identify defi cienci es

inherent

to

ingredients API, excipients

12

(South

Development of multiple-unit pellet system tablets by employing

Africa)

the SeDeM

North-West

API, excipients

12

(South

Development of multiple-unit pellet system tablets by employing

Africa) University

of

ibuprofen ODT tablets

(South

North-West

University

Evolution

theory

Africa)

University

materials

monohydrate

disintegrating agents

University

the physical property of the raw

paramet ers bas ed on percolation

AC C

Tirgu

12

SeDeM was used to understand

theophylline, α-lactose

Ibuprofen

of Medi cine and

12

EP T

University

Panax

notoginseng saponins

Medicine

Modified SeDeM diagrams for

MCC granules of 15 lotes.

ED

Beijing

Application

physical characteristics of excipients

hydrochloride, HPMC

Beijing

An

SeDeM-ODT

MA

Technology

The SeDeM used to know

SC

of

Barcelona

Mureş

2015

NU

University

Pharmacy

The SeDeM used to know physical characteristics of excipients

RI

Barcelona

PT

assessment

the SeDeM of

API, excipients

12

SeDeM used to investigate the

ACCEPTED MANUSCRIPT Seville

suitability to be processed through a direct compression process

Beijing University of Chinese

Ginkgo

leaf

16

SeDeM was used to understand

granules

2017

[41]

2018

[42]

the physical property of the granule

Medicine Beijing University of Chinese

Panax notoginseng

12

SeDeM was used to understand

saponins

the physical property of the raw materials

PT

Medicine

Table 2 Transformation equations for twelve parameters in the SeDeM. Symbol

Unit

Equation

NU

Da

g ﹒

MA

Bulk density

Acceptable

Equation

ranges

convert

RI

Parameter

SC

Incidence

to

values

to SeDeM radius

values

Da=m/Va

0-1

10v

Da=m/Vc

0-1

10v

mL-1

ED

Dimension

EP T

Tapped density

Inter-particle

Dc

g ﹒ mL-1

Ie

-

Ie=Dc-Da/Dc*Da

0-1.2

10v/1.2

IC

%

IC= ((Dc − Da) /

0-50

v/5

AC C

porosity

Compressibility

Flowability

Carr’s index

Dc) × 100

Cohesion index

Icd

N

Experimental

0-200

v/20

Hausner ratio

IH

-

IH=Dc/Da

3-1

(30-10v)/2

Angle of response

α

︒

Experimental

50-0

10-(v/5)

Powder flow

t’’

s

Experimental

20-0

10-(v/2)

ACCEPTED MANUSCRIPT

Loss on drying

%HR

%

Experimental

10-0

10-v

Hygroscopicity

%H

%

Experimental

20-0

10-(v/2)

Particle <50 μm

%Pf

%

Experimental

50-0

10-(v/5)

Homogeneity

Iθ

-

Iθ = Fm / (100 +

0-0.02

500v

Lubricity/Stability

ΔFmn )

RI

index

SC

Table 3 Calculation of r for loss on drying Description

Range (a)

Range (b)

Range (c)

Range value interval

0-2

3-10

2-3

5-0

10-0

10

5

10

2

10

4

0-10

MA

Radius (r) range to be applied

NU

Range of values

PT

Lubricity/Dosage

Symbol

Radius top value

vmax

Range top value

vmin

Range minimum value

0

3

2

v

Experimental value

v

v

v

r = (Rmaxv)/(vmax)

r= (Rmax(vmax-v))/(vmax-vmin)

EP T

AC C

Equations

ED

Rmax

r = radius value calculated

Table 4 Additional parameters used in the SeDeM-ODT Incidence

Parameter

Symbol

Unit

Equation

Acceptable

Equation to

ranges

convert

values

to

ACCEPTED MANUSCRIPT

SeDeM

radius

values

Effervescence

Experimental

0-5

(5-v)*2

time

DCD

min

Experimental

0-3

(3-v)*3333

time

DSD

min

Experimental

Disintegration

RI

with disk

0-3

(3-v)*3333

SC

Disintegrability

min

PT

Disintegration

DE

NU

without disk

Dc

Ie

IC

M in

0.1517

0.2506

0.1517

9.80

M ax

0.9119

1.3699

3.1485

Icd

IH

α

t’’

%HR

%H

%Pf

Iθ

0

1.1087

26.86

6.85

0.0083

0

0.84

0

1.9139

59.14

-*

17.96

39.57

100

0.0044

ED

Da

MA

Table 5 The limits of 12 parameters in the iTCM database.

EP T

47.75

513.35

AC C

*:The material can not flow down from the funnel.

ACCEPTED MANUSCRIPT Figure captions Figure 1 The number of published article about the SeDeM from the year of 2005 to present. Figure 2 The SeDeM diagram with 12 parameters.

PT

Figure 3 SeDeM diagrams for the API and the excipient. Dashed line indicates the

RI

excipient that provides suitable dimension to the final mixture with the API (in yellow

Figure 4 The SeDeM-ODT with 15 parameters.

SC

shadow).

NU

Figure 5 A summary on how to apply the SeDeM in the formulation design.

MA

Figure 6 The manufacturing classification system (MCS).

AC C

EP T

ED

Figure 6 An overview of the iTCM database.

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7