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Process Systems Engineering

Decision support method for the choice between batch and continuous technologies in solid drug product manufacturing Kensaku Matsunami, Takuya Miyano, Hiroaki Arai, Hiroshi Nakagawa, Masahiko Hirao, and Hirokazu Sugiyama Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.7b05230 • Publication Date (Web): 11 Apr 2018 Downloaded from http://pubs.acs.org on April 16, 2018

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Industrial & Engineering Chemistry Research

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Decision support method for the choice between

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batch and continuous technologies in solid drug

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product manufacturing

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Kensaku Matsunami, † Takuya Miyano, ‡ Hiroaki Arai, ‡ Hiroshi Nakagawa, ‡ Masahiko Hirao, †

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and Hirokazu Sugiyama*, †

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†Department of Chemical System Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-

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ku, Tokyo, 113-8656, Japan

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‡Formulation Technology Research Laboratories, Pharmaceutical Technology Division, Daiichi

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Sankyo Co., Ltd., 1-12-1, Shinomiya, Hiratsuka, Kanagawa, 254-0014, Japan

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Corresponding Author

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*Tel.: +81 3 5841 7227. Fax: +81 3 5841 7227. E-mail: [email protected].

ACS Paragon Plus Environment

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Industrial & Engineering Chemistry Research 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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ABSTRACT: This work presents a decision support method for the choice between batch and

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continuous technologies in solid drug product manufacturing based on the economic evaluation.

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The method consists of four steps: (I) modeling of operating costs, (II) evaluation, (III)

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sensitivity analysis, and (IV) interpretation, with iterations. For a given design situation,

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manufacturing processes are modeled and evaluated with consideration for the characteristics of

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the two technologies. The sensitivity of the input parameters is analyzed; after interpreting all

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results, the economically preferable technology is suggested. As a case study, the method was

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applied to a situation where a new product was in the late development stage, and one of the two

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technologies needs to be chosen. After executing the four steps, the comparison result of the net

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present cost was obtained as the decision support information.

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1.

Introduction

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In the pharmaceutical industry, continuous manufacturing technology is attracting the attention

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of numerous researchers as well as industrial experts.1 Conventionally, pharmaceuticals are

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produced in batch processes where the product quality is controlled by sampling, offline

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laboratory analyses, and product release to the next process. This classical approach is nowadays

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being complemented by quality-by-design, along with the development of process analytical

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technology (PAT), the online sensing technology. Many of the PATs apply near-infrared (NIR)

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methods for continuous measuring of critical attributes that affect product quality, such as water

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content,2 blend uniformity,3 or bulk density.4 Other spectroscopic techniques such as Raman

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spectroscopy,5 UV–vis,6 and Terahertz spectroscopy7 are also implemented in the pharmaceutical

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manufacturing processes. The advancement of PAT enables drug producers to achieve real-time

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release of the product, and moreover, continuous manufacturing. The application of continuous

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technology is not limited to mass production of inexpensive products, but is also possible in

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small-scale production, which is in line with personalized healthcare, a recent trend in the

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pharmaceutical industry.

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Continuous technology has already become an actual alternative for producing solid drug

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products such as tablets and capsules. In July 2015, the US Food and Drug Administration

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(FDA) gave an approval to Vertex to adopt continuous technology in the manufacturing line of

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Orkambi®. Next, in April 2016, the FDA approved a change from batch to continuous

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manufacturing for Prezista® produced in a Janssen facility in Puerto Rico. In the literature,

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numerous contributions are found for granulation, the key unit operation for converting inlet

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powder materials to granules. Recent research shows experimental results on granule size

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distribution,8,9 drug hydrophobicity,10 or dissolution11 in order to present the performance of


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continuous granulation. Other unit operations are also studied: e.g., application of modeling

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approaches such as the population balance model to a drying process,12,13 or the use of the

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discrete element method in a mixing process,14 and a coating process.15 Some contributions

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investigated the entire process of continuous manufacturing. Sundaramoorthy et al.16,17

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demonstrated mathematical capacity planning under clinical trials uncertainty; Boukouvala et

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al.18 presented dynamic flowsheet modeling and sensitivity analysis. Research on PAT in solid

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drug product manufacturing is moving forward. For instance, Muteki et al.19 proposed a

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calibration-free/minimum approach for predicting mixture component; Singh et al.20–24

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conducted design/implementation of new control systems. Continuous technology is studied for

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other types of pharmaceutical products, such as sterile drug products of biopharmaceuticals25 or

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active pharmaceutical ingredients (APIs).26–28

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Now that continuous technology is becoming real for the industry, it is necessary to evaluate

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the actual merits of introducing the new technology as compared to the conventional batch

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technology. Some authors reflected such a need in the comparative studies on both technologies.

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Järvinen et al.29 compared product quality of granules and tablets through experiments, and

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investigated the similarity and differences in quality for the two technologies. With the aim of

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environmental comparison, Lee et al.30 conducted life-cycle assessment on the synthesis of 4-D-

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erythronolactone; De Soete et al.31 performed an exergy-based sustainability assessment on tablet

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manufacturing. Regarding economic performance, Schaber et al.32 estimated the production cost

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of both batch and continuous tablet manufacturing processes starting from an organic

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intermediate. The authors calculated capital and operating expenditure considering raw material,

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labor, quality assurance, utilities, and waste disposal costs, and concluded that continuous

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technology was economically advantageous in the case study. With a focus on API


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manufacturing, Denčić et al.33 compared total production cost and other process performance

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such as yield, solvent waste, and feasibility aspects of batch and continuous technologies. Further

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on API manufacturing, Jolliffe and Gerogiorgis presented continuous manufacturing processes of

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ibuprofen34 and artemisinin,35 and presented a comprehensive economic comparison with the

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batch processes.36 To support the actual decision-making on the technology choice, it is desired

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to advance the methodological development beyond studying individual cases.

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In this work, we present a decision support method for the choice between batch and

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continuous technologies in solid drug product manufacturing based on economic evaluation. The

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method consists of four steps: (I) modeling of operating costs, (II) evaluation, (III) sensitivity

83

analysis, and (IV) interpretation, and includes iterations. The final output of the method is the

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comparison result of the net present cost after the product launch, which serves as decision

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support information. As a basis for execution of the method, we developed a set of standard

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models to calculate annual operating cost, and defined the points to incorporate in the calculation

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model of the annual production amount. To demonstrate the proposed method, a case study was

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performed assuming a design situation where the choice of either technology is made

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considering the peak demand and the price of the API. The data used for the calculation were

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provided and reviewed by the industrial coauthors. In this paper, all the equations and parameter

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values are reported (see also Supporting Information) so that the presented results can be

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reproduced, and also that the method can be executed using different input values. An earlier

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version of this work was partly presented in the 27th European Symposium on Computer-Aided

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Process Engineering.37

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2.

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2.1. Method overview

Method


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Figure 1 shows the proposed method. The initial input of step I is a design situation, in which

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either technology, batch or continuous, needs to be chosen, e.g., for a new tablet product in the

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late development stage. In step I, models of operating costs are created for the processes using

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each technology, so that the characteristics of the two technologies in the manufacturing stage

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are reflected in the evaluation. Using these models, an economic evaluation is conducted in step

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II. In step III, a sensitivity analysis is performed to quantify the effect of the input parameter

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values on the evaluation results. Finally, in step IV, the results of the evaluation and the

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sensitivity analysis are interpreted to explore the necessity of iterating the previous steps. The

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final outcome of the method is the decision support information on the choice of the technology.

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2.2. Technology overview

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Before describing the details of the method, this section provides the overview of the two

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technologies. Figure 2 shows a general scheme of pharmaceutical tablet manufacturing using wet

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granulation, which consists of weighing, granulation, blending, compression, and coating

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processes. In batch technology, each process is performed batchwise with a specific batch size

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such as 300 kg/lot, whereas in continuous technology these processes are interconnected and run

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at a constant rate, e.g., 25 kg/h. Figure 2 also displays supporting processes such as testing,

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disposal, cleaning, maintenance of PAT and heat, ventilation, and air conditioning (HVAC). As

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to testing, in-process control is performed during manufacturing, which is normally done by a

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classical sampling approach in batch technology, and, if applicable, by PAT. In continuous

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technology, the dependence on real-time monitoring is so high that PAT is inevitable for the in-

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process control. After manufacturing, the products undergo release testing to become the final

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products by performing various laboratory tests such as content uniformity test, dissolution test,

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and microbial limit test. Losses generated during manufacturing are collected and disposed


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according to the company protocol after the production. Cleaning is another critical

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postproduction process, where the machine, in particular the product contacting surface, is

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cleaned by solvents including purified water. In case of campaign manufacturing, where multiple

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lots are produced in sequence, the cleaning process is performed after the end of the campaign.

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Maintenance of PAT is required to calibrate the PAT model on a regular basis; HVAC is

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installed to keep temperature, humidity and cleanliness of the manufacturing space. All the

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above mentioned processes and technologies need to be validated,38 i.e., the compliance to the

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Good Manufacturing Practice (GMP) is proven, before the commercial production can start.

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Because of the vast efforts required for changes, the conditions that are once validated will

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remain the same for the product lifetime unless required. The validation applies to batch size as

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well as the maximum continuous run time. But for the continuous technology, the run time can

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be changed within the validated maximum run time according to the ongoing GMP-related

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discussions.39,40,41

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The difference between continuous and batch technologies can be summarized in the following

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five points that may affect the evaluation of operating cost. First, in batch technology there is a

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fixed batch size, whereas under the current regulation, continuous technology can easily deal

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with demand change by tuning the continuous run time. Second, the number of operators in

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continuous technology is smaller than that in batch technology because the machine in the

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former is so compact that manual transfer of materials is not needed. This size advantage leads to

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a third characteristic: namely, that the manufacturing space for continuous technology is smaller

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than that for batch technology. Fourth, the continuous technology requires PAT maintenance that

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costs man-hours.42 Finally, continuous technology needs some time (which can be several tens of


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minutes) until the machine operation is sufficiently stable. During this start-up operation, the

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precious materials are disposed.

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2.3. Step I: Modeling of operating costs

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In step I, models are created so that the abovementioned characteristics of the two

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technologies, such as product yield, numbers of operators, or required space for manufacturing,

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can be reflected in the evaluation. We developed a set of standard models to calculate the

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operating cost of the processes using the two technologies. The models were created on the

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assumption that the products obtained by both technologies are pharmacologically equivalent for

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the patient needs. The annual operating cost of the ith year after the launch of the product, C(i)

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[$/yr], is defined in Eq. (1).

= Material cost + Disposal cost + Labor cost + Utility cost + Capacity cost

= !,# $%&'()*+,# + %!'+++,# , #

+ !,+'!-. %+'!-. / + ( +&'+! %!'+++,#

(1)

#

+ !0' $1.)2*) .3 + 1*!. .3 + 1+ .3

+ 1456 , + 7859 : ;7859 + *&* <

152 153

The parameters Cmaterial, j [$/kg], Cmaterial, solvent [$/kg], Mproducts, j(i) [kg/yr], Mlosses, j(i) [kg/yr],

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and Msolvent(i) [kg/yr] represent cost of raw material j, raw material cost of solvent, the annual

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amount of material j used to make the product, amount of annual losses of material j, and amount

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of solvent used annually, respectively. The suffix j is an element of the API, binder, coating


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agent, disintegrant, excipient, or lubricant. Solvent, which is typically water, is used to dissolve

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binders or coating agents to make the solutions to add to the granulation or coating processes,

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respectively. The parameter Cdisposal [$/kg] represents the cost to dispose a unit amount of loss,

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which was assumed to be independent of the type of materials. The parameter Clabor [$/man/h] is

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the labor rate, and the parameters Wmanufacturing(i) [man-hour/yr], Wcleaning(i) [man-hour/yr],

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Wtesting(i) [man-hour/yr], and WPAT(i) [man-hour/yr] represent annual man-hours for

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manufacturing, cleaning, testing, and PAT maintenance, respectively. The latter is assumed to be

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conducted once a year, and the man-hours of the PAT maintenance are not affected by the

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quantities produced. The parameters CHVAC [$/m2/h], A [m2], THVAC(i) [h/yr] represent HVAC

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cost, manufacturing space, which is covered by HVAC, and HVAC running time, respectively.

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As HVAC is known as a dominant utility for maintaining a clean manufacturing environment,43

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other utilities such as water or electricity were not included in this equation. The parameter

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Ccapacity(i) [$/yr] represents capacity cost, i.e., the loss of profits from capacity displaced by the

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new product as an additional operating cost. If the production amount of the new product is so

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large that the existing products have to be produced by a third party, and if a commission

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expense needs to be paid, this additional cost will be covered by Ccapacity(i).

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Eqs. (2)–(8) determine the dependency of Mproducts, j(i), Mlosses, j(i), Msolvent(i) Wmanufacturing(i),

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Wcleaning(i), Wtesting(i), and THVAC(i) on the annual production amount, Nprod(i) [tablet/yr]. All

175

subequations of Eqs. (2)–(8) are presented in Eqs. (S1)–(S11) in Supporting Information.

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%&'()*+,# = =# >?&'( %!'+++,# =

1 >>!'+++,# ?&'( >!'

%+'!-. = =+'!-. >?&'(

(2) (3) (4)


9


1.)2*) .3 = 1*!. .3 = 1+ .3 = ;7859 = 177

1 >A.)2*) .3 ?&'( >!'

1 >A*!. .3 ?&'( >!' ?*& 3.

1 >A+ .3 ?&'( >!'

1 >B*& 3. ?&'( >!' ?*& 3.

Page 10 of 48

(5) (6) (7) (8)

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The parameters αj [–], m [kg/tablet], mlot [kg/lot], mlosses, j [kg/lot], and αsolvent [–] represent the

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mass fraction of material j in the product, weight of one tablet, lot size, total amount of losses of

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material j in one manufacturing lot, and the mass ratio of the solvent to the product used in the

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granulation and coating processes, respectively. The parameters wmanufacturing [man-hour/lot],

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Ncampaign [lot/campaign], wcleaning [man-hour/campaign], wtesting [man-hour/lot], and tcampaign

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[h/campaign] stand for man-hours of one-lot manufacturing, number of lots in one campaign

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manufacturing, man-hours of one cleaning, man-hours of testing in one manufacturing lot, and

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total time needed for one campaign manufacturing, respectively. In this paper, mlot for batch

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technology is defined as the production amount in one lot, hereafter termed as batch size V

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0*C [kg/lot]. The parameter V is equivalent to >!' . The parameter mlot for continuous technology,

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*'. .)')+ *'. .)')+ >!' , is calculated by using the validated run time ;-! [h] in continuous

189

technology. It was assumed that all the lots in year i would be manufactured for the time of

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*'. .)')+ *'. .)')+ ;-! except for the last lot. The run time for the last lot in year i, ;+ [h/lot], is ()

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adjusted using Eq. (S25) in Supporting Information, in order to produce Nprod(i). For the

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parameter mlosses, j, five types of causes are considered: material sticking to the inner surface, and

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sampling for in-process control in batch technology; tablets produced until the machine runs in a


10

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stable manner, and remaining raw material in the feeder after production in continuous

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technology; and tablets produced in compression testing for both technologies. The parameter

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wmanufacturing(i) depends on the number of operators and manufacturing time; wcleaning(i) depends

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on the number of operators and cleaning time. The parameter Wcleaning(i) considers the frequency

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of cleaning, which is expressed as

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process control in batch technology, and release testing in both technologies. The parameter

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tcampaign is an all-inclusive time for one manufacturing campaign covering not only the actual

201

manufacturing time, but also cleaning, weekends, and buffer time. HVAC systems are

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considered to run continuously during tcampaign.

DEFGHI J

DKHL EMNOFNPQR

in Eq. (6). The test for wtesting consists of in-

203

As can be seen in Eqs. (2)–(8), Nprod(i) is a key parameter for calculating C(i). To determine

204

Nprod(i), we regard the following three industry-specific practices as worth incorporating in the

205

model. First, a specific number of lots, which is typically three in the industry, are produced at

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the launch of the process for process validation. Second, the production amount is decided to

207

secure sufficient inventory and avoid drug shortages. Third, there is the shipping deadline, which

208

should be earlier than the expiration date. Under these common conditions to both technologies,

209

the determined value of Nprod(i) would be different because of the flexibility of each technology.

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In batch technology, the production quantity responds to the demand amount stepwise, whereas

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in continuous technology, the production quantity can change continuously. For example, if the

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demand has an amount that corresponds to a quantity of 1.1 lots, batch technology has to produce

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2 lots, whereas continuous technology can produce the exact quantity by adjusting the

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continuous run time. Because the demand changes over the lifetime of a drug product, Nprod(i)

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will be different between the two technologies, even when the demand profile is the same.


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In the model as well as in the case study, one year was set as the unit interval of time. This is

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because pharmaceutical companies typically use annually estimated data for a long-range

218

planning like 30 years, which is the assumed situation in the case study. If a shorter unit interval,

219

such as quarter or month, is more appropriate, the model can be used by defining i as the

220

corresponding time unit.

221

2.4. Step II: Evaluation

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In step II, an economic evaluation is conducted. In this study, the net present cost (NPC) [$],

223

was chosen as the standard objective function to evaluate the economic performance after the

224

launch. The NPC of technology, NPC [$] can be calculated using Eq. (9):

225

?S = ∑ZJ[\ VWXY , UJ

(9)

226

where τ [yr] and r [–] represent the selling period from launch and interest rate, respectively.

227

Similar to net present value (NPV), the indicator NPC considers the time value of money, but

228

excludes capital cost and revenue, which is different from NPV. This modification is suitable for

229

design situations as introduced in the case study, where the facility was supposed to have the two

230

technologies installed.

231

The technology selection can be expressed as the optimization problem shown in Eq. (10): *'. .)')+ min ?S_B`aℎ, ;-! c

s.t.

B`aℎ ∈ ebatch, continuoush

(10)

*'. .)')+ ; . ≤ ;-! ≤ ;j ,

kl2 , l* = 0,

232


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where tech, xf, xc, and g(x) represent batch or continuous, vectors of the input constraint

234

parameters that are fixed (f) or changeable (c) at the stage of the decision-making, and a vector

235

of constraint functions, respectively. The parameters Tmin [h/lot] and Tmax [h/lot] represent the

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minimum and maximum values of the validated continuous run time, respectively. The

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*'. .)')+ *'. .)')+ parameter ;-! is to be optimized because ;-! determines the production amount

238

of the three validation lots, overproduction of which could lead to unnecessary discard. Too

239

*'. .)')+ small ;-! could lead to frequent changeover, and thus increase of the cost. We specified

240

*'. .)')+ tech and ;-! as the optimization parameters because (a) the remaining parameters are

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related with the process given and/or the product quality and thus cannot be freely optimized,

242

and (b) this could be a typical setup of the decision-making in the future pharmaceutical industry.

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In the next step, sensitivity analysis is offered in order to investigate how the solution is affected

244

if the elements of xc were given differently.

245 246

The overall comparison indicator y [$] was defined to compare the economic performance of the two technologies as in Eq. (11): *'. .)')+ n = min ?S_continuous, ;-! c − ?Sbatch,

(11)

247

By analyzing whether the obtained y is positive or negative, a tentative conclusion is drawn on

248

which technology to choose.

249

2.5. Step III: Sensitivity analysis

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The sensitivity analysis is conducted to quantify the effects of the values in the input constraint

251

parameters on the evaluation. The elements of xc are subject to the analysis here. One example is

252

manufacturing rate of continuous technology that could be changed/given differently if the target

253

product quality cannot be achieved with the intended rate. Eq. (12) is a general description of the


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relation between xk, an input parameter k that is an element of xc, and the output function y, e.g.,

255

the overall comparison indicator given in Eq. (11):

n = pqr .

(12)

256 257

In this step, two kinds of indices were introduced. The first index was δyk [$], the response to

258

the perturbation of the input parameter k, δxk, given in Eq. (13); the second was ∆yk [$], the

259

response to a possible change in the input parameter k, ∆xk, given in Eq. (14):

snr =

max

u∈evwx ,yvwx h

∆nr = maxw OPR w x

260

z{p_qr. ! + |c − p_qr. ! c{}

ON x wx

pqr − minw OPR w x

ON x wx

(13)

pqr ,

(14)

where qr. ! , sqr , qrj and qr . represent initial value, perturbation, maximum, and minimum

261

values of the input parameter k, respectively. The parameter | is a placeholder for δxk. If the

262

value of the output function increases or decreases monotonously according to the increase in the

263

value of k, ∆yk [$] can also be expressed as given in Eq. (15):

264

∆nr = {pqrj − p_qr . c {.

(15)

265

To simplify the calculation effort, Eq. (15) can be used as long as there is no significant

266

difference between the results of Eqs. (14) and (15). After the calculation of δyk and ∆yk, the

267

parameters are classified according to the effect on the result. If both δyk and ∆yk are large, the

268

parameter xk is classified as a high-impact parameter. Additionally, if either δyk or ∆yk is large, it

269

could be worth investigating the cause in detail.

270

2.6. Step IV: Interpretation

271

In the last step, all the results obtained in the previous steps are interpreted to produce decision

272

support information as the final output of the method. The main task here is to examine the


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273

necessity of iterating the previous steps based on all the obtained results. First, the evaluation

274

results in step II are interpreted, and if necessary, step I is conducted again to adjust the model.

275

Next, the result of the sensitivity analysis in step III is interpreted, in particular on the high-

276

impact parameters. One interesting analysis would be to change the input parameter values and

277

investigate the influence on the temporary conclusion obtained in step II. The step of iteration is

278

performed until the results can be judged sufficiently solid to suggest which technology is

279

economically preferable in the given design situation.

280

3.

281

3.1. Design situation

Case Study

282

The objective of this case study was to demonstrate the proposed method under certain design

283

situation that is likely to exist in the future. The following items were assumed as the design

284

situation: (i) a new tablet product is assumed, which is in the later development stage; (ii) the

285

selling period is assumed to be 30 years; (iii) there is a facility that is equipped with both batch

286

and continuous technologies where the capacity is so sufficient that Ccapacity(i) can be assumed as

287

zero; and (iv) the technology needs to be chosen based on the estimates of the peak demand

288

j amount of the API during , ?(.( , and the price of the API, !,54 . For the batch

289

technology, V is fixed at 300 kg; and for the continuous technology, Tmin and Tmax are set as 7

290

and 20 h, respectively. We regarded the values of Tmin and Tmax, which correspond to one to three

291

shift operation, as realistic for the actual continuous manufacturing, and thus adopted as the input

292

values. In general, the demand amount and the price of APIs on the market have a wide range,

293

e.g., 106 to 109 tablets/yr and $10 to 104/kg, respectively. In our calculation, this entire range was

294

applied; to demonstrate the execution of the method, we used the predetermined estimates of


15


295

Page 16 of 48

j and !,54 , respectively. Hereafter, the 5.0 × 107 tablets/yr and $1,000/kg for ?(.(

296

j vector of _?(.( , !,54 c = 5.0 × 10 , 1.0 × 10 was denoted as point P.

297

3.2. Step I: Modeling of operating costs

298

For the process shown in Figure 2, the models for evaluating annual operating cost were

299

defined as Eqs. (1)–(8). The values of the parameters used in the calculations were shown in

300

Table S1 and Table S2 in Supporting Information. The entire dataset was provided and reviewed

301

by the industrial coauthors, which included typical manufacturing data for batch technology, and

302

estimated values for continuous technology. For the demonstration purpose of the model, the

303

data quality was judged sufficient. The parameters A, ∑# >!'+++,# , tcampaign, wcleaning, wmanufacturing,

304

wtesting, WPAT were defined differently in the two technologies. As to the in-process control,

305

sampling was set as the method in the batch technology whereas in the continuous technology

306

PAT was employed. With these models and parameter settings, the following characteristics of

307

the two technologies could be reflected in the calculation of C(i). For the same value of Nprod(i),

308

the material and disposal costs of the continuous technology were larger than for batch

309

technology because of the input values of mlosses. The labor cost, except for the term Clabor WPAT,

310

and utility cost for continuous technology was smaller than for batch technology because of the

311

input values of wcleaning, wmanufacturing, wtesting, and A. The cost Clabor WPAT for continuous

312

technology was higher than for batch technology because of the number of PAT, nPAT (see Table

313

S2 in Supporting Information).

314

To determine Nprod(i) as a key parameter in the calculation of C(i), a demand profile of 30

315

years was assumed (Figure 3). This is a typical profile of pharmaceutical tablets according to

316

.' [–], which is the normalized industrial expert knowledge. The vertical axis shows ?(.(

317

demand amount in year i after the launch, as given by Eq. (16):


16

Page 17 of 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


.' = ?(.(

EIONRI J ON EIONRI

,

(16)

318

where ?(.( [tablet/yr] represents the demand amount in year i. The reason for the peak in

319

Figure 3, which is found in year i = 10, is the expiration of the patent. By specifying the value of

320

j ?(.( , the value of ?(.( can be defined, and then the value of ?&'( is calculated.

321

Here, the industry-specific practices mentioned earlier were concretized as follows. (i) Three lots

322

are produced at the launch, i.e., year i = 0, for process validation; (ii) at least a half year of

323

inventory is maintained; and (iii) the shipping deadline is one year before the expiration date,

324

which is three years after the production, and the expired tablets are disposed of. Under these

325

conditions, the models for calculating ?&'( were defined as per Eqs. (17) and (18):

0*C ?&'(

0*C 0*C ? 0*C .-_.( − ? .-_!+ − ? .-_!' 0*C >!'

*'. .)')+ .)')+ *'. .)')+ .)')+ = ? *'. − ? *'. , ?&'( .-_!'++V,# in Supporting Information). This

352

product loss was the key contributor to the result in the area where the material cost was

353

dominant.

354

*'. .)')+ The optimal ;-! that yielded the minimum NPC for continuous technology was also

355

obtained for the entire area of Figure 4. The result is shown in Figure S1 in Supporting

356

*'. .)')+ Information. In most of the area, the optimal ;-! was Tmax, i.e., 20 h, however, a

357

monotonic decrease was observed to the upper-left area. This decreasing trend is because too


18

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358

*'. .)')+ leads to overproduction in the first three lots for process validation, and the long ;-!

359

unnecessary discard of expensive products.

360

The preferable technology can be suggested in Figure 4 by applying the predictions of the peak

361

demand amount and the price of API. At point P, continuous technology is found to be

362

advantageous, which is the tentative conclusion of the step.

363

3.4. Step III: Sensitivity analysis

364

Sensitivity analysis was performed for the elements of xc using the result of y at point P. The

365

response to perturbation, δyk [$], was calculated using Eq. (13) with setting δxk as 1% of the

366

initial value; the response to possible change, ∆yk [$], was calculated using Eq. (15) with setting

367

qr . and qrj based on industrial expert knowledge.

368

Figure 5 shows the obtained results. Two parameters, namely, v, the manufacturing rate in

369

continuous technology (initial value: 25 kg/h) and Ncampaign (initial value: 5 lots), were classified

370

*'. .)')+ as high-impact parameters. The parameter v affects the >!' (see Eq. (S11) in Supporting

371

Information), and then Mlosses, j(i), Wmanufacturing(i), Wcleaning(i), Wtesting(i), and THVAC(i) (see Eqs. (3)

372

and (5)–(8)); the parameter Ncampaign affects D

373

DEFGHI J

KHL EMNOFNPQR

, and then Wcleaning, and THVAC (see Eqs.

0*C (6) and (8)). There were two parameters that showed a large ∆yk, i.e., ∑# >!'++,# , and

374

*'. .)')+ ∑# >!'++V,# . These two parameters that are associated with product losses were identified

375

because of the large ranges of ∆x set for these parameters (see Table S2).

376

3.5. Step IV: Interpretation

377

The sensitivity analysis in step III extracted v and Ncampaign as the high-impact parameters. We

378

repeated step II to investigate whether and how far the temporary conclusion could be affected

379

by changing the input values of these parameters. Figure 6 shows the results of changing v from


19


Page 20 of 48

380

25 kg/h to 10 kg/h (Figure 6 (a)) and 19 kg/h (Figure 6 (b)). As these graphs suggest, the

381

continuous-preferred area becomes larger along with the increase in v. This change leads to the

382

*'. .)')+ (see Eq. (S11)), the decrease in %!'+++,# , %+'!-. , increase in >!'

383

1.)2*) .3 , 1*!. .3 , 1+ .3 , and ;7859 (see Eqs. (3) and (5)–(8)), and finally

384

the decrease in C(i) of continuous technology (see Eq. (1)). At point P, the preferability of the

385

technology changes from continuous to batch at around v = 19 kg/h. This result indicates the

386

importance of v, determination of which requires consideration of various factors such as

387

properties of the raw materials. For example, if the raw materials have high wettability, there is a

388

risk that its flowability would decrease, and that the maintenance of v becomes difficult. In the

389

case of selecting continuous technology at point P, the actual value of v should be larger than 19

390

kg/h, which needs to be consolidated, e.g., through thorough experimental investigations.

391

The result of changing Ncampaign is shown in Figure 7 (from 5 lots/campaign to 2 is shown in

392

Figure 7 (a) and to 8 lots/campaign in Figure 7 (b)). The batch-preferred area is extended when

393

the value of Ncampaign in batch technology becomes larger. This change leads to the decrease in

394

Nprod(i)/Ncampaign, Wcleaning(i), and THVAC(i) (see Eqs. (6) and (8)), and the decrease in C(i) of batch

395

technology (see Eq. (1)). At point P, the continuous technology is always better, i.e., the decision

396

can be made without determining the actual value of Ncampaign in batch technology. As can be

397

seen in this case, some parameters may not affect the choice even if they were classified as high-

398

impact parameters in the sensitivity analysis.

399

After executing all the steps, the decision support information was obtained as follows. “At the

400

predetermined estimates, continuous technology is economically preferable as long as the actual

401

value of v is larger than 19 kg/h.”

402

4.

Discussion


20

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403


4.1. Role of the method in drug development and manufacturing

404

The most important role of the presented method is to support decision-making on the

405

technology choice considering economic performance. The standard model introduced in the

406

paper can incorporate major differences in the evaluation between the two technologies, such as

407

flexibility in lot size. As soon as the estimation of peak demand and API price is available, an

408

economically preferable technology can be suggested at any stage of the drug development and

409

manufacturing. As the prerequisite for executing the evaluation, industrial expert knowledge is

410

required to set a large number of parameter values, which was 83 in the case study. The method

411

defines the sensitivity analysis to help identify the high-impact parameters that, among large

412

numbers of input parameters, require certainty for producing reliable decision support

413

information. By integrating other critical aspects such as quality, safety, or occupational health,

414

the actual decision-making would become more rationalized.

415

4.2. Improvement opportunities for continuous technology

416

In the case study, several opportunities were observed for improving continuous technology

417

*'. .)')+ , resulted toward the future. From Figure 4, the loss during the start-up operation, ∑# >!'++V,#

418

in the steep increase in y in the upper-right area where blockbusters would find their place. To be

419

competitive in this lucrative area, quick stabilization in the start-up operation will be the key

420

opportunity. From Figures 4 and 5, the landscape changed drastically depending on the value of

421

v. This indicates that continuous technology should have the capability to maintain a high

422

manufacturing rate from granulation to tableting while dealing with various properties of inlet

423

materials such as wettability.

424

4.3. Application of the method in other design situations


21


Page 22 of 48

425

An additional analysis was performed for different values of V, which was fixed at 300 kg/lot

426

in the case study. Figure 8 (a) and (b) shows the evaluation results of y at V = 100 and 500 kg/lot,

427

respectively, where the parameters for continuous technology were kept the same. Comparing

428

Figures 4 and 8 (a), an extension of the continuous-preferred area is observed, which was caused

429

by the increase in Nprod(i)/V and the resulting increase in C(i) in batch technology (see Eqs. (3)

430

and (5)–(8)). Additionally, the continuous-preferred area in the upper-left part of Figure 4

431

disappeared in Figure 8 (a). This result was caused by the fact that increment of batch size

432

change according to demand becomes smaller, which makes the batch technology more flexible

433

at V = 100 kg/lot. For the case of V = 500 kg/lot, the opposite tendencies can be observed

434

because of the decrease in Nprod(i)/V, and the increase in the increment in batch size change. At

435

the point of P, the maximum y is given by the batch technology at V = 500 kg/lot (Figure 8 (b)).

436

Figures 4 and 8 (a, b) correspond to the result at step II when V was open in the given design

437

situation. This is possible when the development stage is earlier than the stage investigated in the

438

case study where V was fixed. The temporary conclusion at point P would be to suggest batch

439

technology with V = 500 kg/lot; i.e., the method can be applied to other design situations.

440

5.

Conclusions and Outlook

441

In this article, we presented a decision support method for the choice between batch and

442

continuous technologies in solid drug product manufacturing based on economic evaluation. The

443

method comprises four steps of modeling of operating costs, evaluation, sensitivity analysis, and

444

interpretation, to produce decision support information under the given design situation. As a

445

basis for executing the method, we developed a set of standard models to calculate annual

446

operating cost, and defined the points to incorporate in the calculation model of the annual

447

production amount. This generic model considered costs of materials, disposal, labor, and utility


22

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448

where the two technologies showed different characteristics, and enabled calculation of NPC and

449

the overall comparison indicator. The sensitivity analysis was defined to identify important

450

parameters that need to be set appropriately with analyzing the responses to perturbation and

451

possible changes in the input parameters. The step of interpretation had the role of investigating

452

the necessity to iterate the previous steps before producing solid decision support information as

453

the final output.

454

We demonstrated the method in the case study with assuming a design situation where either

455

technology needs to be chosen considering the peak demand and the price of API. Using the data

456

that were provided and reviewed by the industrial coauthors, the four steps of the method could

457

be completed, and the following quote was obtained as the decision support information. “At the

458

predetermined estimates, i.e., 5.0 × 107 tablets/yr and $1,000/kg for peak demand amount and

459

API price, respectively, the continuous technology is economically preferable as long as the

460

actual manufacturing rate is larger than 19 kg/h.” In the contour line graph obtained in step II,

461

continuous technology was economically preferable for the products that have high demand and

462

low price, e.g., generics, or low demand and high price, e.g., orphan drugs. The obtained results

463

and suggestions are dependent on the input parameter values, however, we could explain the

464

result with the characteristics of the two technologies. In the case study result, quick stabilization

465

in the start-up operation and a high manufacturing rate throughout the entire process were found

466

as the key improvement opportunities for continuous manufacturing.

467

The actual decision-making in solid drug product development/manufacturing needs to cover

468

various aspects. Regarding economic evaluation, costs for investment as well as clinical

469

development are relevant; the revenue may differ because the time duration of the regulatory

470

process would be different for batch and continuous technologies. Another critical aspect would


23


Page 24 of 48

471

be product quality. Depending on the choice of technology, the quality, such as particle size

472

distribution of granules or dissolution profile of the tablet, would be different. In addition, the

473

aspect of safety and occupational health would be critical, because the process might well

474

involve a highly potent powder API. The available information would differ depending on the

475

stage of the drug development and manufacturing, which is another complexity to be considered.

476

Lastly, there is an expansion possibility towards advanced sensitivity analysis such as

477

multivariate and distribution-based sensitivity analysis. Actually we proceeded with the

478

incorporation of parameter uncertainty in the result using Monte Carlo simulation, and will

479

present the first result in a six-page conference proceedings paper.44 Uncertainty-conscious

480

modeling and evaluation will enable expansion to the earlier design phases where degrees of

481

freedom is higher whereas the available information is more limited.

482 483

ASSOCIATED CONTENT

484

Supporting Information. Details of equations and the values of parameters are given in

485

Supporting Information. This information is available free of charge via the Internet at

486

http://pubs.acs.org/.

487 488

AUTHOR INFORMATION

489

Corresponding Author

490

*Tel.: +81 3 5841 7227. Fax: +81 3 5841 7227. E-mail: [email protected].

491

Funding Sources


24

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492


493 494

Japan Society for the Promotion of Science, Grant-in-Aid for Young Scientists (A) No. 17H04964 and Grant-in-Aid for Young Scientists (B) No. 26820343

495

Ministry of Education, Culture, Sports, Science and Technology, Global Leader Program for Social Design and Manage

496

497

Notes

498

The authors declare no competing financial interest.

Nagai Foundation Tokyo, Research Grant 2017

499 500

ACKNOWLEDGMENT

501

Financial support by Grant-in-Aid for Young Scientists (B) No. 26820343 and (A) No.

502

17H04964 from the Japan Society for the Promotion of Science, and by Research Grant 2017

503

from the Nagai Foundation Tokyo, as well as discussions with Mr. Yasuhiro Suzuki from Daiichi

504

Sankyo Co., Ltd. and Mr. Hiroaki Inoue from Daiichi Sankyo Propharma Co., Ltd. are gratefully

505

acknowledged. This research was supported through the Leading Graduates Schools Program,

506

“Global Leader Program for Social Design and Management,” by the Ministry of Education,

507

Culture, Sports, Science and Technology.

508


25


509

Page 26 of 48

Nomenclature Variables

:

Manufacturing space covered by HVAC [m2]

Annual operating cost [$/yr]

|

*&* <

Placeholder of sqr

Capacity cost [$/yr]

( +&'+!

Cost to dispose unit amount of loss [$/kg]

!0'

Labor rate [$/man/h]

!,+'!-.

Material cost of solvent [$/kg]

7859

!,# i

>

%!'+++,# >!'+++,# 0*C >!'++V,# 0*C >!'++,# 0*C >!'++,# 0*C >!'++,# 0*C >!'++,# 0*C >!'++,# 0*C >!'++,#

HVAC cost [$/m2/h]

Raw material cost of material j [$/kg]

Production time after the launch [yr] Weight of one tablet [kg/tablet] Amount of material j that ends up in losses annually [kg/yr] Total amount of losses of material j from one-lot manufacturing [kg/lot] Amount of loss of material j caused by sampling in granulation in batch technology [kg/lot] Amount of loss of material j caused by sticking in granulation in batch technology [kg/campaign] Amount of loss of material j caused by sampling in blending in batch technology [kg/lot] Amount of loss of material j caused by sticking in blending in batch technology [kg/campaign] Amount of loss of material j caused by sampling in compression in batch technology [kg/lot] Amount of loss of material j caused by compression testing in compression in batch technology [kg/lot] Amount of loss of material j caused by sampling in coating in batch technology [kg/lot]


26

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0*C >!'++,#

*'. .)')+ >!'++V,# *'. .)')+ >!'++,# *'. .)')+ >!'++,# >!'

%&'()*+,#

%+'!-. ?*& 3.

Amount of loss of material j caused by other reasons in coating in batch technology [kg/campaign] Amount of loss of material j generated during the start-up operation in continuous technology [kg/lot] Amount of loss of material j remaining in the feeder in continuous technology [kg/campaign] Amount of loss of material j caused by compression testing in compression in continuous technology [kg/lot] Lot size [kg/lot] Amount of material j used to make the product annually [kg/yr] Amount of solvent used annually [kg/yr] Number of lots in one campaign manufacturing [lot/campaign]

.' ?(.(

Normalized demand amount [‒]

j ?(.(

Peak demand amount during [tablet/yr]

? .-_!+

Amount of inventory that was produced one year before the ith year [tablet/yr]

?(.( ?( +&'+! ? .-_.( ? .-_

Decision Support Method for the Choice between ... - ACS Publications

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