Continuous manufacturing process monitoring of pharmaceutical solid dosage form: A case study

Journal Pre-proof Continuous manufacturing process monitoring of pharmaceutical solid dosage form: A case study Yves Roggo, Victoria Pauli, Morgane Jelsch, Laurent Pellegatti, Frantz Elbaz, Simon Ensslin, Peter Kleinebudde, Markus Krumme

PII:

S0731-7085(19)32037-0

DOI:

https://doi.org/10.1016/j.jpba.2019.112971

Reference:

PBA 112971

To appear in:

Journal of Pharmaceutical and Biomedical Analysis

Received Date:

20 August 2019

Revised Date:

1 November 2019

Accepted Date:

1 November 2019

Please cite this article as: { doi: https://doi.org/ This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier.

Continuous manufacturing process monitoring of pharmaceutical solid dosage form: a case study

Yves Roggo a * [email protected], Victoria Pauli a, Morgane Jelsch a, Laurent

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Novartis Pharma AG, Continuous Manufacturing (CM) Unit, CH-4002 Basel, Switzerland.

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Pellegatti a, Frantz Elbaz a, Simon Ensslin a, Peter Kleinebudde b, Markus Krumme a

Institute of Pharmaceutics and Biopharmaceutics, Heinrich Heine University, Universitaetsstr.

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1, 40225 Dusseldorf, Germany

Corresponding author at: Novartis Pharma AG, WSJ-27.4.021.01, Continuous Manufacturing

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(CM) Unit, CH-4002 Basel, Switzerland, Tel: +41 61 32 40 942.

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Graphical Abstract

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Process Analytical Technology e.g. API content (Blend uniformity and content uniformity)

In Process Control e.g. hardness, thickness, mass, disintegration, loss on drying, particle size distribution

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Process data monitoring (Multivariate data analysis) e.g granulation torque, dryer temperature, tablet compression force

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Continuous manufacturing process monitoring Solid dosage forms

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Analytical Technology, Multivariate Data Analysis, Process Data Science, Process Data Analytics

Highlights

Real-world example of process monitoring of a continuous production line used for clinical supply Process optimization based on Design of Experiments

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Application of innovative Process Analytical Technology (PAT) equipment Three independent data sources are used for process monitoring: Multivariate Data analysis of process data, PAT in-line sensors and In Process Control (IPC) Demonstration of the robustness of continuous wet granulation process with a real formulation of a product in development

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Analytical Technology, Multivariate Data Analysis, Process Data Science, Process Data Analytics

Abstract

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Continuous Manufacturing (CM) of pharmaceutical drug products is a rather new approach within the pharmaceutical industry. In the presented paper, a GMP continuous wet granulation line used for clinical production of solid dosage forms was investigated with a thorough monitoring strategy regarding process performance and robustness. The line was composed of the subsequent continuous unit operations feeding – twin-screw wet-granulation – fluid-bed drying – sieving and tableting; the formulation of a new pharmaceutical entity in development was selected for this study. In detail, a Design of Experiments (DoE) was used to evaluate the impact of the three main factors (amount of water, filling rate, and shear force in twin-screw granulator) on the tablet quality. The process was monitored via in-process control (IPC) tests (e.g. weight, hardness, disintegration, and loss-on-drying), Process Analytical Technologies (PAT), and through the analysis of the process parameters (multivariate process control). The tested formulation was very robust to the large process variation of the DoE: all IPC results were in specification, the PAT probes provided stable results for the content uniformity and no critical variations can be detected in the process parameters. An adequate monitoring strategy was presented and the robustness of the process with one formulation has been demonstrated. In summary, this continuous process in combination with smart formulation development allows the robust production of constant quality tablets. The synergy between PAT, process data science and IPC creates an adequate monitoring framework of the continuous manufacturing line.

Abbreviations

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API - active pharmaceutical ingredient  CM - Continuous Manufacturing  FDA – Food and Drug Administration  LC – Label Claim  LOD - loss-on-drying  IPC – In Process Control  NIRS - Near Infrared Spectroscopy  PAT - process analytical technology  PCA - principal component analysis  PLSR - partial least squares regression  PSD - particle size distribution  RMSEC - root mean square error of calibration  RMSECV - root mean square error of cross validation  TSG - twin-screw granulation.

Keywords 3|Page

Continuous manufacturing, Solid Dosage Form, Process Monitoring, Process 1. Introduction

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Continuous Manufacturing (CM) of pharmaceutical drug products is a new approach within the pharmaceutical industry, opposing traditional batch manufacturing processes based on its potential to increase manufacturing flexibility and efficiency. In CM, all process units are directly connected to each other. Starting material is continuously charged into the first process unit at the beginning of the line, while final product is simultaneously discharged at the end. Literature on CM suggests that the process requires sophisticated process control strategies, to know at all times the current process state and to ensure consistent product quality within specification at all times. Such process control strategy can be designed from suitable in-line and/or at-line PAT-tools (PAT = process analytical technologies) that deliver real-time information about the process state and/or product quality, in combination with associated control loops that facilitate the adaption of critical process parameters accordingly in real-time [1-3]. However, extensive experience of the presenting authors suggest that the robustness of a CM-process is highly product and formulation dependent, and hence can be influenced through the process design, depending on the product needs. Some products might require a tight control of process parameters during granulation, drying, and tableting. As small deviations can have a significant impact on the final drug product quality like disintegration time (caused by too many fines or too coarse granules produced during granulation, when small changes in liquid-to-solid ratio were to occur; as described for example in [1]), or varying amounts of water during granulation could influence polymorph changes of the drug substance. However, other products might not require such a strict control strategy, as the formulation and hence the final drug product quality demonstrates robustness towards certain process variations.

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Near Infrared Spectroscopy (NIRS) has become a popular qualitative and quantitative PAT-tool in the pharmaceutical industry, as it is a safe, fast, and non-destructive method which does not require sample preparation. The integration of NIRS into pharmaceutical manufacturing was initially endorsed by the US Food and Drug Administration (FDA) as part of their PAT initiative in 2004 [2, 4]. Nowadays, NIRS is frequently applied for the identification of raw materials, inline monitoring of process steps like blending, granulation, and drying by compound quantification, as well as for process troubleshooting [5-10]. The main objective of this paper is to propose a monitoring strategy of the CM production line. Three main sources of information are reviewed in this study: IPC, process parameters and PAT values. One formulation of a new product in development was selected and a Design of Experiments was performed in order to evaluate the robustness of the continuous process.

2. Materials and Methods 4|Page

2.1. Formulation The formulation contained 40.0 % of Active Pharmaceutical Ingredient (API), 20.0 % Microcrystalline cellulose PH102, 23.0 % Calcium hydrogenphosphate anhydrous, 5.5 % Natrium carboxymethylcellulose, 5.0 % Sodium stearyl fumarate, 4.6% Polyvinyl pyrrolidon (PVP), 1.9% Sodium lauryl sulfate (SLS) (all excipients supplied by Novartis Pharma AG, Stein, Switzerland).

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Purified water at room temperature was used as granulation liquid. The targeted tablet weight was 600 mg with a targeted label claim (LC) of 240 mg API/tablet (LC=100%).

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2.2. Process

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The main process units of the continuous wet granulation line are shown in Figure 1.

Figure 1: Continuous wet granulation process and main operation units. (PTS=powder transfer system, IPC=in process control)

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2.3. Preparation of powder blends 60 kg of powder blend was prepared in batch mode by weighing all ingredients into a 100 L container and blending two times for 14 minutes at 7 rpm with a Pharma Telescope Blender PTM 300 (LB Bohle GmbH, Ennigerloh, Deutschland). Between the two blending steps, the powder was sieved through a 1.25 mm-mesh sieve (Oscillowitt-Lab type MF-lab, Frewitt, Granges-Paccot, Switzerland) to break agglomerates.

2.4. Twin-screw wet granulation Continuous wet-granulation was performed on a Thermo Fisher Pharma 16 Twin Screw 5|Page

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Granulator (TSG) (Thermo Fisher Scientific, Karlsruhe, Germany) with screw diameter (D) of 16 mm and screw length of 53 ¼ x D. Three different screw configurations were tested as part of the DoE, varying in the length of the kneading zones to induce low-, mid-, and high shear forces on the granules. The powder blend was fed into the barrel through the first feeding port by a lossin-weight powder feeder (K-Tron T20, Coperion K-Tron GmbH, Niederlenz, Switzerland). Granulation liquid at room temperature was fed through a 2.5 mm wide nozzle in the third port by a custom made dispensing pump system (based on Watson Marlow, Zollikon, Switzerland). Details on the three investigated screw designs and the barrel setup are summarized in Supplementary material - figure 1. Barrel temperature was set to 35°C in all trials. Screw speed was varied between 450 – 600 rpm, solid feed rate was varied between 3.0 – 4.0 kg/h, and liquid feed rate varied between 450 – 1200 g/h. Depending on factor combination, these settings resulted in wet granules water contents of 13.0 % – 23.1 % and granulator screw fill levels (channel fill level) of 8.5 – 15.2 % (see 2.7 - Channel fill level, for details on how to calculate the fill level).

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2.5. Continuous fluid-bed drying

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Continuous fluid-bed drying of wet granules was performed on a Glatt GPCG 2 CM fluid-bed dryer (Glatt GmbH, Binzen, Germany), directly connected to the twin-screw granulator [1]. Drying settings were as follows: 80 °C drying temperature, 100 m3/h air flow rate, dryer rotation speed 17 rph. Before starting the first trial of the day, or in case the process was stopped inbetween, the empty dryer was pre-heated for 60 minutes at the intended drying temperature and airflow settings.

2.6. Sieving and tableting

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Dried granules were sieved through a 1.25 mm sieve (Oscillowitt-Lab type MF-lab, Frewitt, Granges-Paccot, Switzerland) directly before compaction in a rotary tablet press (Calibration 1200i, FETTE Compacting, Schwarzenbek, Germany), with oblong punches of 12 mm x y mm at tableting speeds 5000 – 6667 tablets/hour. Sieving and tableting was performed after granulation and drying was finished, hence granules were collected in a PE bag after exiting the dryer and then fillied manually into the sieve.

2.7. Channel fill level

Channel fill level ф was calculated according to eq.1 [11] where 𝑚̇ is the powder feed rate, N is the screw speed, 𝑉𝐹 is the free volume of the channels and equals 64.5 cm3 for the used screw of 53 ¼ L/D, 𝑆𝐿 is the lead length of the screw (i.e., the axial advancement of the screw in one 360° turn), L is the screw length, ̅̅̅ 𝜌𝐵 is the mean bulk density of the powder, and 𝑛𝑣 is the volumetric efficiency of the scew to convey powder. For all calculations it was assumed that ̅̅̅ 𝜌𝐵 is equal to the lab-determined tapped powder bulk density and that the screw is 100 % effective in 6|Page

conveying powder (i.e. 𝑛𝑣 =1). More details on the calculations can be found in [11]. eq. 1

ф=

𝑚̇ 𝑛𝑣 ̅̅̅̅(𝑉 𝜌𝐵 𝐹

𝑆𝐿 )𝑁 𝐿

2.8. Design of Experiments

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The process factors wet granule water content, granulator screw filling rate, and shear force were investigated by means of DoE to evaluate their impact on product quality. A D-optimal experimental design (performed with the MODDE Software (Umetrics, Umea, Sweden) on two levels was selected, resulting in eight experiments and three repetitions at center point settings. The trials were performed on 5 consecutive days, each parameter setting was tested for approximately 60 minutes. Table 1 summarizes the experimental design matrix and Table 2 the factor levels.

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The experiment aimed to investigate the influence of the process factors on drug products critical quality attributes (CQAs) and other quality characteristics. In detail, intermediate CQAs of the dried granules are particle size distribution (PSD) and moisture content (loss-on-drying, LOD). Final drug product CQAs and other important quality characteristics are aspect, individual and mean weight, thickness, hardness, friability, and disintegration time.

Amount of water + + 0 0 + + 0

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Shear force + + 0 + + 0 0

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Run Order 1 2 3 4 5 6 7 8 9 10 11

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Table 1: Design of Experimentss: D-optimal experimental design (the execution were randomized during the production campaign) Filling rate + + 0 + 0 + 0

Table 2: Investigated factor levels factor

Level Low (-)

Center point (0)

Level High (+)

unit

Wet granules water content

13.0

18.4

23.1

%

Granulator screw fill level ф

19.0

26.9

34.0

%

Granulation shear force*

Low (screw design A)

Medium (screw design B)

High (screw design C)

N/A

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*shear force was varied by varying the screw design

2.9. Process monitoring 2.9.1. In Process Control (IPC)

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The IPC corresponds to 8 different tests: Loss on drying (LOD), Particle Size Distribution (PSD), hardness, disintegration time, friability, thickness, tablet average weight and tablet visual aspect. Loss on drying (LOD) was analyzed with a Mettler HX-HS 153 (Mettler Toledo, Greifensee, Switzerland) by drying approximately 5 g of samples at 106 °C, until the drying rate was < 1 mg/ 50 s. PSD was measured by CamSizer XT equipped with an X-Jet module operating at 30 kPa dispersion pressure (Retsch Technology Haan Germany), by measuring approximately 5-10 g of sample. Tablet hardness was measured with Pharmatron MultiTest 50 (Sotax, Aesch, Switzerland). Tablet disintegration time with Disitest 50 (Sotax, Aesch, Switzerland) in water (performed according to USP41 [12, 13]). Friability was done according to USP41 [13] with an Ischi AE-2 instrument (Charles Ischi AG, Zuchwil, Switzerland). Tablet thickness was measured with a digital calliper digital cal 150mm IP67 (TESA AG, Renens, Switzerland), tablet average weight was determined by weight balance AX205 (Mettler Toledo, Greifensee, Switzerland).

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LOD and PSD of the dry blend was measured before starting the experiment. Dried granules were sampled during the first, middle, and last dryer rotation of each trial, by collecting one full rotation of granules (i.e. ten process chambers in the continuous FBD) in a PE bag. The granule sample was thoroughly mixed before LOD and PSD was analyzed as described above. Tablets were sampled from beginning, middle, and end of each trial by collecting the required amount of tablets at the tablet press outlet. An overview of all performed IPCs and sampled amounts is listed in Table 3. Table 3: In Process Control Strategy (LOD: Loss On Drying, PSD: Particle Size Determination)

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Processss Step Blending

Drying

IPC tests

amount

Time interval

LOD

~5g

PSD

~ 5-10 g

LOD

~5g

each dryer rotation

PSD

~ 5-10 g

beginning, middle, end

once after blending

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Tableting

Aspect

125 tablets

Individual and mean weight

20 tablets

Thickness

5 tablets

Hardness

10 tablets

Friability

6.5 g(11 tablets)

Disintegration time

6 tablets

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2.9.2. PAT

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Three NIRS probes from two different instruments were installed in-line to analyze dried granules and tablets in three locations of the CM line: A SentroPAT FO instrument (Sentronic®, Dresden, Germany) configured with two immersion probes (SentroProbe DR LS NIR) was used for the monitoring of granules after the fluid-bed dryer (FBD) and of sieved granules in the tablet press feed frame. Spectra were measured in reflection mode using 60 scans of 0.011s, a 2 nm resolution, and a spectral range of 1150-2200 nm. A VisioNIR LS instrument (VisioTec GmbH, Laupheim, Germany) was used for 100 % in-line control of tablet content uniformity. Spectra of tablets were measured in reflection mode in the tablet press using one scan of 0.004s, an 8 cm−1 resolution, and a spectral range of 1100-2100 nm.

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With both instruments, a background spectrum was acquired before the run for in-line acquisition. The instruments and the installation of the three NIR probes are presented in Figure 2.

Figure 2: Installation of the Near Infrared probes in the tablet press and fluid-bed dryer. (A) Sentro PAT FO for granules blend uniformity after FBD (B) Sentro PAT FO for granules blend uniformity in the tablet press feed frame, with light source (1) and probe head (2) inserted into the tablet press feed frame (VB)(3). VisioNIR for content uniformity of tablets after compression with the probe head (4) installed between the upper punch (5) and tablet (6) being ejected by lower punch (7). Figure adapted from [14]. 9|Page

2.9.3. NIR method development and validation For blend uniformity assessment in the tablet feed frame, offline spectra of granules containing 70 %, 100 % and 130% of the label claim API content were acquired. Offline spectra were taken with the probe directly submerged into a granule sample at three different positions and five spectra per position. The data set for content uniformity of granules contained 63 spectra. Model was verified by cross validation only due a low number of samples.

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For tablet content uniformity assessment, offline spectra of tablets containing 70 %, 100 % and 130% of the label claim API content were acquired. Spectra were taken in static mode from 10 tablets per label claim with 50 repetitions. The full dataset containing 1500 tablet spectra (=3 Label claims *10 tablets*50 acquisitions) and 400 blind stamps, was split randomly into two independent calibration and validation sets, each containing 50 % of the overall data.

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For method development, raw spectra were preprocessed via standard normal variate (SNV) combined with first derivative (gap of 5) for spectral adjustment and reduction of noise. Partial least squares (PLS) regression was carried out on the preprocessed NIRS spectra with a full cross-validation [15, 16]. Calibrations were assessed via linearity (R2, slope and bias) and accuracy (RMSEC/RMSECV). R2 greater or equal to 0.95 should be observed while the slope and intercept are expected to be as close as possible to 1, and 0, respectively [17]. Accuracy was demonstrated by evaluating root mean square error of calibration (RMSEC), and of crossvalidation (RMSECV), and a graphical evaluation of residues [17].

2.9.4. Process Parameters

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14 potentially critical process parameters (pCPPs) were selected for process monitoring, as listed in Table 4. Selection was done based on a previously conducted risk evaluation. Each pCPP was monitored by considering the parameters actual process values over time (every 30s). During the 11 DoE runs, 4256 timestamps were recorded. Table 4: Process parameters

pCPP powder feed rate feeder screw speed liquid feed rate granulator barrel temperature zone 2 granulator screw speed granulator torque inlet air temperature outlet air temperature inlet airflow tower inlet humidity

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Process unit Feeder Extruder

Dryer

Unit kg/h rpm kg/h °C rpm Nm °C °C m3/h g/kg

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outlet humidity PD Product filter (pressure difference) PD product (pressure difference) dryer rotation speed

g/kg Pa Pa rph (rotation per hours)

2.9.5. Software for computation The Design of Experiments was prepared and analyzed with MODDE software (version 11.0.1, Umetrics/Sartorius, Umea, Sweden).

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Sentro Suite package version 2 (Sentronic®, Dresden, Germany) was used for spectra acquisition in the tablet feed frame and after the fluid-bed dryer. The calibration for the Sentronic spectrometer was developed with SIMCA software (version 13.3, Umetrics/Sartorius, Umea, Sweden).

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Spectra acquisition of tablets in the tablet press was done with the NovaPAC/NovaMath package (Prozess Technologie Inc. ®, St. Louis, Missouri, USA). Unscrambler® version 10.5 (CAMOs Software AS, Oslo, Norway) was used for the preprocessing and for the the PLS computation . Hierarchical Calibration development module (version 1.5 - CAMOs Software AS, Oslo, Norway) was used to create the in-line calibration.

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Process Data Analytics (e.g. Chemometric methods) were computed with Python 3.4 (Anaconda Package version 4.5.4, Continuum Analytics) using the Spyder (The Scientific Python Development EnviRonment) 3.1.4 interface. The data structures and analysis tools were provided by Panda 0.20.1 while the fundamental package for scientific computing with Python was included in Numpy 1.14.5. The Scikit-lear 0.18.1 toolbox enabled the computation of the PCA and the PLS. The graphics were displayed with Matplotlib 2.0.2 and the derivative pretreatments with Scipy 0.19.0.

3. Results and discussion 3.1. IPC results

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IPC results from dried granules and tablets have been summarized in Figure 3 A and B, respectively. Dried granules target LOD is calculated as the dry blend LOD0 = 1.84 % ± 0.5%. Granules LOD remained within their target specification for all DoE trials and no significant differences were observed between trials, regardless of the induced variations in wet granules moisture content (Figure 3 A (left)). Dried granules PSD varied slightly between the different trials (Figure Figure 3 A (right)). Tablet characteristics thickness, hardness, disintegration time and mass varied slightly between the different trials (Figure 3 B). Generally, all tablets showed acceptable hardness > 100N and acceptable disintegration time of ≤ 15 minutes, based on internal requirements for uncoated tablets. The variation in tablet mass demonstrated to be not-critical. All samples passed the test 11 | P a g e

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on content uniformity of dosage forms via mass variation with all acceptance values AV ≤ 4 ̅ = average API (calculated as 𝐴𝑉 = |𝐿𝐶 − 𝑋̅| + 𝑘 ∙ 𝑠; k=2.4, LC=label claim of 240mg API, X content of sample based on tablet weight, s = standard deviation in API content based on weight. USP requires AV ≤ 15 to pass; see [18] for details). The significance of the observed variations in granule and tablet characteristics in relation to the performed DoE will be assessed in the next chapter.

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Figure 3: Summary of IPC results from (A) dried granules and (B) tablets. *AV= Acceptance Value based on USP uniformity of dosage units via weight variation test.

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3.2. DoE statistical analysis

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For a more detailed analysis of the response variation, statistical DoE analysis was performed. Figure 4 shows a correlation map between process variables shear force, water content, and filling rate and corresponding IPC results.

Figure 4: Correlation between process parameters and responses (in blue) and between responses

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correlations (in green). Light blue and light green indicate correlation of medium significance (0.3-0.7); Dark blue and dark green indicate correlations of high significance (>0.7).

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Observed factor-response correlations were the highest for shear force and PSD (X10/ X50) with absolute correlation > 0.7. Furthermore, shear force with mass and shear force with PSD X90 showed correlation of medium significance (0.3 - 0.7). Likewise, filling rate with thickness, hardness and granules’ LOD as well as water content with PSD (X10, X90) showed medium correlation significance (Figure 4).

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Additionally, logical observations were made regarding intra-response correlation. For example, tablet thickness correlates positively with tablet mass, hardness correlates positively with disintegration time, PSD correlates with disintegration time, and LOD correlates with tablet hardness (Figure 4). The three quantiles of PSD are correlated as well. In summary, increased shear force increases granules PSD. However, since all tablets passed the test on content uniformity via weight variation, it can be denoted as not critical. Increased water content increased PSD, but in turn had no significant effect on the final tablets quality, signifying non-criticality. Increased fill rate results in decreased thickness, hardness, and LOD. However, none of these quality characteristics was found outside of specification, indicating that also fill rate is a not-critical process parameter. Consequently, while small correlations were found between process parameters and responses, no critical effects were observed. Hence, all process parameters were found to be not critical and the process itself demonstrated excellent robustness. 14 | P a g e

3.3. PAT sensors: Calibration, Spectral analysis and API content monitoring The calibrations of PAT sensors were initially performed in the laboratory with offline spectra. This study has been done with a product in early phase development. Therefore, the calibration was computed with a low number of samples due to drug substance availability. The validation of the PAT methods will be performed at a later stage of the development process.

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Figure 5 and 6 presents an overview of the calibration information (i.e. spectra, PLS score plot and regression) for the two API content calibrations. One calibration is used for blend uniformity determination after the fluid-bed dryer and tablet feed frame (Figure 5). The regression results are the following: the slope is 1, the bias is 0 and R2 is 0.996. The second calibration is the one for content uniformity of tablets in the tablet press (Figure 6). The slope is 0.998, bias is 0.21 and the R2 is 0.998. In both cases, a low prediction error was achieved: RMSECV is 1.12% for the tablet and 1.25 % for granules.

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Figure 5: NIR spectra and calibration for the fluid-bed dryer and tablet feeder (A) Spectra after preprocessing (SNV and first derivative) (B) PLS score plot

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(C) PLS regression calibration and cross validation

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(A) Spectra after preprocessing (SNV) (B) PLS score plot

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(C) PLS regression calibration and cross validation

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Figure 6: NIR spectra and calibration for the tablets

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During the process Design of Experiments, spectra were acquired in-line. After the fluid-bed dryer, high absorbance spectra are observed when filling of the chute is low (Supplementary material - Figure 2). These spectra were removed for the computation, by applying a threshold (0.35) on the first wavelength absorbance. In the tablet freed frame, bad acquisitions can be observed due to the scan of metal paddle (Supplementary material - Figure 2). The metal paddle spectra were not taken in account. Concerning the tablets, a decrease in spectral intensity was observed during the 2 weeks of the measurement campaign. However, this intensity change was corrected by the normalization (Supplementary material - Figure 3). Globally, an adequate spectral quality was achieved with the two PAT equipments in order to monitor the CM process.

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The figure 7 present the predicted API content (% of label claim) for run 8. The table 5 present the mean values and the standard deviation of the PAT results. The API values show a stable process without drift. Moreover, the mean and standard deviation values for each run appears to be similar for the 11 runs of the Design of Experiments. Even if small spectra changes can be observed, the NIR calibration appears to be robust and can be applied for process monitoring. The API content of the 11 runs is very constant for the three measuring points (dried granules after dryer, in feed frame and tablets). Intra batch variability is extremely low in the tablet feed frame (RSD < 2%). Probably some sticking issue during the measure or probe fouling can be observed, but was not investigated further. For tablets, variations in the content can be observed (RSD <3%). This variation is due to the repeatability of the measurement and due to the high acquisition speed. The highest intra16 | P a g e

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batch variability is observed after the fluid-bed dryer (RSD max= 17.6%) probably due to the process, the size of the probe head in relation to the granule size, and variabilities between chambers of the dryer. The probe position or the synchronization of the measurement with the process can probably be improved in order to improve the precision of the measurement.

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Time

Figure 7: Example of PAT results (run 8) after fluid-bed dryer (A)– total time for granulation and drying is of approx. 45 min (B) Feed frame (C) Tablet – total time for tableting is of 10 min

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Table 5: Overview of PAT results Run #

API Content after dryer Mean value (Standard deviation) in %

API Content in tablet

API Content in tablet

feed frame

Mean value (Standard

Mean value (Standard

deviation) in %

deviation) in % 90.5 % (4.0%)

97.1 (0.6)

N.A.

2

87.7 (7.6%)

97.0 (0.4)

96.7 (2.8)

3

108.8 (10.6%)

98.3 (0.5)

4

103.7 (8.1%)

100.4 (1.2)

5

107.1 (14.9%)

100.4 (0.3)

6

92.5 (15.9%)

96.6 (1.6)

7

98.7 (8.6%)

N.A.

8

100.2 (17.6%)

100.6 (0.3)

100.9 (2.2)

9

105.6 (12.9%)

104.5 (1.6)

100.2 (2.5)

10

106.2 (14.3%)

103.2 (0.4)

100.2 (2.2)

11

100.5 (13.3%)

N.A.

100.4 (2.3)

99.3 (2.3)

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100.13 (3.0) 101.8 (2.5) N.A.

99.9 (2.5)

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3.4. Univariate analysis of process data

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The last part deals with the analysis of process parameters in order to monitor the continuous process. First, the correlation between parameters was evaluated. Supplementary material Figure 4 presents the correlation map, where it is shown that the 3 temperatures (barrel, inlet and outlet) are correlated. Inlet and outlet humidity are also correlated, and air flow, temperature, and humidity have an impact on the filter pressure. Next, the main parameters for the three process units (feeding, granulation, and drying) were identified and displayed in univariate manner. In detail, the following parameters were analyzed: powder feed rate (feeder), screw speed (feeder), liquid feed rate (feeder), torque (granulation), outlet humidity (dryer) and inlet humidity (dryer). Figure 8 and Supplementary material - Figure 5 present the main parameters of the solid feeder. Generally, the powder feed rate is set by the Design of Experiments and the value should be 18 | P a g e

constant, whereas the screw speed can increase during the process i.e. when the feeder fill volume is lower. The liquid feed rate is plotted in Supplementary material - Figure 6. This parameter is set by the Design of Experiments. Therefore, during a run the values of the liquid feed rate is very stable. Torque is another important parameter during granulation that is impacted by the process parameters and the formulation (see Figure 9). The three target runs (#5, #8 and #11) have a similar torque profile, while the other trials at different parameters differed. Moreover, a process stop can be observed during run #2 with an increase of the torque value after the restart, potentially caused by material deposition on the granulator barrel during the stop.

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In the dryer, outlet humidity is highly impacted by the process parameters, especially the overall water amount (Figure 10), since a correlation between liquid feed rate and outlet humidity was observed. This agrees with the previous observation that the moisture content of wet granules has no significant impact on the dried granules LOD, as the excess water is removed during drying. Concerning the three target runs, batch 8 had a higher outlet humidity than batch 5 and 11. However, as it correlated with an increase of the room humidity (i.e. inlet humidity – Figure 11), this observation is not linked to a change in the process but to change in the environment. Therefore, to consider only process specific values, a new indicator was created (Outlet Humidity – Inlet Humidity, see Figure 12). After correction, the new parameter is specific of the water content added to the process.

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The visualization of single process parameter shows an intra batch stability. Significant inter batch differences can be used to monitor the process with a simple approach. As an example, a biplot representing the granulation torque and the outlet humidity provided an adequate visualization of the different runs of the Design of Experimentss (Figure 13): the cluster between the target batches and the other batch is clearly visible. The use of the new variable (outlet – inlet humidity) shows that the three targets batches are similar and the process robust. This variable hides the deviation in outlet humidity, which was considered as not critical in our study.

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Process Data Analytics is a key element of the monitoring strategy together with PAT and IPC results. The process data analysis was performed retrospectively in this study. The profile of the three target batches can be used as golden batches during the next campaign. Univariate control chart can be applied to detect process variation.

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Figure 8: Univariate control plot for solid feed rate

Figure 9: Univariate control plot for granulator torque

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Figure 10: Univariate control plot for outlet humidity

Figure 11: Univariate control plot for inlet humidity – Run #8 is clearly an outlier

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Figure 12: Univariate control plot for the new created variable outlet humidity – inlet humidity

Figure 13: Biplot control chart - Torque vs Humidity. Top: with all variable (Run 8 is an outlier). Down: Creation of a new process variable - inlet humidity replaced by (Outlet –inlet humidity) – all the target 22 | P a g e

runs (5, 8 and 11) are in one cluster

3.5. Multivariate analysis of process data

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Multivariate methodology can be applied for process monitoring. To test their applicability in the presented work, two different approaches were evaluated: Principal Component Analysis and Partial Least Squares regression (Batch MSPC approach). Both approaches were evaluated with two different data sets: Dataset #1 contained all the process data including stop and start-up of each batch; dataset #2 contained only the steady state data without stop or start-up.

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The statistical methods are highly influenced by the stop and start-up data (see Figure 14 A for the PCA). Furthermore, it is influenced by parameters that evolve from one run to the next, like increasing filter pressure (caused by cumulative particle deposition during the campaign, as no cleaning was performed between the different runs). Here, the first principal component is highly correlated to the increase in filter pressure, even though this increase is simply correlated to time, and not correlated to the process parameters applied (see Figure 14 B). As the multivariate analysis is highly influenced by the start-stop data and the objective is more focused on obtaining a multivariate method for steady-state, only dataset #2 was applied for PLS analysis. To remove this impact, batch multivariate process control for steady state process (PLS regression with process maturation variable i.e. process time) was applied. It allows to remove the impact of filter pressure increase, as shown in Figure 15. Run 8 is indicated to be a separated cluster, which is due to the difference in outlet humidity as described above. Asides from this observation, no other significant differences can be observed, suggesting that batch-MSPC is an adequate tool for process monitoring (Figure 15). It allows to detect small variations in process parameters (like humidity change) even if all IPC results are in specification (Figure 15-A). When using the new variable (outlet – inlet humidity), this impact was removed and the three targets batches demonstrated to be similar and the process robust (see Figure 15-B). Since the increase in inlet humidity had no significant impact on product quality, this approach is justified. However, this might change with other products and drying processes and hence needs to be evaluated on a case-by-case basis. Overall, batch-MSPC is a very sensitive tool for process monitoring that can ensure product quality. The batch-MSPC model can be saved and applied during the future campaign in order to monitor the process.

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Figure 14: PCA score plot (A) full data set (B) steady state only

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Figure 15: PLS score plot (Batch mspc results – PC1 vs PC2) - steady state only (A) Full data set with inlet humidity (B) New variable (Outlet – Inlet humidity)

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4. Conclusion

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A Design of Experiments has been used in order to propose a monitoring strategy of a continuous manufacturing line. During this DoE, the impact of the three main factors have been evaluated (amount of water, filling rate, and shear force). The CM process monitoring was done by 3 ways: in-process control tests (e.g. weight, hardness, disintegration, and loss-on-drying), Process Analytical Technologies (e.g. content uniformity by NIRS), and by the analysis of the process parameters by Process Data Analytics (e.g. univariate and multivariate process control). The tested formulation was very robust to the large process variation of the DoE: all IPC results were in specification, the PAT probes provided stable results and no critical variations can be detected in the process parameters. The synergy between PAT, process data science and IPC creates an adequate monitoring framework of the continuous manufacturing line.

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