Industry Perspectives on Process Analytical Technology: Tools and

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Organic Process Research & Development

INDUSTRY PERSPECTIVES ON PROCESS ANALYTICAL TECHNOLOGY: TOOLS AND APPLICATIONS IN API MANUFACTURING

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Shailendra Bordawekar , Arani Chanda , Adrian M. Daly , Aaron W. Garrett , John P. Higgins , Mark A. 6 6 7 1 2* 8 LaPack , Todd D. Maloney , James Morgado , Samrat Mukherjee , John D. Orr , George L. Reid III , 9 7 Bing-Shiou Yang , and Howard W. Ward II 1 2 3 4 5 6 7 8 9

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Process R&D, GPRD, AbbVie Inc. Analytical Research Laboratories-US, Pharmaceutical Science & Technology, Eisai Inc. Process Analytical Sciences Group, Global Technology Services, Pfizer Global Supply. Global Quality Laboratories, Eli Lilly and Company Process Analytical Technology, Merck and Co., Inc. Small Molecule Design & Development, Eli Lilly and Company Analytical Research and Development, Pfizer Inc., Worldwide Research and Development 92 Castle Hill Road, Pawcatuck, CT 06379 Chemical Development/Material and Analytical Sciences, Boehringer-Ingelheim Pharmaceuticals, Inc. Corresponding author. tel.: (978) 738-2736 E-mail address: [email protected]

ACS Paragon Plus Environment


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Table of Contents Graphic

B a

On-Line Near IR Process Analytics


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1.

ABSTRACT

The IQ Consortium reports on the current state of process analytical technology (PAT) for active pharmaceutical ingredient (API) manufacturing in branded pharmaceutical companies.

The article

describes the application of PAT in manufacturing and provides representative examples in four common pharmaceutical unit operations: reaction and work-up, crystallization, drying, and milling.

Key words: Process Analytical Technologies, API manufacture, case studies.



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2.

INTRODUCTION

This is the second paper on the state of process analytical technologies (PAT) in the pharmaceutical industry from the perspective of members of the International Consortium for Innovation & Quality in Pharmaceutical Development (IQ Consortium; http://www.iqconsortium.org; a group comprised of representatives from 37 branded pharmaceutical companies). The first publication focused on PAT tools 1

and applications in API development, and the focus of this paper is primarily on sharing current experiences of the use of PAT tools for API manufacture (e.g., PAT in a GMP environment). The goal of these papers is to communicate, from a pharmaceutical industry perspective, how PAT is being utilized and implemented across the industry. We strive to increase discussion, acceptance, and adoption of PAT tools in drug development and manufacturing (as appropriate) using these representative case studies. PAT tools are heavily applied in pharmaceutical workflows that underpin drug substance and dosage form development, scale-up, and manufacture.

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These PAT tools are often used in

manufacturing for business reasons (to facilitate process transfer, scale-up, fault-detection, process monitoring and continuous improvement) and as controls when appropriate. To ensure product quality and gain manufacturing flexibility, industry identifies and implements controls [often, using a Quality by 6

Design (QbD) approach to identify and mitigate risks], irrespective of the type or location of the analytics and controls, or whether additional cost benefits could be achieved using in-situ analytics and real-time control.

2.1.

Why use PAT

The FDA considers "PAT to be a system for designing, analyzing, and controlling manufacturing through timely measurements (i.e., during processing) of critical quality and performance attributes of raw and inprocess materials and processes, with the goal of ensuring final product quality.” The goal of PAT is to enhance understanding and control the manufacturing process. Consequently, the tools should be used for gaining process understanding and can also be used to meet the regulatory requirements for 7

validating and controlling the manufacturing process.

PAT is used throughout the pharmaceutical


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lifecycle (discovery to loss of exclusivity), though often for different purposes.

Table 1 provides an

overview of the different and common drivers for the use of PAT during development and manufacturing.

Table 1. PAT Drivers Throughout the Pharmaceutical Lifecycle. Key PAT drivers in development and manufacturing

Key PAT drivers in manufacturing

Enables and speeds process development (real-time assessments allowing more rapid conclusions and next steps to be drawn from experiment).

Improves safety (reduced worker exposure to hazardous materials).

Enables process control leading to improved (more consistent) product quality. Control implies the PAT will be used in a decision making role, and therefore must be cGMP compliant.

Data-rich experiments (potential to evaluate multiple components or attributes simultaneously).

Minimizes or eliminates sampling errors and samplingrelated biases, since the analyzer or sampling system is in continuous process contact. (Manually collected samples are always at risk of being mislabeled or mis-handled.)

Enables cost savings (improved equipment utilization, reduced off-line QC testing).

Enables process understanding (may reveal previously unknown process components, mechanisms, and relationships between variables).

Enables in-situ and real-time analytics (reduced analytical data turnaround time, and enables continuous processing).

Enables continuous quality verification (CQV) and real-time release (RTR).

Assists in efficient scale up to pilot and manufacturing plants and reduces process cycle time (improves plant utilization).

Enables data collection and process understanding at point of operation for business purposes (scale-up, process signature, fault detection, forensics, end-point detection, and continuous process improvement).

Key PAT drivers in development

Enables efficient process development and optimization (potential to evaluate multiple components or attributes simultaneously and consistently throughout the development cycle).

PAT provides a very good approach to ensuring chemical reactions are well controlled and proceed within typical trajectories and specifications. The more frequent measurements and automated analysis (as compared to standard off-line lab-based analyses) allows faster identification of atypical process events, as well as facilitating real-time process monitoring and control of the (batch or continuous) process. PAT also can provide measurements of parameters that are difficult or undesirable to measure with standard off-line techniques in the manufacturing setting (e.g., PAT use provides advantages over off-line



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techniques when processes include the presence of highly hazardous materials, high pressure systems, high or low temperature systems, transient intermediates, components that change when exposed to ambient atmospheric conditions, and heterogeneous systems).

As an investigational new drug progresses through the phases of pharmaceutical development towards commercial manufacture, the role of PAT shifts from the pursuit of process understanding towards a means of process control. This shift in purpose alters the demands from the PAT method. The principles of Fit-for-Purpose and robustness become paramount. To meet these demands, the analytic technique employed may migrate from a sophisticated system that generates complex data to a simple and straightforward measurement that has experimentally been shown to correlate with the attribute or property of interest.

2.2.

How to develop a PAT method: the PAT workflow

The development of an in-situ analytical method progresses in a logical step wise sequence.

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The

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previous PAT article on development activities showed a workflow progressing through step 4, this article continues and shows the entire PAT workflow (Figure 1).

In the initial steps of this workflow, an

appropriate technique (one that is available at the manufacturing site and can be applied to the process) is selected and the performance characteristics are demonstrated to meet or exceed the measurement requirements for the method. The subsequent steps (5-8) relate to activities required for GMP use, including method validation, method transfer and demonstration of method performance, on-going maintenance, and ultimately, decommissioning, when the method is no longer appropriate or required.


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Figure 1. Method lifecycle workflow. (Adapted from reference 1. Copyright 2014 American Chemical Society.)

2.3.

API process workflow

Typical API unit operations, measurement needs, and precedented PAT tools used in a development setting have been disclosed.

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These same unit operations, measurement needs, and PAT tools are

applicable in a manufacturing setting. The main part of this paper will describe representative examples of how industry applies the PAT toolbox in four common pharmaceutical unit operations (reaction and work-up, crystallization, drying, and milling).



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3.

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TRANSFER TO MANUFACTURING

During process development, PAT will have been used for process understanding or control.

As

experience is gained at laboratory and pilot scale, the value of each individual PAT application to the process will start to become apparent. Prior to the technical transfer of the process to the commercial manufacturing equipment, it is useful to carry out a an evaluation of which PAT applications are valuable during commercial manufacturing. This assessment can use information from the development of the process to determine and justify the PAT applications that should be implemented in the commercial process. A number of factors can be used as criteria in this risk assessment, including, but not limited to, impact of the PAT application on product quality, process efficiency, process safety and installation cost. Once agreement has been reached on the prioritized PAT application(s) then the lifecycle of the method, as described in Figure 1, can be followed. Some of the key aspects of Step 5-8 are described below.

3.1.

Method Risk Assessment

The risk assessment (RA) process is an integral part of quality risk management as depicted in Figure 2. It consists of the identification, analysis, and evaluation of risks that may be part of a (manufacturing or measurement) process. In the case of a PAT application, RA can be employed as a tool at various stages in the development of a method, and is especially useful to facilitate identification of operational differences that may impact the transfer and routine use of the method.


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Figure 2. Overview of a typical quality risk management process. (Reproduced with permission from reference 9. Copyright 2005 International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use.)

An RA may be implemented for any analytical measurement technique. Risk identification typically starts with dissecting the unit operation into its focus areas (e.g., equipment, analysis software, sample presentation). These focus areas are further dissected into analytical actions (e.g., calibrate, measure, analyze) with their associated parameters and response attributes (e.g., spectral range, composition of standards and samples, preprocessing conditions). An abbreviated method deconstruction example for reaction monitoring is highlighted in Table 2. Parameters are then evaluated with respect to attributes deemed critical to the measurement. These evaluations can be done via simplistic Ishikawa (fishbone) diagrams or more comprehensive cause and effect matrixes (C & E) or failure mode effect analysis (FMEA) matrixes, as well as other approaches. The aim is to identify the critical parameters that have the highest probability of adversely impacting the attributes and ultimately the greatest potential to contribute to measurement variability and bias. Identified parameters can then be evaluated experimentally, often through multivariate experimental designs, to determine their degree of impact and acceptable



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operational boundaries. If operational tolerances are determined to be unacceptable, alternate conditions or measurement techniques may be required.

Table 2. Analytical Method Deconstruction Example Analytical Unit Operation

Analytical Actions

Inputs Equipment Analysis software Probe material

Reaction monitoring (MIR)

Calibrate

Port location

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Standards & composition

Trend plot

Spectrometer model

Limits test

Spectral range

Quantitative result

Spectrometer resolution

Material ID & lots Rxn conditions

Parameters

Measure Analyze

Background collection Measurement frequency Preprocessing conditions Univariate or multivariate

Risk assessments, performed as part of a method transfer, often focus on parameters associated with the ruggedness of the methodology (e.g., solvent and raw material lots, reactor port location, reactor configuration) as opposed to robustness parameters evaluated during method development.

These

ruggedness parameters may be considered to contribute to the noise of the system during early risk assessments. Identifying differences in how analytical actions are performed and interpreted between the sending and receiving sites is paramount. Assumptions on how the method will be implemented at the receiving site often represent the greatest risk to method transfer; a risk assessment framework that facilitates clear communication of the methodology, its nuances, and parameter sensitivities can lead to a successful method transfer.

3.2.

Equipment and Method Qualification

Arising from the risk assessments described above, instrument and method specifications will have been developed. The subsequent purchase, qualification and installation of the instrument and associated software should follow standard pharmaceutical industry approaches.


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After qualification of the instrumentation it is then necessary to qualify the PAT method. This begins with an assessment of the intended quality impact of the PAT method; the assessment outcome will define extent of method qualification and validation required.

PAT method validation should follow similar

approaches to traditional analytical method validation, however some changes may be required due to instrumentation and process environment constraints. It is important to carry out sufficient qualification to ensure the PAT method is fit for the intended purpose. Once qualification is complete it is important to establish methodology for ongoing evaluation of PAT method performance and validity. Only after all these steps have been completed should the PAT method be put into routine use. In cases where the PAT method is replacing an existing off-line method, the impact of this change on the validated manufacturing process must also be considered.

4.

CASE STUDIES OF PAT APPLICATIONS PERFORMED DURING MANUFACTURE

4.1.

Reaction and Work-up

4.1.1. On-line mid-infrared spectroscopy (MIR) use in enzymatic reaction process control Enzyme catalysis was used to realize a safe, environmentally friendly, and low cost synthesis of an API. During the enzyme catalyzed reaction, a by-product was generated that could slow down the reaction and reduce yield. The reaction by-product level was monitored in real-time using on-line Fourier transform mid-infrared (FTIR) spectroscopy to ensure its efficient removal and minimal loss of a volatile reactant, as shown in Figure 3. The reaction by-product was removed using a combination of vacuum and nitrogen sweep and, therefore, real-time monitoring was required to ensure adequate process control (maintaining reaction by-product below a critical level) and to drive the reaction to completion. In addition, the on-line FTIR was used to measure the extent of reaction and determine the reaction end-point. The benefits of implementing PAT for this application included real-time process control to ensure optimum yield, as well as, cycle time reduction through elimination of sampling and lab testing.

Due to the specificity of mid-infrared spectroscopy, separate calibrations could be developed for analyzing both the reaction by-product, as well as, the free-base end product. Simple multiple linear



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regression (MLR) techniques using peak ratios were employed to develop calibration models. The FTIR instrument was pre-calibrated using a laboratory scale reaction set-up before deployment in the factory. A calibration model was developed by collecting spectral data and correlating it to GC and HPLC measurements from samples pulled during the dynamic process. The FTIR instrument was subsequently deployed in the factory and the probe inserted in a recycle loop of the full-scale reactor. The method was performance qualified at manufacturing scale using data from multiple samples pulled at various reaction maturities across three batches. After performance qualification, the on-line FTIR was released for use as the primary method for process control and end-point determination.

The calibration models were robust and no adjustments were needed despite scale change (lab to full scale), detector change, and optical re-alignment of the instrument. As on-going verification is a key element for spectroscopic PAT methods, a means for spectral outlier detection is a necessary component to ensure suitable method performance. As the methods to determine reaction by-product and free-base product were developed using peak ratio calibrations as opposed to partial least squares (PLS) models, a dynamic spectral outlier detection mechanism was implemented using principle component analysis (PCA).

The on-line FTIR methods have been deployed for the first production campaign at the

manufacturing site and will be used for the future high volume API manufacturing.


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Figure 3. On-line FTIR spectra of the enzymatic reaction and resulting profiles of the by-product and product production.

4.1.2. On-line NIR use in control of water content in a continuous process reaction Precise control of the water content in a Continuous Conversion Reactor (CCR) was required for optimum yield and reactor performance.

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Under conditions of elevated water levels, an increase in a competing

hydrolysis reaction and an increase in product solubility in the mother liquor (both leading to product losses) occurred. Under conditions of lower water levels, product insolubility necessitated a shutdown of the reactor for cleaning. The water content was monitored by sampling the process every hour and performing an off-line Karl Fischer (KF) titration. Based upon the lab results, an operator would manually adjust the water content in the reactor.

A need for tighter control of this critical process parameter (CPP) was the driving force behind this project. In-situ NIR spectroscopy was selected to monitor and control the water content. The NIR probe was installed in the recycle/sampling line of the CCR to enable continuous contact with the reaction mixture. NIR spectra and the corresponding KF titration results (1.0-1.8 wt%) were obtained over several weeks and under varying conditions (Figure 4). A mean-centered PLS model containing three latent variables was created and validated using an independent test set of samples.



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KF NIR

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(sampling rate once per 200 seconds)

Figure 4. Comparison of NIR and KF results.

In-situ NIR-predicted water values were sent to the distributed control system (DCS) controlling the water valve every 200 seconds, and provided more representative (real-time and eighteen assessments per hour) water results than the once per hour off-line KF assays. The increased number of water predictions in conjunction with automated feedback control of the reaction water valve improved the process yield and decreased the number of process shutdowns.

The use of NIR spectroscopy for water content

allowed for the reduction in the manual sampling of a heated, pressurized reactor for off-line KF testing to once per eight hours. While not necessary, the limited off-line testing helped with the acceptance of the new NIR application. The NIR with automated feedback control led to a more effective control of the water content and improved process performance. The system was implemented for the final six months of the production campaign and the savings due to improved process performance and reduced resource requirements was estimated to be $500,000 over that time period. Additionally, operator exposure and the possibility of spills were reduced, due to reduced manual sampling from the heated and pressurized reactor.

4.1.3. On-line HPLC use in continuous process reaction control


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On-line HPLC was used to monitor process impurities and reaction completion for a continuous high pressure hydrogenation, reductive amination reaction. In addition to monitoring impurities and product formation, on-line HPLC enabled measurement of the process transition (F curve) during reaction start-up and shutdown.

The driver for using on-line HPLC over other reaction monitoring techniques for this process was the need to monitor a key isomeric impurity. The isomeric impurity is typically controlled via starting material specifications, but is predominantly formed in-situ, only reduced to 50% during isolation, and must be monitored throughout the process. Additionally, the ability to rapidly observe product transition ensures the catalyst has not been poisoned or plated out during start-up.

On-line HPLC was coupled to the process via a dilution cart to sample the process without perturbing flow. The process was sampled every 30 minutes during transition and every hour during steady state operation (reactor residence time ~13 hours). Over a three week campaign, the on-line HPLC sampled the process over 500 times, reporting impurities and reaction completion data for each assay (Figure 5).

Figure 5. On-line HPLC data collected over a 21 day campaign.



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In addition to reporting impurities and reaction completion, the on-line HPLC immediately reports process fluctuations that may occur during solvent and starting material recharges or adjustments of feed pump flow rates. If sampled manually, these fluctuations would not be detected for approximately 24 hours, due to the time required for off-line analysis and verification of results. This delay would put large amounts of product at risk.

On-line HPLC lowered the cost of the process by significantly reducing the number of samples tested offline in the quality control laboratories (QCL) and further demonstrated future cost savings by matching the QCL results reported for the daily manual sample pulls. Using on-line HPLC to monitor the process also ensured that a state of control was maintained and the quality of the product met specifications throughout the process. The data for each assay was automatically processed, enabling continuous trend plotting of the data, relative to process controls and starting material feeds, and enabling process changes to be made in near real time.

4.1.4. Control of reaction endpoint using a turbidity meter – an example of a simple sensor A new process for the manufacture of an approved large volume API contained a chemical reaction where the starting material was only sparingly soluble in the reaction matrix. During development of the process it was established, using mid-infrared spectroscopy and off-line HPLC, that the reaction was complete once all the starting material was dissolved. At manufacturing scale, it was desirable to move on to the next processing step without off-line analysis. Rather than install an expensive process MIR spectrometer, it was demonstrated in the laboratory that a turbidity meter could be employed to accurately determine the point of starting material dissolution and thus the end of reaction (Figure 6). A process turbidity meter was installed in the manufacturing plant and the correlation between a stable turbidity reading and off-line reaction completion data was demonstrated. The turbidity meter is now the analytical tool used for the in-process control test. This application had significantly lower initial costs, as well as, requiring much less ongoing monitoring and maintenance than use of a spectroscopic PAT. It is clear that, when appropriate, the use of simple analytical sensors in PAT can be just as valuable as spectroscopic PAT applications.


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Figure 6. Turbidity measurement (NTU) used to determine reaction completion.

4.2.

Crystallization

4.2.1: On-line NIR use in crystal form control Careful control of water concentration during dissolution and subsequent crystallization of key reaction products or intermediates is frequently necessary in order to achieve desired final product physical properties, while maintaining acceptable yield and cycle time. For instance, the API synthetic route for a recent compound in pharmaceutical development included conversion of a dihydrate intermediate to an anhydrous form in the final dissolution and crystallization step, to obtain the desired polymorphic form, crystal shape, and particle size distribution in the final product. Unfortunately, the original control strategy was not robust, as it was based on results from an off-line KF titration assay that was highly variable due to heterogeneity of samples obtained from the 80 °C process. An early attempt to improve the control strategy incorporated a step to dry the dihydrate intermediate to an anhydrate, before assay, and then determine the appropriate amount of water to add before crystallization. This led to improved water control, but significantly increased the cycle time.

Therefore, a PAT-based control strategy was

developed and implemented.



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Both diffuse reflectance NIR (Figure 7) and attenuated total reflection MIR were evaluated as on-line measurement technologies for the real-time determination of water concentration in the crystallization tank. Bench scale studies focused on a relative comparison of specificity, sensitivity, accuracy, and robustness of the two techniques. Measurement capability assessment results, as well as, practical considerations and risks associated with the manufacturing facility installation (e.g., long-run fiber optics providing the flexibility to locate the analyzer in a remote general purpose area, to enhance safety and promote efficient work flow) were considered and NIR was selected as the most suitable technology.

Figure 7. Overlay of NIR spectra of representative samples with water ranging from 0.1% to 3.7%.

Following technology selection and implementation, the PAT system was extensively tested during several pilot- and full-scale campaigns. Ultimately, the PAT platform was refined and integrated into the GMP process control strategy. This enabled the use of the dihydrate as the starting material for the final crystallization step, and reproducibly reduced dihydrate drying time from 78 hours to 5.4 hours.

4.2.1. On-line NIR use in crystallization seed-point determination During through-processing of an API, residuals from upstream steps will affect API solubility, and hence, the seeding temperature of the subsequent crystallization. On-line NIR spectroscopy (as shown in Figure 8) was implemented for the determination of batch composition so that the crystallization process could be effectively controlled. On-line NIR provides real-time information of batch composition for predicting the seeding temperature based on an experimentally-derived solubility map. Effective process control


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prevents seed dissolution or batch crash-out and ensures robustness of the crystallization. Additional benefits of the application included cycle time reduction through elimination of sampling and lab testing and the resulting increased productivity enables a capacity increase of approximately 100 metric tons/year. Four separate laboratory in-process assays (HPLC, KF titraion, and two GC methods) were replaced with a single on-line, real-time measurement.

After the final reaction step and subsequent solvent switch, on-line NIR was used to measure the batch composition including the levels of API free base, water, and two additional solvents. Based on the measurements, the batch composition was adjusted in a feedback control scheme. After all component adjustments were completed, a final composition measurement was made and the seeding temperature of the crystallization was calculated using a process model, enabling feed-forward control of the process.

The on-line NIR instrument was calibrated at laboratory scale using a constrained mixture design. Stock solutions were obtained from pilot and full-scale batches and component concentrations were varied through spiking and dilution experiments. PLS models were developed for the four individual components of interest as shown in Figure 9. The system was then deployed at manufacturing scale and the PLS models updated using data obtained from three full-scale batches. The methods were then prospectively performance qualified on three independent batches and subsequently released for use. The on-line NIR methods were deployed for routine manufacturing and were used for high volume API production. As part of on-going verification of the on-line NIR methods, multivariate outlier diagnostics were deployed to ensure the quality of each prediction produced by the PLS models.

T

Figure 8. Schematic of on-line NIR implementation for determination of seed point.



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Figure 9. Predicted vs. measured results for the four calibration models.

4.3.

Drying

4.3.1. Mobile mass spectrometer use in API drying control Drying of pharmaceutical compounds (APIs and intermediates) is an important unit operation in the pharmaceutical industry. It is used to control API residual solvent levels to ensure API quality, and it is used to control chemical intermediate residual solvent levels, when subsequent chemical conversions could be affected. In the manufacturing setting, drying is often one of the most time-consuming (costly) process steps, and can sometimes be the overall process bottleneck. The application of PAT tools to enhance the understanding of complex drying processes, including examples of using NIR to monitor


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dehydrating and form conversion, was previously reviewed.

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Here, another case is presented wherein

on-line mass spectrometry was used to monitor a drying process in the manufacturing setting, resulting in reduced drying cycle time and enhanced process control.

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Due to the differences in physiochemical properties of filtered wet cakes and differences in desired attributes of dried pharmaceutical products, there is no universal drying protocol applicable for all pharmaceutical compounds.

Off-line sampling with Loss on Drying (LOD) analysis is the most

conventional way to determine the drying end-point.

However, this off-line methodology is time-

consuming, especially when dealing with large manufacturing batches that involve cooling of the dryer for sampling (safety concern), waiting for the analysis turnaround, and re-heating for further drying, if the endpoint specification is not met. Additionally, the potential extended drying time experience with off-line analysis approaches may adversely affect product quality. With these drivers, a PAT enabled on-line mass spectrometrometric method was developed in a manufacturing plant to monitor an API drying process and determine its end-point. The example shown here was applied on a vacuum paddle dryer used to dry the API.

A mass spectrometer was connected to the gas exit of the dryer and the

concentration of solvent to be removed was measured in-situ.

The correlations between data from conventional LOD and on-line mass spectrometric analyses were first established in the laboratory. While the on-line mass spectrometer continuously analyzed the solvent partial pressure (H2O) in the head space of the dryer, samples were pulled every 15 minutes and analyzed by LOD. Based on the established correlation, the end point for drying was determined to be a partial pressure of H2O ≤15 mbar [as correlated to the (off-line) LOD ≤6% specification]. In the implementation in the manufacturing plant, the readout from the mass spectrometer was connected to a display in the central control room. The criterion for the drying end-point is constantly checked, starting from the heating of the batch (to 55 °C) under vacuum. Once the drying end-point target is reached, the cooling of the dryer to ≤45 °C (the temperature that the batch can be unloaded or sampled) will be automatically initiated and a voice message will be sent to inform the operators that the batch is ready for unloading. If the end-point is reached even before the batch is heated above 45 °C, further heating will be stopped and the batch will be ready for unloading.



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Figure 10 shows the comparison of drying time based on the end-point determination with the conventional LOD method and the on-line mass spectrometric PAT method. The total drying time is the combination of the heating and cooling time. After the automated PAT mass spectrometer system was installed to continuously monitor and control the drying process, the average drying time (including the heating and cooling of the dryer) was reduced by more than 1 hr (~38% reduction). This reduction in cycle time was mainly attributed to the auto-detection of the drying end-point by the PAT tool that triggered the ending of the drying cycle (cooling or stopped heating) without operator intervention or waiting for the off-line analyses. For some of the PAT supported batches, cooling time was eliminated as the drying end-point was already reached based on mass spectrometric data and no further heating was conducted.

With the conventional LOD method, all the batches had to be heated up to reach the

designated temperature and pressure then cooled to 45 °C for sampling for off-line analyses. Along with the reduced drying time, the hold-up of the material to be dried under elevated temperature was also reduced, minimizing the potential of adversely affecting quality. This PAT-enabled system also alleviated unnecessary heating and cooling cycles, reducing energy costs. The shorter cycle time improved the utilization of the dryer. The PAT mass spectrometric method has been routinely used for the drying process in the manufacturing setting.


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10:01 Cooling time [h] Drying time [h]

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Batch Figure 10. The total drying time [heating (red) and cooling (blue)] for the manufacturing batches with conventional LOD and PAT supported mass spectrometric end-point determination.

4.4.

Milling

Particle size distribution (PSD) is an important physical property attribute for API in most solid dosage formulations. Depending on the criticality of an API’s PSD on the drug product formulation, it may be filed as a critical quality attribute in the registration. Nevertheless, measurement and control of PSD for APIs used in solid dosage formulations is a common activity. While some API processes employ controls for 1

PSD in upstream unit operations via slurry milling or during crystallization, others use a combination of upstream controls or dry milling as the final control point.

4.4.1. Particle size analyzer use for real-time measurement and control during milling In the current case study, an API process with PSD target (non-critical parameter) of Dv50 in the range of 50 to 65 µm was designed to ensure consistent process performance in drug product manufacture;



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although a much wider PSD range was established as an acceptable range.

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13

The API after

crystallization and drying is subjected to an impact mill to achieve the desired PSD. During process development, a laser-diffraction based PSD analyzer was used to measure the PSD off-line prior to milling and during the milling operation. The pre-milling PSD measurement helps to set the appropriate milling conditions that primarily include feed rate, mill speed and screen size, while an in-process sample from the initial phase of milling (from about 1-2 kg) helps to confirm the PSD outcome before the entire batch is milled.

Depending on the potential for lot-to-lot variability in PSD of the dried product, an

adjustment of mill speed was found to be sufficient to achieve the PSD target.

The above arrangement of pulling a sample for off-line measurement of PSD to confirm or adjust mill speed is not operationally efficient and could result in loss of precious API. For large scale commercial manufacturing, employing a particle size analyzer as a real-time PAT and enabling feedback control of milling speed based on PSD was sought as an efficient and streamlined mechanism to ensure the API quality attribute without significant manual interventions. A laser-diffraction based PSD analyzer was installed in a closed loop where representative sample from the main product line was continuously aspirated by nitrogen using a Venture Educator, dispersing the sample into the measurement volume, and returning the material back to the product stream (see Figure 11).


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Figure 11. PSD analyzer installed in a closed loop with measurement.

an impact mill for real-time particle size

In order to ensure consistency between the historical offline PSD data and the on-line PSD data, studies were conducted to correlate off-line vs. on-line measurements and to optimize various parameters including nitrogen flow and flute setting to ensure proper sample introduction into the measurement chamber. To achieve feedback control of the mill speed based on difference between measured particle size D50 and the target it was also important to study the mill speed sensitivity on the D50 outcome. A large step change in mill speed would lead to over-correction of the milled particle sizes resulting in larger oscillations around the target while a small step change could lead to slow response. For this mill and API, an appropriate step change was determined to be 50 rpm that resulted in an average change in D50 of 1.2 µm. Communication was established between the on-line PSD analyzer and the mill such that the analyzer collected PSD data at 1-sec intervals and provided a 30-sec rolling average to the mill PLC. For this process, a D50 dead band of ±2 µm was applied around the D50 set point within which no change was made to the mill speed. A particle size deviation alarm of ±20 µm was applied based on acceptability in downstream formulation. Figure 12 (left) depicts how the automated system performed in achieving a set point of 58 µm from a starting mill speed of 2600 rpm which was significantly far from the expected mill speed based on calibration studies. A tight dead band of 56-60 µm was met within minutes and the D50



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rolling average remained stable. Further demonstration studies in the facility also included challenging the feedback control with changing set points, as shown in Figure 12 (right).

Figure 12. Left: Feedback control to particle size D50 target of 58 µm. Right: The automated milling system was challenged by changing the set point (SP) to 50 µm and then back to 58 µm. Abbreviations: Avg = rolling average, UCL/LCL = upper/lower control limit, UDB/LDB = upper/lower dead band.

This automated milling system was qualified at manufacturing scale where further studies were conducted to fine tune the mill response and to ensure seamless connectivity within the elements of the automated system. This PAT-based feedback control was integrated in the API process where the inprocess milling sample was removed, thus resulting in better control, reduction in cycle time, and improved monitoring and troubleshooting capability.

4.5.

Simple Sensors Use in Conjunction with Multivariate Statistical Process Control

A second important facet of PAT, in which simple sensors are employed is in multivariate statistical process control (MSPC). The basic idea of MSPC is to feed a large number of simple data streams into a statistical model that visualizes the status of a process in real-time. The data being fed into the model are typically process parameters such as reactant feed rates, stir speed, pressure, temperature, etc. Although the data being fed into the model may be individually univariate, the large volume of different data streams enables the advantages of multivariate data analysis including the identification of relationships between different process variables and the ability to detect when the process as a whole


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has deviated from historic trends. MSPC works well when the measurements fed into the model are directly indicative of the state of the process as is the case in many API unit operations. Since MSPC can be a powerful tool for early fault detection and isolation, process upsets can be detected before they become catastrophic faults that implicate the quality of a batch. Timely correction of faults within a batch leads to a reduction in true deviations; this ultimately drives process consistency.

The example shown below is for the constant volume distillation of an API. The length of the process varies from 12-15 hours. There are a total of 16 variables in the multivariate model including jacket, still, and batch temperatures, condenser flow, vacuum, and solvent totalizer. An effective model for process monitoring is built on a limited number of batches (

Industry Perspectives on Process Analytical Technology: Tools and

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