Case Studies in the Applicability of Drug Substance Design Spaces

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Case Studies in the Applicability of Drug Substance Design Spaces Developed at Laboratory Scale to Commercial Manufacturing Nicholas Murray Thomson, Kevin Seibert, Srinivas Tummala, Shailendra Bordawekar, William F Kiesman, Erwin Irdam, Brian Phenix, and Daniel Kumke Org. Process Res. Dev., Just Accepted Manuscript • DOI: 10.1021/op500187u • Publication Date (Web): 18 Aug 2014 Downloaded from http://pubs.acs.org on August 22, 2014

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Organic Process Research & Development

Case Studies in the Applicability of Drug Substance Design Spaces Developed at Laboratory Scale to Commercial Manufacturing Nicholas M. Thomson*#, Kevin D. Seibertŧ, Srinivas Tummala¥, Shailendra Bordawekar†, William F. Kiesman‡, Erwin A. Irdam‡, Brian Phenixᵼ and Daniel Kumke€ #

Chemical Research and Development, Pfizer Inc., Eastern Point Road, Groton, CT, 06340,

USA. ŧ

Small Molecule Design and Development, Eli Lilly and Co., Lilly Technology Center,

Indianapolis, IN 46285, USA. ¥

Chemical Development, Bristol-Myers Squibb Company, One Squibb Dr, New Brunswick, NJ,

08903, USA.

†

Process Research and Development, AbbVie, 1 North Waukegan Road, North Chicago, IL

60064.

‡

Chemical Process Research and Development, Biogen Idec, 14 Cambridge Center, Cambridge,

MA 02142, USA.

ACS Paragon Plus Environment ACTIVE/ 75376451.1

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ᵼ

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Chemical Development, Vertex Pharmaceuticals Incorporated, 50 Northern Avenue, Boston,

MA, 02210, USA. €

Chemical Process Development and Commercialization, Merck, 126 E. Lincoln Ave, Rahway,

NJ, 07065, USA. KEYWORDS Design Space, Verification, Quality by Design ABSTRACT A number of strategies have been employed within the pharmaceutical industry in order to mitigate the risk of applying design space boundaries developed on laboratory scale to commercial drug substance manufacturing. The following article presents a number of case histories from members of the International Consortium for Innovation and Quality in Pharmaceutical Development (IQ), with the aim of exemplifying strategies used to confirm applicability of design spaces developed at laboratory scale. The strategies presented have a common aim of ensuring that appropriate quality standards are developed, maintained and enhanced during the product lifecycle whilst delivering rapid and cost effective mechanisms for drug substance commercialization.


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Table of contents graphic

INTRODUCTION The material in this manuscript was developed with the support of the International Consortium for Innovation and Quality in Pharmaceutical Development (IQ). IQ is a not-forprofit organization of pharmaceutical and biotechnology companies with a mission of advancing science-based and scientifically-driven standards and regulations for pharmaceutical and biotechnology products worldwide. Today, IQ represents 35 pharmaceutical and biotechnology companies. Please visit www.iqconsortium.org for more information. During the development and scale-up of drug substance processes, establishing the functional relationship of process parameters and material attributes to critical quality attributes, through quality risk management approaches, can support the development of an appropriate


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control strategy. A core element of the control strategy may be a proposed design space in which the multivariate interaction of process parameters and their impact on quality is well understood, as defined in ICH documents Q8 and Q11. A design space is typically developed at laboratory scale, relative to a commercial batch size. Whilst a design space should be demonstrated to be appropriate for use at commercial scale, there is currently not a clear agreement amongst industry and regulators as to how to achieve this goal. In recent years, a number of concerns have been expressed by regulators regarding the mechanism for demonstrating the applicability of a design space on scale. A recent ‘Questions and Answers on Design Space Verification’ document1 provided greater clarity and insight from a joint FDA and EMA perspective. This paper seeks to publish explicit case histories in order to further define and exemplify approaches for design space verification. The International Consortium for Innovation and Quality in Pharmaceutical Development (IQ) have developed a Quality by Design (QbD) Working Group with the aim of sharing strategies and practices to successfully ensure a design space is appropriate for use at commercial scale. Two recent publications2, 3 proposed a framework to address risks associated with process scale-up and act as the foundation for science based strategies that can be used to guide development of design spaces that are suitable up through commercial scale. The IQ QbD Working Group, consisting of over 30 member companies, are in general agreement with, and wish to exemplify this approach through the publication of this paper. The following case histories aim to demonstrate a number of risk and science based strategies for drug substance subscale design space mapping and the applicability of design space on scale. BACKGROUND


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During the development lifecycle for pharmaceutical compounds, continued refinement, optimization, and risk mitigation occur as part of the overall development process. As processes mature to a stage where commercialization occurs, a more structured and detailed evaluation of the effects of scale-up with regard to perturbation and sensitivity is generally undertaken in order to understand process performance, support a robust control strategy and assure quality. With increased pressure to assure quality at commercial scale, to reduce both costs and cycle time, and to improve overall throughput, there is a greater need to understand the limits of process sensitivity to scale and long term manufacturability. Business constraints generally preclude the options of demonstrating process robustness by sequential increases in scale and by running large scale experiments at different points within a design space. Designing a comprehensive set of commercial scale experiments is not feasible nor practical.

Therefore, an understanding of

the multivariate interactions between process inputs, such as physical properties and quality of raw materials, and processing parameters (with the identification of key features of a process that may lead to scale sensitivities) must be studied and mapped ahead of the final process scale-up activities. Thorough laboratory development, process analysis, and mathematical modeling can be undertaken to assure that process scale-up and long-term process robustness is achieved, including focused attention upon certain scale-dependent unit operations. Examples may include the study of mixing rates, mass and heat transfer, off gassing rates, reaction rates, phase separation and processing time. Further verification on scale of the studied multivariate space should not be necessary if a detailed enough research scale analysis of the processes involved has been undertaken, especially when other aspects of the control strategy and Pharmaceutical Quality System assure quality and mitigate impact to the patient.


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Given the depth of knowledge around the science and engineering principles governing scale up, it is appropriate to design manufacturing processes in a scale down manner with attention to scale up risks. Furthermore, the current state of established science and engineering principles around scale sensitivity precludes testing at scale in a vast number of cases. For those cases where there is significant risk of scale sensitivity, there are ways to test for sensitivity to scale at the laboratory scale for the specific process.

At commercial scale, successful

performance serves to substantiate the lifecycle process for normal operating ranges, reflecting preferred regions of the design space established for operational and business purposes (rather than edges of design space which are often set to reflect knowledge of suitability from a quality stand point). It should also be noted that not all scale dependent parameters have an impact on quality. In these cases, assessment is based on operational or business need. The strategies employed within these case histories rely primarily on statistically designed experiments, empirical and mechanistic models, univariate experiments, first principles and prior knowledge. These strategies encompass a risk based approach to assess potential parameter scale dependency. A number of strategies that mitigate risk are presented, including the potential for use of platform technologies, simple scaling factors or detailed modeling. When coupled with subsequent scale-up data and an understanding of process variability and sensitivity through continued process verification, and including management of post approval changes within an acceptable Pharmaceutical Quality System, it has become possible to reach agreement between industry and regulators from a number of regions that a design space is appropriate for use at commercial scale. This is greatly preferred over approaches that rely on batch manufacture and empirical experimentation at commercial scale, given the complexity of the multivariate parameter interactions.


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Case History 1 During the development of the Pfizer product Xeljanz©, the first step of the drug substance manufacture involves the coupling of a secondary amine with an aryl chloride, Scheme 1. During development of this process, prior knowledge and risk assessment tools were utilized to identify potential scale dependencies.

The reaction passes through three distinct

heterogeneous phases and hence one area of potential scale dependency was the effect of mixing. A range of experiments were designed to assess the mixing conditions, including multi-factorial experimental design to understand the interaction with other parameters. Experiments were executed in equipment with minimal baffling (swirling flow) and low dispersion of the solid phase, representing a worst case mixing scenario on scale. The results showed no significant effect on the rate of reaction or the quality of product for mixing or other parameters explored in the multi-factorial design, so therefore a lack of dependency on mixing parameters that have the potential to change on scale. In order to further explore the impact of agitation rate, a series of univariate experiments were conducted whereby modulation of the agitation rate from 300rpm to 1200rpm also demonstrated a lack of functional relationship to quality attributes. The lack of scale dependency of mixing was confirmed during development through prosecution of the step at a range of scales, in different vessels, stirring rates and baffle configurations. In all cases, the appropriate drug substance specification was met and all critical quality attributes were within control.

Modeling studies were developed and assisted in determining appropriate vessel

configuration in order to manage other non quality related, business requirements of the process.


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Me Cl

Cl

Me

Me

N

water, K2CO3

+ N

N H

Me

N H

N

Ph

Ph

N Cl

•2HCl

N

N

N

N H

Scheme 1. First Step of Pfizer Xeljanz© Synthesis During the registration process, this scientific rationale and justification was accepted as the basis for confirming acceptability of the design space on commercial scale, in the majority of regions, although regulatory expectations, concerns, and resolutions varied from country to country. In one instance, there was a general request for verification of design space through large scale experimentation. The issue was resolved through demarcation of the verification of Normal Operating Ranges (NOR) provided prior to approval versus the verification of potential changes within the design space throughout the lifecycle of the product, to be managed within the internal PQS. A similar approach was recently published by Pfizer4, outlining a three step process, including initial verification of the NOR, management of change within the design space and specific elements of the product control strategy that ensure quality. Essential to this paradigm is the understanding of the internal PQS system applied throughout the lifecycle of the product, therefore positioning verification of design space as a lifecycle activity.

Case History 2 During the development of an esterification process used to manufacture a drug substance within Biogen Idec, regulators requested analytical confirmation that a potential side reaction


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between sulfuric acid and methanol did not form the genotoxic impurity dimethyl sulfate (DMS) within the proposed design space (Scheme 2).

Scheme 2. Overview of Biogen drug substance esterification process and potential side reactions to form sulfate esters To examine the possibility of DMS formation, a three-pronged approach was taken to link small scale experimentation to large scale process control. First, kinetic experiments were performed at small scale, in the absence of the drug substance, to examine the rates of esterification of sulfuric acid in methanol under the esterification conditions employed in the manufacturing plant. Using a carefully designed series of 1H NMR experiments, all of the rate constants for the formation and degradation of monomethyl sulfate (MMS) and DMS were determined.5 Interestingly, DMS was found to form about 10,000 times more slowly than it


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could be consumed by water in the reaction mixture (k2 = 4.9 x 10-9 L/mole-sec versus k-2 = 1.3 x 10-4 L/mole-sec). At steady-state, assuming completely anhydrous reaction conditions, the model predicted a maximum of 4 ppm of DMS could form within the process solution. The second set of lab scale experiments focused on determining what effect if any the drug substance would have on the predicted DMS formation. Spiking experiments with 250-fold higher concentration than predicted by the model (4 ug/mL) were conducted under scaled down production conditions and it was found that, in the presence of the drug substance, DMS rapidly degraded in the reaction solution below the limit of detection for the method (10) countries where the new drug application was filed, the initial acceptance of the argument of scale-independence was not universal. One health authority, for example, while acknowledging that several scale-up batches were carried out within the design space that afforded product of acceptable quality, pointed out that these commercial-scale batches were not necessarily manufactured at the edges of the design space. BMS was requested to provide confirmation that the proposed design space verified at laboratory scale was still valid at commercial scale (i.e.: the edges of the design space were confirmed to be valid at commercial scale). The design space proposal for both steps was finally accepted upon providing data for pilot-scale batches that demonstrated the scale independence. Thus, batches, carried out at >30% scale of the proposed commercial scale under conditions close to the high risk corners of the design space (as informed by the model), afforded material of acceptable quality. The authors believe that such confirmation is unnecessary prior to approval based on scientific merits, and could be accommodated within a PQS based lifecycle approach, if there was ever a business need for post approval change to that region of the design space. Another health authority requested BMS to explain whether differences in manufacturing scale might lead to different process risk assessment results for every parameter for which no mechanistic model assessment was undertaken. In this case, scientific rationale for scale-


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independence of each process parameter based on chemical and engineering fundamentals was an acceptable response. Case History 4 This case study involves the application of a mechanistic model, together with first principles understanding of a scale dependent process parameter, in developing the control strategy for a small molecule drug substance at AbbVie. The penultimate intermediate in the synthesis of drug substance included catalytic reduction of a dinitro aromatic compound using hydrogen. The reaction pathway for reduction of nitro aromatic compounds is known to proceed through two reaction intermediates: an aryl C-nitroso and an aryl hydroxylamine compound8. In the case of substrates containing two nitro groups, such as the one referenced in this case study, eight reaction intermediates are possible during conversion to the corresponding diamine product. None of these intermediates had ever been observed in the isolated diamine under nominal process conditions. Moreover, a majority of these reaction intermediates are transient and unstable, and therefore cannot be obtained as isolated compounds. Since these reaction intermediates cannot be isolated and assessed in an Ames assay, they were assumed to be genotoxic and a corresponding control strategy was developed. Analytical tests cannot be used to evaluate the presence or absence of the intermediates since reference materials for these cannot be prepared. In lieu of analytical testing, a justification for kinetic control of reaction intermediates was developed through a detailed study of the hydrogenation reaction kinetics. This control strategy ensures that no single reaction intermediate will be greater than 5 ppm in the isolated diamine product.


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Reaction kinetics experiments were conducted at the laboratory scale using significantly retarded reaction rates compared to the nominal process to afford measurable concentration of the transient reaction intermediates. The reaction rate was primarily attenuated by using lower amount of catalyst. During these experiments, six of the eight possible reaction intermediates were detected by periodic anaerobic sampling in an inert atmosphere followed by immediate analysis by LC-MS. Of these six detected intermediates, three were fleetingly observed at trace levels before becoming undetectable. The time course data for the three most significant observed intermediates were used to construct a kinetic model of the reaction consisting of four consecutive pseudo first order reactions. The individual reaction steps can be modeled as pseudo first order because the experiments were conducted at high mass transfer rates, i.e. the reaction rate was not limited by the ability to deliver hydrogen to the reaction solution. The rate constants regressed from the model were used to predict concentration profiles of the transient reaction intermediates and to demonstrate that, within the parameter ranges proposed for the commercial process, no reaction intermediate could be present above 5 ppm in the reaction solution. This strategy was used to justify the ranges for process parameters, including catalyst amount, temperature, hydrogen pressure and minimum reaction time, with adequate safety margins built in to each of these ranges. A key element of the above control strategy is ensuring adequate mass transfer rate of hydrogen to the reaction solution. A minimum value of gas-liquid mass transfer coefficient (kLa) is designated as a critical process parameter so as to ensure that the reaction rate is not limited by hydrogen supply to the reaction solution. To address this aspect of the control strategy, mass transfer rates were measured in the commercial hydrogenation reactor across the proposed temperature and pressure ranges. The effect of agitation rate on kLa was studied using hydrogen


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uptake measurements. The agitation rate under the process conditions (reaction temperature, hydrogen pressure and reactor fill level) required to achieve an acceptable kLa was implemented as an operational control in the manufacturing process. The key elements of the control strategy for ensuring less than 5 ppm of the potentially genotoxic reaction intermediates include, critical process parameters that determine the intrinsic reaction rate (i.e. temperature, hydrogen pressure and catalyst amount), reaction time and the hydrogen gas-liquid mass transfer coefficient (kLa). This approach was viewed favorably by one regulatory authority during a pre-filing meeting. Case History 5 The development of the enhanced submission for Arzoxifene Hydrochloride at Eli Lilly involved the generation of data to support the proposed design space and involved several objectives including the elimination of any scale sensitivity from the data package and the development of a process that would be portable throughout the lifecycle of the product. Significant components of this data package included understanding parameter criticality in order to reduce and appropriately study multivariate effects in a designed experimental set and thoroughly understanding and articulating the important elements of the process control strategy. The methodology for determining parameter criticality by Eli Lilly has been the subject of several publications9. The basis for the determination of what constitutes a critical parameter from a non-critical parameter comes from understanding the impact of a parameter on critical quality attributes (CQAs) when perturbed in a univariate fashion. For example, Figure 1 shows possible results from a series of univariate experiments or model predictions and the impact of those perturbations on an individual CQA, in this case an


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impurity level. If an analysis of the response to that perturbation resulted in a minimal impact to the CQA across a range (denoted as 6σ in Figure 1a) then one might infer that the parameter could be designated as low risk. Conversely, a significant response to the perturbation of the parameter as in Figure 1c, would result in this parameter being given a designation of high risk. As part of this analysis, an underlying assumption has been made that all failures used to designate individual parameter risk would be considered only in a univariate fashion, as multiparameter failures are not only unlikely when considering common cause variability but would be too experimentally burdensome in evaluating all possible failure modes regardless of how unlikely an occurrence might be.

1a

1b

1c

Figure 1. Responses to perturbations of a parameter on CQAs and the resulting designation of risk Scale independence of the analysis becomes significant as one chooses the breadth of the range to study.

A survey of equipment across Eli Lilly’s manufacturing infrastructure was

undertaken to determine the routine operating capability (or 1σ values) of various equipment types and has been the subject of many internal studies. The methodology used in determining the baseline capability for equipment, relative to a variety of parameter types was the subject of


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one published study10. From this analysis, a baseline equipment capability has been chosen, and a weighted value for the equipment capability is used as the basis by which the breadth of a parameter range is studied.

This choice of weighting factors is also summarized in published

studies9. As an example, temperature of a reaction is perturbed, while holding all other reaction parameters constant, including equivalents of reagents, volumes of solvent, pressures, reaction times etc. A review of equipment has shown a baseline capability of control to within +/- 2°C to be consistent with nearly all reactors, independent of size, type of control, material of construction, heat transfer fluid etc. Based on previous analysis, a weighting factor of six will encompass 99.5% of all common cause variability for even the most non-standard distributed parameters. Studies for temperature therefore were perturbed to +/- 12°C from setpoint to study the direct impact of temperature on final product CQAs. A minimal impact would result in the parameter having a low risk, whereas significant impact, or an inability to perturb the parameter to this extent without failing CQAs would therefore result in the elevation of the risk level for this parameter. Ultimately, all parameters for a process were studied in this fashion to determine a parameter risk level.

Parameters determined to have medium to high risk were therefore

classified as critical parameters while all other parameter were classified as low risk and labeled as simply ‘process parameters’. The ability to study a particular range, independent of scale, allows for the processes to be scaled up and transferred to the manufacturing organization with minimal risk for process failure.

Additionally, movement of processes to other reactors within Lilly do not require re-


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validation of the process, but instead, assurance that the equipment being utilized meets the same criteria for operating capability as used by development for the risk assessment and criticality designation. The parameters demonstrating some propensity to impact the CQAs of the resulting drug substance, either directly or indirectly, by impacting the in-process specifications or intermediate specifications from the univariate analyses were further analyzed. A designed set of experiments was carried out on the medium and high risk process parameters for the mapping of the design space.

Ranges tested in the design space did not need to correspond to ranges tested in the

criticality analyses. While design space parameter choices were determined from the univariate analyses, the magnitude of the ranges were not necessarily tied to the criticality analysis. It is the goal of the design space analysis to elucidate a space in which all combinations of process parameters tested will result in final product meeting the CQAs for the drug substance. It was not the intention to map the entire knowledge space around a given process. For that reason, it was prudent to choose a design space that is restricted within the proven acceptable ranges for some or all of the studied parameters to ensure all combinations of the process parameters will yield acceptable quality product. It was understood from this process that the region defined by the design space yields a loci of combinations that are acceptable for a target set point. While a shift in target set points within the design space will result in minimal reporting requirements, in the event of a set-point shift, a potential repeat of appropriate univariate experiments may need to be conducted to ensure the shift did not result in a non-critical process parameter becoming critical. A regulatory update would be required if the shift resulted in a non-critical parameter becoming critical.


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For the purposes of this development program, four synthetic steps were broken into six individual design spaces which were studied independently. This was possible given the well characterized intermediate solids or solutions, which could in turn be assigned intermediate specifications that must be met as well. Additionally, developing the design space around smaller sub-portions of the overall synthesis was consistent with our operating philosophy that espouses that the likelihood of multi-parameter failures, in this case from step to step, were highly unlikely and therefore did not warrant further study for potential interactions. The final step in the mapping of the design space was the bracketing experiments. Based on the multivariate analysis, one or more ‘worst case’ scenarios could be determined for the studied response(s).

When analyzing multiple responses (i.e. impurities, particle size, crystal

form, etc.), it is entirely possible that conditions resulting in the worst case for one response could be different than the conditions resulting in the worst case for another response. Thus, one or more ‘worst case’ scenarios were identified from the multivariate analysis. A final set of bracketing experiments are run at the worst case(s) from the multivariate analysis with all other low risk parameters perturbed in the direction of their best and worst case performance based on the univariate studies.

Execution of these bracketing experiments also ensured that potential

interactions that were not fully captured in the univariate analyses would become obvious and trigger additional studies. Even though the decision was made to stop commercialization activity, feedback on our approach was obtained from the US FDA’s Office of New Drug Quality Assessment. Several questions were submitted to the agency and responses were provided back to Eli Lilly. Questions posed included; the acceptability of the use of a weighted operational variability in determining parametric risk, acceptance of our method of reporting changes in the design space


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post-approval, and a question regarding makeup of the design space and the inclusion of only the interactions of parameters designated as medium and high based on our previous analysis. The agency agreed with our approach and proposals with the caveat that the adequacy of any information would be evaluated during the NDA review cycle.

Case History 6 Merck has recently undergone development, validation and regulatory submissions (with favorable acceptances) of an alternate synthetic route for the manufacture of sitagliptin phosphate monohydrate, the primary drug substance of Januvia© and Janumet©. This alternate manufacturing route is based on the use of enzyme biocatalysis to perform a key enantiomeric transformation of an advanced intermediate, forming the final drug substance. Furthermore, the extreme enantiomeric selectivity afforded by the enzyme biocatalyst negates the need for a chiral upgrade isolation.

Due to the through-process nature of the alternate chemistry process,

particular challenges stem from variability in the post biotransformation solution composition and its impact on crystallization performance, including the strong dependence of final drug substance particle size, a critical quality attribute, on the optimal seeding temperature. To accommodate the solution composition variability, Merck has developed a mechanism by which the optimal dissolution and seeding temperatures are determined in real-time using a dynamic feed-forward process control model, facilitated through NIR spectroscopy. This set of models is intended to take measured batch composition data and inform appropriate crystallization conditions. The models were developed at laboratory scale using PAT technologies (FTIR) for the rapid collection of solubility data to aid in mapping drug substance solubility as a function of solvent composition for a 4-solvent system. This map (which is


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expected to be scale independent due to the equilibrium nature of drug substance solubility) was then coupled with empirical modeling tools for the translation of the acquired solubility map into operational parameter ranges and assay specifications to be implemented in full-scale demonstration and subsequent regulatory submissions. A key facet of the development of the design space and associated control strategy was the execution of a series of formal risk assessments. It is important to note that while the vast majority of parameters assessed during risk assessment activities were determined to be insensitive to scale and/or equipment type, there was a small subset of parameters that warranted further evaluation with respect to scale and site specific considerations. In those cases, it became important to define the design space using scale and equipment appropriate experimentation. As an outcome of the process risk assessment, mixing considerations were identified as requiring further investigation into their scale dependency. As the potential for mixing parameters to have multifactor interactions was unknown at the time of the risk assessment, the primary investigational strategy for these risks was to incorporate a mixing parameter (impeller tip speed) into a laboratory scale preliminary crystallization DoE to serve as a surrogate for mixing hydrodynamics. The results of the preliminary investigation indicated that tip speed did not have a strong impact on particle size. Furthermore, tip speed was also carried as a factor into a subsequent laboratory scale crystallization DoE, where it was again confirmed that this factor was insignificant, as well as free of significant interactions with other operational factors. However, since the risk assessment had identified other potential mixing considerations (e.g., impeller design, vessel geometries etc.), it was decided that the set of confirmatory runs identified for design space definition would be run at pilot scale as this would also allow the opportunity to observe mixing hydrodynamic variations across scale that are not exclusively


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related to impeller tip speed. Furthermore, it was determined that pilot scale experimentation offered the ability to more accurately simulate the geometries and operational activities representative of full scale manufacture. The results of the pilot scale confirmatory runs were in agreement with the results observed at lab scale during the final crystallization DoE. Therefore, mixing sensitivity and its effect on particle size was confirmed to be both insignificant and independent of scale. In addition, as a diverse range of mixing conditions (tip speeds, impeller types, vessel geometries) had been investigated over the course of lab and pilot scale experimentation and mixing insignificance was confirmed throughout, this further reinforces that the conclusions are scale/site independent. The lack of scale or equipment sensitivity for mixing hydrodynamics is also supported by the fact that validation batches conducted at multiple commercial facilities produced material with acceptable particle size. It is noteworthy to mention that as part of site-specific implementation, good engineering principles were applied to evaluate equipment selection, which in some cases, included supplementing knowledge gained during development with additional, site-specific understanding. For example, equipment mixing characteristics were considered as part of process train selection. For the crystallizer, this typically included additional experimentation/process modeling to support implementation of the control strategy at the site as appropriate. At the time of the process risk assessment, process knowledge and thermodynamic first principles indicated that dissolution temperature would be a strong function of solution composition. Therefore, variability in batch composition was expected to play a role in crystallization performance and its subsequent impact on drug substance CQAs.

Although

solubilisation temperature is intrinsically a thermodynamic property of a particular system (and hence, not expected to be site or scale dependent), in this case operational control of batch


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dissolution during the crystallization had the potential to depend on equipment/scale specifics. As an example, while visual confirmation or the use of PAT (i.e. FBRM) to monitor batch dissolution may be suitable for laboratory experimentation, alternate control strategies may be preferable at commercial scale. It was proposed that a model be developed to predict a particular batch’s dissolution temperature (and the associated seeding temperature) based on a set of inprocess assays that characterized the composition and concentration of the system. Once the model and in-process assays were developed, they were included as part of the holistic control strategy, which the commercial facilities then incorporated into site implementation activities via appropriate quality systems, thus ensuring that the commercial process will routinely deliver quality drug substance in a flexible and robust manner. While not sought by all regulatory agencies, Merck was asked by one regulatory agency (EMA) to provide verification details for the proposed control strategy for the crystallization step, with a focus on the design space and inprocess controls that govern the dissolution and seed point of the crystallization. In particular, there was a strong emphasis on demonstrating that the design space, as implemented at scale, was suitably verified. Risk assessment, combined with process understanding gained through a rigorous development program, as well as site implementation considerations, indicated that the prediction of the crystallization solution’s dissolution temperature was the element of the process which warranted the most significant level of verification. Dissolution temperature model verification addressed considerations of edge of failure, measurement error and process factor variability and verified the proposed dissolution temperature model and its associated design space, in light of these considerations. A testing regimen using a separate verification data set (generated at laboratory scale) was found to be statistically equivalent to the model prediction, verifying the suitability of the model for dissolution temperature prediction. Additionally, a


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series of Monte Carlo simulations aimed at quantifying the impact that model and measurement error (both analytical measurement error and process factor or charge accuracy) may contribute across the design space were conducted and confirmed the appropriateness of the dissolution and seeding temperature models as implemented. Additionally, from the standpoint of continued verification, the crystallization’s design space has been developed with the primary intent of ensuring the final drug substance particle size is consistently delivered. Particle size is one of a series of critical quality attributes for the final drug substance, and delivery of material meeting the measurement specifications is expected to serve as continued verification of final drug substance quality. Indirectly, it is also expected to continually verify the suitability of the proposed control strategy. In terms of dissolution temperature model maintenance, the model is considered to be sufficiently accurate and therefore does not require on-going model maintenance, provided the dissolution model continues to suggest seed temperature ranges that consistently produce material of the appropriate end-product particle size. Observations outside the specified particle size limits of the end-product test will be investigated according to site deviation management procedures. In the unlikely event that the root cause is determined to be dissolution temperature model inaccuracy, the model would be updated accordingly following site change control procedures and the regulatory authority will be notified according to local regulations. Case History 7 This final case study from Vertex Pharmaceuticals Incorporated illustrates the successful application of chemical engineering science principles, including the prospective use of established mixing design correlations, heat transfer calculations, and applied thermodynamics, to support the validity of using laboratory-scale experimentation for design space verification. As


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part of the Design Space definition process, each step/unit operation in the drug substance synthetic process was subjected to an engineering-based risk assessment to identify potential scale-up issues and their potential impact, if any, on critical quality attributes (CQAs). The assessment was based on established chemical engineering science principles and sought to determine the impact of changes to relevant transport phenomenon, reaction kinetics, etc. which might arise as a result of moving the unit operations defined by the laboratory-derived design spaces to commercial scale. The case study in question involves a drug substance manufacturing process where one of its synthetic steps involves a two-phase heterogeneous coupling reaction. Two potential scaledependent phenomena were identified during the scale-up risk assessment of this step: the ability to suspend solids during the course of the reaction and the ability of the commercial reactor to adequately remove the heat generated by the reaction. During the reaction, the starting material is out of solution and the reaction begins as a heterogeneous mixture. Approximately one hour into the reaction, upon consumption of a portion of the starting material, the reaction becomes homogeneous and remains so for the duration of the reaction. The ability of the reactor system to adequately suspend the solids was a potential scale dependent concern and was assessed using a well-established, just-suspended mixing correlation.11 The just suspended correlation uses vessel and impeller information, physical property information for the suspension medium and suspended solids, and the mass fraction of suspended solids to estimate the minimum impeller speed (Njs) required to suspend the solids off the bottom of the vessel. For the specific case of the coupling reaction, this calculation was performed using vessel and impeller information from the commercial reactor and measured or estimated values for the physical properties of the suspension medium and suspended solids. For the purposes of the calculation, the scenario


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investigated assumed all the starting material was out of solution—simulating the worst case (most difficult) suspension scenario during the first hour of the reaction. In addition, the sensitivity of the minimum impeller speed to the measured particle size range of the starting material was examined. The resulting minimum impeller speed calculated from the Zwietering correlation was 37-46 rpm, well within the maximum 110 rpm capability of the manufacturing scale reaction vessel used for the coupling reaction. As a result of the just-suspended calculations described above, the observed suspension of solids at the pilot and manufacturing scales, and the comparable performance of the coupling chemistry at the lab, pilot, and manufacturing scales, failure to adequately suspend the starting material solids in the first hour of the coupling reaction was deemed to have a low potential to impact the quality of the coupling reaction product and, hence, drug substance CQAs. The second potential scale-up issue that was identified in the engineering risk assessment was the ability of the commercial scale reactor to adequately remove the heat generated by the coupling reaction. Failure to adequately remove the heat released by the reaction has the potential to impact the ability to control, at the manufacturing scale, the reaction temperature within the laboratory-established reaction normal operating and design space ranges. The potential of inadequate heat transfer to impact the quality of the coupling reaction product and drug substance CQAs upon scale-up was evaluated in a three-step process. First, the heat of reaction of the coupling reaction was measured via reaction calorimetry. Second, the overall heat transfer coefficient of the manufacturing scale reaction vessel was regressed from plant data and knowledge of the vessel geometry. Lastly, the temperature of the coupling reaction during the time course of the reaction was modeled using reaction kinetics, the measured heat of reaction,


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and the regressed heat transfer coefficient to demonstrate that the heat transfer capability of the commercial vessel was adequate to remove the heat from the coupling reaction exotherm. The laboratory-scale design space experiments were conducted under isothermal reaction conditions and established that material of acceptable quality is produced if the coupling reaction temperature is maintained within the defined design space. Simulation of the plant heating profile and the associated reaction exotherm demonstrated that the manufacturing reaction vessel could be maintained within the laboratory-defined temperature ranges. As a result of the heat transfer analysis, temperature control for the coupling reaction at the commercial manufacturing scale was assessed to be adequate and the temperature of the coupling reaction can be maintained within the established design space upon scale-up from the laboratory to manufacturing scale. As a consequence, the reaction kinetics and the design space relationships developed at the laboratory scale are representative of the behavior of the coupling reaction at the manufacturing scale. As a result, inability to adequately control the temperature of the coupling reaction at the manufacturing scale and keep it within the defined operating range was deemed to be low risk. As a result of the heat transfer calculations described above, the successful temperature control at the pilot and manufacturing scales, and the comparable performance of the coupling chemistry at the lab, pilot, and manufacturing scales, failure to adequately control temperature was deemed to have a low potential to impact the quality of the coupling reaction product and, hence, drug substance CQAs. The combination of the engineering design calculations and data from the pilot and commercial scales, then, served as the design space verification for this step for the submission.


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Given the analysis described above, the design space for the coupling reaction was determined to be scale independent and no further design space verification is required. Movement of the step to different equipment, sites, etc. would be handled under appropriate change control procedures and, as part of that change control, the just-suspended and heat transfer calculations described above would be repeated to ensure the laboratory-derived design spaces remained applicable to the new equipment configuration. In summary, this case study illustrates the successful application of fundamental chemical engineering science principles to support the validity of using laboratory-scale experimentation and appropriate engineering based correlations and calculations to define design spaces at the commercial scale. Other elements of control strategies supporting verification of design space at scale. The need to run design space verification experiments at commercial scale can be obviated by control strategies developed on the basis of sound process understanding. Fundamental process understanding of scale-dependent phenomena can be generated through the use of appropriate predictive scaled-down experiments and first principles model predictions. In addition, where appropriate, the use of Process Analytical Technologies (PAT) and trending of process parameters may be used to provide further confirmation of the process performance at commercial scale. Thus, PAT provides an additional tool to assist with risk management. For instance, in a gas-liquid reaction, the gas uptake rate may be monitored to ensure the reaction progress is as expected. A PAT tool may be used to track certain drug substance attributes that provide confirmation of the process performance during movements within the design space.


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Ultimately, other elements of the control strategy, such as intermediate and drug substance specifications are assessed to monitor and confirm process performance at commercial scale.

Conclusion

The case histories herein represent an array of options for supporting the applicability of a design space at commercial scale through science and risk based assessment and understanding. They demonstrate a variety of options, such as, the use of statistically designed multivariate experiments, univariate experiments, models (both empirical and mechanistic), first principles and prior knowledge to assess and understand potentially scale dependent unit operations such as mixing rates, off gassing rates, reaction rates, mass transfer, heat transfer and processing times. This allows for the design and demonstration of processes that can be successfully run across a range of scales based on an understanding of the functional relationships of process parameters and material attributes to quality. The outcome of such a science and risk based assessment should be a focus on the true risk of scale dependency rather than a broad expectation for verification across a broad design space at commercial scale during development. Verification in commercial scale batch execution can be considered as an important element of a lifecycle approach that is typically managed within a company’s Pharmaceutical Quality System and can be outlined in a protocol for future regulatory submissions.1 This publication was developed with the support of the International Consortium for Innovation and Quality in Pharmaceutical Development (IQ).

IQ is a not-for-profit organization of

pharmaceutical and biotechnology companies with a mission of advancing science-based and scientifically-driven standards and regulations for pharmaceutical and biotechnology products worldwide. Please visit www.iqconsortium.org for more information.


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AUTHOR INFORMATION Corresponding Author *[email protected] Author Contributions The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. REFERENCES 1. http://www.ema.europa.eu/docs/en_GB/document_library/Other/2013/11/WC500153784. pdf, October 2013. 2. Garcia, T.P.; McCurdy, V.; Watson, T.J.N.; am Ende, M.; Butterell, P. ; Vukovinsky, K.; Chueh, A.; Coffman, J.; Cooper, S.; Schuemmelfeder, B.; J. Pharm. Innov., 2012, 7, 1318. 3. am Ende, D.J.; Seymour, C.B.; Watson, T.J.N.; J. Pharm. Innov., 2012, 5, 72-78. 4. Watson, T.J.N.; Bonsignore, H.; Callaghan-Manning, E.A.; Colgan, S.T.; Fitzsimons, P.; Garcia, T.P.; Groskoph, J.G.; McMahon, M.E.; Lynch, M.P.; Nosal, R.; Singer, R.A.; Thomson, N.M.; Sluggett. G.W.; Schulz, D.J.; Twohig, S.; J. Pharm. Innov., 2013, 8, 67– 71. 5. Guzowski, J.P. Jr.; Delaney, E.J.; Humora, M.J.; Irdam, E.; Kiesman, W.F.; Kwok, A.; Moran, A.D.; Org. Process Res. Dev. 2012, 16, 232−239. 6. Hallow, D.M.; Mudryk, B.M.; Braem, A.D.; Tabora, J.E.; Lyngberg, O.K.; Bergum, J.S.; Rossano, L.T; Tummala, S.; J. Pharm. Innov., 2010, 5, 193-203.


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7. Burt, J.L.; Braem, A.D.; Ramirez, A.; Mudryk, B.M; Rossano, L.T.; Tummala, S.; J. Pharm. Innov.; 2011, 6, 181-192. 8. Haber, F. Z.; Elektrochem. Angew. Phys. Chem. 1898, 4, 506.

9. Seibert, K. D.; Sethuraman, S.; Mitchell, J.D.; Griffiths, K.L.; McGarvey, B.; J Pharm Innov.; 2008, 2, 105-112. 10. Mitchell, J.D.; Abhinava, K.; Griffiths, K.L.; McGarvey, B.; Seibert, K.D.; Sethuraman, S.; Ind. Eng. Chem. Res., 2008, 47, 6612–6621. 11. Zwietering, T. N.; Chemical Engineering Science, 1958, 8, 244-253.


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Case Studies in the Applicability of Drug Substance Design Spaces

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