Case Studies in the Applicability of Drug Substance Design Spaces

Aug 18, 2014 - A number of strategies have been employed within the pharmaceutical industry in order to mitigate the risk of applying design space bou...
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Case Studies in the Applicability of Drug Substance Design Spaces Developed on the Laboratory Scale to Commercial Manufacturing Nicholas M. Thomson,*,† Kevin D. Seibert,‡ Srinivas Tummala,§ Shailendra Bordawekar,∥ William F. Kiesman,⊥ Erwin A. Irdam,⊥ Brian Phenix,# and Daniel Kumke∇ †

Chemical Research and Development, Pfizer Inc., Eastern Point Road, Groton, Connecticut 06340, United States Small Molecule Design and Development, Eli Lilly and Co., Lilly Technology Center, Indianapolis, Indiana 46285, United States § Chemical Development, Bristol-Myers Squibb Company, One Squibb Drive, New Brunswick, New Jersey 08903, United States ∥ Process Research and Development, AbbVie, 1 North Waukegan Road, North Chicago, Illinois 60064, United States ⊥ Chemical Process Research and Development, Biogen Idec, 14 Cambridge Center, Cambridge, Massachusetts 02142, United States # Chemical Development, Vertex Pharmaceuticals Incorporated, 50 Northern Avenue, Boston, Massachusetts 02210, United States ∇ Chemical Process Development and Commercialization, Merck, 126 East Lincoln Avenue, Rahway, New Jersey 07065, United States ‡

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 Verif ication document1 provided greater clarity and insight from a joint FDA and EMA perspective. This communication 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) has 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 on the commercial scale. Two recent publications2,3 proposed a framework to address risks associated with process scale-up and to 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, is in general agreement with and wishes to exemplify this approach through the publication of this communication. 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.

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 the laboratory scale to commercial drug substance manufacturing. The following communication 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 on the 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 costeffective mechanisms for drug substance commercialization.

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INTRODUCTION The material in this communication 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 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 on the laboratory scale, relative to a commercial batch size. Whilst a design space should be demonstrated to be appropriate for use on the commercial scale, there is currently © 2014 American Chemical Society

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BACKGROUND

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 scaleup with regard to perturbation and sensitivity is generally undertaken in order to understand process performance, to Special Issue: Application of ICH Q11 Principles to Process Development Received: June 11, 2014 Published: August 18, 2014 925

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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

support a robust control strategy, and to ensure quality. With increased pressure to ensure quality on the 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 or 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 ensure that process scale-up and long-term process robustness is achieved, including focused attention on 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 ensure quality and mitigate impact to the patient. 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 on 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 on the laboratory scale for the specific process. On the 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 on the commercial scale. This is greatly preferred over approaches that rely on batch manufacture and empirical experimentation on the commercial scale, given the complexity of the multivariate parameter interactions.

Scheme 1. First Step of Pfizer Xeljanz Synthesis

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 was designed to assess the mixing conditions, including multifactorial 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 multifactorial design, therefore indicating 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 was conducted whereby modulation of the agitation rate from 300 to 1200 rpm 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. During the registration process, this scientific rationale and justification was accepted as the basis for confirming acceptability of the design space on the 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 Pfizer,4 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. 926

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CASE HISTORY 2

Scheme 3. Additional Degradation Pathway for DMS during Drug Substance Manufacture

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

The modeling, spiking, and drug substance testing data were summarized in the process development section of the CTD (3.2.S.2.6) and presented to worldwide regulatory authorities as justification that DMS could not be formed under the reaction conditions employed to manufacture the drug substance and therefore no release testing was required for DMS in the final drug substance. The FDA agreed with the risk assessment and did not require a release test for the drug substance. The EMA, upon review, requested that skip testing (testing of preselected batches and/or at predetermined intervals) be performed for a period of time for commercial drug substance and that those results combined with the scaled-down model data would complete a risk justification package that could support removal of the test.

Scheme 2. Overview of Biogen Drug Substance Esterification Process and Potential Side Reactions To Form Sulfate Esters

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CASE HISTORY 3 Two recent examples from Bristol-Myers Squibb (BMS) outline the use of mechanistic models for guiding design space development. The first example involves the preparation of a final intermediate for a pharmaceutical drug candidate (Scheme 4).6 The final intermediate step includes two sequential reactions: the desired cyclization of a 5-chloropentanamide and the undesired ethanolysis of the resulting lactam. As illustrated in Scheme 4a, the desired reaction pathway involves (i) the reversible deprotonation of the substrate, intermediate A, by sodium ethoxide base to form its corresponding sodium salt and (ii) the subsequent intramolecular cyclization of the sodium salt of intermediate A to form the lactam, intermediate B. The final reaction included in the scheme (Scheme 4b) is the undesired ethanolysis that forms impurity C. Since impurity C and its derivative formed in the drug substance step are not purged well in the final isolations of the intermediate and the drug substance, respectively, this impurity was classified as being critical to quality. The second example7 involves an amidation reaction executed under basic conditions in the presence of an amidation reagent to make the drug substance (Scheme 5). The amidation reagent is deprotonated in the presence of base, generating the active species that reacts with the input material to afford the drug substance. The desired transformation is achieved through a sequence of equilibria (abbreviated into a single step for clarity in Scheme 5), and an imide species is generated as a byproduct. In addition to the desired amidation, another functional group in the drug substance molecule can undergo an undesired amidation, resulting in the formation of a bis-amide species, which is the key impurity in the drug substance manufacturing process. Development of the control strategy for the impurity profile (CQA) within the quality by design approach for both case studies involved the following: (1) Risk assessment and prioritization of factors relative to their impact on impurity levels; (2) multivariate evaluation of select factors to identify statistically significant interactions;

To examine the possibility of DMS formation, a threepronged approach was taken to link small-scale experimentation to large-scale process control. First, kinetic experiments were performed on the 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 could be consumed by water in the reaction mixture (k2 = 4.9 × 10−9 L/mol·s versus k−2 = 1.3 × 10−4 L/mol·s). 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 μg/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

Scheme 5. Process Chemistry Overview for BMS Example 2

(3) development and utilization of mechanistic models to predict in-process impurity levels within a multivariate parameter space; (4) correlating in-process impurity levels with those in the isolated product; (5) identification of a subset of the parameter space that affords CQA compliance; (6) experimental verification of the proposed design space on the laboratory scale. For each case study, the multivariate evaluation of the select factors was carried out via a factorial design of experiments (DOE). In addition, mechanistic models were developed to predict reaction trajectories as well as levels of impurity formation. These models were subsequently used to “guide” the selection of a fixed design space, as opposed to using the model directly to define a dynamic design space. The fixed design space provided sufficient flexibility in addition to being operationally straightforward. Finally, the design space was 928

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Figure 1. Responses to perturbations of a parameter on CQAs and the resulting designation of risk.

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 on the laboratory scale was still valid on the commercial scale (i.e., the edges of the design space were confirmed to be valid on the 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 independence of each process parameter based on chemical and engineering fundamentals was an acceptable response.

ensures that no single reaction intermediate will be greater than 5 ppm in the isolated diamine product. Reaction kinetics experiments were conducted on the laboratory scale using significantly retarded reaction rates compared to those of 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 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 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 prefiling meeting.

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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 compound.8 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, the 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 because 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 929

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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 publications.9 The basis for the determination of what constitutes a critical parameter or a noncritical 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 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 being 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 also would be too experimentally burdensome in evaluating all possible failure modes regardless of how unlikely an occurrence might be. 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 one published study.10 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 studies.9 As an example, the temperature of a reaction is perturbed while holding all other reaction parameters constant, including equivalents of reagents, volumes of solvent, pressures, reaction times, and so forth. 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, and so on. On the basis of previous analysis, a weighting factor of 6 will encompass 99.5% of all common cause variability for even the most nonstandard distributed parameters. Studies for temperature therefore were perturbed to ±12 °C from set point 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, whereas all other parameter were classified as being 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 does not require revalidation of the process but, instead, assurance that the equipment being utilized meets the same criteria for operating capability as that 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 that 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. Although a shift in target set points within the design space will result in minimal reporting requirements, in the event of a setpoint shift, a potential repeat of appropriate univariate experiments may need to be conducted to ensure that the shift did not result in a noncritical process parameter becoming critical. A regulatory update would be required if the shift resulted in a noncritical parameter becoming critical. 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 subportions of the overall synthesis was consistent with our operating philosophy that espouses that the likelihood of multiparameter 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. On the basis of 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 930

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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 sitespecific 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. Since 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 laboratoryscale crystallization DoE, where it was again confirmed that this factor was insignificant as well as free of significant interactions with other operational factors. However, because 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 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 fullscale manufacture. The results of the pilot-scale confirmatory runs were in agreement with the results observed on the lab scale during the final crystallization DoE. Therefore, mixing sensitivity and its effect on particle size were confirmed to be both insignificant and independent of scale. In addition, because 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 solubilization 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 dissolution during the crystallization had the potential to depend on equipment/scale specifics. As an example, while

response could be different than the conditions resulting in the worst case for another response. Thus, one or more worst-case scenarios was identified from the multivariate analysis. A final set of bracketing experiments is 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 U.S. 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 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.

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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 to inform appropriate crystallization conditions. The models were developed on the 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 four-solvent system. This map (which is 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 931

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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 on the 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 in-process 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. Although 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 in-process 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 on 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 that 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 on the laboratory scale) was found to be statistically equivalent to the model prediction, verifying the suitability of the model for dissolution temperature prediction. Additionally, a 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 was 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 ongoing 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.

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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 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, and other factors that might arise as a result of moving the unit operations defined by the laboratory-derived design spaces to the 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 scale-dependent 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 1 h 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 investigated assumed that all 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 on 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 commercialscale reactor to adequately remove the heat generated by the 932

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OTHER ELEMENTS OF CONTROL STRATEGIES SUPPORTING VERIFICATION OF DESIGN SPACE ON SCALE The need to run design space verification experiments on the 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 on the 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 that 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. Ultimately, other elements of the control strategy, such as intermediate and drug substance specifications, are assessed to monitor and confirm process performance on the commercial scale.

coupling reaction. Failure to adequately remove the heat released by the reaction has the potential to impact the ability to control, on 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, 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 on 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 the manufacturing scale. As a consequence, the reaction kinetics and the design space relationships developed on the laboratory scale are representative of the behavior of the coupling reaction on the manufacturing scale. As a result, inability to adequately control the temperature of the coupling reaction on the manufacturing scale and to 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 on the pilot and manufacturing scales, and the comparable performance of the coupling chemistry on 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. 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, and so on 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 on the commercial scale.

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CONCLUSIONS The case histories herein represent an array of options for supporting the applicability of a design space on the 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 on the 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

*E-mail: Nick.Thomson@pfizer.com. Author Contributions

The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. 933

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Notes

The authors declare no competing financial interest.

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REFERENCES

(1) Questions and Answers on Design Space Verification. http:// www.ema.europa.eu/docs/en_GB/document_library/Other/2013/ 11/WC500153784.pdf. (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. Innovation 2012, 7, 13−18. (3) am Ende, D. J.; Seymour, C. B.; Watson, T. J. N. J. Pharm. Innovation 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. Innovation 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. Innovation 2010, 5, 193−203. (7) Burt, J. L.; Braem, A. D.; Ramirez, A.; Mudryk, B. M.; Rossano, L. T.; Tummala, S. J. Pharm. Innovation 2011, 6, 181−192. (8) Haber, F. Z. Z. Elektrochem. Angew. Phys. Chem. 1898, 4, 506. (9) Seibert, K. D.; Sethuraman, S.; Mitchell, J. D.; Griffiths, K. L.; McGarvey, B. J. Pharm. Innovation 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. Chem. Eng. Sci. 1958, 8, 244−253.

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