Article pubs.acs.org/OPRD
Development of a Highly Automated Workflow for Investigating Polymorphism and Assessing Risk of Forming Undesired Crystal Forms within a Crystallization Design Space Joshua A. Selekman,*,† Daniel Roberts,‡ Victor Rosso,† Jun Qiu,† Joseph Nolfo,§ Qi Gao,‡ and Jacob Janey† †
Chemical Development, Bristol-Myers Squibb Company, One Squibb Drive, New Brunswick, New Jersey 08903, United States Drug Product Science & Technology, Bristol-Myers Squibb Company, One Squibb Drive, New Brunswick, New Jersey 08903, United States § Research Informatics & Automation, Bristol-Myers Squibb Company, P.O. Box 4000, Princeton, New Jersey 08543, United States ‡
S Supporting Information *
ABSTRACT: A critical component of defining the feasible design space for an active pharmaceutical ingredient (API) crystallization is isolating the preferred polymorph or crystal form of the compound, which defines many of the compound’s performance-defining attributes such as solubility and bioavailability. While automated platforms and workflows exist to support many facets of pharmaceutical process development, few workflows aim to systematically investigate polymorphism and its kinetics as a function of crystallization conditions, thus providing a measure of risk associated with forming an undesired polymorph. Herein, we describe the development and application of a novel automated workflow, designed to interrogate a multidimensional crystallization design space using parallel experimentation to provide resolution around the feasible design space while simultaneously evaluating risk of forming undesired polymorphs. In addition, we describe a case study highlighting how data generated by this highly automated form analysis workflow can be leveraged to advance crystallization development of an API.
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INTRODUCTION Crystallization is one of the most crucial separation and purification processes in the pharmaceutical industry, used to control numerous chemical and physical properties of process intermediates and, perhaps most importantly, the active pharmaceutical ingredient (API). In addition to controlling attributes such as purity, yield, and dry powder properties, such as particle size, morphology, and surface area, during the manufacturing of the drug substance, it is also essential for crystallization processes to control the solid-state (crystal) form of the isolated API. Most organic compounds have the potential to exist in multiple solid-state forms, or polymorphs, which all have the same chemical composition, but each possesses a unique set of physical and biopharmaceutical properties, including solubility and bioavailability.1,2 It is therefore imperative to develop robust crystallization processes that generate the desired crystal form of an API and thus the expected physicochemical properties of that API, particularly in cases where it is possible to isolate multiple metastable polymorphs and forms (e.g. hydrates, solvates, etc.) from the crystallization operating space.3 While it is not common to require strict control over powder properties or the isolation of a particular crystal form for intermediates, the crystallization and isolation of one polymorph of an intermediate versus another can impact impurity purging efficiency, supersaturation, desaturation profile, filtration efficiency, overall cycle time, process “greenness”, yield, and other process metrics. For either an API or a process intermediate, it is critical during crystallization © XXXX American Chemical Society
development to understand how the crystal forms present in the process stream can vary as a function of process conditions (e.g. temperature, solvent composition, etc.) to potentially avoid the appearance or disappearance of a crystal form at scale.3−9 Moreover, it is crucial to understand polymorphism within a crystallization design space and using this information, evaluate or even quantify the risk associated with forming an unexpected polymorph within this space. This knowledge of polymorphism not only allows for a thorough risk evaluation within a planned operating space, but also provides knowledge of polymorph behavior in case operation of a crystallization deviates from the planned center point conditions. Automated parallel experimentation allows for the development and execution of high-throughput workflows to support various facets of pharmaceutical development including chemical reactions and crystallization.10 Besides productivity enhancements and minimization of material usage, the output generated from these automated workflows is comprised of internally consistent, high-fidelity data sets which allow for the mapping of the feasible design space. Furthermore, the integration of automated sampling through the use of programmable hardware and streamlined data collection, storage, and analysis lends itself to studying trends in process kinetics. For these reasons, laboratory automation has proven to be a valuable tool for supporting activities from drug Received: October 29, 2015
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DOI: 10.1021/acs.oprd.5b00346 Org. Process Res. Dev. XXXX, XXX, XXX−XXX
Organic Process Research & Development
Article
cases where a slurry or suspension does not persist, additional compound is added manually. Magnetic stir bars are added to each vial, and the vials are then placed in 24-well metal plates. Each plate is mounted onto a magnetic stir plate or loaded on a heating/cooling/stirring deck on an automated platform. The temperature and stir rate are then programmed according to the experimental design. Custom Filtration Manifold. To collect solids from slurries in an automated, high-throughput manner for powder X-ray diffraction (PXRD) analysis, a vacuum manifold consisting of a 96-well array was designed and subsequently constructed. The manifold, designed to increase the automated functionality of the highly automated form analysis process, mounts directly into a standard heat/cool/stir deck position on the Freeslate Core Module 3 robot unit. This allows for automated slurry dispense while vacuum is pulled, reducing the solvent removal time from slurries and minimizing manual setup requirements before analyzing wet cake samples via PXRD. The chosen design is comprised of three main components: a lower vacuum manifold, middle plate, and upper funnel plate (see Figure S1). The vacuum manifold and middle plate are fabricated from precision ground, Type 304 stainless steel. This grade of stainless steel provides adequate chemical resistance and rigidity, while the ground surfaces enable the two plates to precisely mate, which is a critical factor for a vacuum seal. The upper funnel plate is fabricated from polyether ether ketone (PEEK), a thermoplastic with excellent mechanical and chemical resistance properties, which is of particular importance as slurries are primarily in contact with this section of the manifold apparatus during filtration. This upper funnel plate component was also precisely machined to ensure a vacuum seal with the middle plate with the use of embedded O-rings, which also prevent cross-contamination of solvent during the filtration process. During assembly, a sheet of cellulosic filter paper is placed between the upper funnel plate and the middle plate, allowing for solvent to pass through when vacuum is applied thereby retaining solid wet cakes in each individual well hence preventing cross-contamination. All three plates are fastened together with standard machine screws and nuts on threaded posts. Slurry Dispense and Filtration. At each relevant time point, as defined by the experimental design, slurry samples are taken and loaded onto the custom filtration manifold with one sample per well. Slurry transfer was performed using the liquid handling capabilities of the automated equipment. Specifically, a 16 gauge needle pierces through the septum in the vial cap and extracts a designated volume of slurry to carry (typically 50− 100 μL). The slurry sample is then dispensed at a designated location on the filtration manifold while vacuum is actively pulled through the manifold. For automated slurry sampling, a backing solvent is chosen based on the solvent(s) used in each experiment. Time between individual samples is ∼90 s using automated equipment. In cases where 18 wt % water,
Figure 4. Cluster analysis of PXRD patterns collected at t = 0.5 h (top) and at t = 48 h (bottom).
while exhibiting a pattern similar to the preferred form, exhibited additional peaks in samples starting at t = 2 h. These additional peaks persisted and were consistently observed at the same 2θ positions at t = 24 and 48 h as well (Figure 4, bottom panel). These observations indicate that there is a significant level of risk in forming an undesirable crystal form if water content in the slurry exceeds 18% by weight. Third, in 0 wt % water (100% MeCN), the material in the solid state appeared D
DOI: 10.1021/acs.oprd.5b00346 Org. Process Res. Dev. XXXX, XXX, XXX−XXX
Organic Process Research & Development
Article
wide variety of recurring challenges in small-molecule pharmaceutical development. To ultimately conquer these challenges, a level of understanding is required as to how the stability of a polymorph can change as a function of various crystallization parameters. Laboratory automation provides a means to generate large, data-rich, internally consistent data sets that are difficult to replicate by standard means. Herein, we have demonstrated how leveraging automated technologies allows for the exploration of a design space and therefore a means to understand polymorphism as a function of various crystallization parameters as well as time. Furthermore, comprehensive data sets from automated, high-throughput studies can help guide researchers in pharmaceutical process development in terms of where in the multidimensional design space it might be advantageous to focus future experimental efforts for crystallization design and optimization. In addition to supporting process development for drug substance manufacturing, this type of workflow has potential for other applications in drug product development including, but not limited to, the following applications: Supplementing current workflows to investigate polymorphism for early stage assets to systematically determine feasible solvent systems for isolating a particular crystal form, investigating polymorphism as a function of coprocessing parameters, identifying polymorph changes under various formulation processing conditions for drug product manufacturing. These are just a few potential applications where this highly automated form analysis workflow has the potential to significantly impact pharmaceutical development in the near future.
to have lost all crystallinity, as no crystalline peaks were observed at t = 2, 24, and 48 h. This observation is of particular importance since, following isolation, the processing included a 100% MeCN cake wash prior to drying. While the time-scale for the observed form conversions under high (>18 wt %) water and pure acetonitrile conditions in this case study are perhaps not directly translatable to manufacturing scales, the observation of the form conversion itself provides critical knowledge for future processing. Specifically, the observed form conversions in this screening study indicates that there is a significant degree of risk when operating at these conditions, although the rate at which this form conversion would occur would likely be slower at pilot or manufacturing scales. To assess risk of isolating any form other than the preferred form of the API, a discrete “risk map” (Figure 5) was generated based on the PXRD data to highlight areas where the preferred form was exclusively observed (blue) and where other patterns were observed (red).
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ASSOCIATED CONTENT
S Supporting Information *
The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.oprd.5b00346. Additional images and specifications of the filtration apparatus described herein (PDF)
Figure 5. Risk map as a function of water content and time illustrating where the preferred form was observed (blue) versus where other patterns were observed (red) based on the PXRD results.
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AUTHOR INFORMATION
Corresponding Author
As a result of this highly automated form analysis study, it was determined that there was little to no risk of forming undesirable polymorphs within the operating range of the crystallization procedure, between the dissolution and isolation point. However, the cake washing procedure was changed from 100% MeCN to 99.5% MeCN containing 0.5 wt % water as a result of this study, since as little as 0.25 wt % of water in MeCN allowed maintenance of the desired crystalline form of the API (Figure 5). In addition, this study motivated additional experimentation to further characterize the API under pure acetonitrile and high water (>18 wt %) conditions in case these observations are encountered in future processing of this API.
ACKNOWLEDGMENTS The authors would like to acknowledge Chester Markwalter and Nathaniel Kopp for their contributions in collaborating on the work with the API in the case study; Freeslate for their help in supporting Core Module 3 operation; and Chemglass for their help in generating the prototype filtration apparatus to pilot this new workflow.
CONCLUSIONS For a majority of organic, small molecule APIs, challenges often arise in crystallization robustness to ensure the correct polymorph is isolated. These issues are preferably encountered during development as opposed to during the manufacturing of an API. While different APIs and process intermediates have distinct challenges in the realm of polymorphism, the highly automated form analysis workflow has the modularity and adaptability to investigate polymorphism and help generate knowledge early in the development process to overcome a
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*Phone: (732) 227-5441. E-mail:
[email protected]. Notes
The authors declare no competing financial interest.
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DOI: 10.1021/acs.oprd.5b00346 Org. Process Res. Dev. XXXX, XXX, XXX−XXX
