While this does not mean that XRPD should be used to the exclusion of other experimental techniques when studying solid forms, X-ray diffraction (XRD) has applications throughout the drug development and manufacturing process, ranging from discovery studies to lot release. The utility of XRD becomes evident when one considers the direct relationship between the measured XRD pattern and the structural order and/or disorder of the solid.
XRPD provides information about the structure of the underlying material, whether it exhibits long-range order as in crystalline materials or short-range order as in glassy or amorphous materials. This information is unique to each structure—whether crystalline or amorphous—and is encoded in the uniqueness of the XRPD pattern collected on a well-prepared sample of the material being analyzed.
One must draw a distinction between crystalline materials, which give rise to XRPD patterns with numerous well-defined sharp diffraction peaks, and glassy or amorphous materials, whose XRPD patterns contain typically three or fewer broad maxima (X-ray amorphous halos). In practice, when using XRPD, one can usually measure a sequence of crystalline materials that are progressively more disordered, ultimately resulting in glass. A classification system has been proposed by Wunderlich (Table 1) to describe the type of structural order and molecular packing present in molecular organic solid forms using three order parameter classes: translation, orientation, and conformation.1
XRPD can be used to identify and characterize solid forms of a given molecule exhibiting long-range crystalline order (e.g., polymorphs, solvates, co-crystals, and salts) by their unique combination of order parameters. Amorphous solid forms do not exhibit any long-range order but are identifiable and characterized by their unique local molecular order, apparent in the X-ray amorphous diffraction pattern.2
Given XRPD’s sensitivity to structural order, some of its typical applications in the analysis of solid-state properties of a drug substance or product include:
- Identification of existing forms of the API;
- Characterization of the type of order present in the API (crystalline and/or amorphous);
- Determination of physical and chemical stability;
- Identification of the solid form of the API in the drug product;
- Identification of excipients present in a drug product;
- Monitoring for solid-form conversion upon manufacturing;
- Detection of impurities in a drug product; and
- Quantitative analysis of a drug product.
Applications in Drug Development
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TABLE 1. Types of Solid Forms Described by the Wunderlich Classification System
API CharacterizationGuidelines from regulatory authorities regarding the need for characterization of a drug substance under development have been clearly stated. Below is an example relating to the issue of polymorphism: “Polymorphic forms of a drug substance can have different chemical and physical properties, including melting point, chemical reactivity, apparent solubility, dissolution rate, optical and mechanical properties, vapor pressure, and density. These properties can have a direct effect on the ability to process and/or manufacture the drug substance and the drug product, as well as on drug product stability, dissolution, and bioavailability. Thus, polymorphism can affect the quality, safety, and efficacy of the drug product.”3
While there are a number of methods to characterize polymorphs of a drug substance, the two broadly accepted methods for providing unequivocal proof of polymorphism that are recognized by the U.S. Food and Drug Administration are single-crystal XRD and XRPD.4 Other techniques like thermal or spectroscopic methods can be helpful in further characterizing drug products, but only X-ray provides the necessary structural information to uniquely identify different polymorphs. Therefore, in early drug development, XRPD is often used as a primary experimental technique and a means of differentiating among experimentally generated materials. Fully characterizing any material requires the use of complementary techniques (thermal or spectroscopic) but X-ray is typically done first because it is fast, is nondestructive, requires little material, and provides the necessary structural information.
Databases of known XRPD patterns for various pharmaceutical materials are published annually by the International Centre for Diffraction Data and the Cambridge Crystallographic Data Centre, which publishes the Cambridge Structural Database.Synchrotron XRD has frequently been used to characterize pharmaceutical materials in applications that require additional sensitivity not provided by laboratory X-ray diffractometers (e.g., crystallization monitoring).5-6 The tradeoff is the greater expense and time investment typically associated with such measurements. Because such applications tend to be specialized, this section will focus primarily on laboratory XRPD methods.
Qualitative Analysis of Materials (Phase Identification)Because every structurally different crystalline material exhibits a unique XRPD pattern upon analysis, the use of XRPD for phase identification was recognized early and remains the most common application of XRPD to pharmaceuticals.7 This so-called qualitative analysis typically refers either to the initial characterization of material not previously analyzed by XRPD or to the identification of a phase or phases in a sample of material by comparison to reference patterns. Reference patterns are previously collected XRPD patterns of the same material.
Where available, XRPD patterns calculated from, for example, single-crystal structures can be substituted, but one should remember that the temperature at which the pattern is calculated can have a significant effect on the calculated XRPD profile. When dealing with mixtures of phases, qualitative analysis can provide an estimate of the relative proportions of different phases in the sample, usually based on the comparison of peak intensities for characteristic peaks of the different phases.
Due to sample artifacts such as preferred orientation and poor particle statistics, this type of analysis should never be confused with quantitative analysis of mixtures. Databases of known XRPD patterns for various pharmaceutical materials are published annually by the International Centre for Diffraction Data and the Cambridge Crystallographic Data Centre, which publishes the Cambridge Structural Database.
When comparing XRPD data of crystalline samples, one notes any differences in peak positions (to within a certain precision, e.g., 0.1 º2Ø) which correspond to structural differences between the samples.8 Intensities are generally not relied upon for qualitative analysis due to the previously mentioned instrument and sample artifacts, although they have to be used to some degree to allocate the peak positions (based on local maxima).
It is not uncommon for two patterns to share some but not all of the peak positions. This can be a coincidence, or it can happen because one of the samples is a mixture of multiple phases, including the phase in the other sample. Experience and data from complementary experimental techniques are needed to resolve such ambiguous cases. It should also be noted that, at higher 2Ø values, peaks of most organic materials become considerably overlapped, which makes determining their exact positions difficult. Therefore, freestanding peaks at low angles are the primary means of differentiating structures, and XRPD data above approximately 30 º2Ø are rarely useful for qualitative analysis.
Figure 1 shows XRPD patterns of two crystalline polymorphs of sulfamerazine. The patterns in Figure 1 were collected from material crystallized in glass capillaries during a polymorphism screen. A polymorphism screen is typically run early in the drug development process to identify and (partially) characterize the different polymorphs of a drug substance. Assuming XRPD was the first analytical technique used on the samples, the data in Figure 1 could be used to make a qualitative assessment regarding the probable nature of the material generated during crystallization experiments. Therefore, one could designate the first of the patterns crystalline “Pattern A” and the other crystalline “Pattern B,” noting the sharp peaks and lack of diffuse halos as a sign of crystallinity and the structural differences, as evidenced by the different peak positions in each pattern.
There is insufficient information at this stage to designate either pattern as a polymorph of the material; they could be, for example, a solvate, a hydrate, or a mixture of two or more polymorphs. It is clear, however, that both materials are crystalline and structurally different. Further characterization using thermal methods (TGA, DSC), for example, would confirm that these materials are not solvates or mixtures but actual polymorphs and would aid in determining the thermodynamically stable polymorph. XRPD provides information about the structure of materials, not thermodynamics, although variable-temperature XRPD has been used to study changes in structure at different temperatures.
PANalytical’s Empyrean X-ray diffractometer is a 2011 winner of an R&D 100 award in the ‘winning technology’ category.
Therefore, the first application for XRPD during drug development is typically to identify the materials generated using different experimental methodologies, often in automated, high throughput screening environments.9-11 To simplify this pattern recognition problem, which often involves hundreds or thousands of experimental data sets per screen, people have developed various computational approaches to recognize, sort, and classify unknown XRPD patterns, either through comparison to a known database of materials or simply within the experimental set of unknown patterns.12-15 The latter often uses an approach called hierarchical clustering.16-17
XRPD data are often cataloged in databases using the so-called Hanawalt system.18-19 In this system, the data are stored as d versus I/Imax pairs. The use of d-space eliminates the need to specify the radiation source wavelength and allows comparison between laboratories using different instrumentation.
A similar system is often used for intellectual property filings. However, there is considerable structural information available in a typical XRPD pattern that can be used to characterize the material. Making use of this information usually requires high quality laboratory data and the use of advanced computational methods.
- Wunderlich B. A classification of molecules, phases, and transitions as recognized by thermal analysis. Thermochim Acta. 1999;340/341:37-52.
- Yu L. Amorphous pharmaceutical solids: preparation, characterization and stabilization. Adv Drug Deliv Rev. 2001;48(1):27-42.
- U.S. Food and Drug Administration. Center for Drug Evaluation and Research. Guidance for Industry: ANDAs: Pharmaceutical Solid Polymorphism Chemistry, Manufacturing, and Controls Information. FDA. Available at: www.fda.gov/OHRMS/DOCKETS/98fr/2004d-0524-gdl0001.doc. Accessed Aug. 3, 2011.
- Brittain HG. Polymorphism in Pharmaceutical Solids. New York: Marcel Dekker, Inc; 1999.
- Varshney DB, Kumar S, Shalaev EY, et al. Solute crystallization in frozen systems–use of synchrotron radiation to improve sensitivity. Pharm Res. 2006;23(10):2368-2374.
- Blagden N, Davey R, Song M, et al. A novel batch cooling crystallizer for in situ monitoring of solution crystallization using energy dispersive X-ray diffraction. Cryst Growth Des. 2002;3(2):197-201.
- Jenkins R, Snyder RL. Introduction to X-ray powder diffractometry. In: Winefordner JD, editor. Chemical analysis. Vol. 138. New York: John Wiley & Sons; 1996.
- United States Pharmacopeial Convention. General chapter 941: X-ray diffraction. In: USP 31-NF 26. Rockville, Md.: United States Pharmacopeial Convention; 2008:374.
- Hertzberg RP, Pope AJ. High-throughput screening: new technology for the 21st century. Curr Opin Chem Biol. 2000;4(4): 445-451.
- Barberis A. Cell-based high-throughput screens for drug discovery. Eur Biopharm Rev website. Winter 2002. Available at: www.samedanltd.com/magazine/12/issue/43/article/1231. Accessed August 3, 2011.
- Johnston PA, Johnston PA. Cellular platforms for HTS: three case studies. Drug Discov Today. 2002;7(6):353-363.
- Ivanisevic I, Bugay DE, Bates S. On pattern matching of X-ray powder diffraction data. J Phys Chem B. 2005;109(16):7781-7787.
- Marquart RG, Katsnelson I, Milne GWA, et al. A search-match system for X-ray powder diffraction data. J Appl Cryst. 1979;12(6): 629-634.
- Gurley K, Kijewski T, Kareem A. First- and higher-order correlation detection using wavelet transforms. J Eng Mech. 2003; 129(2):188-201.
- Gilmore CJ, Barr G, Paisley J. High-throughput powder diffraction. I. A new approach to qualitative and quantitative powder diffraction pattern analysis using full pattern profiles. J Appl Cryst. 2010;37:231-242.
- Johnson SC. Hierarchical clustering schemes. Psychometrika. 1967;32(3):241-254.
- Borgatti SP. How to explain hierarchical clustering. Connections. 1994;17(2):78-80.
- Hanawalt JD, Rinn HW, Frevel LK. Chemical analysis by X-ray diffraction. Ind Eng Chem Anal Ed. 1938;10(9):457-512.
- Byrn SR, Pfeiffer RR, Stowell JG.