Solid-State Chemistry: A Technical Forum

Understanding the solid-state properties of an active pharmaceutical ingredient (API) is critical in formulation development and a finished drug product. Patricia Van Arnum, senior editor, asked several leading experts to share their insight into this field: Menno A. Deij, head of technology development at Avantium Pharma (Amsterdam); Chris Frampton, chief scientific officer of SAFC Pharma's Pharmorphix Solid State Services (Cambridge, UK); and Noel Hamill, team leader in the physical sciences group at Almac (Craigavon, UK).

Patricia Van Arnum

Crystal-structure prediction

PharmTech » Can you outline the major issues involved in polymorphism and advances in crystal-structure prediction?

» Deij: A lot of effort goes into understanding the crystalline solid phase of the API. The pharmaceutical industry is required to screen for polymorphism, that is, the ability of a molecule to exist as two or more crystalline phases that have different arrangements and/or conformations of the molecules in the crystal lattice (1). At room temperature, there is only one polymorph that fits the definition of the most stable; all other polymorphs are metastable and may convert to the most stable form (2). Properties that can differ among solid forms include color, melting point, spectral properties, solubility, crystal shape, water sorption and desorption properties, particle size, hardness, drying characteristics, flow and filterability, compressibility, and density. These variations can lead to differences in dissolution rate, oral absorption, bioavailability, toxicology and clinical results, hence both safety and efficacy are impacted (3, 4). The potential formation of multiple polymorphs of an API is particularly challenging when producing solid oral- dosage drug products. To make solid oral-dosage forms with crystalline APIs, pharmaceutical companies prefer to use the most stable polymorph to prevent the conversion to a less soluble polymorph, which can affect the efficacy of a drug product (5).

In an ongoing effort over the past 20 years, the academic community has pursued the prediction of crystal polymorphs starting from the molecular structure (6). Ideally, a researcher would like to search through the complete range of crystal structures and evaluate their free energies accurately as a function of temperature and pressure to obtain the global minimum energies of optimal crystal structures. The crystal-structure prediction process starts with the generation of millions of trial crystal structures, followed by the optimization of these structures and ranking according to their lattice energy as a first approximation to the lattice free energy. The crystal structure with the lowest lattice energy is expected to correspond to the most stable polymorph that can be found experimentally. The differences between the stable and metastable forms is typically on the order of only a few kJ/mol.

In the past, the lattice-energy ranking was based on molecular mechanics and generic forcefields. In this approach, molecules are modeled as balls and springs, representing the atoms and bonds. The forcefield is a large table that describes the balls' and springs' intramolecular behavior during bond stretching, bond bending, out-of-plane bending, and torsional rotation. The intermolecular interactions are described as charge–charge interactions and induced dipole–dipole interactions (van der Waals interactions). These forcefields are based on empirical data with fitted parameters. Forcefields are meant to be generic and able to describe a lot of different situations. Because the problem of crystal-structure prediction requires very accurate energy rankings, it turns out that the precision and accuracy of these forcefields is not sufficient.

The most recent developments, however, introduced by Marcus Neumann and his company, Avant-garde Materials Simulation, do not use generic forcefields. Advanced quantum-mechanical calculations (dispersion-corrected density functional theory or d-DFT) are used instead to develop a forcefield that is customized specifically for the molecule at hand (7, 8). This forcefield is then used in the geometry optimization of the thousands of crystal structures generated. The most promising structures with the lowest lattice energy are reranked with d-DFT, which results in an energy ranking that is of unprecedented accuracy and precision, giving the required confidence in the computational results.

A blind test of crystal-structure prediction technology is organized every two to three years by the Cambridge Crystallographic Data Center. The latest results of the 2007 blind test show that the approach championed by M. Neumann in cooperation with F. Leusen and J. Kendrick from the Institute of Pharmaceutical Innovation at the University of Bradford (Bradford, UK), which generated and optimized the crystal structures using the quantum mechanics-derived forcefields followed by final energy ranking using the d-DFT calculations, allows for the accuracy required (9, 10). In this latest blind test, they were the only group out of 14 participants to predict all four challenges correctly.

Avantium Pharma recently started to offer computational polymorph prediction services in collaboration with Avant-garde Materials Simulation. The computational results can be used for various purposes: a confirmation of experimental results, as a guide during experimental screening, and to understand the crystal structures of the polymorphic forms generated. This is the first time that the field of crystal-structure prediction has advanced enough to apply the technology to pharmaceutically relevant molecules. Multiple component crystals (hydrates, solvates, and cocrystals) can be handled as well.

All these efforts are geared toward the reduction of the risk that pharma companies run during drug development: when you know which the most stable structure is, you know what to look for in the experimental screening for polymorphs, and you will also know when to stop searching. You can also assess the likelihood of a competitor finding a developable metastable form and protect your intellectual property before this happens.

Chiral APIs and hydrated compounds

PharmTech » Can you outline some recent advances in X-ray crystallography in use for certain types of APIs?

» Frampton: One of the key regulatory requirements for a chiral API is proof of absolute stereochemistry. There are several analytical technologies available for this task; however, the most popular method is that of single-crystal X-ray diffraction. Traditional crystallography requires single crystals that have dimensions of at least 100 μm in size. For some molecules, it is impossible to grow crystals this large with sufficiently good quality to determine the crystal structure. Although the newest generation of single crystal X-ray diffractometers can work with crystals down to about 50 μm, sometimes even with these instruments, structure and stereochemistry determination is not possible.

In cases such as these, the answer lies in using a synchrotron X-ray facility to provide the radiation source. The flux provided by a synchrotron is far greater than a standard X-ray source, and thus it can be used to generate diffraction patterns from much smaller crystals, down to about 5 to 7 μm.

For example, Diamond Light Source's (Didcot, UK) new synchrotron facility has important industrial applications such as determining absolute stereochemistry of pharmaceutical compounds. [Diamond Light Source is a joint venture funded by the UK government via the Science and Technology Facilities Council and the Wellcome Trust]. Looking at ~5 μm crystals is at the edge of the capabilities of even a third-generation synchrotron such as Diamond. The typical experiments carried out there use a shutter speed of 1 s, but to see a diffraction pattern for such a tiny speck of matter, much longer exposures are required, on the order of 10 s per frame. This longer time is because, in general, pharmaceutical molecules are largely made up of lighter atoms (i.e., carbon, hydrogen, nitrogen, and oxygen), with the occasional heavier atoms such as fluorine, chlorine, sulfur, or phosphorus. These lighter atoms have fewer electrons that interact with X-rays to form diffraction patterns.

X-ray crystal structures can also be the only way to determine the connectivity of the crystals, for example, how water of hydration fits in between the drug molecules. Sodium diclofenac is a good example. Although the drug dates back to the 1960s, the first crystal structure was solved in 2002, and it suggested that the crystal is a pentahydrate. Yet thermogravimetric and Karl Fischer analysis consistently suggest 4.83 and 4.69 molecules of water respectively, never 5. By running the experiment at 120 K, the diffraction patterns are sharper, and it became clear that the ratio of drug molecules to water molecules is 4:19, giving a 4.75 hydrate, which matches up with analytical results. The crystal structure of sodium diclofenac anhydrate (see Figure 1) was solved for the first time with data collected at the new Diamond synchrotron facility using a needle crystal of dimensions of 50 X 5 X 3 μm.

Figure 1: View of the anhydrous sodium diclofenac structure down the b-axis of the unit cell. (FIGURE 1 IS COURTESY OF DIAMOND LIGHT SOURCE)

Solid-state chemistry at work

PharmTech » What are some common problems revealed by solid-state chemistry applications?

» Hamill: The need to investigate the solid-state properties of an API is well-known. Stories of solid forms appearing late in development or after launch with little or no bioavailability have been widely reported in the literature (11, 12). Since the requirement to investigate polymorphs was enshrined in the regulatory guideline ICH Q6A in 1999, the pharmaceutical industry has turned this former problem of solid-form diversity into an opportunity (1). Novel solid forms such as cocrystals and amorphous solid dispersions are valuable resources in rescuing poorly bioavailable drugs in addition to generating intellectual property. However, some common problems may arise such as batch-to-batch variability, development of the wrong form, and identification of polymorphs late in development.

Batch-to-batch variability. Physical properties can vary widely between batches; sometimes this can be attributed to differences in crystalline forms or mixtures of amorphous and crystalline forms. Usually, these are first noticed as a failed dissolution test or a poor filtration. In one client example, batches of API used in preclinical and Phase I studies were found to consist of three polymorphs, amorphous material, and a suspected hydrate. Variances in particle-size distribution and crystal habit are the most common issues encountered and suggest that crystallizations at scale are still under inadequate control.

Developing the wrong form. Delaying the polymorph screen as long as possible is a common cost-saving strategy used by smaller companies, particularly where poor solubility was not a complication in early-phase work. The delay carries significant risks in late phase. For example, one company relying on in-house expertise only identified the most stable form late in Phase II, by which point the decision had been taken to progress a metastable form. At Phase III, it was discovered that a mixture of two metastable forms was being produced in the plant. Although Almac was able to assist in solving this problem, the late-stage discovery added the cost of controlling and analyzing the future batches, which would have exceeded the cost of outsourcing a comprehensive screen at an earlier stage.

Late discoveries. Although ICH Q6A requires that a polymorph screen be performed on new drug candidates, it does not specify how much effort should be applied. The most stable form, polymorphs or solvates, can still appear late in development, sometimes after a client or contractor has already done a screen. Worse still, screening may not be performed on key intermediates or registered starting materials, which poses an expensive risk to fixed routes in late-phase projects. For example, one supplier produced an insoluble stable form of a key raw material for a launched compound on their eighty-sixth plant batch. Generic manufacturers have often encountered new forms of established compounds, for which no screening data may exist, leaving them scrambling for answers.

Several factors may contribute to these problems. One may relate to a reliance on automated high throughput (HT) screening. Well plates are prone to cross-contamination, and the number of nucleation techniques that can be automated is limited. The API is experiencing conditions during processing that are not being mimicked in the screening methodology. Fundamentally, a diversity of nucleation techniques maximizes the chance of finding new forms and accessing all of these is not possible with HT screening alone (13).

Another factor relates to increased in-licensing of drug candidates. In their haste to get a product for sale, biotech and virtual companies can be tempted to delay screening or do it on a limited basis. Fortunately, this approach is gradually changing to the view that a good solid-state technical package is not only a valuable asset, but also comes with the benefit of new intellectual property.

There may also be a lack of integrated solid-state awareness. The synergy created by having synthetic chemists, solid-state specialists, and formulators working together cannot be underestimated. The impact of the solid state does not end with the screening at Phase I. Based on our experience, changes in impurity profiles, processing conditions, and excipients can influence the solid form of the API, requiring careful monitoring at all stages of drug development.

Patricia Van Arnum is a senior editor at Pharmaceutical Technology, 485 Route One South, Bldg F, First Floor, Iselin, NJ 08830 tel. 732.346.3072, pvanarnum@advanstar.com

References

1. ICH, Q6A Specifications: Test Procedures and Acceptance Criteria for New Drug Substances and New Drug Products: Chemical Substances (1999).

2. H.G. Brittain, Ed., Polymorphism in Pharmaceutical Solids (2nd edition, Informa Healthcare, London).

3. J. Haleblian and W. McCrone, J. Pharm. Sci., 58 (58), 911–929 (1969).

4. T.L. Threlfall, Analyst, 120 (10) 2435–2460 (1995).

5. D. Singhal D and W. Curatolo, Adv. Drug Delivery Rev. 56 (5) 335–347 (2004).

6. S.L. Price, Acc. Chem. Res. 42 (1) 117–126 (2009).

7. M.A. Neumann and M.A. Perrin, J. Phys. Chem. B, 109 (32), 15531–15541 (2005).

8. M.A. Neumann, J. Phys. Chem. B, 112 (32), 9810–9829 (2008).

9. M.A. Neumann, F.J.J. Leusen, and J. Kendrick, Angew. Chem. Int. Ed. 47 (13), 2427–2430 (2008).

10. G.M. Day et al., Acta Cryst. B 65 (2) 107–125 (2009).

11. S.R.Chemburkar et al., Org. Process Res. Dev. 4 (5), 413–417 (2000).

12. Y. Kobayashi et al., "Int. J. Pharm 193 (2), 137–146 (2000).

13. J. Aaltonen et al., Eur. J. Pharm. Biopharm. 71 (1), 23–37 (2009).

For additional insight on screening strategies in solid-state chemistry, see here.