Alastair J. Florence
Solid-State Research Group, Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, U.K.
INTRODUCTION
The development of experimental methods for increasing the throughput and effi - ciency of all stages of drug discovery and development is an important area of research within the pharmaceutical industry. In the context of physical form discov- ery of pharmaceuticals, the fundamental aim when establishing a rigorous experi- mental crystallization search strategy is to achieve as wide a coverage of crystallization conditions and methods as possible within the constraints of available time, material, and resources. For the purposes of this chapter, physical form will be taken to mean any solid form of the compound being examined, whether crystalline (polymorphs, solvates, salts, and co-crystals) or non-crystalline (amorphous). Automated crystal- lization approaches allow the basic process steps involved in a typical manual solu- tion recrystallization method ( Fig. 1 ) to be effi ciently replicated over large numbers of parallel experiments. In principle, this enables the routine implementation of search strategies designed to maximize the diversity of crystallization conditions tested, allowing the experimental search to be carried out over a fi ner grid (e.g., a larger solvent library).
A signifi cant driver for the development and application of automated crystal- lization approaches has come from the need during pre-clinical drug development of a new chemical entity to identify the complete range of solid forms, and their associated properties, as quickly and effi ciently as possible (1–4). It is also necessary to control polymorphism in generic product development and manufacture (5).
However, the selection of effective approaches for fi nding polymorphs can also be complicated by the limited, sometimes milligram, amounts of compound available during the earliest stages of drug development for the initial assessment of polymor- phism (4,6). In this context, automated and parallel crystallization methodologies offer distinct advantages.
A comprehensive knowledge of solid-state diversity can add considerable value to the product development cycle, informing the control of raw material crys- tallization, scale-up, formulation and product manufacturing processes, and mini- mizing the risk of unexpected polymorphs appearing during later stages in the compound’s development and production. The enumeration of all solid forms of a novel active pharmaceutical ingredient (API) at an early stage in its development also adds signifi cant value by supporting robust intellectual property (IP) protection (7).
In one high-throughput crystallization study summarized in Table 1 , the authors estimated a 120-fold increase in effi ciency over manual approaches alone realized through a combination of the increased number of trials carried out in par- allel and the increased speed at which individual experiments were implemented (9).
5
The results in Table 1 highlight some important issues when considering the value of developing and implementing automated or high-throughput crystallization technologies. For example, the high-throughput approach was signifi cantly more effective in fi nding diverse crystalline forms of MK-996 within a much shorter period of time than with manual methods. This was due to the greater search-space accessed using the high-throughput approach. The use of parallel approaches also enabled the entire study to be completed in an eighth of the time compared with manual methods and from only half the total amount of compound. The increased scale of the investigation also increases confi dence that all forms have been observed and provides information on the reproducibility with which specifi c polymorphs can be obtained. The search on MK-996 (9) also generated one form, form D, that the original study (8) identifi ed as being diffi cult to obtain once a more stable form I had been produced. High-throughput studies on acetaminophen (10) and ritonavir (11) also demonstrate the benefi t of high-throughput methods for identifying multiple sets of conditions that produce each specifi c form.
High-throughput crystallization studies rely on large numbers of small-scale individual crystallizations (e.g., microliter scale crystallizations utilizing micrograms of API) carried out in parallel using multi-well plate or rack formats. These approaches rely on the extensive use of automation to carry out thousands of crystallizations as part of the screen, utilizing robotics and computer control to implement some or all
Solid Solvent
Dissolve
Filter
Crystallize
Reclaim solid
Use a range of different solvents or solvent mixtures Vary amount to
alter concentration and supersaturation
Remove seed crystals of starting material prior to inducing crystallization Controlled cooling / evaporation / anti-solvent
Identify and characterize
Remove solid and prepare for analysis Temperature / stirring Select solution components
FIGURE 1 Process steps and variables in a typical manual solution crystallization experiment. In the context of an experimental physical form search, the aim is to explore factors that may infl uence the thermodynamic and/or kinetic control of nucleation and crystal growth.
aspects of each crystallization process. Many systems are available for automated liquid handling, for example, and these have been applied to great effect in structural biology to screen protein nucleation and crystal growth conditions for the production of diffraction quality samples (12). Whatever the type of solute being studied, the development of high-throughput methods has focused on crystallization techniques that are well suited to automation, miniaturization, and parallelization. For small mol- ecule pharmaceuticals, automated solution crystallization has been the most widely applied to date. This section will outline methods and strategies based on high- throughput, automated, or parallel technologies that have been developed to increase the throughput and effi ciency of crystallization searches for solid forms of APIs.
THE EXTENT OF PHYSICAL FORM DIVERSITY
The crystal energy landscape describes all possible crystal structures that a molecule may adopt (13). To isolate all of the possible forms requires an extensive experimental survey, and the more rigorous the search, the more confi dent we can be that all rele- vant forms have been observed. For practical purposes, “relevant forms” might only include the most stable form (14) and those that may be produced during the specifi c synthetic, bulk crystallization, and product manufacturing processes to which the compound will be exposed or, perhaps only those forms that are stable at ambient temperature. Unfortunately, it is not currently possible to know at the outset of any experimental search exactly how many polymorphs a given compound will actually produce. Indeed, some molecules may not display polymorphism, Pigment Yellow 74 (15), for example, regardless of the number and type of crystallizations that are carried out, although others may produce many polymorphs and solvates.
Although methods for crystal structure prediction (CSP) have advanced con- siderably in recent years, they are not currently able to cope routinely with the molec- ular complexity of many large, conformationally fl exible pharmaceutical APIs.
However, as CSP technologies continue to advance so there is increased potential to exploit theoretical structure prediction as a tool to inform and interpret experimental searches. For example, a CSP study may indicate that a molecule is likely to be poly- morphic, establishing the need to implement a rigorous search. Or, where an experi- mental search fi nds the most probable predicted form(s), this would increase confi dence that all of the likely forms that fall within the scope of the CSP search had been observed (e.g., Z ′ < 1 structures, most common space groups). Experimental searches can also locate forms that typically lie outside the scope of typical CSP TABLE 1 A Comparison of Performance Criteria for Manual and High-Throughput Crystallization Studies on the Compound MK-996 (8)
Manual methods High-throughput methods Number of crystallizations
implemented 100 1500
Time scale of study Approximately 32 weeks 4 weeks
Total amount of compound used More than 10 grams Less than 5 grams
Number of crystalline forms observed 9 18
Source : Data taken from Ref. (9).
searches (e.g., Z ′ > 1 structures, less common space groups, disordered solids, solvates).
Alternatively, if the most probable forms from the CSP are not actually observed by experiment, this may suggest that kinetic, rather than thermodynamic, factors deter- mine which of the energetically feasible crystal structures are observed. The potential value of CSP is not restricted to polymorphs but can also be used to provide a more complete view of the favorable motifs that underpin solvate formation. For example, an automated parallel crystallization study of the thiazide diuretic, hydrochloro- thiazide, found two polymorphs and seven solvates (16). An associated CSP study generated ca. 60 energetically feasible crystal structures, including both polymorphs.
The study identifi ed, a range of recurrent bimolecular hydrogen-bonded motifs in the predicted structures that were also observed among the experimental solvate crystal structures.
Maximizing the Number of Physical Forms Observed
The actual number of solid forms identifi ed by an experimental screen may repre- sent only a subset of all thermodynamically feasible structures. The temperature, pressure, and supersaturation (solubility) ranges that are practically accessible in an experimental search, as well as the chemical stability of the compound, all limit the diversity of crystallization conditions to which a given molecule can be exposed, and so potentially restrict the range of forms that can be observed.
Figure 2 shows a schematic representation of an idealized “experimental space”
for a physical form search that combines several crystallization techniques. The outer box represents all possible structures that the molecule could adopt under all condi- tions, and the inner box represents the subset of all forms that are of direct relevance to pharmaceutical applications. The shaded areas represent experimental space acces- sible by each crystallization technique. Some forms may be observed only by one particular method, whereas others may be produced by several different techniques.
Thus, the total number of forms observed may, in principle, be increased by maxi- mizing the total shaded area, or diversity of crystallization conditions, covered in the screen. This can be achieved by (i) combining multiple crystallization techniques and (ii) maximizing the scope, or breadth of conditions tested, by each technique.
Automated parallel approaches to crystallization can help to achieve these aims.
For example, the anti-epileptic compound carbamazepine has four known polymorphic forms (17–20) in addition to a wide variety of solvates (21–23) and co-crystals (24–28) ( Table 2 ). Although the compound has been subjected to a wide range of crystallization studies with thousands of individual crystallizations, the C -centered polymorph form IV, has only been recovered by recrystallization from solution in the presence of polymers (19,29).
Crystallization Methods
There are many ingenious means of recrystallization that have been demonstrated to be effective for exploring solid-state diversity. These include vapor (30) or liquid–
liquid (31) diffusion, sublimation (32), thermal analysis (33) and hot-stage micros- copy (34), slurrying (35), contact line crystallization (36), potentiometric cycling (37), neat and solvent-assisted grinding (38,39), high-pressure crystallization (40,41), epitaxial growth on crystalline substrates (42) or templating using various materials (29,43–48), supercritical fl uids (49), laser-induced nucleation (50), and capillary crystallization (51). Clearly, with such considerable variety of experimental approaches to choose from, it is not logistically possible to cover all possibilities
Other methods [e.g., suspensions
(slurries);
emulsions;
supercritical fluids;
high-P]
Additives (e.g., impurities
/ polymers) All pharmaceutically relevant forms
All possible crystalline forms
Solution crystallization
Desolvating solvates
Thermal methods (e.g., solid-state transformations;
recrystallization from the melt) Overlap of
experimental space (i.e., same forms
found)
FIGURE 2 Schematic illustration of the relationship between all possible crystalline forms and the coverage of experimental space using different techniques.
TABLE 2 Structural Parameters for the Four Reported Polymorphic Forms of Carbamazepine [5H-dibenzo(b,f)azepine-5-carboxamide; C 15 H 12 N 2 O, Mol. Wt. = 236.3]
Form Crystal system/
space group
a , b , c (Å) a , b , g (°)
I Triclinic, 5.171(1), 20.574(2), 22.245(2) 84.12(4), 88.01(4), 85.19(4)
II Trigonal, 35.454(3), 35.454(3), 5.253(1) 90, 90, 120
III Monoclinic, P 2 1 / n 7.537(1), 11.156(2), 13.912(3) 90, 92.86(2), 90 IV Monoclinic, C 2/ c 26.609, 6.927, 13.957 90, 109.72, 90
3 R
systematically in a grid-type search. So, although the application of multiple experimental techniques remains an important aspect of comprehensive poly- morph screening, crystallization from solution remains the most widely employed method in automated large-scale crystallization studies. The majority of typical steps in solution crystallization ( Fig. 1 ) are readily amenable to automation, allow- ing various parameters that may infl uence nucleation and crystal growth from solution to be rigorously tested using effi cient parallel experimental approaches.
Automated Parallel Crystallization
The inherent challenges of implementing large numbers of experiments under dif- ferent conditions in a comprehensive search can be addressed by carrying out experiments in parallel. This is at the core of high-throughput polymorph screening methods that combine parallel experiments with automation and miniaturization to maximize effi ciency and provide an early indication of the extent of polymor- phism and solvate formation in the compound being studied (3,9–11,52,53). Figure 3 shows an overview of the main components of a physical form search strategy, spanning the selection of crystallization methods and experimental variables, the implementation of individual crystallizations and identifi cation of samples to the complete characterization of all solid forms that are discovered.
The design phase involves the selection of specifi c methods and diverse con- ditions to be used in the screen. Once crystallization protocols have been estab- lished, these are translated into instrument controls to allow the robotic platform to implement the required experiments. Visual, or optical, inspection of individual crystallizations allows the presence of recrystallized solid to be detected, often trig- gering a sample preparation or retrieval step (e.g., removal of solution). Samples are next subjected to physical analysis to identify each specifi c form produced from the experiments performed. Finally, once suffi cient sample is available, each novel form produced can then be subjected to further characterization of physicochemical properties, allowing the relationships between them to be established. Given the potential scope of these activities, electronic data management tools are essential to deal with the large volumes of data generated during each of the above stages of the screen. The successful application of high-throughput, automated, and parallel crystallization methods combined with various design strategies has been demon- strated for a range of organic compounds over the last decade (1,54–56) and some examples are highlighted in Table 3 .
Parallelization does not necessarily require sophisticated and expensive auto- mated instruments; small-scale formats utilizing multi-well plates combined with manual multi-channel pipettes for liquid dispensing can also provide a versatile platform for crystallization screening ( Fig. 4 ). Simple quartz glass plates with indi- vidual wells can be used for crystallization screening, giving good chemical resistance to organic solvents. Accurate dosing of solute can be achieved by dispensing stock solution onto the plate and drying. Solvents can then be dispensed in a combinatorial manner across the well to produce the crystallization solutions. In Figure 4 , for example, in direction 1 a series of 12 solvents with increasing polarity may be used, whereas in direction 2, the amount of solid introduced can be reduced sequentially in each row to vary concentration. The plate containing 96 individual crystallizations can then be stored in a controlled environment to allow cooling or evaporation and on the appearance of solid; the plate can be analysed by microscopy, Raman, or X-ray powder diffraction (XRPD).
Design selection of methods to be used selection of experimental conditions to be varied (e.g., T, concentration) solvent selection (library design)
Screen automated parallel solution crystallization capillary crystallization crystallization from the melt / amorphous thermal transformations polymer heteronuclei manual crystallization methods high pressure crystallization
Identify visual inspection / image analysis vibrational spectroscopy (e.g., Raman) micro-XRPD (GADDS; area detectors) XRPD DSC
Characterize forms solubility and dissolution relative thermodynamic stability hygroscopicity determine crystal structures (single crystal X-ray diffraction or structure determination from powder diffraction data)
Data management
implementation of experimental protocols equipment / instrument interface acquisition and analysis of analytical data sets
crystal structure data data-mining
reporting
FIGURE 3 An overview of the main process elements in a comprehensive physical form discovery approach. Abbreviations : DSC, differential scanning calorimetry; GADDS, general area diffraction detector system; XRPD, X-ray powder diffraction.
1 2 3 4 5 6 7 8 9 10 11 12
A B C D E F G H
1
2
FIGURE 4 A typical 96-position plate/rack format capable of supporting parallel crystallizations (54).
TABLE 3 Examples of High-Throughput, Automated, and/or Parallel Crystallization Studies on Pharmaceutical Compounds CompoundMethodologyAnalytical methodsForms observedNotes Acetaminophen (10)Solution (evaporation) and melt crystallization 24 solvents in mixtures Optical imaging Raman XRPD
3 polymorphs2592 crystallizations (varying solvent, concentration, and temperature in triplicate) Identifi ed reproducible conditions for producing form II 3-azabicyclo (3.3.1) nonane-2,4-dione (57)Solution crystallization (controlled cooling and evaporation) Thermal transformations
Microscopy XRPD DSC/variable- temperature capillary XRPD 2 polymorphs 1 plastic crystalline phase 2 solvates
182 crystallizations 67 solvents Carbamazepine (29)Polymer heteronuclei in methanol solutions (evaporation)RamanForm IV84 different polymers over 3 trials Carbamazepine (45)Solution crystallization on polymer microarraysMicroscope Raman2 polymorphs128 crystallizations Carbamazepine printed in DMSO onto polymer spots 6.5 mg of active used in total Carbamazepine (22)Solution crystallization (controlled cooling and evaporation)XRPD3 polymorphs 9 solvates594 crystallizations (3–5 mL) 66 solvents 5 conditions 3,4-dichloro nitrobenzene (58)Solution crystallization (controlled cooling)XRPD1 polymorph 2 solvates224 crystallizations 64 solvents Controlled cooling 5-HT4 antagonist (59)96-well plate (no automation) polymorph and salt screening Solution evaporation (salt selection) with slurrying
Microscopy Raman2 polymorphs 2 hydrates (total of 16 crystalline forms of 5 salts)
12 solvents, 15 acids Solids dispensed in methanol solution. Upon drying 200 µ L of each crystallization solvent added to each well Hydrochlorothiazide (16)Solution crystallization (controlled cooling and evaporation)XRPD2 polymorphs 7 solvates642 crystallizations (3–5 mL) 67 solvents 4 conditions used Mefenamic acid (60)Crystallization by evaporation of nano- and picoliter solution droplets on self-assembled monolayers
Microscope Raman XRPD 2 polymorphsSupports high-supersaturation levels and concomitant crystallization of forms from confi ned volumes
MK-996 (potassium salt) (9)Solution crystallization in blocks of 96 capillary tubes by coolingRaman XRPD9 polymorphs 9 solvates1–3 mg of compound per crystallization 1440 crystallizations 96 slurry experiments 21 solvents (in mixtures) 2-[(2-nitrophenyl) amino]-3- thiophenecarbonitrile (ROY) (45)
Solution crystallization on polymer microarraysMicroscope Raman4 polymorphs128 crystallizations 1 solvent ROY printed in NMP/acetone solution onto polymer spots 2-[(2-nitrophenyl) amino]-3- thiophenecarbonitrile (ROY) (9) Solution crystallization in blocks of 96 capillary tubes (cooling)Raman XRPD4 polymorphs3100 solution crystallizations 24 solvents alone or in mixtures; 2 concentrations; 2 temperatures 1–2.5 mg of ROY per crystallization Ritonavir (11)Solution crystallization using 24 solvents in mixtures (cooling)
Optical imaging Raman3 polymorphs 2 solvates ∼ 2000 crystallizations, 2 g of material used for entire screen Individual solution volumes used = 50 µ L Sertraline (61)96-well blocks for capillary crystallizations Controlled cooling Raman XRPD18 crystalline salts Polymorphs of HCl, HCBr, benzoate, and mesylate salts
3456 crystallizations with sertraline dispensed in methanol in solution. Salt formers added in solution form and then solutions evaporated Crystallization solvents added using a 32-channel liquid dispenser Sulfamethoxazole (45)Polymer microarrays 768 crystallizationsMicroscope Raman2 polymorphsCompound printed in methanol or ethanol onto polymer spots Individual solution volumes used = 50 µ L Sulfathiazole (60)Solution evaporation on self- assembled monolayersMicroscope Raman XRPD
4 polymorphsCrystallization of nano- and picoliter solution droplets on self-assembled monolayers Tamoxifen (62)Salt section and polymorphism study 96-well plate using 12 solvents and 6 acids in different stoichiometries Solution crystallization (evaporation)
Raman12 crystalline salt forms identifi ed including polymorphs 25–50 µ L volumes of API (100 mg used) and acid solutions mixed in wells, heated, and allowed to evaporate Abbreviations : API, active pharmaceutical ingredient; DMSO, dimethyl sulfoxide; DSC, differential scanning calorimetry; XRPD, X-ray powder diffraction.
TABLE 4 Potential Disadvantages and Advantages of Automated Crystallization Approaches
Disadvantages Advantages
High initial costs of hardware, varies with
•
the extent of control and integration offered (initial hardware costs can range from £20,000–
£500,000)
Need for bespoke software if multiple
•
platforms used incombination (for experimental design;
instrument control; data analysis;
information management)
Diffi culties in miniaturizing key steps
•
(e.g., fi ltration of suspensions under temperature control)
Potentially high maintenance costs
•
High cost of development or
•
implementation of new or bespoke features to pre-existing systems
Repeatability of steps under direct computer
•
control: implement multiple protocols (e.g., controlled cooling or evaporation across a large solvent library, isothermal
crystallization using anti-solvent) Manageability: ease of implementation of
•
combinatorial experiments with integrated data management
Reduced wastage of materials
•
Integration with information management
•
systems and data repositories
Increased productivity: greater number and
•
throughput of crystallizations
Reduced labor for equivalent numbers of
•
experiments
Increased scale of experimental search and
•
diversity of conditions Increased effi ciency
•
Other multi-position formats are also in use including heating blocks for capillary crystallization and, for example, 96-position racks with individual crystal- lization inserts in each position. Robotics can be used to prepare each sample solution, monitor wells, and transfer either entire plates or individual inserts for analysis once crystallization has occurred. Although automation may not be suitable for all types of crystallization, in general, it can support improved throughput, more accurate control of conditions, and greater reproducibility of conditions compared with manual techniques. A summary of various pros and cons of automated crystallization systems is listed in Table 4 .
Experimental Variables for Solution Crystallization
The sections below highlight several experimental variables that may infl uence the thermodynamic and kinetic control of solution crystallizations, and are therefore commonly utilized in the automated parallel crystallization searches.
Solvent and Solvent Selection
Variation of solvent identity can be a relatively straightforward means of design- ing diversity into the crystallization screen and manipulating physical form out- come (63,64). Solvent properties such as viscosity, surface tension, and density are key parameters in nucleation and crystal growth (65), and can be readily varied by using a diverse solvent library comprising many individual solvent types and/or solvent mixtures (1,3,56). Mixed solvent systems or co-solvents can be dispensed to increase the variety of solution properties and to address poor solubility. Chem- ical compatibility with the solute may also need to be taken into account during solvent selection.
In pharmaceutical development the safety of solvents can also be an impor- tant consideration. For example, the International Conference on Harmonization