What is the role of crystal structure prediction in pharmaceutical polymorph screening?

Crystal structure prediction generates hypothetical crystal packings for a molecule and ranks them according to factors such as lattice energy and density. The resulting crystal-energy landscape helps determine whether experimentally observed polymorphs are likely to include the most thermodynamically stable form. It can also reveal low-energy structures that have not yet been observed experimentally, indicating that additional polymorphs may be accessible under different crystallization conditions. This makes CSP useful not only for discovering solid forms but also for evaluating the risk that an unexpected form could emerge later in development, manufacturing, or storage.

Why is polymorph screening important in pharmaceutical development?

Different polymorphs of the same active pharmaceutical ingredient can have significantly different physical and chemical properties, including solubility, hygroscopicity, compressibility, thermal stability, and mechanical behavior. These differences can affect bioavailability, formulation design, manufacturing performance, stability, regulatory strategy, and intellectual property. Screening objectives can also change throughout development. Early work may focus on finding the most thermodynamically stable form, while later studies may seek a form with properties better suited to drug delivery or identify additional forms that could emerge during manufacturing and storage. Comprehensive polymorph screening therefore helps reduce technical and commercial risk throughout a drug's development.

How can crystal structure prediction and PXRD be combined to identify pharmaceutical polymorphs?

CSP generates candidate crystal structures that can be stored as crystallographic information files and used to calculate theoretical PXRD patterns. These simulated patterns can then be compared directly with experimental PXRD data from solid forms produced during polymorph screening. The experimental powder pattern can also be indexed to determine parameters such as the unit cell and Bravais lattice. Candidate structures can therefore be evaluated using both diffraction-pattern similarity and agreement between predicted and experimentally derived crystallographic parameters. This combination provides more robust candidate identification than relying on powder-pattern matching alone.

Can PXRD and crystal structure prediction determine a crystal structure when suitable single crystals are unavailable?

Yes. CSP-generated structures can provide starting models that are refined directly against experimental powder diffraction data. This is particularly useful for pharmaceutical compounds because solid-form screening often produces small quantities of powder rather than large, well-formed crystals suitable for single-crystal X-ray diffraction. After likely CSP candidates are identified through powder-pattern and unit-cell comparisons, the closest candidate can be refined using whole-pattern fitting. Parameters such as lattice dimensions, peak profiles, scale factors, and molecular structural variables can be adjusted to obtain a structure consistent with the experimental PXRD data.

How was the CSP-assisted PXRD approach demonstrated using cimetidine?

PXRD data from cimetidine powder were indexed to determine the experimental unit cell, space group, and related crystallographic information. A targeted CSP search then generated possible crystal structures using both fixed-lattice and global search strategies. Candidate structures underwent geometry optimization and energy ranking, followed by quantitative comparison of their simulated PXRD patterns with the experimental pattern. Five promising candidates were selected for further evaluation. Comparison with the experimentally indexed Bravais lattice identified the strongest candidate, which was subsequently refined against the complete powder diffraction pattern. The refinement achieved an Rwp of 4.59%, and the resulting powder-derived molecular structure showed excellent agreement with the published single-crystal structure of cimetidine.

Why is indexing the experimental PXRD pattern useful when matching CSP structures?

Comparing simulated and experimental diffraction patterns alone can be complicated by differences between predicted and measured lattice parameters. CSP structures often represent static or low-temperature lattices, whereas experimental PXRD measurements are commonly performed under ambient conditions. These differences can shift diffraction peak positions even when the underlying structures are closely related. Indexing the measured pattern provides experimental unit-cell and Bravais-lattice information. Comparing those parameters with CSP-derived cells adds an independent crystallographic criterion for identifying the correct structure and can accommodate differences in crystallographic axis settings through appropriate cell transformations.

How can CSP-assisted PXRD support automated high-throughput pharmaceutical solid-form screening?

CSP can generate a database of predicted structures and corresponding CIF files before or alongside experimental screening. As PXRD patterns are collected from solid-form screening samples, the measured patterns can be compared automatically with calculated patterns from the CSP database. Candidate structures can then be ranked and the most promising matches passed to further analysis and refinement. This approach turns CSP from a primarily predictive exercise into a practical screening tool. Integrating automated PXRD measurement, pattern analysis, candidate matching, and structural refinement can make it feasible to evaluate large numbers of pharmaceutical samples quickly and consistently while also providing structural information about newly observed forms.

Read Article

X-ray Powder Diffraction and Crystal Structure Prediction for Polymorph Screening and Structure Solution in Pharmaceutical Development

Simon Bates, R. Alex Mayo, Zhuocen Yang, Baimei Shi and Akihiro Himeda

Summer 2026 Volume 42, No. 2 , 24-28

Crystal structure prediction (CSP) is increasingly relevant to pharmaceutical solid-form development because it provides a computational view of the crystal-energy landscape and helps assess whether experimentally observed polymorphs are likely to represent the most stable or otherwise accessible crystal forms. When used together with powder X-ray diffraction (PXRD), integrated CSP workflows also offers a practical route to structure solution from powder data in cases where suitable single crystals are not available. In this article, a CSP-assisted PXRD workflow is outlined using cimetidine as a demonstration sample. The X-ray powder data were measured on a Rigaku SmartLab system, with the CSP candidates and landscape determined from the molecular structure of cimetidine by XtalPi. Targeted CSP candidate structures were selected from the CSP landscape using two scoring parameters: comparison of simulated powder patterns with the measured powder pattern, and the similarity of the indexed unit cell from the measured data with the CSP unit cell. The candidate structures were then refined in SmartLab Studio II by whole-pattern profile fitting with the unit-cell lattice parameters and molecular Z-matrix parameters as variables. The resulting fit showed strong agreement with the measured data and the refined structure overlaid well with the published single-crystal structure. The same workflow can be extended to automated high-throughput PXRD screening by comparing measured patterns against a database of CSP-derived CIF files in real time.

Highlights

Crystal structure prediction (CSP) complements powder X-ray diffraction (PXRD) by revealing the crystal-energy landscape and identifying plausible polymorph structures, including potentially stable forms not yet observed experimentally.
Combining indexed PXRD data with simulated diffraction patterns from CSP candidates provides a practical route to crystal structure solution when suitable single crystals are unavailable.
A cimetidine demonstration produced a refined powder-derived structure with an Rwp of 4.59% and excellent agreement with the known single-crystal structure, supporting the approach for automated, high-throughput pharmaceutical polymorph screening.

Summary

Polymorphism is a significant concern in pharmaceutical development because different crystal forms of the same active pharmaceutical ingredient can have different solubility, hygroscopicity, compressibility, thermal stability, and mechanical properties. These differences can affect formulation, manufacturing, bioavailability, regulatory strategy, intellectual property, and long-term product performance. Polymorph screening therefore serves both to identify desirable solid forms and to reduce the risk of unexpected forms appearing later in development or manufacturing.

Crystal structure prediction provides a computational complement to experimental polymorph screening. CSP generates hypothetical crystal packings and ranks them using properties such as lattice energy and density, creating a crystal-energy landscape. This makes it possible to assess whether an experimentally observed form is likely to be close to the thermodynamic minimum and whether other low-energy, potentially accessible polymorphs may remain undiscovered.

Combining CSP with PXRD extends its usefulness further. Theoretical diffraction patterns calculated from CSP-generated structures can be compared with measured powder patterns, rapidly eliminating unlikely structures. Indexing the experimental PXRD pattern provides additional information about the unit cell and Bravais lattice, allowing candidates to be evaluated by both diffraction-pattern similarity and crystallographic compatibility.

This approach was demonstrated with cimetidine. Experimental PXRD data were indexed, while a targeted CSP search generated and ranked possible structures. Five promising candidates were selected based on agreement between their calculated and measured diffraction patterns. Comparison with the indexed lattice identified the strongest candidate, which was then refined against the experimental PXRD data using unit-cell, profile, scale, and molecular structural parameters.

The refinement produced an Rwp of 4.59%, and the resulting molecular structure showed excellent agreement with the previously determined single-crystal structure of cimetidine. This demonstrates that CSP-derived structures can provide effective starting models for structure determination directly from powder data.

The workflow is particularly valuable when crystallization produces only powders, when single crystals are too small or defective for conventional structure determination, or when rapid analysis is needed. It can also be extended to automated high-throughput solid-form screening, in which experimental PXRD patterns are compared in real time with databases of CSP-generated structures.

Frequently asked questions

: Crystal structure prediction generates hypothetical crystal packings for a molecule and ranks them according to factors such as lattice energy and density. The resulting crystal-energy landscape helps determine whether experimentally observed polymorphs are likely to include the most thermodynamically stable form. It can also reveal low-energy structures that have not yet been observed experimentally, indicating that additional polymorphs may be accessible under different crystallization conditions. This makes CSP useful not only for discovering solid forms but also for evaluating the risk that an unexpected form could emerge later in development, manufacturing, or storage.
: Different polymorphs of the same active pharmaceutical ingredient can have significantly different physical and chemical properties, including solubility, hygroscopicity, compressibility, thermal stability, and mechanical behavior. These differences can affect bioavailability, formulation design, manufacturing performance, stability, regulatory strategy, and intellectual property.

Screening objectives can also change throughout development. Early work may focus on finding the most thermodynamically stable form, while later studies may seek a form with properties better suited to drug delivery or identify additional forms that could emerge during manufacturing and storage. Comprehensive polymorph screening therefore helps reduce technical and commercial risk throughout a drug's development.
: CSP generates candidate crystal structures that can be stored as crystallographic information files and used to calculate theoretical PXRD patterns. These simulated patterns can then be compared directly with experimental PXRD data from solid forms produced during polymorph screening.

The experimental powder pattern can also be indexed to determine parameters such as the unit cell and Bravais lattice. Candidate structures can therefore be evaluated using both diffraction-pattern similarity and agreement between predicted and experimentally derived crystallographic parameters. This combination provides more robust candidate identification than relying on powder-pattern matching alone.
: Yes. CSP-generated structures can provide starting models that are refined directly against experimental powder diffraction data. This is particularly useful for pharmaceutical compounds because solid-form screening often produces small quantities of powder rather than large, well-formed crystals suitable for single-crystal X-ray diffraction.

After likely CSP candidates are identified through powder-pattern and unit-cell comparisons, the closest candidate can be refined using whole-pattern fitting. Parameters such as lattice dimensions, peak profiles, scale factors, and molecular structural variables can be adjusted to obtain a structure consistent with the experimental PXRD data.
: PXRD data from cimetidine powder were indexed to determine the experimental unit cell, space group, and related crystallographic information. A targeted CSP search then generated possible crystal structures using both fixed-lattice and global search strategies. Candidate structures underwent geometry optimization and energy ranking, followed by quantitative comparison of their simulated PXRD patterns with the experimental pattern.

Five promising candidates were selected for further evaluation. Comparison with the experimentally indexed Bravais lattice identified the strongest candidate, which was subsequently refined against the complete powder diffraction pattern. The refinement achieved an Rwp of 4.59%, and the resulting powder-derived molecular structure showed excellent agreement with the published single-crystal structure of cimetidine.
: Comparing simulated and experimental diffraction patterns alone can be complicated by differences between predicted and measured lattice parameters. CSP structures often represent static or low-temperature lattices, whereas experimental PXRD measurements are commonly performed under ambient conditions. These differences can shift diffraction peak positions even when the underlying structures are closely related.

Indexing the measured pattern provides experimental unit-cell and Bravais-lattice information. Comparing those parameters with CSP-derived cells adds an independent crystallographic criterion for identifying the correct structure and can accommodate differences in crystallographic axis settings through appropriate cell transformations.
: CSP can generate a database of predicted structures and corresponding CIF files before or alongside experimental screening. As PXRD patterns are collected from solid-form screening samples, the measured patterns can be compared automatically with calculated patterns from the CSP database. Candidate structures can then be ranked and the most promising matches passed to further analysis and refinement.

This approach turns CSP from a primarily predictive exercise into a practical screening tool. Integrating automated PXRD measurement, pattern analysis, candidate matching, and structural refinement can make it feasible to evaluate large numbers of pharmaceutical samples quickly and consistently while also providing structural information about newly observed forms.