Crystal Engineering of Pharmaceutical Cocrystals

Crystal engineering of pharmaceutical cocrystals involves the deliberate design and synthesis of multi-component crystalline materials in which an active pharmaceutical ingredient (API) is co-crystallised with one or more neutral molecular partners. Building on principles of supramolecular chemistry, this discipline seeks to tailor physicochemical properties such as solubility, stability, dissolution rate and mechanical behaviour without altering the primary pharmacological activity of the API. By exploiting predictable noncovalent interactions—most commonly hydrogen bonds but also π–π stacking, halogen bonding and van der Waals contacts—researchers rationalise the assembly of stoichiometric complexes that can overcome challenges presented by poorly soluble or metastable drug forms. Progress in in silico methods for free-energy calculation, combined with experimental high-throughput crystallisation and advanced characterisation tools, has transformed cocrystal screening from largely empirical approaches to data-informed strategies. The global significance of pharmaceutical cocrystals is underscored by their application in enhancing oral bioavailability, improving tabletability and enabling controlled-release profiles. Recent developments are directed towards predictive modelling of multi-component energy landscapes, mechanistic understanding of solid-state reactivity, and the integration of machine-learning algorithms to accelerate discovery. This evolving field brings together crystallographers, computational scientists and formulation experts to deliver co-crystal forms with optimised performance and regulatory compliance.

Research from Nature Portfolio

Recent studies have advanced computational prediction of multi-component crystal stability by refining free-energy calculation protocols. An improved benchmark combining experimental solid–solid free-energy differences with high-precision in silico models now enables placement of cocrystals and solvates on a unified thermodynamic landscape, with standard errors reduced to 1–2 kJ mol⁻¹. The methodological framework allows direct comparison of parallel hydrate and anhydrate stoichiometries, facilitating rational selection of coformers and prediction of humidity-dependent phase behaviour.

Machine-learning approaches have been deployed to predict NMR chemical shifts in molecular solids, achieving density functional theory accuracy at a fraction of the computational cost. Trained on local atomic environments, these models can rapidly screen candidate cocrystal structures by matching predicted and experimental shifts, streamlining structural validation. This strategy has been demonstrated by unambiguous assignment of polymorphs and co-crystal forms in pharmaceutically relevant systems, highlighting the potential of data-driven spectral prediction in co-crystal characterisation.

Crystal Engineering of Pharmaceutical Cocrystals publication trend

The graph below shows the total number of articles in crystal engineering of pharmaceutical cocrystals across all publications each year (not limited to Nature Index journals).

Technical terms

Cocrystal: A crystalline solid composed of an API and a neutral coformer held together by noncovalent interactions in a defined stoichiometry.

Crystal engineering: The design and synthesis of solid materials with targeted properties through control of intermolecular interactions.

Noncovalent interactions: Weak forces including hydrogen bonding, π–π stacking and van der Waals interactions that govern molecular assembly in crystals.

Free-energy landscape: A thermodynamic surface mapping the relative stabilities of different crystal forms as a function of environmental variables.

Mechanochemistry: Chemical transformation induced by mechanical energy, often used to synthesise cocrystals without solvents.

Crystal structure prediction (CSP): Computational methods for forecasting the arrangement and relative energies of molecules in a crystalline solid.

Solid-state NMR: A spectroscopic technique that probes local atomic environments in non-solution samples to elucidate structure and dynamics.

References

1. Predicting crystal form stability under real-world conditions. Nature (2023).
2. Chemical shifts in molecular solids by machine learning. Nature Communications (2018).
3. Opportunities and Challenges in Applying Solid‐State NMR Spectroscopy in Organic Mechanochemistry. Advanced Materials (2023).
4. Report on the sixth blind test of organic crystal structure prediction methods. Acta Crystallographica Section B: Structural Science, Crystal Engineering and Materials (2016).
5. The Cambridge Structural Database. Acta Crystallographica Section B: Structural Science, Crystal Engineering and Materials (2016).