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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteYes, crystal-structure calculations are becoming useful for pharmaceutical solid-form decisions, but they are not yet an oracle for every crystallization outcome. A method reported by Firaha and colleagues in Nature on 8 November 2023 calculates the relative free energy of hydrate, anhydrate and polymorph structures while accounting for temperature and relative humidity. In its validation set, it reproduced hydrate–anhydrate transition humidities to an average factor of 1.7, with free-energy standard errors of about 1–2 kJ mol−1 for industrially relevant compounds. Those uncertainties are small enough to rank likely stability in many cases, yet too large for reliably predicting an exact transition temperature or which form will actually nucleate in a vessel.
What the 2023 study actually predicts
The paper, “Predicting crystal form stability under real-world conditions,” introduces the TRHu(ST) 23 approach for comparing molecular-crystal free energies as temperature and relative humidity change. Rather than treating a crystal as a static structure at absolute zero, the calculation combines several contributions that matter under manufacturing and storage conditions:
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- Electronic-structure energies for the candidate crystal structures.
- Many-body dispersion interactions, which are important for intermolecular attraction in molecular solids.
- Finite-temperature vibrational free energies.
- Additional molecular corrections and computational shortcuts for phonon calculations.
- A common energy landscape for hydrates with different water stoichiometries and for anhydrous forms.
The result is a free-energy difference between candidate forms at a specified temperature and humidity. A lower free energy means greater thermodynamic stability under that modeled condition. The method therefore addresses a question such as “which of these known structures should be more stable at 25 °C and 60% relative humidity?” It does not, by itself, answer “which structure will appear first when this solution is cooled?”
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Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →The authors implemented the calculations in GRACE 3.0, a commercial package licensed from Avant-garde Materials Simulation (AMS). The paper says the extracted calculation library is available from AMS on request; it does not establish a public price, trial or referral offer. The Nature paper describes the implementation and method.
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Why temperature and humidity change the answer
Crystal forms exchange stability as their surroundings change. Heating increases vibrational contributions to free energy, while water vapor can favor a hydrate, destabilize an anhydrate or shift the balance between hydrates containing different numbers of water molecules. A model that compares only electronic energies at 0 K can miss those effects.
TRHu(ST) 23 puts hydrate and anhydrate structures on the same thermodynamic footing, then evaluates their free-energy difference across a grid of temperatures and relative humidities. The output can indicate where a phase transition is probable. This matters in global development because humidity is not a single laboratory constant: as AMS co-chief executive Elsa Gheziel explained to Chemistry World, “If you have a lab in the UK the humidity will not be the same as in Hong Kong.” That observation is about differing crystallization environments, not a quantified result from the Nature benchmark. Chemistry World’s report provides the industry context.
For a development team, the practical implication is to model the humidity and temperature range that a material will actually encounter: crystallization, drying, milling, packaging, warehouse storage and use. A stability ranking at one humidity should not be silently generalized to another.
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The study reports meaningful but finite uncertainty. For industrially relevant compounds, free-energy differences had standard errors of roughly 1–2 kJ mol−1. When that uncertainty is comparable to the free-energy gap between two forms, their ranking should be treated as conditional rather than definitive.
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| Benchmark or error measure | Reported result | How to interpret it |
|---|---|---|
| Free-energy differences | Approximately 1–2 kJ mol−1 standard error | Study result for industrially relevant compounds; close candidates may remain difficult to distinguish. |
| Hydrate–anhydrate transition relative humidity | Average factor of 1.7 between calculated and observed values | Validation-set performance, not a guarantee for every compound or humidity range. |
| Energy error per water molecule | 0.641 kJ mol−1 standard deviation | Reported component of the study’s error analysis. |
| Energy error per non-water atom | 0.191 kJ mol−1 standard deviation | Reported component of the study’s error analysis. |
| Radiprodil anhydrate–hydrate transition temperature | 183 K, 1σ predicted error | An uncertainty illustration; the authors describe it as representative of observations in confidential contract research studies, not as typical accuracy for all compounds. |
| Accuracy needed for a 10 K transition-temperature prediction | Estimated further improvement by a factor of 20 | The authors’ estimate of the gap between current accuracy and that target. |
The 1.7 humidity factor is useful for triage, but it is not the same as a prediction within 1.7 percentage points of relative humidity. It is a multiplicative comparison between predicted and observed transition humidities. Likewise, a 1–2 kJ mol−1 standard error describes the study’s calculations and benchmark; it is not a universal performance specification for every molecule, crystal structure or software implementation.
What was in the validation set?
Firaha and colleagues compared calculations with three types of experimental evidence:
- 12 free-energy differences inferred from solubility ratios.
- Four reversible transitions between anhydrate polymorphs.
- 21 reversible hydrate–anhydrate phase transitions.
The dataset combined literature measurements with contributions from academic and industrial collaborators. Complete paired structural and thermodynamic data are difficult to obtain, especially for metastable polymorphs. That makes the benchmark valuable but not a census of every pharmaceutical solid form. A result established for a reversible transition with well-characterized structures may not transfer directly to a poorly characterized or kinetically trapped form.
The primary account and bibliographic details are available from Nature and the PubMed record.
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Why pharmaceutical companies care about crystal form
A drug molecule’s solid form is part of the product’s performance, not merely a crystallography detail. Different polymorphs or hydrate states can have different:
- Solubility and dissolution behavior.
- Chemical and physical stability.
- Melting behavior and processing window.
- Powder flow, compactability and tablet-manufacturing characteristics.
- Bioavailability and therefore formulation performance.
A form that is thermodynamically stable may dissolve more slowly than a metastable form; a hydrate may gain or lose water during drying or storage; and a transition can alter particle properties after a process has been validated. These changes can affect scale-up, shelf life and regulatory comparability.
Chemistry World recounts ritonavir as a cautionary example: after launch, a slower-nucleating but more stable polymorph appeared and disrupted supply. That episode demonstrates why unrecognized solid forms can become a manufacturing crisis. It does not mean the 2023 calculation was used to predict or prevent the ritonavir event. The report gives the historical account.
What a development team can do with the result
The method is most useful as one part of a solid-form workflow. A practical sequence is:
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- Define the candidate set. Assemble experimentally solved polymorphs, plausible hydrates and anhydrates, and any computationally generated structures that pass chemical and crystallographic checks.
- Specify the environment. Set the temperature and relative-humidity ranges relevant to crystallization, drying, storage and use. Do not substitute a single room condition for the full process envelope.
- Calculate free energies. Run the finite-temperature, humidity-aware calculations for each candidate and retain the reported uncertainty, not only the central ranking.
- Map probable transitions. Identify where free-energy curves cross and flag regions in which a hydrate or polymorph could become more stable.
- Prioritize experiments. Test high-risk crossings, closely ranked forms and conditions where the uncertainty band overlaps. Use humidity-controlled experiments, thermal analysis, diffraction and water-content measurements as appropriate.
- Feed observations back into the model. An experimentally observed transition or an absent form can refine the candidate list and the process strategy, but it does not turn a thermodynamic calculation into a kinetic model.
This sequence turns computation into a decision aid: it can reduce the number of conditions that deserve intensive testing and highlight forms that warrant surveillance. It should not be used to waive experimental confirmation of a drug substance’s identity, purity, stability or manufacturing behavior.
Thermodynamic stability is not crystallization likelihood
The central limitation is the distinction between equilibrium and pathway. Free energy tells you which form is favored if the system can equilibrate under the modeled conditions. Crystallization also depends on nucleation barriers, solvent composition, impurities, mixing, cooling or evaporation rate, seeding, particle history and residence time. A less stable form can appear first because it has a lower kinetic barrier, while a thermodynamically favored structure may be difficult to nucleate.
Matteo Salvalaglio of University College London, who was not involved in the work, summarized the unresolved issue by saying that “predicting the likelihood of observing polymorphs, and the conditions in which they can be obtained remains an open research question.” The paper’s co-author Matthias Neumann is interested in finding ways to crystallize structures predicted computationally but not yet observed experimentally. Those comments frame the next problem: connecting a stability landscape to a reproducible crystallization recipe.
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Consequently, a calculation that ranks Form A below Form B should be reported as a stability result under stated conditions. It should not be rewritten as proof that Form A will be the first or only material produced in the plant.
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Computational cost and operational expectations
The Nature paper set a design target of about one day on 1,000 CPU cores for a single calculation. That figure describes the study’s computational-cost target, not a guaranteed hardware requirement or runtime for every molecule and workflow. Actual effort depends on the number of candidate structures, phonon calculations, convergence settings and the amount of uncertainty analysis.
Large candidate sets can therefore become expensive even when each individual calculation is tractable. Teams should reserve high-cost calculations for forms that matter to a decision: candidates near a stability crossover, hydrates likely to encounter changing humidity, or polymorphs whose properties could alter formulation or process control.
What the method still cannot establish
- Every possible polymorph. The calculation compares supplied or generated structures; it cannot guarantee that the candidate list contains every experimentally accessible arrangement.
- An exact transition point. The authors explicitly say the current accuracy supports probable transitions rather than precise points. Predicting transition temperatures within 10 K was estimated to require a 20-fold accuracy improvement.
- Nucleation rate or yield. Free energy does not provide the time required for a form to appear or the fraction obtained in a particular crystallization run.
- Process robustness. A predicted ranking does not specify agitation, solvent, seeding, cooling profile or drying endpoint.
- Universal error bars. The reported uncertainties come from this method and its benchmark; they should not be promoted to guarantees for unrelated molecules or implementations.
These limits are not reasons to discard the approach. They define the claims that can be made responsibly: computational ranking and transition-risk assessment, followed by targeted experimental work.
How to read the headline claim that the problem is “solved”
The Chemistry World article describes crystal-structure prediction as “more or less a solved problem” as Neumann’s view, then contrasts that statement with Salvalaglio’s caveat about polymorph occurrence and crystallization conditions. The two comments address different questions. Predicting the energy and geometry of a proposed crystal can be highly capable; predicting which of many forms will actually emerge in a real experiment remains broader and harder. Treating the first statement as scientific consensus would overstate what the study demonstrates.
What this means for pharma decisions
For early development, TRHu(ST) 23 can help rank known forms under realistic humidity and temperature, expose hydrate–anhydrate risk and direct experiments toward the most consequential conditions. For late development, it can support a documented rationale for monitoring a form transition, but measured solid-state behavior remains the basis for specifications and control strategy.
The strongest use case is therefore hybrid: calculate first to understand the thermodynamic landscape, then experiment where uncertainty, kinetics or business risk is high. That approach captures the method’s practical value without confusing a more realistic free-energy model with a complete predictor of crystallization.
Quick Recap
Sources and further reading
- Dzmitry Firaha and colleagues, “Predicting crystal form stability under real-world conditions,” Nature 623, 324–328 (8 November 2023): https://www.nature.com/articles/s41586-023-06587-3.
- Anna Demming, “Crystal structure predictions get real with accurate calculations that will aid pharma,” Chemistry World (13 November 2023): https://www.chemistryworld.com/news/crystal-structure-predictions-get-real-with-accurate-calculations-that-will-aid-pharma/4018412.article.
- PubMed bibliographic record, PMID 37938708: https://pubmed.ncbi.nlm.nih.gov/37938708/.
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