Physical properties (stability, solubility, etc.), critical to the performance of pharmaceutical and functional materials, are known to strongly depend on the solid-state form and environmental factors, such as temperature and relative humidity. Recognising that late appearing, more stable forms can lead to disappearing polymorphs and potentially market withdrawal of a life-saving medicine, the pharmaceutical industry has heavily invested in solid form screening platforms.
Quantitatively measuring the free energy differences between crystalline forms is no small challenge. Metastable crystal forms can be difficult to prepare in pure form and they are frequently susceptible to converting to more stable forms. Thus, having the ability to computationally model free energies means that the risks posed by physical instability can be understood and mitigated for all systems, including those that are experimentally intractable. The lack of reliable experimental benchmark data has been a major bottleneck in developing computational methods for accurately predicting solid-solid free energy differences. Reports in the literature are sparse and much of the experimental data on free energy determinations for molecules of pharmaceutical interest is simply not in the public domain.
To overcome this challenge, experts in academia and industry have compiled the first ever reliable experimental benchmark of solid-solid free energy differences for chemically diverse, industrially relevant systems. They then predicted these free energy differences using several methods pioneered by the group of Prof. Alexandre Tkatchenko within the Department of Physics and Materials Science at the University of Luxembourg, and further improved by Dr. Marcus Neumann and his team of researchers at Avant-garde Materials Simulation. Without using any empirical input, these calculations leveraging high performance computing (HPC) were able to predict and explain data from seven pharmaceutical companies with surprising accuracy. The potential future implications of this work are manifold, and this latest development is just one of many potential application of quantum mechanical calculations in the pharmaceutical industry.
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