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Background:

For API optimization, it is essential to select the most thermodynamically stable polymorph for product development. The most stable form should be employed in the marketed formulation to prevent polymorphic alterations during manufacturing or storage, which can alter the bio-availability of the API. National regulatory agencies are increasingly demanding appropriate description of the solid state of the API employed in the formulation. 

Existing polymorph-screening processes include chemical-based crystallization methods and analysis of multiple polymorphic forms using powder x-ray diffraction, microscopy, thermal, spectroscopy and other techniques.

Computational prediction of polymorphs is an emerging approach that has clear potential to predict which polymorph of a crystal-form drug will be present under certain conditions. However current computational techniques are typically based on general-purpose force-fields, which have often neither been tested nor optimized to reproduce the various polymorphs forms (including stability ranking) through experiment.

Breakthrough Technology:

Based on the breakthrough research work of its lead professor with extensive first-hand experience in pharma-industry drug-design, Biosimulytics offers state-of-the-art CSP and SBDD global-optimization methods to evaluate the thermodynamic stability of crystalline drugs, associated agents, and protein-ligand complexes. By taking a systematic, rigorous and holistic approach, this unique in-silico CSP and global-optimization method estimates free energies at the temperatures and pressure of interest for manufacturing (crystallization) and storage, as well as in drug design. In addition to world-leading water-simulation capabilities (of interest to ubiquitous hydrates) these approaches also perform real-time molecular-dynamics simulation of crystal-structure growth/development from the melt upon seeded polymorph-form nanocrystals. This enables scrutiny of development of (kinetically-limited) less-perfect crystal forms, together with analysis of shape, habit, stacking faults, kinetic metastability and dynamic polymorph interconversion, mimicking real-world conditions closely.

Biosimulytics builds upon existing force-field (re)-parameterization methods, to more accurately predict polymorphs and metastable forms of crystalline drugs or associated agents by providing first-principles insights, handling up to 1000 atoms, on a linear-scaling basis.  With larger molecules, this approach can also factor in system-sizing implications on polymorphic forms in addition to pressure and temperature considerations, via (quantal) dynamical simulations (for manufacture and storage conditions).