Orion Formulations Suite

The Orion Formulations Suite, developed by OpenEye Scientific (now part of Cadence), is a cloud-based drug formulation software designed to help researchers identify promising formulation candidates early in the drug development process. By combining robust crystal structure prediction (CSP) workflows with intrinsic solubility calculations, the suite empowers drug development teams to explore the polymorph landscape more thoroughly and efficiently than traditional experimental methods allow.

Built on the Orion® platform and powered by Amazon Web Services (AWS), the suite is purpose-built for pharmaceutical researchers who need fast, accurate, and scalable computational tools. Its validated CSP methodology was developed in collaboration with researchers at GlaxoSmithKline using drug-like molecules from real pharmaceutical settings, ensuring scientific rigor and practical applicability.

Key Benefits

• Robust science in easy-to-use workflows: Researchers are guided through a series of steps that apply crystal-specific force fields and Quantum Mechanics (QM) energy models to eliminate high-energy, unstable crystal packings and identify tightly packed, low-energy structures suitable for formulation.
• Fewer late-stage hurdles: By computationally exploring the polymorph landscape early, the suite reduces the likelihood of discovering unexpected polymorphs during later development stages or after regulatory approval, minimising risks of safety concerns, drug recalls, and financial losses.
• Fast and accurate results at scale: Leveraging AWS infrastructure and novel parallelisation methods, Orion delivers results quickly even for large datasets. Processes that previously took days can now be completed in hours, freeing researchers from hardware and compute-time constraints.

The Four-Stage Crystal Structure Prediction Workflow

1. 3D Conformer Generation from the 2D Structure: Predicts the potential 3D conformations of a molecule that might be observed under experimental conditions. A QM energy model performs automated torsion scanning of all rotatable bonds, and users can filter to retain only tautomers predicted to be accessible within a defined energy range.
2. Random Packing and Intermolecular Energy Force Field (IEFF) Optimisation: Uses OpenEye's proprietary multipole-based IEFF to perform fast and accurate energy calculations, eliminating high-energy, unstable crystal packings and identifying low-energy, tightly packed structures that are more likely to be stable formulation candidates.
3. QM Optimisation Using a Dimer-Expansion Approach: Narrows the list of crystal candidates by examining short-range, mid-range, and long-range interactions and their relative importance to crystal stability. A unique, highly parallelisable dimer-expansion approach enables QM optimisation of crystal packings in unprecedented timeframes.
4. Finite Temperature Corrections: Predicts how temperature will affect the stability of promising crystal structures. OpenEye's dimer-expansion approach makes crystal entropy calculations highly parallelisable, enabling prospective estimation of crystal stability at room temperature.

Additional Capabilities and Resources

• Support for both single-component and multi-component CSP workflows, as demonstrated in recorded scientific presentations by OpenEye's development team.
• Validated results illustrated through analysis of acetaminophen crystal structures, comparing predicted structures against experimentally discovered polymorphs using QM energy and RMSD20 metrics.
• Access to a white paper detailing how to accelerate drug development using crystal structure prediction methodologies.
• An option to engage OpenEye's team for crystal structure prediction delivered as a managed service, for organisations that prefer not to run workflows independently.

The Orion Formulations Suite is deployed on the cloud via AWS, enabling massive computational scaling without local hardware investment. It is suitable for pharmaceutical and biotechnology organisations seeking to de-risk formulation development, reduce costly late-stage failures, and bring better drug candidates to market more efficiently.