Head of Controlled Release, Senior Manager MilliporeSigma Darmstadt, Hessen, Germany
We propose the combination of targeted computational tools and formulation development to minimize the risk of polymorphism in drug development.
Most molecules are crystalline in nature, and so the crystalline solid-state is a crucial topic to address in the development of oral solid dosage forms (OSDs). Specifically, early understanding of the polymorph landscape is essential and reduces risk. The polymorphic form of a molecule effects a wide range of properties including: galenical properties, solubility, stability, bioavailability and intellectual property. Unexpected changes in polymorphism throughout development can lead to costly and potentially hazardous consequences for pharmaceutical companies and patients.
One approach to minimize this risk is computational crystal structure prediction (CSP). CSP refers to an algorithm that seeks to predict 3D crystal structures starting from only the 2D chemical diagram. This is accomplished by predicting all the possible 3D molecular conformations of a molecule and packing them into potential crystal structures. Subsequent quantum mechanical energy optimization is used to identify the lowest energy polymorph and other low energy crystal structures (potential metastable polymorphs). Predicted crystal structures can be compared to experimental data to determine if the most stable form of a drug candidate has been discovered. When the thermodynamically stable experimental structure coincides with the predicted lowest energy structure, the solid form is said to be “de-risked.”
Therefore, CSP is an important component of a multi-pronged OSD development process and can provide assurance that the solid state of a molecule exists in a stable and low-risk energetic environment. However, for certain molecules, many stable polymorphs may exist in close proximity on the energetic landscape. Furthermore, the thermodynamically stable form can change as a function of temperature and pressure. In this case, even with effective crystal engineering, the risk of undesired polymorphic transitions remains upon manufacturing, storage and transport. In these cases, we have demonstrated the potential of mesoporous silica excipients to provide a consistent solid state for molecules with high risks for polymorphism related changes. Mesoporous silica materials can act as a molecular sponge, adsorbing at-risk compounds within nanosized pores. We have shown that in this process a stable, consistent solid state is obtained every time. Furthermore, we have demonstrated how this process is able to homogenize particle properties for diverse molecules, allowing for the development of a templated formulation.
This presentation session will present the concepts of crystal structure and polymorphism in drug development of OSDs, introduce CSP as an option to minimize solid-state risks and, finally, will describe how mesoporous silica can be used as a technology to homogenize a solid form when at risk of unstable polymorphism. All of the above will be supported by a range of case studies from a diverse and highly relevant chemical space. Finally, we will propose an integrated workflow to combine computational and formulation technologies to optimize the development of orally delivered small molecules.
Learning Objectives:
Explain why robust solid-state screening and development is essential in development of oral solid dosage forms
Understand how computational crystal structure prediction can be used to de-risk development of OSDs by identification of stable and unstable polymorphs
Consider that innovative excipients, such as mesoporous silica, can be used to homogenize solid-state when no stable polymorphs can be identified
A workflow is shown how to deal with polymorphism and overcome the challenge in drug development of small molecules for oral applications
