Challenges in Drug Formulation Development
Whether its selecting the dosage form, optimal drug loading, or compatible excipients, drug formulation can be a lengthy process. Optimizing these formulation choices is key for developing a drug product that can be used for phase III clinical trials, batch release testing, and ultimate market release. Ensuring therapeutic efficacy through drug bioavailability, stability, and release performance are a few of the essential areas to satisfy during formulation development.
Microstructure properties including particle size, porosity, phase dispersion, and crystallinity are all key to optimizing these efficacy parameters. Unfortunately, the final microstructure of a drug product can be a black box, with little known about the arrangement of internal ingredients. It can thus be difficult to understand the effects of different formulations and processes on drug release, slowing down the formulation decision cycle. To fully understand these properties, 3D techniques with high sensitivity and resolution are required.
The DigiM Solution - Advancing Formulation Selection
At DigiM, we apply a family of high-resolution 3D imaging techniques to visualize and understand the connectivity of internal drug product ingredients. With advanced AI analytics, complex material phases full be fully quantified and understood, such as the size and distribution of crystalline and amorphous domains in an amorphous solid dispersion (ASD) tablet. For controlled and long-acting dosage forms, where drug release mechanisms must be optimized through formulation, our techniques have thoroughly evaluated the interplay of the drug, polymer, and porosity network. In evaluating bioequivalence of complex dosage forms, AI analytics can quantify Q3 microstructural bioequivalence properties and their association with formulation parameters. The effects of different drug intermediates on the final dosage form can be understood by comparing downstream and upstream samples.
Our microstructure insights have been applied to a variety of dosage forms and intermediates, validating key efficacy parameters and guiding formulation decisions.
At DigiM, we have analyzed a variety of dosage forms and formulations. In this amorphous solid dispersion case study, machine learning detected recrystallized API regions with high sensitivity.