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Optimization of Spray-Drying Parameters for Formulation Development at Preclinical Scale.

Marika Nespi1, Robert Kuhn1, Chun-Wan Yen2

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Summary

Optimized spray-drying parameters for amorphous solid dispersions (ASDs) significantly reduce development time and resource needs. Preset values for solid load and spray gas flow enable rapid, reproducible ASD formulation for early drug development.

Keywords:
particle sizepreclinical formulationspray-drying, amorphous solid dispersionyields

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Area of Science:

  • Pharmaceutical Sciences
  • Materials Science
  • Chemical Engineering

Background:

  • Spray-drying dispersion (SDD) is crucial for creating amorphous solid dispersions (ASDs) to enhance the bioavailability of poorly soluble drugs.
  • The complex nature of spray-drying processes often leads to extended development timelines and high resource demands, hindering preclinical development.

Purpose of the Study:

  • To establish optimized, preset spray-drying parameters for the early development of amorphous solid dispersions (ASDs).
  • To create a versatile and efficient spray-drying approach applicable across various formulations and scales, reducing individual optimization needs.

Main Methods:

  • A mini-Design of Experiments (DoE) study was conducted using a mini spray dryer to assess the impact of solid load and atomizing spray gas flow on ASD critical quality attributes.
  • Optimized parameters were validated across diverse drug-polymer combinations, drug loadings, and batch sizes.

Main Results:

  • A 5% solid load and 35 mm height atomizing spray gas flow were identified as optimal preset parameters.
  • Utilizing these parameters consistently yielded high-quality ASDs with good yields and uniform particle size distribution across varied formulations.
  • The strategy demonstrated successful translation into a reproducible and efficient spray-drying platform.

Conclusions:

  • Preset spray-drying parameters offer a rapid, material-saving, and efficient approach for early-stage amorphous solid dispersion development.
  • This method significantly reduces development timelines and resource requirements, making it ideal for preclinical stages.
  • The established parameters provide a robust foundation for developing diverse ASD formulations.