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Selection of Silica Type and Amount for Flowability Enhancements via Dry Coating: Contact Mechanics Based Predictive Approach

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Abstract

Purpose

To investigate the effect of dry coating the amount and type of silica on powder flowability enhancement using a comprehensive set of 19 pharmaceutical powders having different sizes, surface roughness, morphology, and aspect ratios, as well as assess flow predictability via Bond number estimated using a mechanistic multi-asperity particle contact model.

Method

Particle size, shape, density, surface energy and area, SEM-based morphology, and FFC were assessed for all powders. Hydrophobic (R972P) or hydrophilic (A200) nano-silica were dry coated for each powder at 25%, 50%, and 100% surface area coverage (SAC). Flow predictability was assessed via particle size and Bond number.

Results

Nearly maximal flow enhancement, one or more flow category, was observed for all powders at 50% SAC of either type of silica, equivalent to 1 wt% or less for both the hydrophobic R972P or hydrophilic A200, while R972P generally performed slightly better. Silica amount as SAC better helped understand the relative performance. The power-law relation between FFC and Bond number was observed.

Conclusion

Significant flow enhancements were achieved at 50% SAC, validating previous models. Most uncoated very cohesive powders improved by two flow categories, attaining easy flow. Flowability could not be predicted for both the uncoated and dry coated powders via particle size alone. Prediction was significantly better using Bond number computed via the mechanistic multi-asperity particle contact model accounting for the particle size, surface energy, roughness, and the amount and type of silica. The widely accepted 200 nm surface roughness was not valid for most pharmaceutical powders.

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Acknowledgments

The authors thank Zhixing Lin for his valuable input during the writing of this paper.

Funding

The authors are thankful for financial support from National Science Foundation under grant IIP-1919037, IIP-2137209. RND gratefully acknowledges International Fine Particle Research Institute’s (IFPRI) financial support.

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Authors and Affiliations

  1. New Jersey Center for Engineered Particulates, New Jersey Institute of Technology, Newark, NJ, 07102, USA

    Kuriakose T. Kunnath, Siddharth Tripathi, Sangah S. Kim, Liang Chen, Kai Zheng & Rajesh N. Davé

Authors
  1. Kuriakose T. Kunnath
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  2. Siddharth Tripathi
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  3. Sangah S. Kim
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  4. Liang Chen
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  5. Kai Zheng
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  6. Rajesh N. Davé
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Contributions

Kuriakose T. Kunnath: Conceptualization (equal); Writing – original draft (lead); Methodology; Investigation; Validation; Data Curation; Formal analysis; Visualization.

Siddharth Tripathi: Data Curation; Formal analysis; Visualization; Writing – review & editing.

Sangah S. Kim: Writing – review & editing.

Liang Chen: Supervision (support).

Kai Zheng: Supervision (support).

Rajesh N. Dave: Funding Acquisition (lead); Conceptualization (equal); Writing – review, editing and rewriting (lead); Data curation (equal); Formal Analysis (equal); Supervision (lead).

Corresponding author

Correspondence to Rajesh N. Davé.

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The authors have no conflicts of interest or sources of competing interest to declare.

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Highlights

• Flow improved by 1+ regimes via dry coating for all 19 APIs/excipients (5-200 mm).

• 50 %SAC silica (1 wt% or less) led to optimal flow (FFC) increase for all powders.

• Hydrophobic-R972P and hydrophilic-A200 silica perform well, R972P slightly better.

• The granular Bond number better predicts flowability/FFC than the particle size.

• Surface roughness is a dominant factor for flow, 200 nm value not universally valid.

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Kunnath, K.T., Tripathi, S., Kim, S.S. et al. Selection of Silica Type and Amount for Flowability Enhancements via Dry Coating: Contact Mechanics Based Predictive Approach. Pharm Res 40, 2917–2933 (2023). https://doi.org/10.1007/s11095-023-03561-6

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