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22 results on '"Jara, Miguel O."'

8. Correction to “Inhaled JAK Inhibitor GDC-0214 Nanoaggregate Powder Exhibits Improved Pharmacokinetic Profile in Rats Compared to the Micronized Form: Benefits of Thin Film Freezing”

Author

Moon, Chaeho, primary, Sahakijpijarn, Sawittree, additional, Maier, Esther Y., additional, Taft, David R., additional, Jara, Miguel O., additional, Praphawatvet, Tuangrat, additional, Manandhar, Rachana, additional, Shetty, Nivedita, additional, Lubach, Joseph, additional, Narang, Ajit, additional, Nagapudi, Karthik, additional, and Williams, Robert O., additional

Published
2024
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14. Niclosamide inhalation powder made by thin-film freezing: Multi-dose tolerability and exposure in rats and pharmacokinetics in hamsters

Author

Jara, Miguel O., primary, Warnken, Zachary N., additional, Sahakijpijarn, Sawittree, additional, Moon, Chaeho, additional, Maier, Esther Y., additional, Christensen, Dale J., additional, Koleng, John J., additional, Peters, Jay I., additional, Hackman Maier, Sarah D., additional, and Williams III, Robert O., additional

Published
2021
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21. Finding key nanoprecipitation variables for achieving uniform polymeric nanoparticles using neurofuzzy logic technology.

Author

Jara, Miguel O., Catalan-Figueroa, Johanna, Landin, Mariana, and Morales, Javier O.

Abstract

Nanoprecipitation is a simple and fast method to produce polymeric nanoparticles (Np); however, most applications require filtration or another separation technique to isolate the nanosuspension from aggregates or polydisperse particle production. In order to avoid variability introduced by these additional steps, we report here a systematic study of the process to yield monomodal and uniform Np production with the nanoprecipitation method. To further identify key variables and their interactions, we used artificial neural networks (ANN) to investigate the multiple variables which influence the process. In this work, a polymethacrylate derivative was used for Np (NpERS) and a database with several formulations and conditions was developed for the ANN model. The resulting ANN model had a high predictability (> 70%) for NpERS characteristics measured (mean size, PDI, zeta potential, and number of particle populations). Moreover, the model identified production variables leading to polymer supersaturation, such as mixing time and turbulence, as key in achieving monomodal and uniform NpERS in one production step. Polymer concentration and type of solvent, modifiers of polymer diffusion and supersaturation, were also shown to control NpERS characteristics. The ANN study allowed the identification of key variables and their interactions and resulted in a predictive model to study the NpERS production by nanoprecipitation. In turn, we have achieved an optimized method to yield uniform NpERS which could pave way for polymeric nanoparticle production methods with potential in biological and drug delivery applications. [ABSTRACT FROM AUTHOR]

Published
2018
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