Graduate Student Wins Two AAPS Best Abstract Awards

Mustafa BookwalaMustafa Bookwala, a Ph.D. candidate studying pharmaceutics in the Duquesne University Graduate School of Pharmaceutical Sciences, has won two American Association of Pharmaceutical Sciences (AAPS) Best Abstract Awards and the opportunity to present his research at the 2021 AAPS PharmSci 360 Meeting this October in Philadelphia.

The Best Abstract Awards bring attention to the most exciting research to be found in posters based on abstracts that are submitted and screened before the event, according to AAPS. Approximately 10 percent of all abstract submissions receive the distinction.

"I feel highly honored to receive not one but two 2021 AAPS Best Abstract Awards from the American Association of Pharmaceutical Scientists," Bookwala says. "Gaining recognition from such an established organization, which is a cornerstone of pharmaceutical science, reinforces the meaningful impact of our work on the scientific community which ultimately translates to bettering patient lives–one bit at a time. This work and recognition would not be possible without the constant support and encouragement from my advisor, Dr. Peter L. D. Wildfong, and also Dr. Ira S. Buckner."

More information about Mustafa's research is described below.

Interpreting the Physicochemical Meaning of a Molecular Descriptor Predictive of Dispersion Formation in PVPva Co-Polymer

Purpose: Poorly water-soluble drugs can be formulated as amorphous solid dispersions (ASDs) for drug delivery, allowing improvement in API apparent aqueous solubility leading to potential increase in bioavailability. Oftentimes, this approach requires trial-and-error experiments to determine the viability of the strategy. Our group identified a molecular descriptor R3m which has high accuracy predicting ASD formation in the co-polymer polyvinylpyrrolidone-vinyl acetate (PVPva) for 15 library API, at two drug-loadings using three manufacturing methods, making the process more scientifically driven. Since R3m is a complex molecular descriptor, direct physical interpretation was not obvious, although its meaning is more evident after systematic analysis. The goal of this work was to demonstrate how the molecular attributes of an API combine to R3m values above/below the critical value predictive of dispersion formation in PVPva. This fundamental understanding will increase the utility of the R3m descriptor, ultimately reducing the time for a drug molecule to reach the patient.

Revisitation of a Critical Dislocation Density Model for Milling-Induced Disordering using Simulated Shear Moduli

Purpose: Unanticipated mechanically activated transformations of API pose potential risk of inaccurate dose delivery and regulatory implications. Previous work from our lab on milling-induced disordering potential suggested that physico-mechanical properties could be used to predict whether or not a small molecule organic crystal would become amorphous following continuous impact milling. Use of simulated shear modulus, provided a consistently determined value comparable to experimentally determined moduli, allowing application of mechanical modeling to an expanded dataset. The critical dislocation density model for predicting milling-induced disordering potential for an expanded library was successfully applied to foresee mechanically induced amorphization. This work helps in deep process understanding and providing effective drug products to the patient.

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