263920 Critical Quality Attributes (CQAs) of Biodegradable Polymer Matrices and Particles: Impact of a Recent Mathematical Model

Thursday, November 1, 2012: 1:20 PM
Allegheny II (Westin )
Sam N. Rothstein, Department of Chemical Engineering, University of Pittsburgh, Qrono Inc., Pittsburgh, PA and Steven R. Little, Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, PA; Bioengineering, University of Pittsburgh, Pittsburgh, PA; Immunology, University of Pittsburgh, Pittsburgh, PA; McGowan Institute for Regenerative Medicine, Pittsburgh, PA

Long-acting controlled release formulations address the top causes of non-compliance, a problem responsible for 10% of hospitalizations and over $100 billion in avoidable annual medical expenses.  These formulation are also among the most difficult and costly formulations to design and produce because of their duration, which can extend to weeks or months, and the complexity of their drug-polymer compositions, which makes them difficult to model.  Efforts to expedite the development and quality assurance of these formulations currently include temperature accelerated release testing methods and system specific transport or stochastic models.   Methods or models with great efficiency, flexibility, and predictive power are desirable for the platform evaluation of polymer matrix-based controlled release formulations. 

Researchers at the University of Pittsburgh have recently developed a robust model of polymer matrix controlled release and applied it to more than 50 formulations.  Specifically, CQAs were evaluated in polyester and polyanhydride microparticles, nanoparticles, large matrices (pellets, rods, discs, tablets) and thin films loaded with agents ranging from small molecules of >300Da up to large protein complexes, and even viruses.  These evaluations have identified five key input parameters: matrix geometry, spatial drug distribution, polymer molecular weight, polymer degradation rate and encapsulated drug size.  Variations in these model inputs have been analyzed for their impact on drug release from microparticles loaded with the antipsychotics risperidone and quetiapine.  Model predictions have also proven useful for the interpolation and extrapolation of in vitro release assays where data collection has been conducted infrequently or terminated early.  Such predictions have confirmed equivalence of two protocols for the detection of enfuviritde release (anti-HIV) from poly(lactide-co-glycolide) microparticles.  In another example, the simulations successfully extrapolated upon preliminary release data from a potentially unstable fatty-acid drug candidate and helped confirm the utility of an early microparticle formulation design.  Software and formulation design services based on this model are currently being commercialized by Qrono Inc. www.Qrono.com

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