Long-term, sustained delivery systems are promising approaches to increase the bioavailability of biotherapeutics and reduce the need for frequent administration of the drug. Further investigation of sustained delivery systems for large molecules is warranted to broaden their use in biotherapeutic applications, but detailed experimental studies on these systems can require excessive amounts of time and resources. The understanding and development of such systems can be accelerated with the aid of mathematical models that can predict the expected drug release profile <i>a priori</i>. However, guaranteeing the accuracy of these models is inherently difficult, and confidence in the predictive capabilities generally relies on some experimental validation of the model. We have developed a methodology to evaluate mathematical models for the prediction of sustained release of macromolecular therapeutics from poly(lactic-co-glycolic acid)-based (PLGA) drug delivery systems. We show that an efficient stochastic optimization algorithm can be used not only to find global minima of such complex models robustly, but also to generate meta-data that allow quantitative evaluation of parameter sensitivity and correlation, which can be used for further model refinement and development. This methodology was used in conjunction with a predictive mathematical model of sustained release to (1) design a desirable, zeroth-order release profile of biotherapeutics in injectable PLGA solvent depot release systems, and (2) compare model predictions to experimental release data and microstructural observations for a model experimental system of implantable, solid PLGA rods. The novel observations for both experimental systems are essential for adequately describing the underlying drug-release mechanisms when designing predictive models such as the one evaluated here. In addition, we directly illustrate how such a predictive model facilitates the development of sustained drug-release systems.

Guided design of sustained release biopharmaceutical systems through model predictions

2019 Spring National Meeting

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