The long time and high cost of translating a newly discovered drug to a drug product that will have the desired performance properties when taken by the patient, are significant factors contributing to the high price of new drugs. The work requires designing the right combination and form of ingredients as well as efficient and robust manufacturing processes. These activities are now carried out largely by trial and error means using traditional equipment and relying on past experience. It is widely recognized by the industry and academia that to change the current mode requires new fundamental science and engineering understanding, new engineering methods and computer-based predictive tools. Additionally, the industry needs a workforce that is trained in these new methods and tools to accelerate the desired transformation.
This project will address both of these needs. It will create a representative set of tools, methods and education materials and build a hub technology, called pharmaHUB for sharing and growing the technology through virtual collaboration. The project will implement a library of predictive models for the design and operation of the most important tablet manufacturing operations, will develop computer-based models for predicting how a drug product, such as a tablet, will break apart and dissolve, and will build virtual patient population models that can be used to predict how effectively a specific drug is transported within the body. The use of this methodology will be demonstrated through case studies with five specific drug products. Based on NSF sponsored HUBzero middleware, pharmaHUB will provide open access via the web and internet to all of the created and contributed tools, methods and education content, allowing easy downloading or direct execution of applications on the server farm. It will also allow researchers in the field to easily upload and install their developments, lectures and educational materials in a simple and transparent fashion thus dramatically accelerating community knowledge growth. The project will draw from the results generated in research efforts such as the NSF ERC on Structured Organic Particulate Systems as well as the work of the National Institute for Pharmaceutical Technology and Education, in which the co-pi's are actively involved.
Broader Impact
The tools and associated education materials, openly accessible on pharmaHUb, will serve as core components of new courses and degree programs in pharmaceutical engineering suitable for both students and professionals.
This project advanced model-based methodologies to support quality by design in pharmaceutical product development and manufacture and facilitated the creation and dissemination of associated educational materials. These tools and capabilities are made accessible to the public through a novel web portal, www.pharmaHUB.org (see Fig 1). In contrast to many consumer products, a drug product must meet both conventional quality attributes such as composition and mechanical property uniformity but also performance requirements in terms of dissolution dynamics, bioavailability, toxicity and stability limits. In addition, patient response to a drug regimen can vary significantly between patients and thus achievement of desired performance may also require drug regimen individualization. Given these unique aspects important in the drug product life cycle, the outcomes of this project cover a wide range of component technologies. Specifically, pharmaHUB offers contributions in five main areas: (1) mathematical models and computing infrastructure to support pharmaceutical manufacturing; (2) simulation methodology for predicting the release profile of tablets when orally administered; (3) physiologically-based pharmacokinetic modeling to predict risk of drug toxicity and stability; (4) model based approach for determining the optimal drug regimen individualized to patient characteristics; and (5) cyberinfrastructure enhancements to enable desired pharmaHUB user functionalities. In addition, an important project outcome is an extensive portfolio of educational materials, including lecture materials, presentations, and active learning environments facilitating understanding of these developments. The collective outcomes were produced by the direct project participants at Purdue, Iowa and Rutgers Universities as well as collaborators from the NSF Engineering Research Center on Structured Organic Particulate Systems, the National Institute of Pharmaceutical Technology and Education and the Polytechnic University of Catalunia. 1. Representative operations covered in the pharmaHUB model library include flow of powders in hoppers, roller compaction, particle-particle adhesion, rotating drum drying, freeze-drying, high shear mixing, and filtration. Tools supporting manufacturing include an integrated database of physical properties of common powders used in tablet formulations, a framework for managing the data associated with laboratory experiments and manufacturing operations (see Fig 2), and a multipurpose tool for planning and scheduling of manufacturing operations. 2.The dissolution of tablets is a critical step in the delivery of the drug to the patient, which depends on the complex interaction of a number of manufacturing, formulation and gastrointestinal factors. This project produced innovative experimental procedures and a set of practical models, which incorporate the features of the physical/chemical mechanisms characteristic of specific types of formulations and require only a limited set of experimental data for model calibration. 3. Understanding of the stability, toxicity and efficacy of a drug can be greatly facilitated through kinetic models, which can simulate the progression of a drug as it adsorbed through the small intestine, distributed by the circulatory system, metabolized in various organs and excreted. A major outcome of this project is a general physiologically based pharmacokinetic simulation framework and associated parameter set for several representative drugs (see Fig 3). The pHUBpk multicompartment framework, which uses Bayesian statistical methodology, is available on pharmaHUB for use in studies such as risk assessment for drug degradation products. The framework has been the basis for several studies including demonstration of the use veterinary animal drug performance data to develop human models of drug performance. Additionally the framework has associated with it six active learning environments, accessible through pharmaHUB, designed to facilitate researcher and student understanding of drug kinetics. 4. Many drugs exhibit high inter-patient variability in therapeutic effectiveness and side effects. As a result, use of a "one-size-its all" dosing approach can be inefficient. Thus in practice dosing is adjusted over time based on physician experience until a suitable dosing regimen is obtained. An important project outcome is a three-stage model-based strategy for drug regimen individualization, which can substantially reduce time to establish an effective regimen.. The approach uses (1) the clinical population data on a given drug to develop a population pharmacokinetic model which includes a statistical distribution function of all model parameters; (2) an optimization formulation to determine the minimum number of blood draws needed from a patient and the optimal time at which those draws should be made; and (3) advanced computational methods to determine the optimum dosing regimen for that patient. This approach has been successfully demonstrated on clinical data with three different drugs. In addition to publications, an implementation of this approach is available on pharmaHUB. 5. This project also contributed cyber-infrastructure developments which not only are embedded in pharmaHUB but also have been migrated to the HUBzero®Platform for Scientific Collaboration. These include the creation of enhancements to the system 3D visualization capabilities, regression testing capability, so that model developers can perform verification and validation of their tools and integration of the Pegasus Workflow Management System which automates linkage and execution of any series of computing tasks.
