Modeling release rates for implantable drugs
Some 50% of people in the United States take at least one prescription drug, a number expected to increase as our population gets older. But there’s another 50% figure that’s of equal or even greater concern — the percentage of patients who don’t take their meds as prescribed.
Implantable, controlled-release systems such as in situ forming implants and microspheres can address this problem. These release platforms, already in use to treat diseases from infections to cancer, are delivered via a single, minimally invasive injection and can accommodate a range of drugs.
In situ forming implants are liquid doses of therapeutics that, when injected, form a solid “depot,” or distribution hub, within the body for localized delivery of medicine. Implantable microspheres are injected initially as solid depots, then provide a similar, sustained release of the medication.
The challenge lies in controlling the release rate of these reabsorbable therapeutics for maximum efficacy. For personalized healthcare to be successful, the cadence at which the medications are delivered into the patient must be predictable and tunable to individualized requirements.
A novel mathematical model can bypass time-consuming and costly experimentation to accurately predict the drug release. The underlying study, “Mechanistic computational modeling of implantable, bioresorbable, drug release systems,” has been published by the journal Advanced Materials.
“Implantable therapeutic delivery systems offer an alternative to current drug administration techniques, allowing for patient-tailored dosage and increasing patient compliance,” said Tamara Kinzer-Ursem, associate dean of graduate and professional education in the Purdue College of Engineering and Marta E. Gross associate professor of biomedical engineering in the Weldon School of Biomedical Engineering. “We developed a mechanistic, computational model that lends significant insight into the underlying physical and chemical processes that determine the timing and amount of drug release.”
The in situ forming implants consist of biodegradable, reabsorbable polymers. Using techniques like Scanning Electron Microscope (SEM) imaging, Diffusion-Weighted Magnetic Resonance (DWI) imaging and Fluorescence Recovery After Photobleaching (FRAP) — along with mathematical modeling — the research team, co-led by Kinzer-Ursem and Luis Solorio, associate professor of biomedical engineering in the Weldon School, conducted 3D-simulated spatial and temporal analysis of the diffusion of water into the release system, hydrolysis breaking the polymer bonds and follow-on drug diffusion out of the depot.
Specifically, researchers investigated short-term drug release as a function of water-mediated polymer phase inversion (where a polymer is converted from a liquid to a solid state) within hours to days, and long-term hydrolysis-mediated degradation and release of the drug implant over the next few weeks. The resultant model can characterize the impacts of issues like polymer solidification, swelling, non-uniform distribution of drug within the polymer and polymer degradation.
Using this “digital twin” of the physical process, researchers and drug developers can rapidly screen for conditions — like implant size and shape, polymer molecular weight, polymer ester bond content and drug pH — that produce the desired drug release profiles. Modeling this virtually will accelerate design of release systems that fit patient-specific clinical needs.
The Python code for the model is available at the Purdue University Research Repository and on the Kinzer-Ursem Lab GitHub repository. The research was supported by the National Institutes of Health’s National Cancer Institute, National Institute of Drug Abuse and National Institute of Biomedical Imaging and Bioengineering; the National Science Foundation; and the Purdue Discovery Undergraduate Interdisciplinary Research Internship program.
“The ability to accurately predict the release rates of injectable medications is a vital advance for personalized medicine,” Kinzer-Ursem said. “Self-medication of repetitive doses is not only rife with low compliance, but is subject to fluctuating drug concentrations at the target site. This model furthers the goal of delivering predictable, injectable drug formulations without the need for surgical implantation.”
Giolando, P. A., Hopkins, K., Davis, B. F., Vike, N., Ahmadzadegan, A., Ardekani, A. M., Vlachos, P. P., Rispoli, J. V., Solorio, L., Kinzer-Ursem, T. L., Mechanistic Computational Modeling of Implantable, Bioresorbable Drug Release Systems. Adv. Mater. 2023, 2301698. https://doi.org/10.1002/adma.202301698