Horst A. von Recum, PhD

Our laboratory combines computational modeling, high-throughput experimentation, and biomaterials engineering to understand how implanted materials interact with drugs, microbes, and surrounding tissues. We aim to use these insights to predict material performance and design more effective therapies.

Predicting polymer–drug interactions. We integrate experimental screening with molecular descriptors, computational modeling, and machine learning to predict how drugs interact with different polymers. Our goal is to identify material–drug combinations that improve drug loading, retention, and release, reducing reliance on trial-and-error development. This work builds on our expertise in affinity-based delivery, which uses reversible molecular interactions to control drug release and enables certain materials to be refilled after implantation.

Biomaterials and the implant-associated microbiome. We investigate how implant materials and antibiotic exposure influence microbial communities and how these communities relate to inflammation, tissue integration, and device performance. By combining microbial profiling with molecular analyses of the host response and computational analysis, we seek to identify relationships that can guide better implant design and infection prevention. Current applications include surgical meshes and neural interfaces.

Drug delivery and regenerative medicine. We translate these findings into materials that provide sustained, localized therapy and support tissue repair. Our platforms include cyclodextrin-based polymers, implant coatings, and injectable delivery systems designed to improve therapeutic effectiveness while limiting systemic exposure.