Matthias Gerhard Wacker

1. Effective treatment of central nervous system (CNS) diseases is limited by the inability of many therapeutic agents to reach their sites of action in the brain. The blood–brain barrier (BBB) res...

Background: Antimicrobial resistance is driving the need for bioactive materials that can control infection without compromising tissue repair. Here, we used a bacteria-primed stem-cell response to discover and engineer a host-protein-templated antimicrobial peptide for local wound delivery.Methods: Adipose-derived stem cells (ASCs) at different stages of differentiation were exposed to bacteria, and the resulting antibacterial phenotype was characterized, including glycolytic activity. Knockdown and antibody neutralization identified the host protein responsible, and its sequence served as the template for iterative peptide design. The lead peptide was assessed for bactericidal activity, mechanism of action, cytotoxicity, hemolysis and resistance development, then formulated in a hydroxypropyl methylcellulose (HPMC) hydrogel and tested in rodent infected-wound models.Results: ASCs acquired reproducible antibacterial activity at an intermediate stage of differentiation after direct bacterial contact, accompanied by enhanced glycolytic activity. ENO3 was required for this phenotype and its sequence was used as the design template. The optimized 21-residue peptide HBC-034 showed rapid, membrane-disruptive bactericidal activity against major ESKAPE pathogens, with minimal cytotoxicity and hemolysis, and no detectable resistance during 28 days of serial passaging. In an HPMC hydrogel, HBC-034 retained activity and reduced bacterial burden in infected wounds, while remaining compatible with wound closure and re-epithelialization.Conclusions: These data establish a host-response-guided route from stem-cell biology to an engineered antimicrobial peptide and a bioactive topical material for infected-wound management.

Clinical translation of nanomedicines remains constrained by a persistent gap between static preclinical assays and the dynamic shear environment of the human vasculature. This review argues that fluid shear stress is an underappreciated contributor to clinical attrition, acting alongside well-established factors including biomarker-target mismatch, enhanced permeability and retention heterogeneity, preclinical-clinical discordance in tumor biology, and manufacturing variability. Circulating nanocarriers traverse a mechanical environment in which wall shear stress spans from 0.1 dyn/cm2 in hepatic sinusoids to over 1000 dyn/cm2 in stenotic regions. Emerging quantitative studies indicate that these forces can modulate carrier stability, protein-corona composition, and drug release in ways that static in vitro assays cannot resolve, with the cholesterol-rigidified bilayer of Doxil and the shear-activated platforms of Korin and colleagues providing representative positive control and mechanistic evidence. We synthesize recent progress at the interface of computational fluid dynamics and machine learning. Physics-informed neural networks, deep operator networks, and graph neural networks feature prominently in this synthesis and underpin an integrated framework for mechanistically informed pharmacokinetic prediction. Reported benchmarks show surrogate models reconstructing hemodynamic fields with under 5% error relative to high-fidelity CFD while reducing data requirements 5- to 10-fold, and convolutional surrogates trained on approximately 1800 patient-specific simulations predicting time-averaged wall shear stress with 2.5% mean absolute error. These surrogate models integrate hemodynamic descriptors (TAWSS, OSI, and RRT) with distributional particle features (D10, D50, and D90). We outline the evidentiary and methodological path toward biomechanically informed nanomedicine development aligned with emerging regulatory frameworks, while acknowledging that routine regulatory acceptance of computational shear modeling remains a future milestone rather than current practice.

Polymeric long-acting injectables (LAIs) are among the most impactful yet least predictable drug delivery systems, as their performance emerges from a widely unresolved combination of polymer microstructure, manufacturing history, and assay conditions. To address this challenge, we introduce a hybrid machine learning framework that combines data-driven prediction with mechanistically relevant release theory to systematically decode LAI performance. Using a curated dataset of 1,537 literature-derived release profiles and 35 descriptors, our random forest model achieved robust performance with a mean (R² = 0.86 ± 0.16). Interpretable SHAP analysis confirmed a critical insight: the release apparatus can shape measured kinetics as strongly as the formulation itself, highlighting a major hurdle in current standardization. The framework's prospective utility was examined through validation using the commercial product Risperdal Consta^®, resolving method‑dependent shifts and achieving the desired outcome. Stress/exploratory testing of newly in-house prepared donepezil-loaded PLGA microspheres (intermediate release) yielded high prediction accuracy (MAE = 0.06), whereas prediction accuracy was notably challenged for slow-release formulations. Further investigation revealed that incorporating only three early-stage measurements reduced prediction error for slow-release formulations, providing a practical route for error control and offering a perspective on the use of literature-derived training models for ongoing formulation prototyping. These findings position hybrid artificial intelligence as a promising strategy for the rational development of polymeric LAIs, bridging polymer science and drug release performance while replacing empirical trial-and-error with data-driven precision.

Topical drug delivery offers a cost-effective and non-invasive alternative to systemic drug administration but faces challenges due to the skin’s barrier properties and the complex rheology of semisolid formulations. This necessitates a deeper understanding of the interplay between formulation components and their impact on drug release and therapeutic efficacy. Emulsifying two-component polymer system, supplied as ready-to-use liquid dispersions of oil and surfactant, is increasingly used to simplify manufacturing processes. However, their interaction with active pharmaceutical ingredients (APIs) can unpredictably alter formulation properties. This study investigates how the model APIs (bupivacaine, lidocaine, and atenolol) influence key formulation properties, such as rheology, drug release, and manufacturing efficiency. A systematic, dose-dependent reduction in viscosity was observed with increasing API concentration, an effect attributed to the disruption of the polymer network via electrostatic interactions. This effect remained consistent across different amine classes and was successfully fitted using an exponential function. While formulation pH did not significantly affect viscosity, lower pH values accelerated drug release, highlighting the interplay between gel microstructure, drug-polymer interactions, and release kinetics within this polymer system. Molecular modeling revealed preferential localization of ionized APIs at the polymer-oil interface, while unionized APIs adsorbed onto the oil surface. Detachment force simulations further quantified these interactions. Ex-vivo skin permeation studies confirmed the influence of viscosity on drug permeation, with lower viscosity gels exhibiting faster permeation rates. Finally, in a novel molecule-to-manufacturing approach, these multi-scale insights were integrated into a “Virtual Factory” model. This model successfully predicted the impact of API concentration on manufacturing parameters, offering a valuable tool to optimize equipment selection, process parameters, and energy consumption. This work provides a comprehensive framework for the rational design of topical systems, connecting molecular interactions to final manufacturing outcomes. Graphical abstract