Lipid headgroup and side chain architecture determine manganese-induced dose dependent membrane rigidification and liposome size increase

Leveraging machine learning to streamline the development of liposomal drug delivery systems

Drug delivery systems efficiently and safely administer therapeutic agents to specific body sites. Liposomes, spherical vesicles made of phospholipid bilayers, have become a powerful tool in this field, especially with the rise of microfluidic manufacturing during the COVID-19 pandemic. Despite its efficiency, microfluidic liposomal production poses challenges, often requiring laborious, optimization on a case-by-case basis. This is due to a lack of comprehensive understanding and robust methodologies, compounded by limited data on microfluidic production with varying lipids. Artificial intelligence offers promise in predicting lipid behaviour during microfluidic production, with the still unexploited potential of streamlining development. Herein we employ machine learning to predict critical quality attributes and process parameters for microfluidic-based liposome production. Validated models predict liposome formation, size, and production parameters, significantly advancing our understanding of lipid behaviour. Extensive model analysis enhanced interpretability and investigated underlying mechanisms, supporting the transition to microfluidic production. Unlocking the potential of machine learning in drug development can accelerate pharmaceutical innovation, making drug delivery systems more adaptable and accessible.

Differential interactions of essential and toxic metal ions with biologically relevant phosphatidic acid and phosphatidylserine membranes

Metal pollutants are a growing concern due to increased use in mining and other industrial processes. Moreover, the use of metals in daily life is becoming increasingly prevalent. Metals such as manganese (Mn), cobalt (Co), and nickel (Ni) are toxic in high amounts whereas lead (Pb) and cadmium (Cd) are acutely toxic at low µM concentrations. These metals are associated with system dysfunction in humans including cancer, neurodegenerative diseases, Alzheimer's disease, Parkinson's disease, and other cellular process'. One known but lesser studied target of these metals are lipids that are key membrane building blocks or serve signalling functions. It was shown that Mn, Co, Ni, Pb, and Cd cause rigidification of liposomes and increase the phase transition in membranes composed of both saturated or partly unsaturated phosphatidic acid (PA) and phosphatidylserine (PS). The selected metals showed differential effects that were more pronounced on saturated lipids. In addition, more rigidity was induced in the biologically relevant liquid-crystalline phase. Moreover, metal affinity, induced rigidification and liposome size increases also varied with the headgroup architecture, whereby the carboxyl group of PS appeared to play an important role. Thus, it can be inferred that Mn, Co, Ni, Cd, and Pb may have preferred binding coordination with the lipid headgroup, degree of acyl chain unsaturation, and membrane phase.

Gadolinium Effects on Liposome Fluidity and Size Depend on the Headgroup and Side Chain Structure of Key Mammalian Brain Lipids

The lanthanide metal gadolinium has been used in the healthcare industry as a paramagnetic contrast agent for years. Gadolinium deposition in brain tissue and kidneys has been reported following gadolinium-based contrast agent administration to patients undergoing MRI. This study demonstrates the detrimental effects of gadolinium exposure at the level of the cell membrane. Biophysical analysis using fluorescence spectroscopy and dynamic light scattering illustrates differential interactions of gadolinium ions with key classes of brain membrane lipids, including phosphatidylcholines and sphingomyelins, as well as brain polar extracts and biomimetic brain model membranes. Electrostatic attraction to negatively charged lipids like phosphatidylserine facilitates metal complexation but zwitterionic phosphatidylcholine and sphingomyelin interaction was also significant, leading to membrane rigidification and increases in liposome size. Effects were stronger for fully saturated over monounsaturated acyl chains. The metal targets key lipid classes of brain membranes and these biophysical changes could be very detrimental in biological membranes, suggesting that the potential negative impact of gadolinium contrast agents will require more scientific attention.

Impact of Biogenic and Chemogenic Selenium Nanoparticles on Model Eukaryotic Lipid Membranes

Microbial nanotechnology is an expanding research area devoted to producing biogenic metal and metalloid nanomaterials (NMs) using microorganisms. Often, biogenic NMs are explored as antimicrobial, anticancer, or antioxidant agents. Yet, most studies focus on their applications rather than the underlying mechanism of action or toxicity. Here, we evaluate the toxicity of our well-characterized biogenic selenium nanoparticles (bSeNPs) produced by the Stenotrophomonas maltophilia strain SeITE02 against the model yeast Saccharomyces cerevisiae comparing it with chemogenic SeNPs (cSeNPs). Knowing from previous studies that the biogenic extract contained bSeNPs in an organic material (OM) and supported here by Fourier transform infrared spectroscopy, we removed and incubated it with cSeNPs (cSeNPs_OM) to assess its influence on the toxicity of these formulations. Specifically, we focused on the first stages of the eukaryotic cell exposure to these samples─i.e., their interaction with the cell lipid membrane, which was mimicked by preparing vesicles from yeast polar lipid extract or phosphatidylcholine lipids. Fluidity changes derived from biogenic and chemogenic samples revealed that the bSeNP extract mediated the overall rigidification of lipid vesicles, while cSeNPs showed negligible effects. The OM and cSeNPs_OM induced similar modifications to the bSeNP extract, reiterating the need to consider the OM influence on the physical-chemical and biological properties of bSeNP extracts.

Lipid Structure Determines the Differential Impact of Single Metal Additions and Binary Mixtures of Manganese, Calcium and Magnesium on Membrane Fluidity and Liposome Size

Unilamellar vesicles of the biologically relevant lipids phosphatidic acid (PA) and phosphatidylserine (PS) with fully saturated (DM-) or partly unsaturated (PO-) acyl side chains were exposed to Ca, Mn and Mg in single metal additions; in equimolar mixtures or by sequential additions of one metal at a time. Laurdan generalized polarization measured the membrane fluidity, while dynamic light scattering reported liposome size changes complemented by zeta potential. All metals induced membrane rigidity and increased liposome sizes across all systems. Mn had the strongest effect overall, but Mg was comparable for DMPS. Lipid side chain architecture was important as GP values for binary mixtures were higher than expected from the sum of values for single additions added to POPS but smaller for DMPS. Sequential additions were predominantly different for Ca:Mg mixtures. Mn induced the strongest increase of liposome size in saturated lipids whereas Ca effects dominated unsaturated matrices. Binary additions induced larger sizes than the sum of single additions for POPS, but much lower changes in DMPA. The order of addition was relevant for PS systems. Thus, lipid structure determines metal effects, but their impact is modulated by other ions. Thus, metal effects may differ with the local lipid architecture and metal concentrations within cells.