Lipid-mediated delivery of active pharmaceutical ingredients (API) opened new possibilities in advanced therapies. By encapsulating an API into a lipid nanocarrier (LNC), one can safely deliver APIs not soluble in water, those with otherwise strong adverse effects, or very fragile ones such as nucleic acids. However, for the rational design of LNCs, a detailed understanding of the composition-structure-function relationships is missing. This review presents currently available computational methods for LNC investigation, screening, and design. The state-of-the-art physics-based approaches are described, with the focus on molecular dynamics simulations in all-atom and coarse-grained resolution. Their strengths and weaknesses are discussed, highlighting the aspects necessary for obtaining reliable results in the simulations. Furthermore, a machine learning, i.e., data-based learning, approach to the design of lipid-mediated API delivery is introduced. The data produced by the experimental and theoretical approaches provide valuable insights. Processing these data can help optimize the design of LNCs for better performance. In the final section of this Review, state-of-the-art of computer simulations of LNCs are reviewed, specifically addressing the compatibility of experimental and computational insights.

Biological membranes act as barriers or reservoirs for many compounds within the human body. As such, they play an important role in pharmacokinetics and pharmacodynamics of drugs and other molecular species. Until now, most membrane/drug interactions have been inferred from simple partitioning between octanol and water phases. However, the observed variability in membrane composition and among compounds themselves stretches beyond such simplification as there are multiple drug–membrane interactions. Numerous experimental and theoretical approaches are used to determine the molecule–membrane interactions with variable accuracy, but there is no open resource for their critical comparison. For this reason, we have built Molecules on Membranes Database (MolMeDB), which gathers data about over 3600 compound–membrane interactions including partitioning, penetration and positioning. The data have been collected from scientific articles published in peer-reviewed journals and complemented by in-house calculations from high-throughput COSMOmic approach to set up a baseline for further comparison. The data in MolMeDB are fully searchable and browsable by means of name, SMILES, membrane, method or dataset and we offer the collected data openly for further reuse and we are open to further additions. MolMeDB can be a powerful tool that could help researchers better understand the role of membranes and to compare individual approaches used for the study of molecule/membrane interactions.

Liposomal formulations can be advantageous in many scenarios such as targeted delivery to reduce the systemic toxicity of highly potent active pharmaceutical ingredients (APIs), to increase drug bioavailability by prolonging systemic circulation, to protect labile APIs from degradation in the gastrointestinal tract, or to improve skin permeation in dermal delivery. However, not all APIs are suitable for encapsulation in liposomes. Some of the issues are too high permeability of the API across the lipid bilayer, which may lead to premature leakage, too low permeability, which may hinder the drug release process, or too strong membrane affinity, which may reduce the overall efficacy of drug release from liposomes. Since the most reliable way to test API encapsulation and release from liposomes so far has been experimental, an in silico model capable of predicting API transport across the lipid bilayer might accelerate formulation development. In this work, we demonstrate a new in silico approach to compute the temperature-dependent permeability of a set of compounds across the bilayer of virtual liposomes constructed by molecular dynamics simulation. To validate this approach, we have conducted a series of experiments confirming the model predictions using a homologous series of fluorescent dyes. Based on the performance of individual molecules, we have defined a set of selection criteria for identifying compatible APIs for stable encapsulation and thermally controlled release from liposomes. To further demonstrate the in silico-based methodology, we have screened the DrugBank database, identified potent drugs suitable for liposome encapsulation and successfully carried out the loading and thermal release of one of them – the antimicrobial compound cycloserine.