Nanoparticles (NPs) are increasingly explored for targeted skin penetration, particularly for pharmaceutical and cosmetic applications. However, the complex system between NP properties, skin structure, and experimental conditions poses significant challenges in predicting their penetration depth and pathways. To what depth do NPs penetrate the skin, and which pathways do they follow? These are the questions which we tried to answer in this paper. A n in‐silico human skin model based on 20 years of literature on NPs skin penetration is developed. The model incorporates 19 independent parameters, including a wide range of NP properties, skin across species, and test conditions. Using random forest analysis coupled with Kennard‐Stone sorting, the model achieves a high predictive accuracy of 95%. The study identifies hair follicle diameter as the most critical factor influencing NP penetration across skin layers, surpassing other skin properties, NP properties, or experimental variables. Pig and rabbit skin are the most suitable models for simulating human skin in NP penetration studies. Additionally, the in‐silico model reveals that NPs in emulsions and oil‐based media predominantly follow the intercellular and transappendageal route. In contrast, those embedded in aqueous media favor the intracellular route. These findings offer insights for optimizing NP‐based drug delivery systems.

In the growing landscape of interest in natural surfactants, selecting the appropriate one for specific applications remains challenging. The extensive, yet often unsystematized, knowledge of microbial surfactants, predominantly represented by rhamnolipids (RLs), typically does not translate beyond the conditions presented in scientific publications. This limitation stems from the numerous variables and their interdependencies that characterize microbial surfactant production. We hypothesized that a computational recipe for biosynthesizing RLs with targeted applicational properties could be developed from existing literature and experimental data. We amassed literature data on RL biosynthesis and micellar solubilization and augmented it with our experimental results on the solubilization of triglycerides (TGs), a topic underrepresented in current literature. Utilizing this data, we constructed mathematical models that can predict RL characteristics and solubilization efficiency, represented as logP_RL = f(carbon and nitrogen source, parameters of biosynthesis) and logMSR = f(solubilizate, rhamnolipid (e.g. logP_RL), parameters of solubilization), respectively. The models, characterized by robust R² values of respectively 0.581–0.997 and 0.804, enabled the ranking of descriptors based on their significance and impact—positive or negative—on the predicted values. These models have been translated into ready-to-use calculators, tools designed to streamline the selection process for identifying a biosurfactant optimally suited for intended applications.

The efficiency of micellar solubilization is dictated inter alia by the properties of the solubilizate, the type of surfactant, and environmental conditions of the process. We, therefore, hypothesized that using the descriptors of the aforementioned features we can predict the solubilization efficiency, expressed as molar solubilization ratio (MSR). In other words, we aimed at creating a model to find the optimal surfactant and environmental conditions in order to solubilize the substance of interest (oil, drug, etc.). We focused specifically on the solubilization in biosurfactant solutions. We collected data from literature covering the last 38 years and supplemented them with our experimental data for different biosurfactant preparations. Evolutionary algorithm (EA) and kernel support vector machines (KSVM) were used to create predictive relationships. The descriptors of biosurfactant (logP_BS, measure of purity), solubilizate (logP_sol, molecular volume), and descriptors of conditions of the measurement (T and pH) were used for modelling. We have shown that the MSR can be successfully predicted using EAs, with a mean R²_val of 0.773 ± 0.052. The parameters influencing the solubilization efficiency were ranked upon their significance. This represents the first attempt in literature to predict the MSR with the MSR calculator delivered as a result of our research.