Synthesis of Two Methotrexate Prodrugs for Optimizing Drug Loading into Liposomes

Machine learning instructed microfluidic synthesis of curcumin-loaded liposomes

The association of machine learning (ML) tools with the synthesis of nanoparticles has the potential to streamline the development of more efficient and effective nanomedicines. The continuous-flow synthesis of nanoparticles via microfluidics represents an ideal playground for ML tools, where multiple engineering parameters - flow rates and mixing configurations, type and concentrations of the reagents - contribute in a non-trivial fashion to determine the resultant morphological and pharmacological attributes of nanomedicines. Here we present the application of ML models towards the microfluidic-based synthesis of liposomes loaded with a model hydrophobic therapeutic agent, curcumin. After generating over 200 different liposome configurations by systematically modulating flow rates, lipid concentrations, organic:water mixing volume ratios, support-vector machine models and feed-forward artificial neural networks were trained to predict, respectively, the liposome dispersity/stability and size. This work presents an initial step towards the application and cultivation of ML models to instruct the microfluidic formulation of nanoparticles.

Methotrexate recognition by the human reduced folate carrier SLC19A1

Folates are essential nutrients with important roles as cofactors in one-carbon transfer reactions, being heavily utilized in the synthesis of nucleic acids and the metabolism of amino acids during cell division^1,2. Mammals lack de novo folate synthesis pathways and thus rely on folate uptake from the extracellular milieu³. The human reduced folate carrier (hRFC, also known as SLC19A1) is the major importer of folates into the cell^1,3, as well as chemotherapeutic agents such as methotrexate^4-6. As an anion exchanger, RFC couples the import of folates and antifolates to anion export across the cell membrane and it is a major determinant in methotrexate (antifolate) sensitivity, as genetic variants and its depletion result in drug resistance^4-8. Despite its importance, the molecular basis of substrate specificity by hRFC remains unclear. Here we present cryo-electron microscopy structures of hRFC in the apo state and captured in complex with methotrexate. Combined with molecular dynamics simulations and functional experiments, our study uncovers key determinants of hRFC transport selectivity among folates and antifolate drugs while shedding light on important features of anion recognition by hRFC.