Computationally guided design and synthesis of dual‐drug loaded polymeric nanoparticles for combination therapy

Abstract

Abstract Single‐drug therapies or monotherapies are often inadequate, particularly in the case of life‐threatening diseases like cancer. Consequently, combination therapies emerge as an attractive strategy. Cancer nanomedicines have many benefits in addressing the challenges faced by small molecule therapeutic drugs, such as low water solubility and bioavailability, high toxicity, etc. However, it remains a significant challenge in encapsulating two drugs in a nanoparticle. To address this issue, computational methodologies are employed to guide the rational design and synthesis of dual‐drug‐loaded polymer nanoparticles while achieving precise control over drug loading. Based on the sequential nanoprecipitation technology, five factors are identified that affect the formulation of drug candidates into dual‐drug loaded nanoparticles, and then screened 176 formulations under different experimental conditions. Based on these experimental data, machine learning methods are applied to pin down the key factors. The implementation of this methodology holds the potential to significantly mitigate the complexities associated with the synthesis of dual‐drug loaded nanoparticles, and the co‐assembly of these compounds into nanoparticulate systems demonstrates a promising avenue for combination therapy. This approach provides a new strategy for enabling the streamlined, high‐throughput screening and synthesis of new nanoscale drug‐loaded entities.

Keywords

• combination therapy
• machine learning
• polymeric nanoparticle