We will start off describing lipid nanoparticle - nucleic acid (LNPna) drug formulation, technology and delivery strategies along with a problem statement highlighting - how the limited understanding of the structure and behavior of LNPna systems during the formulation development, storage, and in vivo delivery negatively impacts the overall viability of a drug program. The understanding of the structural aspects of the LNPna helps in the selection of the excipients, encapsulation efficiency, efficacy, improvement in the drug targeting and hence the development for new therapeutic areas.
Next we will describe a number of computer modeling technologies, particularly physics-based approaches, for characterizing lipid nanoparticle behavior and structure at the intraparticle - molecular interaction, whole particle and particle-environment levels to enable more effective formulation, drug production and delivery strategies. Illustrative examples of the simulation of the self-assembly of LNPna structures for actual formulations will be part of the talk.
Distinct case studies, illustrating how drug products would be influenced when: 1. The compositional and pH dependence of the LNP structure is unknown, 2. Seeking to improve the endosomal release of the mRNA, 3. Calculating the apparent pKa values of ionizable lipids, 4. There are challenges with incorporating longer nucleic acid sequences into the LNP. Additionally, using modeling to inform targeting research will be discussed. The studies will illustrate the complementary use of a number of computational technologies to address these LNPna challenges, including all-atom simulations, coarse-grained simulations, molecular mechanics, ML and quantum Submission Title: Computational modeling for lipid nanoparticle formulation development, efficacy and targeting mechanical.
The methodology and current limitations thereof will also be described with examples, namely, 1. Relevant issues that are not addressed, 2. Applicability (complexity) and methodology limitations, 3.Accuracy and reliability
Learning Objectives:
Understand the main challenges in LNPna research and development and how gaps in our knowledge limit progress in improving efficacy and diversification of ailments treated and treatment strategies.
Identify projects for which computation tools in combination with experimental techniques can contribute to formulation selection and the optimization of the manufacturing process.
Know the limitations of computational techniques in order to assess when to apply them, their potential impact, and how to interpret the results.
