AI-Designed Lipid Nanoparticle Formulation and Screening for Advanced Drug Delivery

Lipid nanoparticles (LNPs) have been one of the most clinically verified platforms for drug and nucleic acid delivery, and their essential roles have been recognized. Such effectiveness of LNPs highlights their therapeutic promise; however, rational design of LNP formulations is a malign multivariate challenge. In the traditional optimization of lipid composition, molar ratios, particle size, surface charge, and encapsulation efficiency, the trial-and-error strategy is resource- and time-consuming. Space-ionizable lipids, helper lipids, PEGylated lipids, and therapeutic payloads pose a combinatorial challenge that require more clever data-driven methods. Simultaneously, artificial intelligence (AI) and machine learning (ML) have shown disruptive power across the pharmaceutical sciences enabling predictive modelling, pattern recognition, and automated decision-making at a composition scale not possible with conventional means.Although there has been rapid progress in LNP technology and AI, the systematic integration of AI-driven design methods into experimental LNP screening pipelines is still at its infancy. Key bottlenecks remain in (i) supporting the generation of universally applicable, validated predictive models that capture LNP physicochemical properties and in-vivo performance; (ii) harnessing the potential of generative AI for de novo design of lipids and formulations; and (iii) establishing standardized, reproducible pipelines that translate computational predictions to high-throughput experimental validation. This Research Topic seeks to confront these issues by gathering new research at the intersection of AI and LNP science. Contributions focusing on new AI/ML frameworks, datasets, and experimental validation that will enable the rational design, screening, and optimization of LNPs for therapeutic applications are welcomed. The overall objective is to facilitate the translation of AI-driven LNP formulations from computational realization into preclinical and clinical settings, speeding up timelines and improving outcomes for patients in need.This Research Topic welcomes Original Research, Reviews, Systematic Reviews, and Perspective articles including but not limited to the following themes:• Machine learning and deep learning models for predicting LNP physicochemical properties (size, PDI, zeta potential, encapsulation efficiency)• Generative AI and molecular design of novel ionizable and helper lipids• AI-guided high-throughput screening and automated formulation optimization• Integration of multi-omics data for organ-targeted LNP design• Explainable AI (XAI) approaches for LNP formulation transparency• Digital twin models and in silico–in vitro correlation for LNPs• AI-assisted scale-up strategies and process analytical technology (PAT) for LNP manufacturing• Regulatory and ethical considerations for AI-designed nanomedicinesWe encourage submissions that combine computational (AI/ML) methodologies with experimental LNP validation and those related to clinical translation, regulatory pathways, or open-access datasets for LNP formulation science.In silico submissions must demonstrate methodological rigor and, where applicable, experimental validation or integration with in vitro data. Topic editors: Dr. Ali Sartaj, Mohammad Kashif Iqubal, Md Meraj Anjum, Dinesh Kumar Source: https://www.frontiersin.org/research-topics/80393/ai-designed-lipid-nanoparticle-formulation-and-screening-for-advanced-drug-delivery