Automated high-throughput preparation and characterization of oligonucleotide-loaded lipid nanoparticles

RP-CAD for Lipid Quantification: Systematic Method Development and Intensified LNP Process Characterization

Lipid nanoparticles (LNPs) and their versatile nucleic acid payloads bear great potential as delivery systems. Despite their complex lipid composition, their quality is primarily judged by particle characteristics and nucleic acid encapsulation. In this study, we present a holistic reversed-phase (RP)-charged aerosol detection (CAD)-based method developed for commonly used LNP formulations, allowing for intensified LNP and process characterization. We used an experimental approach for power function value (PFV) optimization termed exploratory calibration, providing a single PFV (1.3) in an appropriate linearity range for all six lipids. Followed by the procedure of method calibration and validation, linearity (10-400 ng, R2 > 0.996), precision, accuracy, and robustness were effectively proven. To complement the commonly determined LNP attributes and to evaluate the process performance across LNP processing, the developed RP-CAD method was applied in a process parameter study varying the total flow rate (TFR) during microfluidic mixing. The RP-CAD method revealed a constant lipid molar ratio across processing but identified deviations in the theoretical lipid content and general lipid loss, which were both, however, entirely TFR-independent. The deviations in lipid content could be successfully traced back to the lipid stock solution preparation. In contrast, the observed lipid loss was attributable to the small-scale dialysis following microfluidic mixing. Overall, this study establishes a foundation for employing RP-CAD for lipid quantification throughout LNP processing, and it highlights the potential to extend its applicability to other LNPs, process parameter studies, or processes such as cross-flow filtration.

Self-Driving Laboratories for Chemistry and Materials Science

Self-driving laboratories (SDLs) promise an accelerated application of the scientific method. Through the automation of experimental workflows, along with autonomous experimental planning, SDLs hold the potential to greatly accelerate research in chemistry and materials discovery. This review provides an in-depth analysis of the state-of-the-art in SDL technology, its applications across various scientific disciplines, and the potential implications for research and industry. This review additionally provides an overview of the enabling technologies for SDLs, including their hardware, software, and integration with laboratory infrastructure. Most importantly, this review explores the diverse range of scientific domains where SDLs have made significant contributions, from drug discovery and materials science to genomics and chemistry. We provide a comprehensive review of existing real-world examples of SDLs, their different levels of automation, and the challenges and limitations associated with each domain.

Simple Scattering: Lipid nanoparticle structural data repository

Lipid nanoparticles (LNPs) are being intensively researched and developed to leverage their ability to safely and effectively deliver therapeutics. To achieve optimal therapeutic delivery, a comprehensive understanding of the relationship between formulation, structure, and efficacy is critical. However, the vast chemical space involved in the production of LNPs and the resulting structural complexity make the structure to function relationship challenging to assess and predict. New components and formulation procedures, which provide new opportunities for the use of LNPs, would be best identified and optimized using high-throughput characterization methods. Recently, a high-throughput workflow, consisting of automated mixing, small-angle X-ray scattering (SAXS), and cellular assays, demonstrated a link between formulation, internal structure, and efficacy for a library of LNPs. As SAXS data can be rapidly collected, the stage is set for the collection of thousands of SAXS profiles from a myriad of LNP formulations. In addition, correlated LNP small-angle neutron scattering (SANS) datasets, where components are systematically deuterated for additional contrast inside, provide complementary structural information. The centralization of SAXS and SANS datasets from LNPs, with appropriate, standardized metadata describing formulation parameters, into a data repository will provide valuable guidance for the formulation of LNPs with desired properties. To this end, we introduce Simple Scattering, an easy-to-use, open data repository for storing and sharing groups of correlated scattering profiles obtained from LNP screening experiments. Here, we discuss the current state of the repository, including limitations and upcoming changes, and our vision towards future usage in developing our collective knowledge base of LNPs.

Antisense and Functional Nucleic Acids in Rational Drug Development

This review is focused on antisense and functional nucleic acid used for completely rational drug design and drug target assessment, aiming to reduce the time and money spent and increase the successful rate of drug development. Nucleic acids have unique properties that play two essential roles in drug development as drug targets and as drugs. Drug targets can be messenger, ribosomal, non-coding RNAs, ribozymes, riboswitches, and other RNAs. Furthermore, various antisense and functional nucleic acids can be valuable tools in drug discovery. Many mechanisms for RNA-based control of gene expression in both pro-and-eukaryotes and engineering approaches open new avenues for drug discovery with a critical role. This review discusses the design principles, applications, and prospects of antisense and functional nucleic acids in drug delivery and design. Such nucleic acids include antisense oligonucleotides, synthetic ribozymes, and siRNAs, which can be employed for rational antibacterial drug development that can be very efficient. An important feature of antisense and functional nucleic acids is the possibility of using rational design methods for drug development. This review aims to popularize these novel approaches to benefit the drug industry and patients.

Applying artificial intelligence and computational modeling to nanomedicine

Achieving specific and targeted delivery of nanomedicines to diseased tissues is a major challenge. This is because the process of designing, formulating, testing, and selecting a nanoparticle delivery vehicle for a specific disease target is governed by complex multivariate interactions. Computational modeling and artificial intelligence are well-suited for analyzing and modeling large multivariate datasets in short periods of time. Computational approaches can be applied to help design nanomedicine formulations, interpret nanoparticle-biological interactions, and create models from high-throughput screening techniques to improve the selection of the ideal nanoparticle carrier. In the future, many steps in the nanomedicine development process will be done computationally, reducing the number of experiments and time needed to select the ideal nanomedicine formulation.

A fluorescent splice-switching mouse model enables high-throughput, sensitive quantification of antisense oligonucleotide delivery and activity

While antisense oligonucleotides (ASOs) are used in the clinic, therapeutic development is hindered by the inability to assay ASO delivery and activity in vivo. Accordingly, we developed a dual-fluorescence, knockin mouse model that constitutively expresses mKate2 and an engineered EGFP that is alternatively spliced in the presence of ASO to induce expression. We first examined free ASO activity in the brain following intracerebroventricular injection revealing EGFP splice-switching is both ASO concentration and time dependent in major central nervous system cell types. We then assayed the impact of lipid nanoparticle delivery on ASO activity after intravenous administration. Robust EGFP fluorescence was observed in the liver and EGFP+ cells were successfully isolated using fluorescence-activated cell sorting. Together, these results show the utility of this animal model in quantifying both cell-type- and organ-specific ASO delivery, which can be used to advance ASO therapeutics for many disease indications.

Quality by Design (QbD) and Design of Experiments (DOE) as a Strategy for Tuning Lipid Nanoparticle Formulations for RNA Delivery

The successful development of nonviral delivery systems for nucleic acids has been reported extensively over the past years. Increasingly employed to improve the delivery efficiency and therapeutic efficacy of RNA are lipid nanoparticles (LNPs). Many of the various critical formulation parameters can affect the quality attributes and effectiveness of these nano-formulations. Therefore, the systematic drug development approach (QbD) and multivariate design and statistical analysis (DOE) can be very helpful and recommended for the optimization of the composition and production of RNA-LNPs. This review addresses the concepts and applications of QbD and/or DOE for the development of lipid nanoparticles for the delivery of different types of RNA, reporting examples published in the ten recent years presenting the latest trends and regulatory requirements as well as the modern mathematical and statistical design methods. As the topic explored in this review is a novel approach, the full QbD has been described in only a few papers, and a few refer only to some aspects of QbD. In contrast, the DOE approach has been used in most of the optimization works. Different approaches and innovations in DOE have been observed. Traditional statistical tests and modeling (ANOVA, regression analysis) are slowly being replaced by artificial intelligence and machine learning methods.

Lipid nanoparticles for siRNA delivery in cancer treatment

RNA-based therapies, and siRNAs in particular, have attractive therapeutic potential for cancer treatment due to their ability to silence genes that are imperative for tumor progression. To be effective and solve issues related to their poor half-life and poor pharmacokinetic properties, siRNAs require adequate drug delivery systems that protect them from degradation and allow intracellular delivery. Among the various delivery vehicles available, lipid nanoparticles have emerged as the leading choice. These nanoparticles consist of cholesterol, phospholipids, PEG-lipids and most importantly ionizable cationic lipids. These ionizable lipids enable the binding of negatively charged siRNA, resulting in the formation of stable and neutral lipid nanoparticles with exceptionally high encapsulation efficiency. Lipid nanoparticles have demonstrated their effectiveness and versatility in delivering not only siRNAs but also multiple RNA molecules, contributing to their remarkable success. Furthermore, the advancement of efficient manufacturing techniques such as microfluidics, enables the rapid mixing of two miscible solvents without the need for shear forces. This facilitates the reproducible production of lipid nanoparticles and holds enormous potential for scalability. This is shown by the increasing number of preclinical and clinical trials evaluating the potential use of siRNA-LNPs for the treatment of solid and hematological tumors as well as in cancer immunotherapy. In this review, we provide an overview of the progress made on siRNA-LNP development for cancer treatment and outline the current preclinical and clinical landscape in this area. Finally, the translational challenges required to bring siRNA-LNPs further into the clinic are also discussed.

Microfluidic Devices: A Tool for Nanoparticle Synthesis and Performance Evaluation

The use of nanoparticles (NPs) in nanomedicine holds great promise for the treatment of diseases for which conventional therapies present serious limitations. Additionally, NPs can drastically improve early diagnosis and follow-up of many disorders. However, to harness their full capabilities, they must be precisely designed, produced, and tested in relevant models. Microfluidic systems can simulate dynamic fluid flows, gradients, specific microenvironments, and multiorgan complexes, providing an efficient and cost-effective approach for both NPs synthesis and screening. Microfluidic technologies allow for the synthesis of NPs under controlled conditions, enhancing batch-to-batch reproducibility. Moreover, due to the versatility of microfluidic devices, it is possible to generate and customize endless platforms for rapid and efficient in vitro and in vivo screening of NPs' performance. Indeed, microfluidic devices show great potential as advanced systems for small organism manipulation and immobilization. In this review, first we summarize the major microfluidic platforms that allow for controlled NPs synthesis. Next, we will discuss the most innovative microfluidic platforms that enable mimicking in vitro environments as well as give insights into organism-on-a-chip and their promising application for NPs screening. We conclude this review with a critical assessment of the current challenges and possible future directions of microfluidic systems in NPs synthesis and screening to impact the field of nanomedicine.

Process Robustness in Lipid Nanoparticle Production: A Comparison of Microfluidic and Turbulent Jet Mixing

The recent clinical and commercial success of lipid nanoparticles (LNPs) for nucleic acid delivery has incentivized the development of new technologies to manufacture LNPs. As new technologies emerge, researchers must determine which technologies to assess and how to perform comparative evaluations. In this article, we use a quality-by-design approach to systematically investigate how the mixer technology used to form LNPs influences LNPstructure. Specifically, a coaxial turbulent jet mixer and a staggered herringbone microfluidic mixer were systematically compared via matched formulation and process conditions. A full-factorial design-of-experiments study with three factors and three levels was executed for each mixer to compare process robustness in the production of antisense oligonucleotide (ASO) LNPs. ASO-LNPs generated with the coaxial turbulent jet mixer were consistently smaller, had a narrower particle size distribution, and had a higher ASO encapsulation as compared to the microfluidic mixer, but had a greater variation in internal structure with less ordered cores. A subset of the study was replicated for mRNA-LNPs with comparable trends in particle size and encapsulation, but more frequent bleb features for LNPs produced by the coaxial turbulent jet mixer. The study design used here provides a road map for how researchers may compare different mixer technologies (or process changes more broadly) and how such studies can inform process robustness and manufacturing control strategies.

Microfluidics for nano-drug delivery systems: From fundamentals to industrialization

In recent years, owing to the miniaturization of the fluidic environment, microfluidic technology offers unique opportunities for the implementation of nano drug delivery systems (NDDSs) production processes. Compared with traditional methods, microfluidics improves the controllability and uniformity of NDDSs. The fast mixing and laminar flow properties achieved in the microchannels can tune the physicochemical properties of NDDSs, including particle size, distribution and morphology, resulting in narrow particle size distribution and high drug-loading capacity. The success of lipid nanoparticles encapsulated mRNA vaccines against coronavirus disease 2019 by microfluidics also confirmed its feasibility for scaling up the preparation of NDDSs via parallelization or numbering-up. In this review, we provide a comprehensive summary of microfluidics-based NDDSs, including the fundamentals of microfluidics, microfluidic synthesis of NDDSs, and their industrialization. The challenges of microfluidics-based NDDSs in the current status and the prospects for future development are also discussed. We believe that this review will provide good guidance for microfluidics-based NDDSs.

Lipid nanoparticle delivery limits antisense oligonucleotide activity and cellular distribution in the brain after intracerebroventricular injection

Antisense oligonucleotide (ASO) therapeutics are being investigated for a broad range of neurological diseases. While ASOs have been effective in the clinic, improving productive ASO internalization into target cells remains a key area of focus in the field. Here, we investigated how the delivery of ASO-loaded lipid nanoparticles (LNPs) affects ASO activity, subcellular trafficking, and distribution in the brain. We show that ASO-LNPs increase ASO activity up to 100-fold in cultured primary brain cells as compared to non-encapsulated ASO. However, in contrast to the widespread ASO uptake and activity observed following free ASO delivery in vivo, LNP-delivered ASOs did not downregulate mRNA levels throughout the brain after intracerebroventricular injection. This lack of activity was likely due to ASO accumulation in cells lining the ventricles and blood vessels. Furthermore, we reveal a formulation-dependent activation of the immune system post dosing, suggesting that LNP encapsulation cannot mask cellular ASO backbone-mediated toxicities. Together, these data provide insights into how LNP encapsulation affects ASO distribution as well as activity in the brain, and a foundation that enables future optimization of brain-targeting ASO-LNPs.

Predicting surfactant phase behavior with a molecularly informed field theory

HYPOTHESIS

The computational study of surfactants and self-assembly is challenging because 1) models need to reflect chemistry-specific interactions, and 2) self-assembled structures are difficult to equilibrate with conventional molecular dynamics. We propose to overcome these challenges with a multiscale simulation approach where relative entropy minimization transfers chemically-detailed information from all-atom (AA) simulations to coarse-grained (CG) models that can be simulated using field-theoretic methods. Field-theoretic simulations are not limited by intrinsic physical time scales like diffusion and allow for rigorous equilibration via free energy minimization. This approach should enable the study of properties that are difficult to obtain by particle-based simulations.

SIMULATION WORK

We apply this workflow to sodium dodecylsulfate. To ensure chemical fidelity we present an AA force field calibrated against interfacial tension experiments. We generate CG models from AA simulation trajectories and show that particle-based and field-theoretic simulations of the CG model reproduce AA simulations and experimental measurements.

FINDINGS

The workflow captures the complex balance of interactions in a multicomponent system ultimately described by an atomistic model. The resulting CG models can study complex 3D phases like double or alternating gyroids, and reproduce salt effects on properties like aggregation number and shape transitions.

Comparative study of lipid nanoparticle-based mRNA vaccine bioprocess with machine learning and combinatorial artificial neural network-design of experiment approach

To develop a combinatorial artificial-neural-network design-of-experiment (ANN-DOE) model, the effect of ionizable lipid, an ionizable lipid-to-cholesterol ratio, N/P ratio, flow rate ratio (FRR), and total flow rate (TFR) on the outcome responses of mRNA-LNP vaccine were evaluated using a definitive screening design (DSD) and machine learning (ML) algorithms. Particle size (PS), PDI, zeta potential (ZP), and encapsulation efficiency (EE) of mRNA-LNP were optimized within a defined constraint (PS 40-100 nm, PDI≤0.30, ZP≥(±)0.30 mV, EE≥70%), fed to ML algorithms (XGBoost, bootstrap forest, support vector machines, k-nearest neighbors, generalized regression-Lasso, ANN) and prediction was compared to ANN-DOE model. Increased FRR decreased the PS and increased ZP, while increased TFR increased PDI and ZP. Similarly, DOTAP and DOTMA produced higher ZP and EE. Particularly, a cationic ionizable lipid with an N/P ratio ≥6 provided a higher EE. ANN showed better predictive ability (R²=0.7269-0.9946), while XGBoost demonstrated better RASE (0.2833-2.9817). The ANN-DOE model outperformed both optimized ML models by R²=1.21% and RASE=43.51% (PS prediction), R²=0.23% and RASE=3.47% (PDI prediction), R²=5.73% and RASE=27.95% (ZP prediction), and R²=0.87% and RASE=36.95% (EE prediction), respectively, which demonstrated that ANN-DOE model was superior in predicting the bioprocess compared to independent models.

Self-driving laboratories: A paradigm shift in nanomedicine development

Nanomedicines have transformed promising therapeutic agents into clinically approved medicines with optimal safety and efficacy profiles. This is exemplified by the mRNA vaccines against COVID-19, which were made possible by lipid nanoparticle technology. Despite the success of nanomedicines to date, their design remains far from trivial, in part due to the complexity associated with their preclinical development. Herein, we propose a nanomedicine materials acceleration platform (NanoMAP) to streamline the preclinical development of these formulations. NanoMAP combines high-throughput experimentation with state-of-the-art advances in artificial intelligence (including active learning and few-shot learning) as well as a web-based application for data sharing. The deployment of NanoMAP requires interdisciplinary collaboration between leading figures in drug delivery and artificial intelligence to enable this data-driven design approach. The proposed approach will not only expedite the development of next-generation nanomedicines but also encourage participation of the pharmaceutical science community in a large data curation initiative.

Increasing the siRNA knockdown efficiency of lipid nanoparticles by morphological transformation with the use of dihydrosphingomyelin as a helper lipid

Lipid nanoparticles (LNPs), comprising ionizable lipids, helper lipids, cholesterol, and PEG lipids, can act as delivery carriers for nucleic acids and have achieved clinical success in the delivery of siRNA and mRNA. It has been shown that the morphology of LNPs varies depending on their lipid composition, but the influence of their morphology on nucleic acid efficacy has not been fully elucidated. In this study, we used our previously developed novel lipid, dioleoylglycerophosphate-diethylenediamine conjugate (DOP-DEDA), to create pH-responsive LNPs (DOP-DEDA LNPs). We evaluated the morphology of DOP-DEDA LNPs composed of different helper lipids and the knockdown efficiency of small interfering RNA (siRNA). A distinctive difference in morphology was observed between DOP-DEDA LNPs of different helper lipids. Significant differences were also observed in the apparent pK_a of DOP-DEDA LNPs and the knockdown efficiency of siRNA, which may be due to the difference in the localization of DOP-DEDA molecules in DOP-DEDA LNPs. These findings suggest that changing helper lipids alters the morphology of the DOP-DEDA LNP system, which affects the apparent pK_a and knockdown efficiency of siRNA.

Impact of non-ionizable lipids and phase mixing methods on structural properties of lipid nanoparticle formulations

Lipid nanoparticles (LNPs) have been widely investigated for nucleic acid therapeutic delivery, and demonstrated their potential in enabling new mRNA vaccines. LNPs are usually formulated with multi-lipid components and the composition variables may impact their structural properties. Here, we investigated the impact of helper lipids on physicochemical properties of LNPs using a Design of Experiments (DoE) definitive screening design. Phospholipid head group, degree of unsaturation, ratio to cholesterol as well as PEG-lipid content were varied and a series of 14 LNPs were prepared by microfluidic- and solvent-injection mixing. Solvent-injection mixing by a robotic liquid handler yielded 50-225 nm nanoparticles with highly ordered, ∼5 nm inter-lamellar spacing as measured by small angle X-ray scattering (SAXS) and confirmed by cryo-transmission electron microscopy (cryo-EM). In contrast, microfluidic mixing resulted in less ordered, notably smaller (50-75 nm) and more homogenous nanoparticles. Significant impacts of the stealth-lipid DSPE-PEG2000 on nanoparticle size, polydispersity and encapsulation efficiency of an oligonucleotide cargo were observed in LNPs produced by both methods, while varying the phospholipid type and content had only marginal effect on these physicochemical properties. These findings suggest that from a physicochemical perspective, the design space for combinations of helper lipids in LNPs may be considerably larger than anticipated based on the conservative formulation composition of the currently FDA-approved LNPs, thereby opening opportunities for screening and optimization of novel LNP formulations.

Ionizable drug delivery systems for efficient and selective gene therapy

Gene therapy has shown great potential to treat various diseases by repairing the abnormal gene function. However, a great challenge in bringing the nucleic acid formulations to the market is the safe and effective delivery to the specific tissues and cells. To be excited, the development of ionizable drug delivery systems (IDDSs) has promoted a great breakthrough as evidenced by the approval of the BNT162b2 vaccine for prevention of coronavirus disease 2019 (COVID-19) in 2021. Compared with conventional cationic gene vectors, IDDSs can decrease the toxicity of carriers to cell membranes, and increase cellular uptake and endosomal escape of nucleic acids by their unique pH-responsive structures. Despite the progress, there remain necessary requirements for designing more efficient IDDSs for precise gene therapy. Herein, we systematically classify the IDDSs and summarize the characteristics and advantages of IDDSs in order to explore the underlying design mechanisms. The delivery mechanisms and therapeutic applications of IDDSs are comprehensively reviewed for the delivery of pDNA and four kinds of RNA. In particular, organ selecting considerations and high-throughput screening are highlighted to explore efficiently multifunctional ionizable nanomaterials with superior gene delivery capacity. We anticipate providing references for researchers to rationally design more efficient and accurate targeted gene delivery systems in the future, and indicate ideas for developing next generation gene vectors.

Challenges and emerging trends in liquid chromatography-based analyses of mRNA pharmaceuticals

Lipid encapsulated messenger RNA (LNP mRNA) has garnered a significant amount of interest from the pharmaceutical industry and general public alike. This attention has been catalyzed by the clinical success of LNP mRNA for SARS-CoV-2 vaccination as well as future promises that might be fulfilled by the biotechnology pipeline, such as the in vivo delivery of a CRISPR/Cas9 complex that can edit patient cells to reduce levels of low-density lipoprotein. LNP mRNAs are comprised of various chemically diverse molecules brought together in a sophisticated intermolecular complex. This can make it challenging to achieve thorough analytical characterization. Nevertheless, liquid chromatography is becoming an increasingly relied upon technique for LNP mRNA analyses. Although there have been significant advances in all types of LNP mRNA analyses, this review focuses on recent developments and the possibilities of applying anion exchange (AEX) and ion pairing reversed phase (IP-RP) liquid chromatography for intact mRNAs as well as techniques for oligo mapping analysis, 5' endcap testing and lipid compositional assays.

Re-envisioning the design of nanomedicines: harnessing automation and artificial intelligence

INTRODUCTION

Interest in nanomedicines has surged in recent years due to the critical role they have played in the COVID-19 pandemic. Nanoformulations can turn promising therapeutic cargo into viable products through improvements in drug safety and efficacy profiles. However, the developmental pathway for such formulations is non-trivial and largely reliant on trial-and-error. Beyond the costly demands on time and resources, this traditional approach may stunt innovation. The emergence of automation, artificial intelligence (AI) and machine learning (ML) tools, which are currently underutilized in pharmaceutical formulation development, offers a promising direction for an improved path in the design of nanomedicines.

AREAS COVERED

the potential of harnessing experimental automation and AI/ML to drive innovation in nanomedicine development. The discussion centers on the current challenges in drug formulation research and development, and the major advantages afforded through the application of data-driven methods.

EXPERT OPINION

The development of integrated workflows based on automated experimentation and AI/ML may accelerate nanomedicine development. A crucial step in achieving this is the generation of high-quality, accessible datasets. Future efforts to make full use of these tools can ultimately contribute to the development of more innovative nanomedicines and improved clinical translation of formulations that rely on advanced drug delivery systems.

A tale of nucleic acid-ionizable lipid nanoparticles: Design and manufacturing technology and advancement

INTRODUCTION

Ionizable lipid nanoparticles (LNPs) have been proven to have high encapsulation, cellular uptake, and effective endosomal escape and are therefore promising for nucleic acid delivery. The combination of ionizable lipids, helper lipids, cholesterol, and PEG lipids advances nucleic acid-ionizable LNPs and distinguishes them from liposomes, SLNs, NLCs, and other lipid particles. Solvent injection and microfluidics technology are the primary manufacturing techniques for commercialized ionizable LNPs. Microfluidics technology limitations restrict the rapid industrial scale-up and therapeutic effectiveness of ionized LNPs. Alternative manufacturing technologies and target-specific lipids are urgently needed.

AREA COVERED

This article provides an in-depth update on the lipid compositions, clinical trials, and manufacturing technologies for nucleic acid-ionizable LNPs. For the first time, we updated the distinction between ionizable LNPs and other lipid particles. We also proposed an alternate thermocycling technology for high industrial scale-up and the stability of nucleic acid-ionizing LNPs.

EXPERT OPINION

Nucleic acid-ionizable LNPs have a promising future for delivering nucleic acids in a target-specific manner. Though ionizing LNPs are in their early stages, they face several challenges, including only hepatic delivery, a short shelf life, and ultra-cold storage. In our opinion, ligand-based, target-specific synthesized novel lipids and advanced manufacturing technologies can easily overcome the restrictions and open up a new approach for improved therapeutic efficacy for chronic disorders.

Spectroscopy-Based Local Modeling Method for High-Throughput Quantification of Nucleic Acid Loading in Lipid Nanoparticles

Lipid nanoparticles (LNPs) are the most widely investigated delivery systems for nucleic acid-based therapeutics and vaccines. Loading efficiency of nucleic acids may vary with formulation conditions, and it is considered one of the critical quality attributes of LNP products. Current analytical methods for quantification of cargo loading in LNPs often require external standard preparations and preseparation of unloaded nucleic acids from LNPs; therefore, they are subject to tedious and lengthy procedures, LNP stability, and unpredictable recovery rates of the separated analytes. Here, we developed a modeling approach, which was based on locally weighted regression (LWR) of ultraviolet (UV) spectra of unpurified samples, to quantify the loading of nucleic acid cargos in LNPs in-situ. We trained the model to automatically tune the training library space according to the spectral features of a query sample so as to robustly predict the nucleic acid cargo concentration and rank loading capacity with similar performance as the more complicated experimental approaches. Furthermore, we successfully applied the model to a wide range of nucleic acid cargo species, including antisense oligonucleotides, single-guided RNA, and messenger RNA, in varied lipid matrices. The LWR modeling approach significantly saved analytical time and efforts by facile UV scans of 96-well sample plates within a few minutes and with minimal sample preprocessing. Our proof-of-concept study presented the very first data mining and modeling strategy to quantify nucleic acid loading in LNPs and is expected to better serve high-throughput screening workflows, thereby facilitates early-stage optimization and development of LNP formulations.

Predictive high-throughput screening of PEGylated lipids in oligonucleotide-loaded lipid nanoparticles for neuronal gene silencing

Lipid nanoparticles (LNPs) are gaining traction in the field of nucleic acid delivery following the success of two mRNA vaccines against COVID-19. As one of the constituent lipids on LNP surfaces, PEGylated lipids (PEG-lipids) play an important role in defining LNP physicochemical properties and biological interactions. Previous studies indicate that LNP performance is modulated by tuning PEG-lipid parameters including PEG size and architecture, carbon tail type and length, as well as the PEG-lipid molar ratio in LNPs. Owing to these numerous degrees of freedom, a high-throughput approach is necessary to fully understand LNP behavioral trends over a broad range of PEG-lipid variables. To this end, we report a low-volume, automated, high-throughput screening (HTS) workflow for the preparation, characterization, and in vitro assessment of LNPs loaded with a therapeutic antisense oligonucleotide (ASO). A library of 54 ASO-LNP formulations with distinct PEG-lipid compositions was prepared using a liquid handling robot and assessed for their physiochemical properties as well as gene silencing efficacy in murine cortical neurons. Our results show that the molar ratio of anionic PEG-lipid in LNPs regulates particle size and PEG-lipid carbon tail length controls ASO-LNP gene silencing activity. ASO-LNPs formulated using PEG-lipids with optimal carbon tail lengths achieved up to 5-fold lower mRNA expression in neurons as compared to naked ASO. Representative ASO-LNP formulations were further characterized using dose-response curves and small-angle X-ray scattering to understand structure-activity relationships. Identified hits were also tested for efficacy in primary murine microglia and were scaled-up using a microfluidic formulation technique, demonstrating a smooth translation of ASO-LNP properties and in vitro efficacy. The reported HTS workflow can be used to screen additional multivariate parameters of LNPs with significant time and material savings, therefore guiding the selection and scale-up of optimal formulations for nucleic acid delivery to a variety of cellular targets.

Development of a high-throughput platform for screening lipid nanoparticles for mRNA delivery

mRNA lipid nanoparticles (LNPs) are at the forefront of nucleic acid intracellular delivery, as exemplified by the recent emergency approval of two mRNA LNP-based COVID-19 vaccines. The success of an LNP product largely depends on the systematic optimisation of the four lipidic components, namely the ionisable lipid, PEG lipid, structural and helper lipids. However, the in vitro screening of novel lipidic components and LNP compositions is limited by the low-throughput of LNP preparation. To address these issues, we herein present an automated high-throughput screening platform to select novel ionisable lipids and corresponding LNPs encapsulating mRNA in vitro. This high-throughput platform employs a lab-based automated liquid handling system, amenable to high-throughput (up to 384 formulations per plate and several plates per run) and allows precise mixing and reproducible mRNA LNP preparation which ensures a direct head-to-head comparison of hundreds and even thousands of novel LNPs. Most importantly, the robotic process has been successfully applied to the screening of novel LNPs encapsulating mRNA and has identified the same novel mRNA LNP leads as those from microfluidics-mixing technology, with a correlation coefficient of 0.8751. This high-throughput platform can facilitate to narrow down the number of novel ionisable lipids to be evaluated in vivo. Moreover, this platform has been integrated into a fully-automated workflow for LNP property control, physicochemical characterisation and biological evaluation. The high-throughput platform may accelerate proprietary lipid development, mRNA LNP lead optimisation and candidate selection to advance preclinical mRNA LNP development to meet urgent global needs.

Chemistry of Lipid Nanoparticles for RNA Delivery

Lipid nanoparticles (LNPs) are a type of lipid vesicles that possess a homogeneous lipid core. These vesicles are widely used in small-molecule drug and nucleic acid delivery and recently gained much attention because of their remarkable success as a delivery platform for COVID-19 mRNA vaccines. Nonetheless, the utility of transient protein expression induced by mRNA extends far beyond vaccines against infectious diseases─they also hold promise as cancer vaccines, protein replacement therapies, and gene editing components for rare genetic diseases. However, naked mRNA is inherently unstable and prone to rapid degradation by nucleases and self-hydrolysis. Encapsulation of mRNA within LNPs protects mRNA from extracellular ribonucleases and assists with intracellular mRNA delivery.In this Account, we discuss the core features of LNPs for RNA delivery. We focus our attention on LNPs designed to deliver mRNA; however, we also include examples of siRNA-LNP delivery where appropriate to highlight the commonalities and the dissimilarities due to the nucleic acid structure. First, we introduce the concept of LNPs, the advantages and disadvantages of utilizing nucleic acids as therapeutic agents, and the general reasoning behind the molecular makeup of LNPs. We also briefly highlight the most recent clinical successes of LNP-based nucleic acid therapies. Second, we describe the theory and methods of LNP self-assembly. The common idea behind all of the preparation methods is inducing electrostatic interactions between the nucleic acid and charged lipids and promoting nanoparticle growth via hydrophobic interactions. Third, we break down the LNP composition with special attention to the fundamental properties and purposes of each component. This includes the identified molecular design criteria, commercial sourcing, impact on intracellular trafficking, and contribution to the properties of LNPs. One of the key components of LNPs is ionizable lipids, which initiate electrostatic binding with endosomal membranes and facilitate cytosolic release; however, the roles of other lipid components should not be disregarded, as they are associated with stability, clearance, and distribution of LNPs. Fourth, we review the attributes of LNP constructs as a whole that can heavily influence RNA delivery. These attributes are LNP size, charge, internal structure, lipid packing, lipid membrane hydration, stability, and affinity toward biomacromolecules. We also discuss the specific techniques used to examine these attributes and how they can be adjusted. Finally, we offer our perspective on the future of RNA therapies and some questions that remain in the realm of LNP formulation and optimization.

Delivery of CRISPR-Cas9 system for screening and editing RNA binding proteins in cancer

RNA-binding proteins (RBPs) play an important role in RNA metabolism, regulating the stability, localization, and functional dynamics of RNAs. Alternation in the RBP-RNA network has profound implications in cellular physiology, and is related to the development and spread of cancer in certain cases. To regulate the expression of specific genes and their biological activities, various strategies have been applied to target RBPs for cancer treatments, including small-molecule inhibitors, small-interfering RNA, peptides, and aptamers. Recently, the deployment of the CRISPR-Cas9 technology has provided a new platform for RBP screening and regulation. This review summarizes the delivery systems of the CRISPR-Cas9 system and their role in RBP-based cancer therapeutics, including identification of novel RBPs and regulation of cancer-associated RBPs. The efficient delivery of the CRISPR-Cas9 system is important to the profound understanding and clinical transition of RBPs as cancer therapeutic targets.