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

Sarode, Apoorva; Fan, Yu–Chen; Byrnes, Amy E.; Hammel, Michal; Hura, Greg L.; Fu, Yige; Kou, Ponien; Hu, Chloe; Hinz, Flora I.; Roberts, J.; Koenig, Stefan G.; Nagapudi, Karthik; Hoogenraad, Casper C.; Chen, Tao; Leung, Dennis; Yen, Chun-Wan

doi:10.1039/d1na00712b

Cited by 35 publications

(45 citation statements)

References 87 publications

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“…Theoretically, an N/P ratio near or slightly lower than 1 (charge balance point) would ensure relatively high loading capacity while saving cargo inputs by reducing the unloaded concentrations. However, determination of the optimal formulation condition requires more evaluations of other formulation attributes and downstream efficacy outcomes. − Here, we demonstrated the LWR model can robustly predict % loading of nucleic acid cargoes in LNPs and rank their loading capacity in the 1st-tier HTS scenario, facilitating a facile and automated in-line quality assessment of LNP formulations.…”

Section: Resultsmentioning

confidence: 95%

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

Fan¹,

Shi²,

Ma³

et al. 2022

Anal. Chem.

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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.

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Section: Resultsmentioning

confidence: 95%

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

Fan¹,

Shi²,

Ma³

et al. 2022

Anal. Chem.

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“…This suggests the potential for small-scale automated screening of drug formulations followed by translation and scale-up of production methods. This was further demonstrated in a follow-up study using the same automated high-throughput workflow to identify optimal LNP formulations for gene silencing [42]. Similar high-throughput approaches have also been used to screen and prepare LNPs encapsulating mRNA, as well as to improve methods of quantifying the loading of nucleic acid cargos [43,44].…”

Section: Laboratory Automation -Strengthening Data Productionmentioning

confidence: 95%

Re-envisioning the Design of Nanomedicines: Harnessing Automation and Artificial Intelligence

Zaslavsky

Bannigan

Allen

2022

Preprint

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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 This article highlights 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 experiments 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.

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“…20 Miniaturization of the nanoprecipitation process onto a 96-well plate format using a liquid handling robot has been reported in an effort to screen the lipid composition for mRNA-loaded lipid nanoparticles. 21,22 Moreover, it has also been demonstrated that nanomedicines screened in this manner can be scaled up on microfluidics platforms, thus eradicating potential downstream manufacturing concerns. 21,22 It is straightforward to envision the development of similar workflows for small molecules, proteins, or peptides given the necessary equipment to characterize the resulting nanomedicines.…”

Section: A Physical Platform For High-throughput Nanoparticle Develop...mentioning

confidence: 99%

“…21,22 Moreover, it has also been demonstrated that nanomedicines screened in this manner can be scaled up on microfluidics platforms, thus eradicating potential downstream manufacturing concerns. 21,22 It is straightforward to envision the development of similar workflows for small molecules, proteins, or peptides given the necessary equipment to characterize the resulting nanomedicines. For instance, drug loading capacity (DLC) and encapsulation efficiency (EE) can be automated with appropriate extraction methods and analysis via high-pressure liquid chromatography (HPLC).…”

Section: A Physical Platform For High-throughput Nanoparticle Develop...mentioning

confidence: 99%

“…Dynamic light scattering plate readers enable the high throughput measurement of particle size. 21,22 Therefore, all of the necessary equipment and components to conduct such high-throughput experiments already exist. In the NanoMAP framework, we unite these components in order to systematically prepare and characterize nanomedicines in an automated fashion (Figure 2).…”

Section: A Physical Platform For High-throughput Nanoparticle Develop...mentioning

confidence: 99%

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Self-Driving Laboratories: A Paradigm Shift in Nanomedicine Development

Hickman

Bannigan

Bao

et al. 2022

Preprint

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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.

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

Abstract: Development of a high throughput screening workflow to characterize the impact of unique PEG-lipid parameters on oligonucleotide-loaded lipid nanoparticle structure and function.

Cited by 35 publications

References 87 publications

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

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

Re-envisioning the Design of Nanomedicines: Harnessing Automation and Artificial Intelligence

Self-Driving Laboratories: A Paradigm Shift in Nanomedicine Development

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