High-Throughput Formulation Screening To Fine-Tune Performance Characteristics of Nanomedicines

The widespread and successful rollout of COVID-19 mRNA vaccines – underpinned by lipid nanoparticle (LNP) technology – has proven the viability of nanomedicines, attracting huge investment and interest for further research. The market continues to boom, and recent scientific breakthroughs have shown LNPs to be effective drug delivery systems. However, the ability to screen large numbers of different formulations is essential in order to fine-tune a plethora of performance characteristics that depend on precise particle size, shape and structure.

Automated systems are ideal for this and can overcome the challenges of typical low-throughput LNP preparation methods, which have, so far, limited screening options.¹ This article discusses how marrying microfluidics with automation can provide the necessary level of control to perform high‑throughput LNP formulation screening in a cost-effective and timely manner, and how this approach can accelerate the development of new nanomedicines.

The promise of nanomedicine

Nanomedicine has the potential to overcome the undesirable properties of many conventional drugs – including poor pharmacokinetics, restricted bioavailability and high toxicity – that limit their clinical use.² At the nanoscale, a therapeutic can be tailored to specific diseases, and can pinpoint and selectively destroy cancer cells while leaving surrounding healthy cells alone. Using LNPs as drug delivery systems is an exciting method to improve the therapeutic index of a drug, and the viability of the technology was validated by the success of LNP-mRNA COVID-19 vaccines.

This method has received significant research interest in the drug delivery space and offers key advantages over other methods: LNPs can encapsulate a range of payloads with high efficiency; stabilize the drug deliverable; and help particle entry into targeted cells. The success of an LNP, however, lies in its specific formulation, which can significantly affect its properties. Furthermore, a nanomedicine is intended for clinical applications, meaning that LNPs must be consistent and optimized for safe and efficient use in humans.

Researchers in this space are in pursuit of the optimal formulation for each application to turn promising encapsulated biologics into effective and viable therapeutics, whether for a vaccine, gene therapy or cancer treatment. To do this, they need to be able to reproducibly create uniform particles with finely tuned performance characteristics.³

Particular particles

Careful engineering of LNPs is critical to ensure they can perform a complicated series of actions, including transport across cell membranes and intracellular release mechanisms, all while keeping the therapeutic cargo intact. To provide a final nanomedicine with high biological efficacy, LNP performance characteristics must be optimized for the successful transfection of human cells. Size is an important parameter to balance for optimal uptake and delivery, and the best-performing formulations are between 75 and 95 nm.^4,5 The encapsulation efficiency (EE) – or “cargo loading” – describes how much of the biologic ends up inside the lipid carrier, and over 90% EE is typically desired.⁶ Values of 0.2 and below are commonly deemed acceptable for the polydispersity index (PDI), which describes the degree of non‑uniformity of a size distribution of particles.⁷ The ideal performance criteria can be targeted by altering the liposomal nanoformulation of the particles.

In pursuit of the perfect nanoformulation

The biological efficacy of an LNP product largely depends on the systematic optimization of four lipidic components – ionizable lipids, phospholipids, PEGylated lipids and cholesterol – that each provide unique functionalities of particle performance (Table 1).^1,8 Lipid material can be altered in terms of molar ratio and type – with multiple options in clinical use for three of the categories – and to test all the combinations in Table 1, this would already require a minimum of 24 experiments. However, the number of experiments skyrockets when considering other factors that can be tweaked to finely tune performance characteristics. These factors include the cargo concentration, length or sequence; the charge ratio of the particle; as well as process parameters, including total flow rate, reagent mixing kinetics, and the pH, type or concentration of the buffer.

Table 1: Lipid categories

The enormous experimental space

To identify the sweet spot of particle performance, small quantities of every permutation of formulation parameters must be synthesized. Physiochemical characterization of each batch is then performed during screening runs, before the most promising nanoformulations are selected to take forward. This is a non‑trivial process largely reliant on trial and error that requires potentially billions of investigations. The experimental process must also be consistent and needs process validation, leading to high costs of materials and consumables, as well as significant demands on labor and time. Low‑throughput, manual screening processes introduce large bottlenecks in downstream assays and characterization steps. Without automation, there is a danger of losing the perfect nanoformulation in the noise, and innovation could be stunted.³

Automation and control in formulation screening

In the absence of automation in downstream screening processes, researchers face a lengthy and costly uphill battle. There is a demand for a platform that can integrate rapid downstream processing with the controlled synthesis of trial nanoformulations, including the capability to automate both the screening steps and process parameter adjustments. Microfluidic devices can achieve this by manipulating fluids on the micrometer scale, generating reproducible and monodisperse nanoformulations. These technologies have been used to synthesize LNPs with more controlled physical properties but, until now, this method has not been able to offer sufficient automation.⁹

Existing microfluidic systems, when used for formulation screening, enable only one experiment to be performed at a time before experimental parameters need to be changed manually. However, modern microfluidic platforms overcome this by using automation, enabling effective and efficient high-throughput screening of LNP formulations. Recent developments have seen innovative platforms come to market that significantly accelerate screening timeframes, offering superior process consistency, increased automation and minimized running costs. These systems work with a 96 well plate sample format – completing up to 96 experiments in around 6 hours – that is compatible with existing upstream and downstream workflows, permitting easy transfer between all stages of particle production. With automatic washing between experiments and the use of reusable microfluidic chips, such platforms offer great flexibility and require very little intervention by the user. A single system can be used for both process optimization and continuous production, making the transfer from screening to scale-up seamless and empowering researchers to speed up the discovery phase of nanomedicines.

Conclusion

Nanomedicine has the potential to transform patient care, and encapsulating biologic material is now an integral part of early-stage development of genetic medicines and vaccines. To bring an LNP-based therapeutic to the global market, researchers need to consider how quickly they can go from screening nanoformulations through to translation into the clinic, and ultimately to commercialization. To reliably and reproducibly synthesize LNPs with ideal performance characteristics, but without prohibitively high costs and lengthy development periods, there is a clear need for automated high-throughput screening platforms that offer excellent control over synthesis. Novel microfluidics systems meet this need by combining controlled synthesis of LNP formulations with automated high-throughput formulation screening. This can accelerate the development of therapeutics that rely on lipid-based drug delivery mechanisms, advancing the delivery of genetic medicines and cancer treatments to meet urgent global needs.  

About the author:

Ben Knappett received his MChem degree from Durham University and completed a PhD in nanoparticle synthesis and characterization at the University of Cambridge. Ben started working for Particle Works in 2016, developing nanoparticle and microparticle products using microfluidics technology, which covered a wide range of material types. Ben moved into his current position as the Head of Science and Applications when the Particle Works brand relaunched in 2021 as a provider of automated nanoparticle synthesis platforms. This role entails leading a team of scientists who specify and test new Particle Works systems, create content for applications and support customers with installation and training, as well as post-purchase applications. Ben and his lab team also run proof of principle studies to demonstrate the capabilities of Particle Works’ systems with customer materials.