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Scientists Discover Way to Send Information into Black Holes Without Using Energy

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New Robotic Platform Could Speed Up mRNA Drug Delivery Design by 100 Times

Artificial Intelligence (AI) is already transforming the way scientists design new medicines. In recent years, AI systems have helped researchers create potential drugs for antibiotic-resistant infections and even rare genetic diseases. However, when it comes to designing the tiny delivery systems that carry these drugs into human cells, progress has been slower.

One of the most important delivery systems in modern medicine is lipid nanoparticles (LNPs). These microscopic fat-based particles protect fragile genetic material and transport it safely into cells. LNPs played a crucial role in the development of mRNA vaccines during the COVID-19 pandemic, proving that they can revolutionize the field of medicine.

Despite their importance, designing better lipid nanoparticles remains a major challenge. Scientists must carefully balance several chemical ingredients, and even small changes in these ingredients can significantly affect how the particles behave inside the body.

Now, engineers at the University of Pennsylvania have developed a new automated system called LIBRIS, which may dramatically accelerate LNP research. According to the researchers, this new platform could speed up the development of lipid nanoparticles by up to 100 times.

Why Lipid Nanoparticles Matter

Lipid nanoparticles are tiny spherical particles made from specialized lipids, or fat-like molecules. Their main job is to deliver genetic instructions—such as messenger RNA (mRNA)—into cells.

Without these nanoparticles, many modern therapies would not work. Genetic materials like mRNA are extremely fragile and can quickly break down in the body. LNPs act as protective carriers that shield the genetic material and help it enter cells safely.

Once inside the cell, the genetic instructions can guide the cell to produce therapeutic proteins. This technology has opened the door to new treatments for diseases including:

  • Infectious diseases

  • Genetic disorders

  • Cancer

  • Immune system conditions

The success of mRNA vaccines during the COVID-19 pandemic showed how powerful this technology can be. However, scientists believe LNPs could be used for many more therapies if their design can be improved.

The Challenge of Designing LNPs

Creating an effective lipid nanoparticle is not simple. Each LNP contains several different components, including:

  • Ionizable lipids, which help deliver genetic material into cells

  • Helper lipids, which stabilize the particle structure

  • Cholesterol, which strengthens the particle membrane

  • PEG-lipids, which improve stability in the bloodstream

The proportions of these ingredients determine how well the nanoparticle performs. Even small adjustments in the ratios can change how efficiently the particle enters cells, releases its cargo, or interacts with the immune system.

Because of this complexity, scientists still do not fully understand how different chemical combinations influence biological results.

The potential design space is enormous. Researchers estimate that there could be around 10¹⁵ possible LNP formulations—a number far too large to test manually.

To explore such a huge space, scientists want to use AI and machine learning. These systems can identify patterns in large datasets and predict which designs may work best. But there is a major obstacle: not enough data exists.

The Data Bottleneck

Creating datasets for machine learning requires generating and testing many different nanoparticle formulations. The process typically involves three main steps:

  1. Designing new ionizable lipids

  2. Formulating the lipid nanoparticles by mixing ingredients

  3. Testing the nanoparticles in cells or biological models

Interestingly, the first and last steps are already highly scalable. Scientists can synthesize thousands of new lipids and test many nanoparticle designs simultaneously.

The real bottleneck lies in the formulation step, where nanoparticles are physically created.

Traditionally, scientists either mix ingredients by hand or use small microfluidic devices—tiny chips with narrow channels that allow fluids to mix under controlled conditions.

However, both approaches are slow.

Manual preparation is labor-intensive and inconsistent, while existing microfluidic systems typically produce only tens to hundreds of formulations per hour. After each experiment, researchers must clean the equipment before starting again, which slows the process even further.

Even robotic systems have limitations. While robotic liquid handlers can prepare many chemical ingredients, they often rely on less controlled mixing methods, leading to inconsistent nanoparticle quality.

Because of these challenges, scientists have struggled to generate the large, standardized datasets needed to train AI models.

Introducing LIBRIS

To solve this problem, the research team developed LIBRIS, which stands for Lipid nanoparticle Batch production via Robotically Integrated Screening.

LIBRIS is essentially a small automated factory designed specifically for producing lipid nanoparticles.

The system includes tubes that carry different LNP components into a glass microfluidic chip enclosed in an aluminum housing. Inside the chip, the components mix together in tiny channels under precisely controlled pressure.

Below the chip, a moving plastic plate collects the newly formed nanoparticles into individual wells for further testing.

What makes LIBRIS special is its parallel design.

Unlike traditional systems that produce one formulation at a time, the LIBRIS chip contains multiple channels that allow it to generate up to eight different nanoparticle formulations simultaneously.

Because the channels can be cleaned quickly between runs, the system can operate almost continuously.

As a result, LIBRIS can create around 1,000 nanoparticle formulations per hour, which is roughly 100 times faster than many existing microfluidic methods.

Unlocking AI-Driven Drug Delivery

The ability to rapidly produce large libraries of nanoparticles could be a game-changer for AI-driven drug discovery.

Machine learning models rely on massive datasets to detect patterns. If scientists can generate thousands—or even millions—of nanoparticle designs and measure their biological performance, AI could begin identifying the relationships between chemical structure and therapeutic effectiveness.

This would allow researchers to move beyond simple trial-and-error experimentation.

Instead of testing random combinations, scientists could use AI to predict which formulations are most likely to succeed before they are even created.

Over time, this approach could dramatically accelerate the development of new therapies.

Moving Toward Rational Nanoparticle Design

Until now, most LNP development has relied on screening large libraries of particles and observing which ones work best.

While this strategy has produced important breakthroughs, including the lipid nanoparticles used in approved mRNA vaccines, it does not allow scientists to predict the behavior of new formulations in advance.

The ultimate goal is something called rational design.

In rational design, researchers would start by defining the desired properties of a nanoparticle—for example, targeting a specific organ, delivering a certain genetic therapy, or avoiding immune reactions.

AI models could then suggest the exact chemical composition needed to achieve those properties.

LIBRIS represents an important step toward making this vision possible.

By rapidly generating large and precisely controlled nanoparticle libraries, the system can help produce the datasets needed to train powerful predictive algorithms.

The Future of mRNA Therapies

The development of LIBRIS comes at a time when interest in mRNA medicine is growing rapidly.

Researchers are exploring mRNA treatments for a wide range of conditions, including:

  • Cancer vaccines

  • Rare genetic disorders

  • Autoimmune diseases

  • Heart disease

However, many of these therapies require improved delivery systems to work effectively.

Better lipid nanoparticles could help target specific organs, increase treatment efficiency, and reduce side effects.

By combining high-speed nanoparticle production with AI-driven analysis, scientists may soon unlock entirely new possibilities in genetic medicine.

A Foundation for the Next Generation of Medicine

The LIBRIS platform represents more than just a faster laboratory tool. It could become a key foundation for the next generation of precision medicine.

By solving the data bottleneck that has slowed LNP research, this robotic microfluidic system may allow AI to finally play a major role in designing nanoparticle delivery systems.

If successful, the approach could dramatically accelerate the creation of safer, more effective mRNA therapies.

As researchers continue improving both AI models and experimental tools, the dream of designing custom nanoparticles for specific diseases may soon become a reality.

Reference: Andrew R. Hanna et al, Automated and Parallelized Microfluidic Generation of Large and Precisely Defined Lipid Nanoparticle Libraries, ACS Nano (2025). DOI: 10.1021/acsnano.5c15613

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