It may be difficult to fathom, but that little device you carry in your pocket to play Candy Crush and read your social media feeds has enough computing power to put supercomputers from just a few decades ago to shame. Similarly, today’s desktop computers have come a long way in raw processing power, artificial intelligence, and machine learning. Now researchers are harnessing this power to improve how we develop critical new drugs faster and cheaper.
In our ultimate goal to find the perfect drugs to cure our ailments, pharma companies have developed massive drug screening facilities to test thousands of potential drugs against the target. The typical screening facility now looks more like an automated manufacturing plant for computer chips.
Despite these advances, the development process is still extremely complex. Although a drug may act perfectly on its intended target, it may have unwanted and unseen consequences somewhere else, known in the industry as toxicity.
A perfect example is cardiotoxicity from off-target drug interactions. Electrical signals in the heart are maintained by proteins in heart cells known as ion channels. A heartbeat is regulated by an intricate interplay as these ion channels conduct sodium, potassium and calcium in and out of the cells. If a drug interferes with this coordinated effort, it can lead to arrhythmias and heart attacks. Several drugs targeting common ailments (e.g. the antihistamine drug terfenadine) were pulled from the market due to safety issues after the discovery of their detrimental interaction with ion channels in the heart (in this case, the hERG ion channel).
To identify these interactions early in the development process, high-throughput assays have been developed to measure drug interactions with hERG and other ion channels. A successful technology used for this is the Automated Patch Clamp (APC), where engineered cells carrying these ion channels are subjected to various drugs and their interactions are quantified.
Read about IonFlux, a microfluidics-based APC and Potassium channels
Although experimental observation is key to understanding hERG-induced cardiotoxicity, computational models using artificial intelligence are now possible. These models have the potential to improve both the speed and efficiency of the current test-based methods. Researchers at the University of Alberta were able to build a computational model that can complement the experimental screening process in successfully predicting effects of drugs based on their molecular structure.
Their ground-breaking research has been described in a Nature Scientific Reports article. The study compares several standard drugs experimentally evaluated with automated patch clamp to their predicted interactions via computational modeling. According to the researchers, “given the results, our workflow would not only be useful for predicting drug-mediated hERG liability but can also be used as a complementary protocol for designing functional groups when optimizing the drug-like leads to reduce their hERG liability.”
A structure-based computational workflow to predict liability and binding modes of small molecules to hERG. |
Dr. Khaled H. Barakat presented the computational model in a webinar shortly after the paper was published. The paper is co-authored with Dr. Michael Houghton, 2020 Nobel Prize Laureate in Physiology or Medicine.
Click to access to prerecorded webinar
Other researchers are attempting to translate this predictive capability from drug development to personalized diagnostics. A recent study uses a combination of experimental results and patient genetic information to adapt the model to each patient. This approach could provide the foundation for a computational model to provide personalized patient treatment for cardiac arrhythmias.
Genetic algorithm-based personalized models of human cardiac action potential D.N. Smirnov et al. PLOS ONE. May.11.2020 |
Applications of multiscale computational models and analytics are not restricted to cardiac disorders. From cancer treatment to brain disease, computational models are providing a better understanding of drug interaction and development. Although still far from perfect, these approaches represent a solid foundation for implementing effective personalized medicine.
Ali Yehia has a Ph.D. in Cardiac Electrophysiology from McGill University. He has been involved in ion channel screening using Automate Patch Clamp since 2006. He joined Fluxion Biosciences in 2012, where he developed ion channel assays and IonFlux Mercury systems. He was the Chief Scientific Officer at Fluxion before moving to Cell Microsystems as the new Senior Director of the IonFlux product.
In the ongoing battle against antibiotic-resistant bacteria, or “superbugs,” scientists are constantly seeking innovative tools to understand the mechanisms of resistance and develop effective treatments. One such groundbreaking technology is shear flow systems, which have emerged as game-changers in microbiological research. By simulating the dynamic conditions of bacteria’s natural environments, such as the human body or food processing, shear flow systems provide unique insights into bacterial behavior and resistance mechanisms that traditional static methods simply cannot achieve. In this blog post, we’ll explore the benefits of using shear flow systems to combat antibiotic resistance and develop next-generation therapies.
Antibiotic resistance is a complex phenomenon influenced by a variety of factors, including genetic mutations, horizontal gene transfer, and the formation of biofilms. Traditional laboratory methods often fail to capture the dynamic nature of these processes, leading to incomplete or misleading results. Shear flow systems address this limitation by providing a more realistic environment for bacterial growth and interaction. Here’s how:
1. Mimicking Real-World Conditions
In the human body, bacteria are rarely in a static state. They are constantly exposed to fluid flow, such as blood circulation or urine flow, which influences their behavior and resistance mechanisms. Shear flow systems can replicate these conditions, allowing researchers to study how bacteria respond to antibiotics under realistic physiological conditions. This is critical for understanding how resistance develops and persists in vivo. This benefit was recently highlighted in a webinar presented by Dr. Katharina Richter, a microbiology researcher from the University of Adelaide in Australia. Using a high-throughput BioFlux Shear Flow System, Dr. Richter and her team were able to test 3 different methods of superbug treatments under physiological conditions.
2. Studying Biofilm Formation and Resistance
Biofilms—structured communities of bacteria encased in a protective matrix—are a major contributor to antibiotic resistance. Biofilms are notoriously difficult to treat because they shield bacteria from antibiotics and the immune system. Shear flow systems enable researchers to study biofilm formation in real time, observing how bacteria adhere to surfaces, form microcolonies, and develop resistance under flow conditions. For example, using a BioFlux, Dr. Richter was able to leverage high-resolution imaging to obtain unprecedented insight into the biofilm killing and prevention efficacy of a copper and diethyldithiocarbamate (DDC) combination nanoparticles¹. The group is currently investigating the use of a Cu-DDC infused gel that can be injected into wounds that are at high risk for chronic infection, such as hernia.
Imagine working with a rare patient-derived cell samples. In a traditional setup, you might need 1 milliliter of sample (equivalent to about 20 drops) to run an experiment. With a microfluidic system, you could potentially get the same results with just 10 microliters (about one-half of a single drop). This efficiency opens up entirely new research possibilities, especially in fields where sample availability is a limiting factor.
Whether due to rarity, cost, or difficulty in obtaining samples, the ability to conduct meaningful experiments with limited sample volumes has long been a goal in biological and chemical research. In this quest for more efficient and precise laboratory techniques, microfluidic shear flow systems have emerged as game-changing tools. These systems are not only reducing the amount of sample required for experiments but are also proving their versatility across a wide range of sample types.
Traditional experimental setups, including static and non-microfluidic shear flow, often demand significant amounts of material, which can be both costly and impractical. This is especially true when working with precious or limited samples such as rare biological specimens, expensive chemicals, or newly synthesized compounds. Enter microfluidic shear flow systems – an elegant solution that’s revolutionizing how investigators approach assays. These miniaturized platforms are proving invaluable across multiple disciplines by dramatically reducing sample requirements from milliliters down to microliters, while maintaining experimental integrity. This up to 1000-fold reduction in sample volume is particularly crucial when working with substances, such as patient-derived cells or novel pharmaceuticals. By significantly reducing the volume of reagents and samples needed, these systems cut costs and make experiments more sustainable.
Equally as impressive as their sample and reagent reduction ability, is the versatility of microfluidic shear flow systems to adapt to different types of samples.
The ability to work with multiple sample types in the same system allows researchers to explore different experimental conditions without the need for multiple specialized setups. By precisely controlling channel geometries and flow rates, researchers can subject different sample types to well-defined shear conditions, enabling detailed studies of cellular mechanics, protein interactions, and material properties.
By Jessica Hartman, Ph.D.
Toiling in the lab
With a deadline on its way
To the scope I go
On a Saturday
I don’t see a clone
I’ve looked for one that’s right
What misery these data bring,
I’ll have to look all night,
Oh! Single cells, single cells, dilutions aren’t the way
Oh, I wish I had an AIR to pick a clone, to-da-ay!
Single cells, single cells, I need a better way
Use a Raft to grow a clone or be sad this holiday!
Use a Raft to grow a clone or be sad this holiday!
Now it’s getting late
My spirits are so low
My PI will hate
If my cells don’t grow
My eyesight’s getting dim
A clone I cannot see
My chances are so slim
I need CellRaftA microscale polystyrene growth surface within an array used to spatially segregate cells/colonies while maintaining shared media access, enabling imaging over time and targeted isolation. More Cytometry
Oh! Single cells, single cells, sorters aren’t the way
Oh, I wish I had an AIR to pick a clone, to-da-ay!
Single cells, single cells, I need a better way
Use a Raft to grow a clone or be sad this holiday!
Use a Raft to grow a clone or be sad this holiday!
Oh! Single cells, single cells, dispensers aren’t the way
Oh, I wish I had an AIR to pick a clone, to-da-ay!
Single cells, single cells, I need a better way
Use a Raft to grow a clone or be sad this holiday!
Use a Raft to grow a clone or be sad this holiday!
“Single Cell” vocalist: Virginia Laurie
