AI Speeds up Drug Polymorph Screening, Preventing Medication Mishaps
Table of Contents
Skoltech AI is at the forefront of pharmaceutical innovation, pioneering the use of machine-learned interatomic potentials to dramatically accelerate the screening process for molecular crystal polymorphs. This groundbreaking approach, detailed in a study published in Physical Chemistry Chemical Physics and supported by Russian Science Foundation Grant No. 23-13-00332, seeks to prevent costly drug recalls and safeguard public health by addressing potential issues stemming from unexpected polymorph behavior. The research emphasizes ensuring drug efficacy and stability through the accurate prediction of stable polymorphs, drawing crucial lessons from past incidents such as the 2008 rotigotine recall.
Molecular crystal polymorphism, while a complex term, describes a phenomenon many have indirectly encountered. A familiar example is the change in texture of a chocolate bar left out for too long. Even though the chemical composition remains largely the same, the cocoa butter molecules rearrange into a different crystal structure, altering the chocolate’s texture and appearance. This same principle applies to medications, where changes in crystal structure can significantly impact their effectiveness and safety.
Understanding Polymorphs and Their Impact
Polymorphs are distinct molecular crystal structures formed by the same compound under varying conditions. Cocoa butter,for instance,exists in six different forms. Chocolate manufacturers meticulously control temperature to maximize the presence of the “tasty” polymorph, ensuring a shiny, smooth, and melt-in-your-mouth experience. Though, prolonged storage, especially under suboptimal conditions, can cause this desirable polymorph to transform into a less palatable one.Similarly, the molecules in pills can undergo polymorphic changes, affecting their properties and, consequently, the drug’s performance.
The implications of polymorphism in pharmaceuticals are profound. A important example is the case of rotigotine, a medication used to treat Parkinson’s disease. Research Scientist Nikita Rybin from Skoltech AI explained the impact of polymorphs on drug efficacy:
As 1985, drug manufacturers had only been aware of one polymorphic form of rotigotine — a medication prescribed for the treatment of Parkinson’s disease. In 2008, though, the revelation of a considerably more stable and less soluble polymorph prompted a massive drug recall wiht huge economic losses and public health repercussions. Solubility is one of those properties that are essential for the medication to have its intended effect, and yet it depends on the crystal structure assumed by the molecules in the pill or, in this case, the transdermal patch, rather then the drug’s chemical makeup.Nikita Rybin, Research Scientist, Skoltech AI
The 2008 rotigotine recall serves as a stark reminder of the potential consequences of overlooking polymorph behavior during drug advancement. The discovery of a new, more stable, and less soluble polymorph led to significant financial losses for the manufacturer and raised concerns about the drug’s effectiveness in patients.
Machine Learning for Accelerated Screening
To overcome these challenges, Rybin and his colleagues are pioneering the use of machine-learned interatomic potentials to accelerate polymorph screening. The team tested their technique using glycine and benzene molecules, successfully predicting the stable polymorphs of these compounds with modest computational resources.
Traditional methods of polymorph prediction involve direct quantum mechanical computations, a “brute force” approach that has proven triumphant in contests like the Crystal Structure Prediction Blind Test. However, as Rybin notes, this method is often impractical for pharmaceutical companies:
You can predict it the hard way, by doing direct quantum mechanical computations. Indeed,such a brute force approach has recently triumphed at the Crystal Structure Prediction Blind Test contest,held by the Cambridge-based nonprofit CCDC every year sence the infamous rotigotine story.This is not feasible for pharmaceutical companies, though. They have to screen millions of drug candidates, and full quantum mechanical simulations — just like actual wet experiments — are only an option for, perhaps, dozens of preselected molecules. So, peopel are exploring ways to speed up this procedure.Nikita Rybin, research Scientist, Skoltech AI
Machine-learned interatomic potentials offer a compelling solution by training on the results of smaller-scale calculations performed with full quantum mechanical accuracy. This allows the resulting model to bypass the computationally demanding direct calculations, significantly speeding up the screening process and making it feasible for pharmaceutical companies to analyze a much larger number of drug candidates.
Expanding Applications and Future Directions
The Laboratory of Artificial Intelligence for Materials Design at Skoltech AI, led by professor Alexander shapeev, has previously applied machine-learned potentials to accelerate the search for salts for next-generation nuclear power plants and industrial metal alloys for aerospace technology. This latest research extends the technique’s application to molecular crystals, demonstrating its broad potential for drug design and development.
The Skoltech team’s work could enable medical research centers and pharmaceutical companies to thoroughly test the physical properties of drug compounds, identifying potential insolubility issues and degradation risks early in the development process. This proactive approach could prevent future incidents like the rotigotine recall, saving time, resources, and potentially safeguarding public health. The team plans to further develop the technique to account for environmental parameters such as ambient humidity and to apply it to more complex pharmaceutically significant molecules.
By thoroughly testing the physical properties of the active compounds of drugs in the form of pills or patches, medical research centers and R&D departments of pharma companies will be able to check for insolubility issues, potential degradation in open-air conditions or upon heating, etc., and avoid possible mishaps, such as the one that involved rotigotine. To make this a reality, the Skoltech team intends to move on to more intricately structured pharmaceutically significant molecules and to develop the proposed technique so as to account for ambient humidity and other environmental parameters.
Did you know that seemingly insignificant changes in a drug’s crystal structure can lead to devastating health consequences adn massive recalls? This is the critical issue of pharmaceutical polymorphism, and today we’ll explore how artificial intelligence is transforming drug development to prevent such disasters.
Interviewer: Dr. Anya Sharma, a leading expert in pharmaceutical crystallography, welcome to World Today News. The recent Skoltech AI research highlights the use of machine-learned interatomic potentials to predict drug polymorphs. Can you explain the meaning of this development for the pharmaceutical industry?
Dr. sharma: Thank you for having me. The Skoltech research is groundbreaking because it tackles a long-standing challenge in drug development: polymorphism. Polymorphism, simply put, refers to the existence of different crystal structures for the same chemical compound. These different forms, or polymorphs, can dramatically alter a drug’s properties, including its solubility, stability, and bioavailability. This means one polymorph might be readily absorbed by the body while another might be nearly insoluble, rendering the medication ineffective. Predicting these polymorphs accurately is crucial to ensure a medicine’s safety and efficacy. The machine-learning approach allows for a notable speed-up in the screening of potential polymorphs, replacing computationally expensive option methods with a much more efficient process. This is especially beneficial for pharmaceutical firms in the drug finding process, as the screening of millions of compounds is feasible.
Interviewer: The 2008 rotigotine recall is frequently cited as a stark example of the consequences of failing to identify problematic polymorphs. Can you elaborate on this case and its impact?
dr. Sharma: The rotigotine case perfectly illustrates the high stakes involved. Initially, only one polymorph of rotigotine, a drug for Parkinson’s disease, was known. The discovery of a second, more stable, and far less soluble polymorph resulted in a massive recall. This dramatically increased costs and, more importantly, raised serious concerns about patient safety and the effectiveness of the treatment. This incident highlighted the urgent need for more robust and efficient methods for polymorph prediction and screening—which is exactly what the AI-driven methodology is aiming to provide.
Interviewer: How dose this new AI-based approach differ from conventional methods for predicting polymorphs? What are its key advantages?
Dr.Sharma: Traditional methods often rely on computationally intensive quantum mechanical calculations —a “brute-force” strategy. While powerful techniques like those that use density functional theory (DFT) have proved successful, they’re simply too slow and resource-intensive for the pharmaceutical industry’s needs. The new approach uses machine learning to train predictive models on the outcome of limited, accurate quantum mechanical calculations, creating a highly efficient process for quickly identifying potential polymorphs.This means pharmaceutical companies can now analyze a much larger number of drug candidates much faster, resulting in better drugs with fewer unintended consequences.
Interviewer: The article mentions the submission of this technique beyond pharmaceuticals; in salt screening for nuclear power plants, such as. Can you discuss the broader implications of this AI-driven methodology?
Dr. Sharma: Absolutely. The underlying principle—using machine learning to predict the properties of materials based on their atomic structure—is widely applicable. The Skoltech team’s work demonstrates the versatile nature of this technology. Its ability to predict crystalline structures is valuable in various fields requiring precise material design that deal with solids. The ability to efficiently screen and predict the stability and properties of crystalline materials will positively impact fields ranging from materials science for the aerospace sector to the development of more effective catalysts.
interviewer: What are the next steps in developing and refining this AI-driven polymorph prediction technology? What challenges might the researchers face?
Dr. Sharma: Further development will likely focus on improving model accuracy and expanding its scope. Researchers will strive to incorporate environmental factors like humidity and temperature into their models. This will generate more accurate data that mirrors the real-world behavior of drug polymorphs which is crucial for accurate estimations. additionally, scaling this technology to handle more complex molecules common in modern pharmaceuticals will require substantial computational power and even more refined algorithms. Overcoming these challenges will lead to a significant enhancement in predictive power.
Interviewer: what is your overall assessment of the impact of this research on the future of drug development and patient safety?
Dr. Sharma: This work represents a major leap forward in pharmaceutical development. The ability to rapidly and accurately predict polymorph behavior will undoubtedly lead to safer, more effective medications.By identifying potential problems early in the development process, we can prevent costly recalls and ensure patients receive high-quality, reliable medicines. This proactive approach, made possible through the use of machine learning, marks a significant advance toward improving healthcare for everyone.
Interviewer: Thank you, Dr. Sharma, for sharing your insights.This is truly fascinating work with extremely positive future implications.
Final Thought: The development of AI-driven polymorph prediction holds immense promise for the future of pharmaceutical development. By accelerating screening and improving the accuracy of polymorph predictions, this technology helps in building safer, more effective drugs and ultimately, protects patient health and well-being. Share your thoughts about this innovative development in the comments below!