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In a groundbreaking development that could transform the landscape of drug discovery and development, researchers at Pohang University of Science and Technology (POSTECH) have harnessed the power of machine learning to predict a drugs chances of approval before clinical trials even begin. Their findings, recently published in the esteemed journal EBioMedicine, offer a promising solution Read more

In a groundbreaking development that could transform the landscape of drug discovery and development, researchers at Pohang University of Science and Technology (POSTECH) have harnessed the power of machine learning to predict a drugs chances of approval before clinical trials even begin. Their findings, recently published in the esteemed journal EBioMedicine, offer a promising solution to one of the pharmaceutical industrys most pressing challenges the high rate of drug candidates that fail during clinical trials despite showing promise in preclinical testing.

The pursuit of new pharmaceuticals is not merely a scientific endeavor but a vital mission that affects the health and well-being of humanity at large. The development of innovative drugs is instrumental in advancing medical treatments, preventing diseases, and ultimately improving the quality of life for individuals around the globe. However, the arduous journey from laboratory discovery to market availability is fraught with obstacles and uncertainties.

One of the most significant hurdles in drug development is the staggering economic losses incurred when a drug candidate fails during clinical trials. These trials involve diverse population groups and are designed to assess the safety and efficacy of a drug in real-world scenarios. Even when a drug has shown exceptional promise in preclinical stages, the transition to clinical trials can reveal unexpected challenges, leading to setbacks that cost pharmaceutical companies billions of dollars.

To address this critical issue, it is imperative to understand why certain drugs, despite passing rigorous preclinical testing, falter during clinical trials. Moreover, there is a pressing need to develop methods that can predict a drugs chances of approval before embarking on these costly and time-consuming trials.

Enter Professor Sanguk Kim and PhD candidate Minhyuk Park, leading a research team at POSTECHs Department of Life Sciences. Leveraging the power of machine learning, they have achieved remarkable success in predicting potential drug outcomes and side effects before clinical trials commence.

The crux of their groundbreaking research lies in addressing a fundamental discrepancy in drug effects observed between cell lines and animals, commonly used in preclinical testing, and their ultimate impact on humans. This discrepancy arises from variations in how drug target genes function and are expressed in cells as opposed to humans. Neglecting this critical difference can lead to severe and unanticipated side effects when drugs are administered to actual patients, deviating significantly from the promising results seen in laboratory settings.

The researchers at POSTECH tackled this challenge head-on by focusing on the disparities in drug effects between cells and humans. Their approach involved a comprehensive analysis of the CRISPR-Cas9 knockout and loss-of-function mutation rate-based gene perturbation effects in cells and humans, respectively. By evaluating this discrepancy, they aimed to predict the likelihood of a drugs approval, drawing from a dataset that included 1404 approved drugs and 1070 unapproved drugs.

To further validate the risk associated with drug targets exhibiting the cells/humans discrepancy, the researchers delved into the targets of drugs that had previously failed in clinical trials or been withdrawn from the market due to safety concerns. This meticulous analysis provided crucial insights into the factors contributing to drug failures and enabled the research team to refine their predictive models.

What sets this research apart from conventional approaches is its integration of both chemical and genetic strategies. While traditional methods primarily rely on a drugs chemical properties to predict its success, the POSTECH team recognized the significance of genetic differences between preclinical models and humans. By harmonizing these two facets, they achieved a level of accuracy previously unattainable in drug safety and success predictions.

The implications of this research are nothing short of revolutionary. Machine learnings ability to predict a drugs chances of approval with a high degree of accuracy has the potential to reshape the pharmaceutical industry. By providing pharmaceutical companies with a tool to make more informed decisions about which drug candidates to advance to clinical trials, this technology has the potential to reduce the risk of costly failures and accelerate the development of safe and effective drugs.

As with any transformative technology, the use of machine learning in drug development raises important ethical considerations. Ensuring the privacy and security of patient data used in these predictive models is paramount. Additionally, regulatory agencies will need to adapt to accommodate the use of these innovative approaches in the drug approval process, striking a balance between innovation and safety.

The work conducted by Professor Sanguk Kim, Minhyuk Park, and their team at POSTECH represents a significant step forward in drug development. Their integration of machine learning, genetic insights, and chemical properties promises to revolutionize the way pharmaceuticals are discovered and developed, ultimately benefiting not only the industry but also the health and well-being of individuals worldwide. The journey from laboratory discovery to clinical approval may soon become a more efficient and predictable path, ushering in a new era of medical innovation.

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