Researchers Utilize Machine Learning to Streamline Drug Formulation Development - medtigo



Researchers Utilize Machine Learning to Streamline Drug Formulation Development

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According to Science Daily, the viability of utilizing machine learning algorithms to assist the creation of long-acting injectable pharmaceutical formulations has been successfully studied by researchers at the University of Toronto. By reducing the time and money necessary to produce potentially life-changing new medicines, machine learning algorithms have the potential to speed their release.  

The lead investigators of this interdisciplinary project are Alán Aspuru-Guzik, a chemistry and computer science professor at the University of Toronto, and Christine Allen, an associate professor in the department of pharmaceutical sciences. Both experts are members of the Acceleration Consortium, a global program that employs artificial intelligence and automation to accelerate the development of environmentally friendly materials and chemicals.  

“This work is a significant step toward data-driven medication formulation innovation, with an emphasis on long-acting injectables,” said Christine Allen, professor of pharmaceutical sciences at the University of Toronto’s Leslie Dan Faculty of Pharmacy.   

“Machine learning has made significant advances in searching for novel compounds with medicinal promise. The same ideas are being used to enhance medical treatments and medications. “Long-acting injectables (LAI), a sophisticated drug delivery technology, are one of the most promising therapeutics for chronic disorders.

It has been demonstrated that giving drugs at the site of action improves adherence, reduces adverse effects, and boosts efficacy. To get the best medicine released over time, thorough research must create and describe a wide range of formulation choices. Because of this process of trial and error, the development of LAIs has been slower than that of standard drugs.  

“Because of AI, science will never be the same. Accelerates optimization and discovery. This shows the importance of multidisciplinary research in identifying how to distribute drugs appropriately.” Alán Aspuru-Guzik of the University of Toronto, Vector Institute CIFAR Artificial Intelligence Research Chair, issued a statement. 

Eleven models were developed and tested to see if machine learning could correctly predict drug release. Multiple linear regression (MLR), random forest, light gradient boosting machine (lightGBM), and neural networks were among them. A panel of machine learning models was trained using data from the authors’ and other research groups’ investigations.  

“We divided the data set in half for constructing and assessing a model. To proceed, we had the models forecast the test set, which we then compared to previous experimental findings. The most accurate projections came from tree-based models, particularly lightGBM.” Pauric Bannigan, a research associate at the University of Toronto’s Leslie Dan Faculty of Pharmacy, made this comment.  

The researchers used sophisticated analytical approaches to derive design requirements from the lightGBM model to apply these predictions and demonstrate how machine learning models may influence LAI design. A novel low-acid-index (LAI) formulation of a medication for the treatment of ovarian cancer was created.

Bannigan contended that after a model has been trained, it may inform the construction of new systems by giving design criteria. The drug release rate was examined to validate the lightGBM model’s predictions. “We were ecstatic when we discovered the extended-release version. It may have taken multiple attempts to get this release profile, but owing to machine learning, we only required one, “they said.  


Allen and the study team have submitted their datasets and code to Zenodo as part of an initiative to promote the production of publicly available datasets to enable machine learning in the pharmaceutical sciences. Bannigan stated that this study aimed to minimize the barrier to bringing machine learning to the pharmaceutical business. “All of the information we’ve gathered is open to the public so that others may build on it. Machine learning must be used to develop novel treatments in the future.” 


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