TL;DR:
- Researchers at POSTECH employ machine learning to predict drug approval outcomes in clinical trials.
- Despite promising preclinical results, many drugs face obstacles in diverse human clinical trials.
- The discrepancy in drug effects between cells and humans is a critical factor leading to unforeseen side effects.
- The research team utilized CRISPR-Cas9 and gene perturbation analysis to understand these disparities.
- Their innovative machine learning approach integrates both chemical and genetic factors to enhance drug safety and success predictions.
Main AI News:
The pursuit of novel pharmaceuticals stands as a linchpin in the continuous quest for groundbreaking medical treatments and the safeguarding of global health. However, even when potential drugs exhibit promise and efficacy in cellular and animal models, they frequently confront insurmountable challenges when subjected to human clinical trials.
In the realm of drug development, a single setback during clinical trials, a phase characterized by its diverse participant pool, can translate into substantial economic setbacks. Addressing this critical issue mandates a comprehensive understanding of why certain drug candidates, despite demonstrating potential in preclinical stages, falter when exposed to the complexities of human trials. Moreover, there exists an urgent need to anticipate a drug’s likelihood of approval in the clinical trial arena.
Recent strides in this direction have been made by a distinguished research team spearheaded by Professor Sanguk Kim, hailing from the Department of Life Sciences within the School of Convergence Science and Technology, and Minhyuk Park, a diligent PhD candidate in the same department, both affiliated with Pohang University of Science and Technology (POSTECH). Their groundbreaking work, unveiled in the esteemed pages of EBioMedicine, a publication under the umbrella of The Lancet Discovery Science, harnesses the power of machine learning to forecast potential drug outcomes and associated side effects before clinical trials commence.
Traditionally, the evaluation of drug candidates primarily unfolds on cellular lines and animal models, offering a preliminary glimpse into the drug’s efficacy and potential toxicity. However, significant variations may surface due to the intricate interplay of drug target genes between cells and their manifestation in the human organism. Overlooking this fundamental disparity can culminate in unforeseen and severe adverse effects when administered to real patients, diverging starkly from the predictions derived from laboratory settings.
In their extensive investigation, the research team meticulously scrutinized the disparities in drug effects between cells and humans. To gauge these disparities and discern their impact on predicting drug approval, they embarked on an analysis encompassing CRISPR-Cas9 knockout techniques and the rate-based gene perturbation effects in cells versus humans. Additionally, the team sought to substantiate their findings by scrutinizing the drug targets of compounds that had faced setbacks or withdrawals during clinical trials due to safety concerns.
Armed with this invaluable insight, the researchers devised an innovative machine learning methodology tailored to anticipate drug approvals during clinical trials. Conventional approaches had hitherto centered primarily on a drug’s chemical properties, largely disregarding the underlying genetic variances between cellular systems and the human body. In stark contrast, this pioneering team melded both chemical and genetic strategies, elevating the precision of their predictions pertaining to drug safety and success in clinical trials.
Conclusion:
The integration of machine learning in drug development, as demonstrated by the research at POSTECH, holds the potential to significantly mitigate economic losses caused by failed clinical trials. This approach, which considers both chemical properties and genetic differences, can enhance the accuracy of drug approval predictions and streamline the pharmaceutical market’s quest for innovative treatments, ultimately benefiting both the medical industry and global health.
