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for a clinical trial
Photo by Esther Dyson
Researchers have devised a method for predicting whether experimental drugs will fail clinical trials due to excessive toxicity.
They say the method, known as PrOCTOR, upends conventional wisdom about the criteria on which to evaluate new drugs’ safety.
Rather than exclusively examining molecular structure to determine viability, PrOCTOR combines a host of structural features and features related to how the drug binds to molecules in the body.
“We looked more broadly at drug molecule features that drug developers thought were unimportant in predicting drug safety in the past. Then, we let the data speak for itself,” said study author Olivier Elemento, PhD, of Weill Cornell Medicine in New York, New York.
Dr Elemento and his colleagues described PrOCTOR in Cell Chemical Biology.
PrOCTOR was inspired by an approach that baseball statisticians adopted to better predict which players would be successful offensively. Instead of relying on collective wisdom from baseball insiders, statisticians decided to use an objective numbers analysis to measure in-game productivity, a strategy known as “Moneyball.”
Similarly, Dr Elemento and his colleagues developed a computational method that analyzes data from 48 different features of a drug—from molecular weight to details about its target—to determine whether it would be safe for clinical use.
Using machine learning, the researchers trained PrOCTOR on drugs approved by the US Food and Drug Administration (FDA) as well as drugs that failed clinical trials due to toxicity problems.
Based on this information, the researchers created “PrOCTOR scores” that could help distinguish drugs approved by the FDA from those that failed for toxicity.
They tested PrOCTOR on hundreds of additional drugs approved in Europe and Japan and using side effect data on approved drugs collected by the FDA.
The researchers said PrOCTOR was able to accurately recognize toxic side effects that were a consequence of a drug’s chemical features and its target. Records revealing that many of these drugs had failed clinical trials supported PrOCTOR’s accuracy.
“We were able to find several features that led to a very predictive model,” Dr Elemento said. “Hopefully, this approach could be used to determine whether it’s worth pursuing a drug prior to starting human trials.” 
for a clinical trial
Photo by Esther Dyson
Researchers have devised a method for predicting whether experimental drugs will fail clinical trials due to excessive toxicity.
They say the method, known as PrOCTOR, upends conventional wisdom about the criteria on which to evaluate new drugs’ safety.
Rather than exclusively examining molecular structure to determine viability, PrOCTOR combines a host of structural features and features related to how the drug binds to molecules in the body.
“We looked more broadly at drug molecule features that drug developers thought were unimportant in predicting drug safety in the past. Then, we let the data speak for itself,” said study author Olivier Elemento, PhD, of Weill Cornell Medicine in New York, New York.
Dr Elemento and his colleagues described PrOCTOR in Cell Chemical Biology.
PrOCTOR was inspired by an approach that baseball statisticians adopted to better predict which players would be successful offensively. Instead of relying on collective wisdom from baseball insiders, statisticians decided to use an objective numbers analysis to measure in-game productivity, a strategy known as “Moneyball.”
Similarly, Dr Elemento and his colleagues developed a computational method that analyzes data from 48 different features of a drug—from molecular weight to details about its target—to determine whether it would be safe for clinical use.
Using machine learning, the researchers trained PrOCTOR on drugs approved by the US Food and Drug Administration (FDA) as well as drugs that failed clinical trials due to toxicity problems.
Based on this information, the researchers created “PrOCTOR scores” that could help distinguish drugs approved by the FDA from those that failed for toxicity.
They tested PrOCTOR on hundreds of additional drugs approved in Europe and Japan and using side effect data on approved drugs collected by the FDA.
The researchers said PrOCTOR was able to accurately recognize toxic side effects that were a consequence of a drug’s chemical features and its target. Records revealing that many of these drugs had failed clinical trials supported PrOCTOR’s accuracy.
“We were able to find several features that led to a very predictive model,” Dr Elemento said. “Hopefully, this approach could be used to determine whether it’s worth pursuing a drug prior to starting human trials.”
for a clinical trial
Photo by Esther Dyson
Researchers have devised a method for predicting whether experimental drugs will fail clinical trials due to excessive toxicity.
They say the method, known as PrOCTOR, upends conventional wisdom about the criteria on which to evaluate new drugs’ safety.
Rather than exclusively examining molecular structure to determine viability, PrOCTOR combines a host of structural features and features related to how the drug binds to molecules in the body.
“We looked more broadly at drug molecule features that drug developers thought were unimportant in predicting drug safety in the past. Then, we let the data speak for itself,” said study author Olivier Elemento, PhD, of Weill Cornell Medicine in New York, New York.
Dr Elemento and his colleagues described PrOCTOR in Cell Chemical Biology.
PrOCTOR was inspired by an approach that baseball statisticians adopted to better predict which players would be successful offensively. Instead of relying on collective wisdom from baseball insiders, statisticians decided to use an objective numbers analysis to measure in-game productivity, a strategy known as “Moneyball.”
Similarly, Dr Elemento and his colleagues developed a computational method that analyzes data from 48 different features of a drug—from molecular weight to details about its target—to determine whether it would be safe for clinical use.
Using machine learning, the researchers trained PrOCTOR on drugs approved by the US Food and Drug Administration (FDA) as well as drugs that failed clinical trials due to toxicity problems.
Based on this information, the researchers created “PrOCTOR scores” that could help distinguish drugs approved by the FDA from those that failed for toxicity.
They tested PrOCTOR on hundreds of additional drugs approved in Europe and Japan and using side effect data on approved drugs collected by the FDA.
The researchers said PrOCTOR was able to accurately recognize toxic side effects that were a consequence of a drug’s chemical features and its target. Records revealing that many of these drugs had failed clinical trials supported PrOCTOR’s accuracy.
“We were able to find several features that led to a very predictive model,” Dr Elemento said. “Hopefully, this approach could be used to determine whether it’s worth pursuing a drug prior to starting human trials.”