Discovering effective drug combinations may now be easier thanks to a screening platform made public today by St. Jude Children’s Research Hospital scientists. Many diseases, including cancers, require combinations of drugs whose effects are more than the sum of their parts to create effective treatment regimens. However, the number of new drugs and potential combinations has exploded, making classical screening methods impractical. To address that need, the St. Jude researchers created Combocat, a platform that combines machine learning and specialized liquid handling technology to enable larger combination screens. The platform’s capabilities were published in Nature Communications.
“The field of drug discovery has lacked a way to deal with the sheer number of potential combinations, which would require impractical amounts of experimental materials to screen. We designed Combocat to use minimal resources and enable scientists to test massive numbers of drug combinations, rapidly nominating those most likely to have synergistic effects to explore further.”
Paul Geeleher, senior co-corresponding author, PhD, St. Jude Department of Computational Biology
“By tying together leading methods in machine learning and drug dispensing technology to power high-throughput combination screening, we performed experiments on a scale that was not feasible before,” said first and co-corresponding author Charlie Wright, PhD, St. Jude Department of Computational Biology.
As a proof of principle of the platform’s capabilities, the researchers tested 9,045 pairs of drugs against a neuroblastoma cancer cell line. The screen uncovered multiple drug pairs with strong synergistic effects, and the top findings were confirmed with additional experiments. The results demonstrated that Combocat can efficiently uncover promising drug combinations at scale.
To achieve scale, Combocat integrates miniaturized drug dispensing with machine learning. For drug dispensing, sonic technology enables customized and efficient experimental layouts. “We incorporated acoustic liquid handlers that use sound waves to transfer tiny droplets of drugs very precisely,” Wright said. “They use the exact minimum of each liquid you need, allowing for the use of far less material than conventional pin or pipette-based techniques, and increasing the number of testable combinations.”
Machine learning helps fill in the picture, using one of Combocat’s two modes, “dense mode” to inform its “sparse mode.” In dense mode, the researchers measured every possible dose pairing for each drug combination. Alongside this dense mode, a sparse mode allows for even tighter resource management by predicting the full results from only a small fraction of the original data. The sparse mode model was trained on hundreds of drug combination experiments generated with the platform’s dense mode approach. When the researchers compared the machine learning predictions to measured values, they found them to be highly consistent, providing confidence in the approach.
“We created two screening ‘modes’, with something of a tradeoff between them,” Geeleher said. “We optimized the sparse mode approach for scale, but it trades detail for efficiency, while we optimized the dense mode to obtain ultra-reliable measurements, which can’t scale. However, we showed that Combocat can combine them to analyze more combinations and validate them more rapidly than traditional approaches.”
Combocat continues a St. Jude legacy of drug combination therapy innovation, providing a way to easily screen combinations not just for cancer researchers, but for any disease in need of new treatments.
“We’ve created a platform that’s free, open-source and highly usable that could become a strong standard in the drug combination discovery field,” Geeleher said. “Combocat can help expedite the identification of potentially safe and effective drug combinations, which could ultimately yield useful and potentially practice-changing new drug combinations in the clinic.”
(Newswise/R)
