Big data enters the fray in the war against cancer as pharmaceutical laboratories combine drugs like cocktails to kill cancerous cells
- Drug cocktails are effective in suppressing HIV/Aids, and combination therapy is increasingly being applied to the battle front in the war on cancer
- Pharmaceutical companies are using big data analysis and machine learning to identify the most appropriate mix out of millions of possible drug combinations
Pharmaceutical researchers are using big data analysis and machine learning to help them find the most appropriate cocktail of drugs to fight cancer, as combination therapy – the use of two or more drugs to treat a disease – is increasingly being pursued in their quest to find a cure.
Unlike the cocktail used in fighting HIV/Aids, there are millions of possible drug combinations to consider for fighting different types of cancerous cells, so finding the right pair or trio is akin to finding needles in a haystack, according to Martin Culjat, senior vice-president, scientific & regulatory Affairs, at San Diego, California-based Dthera Sciences.
There are over 300 approved cancer drugs, with thousands in the approval pipeline, that can be combined to target some 4.5 million cancer-coding gene mutations that have been identified, he wrote in a curematch.com blog post.
To help drug developers find more promising targets, technology firms are using big-data and machine learning tools to narrow them down.
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Engine Biosciences, a Singapore-based firm that helps clients discover drugs faster, is tapping the opportunities from unmet demand in diseases that are particularly prevalent in Asia, such as liver cancer, its chief executive Jeffrey Lu said during the China Healthcare Investment Conference last week.