Combination data finds a possible path to personalised medicine

February 25, 2015 − by Suzanne Elvidge − in Big data in research, Data mining, Drug development − No Comments

Personalised medicine is the Holy Grail of many drug development programmes, particularly in cancer medicine, where different drugs can be combined to improve efficacy and battle the development of resistance, which is the cause of 90% of the cases where metastatic cancer treatment fails. However, finding the right combination and ratio of drugs for any one patient, that takes into account the mix of cells found in tumours, and balances the safety and toxicity, is a huge challenge.

Researchers from the University of California, Los Angeles are working on a drug discovery platform that could tackle these issues. The research was published in ACS Nano. The platform, Feedback System Control.II, or FSC.II, creates combinations of three nanotechnology-enhanced medications and one standard therapeutic. These are simultaneously optimised for efficacy against multiple breast cancer cell lines and safety against multiple control cell lines.

The tool looks at drug efficacy tests and analyzes the physical traits of cells and other biological systems to create personalized ‘maps’. These are designed to show the most effective and safest drug-dose combinations. The system doesn’t require genetic information; if a drug begins to fail, FSC.II can recommend a new combination.

“Drug combinations are conventionally designed using dose escalation,” said Dean Ho of the Jane and Jerry Weintraub Center for Reconstructive Biotechnology at the School of Dentistry at UCLA. “Until now, there hasn’t been a systematic way to even know where the optimal drug combination could be found, and the possible drug-dose combinations are nearly infinite. FSC.II circumvents all of these issues and identifies the best treatment strategy.”

The researchers looked at combinations of four commonly-used breast cancer drugs (doxorubicin, mitoxantrone, bleomycin and paclitaxel) in breast cancer, as well as assessing the use of nanodiamonds, which are by-products of conventional mining and refining operations. Nanodiamonds bind tightly to drugs, which makes it more difficult for the cancer cells to pump the drugs out.

The combinations of drugs that included nanodiamond formulations were safer and more effective than the drug-only combinations. The optimised nanodrug combinations were also better than randomly designed nanodrug combinations.

“This study has the capacity to turn drug development, nano or non-nano, upside-down,” says Ho. “Even though FSC.II now enables us to rapidly identify optimized drug combinations, it’s not just about the speed of discovering new combinations. It’s the systematic way that we can control and optimize different therapeutic outcomes to design the most effective medicines possible.”

According to the researchers, FSC could be used for both nanotechnology-modified and unmodified therapeutic optimizations to create phenotypic personalized medicine.

“This optimized nanodrug combination approach can be used for virtually every type of disease model and is certainly not limited to cancer,” says Chih-Ming Ho. “Additionally, this study shows that we can design optimized combinations for virtually every type of drug and any type of nanotherapy.”





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