Scientists develop Computer Program to help repurpose Drugs to Combat new Disease

Scientists based in Boston have developed a computer program to help reveal both good and bad side effects of known drugs on disease. The study was published in Nature Communications and is currently available to read online. The scientists hope that their research could be used to find new uses for drugs which have already undergone trials and are on the market. In order to demonstrate their findings, the researchers concentrated on two different drugs, which have been developed to help two different conditions, however both have an effect on the cardiovascular system, one for benefit, the other raises risk.

Scientists develop Computer Program to help repurpose Drugs to Combat Disease

Traditionally, drugs have been developed for the market to treat a particular disease or condition. The scientists claimed that this approach risks falling short of using the already developed drugs to their full potential. Equally, focusing on just one disease may not enable scientists to recognise possible toxicity or other effects of a drug, or unintended benefits that may occur. Some studies have been recently published that have found a new use for a previously researched drug. These could be found via network-based drug-disease proximity that links the drugs and the disease they target but it is important that the new use for the drug has been properly validated. 

The team of scientists realised that because the drugs had already been approved and were in use for clinical practice, that it would be possible to use the large-scale patient data which is normally collected during routine healthcare. The data is already used to discover evidence for harm, use, effectiveness and the value of medications and is added to the body of evidence already published through randomised, controlled trials. These are usually run to enable the drug to be approved, but can be limited to small numbers of participants, a short time-scale and follow-up and a lack of representation of the general populations who will be prescribed the drug.

In order to take full advantage of this data, the scientists developed a systems pharmacology-based platform which enabled them to quantify the interaction between drug and disease through the longitudinal data of over 220 million patients. They used lab studies of human vascular cells to qualify possible drug mechanisms, checking the effects of two drugs on the cardiovascular system. The drugs they chose were not currently used to treat heart problems: carbamazepine, which is prescribed to treat epilepsy and neuropathic pain and hydroxycholoquine, which is used to treat rheumatoid arthritis and malaria.

The scientists found that carbamazepine was associated with an increased risk of developing cardiovascular disease when compared to the other drugs. It is a widely used drug, but it has previously been reported to aggravate heart problems. The patient data validated their hypothesis, although further studies are needed. 

Hydroxycholoquine, on the other hand, was found to offer a protective effect against cardiovascular disease. It is a drug that was approved many years ago, but only recently scientists have found what makes it so effective as it suppresses inflammatory responses. Some of that protective response has now been found to be effective against cardiovascular disease. Again, these results were borne out through the patient data, although more research is needed to discover whether the drug can improve outcomes for other patients who may have other risk factors for heart disease.

The scientists concluded that their strategy to discover new drug-disease indications, learn about undesirable side effects and understand the potential way that these approved drugs work could help fill a gap in patient care and drug development. they felt that their system was over 70% accurate. The tools that they used to assess the drugs have been made available for other scientists to use.

Feixiong Cheng, et al., Network-based approach to prediction and population-based validation of in silico drug repurposing, Nature Communications, 2018; 9 (1)

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