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Cancer drug prices must come down, say leading research institutes

Cancer drug prices must come down, say leading research institutes. Science & Technology World Website

The high price of new cancer drugs is indefensible and unsustainable, say two of the world’s leading cancer research institutions, who propose a different way to develop them that could sideline big pharma.

“There is a clear and urgent necessity to lower cancer drug prices to keep lifesaving drugs available and affordable to patients,” say leading scientists from the Institute of Cancer Research in the UK and the University of Texas MD Anderson Cancer Center, where many important new cancer drugs have been invented, in a paper in the journal Cell.

In the US, cancer bills are the leading cause of personal bankruptcy, while in the UK, drugs that might prolong life are rejected for NHS use because of their price. Many new drugs have to be used in combination, adding to the cost. Treatment with the two new immunotherapy drugs nivolumab and ipilimumab costs $252,000, which is more than the median cost of a US home ($240,000 in 2016), they write.

Fantastic scientific work is going on – for instance, in sequencing cancer genomes – which should lead to advances in treatment, said Prof Paul Workman, chief executive of the Institute of Cancer Research in London, which is the world’s most successful academic cancer drug discovery organisation. “All this invention is meaningless if patients cannot afford these drugs,” he said.

“It is unsustainable. For those of us involved in research, it is disturbing that the amount of research that goes on and the success that is made is not translated into treatment for patients. And for patients it is a terrible situation.”

Pharmaceutical companies used to justify their prices by pointing to the high cost of clinical trials involving many thousands of patients. But that is no longer always necessary, the scientists say in their paper. The new targeted drugs require a test for a genetic biomarker to see whether patients will respond or not. That means the drug can be trialled on far fewer people. The drug crizotinib, used for advanced lung cancer, was approved following a trial involving only 347 patients, they point out. Trastuzumab (Herceptin) was first approved for advanced breast cancer and later for early breast cancer, increasing the market for the company but with no reduction in price.

“Some drugs are tested on 50 or 100 patients and yet these drugs still go to Nice [the National Institute for Healthand Care Excellence, which decides whether the NHS can afford a new drug] at the maximum price,” said Workman.

Workman, together with colleagues from the US and the Netherlands, proposes that academic discovery centres like his should forge relationships with new commercial partners – probably not the major drug companies but smaller biotech or generic drug firms.

Academics should take greater control of the drugs they discover, they argue, and join with small companies that will agree to cap the price when the drug reaches the market. They would not have the expectation of big profit margins, as the major pharmaceutical companies do. But in an era where drugs are tested on smaller populations and genetic testing means they are more likely to be effective, they would not need to “cost in” all the failed attempts at producing blockbusters, as the big companies do.

Workman said the institute was already talking to small companies about the possibility of a new way of developing more affordable cancer drugs. He believes other scientists will support the ideas in the paper. “We’re calling for a more mature and open conversation about how this could be done and offering a solution,” he said.

Soon, artificial intelligence could check your skin for cancer

Soon, artificial intelligence could check your skin for cancer

Artificial intelligence that diagnoses skin cancer as well as a dermatologist could one day rest in the palm of your hand.

A team from the Stanford Artificial Intelligence Laboratory set up a machine-learning AI to teach itself to diagnose cancer by looking at skin lesions just as a doctor would during an exam.

The team tested the algorithm against 21 board-certified dermatologists, and it performed just as well as its human counterparts.

"We realized it was feasible, not just to do something well, but as well as a human dermatologist," Sebastian Thrun, an adjunct professor in the Stanford Artificial Intelligence Laboratory, said in a statement. "That's when our thinking changed. That's when we said, 'Look, this is not just a class project for students, this is an opportunity to do something great for humanity.'"

Thrun is perhaps better known as one of the founders of both Udacity and Google X. A paper on the system co-authored by him and his colleagues appeared in the journal Nature Wednesday.

The researchers envision making the algorithm compatible with smartphones, putting skin cancer diagnosis into the hands of just about anyone anywhere.

"Early detection is critical, as the estimated five-year survival rate for melanoma drops from over 99 percent if detected in its earliest stages to about 14 percent if detected in its latest stages," the paper reads. "We developed a computational method which may allow medical practitioners and patients to proactively track skin lesions and detect cancer earlier."

The AI is a convolutional neural network, the same type of system Google uses to wallop masters of the ancient game Go.

The researchers used one of Google's AI systems -- the GoogleNet Inception v3 CNN architecture, which had already been taught to visually identify different objects. But it hadn't yet been trained to tell the difference between a malignant carcinoma and a harmless skin lesion, so the team pulled together a dataset to transform the neural network into a digital dermatologist.

"There's no huge dataset of skin cancer that we can just train our algorithms on, so we had to make our own," said Brett Kuprel, co-lead author of the paper and a graduate student in the Thrun lab. "We gathered images from the internet and worked with the medical school to create a nice taxonomy out of data that was very messy -- the labels alone were in several languages, including German, Arabic and Latin."

The coders collaborated with dermatologists and others in the medical field on the project.

"Advances in computer-aided classification of benign versus malignant skin lesions could greatly assist dermatologists in improved diagnosis for challenging lesions and provide better management options for patients," said Susan Swetter, professor of dermatology and co-author of the paper. "However, rigorous prospective validation of the algorithm is necessary before it can be implemented in clinical practice, by practitioners and patients alike."

Last year, Google announced that it trained an algorithm to spot diabetic retinopathy, which can cause blindness. Fortunately, while we wait for these sort of "doctor's helpers" forms of A.I. to hit hospitals and clinics, we can bide our time by letting the same sorts of systems kick our butts at Go and all sort of other games.

New way to test cancer drugs boosts hopes for personalised treatment

New way to test cancer drugs boosts hopes for personalised treatment. Science & Technology World Website

Hopes that doctors will one day offer personalised treatments for cancer patients have received a boost from a landmark study into the ability of a huge range of drugs to destroy different forms of the disease.

In the largest project of its kind, researchers tested how well 265 old and new drugs worked on cancer cells that harboured most of the important DNA mutations that drive various types of tumours.

In hundreds of cases, the scientists found that drugs which were either already in use or under development in laboratories destroyed cells when they carried key DNA mutations found in many different kinds of cancer.

The findings show how existing drugs could potentially be repurposed to treat new groups of patients, or be more effective in people whose tumours have specific genetic abnormalities. The work will also help researchers design drugs that can target cancers based on their DNA signatures.

“We want to understand which patients will respond to which cancer drug, and find the mutations that will allow us to select patients that will be most likely benefit,” said Matthew Garnett, a cancer biologist who led the research at the Sanger Institute near Cambridge.

The researchers began by gathering details on the genetic mutations known to cause cancer in 11,000 tissue samples that represented 29 different tumour types. Armed with the catalogue of mutations, they went on to identify 1,001 batches of cancer cells used in lab work that between them carried nearly all of the key mutations.

For the next step, the team treated the cells with 265 cancer drugs and noted which mutations most successfully predicted whether a drug killed the cancer cells or left them unscathed. In the future, the findings could inform genetic tests that allow doctors to personalise the treatments patients receive.

“There are so many genetic alterations in cancers, and there are so many drugs, that we can’t possibly test them all on patients. It’s not feasible, and it’s not ethically appropriate,” Garnett. “Cancer is so diverse that we really need to look across large sets of these cells to understand which patients will respond to a drug and which won’t.”

The study, published in the journalCell, confirms much that is already known about the mutations that make cancer cells susceptible to drugs. But it found plenty of completely new links between abnormal DNA and drug effects too, which will now be pored over in the hope of developing more powerful cancer treatments.

The findings already point to a way in which one cancer drug might be used more effectively. Mitomycin C is used to treat bladder cancer, but according to the study the drug is particularly effective against a subset of bladder tumours that carry a certain mutation. “With these kinds of data, we may be able to explain why some patients fare better than others,” Garnett said.

But more work is needed before the results can begin to help patients. The discoveries made with the cancer cells must be checked in animals before human clinical trials. “This will steer decisions about drug development immediately, and it’s possible that in two to five years, it will start to inform clinical practice,” Garnett added.

The Sanger team is now creating a web portal to share their data. Allowing cancer researchers around the world to see which batches of cancer cells most closely mirror the patient tumours they hope to treat.

Key Words : Cancer drug price



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