Category: Methodology

Muyembe Had Knowledge of an Ebola Cure Before Clinical Trial

(p. A1) In a medical breakthrough that compares to the use of penicillin for war wounds, two new drugs are saving lives from the virus and helping uncover tools against other deadly infectious diseases. They were proven effective in a gold-standard clinical trial conducted by an international coalition of doctors and researchers in the middle of armed violence.

. . .

(p. A10) Dr. Muyembe set out on his path to an Ebola treatment during the 1995 outbreak. He transferred blood from five survivors to eight patients, hoping that the antibodies that kept some people alive would keep others from dying. Seven of the patients who received the blood transfusion recovered.

He published the results in a scientific journal in 1999. Other researchers said the study was small and had failed to include a control group, a comparison set of patients who weren’t given the treatment, to fully test its efficacy.

For the full story, see:

Betsy McKay. “From a War Zone Came an Unexpected Cure for Ebola.” The Wall Street Journal (Thursday, October 31, 2019): A1 & A10.

(Note: ellipsis added.)

(Note: the online version of the story has the date Oct. 30, 2019, and has the title “‘Ebola Is Now a Disease We Can Treat.’ How a Cure Emerged From a War Zone.”)

“Our Creative Yield Increases with Age”

(p. C1) . . . precocious achievement is the exception, not the norm. The fact is, we mature and develop at different rates. All of us will have multiple cognitive peaks throughout our lives, and the talents and passions that we have to offer can emerge across a range of personal circumstances, not just in formal educational settings focused on a few narrow criteria of achievement. Late bloomers are everywhere once you know to look for them.

. . .

What about creativity and innovation? That realm must belong to the young, with their exuberance and fresh ideas, right? Not necessarily. For instance, the average age of scientists when they are doing work that eventually leads to a Nobel Prize is 39, according to a 2008 Northwestern University study. The average age of U.S. patent applicants is 47.

Our creative yield increases with age, says Elkhonon Goldberg, a clinical professor of neurology at New York University. Dr. Goldberg thinks that the brain’s right and left hemispheres are connected by a “salience network” that helps us to evaluate novel perceptions from the right side by comparing them to the stored images and patterns on our left side. Thus a child will have greater novel perceptions than a middle-aged adult but will lack the context to turn them into creative insights.

Take Ken Fisher, who today runs Fisher Investments, a stock fund with $100 billion under management and 50,000 customers. After graduating from high school, he flunked out of a junior college. “I had no particular direction,” he said. He went back to school to study forestry, hoping for a career outdoors, but switched to economics and got his degree in 1972. In his early 20s, he hung out his shingle as a financial adviser, following his father’s career. To bring in extra money, he took construction jobs, and he played slide guitar in a bar. But he also read and read: “Books about management and business—and maybe thirty trade magazines a month for years,” he says. By the time he reached his 30s, an idea had gelled that would make him his fortune. As he puts it, during that period of reflection, “I developed a theory about valuing companies that was a bit unconventional.”

For the full commentary, see:

Rich Karlgaard. “It’s Never Too Late to Start a Brilliant Career; Our obsession with early achievement shortchanges people of all ages. Research shows that our brains keep developing deep into adulthood and so do our capabilities.” The Wall Street Journal (Saturday, May 4, 2019): C1-C2.

(Note: ellipses added.)

(Note: the online version of the commentary has the date May 3, 2019, and has the same title as the print version.)

The the passages quoted above, are from a commentary that is adapted from:

Karlgaard, Rich. Late Bloomers: The Power of Patience in a World Obsessed with Early Achievement. New York: Currency, 2019.

The research by Elkhonon Goldberg, mentioned above, is described in:

Goldberg, Elkhonon. Creativity: The Human Brain in the Age of Innovation. New York: Oxford University Press, 2018.

Genetic Diversity Limits Number of Patients for Large Randomized Trials

(p. A9) . . . in the era of personalized medicine, where care can be tailored to a person’s genetic make-up and doctors analyze a patient’s DNA to figure out treatments, big trials are falling out of favor.

. . .

To Ursula Matulonis, who treats ovarian cancer and other women’s cancers at Dana-Farber Cancer Institute in Boston, the debate over trial size has a special urgency: Many of her patients are desperately sick.

“You can’t wait years to get these medications approved. What we are dealing with are women with cancers and their lifespans are limited. They need medications and they need them now, and they are not looking to wait for five years,” says Dr. Matulonis, chief of gynecologic oncology.

That is why flexibility in a trial’s size is crucial, she contends. “As we become more genetically astute, and understand a type of cancer better, I think those large randomized trials will be hard to do. There won’t be that many patients,” that fit into one big group, she added.

One of her patients, Janet Sheehan, is grateful for the small clinical trial she has taken part in for the past five years. Ms. Sheehan, a 63-year-old nurse near Boston, was diagnosed with advanced ovarian cancer a dozen years ago. It has come back three times, and at one point she learned that she had a mutation in the BRCA1 gene which indicates a strong predisposition to breast and ovarian cancer. Dana-Farber suggested in 2013 that she go on a randomized 90-person trial for a drug named Olaparib that showed promise among women with a BRCA1 gene mutation.

She has been taking capsules twice a day and going for check-ups every 28 days since then. Despite side-effects, she has been able to work and carry on. “I have seen my children [grow] and I have seen grandchildren I didn’t have then,” she says. Ms. Sheehan was on a randomized trial where both groups of patients received treatment with Olaparib. One group got the drug only, the other received Olaparib in combination with another drug, her doctor said, adding, “there was no placebo.”

In remission, Ms. Sheehan has become a fan of small trials that offer women such as herself options. She also is a realist. If Olaparib fails, she hopes other trials now going on may yield treatments for her.

For the full commentary, see:

Lucette Lagnado. “Is the Big Clinical Trial Obsolete? The New York Times (Wednesday, May 30, 2018): A9-A10.

(Note: ellipses added; bracketed word in original.)

(Note: the online version of the commentary has the date May 29, 2018, and has the title “Are Big Clinical Trials Relevant? Researchers Disagree.” The sentence that starts with “In remission,” was in the online version, but not the print version. )

Mandated Long Clinical Trials Favor Trivial Incremental Drugs and Impede Magic Bullet Cures

(p. B1) AstraZeneca PLC’s new cancer research chief, José Baselga, wants the company to prioritize early-stage cancers over advanced disease when developing new cancer drugs. If successful, his unorthodox strategy could reap rewards for both patients—the potential to cure cancer is much greater when it is treated early—and company coffers.

The approach turns the tried-and-tested model of cancer drug development on its head. Typically, drug companies aim their new cancer drugs at patients with advanced forms of the disease who have exhausted other treatment options. Of the more than 30 new drugs for solid tumors approved for sale in the U.S. since the start of 2014, just two targeted early cancer.

That is largely because there is a clear-cut case for testing new drugs on patients with advanced cancer, as they don’t have other options. What’s more, measuring a new medicine’s effect in advanced cancer is straightforward: a meaningful extension in survival can usually be measured in months. Such patients are also often more willing to try experimental drugs, and regulators have smoothed the path for treatments that show they can prolong lives by delaying tumor growth in advanced cancer.

. . .

(p. B5) “One thing with early stage disease, you have to be able to cure patients,” said Daniel Chen, who spent more than a decade running cancer drug development projects at Roche Holding AG. “The majority of cancer drugs delay cancer growth, they don’t cure patients.” Dr. Chen is now chief medical officer at biotech startup IGM Biosciences Inc.

Running clinical trials could also be difficult, as it would involve persuading patients to try experimental drugs when they might already be cured.

Another challenge is measuring the drug’s effectiveness. In patients whose cancer is diagnosed and treated early, it would take years to determine whether a new drug meaningfully extended survival, making for very long clinical trials.

For the full story, see:

Denise Roland. “Drug Giant Tests Bold Tactic to Battle Cancer.” The Wall Street Journal (Tuesday, May 28, 2019): B1 & B5.

(Note: ellipsis added.)

(Note: the online version of the story has the date May 27, 2019, and has the title “Drug Giant Tries New Tactic to Fight Cancer.”)

Sulston Earned Nobel, Not by “Bold Theories,” But by “Gathering Data for the Sake of Seeing the Whole Picture”

(p. A9) The nematode worm known as C. elegans is only a millimeter long and leads what appears to be a fairly dull existence. It eats bacteria, wriggles around and reaches adulthood in three days. “It consists basically of two tubes, one inside the other,” the English biologist John Sulston wrote in a memoir.

Although some colleagues thought he was wasting time, Dr. Sulston for years spent up to eight hours a day peering through microscopes at these worms. His findings on the genetics of worms won him a Nobel Prize for physiology in 2002.

. . .

His work didn’t involve “bold theories or sudden leaps of understanding,” he wrote in a 2002 memoir, “The Common Thread.” Instead, he saw his role as “gathering data for the sake of seeing the whole picture.”

For the full obituary, see:

James R. Hagerty. “Exhaustive Study of a Worm Ended in Nobel Prize.” The New York Times (Saturday, March 17, 2018): A9.

(Note: ellipsis added.)

(Note: the online version of the obituary has the date March 16, 2018, and has the title “Sulston’s Work on Lowly Worm Led to Major Role in Mapping Human Genome.”)

Sulston’s 2002 memoir, mentioned above, is:

Sulston, John, and Georgina Ferry. The Common Thread: A Story of Science, Politics, Ethics and the Human Genome. Washington, DC: Joseph Henry Press, 2002.

“Results Are Often Suspiciously Consistent with the Political Dispositions of the Modeler”

(p. 14) For scholars and popular writers alike, the Great Depression has long been a kind of economic Rorschach test. Free marketers look at the economic disaster and blame the Smoot-Hawley tariff, which inaugurated a global trade war; monetarists attack the Federal Reserve for its tight-money policies; Keynesians berate Herbert Hoover for his attempts to balance the budget as the crisis worsened.

. . .

Generally, . . . , Morris is remarkably evenhanded, giving both sides of scholarly debates in deep detail. This is particularly the case in his coverage of the New Deal, where he weighs the practical effects of the dizzying array of policies begun by Roosevelt, from his devaluation of the dollar to the creation of the Civilian Conservation Corps. And Morris explains in accessible prose how economists have used modeling to study the New Deal (he wryly notes that this “is still a work in progress — if only because results are often suspiciously consistent with the political dispositions of the modeler”).

Continue reading ““Results Are Often Suspiciously Consistent with the Political Dispositions of the Modeler””

Complexity of Drug Discovery Requires More Than A.I.

(p. B1) Every two years, hundreds of scientists enter a global competition. Tackling a biological puzzle they call “the protein folding problem,” they try to predict the three-dimensional shape of proteins in the human body.

. . .

Mohammed AlQuraishi, a biologist who has dedicated his career to this kind of research, flew in early December to Cancun, Mexico, where academics were gathering to discuss the results of the latest contest. As he checked into his hotel, a five-star resort on the Caribbean, he was consumed by melancholy.

The contest, the Critical Assessment of Structure Prediction, was not won by academics. It was won by DeepMind, the artificial intelligence lab owned by Google’s parent company.

. . .

“It is not that machines are going to replace chemists,” said Derek Lowe, a longtime drug discovery researcher and the author of In the Pipeline, a widely read blog dedicated to drug discovery. “It’s that the chemists who use machines will replace those that don’t.”

. . .

(p. 5) Working with two other computer scientists, the DeepMind researcher Rich Evans homed in on protein folding. They found a game that simulated this scientific task. They built a system that learned to play the game on its own, and the results were promising enough for DeepMind to greenlight a full-time research project.

The protein folding problem asks a straightforward question: Can you predict the physical structure of a protein — its shape in three dimensions?

If scientists can predict a protein’s shape, they can better determine how other molecules will “bind” to it — attach to it, physically — and that is one way drugs are developed. A drug binds to particular proteins in your body and changes their behavior.

In the latest contest, DeepMind made these predictions using “neural networks,” complex mathematical systems that can learn tasks by analyzing vast amounts of data. By analyzing thousands of proteins, a neural network can learn to predict the shape of others.

. . .

Mr. Hassabis said DeepMind was committed to solving the protein folding problem. But many experts said that even if it was solved, more work was needed before doctors and patients benefited in any practical way.

“This is a first step,” said David Baker, the director of the Institute for Protein Design at the University of Washington. “There are so many other steps still to go.”

As they work to better understand the proteins in the body, for instance, scientists must also create new proteins that can serve as drug candidates. Dr. Baker now believes that creating proteins is more important to drug discovery than the “folding” methods being explored, and this task, he said, is not as well suited to DeepMind-style A.I.

DeepMind researchers focus on games and contests because they can show a clear improvement in artificial intelligence. But it is not clear how that approach translates to many tasks.

“Because of the complexity of drug discovery, we need a wide variety of tools,” Dr. Alvarez said. “There is no one-size-fits-all answer.”

For the full story, see:

Cade Metz. “Making New Medicines With a Spoonful of A.I.” The New York Times (Wednesday, Feb. 6, 2019): B1 & B5.

(Note: ellipses added.)

(Note: the online version of the story has the date Feb. 5, 2019, and has the title “Making New Drugs With a Dose of Artificial Intelligence.”)