Training plus a placebo may make a drug more effective

The placebo effect can be incredibly powerful, performing nearly as well as carefully designed and tested drugs, substituting for actual surgeries and even generating side effects. But it’s a tricky thing to apply outside of experiments. After all, not everyone will have a strong placebo response, so it’s unethical to use it in place of actual treatments.

Now, some researchers in Germany have figured out a way to harness the placebo effect to increase the impact of a normal drug treatment. The procedure involves getting patients to associate a taste with a powerful drug that has problematic side effects. Once the association is made, the patients were given a mix of normal drugs and a placebo, along with the flavor they’d associated with the drug. This experiment enhanced their response to the drug, providing an avenue to potentially reduce its dose and, thus, its side effects. And the whole thing worked despite the fact that the patients knew exactly what was going on.

The drug at issue, cyclosporine A, is a powerful suppressor of the immune system, which makes it useful for patients who have received organ transplants or who have a strong autoimmune disorder. But the immune system isn’t the only system affected by this drug; it also kills off kidney and nerve cells and causes heart problems and hypertension. These effects are independent of any changes to the immune system, but nobody has figured out a way to target the body’s response specifically to immune cells. As a result, people taking this drug have to carefully balance its useful features against its toxicity.

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A critical analysis of the latest cellphone safety scare

Last night, a fellow editor emailed me a link to yet another study purporting to show that cellphone use could be associated with cancer. This one was worth looking at in more detail, however, because it purported to see an increase in a specific cancer—the same type of cancer that was increased in a problematic US government study.

A quick glance at the study identified significant issues with its primary conclusion. Normally, at this point, the decision would be to skip coverage unless the study picked up unwarranted attention from the rest of the media. (See: Scott Kelly’s DNA). But in this case, we thought we’d describe how we went about evaluating the paper, since it could help more people identify similar issues in the future.

Background checks

The first step in evaluating a scientific paper is to get ahold of a copy of the paper. Fortunately, this one has been placed online by an organization that consistently promotes the idea that cell phones create health risks. The Environmental Health Trust’s involvement shouldn’t be seen as a positive or a negative; they’ve promoted very low-quality material in the past, but the organization would undoubtedly promote higher quality studies if those agreed with its stance.

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Biomedical startup AesculaTech is creating a new, more patient-friendly drug delivery system

“Reverse chocolate” — that’s how AesculaTech co-founder and chief science officer Niki Bayat describes the material created by its proprietary technology. Chocolate is solid until heated, when it melts deliciously into liquid. AesculaTech’s material, on the other hand, is a liquid at low temperatures, turns into a gel when heated and then reaches its final, solid state at body temperature. (If you are having a hard time visualizing the process or are distracted by thoughts of dessert, there’s a gif below that shows it being injected into a 37 degree Celsius water bath).

By changing the composition of the material, AesculaTech is able to control the temperature at which it transitions into different states. While the liquid is transforming into a gel, different compounds, including medications, can be added to it. Bayat and co-founder Andrew Bartynski, who are in Y Combinator’s latest startup batch, say it has a wide range of potential applications, including pharmaceuticals, medical devices, cosmetics and textiles.

First, the material is being used in a treatment for dry eye syndrome. AesculaTech’s founders say the condition affects more than 20 million people in America, who collectively spend $3.5 billion a year treating symptoms that can include a burning, scratchy sensation, discharge and impaired vision. Prescription treatments like Restasis and Xiidra can take weeks or even months to reach full effectiveness, while over-the-counter eye drops bring only minutes of relief and need to be reapplied constantly. AesculaTech’s treatment, however, is designed to be administered by a doctor during a quick, in-office procedure and last for about a year. It is slated to be commercially available by 2019.

Bayat and Bartynski, AesculaTech’s chief executive officer, met in 2012 while doing graduate work in chemical engineering at the University of Southern California and discovered a shared interest in unique classes of materials. As graduation drew closer, they began to think of what they wanted to do next.

“One day I was talking to my dad and I heard from him that he’d been diagnosed with glaucoma, but because he’d had heart surgery, he couldn’t have another one,” says Bayat. “I kept thinking there should be a better way to treat glaucoma and so I started working on this project with Andrew and a few other people.”

The team decided to focus on dry eye syndrome first because it is easier to treat, but they plan to work on glaucoma medication in the future. The treatment starts off as an injectable liquid that is inserted into the patient’s tear duct by a doctor. It turns into a solid after raising to body temperature, forming a tiny plug that keeps tears from draining away from the surface of the eye.

AesculaTech has already performed pre-clinical animal trials that show its dry eye treatment creates statistically significant increases in tears on eyes and are preparing for human trials to bring it closer to approval from the Food and Drug Administration.

Because it only needs to be applied once a year, the treatment addresses another important health issue: medication compliance. Many patients don’t stick to drug regimens for chronic conditions as directed by their physicians even though it reduces the efficacy of their medicines. In the case of antibiotics, patient non-compliance can also impact public health by increasing bacterial resistance. As it branches out beyond ophthalmic treatments, Bayat and Bartynski hope their technology will form the foundation of a new way of taking medication that is more realistic for patients to follow.

“To allow people to get the treatment they need without having to interact with medication on a daily basis is hugely valuable because you deliver the treatment to them continuously so they don’t have to interrupt their daily life or be bound to an eye drop or pill pack,” says Bartynski.

“It’s not only about the treatment of dry eye or glaucoma,” adds Bayat. “We are thinking of a platform for drug delivery technology.”

AesculaTech’s founders say the technology can also be used to create materials for a wide range of products, like cosmetics and smart textiles that are temperature responsive. The startup’s plan is to form partnerships with companies, license their technology and help them bring new products to market. The material hasn’t been tested for food products yet, but Bayat and Bartynski say they haven’t seen any indications that it isn’t edible, so reverse chocolate may one day be more than just a simile.

Pfizer CEO gets 61% pay raise—to $27.9 million—as drug prices continue to climb

As drug giant Pfizer Inc. hiked the price of dozens of drugs in 2017, it also jacked up the compensation of CEO Ian Read by 61 percent, putting his total compensation at $27.9 million, according to financial filings reported by Bloomberg.

Pfizer’s board reportedly approved the compensation boost because they saw it as a “compelling incentive” to keep Read from retiring. He turns 65 in May. As part of the deal, Read has to stay on through at least next March and is barred from working with a competitor for a minimum of two years after that.

According to Bloomberg, Read’s compensation included in part a salary of $1.96 million, a $2.6 million bonus, $13.1 million in equity awards linked to financial goals and stock price, as well as an $8 million special equity award that will vest if the company’s average stock return goes above 25 percent for 30 consecutive trading days before the end of 2022.

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Reverie Labs uses new machine learning algorithms to fix drug development bottlenecks

Developing new medicines can take years of research and cost millions of dollars before they are even ready for clinical trials. Several biotech startups are using machine learning to revolutionize the process and get drugs into pharmacies more quickly. One of the newest is called Reverie Labs, which is part of Y Combinator’s latest batch. The Boston-based company wants to fix a critical bottleneck in the drug development process by speeding up the process of identifying promising molecules using recently published machine learning algorithms.

Reverie Labs’ founders Connor Duffy, Ankit Gupta and Jonah Kallenbach, who named their company after a pivotal detail in the HBO series “Westworld,” explain that its tech analyzes early ideas for molecules from pharmaceutical scientists and suggests possible improvements to shorten the amount of time it takes to reach clinical trials. Duffy says Reverie Labs’ ambition is to “become a full service molecule-as-a-service company.” It’s already partnered with several biotech companies and academic institutes working on treatments for diseases including influenza and cancer.

Reverie Labs specializes in the lead development stage, which is when researchers focus on prioritizing and optimizing molecules so they can go to animal and human clinical trials more quickly. Pharmaceutical scientists need to first identify the proteins that cause a disease and then find molecular compounds that can bind to those proteins. Then it becomes a process of elimination as they narrow down those molecules to ones that not only create the results they want, but are also suitable for animal and human studies.

Before clinical trials can start, however, they need to evaluate molecules very carefully in order to understand things like how they are metabolized by the body and their potential toxicity.

“I’ve heard it compared to juggling eight balls at once or playing whack-a-mole,” says Duffy. “You want your compound to be very safe before you put it in people, you want to be efficacious and go where you want it to go in your body and you don’t want side effects. There are a lot of problems drug companies need to think about before putting a molecule in a human, and when you fix one problem, you often come up with another problem. We want to alleviate that by looking at all problems at the same time.”

Lead development is very labor intensive and requires the work of many medicinal chemists. Reverie Labs’ founders say it often takes more than $100 million and two years per drug before a final selection of molecules are ready for clinical trials. Reverie Labs wants to set itself apart from other startups focused on solving the same problem by taking recently-discovered machine learning techniques, and applying them to drug development.

“The machine learning algorithms we implemented are some of the most promising advances that have been published in the past couple of years,” says Kallenbach.

First, molecules are “featurized,” or turned into representations that work with machine learning algorithms. Reverie Labs’s tech creates proprietary featurizations based on quantum chemical calculations, then uses them to analyze the molecules’ properties and how they may act in the body. Afterwards, it selects molecules that have the potential to do well in clinical trials or suggests new molecules based on what properties scientists need.

In addition to the machine learning algorithms it uses, Reverie Labs founders say one of the startup’s key differentiators is that it trains its models on customers’ proprietary in-house datasets, which means the tech can integrate more smoothly into existing drug development workflows. Reverie Labs’ software also runs on customers’ virtual private clouds, giving them more security.

While using artificial intelligence to develop new drugs seemed almost like science fiction just a few years ago, the space is developing quickly. Last month, BenevolentAI, one of the first companies to apply deep learning to drug discovery, bought biotech company Promixagen’s operations in the United Kingdom, which it says will make it the first artificial intelligence company to cover the entire drug research and development process. Atomwise, another AI-based drug discovery startup, announced at the beginning of this month that it has raised a $45 million Series A. Other notable startups include Nimbus Therapeutics and Recursion Pharmaceuticals.

The process of creating new drugs is currently very complicated, slow and extremely expensive. With so much room for improvement, the work done by various AI-based startups to improve the process don’t necessarily overlap.

“The space doesn’t seem like a zero sum game at all,” says Gupta. “Many players can be involved and the fact that other startups are interested shows that there is legitimacy to the technology.”

“The end result is trying to delivery life-saving cures faster and more cheaply,” adds Duffy. “We don’t really feel any competitiveness. We want everyone to succeed.”

AI trained to spot heart disease risks using retina scan

The idea behind using a neural network for image recognition is that you don’t have to tell it what to look for in an image. You don’t even need to care about what it looks for. With enough training, the neural network should be able to pick out details that allow it to make accurate identifications.

For things like figuring out whether there’s a cat in an image, neural networks don’t provide much, if any, advantages over the actual neurons in our visual system. But where they can potentially shine are cases where we don’t know what to look for. There are cases where images may provide subtle information that a human doesn’t understand how to read, but a neural network could pick up on with the appropriate training.

Now, researchers have done just that, getting a deep-learning algorithm to identify risks of heart disease using an image of a patient’s retina.

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Testosterone may protect men from autoimmune diseases

Testosterone. Source of prostates and testes, muscles and machismo, chest hair, and according to some, even math skills. Its levels are only one of the biological differences between males and females, but they may help to explain another: the discrepancies in the incidence of autoimmune diseases.

Women are three to nine times more likely than men to suffer from autoimmune diseases, including multiple sclerosis (MS), Grave’s disease, celiac disease, systemic lupus erythematous, and rheumatoid arthritis. Not only do women get these diseases at higher rates, they usually get them at younger ages.

Men’s higher testosterone levels—about seven to eight times higher than women’s—have been shown to be protective for MS in both mice and men. But it was not clear exactly how this worked. Recent work in a mouse model of MS has filled in the downstream effectors that mediate testosterone’s protective effects. These effectors might be useful as therapeutics, whereas testosterone use really isn’t, especially for women, who are the ones who need it most.

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This small robotic stingray could be the future of biological bots

 What do you get when you smush a bunch off live heart cells, specialized biomaterials, and electrodes into a tiny, stingray-shaped package? If you said “lunch” than you’re wrong. Instead, you get the first example of bioinspired robotic systems that can imitate nature using both electrical and organic components. The resulting project – a 10mm long robot that can swim… Read More