Opiates cause addiction, new substances do not

They are a blessing for patients suffering from severe pain, but they also have serious side effects: Opioids, and above all morphine, can cause nausea, dizziness and constipation and can also often cause slowed breathing that can even result in respiratory failure. In addition, opiates are addictive — a high percentage of the drug problem in the USA is caused by pain medication, for example.

In order to tackle the unwanted medical and social effects of opioids, researchers all over the world are searching for alternative analgesics. Prof. Dr. Peter Gmeiner, Chair of Pharmaceutical Chemistry is one of these researchers. “We are focusing particularly on the molecular structures of the receptors that dock onto the pharmaceutical substances,” says Gmeiner. “It is only when we understand these on the atomic level that we can develop effective and safe active substances.” Collaborating with an international team of researchers, Prof. Gmeiner discovered an active substance in 2016 that bonds to known opioid receptors and that offers the same level of pain relief as morphine, even though it has no chemical similarity to opiates.

New approach: Adrenaline receptors instead of opioid receptors

Peter Gmeiner is currently following a lead that seems very promising: “Many non-opioid receptors are involved in pain processing, but only a small number of these alternatives have as yet been validated for use in therapies,” he explains. Gmeiner and a team of researchers from Erlangen, China, Canada and the USA have now turned their attention to a new receptor that is responsible for binding adrenaline — the alpha 2A adrenergic receptor. There are already some analgesics that target this receptor such as brimonidine, clonidine and dexmedetomidine. Gmeiner: “Dexmedetomidine relieves pain, but has a strong sedative effect, which means its use is restricted to intensive care in hospital settings and is not suitable for broader patient groups.”

The aim of the research consortium is to find a chemical compound that activates the receptor in the central nervous system without a sedative effect. In a virtual library of more than 300 million different and easily accessible molecules, the researchers looked for compounds that physically match the receptor but are not chemically related to known medication. After a series of complex virtual docking simulations, around 50 molecules were selected for synthesis and testing and two of these fulfilled the desired criteria. They had good bonding characteristics, activated only certain protein sub-types and thus a very selective set of cellular signal pathways, whereas dexmedetomidine responds to a significantly wider range of proteins.

Pain relief without sedation in animal models

By further optimizing the identified molecules, for which extremely high-resolution cryo-electron microscopic imaging was used, the researchers were able to synthesize agonists that produced high concentrations in the brain and reduced the sensation of pain effectively in investigations with animal models. “Various tests confirmed that docking on the receptor was responsible for the analgesic effect,” explains Gmeiner. “We are particularly pleased about the fact that none of the new compounds caused sedation, even at considerably higher doses than those that would be required for pain relief.”

The successful separation of analgesic properties and sedation is a milestone in the development of non-opioid pain medication, especially as the newly-identified agonists are comparatively easy to manufacture and administer orally to patients. However, Prof. Gmeiner has to dampen any hopes of rapid widespread use in human medicine: “We are currently still talking about basic research. The development of medication is subject to strict controls and in addition to significant amounts of funding, it takes a long time. However, these results still make us very optimistic.”

The starting point was data from 58 randomised controlled trials (RCTs) of over 10,000 patients worldwide with chronic low back pain. First, the data relevant to the topic were filtered out of the original manuscripts and then evaluated in groups. When evaluating these data, the researchers examined on the one hand whether and to what extent standard forms of treatment and individualised treatment differ in terms of the result. “Individualised” means that there is some type of personal coaching, where therapists specifically target the potentials and requirements of each patient and decide together with them how their therapy should look.

The study concluded that individualised treatment for chronic back pain led to a significantly increased effect in comparison to standard exercise therapies. The success rate in pain relief was 38 percent higher than with standard treatment. “The higher effort required for individual treatment is worthwhile because patients benefit to an extent that is clinically important,” says lead author Dr Johannes Fleckenstein from the Institute of Sport Sciences at Goethe University Frankfurt.

However, the study went even further. The research team in Frankfurt compared a third group of treatment methods alongside the standard and individualised ones. In this group, individualised training sessions were combined with cognitive behavioural therapy (CBT). This procedure — a type of talk therapy — is based on the assumption that negative thoughts and behaviours surrounding pain tend to exacerbate it. Through CBT, pain patients learn to change the way they handle it. They stop being afraid to move or are taught tactics for coping with pain. This makes them realise that they are by no means helpless. But what does the psychotherapeutic support through CBT actually contribute to the success of the treatment? Analysis of the data revealed the following: When an individualised approach and CBT were combined, the success rate in terms of pain relief was an impressive 84 percent higher than with standard treatment. The combined therapy, also called multimodal therapy, thus led to the best result by far.

Fleckenstein sees in the study “an urgent appeal to public health policy” to promote combined therapies both in terms of patient care and remuneration. “Compared to other countries, such as the USA, we are in a relatively good position in Germany. For example, we issue less prescriptions for strong narcotic drugs such as opiates. But the number of unnecessary X-rays, which, by the way, can also contribute to pain chronicity, and inaccurate surgical indications is still very high.” This is also due, according to Fleckenstein, to economic incentives, that is, the relatively high remuneration for such interventions. The situation is different for organisations working in the area of pain therapy, he says. Although these are not unprofitable, they are not a cash cow for investors either. In his view, it is important here to improve the economic conditions. After all, pain therapy saves a lot of money in the long run as far as health economics are concerned, whereas tablets and operations rarely lead to medium and long-term pain relief.

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Materials provided by Goethe University Frankfurt. Note: Content may be edited for style and length.

It means spending hours in the field listening for calls. Or leaving a recording device in the field to catch what sounds are made — only to spend hours later listening to that audio. Then, repeating this process until there’s enough information to start making population estimates.

But a new model developed by researchers at the University of Georgia aims to streamline this process. By using artificial intelligence to analyze terabytes of recordings for quail calls, the process gives wildlife managers the ability to gather the data they need in a matter of minutes.

“The model is very accurate, picking up between 80% and 100% of all calls even in the noisiest recordings. So, you could take a recording, put it through our model and it will tell you how many quail calls that the recorder heard,” said James Martin, an associate professor at the UGA Warnell School of Forestry and Natural Resources who has been working on the project, in collaboration with the Georgia Department of Natural Resources, for about five years. “This new model allows you to analyze terabytes of data in seconds, and what that will allow us to do is scale up monitoring, so you can literally put hundreds of these devices out and cover a lot more area and do so with a lot less effort than in the past.”

The software represents about five years of work by Martin, postdoctoral researcher Victoria Nolan and numerous key contributors who have worked with a code writer to create the model. It’s also part of a larger shift taking place in the field of wildlife research, where computer algorithms are now assisting with work that once took humans thousands of hours to complete.

Increasingly, computers are getting smarter at, for example, identifying specific noises or certain traits in photos and sound recordings. For researchers such as Martin, it means hours once spent on tasks such as listening to audio or looking at game camera images can now be done by a computer, freeing up valuable time to focus on other aspects of a project.

The new tool can also be a valuable resource for state and federal agencies looking for information on their quail populations, but with limited funds to spend on any one project. “So, I think this is something states might jump on as far as replacing their current monitoring with acoustic recording devices,” added Martin.

The software’s success was recently documented by the Journal of Remote Sensing in Ecology and Conservation.

As the software gets more use and is exposed to sounds from new geographic areas, Martin said, it gets even “smarter.” As it is, quail offer several different kinds of calls. But when the software is exposed to a variety of sounds that aren’t quail, he said, it’s better able to distinguish the correct calls from the ambient noises of the grasses and trees around them.

Over time, the software will grow more discerning.

“So that’s why you have to keep giving it training data, and when you move geographies, you encounter new sounds that you didn’t train the model for,” he added. “It’s always about adaption.”

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Materials provided by University of Georgia. Original written by Kristen Morales. Note: Content may be edited for style and length.

“This work expands the way we can look at brain activity during sleep,” said Prerau. “By moving beyond traditional notions that break up the complex continuum of sleep into specific categories and waveform classes, we can reveal new types of signals and dynamics that may be important for understanding brain health and disease.”

Scientists typically study brain activity during sleep using the electroencephalogram, or EEG, which measures brainwaves at the scalp. Starting in the mid 1930s, the sleep EEG was first studied by looking at the traces of brainwaves drawn on a paper tape by a machine. Many important features of sleep are still based on what people almost a century ago could most easily observe in the complex waveform traces. Even the latest machine learning and signal processing algorithms for detecting sleep waveforms are judged against their ability to recreate human observation. In this study, the researchers asked: What can we learn if we expand our notion of sleep brainwaves beyond what was historically easy to identify by eye?

One particularly important set of sleep brainwave events are called sleep spindles. These spindles are short oscillation waveforms, usually lasting less than 1-2 seconds, that are linked to our ability to convert short-term memories to long-term memories. Changes in spindle activity have been linked with numerous disorders such as schizophrenia, autism, and Alzheimer’s disease, as well as with natural aging. In this study, rather than looking for spindle activity according to the historical definition, the team developed a new approach to automatically extract tens of thousands of short spindle-like waveform events from the EEG data throughout the entire night. Next, instead of looking at the waveforms in terms of fixed sleep stages (i.e., Wake, REM, and non-REM stages 1-3) as standard sleep studies do, they characterized the full continuum of gradual changes that occur in the brain during sleep. Using all these data, the team created graphical representations called slow oscillation power and phase histograms, which provide a powerful visualization of the activity of all the waveforms as a function of continuous sleep depth and synchronized activity in the cortex. “This further demonstrates the richness of the information that traditional, manual scoring leaves on the table,” said co-author Shaun Purcell, PhD, of the Department of Psychiatry at the Brigham.

When the team looked at a group of healthy participants, each with two nights of sleep recordings, the patterns observed appeared to be almost like fingerprints — highly specific to each person with strong consistency across nights. These results suggest new ways in which brain activity differs from person to person, even within groups of healthy people selected as control groups. The researchers then compared the activity between the healthy subjects and a population of people with schizophrenia, a disorder that reduces spindle activity. Using their approach, the team not only saw the known differences in participants with schizophrenia, but also found differences in other spindle-like waveforms occurring at other frequencies in the brain. This suggests new potential EEG biomarkers of schizophrenia that could be useful in better understanding the mechanisms of the disorder and in the development of targeted treatments.

“This approach is really exciting,” said co-author Dara Manoach, PhD, of the Department of Psychiatry at Massachusetts General Hospital. “We look forward to seeing how we can enhance our understanding, not only of schizophrenia, but also of other neurodevelopmental disorders characterized by differences in sleep, such as autism and pediatric epilepsy.”

“We are just starting to understand the scope of neurodiversity that exists within the general population,” said Prerau. “If we can more accurately characterize the individual differences observed in both neurological health and disease, we can work towards improved diagnostics and treatments.”

Co-authors were Preetish Rath, Tom Possidente, Mingjian He, Shaun Purcell, Dara Manoach, and Robert Stickgold. Michael Prerau was the senior author.

This work was supported by the National Institute of Neurological Disorders and Stroke (R01NS096177) and the National Institute of Aging (R01AG054081 (MJP).

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Materials provided by Brigham and Women’s Hospital. Note: Content may be edited for style and length.