Thursday, July 05, 2012

Ban on Drugs Interferes With Brain Research, So Scientists Get Creative


Reuters ran an article at the end of May that featured David Nutt - professor of neuropsychopharmacology at Imperial College London and a former chief adviser on drugs to the British government - who argued that the international prohibition on psychedelics and other mind-altering drugs over the last 50 years or so has been damaging to brain research and has had "perverse" consequences for our efforts to solve the mysteries of various mental illnesses.

It's important than researchers at his level speak out on this topic - things are shifting slowly in this area, with a lot of new research underway, but it's still exceedingly difficult to get FDA approval in the U.S.

The restrictions on research have forced investigators to be creative. That is exactly what we find in an article from MIT's Technology Review on data mining the internet for users' first-person descriptions of the effects of psychedelic drugs. The obvious resource they tap is erowid.org, a well-known and popular source of user-generated information about the effects of all kinds of psychoactive substances. Very interesting.

Drug bans hamper brain research, says neuroscientist

The British government's former chief drugs adviser, David Nutt, reacts as he speaks during a news conference announcing the formation of the Independent Scientific Committee on Drugs, in London January 15, 2010. REUTERS/Suzanne Plunkett
 LONDON | Thu May 31, 2012 
 
(Reuters) - Bans on drugs like ecstasy, magic mushrooms and LSD have hampered scientific research on the brain and stalled the progress of medicine as much as George Bush's ban on stem cell research did, a leading British drug expert said on Thursday.

David Nutt, a professor of neuropsychopharmacology at Imperial College London and a former chief adviser on drugs to the British government, said the international prohibition of psychedelics and other mind-altering drugs over the past half century has had damaging and "perverse" consequences.
"When a drug becomes illegal, conducting experimental research on it becomes almost impossible," Nutt told reporters at a briefing in London ahead of the publication of his new book "Drugs - without the hot air".

He compared the situation with that in stem cell research under former U.S. President George W. Bush, who banned any new embryonic stem cell studies from 2001 to 2009 - a move many scientists consider held the field back for years.

Nutt said the problem with the current approach to drugs policy globally, which is centered on the banning of substances thought to be most harmful, "is that we lose sight of the fact that these drugs may well give us insights into areas of science which need to be explored and they also may give us new opportunities for treatment."

"Almost all the drugs which are of interest in terms of brain phenomena like consciousness, perception, mood, psychosis - drugs like psychedelics, ketamine, cannabis, magic mushrooms, MDMA - are currently illegal. So there's almost no (scientific)work in this field," Nutt said.

Nutt last year conducted a small human trial to study the effects of psilocybin, the active ingredient in magic mushrooms, on the brain.

Contrary to scientists' expectations, the study found psilocybin doesn't increase but rather suppresses activity in areas of the brain linked to depression, suggesting the drug might be a useful treatment for the debilitating condition.

Nutt said he was forced to "jump through hundreds of hoops" to be able to conduct the study, having to comply with a level of complex, expensive and time-consuming security and regulation that would put most scientists off.

WHAT DRUGS ARE AND WHAT THEY DO

The professor, who was sacked in 2009 in a high-profile row with the British government after he compared the risks of smoking cannabis with those of riding a horse, said he was driven to write the book in the hope of improving understanding of drugs - both legal and illegal, medicinal and recreational.

"There is almost no one in society who doesn't take drugs of some sort. The choices you make in your drug-taking are driven by a complex mixture of fashion, habit, availability and advertising," he said.
"If we understand drugs more, and have a more rational approach to them, we will actually end up knowing more about how to deal with drug harms."

Published on Thursday, the book seeks to explore the science of what a drug is and how it works.

It discusses whether the "war on drugs" did more harm than good - Nutt thinks it did.

And it explores why Britain's Queen Victoria took cannabis - apparently her physician J.R. Reynolds wrote a paper in the Lancet medical journal saying that "when pure and administered carefully, it (cannabis) is one of the most valuable medicines we possess". He prescribed it to the monarch to help her with period pains and after childbirth.

The book also has chapters on why people take drugs now, how harmful they are, where and whether the danger lines should be drawn between legal drugs like tobacco and alcohol, and illegal ones like cannabis and magic mushrooms.

Nutt doesn't dispute that drugs are harmful, but he takes issue with what he says are un-scientific decisions to ban one, like cannabis, while allowing another, like alcohol, to be freely and cheaply available on supermarket shelves.

"Drugs are drugs. They may differ in terms of their brain effects, but fundamentally they are all psychotropic agents," he said. "And it's arbitrary whether we choose to keep alcohol legal and ban cannabis, or make tobacco legal and ban ecstasy. Those are not scientific decisions they are political, moral and maybe even religious decisions."

(Reporting by Kate Kelland, editing by Paul Casciato)
 * * * * * * *

Here is the article on data mining for knowledge of drug effects.

Psychedelic Drug Research and the Data-Mining Revolution

The Web is filled with users' descriptions of the effects of psychedelic drugs. Now neuroscientists are using data-mining techniques to quantify the effects of these drugs on human consciousness.
 

One of the most mysterious problems in neuroscience is the link between brain chemistry and consciousness. How do changes in our neurochemistry influence our perception of the real world? 
This question is hard to tackle for the obvious reason that experiments on humans are notoriously difficult to perform. Not only are the variables hard to pin down but changing them with psychoactive drugs under controlled conditions is fraught with practical, ethical, and moral dilemmas.

That's why the majority of work examining the role of psychoactive drugs on neuropharmacological signaling mechanisms has been done on rats.

But there's a revolution afoot. Today, Jeremy Coyle at the University of California Berkeley and a couple of pals say they've found a new way to study the role of psychoactive drugs on human perception.

These guys point out that in the contrast to the small amount of formal scientific literature in this area, there are large volumes of narrative descriptions of the effects of drugs posted on the web. Their idea is to mine these descriptions using machine learning techniques to identify common features which would allow a quantitative comparison of their effects.

The obvious place to start such an endeavour is a website called erowid.org, which is a well known and popular source of user generated information about the effects of all kinds of psychoactive substances.

Coyle and co confine their investigations to ten drugs ranging from
3,4‐methylenedioxymethamphetamine, better known as ecstacy, and lysergic acid diethylamide, or LSD, to less well known drugs such as N,N‐dipropyltryptamine, sometimes called The Light,  and
2,5‐dimethoxy‐4‐ethylphenethylamine which has the street name Europa.

They collected 1000 narrative reports on these drugs and mined the text for common words, while screening out some words that are common to more than five drugs.

Having identified signature words, they then tested their hypothesis by seeing whether the results could be used to accurately predict which drug the reports referred to.

It turns out that some drug reports are much easier to classify than others. Ecstasy reports tends to use words such as “club”, “hug”, “rub” and “smile", which reflect the social setting in which the drug is often used and the feelings of love and friendliness the drug seems to produce.


Read the whole article.

You can read the whole paper online for free - here is the abstract.



Jeremy R. Coyle
David E. Presti
Matthew J. Baggott

ABSTRACT
 
BACKGROUND: Psychedelic drugs facilitate profound changes in consciousness and have potential to provide insights into the nature of human mental processes and their relation to brain physiology. Yet published scientific literature reflects a very limited understanding of the effects of these drugs, especially for newer synthetic compounds. The number of clinical trials and range of drugs formally studied is dwarfed by the number of written descriptions of the many drugs taken by people. Analysis of these descriptions using machine‐learning techniques can provide a framework for learning about these drug use experiences.

METHODS: We collected 1000 reports of 10 drugs from the drug information website Erowid.org and formed a term‐document frequency matrix. Using variable selection and a random‐forest classifier, we identified a subset of words that differentiated between drugs.
 
RESULTS: A random forest using a subset of 110 predictor variables classified with accuracy comparable to a random forest using the full set of 3934 predictors. Our estimated accuracy was 51.1%, which compares favorably to the 10% expected from chance. Reports of MDMA had the highest accuracy at 86.9%; those describing DPT had the lowest at 20.1%. Hierarchical clustering suggested similarities between certain drugs, such as DMT and Salvia divinorum.

CONCLUSION: Machine‐learning techniques can reveal consistencies in descriptions of drug use experiences that vary by drug class. This may be useful for developing hypotheses about the pharmacology and toxicity of new and poorly characterized drugs.
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