Eating placenta is an age-old practice in China:
What could easily be the most important advance in the pharmacologic treatment of major depression and anxiety disorders is now unfolding. A new investigational drug, currently known as ALKS 5461, could deliver all the mood-enhancing and anxiety-lowering effects that lead people to use opiates like heroin and Oxycontin—without the potential for getting high or addicted. That’s right: ALKS 5461 could be a non-abusable, non-addictive heroin-like compound. ALKS 5461 is actually a combination of two molecules. The first is buprenorphine, which is already used to provide some of the benefits of opiates, without many of the worst side effects, allowing people to get off of street drugs (as an alternative to methadone). The second molecule is now known as ALKS 33—and that’s the magic part. ALKS 33 interferes with the binding of buprenorphine to the receptors that are involved in making people feel euphoric. Those are the receptors also involved in getting people to crave opiates like alcoholics crave alcohol. In a double-blind, placebo controlled study (meaning, the participants had no idea whether they were getting ALKS 5461 or a sugar pill), ALKS 5461 was rapidly effective in relieving symptoms in 32 patients with major depression. All 32 patients responded to the medicine—with results evident by seven days. What’s more, all of them had failed to respond adequately to traditional antidepressants like Prozac or Effexor. Ultimately, I believe ALKS 5461 could revolutionize the pharmacologic treatment of major depression and panic disorder and post-traumatic stress disorder and obsessive-compulsive disorder. It should come as no surprise that ALKS 5461 is the brainchild of scientists at Alkermes Pharmaceuticals, the same company, which invented and markets Vivitrol, a monthly injection that can take away the “high” of using alcohol and street drugs—and in my opinion, ought to be something that every family member of every addict clamors to get their loved one to take. If ALKS 5461 comes to market (and I believe it will), then that scourge we call major depression will be dealt a massive blow. It will still be imperative to use insight-oriented psychotherapy to get to the bottom of what unique psychological issues have fueled each person’s depression, but that should be easier—not harder—when folks aren’t struggling just to get out of bed and over to their psychiatrists’ offices. This is a really big deal.
DEA Finally Admits Marijuana is Medicine:
If you thought they were going to issue a formal apology after decades of flagrant dishonesty, you would be mistaken. But the DEA is at long last conceding Marijuana’s incredible medical value…by giving pharmaceutical companies exclusive permission to make pills out of it. “Marijuana has no scientifically proven medical value.” So stated the United States Drug Enforcement Administration (DEA) on page six of a July 2010 agency white paper, titled “DEA Position on Marijuana.” Yet only four months after the agency committed its “no medical pot” stance to print, it announced its intent to allow for the regulation and marketing of pharmaceutical products containing plant-derived THC — the primary psychoactive ingredient in Cannabis. DEA can try to frame this any way they like, but the bottom line remains that authorizing cultivation for pharmaceutical companies is the end of the debate. Over. Done. Whatever nuanced distinctions the enemies of medical marijuana seek to advance from this point forward will be devastated by the simple fact that new medicines are being made out of marijuana with the blessing of the Drug Enforcement Administration. Conspiracy theories will abound, of course, regarding the potential for a widespread campaign to shut down state-level medical marijuana programs and instead shove expensive pills down the throats of patients, while arresting providers and cultivators who refuse to comply. That isn’t going to happen. As much as the DEA and their corporate co-conspirators might fantasize about it, a full-scale assault on the medical cannabis industry is simply impossible from both a practical and political standpoint. These laws were put in place by the people and they won’t be done away with over our objections. On the contrary, the emergence of cannabis-based pharmaceuticals has real potential to vest corporate interests with a stake in the drug’s overall reputation. Rather than distancing themselves from the origins of their products, manufacturers of THC-based medications will recognize that associating their product with marijuana is in fact a shrewd marketing ploy. Marinol has already done exactly that. People love pot and that’s going to be the key to selling these pills. As a result, we could soon be witnessing a seemingly impossible scenario in which pharmaceutical companies actually share our frustration when some drug war idiot comes along claiming THC causes schizophrenia. Obviously, it’s unlikely that our goals will ever align perfectly with those of the pharmaceutical industry, but they’re clearly better at working with the DEA than we’ll ever be. Rather than viewing the situation as a threat to our continued progress, I think we need to recognize that various forms of industrialization will be the inevitable result of our hard work to de-stigmatize the drug. As that process unfolds, we’ll encounter numerous new and interesting opportunities to reframe the conversation about the dangers of marijuana. Even if this latest move by DEA is nothing more than a cynical attempt to thwart our progress somehow, I imagine it will backfire just as surely as every other tactic they’ve deployed in the drug war debate thus far.
An algorithm designed by US scientists to trawl through a plethora of drug interactions has yielded thousands of previously unknown side effects caused by taking drugs in combination. The work, published today in Science Translational Medicine1, provides a way to sort through the hundreds of thousands of ‘adverse events’ reported to the US Food and Drug Administration (FDA) each year. “It’s a step in the direction of a complete catalogue of drug–drug interactions,” says the study’s lead author, Russ Altman, a bioengineer at Stanford University in California. Pills in pill boxes. A program predicts the potential side-effects of mixing different pills. Although clinical trials are often designed to assess the safety of a drug in addition to how well it works, the size of the trials needed to detect the full range of drug interactions would surpass even the large, late-stage clinical trials sometimes required for drug approval. Furthermore, clinical trials are often done in controlled settings, using carefully defined criteria to determine which patients are eligible for enrollment — including other conditions they might have and which medicines they can take alongside the trial drug. Once a drug hits the market, however, things can get messy as unknown side-effects pop up. And that’s where Altman’s algorithm comes in. “Even if you show a drug is safe in a clinical trial, that doesn’t mean it’s going to be safe in the real world,” says Paul Watkins, director of the Hamner–University of North Carolina Institute for Drug Safety Sciences in Research Triangle Park, North Carolina, who was not involved in the work. “This approach is addressing a better way to rapidly assess a drug’s safety in the real world once it is approved.”
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Altman and his colleagues have been studying drug–drug interactions as a way to understand how a person’s genes influence their response to pharmaceuticals. To do that, he says, you must first have a good picture of the molecular mechanisms that underlie drug responses. “Adverse events are incredibly valuable clues to what these drugs are doing in the body,” Altman says. “They can tell you the other pathways in the cell that are being tickled by these drugs.” But reports of adverse drug events are notoriously prone to bias. For example, cholesterol-lowering treatments are more often taken by older patients, and so conditions associated with ageing, such as heart attack, could be wrongly linked to a drug as a side effect. Altman and his colleagues reduced this bias by adopting an approach sometimes used in observational clinical trials. They developed an algorithm that would match data from each drug-exposed patient to a nonexposed control patient with the same condition. The approach automatically corrected for several known sources of bias, including those linked to gender, age and disease1. The team then used this method to compile a database of 1,332 drugs and possible side effects that were not listed on the labels of those drugs. The algorithm came up with an average of 329 previously unknown adverse events for each drug — far surpassing the average of 69 side effects listed on most drug labels. The team also compiled a similar database looking at interactions between pairs of drugs, which yielded many more possible side effects than could be attributed to either drug alone. When the data were broken down by drug class, the most striking effect was seen when diuretics called thiazides, often prescribed to treat high blood pressure and edema, were used in combination with a class of drugs called selective serotonin reuptake inhibitors, used to treat depression. Compared with people who used either drug alone, patients who used both drugs were significantly more likely to experience a heart condition known as prolonged QT, which is associated with an increased risk of irregular heartbeats and sudden death. A search of electronic medical records from Stanford University Hospital confirmed the relationship between these two drug classes, revealing at roughly 1.5-fold increase in the likelihood of prolonged QT when the drugs were combined, compared to when either drug was taken alone. Altman says that the next step will be to test this finding further, possibly by conducting a clinical trial in which patients are given both drugs and then monitored for prolonged QT. What should the drug regulators do with the thousands of possible side effects Altman and his team uncovered? That is a complex problem, says Watkins, who adds that regulators will have to factor in the availability of alternative treatments and the magnitude and seriousness of the side effect, among other considerations. Altman, who serves as an adviser on the FDA’s Science Board, says that he plans to present his results to the agency. He suggests that the algorithm could be used with the FDA’s existing drug-surveillance programs to remove bias. However, he points out the enormity of the task: “We’ve just released a database with 10,000 or more adverse events,” he says. “I do not expect the FDA to uncritically take these results and add them to every drug label.”