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One of the common considerations when prescribing haloperidol is whether it will prolong the QT interval. This is a measure of the heart rhythm on the EKG that correlates with one’s risk for serious arrhythmias such as torsades de pointes.

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Earlier this year, van den Boogaard et al published one of the largest RCTs to compare haloperidol against placebo (700+ people in both groups).

Their main finding was that prophylactic haloperidol was not helpful for reducing the rate of delirium or improving mortality.

But one of their most interesting results was the safety data. This showed that their dose of haloperidol had no effect on the QT interval and caused no increased rates of extrapyramidal symptoms. Their regimen was haloperidol IV 2 mg every 8 hours, which is equivalent to ~ 10 mg oral haloperidol in one day.

The maximum QT interval was 465 ms in the 2 mg haloperidol group and 463 ms in the placebo group, a non-significant difference with a 95% CI for the difference of -2.0 to 5.0.

Notably, they excluded people with acute neurologic conditions (who may have been more likely to have cardiovascular problems) and people with QTc already > 500 ms, which makes generalization of this finding to those groups a bit tricky.

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Since I did the same analysis for antidepressants yesterday, I figured that I would analyze the receptor binding profiles of antipsychotics today. Here is a visualization:

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And here is a dendrogram based on a clustering of those receptor affinities:

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It turns out that it’s much harder to see a trend in which these classes cluster based on chemical structure like the antidepressants did, but perhaps you will be able to notice some trends:

Here’s my code to reproduce this.

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As I’m trying to learn more about antidepressants, I found it interesting to make a visualization of the receptor binding profiles of some of the better characterized ones, so I thought I would post it here.

Antidepressant receptor binding

Some of these medications aren’t widely used anymore or were never pursued for development, so they are also a window into the history of psychiatry and what could have been. This is how the meds cluster based on their receptor binding:

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One interesting thing about these clusters is that they cut the medications into groups distinguished by their chemical/drug classes:

  • Group #1: TeCAs like mirtazapine and one TCA, doxepin; o
  • Group #2: TCAs like amitryptiline and one TeCA, amoxapine
  • Group #3: SSRIs/SNRIs, like fluoxetine and venlafaxine
  • Group #4: Phenylpiperazines, like trazodone
  • Group #5: NRIs/NDRIs, like atomoxetine and buproprion

Here’s my code to reproduce this.

 

 

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A key question in the treatment of depression is: what is the probability that a given treatment will lead to a sustained remission of symptoms?

One of the largest, most famous studies to address this is called the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial.

From what I understand, the researchers designed the trial to mimic what might happen in a realistic clinical setting.

A patient diagnosed with major depressive disorder (MDD) might be first started on a first-line drug (citalopram). If that didn’t work (because the side effects were untolerable, or the symptoms persisted), then another drug would be chosen, and so on. Here is their algorithm:

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They used a unique randomization strategy, as participants in Level 2 could choose to opt out of the randomization blocks that entailed a) switching off of citalopram, b) augmenting citalopram with a different drug, and/or c) using cognitive therapy.

From the numbers above, you can see that most common option was for participants to opt out of cognitive therapy. This is probably in part accounted for by a selection bias for participants who would enter the citalopram-based trial in the first level.

One of the main outcomes was the remission rates from depression (defined as QIDS-SR16 score of <= 5) at the various stages:

  • For step 1, the remission rate for those not treated for their current episode was 43%, vs 36% for those already treated for their current episodes
  • For step 2, the remission rate was 30%
  • For step 3, the remission rate was 14%
  • For step 4, the remission rate was 13%
  • Assuming that none of the participants existed the study and stayed in treatment, the theoretical remission rate after a maximum of four treatment steps was 67%

They also looked at the 12-month follow up of these same participants. Of those, the proportion without a relapse (defined as QIDS-SR16 >= 11) was ~50% in the participants who had a remission of symptoms following step 1, and ~ 33% in the participants who had a remission of symptoms following step 2.

This data set has been analyzed in many other ways. For example, after unsuccessful treatment with the SSRI citalopram, there was no difference in the remission rates of buproprion, sertraline, and venlafaxine. On the other hand, augmenting citalopram with buproprion led to a greater reduction in symptoms and had fewer side effects compared with augmenting with buspirone.

 

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In the 1940s two Danish researchers, Erik Jacobsen and Jens Hald, tested a series of substances in an attempt to identify drugs that might rid the body of intestinal worms. After one of them worked in rabbits, Jacobsen tried it on himself, as he had a fun little habit of trying all of his invented drugs on himself.

“During the course of self-experimentation” as Larimer reports, both Jacobsen and Hald noted this substance — called disulfiram — led to a substantial increase in their sensitivity to the toxic effects of alcohol.

In late 1947 and 1948, Oluf Martensen-Larsen, an expert on the treatment of alcoholism, was able to convince his colleagues to allow him to try disulfiram in the treatment of alcohol abuse. In this classic paper, he reported on 83 patients that he had treated with disulfiram (also called Antabuse) for the treatment of their alcohol addiction.

At the time the mechanism was not known, but it was known that giving the drug prophylactically led people to become violently ill with hangover-like effects of alcohol. It is now almost certain that its effects are due to the inhibition of aldehyde dehydrogenase, which causes acetaldehyde to build up in the blood stream following alcohol consumption and cause all sorts of unpleasant toxicity.

In cultured cortical neurons, acetaldehyde causes a substantial loss of MAP2-positive neuronal processes, indicative of the fact that the toxicity of acetaldehyde does not spare the CNS:

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PMID: 11132090

One of the patients he described as having a successful reaction to the treatment was a middle-aged woman. After starting on disulfiram, she began to blush after taking only a mouthful of liquor. As Martensen-Larsen reports, “Her abstinence might be explained by her desire to avoid the humiliation associated with the blushing, but she insists that this is not the deciding factor, and that she has lost the taste for wine and spirits.”

Overall, he classifies 32/83 (39%) of patients as successes, 29/83 (35%) as partial successes as long as their blood and urine checks indicated that they will still on the drug, 13/83 (16%) as successes only as long as the physician can successfully encourage them to stay on the drug, and 9/83 (11%) as not responding to the drug, at least in because they refused to continue on it.

Disulfiram is still used today as a part of a comprehensive treatment for alcohol addiction that includes psychosocial factors as well.

References

Larimer R 1952 JAMA Treatment Of Alcoholism with Antabuse. doi:10.1001/jama.1952.03680020013004

Arghya Pal, Raman Deep Pattanayak, Rajesh Sagar. (2015) “Tracing the journey of disulfiram: From an unintended discovery to a treatment option for alcoholism.” Journal of Mental Health and Human Behavior. DOI: 10.4103/0971-8990.164826

Martensen-Larsen O. Treatment of alcoholism with a sensitizing drug. Lancet 1948;2:1004. Back to cited text no.

Wan JY, Wang JY, Wang Y, Wang JY. A comparison between acute exposures to ethanol and acetaldehyde on neurotoxicity, nitric oxide production and NMDA-induced excitotoxicity in primary cultures of cortical neurons. Chin J Physiol. 2000;43(3):131-8.

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This classic 2009 review paper by Fletcher and Frith, currently cited 456 times, attempts to explain the two major positive symptoms of schizophrenia, hallucinations and delusions, as due to a common high-level cognitive mechanism.

But first, they consider one of the simplest hypotheses: might people with schizophrenia have disordered reasoning in general? The authors reject this hypothesis because patients with schizophrenia do not have problems with logic in general; at least in the n = 32 study they cited, control as opposed to deluded people were actually slightly more likely to fall for fallacies in logical questions.

Instead,Fletcher and Frith’s hypothesis relates to a failure to correctly perform conditional reasoning.

As they point out, stimuli that do not challenge a belief are usually ignored, which is often necessary in order to deal with the large number of stimuli in one’s environment.

Intriguingly, in animals, this prediction error-dependent learning is highly dependent on the dopaminergic system. And there is a wealth of evidence showing that the dopamine system is implicated in schizophrenia, including the ventral striatum.

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Berns et al., in a 2001 fMRI study, showed that the bilateral nucleus accumbens (a part of the ventral striatum) is involved in responses to predictable stimuli 

Their hypothesis, then, is that there is a quantitative divergence in the prediction error-dependent learning for every day stimuli in schizophrenia.

This leads normal stimuli to feel unduly important and thus makes properly attending to one’s environment challenging. This can explain delusions, because people must attempt to explain why those stimuli feel so surprising.

This could also make internal thoughts (which are perceived stimuli just like any other) appear more likely to be under external rather than internal control, because they are imbued with a particular sense of surprisingness. This, of course, can explain hallucinations.

Of course, this is just a model and is probably flawed in various ways, but it’s a pretty thorough one and worthy of consideration.

Reference

Fletcher PC, Frith CD. Perceiving is believing: a Bayesian approach to explaining the positive symptoms of schizophrenia. Nat Rev Neurosci. 2009;10(1):48-58.

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Classic Paper: Elkes J, Elkes C. Effect of chlorpromazine on the behavior of chronically overactive psychotic patients. Br Med J. 1954;2(4887):560-5

In 1950, a group of anesthesiologists in France were trying to find new drugs for anesthesia. They tested the newly synthesized drug chlorpromazine on animals (dogs, rodents, and mice) and found that it led to drowsiness and indifference to aversive stimuli.

Since this was the 1950’s, they were able to quickly try it on people as a booster for anesthesia. They found that people who took chlorpromazine did not lose consciousness, but it did have a profound calming effect. Quickly people thought of trying it on patients with psychosis, for which the available treatments were very limited.

This study by Joel Elkes and Charmian Elkes, who were married, was the first to report a placebo-controlled trial on the effect chlorpromazine in psychosis. It appears that the majority of the data collecting and work was done by Charmian, rather than Joel. Screen Shot 2016-04-11 at 8.19.22 PM

They used a classic crossover study design, testing each patient on both chlorpromazine and an inert placebo (although they do not use the word “random”). They used notes written by the doctors and nurses that were blind to the treatment type to decide whether or not the patient had improved.

Of the 23 patients with a type of psychosis in their study, 7 (30%) showed “definite improvement” when they were taking the drug compared to when they were not, 11 (48%) showed “slight improvement,” and 5 (22%) showed “no improvement.”

Other interesting notes from the paper:

  • They describe the effect of chlorpromazine as symptomatic, since the psychosis itself did not abate: “the essentially symptomatic nature of the response has already been stressed, and cannot be overemphasized. Although affect became more subdued, and attitude and behaviour reflected this improvement, the ingrained psychotic thought disorder seemed to be unchanged.”
  • Because of their detailed records, they noted significant weight gain in 9/23 of the patients (in all of whom the drug led to at least a slight improvement), which has been borne out in both chlorpromazine and in the drug class in general: almost all antipsychotics result in weight gain. Of this effect, they say: “For the present we are inclined to attribute this to improved eating habit as the patients became less tense, less preoccupied, or less assaultive; though more direct metabolic effects of the drug cannot be excluded.”
  • They also tried it on 3 patients with senile dementia, all of whom had “no improvement.” This is yet another example of how Alzheimer’s is where drug discovery goes to die.

Notably, the mechanism remained pretty unknown until the mid-1960s, when it was shown that dopamine metabolites correlated with the chlorpromazine dose given to animals. In 1976, Seeman et al. found a nearly perfect correlation (on the log-log scale) between the ability of antipsychotic drugs to displace haloperidol from binding to the dopamine receptor and the clinical dose required for its effect.

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Seeman et al., 1976

Interestingly, you can see in this figure that chloroprazamine actually has one of the less strong dopaminergic affinities and higher doses required for controlling schizophrenia. Despite this, it and its derivatives have on to become some of the most game-changing psychiatric drugs of all time.

References

Shen WW. A history of antipsychotic drug development. Compr Psychiatry. 1999;40(6):407-14.
Elkes J, Elkes C. Effect of chlorpromazine on the behavior of chronically overactive psychotic patients. Br Med J. 1954;2(4887):560-5.
Bak M, Fransen A, Janssen J, Van os J, Drukker M. Almost all antipsychotics result in weight gain: a meta-analysis. PLoS ONE. 2014;9(4):e94112.

Seeman P, Lee T, Chau-wong M, Wong K. Antipsychotic drug doses and neuroleptic/dopamine receptors. Nature. 1976;261(5562):717-9.

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