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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.

Screen Shot 2016-04-24 at 2.47.22 PM

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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Classic Paper: Cade JF. Lithium salts in the treatment of psychotic excitement. Med J Aust 1949; 2:349-352

Screen Shot 2016-04-11 at 5.33.09 PM

some wells in the British Isles were known for their salubrious effects on mental illness; this may have been due to their lithium content

Prior to 1949, treatments for mania were limited. That year, John Cade published a paper showing the usefulness of lithium in treating patients with mania (“psychotic excitement”).

Interestingly enough, the finding was apparently a surprise to Cade. He was studying guinea pigs in order to see whether uric acid added to the convulsive toxicity of urea, but he needed to find a way to make uric acid soluble in water to be able to inject it into the guinea pigs. (Confusingly enough, urea and uric acid have almost nothing to do with one another chemically.)

For this, he used the lithium salt of urate, and was surprised to find that it was protective against the urea-induced convulsions. He then injected lithium carbonate alone into guinea pigs, and noted that after a couple of hours, they became lethargic and unresponsive to stimuli.

Skipping straight from this effect in guinea pigs (not even a disease model!! — this would never be allowed today) to humans, Cade then reports on 10 cases of patients with mania who were successfully treated with lithium, including longitudinal cases of chronic mania where the mania subsided during lithium treatment and recrudesced when lithium was discontinued.

Other interesting aspects of this paper:

  • Cade notes that historically, water from certain wells was associated with improvements in mental illness, and speculates that “it is very likely that their supposed efficacy was a real efficacy and directly proportional to the lithium content of the waters.”
  • Cade notes that lithium treatment “would be much preferred” to what is usually now considered the cruel treatment of prefrontal leucotomy, even though this (1949) was the year that the Nobel prize was awarded for it, and its use continued into the mid-1950s.
  • All of the cases reported on were men between ages 40 and 65 years old, indicating a total lack of evidence for generalization of the effect across more diverse patient populations.

Recent meta-analysis (2013) has shown that antipsychotics are more effective than lithium in the treatment of acute mania (e.g., the standardized mean difference in manic symptoms for haloperidol is -0.56, while for lithium it is -0.37), but lithium is still often used in combination with antipsychotics in the treatment of mania.

Overall, this short paper is among the best I’ve read in terms of scientific puzzle solving, although you could argue that Cade got lucky.

References

Cade JF. Lithium salts in the treatment of psychotic excitement. 1949. Bull World Health Organ. 2000;78(4):518-20.

Cipriani A, Barbui C, Salanti G, et al. Comparative efficacy and acceptability of antimanic drugs in acute mania: a multiple-treatments meta-analysis. Lancet. 2011;378(9799):1306-15.

Doig MT, Heyl MG, Martin DF. Lithium and mental health. J Chem Educ. 1973;50(5):343-5.

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A nice behind-the-scenes look in Noah Gray’s post discussing this article studying connections between the striatum and basal ganglia mice. Here’s what the reviewers were wondering:

[They] also raised general novelty issues, since it is well-known from many brain areas that any manipulation of circuits on a gross level can lead to innervation changes. A somewhat broad damnation, but worth considering nonetheless. This criticism also related to the next, namely that to really make a valuable contribution to the competitive field of circuit development, the authors would need to expand this study further, supplying additional data allowing a better understanding of other components within the circuit. For example, exploring how exactly the corticostriatal inputs influence basal ganglia synaptogenesis. It begged the question: do the authors understand the physiology and timing well enough to predict how their manipulations would affect the striatum, not by disrupting the striatum itself, but through control of the descending cortical circuits?

Here is an earlier BL post on MSNs in the striatum, which the authors of this article selectively silence as a part of their circuit manipulation.

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Leegaard et al’s article is a worthy summary of recent efforts to map the brain’s connections at various levels of detail and the benefits that we would realize from cross-validating them. Here is one example:

Using the same approach, Axer et al. (2011b) show how 3-D [polarized light imaging] derived fiber orientation vectors can subsequently be used as a basis for high-resolution tractography of fiber tracts, potentially suitable for bridging microscopic and macroscopic connectome representations. The importance of correlating various non-invasive MRI derived measurements to cellular-level morphological data is also emphasized by Annese (2012), presenting the perspective that whole-brain histological maps (Figures 1E,F) created using large-scale digital microscopy spanning several histological modalities will support the analysis and interpretation of MRI-based connectivity studies.

They also have an awe-inspiring figure showing off the results of many different new techniques.

A, B = diffusion MRI in vivo; C, D = detailed fiber architecture via 3-d polarized light imaging (ex vivo); E, F = digital histology of tract stained for myelin; G = combined optogenetics and fMRI; H = knife edge scanning microscopy; I = GUI of the Human Connectome Project; J = data mining to reconstruct hippocampal connections; K = connectivity-based cortical parcelation; L, M = GUI of the Connectome Viewer; N = structural network motifs; O = connectome matrix of the rat brain; P = connectome matrix of the rat hippocampus; Q = a model of connectivity in the developing tapdole spinal cord; doi: 10.3389/fninf.2012.00014 

Reference

Leergaard TB, Hilgetag CC and Sporns O (2012) Mapping the connectome: multi-level analysis of brain connectivity. Front. Neuroinform. 6:14. doi: 10.3389/fninf.2012.00014

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Interesting paper by Knafo et al from a couple of months ago that I’ve been meaning to talk about. This group previously made a synthetic peptide (called FGL) that mimics the active site of a cell adhesion molecule involved in neurite outgrowth (NCAM).

When they subcutaneously inject some amount of this molecule into rats, they learn to navigate a water maze faster. So, cognitive enhancement. Not bad.

Down to mechanism. They fix rat hippocampal sections and investigate a variety of neural morphology measures. No significant differences, although the synapse density looks pretty close to significance to me.

left = representative em images showing synapses, right = differences on various types of synapses

Instead, it seems like this peptide is exerting its effects by recruiting more AMPA receptors to the postsynaptic densities of excitatory CA1 cells. Probably their strongest evidence for this is that, in hippocampal slice cultures, more GFP-tagged AMPA receptors are delivered to the synapse 24-36 hrs after the addition of the peptide.

rectification index = ratio of whole-cell clamped responses at -60 and +40 mV; Inf. = cells infected with GFP (green); FGL = memory-enhancing synthetic peptide

So this is an example where neural connectivity, by itself, seems quite unable to explain all the physiology and behavior in their experiments. Of course, it’s possible that concomitant with the functional (but not structural) changes in the hippocampus, there are correlated structural (but not functional) changes elsewhere in the rat’s brain. But that seems less parsimonious.

Reference

Knafo S, Venero C, Sánchez-Puelles C, Pereda-Peréz I, Franco A, et al. (2012) Facilitation of AMPA Receptor Synaptic Delivery as a Molecular Mechanism for Cognitive Enhancement. PLoS Biol 10(2): e1001262. doi:10.1371/journal.pbio.1001262

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The ADNI has collected longitudinal MRI scans and genotypes from ~ 100 individuals with AD, ~200 with MCI, and ~150 healthy eldery controls, making it quite the boon for Alzheimer’s research. In a paper that caught my eye, Silver et al took this data set and did some cool things with it.

First, they did some genotype filtering, such as removing SNPs with a frequency of less than 10% for the less common allele. This means that both alleles for each SNP they include will be relatively common, which ensured that their subsequent regression would have adequate sample sizes for each group. Next, they map the SNPs to genes, and then they map the genes to genetic pathways.

schematic illustration; unfilled squares/circles = genes/SNPs that do not map to any pathways/genes; orange = SNP that maps to more than one pathway; http://arxiv.org/abs/1204.1937

To integrate the brain data, the researchers calculated the change from baseline in each voxel in scans separated by 6 and 18 months. They then did a voxel-wise ANOVA on the rate of change to determine which voxels show differences between AD and healthy controls.

What I find amazing is that even using a conservative correction for multiple comparisons, they still found that 148,023 out of 2,153,231 (7%) of the voxels showed a difference between AD cases and healthy controls. That shows you the extent of AD damage; see below for the specific regions most affected.

top = log p-value distribution for differences in rate of change between AD patients and healthy controls; bottom = final set of voxels that passed the p-value threshold; http://arxiv.org/abs/1204.1937

Finally, they estimate which SNPs and pathways have the strongest associations with changes in AD-affected brain regions. The top three pathways they blame are the chemokine pathway, the jak-stat pathway, and the tight junction pathway.

The first two of these are related to cytokine signaling and thus continue to emphasize the role of inflammation in AD progression. Tight junction proteins have also been associated with AD to explain the loss in BBB integrity. So, although the AD picture remains messy, studies and data sets like this should help.

Reference

Silver M, et al. 2012 Identification of gene pathways implicated in Alzheimer’s disease using longitudinal imaging phenotypes with sparse regression. arXiv:1204.1937v1

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