Joe Biden, Hunter Biden, Stimulant Use Disorders and ARPA-H
April 14, 2021
There was a press release issued by NIDA today, trumpeting a report of a Phase III trial of medication for methamphetamine use disorders. As a very brief recap, we have no approved medications to assist with methamphetamine (or any psychostimulant) use disorder and the distressing health consequences for addiction to cocaine, methamphetamine and some other related drugs are very severe. According to 2019 data from SAMHSA (Table 5.14A), some 534,000 folks over age 25 were in treatment for cocaine use disorder, 566,000 for methamphetamine and this contrasts with 594,000 for heroin, 552,000 for marijuana and 1,957,000 for alcohol. Table 5.1A points out that some 756,000 of folks in this range probably had a cocaine use disorder, 904,000 had a methamphetamine use disorder and 10,959,000 had an alcohol use disorder.
Not everyone who needs treatment gets it. Not even close.
Hunter Biden, the President’s second (and still living) son has recently published a memoir detailing his struggles with addiction to cocaine and alcohol. Joe Biden, the President, issued a recent press release calling for a $6.5 billion launch of an Advanced Research Projects Agency for Health (ARPA-H) within the National Institutes of Health.
With an initial focus on cancer and other diseases such as diabetes and Alzheimer’s, this major investment in federal research and development will drive transformational innovation in health research and speed application and implementation of health breakthroughs.
Notably missing is any prominent mention of substance use disorder in ARPA-H.
On the relative scale of progress in treating cancer and diabetes, and yes even Alzheimer’s, I would argue that treatments for substance use disorders have been woefully under researched. Funding has lagged for the development of treatments and medications both in the public and private sectors. This means novel discovery, of course, but the real glaring deficit is in the routine churning of clinical trial after clinical trial for evaluating pretty run of the mill stuff. As they did in this recent Phase III trial.
Methamphetamine, as they say, is a helluva drug. From Brecht and Herbeck, 2014, we see the following relapse survival curve for a sample of methamphetamine users admitted to the Los Angeles County substance use treatment system. the followup period ranged from 22-90 months over which 23% maintained abstinence from methamphetamine. That means 77% relapsed, with a range of 0-79 months until relapse. As you can see from the below, 36% returned to methamphetamine use immediately upon discharge (Nb, this is not a sample selected for desire to quit), 14% more relapsed by 6 months and a total of 61% had relapsed within a year of entry. The good news, if there is any, is that this should be low hanging fruit. Anything, anything at all, that seems to work will be a huge gain versus the situation at present.
The new trial conducted by Trivedi et al. found that depot injection of naltrexone combined with daily oral buproprion (a cathinone-derivative, aka “bathsalt”) was effective, versus placebo control, in treating methamphetamine use disorder.
“Effective”.
Meaning that within a population of methamphetamine users with “moderate to severe” use disorder who intended to quit, 11.4% responded. Where a response was 3 out of four urine samples negative for methamphetamine during weeks 11-12. Only 1.8% in the placebo group had this “response”. Let’s round that out to 10% efficacy.
Now, the glass is most emphatically half full. Ten percent is not very impressive sounding but it is something. It is some improvement for some folks. Ten percent of the ~904,000 estimated with a methamphetamine use disorder is a lot of people and their families that have improved lives. We are moved by the stories of single individuals- like Hunter Biden, and Nic Sheff and William C. Moyers. Let us apply that same empathy we feel for these men and their relative success at recover to each and every other person with a stimulant use disorder.
And we have nowhere to go but up, with discovery of any additional strategies that, btw, likely will also help with cocaine use disorder.
Do we need DARPA-like innovation? of course. Anti-drug vaccines (something I’ve worked on, for disclosure) have been languishing in a twilight of basic biological efficacy but need a big kick in the pants to advance to real-world efficacy. Wearable technology has several immediately imaginable future uses. Deep brain stimulation. TMS. Individualized therapy based on genotyping. There is no reason to think that we could not go big with ARPA-H for substance use.
It is more than a little bothersome that Joe Biden, who so explicitly ties his interest in Cancer Moonshots and the like to the fate of his older son, does not exhibit the same motivations for the trials of his younger son. Who, btw, is not dead and is at continual risk of relapse given his history.
Trivedi MH, et al. Trial of Bupropion and Naltrexone in Methamphetamine Use Disorder. New England Journal of Medicine. January 14, 2020.
NIH grant application topics by IC
August 13, 2020
As you will recall, the Hoppe et al. 2019 report [blogpost] both replicated Ginther et al 2011 with a subsequent slice of grant applications, demonstrating that after the news of Ginther, with a change in scoring procedures and changes in permissible revisions, applications with Black PIs still suffered a huge funding disparity. Applications with white PIs are 1.7 times more likely to be funded. Hoppe et al also identified a new culprit for the funding disparity to applications with African-American / Black PIs. TOPIC! “Aha”, they crowed, “it isn’t that applications with Black PIs are discriminated against on that basis, no. It’s that the applications with Black PIs just so happen to be disproportionately focused on topics that just so happen to have lower funding / success rates”. Of course it also was admitted very quietly by Hoppe et al that:
WH applicants also experienced lower award rates in these clusters, but the disparate outcomes between AA/B and WH applicants remained, regardless of whether the topic was among the higher- or lower-success clusters (fig. S6).
Hoppe et al., Science Advances, 2019 Oct 9;5(10):eaaw7238. doi: 10.1126/sciadv.aaw7238
If you go to the Supplement Figure S6 you can see that for each of the five quintiles of topic clusters (ranked by award rates) applications with Black PIs fare worse than applications with white PIs. In fact, in the least-awarded quintile, which has the highest proportion of the applications with Black PIs, the white PI apps enjoy a 1.87 fold advantage, higher than the overall mean of the 1.65 fold advantage.
Record scratch: As usual I find something new every time I go back to one of these reports on the NIH funding disparity. The overall award rate disparity was 10.7% for applications with Black PIs versus 17.7% for those with white PIs. The take away from Hoppe et al. 2019 is reflected in the left side of Figure S6 where it shows that the percentage of applications with Black PIs is lowest (<10%) in the topic domains with the highest award rates and highest (~28%) in the domains with the lowest award rates. The percentages are more similar for apps with white PIs, approximately 20% per quintile. But the right side lists the award rates by quintile. And here we see that in the second highest award-rate topic quintile, the disparity is similar to the mean (12.6% vs 18.9%) but in the top quintile it is greater (13.4% vs 24.2% or a 10.8%age point gap vs the 7%age point gap overall). So if Black PIs followed Director Collins’ suggestion that they work on the right topics with the right methodologies, they would fare even worse due to the 1.81 fold advantage for applications with white PIs in the top most-awarded topic quintile!
Okay but what I really started out to discuss today was a new tiny tidbit provided by a blog post on the Open Mike blog. It reports the topic clusters by IC. This is cool to see since the word clusters presented in Hoppe (Figure 4) don’t map cleanly onto any sort of IC assumptions.
All we are really concerned with here is the ranking along the X axis. From the blog post:
…17 topics (out of 148), representing 40,307 R01 applications, accounted for 50% of the submissions from African American and Black (AAB) PIs. We refer to these topics as “AAB disproportionate” as these are topics to which AAB PIs disproportionately apply.
Note the extreme outliers. One (MD) is the National Institute on Minority Health and Health Disparities. I mean… seriously. The other (NR) is the National Institute on Nursing Research which is also really interesting. Did I mention that these two Is get 0.8% and 0.4% of the NIH budget, respectively? The NIH mission statement reads: “NIH’s mission is to seek fundamental knowledge about the nature and behavior of living systems and the application of that knowledge to enhance health, lengthen life, and reduce illness and disability.” Emphasis added. The next one (TW) is the Fogerty International Center which focuses on global health issues (hello global pandemics!) and gets 0.2% of the NIH budget.
Then we get into the real meat. At numbers 4-6 on the AAB Disproportionate list of ICs we reach the National Institute on Child Health and Development (HD, 3.7% of the budget), NIDA (DA, 3.5%) and NIAAA (AA, 1.3%). And clocking in at 7 and 9 we have National Institute on Aging (AG, 8.5%) and the NIMH (MH, 4.9%).
These are a lot of NIH dollars being expended in ICs of central interest to me and a lot of my audience. We could have made some guesses based on the word clusters in Hoppe et al 2019 but this gets us closer.
Yes, we now need to get deeper and more specific. What is the award disparity for applications with Black vs white PIs within each of these ICs? How much of that disparity, if it exists, accounted for by the topic choices within IC?
And lets consider the upside. If, by some miracle, a given IC is doing particularly well with respect to funding applications with Black PIs fairly….how are they accomplishing this variance from the NIH average? What can the NIH adopt from such an IC to improve things?
Oh, and NINR and NIMHHD really need a boost to their budgets. Maybe NIH Director Collins could put a 10% cut prior to award to the other ICs to improve investment in the applying-knowledge-to-enhance-health goals of the mission statement?
Update from CDC on vape-related lung injury: No single cause yet
October 17, 2019
The CDC has posted a MMWR report on the 2019 spate of serious lung injuries reported as a consequence of vaping. The first culprit to hit the news was vitamin E, it turns out this is not a unique factor after all.
Schier and colleagues report: No consistent e-cigarette product, substance, or additive has been identified in all cases, nor has any one product or substance been conclusively linked to pulmonary disease in patients. … authors identified lipids within alveolar macrophages from the three bronchoalveolar lavage (BAL) specimens stained with oil red O. All five patients reported using marijuana oils or concentrates in e-cigarettes, and three also reported using nicotine (3). In a report describing the clinical course and outcomes of six patients from Utah, health care providers described the potential diagnostic utility of identification of lipid-laden macrophages from BAL specimens (4). Among the 53 cases from Illinois and Wisconsin, however, the pathologic findings were heterogeneous. Whereas almost half (24/53) of these patients underwent BAL, seven reports described the use of oil red O stain that identified lipid-laden macrophages
Perrine and colleagues report: Among 514 patients with information on substances used in e-cigarettes, or vaping products, in the 3 months* preceding symptom onset, 76.9% reported using THC-containing products, and 56.8% reported using nicotine-containing products; 36.0% reported exclusive use of THC-containing products, and 16.0% reported exclusive use of nicotine-containing products. *erratum for the original which says “30 days”.
It’s frustrating that the takeaway message so far is that nobody knows if there even is a unique cause or set of causes for the recent spate of lung injuries. We certainly don’t know the cause. We probably don’t even know if the injuries *are* recently occurring or have always been a consequence of vape device use that simply wasn’t connected to the e-cigarette device use. We know how long it took to recognize that cannabis was causing a hyperemesis syndrome, after all.
My suspicion at the start was that it wasn’t anything to do with cannabinoids, specifically. This reported diversity would appear to confirm that. It always seemed more likely to me that if there was a unique cause that appeared to be associated with cannabis vape cartridges that this is a classic case of a third variable. Perhaps a new vehicle constituent or an extraction method that was being used only, or primarily, with cannabis vape preparation. Well, clearly even that is not the case since there seem to be some nicotine-only users who have experienced lung injury.
Keep your eye on PubMed for updates on this health crisis.
Schier JG, Meiman JG, Layden J, et al. Severe Pulmonary Disease Associated with Electronic-Cigarette–Product Use — Interim Guidance. MMWR Morb Mortal Wkly Rep 2019;68:787–790. DOI: http://dx.doi.org/10.15585/mmwr.mm6836e2external icon
Perrine CG, Pickens CM, Boehmer TK, et al. Characteristics of a Multistate Outbreak of Lung Injury Associated with E-cigarette Use, or Vaping — United States, 2019. MMWR Morb Mortal Wkly Rep 2019;68:860–864. DOI: http://dx.doi.org/10.15585/mmwr.mm6839e1
Erratum: Vol. 68, No. 39. MMWR Morb Mortal Wkly Rep 2019;68:900. DOI: http://dx.doi.org/10.15585/mmwr.mm6840a5
BJP issues new policy on SABV
September 4, 2019
The British Journal of Pharmacology has been issuing a barrage of initiatives over the past few years that are intended to address numerous issues of scientific meta-concern including reproducibility, reliability and transparency of methods. The latest is an Editorial on how they will address current concerns about including sex as a biological variable.
Docherty et al. 2019 Sex: A change in our guidelines to authors to ensure that this is no longer an ignored experimental variable. https://doi.org/10.1111/bph.14761 [link]
I’ll skip over the blah-blah about why. This audience is up to speed on SABV issues. The critical parts are what they plan to do about it, with respect to future manuscripts submitted to their journal. tldr: They are going to shake the finger but fall woefully short of heavy threats or of prioritizing manuscripts that do a good job of inclusion.
From Section 4 BJP Policy: The British Journal of Pharmacology has decided to rectify this neglect of sex as a research variable, and we recommend that all future studies published in this journal should acknowledge consideration of the issue of sex. In the ideal scenario for in vivo studies, both sexes will be included in the experimental design. However, if the researcher’s view is that sex or gender is not relevant to the experimental question, then a statement providing a rationale for this view will be required.
Right? Already we see immense weaseling. What rationales will be acceptable? Will those rationales be applied consistently for all submissions? Or will this be yet another frustrating feature for authors in which our manuscripts appear to be rejected on grounds that other papers published seem to suffer from?
We acknowledge that the economics of investigating the influence of sex on experimental outcomes will be difficult until research grant‐funding agencies insist that researchers adapt their experimental designs, in order to accommodate sex as an experimental variable and provide the necessary resources. In the meantime, manuscripts based on studies that have used only one sex or gender will continue to be published in BJP. However, we will require authors to include a statement to justify a decision to study only one sex or gender.
Oh a statement. You know, the NIH has (sort of, weaselly) “insisted”. But as we know the research force is fighting back, insisting that we don’t have “necessary resources” and, several years into this policy, researchers are blithely presenting data at conferences with no mention of addressing SABV.
Overall sex differences and, more importantly, interactions between experimental interventions and sex (i.e., the effect of the intervention differs in the two sexes) cannot be inferred if males and females are studied in separate time frames.
Absolutely totally false. False, false, false. This has come up in more than one of my recent reviews and it is completely and utterly, hypocritically wrong. Why? Several reasons. First of all in my fields of study it is exceptionally rare that large, multi-group, multi-sub-study designs (in single sex) are conducted this way. It is resource intensive and generally unworkable. Many, many, many studies include comparisons across groups that were not run at the same time in some sort of cohort balancing design. And whaddaya know those studies often replicate with all sorts of variation across labs, not just across time within lab. In fact this is a strength. Second, in my fields of study, we refer to prior literature all the time in our Discussion sections to draw parallels and contrasts. In essentially zero cases do the authors simply throw up their hands and say “well since nobody has run studies at the same time and place as ours there is nothing worth saying about that prior literature”. You would be rightfully laughed out of town.
Third concern: It’s my old saw about “too many notes“. Critique without an actual reason is bullshit. In this case you have to say why you think the factor you don’t happen to like for Experimental Design 101 reasons (running studies in series instead of parallel) has contributed to the difference. If one of my peer labs says they did more or less the same methods this month compared to last year compared to five years ago…wherein lies the source of non-sex-related variance which explains why the female group self-administered more cocaine compared with the before, after and in between male groups which all did the same thing? And why are we so insistent about this for SABV and not for the series of studies in males that reference each other?
In conscious animal experiments, a potential confounder is that the response of interest might be affected by the close proximity of an animal of the opposite sex. We have no specific recommendation on how to deal with this, and it should be borne in mind that this situation will replicate their “real world.” We ask authors merely to consider whether or not males and females should be physically separated, to ensure that sight and smell are not an issue that could confound the results, and to report on how this was addressed when carrying out the study. Obviously, it would not be advisable to house males and females in different rooms because that would undermine the need for the animals to be exposed to the same environmental factors in a properly controlled experiment.
NO SHIT SHERLOCK!
Look, there are tradeoffs in this SABV business when it comes to behavior studies, and no doubt others. We have many sources of potential variance that could be misinterpreted as a relatively pure sex difference. We cannot address them all in each and every design. We can’t. You would have to run groups that were housed together, and not, in rooms together and not, at times similar and apart AND MULTIPLY THAT AGAINST EACH AND EVERY TREATMENT CONDITION YOU HAVE PLANNED FOR THE “REAL” STUDY.
Unless the objective of the study is specifically to investigate drug‐induced responses at specific stages of the oestrous cycle, we shall not require authors to record or report this information in this journal. This is not least because procedures to determine oestrous status are moderately stressful and an interaction between the stress response and stage of the oestrous cycle could affect the experimental outcome. However, authors should be aware that the stage of the oestrous cycle may affect response to drugs particularly in behavioural studies, as reported for actions of cocaine in rats and mice (Calipari et al., 2017; Nicolas et al., 2019).
Well done. Except why cite papers where there are oestrous differences without similarly citing cases where there are no oestrous differences? It sets up a bias that has the potential to undercut the more correct way they start Section 5.5.
My concern with all of this is not the general support for SABV. I like that. I am concerned first that it will be toothless in the sense that studies which include SABV will not be prioritized and some, not all, authors will be allowed to get away with thin rationales. This is not unique to BJP, I suspect the NIH is failing hard at this as well. And without incentives (easier acceptance of manuscripts, better grant odds) or punishments (auto rejects, grant triages) then behavior won’t change because the other incentives (faster movement on “real” effects and designs) will dominate.
Yes, the DEA still continues to keep cannabidiol (CBD) on the list of Schedule I drugs. I took this up in December of 2016 and the issues continue.
The new-ish bit, I suppose, is that the FDA approved GW Pharma’s cannabidiol product Epidiolex for Dravet Syndrome and Lennox-Gastaut Syndrome, which involve uncontrollable seizures. This all flows from the “Charlotte’s Web” phenomenon, which was desperate parents seeking help from a specific CBD-dominant strain of cannabis.
This meant that the DEA had to reschedule CBD as a compound with medical application. The reporting from CNBC says Schedule V:
Epidiolex will be classified as a schedule 5 controlled substance, the lowest level, defined as those with a proven medical use and low potential for abuse. Other drugs in this category include some cough medicines containing codeine.
But. However. Not so fast. Apparently the DEA has decided to re-schedule CBD ONLY in the context of FDA approved products. From the same report:
The rescheduling applies to CBD containing no more than 0.1 percent THC, in FDA-approved drug products. Though this allows GW Pharma to sell Epidiolex, it does not broadly apply to CBD.
emphasis added.
This is getting increasingly ridiculous. It is really, really, really clear that CBD does not have the fun recreational drug properties of good old delta-9-tetrahydrocannabinol. It is hard to find much effect of this compound at all*, despite all the quack remedy type products that are illegally on sale in the country at the moment.
I don’t understand how CBD got on the Schedule I list in the first place, nor why the DEA didn’t take this convenient opportunity to re-schedule it altogether.
__
*The anti-seizure properties seem solid.
N.b., As per my usual disclaimer, I may have held, hold, or be seeking to hold research funding involving CBD. Please read my comments with that in mind.
Surgeon General Murthy Issues A Report on Facing Addiction
November 18, 2016
Surgeon General’s Report On Alcohol, Drugs and Health can be found at addiction.surgeongeneral.gov. You may be particularly interested in the Executive Summary [PDF] or the chapter on the Neurobiology of Addiction [PDF].
There was also a brief interview with the Surgeon General on NPR.
A few factoids from the Executive Summary:
In 2015, substance use disorders affected 20.8 million Americans—almost 8 percent of the adolescent and adult population. That number is similar to the number of people who suffer from diabetes, and more than 1.5 times the annual prevalence of all cancers combined (14 million). Of the 20.8 million people with a substance use disorder in 2015, 15.7 million were in need of treatment for an alcohol problem in 2015 and nearly 7.7 million needed treatment for an illicit drug problem.
Substance use disorder treatment in the United States remains largely segregated from the rest of health care and serves only a fraction of those in need of treatment. Only about 10 percent of people with a substance use disorder receive any type of specialty treatment. Further, over 40 percent of people with a substance use disorder also have a mental health condition, yet fewer than half (48.0 percent) receive treatment for either disorder.
Treatment is effective. As with other chronic, relapsing medical conditions, treatment can manage the symptoms of substance use disorders and prevent relapse. Rates of relapse following treatment for substance use disorders are comparable to those of other chronic illnesses such as diabetes, asthma, and hypertension. More than 25 million individuals with a previous substance use disorder are in remission and living healthy, productive lives.
For instance, people who first use alcohol before age 15 are four times more likely to become addicted to alcohol at some time in their lives than are those who have their first drink at age 20 or older. Nearly 70 percent of those who try an illicit drug before the age of 13 develop a substance use disorder in the next 7 years, compared with 27 percent of those who first try an illicit drug after the age of 17. Although substance misuse problems can develop later in life, preventing or even just delaying young people from trying substances is important for reducing the likelihood of more serious problems later on.
Many more people now die from alcohol and drug overdoses each year than are killed in automobile accidents. The opioid crisis is fueling this trend with nearly 30,000 people dying due to an overdose on heroin or prescription opioids in 2014. An additional roughly 20,000 people died as a result of an unintentional overdose of alcohol, cocaine, or non-opioid prescription drugs.
emphasis added.
NPR on trying to create DUI regulation for marijuana
September 6, 2016
NPR had a good segment on this today: The Difficulty Of Enforcing Laws Against Driving While High. Definitely well worth a listen.
I had a few reactions in a comment that ended up being post-length, so here you go.
The major discussion of the segment was two-fold and I think illustrates where policy based on the science can be helpful, even if only to point to what we need to know but do not at present.
The first point was that THC hangs around in the body for a very long time post-consumption, particularly in comparison with alcohol. Someone who is a long term chronic user can have blood THC levels that are…appreciable (no matter the particular threshold for presumed impairment, this is relevant). Some of the best data on this are from the laboratory of Marilyn Huestis when she was, gasp, an intramural investigator at NIDA! There are some attempts in the Huestis work to compare THC and metabolite ratios to determine recency of consumption-that’s a good direction. IMO.
The second argument was about behavioral tolerance. One of the scientist interviewed was quoted along the lines of saying the relationship between blood levels, repetitive use and actual impairment was more linear for alcohol than for THC. Pretty much. There is some evidence for substantial behavioral tolerance, meaning even when acutely intoxicated, the chronic user may have relatively preserved performance versus the noob. There’s a laboratory study here that makes the point fairly succinctly, even if the behavior itself isn’t that complex. As a counterpoint, this recent human study fails to confirm behavioral tolerance in an acute dosing study (see Fig 4A for baseline THC by frequency of use, btw). As that NPR piece noted, it would be very valuable to get some rapid field screen for THC/driving – relevant impairment on a tablet.
CPDD 2016: Thought of the Day
June 14, 2016
There is a lot of focus on cannabis this year. Much more than usual, seemingly.
And everyone talks about how it is the growing legalization (medical and recreational) that is the driving justification.
I find this to be interesting.
By now most of you are familiar with the huge plume of vapor emitted by a user of an e-cigarette device on the streets. Maybe you walked through it and worried briefly about your second-hand vape exposure risk. Some of you may even have been amused to hear your fellow parents tell you with a straight face that their kids “only vape the vehicle for the flavor”. Sure. Ahem.
Nicotine is one thing, but there is also a growing trend to use e-cigarettes to vape marijuana and, allegedly, stimulants such as flakka (alpha-PVP).
As with many emerging drug trends it can be difficult to put solid, peer-reviewed epidemiology on the table to verify these behaviors.
A recent paper reports on some initial estimates on practices among middle- and high-school students.
High School Students’ Use of Electronic Cigarettes to Vaporize Cannabis. Morean ME, Kong G, Camenga DR, Cavallo DA, Krishnan-Sarin S. Pediatrics. 2015 Oct;136(4):611-6. doi: 10.1542/peds.2015-1727. Epub 2015 Sep 7.[PubMed]
The authors surveyed 5 High Schools and 2 middle schools in Connecticut in the spring of 2014. Apparently insufficient middle school data were obtained so the paper focuses on the high school respondents only.
There were three key questions for the purposes of assessing behavior rates. Students were classified as “never used” or “lifetime used” (for ever having tried at least once) for e-cigarette use, for cannabis use (any method) and for cannabis use with an e-cigarette device.
Out of the total sample of 3847 HS students who completed the entire survey (52% female), about 5.4% had used an e-cigarette to self-administer cannabis. If, however, the sample was limited to those who had ever used an e-cigarette, then 18% had used one to administer cannabis. For lifetime cannabis users, it went to 18.4% and for dual e-cigarette and cannabis users, 26.5%.
So while the majority of high school students who have ever tried cannabis have never tried using an e-cigarette to dose themselves, 20% is a sizeable minority.
As always, it will be most interesting to see where these trends go and how they extend to older user groups. It could be that it is something that kids try and abandon (perhaps due to not learning different inhalation topography necessary for the desired high as with nicotine). It may be that older users are loathe to change their established patterns or see no advantages to e-cigarettes. I anticipate that solid data on these trends will be slow to emerge but I’ll be keeping an eye out.
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Relatedly, the research community has been responding to this trend, and I wanted to draw two new papers to your attention.
Marusich and colleagues report from the Wiley group at RTI that they have a new model of flakka (and methamphetamine) delivery that increases locomotor activity and induces place preference in mice.
Pharmacological Effects of Methamphetamine and Alpha-PVP Vapor and Injection, Julie A. Marusich, , Timothy W. Lefever, Bruce E. Blough, Brian F. Thomas, Jenny L. Wiley, 2016, Neurotoxicology, doi:10.1016/j.neuro.2016.05.015
Nguyen and colleagues report from the Taffe group at TSRI that they have a new model of THC delivery that induces hypothermia, hypolocomotion and anti-nociception in rats.
Inhaled delivery of Δ9-tetrahydrocannabinol (THC) to rats by e-cigarette vapor technology, Jacques D. Nguyen, Shawn M. Aarde, Sophia A. Vandewater, Yanabel Grant, David G. Stouffer, Loren H. Parsons, Maury Cole, Michael A. Taffe, 2016, Neuropharmacology,doi:10.1016/j.neuropharm.2016.05.021
A Repost to mark the passing of Nancy Reagan
March 7, 2016
In terms of health and biomedical science, the Reagan Administration left a shameful legacy of refusing to respond to (or acknowledge, really) the HIV/AIDS crisis that blew up during their tenure in office.
As many of you recall, First Lady Nancy Reagan took up drug abuse and substance dependence as one of her signature issues and this is probably one of the other larger Reagan Administration legacies on health.
To mark her passing, I thought I would repost the following which first appeared on the blog 21 July 2008.
If you are a reader of my posts on drug abuse science you will have noticed that it rarely takes long for a commenter or three to opine some version of “The (US) War on Drugs is a complete and utter failure”. Similarly, while Big Eddie mostly comments on the liberty aspects (rather than the effectiveness) of the WoD himself, a commenter to his posts will usually weigh in, commenting to a similar effect.
Now I’m open to all the arguments about personal liberty trade offs, economic costs, sentencing disparities, violations of other sovereign nations and the like. Nevertheless, I’m most interested in the fundamental question of whether the War on Drugs worked. That is, to reduce drug use in the US. For those who believe it has not worked, I have a few figures I would like explained to me.
I’m following up a story I started in a prior post by putting up the long term trends for cocaine use in the US. These data are from the 2006 Volume II monograph which focuses on the 18 yr old and older populations. As you will recall my hypothesis was / is that the Len Bias fatality had a dramatic effect on cocaine use. I still think this is the case and that this explains much of the timing of a reduction in cocaine prevalence observed consistently from the 18 yr old to 45+ age groups. However Len Bias’s death was not an exclusive effect and must be considered in the context of changes in other drug use patterns. That context is something I want to delve into just a little bit.
As always, I depend on the data from the Monitoring the Future survey (www.monitoringthefuture.org) and I am pulling the figures from the 2006 Volume I monograph which focuses on the 8th, 10th and 12th grade populations in contrast to the older age cohorts outlined in the first graph.
Cocaine
First up are the annual prevalence rates for powder cocaine, which I provide for reference to the previous graph for the older age ranges. I apologize for the blurry figures but my imaging skills are not up to any better- luckily, these reports are freely available on the MtF website. (I also encourage you to get the reports yourself because there are slight changes in the questions asked in some cases- if you see a discontinuity in the longitudinal data this is probably why.) The longest term trends are available for 12th graders, additional grades were added into the survey in the early 1990’s. Prevalence of cocaine was reasonably steady in the 1979-1986 interval and it is stunningly apparent that cocaine became less popular with 12th graders after 1986 . It is also clear that it took about 5 additional years for prevalence to drop to the most recent nadir. So it wasn’t all about Len Bias (he died of cocaine-related cardiac complications on June 19, 1986).
So, if it isn’t all about Len Bias, perhaps we should see similar effects on population prevalence of other illicit drugs?
Marijuana and Amphetamine
It seems reasonable to turn our analysis to two perennial high-prevalence drugs for high school populations; marijuana (duh!) and the amphetamines. (In MtF parlance, the amphetamine class is for tablet or other prescription preparations after 1982.) In this case, the prevalences were at peak in the late 1970s and started to decline in the very early 1980s. Interestingly, there is no evidence of a change in the established trends from 1986-1987 as is observed for powder cocaine; I think this supports the Len Bias hypothesis. Nevertheless we can also see this as additional evidence for something else driving drug use downward.
This brings us to what are illicit drugs for most of these populations but, of course, licit drugs for individuals who have reached the legal age; 21 (alcohol) or 18 (cigarettes; this may be a substantial fraction of 12th graders). In theory, we might use these data to try to dissociate the anti-drug messaging from the drug interdiction / legal penalties side of the equation. Not perfect, but at least a hint.
Alcohol
The trends for annual prevalence of alcohol were very stable from 1978-1988 whereupon a decline was observed (questions were altered in 1993, making further comparison tricky). The trends for 5-drinks-in-a-row (currently the definition of a “binge”) in the past two week interval were very stable from 1978-1983 and thereafter exhibited a slow decline until the early 1990s. Very reminiscent of the above mentioned drugs.
Cigarettes
In this case, please note that we’ve shifted to 30-day prevalence rates (any, daily); obviously this is frustrating for direct comparison but this is what they provide in the monographs. Unfortunately the more recent monographs (it is currently on a reliable annual update schedule with available pdfs, the older ones are not available) seem to only start with the 1986 data in the Tables so one is left with their figures for the earlier part of the trends. With that caveat, we can see that cigarette prevalence in the high school population was reasonably stable during the interval in which the prevalence rates for the illicit-for-all drugs mentioned above were in decline.
So Did the War on Drugs Work or Not?
I do think the jury is still out on this one and the problem of shifting definitions about goals and successes is quite difficult. I feel confident the comments will stray afield a bit and explore some of these issues. However, as I intimated at the outset,
for those of you who insist vociferously that the War on Drugs (considered inclusively with the Just Say No, D.A.R.E, main-stream media reporting, and all that stuff that is frequently rolled into a whole by the legalization crowd) is an abject failure…
for those of you who insist vociferously that you cannot tell teenagers anything about the dangers of recreational drugs and expect them to listen to you…
I would like these data explained to me.
__
Update 7/23/08: Followup post from Scott Morgan at StoptheDrugWar.org
Vaping: Known Unknowns
December 21, 2015
Unless you have been hiding under a rock, you know about e-cigarettes. These are devices which deliver a nicotine dose using a battery-heated element which vaporizes propylene glycol, polyethylene glycol, vegetable glycerin (mostly) and/or some other vehicles in which the nicotine has been dissolved.
These devices appeal to users as cessation aids to help quit smoking tobacco and as a safer alternative to cigarettes.
They also appeal to adolescents, apparently.
You will hear the occasional grand pronouncement hit the media circusit with more assertions than questions leaving people wondering.
Here is my general take on just about anything having to do with e-cigarettes: We don’t really know and we need to do some more science to figure it out.
So here are the key questions all amenable to research, some of which is no doubt ongoing.
Do e-cigs help people quit smoking? The question is, in my view, do they do any better than cold turkey (accounting for subpopulations) and are they as effective or better than any other replacement therapy like the gum or patch.
Do e-cigs prolong nicotine use in individuals who would otherwise have quit smoking cigarettes? Very tricky question, this one. But if you have an individual who would have quit smoking but keeps using nicotine via e-cig, you’ve increased harm.
Do e-cigs cause novel harms? In other words, presumably the nicotine harm is the same (once individuals learn how to get their desired nicotine dose from these). But are there constituents of the vehicles, the flavorants or products created by the vaporization process that cause health risks? And no, just showing an ingredient is present is not evidence of harm. We need careful toxicology studies with relevant exposure doses and regimens.
Do e-cigs prevent well-established harms? The chronic smoking of tobacco, typically via the modern cigarette products, has very well established and very bad health consequences. Nicotine exposure is the cause of only a subset of the harms, even if it is the thing responsible for continued use. So getting combusted tobacco smoke exposure out of the situation cannot help but be a huge win. Huge. I don’t see how this can really be argued until and unless we find some whopping big harms of the vapor exposure.
Do e-cigs addict new individuals to nicotine? One of the big fears of those concerned with e-cigs is that early data show that adolescents are more likely to try e-cigs than to try smoking cigarettes. There will be some work showing that daily nicotine users started off with e-cigs rather than tobacco cigarettes but as you know, it is impossible to establish causality with real human populations. The best we have, overwhelmingly likely causal relationships, has to wait on a whole lot of data. Which we won’t have for many years.
Bonus Round:
Are e-cigs used without nicotine or other psychoactive? One parent I know has asserted that perhaps some adolescents are using e-cig devices with just the flavored vehicles and not to ingest nicotine or any other drug. Obviously this goes back to the above question about harms from the vehicle. But it also links to another concern…
Are e-cigs used to deliver other psychoactive drugs? The devices are very readily and broadly available. They are being used with crude marijuana extracts for certain sure. There have been media allegations that they are being used to ingest “flakka” (here, here, here). For a time, one assumes that by pretending to be smoking nicotine or the flavorant (see above) peope will be able to stroll about ingesting illegal substances in public view. Including adolescents, my friends. Yes, kids.
Congress let the NIH drop the HIV/AIDS set-aside: Implications for NIDA?
December 15, 2015
Jocelyn Kaiser reported in Science Insider:
the National Institutes of Health (NIH) today announced it will no longer support setting aside a fixed 10% of its budget—or $3 billion this year—to fund research on the disease. The agency also plans to reprogram $65 million of its AIDS research grant funding this year to focus more sharply on ending the epidemic.
Whoa. Big news. This is an old Congressional mandate so presumably it needs Congress to be on board. More from Kaiser:
The changes follow growing pressure in Congress and from some advocacy groups for NIH to reallocate its funding based on the public health burden a disease causes…. some patient groups and members of Congress have recently asked why AIDS receives disproportionately far more than diseases with higher death rates, such as heart disease and Alzheimer’s….Last year, Congress omitted instructions asking NIH to maintain the 10% AIDS set aside.
Emphasis added. An act by omission is good enough for gov’mint work, eh? Congress is on board.
@jocelynkaiser was kind enough to link to relevant NIH budgetary distributions:
As you can see, NIDA devotes about $300M to HIV/AIDS research. The annual NIDA budget allocation is about $1B so you can see that something on the order of 30% of the NIDA budget is (and has been) devoted to this Congressional Mandate.
Wait, whut? What about that 10% mandate above? Yep, the HIV/AIDS money has not been evenly distributed across the ICs.
Now, I don’t know exactly when and how all of this shook down. It was FY 1987 when the NIAID budget went up by something like 47% when other similarly sized ICs didn’t see such a large percentile increase. Clearly 1986 was when Congress got serious about HIV/AIDS research. We can’t assess the meaning of
AIDS has received 10% of NIH’s overall budget since the early 1990s, when Congress and NIH informally agreed it should grow in step with NIH’s overall budget.
…
NIH must treat AIDS dollars as a distinct pot of money within its overall budget. That is because a 1993 law carved out a separate HIV/AIDS budget, Collins says. And undoing that law would take action by Congress.
from this article. It is a little frustrating, to be frank. But…on to the NIDA situation.
NIDA doesn’t appear in the NIH tables until FY1993 because it didn’t actually join the NIH until 1992. Nevertheless that history page on NIDA notes:
1986: The dual epidemics of drug abuse and HIV/AIDS are recognized by Congress and the Administration, resulting in a quadrupling of NIDA funding for research on both major diseases.
There are many ways of looking at this situation.
Some in the NIDA world who are not all that interested in HIV/AIDS matters complain bitterly about why “A third of our budget is reserved for HIV/AIDS“. Our.
Another way of looking at this would be “If Congress mandated NIH devote 10% of its budget to HIV/AIDS but NIH did this by incorporating NIDA with its existing HIV/AIDS funding then the entire rest of NIH is shirking its response to the mandate on the back of NIDA”.
And yet a final way of looking at this* would be “Dude, NIDA wouldn’t even have this money if not for Congress’ interest in funding HIV/AIDS research so it isn’t ‘our‘ funding being diverted to HIV/AIDS research.”
Is this important? Yes and no.
The news is potentially huge for those who seek to get the HIV/AIDS funding via NIDA grants and for those who seek non-HIV/AIDS funding. It makes matters slightly better for the latter and worse for the former. Right? If there is no special set-aside, the latter folks now have at least a shot at that $300M that had been out of reach for them. This consequently increases the competition for those who have HIV/AIDS relevant proposals. Who are presumably sad right now.
But it all depends on what Collins plans to do with his newly won freedom. Back to Kaiser:
Francis Collins agrees: At a meeting of his Advisory Committee to the Director (ACD) today, he noted that no other disease receives a set proportion of the NIH budget and the argument that AIDS still deserves such a set-aside is “not a defensible one.”
The end of the set-aside has “free[d] us up” to refocus NIH’s AIDs portfolio, Collins says.
However the article only then talks about $65M being reprioritized. What about the rest of the 10% of the ~$30B / yr NIH budget? No idea.
So I want to know a few things. Is the $300M in the NIDA budget that goes to HIV/AIDS part of this 10% overall NIH mandate? If so, will Collins try to claw that back for some other agenda?
If a miracle occurs and it stays within NIDA, will Nora Volkow use this new-found freedom to ease the pressure on the non-HIV/AIDS researchers by letting them (ok, “us”) get a shot at that previously-sequestered pool?
Or will Volkow use it to pay for the latest boondoggle initiatives of ABCD and BRAINI?
The way I hear it, this latter is likely to happen because up to this point all other NIDA initiatives are being squeezed** to make ABCD and BRAINI happen.
Obviously I would prefer to see Volkow choose to use this new freedom a little more democratically by spreading the love across all of the portfolio.
__
*this has been my view for some time now.
**this manifests, IME, as profound pessimism on the part of POs that anything in the grey zone (which is robust reality at no-public-payline-NIDA) will be picked up because all spare change is going to the two aforementioned boondoggles.
Light matters
December 11, 2015
A recent exchange on the Twitter reminded me of an old paper from 1968.
The paper in question is
Scheving LE, Vedral DF, Pauly JE. Daily circadian rhythm in rats to D-amphetamine sulphate: effect of blinding and continuous illumination on the rhythm. Nature. 1968 Aug 10;219(5154):621-2. [PubMed]
The key takeaway message for me is captured in the first figure (click to embiggen), which represents the percentage of rats that died within 24 h of being injected with either 26 mg/kg (darker line) or 30 mg/kg (dotted line) of amphetamine. The X axis depicts the time of day at which the groups were injected and the bar that forms the X axis indicates when the lights were on (6 am to 6 pm) and off.
As you are aware, rats are a nocturnal species and the wiggle trace just above the X-axis confirms this with activity patterns based on noise recording of the colony.
So, back to the point. The only difference across points within a single amphetamine dose is the time of day at which the drug was administered. Mortality rates change from 20% to nearly 80% with the lowest observed during the inactive part of the rats’ day.
Light cycle and circadian phase matter. A lot.
This brings me to a second example, which is from one of the papers in a series of investigations by Dave Roberts at Wake Forest. In
Roberts DC1, Brebner K, Vincler M, Lynch WJ. Patterns of cocaine self-administration in rats produced by various access conditions under a discrete trials procedure. Drug Alcohol Depend. 2002 Aug 1;67(3):291-9. [PubMed]
the authors use a procedure in which rats are allowed to self-administer cocaine 24 h per day. The one major difference from the usual 1-2 h per day type of model is that the number of opportunities for cocaine were limited. These “discrete trial” opportunities ranged from 2-5 per hour and each time the animal was permitted 10 min to make a response once the lever was extended. Each response terminated the discrete trial so animals could only take 2-5 infusion per hour.
The figure that continues the point most effectively is from a set of manipulations in which the discrete trial was set to 3 per hour and the per-infusion dose was varied. The data represent the total cocaine intake per hour so look at the 1.0 and 2.0 mg/kg/infusion doses if you want to figure out how many responses out of the 3 opportunities per hour were being made.
The point is again obvious, namely that circadian factors and light cycle matter a lot to the outcome. Imagine the more typical 1 h or 2 h operant self-administration session for cocaine being placed at various points across the rat’s light cycle. On average, you might expect different mean intakes.
This is going to contribute to replication and reliability issues, particularly if you expect a given mean amount of drug intake.
It gets even tricker if you want to start exploring the effect of different interventions on cocaine self-administration. Who knows if they themselves have circadian-dependent effects or if the interaction with cocaine taking does? Who knows which direction it takes? We don’t know until someone does the study.
And we can all see how much exacting work with light cycles there will be to satisfy ourselves that we know what the influence is. Work that, should it turn out negative, will be nigh on unpublishable.
And to be clear, there are hard practicalities of research that make us ignore these factors at times. Mostly across studies, but sometimes within them. Take the big issue of running behavior in the light or dark cycle of a rat (or mouse). This depends on University Facilities level decision making. Can the rooms be reverse-cycled (technically or at the whim of the animal care department)? Can you get access to the right light-cycle room for your animals for your experiments if you are low on the totem pole (as a lab or within a lab)?
Then there are within-lab factors. Limited numbers of operant boxes and limited numbers of hands. You cannot necessarily squeeze all of your animal work into the prime window of 6 h into dark to 12 h identified in the Roberts paper, above. Maybe this function changes depending on your procedures and you have an even narrower stability window.
So there will be compromises.
But these compromises will most assuredly affect the perceived replicability (aka generalization) of the work.
__
Additional Reading:
Marijuana Use, Abuse and Dependence Increased Over the Past Decade
October 23, 2015
A new paper from Hasin and colleagues at JAMA Psychiatry reviews data:
from NESARC and from the National Institute on Alcohol Abuse and Alcoholism
2012-2013 National Epidemiologic Survey on Alcohol and Related Conditions–III (NESARC-III), a survey of 36,309 new participants.
…
The NESARC field procedureswere similar to those in NESARC-III.
There are really three key observations, although the tables also break down the findings by sex, age, race/ethnicity, education level, etc.
First, past year use of marijuana went from 4.1% to 9.5% of the sampled populations. Interesting, but hey, could just be more people feeling free to try it out, right?
Second finding looked at prevalence of meeting DSM-IV criteria for a Marijuana Use Disorder (including Abuse and Dependence subcategories) in the past year. This measure went from 1.5% to 2.9% of the population.
The third finding is that if you condition only upon those individuals who have tried marijuana at least once in the past year, the rate of a Marijuana Use Disorder went from 35.6% to 30.6%.
This is all relevant to a few themes we’ve discussed before on the blog.
I don’t see how you can view these data other than in a context of growing liberalization of medical marijuana laws and availability of marijuana. This refutes the occasional position struck by the pot fans that changes in legal status and attitude won’t change use rates because everyone who wants to smoke marijuana already does. Clearly the US population undergoes significant changes in exposure to marijuana. In this case only over a decade.
My position has also been that, in general, as you increase the number of people who are exposed to a given drug you are going to see an increase in problems related to that drug. In the absence of other information, we must start our estimate of that rate from what we observe at a given time. The first two numbers in the study confirm this. As use rates increased, so did rates of meeting criteria for DSM-IV diagnosis of a MUD.
The conditional probability measure also addresses this phenomenon, perhaps in an even better way. I have mentioned before that it is really hard to assess conditional probability of dependence between drugs that feature significant base-rate exposure differences. You can’t help but assume there is going to be a curve whereby the more democratic the exposure, the larger will be the occasional user population. That is, I assume some sort of nonlinearity is going to occur against the general estimation I mention above. I presume the lower the incidence of exposure to a given drug, perhaps the higher the conditional probability of dependence and the higher the incidence of exposure, the lower the conditional probability.
In this case, I’d say the change in conditional probability is not that significant. Something around a third of those who smoke marijuana in a given year are meeting criteria for a MUD across a doubling of the incidence of exposure. The curve is still pretty linear although I assume we will be getting another jump in a decade and can see how this curve shapes up.
This estimate of a MUD is really high to my eye, no doubt because it includes abuse and dependence together. Perhaps the data I usually think about (7-9% dependence rate) references dependence without abuse…I have to go check on that. In case you are wondering, the difference really boils down to symptoms of tolerance (diminished effect at same dose, increasing dose to get desired effect) and withdrawal, as well as some indicators of uncontrolled use relative to a person’s intentions.
Now interestingly the authors reference another similar study (NSDUH) that didn’t find an increase in prevalence that was so large- only 12% reported by Pacek et al, 2015. The present authors suggest more detailed questioning in the NESARC approach may explain the difference.
Publisher wants to take journal Open Access
October 12, 2015
Someone forwarded me what appears to be credible evidence that Wiley is considering taking Addiction Biology Open Access.
To the tune of $2,500 per article.
At present this title has no page charges within their standard article size.
This is interesting because Wiley purchased this title quite a while ago at a JIF that was at or below my perception of my field’s dump-journal level.
They managed to march the JIF up the ranks and get it into the top position in the ISI Substance Abuse category. This, IMO, then stoked a virtuous cycle in which people submit better and better work there.
At some point in the past few years the journal went from publishing four issues per year to six. And the JIF remains atop the category.
As a business, what would you do? You build up a service until it is in high demand and then you try to cash in, that’s what.
Personally I think this will kill the golden goose. It will be a slow process, however, and Wiley will make some money in the mean time.
The question is, do most competitors choose to follow suit? If so, Wiley wins big because authors will eventually have no other option. If the timing is good, Addiction Biology makes money early and then keeps on going as the leader of the pack.
All y’all Open Access wackaloons believe this is inevitable and are solidly behind Wiley’s move, no doubt.
I will be fascinated to see how this one plays out.
DrugMonkey 