Tag Archives: drug

First Day: In the Can


I’m referring to the 2nd definition in this link

I was worried, but I think things went well for my first day of classes this semester. This Spring I am teaching my standard Microbiology course as well as a new Ecology course that I am using to teach all the things that I have never managed to squeeze into my normal General-  and Micro- biology classes.

So far I’m still trying to figure out a way that I can move my Micro class as quickly as possible into Immunology – which is my true love in biology. 

In Ecology, we jumped right into the idea of science as a tool for understanding the universe and working on some of the basic mathematics that help us feel comfortable in the conclusions at which we arrive.  

To describe this, we invented ‘VitaMax!’- a name so absurd that I thought I was inventing it, but it turns out that it’s a real thing – actually a couple of them. Who knew? Vitamax, the dietary supplement,  a multi-vitamin for Cystic Fibrosis patients, a green coffee extract for weight loss, and “A single capsule [that] guarantees firm and lasting erections 45 minutes after being taken.”

(by the way,  is it that a single capsule that gets you multiple erections?)

Well, our VitaMax! extends life … or at least that’s what we’re hoping. The data is in and we just need to evaluate whether it meets our strict requirements before getting it on the market.

Here’s the skinny:

VitaMax!® – Live life to the Max!

Without VitaMax, patients have a normal life span.

  1. Look this up and cite your source. If you are male, use the male life expectancy; if you’re female, use the female life expectancy. Assume the following data matches your ‘control’ group.
    1. Life Expectancy__________________

With VitaMax, patients lived:


life expectancy with Vita-Max:























  1. Determine the mean age patients lived to with VitaMax:_________________


  1. Determine the standard deviation of these patients:______________________



  1. Graph your data as either a bar graph or scatter plot.
  2. Look up a formula for computing Z-Score:


  1. What does Z-Score tell us?



  1. Assuming you can use the SD of your patients as representative for that in both the control and experimental group,  compute the Z-score for the average age without VitaMax __________________


  1. How likely is it that this average age without the drug falls within the area of the bell curve described by your experimental group’s data?


  1. Thinking as a scientist, how would you present this data to your company? Try to make a one-sentence statement about its efficacy.


10. Thinking as a marketing person, how would you pitch VitaMax to the public (without lying)? Try to make this also a one-sentence statement .

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Posted by on January 14, 2014 in Uncategorized


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Magic Bullets


from SAFC global

I just saw that the most recent issue of Science is highlighting a favorite topic of mine, antibody-mediated therapy and B cell biology. I’ve done work in both of these (related) fields in the past and remain an advocate of antibody-tageting of therapies using drug conjugates (e.g. trastuzumab–DM1) or initiating cell-specific effects simply by binding (e.g. anti-CD20). 

In the early 20th century, Paul Ehrlich coined the phrase “magische Kugel,” to describe antibodies as ‘magical’ proteins which could unerringly home in on targets to do a variety of things. Today, we can paint tumors with antibody conjugated with fluorescent dyes, deliver toxic chemicals to cells we wish to eliminate or simply activate / deactivate cells through targeting of receptor proteins.

I’m eager to get my hands on this issue and see what’s new (if anything) in the field and what products are currently in the pipeline of various biotech companies. 


(I’m suddenly struck anew with the misery of not having access to Nature and other journals I’ve always had handy. I’m so glad I at least still have Science! )


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Posted by on September 15, 2013 in Uncategorized


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This Week in MicroBiology Class



Instead of starting our chapter on Eukaryotic micro-organisms / parasites, we spent much of Thursday’s class discussing the second Chapter of ‘Vaccinated’. This chapter digs in and discusses how a number of vaccines were tested in the children of the Willowbrook institution in New York. We talked about how researchers must balance the (sometimes) competing interests of doing the best experiments to answer a question and looking out for the interests of those who can not look after themselves (the children of Willowbrook, in this case).

This chapter looked at the work of several investigators; Most evaluating vaccines, but one (Krugman) was also doing experiments to investigate how Hepatitis was spread. His work included the infection of a number of children with live virus, but no attempt at protecting them from infection.

This is presented as the most condemnable work of the lot as it presented no potential benefit to the children. In saying this we define the principle by which other work was done, ‘does the study do no intentional harm and does it provide at least some potential benefit to the subjects?’

This principle provides a challenge to doing the (scientifically) ideal experiment outlined below.

A basic, direct vaccine test would divide patients into two groups (vaccinated and unvaccinated) and then challenge half of each group with live virus (or whatever the vaccine is to protect against).

Ideal results:

vaccinated –> unchallenged –> 100% healthy

vaccinated –> challenged –> 100% healthy

unvaccinated –> unchallenged –> 100% healthy

unvaccinated –>challenged –> 100% sick

However, this means that the researcher would be knowingly (assume s/he is not blinded) injecting unprotected patients with live virus – an obvious ethical issue.

In looking through some old work done to investigate how hepatitis is spread, there was a mention of work conducted in just such a manner:


Bellin and Bailet J. Ped 1952

It’s unclear from this reference to a personal communication what, exactly the word ‘volunteer’ means.

I’ll bring up this paper in class the next time we discuss Vaccinated. I have an interesting person connection to it.

Instead of a experimentally controlled challenge, modern vaccine tests (as the other work described in this chapter) use much larger populations chosen because of their ‘at risk’ nature and then we wait and see if there are statistical differences between the infection rates of each group.

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Posted by on September 6, 2013 in Uncategorized


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Because it was on Dr. Oz, I’m more likely to think it’s a scam

doctor-ozI got something interesting in my inbox the other day. Something that I assume was a  friend’s email address getting hacked – although it’s the least offensive (apparent) hack I’ve ever seen (he says as the viruses circulate around his computer’s RAM).

It was a nearly blank email with a link to a Dr. Oz clip about the weight-loss promoting effects of green coffee extract, which contains high concentrations of chlorogenic acids. These molecules are said to promote weight loss through increasing metabolism.

Being a scientist means being a skeptic. In this case, because I already feel like it must be BS due to its connection with Dr. Oz (an Oprah-elevated proponent of many untested, ‘alternative’ therapies), the challenge for me is to admit the possibility that this stuff may work. So, rather than looking through the data to see if there’s anything to deny the claim, I’m really trying hard to look at the data to see any glimmer  of possibility.

Here’s a link to the Dr. Oz article online. The article was published in the January 2012  Diabetes, Metabolic Syndrome and Obesity, and happily the entire article is available free of charge. So let’s look at the data…

The article examines a “22-week crossover study was conducted to examine the efficacy and safety of a commercial green coffee extract product GCA™ at reducing weight and body mass in 16 overweight adults.” Half of the participants were male and half female – a typical study setup (although I do worry about how data is handled when looking at both sexes together, so let’s pay attention to that.)

Dr. Oz’s website indicates that “The subjects (taking the supplement) lost an average of almost 18 pounds – this was 10% of their overall body weight and 4.4% of their overall body fat.” These are pretty hefty claims, but I could use losing 18lbs, so let’s see where this goes.

The study followed those eight men and eight women for 22 weeks. At the beginning of the study, the average body mass index (BMI) at the start of the study was 28.22 ±  0.91 kg/m2 . Determine your own BMI here.

Note that BMI < 18.5 is underweight

18.5  –  25     healthy weight

25   –   30      overweight

30+               obese

This puts the study participants at the high end of overweight, but ‘preobese’.

Dosages of the green coffee extract and placebo were as follows:

“This study utilized two dosage levels of GCA, as well as a placebo. The high-dose condition was 350 mg of GCA taken orally three times daily. The low-dose condition was 350 mg of GCA taken orally twice daily. The placebo condition consisted of a 350 mg inert capsule of an inactive substance taken orally three times daily.”

I don’t think I’m the first one to point out that it’s hard to have a double blind trial when the dosages are distinguishable (two times vs three times daily). At least the placebo should be indistinguishable from the high dose.

One early eye-catching piece of data is from Table I, that summarizes the data of all precipitants as

BMI (kg/m2) pre study:28.22 ± 0.91        post study:25.25 ± 1.19     change-2.92 ± 0.85**, -10.3%

On average, all subjects lost weight during the study. But this really tells us nothing because we could see a 10% drop in BMI if the test arm lost 20% and then placebo arm stayed the same, or we could see the same thing if the weight loss occurred during ALL arms of the study.

Perhaps this reporting of data is justified by the next statement that participants all rotated through being on high dose, lose dose or placebo with intervening washout periods. Presumably, this makes the most of a small sampling of people, but I do find it harder to be confident about the data. Then again, I have never been involved in any human trial of this kind.

here’s the data:

High Dose arm:

start    BMI (kg/m2) 26.78 ± 1.55  –>    end 26.03 ± 1.36

Low Dose arm:

start    BMI (kg/m2) 26.25 ± 1.37  –>    end 25.66 ± 1.20

placebo arm:

start    BMI (kg/m2) 25.66 ± 1.20  –>  and 26.67 ± 1.72

At first glance this might appear to be pretty good. But let’s graph it out:


the data continue to look great.

Now, with error bars:

ImageHuh. Not so hot anymore.

Also, I’m not how sure this was done, but they get p values for HD p = 0.002, LD p = 0.003, placebo p = 0.384. These stats mean that the HD and LD groups are showing very significant differences, while the placebo group is not. You should be able to see this in the graph with error bars (as an approximation of significance). Unfortunately, I see a whole lot of no nothing. But, perhaps BMI is not the appropriate way to observe weight change (we are, after all not seeing specific weight changes, but changes within a group, i.e. diversity)

Another way to try to see what’s going on is to take a look at the weight data:


The data were presented in a number of other ways, but each of these was confusing and didn’t illustrate any clear conclusion (my interpretation).If the individuals’ data were visualized as a scatter plot, this might show us something – or data for each individuals change while in each group… As it is, we see unclear data with spectacular statistics, but we don’t get to see enough to be convinced of the changes.

Rather than go on and get more and more skeptical, let’s say, although we don’t see a lot here, the data,as reported, would make us want to see a larger study with some revisions for control of diet, exercise monitoring and a change in the way osage is administered so as to maintain the ‘blindness’ of the study.


Posted by on July 22, 2013 in Uncategorized


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A great old government film touting the discovery and promise of Penicillin:

Penicillins are a group of drugs naturally produced by penicillium molds. The antibiotic activity of penicillium was first observed by Alexander Fleming in 1928. He recognized the value of what he was seeing, but was unable to isolate the molecule that mediated the activity and therefore could not perform appropriate trials. It was not until Howard Florey and a team of researchers including Ernst Boris Chain and others from the Sir William Dunn School of Pathology, University of Oxford managed to isolate and purify the substance that its great promise became evident. (later Fleming, Florey and Chain shared the Nobel Prize in Medicine for their work)

Alexander_FlemingA number of naturally occurring penicillins exist, each characterized by a beta-lactam ring joined to a variable R-group. These drugs may be effective in the treatment of certain, susceptible (mostly) gram positive bacteria. The mechanism of action is the inhibition of peptidoglycan crosslinks in the bacterial cell wall, such that organisms cannot produce new cell wall and wind up shedding the wall during division. Without the cell wall, the bacteria is highly susceptible to immunological mechanisms and is readily killed.

ImageUnfortunately, many otherwise susceptible bacteria produce an enzyme (penicillinase / beta-lactamase) that cleaves the beta lactam ring structure leaving it ineffective. This single enzyme can be easily passed from one bacteria to another via a sex pilus or transformation rendering them non-susceptible to the antibiotic.


This enzyme breaks the β-lactam ring and deactivates it’s antibacterial properties. Because beta-lactam is central to the activity of penicillins, cephamycins, and carbapenems, all of these antibiotics can be rendered ineffective by organisms possessing this enzyme.

To counter this beta-lactamase activity, Clavulanic Acid can be used as an inhibitor of this enzyme. Clavulanic acid acts as a competitive ‘suicide inhibitor’, covalently bonding to the active site of the β-lactamase and irreversibly inactivating it. Compounds of the drug (Penicillin) and the enzyme inhibitor (Clavulanic Acid) are available as Amoxicillin under a number of brand names.

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Posted by on May 21, 2013 in Uncategorized


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RadioLab’s Inheritance Podcast

Screen Shot 2012-11-30 at 9.41.46 AMMy son and I just listened to a completely engrossing podcast on inheritance from RadioLab. The episode had three stories about different aspects of inheritance and genetic control. The first didn’t capture my interest nearly as much as the next two, so I won’t discuss it here.

The second story proposed and interesting idea of Lamarckean inheritance based on the extraordinary record-keeping of a far-north town in Sweden. In this town, the church kept amazingly detailed records about births, deaths, disease, health and even crop production year to year. When all these data were analyzed, researchers found a strong correlation between the availability of food to men in the village and the health and wellbeing of that man’s children. What might seem unintuitive is that contrary to what you might think, the children of men who suffered through years of starvation when they were ~9-12 years old fared the best. If dad ate well, your health prognosis was poor. If dad ate poorly, your prognosis was better. The effect even seemed to trace down two generations.

The explanation for this was that at this time in a man’s life he is making the cells that will go on to make sperm. Somehow, these cells can receive genetic imprinting that improves the fitness of the offspring.

Let me stop here. I have to say, I think this is entirely unconvincing. I can think of at least one simpler explanation for these data. Further, I can easily imagine how if it was possible to turn on these beneficial changes, evolution would make this the norm rather than the exception.

Screen Shot 2012-11-30 at 10.19.27 AM

Example data. A) Population lifespan following feast years, 100% survival. B) Population lifespan following famine years, 50% survival.

Consider a population of 100 kids in the target age group during a year of ‘feast.’ 100% of these kids survive and have children. These children have an average lifespan of 50yrs. Given the same group during a year of ‘famine.’ 50% of the kids survive and have children. The children live to an average age of 75 yrs. It appears that the famine during the elder generation improved the fitness of the younger.

But, if we examine the ‘feast’ population again, we might see that they can be broken up into two natural groups, one with a 75yr lifespan (the healthier 50%) and one with a 25yr lifespan (the less healthy). If the famine year selectively kills the weaker kids, then we are simply selecting our way to better health rather than causing it.

Because this is published research I expect that this simple answer was excluded somehow and I hope to find the original work to see that, but the burden of proof rests on the group proposing the more complex explanation.

I’ll see if I can research this a little and write again later, but I wanted to comment right away because I thought that it was an interesting example of how numbers can sometimes lead you astray if you’re not careful.

Oh, and very quickly, the last story…

The last story was about a woman who had adopted a baby girl, Destiny, from a mother who was addicted to drugs and couldn’t support the child. Amazingly, the next three years after that, the same mother gave birth to three more addicted babies that were all adopted by the same family. Because of her frustration about how this woman was so casually bringing more children into the world, one a year, each addicted to heroin et al. at the time of birth, the adoptive mother tried to pass a law to somehow prevent this from happening. When that failed, she worked directly to set up a fund to pay addicted women to undergo surgical sterilization or get long term birth control.

Many saw this as eugenics in action. Personally, I see no convincing connection to eugenics whatsoever based on the fact that the procedure was voluntary and based on a behavior rather than an innate characteristic of the women. Nevertheless, the conversation went places I never expected – mostly because I thought Jad and Robert would not get drawn into such ridiculous speculations and extensions of logic as they did. It was still good listening though.

I highly recommend checking out this episode.

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Posted by on November 30, 2012 in Uncategorized


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