Bringing Up Bobbin

Some of these brands are not like the others.

Some of these brands are only kinda the same.
Can you guess which brands are not like the others?
Now it's time to play our game.
It's time to play our game.

Yeah. Not a fun game to play when it involves your antidepressant medication.

So I've had a rough few weeks. 4 weeks. Month. I've had a rough month. I racked my brain for some time trying to think of why it's been rough. Things have been going well. Bobbin is enjoying school. Tantrums have significantly decreased. Work is challenging but good. Tim isn't snoring as much so I'm getting more sleep :-)

By week 3, I had a hypothesis. When I went to get my new prescription filled at the beginning of the month, I went to a different drug store than I usually do. And that drug store had a different generic substitute for my prescription than my regular drug store. I could tell because the pills were a different shape and colour. But the actual medicine and dosage was the same. I even looked it up online to be sure.

But after 3 weeks I wasn't feeling the same. So I wondered, with all other factors in my life being fairly consistent, is it possible, not all generics are created equal? And the answer is, in the United states, yes. Yes it is possible that not all generics are exactly the same.

I asked my doctor today and he confirmed my suspicions. The FDA allows for a fair bit of variance. There's no way for him to confirm with certainty that the 2nd brand I was using was any less effective than the first; it could be I was just having a challenging month. But given what I described I had experienced, and given that when I went back to the 1st brand and after a week started slowly feeling like I was getting back to normal, it was a strong possibility that the 2nd brand was not equivalent in level of meds.

So how is bioequivalence defined by the FDA? According to Wikipedia,

The United States Food and Drug Administration (FDA) has defined bioequivalence as, "the absence of a significant difference in the rate and extent to which the active ingredient or active moiety in pharmaceutical equivalents or pharmaceutical alternatives becomes available at the site of drug action when administered at the same molar dose under similar conditions in an appropriately designed study.

The FDA considers two products bioequivalent if the 90% CI of the relative mean Cmax, AUC(0-t) and AUC(0-∞) of the test (e.g. generic formulation) to reference (e.g. innovator brand formulation) should be within 80.00% to 125.00% in the fasting state. Although there are a few exceptions, generally a bioequivalent comparison of Test to Reference formulations also requires administration after an appropriate meal at a specified time before taking the drug, a so-called "fed" or "food-effect" study. A food-effect study requires the same statistical evaluation as the fasting study, described above.

Roughly translated to English, what this means is that two products are considered sufficiently equivalent if the average concentration found in the blood of the majority the test subjects who received the generic formulation is within 80% - 125% of the brand formulation in both fasting and fed studies. This means that in the extreme case I could take generic brand X and be absorbing as much 125% of what I would have absorbed if I had taken the same dose as the name brand. And then take generic brand Y and be absorbing only 80% of what I would have absorbed if I had taken the name brand. Another way to look at it, is that a generic pill that is supposed to be 100mg in strength could potentially contain as much as 125mg of the formulation, all of which is absorbed by some number of test subjects, or might contain 100mg of medicine but only 80mg of that gets absorbed by some number of test subjects. That's a difference of 45mg. Ok - it's not quite that straight forward but I failed first year statistics and barely passed the second time I took it, however the point is the same: there can actually be a fairly wide variation in formulations between different generic brands of the same drug; from each other as well as from the name brand.

In the US, however, some insurance policies won't cover the full cost of the name brand, or even cover a portion of the cost. And when you walk into a drug store, you don't know unless you ask, what generic brand they will use to fill the prescription. Or unless you spend an hour sifting through photos and descriptions of the pill shape & imprint online to identify the manufacturer after you get the drug home. But when you do find out that you've reacted differently to a particular generic brand your doctor can write you a perscription either requiring no substitute from the name brand, or explicitly excluding specific generic brands from being used. So that's what's gonna happen next time for me :-)

And the lesson - don't assume all generics are exactly equal. And if you have switched generics, or switched from brand to generic and you notice a difference in how it is affecting you, definitely let your doctor know.

Footnotes for those interested, and/or those who passed statistics the first time around:

In statistics, a confidence interval (CI) is an interval estimate of a population parameter. Instead of estimating the parameter by a single value, an interval likely to include the parameter is given. Thus, confidence intervals are used to indicate the reliability of an estimate. How likely the interval is to contain the parameter is determined by the confidence level or confidence coefficient. Increasing the desired confidence level will widen the confidence interval.

For example, a CI can be used to describe how reliable survey results are. In a poll of election voting-intentions, the result might be that 40% of respondents intend to vote for a certain party. A 95% confidence interval for the proportion in the whole population having the same intention on the survey date might be 36% to 44%. All other things being equal, a survey result with a small CI is more reliable than a result with a large CI and one of the main things controlling this width in the case of population surveys is the size of the sample questioned. Confidence intervals and interval estimates more generally have applications across the whole range of quantitative studies.

In the above, the 95% associated with the confidence interval is called the confidence level of the interval: this is defined formally below.

Occasionally, blood concentration levels are neither feasible or possible to compare the two products (e.g. inhaled corticosteroids), then pharmacodynamic endpoints rather than pharmacokinetic endpoints (see below) are used for comparison. For a pharmacokinetic comparison, the plasma concentration data are used to assess key pharmacokinetic parameters such as area under the curve (AUC), peak concentration (Cmax), time to peak concentration (Tmax), and absorption lag time (tlag). Testing should be conducted at several different doses, especially when the drug displays non-linear pharmacokinetics.

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