Cheese Dip and Hydrochloric Acid

One of the more wide-ranging on my “Lowe’s Laws of the Lab” list is this: The secret of success in synthetic chemistry is knowing what you can afford not to worry about.

That’s because you have to have a cutoff somewhere. There are so many potential things that can affect an experiment, and if you have to sweat every one of them out every time, you’re never going to get anything done. So you need to understand enough to know which parts are crucial and which parts aren’t. I think the beginnings of this law came from my days as a teaching assistant, watching undergraduates carefully weigh out a fivefold excess of reagent. Hmm. Did it matter if they were throwing in 4.75 equivalents or 5.25? Well, no, probably not. So why measure it out drop by drop?

Tom Goodwin, the professor responsible for teaching me inmy first organic chemistry course, once advanced his own solution to this problem. Growing weary of the seemingly endless stream of lab students asking him “Dr. Goodwin, I added X by mistake instead of Y. . .will that make a difference?”, he proposed creating “Goodwin’s Book of Tolerances.” I think he envisioned this as a thick volume like one of those old unabridged dictionaries, something that would live on its own special stand down the hall. “That way,” he told me, “when some student comes up and says ‘Dr. Goodwin, I added cheese dip instead of HCl – will that make a difference?’, I can walk over, flip to page thousand-and-whatever, and say ‘No. Cheese dip is fine.’”

According to him, a solid majority of these questions ended with the ritual phrase “Will that make a difference?” And that’s just what a working chemist needs to know: what will, and what won’t. The challenge comes when you’re not sure what the key features of your system are, which is the case in a lot of medicinal chemistry. Then you have to feel your way along, and be prepared to do some things (and make some compounds) that in retrospect will look ridiculous. (As I’ve said before, though, if you’re not willing to look like a fool, you’re probably never going to discover anything interesting at all).

Another challenge is when the parts of the system you thought were secure start to turn on you. We see that all the time in drug discovery projects – that methyl group is just what you need, until you make some change at the other end of the molecule. Suddenly its suboptimal – and you really should run some checks on these things as you go, rather than assuming that all your structure-activity relationships make sense. Most of them don’t, at some point. An extreme example of having a feature that should have been solid turn into a variable would be that business I wrote about the other week, where active substances turned out to be leaching out of plastic labware.

But if you spend all your time wondering if your vials are messing up your reactions, you'll freeze up completely. Everything could cause your reaction to go wrong, and your idea to keel over. Realize it, be ready for it - but find a way not to worry about it until you have to.

Liable For Generics? You Are Now!

There was a legal ruling last week in California that we’re going to hear a lot more of in this business. Conte v. Wyeth. This case involved metaclopramide, which was sold by Wyeth as Reglan before going off-patent in 1982. The plaintiff had been prescribed the generic version of the drug, was affected by a rare and serious neurological side effect (tardive dyskinesia, familiar to people who’ve worked with CNS drugs) and sued.

But as you can see from the name of the case, this wasn’t a suit against her physician, or against the generic manufacturer. It was a suit against Wyeth, the original producer of the drug, and that’s where things have gotten innovative. As Beck and Herrmann put it at the Drug and Device Law Blog:

The prescribing doctor denied reading any of the generic manufacturer's warnings but was wishy-washy about whether he might have read the pioneer manufacturer's labeling at some point in the more distant past.

Well, since the dawn of product liability, we thought we knew the answer to that question. You can only sue the manufacturer of the product that injured you. Only the manufacturer made a profit from selling the product, and only the manufacturer controls the safety of the product it makes, so only the manufacturer can be liable.

Not any more, it seems. The First District Court of Appeals in San Francisco ruled that Wyeth (and other drug companies) are also liable for harm caused by the generic versions of their drugs. At first glance, you might think “Well, sure – it’s the same drug, and if it causes harm, it causes harm, and the people who put it on the market should bear responsibility”. But these are generic drugs we’re talking about here – they’ve already been on the market for years. Their behavior, their benefits, and their risks are pretty well worked out by the time the patents expire, so we’re not talking about something new or unexpected popping up. (And in this case, we're talking about a drug that has been generic for twenty-six years).

The prescribing information and labeling has been settled for a long time, too, you’d think. At any rate, that’s worked out between the generic manufacturers and the FDA. How Wyeth can be held liable for the use of a product that it did not manufacture, did not label, and did not sell is a mystery to me.

Over at Law and More, a parallel is drawn between this ruling and the history of public nuisance law during the controversy over lead paint; the implication is that this ruling will stand up and be with us for a good long while. But at Cal Biz Lit, the betting is that “this all goes away at the California Supreme Court”. We’ll see, because that’s exactly where it’s headed and maybe beyond that, eventually.

And if this holds up? Well, Beck and Herrmann lay it out in their extensive follow-up post on the issue, which I recommend to those with a legal interest:

Conte-style liability can only drive up the cost of new drugs – all of them. Generic drugs are cheaper precisely because their manufacturers did not incur the cost of drug development – costs which run into the hundreds of millions of dollars for each successful FDA approval. Because they are cheap, generics typically drive the pioneer manufacturer’s drug off the market (or into a very small market share) within a few years, if not sooner. Generic drugs will stay cheap under Conte. But imposing liability in perpetuity upon pioneer manufacturers for products they no longer sell or get any profit from means that the pioneer manufacturers (being for-profit entities) have to recoup that liability expense somewhere. There’s only one place it can come from. That’s as an add-on to the costs of new drugs that still enjoy patent protection.

Exactly right. This decision establishes a fishing license for people to go after the deepest-pocketed defendents. Let’s hope it’s reversed.

Sticking It to Proteins

So, you’re making an enzyme inhibitor drug, some compound that’s going to go into the protein’s active site and gum up the works. You usually want these things to be potent, so you can be sure that you’ve knocked down the enzyme, so you can give people a tiny, convenient pill, and so you don’t have to make heaps of the compound to sell. How potent is potent? And how potent can you get?

Well, we’d like nanomolar. For the non-chemists in the crowd, that’s a concentration measure based on the molecular weight of the compound. If the molecular weight of the drug is 400, which is more typical than perhaps it should be, then 400 grams of the stuff is one mole. And 400 grams dissolved in a liter of solvent to make a liter of solution would then give you a one molar (1 M) solution. (The original version of this post didn't make that important distinction, which I'll chalk up to my not being completely awake on the train ride first thing in the morning. The final volume you get on taking large amounts of things up in a given amount of solvent can vary quite a bit, but concentration is based, naturally, on what you end up with. And it’s a pretty flippin’ unusual drug substance than can be dissolved in water to that concentration, let me tell you right up front). So, four grams in a liter would be 0.01 M, or 10 millimolar, and foru hundred milligrams per liter would be a 1 millimolar solution. A one micromolar solution would be 400 micrograms (0.0004 grams) per liter, and a one nanomolar solution would be 400 nanograms (400 billionths of a gram) per liter. And that’s the concentration that we’d like to get to show good enzyme inhibition. Pretty potent, eh?

But you can do better – if you want to, which is a real question. Taking it all the way, your drug can go in and attach itself to the active site of its target by a real chemical bond. Some of those bond-forming reactions are reversible, and some of them aren’t. Even the reversible ones are a lot tighter than your usual run of inhibitor.

You can often recognize them by their time-dependent inhibition. With a normal drug, it doesn’t take all that long for things to equilibrate. If you leave the compound on for ten, twenty, thirty minutes, it usually doesn’t make a huge difference in the binding constant, because it’s already done what it can do and reached the balance it’s going to reach. But a covalent inhibitor, that’ll appear to get more and more potent the longer it stays in there, since more and more of the binding sites are being wiped out. (One test for reversibility after seeing that behavior is to let the protein equilibrate with fresh blank buffer solution for a while, to see if its activity ever comes back). You can get into hair-splitting arguments if your compound binds so tightly that it might as well be covalent; at some point they're functionally equivalent.

There are several drugs that do this kind of thing, but they’re an interesting lot. You have the penicillins and their kin – that’s what that weirdo four-membered lactam ring is doing, spring-loaded for trouble once it gets into the enzyme. The exact same trick is used in Alli (orlistat), the pancreatic lipase inhibitor. And there are some oncology drugs that covalently attach to their targets (and, in some cases, to everything else they hit, too). But you’ll notice that there’s a bias toward compounds that hit bacterial enzymes (instead of circulating human ones), don’t get out of the gut, or are toxic and used as a last resort.

Those classes don’t cover all the covalent drugs, but there’s enough of that sort of thing to make people nervous. If your compound has some sort of red-hot functional group on it, like some of those nasty older cancer compounds, you’re surely going to mess up a lot of other proteins that you would rather have left alone. And what happens to the target protein after you’ve stapled your drug to it, anyway? One fear has been that it might present enough of a different appearance to set off an immune response, and you don’t want that, either.

But covalent inhibition is actually a part of normal biochemistry. If you had a compound with a not-so-lively group, one that only reacted with the protein when it got right into the right spot – well, that might be selective, and worth a look. The Cravatt lab at Scripps has been looking into what kinds of functional groups react with various proteins, and as we get a better handle on this sort of thing, covalency could make a comeback. Some people maintain that it never left!

The Yield Monster - And Its Friend, The Model Monster

Organic chemisty can be a real high-wire act. If you’re taking a compound along over a multistep sequence, everything has to work, at least to some extent: a twelve-step route to a compound whose last step can’t be made to work isn’t a route to the compound at all. To get the overall yield you multiply all the individual ones, and a zero will naturally take care of everything that came before it.

Even very respectable yields will creep up on you if you have the misfortune to be doing a long enough synthesis. It’s just math – if you have an average 90% yield, which shouldn’t usually be cause for distress, that means that you’re only going to get about 35% of what you theoretically could have after ten steps (0.9 to the tenth). An average 95% yield will run that up to 60% over the same sequence, and there you have one of the biggest reason for the importance of process chemistry groups. Their whole reason to live is to change those numbers, to make sure that they stay that way every time, and without having to do anything crazier than necessary along the way.

When you’re involved in something like this and you know you’re going to be approaching a tricky step, the natural temptation is to try it out on something else first. Model systems, though, can be the road to heartbreak. In the end, there are no perfect models, of anything. If you’re lucky, the conditions you’ve worked out by using your more-easily-available model compound will translate to your precious one. But as was explained to me years ago in grad school, the problem is that if you run your model and it works, you go on to the real system. And if you run your model and it doesn’t work, well. . .you might just go on to the real system anyway, because you’re not sure if your model is a fair one or not. So what’s the point?

This gets to be a real problem in some labs. While ten steps is medium to long for a commercial drug synthesis, it’s just the warmup for a lot of academic ones. Making natural products by total synthesis can take you on up into the twenty- and thirty-step levels, and some go beyond that, most horribly for everyone concerned. In such cases, you’d much rather have several segments of the big honking molecule built separately and then hooked together, rather than run everything in a row.

But what if you spend all that time on the segments, but you can’t put the things together? The most famous example of that I know happened in Nicolaou’s synthesis of Brevetoxin B. The initial disconnection of this terrible molecule into two nearly-as-awful pieces turned out to have been a mistake. Despite repeated attempts, no way could be found to couple the two laboriously prepared pieces to make the whole molecule, and untold man-hours of grad-student and post-doc slave labor had to be ditched for a new approach. If you want to see the approach that worked, here’s a PDF of a talk about it.

But if you go linear, you’re taking the same risk, and the math will absolutely eat you alive. A 90% average yield will ensure that you throw away 95% of your material if you keep going for 28 steps. And keeping a 90% average over twenty-eight steps is just not possible with real-world chemistry, either – and yes, I’ve seen those papers where they do, but I don’t believe them. Do you? Make it 25 steps of average 90%, and three 60% losers, and now you’re down between one and two percent of your material left. Which is no way to live.

I note that the above summary of the Brevetoxin synthesis counts 123 synthetic steps. It calculates an average yield of 91%. A 2004 synthesis from Japan comes to 90 steps with an average yield of 93%.

Crestor: Would It Save Any Lives?

Should millions more people be taking Crestor? That’s a real balancing act. You have a decrease in heart attacks, but from a fairly small incidence rate. So at a minimum, you’ll need to balance the costs of those coronary events versus the cost of paying for all that Crestor. And statins are not without side effects themselves, so you’ll need to adjust your figures for the incidence of rhabdomyolosis, among other things. (For example, is the increased evidence of high blood sugar in the Crestor treatment group a real effect, or not? If so, you’ll need to add a bit of diabetes cost to the spreadsheet). In any case, the cost of getting all these people screened for C-reactive protein levels in the first place needs to be added in as well.

Naturally, as in any of these calculations, you’re going to have to figure how much should be spent to prevent each excess death, once you’ve decided that these deaths can indeed be considered excess. (Unfortunately, the answer cannot always be “as much as it takes”, since there is not enough money in the world to treat everyone for everything, forever). And that brings up another key question: would putting high-CRP patients on Crestor save lives at all?

Well, you’d think so, what with lowering the incidence of those coronary events. But mortality figures are tricky. In all the graphs presented in the NEJM paper, the “deaths from all causes” one is the least compelling. That shouldn’t be a real surprise, since cutting something down in the 1% range isn’t going to bend the curve very much on its own. But if you look closer at the data, things are even fuzzier.

As pointed out to me by a correspondent, the Crestor-treated group for some reason showed a lower death rate from cancer (35 deaths versus 58). It doesn’t seem particularly likely that this is a real effect – I’ve never heard of statins showing a protective effect like this, although if someone knows differently, I’d be glad to hear about it. The paper makes nothing of this comparison, at any rate. Minus this effect, though, the death rate between the two groups might well be within the error bars. The argument for Crestor would then have to be made purely on treatment costs, as in the first paragraph, because you’d be saving few, if any, lives at all.

And maybe there’s a case to be made. I’m not a public health expert, so I don’t know what numbers to put into those calculations. But it’s important to realize, contrary to some of the headlines out there, that it’s actually a hard call to make. I note that AstraZeneca is being cautious about what all this means for sales of Crestor. They’re wise to be.