Blocks & Locks…

Drug Discovery, Part II

So, in my last post, I spoke about the early stages of Drug Discovery and described how Epidemiology is used to identify promising targets for drug intervention.  In this post, I’m going to  describe some aspects of the next stage in the process, namely how we try to identify chemicals that hit these targets and then develop the chemicals into active drugs.

The world is full of chemicals.  The world is MADE of chemicals.  YOU are made of chemicals!  Just think about that.  Everything you are, everything you see is made up of a mind-boggling number of chemical molecules, which are made from a relatively small number of ingredients.    Most of it is made of carbon stuck to some oxygen, some hydrogen, a bit of nitrogen, and a tiny amount of other elements like sulphur, copper, iron, chlorine…..etc.

Locks & BlocksBut the point is, this small number of ingredients can be stuck together in an almost infinite number of ways.  Think of it like Lego, with each element represented by a differently shaped piece.  You don’t need that many different shapes, but you can build anything you like.

Now, the point is, having used the Epidemiological studies I described in Kill “Bill” to identify a promising target for drug intervention, the next step is to try and find a specific chemical structure that will stick to the “Bill” and stop it working.

Basically, think of the “Bill” as being like a lock.  What you need to do, is find a key for that lock – one that fits that lock alone.  So what you need is a chemical structure, built from the  chemical Lego of carbon, hydrogen, oxygen etc, which acts like a key and will slot exactly into the lock.

There are several ways to do this.  One way to find your potential drug is to design a chemical structure that will bind to your target exactly.  So, if you know the shape and structure of your lock, you can use the chemical Lego to try and build a key from scratch.

Also, you can look at Mother Nature.  Nature is great.  There’s loads of stuff there.  Moulds, bacteria, yeasts, mosses, fungi, flowers, berries – seriously, loads of it.  The natural world is packed with a dizzying myriad of different species, which have evolved to survive in a huge range of environments.  And, to do this, different species make molecules which they can use to promote their own survival, molecules which are built up from the chemical Lego.  Now the thing is, some of these constructions have benefit to us, ie. they have medicinal uses.  This aint news – it’s how medicines have been discovered historically.  But, as well as this, because there are so, so many of these natural products out there, it’s also possible that some of them may also act like a naturally occurring key for your “Bill” lock.

Now, whichever way you go, the important bit is to a) find your key and b) make sure that your key fits the lock.  And one of the great things about the modern world is that modern computer power makes this process a lot easier.  Across the world, there are huge databases of chemical structures.  Some of them are from natural products, some of them have been made synthetically.  Now, if you know the structure of your lock, you can search the databases for chemical structures that are likely to bind – ie. search for keys that fit the lock.

Alternatively, maybe you have a chemical compound and you want to know how it works.  In that case, you would look for complex protein structures it might bind to – ie. you already have a key and you search for the lock that it might fit.

Now, whichever way you approach it, you will end up with a key and a lock.  But that aint the end of the process, oh no…..that’s just the start.  Because after all, does the key you’ve found fit only that lock, or does it fit hundreds of other locks too?  How can you tell?

Well, that’s another story……

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Harvey, A., Edrada-Ebel, R., & Quinn, R. (2015). The re-emergence of natural products for drug discovery in the genomics era Nature Reviews Drug Discovery, 14 (2), 111-129 DOI: 10.1038/nrd4510

ResearchBlogging.org

AG McCluskey (2016). Blocks & Locks… Zongo’s Cancer Diaries

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Kill “Bill” !!!

So, in my previous post, Damn statistics, I explained how Epidemiology is used to identify risk factors, by finding correlations between different activities, such as smoking or meat-eating and cases of cancer.  In this post, I’m going to talk about Drug Development, because the way that this type of work gets started uses much of the same techniques I talked about before.

How do researchers go about deciding how to develop a new drug?   Well, obviously, they will want a drug that targets some process or factor that is important for tumour growth, so if you can disrupt the process or factor with the drug, the tumour stops growing.  But how do the researchers decide which processes or factors ARE important for tumour growth or, in contrast, which ones are not that important?

Well, the techniques that I spoke about in Damn Statistics are also used here, in order to identify new targets for Drug Development.  But, rather than looking at differences in lifestyle or environment between different patients, researchers will look for differences between the make up of the tumours themselves.

So, let’s say that there are some breast cancer researchers, who are interested in a specific growth factor, which we’ll call “Bill” (why?  Why not!)  The researchers know that “Bill” is commonly found in breast tissue, so they suspect that it might also be involved in the development of breast cancer.  In other words, in some people, the researchers think that too much “Bill” is made in the breast tissue, which makes the cells start to grow and divide too much and too fast.  If they are right, then the constant “Bill” would make the cell growth run out of control, and….Bingo!  The person gets breast cancer.

If this hypothesis is true, then this would make “Bill” a good target to try and develop a new drug against.  So, the researchers will look to see if the hypothesis is right.  They will do this by having a look at the tumours of previous and current breast cancer patients.

Every patient who is suspected of having cancer will have a biopsy taken – basically, the doctors cut off a bit of the suspect tissue and then run tests to see if it is cancerous or not.  Every patient is asked if the biopsy sample can be kept to help in future research and if they agree, the biopsy tissue is preserved in special tissue banks.  This means that there is a large number of samples from different patients.  As the medical history of all of the patients is known to the researchers, they will know which ones survived and which ones didn’t.  And of the ones that didn’t, they will know whether they succumbed after a few months, or whether they managed to to survive for 1 year, or 2 years, or 5 years…..or maybe even longer.

So, the researchers can use all of this information to test their hypothesis that “Bill” is linked to breast cancer.  What they will do, is take wafer-thin slices of the stored biopsy tissue and stain them with dyes which only stick to “Bill” (it’s a bit more complex than that, obviously, but you get the general idea – I’ll explain in more detail another time).  The upshot is that if “Bill” is there, the tissue will be stained.  If “Bill” is not there, it won’t.  Also, the more “Bill” there is, the stronger the stain will be.  If you do this for, say, 100 samples, you could end up with a range of results.  Some of the samples could have little staining (ie. low levels of “Bill”), some may be heavily stained (ie. high levels of “Bill”) and some might be unstained (ie. no “Bill” at all).  The researchers can then separate out the samples into groups (called “stratification”) with low, high and negative “Bill” levels.

The researchers can then check the records, to see what happened to the patients the biopsies came from.  Did they have a low stage disease (ie. relatively benign tumour that was easily treated), or a high stage disease (ie. aggressive, malignant tumour which needed intense treatment)? Did the patient survive & recover?  If not, how long did they survive?  Months?  Years?  And, do the levels of “Bill” in the biopsy tissues correlate with a particular outcome?

This is the point where the researchers can start to test their hypothesis and see if “Bill” is linked to cancer or not.  So, they might discover that all of the biopsy samples with no “Bill” came from patients with benign, easily treated tumour, while those samples that did have “Bill” came from patients that had aggressive diseases.  Or, they might find that ALL the biopsies had “Bill”, but those with low levels came from patients who lived longer or survived, while those with high “Bill” came from patients who succumbed quickly.  Either of these results would mean that “Bill” correlated with outcome, meaning that the research supported the original hypothesis.

This would give the researchers the confidence to start suggesting that “Bill” could be a good target for drug development, ie. that it would be a good idea to try and find chemicals that stuck to “Bill” and stop it working.
Kill Bill!Here’s an example of how it works.  The figure is taken from this study, which looked at patients with osteosarcoma (bone cancer).  This type of graph is called a Kaplan-Meier plot and shows the effects of a “Bill” called Beta-Catenin.  This particular “Bill” can switch on certain genes and is involved in sticking cells together inside tissues.  But, cancer cells can make too much Beta-Catenin and the Kaplan-Meier plot shows how the survival of patients correlates with the amount of Beta-Catenin their cancers make.

The way you read it is simple.  Look at each “staircase”.  Each time the line “steps down”, it means that a patient has died.  So, the more rapidly the patients die, the steeper the “staircase” looks.  Conversely, the more slowly the patients die, the shallower the “staircase” looks.  So, from this plot it is obvious that the patients with tumours which make a lot of Beta-Catenin (ie. “positive” in the figure), are dying more quickly than the patients with tumours which lack Beta-Catenin (ie. “negative” in the figure).  Now, this would suggest that Beta-Catenin would be a good target for Drug Development and, indeed, a lot of time and effort has gone into making a drug which targets it.

In the same way, many other chemotherapy drugs, both those being used now, and those being designed, started with studies like these:  by looking at different “Bills” to see if a correlation could be found that could make them an attractive target.  The next step is to try and find molecules which stick to the “Bill” and then try and modify the molecule to make it inhibit the “Bill” more effectively and then tweak it enough, so that it can be made as a drug that can be given to patients….

…. But that’s another story…

…..

Lu, Y., Guan, G., Chen, J., Hu, B., Sun, C., Ma, Q., Wen, Y., Qiu, X., & Zhou, Y. (2015). Aberrant CXCR4 and β-catenin expression in osteosarcoma correlates with patient survival Oncology Letters DOI: 10.3892/ol.2015.3535

ResearchBlogging.org
AG McCluskey (2016). Kill “Bill” Zongo’s Cancer Diaries

Damn Statistics!

Damn StatisticsIf you’ve been following this blog for a while, then you will have spotted that I’ve written a lot about risk factors.  This is due to the glut of stories in the Media that have come out recently.  There was the “tall people are more likely to get cancer” one, then the “meat consumption causes cancer” one, then the “alcohol causes cancer” one and then the “Obesity causes cancer” one.

And if you’ve read through those posts, you will notice that I don’t seem all that convinced by the claims.  In each post, I seem to be downplaying the risks….saying that things are not as bad as the reports suggest.  Well, there is a reason for this.  Basically, I think that, in each case, the reports are overly-simplistic and that makes me take them with a pinch of salt.

So, I thought I’d use this post to try and explain why.  What do I think is wrong with these correlations?  Well… nothing, actually.  The correlations themselves are fine.  But when it comes to how the results are interpreted…. that’s a different story.

First off, how are these correlations identified?  Scientists & medics will survey cancer patients, to try and identify common factors in their environment, lifestyle, medical history or genetic makeup, which could be contributing to the onset of disease.  This is known as “Epidemiology“, and it was through the use of this type of scientific study that, eg. smoking and asbestos exposure were identified as risk factors for lung cancer.  It is also how meat consumption, obesity, height and drinking alcohol were studied to see if they correlated with increased risks of cancer.

So, to investigate a potential risk factor, researchers will look at all of the patients with a certain type of cancer.  Then they will pick one factor – let’s say obesity, but it could be anything – and split the patients up into those who are obese and those that aren’t.  Then, they will look to see if more of the patients are obese, or if more of them are of normal weight.  If more colon cancer patients are obese than not, this suggests there is a link between obesity and disease onset.  Or, alternatively, the numbers may be about the same, suggesting no link to disease onset, but they might discover that obese patients are more likely to have a more advanced form of the disease, suggesting a link between obesity and disease progression.

But there area couple of problems with these correlations, and therefore with the subsequent news stories.  The first one, I’ve covered before back in Just One Cornetto: Correlation is not Causation.  Just because obesity might correlate with increased cancer risk, it doesn’t mean that it is a cause.

But the second problem with these correlations is even trickier: they are too simplistic.  In order to make a correlation, you have to pick ONE thing to assess.  It might be obesity, it might be alcohol consumption, it might be meat consumption… Doesn’t matter.  The point is that you isolate ONE of them, and then check the records to see if high or low levels correlate with increased cancer risk, or poorer survival.

But the problem is, by isolating out individual characteristics like this, you run the risk of focusing on the wrong issue and missing what is REALLY important.  This is because individual traits don’t exist in isolation, but are often linked to other characteristics.  For example, in the list above, people who are obese often consume more red meat.  And more alcohol.  So, if you find a correlation between obesity and cancer, it could be that what the REAL risk factor behind this is that overweight people are also more likely to be heavy drinkers, and it is the underlying alcohol consumption which is the real issue.  Or, it may be that, being more likely to be overweight, heavy drinkers are ALSO more likely to eat more red meat and THIS is what you should focus on.

Now, you could try and get to the bottom of this by subdividing the patients further, eg. separating them into “obese + high alcohol”, “obese + low alcohol”, “not obese + high alcohol”, “not obese + low alcohol”.  So, you look at four groups rather than two.  You could go even further, by adding meat consumption into the mix.  But that would double the amount of groups again, so you’d have eight groups to analyse.  Ooh!  But then overweight people also eat more sugar!  So maybe sugar consumption could be looked at too!  So, now you have SIXTEEN groups to analyse….

It’s like cake.  Yes! I said it!  It’s like CAKE!  With a cake, the fewer times you slice it the bigger the portions are, but fewer people get a piece.  But if you slice it more times, more people can get a portion, but the less satisfied everyone will be, because the pieces are so much smaller.

And that is the issue with the type of statistics that are carried out to look for cancer risk factors.  There is only a finite number of patients to start with.  So, the more you split them into groups for analysis, the fewer patients will be in each one.  And you need a LOT of patients if you want to do proper statistics.  So, the more groups you have, the fewer patients are in each one, and the fewer patients per group, the less reliable the statistics will be.  It is a Catch 22.

And THAT is the issue I have with the stories I’ve spoken about before.  The studies themselves are absolutely fine, but in order to do proper statistics, the studies have to concentrate on one factor.  But the fact that only single things can be looked at means that you have to be VERY careful when you are drawing conclusions about what the data is actually telling you.

But I can’t stop thinking about cake now……..(goes off to look for cake).

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Pérez-Hernández,A.I., Catalán, V., Gómez-Ambrosi, J., AmaiaRodríguez, A., & Frühbeck, G. (2014). Mechanisms Linking Excess Adiposity and Carcinogenesis Promotion Frontiers in Endocrinology, 5 DOI: 10.3389/fendo.2014.00065

Bagnardi, V., Rota, M., Botteri, E., Tramacere, I., Islami, F., Fedirko, V., Scotti, L., Jenab, M., Turati, F., Pasquali, E., Pelucchi, C., Bellocco, R., Negri, E., Corrao, G., Rehm, J., Boffetta, P., & La Vecchia, C. (2012). Light alcohol drinking and cancer: a meta-analysis Annals of Oncology, 24 (2), 301-308 DOI: 10.1093/annonc/mds337

Bouvard, V., Loomis, D., Guyton, K., Grosse, Y., Ghissassi, F., Benbrahim-Tallaa, L., Guha, N., Mattock, H., & Straif, K. (2015). Carcinogenicity of consumption of red and processed meat The Lancet Oncology DOI: 10.1016/S1470-2045(15)00444-1

ResearchBlogging.org

AG McCluskey (2016). Damn Statistics Zongo’s Cancer Diaries