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  • Hi, my name is Jim Wells. I'm with the departments of Pharmaceutical Chemistry

  • and Cellular and Molecular Pharmacology at UCSF.

  • And I'm going to tell you today about the process of drug discovery and development

  • in two parts: part one it will be screening of compounds and in that regard I'll be joined by my colleague

  • Michelle Arkin.

  • Hi, I'm going to talk after Jim gives an early history of drug discovery

  • and talks a little bit about target identification, then I'll talk about the process of screening

  • and hit identification.

  • Great, see you in a bit.

  • This slide shows some of the products of modern drug discoveries,

  • such as Lipitor, which is used for cholesterol lowering,

  • or a more recent drug, Gleevec, which an anti-cancer drug.

  • These compounds were discovered through a very rational, systematic

  • process, involves a lot of exciting scientific discoveries

  • as well as a lot of serendipity, luck and hard work.

  • To understand how we found these compounds, it's useful for us to review

  • how drug discovery came to be,

  • what's the sort of brief history of drug discovery, as I'll show you on the following slide.

  • To understand the modern drug discovery development process today

  • it's useful to review the history, briefly,

  • of drug discovery. Prior to 1900, most drugs, in fact only a few really,

  • were identified through human screening.

  • Natural products, for instance, aspirin, was discovered from tree bark.

  • Quinine was discovered. And even illicit drugs like cocaine were discovered.

  • Long about the turn of the century, 1906,

  • the Food and Drug Administration was established

  • because a number of these kinds of potions or elixirs were found to neither be safe nor efficacious

  • and it was necessary to regulate these in a systematic way.

  • And this led to the development of animal based screening, for example, to discover anesthetics,

  • bacterial screening to identify antibiotics, and the like,

  • tissue screening to identify compounds that could react with neurological receptors,

  • like GPCRs, HTS, high throughput screening,

  • now very common discovery technology, as you'll hear a lot more about in this talk,

  • for discovering target-based compounds.

  • And then, lastly, mechanism-based discovery, which was used for HIV drugs and the like

  • as well as molecular and cellular based screening for kinase inhibitors.

  • And finally, genomics, to actually profile patients to determine who will be affected and who won't be affected.

  • So, in fact, this process, the history of drug discovery,

  • had gone from the human to the molecular target

  • and this now in reverse reflects what we actually do today.

  • Shown on this slide is what, in sort of general terms,

  • the modern drug discovery process.

  • And this process starts off with a disease,

  • and from that disease one tries to, through a lot of biochemical

  • cell-based, genetic and other means identify

  • what is the target or the molecular species in a cell, in an organism,

  • that's causing that disease.

  • One then develops a drug to that target, as you can see here,

  • and then, having identified that target, one then needs to identify

  • a compound that will interact with that target in a phase called lead discovery.

  • This is a chemical process where we identify the first compounds

  • that are actually important in modifying a disease

  • and then once compounds are identified, typically in cells and then in animals,

  • they're prepared for clinical trials in this process called drug development,

  • which is, this phase is really about interfacing the compounds that we discovered here

  • to the human biology that we wish to effect here.

  • And if successful, we'll come out of this process with a drug.

  • Now, this process is a long winded process. It typically takes about now about 10-15 years

  • to discover a drug and it's expensive too.

  • It's about half a billion to a billion dollars to develop a drug.

  • So, when you're thinking about the pills that you take in a bottle,

  • think about a shopping mall, because that's easily the cost that it takes to get to the drugs that we end up using.

  • Ok, I wanted to just review quickly what kind of classes, what kind of molecules constitute drugs.

  • There's actually three basic classes and they include

  • the small molecule, organic compounds,

  • typically, these are compounds whose molecular weight is less than five hundred

  • and they're taken, generally, orally,

  • although they can also be taken as an injectable.

  • And they represent the kind of classic drug that you think of when you go to a pharmacy,

  • that you would buy over the counter, for example.

  • There's another class of very important drugs known as the protein therapeutic drugs.

  • These are typically injectable drugs, molecular weights of over ten thousand,

  • often up to a hundred thousand, or even higher.

  • And they are the important class of biotherapeutics

  • and they represent about thirty percent of drug sales today.

  • The other class of drugs, actually one of the very first to be developed

  • are the vaccines. And these are

  • basically viruses, pieces of viruses, that are used to elicit an immune response to a disease.

  • So these are the basic categories and today I'm going to focus on small molecule drug discovery,

  • leaving these other two categories alone for another talk.

  • So, the process of small molecule discovery is a long and winding road.

  • And it starts off with identifying what is the most critical target that's involved

  • in mediating the disease. So, identifying the disease target.

  • Having identified that target, generally a protein target,

  • one then goes through a process known as hit identification, shown here,

  • and the role of hit identification is to get the first compounds that actually engage the target.

  • Which compounds actually bind to the protein of interest

  • and can begin our drug discovery process.

  • From there, taking that isolated protein in a test tube, we need to show that that compound

  • actually works in a cell.

  • And so, this begins this process called hit to lead

  • which is to generate a compound which has cellular potency.

  • The next stage, sort of drawing from there, to a larger scale,

  • is the lead optimization stage.

  • This is a critical stage in which one actually shows that these compounds

  • that have been generated have animal efficacy and actually work in a pharmacological model

  • for the disease. The next stage after that is the IND enabling stage,

  • this is the stage that is preparing compounds for clinical trials.

  • Primarily, it involves animal tox experiments, in addition, chemical synthesis, scale,

  • and formulations experiments, and at the end of this process, one would hope to have a package

  • that you could convince the food and drug administration that you have a compound

  • that is going to be both safe and efficacious when administered to humans.

  • Then begins the all-important human clinical trials

  • if the FDA agrees with you.

  • In the first trail, is for human safety. This is typically done in a dose escalation,

  • kind of trial, with healthy volunteers, although in certain disease settings, like cancer,

  • you can use people with cancer.

  • And the goal of this is really to find out

  • what is the circulatory lifetime of the drug in humans

  • and how safe is the drug if its dose is increased.

  • The next phase, phase two, is involved in determining the efficacy of the drug

  • in a disease setting. So, this would be taking patients with the disease,

  • treating them with your drug at a level that's below any toxicity that was observed in phase one

  • and in ranging doses to find out what is the efficacy of the drug as a function of its dose

  • and what's the best dose to best effect the disease.

  • So, from these small trials, then, one then moves into a much larger, what's called registration trial,

  • phase three, in which one then fixes the dose, fixes the disease,

  • fixes the formulation, and then treats a large number of cohorts, both with and without the drug

  • to determine how effective the drug is. And at the end of this time, if your drug is safe and efficacious, you'll submit

  • what's known as a new drug application, an NDA,

  • to the FDA. They will either, they will review it and agree with you or not,

  • that you have a drug that's ready to go into humans

  • and at the end of that process, you have this pill down here,

  • which will then be launched with great fanfare, because this process is, as you'll see, a very long and arduous one.

  • Ok so I'm going to start at the beginning here with target ID.

  • What's causing the disease? How is it, what is the actual molecular target that we want to go after?

  • This link to the disease of interest.

  • And this is actually a very, very, can be a very long process

  • to find out what causing the disease.

  • Many diseases we don't have a clue as to what their cause is.

  • And in fact, ironically, even with all the tests that we might do to validate a target,

  • the final validation of a target is not known until you get down here with the pill itself,

  • to see if that is actually effective in a human.

  • Ok, so just briefly, what are the general causes of disease, what are the things that we think about.

  • First thing is, I like to think is it a bug or is it in the body?

  • Is it an infectious agent

  • or is it a host imbalance? So, for instance, if it's an infectious agent,

  • that's causing the disease, generally these days, we have sequences of the pathogenic bacteria.

  • We'll find a target that's not in humans

  • and then we'll take that protein target and go after that

  • in the drug discovery process.

  • If however, it's a disease like host imbalance, maybe it's a metabolic disease or cardiovascular disease or cancer

  • you first have to decide is it due, is the disease caused by an underactive protein,

  • for instance, people with diabetes,

  • they're not as responsive to insulin and so by giving them back insulin

  • you can hope to modify and ameliorate that condition.

  • Other diseases, for instance, here, many cancers are caused by overactive proteins

  • such as kinases, and so there's a lot of interest in discovering drugs

  • that would inhibit specific kinases for cancer.

  • Ok, that is just sort of a very skimmed view of this process, but just to give you a sense.

  • Once you have identified the target, this target process actually can be very complex.

  • So, the human genome is vast, there's some twenty thousand genes

  • that code for proteins. And finding exactly which one is causing the disease

  • can be challenging. So, one you've come up with the protein,

  • and the gene that encodes it for that particular disease,

  • you're ready to go on to another very important consideration.

  • Which is that not only do you need to go after the biology of the target,

  • the target itself, which is causing the disease, but that target is

  • itself has to be amenable to small molecule discovery.

  • And by that I mean it has to be something that we think could bind a small molecule.

  • because that in the end is ultimately what we want to do.

  • What I show here is a recent drug target

  • known as the BCR-Abl protein, shown in this space filling view here,

  • and in it is the small molecule known as Gleevec,

  • which was found to bind to it.

  • And you can see that these are, it was thought that this would be a good drug target because it was known

  • that kinases such as this bind ATP, and ATP targets have pockets in them for which we can find small molecule surrogates.

  • And indeed, they did find them for Gleevec.

  • Another property to look at is does the site have a cavity or a hot-spot,

  • an energetic region in the molecule that can bind a small molecule

  • So these kinds of considerations sort of define the druggability of the target.

  • So, first you have to determine the biology of it,

  • and its link to a disease, and next the druggability of the target.

  • based on whether or not we think we can find compounds that will engage the target.

  • Ok, now certain targets really like to bind small molecules.

  • And in fact, one of the favored disease targets are GPCRs,

  • G-coupled protein receptors. They naturally bind small molecules

  • and they've been traditionally great targets for going after for small molecules.

  • and in fact about forty five percent of the approved drugs are GPCRs.

  • Other targets which are of great interest in terms of the biology are kinases, that I had mentioned

  • proteases and protein-protein targets.

  • These targets can bind small molecules, but in general, they bind them weaker

  • than GPCRs and that probably accounts for why we see so much activity in finding GPCR inhibitors than others.

  • We then want to talk about what is it that we're looking for at the end?

  • What is it we hope this drug will be?

  • Well, first of all, most oral drugs, we'd love them to be just a single, daily dose.

  • That you only have to take it once

  • and typically, that would mean something less than a hundred milligrams of the drug that you would take.

  • Just for example, here's a pill bottle of Ibuprofen,

  • in here are tablets which are about in total two hundred milligrams or so, less than half of that is the drug itself

  • because the rest is the formulation

  • for the drug.

  • Now, in order for that to be the case, in order for us to be

  • at a drug dose of a hundred milligrams per day,

  • there's certain molecular properties that a compound's going to have to show.

  • And one of them is it's going to have to bind to the target with a high affinity

  • and selectivity, so that it binds just one target

  • ideally, so that, and does so with a great deal of potency,

  • so that you don't need much compound to trigger that.

  • We can measure that in this process over here, where we show

  • direct binding of a compound to a protein.

  • So, what we can do is we can titrate the compound in, increasing concentration from left to right

  • of the compound and measure the binding ability

  • of the compound to the target

  • and this case we can measure, then, at what concentration we get fifty percent binding

  • and that's called the Kd

  • and in this case it's ten nanomolar. That's a nice binding compound. We would like our compounds to be

  • that potent or more.

  • In addition, we can measure the potency of the compound in a cell,

  • because that's obviously, going to have to bind to the protein in the cell.

  • And in order to do that, we would like potency of this in a cell based assay

  • to be at least a hundred nanomolar

  • for the midpoint here in binding to the cell.

  • If it meets those criteria, then it has the potency potential

  • to be at a once daily, less than a hundred milligrams dose.

  • But there's some other very important considerations too.

  • And we'll get to that in the lead optimization part of this talk.

  • Which is the half-life of the compound in the body.

  • So, we would like the compound to not be cleared too rapidly,

  • the body wants to clear small molecule compounds, does so through the liver and the kidney,

  • and in other means. And we would like that compound to have a half-life in blood

  • of greater than about three hours

  • in order to have an effectiveness over twenty four hours for the drug.

  • Also, we do want the drug once we take it to be orally active,

  • so that we would want the oral uptake to be at least fifty percent of the drug ingested.

  • to be taken up. Ok, well with these considerations in mind,

  • let me just go also into the chemical considerations of the drug

  • that we want. So those are some of the biological considerations,

  • what are some of the chemical considerations.

  • And here we have a list of four guidelines that were provided by Chris Lipinski

  • and his colleagues at Pfizer that studied

  • a whole variety of orally active drugs

  • and identified several properties that are important for making good, potent

  • and orally active drugs. So, for example, one of the things that most orally active drugs

  • one of their properties, is that they have a molecular weight

  • less than five hundred Daltons.

  • So, we would look to be building compounds that are less than that.

  • They have good solubility, things that are not very soluble don't dissolve so well

  • so they don't go through the gut. And so we'd like to have this parameter called cLog P

  • less than 5.

  • Looking at the structure of the drug too,

  • they found that drugs that had fewer than ten hydrogen bond acceptors

  • such as these moieties here, and here, were better at being orally active drugs

  • as were compounds that had fewer than 5 hydrogen bond donors,

  • such as these species here. And so, these are just general guidelines,

  • they're not, they're many drugs that actually break these rules,

  • and more recently, people are realizing that we can finesse these rules,

  • finesse compounds so that they can actually exceed these guidelines.

  • But they do serve as useful guidelines in thinking about it.

  • Ok, the next consideration that one needs to make is that

  • chemical space, that is we now have to move on to finding compounds that will actually engage our target.

  • The chemome, it turns out, as it were, these are compounds less than five hundred molecular weight

  • is a huge chemical space.

  • If we were to calculate, as has been done, all the compounds that can be made

  • with a molecular weight five hundred from carbon, hydrogen, nitrogen and oxygen,

  • typical constituents in an organic drug,

  • one could build compounds that would be about ten to the sixty in diversity.

  • Now, this number ten to the sixty two is greater than the number of particles in the universe,

  • so we're not going to be able to build all these things.

  • And so what we want is we want methods where we can actually search chemical space

  • effectively to find the area where we want the drug to be.

  • Generally, it turns out that most targets will yield hits

  • to the target when we screen on the range of a thousand or a few thousand compounds

  • we'll find a handful of hits from that process.

  • So the more compounds you screen, the better the likelihood is of getting to hits to your target.

  • And there's no way that we're going to screen

  • all the compounds that are out there, that could be made out there

  • because there's just far too much chemical diversity for us to sample everything.

  • In this regard, bringing a small molecule to a big molecule, involves a lot of considerations

  • that I talked about and also involves a lot of faith,

  • the same of kind of faith that Michelangelo had when drawing this picture.

  • And I'd now like to turn it over to my colleague,

  • Michelle Arkin, who's going to tell you about how we actually make this happen

  • in the laboratory.

  • Alright, so today we're going to talk about hit identification

  • so I say this, getting on the board. So, getting a small molecule

  • that has some of the initial properties

  • that you want in your final drug. And as you see, it's

  • very early in the process. And so having a good starting point here

  • with good molecular properties and good selectivity

  • is really going to help as you winnow down based on other properties

  • that Jim will talk about in the second park of his lecture.

  • So, how are we going to identify that initial chemical starting point.

  • One way is to start with a natural substrate and make it drug-like.

  • If you know the substrate, for example, the ATP-like analogues that Jim gave as an example earlier,

  • you can make these more drug like. That's a sensible place to start.

  • You can start with somebody else's starting point

  • . We call this, jokingly, patent busting, but it's also

  • a very interesting way of getting a late stage compound.

  • If you know the liabilities of your competitors

  • or your own compounds, then you can improve those liabilities

  • and really get a better molecule right off the bat.

  • You can also design a hit from scratch, de novo,

  • using structure based design if you really know a lot about your binding site

  • and really know a lot about that protein's structure and this is difficult

  • but it is an area of a lot of improvement and a lot of research.

  • And finally, probably the most widely used method nowadays is screening.

  • HTS stands for high throughput screening,

  • a more technical, and a little bit new approach is called fragment based screening,

  • but we're going to focus today on screening by high throughput screening.

  • And there are two reasons to do this; one is it's very

  • generally applicable, widely used in the industry,

  • and also more and more people in academics are using this technique

  • as well, to find molecules, not only as starting points for drugs,

  • but also as tools to investigate their biology.

  • So you can think, yourself, your own biology, would it be helped by having a small molecule

  • that specifically interrogates that biology

  • and high throughput screening is one approach for getting those tool compounds.

  • So, especially when you're starting with high throughput screening, you have to screen a lot of molecules

  • to find a drug. So, we're starting here, a high throughput screen can be anywhere from a hundred thousand to a million compounds.

  • Maybe even more than a million compounds.

  • And from that you're going to have some hit rate,

  • so most of those molecules will not be active against your biology.

  • But then a large subset will, say five hundred,

  • to five thousand. Then we have other metrics that we're going to use to winnow those compounds

  • down, based on potency, other in vitro properties, to a hundred to five hundred

  • and you can see that the process keeps going down, here you can see that we're doing toxicology and safety studies,

  • and now through clinical trials, we have attrition all the way until we get to that final molecule.

  • So, we're going to be testing a lot of compounds, we're going to be synthesizing a lot of compounds.

  • So, we need to have robust ways of doing that, precisely and with high throughput.

  • So, what do these molecules look like?

  • We start with libraries of drug-like molecules,

  • and they can come from several places. Three common places that these

  • libraries can be assembled from include natural products,

  • so compounds that come out of extracts or now partially purified compounds from fungi and other species,

  • structure design, so if we know something about the molecular targets that we're looking

  • at, for example, GPCRs are a very common target, as Jim mentioned,

  • and there are a lot libraries that have compounds that look like other GPCR agonists or antagonists.

  • So, you again have a designed library based on particular biological and structural classes.

  • And then finally, there are diversity libraries.

  • These are just selections of molecules selected to be as different from each other as possible

  • still trying to fall within these drug-like properties, like Lipinski's rules,

  • as Jim mentioned, or other drug-like parameters that you might consider.

  • And this is a schematic example of what those drugs would look like.

  • The straight lines are scaffolds, so you'll have different shapes to the molecules,

  • and then the different polygons around those are different functional groups.

  • So, you'll have an array of different shapes, different sizes,

  • and different functionalities to come to about half a million compounds

  • plus or minus. You're then going to screen those compounds

  • in some assay that is amenable to screening half a million compounds,

  • and then at the end, hope that you get some number of molecules

  • that bind to the target in the way that you want.

  • So, how do we handle this many compounds?

  • There are two general ways to think about handling them, one is miniaturization and the other is automation.

  • So, in miniaturization, here we have a standard format plate,

  • and each of these wells is another experiment.

  • Like a test tube or an Eppendorf tube, but they're arrayed here in 96, 96 times 4,

  • 384 well format, and even smaller, 4 times smaller than that, fifteen thirty six well format.

  • So, here we can do one thousand five hundred and thirty six experiments

  • on a single plate.

  • And we'll go into the lab in a minute and I'll show you what these plates look like.

  • And then there's automation, so we have automation,

  • that allows us to prosecute many examples of these plates, one right after the other,

  • we have parallel pipetters, so that we can

  • add reagents with high reproducibility to each of these experiments.

  • Robotics to handle those plates and those pipettes,

  • and then plate readers that are able to read all of these experiments in a high-throughput fashion.

  • So, you can hear now and see now that we're in the laboratory,

  • can you hear the hum of activity?

  • We were just talking about miniaturization into small format plates

  • and automation, so I thought we would show you where we keep our libraries

  • and then show you some automation.

  • We have several of these freezers and in each freezer

  • we have five shelves, and in each shelf we have large numbers of racks of compounds.

  • Here you can see they're stored in 96- or 384- well format and they're barcoded

  • so that we can directly go back to our database and find out what's in those wells.

  • So that we can identify the structures of the compounds and we know what to do next.

  • Ok, now let's go look at some automation.

  • Now we're in the automation room, and I wanted to show you

  • what the plates look like.

  • At the small molecule discovery center, we work mostly in 96-well format

  • and 384-well format and different color plates depending on the assay.

  • Here's a 96-well plate used for luminescence assay,

  • you want to have maximum scatter,

  • to get all of your photons from the luminescence.

  • Then we have black plates, here's a 384-well format,

  • the black bottom plate, this is used for fluorescence,

  • which we measure from the top.

  • And then here's a 384-black sided plate with a clear bottom, and this we use for doing high content imaging for microscopy.

  • Here we have the liquid handler, so these liquid handlers are set up to do automatic pipetting,

  • when you have a lot of plates to work with.

  • We have an incubated plate hotel so your cells can stay warm and cozy,

  • in their plates while they're waiting to be processed.

  • And then over in the back, you probably can't see it here, but in the back,

  • we have a large plate hotel that holds tip boxes, so pipette tips,

  • and also plates for processing.

  • So, now we're going to go through one of these routines,

  • so you can see how the pipetter works.

  • Now we're watching an automation run, where we're going to be adding media to a plate.

  • That will eventually contain cells.

  • So you see the 384-well plate is coming out of an incubated plate hotel,

  • this is where the cells can sit while they're being processed,

  • so that they can stay warm and cozy.

  • You'll notice we're going from a 96-well plate into a 384-well plate.

  • So we're using a 96 tip head,

  • And the pipetter is going to pull media from the 96-well plate

  • and pipette it into all four quadrants

  • of the 384-well plate.

  • Now we say thank you to the tips,

  • thank you to the 96-well plate,

  • and we'll send that 384-well plate of cells back into its hotel.

  • And this plate hotel can hold 42 plates at a time.

  • So you can imagine that pipetting 384 experiments by 42 times would be quite laborious without an instrument like this.

  • Fluorescence is one of the most popular assay

  • formats to use both for biochemical and cell based assays.

  • There are a lot great things about fluorescence. It's very sensitive,

  • often in the nanomolar range of sensitivity,

  • it has a very wide dynamic range, so you can see over several orders of magnitude

  • of intensity.

  • And you can also tune the wavelengths so that you can look at multiple probes at one time

  • that fluoresce in different colors or you can look at the interaction between probes.

  • So, naturally, we don't look at fluorescent 384-well plate

  • visually and score it, we use a plate reader.

  • And so here's the very same plate, on that plate reader,

  • collects the data very quickly, and you can see the image coming up on the screen,

  • as we go.

  • The red is going to be the lowest fluorescence

  • and the blue is among the highest fluorescence on the plate.

  • From there, if you're screening a whole bunch of compounds, you can see that you would have a range of colors

  • in between those. And the top and the bottom of this plate show a dose response curve.

  • So that you can see how the fluorescence changes intensity as the concentration drops.

  • So, this allows us to quantify how much fluorescence we have

  • and therefore how much active enzyme or inactive enzyme

  • we have present in the mix.

  • We just looked at some of the automation equipment

  • that we have in the laboratory, showed you the freezers,

  • where the compounds are stored.

  • Now let's talk about what assays you're actually going to put into those little wells.

  • What little miniaturized experiments are we going to do?

  • So that depends very much on your biology and what's known about the biology.

  • Now, if you have a purified protein and you can make an activity that you can visualize,

  • very typically fluorescence or luminescence,

  • you can do a biochemical based assay.

  • So here's a very simple example where we have an enzyme that binds to a substrate

  • and this substrate has this caged fluorophore in green

  • so that it doesn't fluoresce when it's not bound,

  • then when it binds to the enzyme and is activated by the enzyme,

  • is acted on by the enzyme, it releases

  • this fluorophore, which we can now detect as an increase in fluorescence here at 520 nanometers.

  • If we're looking for an activator of this enzyme,

  • we'd be looking for a low signal that becomes a high signal.

  • If we're looking for an inhibitor of the enzyme,

  • we'll be starting with a situation like this, with a high fluorescence signal,

  • and looking for things that inhibit that signal.

  • So there are several instances where a biochemical assay is really an appropriate high-throughput screening assay

  • especially if you know what protein you want to inhibit.

  • Bcr-Abl is a good example, it's the known agent of causing

  • CML. So inhibiting that enzyme is going to inhibit that disease progression.

  • You also need to be able to express and purify that protein in large enough quantities to run many experiments on it

  • and that protein needs to be similar enough to what it is in the cell to make sense.

  • Also, the selectivity among similar proteins is important.

  • So, again, the kinase example. There are hundreds of kinases in the body,

  • some you want to inhibit for your disease, some you don't.

  • They're critically involved in homeostasis.

  • So, if you have a whole panel of those similar proteins,

  • then you can test activity against your protein of interest

  • without having activity against the proteins you're not interested in.

  • Ok, there are other examples. So when you have a biochemical assay,

  • you come out of it knowing the molecular target.

  • You know what protein your compound is binding to.

  • What you don't know coming out of that assay is whether

  • that compound is going to survive in the cell,

  • whether it's still going to bind to your target when it's in the cell,

  • whether it will even be permeable and get into that cell.

  • On the other hand, if you use a cell based assay, then you'll know the opposite.

  • You'll know that the compound is active

  • in the cell, because that's how you identified it,

  • but you won't necessarily know the molecular target,

  • the protein or other biomolecule that that molecule is targeting.

  • So, when would you want to screen in a whole-cell context.

  • There are several cases where you'd want to screen in cells,

  • and this is an area that's really gotten to be much more popular recently,

  • as high throughput cell culture has become available.

  • For example, if you know the molecular target, but you can't isolate from it the cell,

  • GPCRs are a good example, ion channels are an example,

  • and more subtly, some kinases even are regulated

  • by subcellular localization, by time in the cell cycle and if you take that protein

  • out of its context, it might have a very different inhibitory profile

  • then in the cellular context.

  • Also, sometimes your goal is to alter a cellular phenotype.

  • Maybe you're not that interested in the molecular target,

  • but you want to turn on a whole pathway,

  • or you want to kill a bacteria,

  • and you're a little less concerned about how you kill that bacteria

  • or for example, you get your phenotype and you'll use that molecule to understand the biology

  • well enough, use that molecule to tell you what the important targets are

  • to cause that phenotype.

  • For example, death of a bacterium.

  • Also, if we know the pathway that we want to inhibit,

  • but we don't know the best or most druggable target

  • in that pathway, we can allow the molecules to tell us that information

  • by using an assay that measures some parameter at the bottom of that pathway

  • and see which molecules inhibit or activate that function.

  • How are we going to read out these cell based assays?

  • So, remember we have cells cultured in this small, microtiter plates now,

  • and we want to read them out in some way that a plate reader will be able to detect them.

  • There are two very common, basic approaches

  • to looking at cell-based assays.

  • One is to look at a whole well parameter,

  • such as fluorescence or luminescence.

  • So, this would be an example of reporter cell lines,

  • where you have a luciferase or you have a green fluorescent protein

  • that's downstream of a promoter

  • that you want to turn on or off.

  • And if you alter the state of that promoter, then you're going to

  • get more or less of that fluorescent or luminescent protein.

  • Another more recent advance that's getting to be very popular also is to look at those

  • properties cell by cell, and we're going to go through some examples

  • of high-content imaging, where we're looking at an automated microscope

  • so we can look at each cell in that well,

  • and multiple parameters that are affected by the drugs.

  • So this instrument is our high-content screening instrument,

  • it's an In Cell Analyzer, by GE Healthcare.

  • It's basically automated microscopy. It allows to automatically

  • collect data, fluorescence and absorbance, transmitted light data,

  • across several wavelengths, sequentially, in a multi-well plate. Several plates at a time.

  • And then later, we'll analyze that data.

  • So the assay we're going to look at today is a pretty simple, straight-forward assay.

  • It just has one stain, we can do up to six stains.

  • This is just a nuclear stain, which we'll see in blue,

  • and we're looking for trypanosomes. Trypanosomes are parasites,

  • they're the causative agent of Chagas disease, which is a terrible disease

  • that infects people in South America.

  • And these trypanosomes, these little parasites, live inside

  • host cells. So, this assay is going to have a host cell that's infected with parasites

  • and we'll be able to detect and quantify the number of parasites per host cell

  • based on the nucleic acid staining.

  • So, let's get started.

  • So, now Kenny, our high-content screening expert,

  • starting the instrumentation and it's loading the plate

  • in the plate reader, it's setting the objectives,

  • the lenses to 10x objective, the correct filters to detect

  • fluorescence in the blue channel. And now you can see

  • the data are coming up.

  • We're scanning twenty fields per well

  • see here, and as the data are collected, they're coming up on the screen.

  • And they're also being saved into the computer.

  • They'll go to our database for analysis later.

  • You can see the large spots are host nuclei and the smaller spots

  • in some of them are trypanosome kinetoplastid DNA.

  • This is very dense DNA that is next to the nucleus.

  • We just looked at this experiment on the high content imager

  • and now we're going to look at how we analyze that data.

  • So, you saw that we can automatically collect these experiments

  • and in the blue channel, here, we're looking at nucleic acid.

  • We see the large circles here, are the host nuclei and the small dots are kinetoplastid DNA

  • from the parasite. So it's very straightforward for the instrument,

  • for us to teach the instrument which of these blobs belong to host nuclei and

  • which of these blobs belong to the parasite.

  • And here you see circled in green what the instrument has identified as host nucleus

  • and in yellow, circled, what the instrument has identified as kinetoplastid, or parasite DNA.

  • We can then very simply count the number of cells that have parasites in them

  • and look at a reduction in the number of infected cells

  • with drug treatment.

  • Or look at the ratio of parasites to host cells.

  • One really nice thing about an assay like this is instead of a whole well assay, where we just see the properties overall,

  • here we can look as well at the health of the cells.

  • So, are we killing the host nuclei? Are we causing them to be apoptotic?

  • Or to otherwise change their morphology?

  • We don't want to alter that cell, we only want to remove the number of parasites

  • that are infecting those cells.

  • So, we can get a lot of selectivity and some early toxicity information from an experiment like this.

  • So, now we have run our screen and this is an example of a ninety thousand

  • compound screen. So, a sort of moderate size high-throughput screen.

  • And you can see the three types of experiments that you get out of this.

  • Here you have on the y-axis, we have a percent inhibition.

  • So, we're looking at an enzyme that's active at the bottom and

  • fully inhibited, or inactive, at the top.

  • In green, we see what the average fluorescence is,

  • these are maximum signal controls on each plate

  • that show you the variance in the activity of the enzyme across that plate.

  • And then a fully inhibited enzyme, here these are minimum signal controls

  • going across the plate and you see some variation,

  • but overall, this is a minimum signal, a highly inhibited enzyme signal.

  • And it's normalized to the average here, being 100 percent inhibited

  • and the average of the green being 100 percent active.

  • Now, in blue, are the compounds that we've actually screened.

  • So, how do we identify which of these compounds are inhibitors,

  • or partial inhibitors that we want to follow up in further assays.

  • We have an assay parameter that's commonly used that's called a Z-prime value

  • and the Z-prime value measures the width of the minimum signal,

  • the width of the maximum signal,

  • and the distance between them to come up with an assay parameter

  • that varies between negative one and one,

  • one being a perfect assay, point five being adequate for high-throughput screening.

  • To give you an example, this has a Z-prime factor across the entire assay of about point eight.

  • So this is a very high quality assay.

  • Now we go to find hits in that assay, here the grey line is that average

  • minimum signal, it's the average fully inhibited enzyme.

  • And here, this black line, shows you three standard deviations above the noise.

  • So, most of the compounds are totally inactive in this assay,

  • the enzyme was fully active. And then three standard deviations above that

  • these would be considered statistically relevant changes

  • from active or partially inhibited.

  • We would go in and select several of these compounds and test them some more.

  • So the next steps are to re-test those compounds to see if they

  • recapitulate, run does responses of those compounds to see how potent they are,

  • determine the mechanism of inhibition.

  • This is really important aspect of high throughput screening

  • because a lot of compounds will inhibit a cellular function or protein

  • for reasons that are not drug-like. So we need to tease

  • those apart before we put a lot of effort into the compounds.

  • Then once we're confident that the compounds are truly active,

  • and acting in the way we want them to be acting,

  • then we go into optimize.

  • And this is what Jim will be talking about in the next segment of the seminar.

  • So, just to go back to where we started,

  • hit identification takes some time, and there are a lot of experiments that go there.

  • But it's just the first step to discovering a drug.

  • So, if we start here with a hundred thousand to a million compounds,

  • we really want to make sure that what's going through this funnel

  • has a chance of being successful at the end.

Hi, my name is Jim Wells. I'm with the departments of Pharmaceutical Chemistry

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Jim Wells和Michelle Arkin(加州大學舊金山分校)第一部分:藥物發現簡介 (Jim Wells and Michelle Arkin(UCSF) Part 1: Introduction to Drug Discovery)

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    Scott 發佈於 2021 年 01 月 14 日
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