A new mechanism of action for Selective Serotonin Reuptake Inhibitors (SSRIs)?: Pharm 551A, Baudry et al., 2010

This is the last paper for the first section of the class and, as such, it serves as a transition from basic drug discovery work to the next section of the class, the pharmacology of gene expression. The paper, MiR-16 Targets the Serotonin Transporter: A New Facet for Adaptive Responses to Antidepressants, Baudry et al., 2010 Science, is a last minute addition for the class. I had another miRNA and pharmacology paper in this spot before but, when I saw this paper, I couldn’t help myself and switched the papers. Here’s why:

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Targeting GPCR – Gbeta/gamma with small molecules, Pharm 551A: Bonacci et al., 2006

Our understanding of G protein-coupled receptors (GPCRs) has been greatly aided by their relative tractability in terms of pharmacological targeting. These receptors are fairly easy to express in cells and their signaling pathways are amenable to high throughput screening (HTS) technologies. GPCRs couple to a trimeric G-protein structure composed of an alpha subunit and a beta/gamma subunit. The alpha subunit dissociates from beta/gamma upon stimulation of the GPCR and the duration of the alpha subunit signaling is determined by its intrinsic GTPase activity. This GTPase activity can be modulated by regulator of G-protein signaling (RGS) proteins. In terms of GPCR signaling the vast majority of attention has been paid to alpha subunits and part of the reason for this is the availability of molecules (e.g. pertussis and cholera toxins) that target those subunits. Despite this, it is well known that beta/gamma subunits are also capable of generating signaling as these little proteins are known to activate phospholipase C (PLC), PI3Kinase (PI3K) and G-protein receptor kinases (GRKs). Additionally, beta/gamma subunits activate G-protein coupled inwardly rectifying potassium channels (GiRKs) and inhibit certain types of voltage-gated calcium channels (VGCaC). While these signaling mechanisms for beta/gamma are well known, we know relatively little about the physiology of these processes in vivo. This is because we do not have tools to probe the function of beta/gamma pharmacologically. At least not until 2006.

Bonacci et al., Differential targeting of Gbeta/gamma-subunit signaling with small molecules, Science (2006) [free at Science], was the first paper to describe small molecules that target beta/gamma subunits. The first step in discovering these small molecules involved describing a modulatory binding site for beta/gamma function. The authors used phage display of beta/gamma subunits to screen for peptides that bound to these subunits. They discovered a small peptide, SIGK, that bound to a “hotspot” in beta/gamma. They then used this hotspot to screen several thousand molecules computationally for potential binding within this region. They came out of this “virtual screen” with a list of 85 compounds that could then be tested for interference with SIGK binding to beta/gamma. Of these 85 compounds, they found 9 with apparent binding affinity between 0.1 and 60 uM. They then focused on these compounds as potential modulators of beta/gamma signaling.

Agonist-directed trafficking of receptor stimulus, Pharm 551A: Berg et al., 1998

After doing a whole bunch of cutting-edge papers for the class its time to go back in time a bit (like 1998 is ancient but anyways) and do an oldie-but-goodie. This particular paper, “Effector Pathway-Dependent Relative Efficacy at Serotonin Type 2A and 2C Receptors: Evidence for Agonist-Directed Trafficking of Receptor Stimulus”, Berg et al., 1998 Molecular Pharmacology (Free at Mol Pharm) isn’t really a citation classic (with 278 citations according to google scholar), yet, it marks a very significant moment in GPCR pharmacology. I like this paper for two reasons: 1) It moved a major, emerging pharmacology theoretical framework forward toward experimental discovery and 2) I am very fond of the first and last authors.

First to my fondness for the first and last authors, Kelly Berg and Bill Clarke. Bill and Kelly are professors in the Department of Pharmacology at The University of Texas Health Science Center at San Antonio (UTHSCSA). It so happens that I did my PhD in that very department (I started there in 1998). The very first class I took was Bill Clarke’s Principles of Pharmacology course. When I joined the department I was quite sure I wanted to be a pharmacologist but this course drove that point home for me in ways that are difficult to describe. The course was mainly taught by Bill and Kelly (who happen to be married) with Bill doing most of the teaching on basic principles and Kelly doing the teaching on molecular signaling through GPCRs. While I learned an enormous amount about basic pharmacological principles and the ins-and-outs of GPCR signaling in the class my main memories are of the passion for teaching and graduate education that they both passed on to all of us throughout the semester. I like to think that my teaching style came mostly from the two of them and while I am sure I have not yet lived up to their level of excellence, their example consistently gives me a goal to shoot for. In this class I like to use this paper to transition from screening technologies back to pharmacological principles largely because it reminds me to try to live up to what BIll and Kelly imparted to me through their course.

Okay, enough nostalgia, onto the paper…

Identifying novel inhibitors for uncharacterized enzymes, Pharm 551A: Bachovchin et al., 2009

Today is our last paper on high throughput screening (HTS) techniques. We’re back to discovering drugs on this one but the premise is quite different for this particular screen. Whereas other papers we’ve done so far have involved finding novel drugs for known targets or identifying drugs that produce novel behavioral phenotypes, this paper is about finding novel drugs for enzyme targets that are not fully characterized. The paper is: “Identification of selective inhibitors of uncharacterized enzymes by high-throughput screening with fluorescent activity-based probes”, Bachovchin et al., 2009, Nature Biotechnology [PMC].

The genome era has brought in a new age in terms of understanding and identifying the number of genes in mammalian and other genomes. While we have a good idea of where genes lie in genomes and what their structure looks like from all of this sequencing, we do not necessarily understand what these genes do based purely on their sequence. While we can make some good guesses (probably better than a guess) on whether such genes are enzymes, GPCRs or other types of proteins based purely on homology, we cannot necessarily understand their function within cells or whole organisms based purely on sequence data. For almost every protein, we understand how it works within complex systems because we have tools to probe its function. Sometimes these tools involve genetic manipulations or knockdown technologies but more often these tools to probe function depend on pharmacological manipulation of protein function. Therein lies the problem. If you know of an uncharacterized enzyme or receptor and have some idea of its substrates or its receptor class, but not much else, how do you screen for inhibitors or activators of that enzyme or receptor. In the case of enzymes we can generally identify at least one of their substrates based on sequence homology but that doesn’t help you to put together a functional screen in all cases. A way around that is to perform activity based protein profiling (ABPP):

Identifying tumor suppressor genes through an in vivo RNA interference screen, Pharm 551A: Bric et al., 2009

On the agenda for today is another paper about screening (one more to go after this one): Bric et al., (2009) Functional identification of tumor-suppressor genes through an in vivo RNA interference screen in a mouse lymphoma model [PMC]. This one is a different from our other screening papers because this one is not drug based. Rather, the authors have devised a screen to discover tumor suppressor genes in a mouse model of lymphoma. We’ll jump into all of this in a minute but first I want to take a minute to tell you why I chose this paper (which is way outside my area of expertise). Earlier this year I had the pleasure of attending the Rita Allen Foundation Scholars Meeting. This wonderful event was held in Princeton NJ and all of the current Rita Allen Foundation Scholars gave talks or presented posters. There I had the pleasure of learning about the work of Michael Hemann. Michael gave a really fantastic talk about work related to the current paper and I just couldn’t resist including some of this in my class. Having said that, this paper was more difficult to digest than I remembered from his talk and from my initial read of the paper when I was putting the syllabus together. It all seemed so much more simple when he was describing it in his talk, but, then again, that’s the sign of a great talk. We’ll see if I survive this today…

Back to the paper. As with all of the other screening papers we have done so far, the idea is simple: devise a screen for genes that suppress oncogenesis in vivo. To do this the authors have used a widely accepted lymphoma model and an RNA interference-based screen to find potentially novel tumor suppressor genes:

Predicting new molecular targets for known drugs, Pharm 551A: Keiser et al., 2009

On the agenda today is a fascinating paper titled: predicting new molecular targets for known drugs, Keiser et al., Nature 2009 [PMC]. This is one of my favorite papers of the past two years. I have to admit that it took my quite a while to really understand the potential of the methods and findings of the paper and I was torn as to whether to include the paper in the class because it is likely a bit much to digest in a single class period; however, the findings are so cool and the implications of the method are so far-reaching that I ultimately decided I couldn’t pass up the opportunity to do this paper for the class. Let’s dive in…

The idea is simple. Drugs work because they have an action at a molecular target. However, most drugs have side effects and we don’t always know why. Moreover, while we often presume that drugs work because they hit a particular target, this may not actually be true. There are generally several drugs of a particular class but one particular drug may be better for treating a disease. This could be explained by a number of factors and one potential possibility is that a given drug has a mix of activities that is a perfect fit for a particular disorder. The problem is that we don’t necessarily know what that polypharmacological mix is. If there was a computation model that could help us predict appropriate polypharmacologies and off-target hits at the same time we could potentially make large leaps forward in terms of drug development.

This is, more or less, what the author’s set out to do:

The creation of target-specific ‘magic bullets’ has been a therapeutic goal since Ehrlich, and a pragmatic criterion in drug design for 30 years. Still, several lines of evidence suggest that drugs may have many physiological targets. Psychiatric medications, for instance, notoriously act through multiple molecular targets, and this ‘polypharmacology’ is probably therapeutically essential. Recent kinase drugs, such as Gleevec and Sutent, although perhaps designed for specificity, modulate several targets, and these ‘off-target’ activities may also be essential for efficacy. Conversely, anti-Parkinsonian drugs such as Permax and Dostinex activate not only dopamine receptors but also 5-HT2B serotonin receptors, thereby causing valvular heart disease and severely restricting their use.

Drug polypharmacology has inspired efforts to predict and characterize drug–target associations. Several groups have used phenotypic and chemical similarities among molecules to identify those with multiple targets, and early drug candidates are screened against molecular target panels. To predict new targets for established drugs, a previous group looked for side-effects shared between two molecules, whereas another group linked targets by drugs that bind to more than one of them. Indeed, using easily accessible associations, one can map 332 targets by the 290 drugs that bind to at least two of them, resulting in a network with 972 connections (Fig. 1a, below). It seemed interesting to calculate a related map that predicts new off-target effects.

So what does this network look like?

Panel A is a network of known drugs with known targets linked together. It shows, basically, some pharmacological promiscuity between receptors that would be expected by any pharmacologist. For instance, 5HT receptor drugs tend to hit other 5HT receptors and these drugs also tend to have effects at other GPCRs within the amine ligand class. Nuclear receptors and their ligands also group together and on and on. Where this really gets interesting is in panel B:

Rapid behavior-based identification of neuroactive small molecules in the zebrafish, Pharm 551A: Kokel et al., 2010

Today’s paper is a continuation of our discussion on screening compounds for drug discovery: Kokel et al, 2010 Rapid behavior-based identification of neuroactive small molecules in the zebrafish, Nature Chemical Biology [PMC]. Having just returned from the IASP meeting in Montreal I am really pressed for time today so this is going to be brief; however, don’t let my brevity stop you from delving into the details of the paper — its a really fascinating study with major implications for drug discovery.

The problem is a simple one: when screening compounds we typically work on the assumption that the target for the disease is known. This is required because a typical library screen is based on receptor or enzyme activity and not a relevant behavioral phenotype. While this is fine for disorders with well understood targets, this is often not the case for neurobiological disorders where we don’t necessarily know the target beforehand. Hence, a behavior-based screening mechanism that is done in a high throughput fashion/screen (HTS) would be optimal for identifying novel neuroactive compounds. The problem is that behavioral experiments and model organisms are generally not conducive to this sort of thing:

Unlike target-based approaches, phenotype-based screens can identify compounds that produce a desired phenotype without a priori assumptions about their targets. Phenotype-based screens in cultured cells and whole organisms have identified powerful new compounds with novel activities on unexpected targets in vivo. However, it has been difficult to combine chemical-screening paradigms with behavioral phenotyping, perhaps because many well studied behaviors are too variable or occur in animals that are too large for screening in multi-well format.

So how have the authors provided a solution to this problem?

Quantifying biogenic bias in screening libraries, Pharm 551A: Hert et al. 2009

Today’s paper [PMC] is Hert et al., (2009) Quantifying biogenic bias in screening libraries. At issue for todays class is a discussion about one of the first steps in drug discovery, compound library selection and generation. The authors of this paper pose a very interesting question: with the available chemical space (which is massive) how do high throughput screening (HTS) efforts for drug discovery ever succeed?

Chemical space—that is, all possible molecules—is estimated to be greater than 10^60 molecules with 30 or fewer heavy atoms; 10 ug of each would exceed the mass of the observable universe. This figure decreases if criteria for synthetic accessibility and drug likeness are taken into account and increases steeply if up to 35 heavy atoms (about 500 Da) are allowed. Positing even a modest specificity of proteins for their ligand, the odds of a hit in a random selection of 10^6 molecules from this space seem negligible.

So, based on this seemingly impossible complexity, how does HTS ever succeed to begin with. They have at least two hypotheses:

HTS nevertheless does return active molecules for many targets; how does it overcome the odds stacked against it? One might hazard two hypotheses. First, molecules that are formally chemically different can be degenerate to a target, and many derivatives of a chemotype may have little effect on affinity. This behavior, and the polypharmacology of small molecules, undoubtedly contributes to screening hit rates. Such chemical degeneracy seems unlikely, however, to overcome the long odds against screening. A second explanation is that screening libraries are far from random selections, but rather are biased toward molecules likely to be recognized by biological targets. This second hypothesis seems more plausible, as many accessible molecules are likely to resemble or derive from metabolites and natural products. Some of these will have been synthesized to resemble such biogenic molecules, while others will have used biogenic molecules as a starting material.

Pharm 551A: first day of class

Today was the first day of class for Pharm 551A. Not much to talk about other than a brief discussion of the construction of the class and expectations followed up by a quick run through of the basics of drug discovery to prep for the papers we’ll be doing over the next month. We’ll start the online continuation of paper discussion here after Thursday’s class.

If you’re coming over to check out the blog and are in the class here are some links to the pharma blogs I told you about this morning:
Derek Lowe’s In The Pipeline
Pharmalot
and another I forgot to mention: Pharma Conduct, although it may have gone dead because nothing has been posted in some time.

Also, here are some of my previous discussions on drug discovery on this blog, in case you’re interested:
Drug Discovery in Academia
Drug Discovery in Academia and NIH funding

Regular readers of my blog (DM and someone else I’m sure), this is the start of the incorporation of my class into the blog. Yes, its an experiment and, yes, the students agreed that it “might” be useful.