Category Archives: medicinal chemistry

Practical Fragments blog has reviewed our paper!

Our latest fragment-based drug discovery paper against the p97 ATPase has been noticed and reviewed favorably by the widely-read Practical Fragments blog.

Here is an excerpt from that review:

“The protein p97 is important in regulating protein homeostasis, and thus a potential anti-cancer target. But this is no low-hanging fruit: the protein has three domains and assembles into a hexamer. Two domains, D1 and D2, are ATPases. The third (N) domain binds to other proteins in the cell. All the domains are dynamic and interdependent. Oh, and crystallography is tough. Previous efforts have identified inhibitors of the D2 domain, but not the others. Not to be put off by difficult challenges, a group of researchers at the University of California San Francisco (UCSF) led by Michelle Arkin and Mark Kelly have performed fragment screening against the D1 and N domains, and report their adventures in J. Biomol. Screen.

Automate your Topspin NMR workflow

Here is a tip for scientists that need to batch process NMR data quickly and uniformly for analysis.  This approach could be a big time-saver in situations where you have a large series of 1D reference spectra collected by sample automation, for example.  Or in NMR screening applications, where dozens of STD-NMR experiments are being collected during an overnight run.

Hidden away in the Topsin “Processing” menu is a feature called “Serial Processing:”

Screen shot 2014-10-20 at 2.39.51 PMSelect this menu option and you will see the following dialogue:

Screen shot 2014-10-20 at 2.49.37 PMSince this is first time you are doing this operation, you need to select “find datasets” in order to first find the data to process.  In the future, you will have a “list” created for you by the program that you can reuse to reference datasets in combinations that you specify.

When you click “find datasets” you will see this dialogue:

Screen shot 2014-10-20 at 2.40.02 PMSelect the data directory to search from the “data directories” box at the bottom of the window.   (If your NMR data directory is not here, it is because you haven’t added it to the Topspin file brower in the main Topspin window.  Go do that first, and come back and try this operation again.)

Under the “name” field, enter the name of the specific dataset directory you wish to search, or leave it blank to search across many directories.  You can also match on experiment number (EXPNO) or process number (PROCNO).  The check boxes enforce exact matching.  You can select 1D or higher dimensional datasets for processing.  You can also match by date.

When you’ve made your selections, it will look like this:

Screen shot 2014-10-20 at 2.40.46 PMIn this search, I am selecting for all 1D data contained in the “Oct16-2014-p97” subdirectory of my NMR data repository at “/Users/sandro/UCSF/p97_hit2lead/nmr”.

Click “OK” and wait for the results.  Mine look like the following:

Screen shot 2014-10-20 at 2.41.18 PMThe program has found 24 datasets that match my criteria.  At this point, you want to select only those you wish to batch process.  I will select all files like this:

Screen shot 2014-10-20 at 2.41.36 PMNow click “OK” and you are returned to this prompt:

Screen shot 2014-10-20 at 2.42.00 PMNotice that the program has now created a list of datasets for batch processing for you, store in the ‘/var/folders/’ temporary directory.  The list is a text-based list of the filenames you specified by your selection criteria.  You can edit by hand or proceed to the next step.  To proceed, click “next.”  You will now see this dialogue:

Screen shot 2014-10-20 at 2.42.20 PMThis is where the useful, time-saving stuff happens.   This dialogue takes the list you defined and applies whatever custom command sequence you would like to apply to your data.  You define this sequence in the text box at the bottom.  As you can see, I have chosen to perform “lb 1; em; ft; pk.”   This is line broadening = 1, exponential multiplication, fourier transform, and phase correction.  You can also specify a path to a python script for the Topspin API.

Once you have your desired processing commands, click “Execute” and go grab a coffee!  You just saved yourself many minutes of routine processing of NMR spectra.    Hope you find this tip useful and that it can save you some time in your day.

 

Gilead’s innovative approach to Hep C drug, Sovaldi

Hepatitis C virus (HCV) is a single-stranded RNA virus that infects an estimated 180 million people worldwide.

In 2013, Gilead received FDA approval for a new HCV drug, Sovaldi (sofosbuvir), that inhibits viral replication by targeting the virus’s NS5B polymerase.  Sovaldi has shown a very high cure rate (nearly 100% HCV suppression and sustained virological response) in clinical trials of previously untreated patients and has fewer side effects than pegylated-interferon and ribavirin therapies.

Sovaldi is a methyluridine-monophosphate prodrug: it is metabolized in the body back into methyluridine-triphosphate, which acts as a potent substrate mimic and inhibitor of the NS5B polymerase.

What is interesting about Sovaldi is the approach the scientists took to getting the inhibitor into the cell, relying on phosphoramidate prodrug  technology that had been effectively used to develop anti-HIV drugs, but had never been applied before to this class of anti-HCV drugs.

During development, the researchers decided that they needed to deliver the charged methyluridine-monophosphate (rather than the neutral methyluridine)  into the cell on the basis of two key observations:  1) the methyluridine triphosphate is the active compound against HCV NS5B polymerase, while the methyluridine alone is inactive (owing  to very low conversion to monophosphate in vivo) and 2) the methyuridine monophosphated derivative can be anabolized in the cell back to the potent triphosphate form by an endogenous uridine-cytidine monophosphate kinase.

jm-2010-00863x_0003
The active uridine triphosphate (6) can be created when 4 is metabolized to methyluridine-5′-monophosphate. Compound 5 is not phosphorylated and is inactive in cells.

 

The phosphoramidate prodrug technology had never been applied to HCV inhibition until Solvadi.

The idea behind  phosphoramidate prodrug technology is to create a membrane-soluble neutral prodrug derivative that can be metabolized in the liver by carboxylesterase-mediated cleavage and subsequent steps back to the monophosphate form.

The researchers applied the approach and after a significant amount of  SAR investigation and PK/PD studies around the chemical composition of the phosphoramide substituents, they concluded that the structure of compound shown above was the optimal structure to deliver the methyluridine-monophosphate to the liver.

The result is a new generation of highly effective HCV therapeutics with few side effects that can make a significant difference in the lives of patients living with HCV.

 

Why are many drugs aromatic heterocycles?

To the non-specialist in medicinal chemistry (like myself), the abundance of drugs that contain aromatic ring moieties, usually with heteroatoms like N, is somewhat surprising. In fact, in 2012, the top 4 out of 5 drugs by sales contain such groups. 

There are at least a few good reasons why these types of compounds appear so often:

1 Heterocyclic systems are easier to prepare synthetically than all-carbon based aromatic systems and they are easier to modify later.

2  Scaffolds with heterocycles allow the easy introduction of H-bond donors and acceptors to fine-tune the properties of the compound, like binding affinity, solubility, and resistance to metabolism in vivo.

3 Can “template hop” easily off of aromatic ring scaffolds to evolve new IP with the same functionality as a known drug (e.g., Viagra to Levitra).

Can synthetic chemistry specialists give more reasons?  (Post in the comments!) 

Source:  Jordan, A, Roughley, S.  “Drug discovery chemistry: a primer for the non-specialist.”  Drug Disc Today, 14. 2009