medicinal chemistry Archives - Michael's Bioinformatics Blog

March 28, 2018

Breakthrough advances in 2018 so far: flu, germs, and cancer

2018 medicine breakthrough review!

So far this year has seen some pretty important research breakthrough advances in several key areas of health and medicine. I want to briefly describe some of what we’ve seen in just the first few months of 2018.

Flu

A pharmaceutical company in Japan has released phase 3 trial results showing that its drug, Xofluza, can effectively kill the virus in just 24 hours in infected humans. And it can do this with just one single dose, compared to a 10-dose, three day regimen of Tamiflu. The drug works by inhibiting an endonuclease needed for replication of the virus.

Germs

It is common knowledge that antibiotics are over-prescribed and over-used. This fact has led to the rise of MRSA and other resistant bacteria which threaten human health. Although it is thought that bacteria could be a source of novel antibiotics since they are in constant chemical warfare with each other, most bacteria aren’t culture-friendly in the lab and so researchers haven’t been looking at them for leads. Until now.

Malacidin drugs kill multi-drug resistant S. Aureus in tests on rats.

By adopting whole genome sequencing approaches to soil bacterial diversity, researchers were able to screen for gene clusters associated with calcium-binding motifs known for antibiotic activity. The result was the discovery of a novel class of lipo-peptides, called malacidins A and B. They showed potent activity against MRSA in skin infection models in rats.

The researchers estimate that 99% of bacterial natural-product antibiotic compounds remain unexplored at present.

Cancer

2017 and 2018 have seen some major advances with cancer treatment. It seems that the field is moving away from the focus on small-molecule drugs towards harnessing the patient’s own immune system to attack cancer. The CAR-T therapies for pediatric leukemia appear extremely promising. These kinds of therapies are now in trials for a wide range of blood and solid tumors.

A great summary of the advances being made is available here from the Fred Hutchinson Cancer Research Center. Here is how Dr. Gilliland, President of Fred Hutch, begins his review of the advances:

I’ve gone on record to say that by 2025, cancer researchers will have developed curative therapeutic approaches for most if not all cancers.

I took some flak for putting that stake in the ground. But we in the cancer research field are making incredible strides toward better and safer, potentially curative treatments for cancer, and I’m excited for what’s next. I believe that we must set a high bar, execute and implement — that there should be no excuses for not advancing the field at that pace.

This is a stunning statement on its own; but made even more so because it is usually the scientists in the day-to-day trenches of research who are themselves the most pessimistic about the possibility of rapid advances.

Additionally, an important paper came out recently proposing a novel paradigm for understanding and modeling cancer incidence with age. For a long time the dominant model has been the “two-hit” hypothesis which predicts that clinically-observable cancers arise when a cell acquires sufficient mutations in tumor-suppressor genes to become a tumor.

This paper challenges that notion and shows that a model of thymic function decline (the thymus produces T-cells) over time better describes the incidence of cancers with age. This model better fits the data and leads to the conclusion that cancers are continually arising in our bodies, but it is our properly functioning immune system that roots them out and prevents clinical disease from emerging. This model also helps explain why novel cancer immunotherapies are so potent and why focus has shifted to supporting and activating T-cells.

Declining T cell production leads to increasing disease incidence with age.

October 27, 2017

Why is low-dose naltrexone beneficial for many diverse diseases?

Recently, I’ve been doing some research into Hailey-Hailey Disease (HHD). HHD is an autosomal dominant genetic disorder that leads to severe dermatosis. The disease causing variants are located in the ATP2C1 gene, which is a magnesium-dependent, calcium transporting ATPase.

There are unfortunately few treatment options for HHD. Many treatment options have been tried, from corticosteroids to tacrolimus. There are very few HHD patients, and therefore no large scale clinical trials of therapies for this disease.

I came across a paper that shows that a novel approach, low-dose naltrexone (LDN), may be an effective and low-cost therapy for treating HHD. What is more remarkable, however, is the fact that LDN has already been used with success to treat many diseases like fibromyalgia, Crohn’s disease, and HIV.

Here is the complete list of diseases that LDN has been used to treat with some success according to some case reports and small-scale clinical trials:

Atopic eczema

Cholestatic pruritus

Crohn’s Disease

Adenoid cystic tongue carcinoma

Fibromyalgia

HIV

Multiple Sclerosis

Chronic eczema and pruritis

Hailey-Hailey Disease

******

How is LDN effective across so many seemingly unrelated diseases? I can’t really answer that question. We do know that naltrexone is an opioid receptor inhibitor that is used in the treatment of alcohol and opioid abuse at higher doses. At low dose, the mechanism of action is less clear, but some studies suggest increases in beta endorphins and suppression of cytokines using LDN.

As of now, LDN remains an “off-label” use of naltrexone and in the realm of internet anecdotes until more rigorous studies can be completed. Regardless, it is an exciting development in the potential treatment of rare diseases, like HHD.

April 15, 2015

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.

October 20, 2014

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:”

Select this menu option and you will see the following dialogue:

Since 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:

Select 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:

In 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:

The 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:

Now click “OK” and you are returned to this prompt:

Notice 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:

This 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.

April 25, 2014October 22, 2014

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.