Is CB-5083 a promising new weapon against multiple myeloma?

Why care about p97?

In my postdoc work, I participated in a large team effort at designing a small molecule inhibitor of the p97 AAA-ATPase.

A crystal structure of the p97 ATPase.  The D2 domain is shown in dark blue.

The funding for this project came from the National Cancer Institute (NCI) and was premised on the idea that inhibiting p97 in certain types of cancer cells that depend heavily on the endoplasmic-reticulum associated degradation pathway (ERAD) would have the effect of triggering the unfolded-protein response and apoptosis pathways within the rapidly growing tumor cell populations.   This is because p97 is a critical regulator and component of ERAD, and when it is inhibited, the cell experiences unbalanced protein homeostasis and unfolded protein stress.

Drug design is an extremely challenging problem, and even with a large group of researchers it took us several years to find a compound that showed promising inhibition against p97.   Our results were published in ACS Med Chem Letters in 2016.   The compound we discovered, indole amide 3, has high solubility, permeability, and stability.  It binds an allosteric site on the D2 domain  with sub-micromolar affinity.   Unfortunately, it just didn’t have enough binding affinity to be active in vivo.

A different approach yields new promise

At around the same time we were developing our allosteric inhibitor series, another group was developing an ATP competitive D2 domain inhibitor of p97, called CB-5083.  In contrast to our compound, this one binds directly to the D2 ATP enzyme site with nanomolar affinity.

CB-5083.

The compound also demonstrated potent and specific p97 inhibition activity in mouse xenograft models of tumors.

An advance in myeloma cancer therapy

A more recent paper (Nov 2017) shows activity for CB-5083 against multiple myeloma (MM) cell lines and in vivo MM models.  From the abstract:

CB-5083 decreases viability in multiple myeloma cell lines and patient-derived multiple myeloma cells, including those with background proteasome inhibitor (PI) resistance. CB-5083 has a unique mechanism of action that combines well with PIs, which is likely owing to the p97-dependent retro-translocation of the transcription factor, Nrf1, which transcribes proteasome subunit genes following exposure to a PI. In vivo studies using clinically relevant multiple myeloma models demonstrate that single-agent CB-5083 inhibits tumor growth and combines well with multiple myeloma standard-of-care agents.

Standard of care agents, like bortezomib, are proteasome inhibitors (PI).  Using a PI results in broad inhibition of the proteasome system across many cell types, not just tumor cells, and thus a high likelihood of side effects.  p97 is upstream of the proteasome and targeting it is more narrow in scope, because MM cells rely so heavily on the protein homeostasis activities of the ERAD pathway.

Hope for Phase 1 success

CB-5083 was also found to enhance the activity of bortezomib both in vitro and in vivo and also was active in bortezomib-resistance models of MM.  This paves the way for a potential combination therapy or another line of therapy if resistance develops as a result of earlier treatment with PIs.   Clinical trials are now ongoing in Phase 1 for patients who have exhausted other medications.  Hopefully CB-5083 makes it to the market soon, if trials prove it to be safe and efficacious, so that oncologists and patients have another weapon in the fight against MM.

Interactive heatmap: nuclease expression in humans (GTEX data)

I worked on a project recently looking at tissue-specific nuclease expression.   I made this interactive heatmap from the enormous GTEX dataset that looks at just nuclease gene expression (in TPM) across more than 50 tissues in the human body.   It’s fun to play around with the interactive plot.   This is the way data should be presented in 2017.   I used the Plotly Python API for the chart.

Unfortunately, Plotly is now nearly $400/year if you want to use it for anything more than a few charts and there is no free option to keep sensitive research data private.  There should be an exception for academic research, but there isn’t as far as I know.

 

Are deep neural nets “Software 2.0”?

Image from: https://cdn.edureka.co/blog/wp-content/uploads/2017/05/Deep-Neural-Network-What-is-Deep-Learning-Edureka.png

Recent blog posts by Andrej Karpathy at Medium.com and Pete Warden at PeteWarden.com have caused a paradigm shift in the way I think about neural nets.  Instead of thinking of them as powerful machine learning tools, the authors  instead suggest that we should think of neural nets, and in particular, convolution deep nets, as ‘self-writing programs.’   Hence the term, “Software 2.0.”

It turns out that a large portion of real-world problems have the property that it is significantly easier to collect the data than to explicitly write the program. A large portion of programmers of tomorrow do not maintain complex software repositories, write intricate programs, or analyze their running times. They collect, clean, manipulate, label, analyze and visualize data that feeds neural networks.   — Andrej Karpathy, Medium.com

I found this to be a dramatic reversal in my thinking about these techniques, but it opens up a deeper understanding and is much more intuitive.  The fact is that combinations of artificial neurons can be used to model any logical operation.  Therefore you can conceptualize training a neural net as searching programming space for an optimal program that behaves in the way you specify.  You provide the inputs and desired outputs, and the model searches for the optimal program.

This stands in contrast to the “Software 1.0” paradigm where the programmer uses her skill and experience to conceptualize the right combination of specific instructions to produce the desired behavior.   While it seems certain that Software 1.0 and 2.0 will co-exist for a long time, this new way of understanding deep learning is crucial and exciting, in my opinion.

 

 

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

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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.