Why you should think exponentially to grasp the future of medicine

People often assume that the world tomorrow will be pretty much like the world today.  We all have an in-built bias towards linear thinking when we ponder the future.  Although a linear bias was helpful for thousands of years of our evolution, today technology is changing at an exponential pace and in order to better anticipate future market opportunities and technology’s impact on society, it is crucial to think in terms of exponential trends.  This is a point that renowned futurist Ray Kurzweil has made in his many books and speeches for the last several decades. 

We all have an in-built bias towards linear thinking when we ponder the future.

One example of an exponential trend in biology (among many) is the cost per genome sequence (graph below).  As recently as 2001, the cost to sequence a genome was an astronomical $100M.  Between 2001 and 2007, the cost decreased exponentially (a straight line on a log plot), to the point where a genome in 2007 cost only $10M to sequence.  Around 2007, a paradigm shift in technology massively accelerated this exponential process, and the cost decreased even faster than before, hitting just $10K in 2012.

sequencingcosts

The dramatic, exponential gains in price/performance of sequencing technology have unleashed a tidal wave of sequence data.

As economists are fond of saying, when the price falls, more is demanded.  As a result of this massively reduced sequencing price, many more partial and complete genomes are being sequenced than ever before.  The dramatic, exponential gains in price/performance of sequencing technology have unleashed a tidal wave of sequence data.

Rethinking drug action: activating an ion channel to treat Cystic Fibrosis

In my first “Rethinking Drug Action” post, I described how researchers are seeking activators of PARK9, a protein that is mutated in Parkinson’s Disease.  In a similar manner, Ivacaftor, a new drug for Cystic Fibrosis (CF), shifts the paradigm from treating CF symptoms to therapeutic treatment of the underlying cause of the disease: defects in the activity of the CFTR ion channel owing to genetic loss-of-function mutation.

The molecular structure of Ivacaftor (Kalydeco).
The molecular structure of Ivacaftor (Kalydeco).

In this case the mutation is the rare G551D variant (4-5% of all CF patients) that makes CFTR non-responsive to ATP-dependent channel opening.  The more common delta-F508 CFTR mutation is thought to prevent membrane expression of CFTR through misfolding, and indeed, clinical trials showed that ivacaftor alone had no effect on patients with this mutation.

Ivacaftor, a new drug for Cystic Fibrosis (CF), shifts the paradigm from treating CF symptoms to therapeutic treatment of the underlying cause of the disease

However, for patients with the G551D mutant, where CFTR does reach the membrane but is less active than WT, the drug is very efficacious.  In a clinical trial, patients who received ivacaftor were 55% less likely to experience pulmonary exacerbation (defined as a worsening of lung function owing to infection or inflammation) after 48 weeks on the drug.  Other markers of CF were also improved during this period.

The exact mechanism of action of ivacaftor is not known. Interestingly, however, ivacaftor enhances spontaneous ATP-independent activity of both G551D-CFTR and WT-CFTR to a similar magnitude.  In a recent PNAS paper, researchers propose that ivacaftor affects both WT and G551D in the same way, namely by shifting the equilibrium from the closed (C2) state towards the open2 (O2) state, in essence, “wedging” CFTR open.

Proposed mechanism of CFTR gating from the PNAS paper cited below.  Ivacaftor is thought to stabilize the O2 form over the C2 form.
Proposed mechanism of CFTR gating from the PNAS paper cited above. Ivacaftor is thought to stabilize the O2 form over the C2 form.

In the same paper, the researchers propose that the CFTR transmembrane domains (TMD) may be the site of binding for the drug.  In support of this, they note that the drug is relatively hydrophobic and is measured to increase gate opening times regardless of whether it is applied from the cytoplasmic or extracellular side, suggestion membrane permeation and binding to the TMDs.

In a clinical trial, patients who received ivacaftor were 55% less likely to experience pulmonary exacerbation

More studies are needed to prove this mechanism, but it will be very interesting to see how this paradigm-shifting new drug works on the molecular level.  In addition, other compounds are in development that aim to enhance the folding and membrane expression of the more common DF508 mutation.  Perhaps combination therapy with new compounds for DF508 and ivacaftor together will aid those CF patients who currently are not helped by ivacaftor alone.

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

Antifungals and the urgent need for biofilm-specific drugs

Last year I had the opportunity to help write a business plan for a startup company that is taking on a very difficult challenge: finding novel antifungal treatments that target the biofilm, rather than free-living, form of fungal infections.

This is important because recent estimates by the NIH indicate that biofilms are responsible for over 80% of all microbial (bacterial and fungal) infections. For both structural and genetic reasons, biofilms are inherently resistant to antimicrobial therapy and host immune defenses.

Systemic biofilm infections are most frequently seeded from biofilms formed on mucosal surfaces or implanted medical devices, such as catheters. In fact, biofilm-based infections on catheters are the most serious and prevalent life-threatening consequence of biofilms, resulting in systemic invasive infections.

Existing antifungal drugs aim to kill C. albicans, a major fungal pathogen of humans, but they have significant disadvantages:

1) Inefficient at eradicating C. albicans existing in the resistant biofilm-form.

2) Disrupt the intricate microbial balance within the gastrointestinal tract, allowing for other microorganisms to flourish.

3) Can cause nephrotoxicity in the dosages required to have some effect on the biofilm.

Current treatments for fungal biofilm-based infections are ineffective at destroying the biofilm reservoir, and novel therapeutics specifically designed to target the biofilm are desperately needed to treat these prevalent infections.

In a ground-breaking 2012 Cell paper, Nobile and colleagues identified the transcriptional network controlling the process of C. Albicans biofilm formation.  It consists of six transcriptional regulators and over 1,000 target genes (40 of which are predicted to be highly druggable).

Importance of list comprehensions in Python

A beginner to python programming is usually taught to use for loops to do a lot things. The temptation  is to bust out a for loop whenever you need to modify a list or string object, but this quickly leads to complex “loops within loops” code architectures that are hard to read (by humans), even though they may work OK.

A simple example:

>>>test_list = [2,4,6,8]

>>>for x in test_list:

…     new_list.append(x  + 1)

>>>new_list

[3,5,7,9]

A better approach is to take advantage of Python’s built-in list comprehension expressions, with the form ‘x for x in y’.

Example:

>>>new_list = [x+2 for x in test_list]

>>>new_list

[4,6,8,10]

This can be expanded to include conditionals, for example:

>>>stripped_list = [line.strip() for line in line_list if line !=””]

You can also loop over multiple elements like this:

>>>seq1=’abc’

>>>seq2=(1,2,3)

>>>[(x,y) for x in seq1 for y in seq2]

[(‘a’,1),(‘a’,2),(‘a’,3),(‘b’,1),(‘b’,2)….(‘c’,3)]