There is an interesting new post at “In the Pipeline” that summarizes the performance of Google’s “big data” project to track flu trends from search terms. In short, the predictive performance appears to be pretty bad so far, at least compared to what you might have expected given the hype around “big data.” The author raises some key points, including the importance of high-quality data, even in very large datasets. I particularly like this analogy:
“The quality of the data matters very, very, much, and quantity is no substitute. You can make a very large and complex structure out of toothpicks and scraps of wood, because those units are well-defined and solid. You cannot do the same with a pile of cotton balls and dryer lint, not even if you have an entire warehouse full of the stuff.” –In the Pipeline, March 24, 2014
Data filtering and modeling approaches will likely continue to improve, however, and I think this project is worth watching in the future.
I have noticed that rigid docking methods, even when run with high-precision force fields, don’t always capture the correct poses for your true positives. Sometimes a hit will be docked somewhere other than into the site that you specified because the algorithm could not fit the molecule into the rigid receptor. This will cause true positives to be buried at the bottom of your ranked list.
You may want to try introducing receptor flexibility to improve the poses of your true positives. There are two main ways to do this: scale down the Van der Waals interactions to mimic flexibility (i.e., make the receptor atoms “squishy”) or use induced-fit docking (IFD) methods. I have found that while setting a lower threshold for VdW scaling can rescue false negatives (poorly docked true binders), at least in one case, it does not improve the overall ranking of all of the true positives. So it is not a panacea.
Induced fit methods work by mutating away several side chains in the binding pocket, docking a compound, mutating the side chains back, and energy minimizing the structure. Then the compound is re-docked to the minimized structure using a high-precision algorithm. There are two main applications for IFD: (1) improving the pose of a true positive that cannot be docked correctly by rigid docking and (2) rescuing false negatives.
My experience has been that IFD improves the docking scores of true positives and false positives by about the same amount, so the value of running the method on an entire library remains unclear. However, there is much value in running IFD on a true hit where you are not sure the rigid pose is optimal. Often, the improvement in the shape complementarity and number of interactions will be dramatic.
Also, you can use the alternative receptor conformations generated by IFD to a true positive to rescreen your library with faster rigid docking methods. If you are screening on a prospective basis, this approach could help you identify other chemotypes that may bind well but are missed in a first pass rigid docking screen.
It seems intuitive that there would be a large market for new, highly-effective antibiotics. Doctors are warning publicly about the waning effectiveness of today’s antibiotics owing to over-prescription and increased drug resistance.
The linked article even mentions that a course of action could be to provide government incentives to the industry to make new antibiotics. But where the market creates a profit potential, why would government incentives be necessary in the first place?
I had never heard a suitable explanation for this situation until recently, in a conversation, the following theory was advanced: if new wonder drugs are developed, they will be “held back” by doctors seeking to establish last-line-of-defense antibiotics, and will therefore not be heavily prescribed, dramatically limiting profitability.
Does the above explanation make sense? Is there more to the story? Share your thoughts in the comments below.
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.
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.
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).
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 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.
