The question here, really, is what have we done to democracy? What have we turned it into? What happens once democracy has been used up? When it has been hollowed out and emptied of meaning? What happens when each of its institutions has metastasised into something dangerous? What happens now that democracy and the Free Market have fused into a single predatory organism with a thin, constricted imagination that revolves almost entirely around the idea of maximising profit? Is it possible to reverse this process? Can something that has mutated go back to being what it used to be?

While the wording is perhaps a little extreme, Roy brings up an interesting question: what happens to an idea when its practice has drifted far from its original ideals, motivations and practices? The various medical industries in the United States, be they concerned with bio-tech, pharma, med-tech or healthcare, have grown into one of the most conservative and profit-driven sectors in the marketplace. This is all somewhat odd given the very fundamentally social nature of health work in general, but what does this mean for the future?

The interplay between pharamaceutical companies, hospitals and the health insurance system has yielded a care-delivery infrastructure that is grotesquely inefficient. Medical technology and pharma/bio-tech companies have grown tremendous patent portfolios in an attempt to stave away competition and a focus on high-margin products with proprietary reagents has left many areas of need (diseases and geographic regions) woefully neglected. Even technology development programs with a focus on technologies for the developing world follow the same patterns: a focus on controlling intellectual property and exclusive licensing to private-sector partners. While these systems have undoubtedly seen some success, one must wonder whether there mightn’t be a better way.

Why can’t we encourage competition rather than fight it? More players in the market means lower prices and more innovation as a necessity. Why can’t publicly funded research be placed into the public domain? Taxpayer dollars paid for the technology, removing a great deal of the risk that patents normally are meant to reward. Why do we start businesses in this space to maximize financial return? Shooting for financial sustainability and focusing on social return would encourage business practices and technology development that, in the end, would reach the most people quickly and cost-effectively. Isn’t that why we do what we do?

The truth is, there’s no real reason why we can’t do these. The infrastructure that currently exists to start and fund technology development and care delivery efforts are heavily biased to support the current standard, but that only makes things difficult for entrepreneurs- not impossible. Besides, if these things were easy, well, where would the fun be in that?

The dropping reagent costs are promising, but the high cost of instrumentation is still something that’s remained constant across the board when it comes to diagnostic instrumentation. What can we do to get these tools to the people who need them?

Still, a low-cost PCR test would be a big deal, the problem is that Cepheid’s test is most definitely not a low-cost test. In fact, the cheapest PCR readers that Cepheid sells will cost you about $27,000 a unit. You could buy a flow cytometer for that much and you’d save a lot more lives using that kind of cash by doing CD4 counts than TB diagnostics when there are simpler, cheaper solutions for TB already out there.

Interestingly enough, this was developed in collaboration with FIND, a Gates-funded non-profit working on diagnostic technologies for developing countries. This really casts a lot of doubt in my mind about FIND’s priorities when it comes to developing world diagnostics. FIND’s product pipeline for TB diagnostics limits itself to three approaches: squeezing incremental improvements out of microscopy, making better and cheaper PCR/NAAT based tests and developing antibody-based rapid tests.

I think there’s a huge gap between the first category and the second two in terms of their “appropriateness” for the countries they’re trying to help. Microscopy is dirt cheap, its a relatively poor diagnostic test but it scales very easily. Incremental improvements will help, but really, as a diagnostic test its in bad need of an overhaul. PCR/NAATs and antibody tests suffer from a lot of draw-backs, mostly centered around their costly, proprietary, short-lived and temperature sensitive reagents (I shudder to think of how long PCR probes would last at 40C).

As second-line tests, these approaches may bear significant fruit, but the lack of effort in developing the $1 screening test that’s badly needed to replace the aging microscope is extremely troubling. Without an easy-to-use, scalable and cost-effective first-line diagnostic, we will never be able to bring the TB epidemic under control. No amount of amazing species differentiating, drug-resistance detecting, super sensitive diagnostic tests will be able to make a significant, sustainable impact in these markets if their prices can’t drop an order of magnitude or more over the next few years.

Naman pointed me to a really interesting study out of Hopkins on evaluating the cost-effectiveness of potential new TB diagnostics tests. Since I’m currently developing one of said potential new TB diagnostic tests, this was of particular interest.

Global Health is really an engineering field: its all about optimization. With a limited set of resources and finite demand and a limited set of tools available to us, what is the most efficient way to spend our resources to maximize impact? Granted this depends on your definition of impact, but thankfully the DALY is a pretty well accepted metric when it comes to infectious diseases.

In the case of TB, we have a fairly narrow assortment of tests: sputum smear microscopy, mycobacterial culture and PCR. Some folks (I’m looking at you, FIND!) are working on antibody based rapid tests, but these aren’t due out for 2-3 years, optimistically speaking. When you add latent TB to the mix, a couple other techniques come into play including products based on ELISA and ELISPOT, but we’ll leave out latent TB for now.

The three cornerstones of TB diagnostics (yes, yes, I’m leaving out chest X-rays, but that’s screening not diagnostics. Any… stuff… in your lungs will show up right away, but without any of these other techniques, you’d never know if it were TB or nearly any other pulmonary disease) each have their own challenges:

Microscopy is, well, microscopy. Its manual, time-consuming and error-prone. There’s some push to phase out bright field microscopy for fluorescence, but the improvement is relatively incremental. You’re still fundamentally limited by the human operator and how much time he or she is willing to put into scanning a smear at 1,000x (bright field) or 450x (fluorescence) magnification. The up-side? Its the cheapest method we have at around $1.45 a test and even though its pretty hard to become a master microscopist, it doesn’t take much to become proficient.

PCR is God’s Gift to the Geneticists. Its also an amazingly powerful diagnostic tool. The problem? Its way too expensive- if the developing world were to perform as many PCR tests as they do sputum smears it would cost over $4 Billion at $50 a pop… 4x current global TB control expenditures. Infrastructure is also a bit of a problem since reagents are typically temperature sensitive and the assay’s need pretty clean water (heaven forbid any DNase gets in there!).

The basic premise behind culture is fairly straightforward: if you don’t have enough bugs in the sputum to see easily, give em some food and let them grow till there are enough! Unfortunately, M. TB happens to be one of the slowest growing microorganisms out there with a life cycle of about 15-20 hours so it can take up to 6 weeks for enough to grow out… yikes. The good thing is that TB doesn’t kill you very fast and while culture takes a pretty good amount of infrastructure to support, its a lot less than PCR and each test weighs in at around $3-4 per test. On the other hand, every day that a patient walks around un-diagnosed could mean dozens of future cases, not the ideal situation.

So what are Hopkins’ thoughts on this? Basically, if you were to try to introduce a high specificity (95%) test with a fairly high per-test cost ($20) that is somewhat more sensitive than microscopy (70%) its not going to be very cost-effective. In fact, they found that specificity coupled with price was the single most important factor in a new diagnostic test being able to beat the cost-effectiveness of microscopy. Find the people that are very sick very quickly, get them out of the public and on treatment before they spread more and you’re going to get the most bang for your diagnostic buck. Good advice that FIND, the WHO and the Gates Foundation need to hear more of: PCR and antibody tests may be extremely impressive, but until they can become drastically cheaper they’re still likely to be beat out by a cool blue LED and a $20 camera sensor.

Nature published an interesting commentary on the role of patents in genetic testing that was authored last week by several faculty from Duke University. At first intrigued, I was fairly disappointed with the lack of data presented in the article (I guess that’s the difference between a paper and a commentary) but more by the lack of a strong call to action.

The authors discussed a study that showed how unlike pharmaceuticals, the pricing and availability of genetic tests seem to be unaffected by patents. The authors then went on to posit that the key problem with pricing is not IP but the lack of insurance coverage for genetic testing. “Prices would matter less if everyone in the United States were insured for genetic diagnostic tests.” This is a rather dangerous statement to make, especially given the blooming cost of both direct health care and insurance.

Ok, so it all boils down to mutually blocking patents. We see this today in the software industry where the industry giants have enough cross-blocking patents that if the lawyers had their way it would have erupted into a legal bloodbath a while ago. Thankfully, MAD apparently works. The weird thing with genetic testing patents is that biotech-related patents are a lot clearer and easier to enforce than software parents, even though these same companies tend to patent technology in a more focused manner. The question is: what happens next? Do we see widespread patent cross-licensing? Do the legal departments start to flex their muscles? Its a complicated issue. The authors say that universities need to be more transparent about the patents they license out and to whom. while I agree that transparency is the first step towards unraveling the cross-blocking quagmire, there really needs to be more responsible action by Universities in licensing their discoveries and inventions.

IP is really in a sorry state these days in the US. I think its time the open-source movement hit health care.

The latent infection itself is not directly a health concern- it is the risk of conversion, which occurs in an estimated 5% of patients, from a latent to an active infection that worries most health professionals in the developed world. With an estimated prevalence of about 10-15 million individuals in the US alone, this represents over 500,000 potential future active cases even without direct contact or transmission. These numbers are pretty rough estimates though, when it comes to diagnosing latent TB we’re pretty much flying blind. Almost everyone is familiar with the Tuberculin Skin Test (TST), the standard for diagnosing latent TB. By injecting a cocktail of proteins from sterilized cultures just under the skin, we expect to see a patient that has been exposed to TB to mount an immune response and cause swelling at the site of the injection. Given the complexity of the human immune system and how little we really know about it, it isn’t surprising that the test isn’t very reliable. Everything from the BCG vaccine to exposure to non-TB mycobacteria can throw off the results of the test.

Unfortunately, most modern latent tests haven’t improved significantly over the TST like how PCR is revolutionizing active TB diagnosis in the US and other developed countries. Some technologies based on using ELISA to monitor production of the cytokine interferon gamma (IFN-γ) by immune cells in human blood have been brought to market and approved for use. The problem is an extremely complex one, however, and breakthroughs are likely to hinge on a better quantitative understanding of the immune system itself. According to Dr. Stout, while these tests do seem to fare better in patients that have had a BCG vaccination, the root problem still exists.

We don’t know how many people have latent TB. We don’t know how good our tests are because we have no way of knowing how many patients our tests miss. Our tests rely on a somewhat arbitrary cut-off that patients often fluctuate around. In the end, developing a sane policy for dealing with the danger that latent TB poses is going to depend heavily on developing innovative and effective new diagnostics for the infection.

• $3B NSF
• $2B NIH
• $1.9B DOE
• $600M NASA
• $300M NIST

The investment obviously focuses heavily on the Big 2 (Energy and Health), with a substantial amount of funding to the NSF that is sure to reach a broader audience. As someone working in medtech, however, the $2B to the NIH is a little disappointing when compared with some of the numbers further down in this post. That being said, I have every confidence that the NIH will still be able to support some amazing work with that money. It will be interesting to see whether the money that may go to NASA will support their core scientific mission or help expand their support of commercial space ventures like SpaceX and Orbital. I think either scenario is win-win for the broader community of scientists and engineers- not to mention the bulk of humanity (in case we, you know, ever need to leave this place).

It seem’s that Nathan Lewis’ concerns about the skewed funding of Health over Energy may be assuaged by this bill. Some $24B are going to be directed toward funding development of next-generation energy technologies like a high-efficiency grid ($11B, as a shameless plug to my alma mater see: FREEDM), home energy efficiency ($9B), efficiency and renewables research ($2B) and advanced batteries research ($2B).

This is certainly a fairly aggressive investment, and while one can say that STEM always could use more, its certain to deliver huge returns as funding starts to become available and plentiful again. All in all, these numbers still represent only 3.85% of the total bill and its not hard to imagine that it will also be the single most successful group of investments in strengthening the economy and America’s commercial competitive potential in the long run.

There’s been a lot of coverage of a technology out of UCLA called the Lensless Ultra-wide-field Cell monitoring Array based on Shadow imaging (LUCAS) in the media. Wired’s coverage say’s the UCLA group has “modded a cellphone into a portable blood tester capable of monitoring HIV, malaria, leukemia and detecting diseases.” With any technology, especially medical technologies, it is important to understand really what has been achieved rather than the (often misguided) media coverage. LUCAS is essentially a microfluidic chip built directly onto an image sensor (the marketing material shows a cell-phone camera, but the papers describe a higher end CCD array) that images the shadows of cells passing over it.

The first paper describing LUCAS intended it to be a method of counting a homogeneous population of cells with the idea being that some sort of affinity column would be used to sort, say, CD4+ T-cells from blood for HIV/AIDS monitoring. There’s a number of potential problems with chromatographic separation of CD4 cells, although the concept does sound promising. The key point here is that there are a number of simpler, cheaper, and just as effective for counting cells in a homogeneous solution. A Coulter counter, perhaps? A pair of electrodes is a great deal more robust and cost-effective than a CCD array and a simple comparator is a great deal easier to implement than a live-video blob tracking algorithm.

The second key paper, perhaps following the realization that using a CCD array and an image processing algorithm to count particles might have been a bit overkill, sought perhaps to overcome this limitation by looking at diffraction patterns from various particles. While interesting at first, there’s still some glaring issues with this not the least of which is that you’re only going to see morphological differences between cells. In the case of CD4 T-cells where the only difference between your cell of interest and other T-cells is a single protein, you can see why that might be a problem. It seems Ozcan’s group noticed it was a problem too since their proof-of-concept was that they could differentiate between yeast cells, red blood cells, and polymer microbeads.

I don’t mean to disparage the engineering that’s been done here; the technology is nifty and I’ll be looking forward to seeing some cool applications. I just wish the UCLA press folks and other science journalists would do some more homework on what it actually takes to monitor diseases like HIV/AIDS and malaria before proclaiming every single piece of hardware designed for a biological application to be a magical tool that will diagnose every exotic disease we’re afraid of.