Guest post by Paul Jaffe, MAADDSG@aol.com; coordinator, Manhattan Adult Attention Deficit Disorder Support Group; New York, NY, USA. [7/17/08]

Recently, gene-therapy researchers — who have had their ups and downs — scored a point: the restoration of some vision to patients with retinal degeneration [1,2].

To effect this, they used a generally harmless adeno-associated virus (AAV). The goal was to deliver, to the retina, a functioning copy of a defective gene thought to trigger the illness. This viral “vector” was injected through a surgical procedure deemed reasonably safe.

Within gene therapy, AAV vectoring – which may soon turn a corner [3] — is now standard. This includes a well-publicized effort [4,5,6] — and a less-publicized effort [7] — to treat Parkinson’s Disease.

Unlike the above, which might be termed corrective gene therapy, the PD applications are closer to compensatory gene therapy. Here, the aim is to alter the brain so as to mimic a treatment several steps removed from an underlying pathology.

In each PD clinical trial, a gene has been inserted to encode a specific enzyme. These are:

• glutamic acid decarboxylase (GAD), which catalyzes the synthesis of the neurotransmitter gamma-aminobutyric acid (GABA); and
• human aromatic l-amino acid decarboxylase (AADC or hAADC), which does the same for dopamine (DA).

The vectors are known, respectively, as the AAV-GAD and the AAV-AADC.

The PD research might — or might not — succeed. (One vector is in US Phase II testing; the other, in Phase I.) The question here is: might either be used elsewhere? … Continue Reading »

Thinkgene is on the road this week. Regular postings will resume next week.

Eppendorf International, a lab equipment supplier, created this video to promote their automated pipetting machine. No commentary necessary… though we’d like to reward efforts like this with a relevant, organic blog links.

No. Why?

1. It’s not direct to consumer (DTC), it’s through physicians.
2. It’s a service business, not a product business. (Helix Health’s Services page)

Helix’s non-classification as a competitor isn’t a slight, it merely does not compare well with Navigenics, 23andMe, and deCODEme because it sells services (time), not product.

unit profit by size of business

consulting begins profitable, but becomes unwieldy at scale
products require significant capital to develop, but yields huge returns (scale * profit) if it can scale

Services business begin profitable at full capacity (because people do not work at a loss) but do not scale well because experts have limited time to sell. So consulting businesses like Helix Health may achieve consistent, moderate returns, but they don’t have the potential for monopolistic growth that leads to huge exits. Thus, consulting businesses rarely raise venture capital funding which depend on a few huge exits before their fund’s horizon for profitability.

So deCODEme, Navigenics, and 23andMe are “all or nothing” fence-swingers, while Helix Health is not. So the “crossing the chasm” argument applies less to Helix because its survival doesn’t depend on reaching a general market.

Bottom line: if every American ordered a 23andMe test, 23andMe would be Google. If every American tried to schedule an appointment at Helix Health, there would be a very long line.

23andMe has the most will to succeed, followed by Navigenics, followed by deCODEme. All three have sufficient potential funding, so will (and luck) will most decide who will survive The Chasm.

Yesterday, I mentioned a popular business graph called “The Chasm.” The Chasm is start-up business jargon for the difficultly businesses tend to experience growing from a market of early adopters to the general public. This is because customer motivation changes: early adopters buy because they like new technology, but most people buy because they want to solve problems with minimal effort.

Today, DTC (direct to consumer) genomics is still in its “innovators” market phase, though continued coverage in Wired and regulatory attention suggests that the market is approaching an “early adopter” transition. But which genomics start-ups will survive to cross The Chasm to reap the riches of a greater market?

Two factors keep start-ups alive during tough times:

1. Funding: No money, no payroll, no people. Start-ups, particularly venture-funded start-ups, are profitable until they are bigger, more mature companies.
2. Will: How much does a start-up want to succeed, and what do will its leaders lose if it doest? Does your company have the morale and reputation to recruit talent and investment to beat the competition, weather setbacks, and persist through regulatory struggles?

Consider the “big three” DTC start-ups: 23andMe, deCODEme, and Navigenics. Other competitors are possible, but identifying them is speculation. Further, the recent California “legal lab” crackdown seems to have scared away most other scrappier competitors for now.

I think that all three competitors have ample funding… if they have the will to spend it. I argue that 23andMe and Navigenics have that will, while deCODEme may or may not.

The leaders of 23andMe and Navigenics are most personally and publicly invested in the success of their ventures and thus are most likely to succeed. 23andMe wins the accountability metric because if it doesn’t succeed, it will forever be known as “that Google’s wife’s start-up toy with that disgruntled affy chick.” These women probably do not appreciate being known as such, and are powerful and determined enough to prove otherwise. That alone will keep 23andMe around indefinitely. The rest of the 23andMe team is also well featured on the about page. However, the iStockPhoto slideshow on the 23andMe team page needs replacing.

Navigenics team is also very well featured, even better than 23andMe’s team.

At deCODEme, Kári Stefánsson may publicly represent the business, but he’s the CEO of deCODE. Who is personally accountable for the success of deCODEme itself? On both the old and the new versions of the deCODEme website, nobody is named. The new About deCODEme page does feature a photo of the deCODEme team, but the only names are of deCODE researchers publishing papers, not deCODEme management. (they are pretty nice photos, though)

Further, both 23andMe and Navigenics feature recruitment on their websites and actively advertise positions with third parties (a quick Google search confirms this). deCODEme does not.

Finally, deCODEme’s parent company, deCODE, has not been doing well financially and has never reported a profit. It has recently eliminated many positions, is debt-leveraged, has sold-and-leased its American office, it’s stock price is at $1 and cents from about $28 in 2000, and its CEO has warned of ending operations. All of this is bad for morale, and if more cuts must be made, an unprofitable deCODEme is a likely candidate. I doubt deCODEme will ever be eliminated because it’s obviously Kári’s personal initiative, and as far as I can tell, deCODE is Kári. What’s most likely, if things get bad, is that deCODEme will process orders, but languish without growth or direction as 23andMe, Navigenics, and other competitors continue to hire, grow, and improve.

… Continue Reading »

Two weeks ago, we alluded how one could run one’s own genetic tests.

Now, Wired has grabbed the gene baton and has launched a home genomics how-to guide: Check Yourself for Genetic Abnormalities.

Wired is the banner publication of everything early adopter, but don’t expect much early adoption yet: the day belongs to the scrappy hobbyist innovators. Hobbyists are noisy, and they are more than the few wealthy patrons of yesterday’s DTC (direct to consumer) genomics market, but hobbyists are also notoriously frugal. I predict a flat revenue time-of-trials for DTC genomic start-ups amid a boom of grassroots interest until a bigger market meets a falling price.

The famous market graph from Crossing the Chasm, a staple read in any technology start up or MBA program. DTC genomics is still in its enthusiasts phase, though Wired articles suggest market movement towards early adopters.

Worse, as the graph above suggests, this is merely the first market barrier for genomics. The real business challenge lies between the pre-interested and everybody else. Who of the big three DTC genomics start-ups will survive years of meager profits to consistent losses in this slowing economy and hostile American regulatory environment?

We’ll publish a more in-depth how-to soon. Our goal is that with our guide, the average enthusiast will be able to conduct at least one hobby genomics test. Perhaps, in our own small way, we’ll help the industry by rushing the bloom of geepy*, techcrunch-y publicity into an early harvest of paying customers.

*geepy: adj. “geek cheap,” or of how the well-to-do geek considers paying for new technology an engineering challenge to be surmounted, even at great inconvenience, time, and effort

Bits per Base

A commenter mentioned that they heard nucleotide bases took 7 bits to store.

7 bits is the encoding for ASCII characters, which are used to store literal “A T C G”s in a text editor. There are 4 bases, so one only need 2 bits (2² = 4 bases). These bases could be numbered like this:

00 = A
01 = G
10 = C
11 = T

This encoding further has the convenient property that the bits can be inverted to get the complementary DNA strand. Storing bases as ASCII is OK for small, human readable files, but otherwise, it’s a gross waste of storage, bandwidth, and processor resources (about a 350% waste). This “data inflation” could be much worse if the files are encoded using unicode or other bigger character sets most used in foreign countries.

Abbreviations

Josh, my science editor, will disagree about this because “biologists don’t use the Internet” and “base 2 is for nerds,” but PLEASE, define all acronyms and unit abbreviations in a glossary! I was reviewing “A Short Guide to the Human Genome” by Cold Spring Harbor Lab Press, and in a table of chromosome sizes, the data is measured as Mb (with no explanation).

What is “Mb?”

DATA: “Mb” is “megabits,” which is 2²⁰ bits. Each base is two bits, so 2¹⁹ = 524288 bases per unit “Mb.”

BIOLOGY: “Mb” is “megabases,” which is 1,000,000 bases per unit “Mb.”

Either interpretation is valid, and this is a serious problem as biology and computer science continue to collaborate. If NASA and Lockheed Martin can bungle units at the cost of a $327MM Mars Climate Orbiter extraterrestrial nose dive, you can certainly bungle a genomic experiment due to confused units, too.

There’s a joke that biologist’s don’t have new math, so they invent vocabulary to keep others out of their field. Please do not led credence to this joke.

I don’t say that “biologists don’t use the internet”, but it’s generally not an issue to know whether you’re using megabytes or megabases; the context tells you. If you’re dealing with DNA, it just doesn’t make sense to measure it in computational units of storage (ie megabytes), because this is effectively meaningless. If a segment of DNA is, say, 5Mb, the sentence doesn’t really make sense if you had a 5 megabyte fragment of DNA.

I suppose some people may get confused, but I think generally it’s a non-issue. In that particular book, if it’s targeted to people familiar with the field, they will know what Mb stands for. However, if it’s an introductory book, some explanation probably should be given.

The quote my from gchat logs is:

Josh: I disagree, but whatever. CS corrupted the metric system with base 2

Josh:

I recently noted in Napster of Medicine that an entire human genome would fit on a music CD.

How much data IS a human genome?

• 2 bits per base (4 bases = 2²)
• 3,080.4 Mb per human genome [1]
• 700 MB per CD-ROM

(1 human genome) *
(3,080,400,000 bases / 1 human genome) *
(2 bits / 1 base) *
(1 byte / 8 bits) *
(1 MB / 1,048,576 bytes) =

734.4 MB per uncompressed human genome. Easily enough to fit on a 700 MB with basic file compression like gzip.

Actually, while writing this post I invented a technique to get the file size down to about 10MB, but I need to file a patent before disclosing. Sorry. (yes, 10MB, as in, the size of an mp3 song)

NOTE: Commenter “neandrothal” noted that this is the size of a haploid human genome. Humans are diploid: they two of each autosome and two sex chromosomes. So this is the size of a reference haploid human genome, not a complete human individual genome, which would be twice as much data. (2 music CDs) Thanks, neandrothal!

[1] Scherer, Stewart. 2007. A Short Guide to the Human Genome. 6.

MB = megabytes
Mb = megabase