No. of Recommendations: 2
First, Greg, I want to thank you again for the opportunity to read your research. If I had to describe the experience in one phrase, I'd say "Informative delight!"

I had to work pretty hard to come up with criticisms. But since I owe you, here's what meager offerings I have:

Pg 11-12 I don't think it affects the point(s) you're making, but for the sake of accuracy: The explanation of ESTs is a bit misleading/incorrect. DNA->RNA->Protein. The amino acids in a protein are encoded by onlu a SUBSET of the nucleotides in the mRNA (post translational processing/modification occurs). Similarly, RNA sequence represents only a subset of nucleotides in the DNA encoding a gene and its associated elements (promoters, enhancers, other possibly useful 'intronic' sequence such as splice signals, etc.). To make a cDNA library, one isolates RNA, then reverse transcribes it to DNA. Thus, a cDNA library represents the DNA that's 'expressed' in whatever tissue you isolated the RNA from at the time you did it. ESTs are partial sequences dervived by PCR from the cDNA. In other words, ESTs are partial cDNA sequences. ESTs are a quick easy way to get 'representative' sequences from a library. And if something you're interested in matches an EST, then typically you'd want to sequence the cDNA, or better/in addition, have the genomic sequence. Software allows us to predict RNA (and thus cDNA) and protein sequence from genomic sequence, but it's impossible to go backward. You imply the opposite at the bottom of pg 11, start of pg 12. It's sort of like trying to recreate a book from excerpts of excerpts, all the while knowing FOR SURE that the excerpt transcribers routinely (and not randomly) leave out words, phrases, or whole sections. Ideally, you want genomic sequence (the original book) AND the cDNA library (the excerpts) to fully know what's going on.

Pg 12-13 I agree with your overall point, that signaling molecules are bad drugs, but the reasoning and process seems a bit off. Firstly, I would argue that the actual signaling proteins would be, in general, more specific than drugs, which would have the same distribution problems in addition to lack of binding specificity. You are right that it's a pain to deliver therapeutic proteins, but these sorts of problems are also shared by 'conventional' drugs. Ideally, one would like to induce expression of the native signaling molecule gene. And that brings me to the point: The actual genes/molecules that are discovered are not only important as potential drugs, but as potential drug TARGETS. The way the science on a novel protein would actually likely occur is not as you describe, but rather the putative signaling molecule would serve as a target for drug screens. When a drug was found that enhanced the signaling molecule's action, then that's as good as using the molecule itself. And it's specific because if it only works on that molecule, then it can obviously only work where that molecule is already. An alternative approach, more in line with the future of genomics, is to somehow enhance expression of the gene as I mention above. In this case, the DNA (the promoter, perhaps) or more likely a transcription factor (which controls/aids gene expression) is the target. If we're dealing with a genetic disease where the native gene is mutant, then the options are either bypass that gene's function pharmacologically or try true gene therapy, where the native gene is 'replaced'. The options would depend on the particular gene and its function.
That said, you manage to pick several example drugs (fig 7, pg 13) that actually ARE proteins and are very successful (which makes the negative discussion of protein drugs seem weird). This approach is not necessarily 'new'. Insulin is an example of a 'protein' drug that's been around a long time. However, genomics should greatly facilitate the creation of potential therapeutic recombinant protein drugs. But…

I am a bit of a cynic (realist?) when it comes to pharmaceutical benefits derived from genomics, since mostly genomics provides just more potential drug candidates and targets. And the state of things, unfortunately, is that screening all the existing drug candidates and/or using new targets is the main bottleneck right now for most companies. Because of these practical constraints, the benefits of genomics are indirect: a company still will require a lot of basic science into disease mechanisms, but genomics will open unexplored possibilities there. The drugs you use as examples in figure 7 are all basically copies of what would have been called 'factors' 50 years ago. And all were created knowing how the specific gene product already worked. Advances in gene therapy or antisense therapy, which basically do an end-run around traditional pharmaceutical approaches, could change this situation. Seriously: if I saw a sudden desire for a genomics-oriented pharmaceutical company to get into antisense big time, I'd very much perk up.

The patent discussion on pgs 13-16 is superb and very helpful. God, it's wonderful to read such a nice explanation from an obvious expert.

About pg 17, I began to wonder about whether it IS prudent to gamble with regard to the politics and just bet on the company with the most patents covering the greatest number of genes, assuming that the chance that a recombinant version is useful or the gene product makes a good target is fairly unpredictable and therefore effectively random. If so, this would imply that the number of patents is clearly the best indicator of long-term (>10-15 years) success. What do you think?

Pg 17 on is pretty much uniformly excellent. The only suggestion I can make is that you provide some sources of this and updated info, tips to help us do similar research on another company.

Overall: Very interesting, excellent work. I think you've easily met your stated goal of providing the new biotech investor with some guidelines for choosing companies and an example of how DD should be done. It comes across as a labour of love by a smart, well-informed guy. I have not read better from the 'Wise'.
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