A Revolution in R&D
A Revolution in R&D
A Revolution in R&D
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42<br />
POTENTIAL SAVINGS—FROM THEORETICAL TO PRACTICAL<br />
In the first two chapters of this report, we assessed<br />
the potential sav<strong>in</strong>gs for the two waves of the<br />
genomics revolution: first, the substantial sav<strong>in</strong>gs<br />
atta<strong>in</strong>able through genomics technologies; second, the<br />
greater but far less certa<strong>in</strong> sav<strong>in</strong>gs atta<strong>in</strong>able through<br />
genetics approaches, notably disease genetics and<br />
pharmacogenetics. Those assessments show the high<br />
end of the achievable range, and they view the two<br />
waves s<strong>in</strong>gly rather than jo<strong>in</strong>tly; that is, they <strong>in</strong>dicate<br />
discrete and best-case scenarios, which will be difficult<br />
for companies to realize <strong>in</strong> practice and impossible<br />
to comb<strong>in</strong>e.<br />
A more <strong>in</strong>tegrated assessment needs to average out<br />
the achievable range—to take account of worst-case<br />
scenarios too. And it has to analyze the various likely<br />
comb<strong>in</strong>ations of approaches from the two waves,<br />
rather than treat<strong>in</strong>g genomics and genetics<br />
approaches <strong>in</strong> isolation.<br />
Accord<strong>in</strong>g to the comb<strong>in</strong>ation selected, the R&D<br />
value cha<strong>in</strong> as a whole will assume a particular new<br />
form and favor a particular subset of potential drugs.<br />
That is a crucial consideration for a company engaged<br />
<strong>in</strong> build<strong>in</strong>g a portfolio of technologies: the more com-<br />
DRUG R&D VALUE CHAIN ACTIVITIES<br />
Biology<br />
Target ID Target Optimization Validation<br />
Genomic target discovery<br />
Chemical genomics target discovery<br />
Genetic target discovery with pathway analysis<br />
Chemistry<br />
Screen<strong>in</strong>g Optimization<br />
In silico<br />
Traditional<br />
b<strong>in</strong>ations of approaches, the greater the company’s<br />
versatility <strong>in</strong> pursu<strong>in</strong>g different drug subsets.<br />
Draw<strong>in</strong>g once aga<strong>in</strong> on our economic model, we<br />
have exam<strong>in</strong>ed the impact of each feasible<br />
genomics-based approach to target discovery, augmented<br />
by pharmacogenetics and genomics technology<br />
whenever possible. And we have estimated the<br />
realizable value <strong>in</strong> each case: first, by calculat<strong>in</strong>g the<br />
cost, time, and added value for each possible comb<strong>in</strong>ation<br />
of approaches, and then—adjust<strong>in</strong>g for the<br />
percentage of targets each approach is able to<br />
process—by calculat<strong>in</strong>g a weighted average per<br />
drug. The result is three broad scenarios:<br />
• A genomics-based approach: <strong>in</strong>dustrialized target<br />
identification, supplemented where applicable by<br />
downstream genomics technologies (<strong>in</strong> silico<br />
chemistry, <strong>in</strong> silico toxicology, <strong>in</strong> vitro ADME, surrogate<br />
markers, and pharmacogenetics)<br />
• Chemical genomics: <strong>in</strong>dustrialized target identification,<br />
followed by chemistry and traditional validation<br />
conducted <strong>in</strong> parallel, and supplemented<br />
where possible by the downstream technologies<br />
just listed<br />
30%<br />
70%<br />
Development<br />
Precl<strong>in</strong>ical Cl<strong>in</strong>ical<br />
Genomics:<br />
In silico/<strong>in</strong> vitro tests, surrogate markers<br />
Pharmacogenetics<br />
Genomics and pharmacogenetics<br />
Traditional<br />
73%<br />