Drug dis­cov­ery in the age of coro­n­avirus

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De­vel­op­ing new drugs is in­cred­i­bly hard. That’s why, de­spite su­per­hu­man ef­forts from the in­dus­try, we’re still look­ing at 12-18 months min­i­mum be­fore we can re­al­is­ti­cal­ly hope for a vac­cine for Covid-19, and prob­a­bly months be­fore there’s a proven vi­able drug treat­ment.

But our in­creas­ing abil­i­ty to be­gin to in­dus­tri­al­ize the drug dis­cov­ery and de­vel­op­ment process through an en­gi­neer­ing ap­proach means that we have more hope for speed­ing up this process than ever be­fore — and not just to de­feat coro­n­avirus, but to ben­e­fit the de­vel­op­ment of all new med­i­cines in the fu­ture.

The tra­di­tion­al drug de­vel­op­ment process can be bro­ken down in­to two big “tracks” which have changed very lit­tle for decades: pro­phy­lac­tics (like vac­cines) which pre­vent you from get­ting sick; and ther­a­peu­tics (like an­tivi­rals) that help you get bet­ter once you have symp­toms. Nei­ther is easy.

For vac­cines, that usu­al­ly looks like first iden­ti­fy­ing the cor­rect “dead” part of the virus (anti­gen) so that our im­mune sys­tem can de­vel­op the right an­ti­bod­ies; then test­ing for safe­ty and ef­fi­ca­cy (how long do your an­ti­bod­ies last?); then man­u­fac­tur­ing at scale (no mean feat; think about all those flu vac­cines grow­ing in eggs each year!).

De­vel­op­ing ther­a­peu­tics is just as hard, re­quir­ing a deep knowl­edge of the un­der­ly­ing bi­ol­o­gy, in­clud­ing the right tar­get to go af­ter with just the right small mol­e­cules or bi­o­log­ics, with high ef­fi­ca­cy and low tox­i­c­i­ty again demon­strat­ed in clin­i­cal tri­als … and so on. You see why it can take years to un­der­stand all of this — some­times even decades.

But us­ing an en­gi­neer­ing ap­proach to de­vel­op­ing new drugs with the tools we have com­ing on­line to­day is al­ready trans­form­ing this process, mak­ing it faster, more ef­fi­cient and in­creas­ing the odds of suc­cess. A big part of this is us­ing tech­nol­o­gy to au­to­mate and stan­dard­ize how we un­cov­er new knowl­edge about bi­ol­o­gy — the in­dus­tri­al­iza­tion of dis­cov­ery it­self.

Biotech com­pa­nies are do­ing this by build­ing ro­bot­ic wet lab ex­per­i­ment pipelines with au­toma­tion + bioin­for­mat­ics + da­ta sci­ence for rapid mea­sure­ment and analy­sis of in­for­ma­tion in a ful­ly in­dus­tri­al­ized process. So the se­quenc­ing of a virus (now cheap and quick, due to 20 years of ad­vances in se­quenc­ing tech) im­me­di­ate­ly feeds in­to bioin­for­mat­ic tools that iden­ti­fy the key parts of the genome; bioin­for­mat­ic analy­sis in turn speeds up new ideas for how to tar­get the virus, whether in a vac­cine or ther­a­peu­tic vac­cines; new drug can­di­dates are moved in­to ro­bot­ic test­ing mas­sive­ly, and in par­al­lel; and the en­tire process to hu­man clin­i­cal tri­als is loaded up with more good can­di­dates, faster.

This un­der­ly­ing ap­proach is why Mod­er­na was able to come up with a po­ten­tial coro­n­avirus vac­cine at a speed that blew most in­dus­try es­ti­mates out of the wa­ter. In­dus­tri­al­iz­ing dis­cov­ery like this could work much the same way that fac­to­ry work­force vs. hu­man speeds things up, stan­dard­izes process­es, and helps us scale faster and more broad­ly. It al­so great­ly im­proves re­pro­ducibil­i­ty, a huge is­sue in drug dis­cov­ery ex­per­i­ments when even the way you hold the pipette can af­fect the na­ture of the ex­per­i­ment. Now, re-run­ning an ex­per­i­ment starts to look a lot like re-run­ning code — again, eas­i­er, faster, and more ac­cu­rate.

An­oth­er crit­i­cal el­e­ment of the in­dus­tri­al­iza­tion of vac­cine de­vel­op­ment is our new abil­i­ty to use RNA. In­stead of giv­ing you part of the vi­ral pro­tein and say­ing, hey im­mune sys­tem, learn this, an RNA vac­cine gives you RNA code (akin to soft­ware) for your body to make those vi­ral pro­teins it­self, and then de­vel­op an­ti­bod­ies.

Why both­er with this RNA mid­dle man? RNA is re­al­ly, at heart, in­for­ma­tion, and ac­tu­al­ly very easy chem­i­cal­ly to pro­duce — so this is ef­fec­tive­ly scal­ing pro­duc­tion by us­ing your own body as the pro­tein pro­duc­tion fa­cil­i­ty in­stead of a lab mak­ing the pro­tein — syn­the­siz­ing them, ex­press­ing them, grow­ing them in, say, eggs for an en­tire pop­u­la­tion, all of which is slow and dif­fi­cult.

If RNA is like soft­ware, CRISPR is a whole new hard­ware plat­form. Our new abil­i­ty to ed­it ge­net­ic code through bi­o­log­i­cal de­sign tools like CRISPR is an­oth­er ma­jor vec­tor of at­tack. For ex­am­ple, one type of CRISPR—Cas­Rx—on­ly goes af­ter RNA: if you give it a guide RNA se­quence that has a part of what the virus has, it will “search and find” virus RNA and then cut, i.e. de­stroy them (teams like Stan­ley Qi’s are al­ready at work on this).

Now, again, this be­comes an­oth­er bioin­for­mat­ics prob­lem: Can you iden­ti­fy what the right se­quences are? It is al­so a fun­da­men­tal shift be­tween those two tra­di­tion­al drug de­vel­op­ment tracks of vac­cine vs. ther­a­peu­tic: con­cepts like in vi­vo blurs the line be­tween both. This you would ap­ply pro­phy­lac­ti­cal­ly like a vac­cine, be­fore you get the dis­ease, giv­ing your body a new tool that it didn’t have be­fore to fight the virus when it does en­counter it.

In its grand­est pro­phy­lac­tic form, this type of tech­nol­o­gy could po­ten­tial­ly ad­dress not just pre­vi­ous pan­demics, but even fu­ture pan­demics we haven’t even seen yet. Be­cause in the­o­ry, if you did this right, you could iden­ti­fy a se­quence that isn’t just for coro­n­avirus, or this year’s flu, but an en­tire group or cat­e­go­ry of virus­es to “search and de­stroy.”

Be­cause the RNA se­quence cov­ers such a broad spec­trum, to evade de­tec­tion like that, a virus would have to fun­da­men­tal­ly change their bi­ol­o­gy. So the pro­phy­lac­tic treat­ment al­ready liv­ing in us would cov­er not just Covid-19, but al­so SARS, and MERS, and maybe even those rel­a­tive­ly harm­less coro­n­avirus­es that cause the com­mon cold.

If one as­pect of these ap­proach­es is about in­dus­tri­al­iz­ing dis­cov­ery and an­oth­er is about in­dus­tri­al­iz­ing de­sign tools, is there a way to com­bine both, and al­low us to en­gi­neer this process from start to fin­ish? That’s where AI comes in: one of the broad spec­trum new tools we have that can in­dus­tri­al­ize every sin­gle stage of drug de­sign. By in­cor­po­rat­ing ge­nom­ic analy­ses from not just the virus at hand but all known virus­es, AI can help to iden­ti­fy ide­al and po­ten­tial­ly nov­el tar­gets; to iden­ti­fy drugs that can be quick­ly re­pur­posed; to help come up with new “hits” and lead mol­e­cules for nov­el drugs; for lead op­ti­miza­tion of which can­di­dates have the high­est po­ten­tial ef­fi­ca­cy and min­i­mal tox­i­c­i­ty; even to im­prove the ef­fi­cien­cy of run­ning clin­i­cal tri­als.

As we are learn­ing far too painful­ly now, de­vel­op­ing a new ther­a­peu­tic or vac­cine is not just about ac­cu­ra­cy, it’s about speed—and a true mat­ter of life and death. But the good news is, we are fi­nal­ly see­ing drug dis­cov­ery be­gin to ben­e­fit from Moore’s Law. Tech­nol­o­gy and soft­ware tools and mind­sets are bring­ing new forces, tools, and da­ta that will help us speed up and in­dus­tri­al­ize the de­vel­op­ment of drug can­di­dates we have to treat a whole host of our dis­eases — so that maybe, when the next pan­dem­ic hap­pens, we can move much more quick­ly, with much more ef­fi­ca­cy … or even elim­i­nate the pan­demics of the fu­ture.


Vi­jay Pande is the found­ing in­vestor of a16z’s bio fund. He is a for­mer pro­fes­sor of Chem­istry and pro­fes­sor of Struc­tur­al Bi­ol­o­gy at Stan­ford Uni­ver­si­ty where he con­cur­rent­ly di­rect­ed the bio­physics pro­gram.

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Joe Biden (Carolyn Kaster, AP Images)

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Pascal Soriot (AstraZeneca via YouTube)

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