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.

For a look at all End­points News coro­n­avirus sto­ries, check out our spe­cial news chan­nel.

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Despite being early in development, Allogenic NKs are proving to be an attractive new treatment paradigm in oncology. The question of durability of response with allogenic therapies is still an unknown. Fate Therapeutics’ recent phase 1 data for FT516 showed relatively quicker relapses vs already approved autologous CAR-Ts. However, other manufacturers, like Allogene for their allogenic CAR-T therapy ALLO-501A, are exploring novel lymphodepletion approaches to improve persistence of allogenic cells. Nevertheless, allogenic NKs demonstrate a strong value proposition relative to their T cell counterparts due to comparable response rates (so far) combined with the added advantage of a significantly safer AE profile. Specifically, little to no risk of graft versus host disease (GvHD), cytotoxic release syndrome (CRS), and neurotoxicity (NT) have been seen so far with allogenic NK cells (Fig. 1). In addition, being able to harness an allogenic cell source gives way to operational advantages as “off-the-shelf” products provide improved turnaround time (TAT), scalability, and potentially reduced cost. NKs are currently in development for a variety of overlapping hematological indications with chimeric antigen receptor T cells (CAR-Ts) today, and the question remains to what extent they will disrupt the current cell therapy landscape. Click for more details.

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Welcome back to Endpoints Weekly, your review of the week’s top biopharma headlines. Want this in your inbox every Saturday morning? Current Endpoints readers can visit their reader profile to add Endpoints Weekly. New to Endpoints? Sign up here.

For those of you who attended #JPM22 in any shape or form, we hope you had a fruitful time. Regardless of how you spent the past hectic week, may your weekend be just what you need it to be.

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Robert Califf, FDA commissioner nominee (Graeme Sloan/Sipa USA/Sipa via AP Images)

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Michel Vounatsos, Biogen CEO (World Economic Forum/Ciaran McCrickard)

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