We’re poised for a new era of med­ical ad­vances. A key step: un­der­stand­ing the mol­e­c­u­lar ma­chines that gov­ern health and dis­ease

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We wit­nessed an­oth­er im­por­tant ad­vance in sci­ence last year, when Lon­don-based re­searchers at Deep­Mind an­nounced a ma­jor leap for­ward in the ac­cu­ra­cy of an ar­ti­fi­cial in­tel­li­gence (AI) plat­form called Al­phaFold, which is de­signed to rapid­ly and re­li­ably pre­dict the three-di­men­sion­al struc­ture of pro­teins. It is an­oth­er break­through in our un­der­stand­ing of pro­teins in the last 70 years, and one that could help ush­er in a new era of med­i­cine.

Look­ing back to the start of this rev­o­lu­tion, elu­ci­da­tion of the dou­ble he­lix struc­ture of DNA in 1953 be­gan our quest to de­ci­pher the blue­print of life. The ini­tial draft se­quence of the hu­man genome pub­lished in 2000 made avail­able the in­struc­tion man­u­al for the func­tion­al em­bod­i­ment of genes: pro­teins.

In re­cent decades, the three-di­men­sion­al ar­chi­tec­ture en­cod­ed by a pro­tein’s amino acid se­quence has be­come in­creas­ing­ly ac­ces­si­ble thanks to ad­vances in x-ray-based im­ages orig­i­nal­ly used to re­veal the struc­ture of the dou­ble he­lix. For­mi­da­ble new tools like cryo­genic elec­tron mi­croscopy (cryo-EM) al­low us to de­vel­op a so­phis­ti­cat­ed un­der­stand­ing not just of the form of fold­ed pro­teins, but a bet­ter un­der­stand­ing of the “pock­e­tome” at a lev­el of de­tail that can ex­plain pro­tein func­tion and dys­func­tion in dis­ease.

This isn’t just an aca­d­e­m­ic ex­er­cise. De­ter­min­ing a pro­tein’s struc­ture can pro­vide vi­tal clues to ac­cel­er­ate the de­sign of new med­i­cines. This type of in­sight has been of glob­al in­ter­est in 2020. The amaz­ing progress the bio­phar­ma in­dus­try has made in de­vel­op­ing Covid-19 vac­cines and ther­a­peu­tics was pos­si­ble, in part, be­cause sci­en­tists iden­ti­fied the se­quence and struc­ture of the SARS-CoV-2 virus’s spike pro­tein in record time.

The abil­i­ty of sci­en­tists to ac­cu­rate­ly map pro­tein struc­tures — let’s call them the Google Maps of pro­teins — could pro­vide crit­i­cal in­for­ma­tion about their func­tion and dys­func­tion and in­form med­i­cine dis­cov­ery. Re­searchers around the world have been mak­ing steady progress in ob­tain­ing these maps; the num­ber of pub­licly avail­able pro­tein struc­tures quadru­pled in the last 20 years. How­ev­er, the ma­jor­i­ty of pro­teins en­cod­ed by the hu­man genome have not been solved and ob­tain­ing an ac­cu­rate struc­ture can take years of lab time.

Deep­Mind’s Al­phaFold, which was re­cent­ly hon­ored as a run­ner-up in Sci­ence’s pres­ti­gious Break­through of the Year awards, is a ma­jor ad­vance set to be a ma­jor break­through in pro­tein struc­ture pre­dic­tion. It de­ploys an AI al­go­rithm, trained on hun­dreds of known pro­tein folds and struc­tures, to pre­dict un­solved pro­tein struc­tures based sole­ly on the amino acid se­quence. Al­phaFold’s in­cred­i­ble per­for­mance in repli­cat­ing some known pro­tein struc­tures was demon­strat­ed in a re­cent in­ter­na­tion­al com­pe­ti­tion. The im­pli­ca­tion is that AIphaFold could com­plete in days or weeks struc­ture de­ter­mi­na­tion that would typ­i­cal­ly take years for many pro­teins.

Ex­cit­ing as this news is, there are a mul­ti­tude of com­plex steps be­tween solv­ing a three-di­men­sion­al struc­ture, de­ter­min­ing dis­ease mech­a­nisms, and dis­cov­er­ing safe and ef­fec­tive drugs.

When pro­teins are at work they are not sta­t­ic in the way they of­ten ap­pear in sci­en­tif­ic jour­nals, like a sports car parked in the show­room of a deal­er­ship. They are dy­nam­ic, high-per­for­mance ma­chines, whose ex­quis­ite en­gi­neer­ing is most ap­par­ent when they are in mo­tion. Med­i­c­i­nal chemists are at a great ad­van­tage when they have ac­cess to the “map” of a pro­tein and can vi­su­al­ize the bends, twists and folds that oc­cur as pro­teins tran­si­tion from an in­ac­tive to ac­tive state — al­so known as pro­tein mo­tion. The lev­el of de­tail in pro­tein “maps” is crit­i­cal when nav­i­gat­ing the chal­lenges of drug dis­cov­ery. It will be in­ter­est­ing to see whether Al­phaFold pro­vides the cru­cial lev­el of res­o­lu­tion chemists need.

The ef­fi­cient de­sign of drugs that bind to rel­e­vant or­thos­teric or al­losteric pock­ets in pro­teins to mod­i­fy func­tion for ther­a­peu­tic ben­e­fit re­quires res­o­lu­tion at the lev­el of in­di­vid­ual atoms, equiv­a­lent to see­ing in­di­vid­ual hous­es in a Google map. Drug dis­cov­ery sci­en­tists op­ti­mize ini­tial mol­e­cules to achieve the op­ti­mal bal­ance across a broad range of crit­i­cal prop­er­ties in or­der to turn them in­to vi­able clin­i­cal can­di­dates. If de­cod­ing a pro­tein struc­ture is chal­leng­ing, dis­cov­er­ing promis­ing ther­a­peu­tics is a near­ly Her­culean task. Every one of these steps is la­bo­ri­ous and ex­pen­sive. It’s no won­der that it can take many years, and cost many mil­lions, just to get to the point of hav­ing a mol­e­cule se­lect­ed for pre­clin­i­cal stud­ies.

For­tu­nate­ly, ad­vances in com­put­ing pow­er and com­pu­ta­tion­al chem­istry tech­niques have brought us ever-more-so­phis­ti­cat­ed de­sign and bind­ing sim­u­la­tion tools. When cou­pled with de­tailed pro­tein struc­tures and mol­e­c­u­lar dy­nam­ics in­for­ma­tion, these com­pu­ta­tion­al meth­ods en­able med­i­c­i­nal chemists to con­duct key de­sign and op­ti­miza­tion steps “in sil­i­co.”

For ex­am­ple, ac­cu­rate physics-based mod­els can help chemists pri­or­i­tize promis­ing leads from bil­lions of the ap­prox­i­mate­ly 1050 po­ten­tial drug-like mol­e­cules that ex­ist in the vast­ness of chem­i­cal space be­fore tak­ing the cost­ly and time-con­sum­ing step of syn­the­siz­ing and test­ing them in the lab. This less em­pir­i­cal ap­proach, fu­eled by ac­cess to high-res­o­lu­tion pro­tein struc­tures, will ul­ti­mate­ly al­low drug dis­cov­er­ers to de­sign high­er qual­i­ty drugs as well as tool mol­e­cules for use in val­i­da­tion of tar­gets to help pri­or­i­tize ther­a­peu­tic hy­pothe­ses.

My open­ing state­ment as­sert­ed that we’re at the dawn of a new era in med­i­cine. We’ve reached this point thanks to a com­bi­na­tion of un­prece­dent­ed com­put­ing pow­er, ad­vances in mol­e­c­u­lar sim­u­la­tion tech­nolo­gies and new in­sights in­to hu­man bi­ol­o­gy. We can be­gin to in­te­grate our knowl­edge of the genome, the pro­teome, the in­ter­ac­tome, and the pock­e­tome to de­sign drugs with in­creased speed and ac­cu­ra­cy. With tools such as Al­phaFold to speed in­sights in­to the mol­e­c­u­lar ma­chines that gov­ern health and dis­ease, cou­pled with com­pu­ta­tion­al mod­el­ing to aid drug de­sign, the next decades are sure to yield many more med­i­cines.

I liken these con­nect­ed ad­vances to tak­ing the blind­folds off drug hunters. And all of us stand to ben­e­fit.

Karen Akin­sanya is ex­ec­u­tive vice pres­i­dent, chief bio­med­ical sci­en­tist, and head of dis­cov­ery R&D at Schrödinger, Inc.

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Merck has withdrawn its marketing approval for PD-(L)1 inhibitor Keytruda in metastatic small cell lung cancer as part of what it describes as an “industry-wide evaluation” by the FDA of drugs that do not meet the post-marketing checkpoints on which their accelerated nods were based, the company said Monday.

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The global pandemic may have roiled economies, killed hundreds of thousands and throttled entire industries, but the only effect it had on biopharma venture investing was to help turbocharge the field to giddy new heights.

Below you’ll find the new top 100 venture investors in the industry, ranked by the number of deals they were publicly involved in, as tracked by DealForma chief Chris Dokomajilar. The numbers master then calculated the estimated amount of money they put into each deal — divvying up the cash by the number of players — to indicate how they managed their syndicates.

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GSK, Vir's hopes for a Covid-19 an­ti­body fall flat in NIH 'mas­ter pro­to­col' with no ben­e­fit in hos­pi­tal­ized pa­tients

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The NIH has shut down enrollment for GSK and Vir’s antibody VIR-7831 in its late-stage ACTIV-3 trial after the drug showed negligible effect in achieving sustained recovery in hospitalized Covid-19 patients, the partners said Wednesday.

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As Brain­Storm con­tin­ues to tout ‘clear sig­nal’ on ALS drug, the FDA of­fers a rare pub­lic slap­down on the da­ta

A little more than a week after BrainStorm acknowledged that regulators at the FDA had informed them that the biotech needed more data before it could expect to gain an approval for its ALS treatment NurOwn — while still touting a “clear signal” of efficacy and not ruling out an application — the agency has decided to clarify the record in a most unusual statement.

The FDA statement amounts to a straight slap own, offering a different set of efficacy numbers from the company’s public presentation last November and ruling out any chance of statistical significance.

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In­tro­duc­ing End­points FDA+, our new pre­mi­um week­ly reg­u­la­to­ry news re­port led by Zachary Bren­nan

CRLs. 483s. CBER, CDER and RWE. For biopharma professionals, these acronyms command attention because of the fundamental role FDA plays in drug development. Now Endpoints is doubling down on regulatory coverage, and launching a weekly report focusing on developments out of White Oak, with analysis and insight into what it all means.

Coverage will be led by our new senior editor, Zachary Brennan. He joins Endpoints from POLITICO, where he covered pharma. Prior to that he was the managing editor for Regulatory Focus, a news publication from the Regulatory Affairs Professionals Society.

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Soticlestat made it.

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