In­no­va­tion in the clin­ic is pick­ing up steam. How adap­tive de­signs could pave the road to per­son­al­ized med­i­cine

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Nor­mal­ly, when you hear about in­no­va­tion in R&D, the fo­cus is on the “R” and not the “D.” There’s a ten­den­cy to as­sume that lab-based sci­en­tists do the re­al­ly cre­ative re­search, where­as clin­i­cal de­vel­op­ment is a fair­ly straight­for­ward, check-the-box­es ac­tiv­i­ty.

There’s a ker­nel of truth in this oth­er­wise un­fair as­sump­tion. Just as the ba­sic de­sign of a car hasn’t changed much from the cars our grand­par­ents drove — gas and brake ped­als, four tires, and a steer­ing wheel — clin­i­cal tri­als still have time-hon­ored fea­tures like ran­dom­iza­tion, con­trol arms and blind­ing. What is chang­ing, though, is that we’ve be­gun to aug­ment well-es­tab­lished meth­ods with in­no­va­tions that promise to make drug de­vel­op­ment faster, less ex­pen­sive and more suc­cess­ful.

One big el­e­ment in this trans­for­ma­tion is broad­er use of adap­tive study de­signs. In con­ven­tion­al clin­i­cal tri­als, the study pro­to­col is carved in stone at the out­set, and you ex­e­cute that de­sign with no de­vi­a­tions from start to fin­ish. In adap­tive tri­als, you can build flex­i­bil­i­ty in­to the pro­to­col, which al­lows you to change key pa­ra­me­ters of the study in re­sponse to in­com­ing da­ta.

Adap­tive de­signs are typ­i­cal­ly em­ployed in Phase II tri­als, where re­sults from a small group of pa­tients are used to de­cide if a much larg­er Phase III study is war­rant­ed. “Adap­tive” doesn’t mean you can make any change that seems ad­van­ta­geous—the op­tions need to be spec­i­fied in ad­vance. But if the ear­ly da­ta con­form to pre-es­tab­lished cri­te­ria, you can al­ter the size or du­ra­tion of the study, drop or add dos­es to en­sure more pa­tients re­ceive the op­ti­mal dose, or bring in more of the types of pa­tients who seem to be re­spond­ing well to the test drug.

Adap­tive mod­i­fi­ca­tions don’t change the prop­er­ties of the drug mol­e­cule be­ing test­ed, but they im­prove the odds of test­ing it in the right pa­tients, at the right dose, for the right du­ra­tion to show its risks and ben­e­fits. Greater clar­i­ty can re­duce the risk of false-pos­i­tive or false-neg­a­tive re­sults. A false pos­i­tive — think­ing a drug works when it doesn’t — can lead a com­pa­ny to in­vest heav­i­ly in a large study that will ul­ti­mate­ly fail to de­liv­er what pa­tients need. A false neg­a­tive can cast doubt on the val­ue of a ther­a­py and ter­mi­nate de­vel­op­ment of an in­ves­ti­ga­tion­al drug with re­al ben­e­fits for pa­tients Ul­ti­mate­ly, adap­tive de­signs can al­low us to more read­i­ly achieve a per­son­al­ized med­i­cine, in which pa­tients most like­ly to ben­e­fit from a drug are the ones to re­ceive it.

The ba­sic idea of adap­tive clin­i­cal tri­al de­signs isn’t new — clin­i­cal sci­en­tists from acad­e­mia and in­dus­try have been study­ing this con­cept for two decades. But for much of that time, these meth­ods were seen as too un­proven and risky. Sev­er­al years ago, Am­gen de­cid­ed there was more risk in not adopt­ing these new ap­proach­es, which have the po­ten­tial to de­liv­er more suc­cess­ful stud­ies more quick­ly and with low­er de­vel­op­ment costs.

In con­ven­tion­al tri­als, this trio of pri­or­i­ties — cost, speed, and like­li­hood of suc­cess — in­volve trade-offs that make it hard to pur­sue all three goals si­mul­ta­ne­ous­ly. For ex­am­ple, to boost the like­li­hood of suc­cess, you nor­mal­ly need to col­lect more da­ta from more pa­tients, which trans­lates in­to added time and ex­pense. But im­ple­ment­ed cor­rect­ly, adap­tive de­signs can po­ten­tial­ly avoid these trade­offs and fa­cil­i­tate small­er and faster tri­als that re­veal a drug’s true po­ten­tial with more pre­ci­sion.

Two de­vel­op­ments are ac­cel­er­at­ing the trend to­ward adap­tive de­signs. First, the FDA is en­cour­ag­ing clin­i­cal in­no­va­tion and part­ner­ing with com­pa­nies that are will­ing to try new ap­proach­es. For ex­am­ple, Am­gen has an in­ves­ti­ga­tion­al ther­a­py for lu­pus, efavaleukin al­fa (for­mer­ly AMG 592), which is par­tic­i­pat­ing in the FDA’s Com­plex In­no­v­a­tive Tri­al De­sign (CID) Pi­lot Pro­gram. We plan to use an adap­tive de­sign to work to ze­ro in on the op­ti­mal dose and to en­sure more pa­tients re­ceive this op­ti­mal dose to in­crease the like­li­hood that the right dose is se­lect­ed for fu­ture stud­ies.

A re­lat­ed de­vel­op­ment has been greater ac­cess to re­al-world da­ta and ad­vances in com­pu­ta­tion­al meth­ods that use these da­ta to sim­u­late clin­i­cal tri­als. Sim­u­la­tions can’t pre­dict how a drug will per­form in an ac­tu­al clin­i­cal study, but they can show how dif­fer­ent study de­signs are ex­pect­ed to per­form un­der dif­fer­ent sce­nar­ios.

In de­sign­ing any tri­al, you need to make as­sump­tions about a whole range of vari­ables — the ef­fect size of the drug you are test­ing; how long it takes for this ef­fect to emerge; the re­sponse rate to the place­bo or com­para­tor drugs, etc. For our study in lu­pus pa­tients, we ran mil­lions of sim­u­la­tions, plug­ging in dif­fer­ent val­ues for these vari­ables and oth­ers, with the goal of find­ing the de­sign op­tions most like­ly to yield re­li­able re­sults.

In­no­v­a­tive de­signs ex­tend to tri­als where the drug it­self is the el­e­ment that is open to mod­i­fi­ca­tion. The COVID-19 pan­dem­ic has un­der­scored the im­por­tance of speed in drug de­vel­op­ment, and adap­tive plat­form tri­als pro­vide a way to rapid­ly test mul­ti­ple po­ten­tial ther­a­pies us­ing a sin­gle pro­to­col.

As part of the COVID R&D Al­liance, Am­gen is part­ner­ing with Take­da and UCB in the COM­MU­NI­TY study, which will ini­tial­ly test three po­ten­tial treat­ments for pa­tients hos­pi­tal­ized with COVID-19. The de­sign is ef­fi­cient be­cause it us­es same en­try cri­te­ria for all agents be­ing eval­u­at­ed, and it em­ploys a com­mon con­trol arm and a com­mon fu­til­i­ty bar to eval­u­ate for ef­fi­ca­cy. Test drugs can be dropped from the tri­al if they show lack of ef­fi­ca­cy, and new agents can be in­tro­duced quick­ly to take ad­van­tage of the es­tab­lished pro­to­col.

Am­gen is us­ing a sim­i­lar con­cept to in­ves­ti­gate so­tora­sib, a po­ten­tial new tar­get­ed ther­a­py for pa­tients with non-small cell lung can­cer who car­ry a mu­tat­ed gene known as KRAS G12C. Many of these pa­tients have failed to re­spond to stan­dard ther­a­pies, so there is an ur­gent need to eval­u­ate oth­er po­ten­tial treat­ment op­tions rapid­ly. To ac­cel­er­ate test­ing of so­tora­sib in com­bi­na­tion with oth­er can­cer ther­a­pies, we es­tab­lished a 10-arm mas­ter pro­to­col with a high­ly flex­i­ble de­sign. The goal is to de­tect any pos­i­tive ef­fi­ca­cy sig­nals as ear­ly as pos­si­ble, so that the most promis­ing com­bi­na­tions can be quick­ly iden­ti­fied and ex­pand­ed in­to larg­er stud­ies.

Clin­i­cal in­no­va­tion isn’t on­ly a smarter way to do drug de­vel­op­ment, it is bet­ter for pa­tients as well. The soon­er we can de­ter­mine whether an in­ves­ti­ga­tion­al ther­a­py or spe­cif­ic dose works or not, the soon­er we can ei­ther ad­vance that ther­a­py or stop test­ing it in pa­tients. Speed and clar­i­ty are im­por­tant in re­search, but even more im­por­tant to pa­tients search­ing for the right treat­ment for their dis­ease.

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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.

You may remember that at the beginning of this year, Endpoints News set a goal to go broader and deeper. We are still working towards that, and are excited to share that Beth Snyder Bulik will be joining us on Monday to cover all things pharma marketing. You can sign up for her weekly Endpoints MarketingRx newsletter in your reader profile.

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