The role of AI in pre­dic­tive bio­mark­er pa­tient match­ing

The emer­gence of per­son­al­ized med­i­cine is crit­i­cal to can­cer care as it pro­motes tai­lored treat­ments to pa­tients to im­prove out­comes, and im­por­tant­ly lim­it ex­po­sure to non-ef­fi­ca­cious treat­ments. Un­like the one-size-fits-all ap­proach of tra­di­tion­al treat­ments, per­son­al­ized med­i­cines help en­sure pa­tients re­ceive ther­a­pies that are most ef­fec­tive for their spe­cif­ic ge­net­ic make­up and dis­ease pathol­o­gy. The iden­ti­fi­ca­tion and ap­pli­ca­tion of bio­mark­ers – mol­e­cules that in­di­cate bi­o­log­i­cal states – play a piv­otal role in this process.  Ad­vances in the on­col­o­gy space are forg­ing ground in ap­pli­ca­tion of bio­mark­ers for treat­ment de­ci­sions, and oth­er ther­a­peu­tic ar­eas are swift­ly fol­low­ing suit. Over the past decade re­sponse rates to treat­ment are 20% and in­crease to up­ward of 42% when a bio­mark­er is used to se­lect the ap­pro­pri­ate treat­ment. Bio­mark­ers can pre­dict dis­ease pro­gres­sion, re­sponse to treat­ment, and pa­tient out­comes, thus en­abling high­ly in­formed and per­son­al­ized med­ical de­ci­sions across a broad ar­ray of ther­a­peu­tic ar­eas.

How­ev­er, the lack of ef­fec­tive can­cer treat­ment-spe­cif­ic bio­mark­ers and dis­parate sources for bio­mark­er da­ta presents ma­jor hur­dles for clin­i­cal up­take. While bio­mark­ers can help guide clin­i­cians in for­mu­lat­ing the cor­rect reg­i­men at the in­di­vid­ual pa­tient lev­el, the in­dus­try could ben­e­fit from uni­fy­ing da­ta stor­age as well as guide­lines on ap­pli­ca­tion and use of bio­mark­er da­ta.

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Bio­mark­ers can be used across the con­tin­u­um of hema­tol­ogy/on­col­o­gy care, in­clud­ing risk as­sess­ment, screen­ing, and dif­fer­en­tial di­ag­no­sis. Prog­nos­tic bio­mark­ers can pro­vide in­sight in­to the ex­pect­ed course of a dis­ease, while pre­dic­tive bio­mark­ers pro­vide in­sight in­to a pa­tient’s an­tic­i­pat­ed re­sponse to a drug or in­ter­ven­tion. Bio­mark­ers can al­so play a role in ther­a­peu­tic mon­i­tor­ing, pro­vid­ing in­sights in­to dis­ease sta­tus, safe­ty, and the ef­fi­ca­cy of an on­go­ing treat­ment.

Chal­lenges in bio­mark­er pa­tient match­ing

De­spite its po­ten­tial, bio­mark­er-dri­ven pa­tient match­ing faces sig­nif­i­cant chal­lenges. These in­clude:

1. Com­plex da­ta in­te­gra­tion: Bio­mark­er da­ta is of­ten de­rived from and stored in dif­fuse sources, such as ge­nom­ic se­quenc­ing, pro­teomics, ref­er­ence lab­o­ra­to­ries and clin­i­cal tri­al data­bas­es. In­te­grat­ing these mul­ti­lay­ered datasets can be com­plex, time-con­sum­ing and is his­tor­i­cal­ly re­viewed by a la­bo­ri­ous man­u­al process.
2. Vol­ume of da­ta: The sheer vol­ume of bio­mark­er da­ta gen­er­at­ed is over­whelm­ing and re­quires so­phis­ti­cat­ed tools to an­a­lyze and in­ter­pret it mean­ing­ful­ly.
3. Het­ero­gene­ity of dis­eases: Dis­eases like can­cer are high­ly het­ero­ge­neous, re­quir­ing nu­anced un­der­stand­ing of in­di­vid­u­al­ized treat­ment strate­gies.
4. Dy­nam­ic na­ture of bi­ol­o­gy: Bio­mark­ers can change over time and vary among pop­u­la­tions, mak­ing it dif­fi­cult to de­vel­op sta­t­ic rules for pa­tient match­ing.

Ar­ti­fi­cial in­tel­li­gence, with its abil­i­ty to process vast amounts of da­ta and iden­ti­fy pat­terns be­yond hu­man ca­pa­bil­i­ty, stands at the fore­front of re­solv­ing a mul­ti­tude of chal­lenges. More specif­i­cal­ly, ma­chine learn­ing (ML) al­go­rithms — a sub­set of AI that en­ables sys­tems to learn from da­ta — are prov­ing in­stru­men­tal in bio­mark­er pa­tient match­ing.

Tra­di­tion­al bio­mark­er dis­cov­ery meth­ods of­ten fo­cus on sin­gle quan­tifi­able traits, lim­it­ing their abil­i­ty to cap­ture the in­tri­cate com­plex­i­ties of can­cer bi­ol­o­gy. With AI, the con­cept of a bio­mark­er evolves be­yond a sin­gle mea­sure to a gen­er­al­ized pat­tern. This ap­proach al­lows re­searchers to an­a­lyze thou­sands of fea­tures and in­te­grate com­plex da­ta points in­to uni­fied guid­ance. Ap­ply­ing strin­gent method­olo­gies and good ma­chine learn­ing prac­tices, such as pre­vent­ing over­fit­ting and da­ta leak­age, is es­sen­tial to main­tain the ac­cu­ra­cy and re­li­a­bil­i­ty of AI-dri­ven bio­mark­ers.

WCG is ac­tive­ly en­gaged and pur­su­ing AI in clin­i­cal re­search and pre­ci­sion med­i­cine. Through a mul­ti­di­men­sion­al ap­proach, WCG lever­ages its deep do­main ex­per­tise to de­vel­op state-of-the-art, AI-dri­ven so­lu­tions. Con­sid­er­a­tion should be giv­en to the fol­low­ing ar­eas when look­ing for bio­mark­er dri­ven so­lu­tion to pa­tient match­ing:

1. Da­ta in­te­gra­tion and man­age­ment
   Ad­vanced AI tech­niques to in­te­grate and har­mo­nize da­ta from mul­ti­ple sources, stream­lin­ing the process of bio­mark­er dis­cov­ery and ap­pli­ca­tion. These sources in­clude ge­nom­ic da­ta, elec­tron­ic health records (EHRs), and clin­i­cal tri­al data­bas­es. By stan­dard­iz­ing and in­te­grat­ing these da­ta streams, the AI sys­tems can achieve a holis­tic view of the pa­tient’s bi­o­log­i­cal make­up.
2. So­phis­ti­cat­ed al­go­rithm de­vel­op­ment
   Ap­pli­ca­tion of bioin­for­mat­ics ex­perts to de­vel­op so­phis­ti­cat­ed ML al­go­rithms specif­i­cal­ly tai­lored to iden­ti­fy clin­i­cal­ly rel­e­vant bio­mark­ers. These al­go­rithms should be trained on large, an­no­tat­ed datasets to en­sure they can dis­cern sub­tle pat­terns and in­ter­ac­tions that may in­di­cate a suit­able bio­mark­er for a giv­en dis­ease con­di­tion.
3. Pre­dic­tive an­a­lyt­ics
   Pre­dic­tive mod­el­ing is a key com­po­nent of any ap­proach. Us­ing AI al­go­rithms helps pre­dict which pa­tients are most like­ly to re­spond to spe­cif­ic treat­ments based on their unique bio­mark­ers. This not on­ly en­hances treat­ment ef­fi­ca­cy but al­so sig­nif­i­cant­ly re­duces the tri­al-and-er­ror of­ten as­so­ci­at­ed with tra­di­tion­al treat­ment meth­ods.
4. Dy­nam­ic bio­mark­er track­ing
   Un­der­stand­ing that bio­mark­ers evolve over time, the use of AI for con­tin­u­ous mon­i­tor­ing and up­dat­ing of bio­mark­er da­ta is im­per­a­tive. This dy­nam­ic track­ing al­lows for re­al-time ad­just­ments in treat­ment plans, en­sur­ing that pa­tients re­ceive the most rel­e­vant and ef­fec­tive ther­a­pies through­out their treat­ment jour­ney.
5. Pa­tient strat­i­fi­ca­tion
   Pa­tient strat­i­fi­ca­tion is an­oth­er crit­i­cal area where we have depth of ex­per­tise. AI al­go­rithms de­vel­op pro­files by group­ing pa­tients based on their bio­mark­er da­ta, help­ing clin­i­cians to iden­ti­fy sub­groups that are more like­ly to ben­e­fit from cer­tain ther­a­pies. This can be par­tic­u­lar­ly help­ful in clin­i­cal tri­als to en­sure the right par­tic­i­pants are se­lect­ed, there­by in­creas­ing the like­li­hood of tri­al suc­cess.

Eth­i­cal con­sid­er­a­tions and the fu­ture

How­ev­er, with these ad­vance­ments come eth­i­cal con­sid­er­a­tions. Is­sues around pa­tient da­ta pri­va­cy, trans­paren­cy in how pa­tient da­ta is be­ing ap­plied and rig­or­ous pro­tec­tion of pa­tient in­ter­ests. By pro­mot­ing stan­dards for the eth­i­cal use of AI in health­care, the in­dus­try must aim to fos­ter trust and en­sure that tech­no­log­i­cal ad­vance­ments trans­late to tan­gi­ble pa­tient ben­e­fits.

The in­te­gra­tion of AI in bio­mark­er pa­tient match­ing rep­re­sents trans­for­ma­tive leaps in pre­ci­sion med­i­cine. With this tech­nol­o­gy, the pos­si­bil­i­ties for im­proved pa­tient out­comes, greater clin­i­cal tri­al suc­cess, and re­duc­tion of health­care costs are bound­less.

To learn more about WCG and our so­lu­tions, reach out to­day. Un­lock faster re­sults with WCG.

Au­thors:

Mike Ciof­fi,
Se­nior Vice Pres­i­dent,
Clin­i­cal So­lu­tions

Mike Ciof­fi is a Se­nior Vice Pres­i­dent with WCG in Clin­i­cal So­lu­tions and pro­vides unique in­sight and ex­per­tise to in­dus­try lead­ers in sup­port of project de­liv­ery and im­ple­ment­ing glob­al en­ter­prise so­lu­tions. Mike has worked in the phar­ma­ceu­ti­cal in­dus­try for more than 25 years and has ex­ten­sive ex­pe­ri­ence in var­i­ous ther­a­peu­tic ar­eas with an em­pha­sis on CNS in­di­ca­tions and rare dis­ease.
 
 
 
   
 
   
   

Brant Nicks,
Se­nior Vice Pres­i­dent,
Clin­i­cal So­lu­tions

Brant Nicks is a Se­nior Vice Pres­i­dent with WCG in Clin­i­cal So­lu­tions and pro­vides sup­port and over­site for on­col­o­gy tri­als. Brant has worked in clin­i­cal re­search for 28 years in var­i­ous ca­pac­i­ties with Biotech­nol­o­gy com­pa­nies and Con­tract Re­search Or­ga­ni­za­tions with an em­pha­sis on Hema­tol­ogy/On­col­o­gy re­search.