Em­brac­ing a big data rev­o­lu­tion in medicine

Badhri Srini­vasan, Head of Global De­vel­op­ment Op­er­a­tions, No­var­tis, Basel, Can­ton of Basel-Stadt, Switzer­land

BioSpectrum (India) - - BIO CONTENT - Badhri Srini­vasan, Head of Global De­vel­op­ment Op­er­a­tions, No­var­tis, Basel, Can­ton of Basel-Stadt, Switzer­land

The tra­di­tional clin­i­cal tri­als process can be time con­sum­ing and cum­ber­some - it can take one per­son a week to an­a­lyze re­ported study data. If we are able to ap­ply tech­nolo­gies such as Ar­ti­fi­cial In­tel­li­gence (AI) and ma­chine learn­ing, we could rapidly an­a­lyze an en­tire vol­ume of clin­i­cal data in a mat­ter of sec­onds, whilst also achiev­ing deeper in­sights into drug dis­cov­ery and de­vel­op­ment pro­cesses than ever be­fore.

Like many other in­dus­tries, health­care is on the precipice of mas­sive trans­for­ma­tion due to the in­flux of new, emerg­ing tech­nolo­gies. Many, in­clud­ing my­self, see this change as an in­cred­i­ble op­por­tu­nity to im­prove our abil­ity to de­liver ground­break­ing ther­a­pies to pa­tients and do so at a faster rate. A study from BIO showed that less than 10 per cent of treat­ments in de­vel­op­ment are suc­cess­fully brought from the clinic to mar­ket. This would be unacceptable in any other in­dus­try – and is a crit­i­cal part of the rea­son we need to em­brace new tech­nolo­gies that can help us evolve our busi­ness model to be­come more ag­ile and ef­fi­cient as we seek to find bet­ter solutions for pa­tients in need.

At its core, drug de­vel­op­ment is a data-driven en­ter­prise – but cur­rent mod­els for col­lect­ing and cu­rat­ing data do not pro­vide many op­por­tu­ni­ties to tap into the true power data can pro­vide.

Take clin­i­cal tri­als for in­stance. At any given point in time, large phar­ma­ceu­ti­cal com­pa­nies like No­var­tis are run­ning hun­dreds of clin­i­cal tri­als around the world. The pri­mary goal of each trial is to col­lect data that will help us to bet­ter de­fine and as­sess the ways in which our medicines could help im­prove peo­ple’s lives. How­ever, the tra­di­tional clin­i­cal tri­als process can be time con­sum­ing and cum­ber­some - it can take one per­son a week to an­a­lyze re­ported study data. If we are able to ap­ply tech­nolo­gies such as Ar­ti­fi­cial In­tel­li­gence (AI) and ma­chine learn­ing, we could rapidly an­a­lyze an en­tire vol­ume of clin­i­cal data in a mat­ter of sec­onds, whilst also achiev­ing deeper in­sights into drug dis­cov­ery and de­vel­op­ment pro­cesses than ever be­fore.

Over the past two years, we have been work­ing to cre­ate a mod­ern ar­chi­tec­ture and internal in­fra­struc­ture to in­te­grate our vast data re­sources into a uni­fied plat­form that can store and man­age all of our cur­rent, fu­ture, and his­toric data sets.

This sys­tem­atic and com­pre­hen­sive ef­fort forms the foun­da­tion upon which we are max­i­miz­ing our gold­mine of op­er­a­tional and med­i­cal data – one of the most valu­able as­sets we have – to ac­cel­er­ate tri­als, po­ten­tially iden­tify trends or pat­terns, and more seam­lessly progress the in­no­va­tive com­pounds in our pipe­line from early clin­i­cal stages to mar­ket.

In ad­di­tion, we are also look­ing at bet­ter ways to in­te­grate pre­dic­tive an­a­lyt­ics into our ev­ery­day prac­tices. We be­lieve that AI and pre­dic­tive an­a­lyt­ics have the po­ten­tial to sug­gest novel molec­u­lar en­ti­ties with a high prob­a­bil­ity of suc­cess in the clinic. Ap­ply­ing ad­vanced an­a­lyt­ics tech­niques to our wealth of op­er­a­tional data also en­ables real-time, data-driven de­ci­sion-mak­ing in both cur­rent and fu­ture tri­als, which will lead to over­all sys­tem­atic im­prove­ments.

While change of this scale can of­ten be seen as dis­rup­tive, in this case, our trans­for­ma­tion has cre­ated an in­cred­i­ble op­por­tu­nity to take full ad­van­tage of the dif­fer­ent ca­pa­bil­i­ties and re­sources avail­able to us around the world. Our team in In­dia is a great ex­am­ple where the drug de­vel­op­ment team is work­ing closely with aca­demic in­sti­tutes and in­dus­try part­ners to de­velop tech­nol­ogy solutions that will au­to­mate cer­tain data man­age­ment pro­cesses, en­abling quicker, more ac­cu­rate and in­sight­ful in­ter­pre­ta­tion of vi­tal signs and lab data.

One of the most crit­i­cal things we have learned in our own jour­ney to en­hance the ways we de­velop and study medicines is that in­te­grat­ing new sys­tems and tech­nolo­gies is just one com­po­nent of the crit­i­cal change we need to re­al­ize. The other cru­cial piece is chang­ing the mindsets of those in our in­dus­try

– to em­brace trans­for­ma­tion and the col­lab­o­ra­tion nec­es­sary to make the changes needed to con­tinue to be suc­cess­ful. The en­deavor of de­vel­op­ing medicines is a no­ble and crit­i­cal mis­sion – and we have the chance to go about that task in a new and po­ten­tially more ef­fi­cient way. If we are able to em­brace the changes needed – we have an ex­tra­or­di­nary op­por­tu­nity to im­prove the lives of pa­tients around the world.

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