So you go to the doctor and get some tests. The doctor determines that you have high cholesterol and you would benefit from medication to treat it. So you get a pillbox. You have some confidence, your physician has some confidence that this is going to work. The company that invented it did a lot of studies, submitted it to the FDA. They studied it very carefully, skeptically, they approved it. They have a rough idea of how it works, they have a rough idea of what the side effects are. It should be OK. You have a little more of a conversation with your physician and the physician is a little worried because you've been blue, haven't felt like yourself, you haven't been able to enjoy things in life quite as much as you usually do. Your physician says, "You know, I think you have some depression. I'm going to have to give you another pill."
Odete kod doktora i obavite neke analize. Doktor utvrdi da imate visok holesterol i da bi bilo dobro da uzimate lekove kako biste to lečili, pa uzmete pilule. Verujete, vaš lekar veruje da će to da pomogne. Kompanija koja je izumela lek je obavila dosta ispitivanja, podnela ga na odobrenje Upravi za hranu i lekove. Ispitali su ga veoma pažljivo, skeptično i odobrili ga. Imaju izvesnu predstavu o tome kako deluje, o tome koje su nuspojave. Trebalo bi da bude u redu. Dodatno ste pričali sa svojim lekarom i on je malo zabrinut jer ste tužni, niste baš svoji, ne uživate u stvarima u životu kao i obično. Lekar vam kaže: „Znate, mislim da imate depresiju. Moraću da vam dam druge pilule.“
So now we're talking about two medications. This pill also -- millions of people have taken it, the company did studies, the FDA looked at it -- all good. Think things should go OK. Think things should go OK. Well, wait a minute. How much have we studied these two together?
Tako sad govorimo o dva leka. Sa tim pilulama je isto - milioni su ih uzimali, kompanija je ispitivala, Uprava za hranu i lekove je pregledala, sve je u redu. Mislite da će sa ovim da bude sve u redu. Mislite da će i sa ovim da bude sve u redu. Ipak, sačekajte malo. Koliko smo ova dva leka izučavali zajedno?
Well, it's very hard to do that. In fact, it's not traditionally done. We totally depend on what we call "post-marketing surveillance," after the drugs hit the market. How can we figure out if bad things are happening between two medications? Three? Five? Seven? Ask your favorite person who has several diagnoses how many medications they're on.
Pa, to je vrlo teško uraditi. Zapravo, to se po običaju ne radi. Potpuno zavisimo od onoga što zovemo „postmarketinški nadzor“, nakon što lek bude pušten na tržište. Kako možemo da otkrijemo da li se nešto loše dešava između dva leka? Tri? Pet? Sedam? Pitajte svoju omiljenu osobu sa nekoliko dijagnoza koliko lekova uzima.
Why do I care about this problem? I care about it deeply. I'm an informatics and data science guy and really, in my opinion, the only hope -- only hope -- to understand these interactions is to leverage lots of different sources of data in order to figure out when drugs can be used together safely and when it's not so safe.
Zašto je meni stalo do ovog problema? Jako mi je stalo do toga. Ja sam tip koji se bavi informatikom i naukom o podacima, i prema mom mišljenju, jedina nada da razumemo ove interakcije je da usaglasimo mnogo različitih izvora podataka kako bismo otkrili kada se lekovi mogu bezbedno koristiti zajedno, a kada baš i nije bezbedno.
So let me tell you a data science story. And it begins with my student Nick. Let's call him "Nick," because that's his name.
Dopustite da vam ispričam priču o nauci o podacima. Počinje sa mojim studentom Nikom. Zvaćemo ga „Nik“, jer mu je to ime.
(Laughter)
(Smeh)
Nick was a young student. I said, "You know, Nick, we have to understand how drugs work and how they work together and how they work separately, and we don't have a great understanding. But the FDA has made available an amazing database. It's a database of adverse events. They literally put on the web -- publicly available, you could all download it right now -- hundreds of thousands of adverse event reports from patients, doctors, companies, pharmacists. And these reports are pretty simple: it has all the diseases that the patient has, all the drugs that they're on, and all the adverse events, or side effects, that they experience. It is not all of the adverse events that are occurring in America today, but it's hundreds and hundreds of thousands of drugs.
Nik je bio mladi student. Rekao sam: „Znaš, Nik, moramo da razumemo kako lekovi deluju, kako deluju zajedno i kako deluju zasebno, a ne razumemo mnogo o tome. Međutim, Uprava za hranu i lekove je objavila neverovatnu bazu podataka. To je baza podataka o neželjenim događajima. Bukvalno su je postavili na mrežu - javno je dostupna, svi možete da je sada skinete - stotine hiljada izveštaja o neželjenim događajijma od pacijenata, doktora, kompanija, farmaceuta. Ti izveštaji su prilično jednostavni. Tu su sve bolesti koje pacijent ima, svi lekovi koje uzima i svi neželjeni događaji ili nuspojave koje doživljavaju. To nisu svi neželjeni događaji koji se danas javljaju u Americi, ali to su stotine i stotine hiljada lekova.
So I said to Nick, "Let's think about glucose. Glucose is very important, and we know it's involved with diabetes. Let's see if we can understand glucose response. I sent Nick off. Nick came back.
Rekao sam Niku: „Razmotrimo glukozu. Glukoza je veoma važna i znamo da je u vezi sa dijabetesom. Hajde da vidimo da li možemo da razumemo reakciju glukoze.“ Ispratio sam Nika. Vratio se.
"Russ," he said, "I've created a classifier that can look at the side effects of a drug based on looking at this database, and can tell you whether that drug is likely to change glucose or not."
„Ras“, rekao je, „Napravio sam klasifikator koji može da sagleda neželjene efekte leka na osnovu pregleda baze podataka i može nam reći da li postoji šansa da će taj lek promeniti nivo glukoze ili ne.“
He did it. It was very simple, in a way. He took all the drugs that were known to change glucose and a bunch of drugs that don't change glucose, and said, "What's the difference in their side effects? Differences in fatigue? In appetite? In urination habits?" All those things conspired to give him a really good predictor. He said, "Russ, I can predict with 93 percent accuracy when a drug will change glucose."
Uspeo je. Bilo je prosto, na neki način. Uzeo je sve lekove za koje se zna da menjaju nivo glukoze i gomilu lekova koji ne menjaju nivo glukoze i zapitao se: „U čemu je razlika između njihovih nuspojava? Razlike u osećaju premora? Apetitu? U pogledu vršenja mokrenja?“ Sve to u sklopu mu je dalo veoma dobro sredstvo predviđanja. Rekao je: „Ras, mogu da predvidim sa 93 posto verovatnoće tačnosti kada će lek menjati nivo glukoze.“
I said, "Nick, that's great." He's a young student, you have to build his confidence. "But Nick, there's a problem. It's that every physician in the world knows all the drugs that change glucose, because it's core to our practice. So it's great, good job, but not really that interesting, definitely not publishable."
Rekao sam: „Nik, to je sjajno.“ On je mladi student, morate da mu podignete samopouzdanje. „Ipak, Nik, postoji problem. Činjenica je da svaki lekar na svetu zna sve lekove koji menjaju nivo glukoze jer je to u suštini naše prakse. Tako da je to sjajno, odlično obavljeno, ali nije baš naročito zanimljivo, definitivno ne nešto što se može objaviti.“
(Laughter)
(Smeh)
He said, "I know, Russ. I thought you might say that." Nick is smart. "I thought you might say that, so I did one other experiment. I looked at people in this database who were on two drugs, and I looked for signals similar, glucose-changing signals, for people taking two drugs, where each drug alone did not change glucose, but together I saw a strong signal."
Rekao je: „Znam, Ras. Pretpostavio sam da ćeš to reći.“ Nik je pametan. „Pretpostavio sam da ćeš to reći, pa sam sproveo još jedan eksperiment. Posmatrao sam ljude u ovoj bazi podataka koji uzimaju dva leka i tražio sam slične signale, signale za promenu nivoa glukoze, za osobe koje uzimaju dva leka, pri čemu svaki lek sam po sebi ne menja glukozu, ali vidim da zajednički daju jak signal.“
And I said, "Oh! You're clever. Good idea. Show me the list." And there's a bunch of drugs, not very exciting. But what caught my eye was, on the list there were two drugs: paroxetine, or Paxil, an antidepressant; and pravastatin, or Pravachol, a cholesterol medication.
Rekao sam: „O, pametan si. Dobra ideja. Pokaži mi spisak.“ Tu je bila gomila lekova, ne naročito zanimljivo. Ono što mi je privuklo pažnju je da su na spisku bila dva leka: paroksetin ili Paksil, antidepresiv, i pravastatin ili Pravakol, lek protiv holesterola.
And I said, "Huh. There are millions of Americans on those two drugs." In fact, we learned later, 15 million Americans on paroxetine at the time, 15 million on pravastatin, and a million, we estimated, on both. So that's a million people who might be having some problems with their glucose if this machine-learning mumbo jumbo that he did in the FDA database actually holds up. But I said, "It's still not publishable, because I love what you did with the mumbo jumbo, with the machine learning, but it's not really standard-of-proof evidence that we have." So we have to do something else. Let's go into the Stanford electronic medical record. We have a copy of it that's OK for research, we removed identifying information. And I said, "Let's see if people on these two drugs have problems with their glucose."
Rekoh: „Ha! Milioni Amerikanaca koriste ova dva leka.“ Zapravo, kako smo kasnije saznali, 15 miliona Amerikanaca uzimalo je paroksetin u to vreme i 15 miliona pravastatin, a milion, prema našoj proceni, uzimalo je oba. Dakle, to je milion ljudi koji možda imaju probleme sa glukozom ako ovo čudo od mašinskog učenja koje je on sproveo u bazi Uprave zaista drži vodu. Ipak, rekao sam: „I dalje nije za objavljivanje, mada mi se dopada to što si uradio sa tim čudesima, sa mašinskim učenjem, ali to što imamo baš i nije odgovarajuća vrsta dokaza.“ Moramo da uradimo nešto drugo. Hajde da uđemo u elektronski medicinski zapis Stenforda. Imamo njegovu kopiju koja je u redu za istraživanje, uklonili smo informacije za identifikaciju, i rekao sam: „Hajde da vidimo da li osobe koje uzimaju ova dva leka imaju probleme sa glukozom.“
Now there are thousands and thousands of people in the Stanford medical records that take paroxetine and pravastatin. But we needed special patients. We needed patients who were on one of them and had a glucose measurement, then got the second one and had another glucose measurement, all within a reasonable period of time -- something like two months. And when we did that, we found 10 patients. However, eight out of the 10 had a bump in their glucose when they got the second P -- we call this P and P -- when they got the second P. Either one could be first, the second one comes up, glucose went up 20 milligrams per deciliter. Just as a reminder, you walk around normally, if you're not diabetic, with a glucose of around 90. And if it gets up to 120, 125, your doctor begins to think about a potential diagnosis of diabetes. So a 20 bump -- pretty significant.
Postoje hiljade i hiljade ljudi u stenfordskim medicinskim podacima koji uzimaju parokesetin i pravastatin. Međutim, bili su nam potrebni posebni pacijenti. Bili su nam potrebni pacijenti koji su uzimali jedan od tih lekova i imali izmerenu glukozu, zatim dobili drugi lek i imali drugu meru glukoze, a sve to u okviru prihvatljivog vremenskog perioda - otprilike oko dva meseca. Kada smo to uradili, pronašli smo deset pacijenata. Međutim, osmoro od tih deset je imalo porast glukoze kada su dobili drugi P - nazivamo ih P i P - kada su dobili drugi P. Bilo koji može biti prvi, zatim nastupa drugi, nivo glukoze raste za 1,1 mmol/l. Samo da podsetim, normalno se krećete, ako niste dijabetičar, sa glukozom od oko 5. Ako dostigne 6,6 - 6,9, vaš doktor će početi da pomišlja na potencijalnu dijagnozu dijabetesa. Tako da porast od 1,1 prilično ima značaja.
I said, "Nick, this is very cool. But, I'm sorry, we still don't have a paper, because this is 10 patients and -- give me a break -- it's not enough patients."
Rekao sam: „Nik, ovo je veoma zanimljivo, ali, žao mi je, i dalje nemamo rad, jer ovo je deset pacijenata i, ma daj, to nije dovoljno pacijenata.“
So we said, what can we do? And we said, let's call our friends at Harvard and Vanderbilt, who also -- Harvard in Boston, Vanderbilt in Nashville, who also have electronic medical records similar to ours. Let's see if they can find similar patients with the one P, the other P, the glucose measurements in that range that we need.
Stoga smo se zapitali šta možemo da uradimo. Rešili smo da pozovemo naše prijatelje sa Harvarda i Vanderbilta - Harvarda u Bostonu i Vanderbilta u Nešvilu - koji imaju elektronske medicinske podatke slične našim. Hajde da vidimo da li mogu da nađu slične pacijente sa jednim P, drugim P, merama glukoze u opsegu koji nam je potreban.
God bless them, Vanderbilt in one week found 40 such patients, same trend. Harvard found 100 patients, same trend. So at the end, we had 150 patients from three diverse medical centers that were telling us that patients getting these two drugs were having their glucose bump somewhat significantly.
Bog ih blagoslovio, Vanderbilt je pronašao 40 takvih pacijenata za nedelju dana, utvrđena je ista tendencija. Harvard je pronašao 100 pacijenata, ista tendencija. Tako smo na kraju imali 150 pacijenata iz tri različita medicinska centra koji su nam ukazivali da pacijenti koji uzimaju ova dva leka imaju donekle značajan porast glukoze.
More interestingly, we had left out diabetics, because diabetics already have messed up glucose. When we looked at the glucose of diabetics, it was going up 60 milligrams per deciliter, not just 20. This was a big deal, and we said, "We've got to publish this." We submitted the paper. It was all data evidence, data from the FDA, data from Stanford, data from Vanderbilt, data from Harvard. We had not done a single real experiment.
Što je još zanimljivije, izostavili smo dijabetičare, jer dijabetičari već imaju poremećenu glukozu. Kada smo pogledali glukozu dijabetičara, bila je povišena za 3,3 mmol/l, ne samo 1,1. Ovo je bila velika stvar i rekli smo: „Moramo da objavimo ovo.“ Predali smo rad. Sasvim je obuhvatao dokaze zasnovane na podacima, podacima iz Uprave za hranu i lekove, podacima iz Stenforda, iz Vanderbilta i Harvarda. Nismo sproveli nijedan pravi eksperiment.
But we were nervous. So Nick, while the paper was in review, went to the lab. We found somebody who knew about lab stuff. I don't do that. I take care of patients, but I don't do pipettes. They taught us how to feed mice drugs. We took mice and we gave them one P, paroxetine. We gave some other mice pravastatin. And we gave a third group of mice both of them. And lo and behold, glucose went up 20 to 60 milligrams per deciliter in the mice.
Ipak, bili smo nervozni. Zato je Nik otišao u laboratoriju dok je rad bio pod razmatranjem. Našli smo nekog ko se razumeo u laboratorijske stvari. Ja to ne radim. Brinem se o pacijentima, ali ne koristim pipete. Naučili su nas kako da miševima dajemo lekove. Uzeli smo miševe i dali im jedan P, paroksetin. Nekim drugim miševima smo dali pravastatin, a trećoj grupi miševa smo dali oba. I gle čuda, glukoza se popela za 1,1 do 3,3 mmol/l kod miševa. Rad je prihvaćen samo na osnovu informatičkih dokaza,
So the paper was accepted based on the informatics evidence alone, but we added a little note at the end, saying, oh by the way, if you give these to mice, it goes up.
ali smo na kraju dodali malu belešku u kojoj smo naveli da, uzgred, ako ovo date miševima, poveća se.
That was great, and the story could have ended there. But I still have six and a half minutes.
To je bilo sjajno, priča se mogla tu završiti. Međutim, imam još šest i po minuta.
(Laughter)
(Smeh)
So we were sitting around thinking about all of this, and I don't remember who thought of it, but somebody said, "I wonder if patients who are taking these two drugs are noticing side effects of hyperglycemia. They could and they should. How would we ever determine that?"
Sedeli smo tako i razmišljali o ovome i ne sećam se ko se setio toga, ali neko je rekao: „Pitam se da li pacijenti koji uzimaju ova dva leka primećuju nuspojave hiperglikemije. Mogli bi da osete i trebalo bi. Kako bismo uopšte to ustanovili?“
We said, well, what do you do? You're taking a medication, one new medication or two, and you get a funny feeling. What do you do? You go to Google and type in the two drugs you're taking or the one drug you're taking, and you type in "side effects." What are you experiencing? So we said OK, let's ask Google if they will share their search logs with us, so that we can look at the search logs and see if patients are doing these kinds of searches. Google, I am sorry to say, denied our request. So I was bummed. I was at a dinner with a colleague who works at Microsoft Research and I said, "We wanted to do this study, Google said no, it's kind of a bummer." He said, "Well, we have the Bing searches."
Rekli smo, pa, šta ćeš uraditi? Uzimaš jedan lek, jedan ili dva nova leka i dobiješ čudan osećaj. Šta onda radiš? Odeš na Gugl i uneseš dva leka koja uzimaš ili jedan lek koji uzimaš i uneseš „nuspojave“. Šta je to što doživljavate? Rekli smo, u redu, hajde da pitamo Gugl da li hoće da podele sa nama unose pretraga, tako da možemo da ih pogledamo i vidimo da li pacijenti sprovode takve pretrage. Gugl je, nažalost, odbio naš zahtev. Tako da sam se baš osećao loše. Bio sam na večeri sa kolegom koji radi na istraživanjima u Majkrosoftu i rekao sam: „Hteli smo da sprovedemo istraživanje i Gugl je odbio, baš bezveze.“ Odgovorio je: „Pa, mi imamo pretrage sa Binga.“
(Laughter)
(Smeh)
Yeah. That's great. Now I felt like I was --
Da. To je sjajno. Sada sam se osećao kao da -
(Laughter)
(Smeh)
I felt like I was talking to Nick again. He works for one of the largest companies in the world, and I'm already trying to make him feel better. But he said, "No, Russ -- you might not understand. We not only have Bing searches, but if you use Internet Explorer to do searches at Google, Yahoo, Bing, any ... Then, for 18 months, we keep that data for research purposes only." I said, "Now you're talking!" This was Eric Horvitz, my friend at Microsoft.
Kao da ponovo pričam sa Nikom. Radi u jednoj od najvećih kompanija na svetu i već pokušavam da učinim da se oseća bolje. Međutim, rekao je: „Ne, Ras, možda me nisi razumeo. Ne samo da imamo pretrage sa Binga, već ako koristiš Internet Eksplorer da bi pretraživao na Guglu, Jahuu, Bingu, gde god, tada čuvamo te podatke 18 meseci samo u svrhe istraživanja.“ Uzviknuo sam: „To je već druga priča!“ To je bio Erik Horvic, moj prijatelj sa Majkrosofta.
So we did a study where we defined 50 words that a regular person might type in if they're having hyperglycemia, like "fatigue," "loss of appetite," "urinating a lot," "peeing a lot" -- forgive me, but that's one of the things you might type in. So we had 50 phrases that we called the "diabetes words." And we did first a baseline. And it turns out that about .5 to one percent of all searches on the Internet involve one of those words. So that's our baseline rate. If people type in "paroxetine" or "Paxil" -- those are synonyms -- and one of those words, the rate goes up to about two percent of diabetes-type words, if you already know that there's that "paroxetine" word. If it's "pravastatin," the rate goes up to about three percent from the baseline. If both "paroxetine" and "pravastatin" are present in the query, it goes up to 10 percent, a huge three- to four-fold increase in those searches with the two drugs that we were interested in, and diabetes-type words or hyperglycemia-type words.
Tako smo sproveli istraživanje gde smo odredili 50 reči koje bi bilo koja osoba mogla uneti ako imaju hiperglikemiju, kao što su „premor“, „gubitak apetita“, „učestalo mokrenje“, „često piškanje“ - oprostite, ali to je jedna od stvari koje biste mogli uneti. Dakle, imali smo 50 fraza koje smo nazvali „dijabetskim rečima“. Prvo smo ustanovili polaznu liniju. Ispostavilo se da oko 0,5 do 1 posto svih pretraga na internetu obuhvata jednu od ovih reči. To je naša osnova. Ako ljudi unesu „paroksetin“ ili „Paksil“ - to su sinonimi - i jednu od ovih reči, dolazi do porasta od oko dva posto za reči koje odgovaraju dijabetesu ako već znate da je prisutna ta reč „paroksetin“. Ako je u pitanju „pravastatin“, porast je oko tri posto u odnosu na polaznu liniju. Ako su u upitu prisutni i „paroksetin“ i „pravastatin“, porast je oko 10 posto, ogromno trostruko do četvorostruko povećanje u tim pretragama sa dva leka koja su nas zanimala i reči vezanih za dijabetes ili hiperglikemiju.
We published this, and it got some attention. The reason it deserves attention is that patients are telling us their side effects indirectly through their searches. We brought this to the attention of the FDA. They were interested. They have set up social media surveillance programs to collaborate with Microsoft, which had a nice infrastructure for doing this, and others, to look at Twitter feeds, to look at Facebook feeds, to look at search logs, to try to see early signs that drugs, either individually or together, are causing problems.
Objavili smo ovo i pridobilo je pažnju. Razlog zbog kojeg zaslužuje pažnju je što nam pacijenti indirektno govore o svojim nuspojavama kroz svoje pretrage. Izneli smo ovo pred Upravu za hranu i lekove. Bili su zainteresovani. Postavili su programe za nadgledanje društvenih medija kako bi sarađivali sa Majkrosoftom, koji je imao finu infrastrukturu za sprovođenje ovog, i drugima, da bi pregledali unose na Tviteru, unose na Fejsbuku, da bi pregledali unose pretraga, da bi pokušali da uoče rane znake da lekovi, bilo zasebno ili zajedno, stvaraju probleme.
What do I take from this? Why tell this story? Well, first of all, we have now the promise of big data and medium-sized data to help us understand drug interactions and really, fundamentally, drug actions. How do drugs work? This will create and has created a new ecosystem for understanding how drugs work and to optimize their use. Nick went on; he's a professor at Columbia now. He did this in his PhD for hundreds of pairs of drugs. He found several very important interactions, and so we replicated this and we showed that this is a way that really works for finding drug-drug interactions.
Šta ovde smatram značajnim? Zašto sam ispričao ovu priču? Pa, pre svega, sada imamo nadu u podatke velikih i malih razmera koji će nam pomoći da razumemo interakcije lekova i ono što je zaista u osnovi, dejstva lekova. Kako lekovi deluju? Ovo će stvoriti i stvorilo je novi ekosistem za razumevanje dejstva lekova i njihovo najoptimalno korišćenje. Nik je nastavio sa ovim; danas je profesor na Kolumbiji. Ovo je sproveo u svom doktoratu na stotinama parova lekova. Otkrio je nekoliko veoma važnih interakcija i tako smo ovo ponovili i pokazali da je ovaj način zaista delotvoran u pronalaženju interakcija između lekova.
However, there's a couple of things. We don't just use pairs of drugs at a time. As I said before, there are patients on three, five, seven, nine drugs. Have they been studied with respect to their nine-way interaction? Yes, we can do pair-wise, A and B, A and C, A and D, but what about A, B, C, D, E, F, G all together, being taken by the same patient, perhaps interacting with each other in ways that either makes them more effective or less effective or causes side effects that are unexpected? We really have no idea. It's a blue sky, open field for us to use data to try to understand the interaction of drugs.
Međutim, u igri je još par stvari. Ne koristimo samo parove lekova u isto vreme. Kao što sam već rekao, ima pacijenata koji uzimaju tri, pet, sedam, devet lekova. Jesu li oni izučavani imajući u vidu njihovu devetostruku interakciju? Da, možemo da uzemo parove, A i B, A i C, A i D, ali šta ako A, B, C, D, E, F i G zajedno, ako ih uzima isti pacijent, možda međusobno ulaze u interakciju na načine koji ih čine bilo više ili manje efikasnim ili stvaraju neočekivane nuspojave? Zaista nemamo predstavu. To je ogromno otvoreno polje u kome možemo koristiti podatke da bismo pokušali da razumemo interakciju lekova.
Two more lessons: I want you to think about the power that we were able to generate with the data from people who had volunteered their adverse reactions through their pharmacists, through themselves, through their doctors, the people who allowed the databases at Stanford, Harvard, Vanderbilt, to be used for research. People are worried about data. They're worried about their privacy and security -- they should be. We need secure systems. But we can't have a system that closes that data off, because it is too rich of a source of inspiration, innovation and discovery for new things in medicine.
Još dve lekcije. Želim da razmislite o moći koji smo uspeli da proizvedemo podacima od ljudi koji su dobrovoljno prijavili svoje neželjene reakcije preko njihovih farmaceuta, njih samih, njihovih doktora, ljudi koji su dozvolili pristup bazama podataka na Stenfordu, Harvardu, Vanderbiltu kako bi bile korišćene u istraživanju. Ljudi su zabrinuti zbog podataka. Zabrinuti su zbog svoje privatnosti i bezbednosti i treba da budu. Potrebni su nam bezbedni sistemi. Ipak, ne smemo imati sistem koji blokira pristup tim podacima jer su previše bogat izvor inspiracije, inovacije i otkrića za nove stvari u medicini.
And the final thing I want to say is, in this case we found two drugs and it was a little bit of a sad story. The two drugs actually caused problems. They increased glucose. They could throw somebody into diabetes who would otherwise not be in diabetes, and so you would want to use the two drugs very carefully together, perhaps not together, make different choices when you're prescribing. But there was another possibility. We could have found two drugs or three drugs that were interacting in a beneficial way. We could have found new effects of drugs that neither of them has alone, but together, instead of causing a side effect, they could be a new and novel treatment for diseases that don't have treatments or where the treatments are not effective. If we think about drug treatment today, all the major breakthroughs -- for HIV, for tuberculosis, for depression, for diabetes -- it's always a cocktail of drugs.
Poslednje što želim da kažem je da smo ovde otkrili dva leka i to je bila pomalo tužna priča. Dva leka su stvarala probleme. Povećavala su nivo glukoze. Mogli su da prouzrokuju dijabetes kod nekoga ko inače ne bi imao dijabetes, tako da biste hteli da koristite ta dva leka vrlo pažljivo zajedno, možda ne zajedno, doneti drugačije odluke prilikom propisivanja lekova. Međutim, postoji još jedna mogućnost. Mogli smo da otkrijemo dva ili tri leka koji ulaze u interakciju na povoljan način. Mogli smo naći nova dejstva lekova koje nijedan od njih nema zasebno, već zajedno, umesto da uzrokuju nuspojavu, mogu biti novi način lečenja za bolesti koje se ne leče ili gde način lečenja nije delotvoran. Ako razmislite o lečenju lekovima danas, svim većim otkrićima - kod HIV-a, tuberkuloze, depresije, dijabetesa - uvek je tu mešavina lekova.
And so the upside here, and the subject for a different TED Talk on a different day, is how can we use the same data sources to find good effects of drugs in combination that will provide us new treatments, new insights into how drugs work and enable us to take care of our patients even better?
Dobra strana u ovome, kao i tema nekog drugog TED govora nekog drugog dana je kako možemo da koristimo iste izvore podataka da bismo otkrili dobra dejstva lekova u kombinaciji koji će nam pružiti nova lečenja, nove uvide u to kako lekovi deluju i omogućiti nam da se još bolje staramo o našim pacijentima.
Thank you very much.
Hvala vam mnogo.
(Applause)
(Aplauz)