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混合不同药物会如何? – 译学馆
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混合不同药物会如何?

What really happens when you mix medications? | Russ Altman

你去看医生的时候做了一些检查
So you go to the doctor and get some tests.
医生说你的血脂高
The doctor determines that you have high cholesterol
所以吃药会有帮助
and you would benefit from medication to treat it.
那么你就买了一盒药
So you get a pillbox.
你有信心
You have some confidence,
你的医生也有信心 觉得这会对你有帮助
your physician has some confidence that this is going to work.
发明这个的药物公司做过很多研究 呈送到FDA
The company that invented it did a lot of studies, submitted it to the FDA.
他们充满怀疑地研究 后来许可了
They studied it very carefully, skeptically, they approved it.
他们大概地知道这个药的机理
They have a rough idea of how it works,
和药物的副作用
they have a rough idea of what the side effects are.
它应该可以
It should be OK.
你跟你的医生又谈了一会儿
You have a little more of a conversation with your physician
医生对你有点儿担心 因为你有些抑郁
and the physician is a little worried because you’ve been blue,
感觉你不是自己
You haven’t felt like yourself,
你对生活不像以前那么充满兴趣
you haven’t been able to enjoy things in life quite as much as you usually do.
你的医生说 “你知道吗 我觉得你有些抑郁
Physician says, “You know, I think you have some depression.
我会给你另一种药“
I’m going to have to give you another pill.”
所以 现在我们所谈论的是两种药物
So now we’re talking about two medications.
这种药 上百万人服用过
This pill also — millions of people have taken it,
公司做过很多研究 FDA许可的 不会错
the company did studies, the FDA looked at it — all good.
你会想应该没问题
Think things should go OK.
应该没问题
Think things should go OK.
好了 等一下
Well, wait a minute.
我们对两种药物一起服用研究了多少?
How much have we studied these two together?
这可是很难做到的
Well, it’s very hard to do that.
实际上 还从来没有
In fact, it’s not traditionally done.
在药物上市之后 我们完全依赖于“市场后监测”
We totally depend on what we call “post-marketing surveillance,” after the drugs hit the market.
我们如何能够弄清楚
How can we figure out if bad things are happening
在两种 三种或五种药物
between two medications?
混合之后 会有哪些坏处呢?
Three? Five? Seven?
问问你最喜欢的被诊断了 几个不同疾病的人
Ask your favorite person who has several diagnoses
他们在吃多少种药
how many medications they’re on.
我为什么关心这个问题呢?
Why do I care about this problem?
我对这非常在意
I care about it deeply.
我是重视信息和数据的人 真的 以我的意见来说
I’m an informatics and data science guy and really, in my opinion,
唯一的能够理解这些药物相互作用 的希望
the only hope — only hope — to understand these interactions
就是平衡不同来源的数据
is to leverage lots of different sources of data
以便弄清楚药物在一起什么时候安全
in order to figure out when drugs can be used together safely
什么时候不安全
and when it’s not so safe.
让我来告诉你们一些数据科学的故事
So let me tell you a data science story.
它来自于我的学生尼克
And it begins with my student Nick.
我们叫他“尼克” 因为那是他的名字
Let’s call him “Nick,” because that’s his name.
[笑声]
(Laughter)
尼克是一个年轻学生
Nick was a young student.
我说 “你知道吗 尼克 我们需要理解药物的工作机理
I said, “You know, Nick, we have to understand how drugs work
不仅是它们单独作用 还有它们的协同机理
and how they work together and how they work separately,
目前我们还知道得不多
and we don’t have a great understanding.
但FDA已经提供了一个惊人的数据库
But the FDA has made available an amazing database.
是关于反作用事件的数据库
It’s a database of adverse events.
他们发表在互联网上
They literally put on the web —
可公开使用 你现在就可以下载
publicly available, you could all download it right now —
成千上万例的
hundreds of thousands of adverse event reports
从病人 医生 公司 药房的反作用报告
from patients, doctors, companies, pharmacists.
这些报告很简单:
And these reports are pretty simple:
它具有病人所有的疾病
it has all the diseases that the patient has,
他们在用的所有药物
all the drugs that they’re on,
以及他们所经过的所有的 反作用、副作用
and all the adverse events, or side effects, that they experience.
它还不是所有的在美國发生的 反作用事件
It is not all of the adverse events that are occurring in America today,
但它有成百上千种药物
but it’s hundreds and hundreds of thousands of drugs.
所以我对尼克说
So I said to Nick,
“让我们考虑葡萄糖
“Let’s think about glucose.
血糖很重要 我们知道它和糖尿病相关
Glucose is very important, and we know it’s involved with diabetes.
让我们看看是否理解血糖的反应
Let’s see if we can understand glucose response.
我让尼克去做了 尼克又回来了
I sent Nick off. Nick came back.
“罗斯”他说
“Russ,” he said,
“我已经根据这个数据库 创建了一个分类
“I’ve created a classifier that can look at the side effects of a drug
可以查看一个药物的副作用
based on looking at this database,
并告诉你这个药物是否会改变血糖”
and can tell you whether that drug is likely to change glucose or not.”
他这样做了 而且在他来说很简单
He did it. It was very simple, in a way.
他把所有我们知道会改变血糖的药物
He took all the drugs that were known to change glucose
还有很多不会改变血糖的都分了类
and a bunch of drugs that don’t change glucose,
他说 “他们的副作用有不同吗?”
and said, “What’s the difference in their side effects?
在疲劳?胃口?以及排尿习惯方面有不同吗?
Differences in fatigue? In appetite? In urination habits?”
所有这些指标都给予他 一个很好的预测
All those things conspired to give him a really good predictor.
他说 “罗斯 我能以93%的精确度预测
He said, “Russ, I can predict with 93 percent accuracy
一个药物会改变血糖”
when a drug will change glucose.”
我说 “尼克 那很好”
I said, “Nick, that’s great.”
他是一个年轻的学生 你得帮他建立自信
He’s a young student, you have to build his confidence.
“但是尼克 有一个问题
“But Nick, there’s a problem.
要让这世界上的每一个医生都知道 所有改变血糖的药物
It’s that every physician in the world knows all the drugs that change glucose,
这是我们作业的核心
because it’s core to our practice.
很好 做的好 但并不是那么有趣
So it’s great, good job, but not really that interesting,
绝对不能发表
definitely not publishable.”
[笑声]
(Laughter)
他说 “我知道 罗斯 我知道你会那样说”
He said, “I know, Russ. I thought you might say that.”
尼克很聪明
Nick is smart.
“我想到你会这么说 所以我做了另一个试验
“I thought you might say that, so I did one other experiment.
我查看了在这个数据中 服用两种药物的病人
I looked at people in this database who were on two drugs,
我查看相似的 血糖改变信号
and I looked for signals similar, glucose-changing signals,
那些服用两种药物的人
for people taking two drugs,
他们只服用其中任一种药物时 血糖没有改变
where each drug alone did not change glucose,
但一起的时候 我看到了很强的信号 “
but together I saw a strong signal.”
我说”喔!你真聪明 好主意 给我看看列表 “
And I said, “Oh! You’re clever. Good idea. Show me the list.”
有很多药物 但并不令人兴奋
And there’s a bunch of drugs, not very exciting.
但在列表上有两种药物 吸引了我的眼球:
But what caught my eye was, on the list there were two drugs:
paroxetine 或 Paxil 一种抗抑郁药物
paroxetine, or Paxil, an antidepressant;
和pravastatin 或Pravachol 抗胆固醇药物
and pravastatin, or Pravachol, a cholesterol medication.
然后我说 “呵 上百万的美国人都在用这两种药物”
And I said, “Huh. There are millions of Americans on those two drugs.”
事实上 我们后来知道
In fact, we learned later,
一千五百万的美国人在用paroxetine 而同时 一千五百万人服用pravastatin
15 million Americans on paroxetine at the time, 15 million on pravastatin,
我们估计 有一百万人 两者同时服用
and a million, we estimated, on both.
所以是一百万人
So that’s a million people
可能在血糖上会有问题
who might be having some problems with their glucose
但会不会他在FDA数据库的异想天开
if this machine-learning mumbo jumbo that he did in the FDA database
只是瞎猫碰上了死耗子呢?
actually holds up.
但我说 “还是不能发表”
But I said, “It’s still not publishable,
因为我喜欢你用搜索技术
because I love what you did with the mumbo jumbo,
所做出来的奇思妙想
with the machine learning,
但它不是我们真正的标准证据
but it’s not really standard-of-proof evidence that we have.”
所以我们必须再做些其他的
So we have to do something else.
让我们进入斯坦福的医疗记录电子库
Let’s go into the Stanford electronic medical record.
我们拷贝了一份 用在做研究上的话 这是允许的
We have a copy of it that’s OK for research,
我们去掉了个人信息
we removed identifying information.
然后我说 “让我们看看同时服用这两种药物的人
And I said, “Let’s see if people on these two drugs
和他们的血糖问题”
have problems with their glucose.”
在斯坦福的医疗记录里 有成千上万的人
Now there are thousands and thousands of people
在服用paroxetine和pravastatin
in the Stanford medical records that take paroxetine and pravastatin.
但我们需要很特别的病人
But we needed special patients.
我们需要服用其中一种药物的病人 有血糖纪录
We needed patients who were on one of them and had a glucose measurement,
然后在服用第二种以后 有另一次血糖纪录
then got the second one and had another glucose measurement,
并且是在一个比较合理的阶段以内 比如像两个月
all within a reasonable period of time — something like two months.
当我们这样做以后 我们发现了10个病人
And when we did that, we found 10 patients.
然而 10个中有8个在血糖上有变化
However, eight out of the 10 had a bump in their glucose
当他们服用第二个P药物的时候 我们把这个叫做P和P
when they got the second P — we call this P and P —
当他们服用第二个P时
when they got the second P.
可以是任意一个在先 服用第二个后
Either one could be first, the second one comes up,
血糖升高了20mg/dl
glucose went up 20 milligrams per deciliter.
给一个小小的提示
Just as a reminder,
当你没有糖尿病 正常的四处活动时
you walk around normally, if you’re not diabetic,
你的血糖是90
with a glucose of around 90.
如果上升到120 125
And if it gets up to 120, 125,
你的医生就会认为是潜在的糖尿病
your doctor begins to think about a potential diagnosis of diabetes.
所以 一次上升了20单位 相当明显
So a 20 bump — pretty significant.
我说 “尼克 这太好了
I said, “Nick, this is very cool.
但很抱歉 我们还是没有论文
But, I’m sorry, we still don’t have a paper,
因为这是10个病人 而且 让我想想
because this is 10 patients and — give me a break —
我们没有足够的病人”
it’s not enough patients.”
所以我们说 我们还能怎么做呢?
So we said, what can we do?
后来我们决定打电话 给我们在Harvard和Vanderbilt的朋友
And we said, let’s call our friends at Harvard and Vanderbilt,
在波士顿的哈佛和纳什维尔的 范德比尔
who also — Harvard in Boston, Vanderbilt in Nashville,
也都有和我们相似的医疗电子记录
who also have electronic medical records similar to ours.
我们想看看他们是否能够找到 相似的病人
Let’s see if they can find similar patients
服用一种P 然后另一种P
with the one P, the other P, the glucose measurements
并在我们需要的那个范围内 做过血糖检测
in that range that we need.
上帝祝福他们 范德贝尔 在一周内发现40个这样的病人
God bless them, Vanderbilt in one week found 40 such patients,
都有同样的血糖增长
same trend.
哈佛发现100个同样的病人 也有着一样的增长
Harvard found 100 patients, same trend.
所以 最后我们有150个病人 来自三个不同的的医学中心
So at the end, we had 150 patients from three diverse medical centers
这150个病人的记录告诉我们 这些使用这两种药物的病人
that were telling us that patients getting these two drugs
在某种程度上都有血糖的明显改变
were having their glucose bump somewhat significantly.
更令人感兴趣的是 我们没有算上糖尿病人
More interestingly, we had left out diabetics,
因为糖尿病人的血糖本身就是 一本糊涂账
because diabetics already have messed up glucose.
当我们查看糖尿病人的血糖
When we looked at the glucose of diabetics,
它通常是升高60mg以上 而不是只有20
it was going up 60 milligrams per deciliter, not just 20.
这是一个了不起的结果 然后我们说 “我们一定要发表这个结果”
This was a big deal, and we said, “We’ve got to publish this.”
我们呈送了文章
We submitted the paper.
全是数据证据
It was all data evidence,
来自FDA 来自斯坦福
data from the FDA, data from Stanford,
来自范德贝尔 来自哈佛
data from Vanderbilt, data from Harvard.
我们还没做一个实验
We had not done a single real experiment.
但我们很紧张
But we were nervous.
所以当文章在审查阶段 尼克去了实验室
So Nick, while the paper was in review, went to the lab.
我们找到了一些懂得实验的人
We found somebody who knew about lab stuff.
我做不了那个活
I don’t do that.
我看病人 我不用移液器
I take care of patients, but I don’t do pipettes.
他们教我们怎样喂老鼠吃药
They taught us how to feed mice drugs.
我们拿过老鼠 给它们喂一种P paroxetine
We took mice and we gave them one P, paroxetine.
我们又给某些老鼠pravastatin
We gave some other mice pravastatin.
我们给了第三组老鼠两种药
And we gave a third group of mice both of them.
老鼠的血糖
And lo and behold, glucose went up 20 to 60 milligrams per deciliter
升高了20-60毫克/分升
in the mice.
所以 基于尽有信息考据的文章 被接受了
So the paper was accepted based on the informatics evidence alone,
但是我门在文章的结尾 加上了一个小小的注解
but we added a little note at the end,
顺便说一下 如果你给老鼠喂两种药 血糖会升高
saying, oh by the way, if you give these to mice, it goes up.
这太棒了 故事在此应该了结了
That was great, and the story could have ended there.
但我还要讲六分半钟
But I still have six and a half minutes.
[笑声]
(Laughter)
当我们坐在一起 想着这件事时
So we were sitting around thinking about all of this,
我记不得是谁说的了 但有人说:
and I don’t remember who thought of it, but somebody said,
“我好奇那些服用 这两种药的病人
“I wonder if patients who are taking these two drugs
是否注意到自己有高血糖的症状
are noticing side effects of hyperglycemia.
他们理应注意到的
They could and they should.
我们又怎样确定他们 是否真有呢
How would we ever determine that?”
我们说:那你怎么做呢?
We said, well, what do you do?
“如果你在服用一种新药 或者是两种
You’re taking a medication, one new medication or two,
然后你有了一种奇怪的感觉
and you get a funny feeling.
你会怎么做?
What do you do?
你会在谷歌上查找
You go to Google
你会在搜索栏上打出 两种药物的名称
and type in the two drugs you’re taking or the one drug you’re taking,
然后输入”副作用“
and you type in “side effects.”
你会看到什么?”
What are you experiencing?
于是我们说还不错
So we said OK,
我们可以试着问问谷歌 他们能不能与我们分享搜索记录
let’s ask Google if they will share their search logs with us,
然后我们可以通过这些搜索记录
so that we can look at the search logs
进而知道病人是否在做这种搜索
and see if patients are doing these kinds of searches.
很遗憾的是 谷歌拒绝了我们的请求
Google, I am sorry to say, denied our request.
于是我有点闷闷不乐
So I was bummed.
当时我在和一个微软公司的同事吃饭
I was at a dinner with a colleague who works at Microsoft Research
我说:”我们想要做一个调查
and I said, “We wanted to do this study,
但谷歌拒绝了 这真令人烦恼”
Google said no, it’s kind of a bummer.”
他说“哦 我们有必应bing搜索啊”
He said, “Well, we have the Bing searches.”
[笑声]
(Laughter)
是的
Yeah.
这太棒了
That’s great.
我感觉我就像要……了一样
Now I felt like I was —
[笑声]
(Laughter)
我感觉我就像又在和尼克说话了
I felt like I was talking to Nick again.
他在全世界最大的公司工作
He works for one of the largest companies in the world,
我不想伤害他的自信心
and I’m already trying to make him feel better.
但他说:“不 罗斯…… 你可能不知道
But he said, “No, Russ — you might not understand.
我们不只有必应bing
We not only have Bing searches,
但是如果你用IE浏览器 在谷歌上搜索词条
but if you use Internet Explorer to do searches at Google,
或是在雅虎 bing上
Yahoo, Bing, any …
然后我们将这些搜索信息 为了学术目的自动保存18个月
Then, for 18 months, we keep that data for research purposes only.”
我于是说:”你真有两下子!“
I said, “Now you’re talking!”
他叫 Eric Horvitz 我在微软的朋友
This was Eric Horvitz, my friend at Microsoft.
因此我们就这样做了调查
So we did a study
我们先确定了高血糖症患者
where we defined 50 words that a regular person might type in
可能会搜索的50个词条
if they’re having hyperglycemia,
比如”疲劳“”食欲不振“”尿频“等
like “fatigue,” “loss of appetite,” “urinating a lot,” “peeing a lot” —
不好意思 但这些是你可能输入的词语
forgive me, but that’s one of the things you might type in.
于是我们有了50个 叫做“肥胖词语”的词条
So we had 50 phrases that we called the “diabetes words.”
我们先是确定了基线搜索率
And we did first a baseline.
大概0.5-1%的网络搜索
And it turns out that about .5 to one percent
含有一个这些词语
of all searches on the Internet involve one of those words.
这就是我们的底线比率
So that’s our baseline rate.
如果人们输入paroxetine或Paxil —它们是同义词
If people type in “paroxetine” or “Paxil” — those are synonyms —
它们其中的一个
and one of those words,
那么如果搜索者已经知道了 这个药物术语的话
the rate goes up to about two percent of diabetes-type words,
则在肥胖类内容的搜索中 它们出现的概率升高到了大约2%
if you already know that there’s that “paroxetine” word.
如果是pravastatin 概率则超过了基线3%
If it’s “pravastatin,” the rate goes up to about three percent from the baseline.
如果paroxetine和pravastatin同时出现
If both “paroxetine” and “pravastatin” are present in the query,
那么比例则到达了10%
it goes up to 10 percent,
这是在那些肥胖类或高血糖类 搜索中
a huge three- to four-fold increase
出现我们研究的两种药物的概率的
in those searches with the two drugs that we were interested in,
三至四倍的增长
and diabetes-type words or hyperglycemia-type words.
我们发表了这个结果
We published this,
获取了一些注意
and it got some attention.
这个研究值得注意的原因是
The reason it deserves attention
病人在通过他们的网上搜索
is that patients are telling us their side effects indirectly
向我们间接地传达他们的副作用
through their searches.
我们吸引了FDA的注意
We brought this to the attention of the FDA.
他们很感兴趣
They were interested.
他们建立了社交网站监测项目
They have set up social media surveillance programs
和有着可以完成这些项目的设施的
to collaborate with Microsoft,
微软合作
which had a nice infrastructure for doing this, and others,
在推特网页上
to look at Twitter feeds,
脸书上
to look at Facebook feeds,
观察人们的搜索内容
to look at search logs,
以此来发现一种或多种药物 可能在产生问题的
to try to see early signs that drugs, either individually or together,
早期迹象
are causing problems.
那么我们由此学到了什么?为什么讲这个故事?
What do I take from this? Why tell this story?
第一
Well, first of all,
我们现在有了大数据的支持
we have now the promise of big data and medium-sized data
来帮助我们了解药物的相互作用
to help us understand drug interactions
更本上就是药物的机理
and really, fundamentally, drug actions.
药物是怎样起效的?
How do drugs work?
这已经创造了一种新的系统
This will create and has created a new ecosystem
来了解药物的工作原理 以及优化它们的使用
for understanding how drugs work and to optimize their use.
尼克继续从事着这事 他现在是哥伦比亚大学的教授
Nick went on; he’s a professor at Columbia now.
他在他的PhD中研究了 成百对的药物
He did this in his PhD for hundreds of pairs of drugs.
他发现了几种十分重要的药物反应
He found several very important interactions,
于是我们记录了这些结果
and so we replicated this
而且我们展示了这种方法
and we showed that this is a way that really works
在发现药物相互作用上的可行性
for finding drug-drug interactions.
然而 这有几件事
However, there’s a couple of things.
我们不只是研究一对药物
We don’t just use pairs of drugs at a time.
像我之前说的 有的人同时服用 3.5.7.9种药物
As I said before, there are patients on three, five, seven, nine drugs.
他们的九种药物反应有被研究过吗?
Have they been studied with respect to their nine-way interaction?
是的 我们确实可以用排列组合 a和b,a和c,a和d
Yes, we can do pair-wise, A and B, A and C, A and D,
但如果是a,b,c,d,e,f,g全部混在一起呢?
but what about A, B, C, D, E, F, G all together,
它们被同一个患者服用
being taken by the same patient,
可能会和对方反应
perhaps interacting with each other
有可能是让药效增强或是减弱
in ways that either makes them more effective or less effective
更甚是始料不及的副作用?
or causes side effects that are unexpected?
我们真不知道
We really have no idea.
我们可以很自由地使用数据
It’s a blue sky, open field for us to use data
来了解药物的协同机理
to try to understand the interaction of drugs.
另外两个教训:
Two more lessons:
我想让你们想想 我们使用人们
I want you to think about the power that we were able to generate
通过他们的药师 医生或是自己 上传的药物反作用案例
with the data from people who had volunteered their adverse reactions
那些为斯坦福 哈佛和范德比尔数据库 提供了资料的案例
through their pharmacists, through themselves, through their doctors,
来用作研究
the people who allowed the databases at Stanford, Harvard, Vanderbilt,
能够产生的力量有多大
to be used for research.
人们担心自己的数据被泄露
People are worried about data.
他们害怕自己的隐私和信息安全被偷取他们理应这样想
They’re worried about their privacy and security — they should be.
因此我们需要安全的网络系统
We need secure systems.
但是我们不应该容忍那些 垄断这些数据的网络系统
But we can’t have a system that closes that data off,
因为网络资源是在药理方面
because it is too rich of a source
创造灵感 创新和发现的
of inspiration, innovation and discovery
强大资源
for new things in medicine.
我最后想说的是
And the final thing I want to say is,
在这个案例中 我们发现了两种药物 十分遗憾
in this case we found two drugs and it was a little bit of a sad story.
这两种药物实际上产生了麻烦
The two drugs actually caused problems.
它们增加血糖含量
They increased glucose.
它们可能让 原本没有糖尿病的人
They could throw somebody into diabetes
患上糖尿病
who would otherwise not be in diabetes,
所以当你同时使用这两种药时 会千万小心
and so you would want to use the two drugs very carefully together,
分开用时也是
perhaps not together,
订购药物时做出其他选择
make different choices when you’re prescribing.
但也有另一种可能
But there was another possibility.
我们可能可以发现 二至三种药物
We could have found two drugs or three drugs
能通过有益的方式相互反应
that were interacting in a beneficial way.
我们也可以发现药物的新作用
We could have found new effects of drugs
单独不具有的
that neither of them has alone,
但是在一起服用 不是产生副作用
but together, instead of causing a side effect,
而是成为一种新型治疗手段
they could be a new and novel treatment
治疗那些无药可医的病症
for diseases that don’t have treatments
或是旧的治疗方法效果不明显的疾病
or where the treatments are not effective.
如果我们今天纵观药物治疗
If we think about drug treatment today,
所有的重大突破
all the major breakthroughs —
治疗艾滋病 肺结核 抑郁症 或是糖尿病的
for HIV, for tuberculosis, for depression, for diabetes —
都是几种药物的混合疗法
it’s always a cocktail of drugs.
所以我们目前所做的
And so the upside here,
也是TED大会今后探讨的话题
and the subject for a different TED Talk on a different day,
就是我们怎样使用同样的数据资源
is how can we use the same data sources
来寻找药物混合使用后的好处
to find good effects of drugs in combination
这将会为我们提供新的疗法
that will provide us new treatments,
和药物工作原理的新视角
new insights into how drugs work
使我们可以更好地治疗我们的病人
and enable us to take care of our patients even better?
十分感谢
Thank you very much.
[掌声]
(Applause)

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视频概述

如果你为了治疗两种不同病症而服用不同的药物,这就有一个发人省醒的问题:由于药物的协同机理很难研究,你的医生可能不会通晓同时服用的后果。在这个有趣而又易懂的谈话中,Russ Altman揭示了医生是怎样用一种新奇的资源研究未知的药物间作用的:搜索引擎索引。

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视频来源

https://www.youtube.com/watch?v=avuhY7D71sQ

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