????我們是否真的在兩三年之后,，就能實(shí)現(xiàn)治愈癌癥的愿景,？波士頓小型生物科技公司Berg的總裁尼文?納雷因表示，可能真是這樣,。

????憑借億萬富翁,、房地產(chǎn)業(yè)大鱷卡爾?伯格和米奇?格雷提供的資金，納雷因和他帶領(lǐng)的科學(xué)家,、技術(shù)人員和編程人員團(tuán)隊(duì)耗時(shí)6年,，完善并測試了一個(gè)人工智能平臺，納雷因認(rèn)為,，這個(gè)平臺可能很快就會解開癌癥的密碼,，同時(shí)為治療包括帕金森癥在內(nèi)的一系列嚴(yán)重疾病提供有價(jià)值的信息。

????憑借著跟多所大學(xué),、醫(yī)院甚至美國國防部建立的合作關(guān)系,，伯格公司及其超級計(jì)算機(jī)系統(tǒng)已經(jīng)分析了成千上萬的病歷和組織樣本,，以找到有可能全新的藥物靶標(biāo)和生物標(biāo)志。

????經(jīng)過龐大的數(shù)據(jù)計(jì)算,，伯格公司開發(fā)出第一款新藥——BPM 31510,，目前該藥已經(jīng)進(jìn)入臨床測試階段。它可以重組癌細(xì)胞的新陳代謝,，重新教會癌細(xì)胞如何死亡,。在這個(gè)過程中，癌細(xì)胞就會自然死亡,，使患者不必經(jīng)歷對身體傷害極大又十分昂貴的化療過程,。

????到目前為止，伯格公司的主要資源都集中在前列腺癌上,，因?yàn)槟壳坝写罅筷P(guān)于前列腺癌的數(shù)據(jù)可供研究,。不過拜一項(xiàng)最新合作所賜，該公司現(xiàn)在已經(jīng)開始構(gòu)建針對胰腺癌的新模型了,。胰腺癌也是最兇險(xiǎn)的癌癥之一,，目前的存活率只有7%。

????這個(gè)目標(biāo)本身可謂雄心勃勃,，但它還只是冰山的一角,。除了治療前列腺癌和胰腺癌之外，伯格公司還希望分析多種其它疾病的數(shù)據(jù),，包括乳腺癌,。另外，伯格公司還認(rèn)為,，它的人工智能平臺可以根據(jù)病人的特異性制定專門針對個(gè)別患者的治療方案,，從而將掀起一場藥物測試的革命，并顯著降低藥物的負(fù)面作用在臨床實(shí)驗(yàn)和醫(yī)療實(shí)踐中的風(fēng)險(xiǎn),。

????我采訪了卡爾?伯格和納雷因,，探討了該公司的工作機(jī)制，以及他們在未來幾年內(nèi)的目標(biāo),。以下是采訪摘要,。

????財(cái)富：卡爾，你為什么選擇從房地產(chǎn)業(yè)轉(zhuǎn)向醫(yī)療行業(yè),？它的進(jìn)展是否符合你的預(yù)期,？

????卡爾?伯格：我已經(jīng)在風(fēng)投界干了40年了，但我從來沒有觸碰過生物科技領(lǐng)域,，因?yàn)槲覔?dān)心與政府審批有關(guān)的風(fēng)險(xiǎn),。做風(fēng)投本身就不容易，又要多花一番工夫去獲得美國食品藥品監(jiān)督管理局的認(rèn)證，那就會更難,。但大概8年前我曾說過,，現(xiàn)在我不必再做一堆小公司了，而是有能力做一些影響力足夠大甚至有希望改變世界的事,。這個(gè)目標(biāo)激勵了我,，然后我認(rèn)識了尼文，我們就是這樣開始這項(xiàng)事業(yè)的,。

????是尼文說服了你進(jìn)入醫(yī)療行業(yè),，還是你找到了尼文？

????卡爾?伯格：當(dāng)時(shí)我正考慮投資一款護(hù)膚產(chǎn)品,，然后我在邁阿密大學(xué)經(jīng)人介紹認(rèn)識了尼文,。尼文當(dāng)時(shí)是那個(gè)項(xiàng)目的經(jīng)理，那個(gè)項(xiàng)目開始大約一兩個(gè)月后,，尼文給我打電話說：“卡爾,，這款護(hù)膚產(chǎn)品似乎對治療癌癥有效?！蔽艺f：“好吧，如果你治好了誰,，記得讓我知道,。”

????你聽起來好像不太相信,。

????卡爾?伯格：人人都知道,，每種癌癥都是不一樣的，那么這個(gè)東西怎么會有效呢,？在我看來根本就說不通,。這時(shí)尼文說：“我能飛到加州向你展示一下我的成果嗎？”然后他就來了,，經(jīng)過一番交流,，我相信他使用的技術(shù)和方法真的有可能在醫(yī)藥市場掀起一場革命。

????尼文,，你是怎樣讓卡爾?伯格相信,，你那款護(hù)膚產(chǎn)品上有可能治愈癌癥？

????尼文?納雷因：當(dāng)我見到卡爾時(shí),，我們原則上同意,，肯定有辦法建立一個(gè)更高效的醫(yī)療系統(tǒng)，它能夠以非常精確的方式,，將病人與正確的藥物進(jìn)行匹配,。卡爾支持我們將這個(gè)理念引向深入。我們不是利用篩選過的化學(xué)制品治療病人,，而是從人體的細(xì)胞樣本入手去了解人體生物學(xué),，然后據(jù)此研發(fā)藥物的。我們使用的是人工智能,，而不是各種假設(shè),。

????人工智能究竟在這個(gè)過程中起了什么樣的作用？

????尼文?納雷因：如果你從一個(gè)假設(shè)入手,，你就排除了很多其他可能產(chǎn)生真正效果的領(lǐng)域,。有多少次藥物在晚期測試的失敗，是因?yàn)樗脑缙诳蒲胁粔蛟鷮?shí),，或是選擇了錯(cuò)誤的靶標(biāo),？

????在伯格公司，我們只針對一個(gè)組織樣本就建立了超過14萬億個(gè)數(shù)據(jù)點(diǎn),。無論是使用人力,，還是使用傳統(tǒng)的推理假設(shè)模型，要想從所有這些數(shù)據(jù)中摘取有價(jià)值的信息,，都是不可能的,。所以當(dāng)我們構(gòu)建我們所稱的疑問型生物平臺時(shí)，我們使用了人工智能來分析所有數(shù)據(jù),。人工智能可以從病人的生物數(shù)據(jù),、臨床樣本和人口統(tǒng)計(jì)資料中摘取所有的信息，并且可以根據(jù)類似性和差異性進(jìn)行分類和分層,，從而幫助我們了解健康細(xì)胞和病變細(xì)胞之間的差異,。

????14萬億個(gè)數(shù)據(jù)點(diǎn)聽起來有點(diǎn)超負(fù)荷的感覺。

????尼文?納雷因：所以它有兩個(gè)組成部分：首先是生物信息,，然后還有所謂的“組學(xué)”,。我們不僅僅是分析基因組，而是研究一個(gè)組織樣本的所有基因,、蛋白質(zhì),、代謝分子、脂質(zhì),、病歷記錄,、人口統(tǒng)計(jì)學(xué)資料、年齡,、性別等等信息,。我們把人體的3萬個(gè)基因與6萬種蛋白蛋和幾千種脂質(zhì)、代謝分子的信息綜合起來,，然后把這些成分用具有機(jī)器學(xué)習(xí)功能的高階數(shù)學(xué)算法進(jìn)行計(jì)算,，以了解它們的各種關(guān)聯(lián)性和相關(guān)性,。

????組學(xué)是一個(gè)相對較新的術(shù)語，它意味著你不能僅僅盯著基因組,，而是所有的“組”——比如蛋白質(zhì)組,、代謝組等等。雖然可能我們出生就帶著3萬個(gè)基因,，而且這些基因可能還有某些天生的突變,，但這并不是故事的結(jié)尾。你住在紐約市,，暴露在環(huán)境中的不同物質(zhì)里,，你的飲食與阿拉巴馬州的某個(gè)人不一樣，你的睡眠習(xí)慣也與猶他州的某個(gè)人不一樣,。所以我們認(rèn)為,，這些東西應(yīng)該綜合起來，才能完整描繪你的“組學(xué)”,，即你的整體資料,。

????但是這些東西怎樣讓我們治病,？看起來只是一堆數(shù)據(jù)分析而已,。

????尼文?納雷因：我知道你經(jīng)常報(bào)道航空業(yè)，你可能很熟悉航空公司的路線圖了,，它們展示了各個(gè)樞紐城市和目的地之間的聯(lián)系,。在我們的疑問型生物平臺上，所有這些數(shù)據(jù)分析的結(jié)果看起來就像3D版的航空路線圖,。但這些聯(lián)系并不是城市與城市之間的，而是基因與蛋白質(zhì)之間,。然后我們把重點(diǎn)放在那些大的樞紐上,，看看是否出了什么問題。比如如果達(dá)拉斯市是在俄克拉荷馬州境內(nèi),，我們都知道肯定有問題,，這時(shí)人工智能就會把達(dá)拉斯推回北德克薩斯州，然后分析生物學(xué)中的哪些事件可以讓人體重啟正常的流程,。這就是我們的研究重點(diǎn),，即生物的基本元素，以及能讓健康流程重啟的基因和蛋白質(zhì),。

????在真實(shí)世界中,，你利用該平臺取得過成功嗎？

????尼文?納雷因：我們正在測試一款名叫BPM 31510的藥物,，它就是我們利用疑問型平臺研發(fā)的,。目前顯示的結(jié)果非常令人鼓舞。該平臺顯示，新陳代謝越多,，治療就會越有效,。根據(jù)我們對患有某些癌癥的病人的觀察，的確是這樣,。比如我們在一名患有膀胱癌的病人身上測試了這款藥物,，膀胱癌是一種非常兇險(xiǎn)的癌癥，幾乎對所有療法都沒有反應(yīng),。我們在他身上使用了BPM 31510,，該藥以癌細(xì)胞的新陳代謝為靶向，到了第18周,，他的腫瘤已經(jīng)完全消失了,。

????這種療法取得專利了嗎？

????尼文?納雷因：我們把前六年的大部分時(shí)間花在構(gòu)建平臺,、研究各個(gè)重點(diǎn)領(lǐng)域,、對早期藥物進(jìn)行臨床實(shí)驗(yàn)和實(shí)現(xiàn)技術(shù)使用的多樣化上。我們在全球已經(jīng)注冊了500多個(gè)專利,。所以我們在生物學(xué),、數(shù)學(xué)、信息學(xué)上都有專利,，對每個(gè)個(gè)體生物指標(biāo)和藥物靶標(biāo)也都有專利,。總之我們有著非常堅(jiān)實(shí)的知識產(chǎn)權(quán)資產(chǎn),。

????你們的競爭對手是誰,？與他們相比，你們在今后的發(fā)展中處于何種地位,？

????尼文?納雷因：我們經(jīng)常會被問到這個(gè)問題,。也有一些人和機(jī)構(gòu)在做我們正在做的事。他們是一些蛋白質(zhì)和分析學(xué)上的頂尖公司,，但我們目前還沒有發(fā)現(xiàn)哪家公司把有關(guān)的生物學(xué),、組學(xué)研究和臨床能力整合到一個(gè)疑問型平臺上，來對人體產(chǎn)生堅(jiān)實(shí)的理解,，并以一種新的方式開發(fā)藥物,。另外，我們是用數(shù)據(jù)產(chǎn)生假設(shè),，而不是用假設(shè)產(chǎn)生數(shù)據(jù),，所以它是一種不同的方法。我們在這方面還是挺獨(dú)特的——無論是在技術(shù)上還是商業(yè)上,。

????卡爾,，過去幾年里,，你和米奇?格雷一直是伯格公司的唯一投資人，為什么會這樣,？

????卡爾?伯格：如果你在這些東西的早期階段就讓太多人進(jìn)入,，尤其是這個(gè)項(xiàng)目又有比較高的風(fēng)險(xiǎn)，那么你基本上肯定會失敗,，因?yàn)橹灰惺裁词虑槌隽瞬铄e(cuò),，人們就會感到沮喪和擔(dān)心。憑借多年的風(fēng)投經(jīng)歷,，我基本上已經(jīng)處變不驚了,。就算出了大亂子，我也不會那么沮喪,。我知道那就是你需要預(yù)料到的,。

????你們現(xiàn)在打算開放融資了嗎？

????卡爾?伯格：我們當(dāng)然希望做些其他事情,，并且引入新的投資人,。但我們希望在此之前先達(dá)到某一個(gè)點(diǎn)。我認(rèn)為我們離那個(gè)點(diǎn)已經(jīng)非常近了,。（財(cái)富中文網(wǎng)）

????譯者：樸成奎

????審校：任文科

????Could we be just two or three years away from curing cancer? Niven Narain, the president of Berg, a small Boston-based biotech firm, says that may very well be the case.

????With funding from billionaire real-estate tycoon Carl Berg as well as from Mitch Gray, Narain, a medical doctor by training, and his small army of scientists, technicians, and programmers, have spent the last six years perfecting and testing an artificial intelligence platform that he believes could soon crack the cancer code, in addition to discovering valuable information about a variety of other terrible diseases, including Parkinson’s.

????Thanks to partnerships formed with universities, hospitals, and even the U.S. Department of Defense, Berg and its supercomputers have been able to analyze thousands of patient records and tissue samples to find possible new drug targets and biomarkers.

????All this data crunching has led to the development of Berg’s first drug, BPM 31510, which is in clinical trials. The drug acts by essentially reprogramming the metabolism of cancer cells, re-teaching them to undergo apoptosis, or cell death. In doing so, the cancer cells die off naturally, without the need for harmful and expensive chemotherapy.

????So far, Berg has concentrated most of its resources on prostate cancer, given the large amount of data available on the disease. But thanks to recently announced partnerships, the firm is now building a new modeltargeting pancreatic cancer, which is one of the deadliest forms of cancers with a survivorship rate of only 7%.

????Ambitious as that may be, it is really just the tip of the iceberg. In addition to mapping out prostate and pancreatic cancer, Berg hopes to analyze data from a whole host of other diseases, including breast cancer. Additionally, Berg thinks his company’s artificial intelligence platform can also revolutionize drug testing by creating individualized patient-specific treatment options, which he believes will ultimately reduce the risk of adverse drug interactions in clinical trials and hospitals by a significant degree.

????I sat down with Berg and Narain to discuss how the company works and what they hope to accomplish in the next few years. The following interview has been edited for publication.

????Fortune: Carl, why did you decide to move from real estate into healthcare and has it panned out like you thought it would?

????Carl Berg: I have been in the venture capital business for 40 years but I never once touched biotech because I was concerned about the risk associated with government approval – it’s bad enough when you’re doing venture capital but adding one more equation, like getting approval from the FDA [Food and Drug Administration] makes it a lot harder. But about eight years ago I said, instead of getting into a whole bunch of small companies, I am in a position now where I can do something really big in a hope that it changes the world. So that’s what motivated me, and then I met with Niven, and that’s what got it started.

????Did Niven convince you to go into biotech or did you find Niven?

????CB: I was considering a skin care product investment and I was introduced to Niven at the University of Miami. Niven was the project manager and about a couple months into work on this product, Niven called me and said “Carl, this skin care product appears to have an effect on cancer.” To which I said “Sure, whenever you cure somebody, let me know.”

????You didn’t sound very convinced.

????CB: Everybody knows that every cancer is different, so how could this one thing work? That didn’t make any sense to me. And Niven said, “Can I fly out to California and show you my results?” And he came out, and we talked, and I got convinced that the technology he was using and the approach he was taking, could revolutionize the pharmaceutical market.

????Niven, what did you say to convince Carl Berg that your work on skin cream could possibly lead to a cure for cancer?

????Niven Narain: When I met with Carl we were aligned philosophically that there has to be a better way to create a more efficient healthcare system – one that really matches the right patients to the right drugs in a very precise manner. So Carl supported taking this concept to the next level. Instead of treating humans with chemicals, that are screened to become drugs, we actually started with human tissue samples and work to understand the biology and develop drugs based on that. Using AI [artificial intelligence] instead of hypotheses.

????How exactly does artificial intelligence come into play here?

????NN: When you start with a hypothesis, you are dismissing a lot of other areas that might actually have an impact on whatever you are trying to figure out. How many times do we see drugs get to late stage trials and fail because the early science either wasn’t robust enough or focused on the wrong target?

????At Berg, we use AI to create over 14 trillion data points on only one tissue sample. It is actually humanly impossible to go through all this data and use the traditional hypothesis inference model to glean any value out of all of it. So early on when we built what we call an interrogative biology platform using AI to go through all that data. AI is actually able to take all the information from the patient’s biology, clinical samples, and demographics and really categorize which ones are similar and which ones are different and then stratify those in a way that helps us understand the difference between the healthy and diseased.

????Fourteen trillion data points sounds like information overload.

????NN: So there are two components: the upfront biological and there is something called omics. We go much deeper than just analyzing the genome, we look at all the genes in that tissue sample, all the proteins, metabolites, lipids, patients records, demographics, age, sex, gender, etc. We combine the 30,000 genes in the body with about 60,000 proteins and a few hundred lipids, metabolites. Then we take those components and subject them to high order mathematic algorithm that essentially learns, uses machine learning, to learn the various associations and correlations.

????Omics – it’s a fairly new term. It means you’re going beyond just the genome. It means all the omics – proteomics, metabolomics, and proteins. So we may be born with 30,000 genes, and those genes were born with certain mutations, but that’s not the end of the story. You live in New York City, you are exposed to different things in the environment, your diet is different than someone who lives in Alabama and your sleeping habits are different from some who lives in Utah. We believe all of these things have to be put together to tell the whole story of your omics – the full profile of you.

????But how does all of this get us to a cure for anything? Seems like a bunch of number crunching.

????NN: I know you cover the airline industry pretty intently, so you are probably familiar with those airline route maps that show all the connections between hubs cities and destinations. So with the interrogative biology platform, the result of all that number crunching looks similar to a 3D version of those maps. But instead of those connections going between cities, they are going between genes and proteins. We then focus in on the big hubs and see what, if anything, is wrong. For example, in a system, if Dallas is in Oklahoma, obviously we know something is wrong, so the AI helps to push Dallas back into North Texas, and analyze what events happened in the biology to make that a normal process again. This is what we focus in on. The elements within the biology, the genes and proteins that made that a healthy process again.

????Have you had any success using this platform in a real world situation?

????NN: We are in clinical trials for a drug, BPM 31510, which we developed using the interrogative platform. The results we have seen so far have been very encouraging. The platform predicted that the more metabolic, the better the treatment will work. And that is exactly what we are seeing in patients for certain types of cancer. For example, we tested this on a patient who had bladder cancer. It was a very aggressive cancer, which failed to respond to all other therapies. We then put him on BPM 31510, which targeted the metabolism of the cancer cell, and by week 18, the tumor was completely gone.

????Is this a patented process?

????NN: We spent the lion’s share of the first six years building the platform, developing it into various areas of focus, getting our early drugs into clinical trials and diversifying the use of the technology. And we have filed over 500 patents around the world that govern this specific elevated biology. So we have patents on the biological process, on the mathematics, the informatics, on each individual candidate biomarker, and drug targets. It is a very robust IP portfolio.

????Who are your competitors and where are you versus them in taking this process to the next level?

????NN: We get asked that fairly often. There are folks and entities that do pieces of what Berg does. They’re leading companies focused on proteins or analytics, but there isn’t one company we can identify or know of that has taken the biology, the omics, the clinical capability and put it all into an interrogative platform to really allow for a robust understanding of the biology to discover drugs in a different way. Also, we are allowing the data to generate hypotheses instead of hypotheses generating data, so it’s a really different approach. We are fairly unique in that respect – both from a technology, but also from a commercial standpoint.

????Carl, for the last few years, you and Mitch Gray have been the only investors in Berg. How come?

????CB: I’ve learned that if you get too many people in the early stages of these things, especially within something as risky as this was, basically you have failed because people get upset and they get worried when anything goes wrong. Through all the years that I have been doing this I can kind of roll with the punches. If something goes haywire it doesn’t upset me that much. I know that’s what you’re going to expect.

????Are you ready to open things up now?

????CB: We are definitely planning on doing some other things and bringing in other investors, but we thought we ought to get to a certain point before we did that. I think we are now very close to that point.