只需50种药就可以治疗所有疾病？人工智能显威力

在曼哈顿中城瑞典教会狭长的哥特式建筑中，丹尼尔·科恩在雅致而安静的咖啡馆里介绍了一番仿制药后稍事休息。他走到靠近前门的老旧钢琴前坐了下来，然后流畅而完美地弹奏了电影《绿野仙踪》中的插曲《飞越彩虹》。

如果说人体生物学是科学领域里的复杂琴谱，那么科恩就是懂得如何弹奏它的行家。他曾经是法国非营利实验机构Généthon的主导人物，后者在1993年12月绘制了历史上的第一张人类基因组图谱。可以说他是大数据和自动化进入基因研究的“引路人”，因为他和他的团队率先证明了有可能用超高速计算机来加快DNA样本的处理速度。

全世界科学家的工作都以科恩提出的思路为基础，而作为免疫学专业医学博士，他本人已经在研究者和医药公司高管的位置上取得了成功。然而，25年过去了，基因学几乎没能像许多初期创新者期望的那样实现一些改变规则的医学突破。今天，作为巴黎药物初创企业Pharnext的首席执行官及创始人，科恩正在全力探究为何这道彩虹的末端不是一罐金子。

科恩离开钢琴走回来，然后对我说：“人体内任何一种蛋白质都有很多功能，而不只是一种。就像你，作为个体你在人群中有很多种角色，而不只是一个。”科恩描述的现象就是“多效性”，即单个基因可以发挥多种作用，而且这些作用看起来都没有关联。这也是疾病复杂性的一个体现，而且它已经多次挫败为最顽固疾病寻找治疗方法的医学研究者。

科恩不光了解多效性的意义，他还相信Pharnext等制药公司也许很快就能对其加以利用，而且是在人工智能的有力协助下。科恩希望通过参悟人体的复杂性，并通过用人工智能来更有条理地分析并绘制疾病横扫人体时的连锁反应路径来开发出适于众多疾病的药物组合。

科恩和他的团队还在用人工智能来搜寻“老药新用”带来的治疗方法，也就是将已有药物组合起来，从而产生单一药物不具备的疗效。他们的长期目标是用机器学习来理顺自己的后备药品，使其效率远远超过大型制药公司以缓慢著称的研发部门。说到如何实现这一切，科恩的瞌睡眼就会因为激动而睁大。他说：“这很棒，而且很经济。”

谷歌和IBM这样的大企业以及Insilico Medicine、Recursion Pharmaceuticals和BenevolentAI等初创公司都在与Pharnext赛跑。它们都为人工智能工具投入重金，并用这些工具来分析数以百万计的药物样本和患者数据，然后从中找出主要规律。但2007年成立的Pharnext领先大多数竞争对手好几年，要是考虑到过去几十年科恩率先在基因学和多效性方面进行的研究，它的优势就更明显了。

此外，十几年来Pharnext在解决医学难题时对人工智能的应用终于出现了关键拐点，这也许是最重要的。去年10月，该公司称一组药物的三期临床试验取得了积极成果。该组药物代号为PXT3003，用于治疗腓骨肌萎缩症，这是一种罕见的神经退行性疾病，尚未找到治疗方法。该病症的主要诱因是某个基因的复制，而这样的突变会引发一系列“下游”问题，从而使保护神经的施万细胞退化为没有保护作用的干细胞，最终导致神经突触消失。这样人就无法控制肌肉，进而出现肌肉萎缩。

据Pharnext介绍，上述三期临床试验结果（尚未得到同行审核）表明PXT3003不光能稳定病情，而且还有逆转作用，证据是细胞开始再生。科恩指出，此前的疗法只能延缓病情恶化的速度。在服用PXT3003后，统计数据显示患者的两项残疾指标都有了明显好转。基于这些结果，美国食品与药品监督管理局（FDA）在今年2月将FXT3003纳入“快速通道”。这是一种加速审批程序，只有在FDA认为某种药品在治疗某种严重疾病方面展现出“卓越疗效”时才会启动。

In the elegant quiet of the café at the Church of Sweden, a narrow Gothic-style building in Midtown Manhattan, Daniel Cohen is taking a break from explaining genetics. He moves toward the creaky piano positioned near the front door, sits down, and plays a flowing, flawless rendition of “Over the Rainbow.”

If human biology is the scientific equivalent of a complicated score, Cohen has learned how to navigate it like a virtuoso. Cohen was the driving force behind Généthon, the French laboratory that in December 1993 produced the first-ever “map” of the human genome. He essentially introduced Big Data and automation to the study of genomics, as he and his team demonstrated for the first time that it was possible to use super-fast computing to speed up the processing of DNA samples.

Scientists worldwide have built on Cohen’s insights, and Cohen himself, an MD with a Ph.D. in immunology, has gone on to success as a researcher and pharma executive. But a quarter-century later, genomics has yielded few of the kinds of paradigm-changing medical breakthroughs that many of its early innovators hoped for. Today, as chief executive and founder of Paris-based drug startup Pharnext, Cohen is striving to understand why that rainbow hasn’t led to a pot of gold.

“Any protein in the body has many different functions, not only one,” he says, returning from the piano to talk with me, “just as you are a person who has many functions in the population, not just one.” The phenomenon Cohen is describing is “pleiotropy,” the capacity of a single gene to have multiple, seemingly unrelated effects. It is one of the complexities of disease that has repeatedly frustrated medical researchers in their quest for therapies for the most stubborn illnesses.

Cohen not only appreciates pleiotropy’s significance: He believes that Pharnext and other drugmakers may soon exploit it—with a powerful boost from artificial intelligence. By embracing the body’s complexity, and by using A.I. to more methodically analyze and map the way the chain reactions of disease sweep through the body, he hopes to develop combinations of drugs tuned to attack a plethora of medical conditions.

Cohen and his team are also applying A.I. to search for therapies that leverage “repurposing”—combining existing drugs in ways that give them therapeutic powers that each lacks in isolation. Their long-term goal is a drug pipeline that is far more efficient than Big Pharma’s notoriously slow R&D departments—streamlined by machine learning. Cohen’s sleepy gaze widens with enthusiasm when he describes how it’s all coming along. “Très bien,” he says. “Très économique.”

Running in the same race as Pharnext are companies ranging from giants like Google and IBM to startups such as Insilico Medicine, Recursion Pharmaceuticals, and BenevolentAI. All are deeply invested in the tools of A.I., using them to analyze millions of examples of drug and patient data and tease out patterns of significance. But Pharnext, founded in 2007, predates most of those competitors by several years—and has a longer head start when one factors in Cohen’s decades of earlier research in genomics and pleiotropy.

And perhaps most important, Pharnext’s application of A.I. to medical problems over the course of more than a decade has finally reached a critical inflection point. In October, Pharnext reported positive results for a Phase III trial in humans of one of its drug combinations. The compound is PXT3003, a treatment for a neurodegenerative condition called Charcot-Marie-Tooth disease (CMT), a rare disorder for which no cure has been found. The primary cause of CMT is duplication of a single gene, but a whole cascade of bad things ensues “downstream” from that mutation. Schwann cells, which protect nerves, regress into stem cells that don’t do their job. Axons in the nerves begin to die off. Muscles can’t be controlled, and they shrink as a consequence.

According to Pharnext, its Phase III results (which have not yet been peer-reviewed) showed CMT not merely stabilizing under PXT3003 but also being reversed, as cells began regenerating. Previous treatments, Cohen says, had managed only to slow patients’ decline. Under PXT3003, patients showed statistically significant improvement on two measures of disability. Based on those results, in February the U.S. Food and Drug Administration granted Pharnext “fast track” status for that therapy—an accelerated review process, awarded only when the agency thinks a drug demonstrates “superior effectiveness” in treating a serious disease.

当然，这只是针对某一罕见病迈出的有希望的一步。但科技让长期前景变得光明起来，它把药物设计过程缩短了好几年，让Pharnext走上了捷径。预临床测试和临床试验通常需要8至10年，而从零开始开发新药可能让这个过程再延长7年，甚至更长时间。与之相反，就PXT3003而言，人工智能帮助Pharnext挑选了三种现有药物进行再利用，包括治疗肌肉痉挛和肌肉紧张的巴氯芬、解除阿片类药物依赖的纳曲酮以及用于降低血糖水平的缓泻剂山梨糖醇。由于这些药物都已经得到使用，Pharnext就可以跳过通常需要保证药品安全性的一期临床试验，也就是那个“从零开始”的阶段。

FDA的“快速通道”提高了PXT3003最早在2020年上市的可能性，而这只是Pharnext众多项目中的一个。该公司很快就会启动阿尔茨海默病治疗药物的第二次二期临床试验，以及肌萎缩侧索硬化治疗药物的首次二期临床试验，这两个项目也都采用了类似的老药新用方法。

同样重要的是，如果这些试验取得成功，资金实力更强的效仿者将接踵而至。曾经在奥巴马政府中担任卫生及公共服务部部长的凯瑟琳·西贝利厄斯如今是多家医疗保健公司的顾问和董事。她认为这些工作是不断增强的老药新用趋势的一部分。西贝利厄斯说：“所有这些都可能让投资周期大幅缩短，而且有可能形成非常不同的定价水平，并为经济动力一直不足的罕见病带来许多可能性。这非常有吸引力。”哥伦比亚大学Kavli脑科学研究所的主任埃里克·坎德尔曾经获诺贝尔生理学或医学奖，他也是Pharnext的顾问。坎德尔认为Pharnext走在了这种趋势的最前端，其方法“既有原创性，又很强大”。至于这样的方法能否流行起来，坎德尔说：“我们很快就会知道。”

It is, to be sure, only one hopeful step against one rare ailment. Still, technology has shortened Pharnext’s path in ways with promising long-term implications, shaving years off the drug-design timeline. Preclinical testing and clinical trials generally take eight to 10 years, and developing a novel drug completely from scratch can add seven years to the process, sometimes much more. In the case of PXT3003, in contrast, A.I. helped Pharnext select three existing drugs to repurpose: baclofen, a muscle relaxant; naltrexone, used to treat opioid dependence; and sorbitol, a glucose reduction used as a laxative. Because the drugs were already in use, Pharnext could skip the Phase I trials normally required to ensure their safety—and eliminate the “build from scratch” stage.

FDA fast-tracking increases the odds that PXT3003 could be on the market as soon as 2020—and it’s only one of Pharnext’s many projects. The company will soon begin a second Phase II trial of a drug with indications for Alzheimer’s and a first Phase II trial for an ALS therapy, in both cases using a similar repurposed combination.

Just as important: If these experiments succeed, copycats with deeper pockets could follow suit. Kathleen Sebelius, a secretary of Health and Human Services in the Obama administration who is now a consultant and board member for several health care companies, sees the efforts as part of a growing trend to repurpose. “That all leads to the possibility of a lot shorter investment cycle, and potentially a very different pricing point, and lots of possibilities for rare diseases where there just hasn’t been enough of a financial incentive,” she says. “And that has lots of appeal.” Eric Kandel of the Kavli Institute for Brain Science at Columbia University, a winner of the Nobel Prize in physiology or medicine who is an adviser to Pharnext, says that the startup is at the leading edge of the trend, calling its methodology “both original and powerful.” As for whether that approach will catch on widely, Kandel adds, “We should know soon.”

在现代基因研究的“黎明”中，几乎没有人预见到疾病生物学的极端复杂性。当时，许多研究者都把基因视为人体的某种指导手册。Celera Genomics的首席执行官及创始人克雷格·文特尔和美国国立卫生研究院的院长弗朗西斯·柯林斯等先驱人物被喻为“基因捕手”，这个称号鼓励着改革者们在全球各地搜寻那个“银子弹”基因，也就是可以解释并有助于治疗某种疾病的基因。

从某个角度来说，这些研究者发现了真正的宝藏。举例来说，基因学家南希·韦克斯勒花了好几年时间在委内瑞拉拼凑亨廷顿病患者的族谱。这是一种罕见的遗传疾病。她的工作让人们发现可以通过某个基因的突变来预测一个人会不会患上这种疾病。

但科学家很快意识到基因图谱不太像一本指导手册，而更像是宜家家具随附的那种配件目录。此外，研究人员发现，其他“目录”给基因和疾病的关系增添了复杂的变量，比如蛋白质组、DNA编码蛋白、转录组以及所有将DNA转换成蛋白质的核酸。

在“黎明”过后的“早晨”，失望带来了真正的痛苦，因为研究人员认识到癌症和阿尔茨海默病等复杂病症并非由单个基因引起（就连已经确认诱发基因的亨廷顿病也依然无法治愈）。现在，科恩等人发现了简化执念和药物发现减少之间的联系。药物发现减少的具体表现包括FDA批准的新疗法只有十分之一的成功率、螺旋式上升的药物开发成本（塔夫茨大学最近的一项研究被称为“26亿美元的药丸”）以及少量突破性新药直线上升的价格，比如诺华制药的白血病治疗药物Kymriah，它的每个疗程的费用为47.5万美元。

最近，研究人员开始在网络科学的帮助下应付生物的复杂性。这门科学的首席传播者是美国东北大学的教授艾伯特-拉斯洛·巴拉巴西。他在2014年出版的《链接》（Linked）一书让一种观念流行开来，那就是网络理论可以对很多领域做出解释，比如流行趋势，比如性关系，再比如疾病。巴拉巴西等人意识到疾病就像一个坏信号，它通过基因-蛋白质-细胞-组织这样的关系网络传播，直到所有这些“微小变化”展现出与疾病类似的症状。

复杂疾病是多种因素的共同作用，原因是多效性意味着任何蛋白质都可以在人体的不同部位发挥作用。Pharnext等初创公司认为药物也有多效性，它们能同时对人体内一种以上的蛋白质产生影响，并进行一种以上的互动。为找到能够应对复杂疾病的药物组合，可以在数据中发现规律而且蕴含着巨大能力的机器学习必须与病理结构概念结合起来。

这就要求计算机科学家和生物学家的关系出现“进化”。较新的机器学习方法容纳的数据远超以往，它们还可以将信息分级，从而突破关联这个框架。不过，让这些“深度学习”神经网络获得预测能力需要建立一些漂亮的算法。

GNS Healthcare的 CEO及创始人科林·希尔就是这种算法的塑造者之一。这家设在马萨诸塞州坎布里奇市的公司用了18年时间开发出名为REFS的计算机系统，意思是“逆向工程、正向模拟”（reverse engineering, forward simulation）。近年来，GNS Healthcare共筹集资金3800万美元，投资方包括制药巨头安进公司旗下的安进风投和赛尔基因等诸多机构。GNS Healthcare的目标是构建并调整其疾病模型。在最近的一系列试验中，该公司详细介绍了REFS在治疗帕金森病等方面的潜力——多效性因素让现有的帕金森病治疗手段变成了撞大运。GNS Healthcare的首次试验报告发表在2017年的《柳叶刀》医学杂志上。

就帕金森病而言，基因缺陷触发的互动网络具有特定的形状，而运动机能的分解是病情发展的最可靠指标。将帕金森病患者和控制组成员的基因数据输入REFS帮助GNS Healthcare生成了100多个计算机模型，其作用是描述运动机能退化过程中可能发生的情况。这些模型可以发现此前未被人知晓的基因变异，而后者可能就是运动机能加速退化的原因。

At the dawn of modern genetic research, almost no one anticipated the enormous complexity in the biology of disease. Many researchers thought the genome would be a kind of instruction manual for the body. Pioneers such as Celera Genomics’ Craig Venter and Francis Collins of the National Institutes of Health were celebrated as “gene hunters,” a term that evoked crusaders scouring the globe for that one “silver bullet” gene that would explain—and facilitate a cure for—a given disease.

To some extent, these researchers found real treasure. Geneticist Nancy Wexler, for example, spent years in Venezuela compiling family trees of those affected by Huntington’s disease, a rare, inherited condition. Her work led to the discovery of the mutation in a single gene that predicts whether an individual will contract the condition.

But scientists soon realized that genetic maps were less like an instruction manual and more like the parts catalog you get with Ikea furniture. What’s more, researchers discovered other catalogs that added complex variables to the relationships between genes and disease—for example, the proteome, the proteins encoded by DNA, and the transcriptome, all the nucleic acids that convert DNA into proteins.

The morning-after disappointment has proved wrenching, as researchers learned that complex diseases, such as cancer and Alzheimer’s, didn’t yield to a single gene. (Even Huntington’s, its gene identified, has remained untreatable.) Today, Cohen and others see a link between the obsession with simplicity and a decline in drug discovery. That decline shows itself in the 1-in-10 success rate for FDA approval of new therapies; in spiraling costs for drug development (what a Tufts study recently identified as “the $2.6 billion pill”); and in the soaring prices of the few treatments that break new ground, such as the $475,000 cost of a course of treatment with Novartis’s leukemia drug Kymriah.

More recently, researchers have begun to grapple with biological complexity with the help of the science of networks. That science’s chief evangelist is Albert-László Barabási, a professor at Northeastern University whose 2014 book Linked popularized the notion that network theory can explain numerous fields, from fashion trends to sexual relations to disease. Barabási and others realized that disease is like a bad signal that moves through a network of connections from genes to proteins to cells to tissues, until all these “perturbations” manifest as the familiar symptoms of disease.

Complicated diseases are confluences of numerous effects, because pleiotropy means that any given protein can act at different points in the body. Startups like Pharnext assume that drugs can also be pleiotropic, acting on more than one protein and more than one interaction in the body at the same time. To find a drug combination capable of tackling complexity, the enormous power of machine learning, with its ability to spot patterns in data, must be wedded to a sense of the structure by which disease operates.

This, in turn, has required an evolution in the relationship between computer scientists and biologists. Newer machine-¬learning approaches ingest vastly more data and can assemble hierarchies of information that let them go beyond correlation. Still, harnessing these “deep learning” neural networks into a structure that has any predictive power requires some elegant algorithm-building.

Colin Hill, CEO and founder of GNS Healthcare, is one of the builders. His company, based in Cambridge, Mass., has spent 18 years developing a computer system called REFS, which stands for “reverse engineering, forward simulation.” GNS has raised a total of $38 million over the years—from Amgen Ventures, the venture capital arm of the drug giant, along with Celgene and a variety of other investors—to build and fine-tune its models of disease. And in a recent series of trials, first published in 2017 in the medical journal The Lancet, GNS has detailed REFS’s potential when applied to a disease such as Parkinson’s—an ailment in which pleiotropic factors render existing treatments wildly hit-or-miss in their effectiveness.

With Parkinson’s, the network of interactions set in motion by defective genes has a particular shape to it, and the breakdown of motor functioning is the most reliable indication of its progression. Feeding the genetic data of Parkinson’s sufferers and a control group into REFS helped GNS generate over 100 computer models depicting what might be going on as motor function deteriorates. The models can uncover previously unknown genetic mutations that may contribute to the speedup of deterioration.

但这只是第一部分。GNS Healthcare已经用这些发现在计算机上模拟了5000次随机控制试验，这些模拟都是为了预测采用不同治疗方法时病情发展的速度。和寻求同样结果的控制人体实验相比，此类“速度测试”的经济性要高的多。同时，已经和其他药厂联手的GNS Healthcare目前正在申请用类似的方法来对付糖尿病、肌萎缩侧索硬化、多发性骨髓瘤和乳腺癌等等疾病。

作为CEO，科林·希尔说：“我们现在可以在电脑上创建、进行并展示患者及其病情，这样就可以用不同的药物和不同的护理管理干预方法逐一进行检验，然后找出哪种治疗方法对哪些患者起作用。”

换句话说，这样的模拟不光是要找到关联，它还回答了如果……会怎样的问题。如果我们用这种药替代那种药，那么这个病人的情况会如何？这种模拟以及就反向操作得出结论的能力是人工智能应用的一项近期成果。它正在变得越来越重要，而这在很大程度上要归功于该公司的技术顾问朱迪亚·珀尔，后者长期从事人工智能研究，而且是加州大学洛杉矶分校的计算机科学教授。在去年出版的热门书籍《为什么》（The Book of Why）中，珀尔描述了怎样从简单的模式感知中形成真正的智能，机器学习可以辨别出大量的模式，而真正的智能可以基于这些模式进行反向推理并得出结论。脱离了机制观念，光是数据无法做到真正的洞悉。珀尔认为：“数据在因果关系方面非常笨。”希尔则更直接地说：“深度学习并没有那么深。”

But that’s just the first part. GNS has used those findings to create 5,000 different computer simulations of randomized control trials, each aiming to predict how fast the disease would progress with varying approaches to treatment. Such speed-¬testing can be vastly more economical than seeking the same result through controlled human trials. And GNS, in partnership with other drugmakers, is now applying similar approaches to treatments for diabetes, ALS, multiple myeloma, and breast cancer, among other diseases.

“We now have the ability to create and construct, on the computer, representations of human patients and their diseases such that we can now probe, drug by drug, care management intervention by care management intervention, and say what treatments work for which patient,” says Colin Hill, CEO of GNS.

The simulation, in other words, is not just finding correlations: It is answering What if questions. What if we had given drug A instead of drug B to patient X? That ability to simulate and answer counterfactuals is a recent arrival in the practice of A.I. It owes its growing importance in large part to GNS’s technology adviser, Judea Pearl, a longtime A.I. researcher and professor of computer science at UCLA. In a popular volume published last year called The Book of Why, Pearl describes how true intelligence ascends from merely noticing patterns, which machine learning does in spades, to being able to express counterfactual reasoning about what would have happened, based on those patterns. Data alone, disconnected from any idea of a mechanism, doesn’t provide real insight. “Data is profoundly dumb about causality,” claims Pearl. Hill puts it more bluntly: “Deep learning is not that deep.”

今年67岁的丹尼尔·科恩在突尼斯长大，那时他处在犹太人、基督教徒和穆斯林构成的多元化社会中，“他们用非常雅致而且和平的方式生活在一起”。他说这样的经历让他形成了“事物是复合而非复杂”的概念。9岁时科恩随家人移民到了巴黎，并开始热情地学习弹钢琴。后来他意识到，自己作为科学家的影响可能比音乐家大，这让他立刻转投医学，但保持着同样的热情。他曾经是伦敦皇家交响乐团的客座指挥，梦想着率领该乐团演奏柴科夫斯基的《悲怆交响乐》。他开玩笑说：“交响乐团指挥、CEO和科学家体质都由同样的基因控制着。”

此前科恩就曾经把基因学和技术转化为药物方面的成功。他同别人一起创立了Millennium Pharmaceuticals，这是一家肿瘤药物公司，参与开发了多发性骨髓瘤治疗药物Velcade。

科恩乐观地认为Pharnext能通过人工智能取得成功，但他也清楚这项技术的局限。谷歌的人工智能程序AlphaZero可以击败最顶尖的人类围棋选手，而且没有使用此前人类的任何围棋知识。但就像科恩所说，围棋有固定规则，而AlphaZero也完全明白这些规则。在生物学中，人们还没有完全掌握相关规则，而且或许永远也掌握不了，这在一定程度上要归咎于多效性。

Daniel Cohen, now 67, spent his childhood in Tunisia’s heterogeneous society of Jews, Christians, Muslims, “living all together in a very elegant and pacific way.” He credits that experience for his taste for “things that are not complicated, but complex.” When he was 9, Cohen’s family immigrated to Paris, where he pursued the piano avidly. He switched to medicine once he realized he might have a greater impact as a scientist than a musician, but the passion has not left him. He has been a guest conductor at the Royal Philharmonic in London and dreams of leading that ensemble in Tchaikovsky’s Symphony Pathétique. “The predisposition to orchestra conductor, CEO, and scientist are all controlled by the same genes,” he jokes.

Parlaying genomics and technology into pharmaceutical success is something Cohen has done before. He was a cofounder of Millennium Pharmaceuticals, a U.S. oncology-drug maker that helped develop the multiple-myeloma treatment Velcade.

Cohen is bullish that Pharnext can be successful with A.I., but he is also aware of the technology’s limitations. Google’s ¬AlphaZero, an A.I. program, was able to beat the world’s human masters at the Chinese strategy game Go, without using any prior human knowledge. But as Cohen points out, Go has a finite set of rules, which AlphaZero knew completely. In biology, thanks in part to pleiotropy, the rules are not fully known—and may never be.

但设计周密的人工智能已经让Pharnext能够围绕已知规则建立模型并做出相应的选择。在10000种已知药物中，该公司的发现模型用其中的2000种进行搭配，这些药品的专利已经过期，而且已经得到推广，或者说其疗效和安全性都足以向公众销售。

为开发腓骨肌萎缩症治疗药物，Pharnext首先用了约一年时间来构建这种疾病的网络模型，或者说和GNS Healthcare帕金森病图谱类似的框架，从而说明相关基因突变如何引发了一连串神经和肌肉问题。基于这样的机理，计算机模型挑选出了57种候选药物，分别针对这一连串问题中的各个点。Pharnext对这些药物逐一进行体外测试，再从中选出22种在老鼠身上试验的药物，最终形成由三种药物构成的组合进行临床试验。三期临床最近公布的积极结果证明PXT3003正在这一连串问题的多个点上发挥作用。

科恩说，如果没有人工智能模型，预临床测试需要的时间就会多出好几年，而不是Pharnext所花的三年，“从2000种药物[中着手]，我就可以得出所有可能的组合，即10亿种可能性”来进行体外测试。这个“处方”中有无数的错误测试结果和死胡同，意味着好几年的挫折——现在这些都得以避免。

But thoughtfully designed A.I. has enabled Pharnext to build models around the rules that are known and make choices accordingly. Out of the universe of 10,000 known drugs, the company’s discovery model takes in an assortment of 2,000 that are both out of patent and “marketed”—that is, already judged both therapeutically effective and safe enough to be sold to the public.

To develop its CMT drug, Pharnext first spent about a year assembling its network model for the disease—a framework comparable to GNS’s Parkinson’s map, showing how nervous and muscular problems “cascade” from the relevant gene mutation. Based on this mechanism, the computer model arrived at a short list of 57 candidate drugs that addressed various points in the cascade. Pharnext tested those drugs one by one in vitro, generating a shorter list of 22 to be tested in mice, which finally yielded the three-drug combination that went to human clinical trials. The recent positive Phase III results confirmed that the PXT3003 cocktail is acting at various points in the cascade.

Without the A.I. model, many more years of preclinical testing would have been required beyond the three years it took Pharnext, says Cohen. “With 2,000 drugs [to start with], I could produce all possible combinations, a billion possibilities” to test in vitro. That’s a recipe for countless false positives and dead ends—years of frustration, for now forestalled.

Pharnext在巴黎上市，去年10月公布上述三期临床结果后其股价以上涨了一倍多。10年来，该公司已经在研发上投入约1.2亿欧元（约1.35亿美元）。就制药行业的标准而言，这个数字着实不多。Pharnext尚未盈利，但分析师估算，如果PXT3003上市，该公司的收入将从2020年开始飙升。2018年Pharnext实现收入900万欧元（GNS Healthcare未上市，没有披露过开支或收入）。

除了投资者可能获得成功，Pharnext和GNS Healthcare的进步还为人工智能以及与之共同成长的药物学指明了发展方向。理解因果关系以及反向探索问题的能力是人工智能用户长期以来一直想跨越的门槛。随着它们掌握并控制众多让人眼花缭乱的变量，这些初创公司的计算机模型正在朝着这个方向迈进。

就连疾病的基本定义都可能发生改变。随着科学家不断加深了解，这些定义已经变得过于简单。学术期刊《Bioinformatics》在去年的一项研究指出，癌症中的基因变异存在“根本性差异”，而这对癌症治疗产生了不利影响——看似是同一种疾病，或者同一类疾病，而实际上患者之间的相似之处寥寥无几，区别却有很多。人体内的任何变化都绝不等同于单个基因的“开/关”引发的一系列症状，弄清楚了这一点，科技就可以帮助从业者克服药物开发的复杂性。

最后但同样重要的是，这些人工智能推动的工作从经济角度带来了一丝希望。在这个时代，药物开发成本已经成为令人畏惧的障碍，智能算法则有可能在某一天让医药利益相关方从投入药物研究的数万亿美元中获得更多的价值。科恩判断，理论上，通过老药新用，“人们就不需要再设计[新]药物。我的感觉是可以用50种药物来治疗所有的疾病。”这就意味着另一个词的定义也要变了，那就是“发现”。（财富中文网）

本文另一版本刊登在2019年4月出版的《财富》杂志上，题为《在老药中找到新疗法》。

译者：Charlie

审校：夏林

Pharnext’s shares, which trade on the Paris stock exchange, have more than doubled since October’s Phase III results announcement. The company has spent about 120 million euros ($135 million) over the past decade on research and development—a very modest figure by pharma standards. It has never made a profit, but analysts estimate that if PXT3003 reaches the market, revenue—9 million euros in 2018—could soar starting in 2020. (GNS Healthcare is privately held and does not disclose spending or revenue.)

Beyond possible victories for investors, the advances at Pharnext and GNS point the way to A.I.’s growing up—and pharmacology along with it. The ability to reason about causality, and to explore counterfactual questions, is a threshold that users of artificial intelligence have long sought to cross. The computer models at these startups are making a foray in that direction as they manage and tame a bewildering number of variables.

Even the underlying definition of disease may evolve. As scientists are learning, these definitions have been overly simplistic. A study in the journal Bioinformatics last year noted that attempts to treat tumors are hampered by the fact that genetic mutations in cancer are “fundamentally heterogeneous”: What appears as one disease, or class of disease, in fact contains few commonalities and many differences from patient to patient. As it becomes clear that what’s happening in any body differs sharply from the “on/off” model of one gene turning on a set of symptoms, technology can help drug developers wrestle with the complexity.

Last, but hardly least, these A.I.-driven efforts offer a glimmer of economic hope. In an era in which the cost of drug development is a daunting obstacle, smart algorithms may someday enable medical stakeholders to derive more value from the trillions of dollars that have already been spent on drug research. In theory, with repurposing, “you don’t need to design [new] drugs,” Cohen avers. “My feeling is that with 50 drugs, we can treat everything.” That would mean changing yet another definition: the meaning of “discovery.”

A version of this article appears in the April 2019 issue of Fortune with the headline “Finding New Cures in Old Drugs.”