I-Corps: Market Research, Customer Interviews, and Customer Discovery for Novel Cancer Biomarkers

将一种药物从发现推向市场可能是一场昂贵的赌博，平均花费在数十亿美元左右，可能需要10年甚至更长时间。更糟糕的是，研发中的潜在药物中只有一小部分最终进入了市场。临床试验是药物开发过程中的一个关键步骤，在临床试验中，药物被给予患者，以测试它们是否安全，以及它们是否真的会产生预期的结果（即功效）。临床试验有时被称为“死亡之谷”，因为许多有希望的药物在人体试验中未能证明其安全性或有效性，因此被FDA禁止销售。提高临床试验成功率的一种方法是找到一种方法，提前预测哪些患者会对药物产生良好的反应。生物标志物只是个体的一种可测量的特征，可以准确地预测一种特性，如药物反应。因此，在招募患者进行临床试验时，可以检测生物标志物。如果一种实验性药物具有预测性生物标志物，制药公司可以尝试只选择那些具有这种生物标志物的患者，从而增加他们在临床试验中证明其药物有效（具有高疗效）的机会，并最终增加他们的药物通过FDA批准并逃离“死亡之谷”的机会。这对两个主要利益相关者很重要。首先，患者需要更多的药物进入市场，这样他们才能被治愈，同时也需要更多的生物标志物来帮助医生开出对他们个人有效的药物。其次，制药公司希望更多的药物进入市场，这样他们就可以增加研发回报，降低药物生产成本，增加收入。在患者服用药物之前预测药物是否对患者起作用仍处于起步阶段，并且在肿瘤学中被广泛采用。目前发现预测性生物标志物的方法围绕着使用统计数据将患者基因突变与药物反应联系起来。然而，其他的信息，比如基因是由不同的单元组成的，或者基因是相互作用的蛋白质的蓝图，完全被忽视了。包括这些信息，这个团队已经发现了寻找生物标志物的新方法，即使他们看的是相同的数据。该团队目前的进展几乎完全局限于公开可用的数据集（如癌症基因组图谱，癌细胞系百科全书等），但仅凭这些数据就创建了用于阐明生物标志物的算法，并获得了171个以前未知的生物标志物的专利。该团队还创建了一个非商业工具www.cancer3d.org，允许科学家访问其研究目的的分析。该团队的目标是获得制药公司在准备临床试验时生成的专有数据集，分析他们的数据（或为他们提供可以自己分析数据的工具），为他们的实验药物找到新的生物标记物，并将这些生物标记物授权给公司。一个强大的预测癌症药物的生物标志物可以很容易地帮助药物上市，帮助各地的癌症患者，并创造显著的价值；生物标记物的售价高达数千万美元。通过NSF I-Corps项目，该团队希望学习和改进客户识别和发现、客户互动、如何从市场研究转向客户获取、发现团队是否提供了人们想要的产品，并最终改进和学习将研究项目商业化的各个方面。

Bringing a drug from discovery to market can be a costly gamble in the range of billion of dollars on average, potentially taking a decade or more. What makes matters worse is that only a tiny fraction of potential drugs that are pursued end up making it to market. Drugs make it or break it during clinical trials, a critical step in the drug development process where the drugs are given to patients to test if they are safe, and to test if they will actually have the desired outcome (known as efficacy). Clinical trials are sometimes referred to as the "valley of death," because so many promising drugs fail to demonstrate their safety or efficacy in humans that they are tested on, and thus are barred by the FDA from being sold. One way to improve the success rates of clinical trials is find a way to predict, ahead of time, which patients will respond favorably to a drug. A biomarker is simply a measureable characteristic of an individual human that can accurately predict a property, such as drug response. Hence, when recruiting patients for clinical trials, one can test for biomarkers. If an experimental drug has a predictive biomarker, pharmaceutical companies can try to select only those patients who have this biomarker, increasing the chances that they can prove their drug works (has high efficacy) during clinical trials, and ultimately increase the chances that their drug will pass FDA approval and escape the "valley of death." This is important for two main stakeholders. First, patients need more drugs to reach the market so that they can be cured, as well as more biomarkers to help physicians prescribe drugs that will work on them personally. Second, pharmaceutical companies want more drugs to reach the market so that they can increase returns on research and development, reduce drug-production costs, and increase their revenue.Predicting if a drug will work or not in a patient before the patient has ever even taken the drug is still in its infancy, and has been most adopted in oncology. Current approaches to discovering predictive biomarkers revolve around using statistics to correlate mutations in patients' genes to drug response. Yet, additional information, such as the fact that genes are made up of different units or that genes are blueprints for proteins that interact with each other, is completely ignored. Including this information, this team has discovered new approaches to finding biomarkers even though they are looking at the same data. This team's current progress has been almost exclusively limited to publicly available datasets (such as The Cancer Genome Atlas, The Cancer Cell Line Encyclopedia, etc.), but with this data alone algorithms have been created to elucidate biomarkers and patented 171 previously unknown biomarkers. This yteam has also created a non-commercial tool www.cancer3d.org to allow scientists to access its analysis for their research purposes. The team's goal is to gain access to pharmaceutical companies proprietary data sets which they have generated as they prepare for their clinical trials, analyze their data (or provide tools for them where they can analyze it themselves), find new biomarkers for their experimental drug, and license these biomarkers to the company. A strong predictive biomarker for a cancer drug can easily help get a drug to market, help cancer patients everywhere, and create significant value; biomarkers have been sold for tens of millions of dollars. Through the NSF I-Corps program, this team hopes to learn and improve on customer identification and discovery, customer interactions, how to move from market research to customer acquisition, discover if the team is offering a product that people will want, and ultimately improve and learn on every aspect of commercializing a research project.