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代价高昂的赌博，平均花费数十亿美元，可能需要十年或更长时间。更糟糕的是，只有一小部分被追求的潜在药物最终进入市场。 药物在临床试验中成功或失败，这是药物开发过程中的关键步骤，药物被给予患者以测试它们是否安全，并测试它们是否真的具有预期的结果（称为疗效）。 临床试验有时被称为“死亡之谷”，因为许多有前途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来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人类的可测量特征，可以准确预测属性，例如药物反应。因此，当招募患者进行临床试验时，可以测试生物标志物。如果一种实验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，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，并最终增加其药物通过FDA批准的机会，逃离“死亡之谷”。“这对两个主要利益相关者来说很重要。 首先，患者需要更多的药物进入市场，以便他们能够被治愈，以及更多的生物标志物，以帮助医生开出对他们个人有效的药物。 第二，制药公司希望有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有效仍然处于起步阶段，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学将患者基因突变与药物反应相关联。 然而，其他信息，如基因是由不同的单位组成的，或者基因是蛋白质相互作用的蓝图，被完全忽略了。 包括这些信息，该团队已经发现了寻找生物标志物的新方法，即使他们正在查看相同的数据。 该团队目前的进展几乎仅限于公开可用的数据集（如癌症基因组图谱，癌症细胞系百科全书等），但仅凭这些数据，就已经创建了阐明生物标志物的算法，并为171种以前未知的生物标志物申请了专利。 这个yteam还创建了一个非商业性的工具www.cancer3d.org，让科学家可以访问它的分析，用于他们的研究目的。 该团队的目标是获得制药公司专有的数据集，这些数据集是他们在准备临床试验时生成的，分析他们的数据（或为他们提供工具，让他们自己分析），为他们的实验药物找到新的生物标志物，并将这些生物标志物授权给公司。 癌症药物的强预测性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，并创造重大价值;生物标志物已经以数千万美元的价格出售。通过NSF I-Corps计划，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，发现团队是否提供人们想要的产品，并最终改进和学习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.