CEO约稿 | 精准医疗时代下，如何通过生物标记物侦测手段，优化临床试验？
Labros Digonis
作者介绍：
拉布洛斯·戴格尼斯先生是创业公司InSyBio的首席执行官（CEO）。他曾获得华威商学院的工商管理硕士学位（MBA），拥有30多年的高层管理经验，并曾在科技、财务、生命科学等领域长期从事管理咨询工作。目前他正积极带领InSyBio公司在美国迅速发展，并希望将全新的技术和理念带到中国。
1
The process for performing clinical trials for a specific medical treatment or drug is a very hard, time-intense and complex task. Especially for USA, additionally to the laws and procedures to prevent jeopardizing safety rules, the law requires the results of the medical research on drugs to be approved by the US Food and Drug Administration(FDA) and to also be submitted and approved by a database called ClinicalTrials.gov.
对某个特定的疗法或者药物进行临床研究，是一个异常困难、耗费时间且错综复杂的任务。尤其是在美国，除了那些防止违反安全条例的法规和流程，对药物的医学研究结果不但要经过美国食品药品监督局（FDA）的审核通过，还要在审查后提交到临床试验数据库网站ClinicalTrials.gov。
If researchers do not post their results in this database then they are facing financial consequences such as loss of funding. However, an additional level of quality control exists and refers to publication of the results in scientific journals where they are being judged by the scientific community.
如果研究人员不把他们的研究结果发布在这个数据库里，那么他们将面临诸如失去资金支持等一系列财务问题。除此之外，还有另外一套质量监控系统，那就是通过发布这些临床试验结果到科学期刊中，接受科学领域内的同行评审。
Despite this intense process, it has been shown in a recent article [1] that approximately half of the approved clinical trials are going unpublished raising questions about their validity, the efficacy of these treatments and the probability of potentially concealed adverse effects. Additionally, even in scientifically published clinical trials, more than 99% are mentioning serious adverse effects for the treatments or drugs under study [2].
尽管审批过程很严格，但是最近的文章显示【1】，大约有半数被批准进行的临床试验结果并未被发表出来，这让人们对试验的可靠性、疗法的有效性、以及隐瞒副作用的可能性提出了质疑。即使是在科学期刊中发表的临床试验，超过99%的结果中都提到了其测试的疗法或者药物有严重的副作用【2】。
2
A constantly increasing number of clinical trials around the world hav 40 37301 40 15263 0 0 1326 0 0:00:28 0:00:11 0:00:17 3564 40 37301 40 15263 0 0 1202 0 0:00:31 0:00:12 0:00:19 3185e lately started to incorporate in their processes biomarker discovery modules to overcome these issues, making them more trustworthy and getting them in line with the personalized medicine trend (see Figure 1) [3].
近来，世界上越来越多的临床试验都开始结合一些侦测生物标记物的途径来改善这些问题，使试验更加可靠，也顺应了如今个性化医疗的潮流（见图1）【3】。
图1.结合使用了生物标记物的临床试验的占比在过去五年间稳步上升
A striking example is the use of predictive biomarkers for the selection of suitable patients to participate in clinical trials for Alzheimer disease treatments and drugs [4].
一个显著的例子就是，一项近期开展的关于阿尔兹海默症治疗与药物的临床试验，这项试验使用预测生物标记物，来筛选适合参与此次试验的病人【4】。
The discovery and application of predictive biomarkers in clinical trials has three main applications: prediction of therapeutic effect and efficacy, prediction of drug/therapy toxicity and quantification of disease risk.The greatest number of trials that include predictive biomarkers is within oncology, followed by infectious disease and endocrinology.
对预测生物标记物的侦测和应用主要有3个方面：对药物疗效的预测、对药物毒性的预测、以及对疾病风险的量化估算。目前对预测生物标记物使用最多的临床试验，主要集中在癌症肿瘤领域，其次是传染性疾病和内分泌性疾病。
Another important fact which raises the need for incorporating biomarker discovery in the process for clinical trials is that 52% of the failed clinical trials were failed because of unproven efficacy; highlighting thus the importance of understanding disease mechanisms, drug action and pinpointing the need for predictive biomarkers to uncover even small subsets of the population for which these drugs and therapies could be beneficiary.
另一个重要事实，更能体现生物标记物在临床试验过程中的价值，那就是52%的临床研究之所以失败，是因为无法验证其疗效，这更加说明了解疾病机理、药物作用的重要性，也凸显了使用预测生物标记物来发现有效人群的需求。
The biomarker discovery market is the common denominator of 4 large health and nutrition-related markets and has reached 27 Billion Dollars in 2016 with a predicted annual growth rate surpassing 14% (see Figure 2) [4]. Moreover, the Big Data bioinformatics tools and services market is its fastest growing part with annual growth rate of more than 10%.
生物标记物市场介于四大健康和营养相关的市场之间，在2016年达到了270亿美元，年预期增长率超过了14%（见图2）【4】。更重要的是，大数据生物信息学工具和服务市场是其中增长最快的领域，年增长率超过了10%。
图2.生物标记物市场
3
However, at the moment there does not exist a ready-to-use method for the biomarker discovery process with computational tools and methods.
然而就侦测生物标记物的过程而言，目前并没有一个成熟的电脑计算工具和方法。
Existing tools and approaches are fragmented, not standardized, single-task oriented and costly, treat different types of biomarkers (genes, RNA, proteins, clinical biomarkers etc.) using individual processes and are not interconnected.Thus,they present limited scientific deduction but, fortunately,their results can be further improvedand, as indicated by [6], a systems approach is needed to overcome these problems.
当下存在的工具和方法存在诸多问题，比如方法零零碎碎而未标准化、功能单一且费时费力、对不同类别的生物标记物（包括基因、RNA、蛋白质、临床生物标记物等等）采用独立的方法而未考虑其中的联系，所以通过这些不完善的方法得到（用以侦测生物标记物）的结果只能做非常有限的科学判断。所幸的是，这些结果仍有改善的空间，正如参考文献【6】中所言，现在市场需要一个系统而完善的方法来克服这些问题。
InSyBio’s patented breakthrough technology offers unique inside path to an integrated end-to-end accurate systems biomarker discovery process, simultaneous integration of data from different sources and types and has demonstrated ability to locate significantly more common proteins in large scale proteomics datasets.
InSyBio已获得专利的突破性技术，就提供了这样一个准确的、一站式的侦测生物标记物的独特方法。这项技术可以同时整合不同来源和类型的数据，它在大规模蛋白质组学数据库中，已被证明有找到更多常见蛋白的强大能力。
Moreover, InSyBio’s solutions are based on biological networks modeling to integrate complex biological information in many layers (mutations, proteins, clinical variables, peptides etc.) and advanced big data-oriented artificial intelligence methods to allow for the identification of compact predictive biomarkers sets with increased predictive accuracy.
更值得一提的是，InSybio解决问题的途径主要基于以下两方面：1）在不同层面上（诸如突变、蛋白质、临床变量、肽类等等）整合复杂的生物学信息来搭建生物网络模型；2）通过尖端的大数据为导向的人工智能方法，用更高的精准度鉴别出预测生物标记物组合。
The utilization of InSyBio’s solution in clinical trial applications has been recently awarded with the first prize in the category of Big Data for Health in Sanofi’s Tech for Health Labs in the VivaTech 2017 event while it has been validated with a series of scientific publications and cases studies [6-10].
最近， InSyBio开创的技术方案在临床试验中的应用，在一系列的科学文章和案例分析中得到了反复的验证【6-10】，并在赛诺菲公司举办的“Tech for Health Labs in the VivaTech 2017”大会中，荣获医疗健康大数据领域的一等奖。
4
To sum up, the future of the clinical trials in the road to precision medicine, passes through the bottleneck of the discovery of accurate and interpretable predictive biomarkers based on a more systemic approach.
总而言之，未来的临床试验在通向精准医疗的途中，需要克服一个技术瓶颈，那就是基于一个系统化的流程，侦测精确和可靠的预测生物标记物。
At the moment, only eighty stratified therapies require the use of predictive biomarkers to identify patientresponse to therapies [6]. In order to multiply precision medicine therapies and drugs, there is a great need of new computational tools, pipelines and methods which will be able to maximize the exploitation of the potential of modern molecular biology experimental techniques.
目前，只有80个治疗方法要求使用预测生物标记物来鉴定病人对疗法的反应【6】。为了发现更多精准医疗的疗法和药物，我们急需全新的电脑计算工具、流程和方法，才能让现代分子生物学的实验技术潜力完全发挥出来。
The incorporation of systems-based predictive biomarkers in clinical practice has the potential to reshape clinical practice into a more anthropocentric approach which can ease the work of physicians and additionally save money and resources from the healthcare systems as presented in Figure 3.
在临床实践中，结合系统性的生物标记物预测方法，能重塑医疗行业，使之更加以人为本、服务好每一位患者，同时也能减轻医生的工作量，节约医疗系统的资金和资源（如图3所示）。
图 3.基于精准医疗的医疗健康系统【6】
InSyBio is a bioinformatics pioneer company in personalized healthcare which focuses on developing computational frameworks and tools for the analysis of complex life-science and biological data, aiming at the discovery of predictive integrated biomarkers (biomarkers of various categories) with applications in pharma, nutrition and cosmetics industries. Find more details at www.insybio.com
InSyBio是一家行业领先的生物信息公司，活跃于个性化医疗领域，专注于开发全新的电脑计算框架和工具，用于对复杂的生命科学数据的分析。公司旨在侦测和发现能应用于药物开发、营养保健、和化妆品行业的不同类别的预测生物标记物。更多详情，请见公司官网：www.insybio.com。
References：
1. Riveros, C.?et al. Timing and completeness of trial results posted at ClinicalTrials. gov and published in journals,?PLoS Med.?10, e1001566 (2013).
2. Jones, N.. Half of US clinical trials go unpublished.?Nature,?10. (2013)
3. Coney, G. Clinical Trials Intelligence - Biomarker Roles within Clinical Trials: An Analysis of Clinical Trials from 2007-2011 and 2012-2016, Clarivate Analytics, Cortellis, April 2017.
4. Hampel H, Frank R, Broich K, Teipel SJ, Katz RG, Hardy J, et. al. Biomarkers for Alzheimer’s disease: academic, industry and regulatory perspectives. Nat Rev Drug Discov. 2010 Jul;9(7):560-74.
5. MarketsandMarkets (2017), Bioinformatics Market by Sector (Molecular Medicine, Agriculture, Forensic, Animal, Research & Gene Therapy), Product (Sequencing Platforms, Knowledge Management & Data Analysis) & Application (Genomics, Proteomics & Metabolomics) - Global Forecast to 2021, preview at https://goo.gl/3ZBon2
6. Quantiles IMS Report, Upholding the Clinical Promise of Precision Medicine: Current Position and Outlook, May 2017, online available at: http://www.imshealth.com/en/thought-leadership/quintilesims-institute/reports/upholding-the-clinical-promise-of-precision-medicine-current-position-and-outlook
7. Nikitaki, Zacharenia, et al. "Systemic mechanisms and effects of ionizing radiation: A new ‘old’ paradigm of how the bystanders and distant can become the players."?Seminars in cancer biology. Academic Press, 2016, DOI: https://dx.doi.org/10.1016/j.semcancer.2016.02.002.
8. K. Theofilatos, et al. (2016) InSyBio BioNets: A new tool for analyzing biological networks and its application to biomarker discovery, EmbNet Journal, vol. 22, pp. e871, 2016, DOI: http://dx.doi.org/10.14806/ej.22.0.871.
9. A. Korfiati, et al., InSyBio ncRNASeq: An efficient tool for analyzing non-coding RNAs, Embnet Journal, Submitted on December 2016, Accepted-In Print.
10. J. Corthésy and K. Theofilatos et al., Maximizing shotgun proteomics isobaric tagging data output using MS/MS multi-objective optimization algorithm, In Proceedings of the Annual conference of the American Society for Mass Spectrometry (ASMS 2016) in June 2016, San Antonio, USA
11. J. Corthésy and K. Theofilatos, et al., Using a “Quantify then Identify” metabonomic-based pipeline to maximize quantitative proteomic data processing, In Proceedings of the 5th International Conference on Analytical Proteomics (ICAP 2017), 3-6 July 2017, Caparica, Portugal
译者：黄鄂军
本科毕业于厦门大学，后在美国德州大学西南医学中心（UT Southwestern Medical Center）从事癌症和衰老领域的研究，获得博士学位。他长期跟踪医健行业的热点动态，对专利申请、技术转化、管理咨询等领域颇有研究，并一直致力于中美之间医健行业的跨境投资和创新交流。现担任美柏医健研究员和访谈员。
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