CEO約稿｜精準時代下如何通過生物標記物偵測手段，優化臨床試驗？
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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.
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對某個特定的療法或者藥物進行臨床研究，是一個異常困難、耗費時間且錯綜複雜的任務。尤其是在美國，除了那些防止違反安全條例的法規和流程，對藥物的醫學研究結果不但要經過美國食品藥品監督局（FDA）的審核通過，還要在審查后提交到臨床試驗資料庫網站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.
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如果研究人員不把他們的研究結果發布在這個資料庫里，那麼他們將面臨諸如失去資金支持等一系列財務問題。除此之外，還有另外一套質量監控系統，那就是通過發布這些臨床試驗結果到科學期刊中，接受科學領域內的同行評審。
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process, it has been shown in a recent article [1] that approximately half of the approved clinical trials are going unpublished raising questions about their validity, the efficacy of these treatments and the probability of potentially concealed adverse effects.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].
儘管審批過程很嚴格，但是最近的文章顯示【1】，大約有半數被批准進行的臨床試驗結果並未被發表出來，這讓人們對試驗的可靠性、療法的有效性、以及隱瞞副作用的可能性提出了質疑。即使是在科學期刊中發表的臨床試驗，超過99%的結果中都提到了其測試的療法或者藥物有嚴重的副作用【2】。
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A constantly increasing number of clinical trials around the world have 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].
近來，世界上越來越多的臨床試驗都開始結合一些偵測生物標記物的途徑來改善這些問題，使試驗更加可靠，也順應了如今個性化醫療的潮流（見圖1）【3】。
圖1.結合使用了生物標記物的臨床試驗的佔比在過去五年間穩步上升
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].
一個顯著的例子就是，一項近期開展的關於阿爾茲海默症治療與藥物的臨床試驗，這項試驗使用預測生物標記物，來篩選適合參與此次試驗的病人【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.
對預測生物標記物的偵測和應用主要有3個方面：對藥物療效的預測、對藥物毒性的預測、以及對疾病風險的量化估算。目前對預測生物標記物使用最多的臨床試驗，主要集中在癌症腫瘤領域，其次是傳染性疾病和內分泌性疾病。
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 andpinpointing the need for predictive biomarkers to uncover even small subsets of the population for which these drugs and therapies could be beneficiary.
另一個重要事實，更能體現生物標記物在臨床試驗過程中的價值，那就是52%的臨床研究之所以失敗，是因為無法驗證其療效，這更加說明了解疾病機理、藥物作用的重要性，也凸顯了使用預測生物標記物來發現有效人群的需求。
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%.
生物標記物市場介於四大健康和營養相關的市場之間，在2016年達到了270億美元，年預期增長率超過了14%（見圖2）【4】。更重要的是，大數據生物信息學工具和服務市場是其中增長最快的領域，年增長率超過了10%。
圖2.生物標記物市場
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However, at the moment there does not exist a ready-to-use method for the biomarker discovery process with computational tools and methods.
然而就偵測生物標記物的過程而言，目前並沒有一個成熟的電腦計算工具和方法。
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.
當下存在的工具和方法存在諸多問題，比如方法零零碎碎而未標準化、功能單一且費時費力、對不同類別的生物標記物（包括基因、RNA、蛋白質、臨床生物標記物等等）採用獨立的方法而未考慮其中的聯繫，所以通過這些不完善的方法得到（用以偵測生物標記物）的結果只能做非常有限的科學判斷。所幸的是，這些結果仍有改善的空間，正如參考文獻【6】中所言，現在市場需要一個系統而完善的方法來克服這些問題。
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已獲得專利的突破性技術，就提供了這樣一個準確的、一站式的偵測生物標記物的獨特方法。這項技術可以同時整合不同來源和類型的數據，它在大規模蛋白質組學資料庫中，已被證明有找到更多常見蛋白的強大能力。
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解決問題的途徑主要基於以下兩方面：1）在不同層面上（諸如突變、蛋白質、臨床變數、肽類等等）整合複雜的生物學信息來搭建生物網路模型；2）通過尖端的大數據為導向的人工智慧方法，用更高的精準度鑒別出預測生物標記物組合。
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開創的技術方案在臨床試驗中的應用，在一系列的科學文章和案例分析中得到了反覆的驗證【6-10】，並在賽諾菲公司舉辦的「Tech for Health Labs in the VivaTech 2017」大會中，榮獲醫療健康大數據領域的一等獎。
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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.
總而言之，未來的臨床試驗在通向精準醫療的途中，需要克服一個技術瓶頸，那就是基於一個系統化的流程，偵測精確和可靠的預測生物標記物。
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個治療方法要求使用預測生物標記物來鑒定病人對療法的反應【6】。為了發現更多精準醫療的療法和藥物，我們急需全新的電腦計算工具、流程和方法，才能讓現代分子生物學的實驗技術潛力完全發揮出來。
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.
在臨床實踐中，結合系統性的生物標記物預測方法，能重塑醫療行業，使之更加以人為本、服務好每一位患者，同時也能減輕醫生的工作量，節約醫療系統的資金和資源（如圖3所示）。
圖 3.基於精準醫療的醫療健康系統【6】
About InsyBio:
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 atwww.insybio.com
關於InSyBio：InSyBio是一家行業領先的生物信息公司，活躍於個性化醫療領域，專註於開發全新的電腦計算框架和工具，用於對複雜的生命科學數據的分析。公司旨在偵測和發現能應用於藥物開發、營養保健、和化妝品行業的不同類別的預測生物標記物。更多詳情，請見公司官網：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
本期作者：Labros Digonis
拉布洛斯·戴格尼斯先生是創業公司InSyBio的首席執行官（CEO）。他曾獲得華威商學院的工商管理碩士學位（MBA），擁有30多年的高層管理經驗，並曾在科技、財務、生命科學等領域長期從事管理諮詢工作。目前他正積極帶領InSyBio公司在美國迅速發展，並希望將全新的技術和理念帶到中國。
譯者：黃鄂軍
本科畢業於廈門大學，后在美國德州大學西南醫學中心（UT Southwestern Medical Center）從事癌症和衰老領域的研究，獲得博士學位。他長期跟蹤醫健行業的熱點動態，對專利申請、技術轉化、管理諮詢等領域頗有研究，並一直致力於中美之間醫健行業的跨境投資和創新交流。現擔任美柏醫健研究員和訪談員。
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