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？

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.

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.

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process, it has been shown in a recent article [1] that approximately half of the approved clinical trials are going unpublished raising questions about their validity, the efficacy of these treatments and the probability of potentially concealed adverse effects.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].

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].

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].

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.

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.

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%.

However, at the moment there does not exist a ready-to-use method for the biomarker discovery process with computational tools and methods.

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.

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.

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.

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].

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.

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.

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.

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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