精准医学时代的抗肿瘤药物研发

自 2015 年初,美国总统奥巴马在国情咨文中提出了“精准医学计划”,精准医学迅速成为全球医学界热议和关注的焦点。精准医 学改变了人们对于疾病,特别是肿瘤的药物开发、临床试验和治疗策略的认识和工作模式。2016 年美国通过的《21 世纪治愈法案》进一 步强化了精准医学在药物开发中的作用。重点介绍精准医学在肿瘤药物研发领域引起的变革与发展情况,并就精准医学的现状和前景作深 入探讨。     

基于机器学习的药物-靶标相互作用预测*

近年来,随着计算机硬件、软件工具和数据丰度的不断突破,以机器学习为代表的人工智能技术在生物、基础医学和药学等领域的应用不断拓展和融合,极大地推动了这些领域的发展,尤其是药物研发领域的变革。其中,药物-靶标相互作用(drug-target interactions, DTI)的识别是药物研发领域中的重要难题和人工智能技术交叉融合的热门方向,研究人员在DTI预测方面做了大量的工作,构建了许多重要的数据库,开发或拓展了各类机器学习算法和工具软件。对基于机器学习的DTI预测的基本流程进行了介绍,并对利用机器学习预测DTI的研究进行了回顾,同时对不同的机器学习方法运用于DTI预测的优缺点进行了简单总结,以期对开发更加有效的预测算法和DTI预测的发展提供帮助。     

生物标记物与精准医疗研究进展

自2015年精准医疗理念提出后,生物标记物与精准医疗愈加被人们重视。并被广泛应用于病毒、癌症等重大疾病的筛查与治疗研究中。新兴循环生物标记物的应用与开发,在HBV、HPV的检测和肿瘤的靶向药物治疗、细胞治疗、免疫抑制剂、癌症疫苗开发方向上都取得了重大成果。与传统的医疗方法相比,生物标记物为辅助精准医疗的检测治疗模式,无论是在研发还是治疗上都有着显著的优势。因此,综述了生物标记物与精准医疗的应用,并就最近的研究报告重点综述了生物标记物与精准医疗的最新研究进展。     

面向药物发现和精准医疗的基因表达谱分析

作为功能基因组学中重要的组成部分,基因表达谱在生物学、医学和药物研发等多个领域发挥着重要作用.特别是随着精准医疗概念的提出,整合多组学数据用于个性化医疗是未来的发展趋势.本文从基因表达谱的基本概念出发,重点介绍面向药物发现的基因表达谱分析方法,即基于关联图谱的方法、基于基因调控网络的方法和基于多组学数据整合的方法.系统整理了各种方法的研究进展,特别是在抗癌药物研发领域的最新进展,为利用基因表达谱数据进行药物研发提供方法借鉴.     

国际重组蛋白药物市场和研发趋势的分析

从1982年美国批准第一个重组蛋白药物（重组人胰岛素Humulin）上市至今,已过去了四分之一世纪。重组蛋白药物虽仅占全球处方药市场的7％～8％,但却是增长最快的一类。目前,共有82个重组蛋白药物被用于临床,其中“重磅炸弹”15个,占总数的18％。2005年重组蛋白药物销售总额约410亿美元,而其中“重磅炸弹”的销售额合计约270亿美元,占总额的66％。2006年,美国和欧洲批准了第一个肺吸入型胰岛素上市;欧洲批准了第一个由转基因羊生产的重组人抗凝血酶用于临床,并批准了第一个重组蛋白仿制药物上市。重组蛋白药物市场已经从蛹发育为美丽的蝴蝶,但是,这只蝴蝶能够美丽多久,还受到多种因素的制约。本文以美国和欧洲重组蛋白药物市场为主,采用市场细分的方法,从重组蛋白药物种类的销售额入手,分析了市场及研发趋势,将对我们判断市场走向、提供创新思维和制定创新战略有实际的参考价值。     

细胞组学及其在药物研发中的应用

细胞组学(cytomics)是一门基于细胞分析技术的科学,它是在细胞水平对生物体系的研究,具有真实、简单和系统性的特点,在生物医学研究中有很好的应用前景。现对细胞组学的概念、特点和内容进行介绍,并结合药物研发的现状和过程,综述了细胞组学在药物研发各阶段的应用,最后对其前景进行了展望。     

转基因小鼠在抗体药物研发中的应用

自1890年德国学者Behring及日本学者北里发现白喉抗毒素以来,经过120年的发展,抗体药物依靠其安全性、特异性以及强大的功能性已经成为全球药物开发的焦点。抗体药物的发展从免疫原性角度讲,经历了异源性抗体→人源化抗体（包括嵌合抗体）→全人抗体3个阶段。理论上讲,全人抗体是理想的人用药物分子。早在杂交瘤技术诞生初期,     

抗体库技术在抗体药物研发中的应用

单克隆抗体历经近40年曲折迂回的发展过程,完成了从实验室的研究工具到临床上不可或缺的治疗药物的角色转变,逐渐成为生物制药的明星产品。     

机器学习在蛋白质功能预测领域的研究进展

蛋白质是有机生命体内不可或缺的化合物,在生命活动中发挥着多种重要作用,了解蛋白质的功能有助于医学和药物研发等领域的研究。此外,酶在绿色合成中的应用一直备受人们关注,但是由于酶的种类和功能多种多样,获取特定功能酶的成本高昂,限制了其进一步的应用。目前,蛋白质的具体功能主要通过实验表征确定,该方法实验工作繁琐且耗时耗力,同时,随着生物信息学和测序技术的高速发展,已测序得到的蛋白质序列数量远大于功能获得注释的序列数量,高效预测蛋白质功能变得至关重要。随着计算机技术的蓬勃发展,由数据驱动的机器学习方法已成为应对这些挑战的有效解决方案。本文对蛋白质功能及其注释方法以及机器学习的发展历程和操作流程进行了概述,聚焦于机器学习在酶功能预测领域的应用,对未来人工智能辅助蛋白质功能高效研究的发展方向提出了展望。     

光学分子影像技术及其在药物研发领域的应用

光学分子影像技术是一种发展迅速的生物医学影像技术,能够利用生物发光技术或荧光蛋白等,对生物体内特定的生物过程进行无创的定性或定量研究。应用该技术可以对药物进行筛选,选取具有潜在治疗效果的药物进行后续研究,而终止对可能无效药物的研究,同时可以评价药物对肿瘤的代谢、增殖、血管形成、凋亡和组织乏氧等方面的影响。本文主要介绍光学分子影像技术及其在药物研发,尤其是抗肿瘤药物研发领域的应用。     

Machine learning in Magnetic Resonance Imaging: Image reconstruction

Magnetic Resonance Imaging (MRI) plays a vital role in diagnosis, management and monitoring of many diseases. However, it is an inherently slow imaging technique. Over the last 20 years, parallel imaging, temporal encoding and compressed sensing have enabled substantial speed-ups in the acquisition of MRI data, by accurately recovering missing lines of k-space data. However, clinical uptake of vastly accelerated acquisitions has been limited, in particular in compressed sensing, due to the time-consuming nature of the reconstructions and unnatural looking images. Following the success of machine learning in a wide range of imaging tasks, there has been a recent explosion in the use of machine learning in the field of MRI image reconstruction.A wide range of approaches have been proposed, which can be applied in k-space and/or image-space.Promising results have been demonstrated from a range of methods, enabling natural looking images and rapid computation.In this review article we summarize the current machine learning approaches used in MRI reconstruction, discuss their drawbacks, clinical applications, and current trends.     

Artificial intelligence and machine learning for medical imaging: A technology review

Artificial intelligence (AI) has recently become a very popular buzzword, as a consequence of disruptive technical advances and impressive experimental results, notably in the field of image analysis and processing. In medicine, specialties where images are central, like radiology, pathology or oncology, have seized the opportunity and considerable efforts in research and development have been deployed to transfer the potential of AI to clinical applications. With AI becoming a more mainstream tool for typical medical imaging analysis tasks, such as diagnosis, segmentation, or classification, the key for a safe and efficient use of clinical AI applications relies, in part, on informed practitioners. The aim of this review is to present the basic technological pillars of AI, together with the state-of-the-art machine learning methods and their application to medical imaging. In addition, we discuss the new trends and future research directions. This will help the reader to understand how AI methods are now becoming an ubiquitous tool in any medical image analysis workflow and pave the way for the clinical implementation of AI-based solutions.     

Pharmacogenomics of Drug Metabolizing Enzymes and Transporters:Relevance to Precision Medicine

The interindividual genetic variations in drug metabolizing enzymes and transporters influence the efficacy and toxicity of numerous drugs. As a fundamental element in precision med-icine, pharmacogenomics, the study of responses of individuals to medication based on their genomic information, enables the evaluation of some specific genetic variants responsible for an individual’s particular drug response. In this article, we review the contributions of genetic polymorphisms to major individual variations in drug pharmacotherapy, focusing specifically on the pharmacoge-nomics of phase-I drug metabolizing enzymes and transporters. Substantial frequency differences in key variants of drug metabolizing enzymes and transporters, as well as their possible functional consequences, have also been discussed across geographic regions. The current effort illustrates the common presence of variability in drug responses among individuals and across all geographic regions. This information will aid health-care professionals in prescribing the most appropriate treatment aimed at achieving the best possible beneficial outcomes while avoiding unwanted effects for a particular patient.     

Artificial intelligence in radiotherapy

Artificial intelligence (AI) has already been implemented widely in the medical field in the recent years. This paper first reviews the background of AI and radiotherapy. Then it explores the basic concepts of different AI algorithms and machine learning methods, such as neural networks, that are available to us today and how they are being implemented in radiotherapy and diagnostic processes, such as medical imaging, treatment planning, patient simulation, quality assurance and radiation dose delivery. It also explores the ongoing research on AI methods that are to be implemented in radiotherapy in the future. The review shows very promising progress and future for AI to be widely used in various areas of radiotherapy. However, basing on various concerns such as availability and security of using big data, and further work on polishing and testing AI algorithms, it is found that we may not ready to use AI primarily in radiotherapy at the moment.     

AI applications to medical images: From machine learning to deep learning

PurposeArtificial intelligence (AI) models are playing an increasing role in biomedical research and healthcare services. This review focuses on challenges points to be clarified about how to develop AI applications as clinical decision support systems in the real-world context.MethodsA narrative review has been performed including a critical assessment of articles published between 1989 and 2021 that guided challenging sections.ResultsWe first illustrate the architectural characteristics of machine learning (ML)/radiomics and deep learning (DL) approaches. For ML/radiomics, the phases of feature selection and of training, validation, and testing are described. DL models are presented as multi-layered artificial/convolutional neural networks, allowing us to directly process images. The data curation section includes technical steps such as image labelling, image annotation (with segmentation as a crucial step in radiomics), data harmonization (enabling compensation for differences in imaging protocols that typically generate noise in non-AI imaging studies) and federated learning. Thereafter, we dedicate specific sections to: sample size calculation, considering multiple testing in AI approaches; procedures for data augmentation to work with limited and unbalanced datasets; and the interpretability of AI models (the so-called black box issue). Pros and cons for choosing ML versus DL to implement AI applications to medical imaging are finally presented in a synoptic way.ConclusionsBiomedicine and healthcare systems are one of the most important fields for AI applications and medical imaging is probably the most suitable and promising domain. Clarification of specific challenging points facilitates the development of such systems and their translation to clinical practice.     

二代基因测序数据管理和大数据平台在精准医学中的应用

精准医学集合了多种数据,包括组学、临床、环境和行为等,是对疾病进行个性化治疗、预防和管理的科学。随着基因测序费用的大幅下降,人们对肿瘤等疾病的认识从传统病理到分子水平的飞跃等,相关科学的发展和普及推动了精准医学的诞生和发展,将更加深远地影响着人类的健康。本文介绍了精准医学的概念、目的及应用,介绍了二代DNA测序技术在精准医学中的应用,认为基因组学数据、样本管理、数据质量控制标准以及数据管理平台等是实现精准医学的基础,智能化精准医疗将是来的发展方向。进行展望的同时,也认为基因组学海量数据的规模特点、各种健康应用在推动数据管理平台的发展的同时,也对其演进提出了挑战。     

Application of Artificial Intelligence/Machine Vision & Learning for the Development of a Live Single-cell Phenotypic Biomarker Test to Predict Prostate Cancer Tumor Aggressiveness

To assess the usefulness and applications of machine vision (MV) and machine learning (ML) techniques that have been used to develop a single cell-based phenotypic (live and fixed biomarkers) platform that correlates with tumor biological aggressiveness and risk stratification, 100 fresh prostate samples were acquired, and areas of prostate cancer were determined by post-surgery pathology reports logged by an independent pathologist. The prostate samples were dissociated into single-cell suspensions in the presence of an extracellular matrix formulation. These samples were analyzed via live-cell microscopy. Dynamic and fixed phenotypic biomarkers per cell were quantified using objective MV software and ML algorithms. The predictive nature of the ML algorithms was developed in two stages. First, random forest (RF) algorithms were developed using 70% of the samples. The developed algorithms were then tested for their predictive performance using the blinded test dataset that contained 30% of the samples in the second stage. Based on the ROC (receiver operating characteristic) curve analysis, thresholds were set to maximize both sensitivity and specificity. We determined the sensitivity and specificity of the assay by comparing the algorithm-generated predictions with adverse pathologic features in the radical prostatectomy (RP) specimens. Using MV and ML algorithms, the biomarkers predictive of adverse pathology at RP were ranked and a prostate cancer patient risk stratification test was developed that distinguishes patients based on surgical adverse pathology features. The ability to identify and track large numbers of individual cells over the length of the microscopy experimental monitoring cycles, in an automated way, created a large biomarker dataset of primary biomarkers. This biomarker dataset was then interrogated with ML algorithms used to correlate with post-surgical adverse pathology findings. Algorithms were generated that predicted adverse pathology with >0.85 sensitivity and specificity and an AUC (area under the curve) of >0.85. Phenotypic biomarkers provide cellular and molecular details that are informative for predicting post-surgical adverse pathologies when considering tumor biopsy samples. Artificial intelligence ML-based approaches for cancer risk stratification are emerging as important and powerful tools to compliment current measures of risk stratification. These techniques have capabilities to address tumor heterogeneity and the molecular complexity of prostate cancer. Specifically, the phenotypic test is a novel example of leveraging biomarkers and advances in MV and ML for developing a powerful prognostic and risk-stratification tool for prostate cancer patients.     

Requirements and reliability of AI in the medical context

The digital information age has been a catalyst in creating a renewed interest in Artificial Intelligence (AI) approaches, especially the subclass of computer algorithms that are popularly grouped into Machine Learning (ML). These methods have allowed one to go beyond limited human cognitive ability into understanding the complexity in the high dimensional data. Medical sciences have seen a steady use of these methods but have been slow in adoption to improve patient care. There are some significant impediments that have diluted this effort, which include availability of curated diverse data sets for model building, reliable human-level interpretation of these models, and reliable reproducibility of these methods for routine clinical use. Each of these aspects has several limiting conditions that need to be balanced out, considering the data/model building efforts, clinical implementation, integration cost to translational effort with minimal patient level harm, which may directly impact future clinical adoption. In this review paper, we will assess each aspect of the problem in the context of reliable use of the ML methods in oncology, as a representative study case, with the goal to safeguard utility and improve patient care in medicine in general.     

Machine Learning in Healthcare

Recent advancements in Artificial Intelligence (AI) and Machine Learning (ML) technology have brought on substantial strides in predicting and identifying health emergencies, disease populations, and disease state and immune response, amongst a few. Although, skepticism remains regarding the practical application and interpretation of results from ML-based approaches in healthcare settings, the inclusion of these approaches is increasing at a rapid pace. Here we provide a brief overview of machine learning-based approaches and learning algorithms including supervised, unsupervised, and reinforcement learning along with examples. Second, we discuss the application of ML in several healthcare fields, including radiology, genetics, electronic health records, and neuroimaging. We also briefly discuss the risks and challenges of ML application to healthcare such as system privacy and ethical concerns and provide suggestions for future applications.