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药物开发人工智能人工智能药物研发

市场调查报告书

商品编码

1568894

人工智慧驱动的药物定序市场，全球成长机会，2024-2029

AI-based Drug Repurposing Market, Growth Opportunities, Global, 2024-2029

出版日期: 2024年09月09日 | 出版商:

Frost & Sullivan | 英文 54 Pages | 商品交期: 最快1-2个工作天内

价格

简介目录

人工智慧驱动的药物定序正在成为一种向患者提供药物的更快的新方法。

这项研究分析了人工智慧驱动的药物定序的出现，并检验了促进和抑制其采用的因素。传统药物发现的限制导致人们对基于人工智慧的药物再利用的兴趣日益浓厚，它在时间、速度和成本方面具有许多优势。人工智慧驱动的药物定序利用正在针对多种适应症进行研究，包括罕见疾病、肿瘤、代谢疾病、自体免疫疾病和神经退化性疾病。

本研究重点在于机器学习、深度学习和生成式人工智慧等各种人工智慧技术，并考虑它们如何利用人工智慧实现药物定序利用。此外，我们讨论了基于人工智慧的药物定序的主要参与者，包括他们的人工智慧方法、优先疾病领域和未来前景。这项研究检验了推动和限制人工智慧药物定序成长的关键因素，并确定了关键参与者和相关人员可以利用的领域变化所带来的成长机会。

本次调查回答的关键问题

人工智慧药物定序利用发展的关键驱动因素和阻碍因素是什么？
使用人工智慧对每种疾病进行药物定序有何用途？
使用人工智慧进行药物定序的主要趋势是什么？
谁是定序创新者？
资金筹措和伙伴关係关係的状况如何？

使用人工智慧进行药物定序的概述

自 COVID-19 爆发以来，人们对药物重复利用的兴趣增加。药物发现是一个耗时的过程，需要几个步骤，包括标靶识别、先导化合物识别、临床试验和核准。将药物推向市场可能需要 17 年时间和 20 亿美元，而且临床试验的任何阶段都可能失败。药物定序（药物重新定位）是指为已核准的药物确定新的治疗用途。这种方法透过使用核准药物的安全性资料和药理学特征，缩短了核准时间，降低了失败率，并减少了开发时间和成本。

药物再利用已成为一种有吸引力且成功的策略，可以为已核准的药物寻找新的治疗用途。较短的开发时间使其成为对製药公司和患者都有吸引力的方法。近 30% 的再利用药物最终到达患者手中，明显高于传统流程 10% 的成功率。

药物再利用有四种应用：扩大适应症、确定药物在不同治疗领域的新用途、再利用失败或停产的药物、联合治疗。

ML、DL、自然语言处理 (NLP)、预测 AI、预测建模和生成 AI 等技术可分析来自不同来源的大量资料，例如科学文献、索赔资料、电子健康记录(EHR) 和生物资讯资料。透过这样做，我们正在彻底改变药物的再利用。透过分析数百万个资料点，这些技术可以在分子层面上识别药物-蛋白质相互作用，使公司能够识别用于不同疾病适应症的药物。它还分析 EHR 和索赔资料，以提供有关人们仿单标示外使用药物的资讯。因此，人工智慧有助于在药物标靶、疾病机制和新疾病之间建立联繫，公司可以利用现有药物来针对这些新疾病。由于临床试验已经证实了它们的安全性，这些药物将更快地送到患者手中。这个过程对于很少或没有其他治疗选择的罕见疾病特别有益。

利用人工智慧加快药物定序过程，并发现现有药物潜在的新治疗用途。儘管这一过程面临挑战，包括缺乏旧药物的可用资料以及需要进行更多研究以将新药物应用于新的疾病适应症，但人工智慧可以显着加快药物定序过程，并为患者提供新的治疗选择。

调查范围

药物再利用在速度和成本方面比传统药物发现有许多优势。
本报告重点介绍了各种技术，包括机器学习和深度学习 (DL)，公司正在将这些技术用作人工智慧驱动的药物定序利用的架构基础。
该报告重点介绍了基于人工智慧的药物再利用在多种疾病中的应用，包括罕见疾病和代谢疾病。
本报告简要介绍了业界最受欢迎的人工智慧技术。
在这一领域运营的生物製药和人工智慧公司专注于罕见疾病的开发。
本报告重点介绍了该领域的行业主要参与者。

使用 AI 进行药物定序细分

以药物为中心的方法

低分子化合物（本报告涵盖的所有主要参与企业均经营低分子化合物）

以疾病为中心的方法

癌症领域
神经退化性疾病
感染疾病
罕见疾病
自体免疫疾病
代谢性疾病

利用人工智慧促进药物定序的因素

效率的需要

人工智慧采用率增加

应对新威胁的需要

加速罕见疾病研发管线

抑制人工智慧药物定序利用成长阻碍因素

有限的资料

AI模型的可解释性

监管问题

基础设施成本高

This study analyzes the emergence of AI-based drug repurposing and examines the factors driving and hindering adoption. The limitation of traditional drug discovery has led to the growing interest in AI -based drug repurposing, which offers numerous advantages in terms of time, speed, and cost. AI-based drug repurposing has been explored across different disease indications, such as rare diseases, oncology, metabolic diseases, autoimmune diseases, and neurodegenerative diseases.

The study focuses on the different AI-technologies, such as machine learning, deep learning, and generative AI, and how they are enabling AI-based drug repurposing. In addition, the report looks at key participants involved in AI-based drug repurposing, including their AI approaches, disease focus areas, and future outlook. The study examines the key factors driving and restraining the growth of AI-based drug repurposing and identifies the growth opportunities emerging from the changes in this space that key participants and stakeholders can leverage.

Key Questions This Study Answers:

What are the key drivers and restraints in the development of AI-based drug repurposing?
What are the applications of AI-based drug repurposing across disease indication?
What are the key trends in AI-based drug repurposing?
Who are the key innovators, and what are their approaches to AI-based drug repurposing?
What does the funding and partnership landscape look like?

AI-based Drug Repurposing Overview

Interest in drug repurposing has been increasing since the COVID-19 outbreak. Drug discovery is a time-consuming process that requires several stages, including target identification, lead identification, clinical studies, and approval. The process of bringing a drug to market can take 17 years, can cost $2 billion, and can fail at any stage in the clinical study. Drug repurposing, or drug repositioning, identifies novel therapeutic uses for already-approved drugs. This approach shortens the approval time, lowers the failure rate, and uses approved drug safety data and pharmacological profiles, thereby lowering development time and cost.

Drug repurposing has emerged as an appealing and successful strategy for finding novel therapeutic applications for already-approved medications. The shorter timeframe makes the approach attractive for pharmaceutical industries and patients. Almost 30% of repurposed medications eventually reach patients, which is a significant advance over the 10% success rate of conventional processes.

Drug repurposing has the following 4 applications: indication expansion, identification of new uses of drugs in different therapeutic areas, repurposing of failed or discontinued drugs, and combination therapies.

Technologies such as ML, DL, natural language processing (NLP), predictive AI, predictive modeling, and generative AI are revolutionizing drug repurposing by analyzing a large amount of data from different sources, such as scientific literature, claim data, electronic health records (EHRs), and bioinformatics data. These technologies can identify drug–protein interactions at the molecular level by analyzing millions of data points and identifying drugs companies’ use for different disease indications. They analyze EHRs and claim data to provide information on the drugs people use off-label. AI, therefore, helps establish connections between drug targets, disease mechanisms, and novel diseases that companies can target with established drugs. Clinical trials have already confirmed their safety, thus shortening the time these drugs take to reach patients. The process will be especially beneficial for orphan diseases with few or no other treatment options.

AI will speed up the drug repurposing process and uncover the additional potential therapeutic uses of existing drugs. While the process presents challenges, such as a lack of available data for older drugs and the necessity to conduct more studies to apply repurposed drugs for new disease indications, AI could significantly speed up the drug repurposing process, providing patients with novel therapeutic options.

Research Scope

Drug repurposing offers numerous speed and cost benefits over traditional drug discovery.
This report covers the different technologies, such as ML and deep learning (DL), that companies use as the architectural basis for AI-based drug repurposing.
The report covers the applications of AI-based drug repurposing across different diseases, including rare and metabolic diseases.
The report illustrates the landscape of the industry’s most common AI technologies.
Biopharma and AI companies operating in this space focus on rare disease development.
The report covers the industry’s main participants operating in the space.

AI-based Drug Repurposing Segmentation

Drug-centric Approach:

Small Molecules (All main participants covered in this report are doing small molecules)

Disease-centric Approach

Oncology
Neurodegenerative diseases
Infectious diseases
Rare diseases
Autoimmune diseases
Metabolic diseases

AI-based Drug Repurposing Growth Drivers

Need for efficiency

Increased AI adoption

Need to address emerging threats

Accelerated pipeline for rare diseases

AI-based Drug Repurposing Growth Restraints

Limited data

Interpretability of AI models

Regulatory issues

High infrastructure costs

Strategic Imperatives

Why Is It Increasingly Difficult to Grow?
The Strategic Imperative 8™
The Impact of the Top 3 Strategic Imperatives on the AI-based Drug Repurposing Industry
Growth Opportunities Fuel the Growth Pipeline Engine™
Research Methodology

Growth Opportunity Analysis

Scope of Analysis
Segmentation

Growth Generator

Introduction
Drug Repurposing Is More Efficient than Traditional Drug Discovery
How AI-based Drug Repurposing Is Superior to Traditional Drug Discovery
Growth Drivers
Growth Restraints

AI Models Enabling Drug Repurposing

How AI-based Drug Repurposing Works
AI-based Drug Repurposing Approaches
How AI Interprets Large Datasets
Multiple Data Sources Available to Extract Different Types of Data to Determine Drug Repurposing Opportunity
How Companies Use ML in Drug Repurposing
How Companies Use DL in Drug Repurposing