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藥物開發人工智能人工智能藥物研發

市場調查報告書

商品編碼

1568894

基於人工智慧的藥物新用市場，成長機會，全球，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個工作天內

價格

簡介目錄

基於人工智慧的藥物新用成為一種向患者提供藥物的更快的新方法。

這項研究分析了基於人工智慧的藥物新用的出現，並檢驗了促進和抑制其採用的因素。傳統藥物發現的限制導致人們對基於人工智慧的藥物新用的興趣日益濃厚，它在時間、速度和成本方面具有許多優勢。基於人工智慧的藥物新用利用針對多種適應症進行研究，包括罕見疾病、腫瘤、代謝疾病、自體免疫疾病和神經退化性疾病。

本研究重點在於機器學習、深度學習和生成式人工智慧等各種人工智慧技術，並考察如何實現基於人工智慧的藥物新用。此外，討論了基於人工智慧的藥物新用的主要參與者，包括他們的人工智慧方法、優先疾病領域和未來前景。這項研究檢驗了推動和限制人工智慧藥物新用成長的關鍵因素，並確定了關鍵參與者和相關人員可以利用的領域變化所帶來的成長機會。

本次調查回答的關鍵問題

基於人工智慧的藥物新用發展的關鍵促進因素和阻礙因素是什麼？
基於人工智慧的藥物新用的依適應症的用途是什麼？
基於人工智慧的藥物新用的主要趨勢是什麼？
誰是關鍵創新者？基於人工智慧的藥物新用的方法是什麼？
資金籌措和夥伴關係關係的狀況如何？

基於人工智慧的藥物新用的概述

自 COVID-19 爆發以來，人們對藥物再利用的興趣增加。藥物發現是一個耗時的過程，需要幾個步驟，包括標靶識別、先導化合物識別、臨床試驗和核准。將藥物推向市場可能需要 17年時間和20億美元，而且臨床試驗的任何階段都可能失敗。藥物新用（藥物重新定位）是指為已核准的藥物確定新的治療用途。這種方法透過使用核准藥物的安全性資料和藥理學特徵，縮短了核准時間，降低了失敗率，並減少了開發時間和成本。

藥物再利用已成為一種有吸引力且成功的策略，可以為已核准的藥物尋找新的治療用途。較短的開發時間使其成為對製藥公司和患者都有吸引力的方法。近 30%的再利用藥物最終到達患者手中，明顯高於傳統流程 10%的成功率。

藥物再利用有四種應用：擴大適應症、確定藥物在不同治療領域的新用途、再利用失敗或停產的藥物、聯合治療。

ML、DL、自然語言處理（NLP）、預測 AI、預測建模和生成 AI 等技術可分析來自不同來源的大量資料，例如科學文獻、索賠資料、電子健康記錄（EHR）和生物資訊資料。透過這樣做徹底改變藥物的再利用。透過分析數百萬個資料點，這些技術可以在分子層面上識別藥物-蛋白質相互作用，使公司能夠識別用於不同疾病適應症的藥物。它還分析 EHR 和索賠資料，以提供有關人們仿單標示外使用藥物的資訊。因此，人工智慧有助於在藥物標靶、疾病機制和新疾病之間建立聯繫，公司可以利用現有藥物來針對這些新疾病。由於臨床試驗已經證實了它們的安全性，這些藥物將更快送到患者手中。這個過程對於很少或沒有其他治療選擇的罕見疾病特別有益。

利用人工智慧加快藥物新用過程，並發現現有藥物潛在的新治療用途。儘管這一過程面臨挑戰，包括缺乏舊藥物的可用資料以及需要進行更多研究以將新藥物應用於新的疾病適應症，但人工智慧可以顯著加快藥物新用過程，並為患者提供新的治療選擇。

調查範圍

藥物再利用在速度和成本方面比傳統藥物發現有許多優勢。
本報告重點介紹了各種技術，包括機器學習和深度學習（DL），公司將這些技術用作基於人工智慧的藥物新用利用的架構基礎。
本報告重點介紹了基於人工智慧的藥物新用在多種疾病中的應用，包括罕見疾病和代謝疾病。
本報告簡要介紹了業界最受歡迎的人工智慧技術。
在這一領域營運的生物製藥和人工智慧公司致力於罕見疾病的開發。
本報告重點介紹了該領域的行業主要參與者。

使用 AI 進行藥物新用細分

以藥物為中心的方法

低分子化合物（本報告涵蓋的所有主要參與企業均經營低分子化合物）

以疾病為中心的方法

癌症領域
神經退化性疾病
感染疾病
罕見疾病
自體免疫疾病
代謝性疾病

利用人工智慧促進藥物新用的因素

效率的需要

人工智慧採用率增加

應對新威脅的需要

加速罕見疾病研發管線

抑制人工智慧藥物新用利用成長阻礙因素

有限的資料

AI模型的可解釋性

監管問題

基礎設施成本高

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