AI 기반 의약품 재이용 시장 : 성장 기회(2024-2029년)

AI-based Drug Repurposing is emerging as a new and faster approach to bringing drugs to patients.

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

Table of Contents

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

Application Across Disease Indications

• Disease Focus Areas for AI-based Drug Repurposing
• Clinical Pipeline Analysis of Key Participants
• Key Trends-AI-based Drug Repurposing

Key Innovators-AI in Drug Repurposing

• Key Participants in AI-based Drug Repurposing
• Landscape of AI Technologies Used by Key Participants for Drug Repurposing

Funding and Partnerships

• Recent Funding Promoting AI-based Drug Repurposing
• Strategic Collaborations Accelerating the Use of AI-based Drug Repurposing

Growth Opportunity Universe

• Growth Opportunity 1: Collaborative Environment
• Growth Opportunity 1: Collaborative Environment
• Growth Opportunity 2: Focus on Rare Diseases
• Growth Opportunity 2: Focus on Rare Diseases
• Growth Opportunity 3: Focus on Drug Indication Expansion
• Growth Opportunity 3: Focus on Drug Indication Expansion

Next Steps

• Benefits and Impacts of Growth Opportunities
• Next Steps
• Legal Disclaimer