【ICON】2024肿瘤研发新兴疗法和新挑战白皮书

Innovation in Oncology: Accelerating R&D in an Evolving Landscape

Core Content

This document explores the current state and future directions of oncology drug development, highlighting the diversity of therapeutic approaches, challenges in clinical development, and the role of emerging technologies such as AI and ML in the field.

Main Points

Overview of Oncology Drug Development

• R&D Landscape: There is a growing understanding of cancer complexities, leading to the discovery of new biological treatment approaches. Over the last decade, the number of innovative oncology treatment modalities entering clinical trials has significantly increased.
• Therapeutic Approaches: The document reports on 146 individual therapeutic approaches, with the most common being targeted therapies (24%), immunotherapies (23%), and cell and gene therapies (30%).
• Therapeutic Pipeline: 40% of respondents are working in research and development, while 60% are involved in pre-clinical research and beyond. Most oncology developers are working on multiple therapeutic approaches, with 85% developing multiple therapies and 68% developing at least one combination therapy.

Therapeutic Settings and Cancer Staging

• Therapeutic Settings: The most common therapeutic settings are very early (preventative) and late (metastatic) stages.
• Cancer Classification: 65% of therapies are best suited for molecular-genetic classification, indicating a shift away from traditional staging methods.

Clinical Development Challenges

• Common Challenges: 100% of respondents identified at least one clinical trial challenge. The most commonly cited challenges include:
  • Biomarker identification/response prediction (53%)
  • Therapeutic toxicity (47%)
  • Limited efficacy in a subset of patients (42%)
  • Off-target effects (38%)
• Phase-Specific Challenges:
  • Pre-clinical: Dose selection is a major challenge.
  • Early Phase (Phases 1 & 2): Site selection is a primary issue.
  • Late Phase (Phases 2 & 3): Managing complex logistics and patient retention are significant challenges.
  • Post-Market: Drawing statistically significant conclusions from small cohorts is a challenge.

Emerging Therapeutic Approaches

• Top Therapeutic Approaches:
  • Targeted small molecules (29%)
  • mRNA-based cancer vaccines (27%)
  • Gene therapy (26%)
  • Antibody-drug conjugates (25%)
  • Immunomodulatory cytokines (25%)

Key Challenges by Specific Therapeutic Approach

• CAR-T Cell Therapy: 80% of respondents identified challenges with predicting patient response.
• Oncolytic Virus Therapy: Faced challenges with off-target effects (79%), assessment of clinical effect (86%), and delivery system (64%).
• Non-mRNA Cancer Vaccines: Faced challenges with localized therapeutic effects and metabolic stability.
• Cell Therapies (CAR-T and NK Cell): Similar challenges to non-mRNA vaccines.
• Gene Therapy: Faced challenges with localized therapeutic effects, metabolic stability, and off-target effects.

AI and ML in Drug Development

• AI/ML Application: Respondents are using AI and ML to support oncology drug development, particularly in areas that require predictive analytics and data processing.
• Benefits: The most significant benefits of AI/ML include improved biomarker identification, better prediction of patient response, and more efficient trial management.

Key Information

• Survey Participants: 104 professionals from small to large biotech and pharmaceutical companies in North America and Europe.
• Respondent Profile:
  • 71% work for small pharma/biotech companies.
  • 73% have a clinical practice.
  • 54% hold director-level or higher positions.
  • 47% have job functions that include clinical research.
• Clinical Trial Elements:
  • 69% of respondents identified phase 2 or 3 as the most challenging.
  • Half of respondents found site selection challenging.
  • Nearly half struggled with managing complex logistics for therapy supply and manufacture.

Conclusion

The oncology R&D landscape is evolving rapidly with the introduction of new and complex therapeutic approaches. While these innovations offer promising treatments, they also present significant challenges in clinical development, including dose selection, patient retention, and the management of logistics. AI and ML are being increasingly used to address these challenges, and there is a clear shift towards molecular-genetic cancer classification. The future of oncology treatment development is likely to be shaped by these trends, with a focus on combination therapies and more personalized, targeted approaches.