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Last Updated: 01/10/2019

DCTD Convenes Workshop on Cell-based Immunotherapy for Solid Tumors

Workshop on Cell-based Immunotherapy for Solid Tumors

On December 10-11, 2018, DCTD sponsored a Workshop on Cell-based Immunotherapy for Solid Tumors (agenda). The workshop addressed scientific, clinical, and manufacturing issues related to adoptive cell therapies, including transfer of tumor infiltrating lymphocytes (TILs), T cells that have been modified with chimeric antigen receptors (CAR), T cells with engineered T-cell receptors (TCR), and other immune cells.

The symposium brought together extramural researchers, industry representatives, and NCI staff to develop a strategy to meet the needs of the extramural community to advance the field. The meeting can be viewed by NIH Videocast (Day 1 and Day 2).

Meeting Goals

  • Review successes and challenges of adoptive cell therapies for hematological malignancies
  • Examine current use of cell therapies in solid tumor patients
  • Understand scientific, technological, and regulatory challenges to advancing cell-based treatments for solid tumors
  • Develop consensus around bottlenecks preventing the timely implementation of clinical and research studies of cell-based therapies for solid tumors, which might be addressed by NCI action

Keynote Speaker

Malcolm Brenner, MD, PhD, Baylor College of Medicine

Malcolm Brenner, MD, PhD, Baylor College of Medicine
Bit by Bit, Putting it Together: Accelerating Clinical Development of Cellular Therapy for Solid Tumors

 

 

 

Meeting Session Highlights

Scientific Issues

  • Identify best targets - unique vs. shared antigens and levels of expression
  • Understand affinity and cross-reactivity
  • Identify how to improve tumor trafficking and penetration, including noninvasive ways to measure these
  • Overcome the inhibitory tumor microenvironment
  • Understand the benefits and challenges of different cell-based therapy paradigms (When is it best to use CAR T cells vs. TILs vs. engineered TCR T cells vs. CAR natural killer cells, etc?)
  • Determine which animal models can best inform clinical development (non-human primates, canine patients, etc.)

Clinical Development Issues

  • Develop criteria to establish starting dose and escalation scheme
  • Optimize early, feasibility trials using nimble, adaptive trial designs
  • Facilitate later phase, efficacy trials, including trials involving multiple centers
  • Understand and address the challenges of multi-institution trials (tracking, indemnification, infrastructure support)
  • Encourage timely sharing of early trial experience to improve efficiency and risk management
  • Understand the mechanisms of toxicities and develop evidence- and mechanism-based mitigation strategies
  • Harmonize the adverse event (AE) grading system and standards in AE assessment and reporting
  • Develop guidelines for outcome reporting (e.g., inclusion of intent-to-treat analysis)
  • Enhance efforts in immune monitoring and correlative studies

Technology Challenges

  • Inconsistency during manufacturing (starting material through final product)
  • Improve availability of resources (vectors, GMP manufacturing, critical reagents, specialized assays)
  • Risks and benefits of centralized vs. decentralized production; minimize changes in product characteristics during technology transfer and increasing scale
  • Need for specialized training
  • Gene editing promises to be the ultimate technology used to alter immune cells, but ethical and safety concerns must be addressed

Regulatory Challenges

  • Limited experience to guide regulatory decisions because of the few Investigational New Drug (IND) applications for adoptive cell transfer and the lack of clinical experience in treating solid tumors; contrast to the vast experience in hematologic indications with greater than 1,000 hematologic IND applications with the FDA
  • Manufacturing protocols for autologous cells need to be individualized; “quality by design” and matrix approach to product release testing can reduce Quality Assurance burden
  • Preclinical data requirement is driven by the target (e.g., more data are required for novel targets compared to established targets to anticipate potential toxicities of new therapies)