The biggest challenges currently facing companion diagnostic advancement

Abstract

Low productivity in the pharmaceutical industry positions personalized medicine and companion diagnostics as an attractive approach. The oncology community is enthusiastic and optimistic that advances in molecular diagnostics have the potential to materialize the dream of personalized cancer therapy with the aim to: help the drug industry enhance the probability of success and, probably, accelerate time to market; help the diagnostics industry develop diagnostic tests utilizing precious human samples; and support accurate diagnosis and selection of the most efficacious and least toxic therapies. However, this spectacular road is not yet paved and remains facing the following biggest challenges: the need for better understanding of biology and limitations of preclinical models; pre-analytical variables that are likely to alter results; analytical discrepancies; and the wide gap between clinical/pharmaceutical and diagnostic understandings. This article explores these challenges, their possible impacts, and provides some suggestions for mitigation.

Keywords::

• companion diagnostics
• drug development
• in vitro diagnostics
• molecular diagnostics
• oncology
• personalized medicine
• probability of success

Financial & competing interests disclosure

The author is an employee of DrugScan, Inc. The author has no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

No writing assistance was utilized in the production of this manuscript.

Key issues

Issues with personalized medicine and companion diagnostics

• Inadequate understanding of biology.

• Intra- and inter-tumor heterogeneity.

• Inadequate preclinical models used in target and biomarker discovery.

• Pre-analytical variables that are likely to alter biomarker results.

• Improper selection of analytical platform, methodology or reagents.

• Inadequate assay validation or in-production quality control tool.

• Improper selection of a Clinical Research Organization laboratory or a diagnostic partner.

• Misinterpretation of biomarker data due to unfamiliarity with the technology, limited number of observations or overexcitement.

• Misunderstanding the difference between publishable research and clinical requirements.

• Misalignment between different partners within a pharma organization.

• The wide gap between pharma and diagnostic industries.

• Lack of education of medical staff on the topic.

• Lag in reimbursement of companion diagnostics testing.