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Mondaq — United States: New Approaches In Cancer Drug Development

Mondaq — United States: New Approaches In Cancer Drug Development

One of the features that excites me about representing life sciences and other technology innovators is the impact they can have on our society.

I enjoy using the skills I learned in over 30 years of working with business leaders at a global health products company to help those innovators navigate the pathway to the marketplace and to understand the disciplined decision-making process employed by successful companies – while avoiding the “landmines” and detours that can spell the end of a company. See some of our resources for innovators at

Technology is moving forward with breakneck speed.

That has certainly occurred in the process for developing drugs to combat cancers. I’ve had my own family experiences with this disease and applaud efforts to destroy this disease.

In this note, I’ll report on some research into “digital pathology”.

  • It’s a new frontier in interpreting tissue samples necessary for drug development
  • It uses artificial intelligence to seek increase speed and accuracy
  • Regulatory oversight will need to develop to permit and support it
  • As with any new technology, the future is not completely clear or determined – much work remains to be done

In a recent virtual meeting convened by The Friends of Cancer Research (Friends), a significant breakthrough was announced: a new research project dedicated to bolstering the use of digital pathology in oncology drug development. With this announcement came the release of a white paper titled “Supporting the Application of Computational Pathology in Oncology.” The significance of this paper, its insights, and what this means for the future of oncology drug research cannot be understated.

Is Digital Pathology Relevant to the Patient Experience?

As outlined in the report, digital pathology isn’t just a buzzword—it’s an emerging technology with the potential to transform oncology drug development and clinical care. It provides a means for creating digitized images for storage, analysis, and interpretation, which pathologists can then use for various functions such as biomarker interpretation and diagnosis. More impressively, these images can lay the foundation for computational pathology platforms. By leveraging the power of artificial intelligence (AI) and machine learning (ML), these platforms can analyze specific image elements, helping to identify subvisual patterns and phenotypes.

Benefits of AI-Driven Pathology

AI and ML can fast-track the identification of relevant biomarkers, thus leading to more tailored and efficient treatments during clinical trials. The Friends’ white paper highlights the technology’s potential for establishing novel biomarkers, assisting in the quantification of prognostic and predictive biomarkers, and hastening the diagnosis and pathological scoring process.

J. Carl Barrett, PhD, touched upon the transformative potential of this technology. As an authority in multiple medical disciplines, Barrett highlighted how digital pathology can deepen our understanding of cancer networks and evaluate tumor heterogeneity. He eloquently emphasized the shift from reading to measuring. He also pointed out the unintentional silver lining of the COVID-19 pandemic – the acceleration of digital pathology adoption due to the increased reliance on remote work and telemedicine.

Regulating the Revolution

However, with new technology comes the necessity of regulation. The white paper proposes:

  1. Reporting input and platform characteristics for transparency.
  2. Establishing a risk classification framework.
  3. Setting common reference standards.

The push for guidelines is echoed by Megan Doyle, JD, MPH, from Amgen, emphasizing the need for robust validation to foster trust in these tools, especially in clinical trials. Jochen Lennerz, MD, PhD, further suggests that the advancements in drug development might also enhance clinical care for patients.

Challenges Ahead

Brandon D. Gallas, PhD, notes that the journey of integrating computational pathology is not without its obstacles. Inertia remains a significant hurdle in drug development, with industry players preferring a clear path. There are also pressing concerns about the consistency and standardization across computational pathology platforms, regional disparities in digital pathology capabilities, and ethical questions around AI algorithms.

The FDA has been proactive, releasing a discussion paper addressing the growing influence of AI and ML in drug development. It raises essential questions and concerns, urging for a more patient-centered and ethical approach to AI adoption.

The journey towards integrating digital pathology in oncology drug development is exciting yet filled with challenges. The promise it holds can revolutionize how we understand and treat cancer. However, the path to its seamless integration requires collaboration, standardization, and addressing ethical concerns. As we move forward, it’s vital to prioritize patient trust and welfare, ensuring that these technologies are harnessed for their maximum potential for the benefit of all.

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