The power of Predictive AI-based Biomarkers in accelerating Precision Oncology

KEY INSIGHTS

●     Artificial intelligence (AI) when combined with real world data access can accelerate the full potential of precision oncology, and precision medicine more broadly.

●     A rigorous and transparent process is necessary. From the onset, all factors that might influence the development of a successful predictive AI-based biomarker must be taken into consideration in order to maximize the project’s chances of success.

●     Industry players can access new AI resources to reinforce clinical-trial predictive enrichment strategies and inform care pathway decisions with the benefit of tailoring approaches to patient care.

●     A real-world example is included that illustrates the application of a deep-learning model to predict clinical outcomes in patients with advanced non-small cell lung cancer treated by immune checkpoint inhibitors (ICI).

Precision medicine aims to offer a patient, or a subgroup of patients, disease treatment and prevention that has been shown to be optimally efficient or beneficial for them based on their genetic predisposition, their environment, and their lifestyle.

While precision medicine is not a new approach to disease treatment and prevention, progress in biology and disease knowledge combined with improved data-collection technologies has led to the generation of significant amounts of data. AI has the power to extract the relevant information from these vast quantities of data and transform it into actionable knowledge which helps predict disease and target therapy.

At Imagia, we leverage AI to accelerate precision oncology discoveries and unleash valuable insights that will improve patient outcomes — without compromising patient privacy.

AI-based predictive biomarkers: a real-world application

Predictive biomarkers are «used to identify individuals who are more likely than similar individuals without the biomarker to experience a favorable or unfavorable effect from exposure to a medical product or an environmental agent».

As such, predictive biomarkers can be fruitfully applied in clinical-trial settings to improve enrichment strategies. A predictive enrichment strategy uses predictive biomarkers to more rapidly and efficiently identify patients who are more likely to respond to a treatment, as well as to determine cohort stratification. Predictive biomarkers can also assist in informing patient-care decisions, for instance in identifying a treatment that is more likely to benefit the patient.

An example of predictive biomarkers is that of immune checkpoint inhibitor (ICI) drugs, which are novel therapeutic agents used notably in the treatment of patients with non-small cell lung cancer (NSCLC). Capturing the dynamic nature of interactions between a patient’s immune system and a tumor generates volumes of diverse data that are difficult to interpret in any practical way. Using AI in the discovery process is a promising approach to analyze this complex data to tailor treatment plans and improve patient outcomes by putting the patients on an effective treatment faster.

The potential of this application of AI-based biomarkers is well illustrated in this project led by Imagia and our EVIDENS clinician-researchers: Deep learning model to predict clinical outcomes in patients with advanced non-small cell lung cancer treated with immune checkpoint  inhibitors.

The Imagia AI-based biomarker discovery process

At Imagia, we drive the discovery process of an AI-based biomarker in tight collaboration with our pharma partner, from framing the clinical and business needs to the biomarker discovery phase up to the productization strategy.

Our sequential, multidimensional process is driven by a multidisciplinary team of professionals, as well as an extensive network of collaborators through the ecosystem provided by our EVIDENS platform.

Our process is  designed to handle the massive, multimodal data that exists in healthcare — which must be accessed, mined, structured and analyzed — while simultaneously protecting patient privacy.

In order to do so, we leverage our cutting-edge machine learning and medical expertise,  privacy by design approach and robust validation phase. The entire sequence is supported by industrial project management standards.

All factors that could influence the development of a successful predictive AI-based biomarker are taken into consideration in order to maximize the project’s chances of success.

• Clinical need and business value. By first considering the current care pathway approach and the need to optimize the patient management and/or to personalize the therapeutic strategy, we frame the clinical objective and the benefits expected by using AI in the discovery process.
• Intent of use. The biomarker development plan is adjusted according to the context of the AI-based biomarker’s use, for instance in support of a clinical trial, within a real-world-evidence study, to ensure the appropriate usage of a marketed drug or a clinical decision tool.
• Multiple data sources. It could be real-world data accessed via our EVIDENS platform through collaborative partnerships, clinical trials data, patient-reported outcomes, registries, etc. This has an impact on:
  • the level of data curation and preparation needed to generate an AI-ready dataset that meets a regulatory-grade data status,
  • the data linkage,
  • the infrastructure and the access rules that should be considered (on premise or on the cloud),
  • the data governance and security issues, and the compliance to the international standards (HIPAA, PIPEDA, GDPR).
• Data types and quality. Data can be unstructured or structured and can take on many forms — text, images, genomics, among others. That, in combination with the size, representativeness and richness of the datasets, impacts data preprocessing and the scope of the biomarker design process.
• Model development. A multitude of AI approaches can be investigated and used, including self-evolving machine learning, deep radiomics, multimodal learning, transfer learning, or Natural Language Processing (NLP). The selected AI solution is tailored to the biomarker candidate targeted and the data types. Models are first evaluated on their predictive performance, interpretability, possible bias, reproducibility, and generalizability. The statistical plan and methodology are a central point of discussion for the AI model architecture, training and validation. Candidate biomarkers undergo further fit-for-purpose validation and/or are evaluated for clinical benefit.
• Privacy by design. The generalizability and validation of the learnings on various datasets without moving the data from their location is facilitated by our advanced federated learning approach, which allows us to preserve data privacy while scaling learnings. If appropriate, privacy can also be reinforced by using homomorphic encryption.
• Regulatory environment. If the biomarker candidate is destined to become a clinical-grade product, for instance a Software as a Medical Device, a regulatory strategy is prepared and implemented from the proof of concept stage. Imagia Healthcare Inc. is ISO 13485 certified, and can manage the productization step of an AI-based biomarker.

Imagia’s biomarker discovery process is also built on fundamental principles such as  traceability, security and transparency to facilitate AI adoption by clinicians, industry and regulated bodies ensuring the full potential of precision medicine.

This biomarker discovery process is designed to accelerate the discovery and transformation of promising AI-based candidates into robust AI-based biomarkers that can be deployed in clinical settings, where they can accomplish our ultimate goal of improving patient outcomes.

To learn more about Imagia’s biomarker discovery process and how you can infuse value throughout your clinical development program or Real-World Evidence studies with powerful AI capabilities, get in touch with us by writing to [email protected]  or  completing this form.