● 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.
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.
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