With the continuation of AI advancements throughout the rest to 2025, we will continue to see new AI developments become more mainstream in the Biotech Industry, further aiding with deeper understanding of the discovery and development process of drug therapeutics.
AI essentially helps with the process of biomarker discovery, analysis, and validation. It does this through mass analysis of datasets, identifying discrete patterns that human researchers could have missed, and predicting treatment responses based on patients and their individual profiles.
What are Biomarkers?
A biomarker stands for a biological marker, an objective measure that captures what is happening inside an organism or even a cell at a given moment. They can serve as key indicators for abnormal biological processes within your systems, and detect pharmacological responses to therapeutic intervention.
How Do They Work?
Some biomarkers, like proteins, genes or changes in genes can help researcher diagnose cancer, and discover and determine the best treatment choices. Proteins in the blood act as biomarkers to diagnose dementia. A gene called HbA1c is a biomarker to determine blood sugar levels to treat and monitor blood sugar levels.
Biomarkers have to be analyzed to determine their effects. Proteins and genes that act as biomarkers and determine effects in the body have to be broken apart to be able to tell their meaning, and identify the true effect they have in the body.
AI's Role
AI algorithms can help build predictive models to identify patients and what therapies they would most likely benefit from. Ai algorithms can also analyze and identify patterns within larger datasets, leading to earlier and more accurate diagnoses. Image analysis also can be done in the same way, where it can analyze digital pathology slides and medical images to quantify biomarkers.
It also enables continuous tracking of biomarkers, allowing for real time adjustments to treatment plans as biomarkers change and evolve. The ability of AI to integrate data from multiple sources is a fundamental aspect of it that differs from pre-AI treatment strategies and ensures efficiency in time-consuming tasks.
Kelly Abernathy, VP of Clinical Development at Arrivo Bioventures says “Biomarkers and evidence of target engagement are essential in studying disorders where, in the past, we relied only on subjective rating scales. From drug discovery and design where AI is speeding the creation of novel structures, to the ability to compare the profile of a drug candidate across a variety of parameters, to databases of both human subjects with disease and normal volunteers, the amount of information available to drug developers earlier in the process will allow for more efficient and potentially faster development.”
A hybrid technology-human approach to biotech in the next few years is expected, taking hold across healthcare in many aspects of it, such as drug discovery to hospital operation. The key for this, is to maximize utilizing AI to optimize and scale data driven processes while reserving deeply personal tasks for humans.