In our latest interview, we speak with Dr. Justin Devine and Dr. Nicholas Woudberg from SynexaLS about biomarkers, their importance for drug development, and their potential for machine learning. 👇
Sponsored Content by Synexa Life Sciences BVJan 9 2023Reviewed by Maria Osipova insights from industryNicholas Woudber & Justin DevineSynexa Life Sciences In this interview, NewsMedical speaks with Dr. Justin Devine and Dr. Nicholas Woudberg about biomarkers, their importance for drug development, and their potential for machine learning.
As we saw with the.com bubble or, more recently, with Bitcoin, everything goes through a peak of inflated expectations. This often gets followed by a period of disillusionment and, finally, an increasing realism resulting in maximum productivity. Why are clinically relevant biomarkers valuable? Dr. Justin Devine: The analysis of the immune system plays an essential part in routine clinical diagnostics. There are many factors in clinical practice, from the monitoring of CD4 counts in HIV to other areas of flow cytometry that are used in immune diagnostics. Recently, cytokines have played a role in monitoring various aspects of health and disease.
In this regard, a remarkable piece was published by IQVIA in 2019, in which they observed the factors with the most significant impact on clinical productivity. According to IQVIA, biomarkers and patient selection tools would influence clinical development success most until the end of 2023. Pillar one ensures that the drug reaches the target where the pharmacological effect should occur. For instance, if a therapy targets a central nervous system illness, it must be proven that the drug can safely reach the central nervous system.
Pfizer also had 12 drugs that did not pass the three pillars. They had no proof that the drug even got to the target tissue. One example is a dopamine-3 receptor antagonist found in the brain. All 12 of these drugs did not proceed to phase three development. What is Pfizer’s 5Rs principle? Dr. Justin Devine: Pfizer summarized their principles into the 5Rs, which include the right drug, tissue, safety, patient, and commercial potential. There is a high level of overlap in Pfizer's work. For example, the right tissue and showing that the drug is getting to the right place are fundamentally important.
One of the exciting measures in this regard is the tumor mutational burden. The level of mutations in the tumor is an excellent indicator of whether somebody will respond to a checkpoint inhibitor. The first step in this process is looking at target engagement. Target engagement measures the level of impact that therapy has on the immune system and how the immune system interacts with the tumor.
It is essential to understand the people and genetic background to whom the therapy is given because that can significantly impact therapeutic responsiveness. Gene treatments are one of the most important fields of gene therapy, particularly in the central nervous system. Exosomes are also considered because they are a surrogate for a brain biopsy. Pre-analytical factors of exosomes are still challenging, but they represent a very attractive way of understanding both target engagement as well as expression of pharmacology.
AI can dramatically increase efficiencies and target identification, the design of a clinical trial, the manufacturing of drugs, and marketing. However, how does it relate to biomarker discovery? Currently, the only FDA-approved AI algorithms for biomarker identification are in heart and lung cancer. The introduction of bias is one of the primary obstacles to why the usability of these essential technologies has been constrained.
Therefore, there is a limitation regarding the amount of data that AR can be trained on. Many racial and ethnic minorities can be more susceptible to certain cancers. Why should AI and ML be incorporated into biomarker development programs? Dr. Nicholas Woudberg: AI and ML are extremely powerful tools, and there is no doubt that they will continue to become more important parts of our daily lives and medicines. These techniques can help better stratify patients in clinical studies, providing the most accurate signs of remission, reoccurrence, and treatment refraction.
Having a clear sense and being quite honest about what to expect from a given biomarker strategy is essential. Tying a product to a single marker without a deep understanding of the biology behind it can result in repeated failure. Many markers are made or broken between when the samples are drawn from the patient and when they get analyzed in the lab.
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