Recently, Nature Drug Discovery published an article discussing how to find articles that predict the response of immunotherapy. This article argues that the so-called static biomarkers currently used, such as PD-L1, mutation burden, new antigen expression, etc., are difficult to predict immunotherapeutic responses, because small initial differences in a complex system such as the immune system may cause a huge immune response. difference. For example, although the test animals have the same genetic background, similar age, and the same dietary and living conditions, they also have different responses to immunotherapy. The authors suggest that dynamic biomarkers such as changes in gene expression after short-term administration are more likely to predict responses, but such studies are still in infancy and have few published data.
Drug source analysis
Traditional drug development does not pay attention to biomarker prediction response population, and even if there is no predictive index drug sales from the market, there is no predictive biomarker that is more favorable to manufacturers. For example, the largest drug statin in history needs 200 people to avoid a heart attack every year, but the manufacturer has no incentive to figure out which person will benefit. But immunotherapy is completely different from statins. First, immunotherapy seems to be a 0/1 response, and a small number of respondents can survive for a long time while most patients do not. Second, the toxic side effects of these therapies are much larger than that of statins, so that most unresponsive patients are accompanied by enduring side effects. Third, these drugs are generally more expensive, and the same should not be allowed for unresponsive patients to bear such an economic burden. In addition, the relationship between tumor response, recurrence and prognosis is relatively straightforward. Although there are also false advances, 90% of patients have a much lower cardiovascular rate than LDL.
Although immunotherapy is an anticancer drug in clinical use, it is an immune system drug. The authors believe that responsive and non-response are two different immune system states. These two state transitions are like Transformers, only occur through a certain critical point, and are nonlinear. Static markers, as described above, may produce large response differences even if the difference is small, and observation of biomarker changes after a short-term use of the drug is more likely to predict which patients are closer to the critical point of system conversion. This is similar to a football match. Before the game, the two teams looked at the same station, but after a few minutes, they could predict the outcome more reliably. Of course, some teams love to play black for three minutes in the overtime period.
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