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Validation Strategy for Biomarker-Guided Precision/Personalized Medicine
Published in Wei Zhang, Fangrong Yan, Feng Chen, Shein-Chung Chow, Advanced Statistics in Regulatory Critical Clinical Initiatives, 2022
Enrichment aims to utilize patient characteristics, such as demographic, pathophysiologic and genetic, to explore and identify the subpopulations that are more likely to respond to drug or other medical intervention. It can increase study power by decreasing heterogeneity and choosing an appropriate subpopulation. It also can identify a population with different outcome events, i.e., patients with severe disease or those in high-risk disease, which is called prognostic enrichment. In addition, it still can identify the subpopulation capable of responding to the treatment, which is called predictive enrichment.
Clinical Trials
Published in Michael Ljungberg, Handbook of Nuclear Medicine and Molecular Imaging for Physicists, 2022
There are often complementary aspects of a new drug that may be of interest to study even after receiving marketing authorization. This may include further safety data, quality of life, health economics, efficacy in a certain subpopulation of patients, and so forth. Clinical trials conducted within the approved indication but after commercialization, are called phase IV trials.
The Precision Medicine Approach in Oncology
Published in David E. Thurston, Ilona Pysz, Chemistry and Pharmacology of Anticancer Drugs, 2021
Rather than using larger numbers of patients with no means of predicting which ones will respond to a new therapy (i.e., a traditional “all-comers” Phase I clinical trial), the adaptive trial design involves the selection of smaller groups of patients based on a known biomarker so that there is a greater chance of individuals responding to the therapy. Furthermore, based on biomarker readouts, the selection of patients and the dosing regimen can be re-evaluated and possibly re-adapted during the trial to answer questions such as which subpopulation of patients is responding optimally, and which dose of a drug is best for a subpopulation of patients.
Bispecific antibodies for immune cell retargeting against cancer
Published in Expert Opinion on Biological Therapy, 2022
Rebecca P Chen, Kenta Shinoda, Pragya Rampuria, Fang Jin, Tin Bartholomew, Chunxia Zhao, Fan Yang, Javier Chaparro-Riggers
Another avenue of early exploration is to target specific subpopulations of T cells. Current CD3 based TCEs can theoretically activate all CD3+ T cells indiscriminately, including Tregs, which may be counterproductive for immune activation of anti-tumor effector cell responses. Patients with higher frequency of Tregs have reduced response rates toward blinatumomab, due to Treg activation leading to inhibition of CD8+ T cell proliferation and cytolytic activity [32]. An early study exploring a CD3 × CD8 × TAA trispecific did not yield sufficient cytotoxicity [81] but with the advances in other engineering strategies, could present an interesting avenue for subpopulation specificity, although manufacturability and higher cost associated with more complex modalities need to be factored in when weighing the benefits. Another specific example of subpopulation targeting is discussed in the following section.
Focusing on protective factors, resilience and thriving to reduce health disparities and treatment inequities
Published in The American Journal of Drug and Alcohol Abuse, 2022
Craig Field, Jennifer Reingle Gonzalez
Health disparities research is conducted using two key approaches: (1) documenting between-group differences comparing ethnic/racial minorities groups and majority populations; or, (2) documenting within-group differences in a single racial or ethnic group. Characterization of between-group differences is useful to identify existing health disparities; however, it is essential to understand within-group diversity to truly address these inequities. By examining within-group differences, researchers moved beyond factors shared with other racial and ethnic groups and were able to examine factors that are unique to – and vary within – a specific racial/ethnic subpopulation. This approach recognizes that there is significant variability in the 22 Spanish-speaking countries and in the more than 500 federally recognized American Indian/Alaskan Native (AI/AN) tribal communities – each country or community with their own unique cultural values, practices, and historical influences. Examining within-group differences also inherently acknowledges that members of every racial and ethnic subpopulation have people who are at risk for substance use disorders, but not all people of a particular race/ethnicity experience substance use-related problems. To this end, this special issue emphasizes the examination of unique characteristics within subgroups of ethnic minorities as opposed to focusing exclusively on comparison to the majority population.
A signature enrichment design with Bayesian adaptive randomization
Published in Journal of Applied Statistics, 2021
Fang Xia, Stephen L. George, Jing Ning, Liang Li, Xuelin Huang
Recently, the rapid advancement of biomarker studies in oncology has promoted the development and application of precision medicine, previously known as ‘personalized medicine’ [12,25]. Precision medicine targets a subpopulation of patients who are most likely to respond to the treatment based on their characteristics or biomarker profile. Prognostic biomarkers provide information on clinical outcome independently of the treatment received, while predictive biomarkers provide information on clinical outcome for a particular treatment [31]. Prognostic biomarkers can be used to screen good and bad prognostic patients, while predictive biomarkers can be used to measure how likely the patient will respond to a particular treatment. Before using biomarker information in clinical practice, it is essential to test the biomarkers for analytical and clinical validity and clinical utility [33]. For the designs described in this paper, we focus on predictive biomarkers.