Challenges and Opportunities for Biomarker Validation

Computational reactive-diffusive modeling for stratification and prognosis determination of patients with breast cancer receiving Olaparib

Mathematical models based on partial differential equations (PDEs) can be exploited to handle clinical data with space/time dimensions, e.g. tumor growth challenged by neoadjuvant therapy. A model based on simplified assessment of tumor malignancy and pharmacodynamics efficiency was exercised to discover new metrics of patient prognosis in the OLTRE trial. We tested in a 17-patients cohort affected by early-stage triple negative breast cancer (TNBC) treated with 3 weeks of olaparib, the capability of a PDEs-based reactive-diffusive model of tumor growth to efficiently predict the response to olaparib in terms of SUV_max detected at ¹⁸FDG-PET/CT scan, by using specific terms to characterize tumor diffusion and proliferation. Computations were performed with COMSOL Multiphysics. Driving parameters governing the mathematical model were selected with Pearson's correlations. Discrepancies between actual and computed SUV_max values were assessed with Student's t test and Wilcoxon rank sum test. The correlation between post-olaparib true and computed SUV_max was assessed with Pearson's r and Spearman's rho. After defining the proper mathematical assumptions, the nominal drug efficiency (ε_PD) and tumor malignancy (r_c) were computationally evaluated. The former parameter reflected the activity of olaparib on the tumor, while the latter represented the growth rate of metabolic activity as detected by SUV_max. ε_PD was found to be directly dependent on basal tumor-infiltrating lymphocytes (TILs) and Ki67% and was detectable through proper linear regression functions according to TILs values, while r_c was represented by the baseline Ki67-to-TILs ratio. Predicted post-olaparib SUV*_max did not significantly differ from original post-olaparib SUV_max in the overall, gBRCA-mutant and gBRCA-wild-type subpopulations (p > 0.05 in all cases), showing strong positive correlation (r = 0.9 and rho = 0.9, p < 0.0001 both). A model of simplified tumor dynamics was exercised to effectively produce an upfront prediction of efficacy of 3-week neoadjuvant olaparib in terms of SUV_max. Prospective evaluation in independent cohorts and correlation of these outcomes with more recognized efficacy endpoints is now warranted for model confirmation and tailoring of escalated/de-escalated therapeutic strategies for early-TNBC patients.

Regulatory oversight, causal inference, and safe and effective health care machine learning

In recent years, the applications of Machine Learning (ML) in the health care delivery setting have grown to become both abundant and compelling. Regulators have taken notice of these developments and the U.S. Food and Drug Administration (FDA) has been engaging actively in thinking about how best to facilitate safe and effective use. Although the scope of its oversight for software-driven products is limited, if FDA takes the lead in promoting and facilitating appropriate applications of causal inference as a part of ML development, that leadership is likely to have implications well beyond regulated products.

Clinical Oral Disorders in Adults Screening Protocol (CODA-SP) from the 2019 Vancouver IADR Consensus Symposium

BACKGROUND

The Clinical Oral Disorder in Elders (CODE) index was proposed in 1999 to assess the oral health status and treatment needs of older people who typically were edentate or had few natural teeth. Since then, more people are retaining natural teeth into old age and have oral disorders similar to younger adults. In addition, there has been further guidance on screening for disease that includes changes to the clinical indicators of several oral disorders and greater sensitivity to people's concerns about their oral health and care needs.

METHODS

Experts in dental geriatrics assembled at a satellite symposium of the International Association of Dental Research in June 2019 to revise the objectives and content of the CODE index. Before the symposium, 139 registrants were asked for comments on the CODE index, and 11 content experts summarised current evidence and assembled reference lists of relevant information on each indicator. The reference lists provided the base for a narrative review of relevant evidence supplemented by reference tracking and direct searches of selected literature for additional evidence.

RESULTS

Analysis of the evidence by consensus of the experts produced the Clinical Oral Disorders in Adults Screening Protocol (CODA-SP).

CONCLUSIONS

The CODA-SP encompasses multiple domains of physical and subjective indicators with weighted severity scores. Field tests are required now to validate its effectiveness and utility in oral healthcare services, outcomes and infrastructure.

Biomarker Validation: Context and Complexities

Validation of a biomarker-based medical product development tool or clinical test is an evidentiary process that must be tailored to the proposed use. Appropriate data and analyses are needed to demonstrate that the bio-marker meets analytical and clinical performance criteria consistent with favorable benefit: risk balance.

Cochrane's Linked Data Project: How it Can Advance our Understanding of Surrogate Endpoints

Cochrane has developed a linked data infrastructure to make the evidence and data from its rich repositories more discoverable to facilitate evidence-based health decision-making. These annotated resources can enhance the study and understanding of biomarkers and surrogate endpoints.

Managing the Use and Dissemination of Information about Biomarkers: The Importance of Incentive Structures

The use of biomarkers holds great promise for the development of new therapeutics and the acceleration of clinical research. However, biomarkers must be validated - a complex and costly endeavor. Importantly, biomarker validation is meaningfully shaped by economic and policy-driven incentives.