Biomarker Validation: Technological, Clinical and Commercial Aspects

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Digital drug development technologies present an opportunity for more frequent, objective functional outcomes across a range of neurological disorders. In this talk, I provide an overview of our ongoing work in the PD and ALS space evaluating wearable and mobile assessments, including operational aspects, precompetitive alignment across industry and academic partners, and our interactions with regulators. Trials have been limited by brief, episodic examinations relying on surrogate markers and much subjective data as outcomes.

They are either too expensive polysomnography , too inaccurate 6 minute walk , or too burdensome VO2max. Physiology data from wearable sensors, when coupled with deep-learning artificial intelligence, provide the opportunity to replicate these assessments at home. In this presentation physIQ will review their latest proprietary deep learning tool as a potential replacement for VO2max in clinical trials.

Computational and mathematical models of human physiological and pathological processes can be used as a platform to monitor and control disease conditions by cloud computing. Input data are provided by biosensors and wearables, and the data processing using mathematical models is done on the cloud. In this talk I will give an overview and details of cloud computing strategy for precision medicine.

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Ariel V. Atopic dermatitis is often accompanied by unrelenting nighttime pruritus resulting in reduced sleep.

A key aspect to provide value in these assessments is that the methods and devices are vetted and verified to detect clinically meaningful changes. Robustness of sample processing and data analysis procedures is another factor that influences reproducibility of biomarker studies. For example, a high diagnostic accuracy of a peptide signature for ovarian cancer was not confirmed in subsequent independent reanalysis of the original dataset possibly due to variation in sample processing 36 , One report of proteomic biomarker discovery noted that common statistical algorithms run on data with low sample sizes can overfit and yield misleading misclassification rates and that prefiltering variables exacerbated this problem Similarly, a critical review of prognostic microarray studies in cancer revealed that half of the reported prognostic gene signatures were not reproducible due to critical flaws in the data analysis methods These reports highlight the importance of careful assessment of technical soundness and methodological validity and disclosure of information to the research community to enable fair evaluation of reported biomarkers and identification of candidates for further development.

In addition, ensuring reproducibility of bioinformatics analysis is a critical determinant of successful clinical translation of genome-based biomarkers. There have been several efforts to develop informatics infrastructure to address this issue, including public repository of datasets with relevant annotations on biological, clinical, and experimental parameters, analysis software repository, and systems to record whole process of data analysis itself to allow anyone to rerun or modify the analysis to verify robustness of reported findings 40 , Following the discovery phase that typically includes internal validation, candidate biomarkers are adapted to clinically applicable assay platforms, and subjected to two types of validation, namely analytical validation, i.

Analytical validation is typically performed by assaying the same set of samples by both the assay used in the initial discovery and the clinical deployment platform to determine robustness and reproducibility of the measurements. More recently, several multi-gene assays classified as in vitro diagnostic multivariate index assays IVDMIA have been introduced into clinic 13 , 42 , The implementation of gene expression-based multi-gene assays has been a challenging task due to poorer reproducibility of the measurements Currently available tests, such as MammaPrint 45 and Oncotype Dx 12 , are performed in centralized laboratories to minimize technical variability.

Emerging technology such as direct digital counting of transcripts without target amplification could enable more robust gene expression measurements reproducible across individual laboratories 46 , Resequencing of a targeted panel of genes disease-specific, exome, etc. Clinical sequencing is a promising approach, but the interpretation and reporting of incidental findings from non-targeted sequencing is still being debated Capability to analyze formalin-fixed, paraffin-embedded FFPE tissue samples greatly enhances general applicability of biomarker assays 52 - Emergence of highly sensitive assays, e.

These technologies are expected to achieve less-invasive assessment of molecular biomarkers liquid biopsy Another report, assessing the role of CTCs, defined as 5 or more per 7. In addition to their role in diagnosis, circulating cell-free microRNAs are also being currently assessed as a predictive cancer biomarker with some encouraging preliminary reports 59 , After analytical validity is confirmed, the biomarker assay in the clinical deployment platform must be evaluated to confirm its performance in predicting or diagnosing the clinical phenotype or outcome of interest as demonstrated in the discovery and initial validation phase 5 , 21 , Ideally, the biomarker should be evaluated in statistically well-powered prospective trials as performed in the TransATAC study for breast cancer recurrence prediction Clinical utility assessment could also include analysis of clinically meaningful outcome benefit, comparative effectiveness, cost-effectiveness of biomarker-guided clinical care, and assessment of alternatives and availability of the biomarker based on real-world clinical data or mathematical modeling 21 , An analytically and clinically validated biomarker assay is now ready for implementation in clinical care.

This phase includes the following four key elements, which vary widely across regions: regulatory approval, commercialization, coverage by health insurance companies, and incorporation in clinical practice guidelines. In the USA, there are two paths for regulatory approval: in vitro diagnostic device IVD as commercial medical device with k clearance overseen by the FDA, and laboratory developed tests LDT , home-grown assay developed and optimized at a diagnostic lab performing the test, which will likely be regulated by the FDA although current oversight is more limited Clinical biomarker tests must be conducted in diagnostic laboratories certified for Clinical Laboratory Improvement Amendments CLIA and in accordance with state-specific regulations.

Coverage by health insurance is critical for physicians to order the tests. Resources such as the National Comprehensive Cancer Network Biomarkers Compendium 66 are available to access the current recommendation for biomarkers in clinical guidelines HER2 overexpression is similarly predictive of response to trastuzumab in esophago-gastric adenocarcinoma Despite the numerous prognostic biomarkers reported in the literature, only seven biomarkers have been approved by the FDA Center for Devices and Radiological Health Table 2 One of the major reasons is that prognostic prediction itself often does not directly change clinical decision making unless coupled to specific therapeutic options.

Improving the Search for Biomarkers of Early Cancer

Despite this, many other prognostic biomarkers are available through the LDT pathway. Mammaprint is one of the first gene expression signature-based assays based on the measurement of 70 genes to predict breast cancer recurrence after chemotherapy, which was recently adapted for use in FFPE tissue Another gene expression-based assay, Oncotype Dx Breast Cancer Assay measures 21 genes predicting breast cancer recurrence in women with node negative or node positive, ER-positive, HER2-negative invasive breast cancer 12 , Boellaard, R.

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    Biomarkers: Advancing Drug Development, Dominating the Market

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    Biomarker Validation: Technological, Clinical and Commercial Aspects Biomarker Validation: Technological, Clinical and Commercial Aspects
    Biomarker Validation: Technological, Clinical and Commercial Aspects Biomarker Validation: Technological, Clinical and Commercial Aspects
    Biomarker Validation: Technological, Clinical and Commercial Aspects Biomarker Validation: Technological, Clinical and Commercial Aspects
    Biomarker Validation: Technological, Clinical and Commercial Aspects Biomarker Validation: Technological, Clinical and Commercial Aspects
    Biomarker Validation: Technological, Clinical and Commercial Aspects Biomarker Validation: Technological, Clinical and Commercial Aspects
    Biomarker Validation: Technological, Clinical and Commercial Aspects Biomarker Validation: Technological, Clinical and Commercial Aspects
    Biomarker Validation: Technological, Clinical and Commercial Aspects Biomarker Validation: Technological, Clinical and Commercial Aspects
    Biomarker Validation: Technological, Clinical and Commercial Aspects Biomarker Validation: Technological, Clinical and Commercial Aspects
    Biomarker Validation: Technological, Clinical and Commercial Aspects

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