Managing Complex Biomarker Data Archives - Page 4 of 5

November 12, 2020 — In this article, QuartzBio Managing Director Scott Marshall discusses critical steps to integrating clinical, PK, and exploratory data on-study to advance operational and translational insights (without all the manual effort that has become all too common). With our experience working across dozens of clients and hundreds of studies, we’ve identified four key components necessary to efficiently centralize.

November 9, 2020 — Advances in high throughput measurement technologies (omics data) have made it possible, and increasingly affordable, to generate high complexity, high volume data for oncology research. QuartzBio’s Renee Deehan poster, “Reverse Causal Inferencing on Lung Adenocarcinoma Patients Reveals a Stem Cell–Like Molecular Subtype Associated with Pack-Year History,” is an insightful look into an exciting area of opportunity.

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October 1, 2020 — In the months since March 2020, the biotech community has rallied to identify how our unique perspectives and skills can help with the COVID-19 pandemic. The computational biologists at QuartzBio are no different. QuartzBio’s Renee Deehan discusses how using existing published data to build a disease model can answer questions and assist researchers in the fight against COVID-19. Read the article today.

September 24, 2020 — The COVID-19 pandemic has forced clinical trial stakeholders to rethink the paradigm as sponsors adapt to site restrictions, remote monitoring, and updated regulatory guidance. This is especially true in precision medicine programs, which have developed global footprints in order to reach targeted patient populations and rely on specialty labs running complex assays to assess safety, characterize patient response and drug MoA, and provide insights into efficacy. In the absence of consistent and predictable in-person presence, how can sponsors maintain the timely visibility necessary to optimize operations of these complex trials?

September 21, 2020 — What does precision medicine actually mean and how can AI and ML impact it?

QuartzBio’s Renee Deehan and panelists explore AI and ML’s impact on precision medicine and what the industry needs to do to prepare for it, tackle challenges as they evolve and develop appropriate solutions.

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August 27, 2020 — Machine learning and model-based drug discovery is rapidly becoming the norm. Computational biology is now integrating ‘wet’ in vitro/in vivo and ‘dry’ in silico biology accelerating and saving time and resources, at least in the early stages of drug discovery. This MassBio panel discussion explored the new world of digital biology as a way to model potential drug effects more accurately and quickly.

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August 13, 2020 — Cell therapy clinical trials typically generate a significant quantity and diversity of biologic data intended to characterize disease and patient biology, as well as data intended to demonstrate evidence of therapeutic mechanisms. QuartzBio’s Cliff Culver et al., explores the benefits of capturing and integrating molecular biomarker data within clinical trials to build foundations for data assets. Read the article in the latest issue of Applied Clinical Trials Magazine.

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July 27, 2020 — QuartzBio’s Tobias Guennel, PhD, presented the following poster, “A Genetic Enrichment Strategy for Delay of Onset of Alzheimer’s Disease Clinical Trials,” at AAIC 2020.

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July 16, 2020 — Computational approaches using COVID-19 data can be invaluable in quickly generating insights for drug repurposing, target pathway selection, and many other challenges. In this webinar, QuartzBio’s Renee Deehan will discuss how teams are combining deep biological knowledge with computational and machine learning approaches.

The global pandemic caused by SARS-CoV-2 has resulted in an incredible mobilization of biopharma resources and attention.

Agile research groups are recognizing the valuable opportunity for computational approaches to accelerate paths to insight generation. Mechanistically-guided methods have significant potential to inform and expedite critical decisions that would have previously required time consuming and costly de novo research studies.

Join us to learn how teams are combining deep biological knowledge with computational and machine learning approaches.

You’ll see how high resolution modeling of COVID-19 and related conditions enables:

Predictive analysis of drug response for re-purposing or indication expansion
Investigation of mechanism of action for potential therapeutic agents
Analysis of preclinical model recapitulation
Co-therapeutic target pathway selection

Watch the Webinar

June 8, 2020 — As the QuartzBio team’s efforts have progressed with the selection and integration of SARS-CoV-2 / COVID-19 data sets, I am pleased to share our first public update on analysis. This work builds on an evaluation of IFN-α2b treatment, and supports the rationale that interferons are upregulated as part of the antiviral defense, specifically for COVID-19. We are also sharing this work as a demonstration of the potential for translational research and drug repurposing while mitigating the time, expense, and reproducibility challenges inherent in drug/treatment combination discovery. This need for speed and flexibility is, of course, at the top of our minds as we respond to the global pandemic.