To learn more visit:
https://insidescientific.com/webinar/cutting-edge-conversations-fighting-neurodegenerative-diseases/
Evelyn Pyper, MPH discusses how a patient-centered approach to real-world data collection and evidence generation can transform research in neurodegeneration. Neurodegenerative diseases often affect both motor and cognitive function, produce emotional and social changes, and require significant caregiver support, all while stretching across a fragmented healthcare ecosystem. Participatory research that directly obtains patient consent, empowers patients, and simplifies the task of linking multiple data sources, can lead to a more comprehensive capture of medical histories. This presentation briefly explores ways in which patient-centered research can improve understanding of disease diagnoses, symptomatology, and progression.
Real world Evidence and Precision medicine bridging the gapClinosolIndia
Real-world evidence and precision medicine represent complementary forces reshaping the healthcare landscape. The synergy between these realms offers a pathway to more personalized, effective, and patient-centered care. As technology, data analytics, and collaborative initiatives advance, the integration of real-world evidence into precision medicine practices holds the promise of revolutionizing how healthcare is delivered, ensuring that treatments are not only scientifically sound but also tailored to the unique characteristics and experiences of individual patients.
Cutting Edge Conversations: Addressing Orphan and Rare DiseasesInsideScientific
There are over 7,000 rare and orphan diseases known to impact approximately 1 in 17 individuals globally, or 50 million in the EU and USA alone. The development of safe, effective, and accessible therapies against these diseases has been challenged by manufacturing, clinical and regulatory hurdles. Despite these obstacles, increased awareness, greater funding, and new research technologies are driving discoveries in this area. Join this webinar to learn how various research groups are working in this space.
Dr. Zabinski discusses how Artificial Intelligence (AI) and Real-World Datasets (RWD) can work in synergy to address many of the challenges facing rare disease researchers, including better describing real-world epidemiology; identifying meaningful patterns in rare disease patient journeys; and assisting in finding patients, plus those not yet diagnosed. This presentation will briefly explore the ways AI and RWD together can enhance visibility into patient trajectories, improve rare disease patient identification for clinical trial recruitment and observational research, and shorten time to diagnosis.
Dr. Kish discusses how precision medicine is redefining how we evaluate and treat rare diseases. No longer is cancer defined by its organ of origin, tissue or cell type but rather its genotype. Over just a few decades lung cancer has been transformed from one disease affecting 100,000+ in the U.S. annually to dozens of cancers that each inflict just a few thousand. But, finding patients and RWD to improve outcomes can be challenging. Learn how Cardinal Health Real-World Evidence and Insights takes a decentralized approach to identify hard to find patients and RWD.
Real world Evidence and Precision medicine bridging the gapClinosolIndia
Real-world evidence and precision medicine represent complementary forces reshaping the healthcare landscape. The synergy between these realms offers a pathway to more personalized, effective, and patient-centered care. As technology, data analytics, and collaborative initiatives advance, the integration of real-world evidence into precision medicine practices holds the promise of revolutionizing how healthcare is delivered, ensuring that treatments are not only scientifically sound but also tailored to the unique characteristics and experiences of individual patients.
Cutting Edge Conversations: Addressing Orphan and Rare DiseasesInsideScientific
There are over 7,000 rare and orphan diseases known to impact approximately 1 in 17 individuals globally, or 50 million in the EU and USA alone. The development of safe, effective, and accessible therapies against these diseases has been challenged by manufacturing, clinical and regulatory hurdles. Despite these obstacles, increased awareness, greater funding, and new research technologies are driving discoveries in this area. Join this webinar to learn how various research groups are working in this space.
Dr. Zabinski discusses how Artificial Intelligence (AI) and Real-World Datasets (RWD) can work in synergy to address many of the challenges facing rare disease researchers, including better describing real-world epidemiology; identifying meaningful patterns in rare disease patient journeys; and assisting in finding patients, plus those not yet diagnosed. This presentation will briefly explore the ways AI and RWD together can enhance visibility into patient trajectories, improve rare disease patient identification for clinical trial recruitment and observational research, and shorten time to diagnosis.
Dr. Kish discusses how precision medicine is redefining how we evaluate and treat rare diseases. No longer is cancer defined by its organ of origin, tissue or cell type but rather its genotype. Over just a few decades lung cancer has been transformed from one disease affecting 100,000+ in the U.S. annually to dozens of cancers that each inflict just a few thousand. But, finding patients and RWD to improve outcomes can be challenging. Learn how Cardinal Health Real-World Evidence and Insights takes a decentralized approach to identify hard to find patients and RWD.
CORD Rare Drug Conference, June 8 - 9, 2022
Opportunities and Challenges for Data Management Real-World Data and Real-World Evidence
• Patient support programs: Sandra Anderson, Innomar Strategies
• AI for Data Management and Enhancement: Aaron Leibtag, Pentavere
• Patient Support and RWE: Laurie Lambert, CADTH
Real-World Data and Real-World Evidence Webinar
Panelists
Tara Cowling, Medlior
Laurie Lambert, CADTH
Craig Campbell, London Health Sciences
Sandra Anderson, Innomar Strategies
Brad Alyward, Canadian Organization for Rare Disorders
Durhane Wong-Rieger, Canadian Organization for Rare Disorders
Chapter 4 Knowledge Discovery, Data Mining, and Practice-Based Evi.docxchristinemaritza
Chapter 4 Knowledge Discovery, Data Mining, and Practice-Based Evidence
Mollie R. Cummins
Ginette A. Pepper
Susan D. Horn
The next step to comparative effectiveness research is to conduct more prospective large-scale observational cohort studies with the rigor described here for knowledge discovery and data mining (KDDM) and practice-based evidence (PBE) studies.
Objectives
At the completion of this chapter the reader will be prepared to:
1.Define the goals and processes employed in knowledge discovery and data mining (KDDM) and practice-based evidence (PBE) designs
2.Analyze the strengths and weaknesses of observational designs in general and of KDDM and PBE specifically
3.Identify the roles and activities of the informatics specialist in KDDM and PBE in healthcare environments
Key Terms
Comparative effectiveness research, 69
Confusion matrix, 62
Data mining, 61
Knowledge discovery and data mining (KDDM), 56
Machine learning, 56
Natural language processing (NLP), 58
Practice-based evidence (PBE), 56
Preprocessing, 56
Abstract
The advent of the electronic health record (EHR) and other large electronic datasets has revolutionized efficient access to comprehensive data across large numbers of patients and the concomitant capacity to detect subtle patterns in these data even with missing or less than optimal data quality. This chapter introduces two approaches to knowledge building from clinical data: (1) knowledge discovery and data mining (KDDM) and (2) practice-based evidence (PBE). The use of machine learning methods in retrospective analysis of routinely collected clinical data characterizes KDDM. KDDM enables us to efficiently and effectively analyze large amounts of data and develop clinical knowledge models for decision support. PBE integrates health information technology (health IT) products with cohort identification, prospective data collection, and extensive front-line clinician and patient input for comparative effectiveness research. PBE can uncover best practices and combinations of treatments for specific types of patients while achieving many of the presumed advantages of randomized controlled trials (RCTs).
Introduction
Leaders need to foster a shared learning culture for improving healthcare. This extends beyond the local department or institution to a value for creating generalizable knowledge to improve care worldwide. Sound, rigorous methods are needed by researchers and health professionals to create this knowledge and address practical questions about risks, benefits, and costs of interventions as they occur in actual clinical practice. Typical questions are as follows:
•Are treatments used in daily practice associated with intended outcomes?
•Can we predict adverse events in time to prevent or ameliorate them?
•What treatments work best for which patients?
•With limited financial resources, what are the best interventions to use for specific types of patients?
•What types of indi ...
Data Science Deep Roots in Healthcare IndustryDinesh V
Data Science transforms the healthcare industry with impeccable solutions that can improve patient care through EHRs, medical imaging, drug discovery, predictive medicines and genetics and genomics.
The benefits of patient involvement in research and development (RE:ACT Congr...jangeissler
Presentation of Jan Geissler, Director EUPATI and Co-Founder CML Advocates Network, about the benefits of involving patients in research and development, and about EUPATI. Held at RE:ACT Conress 2016 on Research of Rare and Orphan Diseases, organized by the Blackswan Foundation on 12 March 2016 in Barcelona, Spain
Improving health care outcomes with responsible data scienceWessel Kraaij
Keynote presentation by Wessel Kraaij at the Dutch pattern recognition and impage processing society (NVPBV) 29/5/2018, Eindhoven.
This talk discusses
1. trends in health care and respondible data science and their intersection
2. Secure federated analytics on distributed data repositories
3. Generating clinically relevant hypotheses from patient forum discussions.
The Impact of Real-World Data in Pharmacovigilance and Regulatory Decision-Ma...ClinosolIndia
Real-world data (RWD) has gained significant importance in pharmacovigilance and regulatory decision-making processes. Real-world data refers to data collected from routine clinical practice, including electronic health records (EHRs), claims databases, registries, and other sources, outside the controlled environment of clinical trials. Here are some key impacts of real-world data in pharmacovigilance and regulatory decision-making
REAL WORLD DATA SOURCES AND APPLICATIONS IN HEALTH OUTCOMES RESEARCH ClinosolIndia
Health outcomes research aims to assess the real-world effectiveness, safety, and value of healthcare interventions. In recent years, the availability and utilization of real-world data (RWD) have significantly contributed to advancing health outcomes research. This paper explores the various sources of real-world data and their applications in health outcomes research.
Real-world data refers to data collected outside of controlled clinical trials, often generated through routine healthcare delivery, electronic health records (EHRs), claims databases, registries, wearable devices, and patient-reported outcomes. These data sources provide a wealth of information on patient characteristics, treatment patterns, healthcare utilization, and clinical outcomes in real-world settings.
Next-Generation Safety Assessment Tools for Advancing In Vivo to In Vitro Tra...InsideScientific
Join Prof. Victoria Hutter and Dr. Louis Scott as they showcase the application of high-content imaging and advanced cell lines for drug safety assessment.
Safety concerns play a significant role in the unsuccessful progression of candidate compounds in the later stages of drug development. Establishing the connection between in vitro endpoints and human health outcomes is essential.
In this webinar, Prof. Victoria Hutter and Dr. Louis Scott present a novel tool for in vitro safety assessment in drug development. The morph_ONE™ assay provides a human-centric approach to potentially fill specific regulatory gaps concerning safety issues. This tool is capable of profiling both human and rat alveolar macrophages, offering valuable insights for hazard identification and toxicity assessments. By bridging the divide between cellular effects and overall risk, it has the potential to enhance our understanding of safety-related aspects in drug development.
Key Topics Include:
- Explore distinct in vitro screening techniques for evaluating the safety of emerging inhaled products, facilitating early and informed decisions in compound selection and development.
- How high-content image analysis (HCIA) cell painting assays can be used as a forward-looking high-throughput screening tool, distinguishing unique response patterns in alveolar macrophages.
- Understand the use of the ImmuPHAGE™ and ImmuLUNG™ models in conducting customized evaluations focused on inhalation safety.
A Ready-to-Analyze High-Plex Spatial Signature Development Workflow for Cance...InsideScientific
In this webinar, Aditya Pratapa and Lorcan Sherry present a new workflow for analyzing multiplex immunoflurescence images.
Spatial Signatures are a new class of highly predictive biomarkers that measure the interactions and cellular densities of tumor and immune cells that compose the tumor microenvironment. Based on multiplex immunofluorescence, spatial signatures provide a deeper understanding of complex interactions between tumors and the immune system, enabling improved patient stratification for immunotherapies. A significant hurdle to date has been in developing a data analysis workflow that is straightforward and user-friendly to transform the data rich images into meaningful quantitative spatial signatures.
In this webinar, Aditya and Lorcan review the key features of the new PhenoImager HT 2.0 data analysis workflow. This workflow introduces a simplified framework from scanning to analyzing spectrally unmixed multiplex immunofluorescence images generated on the PhenoImager HT platform. The ready-to-analyze data can be directly imported into image analysis software such as Visiopharm. This presentation covers key aspects of data analysis elements such as image QC, segmentation, phenotyping, and verification – all essential for creating outputs that support the development of a spatial signature.
Key Topics Include:
- Understand Akoya’s new HT 2.0 data analysis workflow
- The challenges in multiplex immunofluorescence analysis and the use of AI and cell
lineage segmentation considerations
- Explore OracleBio’s image analysis workflow incorporating Visiopharm
- Evaluation of analysis data to facilitate spatial profiling and interpretation
CORD Rare Drug Conference, June 8 - 9, 2022
Opportunities and Challenges for Data Management Real-World Data and Real-World Evidence
• Patient support programs: Sandra Anderson, Innomar Strategies
• AI for Data Management and Enhancement: Aaron Leibtag, Pentavere
• Patient Support and RWE: Laurie Lambert, CADTH
Real-World Data and Real-World Evidence Webinar
Panelists
Tara Cowling, Medlior
Laurie Lambert, CADTH
Craig Campbell, London Health Sciences
Sandra Anderson, Innomar Strategies
Brad Alyward, Canadian Organization for Rare Disorders
Durhane Wong-Rieger, Canadian Organization for Rare Disorders
Chapter 4 Knowledge Discovery, Data Mining, and Practice-Based Evi.docxchristinemaritza
Chapter 4 Knowledge Discovery, Data Mining, and Practice-Based Evidence
Mollie R. Cummins
Ginette A. Pepper
Susan D. Horn
The next step to comparative effectiveness research is to conduct more prospective large-scale observational cohort studies with the rigor described here for knowledge discovery and data mining (KDDM) and practice-based evidence (PBE) studies.
Objectives
At the completion of this chapter the reader will be prepared to:
1.Define the goals and processes employed in knowledge discovery and data mining (KDDM) and practice-based evidence (PBE) designs
2.Analyze the strengths and weaknesses of observational designs in general and of KDDM and PBE specifically
3.Identify the roles and activities of the informatics specialist in KDDM and PBE in healthcare environments
Key Terms
Comparative effectiveness research, 69
Confusion matrix, 62
Data mining, 61
Knowledge discovery and data mining (KDDM), 56
Machine learning, 56
Natural language processing (NLP), 58
Practice-based evidence (PBE), 56
Preprocessing, 56
Abstract
The advent of the electronic health record (EHR) and other large electronic datasets has revolutionized efficient access to comprehensive data across large numbers of patients and the concomitant capacity to detect subtle patterns in these data even with missing or less than optimal data quality. This chapter introduces two approaches to knowledge building from clinical data: (1) knowledge discovery and data mining (KDDM) and (2) practice-based evidence (PBE). The use of machine learning methods in retrospective analysis of routinely collected clinical data characterizes KDDM. KDDM enables us to efficiently and effectively analyze large amounts of data and develop clinical knowledge models for decision support. PBE integrates health information technology (health IT) products with cohort identification, prospective data collection, and extensive front-line clinician and patient input for comparative effectiveness research. PBE can uncover best practices and combinations of treatments for specific types of patients while achieving many of the presumed advantages of randomized controlled trials (RCTs).
Introduction
Leaders need to foster a shared learning culture for improving healthcare. This extends beyond the local department or institution to a value for creating generalizable knowledge to improve care worldwide. Sound, rigorous methods are needed by researchers and health professionals to create this knowledge and address practical questions about risks, benefits, and costs of interventions as they occur in actual clinical practice. Typical questions are as follows:
•Are treatments used in daily practice associated with intended outcomes?
•Can we predict adverse events in time to prevent or ameliorate them?
•What treatments work best for which patients?
•With limited financial resources, what are the best interventions to use for specific types of patients?
•What types of indi ...
Data Science Deep Roots in Healthcare IndustryDinesh V
Data Science transforms the healthcare industry with impeccable solutions that can improve patient care through EHRs, medical imaging, drug discovery, predictive medicines and genetics and genomics.
The benefits of patient involvement in research and development (RE:ACT Congr...jangeissler
Presentation of Jan Geissler, Director EUPATI and Co-Founder CML Advocates Network, about the benefits of involving patients in research and development, and about EUPATI. Held at RE:ACT Conress 2016 on Research of Rare and Orphan Diseases, organized by the Blackswan Foundation on 12 March 2016 in Barcelona, Spain
Improving health care outcomes with responsible data scienceWessel Kraaij
Keynote presentation by Wessel Kraaij at the Dutch pattern recognition and impage processing society (NVPBV) 29/5/2018, Eindhoven.
This talk discusses
1. trends in health care and respondible data science and their intersection
2. Secure federated analytics on distributed data repositories
3. Generating clinically relevant hypotheses from patient forum discussions.
The Impact of Real-World Data in Pharmacovigilance and Regulatory Decision-Ma...ClinosolIndia
Real-world data (RWD) has gained significant importance in pharmacovigilance and regulatory decision-making processes. Real-world data refers to data collected from routine clinical practice, including electronic health records (EHRs), claims databases, registries, and other sources, outside the controlled environment of clinical trials. Here are some key impacts of real-world data in pharmacovigilance and regulatory decision-making
REAL WORLD DATA SOURCES AND APPLICATIONS IN HEALTH OUTCOMES RESEARCH ClinosolIndia
Health outcomes research aims to assess the real-world effectiveness, safety, and value of healthcare interventions. In recent years, the availability and utilization of real-world data (RWD) have significantly contributed to advancing health outcomes research. This paper explores the various sources of real-world data and their applications in health outcomes research.
Real-world data refers to data collected outside of controlled clinical trials, often generated through routine healthcare delivery, electronic health records (EHRs), claims databases, registries, wearable devices, and patient-reported outcomes. These data sources provide a wealth of information on patient characteristics, treatment patterns, healthcare utilization, and clinical outcomes in real-world settings.
Similar to Fighting Neurodegenerative Diseases (20)
Next-Generation Safety Assessment Tools for Advancing In Vivo to In Vitro Tra...InsideScientific
Join Prof. Victoria Hutter and Dr. Louis Scott as they showcase the application of high-content imaging and advanced cell lines for drug safety assessment.
Safety concerns play a significant role in the unsuccessful progression of candidate compounds in the later stages of drug development. Establishing the connection between in vitro endpoints and human health outcomes is essential.
In this webinar, Prof. Victoria Hutter and Dr. Louis Scott present a novel tool for in vitro safety assessment in drug development. The morph_ONE™ assay provides a human-centric approach to potentially fill specific regulatory gaps concerning safety issues. This tool is capable of profiling both human and rat alveolar macrophages, offering valuable insights for hazard identification and toxicity assessments. By bridging the divide between cellular effects and overall risk, it has the potential to enhance our understanding of safety-related aspects in drug development.
Key Topics Include:
- Explore distinct in vitro screening techniques for evaluating the safety of emerging inhaled products, facilitating early and informed decisions in compound selection and development.
- How high-content image analysis (HCIA) cell painting assays can be used as a forward-looking high-throughput screening tool, distinguishing unique response patterns in alveolar macrophages.
- Understand the use of the ImmuPHAGE™ and ImmuLUNG™ models in conducting customized evaluations focused on inhalation safety.
A Ready-to-Analyze High-Plex Spatial Signature Development Workflow for Cance...InsideScientific
In this webinar, Aditya Pratapa and Lorcan Sherry present a new workflow for analyzing multiplex immunoflurescence images.
Spatial Signatures are a new class of highly predictive biomarkers that measure the interactions and cellular densities of tumor and immune cells that compose the tumor microenvironment. Based on multiplex immunofluorescence, spatial signatures provide a deeper understanding of complex interactions between tumors and the immune system, enabling improved patient stratification for immunotherapies. A significant hurdle to date has been in developing a data analysis workflow that is straightforward and user-friendly to transform the data rich images into meaningful quantitative spatial signatures.
In this webinar, Aditya and Lorcan review the key features of the new PhenoImager HT 2.0 data analysis workflow. This workflow introduces a simplified framework from scanning to analyzing spectrally unmixed multiplex immunofluorescence images generated on the PhenoImager HT platform. The ready-to-analyze data can be directly imported into image analysis software such as Visiopharm. This presentation covers key aspects of data analysis elements such as image QC, segmentation, phenotyping, and verification – all essential for creating outputs that support the development of a spatial signature.
Key Topics Include:
- Understand Akoya’s new HT 2.0 data analysis workflow
- The challenges in multiplex immunofluorescence analysis and the use of AI and cell
lineage segmentation considerations
- Explore OracleBio’s image analysis workflow incorporating Visiopharm
- Evaluation of analysis data to facilitate spatial profiling and interpretation
Molecule Transport across Cell Membranes: Electrochemical Quantification at t...InsideScientific
In this webinar, Dr. Sabine Kuss will discuss the importance of transmembrane molecule exchange and how to detect and quantify membrane transport of molecules in cells.
Complex biological processes, such as the transport of molecules across cell membranes, are difficult to understand using purely biological methodologies. Investigating cellular transport processes is challenging, because of the highly complex chemical composition of cells and the diffusion of molecules in and around cells at low concentrations. The development and advancement of electroanalytical methods over the last two decades has enabled the monitoring of living cells and their interaction with the environment, including external stimuli, such as pharma-molecules.
This presentation emphasizes electrochemical and electrophysiological methods of detection and quantification but also makes a comparison to other bioanalytical approaches. Join us to discover a substantial diversity in methods used to monitor the transport of cell metabolites, crucial for cell survival, and pharmaceutical compounds, involved in cell characteristics such as drug resistance.
Key Topics Include:
- Understanding transmembrane molecule transport through bioanalytical methods
- Electrochemical approaches to monitor molecule transport across cell membranes
- What bioanalytical and especially electrochemical approaches can reveal
- Challenges associated with instrument limitations
Exploring Predictive Biomarkers and ERK1/2 Phosphorylation: A New Horizon in ...InsideScientific
In this webinar, Dr. Victor Arrieta highlights the link between p-ERK activation and improved survival in rGBM patients using anti-PD-1 immunotherapy.
Recurrent glioblastoma (rGBM) has displayed a varied response to anti-PD-1 immunotherapy, necessitating the identification of predictive biomarkers. Through extensive analyses and 3 clinical studies, we have identified that activation of the MAPK/ERK signaling pathway, particularly ERK1/2 phosphorylation (p-ERK), is associated with longer overall survival (OS) in rGBM patients receiving PD-1 blockade. Initially, enrichment of BRAF/PTPN11 mutations was reported in 30% of responsive rGBM patients, prompting the investigation of p-ERK as a potential marker beyond these mutations.
Our research has unraveled an association between p-ERK abundance and better clinical outcomes following PD-1 blockade, with p-ERK mainly localized in tumor cells. Notably, high p-ERK GBMs contained unique microglia and macrophage phenotypes with elevated MHC class II expression, suggesting a novel interplay between MAPK activation and the tumor immune microenvironment.
While these insights establish a pivotal role for p-ERK in predicting PD-1 blockade response in rGBM, the implementation in clinical settings calls for further validation and accuracy. Nonetheless, these findings pave the way for more personalized and effective immunotherapy strategies, emphasizing the significance of the tumor microenvironment and its interaction with therapeutic interventions in GBM.
Key Topics Include:
- The activation of the MAPK signaling pathway, specifically ERK1/2 phosphorylation (p-ERK), is identified as a predictive biomarker for longer overall survival in recurrent glioblastoma (eGBM) patients undergoing PD-1 blockade
- High p-ERK tumors in rGBM present a distinct myeloid cell phenotype with elevated MHC class II expression, signifying a connection between MAPK pathway activation and the immune microenvironment
- The implementation of p-ERK as a predictive biomarker in clinical settings requires further validation and exploration of variables impacting its evaluation
Exploring Estrogen’s Role in Metabolism and the Use of 13C-Labeled Nutrients ...InsideScientific
Dr. Reilly Enos and Dr. Eran Levin discuss estrogen's metabolic impact and how isotopic labeling and 13C-labeled nutrients can be used for animal physiology and nutrition research.
Reilly Enos, PhD – Harnessing the power of estrogen to regulate metabolic processes
Dr. Reilly Enos’ research focuses on the role that sex steroids and their receptors play in regulating metabolic processes, particularly in the setting of obesity. In this webinar, Dr. Enos will discuss his research on tissue-specific fluctuations of sex steroids throughout the estrous cycle in mice, provide insights into the importance of the quantity of estrogen necessary to impact physiological processes, as well as an understanding of the central versus peripheral effects of estrogen action.
Eran Levin, PhD – Unlocking Insights: Utilizing 13C Labeled Nutrients for Cutting-Edge Physiology and Nutrition Research
Dr. Eran Levin will discuss the potential of using 13C-labeled nutrients in physiology and nutrition research in animal models. Specifically, he will share practical tips for designing and conducting experiments using isotopic labeling techniques and demonstrate how they can provide unprecedented insights into metabolic pathways, nutrient utilization, and behaviors in both vertebrate and invertebrate models including insects, reptiles, and mammals.
Key Topics Include:
- The role that estrogen plays in regulating metabolic and behavioral processes in males and females
- The tissue-specific fluctuations of sex steroids throughout the estrous cycle
- Insight into the importance of tissue-specificity in developing hormonal therapies
- The importance of estrogen quantity in regulating physiological processes
- Understand the diverse range of 13C labeled nutrients available
- Specific applications of labeled amino acids in studies of protein metabolism, cellular signaling, and typical nutrient utilization
- How to integrate 13C labeling techniques with respirometry for a comprehensive assessment of metabolic processes, energy expenditure, and substrate utilization in animal models
- How to calculate metabolic rates in free-flying animals using 13C bicarbonate
Longitudinal Plasma Samples: Paving the Way for Precision OncologyInsideScientific
Experts present a cell-free plasma biobank and describe the role of longitudinal plasma samples for cancer research, disease monitoring, and biomarker development.
Through liquid biopsies, it is now possible to repeatedly and non-invasively interrogate the molecular landscape of solid tumors via a blood draw over the whole treatment course. Until now, liquid biopsies can be used for screening, disease monitoring and prognosis. Circulating tumor DNA (ctDNA) and circulating tumor cells (CTCs) have been the most explored targets in this technology for commercial applications up to the present time.
In collaboration with a continuously expanding oncology network, Indivumed Services has established a unique high-quality cell-free plasma biobank that is exclusively focused on collecting longitudinal whole blood samples from cancer patients. This allows molecular insight by providing quick access to longitudinal plasma from cancer patients that have undergone treatment. ctDNA can then be isolated from longitudinal cell-free plasma to allow for monitoring of disease progression by providing diagnostic and prognostic information, potentially in real time.
Key Topics Include:
- Gain insights into Indivumed Services’ longitudinal plasma collection process
- Understand the advantages and benefits of utilizing longitudinal plasma samples for cancer research
- Explore applications of longitudinal plasma samples for biomarker research and development of companion diagnostics
Fully Characterized, Standardized Human Induced Pluripotent Stem Cell Line an...InsideScientific
In this webinar, experts present a standardized stem cell line and its differentiation into neural cells for disease modeling and assay development.
Reproducible research with human induced pluripotent stem cells (iPSCs) depends on thoroughly characterized and quality-controlled cell lines. In this webinar, Dr. Andrew Gaffney and Dr. Erin Knock from STEMCELL Technologies describe the generation of a standardized induced pluripotent stem cell (iPSC) line. Developed with the upcoming ISSCR Standards Initiative characterization guidelines in mind, this highly characterized line is karyotypically stable, demonstrates trilineage differentiation potential, and expresses undifferentiated cell markers. Further, STEMCELL has developed a highly pure, ready-to-use neural progenitor cell product expressing PAX6 and SOX1 over multiple passages.
Dr. Knock shows how these multipotent cells are suitable for customized downstream differentiation to various CNS cell types, such as forebrain neurons, midbrain neurons, and astrocytes. These progenitor cells are the ideal controls for standardizing downstream differentiation protocols, modeling diseases, and assay development.
Key Topics Include:
- Discover how STEMCELL’s induced pluripotent stem cell lines are derived and characterized
- Learn how to differentiate induced pluripotent stem cell lines into all three germ layers
- Explore the features of STEMCELL’s neural progenitor cell product
- Differentiate neural progenitor cells into a variety of neural cell types, including neurons and glia
How to Create CRISPR-Edited T Cells More Efficiently for Tomorrow's Cell Ther...InsideScientific
Ian Foster and Steven Loo-Yong-Kee discuss Artisan Bio's STAR-CRISPR system for optimized gene editing in cell therapy, with a focus on the genetifc modification of T cells for cancer immunotherapy.
Cell therapy is an emerging field with great promise for the treatment of various diseases. One of the most exciting areas of cell therapy is the use of T cells that have been genetically modified to recognize and kill cancer cells. While the use of T cells for cancer immunotherapy has tremendous promise, there is still room for improvement. The efficiency, expansion, and functionality of T cells can be enhanced by genetic modification using the STAR-CRISPR system.
Artisan Bio is a biotechnology company focused on developing a CRISPR-mediated editing platform to improve the efficacy and safety of cell therapy products. In this webinar, we will provide a comprehensive overview of Artisan Bio’s STAR-CRISPR system, which is designed to improve the specificity and efficiency of gene editing for cell therapies. We will explain the system’s key components and how we are using a risk-based approach to optimize and validate the editing platform. The webinar will focus on Artisan Bio’s approach to building T cell OS/APPS through iterative improvements to achieve best-in-class editing capabilities and improved cell health metrics.
Key Topics Include:
- Learn about Artisan Bio’s proprietary high-performance STAR-CRISPR system for improving the specificity and efficiency of gene editing for cell therapies
- Explore Artisan Bio’s risk-based, systems approach to technology development, including how to implement Design of Experiments (DoE) and Quality by Design (QbD) principles to optimize and validate any process
- Case study of the application of QbD to Artisan Bio’s STAR-CRISPR platform to edit T cells for cancer immunotherapy with preliminary data showing improved efficacy, expansion, and functionality
Peripheral and Cerebral Vascular Responses Following High-Intensity Interval ...InsideScientific
Dr. Bert Bond and Max Weston will present an overview on their study investigating the effects high-intensity interval exercise has on cerebrovascular health.
Physical activity reduces the risk of developing cardiovascular diseases (CVD) and dementia. This benefit cannot be explained by changes in traditional CVD risk factors alone, and direct improvements in vascular health are thought to play a key role. However, our understanding of how exercise can be optimized for improvements in blood-vessel health is limited.
High-intensity interval exercise (HIIE) is known to improve peripheral vascular function, and there is a growing interest in the effects of HIIE on cerebrovascular health. However, it is not clear whether the acute improvements in peripheral vascular function following HIIE are also seen in the major blood-vessels of the brain.
In the Bond lab’s study, 30 minutes of HIIE completed at both 75% and 90% V̇O2max improved peripheral vascular function 1 and 3h following exercise in healthy young adults, compared with work-matched continuous moderate-intensity exercise and a sedentary control condition. By contrast, cerebrovascular function was unchanged following all conditions. This is the first study to identify that acute improvements in peripheral vascular function following high-intensity interval exercise are not mirrored by improvements in cerebrovascular function in healthy young adults.
Leveraging Programmable CRISPR-Associated Transposases for Next-Generation Ge...InsideScientific
Dr. Sam Sternberg discusses a novel CRISPR-Cas9 system using programmable, RNA-guided transposase, and highlights its implications for kilobase-scale genome engineering in cell and gene therapies.
The utility of programmable, RNA-guided CRISPR-Cas systems in genome engineering continues to evolve. Nature has afforded scientists novel and diverse gene editing functionality, from nuclease-dependent CRISPR-Cas9 to second-generation base and prime editors that do not produce double-strand breaks.
In this webinar, Dr. Sam Sternberg describes a new CRISPR-Cas9 paradigm relying on nuclease-deficient bacterial transposons that catalyze RNA-guided integration of mobile genetic elements into the genome. The discovery of a fully programmable, RNA-guided transposase lays the foundation for kilobase-scale genome engineering with broad applications for developing cell and gene therapies.
Key Topics Include:
- The basics of first- and second-generation CRISPR-Cas technologies from a scientist at the forefront of their development
- Mechanisms, accommodation, and cell type diversity of CRISPR-Cas programmable transposition
- How transposase factor coordination enables highly specific, genome-wide DNA integration to target sites
- Implications of programmable transposases that obviate the need for DNA double-strand breaks and homologous recombination
Simple Tips to Significantly Improve Rodent Surgical OutcomesInsideScientific
Dr. Marcel Perret-Gentil presents six simple-to-implement techniques to significantly improve surgical outcomes.
You may feel proficient, even confident in performing rodent surgery; however, you may be surprised how simple improvements can have a huge impact to your animal’s recovery and data. The presentation is designed for individuals who have minimal or no rodent surgical skills but is also a great opportunity for those with considerable experience wanting to improve outcomes as well as teach such key principles.
Key Topics Include:
- Improve surgical outcomes that will lessen post-op morbidity and mortality
- Improve data yield after rodent surgery
- Implementation of key principles into a rodent surgical program
Cardiovascular Autonomic Dysfunction in the Post-COVID Landscape: Detection a...InsideScientific
A world-wide spread of the novel Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has triggered a pandemic and generated over 600 million reported cases around the globe. A substantial portion of patients who suffered Corona Virus Disease 2019 (COVID-19) have not recovered completely but continue to experience lingering symptoms for months to years. This novel clinical syndrome has been termed Long COVID or Post-acute Sequalae of COVID-19 (PASC).
Observational studies have indicated that in about one third of cases PASC can be associated with cardiovascular (CV) autonomic dysfunction including postural orthostatic tachycardia syndrome, inappropriate sinus tachycardia, orthostatic hypotension, reflex syncope and microvascular dysfunction. The presence of CV autonomic dysfunction in PASC is important to detect since although frequently overlooked, it may be effectively treated in contrast to many other Long COVID-related symptoms.
This webinar highlights CV dysautonomia as a specific sequalae of acute COVID-19 and guides the audience in the diagnostic work-up of PASC patients with suspected cardiovascular complications.
Creating Better Gene-Edited Cell Lines with the FAST-HDR SystemInsideScientific
Cell lines are the core of biological research. Scientists need cell lines for drug development, basic biology research, safety testing, and biologic therapeutic production. Since the 1980s, genetic manipulation has allowed researchers to tailor cell lines to the experiment or production purpose. Over time, the requirements for these cell lies have risen. In many cases, the cells require multiple genetic edits and must produce data that passes FDA. Moreover, the current funding environment often requires rapid delivery of these cells so scientists can produce data to support further budget and/or investment. This is particularly acute for knock-in cell lines. Current technologies may take months to complete a cell line, allow a limited number of edits, and often have off-target effects that are not suitable for FDA filings. ExpressCells uses its patented FAST-HDR plasmid--along with CRISPR, to address these problems. The FAST-HDR process can precisely knock-in multiple genes (while supporting other types of genetic modifications), ensure precise placement of these edits, and deliver them months faster than competing technologies.
This webinar will discuss the basis of the FAST-HDR technology and illustrate several uses. The first part is a presentation by Oscar Perez-Leal, MD, the inventor of the technology. Oscar will discuss the problems he faced as a researcher and how FAST-HDR was designed to address them. He will outline the details of the technology, the history of its development, and several examples where he used FAST-HDR. The second part is a conversation with Jon Weidanz, PhD. Jon will outline the challenges he faced at AbeXXa and how he selected a FAST-HDR custom cell line for his project. He'll outline the learnings from using this cell line, some of which were unexpected, but valuable to future development.
By attending this program, attendees will:
- Understand the current challenges in creating custom gene-edited cell lines
- Know the technology underlying the FAST-HDR gene-editing system, including its use with CRISPR
- Be able to describe the advantages of the FAST-HDR system
- Learn about several case studies using gene-edited cell lines
Functional Recovery of the Musculoskeletal System Following Injury - Leveragi...InsideScientific
Watch Dr. Sarah Greising discuss the current pathophysiologic understanding of the skeletal muscle remaining following traumatic musculoskeletal injuries.
Volumetric muscle loss (VML) injuries result in the abrupt loss of skeletal muscle fibers, causing chronic functional disability in part due to limited muscle regeneration and vast co-morbidities. With a focus on clinically relevant outcome measurements for skeletal muscle function in both small and large animal models of VML injury, this webinar presents various near-term interventions for the restoration of tissue function following complex injuries. Interventions evaluated focus on regenerative rehabilitation approaches using regenerative pharmaceuticals to correct underlying muscle pathophysiology.
Designing Causal Inference Studies Using Real-World DataInsideScientific
In this webinar, experts provide an overview of causal inference, along with step-by-step guidance to designing these studies using real-world healthcare data.
Causal inference is used to answer cause and effect research questions and yield estimates of effect. Causal study design considerations and statistical methods address the effects of confounding variables and other potential biases and allow researchers to answer questions such as, “Does treatment A produce better patient outcomes compared to Treatment B?”
Causal study interpretations have traditionally been restricted to randomized controlled trials; however, causal inference applied to observational healthcare data is growing in importance, driven by the need for generalizable and rapidly delivered real-world evidence to inform regulatory, payer, and patient/provider decision making. The application of causal inference methods leads to stronger and more powerful evidence. When these techniques are applied to observational data, the results generated are both from and for the real world.
Presenters walk through several real-world case studies including the PCORI-funded BESTMED study and a collaborative study with a prominent pharmacy payer.
Social Media Data: Opportunities and Insights for Clinical ResearchInsideScientific
Many new data are emerging in recent years - real time data is collected through digital health technologies, including apps and wearables, monitoring data, social media data, public datasets, and patient organization data, in addition to primary and secondary datasets.
Real life data are highly informative and can be used to address a range of challenges throughout the product life cycle. Data from social media can generate valuable insights as patients often gather in digital communities to get answers and share their experiences. Conversations on social networks merit special consideration as they can have real world influence over treatment management decisions.
Social media data can reveal the motivations that impact patient healthcare decisions and behaviors through each stage of the care pathway. These data provide both the patient and caregiver perspectives at the same time. For this reason, conversations on social networks offer an opportunity to deepen our understanding on:
- The fears and hopes associated with patient treatments
- Daily needs and difficulties patients are facing in managing their disease
- The impact of disease on patient health related quality of life
- Identification in real life of the stages of the care pathway and patient perceptions
- Reactions to health policies
Watch this webinar for insights on how to collect, use, analyze, and interpret social media data in different contexts. Our experts share knowledge from over fifteen years of successfully developing and adapting algorithms to treat this kind of data.
We Are More Than What We Eat Dietary Interventions Depend on Sex and Genetic ...InsideScientific
To learn more visit: https://insidescientific.com/webinar/we-are-more-than-what-we-eat-dietary-interventions-depend-on-sex-and-genetic-background/
Despite evidence that sex and genetic background are key factors in the response to diet, most studies of how diet regulates metabolic health and even longevity in mice examine only a single strain and sex.
Using multiple strains and both male and female mice, Dr Lamming's team has found that improvements in metabolic health and in longevity in response to reduced levels of protein or specific amino acids strongly depend on sex and strain. While some phenotypes were conserved across strains and sexes, including increased glucose tolerance and energy expenditure, they observed high variability in adiposity, insulin sensitivity, and circulating hormones. Using a multi-omics approach, they identified mega-clusters of differentially expressed hepatic genes, metabolites, and lipids associated with each phenotype, gaining new insight into role of the energy balance hormone FG21 in the response to protein restriction.
Antibody Discovery by Single B Cell Screening on Beacon®InsideScientific
Amy Sheng, PhD provides an overview of antibody screening platforms and presents applications and case studies using the Beacon® platform for antibody discovery.
Single B cell screening is a powerful and efficient strategy for generating antigen-specific monoclonal antibodies. Distinguished with fluorescence-activated B cell sorting, the Beacon® platform is based on plasma cell screening, making it easier to obtain antibody genes.
The Beacon® single-cell optofluidic system combines a unique optoelectro positioning (OEP) technology with novel microfluidic technology. It can be used to accurately select single cells on a chip, perform multiple single-cell assays, and export target cells based on specific results. The Beacon® optofluidic platform preserves the diversity of B cells, generating high-quality positive hits at an early stage of discovery and avoiding the loss of “good clones”.
Key Topics Include:
- B cell differentiation and development
- Pros and cons of mainstream antibody screening platforms
- Mechanisms, applications, and case studies using the Beacon® platform for antibody screening
- Sino Biological’s capacity using the Beacon® platform
Experimental Design Considerations to Optimize Chronic Cardiovascular Telemet...InsideScientific
Phil Griffiths, PhD, presents a summary of chronic cardiovascular telemetry studies and considerations for experimental design.
Ensuring you collect the best and most physiologically accurate data from your chronic telemetry experiments requires careful planning and experimental design. This webinar will give an insight into the practical aspects of designing chronic animal experiments to set you on the best path for success. The benefits of chronic studies, how to select the most appropriate sample size for your study, some basic tips and tricks for data acquisition and handling, and how to ensure high animal welfare are discussed.
Key Topics Include:
- What are the benefits of chronic over acute studies?
- How to decide the best sample sizes and the length of experiments?
- Basic tips for data acquisition and handling
- How to maintain high animal welfare standards
Strategic Approaches to Age-Related Metabolic Insufficiency and Transition in...InsideScientific
In this webinar, Dr. Dennis Turner delves into dementia syndrome, the metabolic changes that occur, and the importance of proper physiological monitoring of animal models.
Brain metabolism transforms with normal aging, and transient, dynamic metabolic insufficiency may underlie critical progression from aging into dementia syndrome and Alzheimer’s disease (AD). Age-related brain metabolism balances vascular-related substrate supply and transport mechanisms into extracellular space to neurons with cellular metabolic needs and utilization. Dynamic metabolic insufficiency can occur when there is intermittent supply-demand mismatch.
Adequacy of neurovascular coupling to provide sufficient cerebral blood flow (CBF) to meet neuronal demand in vivo in a mouse AD model, compared to aged controls were studied. Dr. Turner’s lab analyzed the response to maximal neuronal metabolic demands, spreading depression and anoxia, using imaging, CBF measurements, and oxygen and glucose levels. These in vivo studies require human-similar anesthesia conditions, through monitoring temperature, blood pressure/pulse oximetry, and respiration, to maintain homeostasis. The lab confirmed abnormal neurovascular coupling in a mouse model of AD in response to these metabolic challenges, showing disruption much earlier in dementia than in equivalently aged individuals. Chronic metabolic treatments could influence dementia syndrome progression.
Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
A brief information about the SCOP protein database used in bioinformatics.
The Structural Classification of Proteins (SCOP) database is a comprehensive and authoritative resource for the structural and evolutionary relationships of proteins. It provides a detailed and curated classification of protein structures, grouping them into families, superfamilies, and folds based on their structural and sequence similarities.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
4. 4
Session Objectives
Objective 1: Demonstrate the need for real-world data in neurodegeneration research
Objective 2: Describe patient-centric research and the unique value it brings within this
therapeutic area
Objective 3: Highlight the importance of using unstructured data to construct a complete
picture of patient health
Objective 4: Provide disease-specific challenges & opportunities of RWD collection
5. 5
An Introduction to Real-World Data (RWD)
Sources of Real-World Data
What is Real-World Data?
Real-world data is data derived from
outside the context of traditional RCTs.
Research
questions
Appropriate
analytics
Real-World
Evidence
Medical
records
Prescription
data
Registries Lab data Hospital
visits
Claims data Patient-
generated data
6. 6
Real-World Evidence (RWE) Evolution
Evolving Landscape Evolving Stakeholders Evolving Applications
Increasing number and
complexity of players
Public-private partnerships
Growing role of data
organizations (collection, linkage,
analysis, etc.)
Patient/consumer empowerment
Growing recognition and
acceptance by regulators and
HTA bodies
• FDA RWE Framework & Draft
Guidance Documents
• NICE RWE Framework
• EMA’s Data Analysis and Real
World Interrogation Network
(DARWIN EU)
Proliferation of RWE assets
Uses across the product life
cycle, starting from trial design
and execution
Value based contracting with
payors
Both assessment and
reassessment by HTA bodies
Personalized healthcare
7. 7
RWE Across the Product Life Cycle
Earlier, more proactive RWE strategies enable greater
depth and breadth of data serving research objectives
across the product lifecycle
✔ More data, throughout the patient journey
✔ Ability to identify important trends and subpopulations
✔ Sustained engagement with hard-to-reach patient groups
What are life sciences researchers looking for?
Pre-Clinical Phase I Phase II Phase III Phase IV Ongoing Surveillance
PicnicHealth partners with life sciences researchers to provide connected, complete patient-level data
Unmet need /
disease burden
Natural history
of disease
Post-marketing
safety
Value-based
reassessment
Patient journey
mapping
Economic
burden/HCRU
Treatment
patterns
Cost saving with
treatment
PRO
development/
identification
Patient finding &
recruitment
Comparative
effectiveness
Monitor
prescribing/use
Subpopulation
studies
Patient
experience
External
control arm
Trial design
8. 8
Innovative approaches are needed for progress in
neurodegenerative diseases
We recognize the urgent need for new treatments… To
face that challenge and to accelerate drug development,
we need innovative approaches to better understand these
diseases while also building on current scientific and
research capabilities.
- FDA Commissioner Robert M. Califf, M.D.
“
”
9. 9
The Case for RWE in Neurodegenerative
Diseases
Complexity of disease
Clinical considerations
Time and cost of trials
Burden on patients and caregivers
Diversity/heterogeneity of patients
10. 10
Why Patient-Centric RWD for Neurodegeneration?
• Routinely-collected data can
miss key information
• What happens between clinic
visits matters
• Neuropathological change ≠
clinical significance
• Bringing together data from
disparate sources begins with the
patient
• Important insights come from
cognitive, functional, and
patient-reported measures
• Direct-to-patient channels can
extend to caregivers
• Empowering patients and
caregivers in their care
journey & research inputs
• Minimizing burden
There’s more to the story Data spans sites & specialists Caregivers are involved
11. 11
What does patient-centric RWD look like in practice?
PicnicHealth works directly with patients to create patient-centered RWE
12. 12
With patients at the center, we capture comprehensive and longitudinal data
All Providers
Average 23 providers per patient
● Primary Care
● Specialist Care
● Emergency Care
All Care Sites
Average 8 sites per patient
● Academic & Community
● Inpatient & Outpatient
All Medical Records
Average 91 clinical documents per patient
● Structured data (e.g., EMR)
● Narrative text (e.g., doctor’s notes)
Leveraging unstructured narrative text can identify
more patients vs. using only ICD codes in
structured sections of the EHR. For LN, we were
able to identify 95% more patients!1
1. Tierney M, Rowe C Addition of narrative next abstraction to ICD-based abstraction significantly improves identification
of lupus nephritis in real-world data [abstract]. Arthritis Rheumatol. 2021; 73 (suppl 10).
13. 13
Confidential
Multiple Sclerosis journeys necessitate longitudinal & complete data
PicnicHealth provides:
2,500 patient cohort
7+ years of data per patient
5 providers per patient
3 sites of care per patient
15. 15
PicnicHealth has a strong track record in
MS evidence generation.
Manuscript, JAMIAOpen,
Volume 5, Issue 1
Research Poster,
ACTRIMS 2021Virtual
Forum
Research Poster, ACTRIMS-
ECTRIMS Meeting,
MSVirtual2020
Research Poster, ECTRIMS
Congress 2019
16. 16
Parkinson’s Disease
1,500+ patients
Evolving understanding of Parkinson’s
Disease (PD)
• PD is increasingly recognized as a
complex condition with both motor and
non-motor clinical features, incl.
neuropsychiatric manifestations
• Past registries or chart reviews may not
have captured the right elements to
describe the natural history of PD as we
now understand it
Common data limitations
• Generalizability
• Recency
• Focus on incident or untreated
populations
• Dependence on patient recall
Key data elements
• Treatment histories
• UPDRS parts I and II
• Subjective and objective
measures of disability
• Motor & non-motor symptoms
Insights from Unstructured Data
Unrestricted access to narrative text improves captures of
motor & non-motor symptoms for assembling clinical
phenotypes of PD across multiple stages of disease.
17. 17
PD Cohort: Disease and Treatment Burden
Medication discontinued
73 year old, female
Medication restarted
at increased dose
Identify unmet needs and explore the
reasons for treatment switches and
discontinuations
Explore the presence of psychiatric
comorbidities and association with
poorer PD outcomes
TREATMENTS
MENTAL HEALTH
Can prospectively capture UDPRS
and/or extract data on symptoms and
disability from medical record
PROGRESSION
18. 18
Looking at the whole patient (motor and non-
motor symptoms) is critical
Mental Health Burden in PD
68%
58%
35%
31%
14%
5.60%
0%
10%
20%
30%
40%
50%
60%
70%
80%
Any mental
health disorder
Sleep disorders Anxiety Depression Cognitive
deficiency
Psychosis
19. 19
Alzheimer’s Disease
We are building a cohort of
2,500patients
Learnings from our journey in
Alzheimer’s disease to date :
• Industry interest in MCI and patients
who are yet to have formal diagnosis
• Complexity from specific imaging
needed for diagnosis
• Reimbursement decisions around
diagnostic tests can influence
therapeutic landscape
Common data limitations
• Disease staging not available
• Long disease course requires
sufficient lookback/forward
• Recency – given competitive and
evolving Tx landscape
Key data elements
• Onset of cognitive impairment
• Diagnosis, symptoms &
comorbidities
• Treatment & dosages
• Treatment-emergent AEs
• Biomarkers
Insights from Unstructured Data
We can use narrative text abstraction to access scores of
diagnostic exams (MMSE, MoCA) and other mentions of
physician/neurologist assessment of cognitive impairment.
20. 20
Huntington’s Disease
The current HD landscape
• Large, established research base
through long-standing registries;
but much of this data is siloed and
incomplete.
• No cure or treatment for HD
disease progression. However, Tx’s
exist to help patients manage
movement, cognitive, and
psychiatric symptoms.
Common data limitations
• Hard to connect cognitive
assessments to costs, treatment
patterns, or HCRU
• Limited access to patients for
collection of PROs
• Linked genetic information
Key data elements
• Age of onset, Date of diagnosis
• PROs (e.g. UHDRS-TFC, EQ-
5D, WPAI, HiDEF, DSST)
• Functional assessments
• Cognitive & behavioral
assessments
Insights from Unstructured Data
To inform overall understanding of Huntington's disease, we
can do customized abstraction of assistive device use, weight
loss, cognitive impairment, and onset/progression.
We are building a cohort of
400+patients
21. 21
Targeting Patients across Neurodegenerative Diseases: Challenges &
Opportunities
Challenges Opportunities
• Inconsistent scales for severity staging
• Difficult to define early-stage vs. late-
stage patients at a given time point
• Progression differs across patients
• Challenge of differentiating between
symptoms (e.g. MCI), dementia, and
formal diagnosis (e.g. Alzheimer’s)
• Capture measures of motor function
through wearables, mobile apps
• Capture cognitive, emotional, and social
changes through PROs
• Capture diverse spectrum of disease
stages, severity, symptoms, treatment
status and more complete medication
information through unstructured data
22. 22
What patient-centric research means for patients
Empowering patients and
their caregivers
Inclusion of the patient experience,
not just outcomes
More data = better
understanding of disease
Better characterize quality of
life, not just quantity of life
01
02
03
04
24. 24
Confidential
PicnicHealth sets a new standard for RWE
Traditional RWE Data Aggregators
Aggregate siloed de-identified data sets Work directly with patients
All sites of care for each patient
Single site of care for each patient
Abstraction from doctors notes and reports
Structured data fields only
Retrospective and prospective
Retrospective only
Customizable data model to answer research questions
Fixed data model
Enrichment with imaging files, PROs, and claims data
Stand-alone data
De-centralized enrollment from anywhere in ~5 minutes
Limited to patients in existing data set
25. 25
Confidential
PicnicHealth Cohorts
Fully Enrolled
PD Parkinson’s Disease 1,500+
Patients
SCD Sickle Cell Disease 900+
Patients
MG Myasthenia Gravis 500+
Patients
Enrolling in 2022
Hem Hemophilia A + B 450+
Patients
Pilots Launched
Amyloidosis
Atopic Dermatitis
Berger’s Disease
Cystic Fibrosis
Duchenne’s Muscular Dystrophy
Eosinophilic Esophagitis
Focal Segmental Glomerulosclerosis
Hepatitis B
Human Immunodeficiency Virus
Migraine
Non-alcoholic Steatohepatitis
Pulmonary Arterial Hypertension
Scleroderma
A
AD
BD
CF
DMD
EE
FSGS
Hep
HIV
M
NASH
PAH
S
T1D Type 1 Diabetes
PBC Primary Biliary Cholangitis 300+
Patients
LN Lupus Nephritis 250+
Patients
MS Multiple Sclerosis 2,500+
Patients
IBD Inflammatory Bowel Disease 1,000+
Patients
ALS Amyotrophic Lateral Sclerosis 500+
Patients
AD Alzheimer’s Disease 2,500+
Patients
HD Huntington’s Disease 400+
Patients
ITP
Immune Thrombocytopenic
Purpura
300+
Patients
PNG
Paroxysmal Nocturnal
Hemoglobinuria
150+
Patients
Pom Pompe Disease 75+
Patients
26. 26
Confidential
How to work with PicnicHealth
1: License an
Existing Cohort
PicnicHealth has a cohort
of patients or subset of
patients ready-built that
meets your needs.
2: Request a
New Cohort
The data you need doesn’t
exist, but PicnicHealth can
build a cohort that we all
benefit from.
3: Build A Virtual
Registry
Have patients you wants to study?
Use our platform to manage
enrollment, data collection, and
future engagement and/or linkage.
Custom Curated Deep Dataset
27. 27
Looking Ahead
A challenging, but hopeful future for
neurodegeneration research
An ever-growing patient
population in need ofTx
A large, promising
pipeline for thisTA
28. Watch the Cutting Edge
Conversations Series On Demand
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Editor's Notes
Thank you for joining this session today - and thank you to all of you who play a role the pursuit of treatment for these devastating conditions. So many (myself included) have been personally impacted by neurodegenerative diseases, whether first-hand witnessing its impact on a friend or relative, through serving a role as a caregiver, or from working in this space as a researcher or clinician.
The challenging, non-linear journey from where we are today to future breakthroughs for neurodegenerative conditions is made up of many connected components – from expanding our basic scientific understanding of neurodegenerative diseases and their underlying causes, to identifying better prognostic and diagnostic biomarkers, to finally developing treatment to fight or even cure these diseases.
And whether we are considering epidemiological studies, innovative clinical trial design, or clinical effectiveness of treatments, data – collected from patients in the real world – has a role to play.
Definitions of RWD, RWE
Real-world data is data derived from settings outside of randomized controlled trials (RCTs). It can be collected from a variety of real-world sources. Once real-world data is organized, interpreted or analyzed, and used to draw conclusions about a specific research question, that’s when it becomes evidence
Sources of RWD
Use by different healthcare system stakeholders
Proliferation of RWE policies, players, etc.
FDA RWE Framework & Draft Guidances
The value of RWE spanning clinical development, market access, reimbursement, surveillance, sustained/expanded value demonstration
Right patients being studied
Improved probability of success
Faster, efficient
Right patients receive tx
Q re. trial deisgn: Gene therapy example
Last week, the FDA unveiled its Action Plan for Rare Neurodegenerative Diseases including Amyotrophic Lateral Sclerosis (ALS) – a five-year strategy for improving and extending the lives of people living with rare neurodegenerative diseases by advancing the development of safe and effective medical products and facilitating patient access to novel treatments.
“The effects of rare neurodegenerative diseases are devastating, with very few effective therapeutic options available to patients.”
This action plan, especially including the use of public-private partnerships and direct involvement of patients, will ensure the FDA is working toward meeting the task set forth by Congress to enhance the quality of life for those suffering by facilitating access to new therapies.”
Today, we are talking about RWE in the context of neurodegenerative diseases: a heterogeneous group of disorders that are characterized by the progressive degeneration of the structure and function of the central nervous system or peripheral nervous system. In other words, these diseases involve the break down and even death of nerve cells in the brain or peripheral nerves.
Certain treatments may help relieve some physical or mental symptoms associated with neurodegenerative diseases, we don’t currently have treatments that slow progression or cure these diseases.
Why RWD is conducive to neurodegeneration research
Clinical considerations, e.g. symptoms of interest
Complexity of disease
Complex pathologies
Diverse clinical features
Multiple factors at play, including cognitive impairment and behavioral health changes (often underdiagnosed)
Cost and time to study neurodegenerative processes in clinical trials
Minimize burden on patients and caregivers
Diversity/heterogeneity of patients (e.g. comorbidities, sociodemographics)
Clinical trials often: lack an ethnically, racially or geographically diverse population; exclude those with comorbid conditions or taking concomitant medications
** Key slide; Opportunity here to build up the story from the perspective of a patient.
There’s more to the story – Routinely-collected data misses key information
What happens in between visits matters, especially for this population
What is happening at the cellular level does not always translate into clinical significance
Caregivers are involved – Direct-to-patient channels can extend to caregivers
Data spans sites and specialists – Bringing together disparate data sources starts with the patient
Empowering patients and caregivers – With patients part of this process, it not only means better data, but also benefits to patients (empowerment in care journey; contribution to research; recognition of symptoms/experience/QoL beyond outcomes)
These diseases also place an immense burden on both patients and caregivers. Patient-centricity means engaging and empowering, while not added additional burden.
Introduce the PicnicHealth approach
Working directly with patients; act on their behalf to gather all health records - and information from those records (including unstructured data)
Patient consent drives ability to use data to support clinical care, as well as research
Near term - Patients benefit from improved continuity of care and lessened burden (e.g. keeping track of disparate medical info)
Longer term - This research, enabled by patient consent, can drive improvements in care, availability of innovative treatments, etc.
{Slide from standard pitch deck: “PicnicHealth works directly with patients to create patient-centered RWE”}
PicnicHealth’s strong background in neurodegeneration began with MS
We have built a rich cohort of 2,500 patients, representative of the MS population in the US
We have also demonstrated the value of a large, longitudinal real-world dataset to life sciences partners- notably, working with Roche as part of the FlywheelMS study uncover novel insights about the clinical profile of individuals prior to disease onset.
NOTE:
Many of these data elements would not be found in traditional claims data
Call out examples
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Existing data sources have gaps with real consequences:
Claims: 2–3-year window of data capture
EMR aggregators: Critical information missing
Complexity of assistive device use
E.g. If they don’t file medical claim to purchase device, it’s not captured in claims
Discussing function w/ provider – narrative text use of cane/wheelchair
Our platform also enables us to ask further questions through prospectively-collected surveys on our platform.
Our work in MS can be found in a number of published abstracts/posters/papers.
As part of this work, custom abstraction methods were designed to extract MS-specific variables, including disability measurements, neurologic signs related to progression, disease subtype, and brain MRIs.-- All expertise that we can, and have, applied across other neurodegenerative diseases.
We are taking the things we learned in MS (progression, assisted devices, treatment switching reasons) and applying to other neurodegenerative diseases, while keeping patients at the centre Which we are now launching
We have built a cohort of 1,500 PD patients covering 50 U.S. states.
Unique evidence generation challenges for PD
Evolving understanding of disease: PD is increasingly recognized as a complex condition with both motor and non-motor clinical features, including neuropsychiatric manifestations
Limitations of data sources
Alternative Data Sources are not generalizable
Recency challenges: CMS and VA data sources are not up-to-date and not generalizable.
MJFF registries are focused on incident or untreated populations
Patient informed registries are dependent on patient recall of symptoms and treatments (6-12 months retrospective coverage)
Key data elements – (critical endpoints; but not always done in routine care):
Comprehensive treatment histories can be constructed, including treatment-emergent adverse effects (TEAEs)
UPDRS parts I and II can be prospectively captured
We know that clinicians are gathering this info, but we need to find ways to engage the patients more frequently – can be completed by patient or caregiver
Subjective and objective measures of disability
Unrestricted access to unstructured text/physician’s notes to better capture motor and non-motor symptoms for assembling clinical phenotypes of PD across multiple stages of disease
How use of PH (incl. unstructured data) can overcome these gaps
TREATMENTS: Can look at longitudinal treatment patterns; Prescriptions, dose, and even reasons for discontinuation
MENTAL HEALTH: Can explore mental health burden in PD
PROGRESSION: Can prospectively capture UPDRS (unified Parkinson's disease rating scale) parts I and II
Part 1 = evaluation of mentation, behavior, and mood (non-motor behaviours of daily living)
Part 2 = self-evaluation of the activities of daily life (ADLs) including speech, swallowing, handwriting, dressing, hygiene, falling, salivating, turning in bed, walking, and cutting food
Claims would only tell you – billed another prescription – not onset of symptoms, mental illness/comorbidities;
just show as percentage of cohort. with a title to the point of "looking at the whole patient (motor and non-motor symptoms) is important).
May or may not be formal diagnoses or ICD-codes; Can take a number of approaches (unstructured + coded data)
PicnicHealth is building a cohort of 2,500 patients
In preparing this cohort, we have learned a lot about this space
We have learned from industry the research interest in people with MCI, who do not yet have a formal AD diagnosis; greater opportunity for intervention
This space is made even more complex by the need for specific imaging (e.g. amyloid PET scans) which may not be covered by payers. Accordingly, reimbursement decisions around diagnostic tests can drive strategies for real world evidence generation.
Why RWE? - This is an example of the healthcare system-related factors that are not accounted for in traditional trials, where all patients may receive the same test.
Why PH? - This is just one example of why data from different sources (incl. Imaging, claims, etc.) is needed to tell the full story. E.g. Questions of treatment effectiveness may be biased by the fact that certain patients are being systematically missed (missed diagnosis; missed opportunity to receive treatment, stemming from out-of-pocket costs of PET.) Patient-level data capturing claims, health records, imaging, PROs can help unpack these factors at play
Limitations of common data sources
Disease staging not available
Long disease course requires sufficient lookback/forward
Recency – given competitive and evolving Tx landscape
Key data elements
Onset of cognitive impairment*** Esp. given industry interest in early stages of disease; before formal diagnosis; Caregiver may also notice onset; reliable assessment
Diagnosis
Symptoms & comorbidities
Treatment & dosages
PRO collection
Treatment-emergent AE’s (Critical to research because TEAEs can greatly impact quality of life, follow-on treatment choices, and HCRUs)
How use of PH data (incl. unstructured data) can overcome these gaps
We can use narrative text abstraction to access scores of diagnostic exams (MMSE, MoCA) and other mentions of physician/neurologist assessment of cognitive impairment.
MMSE = Mini-Mental State Exam
MoCA = Montreal Cognitive Assessment
NOTE: **We can also capture relevant biomarkers. ; E.g. CSF biomarker analysis of content including Beta Amyloid ratio.
* Can be captured from a variety of places; Lab, primary care; FLEXIBLE RWE PLAN (whether traditional, blood-based, imaging, or physician notes); PREPARE TO CAST A WIDE NET
We are excited to also be launching a cohort in Huntington’s Disease, a more rare, inherited disease that causes uncontrolled movements, emotional problems, and cognitive impairment.
Limitations of common data sources
There is a large, established research base through long-standing registries–however, much of this data ecosystem is siloed and incomplete
Key data elements:
Age of onset; Date of diagnosis
PROs (e.g. UHDRS-TFC, EQ-5D, WPAI, HiDEF, DSST). UHDRS-TFC= Total Functional Capacity
Functional Assessments (e.g., UHDRS, SDMT, Stroop); UHDRS=Unified Huntington's Disease Rating Scale; SDMT= Symbol Digit Modalities Test
Cognitive assessments (e.g., MMSE, MoCA, PBA-s, PHQ-9)
MMSE = Mini-Mental State Exam
MoCA = Montreal Cognitive Assessment
PBAs = Problem Behaviours Assessment (short form)
PHQ-9=Patient Health Questionnaire [depression]
+Genetic information; Mutation information not captured
With patient consent, we can connect to any data where the patient has data (vs. other data source providers)
Critical endpoints, but not being done in routine care (not feasible; not consistently captured); Explore alternative ways to get at key endpoints ; Learn nuances of designed endpoint
Challenges:
Inconsistent scales used for severity staging
Scales may only cover one dimension of health–Cognitive/Functional/QoL–but not all
Difficult to define early vs. late patients at a given time
Progression differs across patients
Teasing apart symptoms (e.g. MCI) vs. dementia vs. AD
Opportunities:
Can capture measures of motor function through wearables, mobile apps
Can capture cognitive, emotional, and social changes through PROs
Leveraging unstructured data (narrative text abstraction) enables researchers to explore a diverse spectrum of disease stages, severity, symptoms (e.g. onset of MCI, memory loss), treatment statuses and capture more complete medication information.
Take-away: Working with patients enables us to do all this great research, but what is the benefit for patients themselves?
Empowering patients and their caregivers
Inclusion of the patient experience, not just hard outcomes
Better characterize quality of life, not just quantity of life
More data enables better understanding of disease epidemiology, treatment effectiveness, all over a longer time period
Wouldn’t want you to leave this webinar without showing how we do this …
How our studies typically work
Patients sign up electronically in 5 - 10 minutes and consent to PicnicHealth collecting medical records on their behalf
PicnicHealth collects complete historical and prospective records and creates custom, deidentified datasets
Data is enhanced via multi-modal approaches (e.g., ePROs, claims linkage) via patient-centric approach
Patients are empowered to own their health history & receive digitized records via their PicnicHealth timeline
ALS – FDA
Neurodegeneration research is complex–but the future is promising
There is an ever-growing patient population in need of evidence that brings us closer to better diagnosis, management, and treatment of these diseases
Elevated recognition and importance by top researchers and pharma innovators
Over 100 drug assets in development / in trials
Take away: We’re exciting to work in this space and partner with you to generation real world evidence that makes a difference for real patients