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Harnessing AI To Expedite R&D

Resource Tag: Harnessing AI To Expedite R&D

Challenges in the Regulation of AI Software as a Medical Device

Software as a medical device (SaMD) that leverages artificial intelligence (AI) has the opportunity to reshape healthcare. It also raises unique challenges for developers and regulators. As healthcare advances and digital solutions leveraging AI become more prevalent, it is important that medical device regulatory frameworks also advance to match the speed of innovation.

This panel will review key terms related to AI SaMD and describe unique regulatory challenges associated with devices that leverage AI. Additionally, the panel will explore novel regulatory approaches to the regulation of AI SaMD currently under consideration by international regulatory authorities.

Speakers

  • Nathan A. Carrington, Ph.D. Head of Digital Health and Innovation, Roche
  • Pat Baird Sr. Regulatory Specialist – Head of Global Software Standards, Philips
  • Loganathan Kumarasamy, Head of Scientific Informatics, Validation and Compliance services, North America, Zifo R&D

Building the Future of Collaborative Research with Federated Learning

Federated learning is a new machine-learning paradigm where multiple partners can collaborate on complex research questions without centralizing or sharing data outside of their organizations.

This ‘collaborative machine learning’ approach enables data science teams to work on larger and more diverse datasets, previously inaccessible, boosting the predictive power of machine learning algorithms and enhancing AI capabilities. By overcoming privacy and confidentiality concerns, companies can build partnerships and consortia and retain their competitive edge.

For example, the MELLODDY consortium pioneers federated learning-based drug discovery across 10 pharma companies benefiting from the collective insights of the world’s largest cheminformatics data network where each participant retains full confidentiality and governance over their molecular libraries.

Federated learning in healthcare can also facilitate knowledge transfer between medical researchers and data scientists, bridging the gap between AI and clinical care. The HealthChain project is a successful demonstration that an algorithm can be trained on siloed histology images, distributed across different hospitals, to predict treatment responses in breast cancer. Together with clinical, research, and technology partners, we demonstrated improved robustness and performance of the technology over locally trained algorithms.

With the platform deployed and used reliably in a production environment, the stage is set for further collaborative research projects and eventually clinical applications in cancer, heart failure, and other therapeutic areas.

Speakers

  • Victor Dillard, Commercial Operations Director, Owkin
  • Hugo Ceulemans, Scientific Director Discovery Data Sciences, Janssen
  • Dr. Guillaume Bataillon, Pathologist, IUCT Oncopole

Optimizing Kinase Profiling Programs with Deep Learning

Join Genentech and Optibrium for this discussion of Alchemite™, a novel deep learning approach, and its application to optimizing kinase profiling programs. Using Alchemite™ reduces the number of kinase assays required to accurately predict the full kinase selectivity profile, effectively accelerating experimental programs.

The team will demonstrate the method’s performance on a data set of approximately 650 kinases and 10,000 compounds, significantly outperforming state-of-the-art quantitative structure-activity relationship (QSAR) approaches, including multi-target deep learning. Furthermore, we will discuss Alchemite’s unique ability to provide reliable prediction-uncertainty-estimates that enable the selection of the most informative kinase assays and which compounds to test.

Ideal for:

Scientist, Sr. Scientist, Program Manager, Associate Director, Director

Speakers

  • Matt Segall, CEO, Optibrium
  • Samar Mahmoud, Senior Scientist, Optibrium
  • Fabio Broccatelli, Senior Scientist, Genentech

Technical Strategies Against Bias in AI

There is an increasing number of reports discussing the urgent need for addressing bias in decision-making algorithms in healthcare. In fact, a recent JAMA commentary published in 2021 highlighted systemic kidney transplantation inequities for black individuals. With AI-based and machine learning techniques increasingly playing a role in healthcare decision-making, it becomes necessary to discuss not only the ethical implications but solutions and approaches to detect and reduce the impact of computer bias in healthcare.

In this webinar on-demand, industry experts will share lessons learned and discuss possible solutions.

Ideal for:

Manager, Sr. Manager, Director, CSO, CIO, CEO

Speakers

  • Peter Henstock, PhD, Machine Learning & AI Technical Lead, Pfizer
  • Helena Deus, PhD, Biomedical Semantic Solutions Lead, ZS Associates
  • Margi Sheth, R&D Data Policy Director, AstraZeneca
  • Prashant Natarajan, Vice President of Strategy & Products, H20.ai

Best Practices for Artificial Intelligence in Life Sciences Research

Recently, artificial intelligence (AI) and machine learning (ML) technologies in the biotechnology and pharmaceutical industries moved beyond experiments conducted by a few dedicated specialists and entered the industry mainstream. With this transition comes the need to formalize and publish the lessons learned by early adopters. These lessons are not limited to the technology of AI, but rather are the best ways to apply AI methods in a business environment.

Real-World Evidence – Leveraging AI And Analytics For Real Value And Lasting Impact

Real-world evidence is not new, but with advances in processes, technology, policy, and analytics, is becoming more accessible and usable. RWE is being used to drive real outcomes and lasting impact for pharma, patients/subjects, and other participants in the continuum of care. At the foundation of RWE is data – behaviors, patterns, computational biomarkers, phenotypic/genomic data, imaging, outcomes, and social determinants of health.

The RWE trends that are happening in life sciences and biological sciences are driven by

  • Datafication is driven by the availability of diverse data – big, small, and everything in between
  • Competitive advantages
  • Reducing the time for regulatory approvals
  • Cost and outcomes

While data and descriptive analytics have been in vogue for years, advances in processing RWE – in combination with RCTs via data science, machine/deep learning, and advanced analytics – are creating new value for Pharma companies across the board – not just in R&D and pharmacovigilance but also extending into economic value, sales & marketing, affordable therapies, and patient outcomes.

More importantly, with the success of these analytics and AI efforts, we will see an increasing appetite for more types of RWE – beyond EMRs, all-claims, and commercial data sets – into patient-reported experiences, wearables, at-home devices, and implants.

Creating value at scale and achieving lasting impact is important, doable, and repeatable. This presentation will provide practical recommendations on how to put this tsunami of RWE and data variety to work using the IMPACT framework.

We will conclude with a discussion of representative use cases that pharma and biotechnology organizations can use to move the needle from a product focus to customized/personalized therapies, precision medicine, and population health.

Speaker: Prashant Natarajan, Vice President of AI & Analytics Solutions, H2O.ai and Pistoia Alliance AI CoE Advisory Committee Member

Please note: This presentation was originally delivered during the Qiagen Digital Insights hackathon in February 2021 and is being shared with permission. All rights reserved.

Imaging Biomarkers

Biomarkers have become an essential part of the drug discovery and development process. A biomarker-driven approach to developing targeted therapies and patient selection strategies has the potential to increase success in the drug development process, decrease costs, and ultimately improve patient outcomes.

But what about imaging biomarkers? Usually obtained from PET, MRI, and CT scans, they comprise measurements of structural and metabolic features of the body that over time are used to assess disease progression and response to treatment. Imaging biomarkers are an ideal method to draw evidence from retrospective data and can be used both as inclusion criteria—to select relevant cohorts of patients and output data—to quantify responses to treatments.

  • How to use imaging in early clinical trials for an increased confidence in the target and in the new drug discovery?
  • From the investigator perspective, how to best combine standard imaging and advanced, personalized phenotypic endpoints in clinical trials?
  • Radiomics, ML and AI, digital patient, synthetic control arms .. :  Where the future of imaging is?
  • How to massively access real world quality data to create data lake and to develop new imaging markers?

Speakers:

  • Jerome Windsor, PharmD, MBA (Moderator), Advisor, Boston Digital Bio Consulting
  • Karine Seymour, MBA, CIO, Medexprim
  • Tim McCarthy, PhD, MBA, VP and Digital Medicine Head, Pfizer
  • Prof. Laure Fournier, Academic Radiologist, Hôpitaux de Paris
  • Angel Alberich-Bayarri, PhD, CEO, Quibim

AI for Drug Repurposing

Chemical-induced gene expression profiles provide a mechanistic signature of phenotypic response, and are thus promising for drug repurposing. However, the use of such data is limited by their sparseness, unreliability, and relatively low throughput.

Our speakers, Drs. Aleksandar Poleksic and Lei Xie, describe two new computational techniques for prediction of the differential gene expression profiles perturbed by de novo chemicals and inference of drug-disease associations.