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.
Resource Tag: Harnessing AI To Expedite R&D
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.
Minimal Information about an AI Model
Artificial intelligence and machine learning models are used more and more often in the development of pharmaceuticals and as software components in medical devices. However, because there has been a lack of clear reporting standards, many clinically relevant models have been reported with insufficient details to properly assess their risks and benefits.
Historically, this has made the science underlying these products irreproducible, deployment and comparison of AI algorithmic solutions hard, and may lead to the users of these products facing unequal or unforeseen harms. Therefore a standard for reporting of biomedically-relevant AI/ML models is necessary. In this panel discussion we will brainstorm options for the transparent reporting of AI algorithms in biology and medicine.
Participants include Prof. Atul Butte and Dr. Beau Norgot, authors of the MI-CLAIM checklist recently published in Nature, Dr. Xiaoxuan Liu and Prof. Alastair Denniston, founders of the SPIRIT-AI and CONSORT-AI working groups, and Dr. Jen Harrow, Fotis Psomopoulos, and Dr. Tom Lenaerts, who are actively working on the standards for AI and ML in Europe.
Centre of Excellence in Artificial Intelligence & Machine Learning
Introduction to the Centre of Excellence in Artifical Intelligence and Machine Learning project by Vladimir Makarov.
Putting AI into Practice
Artificial Intelligence and Machine Learning has already had some considerable success in drug discovery and development and promises further progress as the quality and breadth of data sets are increased.
This Webinar will focus on some of the AI driven initiatives that are being undertaken and review the challenges and opportunities in putting AI into practice.
How Can Federated AI-ML Learning Support Genomics and Patient Data Analysis to Enable Precision Medicine at Scale
Federated Learning is a learning paradigm that enables collaborative learning without centralizing datasets. In this webinar, NVIDIA will present the concept of FL and discuss how it can help overcome some of the barriers seen in the development of AI-based solutions for pharma, genomics and healthcare. Following the presentation, there will be a panel debate on other elements that could drive the adoption of digital approaches more widely and help answer currently intractable science and business questions.
Automated Molecular Design and the BRADSHAW Platform
This presentation describes how data-driven chemoinformatics methods may automate much of what has historically been done by a medicinal chemist. It explored what is reasonable to expect “AI” approaches might achieve, and what is best left with a human expert. The implications of automation for the human-machine interface are explored and illustrated with examples from Bradshaw, GSK’s experimental automated design environment.
Looking beyond the Hype: Applied AI and Machine Learning in Translational Medicine
Dr. Dennis Wang discusses possible ways to enable ML methods to be more powerful for discovery and to reduce ambiguity within translational medicine, allowing data-informed decision-making to deliver the next generation of diagnostics and therapeutics to patients quicker, at lowered costs, and at scale.
The talk by Dr. Dennis Wang was followed by a panel discussion with Mr. Albert Wang, M. Eng., Head, IT Business Partner, Translational Research & Technologies, Bristol-Myers Squibb.
Looking beyond the Hype: Applied AI and Machine Learning in Translational Medicine
Dr. Dennis Wang discusses possible ways to enable ML methods to be more powerful for discovery and to reduce ambiguity within translational medicine, allowing data-informed decision-making to deliver the next generation of diagnostics and therapeutics to patients quicker, at lowered costs, and at scale.
The talk by Dr. Dennis Wang was followed by a panel discussion with Mr. Albert Wang, M. Eng., Head, IT Business Partner, Translational Research & Technologies, Bristol-Myers Squibb.
Pistoia-Elsevier Datathon Report: Big Data Mining and AI for Drug Repurposing
In the late Fall and Winter of 2018, the Pistoia Alliance in cooperation with Elsevier and charitable organizations Cures within Reach and Mission: Cure ran a datathon aiming to find drugs suitable for treatment of childhood chronic pancreatitis, a rare disease that causes extreme suffering. The datathon resulted in identification of four candidate compounds in a short time frame of just under three months. In this webinar our speakers discuss the technologies that made this leap possible.