WorkDifferentWithAI.com Academic Paper Alert!
Written by Maria Bordukova, Nikita Makarov, Raul Rodriguez-Esteban, Fabian Schmich, Michael P. Menden
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Article Section: none
Publication Date: 2023-10-27
SEO Description:
Presents vision for transforming development through optimized design. Discusses challenges around model interpretability and regulatory acceptance.
AI-Generated Paper Summary
Here are some key takeaways from this review paper on using generative AI to empower digital twins in drug discovery and clinical trials:
Novelty
- The paper provides a comprehensive overview of how generative AI can be used to create digital twins of biological systems at various levels, from individual cells all the way up to full human patients. This is a relatively new area of research.
- The authors highlight some of the latest generative AI architectures like GANs, transformers, diffusion models etc. that have potential to be adapted for creating more complex and realistic digital twins.
- They present a forward-looking vision for how digital twins could transform drug development through applications like accelerating patient recruitment, interim trial analysis, predicting adverse events etc. This goes beyond current limited use cases.
Commercial Applications
- Pharma companies can leverage digital twin technology to conduct virtual drug screens and predict efficacy and toxicity earlier, reducing expensive lab tests.
- Generative AI for digital twins provides an alternative to animal testing, enabling in silico safety and efficacy assessment. This has ethical and cost benefits.
- Clinical trial simulations with digital twins can optimize trial design, accelerate patient recruitment, and enable interim analysis to reduce trial costs and durations.
- Healthcare providers could potentially utilize patient digital twins for more personalized care through counterfactual predictions and discovering intermediate disease states.
- Regulators need to work closely with digital twin researchers to develop appropriate validation frameworks and standards for use in drug approval decisions.
In summary, this review highlights the nascent but promising role of combining generative AI and digital twin concepts to improve various aspects of drug development and clinical care. There are still challenges around model interpretability, multimodal data integration and regulatory acceptance that need to be addressed. Overall, it’s an exciting area with big commercial potential.
Keywords
digital twins, generative AI, drug discovery, clinical trials, deep learning
Author’s Abstract
Introduction: The concept of Digital Twins (DTs) translated to drug development and clinical trials
describes virtual representations of systems of various complexities, ranging from individual cells to
entire humans, and enables in silico simulations and experiments. DTs increase the efficiency of drug
discovery and development by digitalizing processes associated with high economic, ethical, or social
burden. The impact is multifaceted: DT models sharpen disease understanding, support biomarker
discovery and accelerate drug development, thus advancing precision medicine. One way to realize DTs
is by generative artificial intelligence (AI), a cutting-edge technology that enables the creation of novel,
realistic and complex data with desired properties.
Areas covered: The authors provide a brief introduction to generative AI and describe how it facilitates
the modeling of DTs. In addition, they compare existing implementations of generative AI for DTs in
drug discovery and clinical trials. Finally, they discuss technical and regulatory challenges that should be
addressed before DTs can transform drug discovery and clinical trials.
Expert opinion: The current state of DTs in drug discovery and clinical trials does not exploit the entire
power of generative AI yet and is limited to simulation of a small number of characteristics.
Nonetheless, generative AI has the potential to transform the field by leveraging recent develop