Towards a Better Understanding of the Workflows: Modeling Pathology Processes in View of Future AI Integration

Kargl, Michaela; Regitnig, Peter; Müller, Heimo; Holzinger, Andreas

doi:10.1007/978-3-030-50402-1_7

“…This approach aids in predicting system behavior, optimizing processes, and reducing the cost and time required for experimentation and technology transfer in chemical compound development (Erhardt et al, 2019). Moreover, the integration of in vitro-in vivo correlation (IVIVC) models, incorporating nonlinear-mixed effects models and Bayesian frameworks, allows for reliable prediction of in vivo serum concentration-time courses based on in vitro data, supporting formulation changes without additional bioequivalence trials (Kargl et al, 2020). This comprehensive integration enhances process understanding and facilitates more efficient and effective simulations in both laboratory and clinical se ings.…”

Section: Integration Of Modeling Methods For Enhanced Process Underst...mentioning

confidence: 99%

Computational Pharmaceutics: A Comprehensive Review of Past Achievements, Present Applications, and Future Challenges

Chaudhari,

Puri

et al. 2024

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0

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This paper explores the transformative impact of computational pharmaceutics, integrating AI, multi-scale modeling, quantum computing, AI-driven drug discovery, and blockchain frameworks. Molecular dynamics simulations provide insights into atomic behaviors crucial for drug design. Quantum computing reduces computational costs for simulating protein-drug interactions. AI and Graph Neural Networks accelerate drug discovery and personalized medicine. Blockchain frameworks facilitate secure pharmacogenetic data sharing. These advancements revolutionize drug development, offering unprecedented efficiency, innovation, and personalized treatments.

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“…This approach aids in predicting system behavior, optimizing processes, and reducing the cost and time required for experimentation and technology transfer in chemical compound development (Erhardt et al, 2019). Moreover, the integration of in vitro-in vivo correlation (IVIVC) models, incorporating nonlinear-mixed effects models and Bayesian frameworks, allows for reliable prediction of in vivo serum concentration-time courses based on in vitro data, supporting formulation changes without additional bioequivalence trials (Kargl et al, 2020). This comprehensive integration enhances process understanding and facilitates more efficient and effective simulations in both laboratory and clinical settings.…”

Section: Integration Of Modeling Methods For Enhanced Process Underst...mentioning

confidence: 99%

Computational Pharmaceutics: A Comprehensive Review of Past Achievements, Present Applications, and Future Challenges

Chaudhari,

Puri

et al. 2024

Preprint

0

View full text Add to dashboard Cite

This paper explores the transformative impact of computational pharmaceutics, integrating AI, multi-scale modeling, quantum computing, AI-driven drug discovery, and blockchain frameworks. Molecular dynamics simulations provide insights into atomic behaviors crucial for drug design. Quantum computing reduces computational costs for simulating protein-drug interactions. AI and Graph Neural Networks accelerate drug discovery and personalized medicine. Blockchain frameworks facilitate secure pharmacogenetic data sharing. These advancements revolutionize drug development, offering unprecedented efficiency, innovation, and personalized treatments.

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“…These factors, as well as specifying programming languages utilised and clarifying whether these process are fully or semi-automated, need to be considered with a holistic appreciation for the complexity of a pathology department. While the specifics of any one element are vital decisions which must be taken for the introduction of AI and machine leaning, an end-to-end overview is necessary to appreciate the interdependencies of all workflow components [71] .…”

Section: Methodsmentioning

confidence: 99%

QuPath: The global impact of an open source digital pathology system

Humphries

¹

,

Maxwell

²

,

Salto-Tellez

³

2021

Computational and Structural Biotechnology Journal

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QuPath, originally created at the Centre for Cancer Research & Cell Biology at Queen’s University Belfast as part of a research programme in digital pathology (DP) funded by Invest Northern Ireland and Cancer Research UK, is arguably the most wildly used image analysis software program in the world. On the back of the explosion of DP and a need to comprehensively visualise and analyse whole slides images (WSI), QuPath was developed to address the many needs associated with tissue based image analysis; these were several fold and, predominantly, translational in nature: from the requirement to visualise images containing billions of pixels from files several GBs in size, to the demand for high-throughput reproducible analysis, which the paradigm of routine visual pathological assessment continues to struggle to deliver. Resultantly, large-scale biomarker quantification must increasingly be augmented with DP. Here we highlight the impact of the open source Quantitative Pathology & Bioimage Analysis DP system since its inception, by discussing the scope of scientific research in which QuPath has been cited, as the system of choice for researchers.

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Towards a Better Understanding of the Workflows: Modeling Pathology Processes in View of Future AI Integration

Cited by 8 publications

References 28 publications

Computational Pharmaceutics: A Comprehensive Review of Past Achievements, Present Applications, and Future Challenges

Computational Pharmaceutics: A Comprehensive Review of Past Achievements, Present Applications, and Future Challenges

Computational Pharmaceutics: A Comprehensive Review of Past Achievements, Present Applications, and Future Challenges

QuPath: The global impact of an open source digital pathology system

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