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Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
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Clinical development focuses on how the drug will interact with the human body and encompasses four key phases of clinical trials, each serving a specific purpose in assessing the safety and effectiveness of new drugs. These phases overlap and build upon one another. Phase I involves a small group of healthy volunteers (typically 20-80 individuals) or, in cases where significant toxicity is expected, patients with the targeted disease, such as cancer or AIDS. The volunteers are tested for...
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Related Experiment Video

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An AI Approach to Generating MIDD Assets Across the Drug Development Continuum.

Jeffrey S Barrett1, Rahul K Goyal2, Jogarao Gobburu2,3

  • 1Aridhia Bioinformatics, 163 Bath Street, Glasgow, Scotland, G2 4SQ, UK. Jeff.barrett@aridhia.com.

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|July 10, 2023
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Summary
This summary is machine-generated.

Artificial intelligence and machine learning (AI/ML) enhance model-informed drug development (MIDD) by integrating diverse internal and external data. This AI/ML approach improves decision-making, optimizes information value, and increases confidence in drug candidate safety and efficacy.

Keywords:
MIDDartificial intelligenceclinical pharmacology

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Area of Science:

  • Pharmacometrics and Computational Biology
  • Drug Development and Regulatory Science
  • Artificial Intelligence in Healthcare

Background:

  • Model-informed drug development (MIDD) traditionally relies on internal data and structural models for decision-making.
  • Current MIDD approaches often utilize discrete models from individual experiments, limiting holistic disease understanding.
  • Existing data integration and model development methods are predominantly confined to internal company data stores.

Purpose of the Study:

  • To explore the potential of an AI/ML-based approach to enhance Model-Informed Drug Development (MIDD).
  • To demonstrate how AI/ML can leverage diverse data sources, including external data, to improve predictive value in drug development.
  • To highlight the complementary role of AI/ML methodologies alongside traditional modeling for more robust decision-making.

Main Methods:

  • Development and application of AI/ML algorithms to integrate diverse preclinical and clinical data.
  • Utilizing both internal (within-company) and external data sources to inform model development.
  • Employing AI compute platforms to facilitate AI/ML-based MIDD processes.

Main Results:

  • AI/ML-based MIDD demonstrates enhanced predictive value by incorporating broader datasets, including external data.
  • The AI/ML methodology complements traditional modeling, leading to greater fidelity in decision-making.
  • Early pilot studies indicate the potential of AI/ML to optimize information value and increase confidence in drug candidates.

Conclusions:

  • AI/ML-based MIDD has the potential to transform regulatory science and the drug development paradigm.
  • Broader adoption and regulatory support are needed to further refine and validate this AI/ML approach.
  • This approach can significantly increase confidence in drug candidate safety and efficacy through optimized information value.