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Related Concept Videos

Therapeutic Drug Monitoring: Overview and Classification01:16

Therapeutic Drug Monitoring: Overview and Classification

Therapeutic Drug Monitoring (TDM) is a clinical practice that measures specific drug levels in a patient's blood at designated intervals to ensure the drug concentration stays within a therapeutic range. This monitoring is crucial for optimizing individual dosage regimens, enhancing therapeutic efficacy, and minimizing drug-related toxicity. TDM is vital for drugs with narrow therapeutic windows, significant variability in pharmacokinetics, and a clear correlation between plasma levels and...
Therapeutic Drug Monitoring: Affecting Factors01:29

Therapeutic Drug Monitoring: Affecting Factors

Therapeutic Drug Monitoring (TDM) is the clinical practice of measuring specific drug levels in a patient's blood or body tissues to manage and optimize therapy. TDM is crucial for drugs with narrow therapeutic windows, like warfarin and phenytoin, where incorrect doses can lead to treatment failure or severe side effects. This monitoring ensures the dosage administered is within a safe and effective range. The factors affecting therapeutic drug monitoring include:Patient-Specific Factors:a.
Therapeutic Drug Monitoring: Drug Analysis Methods01:26

Therapeutic Drug Monitoring: Drug Analysis Methods

Therapeutic Drug Monitoring (TDM) is a clinical practice that measures specific drug levels in a patient's blood or body tissues to tailor drug therapy effectively. This monitoring is critical for managing drugs with narrow therapeutic indices like digoxin and phenytoin, ensuring they are both safe and effective. For instance, monitoring theophylline levels in asthma patients involves precision and sensitivity to adjust doses according to individual responses to therapy, ensuring efficacy and...
Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions01:15

Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions

PK–PD modeling has significantly influenced FDA regulatory decisions, particularly drug approval, dosage optimization, and labeling. These models integrate pharmacokinetics (PK) and pharmacodynamics (PD) to predict drug behavior and effects, aiding in optimizing dosing regimens and enhancing the probability of clinical trial success.One notable example is Nesiritide (Natrecor®), a recombinant human brain natriuretic peptide for treating acute decompensated congestive heart failure (CHF).

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Augmenting DMTA using predictive AI modelling at AstraZeneca.

Gian Marco Ghiandoni1, Emma Evertsson2, David J Riley1

  • 1Augmented DMTA Platform, R&D IT, AstraZeneca, The Discovery Centre (DISC), Francis Crick Avenue, Cambridge CB2 0AA, UK.

Drug Discovery Today
|March 9, 2024
PubMed
Summary
This summary is machine-generated.

Artificial intelligence and cloud computing streamline drug discovery. AstraZeneca's Predictive Insight Platform (PIP) accelerates the Design-Make-Test-Analyse cycle, reducing the iterations needed for identifying viable drug candidates.

Keywords:
artificial intelligencecloud computingdrug discoverymachine learning

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Area of Science:

  • Drug discovery and development
  • Computational chemistry
  • Pharmaceutical sciences

Background:

  • The traditional Design-Make-Test-Analyse (DMTA) cycle is iterative and can require numerous cycles to identify viable drug candidates.
  • Advancements in artificial intelligence (AI) and cloud computing offer potential to optimize and accelerate the drug discovery process.

Purpose of the Study:

  • To introduce the Predictive Insight Platform (PIP), a novel cloud-native modeling platform developed at AstraZeneca.
  • To discuss the impact, architecture, integration, and usage of PIP within the DMTA framework.
  • To provide insights into the future of AI-driven drug discovery.

Main Methods:

  • Development of a cloud-native modeling platform (PIP).
  • Integration of PIP into the Design-Make-Test-Analyse (DMTA) workflow.
  • Analysis of PIP's impact on each stage of the DMTA cycle.

Main Results:

  • PIP enhances efficiency across all stages of the DMTA cycle.
  • The platform's architecture and integration facilitate seamless data flow and analysis.
  • PIP demonstrates the potential to significantly reduce the number of cycles required for drug candidate identification.

Conclusions:

  • The Predictive Insight Platform (PIP) represents a significant advancement in AI-powered drug discovery.
  • PIP's application within the DMTA cycle accelerates the identification of viable drug candidates.
  • The platform offers valuable insights into the future trajectory of pharmaceutical research and development.