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Related Concept Videos

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

320
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
320
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

416
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
416
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

870
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
870
Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions01:15

Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions

88
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...
88
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

467
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
467
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

701
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
701

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Related Experiment Videos

Scaling predictive modeling in drug development with cloud computing.

Behrooz Torabi Moghadam1, Jonathan Alvarsson, Marcus Holm

  • 1Department of Pharmaceutical Biosciences, ‡Department of Information Technology, and §Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University , SE-751 24 Uppsala, Sweden.

Journal of Chemical Information and Modeling
|December 11, 2014
PubMed
Summary
This summary is machine-generated.

Cloud computing offers a feasible and cost-efficient alternative for predictive modeling in drug discovery, especially for large datasets. It provides scalable computational resources on demand, making advanced modeling accessible without high upfront investments.

Related Experiment Videos

Area of Science:

  • Computational chemistry
  • Cheminformatics
  • Drug discovery

Background:

  • Growing datasets and analysis time impede predictive modeling in drug discovery.
  • High-performance computing (HPC) clusters are effective but costly to acquire and maintain.

Purpose of the Study:

  • To evaluate the feasibility and scalability of cloud computing resources for ligand-based predictive modeling.
  • To compare cloud computing performance against traditional high-performance computing clusters for model building.

Main Methods:

  • Ligand-based modeling was performed on Amazon Elastic Cloud computing resources.
  • Models were trained on open datasets for logP and Ames mutagenicity endpoints.
  • Cloud computing performance was benchmarked against parallelized HPC clusters.

Main Results:

  • Cloud computing is a viable option for large datasets, demonstrating good scalability within cloud instances.
  • While HPC offers faster model building, cloud computing provides a cost-effective and accessible alternative.
  • Cloud computing allows for easy cost quantification and a balance between speed and economy.

Conclusions:

  • Cloud computing presents an attractive, cost-efficient solution for predictive modeling in drug discovery.
  • It democratizes access to computational resources for scientists lacking supercomputer access.
  • On-demand modeling of large datasets is achievable within reasonable timeframes using cloud platforms.