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Related Concept Videos

Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

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Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal...
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Drug Discovery: Overview01:26

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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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Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
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Preclinical development consists of a series of tests that ensure the safety and efficacy of a new therapeutic compound before it is tested in humans. There are four main phases to this process. First, safety pharmacology tests are conducted to ensure the drug does not produce any acutely harmful effects. These tests examine parameters such as bronchoconstriction, cardiac dysrhythmias, blood pressure changes, and ataxia. Next, preliminary toxicological testing is performed to determine the...
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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

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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...
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Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

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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.
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Emerging Landscape of Computational Modeling in Pharmaceutical Development.

Yuriy A Abramov1,2, Guangxu Sun3, Qun Zeng3

  • 1XtalPi, Inc., 245 Main St., Cambridge, Massachusetts 02142, United States.

Journal of Chemical Information and Modeling
|February 28, 2022
PubMed
Summary

Computational modeling aids drug development beyond discovery. This review details its use in process chemistry, analytical R&D, and formulation, including solid form design via virtual screening.

Keywords:
artificial intelligencecomputational chemistrycrystal structure predictionpreclinical and clinical developmentprocess chemistryquantum mechanics

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Area of Science:

  • Pharmaceutical Sciences
  • Computational Chemistry
  • Drug Development

Background:

  • Computational chemistry is established in drug discovery.
  • Computational modeling applications in drug development are less explored.
  • This review focuses on modeling for preclinical and clinical drug development stages.

Purpose of the Study:

  • To review computational modeling workflows in drug development.
  • To highlight computational support across process chemistry, analytical R&D, and formulation.
  • To detail physics-based virtual screening for solid form design.

Main Methods:

  • Literature review of computational modeling applications.
  • Analysis of workflows in process chemistry, analytical R&D, and formulation.
  • Focus on physics-based virtual screening for solid form selection.

Main Results:

  • Computational modeling offers support across key drug development areas.
  • Virtual screening methods are effective for rational solid form design.
  • Specific applications include polymorph, coformer, counterion, and solvent screening.

Conclusions:

  • Computational modeling is crucial for optimizing drug development processes.
  • Physics-based virtual screening enhances solid form selection and design.
  • Integrating computational tools can accelerate and improve drug product development.