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Related Concept Videos

Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

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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.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence...
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Physiological Pharmacokinetic Models: Incorporating Hepatic Transporter-Mediated Clearance01:07

Physiological Pharmacokinetic Models: Incorporating Hepatic Transporter-Mediated Clearance

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Drug transporters are critical in drug absorption, distribution, and excretion processes. They should be included in physiological-based pharmacokinetic (PBPK) models, which help predict human drug disposition. However, predicting this is challenging during drug development, especially when liver transport is involved. However, with a realistic representation of body transport processes, an accurate model may be possible.
A recent model describes pravastatin's hepatobiliary excretion,...
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Quantitative Aspects of Drug-Receptor Interaction01:30

Quantitative Aspects of Drug-Receptor Interaction

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The receptor occupancy theory connects a drug's response to the number of occupied receptors. With higher drug concentrations, more receptors are occupied, leading to increased responses. The formation of drug-receptor complexes involves association and dissociation rates, which reach equilibrium when the forward and backward reactions are equal. The equilibrium association constant (Ka) and its inverse, the equilibrium dissociation constant (Kd), indicate drug affinity. Higher Ka and lower...
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Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

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Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
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Transducer Mechanism: Nuclear Receptors01:31

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Nuclear receptors, or NRs, are unique transcription factors that regulate gene transcription and affect the cellular pathways involved in reproduction, development, or metabolism. Their ability to be stimulated by small lipophilic ligands and control vital cellular processes makes them ideal drug targets. Nearly 10-15% of currently prescribed drugs target these receptors.
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Related Experiment Video

Updated: Jun 6, 2025

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
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Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

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QSAR Regression Models for Predicting HMG-CoA Reductase Inhibition.

Robert Ancuceanu1, Patriciu Constantin Popovici1, Doina Drăgănescu2

  • 1Department of Pharmaceutical Botany and Cell Biology, Faculty of Pharmacy, Carol Davila University of Medicine and Pharmacy, 020021 Bucharest, Romania.

Pharmaceuticals (Basel, Switzerland)
|November 27, 2024
PubMed
Summary

Quantitative structure-activity relationship (QSAR) models were developed to predict HMG-CoA reductase inhibitors for cardiovascular disease treatment. These models identified novel cholesterol-lowering compounds and can aid in understanding herbal extract activities.

Keywords:
HMG-CoA reductaseIris germanicaMACCS fingerprintsQSARfeature selectionmachine learningmlr3molecular descriptorsnested cross-validationstatinsvirtual screening

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A Semi-High-Throughput Adaptation of the NADH-Coupled ATPase Assay for Screening Small Molecule Inhibitors
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Area of Science:

  • Medicinal Chemistry
  • Computational Chemistry
  • Pharmacology

Background:

  • HMG-CoA reductase is a key enzyme in cholesterol synthesis.
  • Inhibitors of HMG-CoA reductase are crucial for treating cardiovascular diseases.

Purpose of the Study:

  • To develop and validate quantitative structure-activity relationship (QSAR) models for human HMG-CoA reductase inhibitors.
  • To identify novel potential inhibitors of HMG-CoA reductase.
  • To explore the application of QSAR models in understanding herbal extract bioactivity.

Main Methods:

  • Utilized nested cross-validation for QSAR model validation.
  • Employed machine learning regression algorithms, feature selection, and molecular descriptors/fingerprints.
  • Screened over 220,000 compounds from the ZINC 15 database using validated QSAR models.

Main Results:

  • Developed 21 high-performing QSAR models (R² ≥ 0.70 or CCC ≥ 0.85).
  • Constructed five ensemble models from the top six QSAR models.
  • Identified 237 compounds with predicted IC50 values ≤ 10 nM, including novel potential inhibitors.
  • An svm-based ensemble model predicted potent inhibitors for approximately 0.08% of screened compounds.

Conclusions:

  • Developed accurate QSAR models for predicting HMG-CoA reductase inhibitors.
  • The models can accelerate the discovery of new cholesterol-lowering drugs.
  • QSAR models show potential for elucidating the cholesterol-lowering mechanisms of herbal extracts.