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Related Concept Videos

Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

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 its...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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...
Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
Pharmacodynamic Models: Overview01:27

Pharmacodynamic Models: Overview

Pharmacodynamic (PD) responses describe the interaction between a drug and its biological target, culminating in a physiological effect. These responses can be classified into different types: continuous variables, such as blood glucose levels; categorical outcomes, like survival rates; and time-to-event metrics, such as disease progression. Understanding and modeling PD responses are critical for optimizing drug efficacy and safety.PD models describe the relationship between drug concentration...
Pharmacodynamic Models: Additive and Proportional Drug Effect Model01:09

Pharmacodynamic Models: Additive and Proportional Drug Effect Model

Drug response models describe how pharmacological agents interact with biological systems to produce measurable effects. Baseline responses are inherent physiological activities without a drug significantly influencing the observed pharmacological outcomes. Depending on the drug response model employed, these baseline responses may combine with the drug's effect in either an additive or proportional manner.Additive Drug Response ModelIn the additive model, the drug effect is independent of the...
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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Related Experiment Video

Updated: Jul 6, 2026

Multi-target Parallel Processing Approach for Gene-to-structure Determination of the Influenza Polymerase PB2 Subunit
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DAPTEV: Deep aptamer evolutionary modelling for COVID-19 drug design.

Cameron Andress1, Kalli Kappel2, Marcus Elbert Villena3

  • 1Department of Computer Science, Brock University, St. Catharines, Canada.

Plos Computational Biology
|July 5, 2023
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Summary

This study introduces DAPTEV, an intelligent method for creating aptamer sequences, overcoming the limitations of traditional Systematic Evolution of Ligands by Exponential Enrichment (SELEX) for drug discovery.

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Area of Science:

  • Biotechnology and Pharmaceutical Sciences
  • Computational Biology
  • Molecular Biology

Background:

  • Traditional drug discovery is expensive, slow, and prone to bias.
  • Aptamers offer high affinity and specificity for molecular targets, but their development is challenging.
  • Systematic Evolution of Ligands by Exponential Enrichment (SELEX) is a conventional, yet inefficient, aptamer development process.

Purpose of the Study:

  • To develop an intelligent computational approach for aptamer sequence generation and evolution.
  • To address the cost, time, and optimization limitations of conventional aptamer development.
  • To support and accelerate aptamer-based drug discovery and development.

Main Methods:

  • Development of a novel intelligent approach named DAPTEV.
  • Utilizing computational methods for generating and evolving aptamer sequences.
  • Testing the approach using the COVID-19 spike protein as a target molecule.

Main Results:

  • DAPTEV demonstrates an ability to generate structurally complex aptamers.
  • The generated aptamers exhibit strong binding affinities to the target.
  • Computational results indicate significant improvements over traditional methods.

Conclusions:

  • DAPTEV offers a promising intelligent solution for aptamer discovery.
  • This approach can potentially reduce costs and time in aptamer-based drug development.
  • The method shows efficacy in generating high-affinity aptamers for specific targets like the COVID-19 spike protein.