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Pharmacodynamic Models: Logarithmic Concentration–Effect Model01:15

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The log-linear model is a pharmacological framework used to describe the relationship between drug concentration and its effect. This model is particularly relevant when the observed effects range between 20% and 80% of the drug’s maximum effect (Emax), where a near-linear relationship is observed between the log of drug concentration and the measured effect. However, the log-linear model does not predict the maximum possible effect (Emax) or the effect at zero drug concentration,...
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The linear concentration–effect model, underpinned by the principle that pharmacological effect (E) is directly proportional to plasma drug concentration (C), emerges as a pivotal simplification of the Emax model for conditions where C is significantly less than EC50. This model portrays a linear trajectory of the concentration–effect relationship when drug levels are markedly below the EC50 threshold.Despite its inherent assumption of continuous effect augmentation with increasing...
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Pharmacodynamic Models: Emax Drug–Concentration Effect Model01:18

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The Emax drug-concentration effect model is central to pharmacodynamics in drug discovery and development. This model is predicated on the receptor occupancy theory, which posits that the effect of a drug is directly related to the number of receptors occupied by the drug and the resultant complex formation.The model describes the reversible interaction between a drug (C) and a receptor (R) to form a drug-receptor complex (RC). The kinetics of this interaction are quantified by an equation that...
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Post-marketing surveillance is a critical component of pharmaceutical regulation, often uncovering unanticipated adverse drug reactions (ADRs) once a drug is widely used over an extended period.
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Pharmacodynamic models are essential tools in understanding the relationship between drug concentrations and their effects on biological systems. By characterizing the dynamics of drug action, these models guide dose selection, optimize therapeutic efficacy, and inform the development of new drugs. Two major classes of pharmacodynamic models include direct effect and indirect response models.Direct Effect ModelsDirect effect models describe the immediate relationship between drug concentration...
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The progression of a drug's impact can be analyzed by examining both the concentration-time course and the effect-time course. The concentration-time course is determined by the drug's half-life and is influenced by factors such as its pharmacokinetics, including absorption, distribution, metabolism, and elimination. The effect of the drug is often related to its concentration in the plasma and is calculated using the maximum drug effect and the plasma concentration that generates 50...
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CORAL: model for no observed adverse effect level (NOAEL).

Andrey A Toropov1, Alla P Toropova, Fabiola Pizzo

  • 1Laboratory of Environmental Chemistry and Toxicology, IRCCS - Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, 20159, Milan, Italy, andrey.toropov@marionegri.it.

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Quantitative structure-activity relationship (QSAR) models were developed to predict the no observed adverse effect level (NOAEL) from repeated dose toxicity tests. This offers a promising alternative to animal testing for chemical safety assessment.

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Area of Science:

  • Toxicology
  • Computational Chemistry
  • cheminformatics

Background:

  • In vivo repeated dose toxicity (RDT) tests assess risks from substance exposure.
  • The no observed adverse effect level (NOAEL) is a key safety indicator derived from RDT.
  • European regulations necessitate alternatives to animal testing, driving QSAR development.

Purpose of the Study:

  • To develop a reliable quantitative structure-activity relationship (QSAR) model for predicting the no observed adverse effect level (NOAEL).
  • To explore alternative methods for assessing chemical safety and reducing in vivo testing.

Main Methods:

  • Utilized a dataset of 140 organic compounds with oral short-term toxicity NOAEL values in rats.
  • Employed CORAL software for QSAR modeling, calculating optimal descriptors using simplified molecular input-line entry systems and Monte Carlo methods.
  • Investigated three different training, calibration, and validation set splits.

Main Results:

  • Developed a QSAR model for predicting NOAEL values.
  • Provided mechanistic interpretation of molecular fragments influencing toxicity.
  • Suggested a probabilistic definition for the model's domain of applicability.

Conclusions:

  • QSAR modeling presents a viable alternative for predicting RDT endpoints like NOAEL.
  • The developed model offers insights into structure-toxicity relationships.
  • The study contributes to the advancement of non-animal testing methods in chemical safety evaluation.