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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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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.
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In biostatistics, data are the observations collected for analysis. There are two main types: parametric and non-parametric. Parametric data, which include continuous (e.g., weight) and discrete numerical data (e.g., number of tablets), assume a particular distribution pattern, often the normal distribution. Non-parametric data do not adhere to a specific distribution and typically comprise nominal (e.g., gender) and ordinal categorical data (e.g., pain scale ratings).
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Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
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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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The Arrhenius equation relates the activation energy and the rate constant, k, for chemical reactions. In the Arrhenius equation, k = Ae−Ea/RT, R is the ideal gas constant, which has a value of 8.314 J/mol·K, T is the temperature on the kelvin scale, Ea is the activation energy in J/mole, e is the constant 2.7183, and A is a constant called the frequency factor, which is related to the frequency of collisions and the orientation of the reacting molecules.
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Species Distribution Modelling: Contrasting presence-only models with plot abundance data.

Vitor H F Gomes1,2, Stéphanie D IJff3,4, Niels Raes3

  • 1Coordenação de Botânica, Museu Paraense Emílio Goeldi, Av. Magalhães Barata 376, C.P. 399, Belém, PA, 66040-170, Brazil.

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Species distribution models (SDMs) using natural history collections (NHCs) can be inaccurate due to spatial bias. A new pipeline improves NHC data quality, providing more reliable species distribution estimates for conservation assessments.

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Area of Science:

  • Ecology and Conservation Biology
  • Computational Biology
  • Biodiversity Informatics

Background:

  • Species distribution models (SDMs) are crucial tools in ecology and conservation.
  • Presence-only SDMs, like MaxEnt, often utilize natural history collections (NHCs) for occurrence data.
  • NHCs can exhibit spatial biases, potentially leading to inaccuracies in SDM predictions.

Purpose of the Study:

  • To evaluate the relationship between NHC distribution, MaxEnt predictions, and a spatial abundance model (IDW) for Amazonian tree species.
  • To introduce and validate a novel pipeline for refining NHC data and constraining species' area of occupancy estimates.
  • To assess the impact of data inconsistencies on SDM outputs and propose mitigation strategies.

Main Methods:

  • Utilized a large plot dataset for Amazonian tree species to build a spatial abundance model using inverse distance weighting (IDW).
  • Compared the spatial distribution of NHCs and MaxEnt predictions against the IDW abundance model.
  • Developed and applied a new data processing pipeline to identify and remove inconsistencies in NHC records.

Main Results:

  • A significant but weak positive correlation was observed between NHC distribution and IDW for 66% of species.
  • SDM predictions showed a significant, albeit weak, positive relationship with IDW for 95% of species, with high sensitivity in both analyses.
  • The proposed pipeline successfully removed approximately 50% of the original NHC records, highlighting data quality issues.

Conclusions:

  • The spatial bias in NHCs can introduce inaccuracies into presence-only SDMs, particularly in large-scale biodiversity assessments.
  • Automatic generation of SDMs without rigorous data checking is problematic.
  • The developed pipeline offers a conservative and reliable method for estimating a species' area of occupancy, suitable for conservation status assessments like IUCN Red List.