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The VCU Virtual Pharmacology Lab: An Online Tool for Graduate Pharmacology Education

Abstract ID 90571 Poster Board 217 The VCU Virtual Pharmacology Lab (https://github.com/VCU-SOM/VPLW) was created to provide a flexible online platform for virtual pharmacology experiments by graduate students in the VCU Department of Pharmacology and Toxicology. The program is written in Python pro...

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Published in:The Journal of pharmacology and experimental therapeutics 2024-06, Vol.389, p.217-217
Main Authors: Negus, Sidney, Percy, James
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description Abstract ID 90571 Poster Board 217 The VCU Virtual Pharmacology Lab (https://github.com/VCU-SOM/VPLW) was created to provide a flexible online platform for virtual pharmacology experiments by graduate students in the VCU Department of Pharmacology and Toxicology. The program is written in Python programming language and utilizes a Jupyter Notebook to provide an interactive computing environment. Calculations to generate simulated data are accomplished using a modification of the Furchgott equation for receptor theory: Receptor Activation = f {Rt * ε * L/(L+KD)}. The program allows the instructor to set values for each independent variable: f (transduction function), Rt (total number of receptors), ε (drug efficacy at the receptor), and KD (drug affinity at the target receptor. The instructor also sets values for a “threshold” amount of receptor activation required to detect an effect and “capacity” level of receptor activation at which the effect peaks and above which further receptor activation fails to produce a further increase in effect. Parameters can be set for up to four separate modules, or “experiments”, to simultaneously model four different pharmacological contexts that could include (a) different drugs that vary in affinity for a receptor, (b) different drugs that vary in efficacy at a receptor, (c) effects of a common drug in systems with different receptor densities, (d) effects of a common drug in systems with different transduction functions, or (e) a common drug in procedures with different thresholds and capacities. Once these values are set, students can then enter doses, and the program generates both a graph and tabular display of data relating dose to effect for all experiments. An additional component extends the platform to studies of drug interactions. Overall, the platform provides an opportunity for students to experiment with different strategies for generating dose-effect curves (i.e. by using different dose increments and/or dose ranges), to determine quantitative parameters of the dose-effect curves they have generated (e.g. ED50 and Emax values), and to learn mechanisms that might contribute to differences between those dose-effect curves. At the poster, three examples will illustrate how the VCU Virtual Pharmacology Lab is used as part of an introductory graduate-level pharmacology course. In the first example, the four different experimental modules will be used to model four drugs with different efficacies at a common rec
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The program is written in Python programming language and utilizes a Jupyter Notebook to provide an interactive computing environment. Calculations to generate simulated data are accomplished using a modification of the Furchgott equation for receptor theory: Receptor Activation = f {Rt * ε * L/(L+KD)}. The program allows the instructor to set values for each independent variable: f (transduction function), Rt (total number of receptors), ε (drug efficacy at the receptor), and KD (drug affinity at the target receptor. The instructor also sets values for a “threshold” amount of receptor activation required to detect an effect and “capacity” level of receptor activation at which the effect peaks and above which further receptor activation fails to produce a further increase in effect. Parameters can be set for up to four separate modules, or “experiments”, to simultaneously model four different pharmacological contexts that could include (a) different drugs that vary in affinity for a receptor, (b) different drugs that vary in efficacy at a receptor, (c) effects of a common drug in systems with different receptor densities, (d) effects of a common drug in systems with different transduction functions, or (e) a common drug in procedures with different thresholds and capacities. Once these values are set, students can then enter doses, and the program generates both a graph and tabular display of data relating dose to effect for all experiments. An additional component extends the platform to studies of drug interactions. Overall, the platform provides an opportunity for students to experiment with different strategies for generating dose-effect curves (i.e. by using different dose increments and/or dose ranges), to determine quantitative parameters of the dose-effect curves they have generated (e.g. ED50 and Emax values), and to learn mechanisms that might contribute to differences between those dose-effect curves. At the poster, three examples will illustrate how the VCU Virtual Pharmacology Lab is used as part of an introductory graduate-level pharmacology course. In the first example, the four different experimental modules will be used to model four drugs with different efficacies at a common receptor. In the second example, the four different experiments will be used to model administration of the same agonist to four different subjects with different tissue parameters f and RT for a common receptor. For both of these examples, graphs and tables will be displayed at the poster. 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Parameters can be set for up to four separate modules, or “experiments”, to simultaneously model four different pharmacological contexts that could include (a) different drugs that vary in affinity for a receptor, (b) different drugs that vary in efficacy at a receptor, (c) effects of a common drug in systems with different receptor densities, (d) effects of a common drug in systems with different transduction functions, or (e) a common drug in procedures with different thresholds and capacities. Once these values are set, students can then enter doses, and the program generates both a graph and tabular display of data relating dose to effect for all experiments. An additional component extends the platform to studies of drug interactions. 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The program is written in Python programming language and utilizes a Jupyter Notebook to provide an interactive computing environment. Calculations to generate simulated data are accomplished using a modification of the Furchgott equation for receptor theory: Receptor Activation = f {Rt * ε * L/(L+KD)}. The program allows the instructor to set values for each independent variable: f (transduction function), Rt (total number of receptors), ε (drug efficacy at the receptor), and KD (drug affinity at the target receptor. The instructor also sets values for a “threshold” amount of receptor activation required to detect an effect and “capacity” level of receptor activation at which the effect peaks and above which further receptor activation fails to produce a further increase in effect. 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