<HashMap><database>BioModels</database><scores/><additional><submitter>Apoorv KM</submitter><curationStatus>Non-curated</curationStatus><modellingApproach>ordinary differential equation model</modellingApproach><levelVersion>L3V1</levelVersion><full_dataset_link>https://www.ebi.ac.uk/biomodels/MODEL2605260001</full_dataset_link><publication_pubmed>8133465</publication_pubmed><isPrivate>false</isPrivate><repository>BioModels</repository><omics_type>Models</omics_type><modelFormat>SBML</modelFormat><tokenised_name>Dayneka1993   Theophylline like 2 compartment PK with indirect PD response</tokenised_name><publication_year>1993</publication_year><submissionId>MODEL2605260001</submissionId><publication_authors>N L Dayneka, V Garg, W J Jusko</publication_authors><first_author>N L Dayneka</first_author><publication>8133465,
                            Four basic models for characterizing indirect pharmacodynamic responses after drug administration have been developed and compared. The models are based on drug effects (inhibition or stimulation) on the factors controlling either the input or the dissipation of drug response. Pharmacokinetic parameters of methylprednisolone were used to generate plasma concentration and response-time profiles using computer simulations. It was found that the responses produced showed a slow onset and a slow return to baseline. The time of maximal response was dependent on the model and dose. In each case, hysteresis plots showed that drug concentrations preceded the response. When the responses were fitted with pharmacodynamic models based on distribution to a hypothetical effect compartment, the resulting parameters were dose-dependent and inferred biological implausibility. Indirect response models must be treated as distinct from conventional pharmacodynamic models which assume direct action of drugs. The assumptions, equations, and data patterns for the four basic indirect response models provide a starting point for evaluation of pharmacologic effects where the site of action precedes or follows the measured response variable.. 4, 21.
                            Department of Pharmaceutics, School of Pharmacy, State University of New York at Buffalo 14260.</publication><submitter_mail>apoorvmasta@gmail.com</submitter_mail><pubmed_abstract>Four basic models for characterizing indirect pharmacodynamic responses after drug administration have been developed and compared. The models are based on drug effects (inhibition or stimulation) on the factors controlling either the input or the dissipation of drug response. Pharmacokinetic parameters of methylprednisolone were used to generate plasma concentration and response-time profiles using computer simulations. It was found that the responses produced showed a slow onset and a slow return to baseline. The time of maximal response was dependent on the model and dose. In each case, hysteresis plots showed that drug concentrations preceded the response. When the responses were fitted with pharmacodynamic models based on distribution to a hypothetical effect compartment, the resulting parameters were dose-dependent and inferred biological implausibility. Indirect response models must be treated as distinct from conventional pharmacodynamic models which assume direct action of drugs. The assumptions, equations, and data patterns for the four basic indirect response models provide a starting point for evaluation of pharmacologic effects where the site of action precedes or follows the measured response variable.</pubmed_abstract><pubmed_title>Comparison of four basic models of indirect pharmacodynamic responses.</pubmed_title><pubmed_authors>Dayneka N L NL, Garg V V, Jusko W J WJ</pubmed_authors></additional><is_claimable>false</is_claimable><name>Dayneka1993 - Theophylline-like 2-compartment PK with indirect PD response</name><description>Two-compartment PK model with Michaelis-Menten clearance and indirect 
PD response (Model IV). Theophylline-like diagnostic benchmark; not 
fitted to clinical data.</description><dates><last_modification>2026-05-26</last_modification><publication>2026-06-27</publication><submission>2026-05-26</submission></dates><accession>MODEL2605260001</accession><cross_references><pubmed>8133465</pubmed><biomodels__db>MODEL2605260001</biomodels__db></cross_references></HashMap>