Assessment of Drug Interactions Relevant to Pharmacodynamic Indirect Response Models

Abstract

The assessment of drug interactions for a simple turnover system when the basic pharmacodynamic response is governed by indirect mechanisms was explored. This report describes a diverse array of possible in vivo pharmacodynamic effects from a combination of two drugs acting by similar or different indirect mechanisms. Various conditions of pharmacodynamic drug combinations were explored mathematically and by simulation: (a) interactions of two drugs acting simultaneously either on the production (k(in)) or on the dissipation (k(out)) processes controlling the in vivo response by competitive (four cases) or non-competitive interaction (six cases); and (b) combinations of two drugs acting on separate k(in) and k(out) processes simultaneously (four cases). A range of different combinations of drug doses was used. Plasma concentration time profiles were generated according to monoexponential disposition. Pharmacodynamic response profiles were simulated using the above conditions and characterized by descriptors such as Area Between Effect (and Baseline) Curve (ABEC) values. The interaction of agents by competitive mechanisms produced net responses that were additive in nature. Response profiles for non-competitive interactions on the same process were both antagonistic (for two drugs with effects that oppose each other) and synergistic (for two drugs that produce the same response). On the other hand, response signatures for agents acting non-competitively on the production and dissipation factors by opposing mechanisms (e.g. inhibiting k(in) plus stimulating k(out)) showed dramatic changes in net effects and produced apparent drug synergy. Net indirect response profiles for joint use of two or more drugs measured by ABEC values may look "additive", "antagonistic", or "synergistic" depending on doses, their intrinsic potencies, the nature of interaction (competitive or non-competitive) as well as their mechanisms of action. These models may help explain changes in pharmacologic responses to two agents in a more rational and mechanistic fashion than older empirical methods.

Pharmacodynamic interactions between recombinant mouse interleukin‐10 and prednisolone using a mouse endotoxemia model

“…Models based on the principle of competitive interaction are inherently more antagonistic in comparison to noncompetitive interaction models. 28 Hence, the values for interaction term, ψ is always lower for competitive interaction.…”

Modeling receptor/gene-mediated effects of corticosteroids on hepatic tyrosine aminotransferase dynamics in rats: dual regulation by endogenous and exogenous corticosteroids

“…The TAT mRNA is produced at a zero-order rate (k s,TATm ) and dissipates with a first-order rate constant, k d,TATm . Nuclear drug-receptor complexes from both CST and MPL were assumed to act on the same process (k s,TATm ) in a non-competitive manner [37] with their respective linear stimulation constants, S TAT,MPL and S TAT,CST . The free fraction of CST was fixed to 0.017 [23,38].…”

Interactions of Everolimus and Sorafenib in Pancreatic Cancer Cells

“…The second pharmacodynamic model employed was a previously described mechanistic model (30), which combines the indirect effect of the drugs on the growth process in order to evaluate interactions of the drugs according to the mechanisms of action of each. The drugs used in this study exert their effects on cells by inhibiting the processes that control their growth.…”

“…The γ reflects the steepness of the concentrationresponse curve. We modified the previously described equation (30) to account for γ values other than 1 and also estimated the baseline growth of cells (R 0 ) rather than fixing it to 100%. For combinations, one drug might alter the actions of the other, thus exerting either antagonism or synergism.…”

“…The three-dimensional surface also allows for visual inspection of the drug combination data. Drug interactions relevant to indirect response models were explored by Earp et al, who explored numerous scenarios depending on the mechanisms of combined drug action (30). The Earp et al mechanistic models can be used for data obtained in vitro by modifying them for steady-state conditions.…”