Recursive model for dose-time responses in pharmacological studies

Abstract

Background Clinical studies often track dose-response curves of subjects over time. One can easily model the dose-response curve at each time point with Hill equation, but such a model fails to capture the temporal evolution of the curves. On the other hand, one can use Gompertz equation to model the temporal behaviors at each dose without capturing the evolution of time curves across dosage. Results In this article, we propose a parametric model for dose-time responses that follows Gompertz law in time and Hill equation across dose approximately. We derive a recursion relation for dose-response curves over time capturing the temporal evolution and then specify a regression model connecting the parameters controlling the dose-time responses with individual level proteomic data. The resultant joint model allows us to predict the dose-response curves over time for new individuals. Conclusion We have compared the efficacy of our proposed Recursive Hybrid model with individual dose-response predictive models at desired time points. We note that our proposed model exhibits a superior performance compared to the individual ones for both synthetic data and actual pharmacological data. For the desired dose-time varying genetic characterization and drug response values, we have used the HMS-LINCS database and demonstrated the effectiveness of our model for all available anticancer compounds. Electronic supplementary material The online version of this article (10.1186/s12859-019-2831-4) contains supplementary material, which is available to authorized users.

Selected research articles from the 2018 International Workshop on Computational Network Biology: Modeling, Analysis, and Control (CNB-MAC)

“…After the workshop, eleven papers [1][2][3][4][5][6][7][8][9][10][11] were accepted for publication in the CNB-MAC 2018 partner journals: BMC Bioinformatics and BMC Genomics. In the following we provide a brief summary of these selected papers.…”

“…Clinical studies often track dose-response curves of subjects over time which can be modeled separately in either time or dose without capturing the simultaneous evolution of the curves. Dhruba et al [1] propose a parametric model to explain the dose-time response behavior and derive a recursive relation to predict dose-response curves over time for individuals using the corresponding dose-time proteomic data. By comparing the proposed recursive approach with individual dose-response predictive models at desired time points, Dhruba et al [1] demonstrate that the recursive methodology provides a superior fit to the dose-time response behavior post drug application for both synthetic experimentation and pharmacological data from the HMS-LINCS database.…”

Using entropy-driven amplifier circuit response to build nonlinear model under the influence of Lévy jump

“…Based on the above methods, nonlinear system models can be established [ 29 , 30 ], and sensitivity analysis [ 31 , 32 ], stability analysis [ 30 , 33 , 34 ] and bifurcation analysis [ 35 – 37 ] can be performed on them. A parametric model of dosetime response is proposed in [ 38 ], demonstrating the effectiveness of our model for all available anticancer compounds. In real life, it is full of randomness, and random disturbance is inevitable.…”

Generalized Additive Mixed Modeling of Longitudinal Tumor Growth Reduces Bias and Improves Decision Making in Translational Oncology

“…A more refined and frequently applied parametric model observes that the growth rate necessarily slows as the tumor itself grows in size. This has inspired a parallel line of work applying Gompertzian growth kinetics to draw insights on tumor biology (17), (18). Other recent proposals include (19), (20), and (21), with a wide-ranging review in (22).…”