Dosing time optimization of antihypertensive medications by including the circadian rhythm in pharmacokinetic-pharmacodynamic models

Figures

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  Fig 1. Summary scheme of the methodology employed. Inclusion and exclusion criteria and PK-PD models developed are described in the methodology section (data search and selection and PK-PD modeling sections, respectively). Preliminarily, local sensitivity and identifiability analyses of the developed PK-PD models indicated the presence of highly correlated parameters, so the circadian rhythm parameters of BP were estimated, and available pharmacokinetic parameters were also established; F (bioavailability), Vd (volume of distribution) and IC50 (50% inhibitory concentration). Thus, the remaining parameters of each model were estimated later. Models that include and do not include circadian kinetic constants and Hill’s coefficient in the effect equation were tested, and the model with the lowest corrected Akaike information criterion (AICc) was selected. Subsequently, the selected model parameters were subjected to an analysis of local sensitivity and identifiability in order to understand their relevance. Finally, simulations at different administration times (Ta) allowed establishing relationships between different optimization objectives and proposing optimal administration times for…

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  Fig 2. Model fitting to the experimental data for non-prodrug models. The figures on the left correspond to awakening/morning administration and on the right to bedtime/evening administration. Each figure shows data with their respective standard errors (SE) for SBP before treatment (light red dots) and DBP (light blue dots), and for SBP after treatment (dark red dots), DBP (dark blue dots). The dotted line represents the model prediction before treatment, and the solid line represents the model prediction of each model after the last dose. Fig 2A and 2B, amlodipine; Fig 2C and 2D, olmesartan; Fig 2E and 2F, telmisartan; Fig 2G and 2H, valsartan; Fig 2I and 2J, lisinopril.

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  Fig 3. Model fitting to the experimental data for prodrug models. The figures on the left correspond to awakening/morning administration and on the right to bedtime/evening administration. Each figure shows data with their respective standard errors (SE) for SBP before treatment (light red dots) and DBP (light blue dots), and for SBP after treatment (dark red dots), DBP (dark blue dots). The dotted line represents the model prediction before treatment, and the solid line represents the model prediction of each model after the last dose. Fig 3A and 3B, enalapril; Fig 3C and 3D, ramipril; Fig 3E and 3F, spirapril; Fig 3G and 3H, perindopril.

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  Fig 4. Local sensitivity summaries for non-prodrug models (A) and prodrug models (B). Sensitivity values are shown stacked for each model variable (Xa: drug available to be absorbed, C: drug concentration, Cm: metabolite concentration, SBP: systolic blood pressure, DBP: diastolic blood pressure).

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  Fig 5. Relationship between dosing time and SBP dipper percentage, average SBP reduction (SBPreduced), and average SBP peaks reduction (SBPpeaks) for amlodipine, nifedipine, telmisartan, olmesartan, and valsartan. The figures on the left show the results for dipper subjects and those on the right for non-dipper. Fig 5A and 5B, amlodipine; Fig 5C and 5D, nifedipine; Fig 5E and 5F, olmesartan; Fig 5G and 5H, telmisartan; Fig 5I and 5J, valsartan. Dosing time (Ta) is represented in colors from 0 to 24 hours after awakening (colorbar).

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  Fig 6. Relationship between dosing time and SBP dipper percentage, average SBP reduction (SBPreduced), and average SBP peaks reduction (SBPpeaks) for lisinopril, enalapril, ramipril, spirapril and perindopril. The figures on the left show the results for dipper subjects and those on the right for non-dipper. Fig 6A and 6B, lisinopril; Fig 6C and 6D, enalapril; Fig 6E and 6F, ramipril; Fig 6G and 6H, spirapril; Fig 6I and 6J, perindopril. Dosing time (Ta) is represented in colors from 0 to 24 hours after awakening (colorbar).