Development of a Digital Interface for Personalized Dosing in Renal Impaired Patients: A Case-Study Using the ACE-Inhibitor, Benazepril

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  • Type:
    Conference Presentation
  • Conference Type:
    AIChE Annual Meeting
  • Presentation Date:
    November 17, 2021
  • Duration:
    18 minutes
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Benazepril is an inhibitor of the angiotensin-converting enzyme (ACE), prescribed for the treatment of high blood pressure. Benazepril clearance is primarily carried out in the liver, where benazeprilat is formed. In turn, benazeprilat is predominantly cleared in the kidney (1). On account of its elimination route, benazeprilat concentration in the tissues and plasma is influenced by the stage of renal insufficiency. The recommended dose adjustment for renal impaired individuals is to start with an initial dose of 5 mg benazepril hydrochloride, but it can be extended to a maximum daily dose of 40 mg (2). Due to this wide range, a personalized dosing-regime could greatly benefit the health status of patients. Population based physiological based pharmacokinetic (PopPBPK) modelling encompasses the application of mechanistic equations aimed to simulate the concentration-time profiles of a given drug in the whole body of individuals presenting distinct anthropometric features depending on their age, sex and race. Thus, it was the aim of this work to simulate the plasma concentration profiles of benazepril and benazeprilat in renal impaired patients presenting distinct anthropometric features. Depending on specific physiological characteristics, we were able to get mechanistic insights into how the severity of the disease impacts the resulting amount of drug in the body and developed an in-silico environment, where a suitable personalized dosing-regime could be determined. Additionally, a graphical user interface of the model is in development so that untrained users (i.e. health professionals) can comfortably and easily design appropriate dosing regimens.

Model development

Mathematical implementation of benazepril’s absorption, distribution, metabolization and excretion (ADME) after dissolution was done in MATLAB (MathWorks, MA, USA). To imitate a real population a virtual one was created, which considers inter-individual variability of anthropometric parameters. For n individuals, the function requires a race-, age-, height- and weight-range as well as gender distribution, where n is the headcount included in the population. One single individual gets created based on a mean dataset of volumes and blood flows, dependent on a given relevant distribution of anthropometric parameters and, respective coefficients of variation for each compartment.


Benazepril is marketed as tablets in its salt form, i.e., benazepril hydrochloride. The drug release constant was estimated directly from the in-silico fit of the in-vitro dissolution profiles of benazepril hydrochloride tablets (3).


To simplify the model only the stomach, small intestine and large intestine compartments were incorporated. The absorption rate constants of the compartments were determined using the effective permeability of the drug and an absorption scale factor (ASF). The effective permeability was calculated based on the apparent permeability value (4). The initial ASFs for the compartments were obtained from GastroPlus® (Simulations Plus, CA, USA). To compensate for the fusion of compartments, the values were optimized.


The equations for the distribution of the eliminating and non-eliminating organs and tissues, and the blood circulation were obtained (5). Benazepril tissue to plasma partition coefficients were calculated with the Rodgers and Rowland method and adapted to the human physiology by scaling them to the volume of distribution of benazepril at steady state (0.124 L/kg in human). The drug was assumed to distribute passively, without saturation.


The metabolization of benazepril to benazeprilat was assumed to take place exclusively in the liver. The time dependent concentration of benazeprilat was calculated based on the unbound concentration of benazepril in the liver through time and its intrinsic clearance. The intrinsic clearance was calculated based on the half-life (0.6 h) and volume of distribution of benazepril (0.124 L/kg), the mass of microsomal proteins per grams of liver (40 mg/g) and the body weight of the subjects.


After metabolization of benazepril, benazeprilat gets excreted by the kidneys and is thus eliminated from the body with the urine. To calculate the excretion of benazeprilat a single-compartment model was created. The elimination of benazeprilat through time was calculated based on the concentration of the metabolite in the venous blood and maximum concentration in the liver, volume of the vein compartment and the glomerular filtration rate (GFR). Finally, to optimize the fit of the model to the clinical data (1) of healthy individuals, a scaling factor was used and fixed throughout the simulations.

Disposition of Benazepril in renal impaired subjects

The model showed to be able to predict plasma concentration profiles of benazepril and benazeprilat identical to the ones observed in clinical studies of healthy patients (6). However, since the objective of this study is to determine the changes of benazepril concentration in renal impairment population, the GFR, a strong indicator in the detection of kidney disease, was adapted, accordingly. With the use of the different stages of impairment (from normal renal function to kidney failure), an initial dose of 10 mg, and a simulation time of 72 hours, plasma concentration profiles were calculated. Although, no significant differences were detected in the concentration-time profiles of benazepril, the same was not true for benazeprilat. Depending on the renal impaired group being simulated, very notable, important differences could be observed on the concentration-time profiles of the metabolite. From extrapolation of the clinical data (1) it was possible to demonstrate that, depending on the individual anthropological features of the patient, severe renal disease and kidney failure could potentially lead to a decrease of 15 mmHg in the diastolic blood pressure. Thus, demonstrated the importance of a personalized dosing regimen able to account for a multitude of factors, such as the initial blood pressure of the patient and ADME of the drug on a specific individual.

Digital interface for personalized dosing

The generation of a user-friendly platform for untrained health professionals to be able to use benazepril model, is also in development. The figure exemplifies a first translation of the model into a graphical user interface, using MATLAB’s built-in application AppDesigner. So far, the platform is made of four parts: (I) home, (II) population, (III) parameter and (IV) simulation. In the population tab the user can input the individual-related data, i.e., race, gender, age, weight and height. After completing this task, the user can switch to the parameter-tab to choose the desired drug (benazepril), which is stored based on the different classes and types of medication. After clicking on the chosen drug all input parameters display on the screen, which can be modified. With the input of administrated dose, time span and clicking the ‘Simulate’ button, results can be analyzed and compared to the clinical data in the simulation-tab. With an extra button on the simulation-tab clinical results can be imported.


Digital pharmaceutical environments, target to support the practice of medicine, promoting positive health outcomes on individuals and across populations. To this end, the systematic application of theoretical frameworks aimed to improve the quality of medical interventions and personalize their application could bring immense cost-effective benefits to health systems. Consequently, a platform that could allow the characterization of the drug pharmacokinetics under different physiological scenarios and pathological conditions could be invaluable in improving clinical decisions.


(1) M. Gengo and E. Brady, “The pharmacokinetics of benazepril relative to other ace inhibitors,” Clin. Cardiol., vol. 14, no. 4 S, pp. 44–50, 1991, doi: 10.1002/clc.4960141807

(2) FDA, “Lotensin® (benazepril hydrochloride) - FULL PRESCRIBING INFORMATION,” ID 3795436, pp. 1–16, [Online]. Available:

(3) Li, B. Wei, S. Fleres, A. Comfort, and A. Royce, “Correlation and prediction of moisture-mediated dissolution stability for benazepril hydrochloride tablets,” Pharm. Res., vol. 21, no. 4, pp. 617–624, 2004, doi: 10.1023/B:PHAM.0000022408.91151.64.

(4) PubChem, “Benazepril | C24H28N2O5 - PubChem.” (accessed Jan. 31, 2021).

(5) A. Peters, “Evaluation of a generic physiologically based pharmacokinetic model for lineshape analysis,” Clin. Pharmacokinet., vol. 47, no. 4, pp. 261–275, 2008, doi: 10.2165/00003088-200847040-00004.

(6) V. Mishina, “Clinical Pharmacology / Biopharmaceutics Review Lotensin,” Clin. Pharmacol. Rev. NDA, vol. 19, no. 851, 2003.

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