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Data-driven personalization of a physiologically based pharmacokinetic model for caffeine: A systematic assessment.

ABSTRACT: Physiologically based pharmacokinetic (PBPK) models have been proposed as a tool for more accurate individual pharmacokinetic (PK) predictions and model-informed precision dosing, but their application in clinical practice is still rare. This study systematically assesses the benefit of using individual patient information to improve PK predictions. A PBPK model of caffeine was stepwise personalized by using individual data on (1) demography, (2) physiology, and (3) cytochrome P450 (CYP) 1A2 phenotype of 48 healthy volunteers participating in a single-dose clinical study. Model performance was benchmarked against a caffeine base model simulated with parameters of an average individual. In the first step, virtual twins were generated based on the study subjects' demography (height, weight, age, sex), which implicated the rescaling of average organ volumes and blood flows. The accuracy of PK simulations improved compared with the base model. The percentage of predictions within 0.8-fold to 1.25-fold of the observed values increased from 45.8% (base model) to 57.8% (Step 1). However, setting physiological parameters (liver blood flow determined by magnetic resonance imaging, glomerular filtration rate, hematocrit) to measured values in the second step did not further improve the simulation result (59.1% in the 1.25-fold range). In the third step, virtual twins matching individual demography, physiology, and CYP1A2 activity considerably improved the simulation results. The percentage of data within the 1.25-fold range was 66.15%. This case study shows that individual PK profiles can be predicted more accurately by considering individual attributes and that personalized PBPK models could be a valuable tool for model-informed precision dosing approaches in the future.

SUBMITTER: Fendt R

PROVIDER: S-EPMC8302243 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND: Propofol is widely used for both short-term anesthesia and long-term sedation. It has unusual pharmacokinetics because of its high lipid solubility. The standard approach to describing the pharmacokinetics is by a multi-compartmental model. This paper presents the first detailed human physiologically based pharmacokinetic (PBPK) model for propofol. METHODS: PKQuest, a freely distributed software routine http://www.pkquest.com, was used for all the calculations. The "standard human" PBPK parameters developed in previous applications is used. It is assumed that the blood and tissue binding is determined by simple partition into the tissue lipid, which is characterized by two previously determined set of parameters: 1) the value of the propofol oil/water partition coefficient; 2) the lipid fraction in the blood and tissues. The model was fit to the individual experimental data of Schnider et. al., Anesthesiology, 1998; 88:1170 in which an initial bolus dose was followed 60 minutes later by a one hour constant infusion. RESULTS: The PBPK model provides a good description of the experimental data over a large range of input dosage, subject age and fat fraction. Only one adjustable parameter (the liver clearance) is required to describe the constant infusion phase for each individual subject. In order to fit the bolus injection phase, for 10 or the 24 subjects it was necessary to assume that a fraction of the bolus dose was sequestered and then slowly released from the lungs (characterized by two additional parameters). The average weighted residual error (WRE) of the PBPK model fit to the both the bolus and infusion phases was 15%; similar to the WRE for just the constant infusion phase obtained by Schnider et. al. using a 6-parameter NONMEM compartmental model. CONCLUSION: A PBPK model using standard human parameters and a simple description of tissue binding provides a good description of human propofol kinetics. The major advantage of a PBPK model is that it can be used to predict the changes in kinetics produced by variations in physiological parameters. As one example, the model simulation of the changes in pharmacokinetics for morbidly obese subjects is discussed.

Project description:Minimal physiologically-based pharmacokinetic (mPBPK) models provide a sensible modeling approach when fitting only plasma (or blood) data yielding physiologically-relevant PK parameters that may provide more practical value than parameters of mammillary models. We propose a second-generation mPBPK model specifically for monoclonal antibodies (mAb) by including (lumping) several essential components of mAb PK used in full PBPK models. These components include convection as the primary mechanism of antibody movement from plasma into tissues and return to plasma with interstitial fluid as the major extravascular distribution space. The model divides tissue spaces into two groups according to their vascular endothelial structure, leaky and tight, which consequently allows discernment of two types and general sites of distribution. This mPBPK model was applied to two mAbs in mice and ten mAbs with linear kinetics in humans. The model captured their plasma PK profiles well with predictions of concentrations in interstitial fluid for two types of tissues. Predictions of tissue concentrations for mAb 7E3 and 8C2 were consistent with actual measurements in mice, indicating the feasibility of this model in assessing extravascular distribution in the two categories of tissues. The vascular reflection coefficients (σ₁) of tight tissues (V(tight)) ranged 0.883-0.987 and coefficients (σ₂) for leaky tissues (V(leaky)) ranged 0.311 to 0.837. The plasma clearance (CL(p)) varied among the mAbs in humans from 0.0054 to 0.03 L/h. In addition, applying this model generates parameters for mAb transcapillary escape rates and assesses major sites of elimination. Four of ten mAbs exhibited better fitting statistics premised on elimination from interstitial fluid than from plasma. This approach allows comparisons of mAb PK when only plasma data are available, provides more realistic parameters and predictions than mammillary models, and may provide an intermediate step towards utilizing full PBPK models for mAbs.

Project description:BackgroundThe angiotensin-converting enzyme (ACE) inhibitors have complicated and poorly characterized pharmacokinetics. There are two binding sites per ACE (high affinity "C", lower affinity "N") that have sub-nanomolar affinities and dissociation rates of hours. Most inhibitors are given orally in a prodrug form that is systemically converted to the active form. This paper describes the first human physiologically based pharmacokinetic (PBPK) model of this drug class.MethodsThe model was applied to the experimental data of van Griensven et. al for the pharmacokinetics of ramiprilat and its prodrug ramipril. It describes the time course of the inhibition of the N and C ACE sites in plasma and the different tissues. The model includes: 1) two independent ACE binding sites; 2) non-equilibrium time dependent binding; 3) liver and kidney ramipril intracellular uptake, conversion to ramiprilat and extrusion from the cell; 4) intestinal ramipril absorption. The experimental in vitro ramiprilat/ACE binding kinetics at 4 degrees C and 300 mM NaCl were assumed for most of the PBPK calculations. The model was incorporated into the freely distributed PBPK program PKQuest.ResultsThe PBPK model provides an accurate description of the individual variation of the plasma ramipril and ramiprilat and the ramiprilat renal clearance following IV ramiprilat and IV and oral ramipril. Summary of model features: Less than 2% of total body ACE is in plasma; 35% of the oral dose is absorbed; 75% of the ramipril metabolism is hepatic and 25% of this is converted to systemic ramiprilat; 100% of renal ramipril metabolism is converted to systemic ramiprilat. The inhibition was long lasting, with 80% of the C site and 33% of the N site inhibited 24 hours following a 2.5 mg oral ramipril dose. The plasma ACE inhibition determined by the standard assay is significantly less than the true in vivo inhibition because of assay dilution.ConclusionIf the in vitro plasma binding kinetics of the ACE inhibitor for the two binding sites are known, a unique PBPK model description of the Griensven et. al. experimental data can be obtained.

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Data-driven personalization of a physiologically based pharmacokinetic model for caffeine: A systematic assessment.

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