A possible role of multidrug resistance-associated protein 2 (Mrp2) in hepatic excretion of PCB126, an environmental contaminant: PBPK/PD modeling

Physiologically-Based Pharmacokinetic Modeling of Blood Clearance of Liver Fluorescent Markers for the Assessment of the Degree of Hepatic Ischemia-Reperfusion Injury. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023;2023:1-6. [PMID: 38082711 DOI: 10.1109/embc40787.2023.10340273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

During liver transplantation, ischemia-reperfusion injury (IRI) is inevitable and decreases the overall success of the surgery. While guidelines exist, there is no reliable way to quantitatively assess the degree of IRI present in the liver. Our recent study has shown a correlation between the bile-to-plasma ratio of FDA-approved sodium fluorescein (SF) and the degree of hepatic IRI, presumably due to IRI-induced decrease in the activity of the hepatic multidrug resistance-associated protein 2 (MRP2); however, the contribution of SF blood clearance via the bile is still convoluted with other factors, such as renal clearance. In this work, we sought to computationally model SF blood clearance via the bile. First, we converted extant SF fluorescence data from rat whole blood, plasma, and bile to concentrations using calibration curves. Next, based on these SF concentration data, we generated a "liver-centric", physiologically-based pharmacokinetic (PBPK) model of SF liver uptake and clearance via the bile. Model simulations show that SF bile concentration is highly sensitive to change in the activity of hepatic MPR2. These simulations suggest that SF bile clearance along with the PBPK model can be used to quantify the effect of IRI on the activity of MRP2.Clinical Relevance- This study establishes the theory necessary to generate a model for predicting the degree of IRI during liver transplantation.

A Δ9-Tetrahydrocannabinol Physiologically-Based Pharmacokinetic Model Development in Humans

BACKGROUND AND OBJECTIVE

∆⁹-Tetrahydrocannabinol (THC) exhibits several therapeutic effects, such as analgesics, anti-emetic, antispastic, and muscle relaxation properties. Knowledge concerning THC disposition in target organs is crucial for THC therapy. The objective of this study was to develop a physiologically-based pharmacokinetic (PBPK) model of THC in humans to characterize tissue-specific pharmacokinetics of THC in organs of interest.

METHODS

The model was extrapolated from the previously developed PBPK model conducted in mice, rats, and pigs. The model consisted of seven compartments: brain, lungs, liver, kidneys, fat, and rapidly perfused and slowly perfused tissues. P-glycoprotein was included in the brain compartment to characterize an efflux of THC from the brain. Physiologic, biochemical, and physicochemical parameters were determined and acquired from the literature. Model validation was performed by comparisons of the predicted and observed THC concentrations acquired from published studies.

RESULTS

The developed PBPK model resulted in good agreement between the predicted and observed THC concentrations across several studies conducted following IV bolus, IV infusion, oral, and smoking and inhalation, with the coefficient of determination (R²) ranging from 0.54 to 0.95.

CONCLUSIONS

A PBPK model of THC in humans was developed. The model could describe THC concentration-time profiles in several dosing scenarios (i.e., IV bolus, IV infusion, oral administration and inhalation).

Development of a Physiologically Based Pharmacokinetic Model of Mitragynine, Psychoactive Alkaloid in Kratom (Mitragyna Speciosa Korth.), In Rats and Humans

Mitragynine is a major psychoactive alkaloid in leaves of kratom (Mitragyna speciosa Korth.). To understand its disposition in organs, this study aimed to develop a physiologically based pharmacokinetic (PBPK) model that predicts mitragynine concentrations in plasma and organ of interests in rats and humans. The PBPK model consisted of six organ compartments (i.e. lung, brain, liver, fat, slowly perfused tissues, and rapidly perfused tissue). From systematic searching, three pharmacokinetic studies of mitragynine (two studies in rats and 1 study in humans) were retrieved from the literature. Berkeley Madonna Software (version 8.3.18) was used for model development and model simulation. The developed PBPK model consisted of biologically relevant features following involvement of (i) breast cancer-resistant protein (BCRP) in brain, (ii) a hepatic cytochrome P450 3A4 (CYP3A4)-mediated metabolism in the liver, and (iii) a diffusion-limited transport in fat. The simulations adequately describe simulated and observed data in the two species with different dosing regimens. PBPK models of mitragynine in rats and humans were successfully developed. The models may be used to guide optimal mitragynine dosing regimens.

Human continuous hydrogen cyanide inhalation predictor with a physiologically based pharmacokinetic (PBPK) model

Hydrogen cyanide (HCN) is volatile and highly toxic with acute and chronic effects on humans. Gaseous HCN enters the atmosphere from natural processes or industrial activities, which lead to human exposure. Effective intervention in cases of HCN inhalation requires an efficient diagnostic tool. The existing physiologically based pharmacokinetic (PBPK) model for HCN cannot clearly simulate continuous HCN inhalation or predict HCN levels in inhaled air. The current study presents a PBPK model for continuous inhalation of HCN, called Human Continuous Cyanide Inhalation Predictor (HCCIP). Since existing data on pharmacokinetics of HCN inhalation are limited, HCCIP utilizes extensive data from the current authors' PBPK model on cyanide ingestion. The structure of HCCIP comprises the lungs, kidneys, liver, and slowly perfused tissue. In both the human body and in exhaled air, HCCIP features the ability to predict concentration-time courses of cyanide. Moreover, HCCIP can predict HCN concentration in inhaled air from known blood cyanide levels. After completion, the results of HCCIP were validated against preexisting published datasets. The simulation results agreed with these datasets, validating the model. The HCCIP model is an effective tool for assessing risk from continuous HCN inhalation, and HCCIP extends the capabilities of air dispersion modeling, such as AERMOD or CALPUFF, to assess HCN risk from specific release sources.

Membrane transporter data to support kinetically-informed chemical risk assessment using non-animal methods: Scientific and regulatory perspectives

Humans are continuously exposed to low levels of thousands of industrial chemicals, most of which are poorly characterised in terms of their potential toxicity. The new paradigm in chemical risk assessment (CRA) aims to rely on animal-free testing, with kinetics being a key determinant of toxicity when moving from traditional animal studies to integrated in vitro-in silico approaches. In a kinetically informed CRA, membrane transporters, which have been intensively studied during drug development, are an essential piece of information. However, how existing knowledge on transporters gained in the drug field can be applied to CRA is not yet fully understood. This review outlines the opportunities, challenges and existing tools for investigating chemical-transporter interactions in kinetically informed CRA without animal studies. Various environmental chemicals acting as substrates, inhibitors or modulators of transporter activity or expression have been shown to impact TK, just as drugs do. However, because pollutant concentrations are often lower in humans than drugs and because exposure levels and internal chemical doses are not usually known in contrast to drugs, new approaches are required to translate transporter data and reasoning from the drug sector to CRA. Here, the generation of in vitro chemical-transporter interaction data and the development of transporter databases and classification systems trained on chemical datasets (and not only drugs) are proposed. Furtheremore, improving the use of human biomonitoring data to evaluate the in vitro-in silico transporter-related predicted values and developing means to assess uncertainties could also lead to increase confidence of scientists and regulators in animal-free CRA. Finally, a systematic characterisation of the transportome (quantitative monitoring of transporter abundance, activity and maintenance over time) would reinforce confidence in the use of experimental transporter/barrier systems as well as in established cell-based toxicological assays currently used for CRA.

Paraquat exposure and Parkinson's disease: A systematic review and meta-analysis

To reconcile and unify available results regarding paraquat exposure and Parkinson's disease (PD), we conducted a systematic review and meta-analysis to provide a quantitative estimate of the risk of PD associated with paraquat exposure. Six scientific databases including PubMed, Cochrane libraries, EMBASE, Scopus, ISI Web of Knowledge, and TOXLINE were systematically searched. The overall odds ratios (ORs) with corresponding 95% CIs were calculated using a random-effects model. Of 7,309 articles identified, 13 case control studies with 3,231 patients and 4,901 controls were included into our analysis. Whereas, one prospective cohort studies was included into our systematic review. A subsequent meta-analysis showed an association between PD and paraquat exposure (odds ratio = 1.64 (95% CI: 1.27-2.13; I² = 24.8%). There is a statistically significant association between paraquat exposure and PD. Thus, future studies regarding paraquat and Parkinson's disease are warranted.

Development of a physiologically based pharmacokinetic model of paraquat

Paraquat (N, N'-dimethyl-4,4'-bipyridium dichloride) is a potent and widely used herbicide in agricultural countries, including Thailand. The presence of this chemical in the body can lead to toxic effects in the liver, kidney, and lung. Pulmonary toxicity has been identified as the main cause of acute toxicity in animals and humans. Chronic exposure to paraquat is associated with Parkinson's disease in humans. Paraquat is transported into the lungs by neutral amino acid transporter. Therefore, a physiologically based pharmacokinetic (PBPK) model of paraquat was developed with a description of the protein transporter mechanism. To develop a PBPK model of paraquat, a pharmacokinetic study of paraquat in rats was selected from the ThaiLIS and Pubmed database. The selected study contained tissue-specific concentration-time course information such as paraquat concentration levels in liver, kidney and lung. Physiologic parameters were acquired from the literature or determined using a Markov-Chain Monte Carlo (MCMC) technique. The developed PBPK model consisted of 5 organ compartments (i.e. kidney, liver, slowly perfused organs, richly perfuse organs and lung), featuring an incorporation of neutral amino acid transporter in the lung. Our model simulations could explain the data from the literature and adequately describe pharmacokinetics of paraquat in the rats. This developed PBPK model may be able help in understanding of paraquat-induced Parkinson's disease as well as in risk assessment of paraquat.

A physiologically-based pharmacokinetic model of methotrexate incorporating hepatic excretion via multidrug-resistance-associated protein 2 (Mrp2) in mice, rats, dogs, and humans

An updated physiologically-based pharmacokinetic (PBPK) model of methotrexate (MTX) was built based on an earlier model developed by Bischoff et al. (1971). MTX has been known to be a substrate of multidrug-resistance-associated protein 2 (Mrp2). A three-dimensional quantitative structure-activity relationship model (3D-QSAR) of Mrp2 was developed by Hirono et al. (2005). In our updated PBPK model of MTX, using the computational chemistry-derived binding affinity (K_m), a Mrp2-mediated biliary excretion process was incorporated as the MTX excretory pathway. Our model simulation results are consistent with numerous datasets obtained from mice, rats, dogs, and humans, at a variety of dose levels. Comparisons were made between our updated PBPK model and the earlier one from Bischoff et al. using a PBPK Index approach. Our new PBPK model was further verified against additional pharmacokinetic datasets from rats under special experimental conditions (cannulated bile duct) and Eisai hyperbirilubinemic rats.

When evolution is the solution to pollution: Key principles, and lessons from rapid repeated adaptation of killifish (Fundulus heteroclitus) populations

For most species, evolutionary adaptation is not expected to be sufficiently rapid to buffer the effects of human‐mediated environmental changes, including environmental pollution. Here we review how key features of populations, the characteristics of environmental pollution, and the genetic architecture underlying adaptive traits, may interact to shape the likelihood of evolutionary rescue from pollution. Large populations of Atlantic killifish (Fundulus heteroclitus) persist in some of the most contaminated estuaries of the United States, and killifish studies have provided some of the first insights into the types of genomic changes that enable rapid evolutionary rescue from complexly degraded environments. We describe how selection by industrial pollutants and other stressors has acted on multiple populations of killifish and posit that extreme nucleotide diversity uniquely positions this species for successful evolutionary adaptation. Mechanistic studies have identified some of the genetic underpinnings of adaptation to a well‐studied class of toxic pollutants; however, multiple genetic regions under selection in wild populations seem to reflect more complex responses to diverse native stressors and/or compensatory responses to primary adaptation. The discovery of these pollution‐adapted killifish populations suggests that the evolutionary influence of anthropogenic stressors as selective agents occurs widely. Yet adaptation to chemical pollution in terrestrial and aquatic vertebrate wildlife may rarely be a successful “solution to pollution” because potentially adaptive phenotypes may be complex and incur fitness costs, and therefore be unlikely to evolve quickly enough, especially in species with small population sizes.

Modelling ecological and human exposure to POPs in Venice lagoon - Part II: Quantitative uncertainty and sensitivity analysis in coupled exposure models

The study is focused on applying uncertainty and sensitivity analysis to support the application and evaluation of large exposure models where a significant number of parameters and complex exposure scenarios might be involved. The recently developed MERLIN-Expo exposure modelling tool was applied to probabilistically assess the ecological and human exposure to PCB 126 and 2,3,7,8-TCDD in the Venice lagoon (Italy). The 'Phytoplankton', 'Aquatic Invertebrate', 'Fish', 'Human intake' and PBPK models available in MERLIN-Expo library were integrated to create a specific food web to dynamically simulate bioaccumulation in various aquatic species and in the human body over individual lifetimes from 1932 until 1998. MERLIN-Expo is a high tier exposure modelling tool allowing propagation of uncertainty on the model predictions through Monte Carlo simulation. Uncertainty in model output can be further apportioned between parameters by applying built-in sensitivity analysis tools. In this study, uncertainty has been extensively addressed in the distribution functions to describe the data input and the effect on model results by applying sensitivity analysis techniques (screening Morris method, regression analysis, and variance-based method EFAST). In the exposure scenario developed for the Lagoon of Venice, the concentrations of 2,3,7,8-TCDD and PCB 126 in human blood turned out to be mainly influenced by a combination of parameters (half-lives of the chemicals, body weight variability, lipid fraction, food assimilation efficiency), physiological processes (uptake/elimination rates), environmental exposure concentrations (sediment, water, food) and eating behaviours (amount of food eaten). In conclusion, this case study demonstrated feasibility of MERLIN-Expo to be successfully employed in integrated, high tier exposure assessment.

A pharmacokinetic model of lopinavir in combination with ritonavir in human

Ritonavir-boosted lopinavir (LPV/r) has been recommended as an alternative regimen for HIV-naive patients who cannot tolerate nevirapine (NVP) and/or efavirenz (EFV). Although combinations of ritonavir and lopinavir have shown higher plasma concentration level of LPV in clinical settings, dosage adjustment is still required to maintain an adequate therapeutic efficacy and reduce side effects. A compartmental pharmacokinetic (PK) model of LPV/r was developed, including a mechanistic description of competitive inhibition. Systematic simulations were performed and predicted plasma drug concentration levels were compared with those from the literature. In particular, the simulated and experimental area under the curve (AUC) based on oral dosing were 76.10 μMol/L, and 76.25 μMol/L, respectively Results from the mathematical model support the hypothesis that the mechanism of LPV/r interaction is due to the competitive inhibition of CYP3A4 in the liver by ritonavir, resulting in an increasing LPV plasma concentration levels. The simulated plasma concentration-time courses were consistent with those from the literature with the goodness of fit (R(2)) of 0.9025 (0.8269-0.9862 95%CI).

A pharmacokinetic drug-drug interaction model of simvastatin and verapamil in humans

BACKGROUND

Verapamil is a calcium channel blocker commonly used in treatments of hypertension. Verapamil and its active metabolite, norverapamil, are known to be CYP3A4 inhibitors. Co-administration of verapamil with CYP3A4 substrates can alter the pharmacokinetics of the substrates. Simvastatin, a commonly used HMG-CoA reductase inhibitor for the treatment of hypercholesterolemia is extensively metabolized by CYP3A4. Therefore, concomitant use of simvastatin and verapamil can increase simvastatin plasma concentration levels, resulting in a higher risk of rhabdomyolysis, a serious adverse drug reaction. Even though, pharmacokinetic data regarding the interaction between both drugs have been published, their use is limited to semiquantitative applications. Therefore, we aimed to develop a mathematical model describing drug-drug interaction between simvastatin and verapamil in humans.

METHODS

Eligible pharmacokinetic interaction study between simvastatin and verapamil in humans was selected from PubMed database. The concentration-time courses from this study were digitally extracted and used for model development.

RESULTS

The drug-drug interaction between simvastatin and verapamil was modeled simultaneously with a two compartment model for verapamil with its active metabolite, norverapamil and a one compartment model for simvastatin with its active form, simvastatin hydroxy acid. The effects of verapamil and norverapamil on pharmacokinetics of simvastatin and its active form, simvastatin hydroxy acid were described by Michaelis-Menten equation. Simulated simvastatin and simvastatin hydroxy acid concentrations obtained from the final model produced a good fit to the dataset from a literature. The final model adequately describes pharmacokinetic interaction between simvastatin and verapamil which can be helpful in prediction of rhabdomyolysis in patients with concurrent use of these drugs.

A pharmacokinetic drug-drug interaction model of simvastatin and clarithromycin in humans

BACKGROUND

Simvastatin is a HMG-CoA reductase Inhibitor and a substrate of CYP3A4. Clarithromycin is a commonly used macrolide antibiotics and a potent inhibitor of CYP3A4. When co-administered with simvastatin, clarithromycin can significantly increase simvastatin plasma concentration levels, thereby, increase the risk of rhabdomyolysis. At present, pharmacokinetic data of the interaction between both drugs are available. However, they are being used for semi-quantitative application only, not for quantitative prediction. We aimed to develop a mathematical model describing a drug-drug interaction between simvastatin and clarithromycin in humans.

METHODS

Selected pharmacokinetic interaction study was obtained from PubMed search. Concentration-time course data were subsequently extracted and used for model development. Compartmental pharmacokinetic interaction model was developed using Advanced Continuous Simulating Language Extreme (ACSLX), a FORTRAN language-based computer program.

RESULTS

The drug-drug interaction between simvastatin and clarithromycin was modeled simultaneously with a parent-metabolite model for clarithromycin and a one-compartment model for simvastatin linked to its active form, simvastatin hydroxy acid. The simulated simvastatin concentrations obtained from the final model displayed satisfactory goodness of fit to the data from the literature.

CONCLUSION

Our model could successfully describe concentration-time course of simvastatin-clarithromycin interaction. The resulting interaction model can be able to use for further development of a quantitative model predicting rhabdomyolysis occurrence in patients concurrently receiving simvastatin and clarithromycin.

Pharmacokinetic modeling of simvastatin, nelfinavir and their interaction in humans

BACKGROUND

Simvastatin, a commonly used HMG-CoA reductase inhibitor, is extensively metabolized by CYP3A4. Therefore, co-administration of simvastatin and CYP3A4 inhibitor can affect simvastatin pharmacokinetics. Nelfinavir, a protease inhibitor, and its major metabolite (M8) are known to be potent CYP3A4 inhibitors. When simvastatin and nelfinavir are co-administered, simvastatin pharmacokinetics is significantly altered and may result in an increased risk of rhabdomyolysis.

OBJECTIVE

To develop a mathematical model describing a drug-drug interaction between simvastatin and nelfinavir in humans.

METHODS

Eligible pharmacokinetic studies were selected from Pubmed database and concentration time course data were digitally extracted and used for model development. Compartmental pharmacokinetic models for simvastatin and nelfinavir were developed separately. A drug-drug interaction model of simvastatin and nelfinavir was subsequently developed using the prior information. Finally, the final drug-drug interaction modeled was validated against observed simvastatin concentrations.

RESULTS

Three compartmental pharmacokinetic models were successfully developed. Simvastatin pharmacokinetics was best described by a one compartment model for simvastatin linked to its active form, simvastatin hydroxy acid. Nelfinavir pharmacokinetics could be adequately described by a one compartment parent-metabolite model. Our final drug-drug interaction model predicted an increase in simvastatin exposure which is in line with clinical observations linking the simvastatin-nelfinavir combination to an increased risk of rhabdomyolysis.

CONCLUSION

Simvastatin-nelfinavir pharmacokinetic interaction can be explained by our final model. This model framework will be useful in further advanced developing other mechanism based drug-drug interaction model used to predict the risk of rhabdomyolysis occurrence in patients prescribed simvastatin and nelfinavir concurrently.

The role of transporters in the pharmacokinetics of orally administered drugs

Drug transporters are recognized as key players in the processes of drug absorption, distribution, metabolism, and elimination. The localization of uptake and efflux transporters in organs responsible for drug biotransformation and excretion gives transporter proteins a unique gatekeeper function in controlling drug access to metabolizing enzymes and excretory pathways. This review seeks to discuss the influence intestinal and hepatic drug transporters have on pharmacokinetic parameters, including bioavailability, exposure, clearance, volume of distribution, and half-life, for orally dosed drugs. This review also describes in detail the Biopharmaceutics Drug Disposition Classification System (BDDCS) and explains how many of the effects drug transporters exert on oral drug pharmacokinetic parameters can be predicted by this classification scheme.

Physiologically based toxicokinetic models and their application in human exposure and internal dose assessment

Human populations may exhibit large interindividual variation in toxicokinetic response to chemical exposures. Rapid developments in dosimetry research have brought medicine and public health closer to understanding the biological basis of this heterogeneity. The toxicokinetic behavior of chemicals is, in part, controlled by the properties of the epithelium surrounding organs, some of which are effective barriers to penetration into the systemic circulation. Physiologically based toxicokinetic (PBTK) models have been developed and used to simulate the mechanism of uptake into the systemic circulation, to extrapolate between doses and exposure routes, and to estimate internal dosimetry and sources of heterogeneity in animals and humans. Recent improvements to PBTK models include descriptions of active transport across biological membranes, carrier-mediated clearance, and fractal kinetics. The expanding area of toxicogenetics has provided valuable insight for delineating toxicokinetic differences between individuals; genetic differences include inherited single nucleotide polymorphisms, copy number variants, and dynamic changes in the methylation pattern of imprinted genes. This chapter discusses the structure of PBTK models and how toxicogenetic information and newer biological descriptions have improved our understanding of variability in response to toxicant exposures.