Whole body physiologically based modelling of β-blockers in the rat: events in tissues and plasma following an i.v. bolus dose

The Role of Gut Microbiota and Its Metabolites in Patients with Heart Failure

Heart failure (HF) is a significant health concern; early detection and prevention are crucial. Recent studies suggest that the gut microbiota and its metabolites may influence HF development and risk factors. We explored this relationship by examining changes in gut microbiota composition and metabolite levels in HF patients. HF patients often exhibit decreased alpha and beta diversity compared to controls, suggesting lower bacterial richness and community variation. Changes in specific bacterial phyla were observed, with decreases in Firmicutes (e.g., Ruminococcus) and Bacteroidetes (e.g., Prevotella) and increases in Proteobacteria (e.g., Escherichia, Shigella, and Klebsiella) and Actinobacteria. Gut-microbiota-related metabolites have been identified, potentially affecting various body systems, including the cardiovascular system. Among these are short-chain fatty acids (SCFAs), betaine, trimethylamine N-oxide (TMAO), phenylalanine, tryptophan-kynurenine, and phenylacetylgutamine (PAGIn). Although SCFAs positively affect our organisms, patients with HF have been observed to experience a decline in bacteria responsible for producing these chemical compounds. There have been indications of possible links between betaine, TMAO, phenylalanine, tryptophan-kynurenine, PAGIn, and heart failure. TMAO and phenylalanine, in particular, show promise as potential prognostic factors. However, their clinical significance has not yet been thoroughly evaluated and requires further investigation.

Oral Cardiac Drug-Gut Microbiota Interaction in Chronic Heart Failure Patients: An Emerging Association

Regardless of the currently proposed best medical treatment for heart failure patients, the morbidity and mortality rates remain high. This is due to several reasons, including the interaction between oral cardiac drug administration and gut microbiota. The relation between drugs (especially antibiotics) and gut microbiota is well established, but it is also known that more than 24% of non-antibiotic drugs affect gut microbiota, altering the microbe's environment and its metabolic products. Heart failure treatment lies mainly in the blockage of neuro-humoral hyper-activation. There is debate as to whether the administration of heart-failure-specific drugs can totally block this hyper-activation, or whether the so-called intestinal dysbiosis that is commonly observed in this group of patients can affect their action. Although there are several reports indicating a strong relation between drug-gut microbiota interplay, little is known about this relation to oral cardiac drugs in chronic heart failure. In this review, we review the contemporary data on a topic that is in its infancy. We aim to produce scientific thoughts and questions and provide reasoning for further clinical investigation.

Utility of Minimal Physiologically Based Pharmacokinetic Models for Assessing Fractional Distribution, Oral Absorption, and Series-Compartment Models of Hepatic Clearance

Minimal physiologically based pharmacokinetic (mPBPK) models are physiologically relevant, require less information than full PBPK models, and offer flexibility in pharmacokinetics (PK). The well-stirred hepatic model (WSM) is commonly used in PBPK, whereas the more plausible dispersion model (DM) poses computational complexities. The series-compartment model (SCM) mimics the DM but is easier to operate. This work implements the SCM and mPBPK models for assessing fractional tissue distribution, oral absorption, and hepatic clearance using literature-reported blood and liver concentration-time data in rats for compounds mainly cleared by the liver. Further handled were various complexities, including nonlinear hepatic binding and metabolism, differing absorption kinetics, and sites of administration. The SCM containing one to five (n) liver subcompartments yields similar fittings and provides comparable estimates for hepatic extraction ratio (ER), prehepatic availability (Fg ), and first-order absorption rate constants (ka ). However, they produce decreased intrinsic clearances (CLint ) and liver-to-plasma partition coefficients (Kph ) with increasing n as expected. Model simulations demonstrated changes in intravenous and oral PK profiles with alterations in Kph and ka and with hepatic metabolic zonation. The permeability (PAMPA P) of the various compounds well explained the fitted fractional distribution (fd ) parameters. The SCM and mPBPK models offer advantages in distinguishing systemic, extrahepatic, and hepatic clearances. The SCM allows for incorporation of liver zonation and is useful in assessing changes in internal concentration gradients potentially masked by similar blood PK profiles. Improved assessment of intraorgan drug concentrations may offer insights into active moieties driving metabolism, biliary excretion, pharmacodynamics, and hepatic toxicity. SIGNIFICANCE STATEMENT: The minimal physiologically based pharmacokinetic model and the series-compartment model are useful in assessing oral absorption and hepatic clearance. They add flexibility in accounting for various drug- or system-specific complexities, including fractional distribution, nonlinear binding and saturable hepatic metabolism, and hepatic zonation. These models can offer improved insights into the intraorgan concentrations that reflect physiologically active moieties often driving disposition, pharmacodynamics, and toxicity.

Exploring the Pharmacokinetic Mysteries of the Liver: Application of Series Compartment Models of Hepatic Elimination

Among the basic hepatic clearance models, the dispersion model (DM) is the most physiologically sound compared with the well-stirred model and the parallel tube model. However, its application in physiologically-based pharmacokinetic (PBPK) modeling has been limited due to computational complexities. The series compartment models (SCM) of hepatic elimination that treats the liver as a cascade of well-stirred compartments connected by hepatic blood flow exhibits some mathematical similarities to the DM but is easier to operate. This work assesses the quantitative correlation between the SCM and DM and demonstrates the operation of the SCM in PBPK with the published single-dose blood and liver concentration-time data of six flow-limited compounds. The predicted liver concentrations and the estimated intrinsic clearance (CLint ) and PBPK-operative tissue-to-plasma partition coefficient (Kp ) values were shown to depend on the number of liver sub-compartments (n) and hepatic enzyme zonation in the SCM. The CLint and Kp decreased with increasing n, with more remarkable differences for drugs with higher hepatic extraction ratios. Given the same total CLint , the SCM yields a higher Kp when the liver perivenous region exhibits a lower CLint as compared with a high CLint at this region. Overall, the SCM nicely approximates the DM in characterizing hepatic elimination and offers an alternative flexible approach as well as providing some insights regarding sequential drug concentrations in the liver. SIGNIFICANCE STATEMENT: The SCM nicely approximates the DM when applied in PBPK for characterizing hepatic elimination. The number of liver sub-compartments and hepatic enzyme zonation are influencing factors for the SCM resulting in model-dependent predictions of total/internal liver concentrations and estimates of CLint and the PBPK-operative Kp . Such model-dependency may have an impact when the SCM is used for in vitro-to-in vivo extrapolation (IVIVE) and may also be relevant for PK/PD/toxicological effects when it is the driving force for such responses.

Whole body physiologically based modelling of β-blockers in the rat: events in tissues and plasma following an i.v. bolus dose

BACKGROUND AND PURPOSE

Whole body physiologically based pharmacokinetic (PBPK) models have been increasingly applied in drug development to describe kinetic events of therapeutic agents in animals and humans. The advantage of such modelling is the ability to incorporate vast amounts of physiological information, such as organ blood flow and volume, to ensure that the model is as close to reality as possible.

EXPERIMENTAL APPROACH

Previous PBPK model development of enantiomers of a series of seven racemic β-blockers, namely, acebutolol, betaxolol, bisoprolol, metoprolol, oxprenolol, pindolol and propranolol, together with S-timolol in rat was based on tissue and blood concentration data at steady state. Compounds were administered in several cassettes with the composition mix and blood and tissue sampling times determined using a D-optimal design.

KEY RESULTS

Closed-loop PBPK models were developed initially based on the application of open loop forcing function models to individual tissues and compounds. For the majority of compounds and tissues, distribution kinetics was adequately characterized by perfusion rate-limited models. For some compounds in the testes and gut, a permeability rate-limited distribution model was required to best fit the data. Parameter estimates of the tissue-to-blood partition coefficient through fitting of individual enantiomers and of racemic pair were generally in agreement and also concur with those from previous steady-state experiments.

CONCLUSIONS AND IMPLICATIONS

PBPK modelling is a very powerful tool to aid drug discovery and development of therapeutic agents in animals and humans. However, careful consideration of the assumptions made during the modelling exercise is essential.