Interspecies prediction of pharmacokinetics and tissue distribution of doxorubicin by physiologically-based pharmacokinetic modeling

PBPK model of pegylated liposomal doxorubicin to simultaneously predict the concentration-time profile of encapsulated and free doxorubicin in tissues

The objective of this study was to develop a physiologically based pharmacokinetic (PBPK) model to predict the concentrations of encapsulated and free doxorubicin in plasma and tissues in mice after intravenous injection of PEGylated liposomes (Doxil®). The PBPK model used in this study contains liposomes and free doxorubicin disposition components. The free doxorubicin disposition component was used to simulate the disposition of free doxorubicin produced by mononuclear phagocyte system (MPS)-degrading liposomes. The liver, spleen, kidneys, and lungs contain an additional MPS subcompartment. These compartments are interconnected through blood and lymphatic circulation. The model was validated strictly by four doses of external observed plasma and tissue concentration-time profiles. The fold error (FE) values were almost all within threefold. The sensitivity analysis revealed that the MPS-related parameters greatly influenced the model. The predicted in vivo distribution characteristics of the doxorubicin liposomes and doxorubicin solution were consistent with the observed values. The PBPK model was established based on the physiological mechanism and parameters of practical significance that can be measured in vitro. Thus, it can be used to study the pharmacokinetic properties of liposomes. This study also provides a reference for the establishment of liposome PBPK model.

Metformin inhibits OCTN1- and OCTN2-mediated hepatic accumulation of doxorubicin and alleviates its hepatotoxicity in mice

Doxorubicin (DOX) is a widely used antitumor agent; however, its clinical application is limited by dose-related organ damage. Because organic cation/carnitine transporters (OCTN1 and OCTN2), which are critical for DOX uptake, are highly expressed in hepatocytes, we aimed to elucidate the role of these transporters in hepatic DOX uptake. The results indicated that inhibitors and RNA interference both significantly reduced DOX accumulation in HepG2 and HepaRG cells, suggesting that OCTN1/2 contribute substantially to DOX uptake by hepatocytes. To determine whether metformin (MET, an inhibitor of OCTN1 and OCTN2) ameliorates DOX-induced hepatotoxicity, we conducted in vitro and in vivo studies. MET (1-100 μM) inhibited DOX (500 nM) accumulation and cytotoxicity in vitro in a concentration-dependent manner. Furthermore, intravenous MET administration at 250 or 500 mg/kg or by gavage at 50, 100, or 200 mg/kg reduced DOX (8 mg/kg) accumulation in a dose-dependent manner in the mouse liver and attenuated the release of alanine aminotransferase, aspartate aminotransferase, and carboxylesterase 1. Additionally, MET reduced the distribution of DOX in the heart, liver, and kidney and enhanced the urinary elimination of DOX; however, it did not increase the nephric toxicity of DOX. In conclusion, our study demonstrated that MET alleviates DOX hepatotoxicity by inhibiting OCTN1- and OCTN2-mediated DOX uptake in vitro (mouse hepatocytes and HepaRG or HepG2 cells) and in mice.

A minimal PBPK model to accelerate preclinical development of drugs against tuberculosis

Introduction: Understanding drug exposure at disease target sites is pivotal to profiling new drug candidates in terms of tolerability and efficacy. Such quantification is particularly tedious for anti-tuberculosis (TB) compounds as the heterogeneous pulmonary microenvironment due to the infection may alter lung permeability and affect drug disposition. Murine models have been a longstanding support in TB research so far and are here used as human surrogates to unveil the distribution of several anti-TB compounds at the site-of-action via a novel and centralized PBPK design framework. Methods: As an intermediate approach between data-driven pharmacokinetic (PK) models and whole-body physiologically based (PB) PK models, we propose a parsimonious framework for PK investigation (minimal PBPK approach) that retains key physiological processes involved in TB disease, while reducing computational costs and prior knowledge requirements. By lumping together pulmonary TB-unessential organs, our minimal PBPK model counts 9 equations compared to the 36 of published full models, accelerating the simulation more than 3-folds in Matlab 2022b. Results: The model has been successfully tested and validated against 11 anti-TB compounds-rifampicin, rifapentine, pyrazinamide, ethambutol, isoniazid, moxifloxacin, delamanid, pretomanid, bedaquiline, OPC-167832, GSK2556286 - showing robust predictability power in recapitulating PK dynamics in mice. Structural inspections on the proposed design have ensured global identifiability and listed free fraction in plasma and blood-to-plasma ratio as top sensitive parameters for PK metrics. The platform-oriented implementation allows fast comparison of the compounds in terms of exposure and target attainment. Discrepancies in plasma and lung levels for the latest BPaMZ and HPMZ regimens have been analyzed in terms of their impact on preclinical experiment design and on PK/PD indices. Conclusion: The framework we developed requires limited drug- and species-specific information to reconstruct accurate PK dynamics, delivering a unified viewpoint on anti-TB drug distribution at the site-of-action and a flexible fit-for-purpose tool to accelerate model-informed drug design pipelines and facilitate translation into the clinic.

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.

Machine learning and artificial intelligence in physiologically based pharmacokinetic modeling

Physiologically based pharmacokinetic (PBPK) models are useful tools in drug development and risk assessment of environmental chemicals. PBPK model development requires the collection of species-specific physiological, and chemical-specific absorption, distribution, metabolism, and excretion (ADME) parameters, which can be a time-consuming and expensive process. This raises a need to create computational models capable of predicting input parameter values for PBPK models, especially for new compounds. In this review, we summarize an emerging paradigm for integrating PBPK modeling with machine learning (ML) or artificial intelligence (AI)-based computational methods. This paradigm includes 3 steps (1) obtain time-concentration PK data and/or ADME parameters from publicly available databases, (2) develop ML/AI-based approaches to predict ADME parameters, and (3) incorporate the ML/AI models into PBPK models to predict PK summary statistics (eg, area under the curve and maximum plasma concentration). We also discuss a neural network architecture "neural ordinary differential equation (Neural-ODE)" that is capable of providing better predictive capabilities than other ML methods when used to directly predict time-series PK profiles. In order to support applications of ML/AI methods for PBPK model development, several challenges should be addressed (1) as more data become available, it is important to expand the training set by including the structural diversity of compounds to improve the prediction accuracy of ML/AI models; (2) due to the black box nature of many ML models, lack of sufficient interpretability is a limitation; (3) Neural-ODE has great potential to be used to generate time-series PK profiles for new compounds with limited ADME information, but its application remains to be explored. Despite existing challenges, ML/AI approaches will continue to facilitate the efficient development of robust PBPK models for a large number of chemicals.

Intravenous Nicotinamide Riboside Administration Has a Cardioprotective Effect in Chronic Doxorubicin-Induced Cardiomyopathy

Doxorubicin, which is widely used to treat a broad spectrum of malignancies, has pronounced dose-dependent side effects leading to chronic heart failure development. Nicotinamide riboside (NR) is one of the promising candidates for leveling the cardiotoxic effect. In the present work, we performed a comparative study of the cardioprotective and therapeutic actions of various intravenous NR administration modes in chronic doxorubicin-induced cardiomyopathy in Wistar rats. The study used 60 mature male SPF Wistar rats. The animals were randomized into four groups (a control group and three experimental groups) which determined the doxorubicin (intraperitoneally) and NR (intravenous) doses as well as the specific modes of NR administration (combined, preventive). We demonstrated the protective effect of NR on the cardiovascular system both with combined and preventive intravenous drug administration, which was reflected in a fibrous tissue formation decrease, reduced fractional-shortening decrease, and better antioxidant system performance. At the same time, it is important to note that the preventive administration of NR had a more significant protective effect on the animal organism as a whole. This was confirmed by better physical activity parameters and vascular bed conditions. Thus, the data obtained during the study can be used for further investigation into chronic doxorubicin-induced cardiomyopathy prevention and treatment approaches.

Plasma Pharmacokinetics and Tissue Distribution of Doxorubicin in Rats following Treatment with Astragali Radix

Doxorubicin (DOX) is an essential component in chemotherapy, and Astragali Radix (AR) is a widely used tonic herbal medicine. The combination of DOX and AR offers widespread, well-documented advantages in treating cancer, e.g., reducing the risk of adverse effects. This study mainly aims to uncover the impact of AR on DOX disposition in vivo. Rats received a single intravenous dose of 5 mg/kg DOX following a single-dose co-treatment or multiple-dose pre-treatment of AR (10 g/kg × 1 or × 10). The concentrations of DOX in rat plasma and six tissues, including heart, liver, lung, kidney, spleen, and skeletal muscle, were determined by a fully validated LC-MS/MS method. A network-based approach was further employed to quantify the relationships between enzymes that metabolize and transport DOX and the targets of nine representative AR components in the human protein−protein interactome. We found that short-term (≤10 d) AR administration was ineffective in changing the plasma pharmacokinetics of DOX in terms of the area under the concentration−time curve (AUC, 1303.35 ± 271.74 μg/L*h versus 1208.74 ± 145.35 μg/L*h, p > 0.46), peak concentrations (Cmax, 1351.21 ± 364.86 μg/L versus 1411.01 ± 368.38 μg/L, p > 0.78), and half-life (t1/2, 31.79 ± 5.12 h versus 32.05 ± 6.95 h, p > 0.94), etc. Compared to the isotype control group, DOX concentrations in six tissues slightly decreased under AR pre-administration but only showed statistical significance (p < 0.05) in the liver. Using network analysis, we showed that five of the nine representative AR components were not localized to the vicinity of the DOX disposition-associated module. These findings suggest that AR may mitigate DOX-induced toxicity by affecting drug targets rather than drug disposition.

Chemotherapeutics-Induced Intestinal Mucositis: Pathophysiology and Potential Treatment Strategies

The gastrointestinal tract is particularly vulnerable to off-target effects of antineoplastic drugs because intestinal epithelial cells proliferate rapidly and have a complex immunological interaction with gut microbiota. As a result, up to 40-100% of all cancer patients dosed with chemotherapeutics experience gut toxicity, called chemotherapeutics-induced intestinal mucositis (CIM). The condition is associated with histological changes and inflammation in the mucosa arising from stem-cell apoptosis and disturbed cellular renewal and maturation processes. In turn, this results in various pathologies, including ulceration, pain, nausea, diarrhea, and bacterial translocation sepsis. In addition to reducing patient quality-of-life, CIM often leads to dose-reduction and subsequent decrease of anticancer effect. Despite decades of experimental and clinical investigations CIM remains an unsolved clinical issue, and there is a strong consensus that effective strategies are needed for preventing and treating CIM. Recent progress in the understanding of the molecular and functional pathology of CIM had provided many new potential targets and opportunities for treatment. This review presents an overview of the functions and physiology of the healthy intestinal barrier followed by a summary of the pathophysiological mechanisms involved in the development of CIM. Finally, we highlight some pharmacological and microbial interventions that have shown potential. Conclusively, one must accept that to date no single treatment has substantially transformed the clinical management of CIM. We therefore believe that the best chance for success is to use combination treatments. An optimal combination treatment will likely include prophylactics (e.g., antibiotics/probiotics) and drugs that impact the acute phase (e.g., anti-oxidants, apoptosis inhibitors, and anti-inflammatory agents) as well as the recovery phase (e.g., stimulation of proliferation and adaptation).