Simulation and prediction of in vivo drug metabolism in human populations from in vitro data

Physiologically based kinetic (PBK) modeling of propiconazole using a machine learning-enhanced read-across approach for interspecies extrapolation

A significant challenge in the traditional human health risk assessment of agrochemicals is the uncertainty in quantifying the interspecies differences between animal models and humans. To work toward a more accurate and animal-free risk determination, new approaches such as physiologically based kinetic (PBK) modeling have been used to perform dosimetry extrapolation from animals to humans. However, the regulatory use and acceptance of PBK modeling is limited for chemicals that lack in vivo animal pharmacokinetic (PK) data, given the inability to evaluate models. To address these challenges, this study developed PBK models in the absence of in vivo PK data for the fungicide propiconazole, an activator of constitutive androstane receptor (CAR)/pregnane X receptor (PXR). A fit-for-purpose read-across approach was integrated with hierarchical clustering - an unsupervised machine learning algorithm, to bridge the knowledge gap. The integration allowed the incorporation of a broad spectrum of attributes for analog consideration, and enabled the analog selection in a simple, reproducible, and objective manner. The applicability was evaluated and demonstrated using penconazole (source) and three pseudo-unknown target chemicals (epoxiconazole, tebuconazole and triadimefon). Applying this machine learning-enhanced read-across approach, difenoconazole was selected as the most appropriate analog for propiconazole. A mouse PBK model was developed and evaluated for difenoconazole (source), with the mode of action of CAR/PXR activation incorporated to simulate the in vivo autoinduction of metabolism. The difenoconazole mouse model then served as a template for constructing the propiconazole mouse model. A parallelogram approach was subsequently applied to develop the propiconazole rat and human models, enabling a quantitative assessment of interspecies differences in dosimetry. This integrated approach represents a substantial advancement toward refining risk assessment of propiconazole within the framework of animal alternative safety assessment strategies.

Personalized Dosing of Medicines for Children: A Primer on Pediatric Pharmacometrics for Clinicians

The widespread use of drugs for unapproved purposes remains common in children, primarily attributable to practical, ethical, and financial constraints associated with pediatric drug research. Pharmacometrics, the scientific discipline that involves the application of mathematical models to understand and quantify drug effects, holds promise in advancing pediatric pharmacotherapy by expediting drug development, extending applications, and personalizing dosing. In this review, we delineate the principles of pharmacometrics, and explore its clinical applications and prospects. The fundamental aspect of any pharmacometric analysis lies in the selection of appropriate methods for quantifying pharmacokinetics and pharmacodynamics. Population pharmacokinetic modeling is a data-driven method ('top-down' approach) to approximate population-level pharmacokinetic parameters, while identifying factors contributing to inter-individual variability. Model-informed precision dosing is increasingly used to leverage population pharmacokinetic models and patient data, to formulate individualized dosing recommendations. Physiologically based pharmacokinetic models integrate physicochemical drug properties with biological parameters ('bottom-up approach'), and is particularly valuable in situations with limited clinical data, such as early drug development, assessing drug-drug interactions, or adapting dosing for patients with specific comorbidities. The effective implementation of these complex models hinges on strong collaboration between clinicians and pharmacometricians, given the pivotal role of data availability. Promising advancements aimed at improving data availability encompass innovative techniques such as opportunistic sampling, minimally invasive sampling approaches, microdialysis, and in vitro investigations. Additionally, ongoing research efforts to enhance measurement instruments for evaluating pharmacodynamics responses, including biomarkers and clinical scoring systems, are expected to significantly bolster our capacity to understand drug effects in children.

Investigating clinical pharmacokinetics of brivaracetam by using a pharmacokinetic modeling approach

The development of technology and the processing speed of computing machines have facilitated the evaluation of advanced pharmacokinetic (PK) models, making modeling processes simple and faster. The present model aims to analyze the PK of brivaracetam (BRV) in healthy and diseased populations. A comprehensive literature review was conducted to incorporate the BRV plasma concentration data and its input parameters into PK-Sim software, leading to the creation of intravenous (IV) and oral models for both populations. The developed physiologically based pharmacokinetic (PBPK) model of BRV was then assessed using the visual predictive checks, mean observed/predicted ratios (Robs/pre), and average fold error for PK parameters including the maximum systemic concentration (Cmax), the area under the curve at time 0 to t (AUC0-∞), and drug clearance (CL). The PBPK model of BRV demonstrated that mean Robs/pre ratios of the PK parameters remained within the acceptable limits when assessed against a twofold error margin. Furthermore, model predictions were carried out to assess how AUC0-∞ is affected following the administration of BRV in individuals with varying degrees of liver cirrhosis, ranging from different child-pugh (CP) scores like A, B, and C. Moreover, dose adjustments were recommended by considering the variations in Cmax and CL in various kidney disease stages (mild to severe).

Molecular Cloning, Expression and Enzymatic Characterization of Tetrahymena thermophila Glutathione-S-Transferase Mu 34

Glutathione-S-transferase enzymes (GSTs) are essential components of the phase II detoxification system and protect organisms from oxidative stress induced by xenobiotics and harmful toxins such as 1-chloro-2,4-dinitrobenzene (CDNB). In Tetrahymena thermophila, the TtGSTm34 gene was previously reported to be one of the most responsive GST genes to CDNB treatment (LD50 = 0.079 mM). This study aimed to determine the kinetic features of recombinantly expressed and purified TtGSTm34 with CDNB and glutathione (GSH). TtGSTm34-8xHis was recombinantly produced in T. thermophila as a 25-kDa protein after the cloning of the 660-bp full-length ORF of TtGSTm34 into the pIGF-1 vector. A three-dimensional model of the TtGSTm34 protein constructed by the AlphaFold and PyMOL programs confirmed that it has structurally conserved and folded GST domains. The recombinant production of TtGSTm34-8xHis was confirmed by SDS‒PAGE and Western blot analysis. A dual-affinity chromatography strategy helped to purify TtGSTm34-8xHis approximately 3166-fold. The purified recombinant TtGSTm34-8xHis exhibited significantly high enzyme activity with CDNB (190 µmol/min/mg) as substrate. Enzyme kinetic analysis revealed Km values of 0.68 mM with GSH and 0.40 mM with CDNB as substrates, confirming its expected high affinity for CDNB. The optimum pH and temperature were determined to be 7.0 and 25 °C, respectively. Ethacrynic acid inhibited fully TtGSTm34-8xHis enzyme activity. These results imply that TtGSTm34 of T. thermophila plays a major role in the detoxification of xenobiotics, such as CDNB, as a first line of defense in aquatic protists against oxidative damage.

Kinetic Characterization of Estradiol Glucuronidation by Liver Microsomes and Expressed UGT Enzymes: The Effects of Organic Solvents

BACKGROUND AND OBJECTIVE

In vitro glucuronidation of 17β-estradiol (estradiol) is often performed to assess the role of uridine 5'-diphospho-glucuronosyltransferase 1A1 (UGT1A1) in xenobiotic/drug metabolism. The objective of this study was to determine the effects of four commonly used organic solvents [i.e., dimethyl sulfoxide (DMSO), methanol, ethanol, and acetonitrile] on the glucuronidation kinetics of estradiol, which can be glucuronidated at C3 and C17 positions.

METHODS

The impacts of organic solvents on estradiol glucuronidation were determined by using expressed UGT enzymes and liver microsomes from both human and animals.

RESULTS

In human liver microsomes (HLM), methanol, ethanol, and acetonitrile significantly altered estradiol glucuronidation kinetics with increased Vmax (up to 2.6-fold) and CLmax (up to 2.8-fold) values. Altered estradiol glucuronidation in HLM was deduced to be attributed to the enhanced metabolic activities of UGT1A1 and UGT2B7, whose activities differ at the two glucuronidation positions. The effects of organic solvents on estradiol glucuronidation were glucuronidation position-, isozyme-, and solvent-specific. Furthermore, both ethanol and acetonitrile have a greater tendency to modify the glucuronidation activity of estradiol in animal liver microsomes.

CONCLUSION

Organic solvents such as methanol, ethanol, and acetonitrile showed great potential in adjusting the glucuronidation of estradiol. DMSO is the most suitable solvent due to its minimal influence on estradiol glucuronidation. Researchers should be cautious in selecting appropriate solvents to get accurate results when assessing the metabolism of a new chemical entity.

Maternal Ezetimibe Concentrations Measured in Breast Milk and Its Use in Breastfeeding Infant Exposure Predictions

BACKGROUND

Lactating mothers taking ezetimibe, an antihyperlipidemic agent, may be hesitant to breastfeed despite the known benefit of breastfeeding to both mother and infant. Currently, no data exist on the presence or concentration of ezetimibe and its main active metabolite, ezetimibe-glucuronide (EZE-glucuronide), in human breast milk.

METHODS

Voluntary breast milk samples containing ezetimibe and EZE-glucuronide were attained from lactating mothers taking ezetimibe as part of their treatment. An assay was developed and validated to measure ezetimibe and EZE-glucuronide concentrations in breast milk. A workflow that utilized a developed and evaluated pediatric physiologically based pharmacokinetic (PBPK) model, the measured concentrations in milk, and weight-normalized breast milk intake volumes was applied to predict infant exposures and determine the upper area under the curve ratio (UAR).

RESULTS

Fifteen breast milk samples from two maternal-infant pairs were collected. The developed liquid chromatography-tandem mass spectrometry (LC-MS/MS) assay showed an analytical range of 0.039-5.0 ng/mL and 0.39-50.0 ng/mL for ezetimibe and EZE-glucuronide, respectively. The measured concentrations in the breast milk samples were 0.17-1.02 ng/mL and 0.42-2.65 ng/mL of ezetimibe and EZE-glucuronide, respectively. The evaluated pediatric PBPK model demonstrated minimal exposure overlap in adult therapeutic dose and breastfed infant simulated area under the concentration-time curve from time zero to 24 h (AUC24). Calculated UAR across infant age groups ranged from 0.0015 to 0.0026.

CONCLUSIONS

PBPK model-predicted ezetimibe and EZE-glucuronide exposures and UAR suggest that breastfeeding infants would receive non-therapeutic exposures. Future work should involve a 'mother-infant pair study' to ascertain breastfed infant plasma ezetimibe and EZE-glucuronide concentrations to confirm the findings of this work.

Comparative Intra-Subject Analysis of Gene Expression and Protein Abundance of Major and Minor Drug Metabolizing Enzymes in Healthy Human Jejunum and Liver

First pass metabolism by phase I and phase II enzymes in the intestines and liver is a major determinant of the oral bioavailability of many drugs. Several studies analyzed expressions of major drug-metabolizing enzymes (DMEs), such as CYP3A4 and UGT1A1 in the human gut and liver. However, there is still a lack of knowledge regarding other DMEs (i.e., "minor" DMEs), although several clinically relevant drugs are affected by those enzymes. Moreover, there is very limited intra-subject data on hepatic and intestinal expression levels of minor DMEs. To fill this gap of knowledge, we analyzed gene expression (quantitative real-time polymerase chain reaction) and protein abundance (targeted proteomics) of 24 clinically relevant DMEs, that is, carboxylesterases (CES), UDP-glucuronosyltransferases (UGT), and cytochrome P450 (CYP)-enzymes. We performed our analysis using jejunum and liver tissue specimens from the same 11 healthy organ donors (8 men and 3 women, aged 19-60 years). Protein amounts of all investigated DMEs, with the exception of CYP4A11, were detected in human liver samples. CES2, CYP2C18, CYP3A4, and UGT2B17 protein abundance was similar or even higher in the jejunum, and all other DMEs were found in higher amounts in the liver. Significant correlations between gene expression and protein levels were observed only for 2 of 15 jejunal, but 13 of 23 hepatic DMEs. Intestinal and hepatic protein amounts only significantly correlated for CYP3A4 and UGT1A3. Our results demonstrated a notable variability between the individuals, which was even higher in the intestines than in the liver. Our intrasubject analysis of DMEs in the jejunum and liver from healthy donors, may be useful for physiologically-based pharmacokinetic-based modeling and prediction in order to improve efficacy and safety of oral drug therapy.

Potential risk of drug-drug interactions of ponatinib via inhibition against human UDP-glucuronosyltransferases

Ponatinib is an efficient oral tyrosine kinase inhibitor (TKI) for T315I-positive Ph + ALL and T315I-positive chronic myeloid leukemia (CML) or BCR-ABL when no other TKIs can be prescribed. In this research, we evaluated the inhibitory effects of ponatinib on human recombinant UDP-glucuronosyltransferases (UGTs) and predicted the magnitude of potential drug-drug interaction (DDI) risk of co-treatment with ponatinib and UGTs substrates by using in vitro-in vivo extrapolation (IVIVE) method. Our study presented that ponatinib showed a broad-spectrum inhibition against UGTs. Particularly, ponatinib exhibited potent inhibitory effects towards UGT1A7, UGT1A1, and UGT1A9 with IC50 values of 0.37, 0.41, and 0.89 μM, respectively, which might lead to clinically significant DDI.

Research in the Field of Drug Design and Development

The processes used by academic and industrial scientists to discover new drugs have recently experienced a true renaissance, with many new and exciting techniques being developed over the past 5-10 years alone. Drug design and discovery, and the search for new safe and well-tolerated compounds, as well as the ineffectiveness of existing therapies, and society's insufficient knowledge concerning the prophylactics and pharmacotherapy of the most common diseases today, comprise a serious challenge. This can influence not only the quality of human life, but also the health of whole societies, which became evident during the COVID-19 pandemic. In general, the process of drug development consists of three main stages: drug discovery, preclinical development using cell-based and animal models/tests, clinical trials on humans and, finally, forward moving toward the step of obtaining regulatory approval, in order to market the potential drug. In this review, we will attempt to outline the first three most important consecutive phases in drug design and development, based on the experience of three cooperating and complementary academic centers of the Visegrád group; i.e., Medical University of Lublin, Poland, Masaryk University of Brno, Czech Republic, and Comenius University Bratislava, Slovak Republic.

The Coming of Age of AI/ML in Drug Discovery, Development, Clinical Testing, and Manufacturing: The FDA Perspectives

Artificial intelligence (AI) and machine learning (ML) represent significant advancements in computing, building on technologies that humanity has developed over millions of years-from the abacus to quantum computers. These tools have reached a pivotal moment in their development. In 2021 alone, the U.S. Food and Drug Administration (FDA) received over 100 product registration submissions that heavily relied on AI/ML for applications such as monitoring and improving human performance in compiling dossiers. To ensure the safe and effective use of AI/ML in drug discovery and manufacturing, the FDA and numerous other U.S. federal agencies have issued continuously updated, stringent guidelines. Intriguingly, these guidelines are often generated or updated with the aid of AI/ML tools themselves. The overarching goal is to expedite drug discovery, enhance the safety profiles of existing drugs, introduce novel treatment modalities, and improve manufacturing compliance and robustness. Recent FDA publications offer an encouraging outlook on the potential of these tools, emphasizing the need for their careful deployment. This has expanded market opportunities for retraining personnel handling these technologies and enabled innovative applications in emerging therapies such as gene editing, CRISPR-Cas9, CAR-T cells, mRNA-based treatments, and personalized medicine. In summary, the maturation of AI/ML technologies is a testament to human ingenuity. Far from being autonomous entities, these are tools created by and for humans designed to solve complex problems now and in the future. This paper aims to present the status of these technologies, along with examples of their present and future applications.

Inter-species differences between humans and other mammals in the in vitro metabolism of carbofuran and the role of human CYP enzymes

This study investigated the metabolic transformation of carbofuran in seven species of mammals using LC-MS/MS and liver microsomes. The results revealed species-specific differences in metabolite formation, indicating the potential role of metabolic pathways in toxicity and risk assessment. The majority of carbofuran was metabolized through the 3-hydroxycarbofuran pathway, with the highest levels observed in dogLM and the lowest in humanLM. Further analysis was conducted to investigate the human cytochrome P450-mediated metabolism of carbofuran, with CYP3A4 being found to be the most efficient enzyme with the highest contribution to the 3-hydroxycarbofuran pathway. Inhibition of CYP3A4 with ketoconazole resulted in a substantial decrease in carbofuran metabolism. In addition, carbofuran exhibited inhibitory effects on human CYP3A4 and CYP2B6, demonstrating the potential for carbofuran to interact with these enzymes. The findings highlight the importance of in vitro screening for metabolic processes and provide insights into the biotransformation of carbofuran.

Real-Time Monitoring Platform for Ocular Drug Delivery

Real-time measurement is important in modern dissolution testing to aid in parallel drug characterisation and quality control (QC). The development of a real-time monitoring platform (microfluidic system, a novel eye movement platform with temperature sensors and accelerometers and a concentration probe setup) in conjunction with an in vitro model of the human eye (PK-Eye™) is reported. The importance of surface membrane permeability when modelling the PK-Eye™ was determined with a "pursing model" (a simplified setup of the hyaloid membrane). Parallel microfluidic control of PK-Eye™ models from a single source of pressure was performed with a ratio of 1:6 (pressure source:models) demonstrating scalability and reproducibility of pressure-flow data. Pore size and exposed surface area helped obtain a physiological range of intraocular pressure (IOP) within the models, demonstrating the need to reproduce in vitro dimensions as closely as possible to the real eye. Variation of aqueous humour flow rate throughout the day was demonstrated with a developed circadian rhythm program. Capabilities of different eye movements were programmed and achieved with an in-house eye movement platform. A concentration probe recorded the real-time concentration monitoring of injected albumin-conjugated Alexa Fluor 488 (Alexa albumin), which displayed constant release profiles. These results demonstrate the possibility of real-time monitoring of a pharmaceutical model for preclinical testing of ocular formulations.

Design and conduct considerations for studies in patients with hepatic impairment

Despite the liver being the primary site for clearance of xenobiotics utilizing a myriad of mechanisms ranging from cytochrome P450 enzyme pathways, glucuronidation, and biliary excretion, there is a dearth of information available as to how the severity of hepatic impairment (HI) can alter drug absorption and disposition (i.e., pharmacokinetics [PK]) as well as their efficacy and safety or pharmacodynamics (PD). In general, regulatory agencies recommend conducting PK studies in subjects with HI when hepatic metabolism/excretion accounts for more than 20% of drug elimination or if the drug has a narrow therapeutic range. In this tutorial, we provide an overview of the global regulatory landscape, clinical measures for hepatic function assessment, methods to stage HI severity, and consequently the impact on labeling. In addition, we provide an in-depth practical guidance for designing and conducting clinical trials for patients with HI and on the application of modeling and simulation strategies in lieu of dedicated trials for dosing recommendations in patients with HI.

Evaluation of a rapid, generic human gestational dose model

Chemical risk assessment considers potentially susceptible populations including pregnant women and developing fetuses. Humans encounter thousands of chemicals in their environments, few of which have been fully characterized. Toxicokinetic (TK) information is needed to relate chemical exposure to potentially bioactive tissue concentrations. Observational data describing human gestational exposures are unavailable for most chemicals, but physiologically based TK (PBTK) models estimate such exposures. Development of chemical-specific PBTK models requires considerable time and resources. As an alternative, generic PBTK approaches describe a standardized physiology and characterize chemicals with a set of standard physical and TK descriptors - primarily plasma protein binding and hepatic clearance. Here we report and evaluate a generic PBTK model of a human mother and developing fetus. We used a published set of formulas describing the major anatomical and physiological changes that occur during pregnancy to augment the High-Throughput Toxicokinetics (httk) software package. We simulated the ratio of concentrations in maternal and fetal plasma and compared to literature in vivo measurements. We evaluated the model with literature in vivo time-course measurements of maternal plasma concentrations in pregnant and non-pregnant women. Finally, we prioritized chemicals measured in maternal serum based on predicted fetal brain concentrations. This new model can be used for TK simulations of 859 chemicals with existing human-specific in vitro TK data as well as any new chemicals for which such data become available. This gestational model may allow for in vitro to in vivo extrapolation of point of departure doses relevant to reproductive and developmental toxicity.

SimRFlow: An R-based workflow for automated high-throughput PBPK simulation with the Simcyp® simulator

SimRFlow is a high-throughput physiologically based pharmacokinetic (PBPK) modelling tool which uses Certara’s Simcyp® simulator. The workflow is comprised of three main modules: 1) a Data Collection module for automated curation of physicochemical (from ChEMBL and the Norman Suspect List databases) and experimental data (i.e.: clearance, plasma-protein binding, and blood-to-plasma ratio, from httk-R package databases), 2) a Simulation module which activates the Simcyp® simulator and runs Monte Carlo simulations on virtual subjects using the curated data, and 3) a Data Visualisation module for understanding the simulated compound-specific profiles and predictions. SimRFlow has three administration routes (oral, intravenous, dermal) and allows users to change some simulation parameters including the number of subjects, simulation duration, and dosing. Users are only expected to provide a file of the compounds they wish to simulate, and in return the workflow provides summary statistics, concentration-time profiles of various tissue types, and a database file (containing in-depth results) for each simulated compound. This is presented within a guided and easy-to-use R Shiny interface which provides many plotting options for the visualisation of concentration-time profiles, parameter distributions, trends between the different parameters, as well as comparison of predicted parameters across all batch-simulated compounds. The in-built R functions can be assembled in user-customised scripts which allows for the modification of the workflow for different purposes. SimRFlow proves to be a time-efficient tool for simulating a large number of compounds without any manual curation of physicochemical or experimental data necessary to run Simcyp® simulations.

In Vitro-In Vivo Extrapolation by Physiologically Based Kinetic Modeling: Experience With Three Case Studies and Lessons Learned

Physiologically based kinetic (PBK) modeling has been increasingly used since the beginning of the 21st century to support dose selection to be used in preclinical and clinical safety studies in the pharmaceutical sector. For chemical safety assessment, the use of PBK has also found interest, however, to a smaller extent, although an internationally agreed document was published already in 2010 (IPCS/WHO), but at that time, PBK modeling was based mostly on in vivo data as the example in the IPCS/WHO document indicates. Recently, the OECD has published a guidance document which set standards on how to characterize, validate, and report PBK models for regulatory purposes. In the past few years, we gained experience on using in vitro data for performing quantitative in vitro–in vivo extrapolation (QIVIVE), in which biokinetic data play a crucial role to obtain a realistic estimation of human exposure. In addition, pharmaco-/toxicodynamic aspects have been introduced into the approach. Here, three examples with different drugs/chemicals are described, in which different approaches have been applied. The lessons we learned from the exercise are as follows: 1) in vitro conditions should be considered and compared to the in vivo situation, particularly for protein binding; 2) in vitro inhibition of metabolizing enzymes by the formed metabolites should be taken into consideration; and 3) it is important to extrapolate from the in vitro measured intracellular concentration and not from the nominal concentration to the tissue/organ concentration to come up with an appropriate QIVIVE for the relevant adverse effects.

Guide to development of compound files for PBPK modeling in the Simcyp population-based simulator

The Simcyp Simulator is a software platform for population physiologically‐based pharmacokinetic (PBPK) modeling and simulation. It links in vitro data to in vivo absorption, distribution, metabolism, excretion and pharmacokinetic/pharmacodynamic outcomes to explore clinical scenarios and support drug development decisions, including regulatory submissions and drug labels. This tutorial describes the different input parameters required, as well as the considerations needed when developing a PBPK model within the Simulator, for a small molecule intended for oral administration. A case study showing the development and application of a PBPK model for ondansetron is herein used to aid the understanding of different PBPK model development concepts.

Application of Physiologically Based Pharmacokinetic Modeling in Preclinical Studies: A Feasible Strategy to Practice the Principles of 3Rs

Pharmacokinetic characterization plays a vital role in drug discovery and development. Although involving numerous laboratory animals with error-prone, labor-intensive, and time-consuming procedures, pharmacokinetic profiling is still irreplaceable in preclinical studies. With physiologically based pharmacokinetic (PBPK) modeling, the in vivo profiles of drug absorption, distribution, metabolism, and excretion can be predicted. To evaluate the application of such an approach in preclinical investigations, the plasma pharmacokinetic profiles of seven commonly used probe substrates of microsomal enzymes, including phenacetin, tolbutamide, omeprazole, metoprolol, chlorzoxazone, nifedipine, and baicalein, were predicted in rats using bottom-up PBPK models built with in vitro data alone. The prediction's reliability was assessed by comparison with in vivo pharmacokinetic data reported in the literature. The overall predicted accuracy of PBPK models was good with most fold errors within 2, and the coefficient of determination (R2) between the predicted concentration data and the observed ones was more than 0.8. Moreover, most of the observation dots were within the prediction span of the sensitivity analysis. We conclude that PBPK modeling with acceptable accuracy may be incorporated into preclinical studies to refine in vivo investigations, and PBPK modeling is a feasible strategy to practice the principles of 3Rs.

IVIVE: Facilitating the Use of In Vitro Toxicity Data in Risk Assessment and Decision Making

During the past few decades, the science of toxicology has been undergoing a transformation from observational to predictive science. New approach methodologies (NAMs), including in vitro assays, in silico models, read-across, and in vitro to in vivo extrapolation (IVIVE), are being developed to reduce, refine, or replace whole animal testing, encouraging the judicious use of time and resources. Some of these methods have advanced past the exploratory research stage and are beginning to gain acceptance for the risk assessment of chemicals. A review of the recent literature reveals a burst of IVIVE publications over the past decade. In this review, we propose operational definitions for IVIVE, present literature examples for several common toxicity endpoints, and highlight their implications in decision-making processes across various federal agencies, as well as international organizations, including those in the European Union (EU). The current challenges and future needs are also summarized for IVIVE. In addition to refining and reducing the number of animals in traditional toxicity testing protocols and being used for prioritizing chemical testing, the goal to use IVIVE to facilitate the replacement of animal models can be achieved through their continued evolution and development, including a strategic plan to qualify IVIVE methods for regulatory acceptance.

Integration of Engineered Delivery with the Pharmacokinetics of Medical Candidates via Physiology-Based Pharmacokinetics

Physiologically based pharmacokinetic (PBPK) modeling is a mechanistic computational model that can be used to predict a drug product's ADME (absorption, distribution, metabolism, and excretion) and pharmacokinetics (PK). In recent years, PBPK modeling and simulation has been used increasingly to address many biopharmaceutics and clinical pharmacology questions, such as the effect of formulations, intrinsic factors (age, organ dysfunction, etc.), and extrinsic factors (comedications, food) on the PK of an investigational drug product. In this chapter, we will briefly introduce various PBPK models for ADME prediction and general procedures for PBPK modeling and simulations. The readers are encouraged to read updated literature on new applications of PBPK modeling and simulation which is still an emerging area in pharmaceutical development.

Opportunities and challenges of physiologically based pharmacokinetic modeling in drug delivery

Physiologically based pharmacokinetic (PBPK) modeling is an important in silico tool to bridge drug properties and in vivo PK behaviors during drug development. Over the recent decade, the PBPK method has been largely applied to drug delivery systems (DDS), including oral, inhaled, transdermal, ophthalmic, and complex injectable products. The related therapeutic agents have included small-molecule drugs, therapeutic proteins, nucleic acids, and even cells. Simulation results have provided important insights into PK behaviors of new dosage forms, which strongly support drug regulation. In this review, we comprehensively summarize recent progress in PBPK applications in drug delivery, which shows large opportunities for facilitating drug development. In addition, we discuss the challenges of applying this methodology from a practical viewpoint.

Cytochrome P450 isoforms contribution, plasma protein binding, toxicokinetics of enniatin A in rats and in vivo clearance prediction in humans

Emerging mycotoxins, such as enniatin A (ENNA), are becoming a worldwide concern owing to their presence in different types of food and feed. However, comprehensive toxicokinetic data that links intake, exposure and toxicological effects of ENNA has not been elucidated yet. Therefore, the present study investigated the in vitro (rat and human) and in vivo (rat) toxicokinetic properties of ENNA. Towards this, an easily applicable and sensitive bioanalytical method was developed and validated for the estimation of ENNA in rat plasma. ENNA exhibited high plasma protein binding (99%), high hepatic clearance and mainly underwent metabolism via CYP3A4 (74%). The in-house predicted hepatic clearance (54 mL/min/kg) and observed in vivo rat clearance (55 mL/min/kg) were comparable. The predicted in vivo human hepatic clearance was 18 mL/min/kg. ENNA underwent slow absorption (T_max = 4 h) and rapid elimination following oral administration to rats. The absolute oral bioavailability was 47%. The toxicokinetic findings for ENNA from this study will help in designing and interpreting toxicological studies in rats. Besides, these findings could be used in physiologically based toxicokinetic (PBTK) model development for exposure predictions and risk assessment for ENNA in humans.

Prediction of Drug-Drug Interaction Between Dabrafenib and Irinotecan via UGT1A1-Mediated Glucuronidation

BACKGROUND

Dabrafenib and irinotecan are two drugs that can be utilized to treat melanoma. A previous in vivo study has shown that dabrafenib enhances the antitumor activity of irinotecan in a xenograft model with unclear mechanism.

OBJECTIVES

This study aims to investigate the inhibition of dabrafenib on SN-38 (the active metabolite of irinotecan) glucuronidation, trying to elucidate the possible mechanism underlying the synergistic effect and to provide a basis for further development and optimization of this combination in clinical research.

METHODS

Recombinant human uridine diphosphate glucuronosyltransferase 1A1 (UGT1A1) and human liver microsomes (HLMs) were employed to catalyze the glucuronidation of SN-38 in vitro. Inhibition kinetic analysis and quantitative prediction study were combined to predict drug-drug interaction (DDI) potential in vivo.

RESULTS

Dabrafenib noncompetitively inhibited SN-38 glucuronidation in pooled HLMs and recombinant UGT1A1 with unbound inhibitor constant (K_i,u) values of 12.43 ± 0.28 and 3.89 ± 0.40 μM, respectively. Based on the in vitro K_i,u value and estimation of kinetic parameters, dabrafenib administered at 150 mg twice daily may result in about a 1-2% increase in the area under the curve (AUC) of SN-38 in vivo. However, the ratios of intra-enterocyte concentration of dabrafenib to K_i,u ([I]_gut/K_i,u) are 2.73 and 8.72 in HLMs and recombinant UGT1A1, respectively, indicating a high risk of intestinal DDI when dabrafenib was used in combination with irinotecan.

CONCLUSION

Dabrafenib is a potent noncompetitive inhibitor of UGT1A1 and may bring potential risk of DDI when combined with irinotecan.

Pharmacokinetics and Disposition of Heparin-Binding Growth Factor Midkine Antisense Oligonucleotide Nanoliposomes in Experimental Animal Species and Prediction of Human Pharmacokinetics Using a Physiologically Based Pharmacokinetic Model

Encapsulating the antisense oligonucleotide drug MK-ASODN with nanoliposomes greatly improved its potency and targeting to the heparin-binding growth factor midkine. The disposition and pharmacokinetic (PK) parameters of MK-ASODN nanoliposomes were studied in monkeys and rats, and the human PK parameters were predicted based on preclinical data using a physiologically based pharmacokinetic (PBPK) model. Following intravenous injection, the drug plasma concentration rapidly declined in a multiexponential manner, and the drug was rapidly transferred to tissues from the circulation. The terminal t_1/2 in plasma was clearly longer than that of the unmodified antisense nucleic acid drug. According to the AUC,MK-ASODN nanoliposomes were mainly distributed in the kidney, spleen, and liver. . MK-ASODN nanoliposomes were highly plasma protein bound, limiting their urinary excretion. Very little MK-ASODN nanoliposomes were detected in urine or feces. The plasma disposition of MK-ASODN nanoliposomes appeared nonlinear over the studied dose range of 11.5–46 mg kg⁻¹. The monkey PBPK model of MK-ASODN nanoliposomes was well established and successfully extrapolated to predict MK-ASODN nanoliposome PK in humans. These disposition and PK data support further development in phase I clinical studies.

Analysis of inhibition kinetics of three beverage ingredients, bergamottin, dihydroxybergamottin and resveratrol, on CYP2C9 activity

Some grapefruit juice (GFJ) ingredients and resveratrol, a fruit-derived phytoalexin, are known to inhibit cytochrome P450 (CYP) 2C9. However, their inhibition modes and detailed inhibition kinetics remain undetermined. This study aimed to investigate the inhibitory effects of two GFJ ingredients, bergamottin (BG) and dihydroxybergamottin (DHB), and resveratrol on CYP2C9 activity in vitro. DHB inhibited CYP2C9 activity, as assessed by warfarin 7-hydroxylation, in a preincubation time-dependent manner (i.e., mechanism-based inhibition; MBI), in the same manner as CYP2C19 and CYP3A4. The maximal inactivation rate (k_inact,max) was 0.0638 min^-1 and 0.12- and 0.26-fold of that for CYP2C19 and CYP3A4, respectively. BG showed both MBI and time-independent competitive inhibition. Resveratrol showed non-competitive inhibition with an inhibition constant (K_i) of 3.64 μM. Unlike the inhibition of CYP2C19 and CYP3A4, resveratrol did not induce MBI. These findings are important for estimating the risk of drug interactions between CYP2C9 substrates and some beverages. (146 words).

A Quantitative Approach to the Prediction of Drug-Drug Interactions Mediated by Cytochrome P450 2C8 Inhibition

BACKGROUND

Ohno and Colleagues proposed an approach for predicting drug-drug interactions (DDIs) mediated by cytochrome P450 (CYP) 3A4 based on the use of the ratio of the inhibited to non-inhibited area under the plasma concentration time curve (AUC) of substrates to estimate the fraction of the dose metabolized via CYP3A4 (contribution ratio, CR) and the in vivo inhibitory potency of a perpetrator (inhibition ratio, IR). This study evaluated the performance of this approach on DDIs mediated by CYP2C8 inhibitors.

RESEARCH DESIGN AND METHODS

Initial estimates of CR and IR of CYP2C8 substrates and inhibitors were calculated for 33 DDI in vivo studies. The approach was externally validated with 17 additional studies. Bayesian orthogonal regression was used to refine the estimates of the parameters. Assessment of prediction success was conducted by plotting observed versus predicted AUC ratios.

RESULTS

Final estimates of CRs and IRs were obtained for 19 CYP2C8 substrates and 23 inhibitors, respectively. The method demonstrated good predictive capacity, with only two values outside of the prespecified limits.

CONCLUSIONS

The approach may help to adapt dose regimens for CYP2C8 substrates when given in combination with CYP2C8 inhibitors and to map the potential DDIs of new molecular entities.

Identification of glutathione-S-transferase m19 and m34 among responsive GST genes against 1-chloro-2,4-dinitrobenzene treatment of Tetrahymena thermophila

Industrial xenobiotic pollutants have toxic effects on diverse organisms in their natural environments. This study aims to identify the Glutathione-S-transferases (GST) from Tetrahymena thermophila that are highly responsive to the treatment of synthetic substrate 1-chloro-2,4-dinitrobenzene (CDNB). The LD50 value of CDNB was determined by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) test as 0.079 mM at 9 h exposure. The glutathione affinity-purified 22 kDa and 23 kDa GSTs from CDNB-treated cells were identified as GSTm19 and GSTm34 with 2D-gel electrophoresis coupled MALDI-Tof MS/MS analysis. The specific activitiy of the affinity-purified GSTs was upregulated upon the treatment of 0.072 mM CDNB with the decreased cell survival. GSTm19 and GSTm34 had also upregulated the mRNA expression under the highest dose treatment. The high cell survival and elevated total GST enzyme activity at 9 h under CDNB doses could be the result of both transcriptional upregulations as well as post-translational modifications. As a result, the cell survival of Tetrahymena thermophila was significantly affected by CDNB exposure in a concentration-dependent manner with the effect of low-dose stimulation and high-dose inhibition.

Are body surface area based estimates of liver volume applicable to children with overweight or obesity? An in vivo validation study

The liver is the primary organ responsible for clearing most drugs from the body and thus determines systemic drug concentrations over time. Drug clearance by the liver appears to be directly related to organ size. In children, organ size changes as children age and grow. Liver volume has been correlated with body surface area (BSA) in healthy children and adults and has been estimated by functions of BSA. However, these relationships were derived from "typical" populations and it is unknown whether they extend to estimations of liver volumes for population "outliers," such as children with overweight or obesity, who today represent one-third of the pediatric population. Using computerized tomography or magnetic resonance imaging, this study measured liver volumes in 99 children (2-21 years) with normal weight, overweight, or obesity and compared organ measurements with estimates calculated using an established liver volume equation. A previously developed equation relating BSA to liver volume adequately estimates liver volumes in children, regardless of weight status.

Novel Bruton's Tyrosine Kinase inhibitor remibrutinib: Drug-drug interaction potential as a victim of CYP3A4 inhibitors based on clinical data and PBPK modeling

Remibrutinib, a novel oral Bruton’s Tyrosine Kinase inhibitor (BTKi) is highly selective for BTK, potentially mitigating the side effects of other BTKis. Enzyme phenotyping identified CYP3A4 to be the predominant elimination pathway of remibrutinib. The impact of concomitant treatment with CYP3A4 inhibitors, grapefruit juice and ritonavir (RTV), was investigated in this study in combination with an intravenous microtracer approach. Pharmacokinetic (PK) parameters, including the fraction absorbed, the fractions escaping intestinal and hepatic first‐pass metabolism, the absolute bioavailability, systemic clearance, volume of distribution at steady‐state, and the fraction metabolized via CYP3A4 were evaluated. Oral remibrutinib exposure increased in the presence of RTV 4.27‐fold, suggesting that remibrutinib is not a sensitive CYP3A4 substrate. The rich PK dataset supported the building of a robust physiologically‐based pharmacokinetic (PBPK) model, which well‐described the therapeutic dose range of 25–100 mg. Simulations of untested scenarios revealed an absence of drug‐drug interaction (DDI) risk between remibrutinib and the weak CYP3A4 inhibitor fluvoxamine (area under the concentration‐time curve ratio [AUCR] <1.25), and a moderate effect with the CYP3A4 inhibitor erythromycin (AUCR: 2.71). Predictions with the moderate and strong CYP3A4 inducers efavirenz and rifampicin, suggested a distinct remibrutinib exposure decrease of 64% and 89%. Oral bioavailability of remibrutinib was 34%. The inclusion of an intravenous microtracer allowed the determination of all relevant remibrutinib PK parameters, which facilitated construction of the PBPK model. This will provide guidance on the selection or restriction of comedications and prediction of DDI risks.

Variability in Human In Vitro Enzyme Kinetics

There are many factors which are known to cause variability in human in vitro enzyme kinetic data. Factors such as the source of enzyme and how it was prepared, the genetics and background of the donor, how the in vitro studies are designed, and how the data are analyzed contribute to variability in the resulting kinetic parameters. It is important to consider not only the factors which cause variability within an experiment, such as selection of a probe substrate, but also those that cause variability when comparing kinetic data across studies and laboratories. For example, the artificial nature of the microsomal lipid membrane and microenvironment in some recombinantly expressed enzymes, relative to those found in native tissue microsomes, has been shown to influence enzyme activity and thus can be a source of variability when comparing across the two different systems. All of these factors, and several others, are discussed in detail in the chapter below. In addition, approaches which can be used to visualize the uncertainty arising from the use of enzyme kinetic data within the context of predicting human pharmacokinetics are discussed.

High-throughput PBTK models for in vitro to in vivo extrapolation

INTRODUCTION

Toxicity data are unavailable for many thousands of chemicals in commerce and the environment. Therefore, risk assessors need to rapidly screen these chemicals for potential risk to public health. High-throughput screening (HTS) for in vitro bioactivity, when used with high-throughput toxicokinetic (HTTK) data and models, allows characterization of these thousands of chemicals.

AREAS COVERED

This review covers generic physiologically based toxicokinetic (PBTK) models and high-throughput PBTK modeling for in vitro-in vivo extrapolation (IVIVE) of HTS data. We focus on 'httk', a public, open-source set of computational modeling tools and in vitro toxicokinetic (TK) data.

EXPERT OPINION

HTTK benefits chemical risk assessors with its ability to support rapid chemical screening/prioritization, perform IVIVE, and provide provisional TK modeling for large numbers of chemicals using only limited chemical-specific data. Although generic TK model design can increase prediction uncertainty, these models provide offsetting benefits by increasing model implementation accuracy. Also, public distribution of the models and data enhances reproducibility. For the httk package, the modular and open-source design can enable the tool to be used and continuously improved by a broad user community in support of the critical need for high-throughput chemical prioritization and rapid dose estimation to facilitate rapid hazard assessments.

Development of Physiologically Based Pharmacokinetic Model for Orally Administered Fexuprazan in Humans

Fexuprazan is a new drug candidate in the potassium-competitive acid blocker (P-CAB) family. As proton pump inhibitors (PPIs), P-CABs inhibit gastric acid secretion and can be used to treat gastric acid-related disorders such as gastroesophageal reflux disease (GERD). Physiologically based pharmacokinetic (PBPK) models predict drug interactions as pharmacokinetic profiles in biological matrices can be mechanistically simulated. Here, we propose an optimized and validated PBPK model for fexuprazan by integrating in vitro, in vivo, and in silico data. The extent of fexuprazan tissue distribution in humans was predicted using tissue-to-plasma partition coefficients in rats and the allometric relationships of fexuprazan distribution volumes (V_SS) among preclinical species. Urinary fexuprazan excretion was minimal (0.29-2.02%), and this drug was eliminated primarily by the liver and metabolite formation. The fraction absorbed (Fa) of 0.761, estimated from the PBPK modeling, was consistent with the physicochemical properties of fexuprazan, including its in vitro solubility and permeability. The predicted oral bioavailability of fexuprazan (38.4-38.6%) was within the range of the preclinical datasets. The C_max, AUC_last, and time-concentration profiles predicted by the PBPK model established by the learning set were accurately predicted for the validation sets.

A latent variable approach to account for correlated inputs in global sensitivity analysis

In drug development decision-making is often supported through model-based methods, such as physiologically-based pharmacokinetics (PBPK). Global sensitivity analysis (GSA) is gaining use for quality assessment of model-informed inference. However, the inclusion and interpretation of correlated factors in GSA has proven an issue. Here we developed and evaluated a latent variable approach for dealing with correlated factors in GSA. An approach was developed that describes the correlation between two model inputs through the causal relationship of three independent factors: the latent variable and the unique variances of the two correlated parameters. The latent variable approach was applied to a set of algebraic models and a case from PBPK. Then, this method was compared to Sobol’s GSA assuming no correlations, Sobol’s GSA with groups and the Kucherenko approach. For the latent variable approach, GSA was performed with Sobol’s method. By using the latent variable approach, it is possible to devise a unique and easy interpretation of the sensitivity indices while maintaining the correlation between the factors. Compared methods either consider the parameters independent, group the dependent variables into one unique factor or present difficulties in the interpretation of the sensitivity indices. In situations where GSA is called upon to support model-informed decision-making, the latent variable approach offers a practical method, in terms of ease of implementation and interpretability, for applying GSA to models with correlated inputs that does not violate the independence assumption. Prerequisites and limitations of the approach are discussed.

Comparison of the drug-drug interactions potential of ibrutinib and acalabrutinib via inhibition of UDP-glucuronosyltransferase

Ibrutinib and acalabrutinib are two Bruton's tyrosine kinase (BTK) inhibitors which have gained Food and Drug Administration (FDA) approval for the treatment of various B cell malignancies. Herein, we investigated the effects of the two drugs on UDP-glucuronosyltransferase (UGT) activities to evaluate their potential risk for drug-drug interactions (DDIs) via UGT inhibition. Our data indicated that ibrutinib exerted broad inhibition on most of UGTs, including a potent competitive inhibition against UGT1A1 with a K_i value of 0.90 ± 0.03 μM, a noncompetitive inhibition against UGT1A3 and UGT1A7 with K_i values of 0.88 ± 0.03 μM and 2.52 ± 0.23 μM, respectively, while acalabrutinib only exhibited weak UGT inhibition towards all tested UGT isoforms. DDI risk prediction suggested that the inhibition against UGT1A1 and UGT1A3 by ibrutinib might bring a potential DDIs risk, while acalabrutinib was unlikely to trigger clinically significant UGT-mediated DDIs due to its weak effects. Our study raises an alarm bell about potential DDI risk associated with ibrutinib, however, the extrapolation from in vitro data to in vivo drug interactions should be taken with caution, and additional systemic study is needed.

Design and conduct considerations for studies in patients with impaired renal function

An impaired renal function, including acute and chronic kidney disease and end‐stage renal disease, can be the result of aging, certain disease conditions, the use of some medications, or as a result of smoking. In patients with renal impairment (RI), the pharmacokinetics (PKs) of drugs or drug metabolites may change and result in increased safety risks or decreased efficacy. In order to make specific dose recommendations in the label of drugs for patients with RI, a clinical trial may have to be conducted or, when not feasible, modeling and simulations approaches, such as population PK modeling or physiologically‐based PK modelling may be applied. This tutorial aims to provide an overview of the global regulatory landscape and a practical guidance for successfully designing and conducting clinical RI trials or, alternatively, on applying modeling and simulation tools to come to a dose recommendation for patients with RI in the most efficient manner.

Current Evidence, Challenges, and Opportunities of Physiologically Based Pharmacokinetic Models of Atorvastatin for Decision Making

Atorvastatin (ATS) is the gold-standard treatment worldwide for the management of hypercholesterolemia and prevention of cardiovascular diseases associated with dyslipidemia. Physiologically based pharmacokinetic (PBPK) models have been positioned as a valuable tool for the characterization of complex pharmacokinetic (PK) processes and its extrapolation in special sub-groups of the population, leading to regulatory recognition. Several PBPK models of ATS have been published in the recent years, addressing different aspects of the PK properties of ATS. Therefore, the aims of this review are (i) to summarize the physicochemical and pharmacokinetic characteristics involved in the time-course of ATS, and (ii) to evaluate the major highlights and limitations of the PBPK models of ATS published so far. The PBPK models incorporate common elements related to the physicochemical aspects of ATS. However, there are important differences in relation to the analyte evaluated, the type and effect of transporters and metabolic enzymes, and the permeability value used. Additionally, this review identifies major processes (lactonization, P-gp contribution, ATS-Ca solubility, simultaneous management of multiple analytes, and experimental evidence in the target population), which would enhance the PBPK model prediction to serve as a valid tool for ATS dose optimization.

Mutation of the Cytochrome P450 CYP360A8 Gene Increases Sensitivity to Paraquat in Daphnia magna

The freshwater crustacean Daphnia magna has traditionally been a model for ecotoxicological studies owing to its sensitivity to many xenobiotics. Because it is used in many toxicity assessments, its detoxification mechanism for xenobiotics is important and requires further study. However, studies related to detoxification genes are limited to transcriptomic profiling, and there are no D. magna mutants for use in the understanding of xenobiotic metabolism in vivo. We report the generation of a D. magna CYP360A8 mutant-the gene is a cytochrome P450 (CYP) clan 3 gene. Based on RNA sequencing of adult D. magna, we found that CYP360A8 has the highest expression level among all CYP genes. At ovarian maturation, its expression level is up-regulated 6-fold compared to the juvenile stages and is maintained thereafter. Using the CRISPR/CRISPR-associated 9 (Cas9) system, we disrupted CYP360A8 by coinjecting CYP360A8-targeting guide RNA and Cas9 proteins into D. magna eggs and established one monoallelic CYP360A8 mutant line. This CYP360A8 mutant had a higher sensitivity to the herbicide paraquat compared to the wild type. We confirmed the up-regulation of CYP360A8 by paraquat. The results demonstrate the role of CYP360A8 in paraquat detoxification. The present study establishes a CYP mutant of D. magna, and this strategy can be a basic platform to document a range of CYP gene-xenobiotic relationships in this species. Environ Toxicol Chem 2021;40:1279-1288. © 2020 SETAC.

Development and evaluation of physiologically based pharmacokinetic drug-disease models for predicting captopril pharmacokinetics in chronic diseases

The advancement in the processing speeds of computing machines has facilitated the development of complex physiologically based pharmacokinetic (PBPK) models. These PBPK models can incorporate disease-specific data and could be used to predict pharmacokinetics (PK) of administered drugs in different chronic conditions. The present study aimed to develop and evaluate PBPK drug-disease models for captopril after incorporating relevant pathophysiological changes occurring in adult chronic kidney disease (CKD) and chronic heart failure (CHF) populations. The population-based PBPK simulator Simcyp was used as a modeling and simulation platform. The visual predictive checks and mean observed/predicted ratios (ratio(Obs/pred)) of the PK parameters were used for model evaluation. The developed disease models were successful in predicting captopril PK in all three stages of CKD (mild, moderate, and severe) and CHF, as the observed and predicted PK profiles and the ratio(obs/pred) for the PK parameters were in close agreement. The developed captopril PBPK models can assist in tailoring captopril dosages in patients with different disease severity (CKD and CHF).

In Vivo-to-In Vitro Extrapolation of Transporter-Mediated Renal Clearance: Relative Expression Factor Versus Relative Activity Factor Approach

About 30% of approved drugs are cleared predominantly by renal clearance (CL_r). Of these, many are secreted by transporters. For these drugs, in vitro-to-in vivo extrapolation of transporter-mediated renal secretory clearance (CL_sec,plasma) is important to prospectively predict their renal clearance and to assess the impact of drug-drug interactions and pharmacogenetics on their pharmacokinetics. Here we compared the ability of the relative expression factor (REF) and the relative activity factor (RAF) approaches to quantitatively predict the in vivo CL_sec,plasma of 26 organic anion transporter (OAT) substrates assuming that OAT-mediated uptake is the rate-determining step in the CL_sec,plasma of the drugs. The REF approach requires protein quantification of each transporter in the tissue (e.g., kidney) and transporter-expressing cells, whereas the RAF approach requires the use of a transporter-selective probe substrate (both in vitro and in vivo) for each transporter of interest. For the REF approach, 50% and 69% of the CL_sec,plasma predictions were within 2- and 3-fold of the observed values, respectively; the corresponding values for the RAF approach were 65% and 81%. We found no significant difference between the two approaches in their predictive capability (as measured by accuracy and bias) of the CL_sec,plasma or CL_r of OAT drugs. We recommend that the REF and RAF approaches can be used interchangeably to predict OAT-mediated CL_sec,plasma Further research is warranted to evaluate the ability of the REF or RAF approach to predict CL_sec,plasma of drugs when uptake is not the rate-determining step. SIGNIFICANCE STATEMENT: This is the first direct comparison of the relative expression factor (REF) and relative activity factor (RAF) approaches to predict transporter-mediated renal clearance (CL_r). The RAF, but not REF, approach requires transporter-selective probes and that the basolateral uptake is the rate-determining step in the CL_r of drugs. Given that there is no difference in predictive capability of the REF and RAF approach for organic anion transporter-mediated CL_r, the REF approach should be explored further to assess its ability to predict CL_r when basolateral uptake is not the sole rate-determining step.

Overcoming the shortcomings of the extended-clearance concept: a framework for developing a physiologically-based pharmacokinetic (PBPK) model to select drug candidates involving transporter-mediated clearance

Introduction:Human pharmacokinetic (PK) prediction can be a significant challenge to drug candidates undergoing transporter-mediated clearance, when only animal data and in vitro human parameters are available in the drug discovery stage.Areas covered:The extended clearance concept (ECC) that incorporates the processes of hepatic uptake, passive diffusion, metabolism and biliary secretion has been adapted to determine the rate-determining process of hepatic clearance and drug-drug interactions (DDIs). However, since the ECC is derived from the well-stirred model and does not consider the liver as a drug distribution organ to reflect the time-dependent variation of drug concentrations between the liver and plasma, it can be misused for compound selection in drug discovery.Expert opinion:The PBPK model consists of a set of differential equations of drug mass balance, and can overcome the shortcomings of the ECC in predicting human PK. The predictability, relevance and reliability of the model and the scaling factors for IVIVE must be validated using either the measured liver concentrations or DDI data with known transporter inhibitors, or both, in monkeys. A human PBPK model that incorporates in vitro human data and SFs obtained from the validated monkey PBPK model can be used for compound selection in the drug discovery phase.

History and Future Perspectives on the Discipline of Quantitative Systems Pharmacology Modeling and Its Applications

Mathematical biology and pharmacology models have a long and rich history in the fields of medicine and physiology, impacting our understanding of disease mechanisms and the development of novel therapeutics. With an increased focus on the pharmacology application of system models and the advances in data science spanning mechanistic and empirical approaches, there is a significant opportunity and promise to leverage these advancements to enhance the development and application of the systems pharmacology field. In this paper, we will review milestones in the evolution of mathematical biology and pharmacology models, highlight some of the gaps and challenges in developing and applying systems pharmacology models, and provide a vision for an integrated strategy that leverages advances in adjacent fields to overcome these challenges.

Measurement of total liver blood flow in intact anesthetized rats using ultrasound imaging

This short report describes the measurement of total liver blood flow in commonly used laboratory rats using the relatively non-invasive approach of ultrasound imaging. A total of 29 rats (n = 26 Wistar-Han, n = 3 Sprague-Dawley) were imaged and both male and female rats were included. The mean (SD) total liver blood flow of all animals combined was 33.3 ± 7.8 mL/min, or 104.3 ± 17.1 mL/min/kg when normalized to observed body weight at the time of imaging. There was a trend for higher unnormalized total liver blood flow as body weight increased and the female rats had, in general, the lowest body weight and total liver blood flow of the animals studied. There were no major differences in total liver blood flow between the small number of Sprague-Dawley rats used in the study and the larger Wistar-Han group. Further research would be needed to accurately characterize any subtle differences in body weight between rats of different strains, sexes, and body weight.

The Way of the Future: Personalizing Treatment Plans Through Technology

Advances in tissue analysis methods, image analysis, high-throughput molecular profiling, and computational tools increasingly allow us to capture and quantify patient-to patient variations that impact cancer risk, prognosis, and treatment response. Statistical models that integrate patient-specific information from multiple sources (e.g., family history, demographics, germline variants, imaging features) can provide individualized cancer risk predictions that can guide screening and prevention strategies. The precision, quality, and standardization of diagnostic imaging are improving through computer-aided solutions, and multigene prognostic and predictive tests improved predictions of prognosis and treatment response in various cancer types. A common theme across many of these advances is that individually moderately informative variables are combined into more accurate multivariable prediction models. Advances in machine learning and the availability of large data sets fuel rapid progress in this field. Molecular dissection of the cancer genome has become a reality in the clinic, and molecular target profiling is now routinely used to select patients for various targeted therapies. These technology-driven increasingly more precise and quantitative estimates of benefit versus risk from a given intervention empower patients and physicians to tailor treatment strategies that match patient values and expectations.

Prediction of pharmacokinetic values of two various dosages of caffeine in premature neonates with apnea

OBJECTIVES

Despite extensive caffeine use in preterm infants, the pharmacokinetics (PKs) data are limited because of the studies are complicated to do in these patients. This research was investigated the PK profile of two various dosages of caffeine in premature neonates.

MATERIALS AND METHODS

The PK values of caffeine in premature neonates with Apnea were predicted by using all of computer-based simulation (Simcyp®), population-based PK, and modeling (P-Pharm®). We assayed the plasma levels of caffeine in two groups. The information was analyzed utilizing nonlinear mixed-effects modeling approach. The PK parameters were assessed simulating virtual clinical considers with subjects got 20 mg. kg-1 of caffeine in both groups, which was followed by a 5 mg. kg-1 once daily in Group 1 or 2.5 mg. kg-1 twice daily in Group 2. All statistical analysis was executed utilizing SSPS issue 19 and a P value of 0.05 was chosen significance.

RESULTS

In the present study, the means CL, volume of distribution, and T1/2 of caffeine in preterm infants were 0.0476 L. h-1, 1.1081 L, 16.2284 h, respectively. Whereas our simulated means by Simcyp were 0.090 L. h-1, 1.841 L, and 14.653 h in Group 1 and 16.223 h in Group 2, respectively.

CONCLUSIONS

There was overall good agreement between predicted and measured PK values in our study. This study provides an initial demonstration of Simcyp simulation advantage on anticipating of PK parameters.

Inhibition of human UDP-glucuronosyltransferase enzyme by belinostat: Implications for drug-drug interactions

Belinostat is a pan-histone deacetylase (HDAC) inhibitor which recently approved for the treatment of relapsed/refractory Peripheral T-cell lymphomas (PTCL). To assess drug-drug interactions (DDIs) potential of belinostat via inhibition of UDP-glucuronosyltransferases (UGTs), the effects of belinostat on UGTs activities were investigated using the non-selective probe substrate 4-methylumbelliferone (4-MU) and trifluoperazine (TFP) by UPLC-MS/MS. Belinostat exhibited a wide range of inhibition against UGTs activities, particularly a potent non-competitive inhibition against UGT1A3, and weak inhibition against UGT1A1, 1A7, 1A8, 2B4 and 2B7. Further, in vitro-in vivo extrapolation (IVIVE) approaches were used to predict the risk of DDI arising from inhibition of UGTs. Our data indicate that the intravenous infusion of belinostat at clinical available dose can contribute a significant increase to the AUC of co-administrated drugs primarily cleared by UGT1A3 or UGT1A1, which will result in potential DDIs. In contrast, oral administrated belinostat is unlikely to cause significant DDIs through inhibition of glucuronidation.

Dose Optimization in Kidney Disease: Opportunities for PBPK Modeling and Simulation

Kidney disease affects pharmacokinetic (PK) profiles of not only renally cleared drugs but also nonrenally cleared drugs. The impact of kidney disease on drug disposition has not been fully elucidated, but describing the extent of such impact is essential for conducting dose optimization in kidney disease. Accurate evaluation of kidney function has been a clinical interest for dose optimization, and more scientists pay attention and conduct research for clarifying the role of drug transporters, metabolic enzymes, and their interplay in drug disposition as kidney disease progresses. Physiologically based pharmacokinetic (PBPK) modeling and simulation can provide valuable insights for dose optimization in kidney disease. It is a powerful tool to integrate discrete knowledge from preclinical and clinical research and mechanistically investigate system- and drug-dependent factors that may contribute to the changes in PK profiles. PBPK-based prediction of drug exposures may be used a priori to adjust dosing regimens and thereby minimize the likelihood of drug-related toxicity. With real-time clinical studies, parameter estimation may be performed with PBPK approaches that can facilitate identification of sources of interindividual variability. PBPK modeling may also facilitate biomarker research that aids dose optimization in kidney disease. U.S. Food and Drug Administration guidances related to conduction of PK studies in kidney impairment and PBPK documentation provide the foundation for facilitating model-based dose-finding research in kidney disease.