Prediction of human pharmacokinetics using physiologically based modeling: a retrospective analysis of 26 clinically tested drugs

A physiologically based pharmacokinetic model of voriconazole in human CNS-Integrating time-dependent inhibition of CYP3A4, genetic polymorphisms of CYP2C19 and possible transporter mechanisms

OBJECTIVES

Voriconazole is a classical antifungal drug that is often used to treat CNS fungal infections due to its permeability through the BBB. However, its clinical use remains challenging because of its narrow therapeutic window and wide inter-individual variability. In this study, we proposed an optimised and validated PBPK model by integrating in vitro, in vivo and clinical data to simulate the distribution and PK process of voriconazole in the CNS, providing guidance for clinical individualised treatment.

METHODS

The model structure was optimised and tissue-to-plasma partition coefficients were obtained through animal experiments. Using the allometric relationships, the distribution of voriconazole in the human CNS was predicted. The model integrated factors affecting inter-individual variation and drug interactions of voriconazole-polymorphisms in the CYP2C19 gene and auto-inhibition and then was validated using real clinical data.

RESULTS

The overall AFE value showing model predicted differences was 1.1420 in the healthy population; and in the first prediction of plasma and CSF in actual clinical patients, 89.5% of the values were within the 2-fold error interval, indicating good predictive performance of the model. The bioavailability of voriconazole varied at different doses (39%-86%), and the optimised model conformed to this pattern (46%-83%).

CONCLUSIONS

Combined with the relevant pharmacodynamic indexes, the PBPK model provides a feasible way for precise medication in patients with CNS infection and improve the treatment effect and prognosis.

Striated muscle: an inadequate soil for cancers

Many organs of the body are susceptible to cancer development. However, striated muscles-which include skeletal and cardiac muscles-are rarely the sites of primary cancers. Most deaths from cancer arise due to complications associated with the development of secondary metastatic tumours, for which there are few effective therapies. However, as with primary cancers, the establishment of metastatic tumours in striated muscle accounts for a disproportionately small fraction of secondary tumours, relative to the proportion of body composition. Examining why primary and metastatic cancers are comparatively rare in striated muscle presents an opportunity to better understand mechanisms that can influence cancer cell biology. To gain insights into the incidence and distribution of muscle metastases, this review presents a definitive summary of the 210 case studies of metastasis in muscle published since 2010. To examine why metastases rarely form in muscles, this review considers the mechanisms currently proposed to render muscle an inhospitable environment for cancers. The "seed and soil" hypothesis proposes that tissues' differences in susceptibility to metastatic colonization are due to differing host microenvironments that promote or suppress metastatic growth to varying degrees. As such, the "soil" within muscle may not be conducive to cancer growth. Gaining a greater understanding of the mechanisms that underpin the resistance of muscles to cancer may provide new insights into mechanisms of tumour growth and progression, and offer opportunities to leverage insights into the development of interventions with the potential to inhibit metastasis in susceptible tissues.

A deep neural network: mechanistic hybrid model to predict pharmacokinetics in rat

An important aspect in the development of small molecules as drugs or agrochemicals is their systemic availability after intravenous and oral administration. The prediction of the systemic availability from the chemical structure of a potential candidate is highly desirable, as it allows to focus the drug or agrochemical development on compounds with a favorable kinetic profile. However, such predictions are challenging as the availability is the result of the complex interplay between molecular properties, biology and physiology and training data is rare. In this work we improve the hybrid model developed earlier (Schneckener in J Chem Inf Model 59:4893-4905, 2019). We reduce the median fold change error for the total oral exposure from 2.85 to 2.35 and for intravenous administration from 1.95 to 1.62. This is achieved by training on a larger data set, improving the neural network architecture as well as the parametrization of mechanistic model. Further, we extend our approach to predict additional endpoints and to handle different covariates, like sex and dosage form. In contrast to a pure machine learning model, our model is able to predict new end points on which it has not been trained. We demonstrate this feature by predicting the exposure over the first 24 h, while the model has only been trained on the total exposure.

A tutorial for model-based evaluation and translation of cardiovascular safety in preclinical trials

Assessment of drug-induced effects on the cardiovascular (CV) system remains a critical component of the drug discovery process enabling refinement of the therapeutic index. Predicting potential drug-related unintended CV effects in the preclinical stage is necessary for first-in-human dose selection and preclusion of adverse CV effects in the clinical stage. According to the current guidelines for small molecules, nonclinical CV safety assessment conducted via telemetry analyses should be included in the safety pharmacology core battery studies. However, the manual for quantitative evaluation of the CV safety signals in animals is available only for electrocardiogram parameters (i.e., QT interval assessment), not for hemodynamic parameters (i.e., heart rate, blood pressure, etc.). Various model-based approaches, including empirical pharmacokinetic-toxicodynamic analyses and systems pharmacology modeling could be used in the framework of telemetry data evaluation. In this tutorial, we provide a comprehensive workflow for the analysis of nonclinical CV safety on hemodynamic parameters with a sequential approach, highlight the challenges associated with the data, and propose respective solutions, complemented with a reproducible example. The work is aimed at helping researchers conduct model-based analyses of the CV safety in animals with subsequent translation of the effect to humans seamlessly and efficiently.

A New Version of the Tissue Composition-Based Model for Improving the Mechanism-Based Prediction of Volume of Distribution at Steady-State for Neutral Drugs

In-vitro models are available in the literature for predicting the volume of distribution at steady-state (Vdss) of drugs. The mechanistic model refers to the tissue composition-based model (TCM), which includes important factors that govern Vdss such as drug physiochemistry and physiological data. The recognized TCM published by Rodgers and Rowland (TCM-RR) and a subsequent adjustment made by Simulations Plus Inc. (TCM-SP) have been shown to be generally less accurate with neutral compared to ionized drugs. Therefore, improving these models for neutral drugs becomes necessary. The objective of this study was to propose a new TCM for improving the prediction of Vdss for neutral drugs. The new TCM included two modifications of the published models (i) accentuate the effect of the blood-to-plasma ratio (BPR) that should cover permeated molecules across the biomembranes, which is lacking in these models for neutral compounds, and (ii) use a different approach to estimate the binding in tissues. The new TCM was validated with a large dataset of 202 commercial and proprietary compounds including preclinical and clinical data. All scenario datasets were predicted more accurately with the TCM-New, whereas all statistical parameters indicate that the TCM-New showed significant improvements in terms of accuracy over the TCM-RR and TCM-SP. Predictions of Vdss were frequently more accurate for the TCM-new with 83% within twofold error versus only 50% for the TCM-RR. And more than 95% of the predictions were within threefold error and patient interindividual differences can be predicted with the TCM-New, greatly exceeding the accuracy of the published models. Overall, the new TCM incorporating BPR significantly improved the Vdss predictions in animals and humans for neutral drugs, and, hence, has the potential to better support the drug discovery and facilitate the first-in-human predictions.

Prediction of pharmacokinetics of an anaplastic lymphoma kinase inhibitor in rat and monkey: application of physiologically based pharmacokinetic model as an alternative tool to minimise animal studies

The pharmacokinetic (PK) and toxicokinetic profile of a drug from its preclinical evaluation helps the researcher determine whether the drug should be tested in humans based on its safety and toxicity.Preclinical studies require time and resources and are prone to error. Moreover, according to the United States Food and Drug Administration Modernisation Act 2, animal testing is no longer mandatory for new drug development, and an animal-free alternative, such as cell-based assay and computer models, can be used.Different physiologically based PK models were developed for an anaplastic lymphoma kinase inhibitor in rats and monkeys after intravenous and oral administration using its physicochemical properties and in vitro characterisation data.The developed model was validated against the in vivo data available in the literature, and the validation results were found within the acceptable limit. A parameter sensitivity analysis was performed to identify the properties of the compound influencing the PK profile.This work demonstrates the application of the physiologically based PK model to predict the PKs of a drug, which will eventually assist in reducing the number of animal studies and save time and cost of drug discovery and development.

ADME characterization and PBK model development of 3 highly protein-bound UV filters through topical application

Estimating human exposure in the safety assessment of chemicals is crucial. Physiologically based kinetic (PBK) models which combine information on exposure, physiology, and chemical properties, describing the absorption, distribution, metabolism, and excretion (ADME) processes of a chemical, can be used to calculate internal exposure metrics such as maximum concentration and area under the concentration-time curve in plasma or tissues of a test chemical in next-generation risk assessment. This article demonstrates the development of PBK models for 3 UV filters, specifically octyl methoxycinnamate, octocrylene, and 4-methylbenzylidene camphor. The models were parameterized entirely based on data obtained from in vitro and/or in silico methods in a bottom-up modeling approach and then validated based on human dermal pharmacokinetic (PK) data. The 3 UV filters are "difficult to test" in in vitro test systems due to high lipophilicity, high binding affinity for proteins, and nonspecific binding, for example, toward plastic. This research work presents critical considerations in ADME data generation, interpretation, and parameterization to assure valid PBK model development to increase confidence in using PBK modeling to help make safety decisions in the absence of human PK data. The developed PBK models of the 3 chemicals successfully simulated the plasma concentration profiles of clinical PK data following dermal application, indicating the reliability of the ADME data generated and the parameters determined. The study also provides insights and lessons learned for characterizing ADME and developing PBK models for highly lipophilic and protein-bound chemicals in the future.

Prediction of human pharmacokinetics from chemical structure: combining mechanistic modeling with machine learning

Pharmacokinetics (PK) is the result of a complex interplay between compound properties and physiology, and a detailed characterization of a molecule's PK during preclinical research is key to understanding the relationship between applied dose, exposure, and pharmacological effect. Predictions of human PK based on the chemical structure of a compound are highly desirable to avoid advancing compounds with unfavorable properties early on and to reduce animal testing, but data to train such models are scarce. To address this problem, we combine well-established physiologically based pharmacokinetic models with Deep Learning models for molecular property prediction into a hybrid model to predict PK parameters for small molecules directly from chemical structure. Our model predicts exposure after oral and intravenous administration with fold change errors of 1.87 and 1.86, respectively, in healthy subjects and 2.32 and 2.23, respectively, in patients with various diseases. Unlike pure Deep Learning models, the hybrid model can predict endpoints on which it was not trained. We validate this extrapolation capability by predicting full concentration-time profiles for compounds with published PK data. Our model enables early selection and prioritization of the most promising drug candidates, which can lead to a reduction in animal testing during drug discovery and development.

Shared learning from a physiologically based pharmacokinetic modeling strategy for human pharmacokinetics prediction through retrospective analysis of Genentech compounds

The quantitative prediction of human pharmacokinetics (PK) including the PK profile and key PK parameters are critical for early drug development decisions, successful phase I clinical trials, and the establishment of a range of doses to enable phase II clinical dose selection. Here, we describe an approach employing physiologically based pharmacokinetic (PBPK) modeling (Simcyp) to predict human PK and to validate its performance through retrospective analysis of 18 Genentech compounds for which clinical data are available. In short, physicochemical parameters and in vitro data for preclinical species were integrated using PBPK modeling to predict the in vivo PK observed in mouse, rat, dog, and cynomolgus monkey. Through this process, the in vitro to in vivo extrapolation (IVIVE) was determined and then incorporated into PBPK modeling in order to predict human PK. Overall, the prediction obtained using this PBPK-IVIVE approach captured the observed human PK profiles of the compounds from the dataset well. The predicted Cmax was within 2-fold of the observed Cmax for 94% of the compounds while the predicted area under the curve (AUC) was within 2-fold of the observed AUC for 72% of the compounds. Additionally, important IVIVE trends were revealed through this investigation, including application of scaling factors determined from preclinical IVIVE to human PK prediction for each molecule. Based upon the analysis, this PBPK-based approach now serves as a practical strategy for human PK prediction at the candidate selection stage at Genentech.

Revisiting the in-vitro and in-vivo considerations for in-silico modelling of complex injectable drug products

Complex injectable drug products (CIDPs) have often been developed to modulate the pharmacokinetics along with efficacy for therapeutic agents used for remediation of chronic disorders. The effective development of CIDPs has exhibited complex kinetics associated with multiphasic drug release from the prepared formulations. Consequently, predictability of pharmacokinetic modelling for such CIDPs has been difficult and there is need for advanced complex computational models for the establishment of accurate prediction models for in-vitro-in-vivo correlation (IVIVC). The computational modelling aims at supplementing the existing knowledge with mathematical equations to develop formulation strategies for generation of predictable and discriminatory IVIVC. Such an approach would help in reduction of the burden of effect of hidden factors on preclinical to clinical translations. Computational tools like physiologically based pharmacokinetics (PBPK) modelling have combined physicochemical and physiological properties along with IVIVC characteristics of clinically used formulations. Such techniques have helped in prediction and understanding of variability in pharmacodynamic parameters of potential generic products to clinically used formulations like Doxil®, Ambisome®, Abraxane® in healthy and diseased population using mathematical equations. The current review highlights the important formulation characteristics, in-vitro, preclinical in-vivo aspects which need to be considered while developing a stimulatory predictive PBPK model in establishment of an IVIVC and in-vitro-in-vivo relationship (IVIVR).

A "middle-out approach" for the prediction of human drug disposition from preclinical data using simplified physiologically based pharmacokinetic (PBPK) models

Simplified physiologically based pharmacokinetic (PBPK) models using estimated tissue-to-unbound plasma partition coefficients (Kpus) were previously investigated by fitting them to in vivo pharmacokinetic (PK) data. After optimization with preclinical data, the performance of these models for extrapolation of distribution kinetics to human were evaluated to determine the best approach for the prediction of human drug disposition and volume of distribution (Vss) using PBPK modeling. Three lipophilic bases were tested (diazepam, midazolam, and basmisanil) for which intravenous PK data were available in rat, monkey, and human. The models with Kpu scalars using k-means clustering were generally the best for fitting data in the preclinical species and gave plausible Kpu values. Extrapolations of plasma concentrations for diazepam and midazolam using these models and parameters obtained were consistent with the observed clinical data. For diazepam and midazolam, the human predictions of Vss after optimization in rats and monkeys were better compared with the Vss estimated from the traditional PBPK modeling approach (varying from 1.1 to 3.1 vs. 3.7-fold error). For basmisanil, the sparse preclinical data available could have affected the model performance for fitting and the subsequent extrapolation to human. Overall, this work provides a rational strategy to predict human drug distribution using preclinical PK data within the PBPK modeling strategy.

Predictive Design and Analysis of Drug Transport by Multiscale Computational Models Under Uncertainty

Computational modeling of drug delivery is becoming an indispensable tool for advancing drug development pipeline, particularly in nanomedicine where a rational design strategy is ultimately sought. While numerous in silico models have been developed that can accurately describe nanoparticle interactions with the bioenvironment within prescribed length and time scales, predictive design of these drug carriers, dosages and treatment schemes will require advanced models that can simulate transport processes across multiple length and time scales from genomic to population levels. In order to address this problem, multiscale modeling efforts that integrate existing discrete and continuum modeling strategies have recently emerged. These multiscale approaches provide a promising direction for bottom-up in silico pipelines of drug design for delivery. However, there are remaining challenges in terms of model parametrization and validation in the presence of variability, introduced by multiple levels of heterogeneities in disease state. Parametrization based on physiologically relevant in vitro data from microphysiological systems as well as widespread adoption of uncertainty quantification and sensitivity analysis will help address these challenges.

Single-time-point estimation of absorbed doses in PRRT using a non-linear mixed-effects model

INTRODUCTION

Estimation of accurate time-integrated activity coefficients (TIACs) and radiation absorbed doses (ADs) is desirable for treatment planning in peptide-receptor radionuclide therapy (PRRT). This study aimed to investigate the accuracy of a simplified dosimetry using a physiologically-based pharmacokinetic (PBPK) model, a nonlinear mixed effect (NLME) model, and single-time-point imaging to calculate the TIACs and ADs of ⁹⁰Y-DOTATATE in various organs of dosimetric interest and tumors.

MATERIALS & METHODS

Biokinetic data of ¹¹¹In-DOTATATE in tumors, kidneys, liver, spleen, and whole body were obtained from eight patients using planar scintigraphic imaging at T1 = (2.9 ± 0.6), T2 = (4.6 ± 0.4), T3 = (22.8 ± 1.6), T4 = (46.7 ± 1.7) and T5 = (70.9 ± 1.0) h post injection. Serum activity concentration was measured at 5 and 15 min; 0.5, 1, 2, and 4 h; and 1, 2, and 3 d p.i.. A published PBPK model for PRRT, NLME, and a single-time-point imaging datum at different time points were used to calculate TIACs in tumors, kidneys, liver, spleen, whole body, and serum. Relative deviations (RDs) (median [min, max]) between the calculated TIACs from single-time-point imaging were compared to the TIACs calculated from the all-time-points fit. The root mean square error (RMSE) of the difference between the computed ADs from the single-time-point imaging and reference ADs from the all-time point fittings were analyzed. A joint root mean square error RMSE_joint of the ADs was calculated with the RSME from both the tumor and kidneys to sort the time points concerning accurate results for the kidneys and tumor dosimetry. The calculations of TIACs and ADs from the single-time-point dosimetry were repeated using the sum of exponentials (SOE) approach introduced in the literature. The RDs and the RSME of the PBPK approach in our study were compared to the SOE approach.

RESULTS

Using the PBPK and NLME models and the biokinetic measurements resulted in a good fit based on visual inspection of the fitted curves and the coefficient of variation CV of the fitted parameters (<50%). T4 was identified being the time point with a relatively low median and range of TIACs RDs, i.e., 5 [1, 21]% and 2 [-15, 21]% for kidneys and tumors, respectively. T4 was found to be the time point with the lowest joint root mean square error RMSE_joint of the ADs. Based on the RD and RMSE, our results show a similar performance as the SOE and NLME model approach.

SUMMARY

In this study, we introduced a simplified calculation of TIACs/ADs using a PBPK model, an NLME model, and a single-time-point measurement. Our results suggest a single measurement might be used to calculate TIACs/ADs in the kidneys and tumors during PRRT.

Pharmacokinetics of Novel Furoxan/Coumarin Hybrids in Rats Using LC-MS/MS Method and Physiologically Based Pharmacokinetic Model

Novel furoxan/coumarin hybrids were synthesized, and pharmacologic studies showed that the compounds displayed potent antiproliferation activities via downregulating both the phosphatidylinositide 3-kinase (PI3K) pathway and the mitogen-activated protein kinase (MAPK) pathway. To investigate the preclinical pharmacokinetic (PK) properties of three candidate compounds (CY-14S-4A83, CY-16S-4A43, and CY-16S-4A93), liquid chromatography, in tandem with the mass spectrometry LC-MS/MS method, was developed and validated for the simultaneous determination of these compounds. The absorption, distribution, metabolism, and excretion (ADME) properties were investigated in in vitro studies and in rats. Meanwhile, physiologically based pharmacokinetic (PBPK) models were constructed using only in vitro data to obtain detailed PK information. Good linearity was observed over the concentration range of 0.01−1.0 μg/mL. The free drug fraction (fu) values of the compounds were less than 3%, and the clearance (CL) values were 414.5 ± 145.7 mL/h/kg, 2624.6 ± 648.4 mL/h/kg, and 500.6 ± 195.2 mL/h/kg, respectively. The predicted peak plasma concentration (Cmax) and the area under the concentration-time curve (AUC) were overestimated for the CY-16S-4A43 PBPK model compared with the experimental ones (fold error > 2), suggesting that tissue accumulation and additional elimination pathways may exist. In conclusion, the LC-MS/MS method was successively applied in the preclinical PK studies, and the detailed information from PBPK modeling may improve decision-making in subsequent new drug development.

Beyond the Michaelis-Menten: Accurate Prediction of Drug Interactions through Cytochrome P450 3A4 Induction

The US Food and Drug Administration (FDA) guidance has recommended several model-based predictions to determine potential drug-drug interactions (DDIs) mediated by cytochrome P450 (CYP) induction. In particular, the ratio of substrate area under the plasma concentration-time curve (AUCR) under and not under the effect of inducers is predicted by the Michaelis-Menten (MM) model, where the MM constant ( K m $$ {K}_{\mathrm{m}} $$ ) of a drug is implicitly assumed to be sufficiently higher than the concentration of CYP enzymes that metabolize the drug ( E T $$ {E}_{\mathrm{T}} $$ ) in both the liver and small intestine. Furthermore, the fraction absorbed from gut lumen ( F a $$ {F}_{\mathrm{a}} $$ ) is also assumed to be one because F a $$ {F}_{\mathrm{a}} $$ is usually unknown. Here, we found that such assumptions lead to serious errors in predictions of AUCR. To resolve this, we propose a new framework to predict AUCR. Specifically, F a $$ {F}_{\mathrm{a}} $$ was re-estimated from experimental permeability values rather than assuming it to be one. Importantly, we used the total quasi-steady-state approximation to derive a new equation, which is valid regardless of the relationship between K m $$ {K}_{\mathrm{m}} $$ and E T $$ {E}_{\mathrm{T}} $$ , unlike the MM model. Thus, our framework becomes much more accurate than the original FDA equation, especially for drugs with high affinities, such as midazolam or strong inducers, such as rifampicin, so that the ratio between K m $$ {K}_{\mathrm{m}} $$ and E T $$ {E}_{\mathrm{T}} $$ becomes low (i.e., the MM model is invalid). Our work greatly improves the prediction of clinical DDIs, which is critical to preventing drug toxicity and failure.

Inferring Therapeutic Targets in Candida albicans and Possible Inhibition through Natural Products: A Binding and Physiological Based Pharmacokinetics Snapshot

Despite being responsible for invasive infections, fungal pathogens have been underrepresented in computer aided therapeutic target mining and drug design. Excess of Candida albicans causes candidiasis, causative of thrush and vaginal infection due to off-balance. In this study, we attempted to mine drug targets (n = 46) using a subtractive proteomic approach in this pathogenic yeast and screen natural products with inhibition potential against fructose-bisphosphate aldolase (FBA) of the C. albicans. The top compound selected on the basis of best docking score from traditional Indian medicine/Ayurvedic library was (4-Hydroxybenzyl)thiocarbamic acid, from the ZINC FBA inhibitor library was ZINC13507461 (IUPAC name: [(2R)-2-hydroxy-3-phosphonooxypropyl] (9E,12E)-octadeca-9,12-dienoate), and from traditional Tibetan medicine/Sowa rigpa was Chelerythrine (IUPAC name: 1,2-Dimethoxy-12-methyl-9H-[1,3]benzodioxolo[5,6-c]phenanthridin-12-ium), compared to the control (2E)-1-(4-nitrophenyl)-2-[(4-nitrophenyl)methylidene]hydrazine. No Ames toxicity was predicted for prioritized compounds while control depicted this toxicity. (4-Hydroxybenzyl)thiocarbamic acid showed hepatotoxicity, while Chelerythrine depicted hERG inhibition, which can lead to QT syndrome, so we recommend ZINC13507461 for further testing in lab. Pharmacological based pharmacokinetic modeling revealed that it has low bioavailability and hence, absorption in healthy state. In cirrhosis and renal impairment, absorption and plasma accumulation increased so we recommend further investigation into this occurrence and recommend high dosage in further tests to increase bioavailability.

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.

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.

An IQ Consortium Perspective on Connecting Dissolution Methods to In Vivo Performance: Analysis of an Industrial Database and Case Studies to Propose a Workflow

Assessment of bioperformance to inform formulation selection and development decisions is an important aspect of drug development. There is high demand in the pharmaceutical industry to develop an efficient and streamlined approach for better understanding and predicting drug product performance to support acceleration of clinical timelines. This manuscript presents an effort from the IQ Formulation Bioperformance Prediction Working Group composed of members from 12 pharmaceutical companies under the IQ Consortium to develop a database around the topic of formulation bioperformance prediction and report findings from the database analysis. Six case studies described in the manuscript demonstrate how bioperformance models were used to predict in vivo performance and to provide guidance addressing questions encountered during oral solid dosage form development. The case studies also described findings of a correlation between in vitro dissolution and in vivo performance and how dissolution data can be incorporated into physiologically based biopharmaceutical modeling. Finally, a workflow for how in vitro dissolution data can be utilized to predict clinical bioperformance of oral solid dosage forms is proposed.

In Vitro and In Vivo Bioequivalence Study of 3D-Printed Instant-Dissolving Levetiracetam Tablets and Subsequent Personalized Dosing for Chinese Children Based on Physiological Pharmacokinetic Modeling

Recently, the development of Binder Jet 3D printing technology has promoted the research and application of personalized formulations, which are especially useful for children’s medications. Additionally, physiological pharmacokinetic (PBPK) modeling can be used to guide drug development and drug dose selection. Multiple technologies can be used in combination to increase the safety and effectiveness of drug administration. In this study, we performed in vivo pharmacokinetic experiments in dogs with preprepared 3D-printed levetiracetam instant-dissolving tablets (LEV-IDTs). Bioequivalence analysis showed that the tablets were bioequivalent to commercially available preparations (Spritam^®) for dogs. Additionally, we evaluated the bioequivalence of 3D-printed LEV-IDTs with Spritam^® by a population-based simulation based on the established PBPK model of levetiracetam for Chinese adults. Finally, we established a PBPK model of oral levetiracetam in Chinese children by combining the physiological parameters of children, and we simulated the PK (pharmacokinetics) curves of Chinese children aged 4 and 6 years that were administered the drug to provide precise guidance on adjusting the dose according to the effective dose range of the drug. Briefly, utilizing both Binder jet 3D printing technology and PBPK models is a promising route for personalized drug delivery with various age groups.

Characterization of Preclinical Pharmacokinetic Properties and Prediction of Human PK Using a Physiologically Based Pharmacokinetic Model for a Novel Anti-Arrhythmic Agent Sulcardine Sulfate

PURPOSE

Sulcardine sulfate (Sul) is a novel antiarrhythmic agent with promising pharmacological properties, which is currently being evaluated in several clinical trials as an oral formulation. To meet the medication needs of patients with acute conditions, the injection formulation of Sul has been developed. The objective of this study was to systemically investigate the pharmacokinetic profiles of Sul after intravenous infusion.

METHODS

This research included the plasma protein binding and metabolic stability studies in vitro, plasma pharmacokinetics, biodistribution, excretion studies in animals, and the prediction of the clinical PK of Sul injection using a physiologically based pharmacokinetics (PBPK) model.

RESULTS

The metabolic stability was similarly in dogs and humans but lower in rats. The plasma protein binding rates showed a concentration-dependent manner and species differences. The pharmacokinetic behavior after intravenous administration was linear in rats within the dose range of 30-90 mg/kg, but nonlinear in dogs within 30-60 mg/kg. Sul could be rapidly and widely distributed in multiple tissues after intravenous administration. About 12% of the parent compound were excreted via the urine and only a small fraction via bile and feces,and eight metabolites were found and identified in the rat excretion. The PBPK models were developed and simulated the observed PK date well in both rats and dogs. The PBPK model refined with human data predicted the PK characteristics of the first intravenous infusion of Sul in human.

CONCLUSIONS

Our study systematically explored the pharmacokinetic characteristics of Sul and successfully developed the PBPK model to predict of its clinical PK.

Comparison between lab variability and in silico prediction errors for the unbound fraction of drugs in human plasma

Variability of the unbound fraction in plasma (f_u) between labs, methods and conditions is known to exist. Variability and uncertainty of this parameter influence predictions of the overall pharmacokinetics of drug candidates and might jeopardise safety in early clinical trials. Objectives of this study were to evaluate the variability of human in vitro f_u-estimates between labs for a range of different drugs, and to develop and validate an in silico f_u-prediction method and compare the results to the lab variability.A new in silico method with prediction accuracy (Q²) of 0.69 for log f_u was developed. The median and maximum prediction errors were 1.9- and 92-fold, respectively. Corresponding estimates for lab variability (ratio between max and min f_u for each compound) were 2.0- and 185-fold, respectively. Greater than 10-fold lab variability was found for 14 of 117 selected compounds.Comparisons demonstrate that in silico predictions were about as reliable as lab estimates when these have been generated during different conditions. Results propose that the new validated in silico prediction method is valuable not only for predictions at the drug design stage, but also for reducing uncertainties of f_u-estimations and improving safety of drug candidates entering the clinical phase.

Investigating the glucuronidation and sulfation pathways contribution and disposition kinetics of Bisphenol S and its metabolites using LC-MS/MS-based nonenzymatic hydrolysis method

Despite showing serious health consequences and widespread exposure, the toxicokinetic information required to evaluate the health risks of BPS is insufficient. Thus, we aim to describe the comprehensive toxicokinetics of BPS and its glucuronide (BPS-G) and sulfate (BPS-S) metabolites in rats. Simultaneous quantification of BPS and its metabolites (authentic standards) was accomplished using UPLC-MS/MS method. BPS displayed rapid absorption, extensive metabolism and fast elimination after oral administration. Following intravenous administration, BPS exhibited CL (8.8 L/h/kg) higher than the rat hepatic blood flow rate suggesting the likelihood of extrahepatic clearance. The CL value differed from those reported previously (sheep and piglets) and the probable reason could be attributed to dose- and/or interspecies differences. BPS was extensively metabolized and excreted primarily through urine as BPS-G (∼56%). BPS and BPS-S exhibited a high protein binding capacity in comparison to BPS-G. In in vitro metabolic stability study, BPS was predominantly metabolized through glucuronidation. The predicted in vivo hepatic clearance of BPS suggested it to be a high and intermediate clearance chemical in rats and humans, respectively. The significant interspecies difference observed in the clearance of BPS between rats and humans indicated that toxicokinetics of BPS should be considered for health risk assessment in humans.

Administration in fed state but not controlled release in the colon increases oral bioavailability of DF030263, a promising drug candidate for chronic lymphocytic leukemia

For treatment of chronic cancers, the oral administration route is preferred as it provides numerous advantages over other delivery routes. However, these benefits of oral chemotherapy can be limited due to unfavorable pharmacokinetics. Accordingly, pharmacokinetic development of chemotherapeutic agents is crucial to the improvement of cancer treatment. In this study, assessment and optimization of biopharmaceutical properties of a promising drug candidate for cyclin-dependent kinase 9 (CDK9) inhibitor (DF030263) was performed to promote oral delivery. Oral bioavailability of DF030263 in fasted rats was 23.8%, and a distinct double-peak phenomenon was observed. A two-site absorption windows mechanism was proposed as a possible explanation to the phenomenon. The two-site absorption window hypothesis was supported by in vitro solubility assays in biorelevant fluids with different pH levels, as well as by in silico simulation by GastroPlus™. Controlled release to the colon was conducted in rats in order to exploit the colonic absorption window but did not improve the oral bioavailability. On the other hand, oral administration at postprandial conditions in rats (performed based on the high in vitro solubility in fed state simulated fluid and reduced pH-dependency) resulted in an almost 3-fold increase in bioavailability to 63.6%. In conclusion, this study demonstrates an efficient in vitro-in vivo-in silico drug development approach for improving the oral bioavailability of DF030263, a promising candidate for the treatment of chronic lymphocytic leukemia.

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.

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.

Toxicokinetics of temephos after oral administration to adult male rats

Temephos (Tem) is the larvicide of choice to control mosquito transmission of dengue, Zika, and chikungunya. The toxicokinetic and toxicological information of temephos is very limited. The aim of this work was to determine the toxicokinetics and dosimetry of temephos and its metabolites. Male Wistar rats were orally administered temephos (300 mg/kg) emulsified with saline solution and sacrificed over time after dosing. Temephos and its metabolites were analyzed in blood and tissues by high performance liquid chromatography-diode array detector. At least eleven metabolites were detected, including temephos-sulfoxide (Tem-SO), temephos-oxon (Tem-oxon), temephos-oxon-sulfoxide (Tem-oxon-SO), temephos-oxon-SO-monohydrolyzed (Tem-oxon-SO-OH), 4,4´-thiodiphenol, 4,4´-sulfinyldiphenol, and 4,4´-sulfonyldiphenol or bisphenol S (BPS). The mean blood concentrations of temephos were fitted to a one-compartment model for kinetic analysis. At 2 h, the peak was reached (t_{1/2 abs} = 0.38 h), and only trace levels were detected at 36 h (t_{1/2 elim} = 8.6 h). Temephos was detected in all tissues and preferentially accumulated in fat. Temephos-sulfone-monohydrolyzed (Tem-SO₂-OH) blood levels remained constant until 36 h and gradually accumulated in the kidney. Tem-oxon was detected in the brain, liver, kidney, and fat. Clearance from the liver and kidney were 7.59 and 5.52 ml/min, respectively. These results indicate that temephos is well absorbed, extensively metabolized, widely distributed and preferentially stored in adipose tissue. It is biotransformed into reactive metabolites such as Tem-oxons, Tem-dioxons, and BPS. Tem-SO₂-OH, the most abundant metabolite of temephos, could be used as an exposure biomarker for toxicokinetic modeling. These results could provide critical insight into the dosimetry and toxicity of temephos and its metabolites.

A Novel Experimental and Theoretical Method for Estimating Albumin-Mediated Hepatic Uptake Based on the Albumin Binding Fraction in Plasma and Human PK Prediction Using a Physiologically-Based Pharmacokinetic Approach

Recently, protein-facilitated uptake has been suggested to be an important factor in the precise prediction of the pharmacokinetic (PK) profiles of drugs. In our previous study, a physiologically-based pharmacokinetic (PBPK) approach considering the mechanism of albumin-mediated hepatic uptake was developed for predicting human PK profiles. It was assumed that drugs affected by albumin-mediated hepatic uptake would bind only to albumin, which means that there would be over-estimation of the contribution of protein-facilitated uptake for a drug that could bind to multiple proteins. In this study, we developed a method that can evaluate the albumin binding fraction in plasma considering the affinity for other proteins. Based on the albumin binding fraction, the contribution of albumin-mediated hepatic uptake was theoretically estimated, and then the human PK profiles were predicted by our proposed PBPK approach incorporating this mechanism. As a result, the predicted human PK profiles agreed well with the observed ones, and the absolute average fold error of PK parameters was almost within a 1.5-fold error on average. These findings show the importance of considering protein-facilitated uptake and also suggest that our proposed PBPK approach can be useful in scientific discussions with regulatory authorities.

Qualification and application of liquid chromatography-quadrupole time-of-flight mass spectrometric method for the determination of carisbamate in rat plasma and prediction of its human pharmacokinetics using physiologically based pharmacokinetic modeling

Carisbamate is an antiepileptic drug and it also has broad neuroprotective activity and anticonvulsant reaction. In this study, a liquid chromatography-quadrupole time-of-flight mass spectrometric (LC-qTOF-MS) method was developed and applied for the determination of carisbamate in rat plasma to support in vitro and in vivo studies. A quadratic regression (weighted 1/concentration²), with an equation y = ax² + bx + c, was used to fit calibration curves over the concentration range from 9.05 to 6,600 ng/mL for carisbamate in rat plasma. Preclinical in vitro and in vivo studies of carisbamate have been studied through the developed bioanalytical method. Based on these study results, human pharmacokinetic (PK) profile has been predicted using physiologically based pharmacokinetic (PBPK) modeling. The PBPK model was optimized and validated by using the in vitro and in vivo data. The human PK of carisbamate after oral dosing of 750 mg was simulated by using this validated PBPK model. The human PK parameters and profiles predicted from the validated PBPK model were similar to the clinical data. This PBPK model developed from the preclinical data for carisbamate would be useful for predicting the PK of carisbamate in various clinical settings.

Physiologically Based Pharmacokinetic Modelling for First-In-Human Predictions: An Updated Model Building Strategy Illustrated with Challenging Industry Case Studies

Physiologically based pharmacokinetic modelling is well established in the pharmaceutical industry and is accepted by regulatory agencies for the prediction of drug-drug interactions. However, physiologically based pharmacokinetic modelling is valuable to address a much wider range of pharmaceutical applications, and new regulatory impact is expected as its full power is leveraged. As one example, physiologically based pharmacokinetic modelling is already routinely used during drug discovery for in-vitro to in-vivo translation and pharmacokinetic modelling in preclinical species, and this leads to the application of verified models for first-in-human pharmacokinetic predictions. A consistent cross-industry strategy in this application area would increase confidence in the approach and facilitate further learning. With this in mind, this article aims to enhance a previously published first-in-human physiologically based pharmacokinetic model-building strategy. Based on the experience of scientists from multiple companies participating in the GastroPlus™ User Group Steering Committee, new Absorption, Distribution, Metabolism and Excretion knowledge is integrated and decision trees proposed for each essential component of a first-in-human prediction. We have reviewed many relevant scientific publications to identify new findings and highlight gaps that need to be addressed. Finally, four industry case studies for more challenging compounds illustrate and highlight key components of the strategy.

Ethambutol disposition in humans: Challenges and limitations of whole-body physiologically-based pharmacokinetic modelling in early drug development

Whole-body physiologically based pharmacokinetic (WB-PBPK) models have become an important tool in drug development, as they enable characterization of pharmacokinetic profiles across different organs based on physiological (systems-specific) and physicochemical (drug-specific) properties. However, it remains unclear which data are needed for accurate predictions when applying the approach to novel candidate molecules progressing into the clinic. In this work, as case study, we investigated the predictive performance of WB-PBPK models both for prospective and retrospective evaluation of the pharmacokinetics of ethambutol, considering scenarios that reflect different stages of development, including settings in which the data are limited to in vitro experiments, in vivo preclinical data, and when some clinical data are available. Overall, the accuracy of PBPK model-predicted systemic and tissue exposure was heavily dependant on prior knowledge about the eliminating organs. Whilst these findings may be specific to ethambutol, the challenges and potential limitations identified here may be relevant to a variety of drugs, raising questions about (1) the minimum requirements for prospective use of WB-PBPK models during the characterization of drug disposition and (2) implication of uncertainty for dose selection in humans.

Translational approach from preclinical to clinical: comparison of dose finding methods of a new Bcl2 inhibitor using PK-PD modeling and interspecies extrapolation

The attrition rate of anticancer drugs during the clinical development remains very high. Interspecies extrapolation of anticancer drug pharmacodynamics (PD) could help to bridge the gap between preclinical and clinical settings and to improve drug development. Indeed, when combined with a physiologically-based-pharmacokinetics (PBPK) approach, PD interspecies extrapolation could be a powerful tool for predicting drug behavior in clinical trials. The present study aimed to explore this field for anticipating the clinical efficacy of a new Bcl-2 inhibitor, S 55746, for which dose ranging studies in xenografted mice and clinical data from a phase 1 trial involving cancer patients were available. Different strategies based on empirical or more mechanistic assumptions (based on PBPK-PD modelling) were developped and compared: the Rocchetti approach (ROC); the Orthogonal Rocchetti approach (oROC), a variant of ROC based on an orthogonal regression; the Consistent across species approach, bringing out an efficacy parameter assumed to be consistent across species; and the Scaling species-specific parameters approach, assuming the concentration-efficacy link is the same in mice as in humans, after allometric scaling. Empirical approaches (ROC and oROC) gave similar predictive performances and seemed to overestimate the active S 55746 dose compared to mechanistic approaches, while strategies elaborated from semi-mechanistic concepts and PBPK-PD modelling did not seem to be invalidated by clinical efficacy data. Also, empirical methods only predict a single dose level for the subsequent clinical studies, whereas mechanism-based strategies are more informative about the dose response relationship, highlighting the potential interest of such approaches in drug development.

Investigating the Role of Altered Systemic Albumin Concentration on the Disposition of Theophylline in Adult and Pediatric Patients with Asthma by Using the Physiologically Based Pharmacokinetic Approach

Theophylline is commonly used for the treatment of asthma and has a low hepatic clearance. The changes in plasma albumin concentration occurring in asthma may affect the exposure of theophylline. The aim of the presented work was to predict theophylline pharmacokinetics (PK) after incorporating the changes in plasma albumin concentration occurring in patients with asthma into a physiologically based pharmacokinetic (PBPK) model to see whether these changes can affect the systemic theophylline concentrations in asthma. The PBPK model was developed following a systematic model building approach using Simcyp. The predictions were performed initially in healthy adults after intravenous and oral drug administration. Only when the developed adult PBPK model had adequately predicted theophylline PK in healthy adults, the changes in plasma albumin concentrations were incorporated into the model for predicting drug exposure in patients with asthma. After evaluation of the developed model in the adult population, it was scaled to children on physiologic basis. The model evaluation was performed by using visual predictive checks and comparison of ratio of observed and predicted (R_obs/Pre) PK parameters along with their 2-fold error range. The developed PBPK model has effectively described theophylline PK in both healthy and diseased populations, as R_obs/Pre for all the PK parameters were within the 2-fold error limit. The predictions in patients with asthma showed that there were no significant changes in PK parameters after incorporating the changes in serum albumin concentration. The mechanistic nature of the developed asthma-PBPK model can facilitate its extension to other drugs. SIGNIFICANCE STATEMENT: Exposure of a low hepatic clearance drug like theophylline may be susceptible to plasma albumin concentration changes that occur in asthma. These changes in systemic albumin concentrations can be incorporated into a physiologically based pharmacokinetic model to predict theophylline pharmacokinetics in adult and pediatric asthma populations. The presented work is focused on predicting theophylline absorption, distribution, metabolism, and elimination in adult and pediatric asthma populations after incorporating reported changes in serum albumin concentrations to see their impact on the systemic theophylline concentrations.

The Novel In Vitro Method to Calculate Tissue-to-Plasma Partition Coefficient in Humans for Predicting Pharmacokinetic Profiles by Physiologically-Based Pharmacokinetic Model With High Predictability

Proper prediction of human pharmacokinetic (PK) profiles can accelerate the compound selection in drug discovery. Recently, we reported a robust bottom-up physiologically-based pharmacokinetic (PBPK) approach (J Pharm Sci. 2019 Aug; 108(8):2718-2727), which uses the in vivo rat distribution volume at the steady state (V_ss) to determine human tissue-to-plasma partition coefficients (Kp_tissue). Here, we report on a bottom-up PBPK approach that can simulate the PK profile with both high-throughput and high-predictive accuracy only using in vitro data. In this study, as an alternative parameter of in vivo rat V_ss which was used for the correction of human Kp_tissue, V_{ss, in vitro} was obtained from protein binding data in rats, and the values of V_{ss, in vitro} for 31 reference compounds showed good correlation with the observed rat V_ss (R² = 0.859). Next, rat and human PK profiles of reference compounds were predicted by the bottom-up PBPK approach using Kp_tissue corrected by rat V_{ss, in vitro}. As a result, the absolute average fold errors for pharmacokinetic parameters were almost less than 2, showing that these PK profiles could be accurately predicted using in vitro data. This method enables the screening of promising compounds with good PK profiles in humans at an early stage of drug discovery.

Exposure-Response and Clinical Outcome Modeling of Inhaled Budesonide/Formoterol Combination in Asthma Patients

Exposure-response and clinical outcome (CO) model for inhaled budesonide/formoterol was developed to quantify the relationship among pharmacokinetics (PK), pharmacodynamics (PD) and CO of the drugs and evaluate the covariate effect on model parameters. Sputum eosinophils cationic proteins (ECP) and forced expiratory volume (FEV1) were selected as PD markers and asthma control score was used as a clinical outcome. One- and two-compartment models were used to describe the PK of budesonide and formoterol, respectively. The indirect response model (IDR) was used to describe the PD effect for ECP and FEV1. In addition, the symptomatic effect on the disease progression model for CO was connected with IDR on each PD response. The slope for the effect of ECP and FEV1 to disease progression were estimated as 0.00008 and 0.644, respectively. Total five covariates (ex. ADRB2 genotype etc.) were searched using a stepwise covariate modeling method, however, there was no significant covariate effect. The results from the simulation study were showed that a 1 puff b.i.d. had a comparable effect of asthma control with a 2 puff b.i.d. As a result, the 1 puff b.i.d. of combination drug could be suggested as a standardized dose to minimize the side effects and obtain desired control of disease compared to the 2 puff b.i.d.

In vitro hepatic biotransformation of the algal toxin pectenotoxin-2

We have investigated the in vitro metabolism of pectenotoxin-2 (PTX-2) using primary hepatocytes from Wistar rats in suspension. Purified PTX-2 was rapidly metabolized. Two major and several minor oxidized PTX-2 metabolites were formed, none of which had retention times corresponding to PTX-1, -11, or −13. Hydrolysis products, such as PTX-2 seco acid, were not observed. Preliminary multi-stage LC-MS analyses indicated that the major hepatic PTX-2 metabolites resulted from the insertion of an oxygen atom at the positions C-19 to C-24, or at C-44. The rapid oxidative metabolism may explain the low oral toxicity of PTXs observed in vivo studies.

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PTX-2 is rapidly metabolized in rat hepatocytes.

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Two major and several minor oxidized PTX-2 metabolites were formed.

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The results may explain the low oral toxicity of PTXs observed in vivo studies.

Toxicokinetics of bisphenol S in rats for predicting human bisphenol S clearance from allometric scaling

Previous data obtained in piglets suggested that despite structural analogy with Bisphenol A (BPA), Bisphenol S (BPS) elimination may proceed more slowly, resulting in a much higher systemic exposure to unconjugated BPS than to BPA. Interspecies allometric scaling was applied to predict the toxicokinetic (TK) parameters of BPS, namely plasma clearance in humans from values obtained in animals, and thus contribute to assessment of the human internal exposure to BPS. Allometric scaling was performed using mean BPS plasma clearance values measured in rats after intravenous administration of 5 mg BPS /kg body weight (BW) and those previously obtained in piglets and sheep using identical IV BPS dosing and analytical procedures. The BPS plasma clearance, evaluated at 0.92 L/kg.h in rats, was proportional to species body weight, enabling the prediction of human BPS plasma clearance by extrapolating to a BW of 70 kg. The estimated BPS plasma clearance in humans was thus 0.92 L/min (0.79 L/kg.h), i.e. about two times lower than the previously estimated BPA clearance (1.79 L/min). By increasing systemic exposure to the active moiety of an environmental estrogenic chemical, this less efficient clearance of BPS in humans, as compared with BPA, might worsen the harmful consequences of replacing BPA by BPS.

Prediction of Oral Bioavailability in Rats: Transferring Insights from in Vitro Correlations to (Deep) Machine Learning Models Using in Silico Model Outputs and Chemical Structure Parameters

Oral administration of drug products is a strict requirement in many medical indications. Therefore, bioavailability prediction models are of high importance for prioritization of compound candidates in the drug discovery process. However, oral exposure and bioavailability are difficult to predict, as they are the result of various highly complex factors and/or processes influenced by the physicochemical properties of a compound, such as solubility, lipophilicity, or charge state, as well as by interactions with the organism, for instance, metabolism or membrane permeation. In this study, we assess whether it is possible to predict intravenous (iv) or oral drug exposure and oral bioavailability in rats. As input parameters, we use (i) six experimentally determined in vitro and physicochemical endpoints, namely, membrane permeation, free fraction, metabolic stability, solubility, pK_a value, and lipophilicity; (ii) the outputs of six in silico absorption, distribution, metabolism, and excretion models trained on the same endpoints, or (iii) the chemical structure encoded as fingerprints or simplified molecular input line entry system strings. The underlying data set for the models is an unprecedented collection of almost 1900 data points with high-quality in vivo experiments performed in rats. We find that drug exposure after iv administration can be predicted similarly well using hybrid models with in vitro- or in silico-predicted endpoints as inputs, with fold change errors (FCE) of 2.28 and 2.08, respectively. The FCEs for exposure after oral administration are higher, and here, the prediction from in vitro inputs performs significantly better in comparison to in silico-based models with FCEs of 3.49 and 2.40, respectively, most probably reflecting the higher complexity of oral bioavailability. Simplifying the prediction task to a binary alert for low oral bioavailability, based only on chemical structure, we achieve accuracy and precision close to 70%.

Development and Evaluation of Physiologically Based Pharmacokinetic Drug-Disease Models for Predicting Rifampicin Exposure in Tuberculosis and Cirrhosis Populations

The physiologically based pharmacokinetic (PBPK) approach facilitates the construction of novel drug–disease models by allowing incorporation of relevant pathophysiological changes. The aim of the present work was to explore and identify the differences in rifampicin pharmacokinetics (PK) after the application of its single dose in healthy and diseased populations by using PBPK drug–disease models. The Simcyp^® simulator was used as a platform for modeling and simulation. The model development process was initiated by predicting rifampicin PK in healthy population after intravenous (i.v) and oral administration. Subsequent to successful evaluation in healthy population, the pathophysiological changes in tuberculosis and cirrhosis population were incorporated into the developed model for predicting rifampicin PK in these populations. The model evaluation was performed by using visual predictive checks and the comparison of mean observed/predicted ratios (ratio_(Obs/pred)) of the PK parameters. The predicted PK parameters in the healthy population were in adequate harmony with the reported clinical data. The incorporation of pathophysiological changes in albumin concentration in the tuberculosis population revealed improved prediction of clearance. The developed PBPK drug–disease models have efficiently described rifampicin PK in tuberculosis and cirrhosis populations after administering single drug dose, as the ratio_(Obs/pred) for all the PK parameters were within a two-fold error range. The mechanistic nature of the developed PBPK models may facilitate their extension to other diseases and drugs.

Quantitative Analysis of Tozadenant Using Liquid Chromatography-Mass Spectrometric Method in Rat Plasma and Its Human Pharmacokinetics Prediction Using Physiologically Based Pharmacokinetic Modeling

Tozadenant is one of the selective adenosine A2a receptor antagonists with a potential to be a new Parkinson's disease (PD) therapeutic drug. In this study, a liquid chromatography-mass spectrometry based bioanalytical method was qualified and applied for the quantitative analysis of tozadenant in rat plasma. A good calibration curve was observed in the range from 1.01 to 2200 ng/mL for tozadenant using a quadratic regression. In vitro and preclinical in vivo pharmacokinetic (PK) properties of tozadenant were studied through the developed bioanalytical methods, and human PK profiles were predicted using physiologically based pharmacokinetic (PBPK) modeling based on these values. The PBPK model was initially optimized using in vitro and in vivo PK data obtained by intravenous administration at a dose of 1 mg/kg in rats. Other in vivo PK data in rats were used to validate the PBPK model. The human PK of tozadenant after oral administration at a dose of 240 mg was simulated by using an optimized and validated PBPK model. The predicted human PK parameters and profiles were similar to the observed clinical data. As a result, optimized PBPK model could reasonably predict the PK in human.

Pre-Clinical Testing of Therapies for Traumatic Brain Injury

Despite the large number of promising neuroprotective agents identified in experimental traumatic brain injury (TBI) studies, none has yet shown meaningful improvements in long-term outcome in clinical trials. To develop recommendations and guidelines for pre-clinical testing of pharmacological or biological therapies for TBI, the Moody Project for Translational Traumatic Brain Injury Research hosted a symposium attended by investigators with extensive experience in pre-clinical TBI testing. The symposium participants discussed issues related to pre-clinical TBI testing including experimental models, therapy and outcome selection, study design, data analysis, and dissemination. Consensus recommendations included the creation of a manual of standard operating procedures with sufficiently detailed descriptions of modeling and outcome measurement procedures to permit replication. The importance of the selection of clinically relevant outcome variables, especially related to behavior testing, was noted. Considering the heterogeneous nature of human TBI, evidence of therapeutic efficacy in multiple, diverse (e.g., diffuse vs. focused) rodent models and a species with a gyrencephalic brain prior to clinical testing was encouraged. Basing drug doses, times, and routes of administration on pharmacokinetic and pharmacodynamic data in the test species was recommended. Symposium participants agreed that the publication of negative results would reduce costly and unnecessary duplication of unsuccessful experiments. Although some of the recommendations are more relevant to multi-center, multi-investigator collaborations, most are applicable to pre-clinical therapy testing in general. The goal of these consensus guidelines is to increase the likelihood that therapies that improve outcomes in pre-clinical studies will also improve outcomes in TBI patients.

Novel in vitro dynamic metabolic system for predicting the human pharmacokinetics of tolbutamide

Liver metabolism is commonly considered the major determinant in drug discovery and development. Many in vitro drug metabolic studies have been developed and applied to understand biotransformation. However, these methods have disadvantages, resulting in inconsistencies between in vivo and in vitro experiments. A major factor is that they are static systems that do not consider the transport process in the liver. Here we developed an in vitro dynamic metabolic system (Bio-PK metabolic system) to mimic the human pharmacokinetics of tolbutamide. Human liver microsomes (HLMs) encapsulated in a F127'-Acr-Bis hydrogel (FAB hydrogel) were placed in the incubation system. A microdialysis sampling technique was used to monitor the metabolic behavior of tolbutamide in hydrogels. The measured results in the system were used to fit the in vitro intrinsic clearance of tolbutamide with a mathematical model. Then, a PBPK model that integrated the corresponding in vitro intrinsic clearance was developed to verify the system. Compared to the traditional incubation method, reasonable PK profiles and the in vivo clearance of tolbutamide could be predicted by integrating the intrinsic clearance of tolbutamide obtained from the Bio-PK metabolic system into the PBPK model. The predicted maximum concentration (C_max), area under the concentration-time curve (AUC), time to reach the maximum plasma concentration (T_max) and in vivo clearance were consistent with the clinically observed data. This novel in vitro dynamic metabolic system can compensate for some limitations of traditional incubation methods; it may provide a new method for screening compounds and predicting pharmacokinetics in the early stages, supporting the development of compounds.