Novel physiologically based pharmacokinetic modeling of patupilone for human pharmacokinetic predictions

Recent Progress on Physiologically Based Pharmacokinetic (PBPK) Model: A Review Based on Bibliometrics

Physiologically based pharmacokinetic/toxicokinetic (PBPK/PBTK) models are designed to elucidate the mechanism of chemical compound action in organisms based on the physiological, biochemical, anatomical, and thermodynamic properties of organisms. After nearly a century of research and practice, good results have been achieved in the fields of medicine, environmental science, and ecology. However, there is currently a lack of a more systematic review of progress in the main research directions of PBPK models, especially a more comprehensive understanding of the application in aquatic environmental research. In this review, a total of 3974 articles related to PBPK models from 1996 to 24 March 2024 were collected. Then, the main research areas of the PBPK model were categorized based on the keyword co-occurrence maps and cluster maps obtained by CiteSpace. The results showed that research related to medicine is the main application area of PBPK. Four major research directions included in the medical field were "drug assessment", "cross-species prediction", "drug-drug interactions", and "pediatrics and pregnancy drug development", in which "drug assessment" accounted for 55% of the total publication volume. In addition, bibliometric analyses indicated a rapid growth trend in the application in the field of environmental research, especially in predicting the residual levels in organisms and revealing the relationship between internal and external exposure. Despite facing the limitation of insufficient species-specific parameters, the PBPK model is still an effective tool for improving the understanding of chemical-biological effectiveness and will provide a theoretical basis for accurately assessing potential risks to ecosystems and human health. The combination with the quantitative structure-activity relationship model, Bayesian method, and machine learning technology are potential solutions to the previous research gaps.

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.

Sub-structure-based category formation for the prioritisation of genotoxicity hazard assessment for pesticide residues: Sulphonyl ureas

In dietary risk assessment, residues of pesticidal ingredients or their metabolites need to be evaluated for their genotoxic potential. The European Food Safety Authority recommend a tiered approach focussing assessment and testing on classes of similar chemicals. To characterise similarity and to identify structural alerts associated with genotoxic concern, a set of chemical sub-structures was derived for an example dataset of 74 sulphonyl urea agrochemicals for which either Ames, chromosomal aberration or micronucleus test results are publicly available. This analysis resulted in a set of seven structural alerts that define the chemical space, in terms of the common parent and metabolic scaffolds, associated with the sulphonyl urea chemical class. An analysis of the available profiling schemes for DNA and protein reactivity shows the importance of investigating the predictivity of such schemes within a well-defined area of structural space. Structural space alerts, covalent chemistry profiling and physico-chemistry properties were combined to develop chemical categories suitable for chemical prioritisation. The method is a robust and reproducible approach to such read-across predictions, with the potential to reduce unnecessary testing. The key challenge in the approach was identified as being the need for pesticide-class specific metabolism data as the basis for structural space alert development.

Drug Exposure to Establish Pharmacokinetic-Response Relationships in Oncology

In the oncology field, understanding the relationship between the dose administered and the exerted effect is particularly important because of the narrow therapeutic index associated with anti-cancer drugs and the high interpatient variability. Therefore, in this review, we provide a critical perspective of the different methods of characterising treatment exposure in the oncology setting. The increasing number of modelling applications in oncology reflects the applicability and the impact of pharmacometrics on all phases of the drug development process and patient management as well. Pharmacometric modelling is a worthy component within the current paradigm of model-based drug development, but pharmacometric modelling techniques are also accessible for the clinician in the optimisation of current oncology therapies. Consequently, the application of population models in a hospital setting by generating close collaborations between physicians and pharmacometricians is highly recommended, providing a systematic means of developing and assessing model-based metrics as 'drivers' for various responses to treatments, which can then be evaluated as predictors for treatment success. Characterising the key determinants of variability in exposure is of particular importance for anticancer agents, as efficacy and toxicity are associated with exposure. We present the different strategies to describe and predict drug exposure that can be applied depending on the data available, with the objective of obtaining the most useful information in the patients' favour throughout the full drug cycle. Therefore, the objective of the present article is to review the different approaches used to characterise a patient's exposure to oncology drugs, which will result in a better understanding of the time course of the response and the magnitude of interpatient variability.

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.

New insights into the vancomycin-induced nephrotoxicity using in vitro metabolomics combined with physiologically based pharmacokinetic modeling

Vancomycin is a first-line treatment for invasive infections caused by multidrug-resistant gram-positive bacteria. However, vancomycin-induced nephrotoxicity is an increasing burden, particularly in patients with complex life-threatening conditions. Vancomycin-induced nephrotoxicity associated with clinically relevant exposure on the target site has not been well defined. This study aimed to acquire the concentration of vancomycin in the renal tubules and kidneys in humans using physiologically based pharmacokinetic (PBPK) modeling and simulation. Based upon the exposure of vancomycin in the renal tubule, the toxicity of vancomycin in human renal proximal tubular epithelial cells was examined with the XTT assay and in vitro metabolomics analysis. A rat PBPK model predicting plasma and kidney concentration-time profiles of vancomycin matched the observed behavior after a single administration of 10 mg/kg. The concentration of vancomycin in renal tubules was about 40-50 times higher than that in plasma. The human PBPK model transferred from the rat model predicted renal tubule concentrations of vancomycin as 316.1-2136.6 μg/mL at 500 mg every 6 hours, and 199.0-3932.5 μg/mL at 1000 mg every 12 hours. Vancomycin showed significant nephrotoxicity at 4 mg/mL in XTT assessment. In total, 11 lysophosphatidylcholines and one lysophosphatidylethanolamine were identified by metabolomics analysis. The concentration-dependent increase was evident in the release of lysophospholipids after vancomycin treatment (0.125-4 mg/mL) for 24 hours. Our study revealed the relationship between the exposure of vancomycin in the kidney and toxicity of vancomycin at clinically relevant concentrations achieved from a mechanical PBPK model. A series of lysophospholipids as potential metabolic markers of renal toxicity were identified.

Pulmonary delivery alters the disposition of raloxifene in rats

OBJECTIVE

Pulmonary delivery is an effective way to improve the bioavailability of drugs with extensive metabolism. This research was designed to study the different pharmacokinetic behaviours of small molecule drug after pulmonary delivery and intragastric (i.g) administration.

METHODS

Raloxifene, a selective estrogen receptor modulator with low oral bioavailability (~2%), was chosen as the model drug. Studies were conducted systematically in rats, including plasma pharmacokinetics, excretion, tissue distribution and metabolism.

KEY FINDINGS

Results showed that raloxifene solution dosed by intratracheal (i.t) administration exhibited relatively quick plasma elimination (t_1/2  = 1.78 ± 0.14 h) and undetected absorption process, which was similar with intravenous injection. Compared with i.g administration, the bioavailability increased by 58 times, but the major route of excretion remained faecal excretion. Drug concentration on the bone and the target efficiency were improved by 49.6 times and five times, respectively. Benefited from quick elimination in the lung, chronic toxicity might be ignored.

CONCLUSIONS

Pulmonary administration improved the bioavailability of raloxifene and further increased the distribution on the target organ (bone), with no obvious impact on its excretory pattern.

Challenges Associated With Applying Physiologically Based Pharmacokinetic Modeling for Public Health Decision-Making

The development and application of physiologically based pharmacokinetic (PBPK) models in chemical toxicology have grown steadily since their emergence in the 1980s. However, critical evaluation of PBPK models to support public health decision-making across federal agencies has thus far occurred for only a few environmental chemicals. In order to encourage decision-makers to embrace the critical role of PBPK modeling in risk assessment, several important challenges require immediate attention from the modeling community. The objective of this contemporary review is to highlight 3 of these challenges, including: (1) difficulties in recruiting peer reviewers with appropriate modeling expertise and experience; (2) lack of confidence in PBPK models for which no tissue/plasma concentration data exist for model evaluation; and (3) lack of transferability across modeling platforms. Several recommendations for addressing these 3 issues are provided to initiate dialog among members of the PBPK modeling community, as these issues must be overcome for the field of PBPK modeling to advance and for PBPK models to be more routinely applied in support of public health decision-making.

Utility of physiologically based pharmacokinetic (PBPK) modeling in oncology drug development and its accuracy: a systematic review

PURPOSE

Physiologically based pharmacokinetic (PBPK) modeling, a mathematical modeling approach which uses a pharmacokinetic model to mimick human physiology to predict drug concentration-time profiles, has been used for the discover and development of drugs in various fields, including oncology, since 2000. There have been a few general review articles on the utilization of PBPK in the development of oncology drugs, but these do not include an evaluation of model prediction accuracy. We therefore conducted a systematic review to define the accuracy of PBPK model prediction and its utility throughout all the developmental phases of oncology drugs.

METHODS

A systematic search was performed in the PubMed, PubMed Central and Cochrane Library databases from 1980 to February 2017 for articles (1) written in English, (2) focused on oncology or antineoplastic or anticancer drugs, tumor or cancer or anticancer drugs listed in the U.S. National Institutes of Health and (3) involving a PBPK model. The absolute-average-folding-errors (AAFEs) of the area under the curve (AUC) between predicted and observed values in each article were calculated to assess model prediction accuracy.

RESULTS

Of the 2341 articles initially identified by our search of the databases, 40 were included in the review analysis. These articles reported on six types of studies, i.e. in vivo (n = 4), first-in-human (n = 5), phase II/III clinical trials (n = 9), organ impairment (n = 3), pediatrics (n = 4) and drug-drug interactions (n = 15). AAFEs of the predicted AUC for all groups of studies were within 1.3-fold of each other despite variations in experimental methodologies.

CONCLUSION

PBPK modeling is a potential tool which can be effectively applied throughout all phases of oncology drug development. The number of experimental animals and human participants enrolled in the studies can be reduced using PBPK modeling and PBPK-population-PK modeling. The limited number of publications of unsuccessful model application to date may contribute to bias toward the usefulness of modeling.

Continuous low-dose infusion of patupilone increases the therapeutic index in mouse and rat tumour models

Patupilone is a microtubule-targeted cytotoxic agent with clinical efficacy, but causes diarrhoea in more than 80% of patients. The efficacy and tolerability of patupilone delivered continuously by subcutaneous (s.c.) mini-pumps [(mini-pump dose (MPD)] or by intravenous bolus administration [intravenous bolus dose (IVBD)] were compared preclinically to determine whether the therapeutic index could be improved. The antiproliferative potency in vitro of patupilone was determined by measuring total cell protein. Tumours were grown s.c. in rats (A15) or nude mice (KB31, KB8511) or intracranially in nude mice (NCI-H460-Luc). Efficacy was monitored by measuring tumour volumes, bioluminescence or survival. Toxicity was monitored by body weight and/or diarrhoea. Total drug levels in blood, plasma, tissues or dialysates were quantified ex-vivo by liquid chromatography-mass spectroscopy/mass spectroscopy. Patupilone was potent in vitro with GI50s of 0.24-0.28 nmol/l and GI90s of 0.46-1.64 nmol/l. In rats, a single IVBD of patupilone dose dependently inhibited the growth of A15 tumours, but also caused dose-dependent body weight loss and diarrhoea, whereas MPD achieved similar efficacy, but no toxicity. In mice, MPD showed efficacy similar to that of IVBD against KB31 and KB8511 tumours, but with reduced toxicity. In a mouse intracranial tumour model, IVBD was more efficacious than MPD, consistent with patupilone concentrations in the brain. MPD provided constant plasma levels, whereas IVBD had very high C0/Cmin ratios of 70-280 (rat) or 8000 (mouse) over the dosing cycle. Overall, the correlation of plasma and tumour levels with response indicated that a Cave of at least GI90 led to tumour stasis. Continuous low concentrations of patupilone by MPD increased the therapeutic index in s.c. rodent tumour models compared with IVBD by maintaining efficacy, but reducing toxicity.

Quantitative whole-body autoradiography: past, present and future

Traditional bioanalytical measurements determine concentrations of drug and metabolites in plasma; however, most drugs exert their effects in defined target tissues. As there is no clear relation between concentrations in plasma and those in tissue, alternative methods must be employed to study the absorption, distribution, metabolism and excretion properties of new therapeutic agents. Quantitative whole-body autoradiography is used in the drug development process to determine the distribution and concentrations of radiolabeled test compounds in laboratory animals. Quantitative whole-body autoradiography can provide information on tissue PKs, penetration, accumulation and retention. Although the technique is considered the industry standard for performing preclinical tissue distribution studies, it is perhaps timely, 60 years after the first reported use of the method, to re-assess the technique against modern alternatives.

Interrogating the relationship between rat in vivo tissue distribution and drug property data for >200 structurally unrelated molecules

The ability to explain distribution patterns from drug physicochemical properties and binding characteristics has been explored for more than 200 compounds by interrogating data from quantitative whole body autoradiography studies (QWBA). These in vivo outcomes have been compared to in silico and in vitro drug property data to determine the most influential properties governing drug distribution. Consistent with current knowledge, in vivo distribution was most influenced by ionization state and lipophilicity which in turn affected phospholipid and plasma protein binding. Basic and neutral molecules were generally better distributed than acidic counterparts demonstrating weaker plasma protein and stronger phospholipid binding. The influence of phospholipid binding was particularly evident in tissues with high phospholipid content like spleen and lung. Conversely, poorer distribution of acidic drugs was associated with stronger plasma protein and weaker phospholipid binding. The distribution of a proportion of acidic drugs was enhanced, however, in tissues known to express anionic uptake transporters such as the liver and kidney. Greatest distribution was observed into melanin containing tissues of the eye, most likely due to melanin binding. Basic molecules were consistently better distributed into parts of the eye and skin containing melanin than those without. The data, therefore, suggest that drug binding to macromolecules strongly influences the distribution of total drug for a large proportion of molecules in most tissues. Reducing lipophilicity, a strategy often used in discovery to optimize pharmacokinetic properties such as absorption and clearance, also decreased the influence of nonspecific binding on drug distribution.

Preclinical pharmacokinetic studies of 3-deazaneplanocin A, a potent epigenetic anticancer agent, and its human pharmacokinetic prediction using GastroPlus™

DZNep is a potential epigenetic drug, and exerts potent anti-proliferative and pro-apoptotic effects on broad-spectrum carcinomas via disruption of the EZH2 pathway. Antitumor studies on DZNep have been stuck in the preclinical phase because of the lack of information about its integral pharmacokinetic (PK) properties. To circumvent this problem, we extensively investigated the disposition characteristics of the DZNep in rats. By incorporating the disposition data across species into a whole-body physiologically based pharmacokinetic (PBPK) models using the GastroPlus(TM) software, we simulated human PK properties of DZNep and determined whether DZNep could be developed for human cancer therapy. Firstly, DZNep was found to cause nephrotoxicity in a dose-dependent manner in rats and its safe dose was determined to be 10mg/kg. DZNep showed a short plasma elimination half-life (1.1h) in rats, a low protein binding in plasma (18.5%), a low partitioning to erythrocyte (0.78), and a low intrinsic hepatic clearance in rats and humans. There was extensive tissue distribution and predominant renal excretion (80.3%). The simulated rat PBPK model of DZNep was well-verified with satisfactory coefficients of determination for all the tested tissues (R(2)>0.781). The simulated human PBPK model successfully identified that intravenous administration of DZNep at appropriate dosing regimen could be further developed for human non-small cell lung carcinoma treatments. The present findings provide valuable information regarding experimental or in silico PK characteristics of DZNep in rats and humans, which is helpful to guide future studies of DZNep in both preclinical and clinical phases.

Quantitative whole-body autoradiography, LC-MS/MS and MALDI for drug-distribution studies in biological samples: the ultimate matrix trilogy

The drug-development process requires an understanding of the ADME properties of the novel therapeutic agent. Determination of drug concentrations and identity in excreta (urine and feces) examines the products of these processes. Similar measurements made on plasma, while accurately determining exposure, show only what is being transported around the body. Both activities fail to confirm the nature of components at the pharmacologically relevant matrix - the tissue. Attention is therefore being directed towards methods that can be employed to address this lack in our current methodologies, to provide better quality data on which risk assessments can be made, so that pharmacological models can be refined, and drug safety improved. In this article, we will look at the current methods used to obtain tissue drug and drug metabolite concentrations, and their potential use in drug discovery.

A simplified PBPK modeling approach for prediction of pharmacokinetics of four primarily renally excreted and CYP3A metabolized compounds during pregnancy

During pregnancy, a drug's pharmacokinetics may be altered and hence anticipation of potential systemic exposure changes is highly desirable. Physiologically based pharmacokinetics (PBPK) models have recently been used to influence clinical trial design or to facilitate regulatory interactions. Ideally, whole-body PBPK models can be used to predict a drug's systemic exposure in pregnant women based on major physiological changes which can impact drug clearance (i.e., in the kidney and liver) and distribution (i.e., adipose and fetoplacental unit). We described a simple and readily implementable multitissue/organ whole-body PBPK model with key pregnancy-related physiological parameters to characterize the PK of reference drugs (metformin, digoxin, midazolam, and emtricitabine) in pregnant women compared with the PK in nonpregnant or postpartum (PP) women. Physiological data related to changes in maternal body weight, tissue volume, cardiac output, renal function, blood flows, and cytochrome P450 activity were collected from the literature and incorporated into the structural PBPK model that describes HV or PP women PK data. Subsequently, the changes in exposure (area under the curve (AUC) and maximum concentration (C max)) in pregnant women were simulated. Model-simulated PK profiles were overall in agreement with observed data. The prediction fold error for C max and AUC ratio (pregnant vs. nonpregnant) was less than 1.3-fold, indicating that the pregnant PBPK model is useful. The utilization of this simplified model in drug development may aid in designing clinical studies to identify potential exposure changes in pregnant women a priori for compounds which are mainly eliminated renally or metabolized by CYP3A4.

Case studies for practical food effect assessments across BCS/BDDCS class compounds using in silico, in vitro, and preclinical in vivo data

Practical food effect predictions and assessments were described using in silico, in vitro, and/or in vivo preclinical data to anticipate food effects and Biopharmaceutics Classification System (BCS)/Biopharmaceutics Drug Disposition Classification System (BDDCS) class across drug development stages depending on available data: (1) limited in silico and in vitro data in early discovery; (2) preclinical in vivo pharmacokinetic, absorption, and metabolism data at candidate selection; and (3) physiologically based absorption modeling using biorelevant solubility and precipitation data to quantitatively predict human food effects, oral absorption, and pharmacokinetic profiles for early clinical studies. Early food effect predictions used calculated or measured physicochemical properties to establish a preliminary BCS/BDDCS class. A rat-based preclinical BCS/BDDCS classification used rat in vivo fraction absorbed and metabolism data. Biorelevant solubility and precipitation kinetic data were generated via animal pharmacokinetic studies using advanced compartmental absorption and transit (ACAT) models or in vitro methods. Predicted human plasma concentration-time profiles and the magnitude of the food effects were compared with observed clinical data for assessment of simulation accuracy. Simulations and analyses successfully identified potential food effects across BCS/BDDCS classes 1-4 compounds with an average fold error less than 1.6 in most cases. ACAT physiological absorption models accurately predicted positive food effects in human for poorly soluble bases after oral dosage forms. Integration of solubility, precipitation time, and metabolism data allowed confident identification of a compound's BCS/BDDCS class, its likely food effects, along with prediction of human exposure profiles under fast and fed conditions.