Whole-body physiologically based pharmacokinetic models

The Effect of Inflammatory Bowel Disease and Irritable Bowel Syndrome on Pravastatin Oral Bioavailability: In vivo and in silico evaluation using bottom-up wbPBPK modeling

The common disorders irritable bowel syndrome (IBS) and inflammatory bowel disease (IBD) can modify the drugs' pharmacokinetics via their induced pathophysiological changes. This work aimed to investigate the impact of these two diseases on pravastatin oral bioavailability. Rat models for IBS and IBD were used to experimentally test the effects of IBS and IBD on pravastatin pharmacokinetics. Then, the observations made in rats were extrapolated to humans using a mechanistic whole-body physiologically-based pharmacokinetic (wbPBPK) model. The rat in vivo studies done herein showed that IBS and IBD decreased serum albumin (> 11% for both), decreased PRV binding in plasma, and increased pravastatin absolute oral bioavailability (0.17 and 0.53 compared to 0.01) which increased plasma, muscle, and liver exposure. However, the wbPBPK model predicted muscle concentration was much lower than the pravastatin toxicity thresholds for myotoxicity and rhabdomyolysis. Overall, IBS and IBD can significantly increase pravastatin oral bioavailability which can be due to a combination of increased pravastatin intestinal permeability and decreased pravastatin gastric degradation resulting in higher exposure. This is the first study in the literature investigating the effects of IBS and IBD on pravastatin pharmacokinetics. The high interpatient variability in pravastatin concentrations as induced by IBD and IBS can be reduced by oral administration of pravastatin using enteric-coated tablets. Such disease (IBS and IBD)-drug interaction can have more drastic consequences for narrow therapeutic index drugs prone to gastric degradation, especially for drugs with low intestinal permeability.

Salivary Therapeutic Monitoring of Buprenorphine in Neonates After Maternal Sublingual Dosing Guided by Physiologically Based Pharmacokinetic Modeling

BACKGROUND

Opioid use disorder (OUD) during pregnancy is associated with high mortality rates and neonatal opioid withdrawal syndrome (NOWS). Buprenorphine, an opioid, is used to treat OUD and NOWS. Buprenorphine active metabolite (norbuprenorphine) can cross the placenta and cause neonatal respiratory depression (EC50 = 35 ng/mL) at high brain extracellular fluid (bECF) levels. Neonatal therapeutic drug monitoring using saliva decreases the likelihood of distress and infections associated with frequent blood sampling.

METHODS

An adult physiologically based pharmacokinetic model for buprenorphine and norbuprenorphine after intravenous and sublingual administration was constructed, vetted, and scaled to newborn and pregnant populations. The pregnancy model predicted that buprenorphine and norbuprenorphine doses would be transplacentally transferred to the newborns. The newborn physiologically based pharmacokinetic model was used to estimate the buprenorphine and norbuprenorphine levels in newborn plasma, bECF, and saliva after these doses.

RESULTS

After maternal sublingual administration of buprenorphine (4 mg/d), the estimated plasma concentrations of buprenorphine and norbuprenorphine in newborns exceeded the toxicity thresholds for 8 and 24 hours, respectively. However, the norbuprenorphine bECF levels were lower than the respiratory depression threshold. Furthermore, the salivary buprenorphine threshold levels in newborns for buprenorphine analgesia, norbuprenorphine analgesia, and norbuprenorphine hypoventilation were observed to be 22, 2, and 162 ng/mL.

CONCLUSIONS

Using neonatal saliva for buprenorphine therapeutic drug monitoring can facilitate newborn safety during the maternal treatment of OUD using sublingual buprenorphine. Nevertheless, the suitability of using adult values of respiratory depression EC50 for newborns must be confirmed.

Estimation of volatile organic compound exposure concentrations and time to reach a specific dermal absorption using physiologically based pharmacokinetic modeling

A procedure was proposed to estimate dermal exposures based on a physiologically based pharmacokinetic (PBPK) model developed in rats. The study examined vapor concentrations ranging from 500 to 10,000 ppm for dibromomethane and 2,500 to 40,000 ppm for bromochloromethane. These concentrations were reconstructed based on chemical blood levels measured in 4 hr, with errors varying from 0.0% to 52.0%. The PBPK approach adequately predicted the blood concentrations and helped simulate contaminant transport through the stratum corneum and distribution in the body compartments. The proposed technique made it possible to estimate the skin absorption time (SAT) obtained from acute inhalation toxicity data. An inverse relationship exists between the SAT and exposure concentration. The method can be helpful in toxicology and risk assessment of hazardous volatile organic compounds.

Physiologically Based Pharmacokinetic Modeling in Neonates: Current Status and Future Perspectives

Rational drug use in special populations is a clinical problem that doctors and pharma-cists must consider seriously. Neonates are the most physiologically immature and vulnerable to drug dosing. There is a pronounced difference in the anatomical and physiological profiles be-tween neonates and older people, affecting the absorption, distribution, metabolism, and excretion of drugs in vivo, ultimately leading to changes in drug concentration. Thus, dose adjustments in neonates are necessary to achieve adequate therapeutic concentrations and avoid drug toxicity. Over the past few decades, modeling and simulation techniques, especially physiologically based pharmacokinetic (PBPK) modeling, have been increasingly used in pediatric drug development and clinical therapy. This rigorously designed and verified model can effectively compensate for the deficiencies of clinical trials in neonates, provide a valuable reference for clinical research design, and even replace some clinical trials to predict drug plasma concentrations in newborns. This review introduces previous findings regarding age-dependent physiological changes and pathological factors affecting neonatal pharmacokinetics, along with their research means. The application of PBPK modeling in neonatal pharmacokinetic studies of various medications is also reviewed. Based on this, we propose future perspectives on neonatal PBPK modeling and hope for its broader application.

Assessing Dose-Exposure-Response Relationships of Miltefosine in Adults and Children using Physiologically-Based Pharmacokinetic Modeling Approach

OBJECTIVES

Miltefosine is the first and only oral medication to be successfully utilized as an antileishmanial agent. However, the drug is associated with differences in exposure patterns and cure rates among different population groups e.g. ethnicity and age (i.e., children v adults) in clinical trials. In this work, mechanistic population physiologically-based pharmacokinetic (PBPK) models have been developed to study the dose-exposure-response relationship of miltefosine in in silico clinical trials and evaluate the differences in population groups, particularly children and adults.

METHODS

The Simcyp population pharmacokinetics platform was employed to predict miltefosine exposure in plasma and peripheral blood mononuclear cells (PBMCs) in a virtual population under different dosing regimens. The cure rate of a simulation was based on the percentage of number of the individual virtual subjects with AUCd0-28 > 535 µg⋅day/mL in the virtual population.

RESULTS

It is shown that both adult and paediatric PBPK models of miltefosine can be developed to predict the PK data of the clinical trials accurately. There was no significant difference in the predicted dose-exposure-response of the miltefosine treatment for different simulated ethnicities under the same dose regime and the dose-selection strategies determined the clinical outcome of the miltefosine treatment. A lower cure rate of the miltefosine treatment in paediatrics was predicted because a lower exposure of miltefosine was simulated in virtual paediatric in comparison with adult virtual populations when they received the same dose of the treatment.

CONCLUSIONS

The mechanistic PBPK model suggested that the higher fraction of unbound miltefosine in plasma was responsible for a higher probability of failure in paediatrics because of the difference in the distribution of plasma proteins between adults and paediatrics. The developed PBPK models could be used to determine an optimal miltefosine dose regime in future clinical trials.

Evaluating the Pharmacokinetics of Fentanyl in the Brain Extracellular Fluid, Saliva, Urine, and Plasma of Newborns from Transplacental Exposure from Parturient Mothers Dosed with Epidural Fentanyl Utilizing PBPK Modeling

BACKGROUND AND OBJECTIVE

Fentanyl can mitigate the mother and newborn complications resulting from labor pain. However, fentanyl shows a narrow therapeutic index between its respiratory depressive and analgesic effects. Thus, prenatally acquired high fentanyl levels in the newborn brain extracellular fluid (bECF) may induce respiratory depression which requires therapeutic drug monitoring (TDM). TDM using saliva and urine in newborns can reduce the possibility of infections and distress associated with TDM using blood. The objective of this study was to develop  a physiologically based pharmacokinetic (PBPK) model to predict fentanyl concentrations in different newborn tissues due to intrauterine exposure.

METHODS

A fentanyl PBPK model in adults after intravenous and epidural administration was built, validated, and scaled to pregnancy and newborn populations. The dose that the newborn received transplacentally at birth was calculated using the pregnancy model. Then, the newborn bECF, saliva, plasma, and urine concentrations after such a dose were predicted using the newborn PBPK model.

RESULTS

After a maternal epidural dose of fentanyl 245 µg, the predicted newborn plasma and bECF levels were below the toxicity thresholds. Furthermore, the salivary threshold levels in newborns for fentanyl analgesic and respiratory depression effects were estimated to be 0.39 and 14.7-18.2 ng/ml, respectively.

CONCLUSION

The salivary TDM of fentanyl in newborns can be useful in newborns exposed to intrauterine exposure from parturient females dosed with epidural fentanyl. However, newborn-specific values of µ-opioid receptors IC50 for respiratory depression are needed.

The Analysis of Pethidine Pharmacokinetics in Newborn Saliva, Plasma, and Brain Extracellular Fluid After Prenatal Intrauterine Exposure from Pregnant Mothers Receiving Intramuscular Dose Using PBPK Modeling

BACKGROUND AND OBJECTIVE

Pethidine (meperidine) can decrease labor pain-associated mother's hyperventilation and high cortisol-induced newborn complications. However, prenatal transplacentally acquired pethidine can cause side effects in newborns. High pethidine concentrations in the newborn brain extracellular fluid (bECF) can cause a serotonin crisis. Therapeutic drug monitoring (TDM) in newborns' blood distresses them and increases infection incidence, which can be overcome by using salivary TDM. Physiologically based pharmacokinetic (PBPK) modeling can predict drug concentrations in newborn plasma, saliva, and bECF after intrauterine pethidine exposure.

METHODS

A healthy adult PBPK model was constructed, verified, and scaled to newborn and pregnant populations after intravenous and intramuscular pethidine administration. The pregnancy PBPK model was used to predict the newborn dose received transplacentally at birth, which was used as input to the newborn PBPK model to predict newborn plasma, saliva, and bECF pethidine concentrations and set correlation equations between them.

RESULTS

Pethidine can be classified as a Salivary Excretion Classification System class II drug. The developed PBPK model predicted that, after maternal pethidine intramuscular doses of 100 mg and 150 mg, the newborn plasma and bECF concentrations were below the toxicity thresholds. Moreover, it was estimated that newborn saliva concentrations of 4.7 µM, 11.4 µM, and 57.7 µM can be used as salivary threshold concentrations for pethidine analgesic effects, side effects, and the risk for serotonin crisis, respectively, in newborns.

CONCLUSION

It was shown that saliva can be used for pethidine TDM in newborns during the first few days after delivery to mothers receiving pethidine.

Enhancing Atorvastatin In Vivo Oral Bioavailability in the Presence of Inflammatory Bowel Disease and Irritable Bowel Syndrome Using Supercritical Fluid Technology Guided by wbPBPK Modeling in Rat and Human

Inflammatory bowel disease (IBD) and irritable bowel syndrome (IBS) are common disorders that can change the body's physiology and drugs pharmacokinetics. Solid dispersion (SD) preparation using supercritical fluid technology (SFT) has many advantages. Our study aimed to explore the effect of IBS and IBD on atorvastatin (ATV) pharmacokinetics, enhance ATV oral bioavailability (BCS II drug) using SFT, and analyze drug-disease-formulation interaction using a whole-body physiologically based pharmacokinetic (wbPBPK) model in rat and human. A novel ATV formulation was prepared using SFT and characterized in vitro and in vivo in healthy, IBS, and IBD rats. The resulting ATV plasma levels were analyzed using a combination of conventional and wbPBPK approaches. The novel formulation increased ATV solubility by 20-fold and resulted in a zero-order release of up to 95%. Both IBS and IBD increased ATV exposure after oral and intravenous administration by more than 30%. The novel SFT formulation increased ATV bioavailability by 28, 14, and 18% in control, IBD, and IBD rat groups and resulted in more consistent exposure as compared to raw ATV solution. Higher improvements in ATV bioavailability of more than 2-fold upon receiving the novel SFT formulation were predicted by the human wbPBPK model as compared to receiving the conventional tablets. Finally, the established wbPBPK model could describe ATV ADME in the presence of IBS and IBD after oral administration of raw ATV and using the novel SFT formula and can help scale the optimized ATV dosing regimens in the presence of IBS and IBD from rats to humans.

The Development of a PBPK Model for Atomoxetine Using Levels in Plasma, Saliva and Brain Extracellular Fluid in Patients with Normal and Deteriorated Kidney Function

BACKGROUND

Atomoxetine is a treatment for attention-deficit hyperactivity disorder. It inhibits Norepinephrine Transporters (NET) in the brain. Renal impairment can reduce hepatic CYP2D6 activity and atomoxetine elimination which may increase its body exposure. Atomoxetine can be secreted in saliva.

OBJECTIVE

The objective of this work was to test the hypothesis that atomoxetine saliva levels (sATX) can be used to predict ATX brain Extracellular Fluid (bECF) levels and their pharmacological effects in healthy subjects and those with End-Stage Renal Disease (ESRD).

METHODS

The pharmacokinetics of atomoxetine after intravenous administration to rats with chemically induced acute and chronic renal impairments were investigated. A physiologically-based pharmacokinetic (PBPK) model was built and verified in rats using previously published measured atomoxetine levels in plasma and brain tissue. The rat PBPK model was then scaled to humans and verified using published measured atomoxetine levels in plasma, saliva, and bECF.

RESULTS

The rat PBPK model predicted the observed reduced atomoxetine clearance due to renal impairment in rats. The PBPK model predicted atomoxetine exposure in human plasma, sATX and bECF. Additionally, it predicted that ATX bECF levels needed to inhibit NET are achieved at 80 mg dose. In ESRD patients, the developed PBPK model predicted that the previously reported 65% increase in plasma exposure in these patients can be associated with a 63% increase in bECF. The PBPK simulations showed that there is a significant correlation between sATX and bECF in human.

CONCLUSION

Saliva levels can be used to predict atomoxetine pharmacological response.

Artemisinin Cocrystals for Bioavailability Enhancement. Part 2: In Vivo Bioavailability and Physiologically Based Pharmacokinetic Modeling

We report the evaluation and prediction of the pharmacokinetic (PK) performance of artemisinin (ART) cocrystal formulations, that is, 1:1 artemisinin/orcinol (ART-ORC) and 2:1 artemisinin/resorcinol (ART²-RES), using in vivo murine animal and physiologically based pharmacokinetic (PBPK) models. The efficacy of the ART cocrystal formulations along with the parent drug ART was tested in mice infected with Plasmodium berghei. When given at the same dose, the ART cocrystal formulation showed a significant reduction in parasitaemia at day 4 after infection compared to ART alone. PK parameters including C_max (maximum plasma concentration), T_max (time to C_max), and AUC (area under the curve) were obtained by determining drug concentrations in the plasma using liquid chromatography-high-resolution mass spectrometry (LC-HRMS), showing enhanced ART levels after dosage with the cocrystal formulations. The dose-response tests revealed that a significantly lower dose of the ART cocrystals in the formulation was required to achieve a similar therapeutic effect as ART alone. A PBPK model was developed using a PBPK mouse simulator to accurately predict the in vivo behavior of the cocrystal formulations by combining in vitro dissolution profiles with the properties of the parent drug ART. The study illustrated that information from classical in vitro and in vivo experimental investigations of the parent drug of ART formulations can be coupled with PBPK modeling to predict the PK parameters of an ART cocrystal formulation in an efficient manner. Therefore, the proposed modeling strategy could be used to establish in vitro and in vivo correlations for different cocrystals intended to improve dissolution properties and to support clinical candidate selection, contributing to the assessment of cocrystal developability and formulation development.

Utilization of physiologically-based pharmacokinetic model to assess disease-mediated therapeutic protein-disease-drug interaction in immune-mediated inflammatory diseases

It is known that interleukin-6 (IL-6) can significantly modulate some key drug-metabolizing enzymes, such as phase I cytochrome P450s (CYPs). In this study, a physiologically-based pharmacokinetic (PBPK) model was developed to assess CYPs mediated therapeutic protein drug interactions (TP-DIs) in patients with immune-mediated inflammatory diseases (IMIDs) with elevated systemic IL-6 levels when treated by anti-IL-6 therapies. Literature data of IL-6 levels in various diseases were incorporated in SimCYP to construct respective virtual patient populations. The modulation effects of systemic IL-6 level and local IL-6 level in the gastrointestinal tract (GI) on CYPs activities were assessed. Upon blockade of the IL-6 signaling pathway by an anti-IL-6 treatment, the area under plasma concentration versus time curves (AUCs) of S-warfarin, omeprazole, and midazolam were predicted to decrease by up to 40%, 42%, and 46%, respectively. In patients with Crohn's disease and ulcerative colitis treated with an anti-IL-6 therapy, the lowering of the elevated IL-6 levels in the local GI tissue were predicted to result in further decreases in AUCs of those CYP substrates. The propensity of TP-DIs under comorbidity conditions, such as in patients with cancer with IMID, were also explored. With further validation with relevant clinical data, this PBPK model may provide an in silico way to quantify the magnitude of potential TP-DI in patients with elevated IL-6 levels when an anti-IL-6 therapeutic is used with concomitant small-molecule drugs. This model may be further adapted to evaluate the CYP modulation effect by other therapeutic modalities, which would significantly alter levels of proinflammatory cytokines during the treatment period.

A Mechanistic Site-Of-Action Model: A Tool for Informing Right Target, Right Compound, And Right Dose for Therapeutic Antagonistic Antibody Programs

Quantitative modeling is increasingly utilized in the drug discovery and development process, from the initial stages of target selection, through clinical studies. The modeling can provide guidance on three major questions–is this the right target, what are the right compound properties, and what is the right dose for moving the best possible candidate forward. In this manuscript, we present a site-of-action modeling framework which we apply to monoclonal antibodies against soluble targets. We give a comprehensive overview of how we construct the model and how we parametrize it and include several examples of how to apply this framework for answering the questions postulated above. The utilities and limitations of this approach are discussed.

A physiologically-based pharmacokinetic model to describe antisense oligonucleotide distribution after intrathecal administration

Antisense oligonucleotides (ASOs) are promising therapeutic agents for a variety of neurodegenerative and neuromuscular disorders, e.g., Alzheimer's, Parkinson's and Huntington's diseases, spinal muscular atrophy (SMA) and amyotrophic lateral sclerosis (ALS), caused by genetic abnormalities or increased protein accumulation. The blood-brain barrier (BBB) represents a challenge to the delivery of systemically administered ASOs to the relevant sites of action within the central nervous system (CNS). Intrathecal (IT) delivery, in which drugs are administered directly into the cerebrospinal fluid (CSF) space, enables to bypass the BBB. Several IT-administered ASO therapeutics have already demonstrated clinical effect, e.g., nusinersen (SMA) and tofersen (ALS). Due to novelty of IT dosing for ASOs, very limited pharmacokinetic (PK) data is available and only a few modeling reports have been generated. The objective of this work is to advance fundamental understanding of whole-body distribution of IT-administered ASOs. We propose a physiologically-based pharmacokinetic modeling approach to describe the distribution along the neuroaxis based on PK data from non-human primate (NHP) studies. We aim to understand the key processes that drive and limit ASO access to the CNS target tissues. To elucidate the trade-off between parameter identifiability and physiological plausibility of the model, several alternative model structures were chosen and fitted to the NHP data. The model analysis of the NHP data led to important qualitative conclusions that can inform projection to human. In particular, the model predicts that the maximum total exposure in the CNS tissues, including the spinal cord and brain, is achieved within two days after the IT injection, and the maximum amount absorbed by the CNS tissues is about 4% of the administered IT dose. This amount greatly exceeds the CNS exposures delivered by systemic administration of ASOs. Clearance from the CNS is controlled by the rate of transfer from the CNS tissues back to CSF, whereas ASO degradation in tissues is very slow and can be neglected. The model also describes local differences in ASO concentration emerging along the spinal CSF canal. These local concentrations need to be taken into account when scaling the NHP model to human: due to the lengthier human spinal column, inhomogeneity along the spinal CSF may cause even higher gradients and delays potentially limiting ASO access to target CNS tissues.

Physiologically-based pharmacokinetic model for alectinib, ruxolitinib, and panobinostat in the presence of cancer, renal impairment, and hepatic impairment

Renal (RIP) and hepatic (HIP) impairments are prevalent conditions in cancer patients. They can cause changes in gastric emptying time, albumin levels, hematocrit, glomerular filtration rate, hepatic functional volume, blood flow rates, and metabolic activity that can modify drug pharmacokinetics. Performing clinical studies in such populations has ethical and practical issues. Using predictive physiologically-based pharmacokinetic (PBPK) models in the evaluation of the PK of alectinib, ruxolitinib, and panobinostat exposures in the presence of cancer, RIP, and HIP can help in using optimal doses with lower toxicity in these populations. Verified PBPK models were customized under scrutiny to account for the pathophysiological changes induced in these diseases. The PBPK model-predicted plasma exposures in patients with different health conditions within average 2-fold error. The PBPK model predicted an area under the curve ratio (AUCR) of 1, and 1.8, for ruxolitinib and panobinostat, respectively, in the presence of severe RIP. On the other hand, the severe HIP was associated with AUCR of 1.4, 2.9, and 1.8 for alectinib, ruxolitinib, and panobinostat, respectively, in agreement with the observed AUCR. Moreover, the PBPK model predicted that alectinib therapeutic cerebrospinal fluid levels are achieved in patients with non-small cell lung cancer, moderate HIP, and severe HIP at 1-, 1.5-, and 1.8-fold that of healthy subjects. The customized PBPK models showed promising ethical alternatives for simulating clinical studies in patients with cancer, RIP, and HIP. More work is needed to quantify other pathophysiological changes induced by simultaneous affliction by cancer and RIP or HIP.

Quantitative analysis of an impact of P-glycoprotein on edoxaban's disposition using a human physiologically based pharmacokinetic (PBPK) model

The purpose of this study was to evaluate the impact of P-glycoprotein (P-gp) efflux on edoxaban absorption in gastrointestinal tracts quantitatively by a physiologically based pharmacokinetic (PBPK) model constructed with clinical and non-clinical observations (using GastroPlus™ software). An absorption process was described by the advanced compartmental absorption and transit model with the P-gp function. A human PBPK model was constructed by integrating the clinical and non-clinical observations. The constructed model was demonstrated to reproduce the data observed in the mass-balance study. Thus, elimination pathways can be quantitatively incorporated into the model. A constructed model successfully described the difference in slopes of plasma concentration (Cp)-time curve at around 8 - 24 hr post-dose between intravenous infusion and oral administration. Furthermore, the model without P-gp efflux activity can reproduce the Cp-time profile in the absence of P-gp activity observed from the clinical DDI study results. Since the difference of slopes between intravenous infusion and oral administration also disappeared by the absence of P-gp efflux activity, P-gp must be a key molecule to govern edoxaban's PK behavior. The constructed PBPK model will help us to understand the significant contribution of P-gp in edoxaban's disposition in gastrointestinal tracts quantitatively.

Pharmacokinetics and pharmacodynamics of therapeutic antibodies in tumors and tumor-draining lymph nodes

The signaling axis from the primary tumor to the tumor-draining lymph node (TDLN) has emerged as a crucial mediator for the efficacy of immunotherapies in neoadjuvant settings, challenging the primary use of immunotherapy in adjuvant settings. TDLNs are regarded as highly opportunistic sites for cancer cell dissemination and promote further spread via several primary tumor-dependent mechanisms. Lesion-level mixed responses to antibody immunotherapy have been traced to local immune signatures present in the TDLN and the organ-specific primary tumors that they drain. However, the pharmacokinetics (PK) and biodistribution gradients of antibodies in primary tumors and TDLNs have not been systemically evaluated. These concentration gradients are critical in ensuring adequate antibody pharmacodynamic (PD) T-cell activation and/or anti-tumor response. The current work reviews the knowledge for developing physiologically-based PK and pharmacodynamic (PBPK/PD) models to quantify antibody biodistribution gradients in anatomically distinct primary tumors and TDLNs as a means to characterize the clinically observed heterogeneous responses to antibody therapies. Several clinical and pathophysiological considerations in modeling the primary tumor-TDLN axis, as well as a summary of both preclinical and clinical PK/PD lymphatic antibody disposition studies, will be provided.

Saliva versus Plasma Therapeutic Drug Monitoring of Gentamicin in Jordanian Preterm Infants. Development of a Physiologically-Based Pharmacokinetic (PBPK) Model and Validation of Class II Drugs of Salivary Excretion Classification System

Gentamicin has proven to be a very successful treatment for bacterial infection, but it also can cause adverse effects, especially ototoxicity, which is irreversible. Therapeutic drug monitoring (TDM) in saliva is a more convenient non-invasive alternative compared to plasma. A physiologically-based pharmacokinetic (PBPK) model of gentamicin was built and validated using previously-published plasma and saliva data. The validated model was then used to predict experimentally-observed plasma and saliva gentamicin TDM data in Jordanian pediatric preterm infant patients measured using sensitive LCMS/MS method. A correlation was established between plasma and saliva exposures. The developed PBPK model predicted previously reported gentamicin levels in plasma, saliva and those observed in the current study. A good correlation was found between plasma and saliva exposures. The PBPK model predicted that gentamicin in saliva is 5-7 times that in plasma, which is in agreement with observed results. Saliva can be used as an alternative for TDM of gentamicin in preterm infant patients. Exposure to gentamicin in plasma and saliva can reliably be predicted using the developed PBPK model in patients.

Pharmacokinetics and population pharmacokinetics in pediatric oncology

Pharmacokinetic research has become increasingly important in pediatric oncology as it can have direct clinical implications and is a crucial component in individualized medicine. Population pharmacokinetics has become a popular method especially in children, due to the potential for sparse sampling, flexible sampling times, computing of heterogeneous data, and identification of variability sources. However, population pharmacokinetic reports can be complex and difficult to interpret. The aim of this article is to provide a basic explanation of population pharmacokinetics, using clinical examples from the field of pediatric oncology, to facilitate the translation of pharmacokinetic research into the daily clinic.

Recent advances in physiologically based pharmacokinetic and pharmacodynamic models for anticancer nanomedicines

Nanoparticles (NPs) have distinct pharmacokinetic (PK) properties and can potentially improve the absorption, distribution, metabolism, and elimination (ADME) of small-molecule drugs loaded therein. Owing to the unwanted toxicities of anticancer agents in healthy organs and tissues, their precise delivery to the tumor is an essential requirement. There have been numerous advancements in the development of nanomedicines for cancer therapy. Physiologically based PK (PBPK) models serve as excellent tools for describing and predicting the ADME properties and the efficacy and toxicity of drugs, in combination with pharmacodynamic (PD) models. The recent preliminary application of these modeling approaches to NPs demonstrated their potential benefits in research and development processes relevant to the ADME and pharmacodynamics of NPs and nanomedicines. Here, we comprehensively review the pharmacokinetics of NPs, the developed PBPK models for anticancer NPs, and the developed PD model for anticancer agents.

Pivotal Considerations for Optimal Deployment of Healthy Volunteers in Oncology Drug Development

Oncology drug development is among the most challenging of any therapeutic area, with first-in-human trials expected to deliver information on both safety and activity. Until recently, therapeutic approaches in oncology focused on cytotoxic chemotherapy agents, ruling out even the possibility of enrolling normal healthy volunteers (NHVs) in clinical trials due to safety considerations. The emergence of noncytotoxic modalities, including molecularly targeted agents with more favorable safety profiles, however, has led to increasing numbers of clinical pharmacology studies of these agents being conducted in NHVs. Beyond rapid enrollment and cost savings, there are other advantages of conducting specific types of studies in NHVs with the goal of more appropriate dosing decisions in certain subsets of the intended patient populations, allowing for enrollment of such patients in therapeutic trials from which they might otherwise have been excluded. Nevertheless, the decision must be carefully weighed against potential disadvantages, and although the considerations surrounding conduct of clinical trials using NHVs are generally well-defined in most other therapeutic areas, they are less well-defined in oncology.

Automated proper lumping for simplification of linear physiologically based pharmacokinetic systems

Physiologically based pharmacokinetic (PBPK) models are an important type of systems model used commonly in drug development before commencement of first-in-human studies. Due to structural complexity, these models are not easily utilised for future data-driven population pharmacokinetic (PK) analyses that require simpler models. In the current study we aimed to explore and automate methods of simplifying PBPK models using a proper lumping technique. A linear 17-state PBPK model for fentanyl was identified from the literature. Four methods were developed to search the optimal lumped model, including full enumeration (the reference method), non-adaptive random search (NARS), scree plot plus NARS, and simulated annealing (SA). For exploratory purposes, it was required that the total area under the fentanyl arterial concentration–time curve (AUC) between the lumped and original models differ by 0.002% at maximum. In full enumeration, a 4-state lumped model satisfying the exploratory criterion was found. In NARS, a lumped model with the same number of lumped states was found, requiring a large number of random samples. The scree plot provided a starting lumped model to NARS and the search completed within a short time. In SA, a 4-state lumped model was consistently delivered. In simplify an existing linear fentanyl PBPK model, SA was found to be robust and the most efficient and may be suitable for general application to other larger-scale linear systems. Ultimately, simplified PBPK systems with fundamental mechanisms may be readily used for data-driven PK analyses.

Americium Systemic Biokinetic Model for Rats

In this work, a baseline compartmental model of the distribution and retention of americium in the rat for a systemic intake was derived. The model was derived from data obtained from a study designed to evaluate the behavior of americium in the first 28 days after incorporation. A pharmacokinetic (PK)-front-end modeling approach was used to specify transfer to and from the extracellular fluids (ECF) in the various tissues in terms of vascular flow and volumes of ECF. Back-end rates representing transport into and out of the cells were determined empirically. Uncertainties in transfer rates were investigated using Markov chain Monte Carlo (MCMC). The combination of PK-front-end model and the back-end model structure used allowed for extrapolation to the earliest times with small uncertainty. This approach clearly demonstrated the rapid transfer of material from ECF to liver and bone. This model provides a baseline for modeling the action of decorporation agents, such as DTPA.

Revisiting a physiologically based pharmacokinetic model for cocaine with a forensic scope

A whole-body permeability-rate-limited physiologically based pharmacokinetic (PBPK) model for cocaine was developed and adjusted with the pharmacokinetic data from studies with animals and reparametrized scaling to humans with the aim to predict the concentration-time profiles of the drug in blood and different tissues in humans. Estimated time course concentrations could be used as an interpretation tool by forensic toxicologists. The model estimations were compared successfully with pharmacokinetic parameters and time to peak for some effects reported in the literature. Once developed, the PBPK model was employed to predict the time course tissue concentrations reported in previous distribution studies introducing individualizing data. The heart and brain concentrations estimated by the model match adequately with the time and duration of some effects such as chronotropic and psychoactive effects, respectively. This work is the first attempt for employing PBPK modeling as a tool for forensic interpretation. Future modeling of other cocaine metabolite profiles or interaction when co-administered with other substances, such as alcohol, might be developed in the future.

Suggestions for Model-Informed Precision Dosing to Optimize Neonatal Drug Therapy

Evidence for dosing, efficacy, and safety of most medications used to treat neonates is sparse. Thus, dosing is usually derived by extrapolation from adult and pediatric pharmacologic data with scaling by body weight or body surface area. This may lead to drug dosing that is unsafe or ineffective. However, new strategies are being developed and studied to dose medications in critically ill neonates. Mass spectroscopy technology capable of quickly analyzing drug levels is readily available. Software that integrates population pharmacokinetics and pharmacodynamics with data from sparse samples from neonates allows for timely adjustments of dosing to achieve the desired effect while minimizing adverse outcomes. Some genetic polymorphisms that affect drug response in neonates have also been reported. This review highlights aspects of drug response and how it is impacted by prematurity, assesses pharmacogenomic studies in neonates, and offers suggestions for innovative pharmacokinetic/pharmacodynamic model-based approaches that combine population- or physiology-based pharmacology data, Bayesian analysis, and electronic decision support tools for precision dosing in neonates while illustrating examples where this approach can be used to optimize medical therapy in neonates. Barriers to implementing precision dosing in neonates and how to overcome them are also discussed.

Prediction of tissue concentrations of monoclonal antibodies in mice from plasma concentrations

The objectives of this study were to develop and evaluate allometric methods for predicting tissue-to-plasma partition coefficients (Kp) in mice from experimentally determined in-vivo volume of distribution at steady state (Vss) for monoclonal antibodies (mAbs). The Vss was allometrically predicted (using a fixed exponent 1.0 or 0.9) in a given tissue of the mice. The Kp was predicted using Vss and tissue specific physiological parameters. In total, Kp values were predicted for 20 mAbs, 121 tissues, and 665 tissue concentrations. The predicted Kp values and tissue concentrations were compared with the experimental results as well as an empirically predicted antibody biodistribution coefficient (ABC). Comparison of the predicted Kp values by the two proposed methods with experimentally determined Kp values indicated that 64-75% of the predicted Kp values were within two-fold prediction error. For 665 tissue concentrations, 63%, 74%, and 48% tissue concentration ratio were within 0.5-2 fold prediction error by exponent 1.0, exponent 0.9, and ABC, respectively. The proposed allometric methods are better than ABC method for the prediction of tissue Kp values and tissue concentrations. The proposed methods can reasonably predict tissue concentrations of mAbs using plasma concentration gathered at early stage of biologics development.

Application of Physiologically Based Pharmacokinetic Modeling in Understanding Bosutinib Drug-Drug Interactions: Importance of Intestinal P-Glycoprotein

Bosutinib is an orally available Src/Abl tyrosine kinase inhibitor indicated for the treatment of patients with Ph+ chronic myelogenous leukemia at a clinically recommended dose of 500 mg once daily. Clinical results indicated that increases in bosutinib oral exposures were supraproportional at the lower doses (50-200 mg) and approximately dose-proportional at the higher doses (200-600 mg). Bosutinib is a substrate of CYP3A4 and P-glycoprotein and exhibits pH-dependent solubility with moderate intestinal permeability. These findings led us to investigate the factors influencing the underlying pharmacokinetic mechanisms of bosutinib with physiologically based pharmacokinetic (PBPK) models. Our primary objectives were to: 1) refine the previously developed bosutinib PBPK model on the basis of the latest oral bioavailability data and 2) verify the refined PBPK model with P-glycoprotein kinetics on the basis of the bosutinib drug-drug interaction (DDI) results with ketoconazole and rifampin. Additionally, the verified PBPK model was applied to predict bosutinib DDIs with dual CYP3A/P-glycoprotein inhibitors. The results indicated that 1) the refined PBPK model adequately described the observed plasma concentration-time profiles of bosutinib and 2) the verified PBPK model reasonably predicted the effects of ketoconazole and rifampin on bosutinib exposures by accounting for intestinal P-glycoprotein inhibition/induction. These results suggested that bosutinib DDI mechanism could involve not only CYP3A4-mediated metabolism but also P-glycoprotein-mediated efflux on absorption. In summary, P-glycoprotein kinetics could constitute an element in the PBPK models critical to understanding the pharmacokinetic mechanism of dual CYP3A/P-glycoprotein substrates, such as bosutinib, that exhibit nonlinear pharmacokinetics owing largely to a saturation of intestinal P-glycoprotein-mediated efflux.

A Population WB-PBPK Model of Colistin and its Prodrug CMS in Pigs: Focus on the Renal Distribution and Excretion

PURPOSE

The objective was the development of a whole-body physiologically-based pharmacokinetic (WB-PBPK) model for colistin, and its prodrug colistimethate sodium (CMS), in pigs to explore their tissue distribution, especially in kidneys.

METHODS

Plasma and tissue concentrations of CMS and colistin were measured after systemic administrations of different dosing regimens of CMS in pigs. The WB-PBPK model was developed based on these data according to a non-linear mixed effect approach and using NONMEM software. A detailed sub-model was implemented for kidneys to handle the complex disposition of CMS and colistin within this organ.

RESULTS

The WB-PBPK model well captured the kinetic profiles of CMS and colistin in plasma. In kidneys, an accumulation and slow elimination of colistin were observed and well described by the model. Kidneys seemed to have a major role in the elimination processes, through tubular secretion of CMS and intracellular degradation of colistin. Lastly, to illustrate the usefulness of the PBPK model, an estimation of the withdrawal periods after veterinary use of CMS in pigs was made.

CONCLUSIONS

The WB-PBPK model gives an insight into the renal distribution and elimination of CMS and colistin in pigs; it may be further developed to explore the colistin induced-nephrotoxicity in humans.

The Effect of Total Tumor Volume on the Biologically Effective Dose to Tumor and Kidneys for 177Lu-Labeled PSMA Peptides

The aim of this work was to simulate the effect of prostate-specific membrane antigen (PSMA)-positive total tumor volume (TTV) on the biologically effective doses (BEDs) to tumors and organs at risk in patients with metastatic castration-resistant prostate cancer who are undergoing ¹⁷⁷Lu-PSMA radioligand therapy. Methods: A physiologically based pharmacokinetic model was fitted to the data of 13 patients treated with ¹⁷⁷Lu-PSMA I&T (a PSMA inhibitor for imaging and therapy). The tumor, kidney, and salivary gland BEDs were simulated for TTVs of 0.1-10 L. The activity and peptide amounts leading to an optimal tumor-to-kidneys BED ratio were also investigated. Results: When the TTV was increased from 0.3 to 3 L, the simulated BEDs to tumors, kidneys, parotid glands, and submandibular glands decreased from 22 ± 15 to 11.0 ± 6.0 Gy_1.49, 6.5 ± 2.3 to 3.7 ± 1.4 Gy_2.5, 11.0 ± 2.7 to 6.4 ± 1.9 Gy_4.5, and 10.9 ± 2.7 to 6.3 ± 1.9 Gy_4.5, respectively (where the subscripts denote that an α/β of 1.49, 2.5, or 4.5 Gy was used to calculate the BED). The BED to the red marrow increased from 0.17 ± 0.05 to 0.32 ± 0.11 Gy₁₅ For patients with a TTV of more than 0.3 L, the optimal amount of peptide was 273 ± 136 nmol and the optimal activity was 10.4 ± 4.4 GBq. Conclusion: This simulation study suggests that in patients with large PSMA-positive tumor volumes, higher activities and peptide amounts can be safely administered to maximize tumor BEDs without exceeding the tolerable BED to the organs at risk.

A generic whole body physiologically based pharmacokinetic model for therapeutic proteins in PK-Sim

Proteins are an increasingly important class of drugs used as therapeutic as well as diagnostic agents. A generic physiologically based pharmacokinetic (PBPK) model was developed in order to represent at whole body level the fundamental mechanisms driving the distribution and clearance of large molecules like therapeutic proteins. The model was built as an extension of the PK-Sim model for small molecules incorporating (i) the two-pore formalism for drug extravasation from blood plasma to interstitial space, (ii) lymph flow, (iii) endosomal clearance and (iv) protection from endosomal clearance by neonatal Fc receptor (FcRn) mediated recycling as especially relevant for antibodies. For model development and evaluation, PK data was used for compounds with a wide range of solute radii. The model supports the integration of knowledge gained during all development phases of therapeutic proteins, enables translation from pre-clinical species to human and allows predictions of tissue concentration profiles which are of relevance for the analysis of on-target pharmacodynamic effects as well as off-target toxicity. The current implementation of the model replaces the generic protein PBPK model available in PK-Sim since version 4.2 and becomes part of the Open Systems Pharmacology Suite.

Designer bacteria as intratumoural enzyme biofactories

Bacterial-directed enzyme prodrug therapy (BDEPT) is an emerging form of treatment for cancer. It is a biphasic variant of gene therapy in which a bacterium, armed with an enzyme that can convert an inert prodrug into a cytotoxic compound, induces tumour cell death following tumour-specific prodrug activation. BDEPT combines the innate ability of bacteria to selectively proliferate in tumours, with the capacity of prodrugs to undergo contained, compartmentalised conversion into active metabolites in vivo. Although BDEPT has undergone clinical testing, it has received limited clinical exposure, and has yet to achieve regulatory approval. In this article, we review BDEPT from the system designer's perspective, and provide detailed commentary on how the designer should strategize its development de novo. We report on contemporary advancements in this field which aim to enhance BDEPT in terms of safety and efficacy. Finally, we discuss clinical and regulatory barriers facing BDEPT, and propose promising approaches through which these hurdles may best be tackled.

Quantitative systems pharmacology and the personalized drug-microbiota-diet axis

Precision medicine is an emerging paradigm that aims at maximizing the benefits and minimizing the adverse effects of drugs. Realistic mechanistic models are needed to understand and limit heterogeneity in drug responses. While pharmacokinetic models describe in detail a drug's absorption and metabolism, they generally do not account for individual variations in response to environmental influences, in addition to genetic variation. For instance, the human gut microbiota metabolizes drugs and is modulated by diet, and it exhibits significant variation among individuals. However, the influence of the gut microbiota on drug failure or drug side effects is under-researched. Here, we review recent advances in computational modeling approaches that could contribute to a better, mechanism-based understanding of drug–microbiota–diet interactions and their contribution to individual drug responses. By integrating systems biology and quantitative systems pharmacology with microbiology and nutrition, the conceptually and technologically demand for novel approaches could be met to enable the study of individual variability, thereby providing breakthrough support for progress in precision medicine.

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The response to drug treatment is highly variable among individuals.

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Pharmacokinetic models have been used to accelerate drug discovery but do not account for a person's diet and gut microbiota.

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Here, we propose combining constraint-based and pharmacokinetic modeling to capture also dietary and gut microbial metabolism.

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Such integrated models will enable the individual-specific prediction of drug response.

An extended vascular model for less biased estimation of permeability parameters in DCE-T1 images

One of the key elements in dynamic contrast enhanced (DCE) image analysis is the arterial input function (AIF). Traditionally, in DCE studies a global AIF sampled from a major artery or vein is used to estimate the vascular permeability parameters; however, not addressing dispersion and delay of the AIF at the tissue level can lead to biased estimates of these parameters. To find less biased estimates of vascular permeability parameters, a vascular model of the cerebral vascular system is proposed that considers effects of dispersion of the AIF in the vessel branches, as well as extravasation of the contrast agent (CA) to the extravascular-extracellular space. Profiles of the CA concentration were simulated for different branching levels of the vascular structure, combined with the effects of vascular leakage. To estimate the permeability parameters, the extended model was applied to these simulated signals and also to DCE-T1 (dynamic contrast enhanced T₁ ) images of patients with glioblastoma multiforme tumors. The simulation study showed that, compared with the case of solving the pharmacokinetic equation with a global AIF, using the local AIF that is corrected by the vascular model can give less biased estimates of the permeability parameters (K^trans , v_p and K_b ). Applying the extended model to signals sampled from different areas of the DCE-T1 image showed that it is able to explain the CA concentration profile in both the normal areas and the tumor area, where effects of vascular leakage exist. Differences in the values of the permeability parameters estimated in these images using the local and global AIFs followed the same trend as the simulation study. These results demonstrate that the vascular model can be a useful tool for obtaining more accurate estimation of parameters in DCE studies.

A parametric model of the brain vascular system for estimation of the arterial input function (AIF) at the tissue level

In this paper, we introduce a novel model of the brain vascular system, which is developed based on laws of fluid dynamics and vascular morphology. This model is used to address dispersion and delay of the arterial input function (AIF) at different levels of the vascular structure and to estimate the local AIF in DCE images. We developed a method based on the simplex algorithm and Akaike information criterion to estimate the likelihood of the contrast agent concentration signal sampled in DCE images belonging to different layers of the vascular tree or being a combination of different signal levels from different nodes of this structure. To evaluate this method, we tested the method on simulated local AIF signals at different levels of this structure. Even down to a signal to noise ratio of 5.5 our method was able to accurately detect the branching level of the simulated signals. When two signals with the same power level were combined, our method was able to separate the base signals of the composite AIF at the 50% threshold. We applied this method to dynamic contrast enhanced computed tomography (DCE-CT) data, and using the parameters estimated by our method we created an arrival time map of the brain. Our model corrected AIF can be used for solving the pharmacokinetic equations for more accurate estimation of vascular permeability parameters in DCE imaging studies.

Physiologically Based Simulations of Deuterated Glucose for Quantifying Cell Turnover in Humans

In vivo [6,6-²H₂]-glucose labeling is a state-of-the-art technique for quantifying cell proliferation and cell disappearance in humans. However, there are discrepancies between estimates of T cell proliferation reported in short (1-day) versus long (7-day) ²H₂-glucose studies and very-long (9-week) ²H₂O studies. It has been suggested that these discrepancies arise from underestimation of true glucose exposure from intermittent blood sampling in the 1-day study. Label availability in glucose studies is normally approximated by a “square pulse” (Sq pulse). Since the body glucose pool is small and turns over rapidly, the availability of labeled glucose can be subject to large fluctuations and the Sq pulse approximation may be very inaccurate. Here, we model the pharmacokinetics of exogenous labeled glucose using a physiologically based pharmacokinetic (PBPK) model to assess the impact of a more complete description of label availability as a function of time on estimates of CD4+ and CD8+ T cell proliferation and disappearance. The model enabled us to predict the exposure to labeled glucose during the fasting and de-labeling phases, to capture the fluctuations of labeled glucose availability caused by the intake of food or high-glucose beverages, and to recalculate the proliferation and death rates of immune cells. The PBPK model was used to reanalyze experimental data from three previously published studies using different labeling protocols. Although using the PBPK enrichment profile decreased the 1-day proliferation estimates by about 4 and 7% for CD4 and CD8+ T cells, respectively, differences with the 7-day and 9-week studies remained significant. We conclude that the approximations underlying the “square pulse” approach—recently suggested as the most plausible hypothesis—only explain a component of the discrepancy in published T cell proliferation rate estimates.

A whole-body physiologically based pharmacokinetic (WB-PBPK) model of ciprofloxacin: a step towards predicting bacterial killing at sites of infection

The purpose of this study was to develop a whole-body physiologically based pharmacokinetic (WB-PBPK) model for ciprofloxacin for ICU patients, based on only plasma concentration data. In a next step, tissue and organ concentration time profiles in patients were predicted using the developed model. The WB-PBPK model was built using a non-linear mixed effects approach based on data from 102 adult intensive care unit patients. Tissue to plasma distribution coefficients (Kp) were available from the literature and used as informative priors. The developed WB-PBPK model successfully characterized both the typical trends and variability of the available ciprofloxacin plasma concentration data. The WB-PBPK model was thereafter combined with a pharmacokinetic-pharmacodynamic (PKPD) model, developed based on in vitro time-kill data of ciprofloxacin and Escherichia coli to illustrate the potential of this type of approach to predict the time-course of bacterial killing at different sites of infection. The predicted unbound concentration-time profile in extracellular tissue was driving the bacterial killing in the PKPD model and the rate and extent of take-over of mutant bacteria in different tissues were explored. The bacterial killing was predicted to be most efficient in lung and kidney, which correspond well to ciprofloxacin's indications pneumonia and urinary tract infections. Furthermore, a function based on available information on bacterial killing by the immune system in vivo was incorporated. This work demonstrates the development and application of a WB-PBPK-PD model to compare killing of bacteria with different antibiotic susceptibility, of value for drug development and the optimal use of antibiotics .

Addressing Adherence Using Genotype-Specific PBPK Modeling-Impact of Drug Holidays on Tamoxifen and Endoxifen Plasma Levels

Introduction: Tamoxifen is one of the most common treatment opportunities for hormonal positive breast cancer. Despite its good tolerability, patients demonstrate decreasing adherence over years impacting on therapeutic success. PBPK modeling was applied to demonstrate the impact of drug holidays on plasma levels of tamoxifen and its active metabolite endoxifen for different CYP2D6 genotypes.

Materials and Methods: A virtual study with 24,000 patients was conducted in order to investigate the development of tamoxifen steady-state kinetics in patient groups of different CYP2D6 genotypes. The impact of drug holidays on steady-state kinetics was investigated assuming changing drug holiday scenarios.

Results: Drug holidays in CYP2D6 extensive and intermediate metabolizers (EMs, IMs) exceeding 1 month lead to a decrease of endoxifen steady-state trough levels below the 5th percentile of the control group. Assuming drug holidays of 1, 2, or 3 months and administering a fixed-dose combination of 20 mg tamoxifen and 3 mg endoxifen EMs demonstrated re-established endoxifen steady-state trough levels after 5, 8, and 9 days. IMs receiving the same fixed-dose combination demonstrated re-established endoxifen steady-state trough levels after 7, 10, and 11 days.

Discussion: The PBPK model impressively demonstrates the impact of drug holidays in different CYP2D6 genotypes on PK. Population simulation results indicate that drug holidays of more than 2 weeks cause a tremendous decrease of plasma levels despite the long half-life of tamoxifen. To improve therapeutic success, PBPK modeling allows identifying genotype-specific differences in PK following drug holidays and adequate treatment with loading doses.

Review on crosstalk and common mechanisms of endocrine disruptors: Scaffolding to improve PBPK/PD model of EDC mixture

Endocrine disruptor compounds (EDCs) are environment chemicals that cause harmful effects through multiple mechanisms, interfering with hormone system resulting in alteration of homeostasis, reproduction and developmental effect. Many of these EDCs have concurrent exposure with crosstalk and common mechanisms which may lead to dynamic interactions. To carry out risk assessment of EDCs' mixture, it is important to know the detailed toxic pathway, crosstalk of receptor and other factors like critical window of exposure. In this review, we summarize the major mechanism of actions of EDCs with the different/same target organs interfering with the same/different class of hormone by altering their synthesis, metabolism, binding and cellular action. To show the impact of EDCs on life stage development, a case study on female fertility affecting germ cell is illustrated. Based on this summarized discussion, major groups of EDCs are classified based on their target organ, mode of action and potential risk. Finally, a conceptual model of pharmacodynamic interaction is proposed to integrate the crosstalk and common mechanisms that modulate estrogen into the predictive mixture dosimetry model with dynamic interaction of mixture. This review will provide new insight for EDCs' risk assessment and can be used to develop next generation PBPK/PD models for EDCs' mixture analysis.

Systems pharmacology and enhanced pharmacodynamic models for understanding antibody-based drug action and toxicity

Pharmacokinetic (PK) and pharmacodynamic (PD) models seek to describe the temporal pattern of drug exposures and their associated pharmacological effects produced at micro- and macro-scales of organization. Antibody-based drugs have been developed for a large variety of diseases, with effects exhibited through a comprehensive range of mechanisms of action. Mechanism-based PK/PD and systems pharmacology models can play a major role in elucidating and integrating complex antibody pharmacological properties, such as nonlinear disposition and dynamical intracellular signaling pathways triggered by ligation to their cognate targets. Such complexities can be addressed through the use of robust computational modeling techniques that have proven powerful tools for pragmatic characterization of experimental data and for theoretical exploration of antibody efficacy and adverse effects. The primary objectives of such multi-scale mathematical models are to generate and test competing hypotheses and to predict clinical outcomes. In this review, relevant systems pharmacology and enhanced PD (ePD) models that are used as predictive tools for antibody-based drug action are reported. Their common conceptual features are highlighted, along with approaches used for modeling preclinical and clinically available data. Key examples illustrate how systems pharmacology and ePD models codify the interplay among complex biology, drug concentrations, and pharmacological effects. New hybrid modeling concepts that bridge cutting-edge systems pharmacology models with established PK/ePD models will be needed to anticipate antibody effects on disease in subpopulations and individual patients.

Physiologically based pharmacokinetic models of small molecules and therapeutic antibodies: a mini-review on fundamental concepts and applications

The mechanisms of absorption, distribution, metabolism and elimination of small and large molecule therapeutics differ significantly from one another and can be explored within the framework of a physiologically based pharmacokinetic (PBPK) model. This paper briefly reviews fundamental approaches to PBPK modeling, in which drug kinetics within tissues and organs are explicitly represented using physiologically meaningful parameters. The differences in PBPK models applied to small/large molecule drugs are highlighted, thus elucidating differences in absorption, distribution and elimination properties between these two classes of drugs in a systematic manner. The absorption of small and large molecules differs with respect to their common extravascular routes of delivery (oral versus subcutaneous). The role of the lymphatic system in drug distribution, and the involvement of tissues as sites of elimination (through catabolism and target mediated drug disposition) are unique features of antibody distribution and elimination that differ from small molecules, which are commonly distributed into the tissues but are eliminated primarily by liver metabolism. Fundamental differences exist in the ability to predict human pharmacokinetics based upon preclinical data due to differing mechanisms governing small and large molecule disposition. These differences have influence on the evolving utilization of PBPK modeling in the discovery and development of small and large molecule therapeutics.

Microfluidic-Based Multi-Organ Platforms for Drug Discovery

Development of predictive multi-organ models before implementing costly clinical trials is central for screening the toxicity, efficacy, and side effects of new therapeutic agents. Despite significant efforts that have been recently made to develop biomimetic in vitro tissue models, the clinical application of such platforms is still far from reality. Recent advances in physiologically-based pharmacokinetic and pharmacodynamic (PBPK-PD) modeling, micro- and nanotechnology, and in silico modeling have enabled single- and multi-organ platforms for investigation of new chemical agents and tissue-tissue interactions. This review provides an overview of the principles of designing microfluidic-based organ-on-chip models for drug testing and highlights current state-of-the-art in developing predictive multi-organ models for studying the cross-talk of interconnected organs. We further discuss the challenges associated with establishing a predictive body-on-chip (BOC) model such as the scaling, cell types, the common medium, and principles of the study design for characterizing the interaction of drugs with multiple targets.

Use of a semi-physiological pharmacokinetic model to investigate the influence of itraconazole on tacrolimus absorption, distribution and metabolism in mice

1. The aim of this study was to investigate the influence of itraconazole (ITCZ) on tacrolimus absorption, distribution and metabolism by developing a semi-physiological pharmacokinetic model of tacrolimus in mice. 2. Mice were randomly divided into four groups, namely control group (CG, taking 3 mg kg^-1 tacrolimus only), low-dose group (LDG, taking tacrolimus with 12.5 mg kg^-1 ITCZ), medium-dose group (MDG, taking tacrolimus with 25 mg kg^-1 ITCZ) and high-dose group (HDG, taking tacrolimus with 50 mg kg^-1 ITCZ). 3. Liver clearance (CL_li) decreased significantly (**p < 0.01) in LDG (35.3%), MDG (45.2%) and HDG (58.7%) mice compared to CG mice. With respect to gut clearance (CL_gu), significant (**p < 0.01) decrease was also revealed in LDG (35.9%), MDG (50.2%) and HDG (64.6%) mice. A significant (**p < 0.01) higher tacrolimus brain-to-blood partition coefficient (K_t,br) was found in MDG (25.3%) and HDG (55.9%) mice than in CG mice. Moreover, a significant (*p < 0.05) increase (16.3%) was found in the absorption rate constant (K_a) in HDG mice compared to CG mice. There was a significant (**p < 0.01) association between ITCZ dose and the change in CL_gu (ΔCL_gu, r= -0.790), the change in CL_li (ΔCL_li, r= -0.787) and the change in K_t,br (ΔK_t,br, r = 0.727), while the association between ITCZ dose and the change in K_a (ΔK_a) was not significant (p > 0.05). 4. These findings could be useful in predicting the efficacy and toxicity of tacrolimus, and drug-drug interaction of ITCZ and tarcolimus in human.

Physiologically Based Pharmacokinetic (PBPK) Model for Biodistribution of Radiolabeled Peptides in Patients with Neuroendocrine Tumours

Objective(s):

The objectives of this work was to assess the benefits of the application of Physiologically Based Pharmacokinetic (PBPK) models in patients with different neuroendocrine tumours (NET) who were treated with Lu-177 DOTATATE. The model utilises clinical data on biodistribution of radiolabeled peptides (RLPs) obtained by whole body scintigraphy (WBS) of the patients.

Methods:

The blood flow restricted (perfusion rate limited) type of the PBPK model for biodistribution of radiolabeled peptides (RLPs) in individual human organs is based on the multi-compartment approach, which takes into account the main physiological processes in the organism: absorption, distribution, metabolism and excretion (ADME). The approach calibrates the PBPK model for each patient in order to increase the accuracy of the dose estimation. Datasets obtained using WBS in four patients have been used to obtain the unknown model parameters. The scintigraphic data were acquired using a double head gamma camera in patients with different neuroendocrine tumours who were treated with Lu-177 DOTATATE. The activity administered to each patient was 7400 MBq.

Results:

Satisfactory agreement of the model predictions with the data obtained from the WBS for each patient has been achieved.

Conclusion:

The study indicates that the PBPK model can be used for more accurate calculation of biodistribution and absorbed doses in patients. This approach is the first attempt of utilizing scintigraphic data in PBPK models, which was obtained during Lu-177 peptide therapy of patients with NET.