Whole body pharmacokinetic models

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

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

PBPK modeling to support risk assessment of pyrethroid exposure in French pregnant women

BACKGROUND

Pyrethroids are widely used pesticides and are suspected to affect children's neurodevelopment. The characterization of pyrethroid exposure during critical windows of development, such as fetal development and prenatal life, is essential to ensure a better understanding of pyrethroids potential effects within the concept of Developmental Origins of Health and Disease.

OBJECTIVE

The aim of this study was to estimate maternal exposure of French pregnant women from biomonitoring data and simulate maternal and fetal internal concentrations of 3 pyrethroids (permethrin, cypermethrin and deltamethrin) using a multi-substance pregnancy-PBPK (physiologically based pharmacokinetics) model. The estimated maternal exposures were compared to newly proposed toxicological reference values (TRV) children specific also called draft child-specific reference value to assess pyrethroid exposure risk during pregnancy i.e. during the in utero exposure period.

METHODS

A pregnancy-PBPK model was developed based on an existing adult pyrethroids model. The maternal exposure to each parent compound of pregnant women of the Elfe (French Longitudinal Study since Childhood) cohort was estimated by reverse dosimetry based on urinary biomonitoring data. To identify permethrin and cypermethrin contribution to their common urinary biomarkers of exposure, an exposure ratio based on biomarkers in hair was tested. Finally, exposure estimates were compared to current and draft child-specific reference values derived from rodent prenatal and postnatal exposure studies.

RESULTS

The main contributor to maternal pyrethroid diet intake is cis-permethrin. In blood, total internal concentrations main contributor is deltamethrin. In brain, the major contributors to internal pyrethroid exposure are deltamethrin for fetuses and cis-permethrin for mothers. Risk is identified only for permethrin when referring to the draft child-specific reference value. 2.5% of the population exceeded permethrin draft child-specific reference value.

CONCLUSIONS

A new reverse dosimetry approach using PBPK model combined with human biomonitoring data in urine and hair was proposed to estimate Elfe pregnant population exposure to a pyrethroids mixture with common metabolites.

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 physiologically-based pharmacokinetic model for predicting doxorubicin disposition in multiple tissue levels and quantitative toxicity assessment

Doxorubicin is a widely-used chemotherapeutic drug, however its high toxicity poses a significant challenge for its clinical use. To address this issue, a physiologically-based pharmacokinetic (PBPK) model was implemented to quantitatively assess doxorubicin toxicity at cellular scale. Due to its unique pharmacokinetic behavior (e.g. high volume of distribution and affinity to extra-plasma tissue compartments), we proposed a modified PBPK model structure and developed the model with multispecies extrapolation to compensate for the limitation of obtaining clinical tissue data. Our model predicted the disposition of doxorubicin in multiple tissues including clinical tissue data with an overall absolute average fold error (AAFE) of 2.12. The model's performance was further validated with 8 clinical datasets in combined with intracellular doxorubicin concentration with an average AAFE of 1.98. To assess the potential cellular toxicity, toxicity levels and area under curve (AUC) were defined for different dosing regimens in toxic and non-toxic scenarios. The cellular concentrations of doxorubicin in multiple organ sites associated with commonly observed adverse effects (AEs) were simulated and calculated the AUC for quantitative assessments. Our findings supported the clinical dosing regimen of 75 mg/m2 with a 21-day interval and suggest that slow infusion and separated single high doses may lower the risk of developing AEs from a cellular level, providing valuable insights for the risk assessment of doxorubicin chemotherapy. In conclusion, our work highlights the potential of PBPK modelling to provide quantitative assessments of cellular toxicity and supports the use of clinical dosing regimens to mitigate the risk of adverse effects.

The Application of a Physiologically Based Toxicokinetic Model in Health Risk Assessment

Toxicokinetics plays a crucial role in the health risk assessments of xenobiotics. Classical compartmental models are limited in their ability to determine chemical concentrations in specific organs or tissues, particularly target organs or tissues, and their limited interspecific and exposure route extrapolation hinders satisfactory health risk assessment. In contrast, physiologically based toxicokinetic (PBTK) models quantitatively describe the absorption, distribution, metabolism, and excretion of chemicals across various exposure routes and doses in organisms, establishing correlations with toxic effects. Consequently, PBTK models serve as potent tools for extrapolation and provide a theoretical foundation for health risk assessment and management. This review outlines the construction and application of PBTK models in health risk assessment while analyzing their limitations and future perspectives.

The pharmacokinetic-pharmacodynamic modelling framework as a tool to predict drug resistance evolution

Pharmacokinetic-pharmacodynamic (PKPD) models, which describe how drug concentrations change over time and how that affects pathogen growth, have proven highly valuable in designing optimal drug treatments aimed at bacterial eradication. However, the fast rise of antimicrobial resistance calls for increased focus on an additional treatment optimization criterion: avoidance of resistance evolution. We demonstrate here how coupling PKPD and population genetics models can be used to determine treatment regimens that minimize the potential for antimicrobial resistance evolution. Importantly, the resulting modelling framework enables the assessment of resistance evolution in response to dynamic selection pressures, including changes in antimicrobial concentration and the emergence of adaptive phenotypes. Using antibiotics and antimicrobial peptides as an example, we discuss the empirical evidence and intuition behind individual model parameters. We further suggest several extensions of this framework that allow a more comprehensive and realistic prediction of bacterial escape from antimicrobials through various phenotypic and genetic mechanisms.

Development of a Minimalistic Physiologically Based Pharmacokinetic (mPBPK) Model for the Preclinical Development of Spectinamide Antibiotics

Spectinamides 1599 and 1810 are lead spectinamide compounds currently under preclinical development to treat multidrug-resistant (MDR) and extensively drug-resistant (XDR) tuberculosis. These compounds have previously been tested at various combinations of dose level, dosing frequency, and route of administration in mouse models of Mycobacterium tuberculosis (Mtb) infection and in healthy animals. Physiologically based pharmacokinetic (PBPK) modeling allows the prediction of the pharmacokinetics of candidate drugs in organs/tissues of interest and extrapolation of their disposition across different species. Here, we have built, qualified, and refined a minimalistic PBPK model that can describe and predict the pharmacokinetics of spectinamides in various tissues, especially those relevant to Mtb infection. The model was expanded and qualified for multiple dose levels, dosing regimens, routes of administration, and various species. The model predictions in mice (healthy and infected) and rats were in reasonable agreement with experimental data, and all predicted AUCs in plasma and tissues met the two-fold acceptance criteria relative to observations. To further explore the distribution of spectinamide 1599 within granuloma substructures as encountered in tuberculosis, we utilized the Simcyp granuloma model combined with model predictions in our PBPK model. Simulation results suggest substantial exposure in all lesion substructures, with particularly high exposure in the rim area and macrophages. The developed model may be leveraged as an effective tool in identifying optimal dose levels and dosing regimens of spectinamides for further preclinical and clinical development.

Simulation of optimal dose regimens of photoactivated curcumin for antimicrobial resistance pneumonia in COVID-19 patients: A modeling approach

Background

Secondary antimicrobial resistance bacterial (AMR) pneumonia could lead to an increase in mortality in COVID-19 patients, particularly of geriatric patients with underlying diseases. The comedication of current medicines for AMR pneumonia with corticosteroids may lead to suboptimal treatment or toxicities due to drug-drug interactions (DDIs).

Objective

This study aimed to propose new promising dosage regimens of photoactivated curcumin when co-administered with corticosteroids for the treatment of antimicrobial resistance (AMR) pneumonia in COVID-19 patients.

Methods

A whole-body physiologically-based pharmacokinetic (PBPK) with the simplified lung compartments model was built and verified following standard model verification (absolute average-folding error or AAFEs). The pharmacokinetic properties of photoactivated were assumed to be similar to curcumin due to minor changes in physiochemical properties of compound by photoactivation. The acceptable AAFEs values were within 2-fold. The verified model was used to simulate new regimens for different formulations of photoactivated curcumin.

Results

The AAFEs was 1.12-fold. Original formulation (120 mg once-daily dose) or new intramuscular nano-formulation (100 mg with a release rate of 10/h given every 7 days) is suitable for outpatients with MRSA pneumonia to improve patient adherence. New intravenous formulation (2000 mg twice-daily doses) is for hospitalized patients with both MRSA and VRSA pneumonia.

Conclusion

The PBPK models, in conjunction with MIC and applied physiological changes in COVID-19 patients, is a potential tool to predict optimal dosage regimens of photoactivated curcumin for the treatment of co-infected AMR pneumonia in COVID-19 patients. Each formulation is appropriate for different patient conditions and pathogens.

Evaluation of drug-drug interaction between rilpivirine and rifapentine using PBPK modelling

Tuberculosis remains the leading cause of death among people living with HIV. Rifapentine is increasingly used to treat active disease or prevent reactivation, in both cases given either as weekly or daily therapy. However, rifapentine is an inducer of CYP3A4, potentially interacting with antiretrovirals like rilpivirine. This in silico study investigates the drug-drug interaction (DDI) magnitude between daily oral rilpivirine 25 mg with either daily 600 mg or weekly 900 mg rifapentine. A physiologically based pharmacokinetic (PBPK) model was built in Simbiology (Matlab R2018a) to simulate the drug-drug interaction. The simulated PK parameters from the PBPK model were verified against reported clinical data for rilpivirine and rifapentine separately, daily rifapentine with midazolam, and weekly rifapentine with doravirine. The simulations of concomitant administration of rifapentine with rilpivirine at steady-state lead to a maximum decrease on AUC_0-24 and C_trough by 83% and 92% on day 5 for the daily rifapentine regimen and 68% and 92% for the weekly regimen on day 3. In the weekly regimen, prior to the following dose, AUC_0-24 and C_trough were still reduced by 47% and 53%. In both simulations, the induction effect ceased 2 weeks after the interruption of rifapentine's treatment. A daily double dose of rilpivirine after initiating rifapentine 900 mg weekly was simulated but failed to compensate the drug-drug interaction. The drug-drug interaction model suggested a significant decrease on rilpivirine exposure which is unlikely to be corrected by dose increment, thus coadministration should be avoided.

The LEAP Process: Streamlining the Development of Long-Acting Products and Formulations for Infectious Diseases

Developing long-acting products and formulations for infectious diseases is a nontrivial undertaking that is frequently classified as high risk and low reward by the pharmaceutical industry. The Long-Acting/Extended Release Antiretroviral Research Resource Program (LEAP) was founded in 2015 with the support of the National Institutes of Health to encourage, promote, and accelerate the development of such products. Assessment methodology for any new proposal brought to this group is part of a framework-the LEAP Process-that includes a landscape analysis of what is currently available in the public domain. This is followed by in silico modeling and simulation offered as a service to the relevant scientific community. A variety of preclinical and clinical outcome metrics are applied to each new agent as part of a continuous feedback loop to improve product characteristics. This allows us to catalog knowledge gaps and barriers that can be addressed by engaged stakeholders. Results are communicated in scientific articles, reviews, and position papers. This undertaking serves to de-risk discovery, development, and implementation by bridging the gaps between academic, regulatory, and industrial investigators, and by engaging those in the community who will be the eventual users of these medicines. The LEAP Process has supported formulations now approved for human immunodeficiency virus, as well as products in clinical and preclinical development for tuberculosis and hepatitis viruses B and C.

Translational pharmacokinetics of a novel bispecific antibody against Ebola virus (MBS77E) from animal to human by PBPK modeling & simulation

The goal of this study was to construct a PBPK model to accelerate the translation of MBS77E, a humanized bispecific antibody against the Ebola virus. In-depth nonclinical pharmacokinetic studies in rats, monkeys, wild-type mice and transgenic mice were conducted. The pH-dependent affinities (K_D) of MBS77E to recombinant FcRn of different species were determined by surface plasmon resonance analysis. A mechanistic whole-body PBPK model of MBS77E was developed and validated in the assessment of PK profiles and tissue distributions in preclinical models. This PBPK model was finally used to predict human PK behaviors of MBS77E. Simulations from the PBPK model with measured and fitted parameters were able to yield good predictions of the serum and tissue pharmacokinetic parameters of MBS77E within 2-fold errors. The predicted serum concentration in humans was able to maintain a sufficiently high level for more than 14 days after 50 mg/kg i.v. administrating. This achievement unlocks that PBPK modeling is a powerful tool to gain insights into the properties of antibody drugs. It guided experimental efforts to obtain necessary information before entry into humans.

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

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

Physiologically Based Pharmacokinetic (PBPK) Model of Gold Nanoparticle-Based Drug Delivery System for Stavudine Biodistribution

Computational modelling has gained attention for evaluating nanoparticle-based drug delivery systems. Physiologically based pharmacokinetic (PBPK) modelling provides a mechanistic approach for evaluating drug biodistribution. The aim of this work is to develop a specific PBPK model to simulate stavudine biodistribution after the administration of a 40 nm gold nanoparticle-based drug delivery system in rats. The model parameters used have been obtained from literature, in vitro and in vivo studies, and computer optimization. Based on these, the PBPK model was built, and the compartments included were considered as permeability rate-limited tissues. In comparison with stavudine solution, a higher biodistribution of stavudine into HIV reservoirs and the modification of pharmacokinetic parameters such as the mean residence time (MRT) have been observed. These changes are particularly noteworthy in the liver, which presents a higher partition coefficient (from 0.27 to 0.55) and higher MRT (from 1.28 to 5.67 h). Simulated stavudine concentrations successfully describe these changes in the in vivo study results. The average fold error of predicted concentrations after the administration of stavudine-gold nanoparticles was within the 0.5–2-fold error in all of the tissues. Thus, this PBPK model approach may help with the pre-clinical extrapolation to other administration routes or the species of stavudine gold nanoparticles.

Integration of advanced methods and models to study drug absorption and related processes: An UNGAP perspective

This collection of contributions from the European Network on Understanding Gastrointestinal Absorption-related Processes (UNGAP) community assembly aims to provide information on some of the current and newer methods employed to study the behaviour of medicines. It is the product of interactions in the immediate pre-Covid period when UNGAP members were able to meet and set up workshops and to discuss progress across the disciplines. UNGAP activities are divided into work packages that cover special treatment populations, absorption processes in different regions of the gut, the development of advanced formulations and the integration of food and pharmaceutical scientists in the food-drug interface. This involves both new and established technical approaches in which we have attempted to define best practice and highlight areas where further research is needed. Over the last months we have been able to reflect on some of the key innovative approaches which we were tasked with mapping, including theoretical, in silico, in vitro, in vivo and ex vivo, preclinical and clinical approaches. This is the product of some of us in a snapshot of where UNGAP has travelled and what aspects of innovative technologies are important. It is not a comprehensive review of all methods used in research to study drug dissolution and absorption, but provides an ample panorama of current and advanced methods generally and potentially useful in this area. This collection starts from a consideration of advances in a priori approaches: an understanding of the molecular properties of the compound to predict biological characteristics relevant to absorption. The next four sections discuss a major activity in the UNGAP initiative, the pursuit of more representative conditions to study lumenal dissolution of drug formulations developed independently by academic teams. They are important because they illustrate examples of in vitro simulation systems that have begun to provide a useful understanding of formulation behaviour in the upper GI tract for industry. The Leuven team highlights the importance of the physiology of the digestive tract, as they describe the relevance of gastric and intestinal fluids on the behaviour of drugs along the tract. This provides the introduction to microdosing as an early tool to study drug disposition. Microdosing in oncology is starting to use gamma-emitting tracers, which provides a link through SPECT to the next section on nuclear medicine. The last two papers link the modelling approaches used by the pharmaceutical industry, in silico to Pop-PK linking to Darwich and Aarons, who provide discussion on pharmacometric modelling, completing the loop of molecule to man.

Predicting Antimicrobial Activity at the Target Site: Pharmacokinetic/Pharmacodynamic Indices versus Time-Kill Approaches

Antibiotic dosing strategies are generally based on systemic drug concentrations. However, drug concentrations at the infection site drive antimicrobial effect, and efficacy predictions and dosing strategies should be based on these concentrations. We set out to review different translational pharmacokinetic-pharmacodynamic (PK/PD) approaches from a target site perspective. The most common approach involves calculating the probability of attaining animal-derived PK/PD index targets, which link PK parameters to antimicrobial susceptibility measures. This approach is time efficient but ignores some aspects of the shape of the PK profile and inter-species differences in drug clearance and distribution, and provides no information on the PD time-course. Time–kill curves, in contrast, depict bacterial response over time. In vitro dynamic time–kill setups allow for the evaluation of bacterial response to clinical PK profiles, but are not representative of the infection site environment. The translational value of in vivo time–kill experiments, conversely, is limited from a PK perspective. Computational PK/PD models, especially when developed using both in vitro and in vivo data and coupled to target site PK models, can bridge translational gaps in both PK and PD. Ultimately, clinical PK and experimental and computational tools should be combined to tailor antibiotic treatment strategies to the site of infection.

PBPK Modeling and Simulation and Therapeutic Drug Monitoring: Possible Ways for Antibiotic Dose Adjustment

Pharmacokinetics (PK) is a branch of pharmacology present and of vital importance for the research and development (R&D) of new drugs, post-market monitoring, and continued optimizations in clinical contexts. Ultimately, pharmacokinetics can contribute to improving patients’ clinical outcomes, helping enhance the efficacy of treatments, and reducing possible adverse side effects while also contributing to precision medicine. This article discusses the methods used to predict and study human pharmacokinetics and their evolution to the current physiologically based pharmacokinetic (PBPK) modeling and simulation methods. The importance of therapeutic drug monitoring (TDM) and PBPK as valuable tools for Model-Informed Precision Dosing (MIPD) are highlighted, with particular emphasis on antibiotic therapy since dosage adjustment of antibiotics can be vital to ensure successful clinical outcomes and to prevent the spread of resistant bacterial strains.

Mechanistic Modeling of Placental Drug Transfer in Humans: How Do Differences in Maternal/Fetal Fraction of Unbound Drug and Placental Influx/Efflux Transfer Rates Affect Fetal Pharmacokinetics?

Background: While physiologically based pharmacokinetic (PBPK) models generally predict pharmacokinetics in pregnant women successfully, the confidence in predicting fetal pharmacokinetics is limited because many parameters affecting placental drug transfer have not been mechanistically accounted for. Objectives: The objectives of this study were to implement different maternal and fetal unbound drug fractions in a PBPK framework; to predict fetal pharmacokinetics of eight drugs in the third trimester; and to quantitatively investigate how alterations in various model parameters affect predicted fetal pharmacokinetics. Methods: The ordinary differential equations of previously developed pregnancy PBPK models for eight drugs (acyclovir, cefuroxime, diazepam, dolutegravir, emtricitabine, metronidazole, ondansetron, and raltegravir) were amended to account for different unbound drug fractions in mother and fetus. Local sensitivity analyses were conducted for various parameters relevant to placental drug transfer, including influx/efflux transfer clearances across the apical and basolateral membrane of the trophoblasts. Results: For the highly-protein bound drugs diazepam, dolutegravir and ondansetron, the lower fraction unbound in the fetus vs. mother affected predicted pharmacokinetics in the umbilical vein by ≥10%. Metronidazole displayed blood flow-limited distribution across the placenta. For all drugs, umbilical vein concentrations were highly sensitive to changes in the apical influx/efflux transfer clearance ratio. Additionally, transfer clearance across the basolateral membrane was a critical parameter for cefuroxime and ondansetron. Conclusion: In healthy pregnancies, differential protein binding characteristics in mother and fetus give rise to minor differences in maternal-fetal drug exposure. Further studies are needed to differentiate passive and active transfer processes across the apical and basolateral trophoblast membrane.

Spreadsheet-Based Minimal Physiological Models for the Prediction of Clearance of Therapeutic Proteins in Pediatric Patients

There is a growing interest in the use of physiologically based pharmacokinetic (PBPK) models as clinical pharmacology drug development tools. In PBPK modeling, not every organ or physiological parameter is required, leading to the development of a minimal PBPK (mPBPK) model, which is simple and efficient. The objective of this study was to streamline mPBPK modeling approaches and enable straightforward prediction of clearance of protein-based products in children. Four mPBPK models for scaling clearance from adult to children were developed and evaluated on Excel spreadsheets using (1) liver and kidneys; (2) liver, kidneys, and skin; (3) liver, kidneys, skin, and lymph; and (4) interstitial, lymph, and plasma volume. There were 35 therapeutic proteins with a total of 113 observations across different age groups (premature neonates to adolescents). For monoclonal and polyclonal antibodies, more than 90% of observations were within a 0.5- to 2-fold prediction error for all 4 methods. For nonantibodies, 79% to 100% of observations were within the 0.5- to 2-fold prediction error for the 4 different methods. Methods 1 and 4 provided the best results, >90% of the total observations were within the 0.5- to 2-fold prediction error for all 3 classes of protein-based products across a wide age range. The precision of clearance prediction was comparatively lower in children ≤2 years of age vs older children (>2 years of age) with methods 1 and 4 predicting 80% to 100% and 75% to 90% of observations within the 0.5- to 2-fold prediction error, respectively. The results of the study indicated that mPBPK models can be developed on spreadsheets, with acceptable performance for prediction of clearance.

Prediction of dolutegravir pharmacokinetics and dose optimization in neonates via physiologically based pharmacokinetic (PBPK) modelling

BACKGROUND

Only a few antiretroviral drugs (ARVs) are recommended for use during the neonatal period and there is a need for more to be approved to increase treatment and prophylaxis strategies. Dolutegravir, a selective integrase inhibitor, has potential for treatment of HIV infection and prophylaxis of transmission in neonates.

OBJECTIVES

To model the pharmacokinetics of dolutegravir in neonates and to simulate a theoretical optimal dosing regimen.

METHODS

The physiologically based pharmacokinetic (PBPK) model was built incorporating the age-related changes observed in neonates. Virtual neonates between 0 and 28 days were simulated. The model was validated against observed clinical data for raltegravir and midazolam in neonates, prior to the prediction of dolutegravir pharmacokinetics.

RESULTS

Both raltegravir and midazolam passed the criteria for model qualification, with simulated data within 1.8-fold of clinical data. The qualified model predicted the pharmacokinetics for several multidose regimens of dolutegravir. Regimen 6 involved 5 mg doses with a 48 h interval from Day 1-20, increasing to 5 mg once daily on Week 3, yielding AUC and Ctrough values of 37.2 mg·h/L and 1.3 mg/L, respectively. These exposures are consistent with those observed in paediatric patients receiving dolutegravir.

CONCLUSIONS

Dolutegravir pharmacokinetics were successfully simulated in the neonatal PBPK model. The predictions suggest that during the first 3 weeks of life a 5 mg dose administered every 48 h may achieve plasma exposures needed for therapy and prophylaxis.

The impact of the intestinal microbiota and the mucosal permeability on three different antibiotic drugs

BackgroundThe totality of bacteria, protozoa, viruses and fungi that lives in the human body is called microbiota. Human microbiota specifically colonizes the skin, the respiratory and urinary tract, the urogenital tract and the gastrointestinal system. This study focuses on the intestinal microbiota to explore the drug-microbiota relationship and, therefore, how the drug bioavailability changes in relation to the microbiota biodiversity to identify more personalized therapies, with the minimum risk of side effects. MethodsTo achieve this goal, we developed a new mathematical model with two compartments, the intestine and the blood, which takes into account the colonic mucosal permeability variation - measured by Ussing chamber system on human colonic mucosal biopsies - and the fecal microbiota composition, determined through microbiota 16S rRNA sequencing analysis. Both of the clinical parameters were evaluated in a group of Irritable Bowel Syndrome patients compared to a group of healthy controls. Key ResultsThe results show that plasma drug concentration increases as bacterial concentration decreases, while it decreases as intestinal length decreases too. ConclusionsThe study provides interesting data since in literature there are not yet mathematical models with these features, in which the importance of intestinal microbiota, the "forgotten organ", is considered both for the subject health state and in the nutrients and drugs metabolism.

A Δ9-Tetrahydrocannabinol Physiologically-Based Pharmacokinetic Model Development in Humans

BACKGROUND AND OBJECTIVE

∆⁹-Tetrahydrocannabinol (THC) exhibits several therapeutic effects, such as analgesics, anti-emetic, antispastic, and muscle relaxation properties. Knowledge concerning THC disposition in target organs is crucial for THC therapy. The objective of this study was to develop a physiologically-based pharmacokinetic (PBPK) model of THC in humans to characterize tissue-specific pharmacokinetics of THC in organs of interest.

METHODS

The model was extrapolated from the previously developed PBPK model conducted in mice, rats, and pigs. The model consisted of seven compartments: brain, lungs, liver, kidneys, fat, and rapidly perfused and slowly perfused tissues. P-glycoprotein was included in the brain compartment to characterize an efflux of THC from the brain. Physiologic, biochemical, and physicochemical parameters were determined and acquired from the literature. Model validation was performed by comparisons of the predicted and observed THC concentrations acquired from published studies.

RESULTS

The developed PBPK model resulted in good agreement between the predicted and observed THC concentrations across several studies conducted following IV bolus, IV infusion, oral, and smoking and inhalation, with the coefficient of determination (R²) ranging from 0.54 to 0.95.

CONCLUSIONS

A PBPK model of THC in humans was developed. The model could describe THC concentration-time profiles in several dosing scenarios (i.e., IV bolus, IV infusion, oral administration and inhalation).

Hepatotoxicological potential of P-toluic acid in humanised-liver mice investigated using simplified physiologically based pharmacokinetic models

p-Toluic acid, a metabolite of organic solvent xylene, has a high reported no-observed-effect level (NOEL, 1000 mg/kg) in rats, possibly because of direct glycine conjugation to methylhippuric acid. In this study, plasma levels of p-toluic acid and its glycine conjugate in mice and humanised-liver mice were evaluated after oral administrations.Although rapid conversion of p-toluic acid to its glycine conjugate was evident from mouse plasma concentrations, the biotransformation of p-toluic acid was slower in humanised-liver mice. The input parameters for physiologically based pharmacokinetic (PBPK) models were determined using fitting procedures to create PBPK-generated plasma concentration curves.The PBPK-modelled hepatic concentrations of p-toluic acid in humanised-liver mice were higher than those observed in plasma. PBPK-modelled hepatic and plasma concentrations of p-toluic acid also indicated slow elimination in humans.These results suggest that rapid conjugations of p-toluic acid reportedly observed in rats could result in overestimation of NOELs for conjugatable chemicals when extrapolated to humanised-liver mice or humans.

Multi-organ-on-a-chip for pharmacokinetics and toxicokinetic study of drugs

Introduction: Accurate prediction of pharmacokinetic (PK) and toxicokinetics (TK) of drugs is imperative for successful development of new pharmaceutics. Although conventional in vitro methods for predicting the PK and TK of drugs are well established, limitations still exist and more advanced chip-based in vitro platforms combined with mathematical models can help researchers overcome the limitations. Areas covered: We will review recent progress in the development of multi-organ-on-a-chip platforms for predicting PK and TK of drugs, as well as mathematical approaches that can be combined with these platforms for experiment design, data analysis and in vitro-in vivo extrapolation (IVIVE) for application to humans. Expert opinion: Although there remain some challenges to be addressed, the remarkable progress in the area of multi-organ-on-a-chip in recent years indicate that we will see tangible outcomes that can be utilized in the pharmaceutical industry in near future.

Prediction of Drug Clearance from Enzyme and Transporter Kinetics

Accurate estimation of in vivo clearance in human is pivotal to determine the dose and dosing regimen for drug development. In vitro-in vivo extrapolation (IVIVE) has been performed to predict drug clearance using empirical and physiological scalars. Multiple in vitro systems and mathematical modeling techniques have been employed to estimate in vivo clearance. The models for predicting clearance have significantly improved and have evolved to become more complex by integrating multiple processes such as drug metabolism and transport as well as passive diffusion. This chapter covers the use of conventional as well as recently developed methods to predict metabolic and transporter-mediated clearance along with the advantages and disadvantages of using these methods and the associated experimental considerations. The general approaches to improve IVIVE by use of appropriate scalars, incorporation of extrahepatic metabolism and transport and application of physiologically based pharmacokinetic (PBPK) models with proteomics data are also discussed. The chapter also provides an overview of the advantages of using such dynamic mechanistic models over static models for clearance predictions to improve IVIVE.

A GFR-Based Method to Predict the Effect of Renal Impairment on the Exposure or Clearance of Renally Excreted Drugs: A Comparative Study Between a Simple GFR Method and a Physiologically Based Pharmacokinetic Model

Objective

The objective of this study was to compare the predictive performances of a glomerular filtration rate (GFR) model with a physiologically based pharmacokinetic (PBPK) model to predict total or renal clearance or area under the curve of renally excreted drugs in subjects with varying degrees of renal impairment.

Methods

From the literature, 11 studies were randomly selected in which total or renal clearance or area under the curve of drugs in subjects with different degrees of renal impairment were predicted by PBPK models. In these published studies, drugs were given to subjects intravenously or orally. The PBPK model was generally a whole-body model whereas the GFR model was as follows: Predicted total clearance (CL_T) = CL_T in healthy subjects × (GFR in RI/GFR in H), Predicted AUC = AUC in healthy subjects × (GFR in H/GFR in RI), where H is the healthy subjects and RI is renal impairment. The predicted clearance or area under the curve values using PBPK and GFR models were compared with the observed (experimental pharmacokinetic) values. The acceptable prediction error was within the 0.5- to 2-fold or 0.5- to 1.5-fold prediction error.

Results

There were 33 drugs with a total number of 101 observations (area under the curve, total and renal clearance in subjects with mild, moderate, and severe renal impairment). From PBPK and GFR models, out of 101 observations, 94 (93.1%) and 96 (95.0%) observations were within the 0.5- to 2-fold prediction error, respectively.

Conclusions

This study indicates that the predictive power of a simple GFR model is similar to a PBPK model for the prediction of clearance or area under the curve in subjects with renal impairment. The GFR method is simple, robust, and reliable and can replace complex empirical PBPK models.

Computational Approaches in Preclinical Studies on Drug Discovery and Development

Because undesirable pharmacokinetics and toxicity are significant reasons for the failure of drug development in the costly late stage, it has been widely recognized that drug ADMET properties should be considered as early as possible to reduce failure rates in the clinical phase of drug discovery. Concurrently, drug recalls have become increasingly common in recent years, prompting pharmaceutical companies to increase attention toward the safety evaluation of preclinical drugs. In vitro and in vivo drug evaluation techniques are currently more mature in preclinical applications, but these technologies are costly. In recent years, with the rapid development of computer science, in silico technology has been widely used to evaluate the relevant properties of drugs in the preclinical stage and has produced many software programs and in silico models, further promoting the study of ADMET in vitro. In this review, we first introduce the two ADMET prediction categories (molecular modeling and data modeling). Then, we perform a systematic classification and description of the databases and software commonly used for ADMET prediction. We focus on some widely studied ADMT properties as well as PBPK simulation, and we list some applications that are related to the prediction categories and web tools. Finally, we discuss challenges and limitations in the preclinical area and propose some suggestions and prospects for the future.

Physiologically based pharmacokinetic-pharmacodynamic evaluation of meropenem plus fosfomycin in paediatrics

AIMS

The objective of the current study was to evaluate paediatric dosing regimens for meropenem plus fosfomycin that generate sufficient coverage against multidrug-resistant bacteria.

METHODS

The physiologically based pharmacokinetic (PBPK) models of meropenem and fosfomycin were developed from previously published pharmacokinetic studies in five populations: healthy subjects of Japanese origin, and healthy adults, geriatric, paediatric and renally impaired of primarily Caucasian origins. Pharmacodynamic (PD) analyses were carried out by evaluating dosing regimens that achieved a ≥90% joint probability of target attainment (PTA), which was defined as the minimum of the marginal probabilities to achieve the target PD index of each antibiotic. For meropenem, the percentage of time over a 24-hour period wherein the free drug concentration was above the minimum inhibitory concentration (fT > MIC) of at least 40% was its PD target. The fosfomycin PD index was described by fAUC/MIC of at least 40.8.

RESULTS

For coadministration consisting of 20 mg/kg meropenem q8h as a 3-hour infusion and 35 mg/kg fosfomycin q8h also as a 3-hour infusion in a virtual paediatric population between 1 month and 12 years of age with normal renal function and a corresponding body weight between 3 and 50 kg, a joint PTA ≥ 90% is achieved at MICs of 16 and 64 mg/L for meropenem and fosfomycin coadministration, respectively, against Klebsiella pneumoniae and Pseudomonas aeruginosa.

CONCLUSION

The current study identified potentially effective paediatric dosing regimens for meropenem plus fosfomycin coadministration against multidrug-resistant bacteria.

Predicting Pharmacokinetics of a Tenofovir Alafenamide Subcutaneous Implant Using Physiologically Based Pharmacokinetic Modelling

Long-acting (LA) administration using a subcutaneous (s.c.) implant presents opportunities to simplify administration of antiretroviral drugs, improve pharmacological profiles, and overcome suboptimal adherence associated with daily oral formulations. Tenofovir alafenamide (TAF) is a highly potent nucleoside reverse transcriptase inhibitor (NRTI) and an attractive agent for LA delivery, with a high potency and long intracellular half-life. The aim of this study was to predict minimum TAF doses required to achieve concentrations effective for HIV preexposure prophylaxis (PrEP). Daily drug release requirements were then ascertained by averaging across the dosing interval. A TAF physiologically based pharmacokinetic (PBPK) model was developed and partially qualified against available oral single- and multiple-dose pharmacokinetics. The models were assumed to be qualified when simulated values were within 2-fold of the observed mean. TAF s.c. implants were simulated in five hundred individuals, reporting predicted TAF plasma and tenofovir (TFV) plasma concentrations for various release rates. Intracellular TFV diphosphate (TFV-DP) concentrations were also simulated in peripheral blood cells and cervical and rectal tissues. The minimum dose predicted to achieve intracellular TFV-DP levels above a target concentration of 48 fmol/10⁶ cells for a month was identified. TAF, TFV, and TFV-DP concentrations for release rates between 1.0 and 1.6 mg/day were simulated. The PBPK model indicated that a minimum release of 1.4 mg/day TAF is necessary to achieve TFV-DP concentrations above the identified target in peripheral blood mononuclear cells (PBMCs). TFV-DP cervical and rectal tissue concentrations were predicted to be between 1.5 and 2.0 fmol/10⁶ cells and 0.9 and 1.1 fmol/10⁶ cells, respectively, for release rates between 1.3 and 1.6 mg/day. These simulations provide target minimum doses for LA TAF PrEP in humans. Based on the generated results, multiple implants delivering a total of 1.4 mg/day of TAF subcutaneously could provide protection levels for approximately 6 months to 1 year. This modeling may inform future design of s.c. implants to mitigate adherence issues for effective PrEP applications.

Predicting Drug-Drug Interactions Between Rifampicin and Long-Acting Cabotegravir and Rilpivirine Using Physiologically Based Pharmacokinetic Modeling

BACKGROUND

Cabotegravir and rilpivirine are 2 long-acting (LA) antiretrovirals that can be administered intramuscularly; their interaction with rifampicin, a first-line antituberculosis agent, has not been investigated. The aim of this study was to simulate and predict drug-drug interactions (DDIs) between these LA antiretroviral agents and rifampicin using physiologically based pharmacokinetic (PBPK) modeling.

METHODS

The designed PBPK models were qualified (according to European Medicines Agency guidelines) against observed data for oral formulations of cabotegravir, rilpivirine, and rifampicin. Induction potential of rifampicin was also qualified by comparing the DDI between oral cabotegravir and oral rilpivirine with rifampicin. Qualified PBPK models were utilized for pharmacokinetic prediction of DDIs.

RESULTS

PBPK models predicted a reduction in both area under the curve (AUC0-28 days) and trough concentration (Ctrough, 28th day) of LA cabotegravir of 41%-46% for the first maintenance dose coadministered with 600 mg once-daily oral rifampicin. Rilpivirine concentrations were predicted to decrease by 82% for both AUC0-28 days and Ctrough, 28th day following the first maintenance dose when coadministered with rifampicin.

CONCLUSIONS

The developed PBPK models predicted the theoretical effect of rifampicin on cabotegravir and rilpivirine LA intramuscular formulations. According to these simulations, it is likely that coadministration of rifampicin with these LA formulations will result in subtherapeutic concentrations of both drugs.

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.

CardiacPBPK: A tool for the prediction and visualization of time-concentration profiles of drugs in heart tissue

BACKGROUND AND OBJECTIVE

Prediction of drug concentration in heart tissue is important in terms of drug safety and efficacy. This work presents the Open-Source CardiacPBPK platform for the prediction of the time-concentration profile of drugs, which could potentially reduce the risk of drug development failure due to cardiotoxicity. The objective of the CardiacPBPK development is to accelerate and simplify the in-silico toxicological assessment of new drugs, and to provide supportive material for the research community to use.

METHODS

The CardiacPBPK software provides a modular implementation of the PBPK model of heart tissue. It can be easily accessed via the Internet or installed locally. The graphical user interface and tabular design are easy to configure and use.

RESULTS

CardiacPBPK is a tool designed to predict and visualize the time-concentration profiles of a parent compound, and one metabolite, in venous plasma and heart tissue after oral or intravenous drug administration. CardiacPBPK is built on the R-environment framework and supports shiny application features such as interactive visualization of the results, and web applications interface by default. A shiny application refers to a computer program created with the use of shiny package in R. The application is freely available at https://github.com/jszlek/CardiacPBPK and https://sourceforge.net/projects/cardiacpbpk/. This open-source application runs on all platforms supporting R-environment (Linux, Windows, Mac OS X, Solaris).

CONCLUSIONS

We demonstrate the application of CardiacPBPK by simulating the study of amitriptyline intoxication in the case of CYP2D6 genetic polymorphism.

Modelling the intradermal delivery of microneedle array patches for long-acting antiretrovirals using PBPK

INTRODUCTION

Existing HIV therapy using oral antiretrovirals (ARVs) can result in pill fatigue and sub-optimal adherence. Microneedle array patches (MAPs) offer non-invasive, blood-free and painless drug delivery, and may improve patient adherence. The objective of this study was to develop a novel physiologically-based pharmacokinetic (PBPK) model to simulate the systemic pharmacokinetics of cabotegravir and rilpivirine MAPs using the intradermal route.

METHODS

The developed PBPK models were qualified against observed pharmacokinetic data after intramuscular (IM) and intradermal administration of long-acting nanoformulated rilpivirine to rats, and for IM administration of both drugs to healthy adults. Qualified models were then utilised to estimate suitable MAP characteristics (e.g. nanoformulation dose and release rates) and inform dosing strategies to maintain plasma concentrations above target trough concentrations for the designated dosing interval.

RESULTS

PBPK models simulated q4-weekly loading and maintenance doses of 360 mg and 180 mg for long-acting formulated cabotegravir between the release rates of 1 × 10^-3-3 × 10^-3h^-1 and 1 × 10^-3-1.5 × 10^-3h^-1 respectively, for a 70 kg adult. Estimated patch size was 60 cm² for a 360 mg dose of cabotegravir. For q4-weekly dosing, rilpivirine required a 1080 mg loading dose and a 540 mg maintenance dose with release rates of 1.5 × 10^-3-2.5 × 10^-3h^-1 and 5 × 10^-4-1 × 10^-3h^-1, respectively. Weekly dosing was also evaluated to assess the potential application from a smaller patch size. The ability to self-administer via a patch that is only left in place for a short duration makes longer durations less important than for some other long-acting approaches. Weekly cabotegravir required 60 mg between release rates 7 × 10^-3-9 × 10^-3h^-1 and rilpivirine required 270 mg and 180 mg respectively between release rates of 7 × 10^-3-9 × 10^-3h^-1.

DISCUSSION

This model estimated optimal dose and release rates for cabotegravir and rilpivirine MAPs. Our approach provides a computational platform to support rational development of intradermal administration strategies to tackle problems associated with chronic oral ARV administration.

Population pharmacokinetic reanalysis of a Diazepam PBPK model: a comparison of Stan and GNU MCSim

The aim of this study is to benchmark two Bayesian software tools, namely Stan and GNU MCSim, that use different Markov chain Monte Carlo (MCMC) methods for the estimation of physiologically based pharmacokinetic (PBPK) model parameters. The software tools were applied and compared on the problem of updating the parameters of a Diazepam PBPK model, using time-concentration human data. Both tools produced very good fits at the individual and population levels, despite the fact that GNU MCSim is not able to consider multivariate distributions. Stan outperformed GNU MCSim in sampling efficiency, due to its almost uncorrelated sampling. However, GNU MCSim exhibited much faster convergence and performed better in terms of effective samples produced per unit of time.

Prediction of Human Nonlinear Pharmacokinetics of a New Bcl-2 Inhibitor Using PBPK Modeling and Interspecies Extrapolation Strategy

S 55746 ((S)-N-(4-hydroxyphenyl)-3-(6-(3-(morpholinomethyl)-1,2,3,4-tetrahydroisoquinoline-2-carbonyl)benzo[d][1,3]dioxol-5-yl)-N-phenyl-5,6,7,8-tetrahydroindolizine-1-carboxamide) is a new selective Bcl-2 (B-cell lymphoma 2) inhibitor developed by Servier Laboratories and used to restore apoptosis functions in cancer patients. The aim of this work was to develop a translational approach using physiologically based (PB) pharmacokinetic (PK) modeling for interspecies extrapolation to anticipate the nonlinear PK behavior of this new compound in patients. A PBPK mouse model was first built using a hybrid approach, defining scaling factors (determined from in vitro data) to correct in vitro clearance parameters and predicted Kp (partition coefficient) values. The qualification of the hybrid model using these empirically determined scaling factors was satisfactorily completed with rat and dog data, allowing extrapolation of the PBPK model to humans. Human PBPK simulations were then compared with clinical trial data from a phase 1 trial in which the drug was given orally and daily to cancer patients. Human PBPK predictions were within the 95% prediction interval for the eight dose levels, taking into account both the nonlinear dose and time dependencies occurring in S 55746 kinetics. Thus, the proposed PK interspecies extrapolation strategy, based on preclinical and in vitro information and physiologic assumptions, could be a useful tool for predicting human plasma concentrations at the early stage of drug development.

A Spatio-Temporal Exposure-Hazard Model for Assessing Biological Risk and Impact

We developed a simulation model for quantifying the spatio-temporal distribution of contaminants (e.g., xenobiotics) and assessing the risk of exposed populations at the landscape level. The model is a spatio-temporal exposure-hazard model based on (i) tools of stochastic geometry (marked polygon and point processes) for structuring the landscape and describing the exposed individuals, (ii) a dispersal kernel describing the dissemination of contaminants from polygon sources, and (iii) an (eco)toxicological equation describing the toxicokinetics and dynamics of contaminants in affected individuals. The model was implemented in the briskaR package (biological risk assessment with R) of the R software. This article presents the model background, the use of the package in an illustrative example, namely, the effect of genetically modified maize pollen on nontarget Lepidoptera, and typical comparisons of landscape configurations that can be carried out with our model (different configurations lead to different mortality rates in the treated example). In real case studies, parameters and parametric functions encountered in the model will have to be precisely specified to obtain realistic measures of risk and impact and accurate comparisons of landscape configurations. Our modeling framework could be applied to study other risks related to agriculture, for instance, pathogen spread in crops or livestock, and could be adapted to cope with other hazards such as toxic emissions from industrial areas having health effects on surrounding populations. Moreover, the R package has the potential to help risk managers in running quantitative risk assessments and testing management strategies.

Exploration of sexual dimorphism and inter-individual variability in multivariate parameter spaces for a pharmacokinetic compartment model

Pharmacokinetic models are particularly useful to study the underlying and complex physiological mechanisms contributing to clinical differences across patient subgroups or special populations. Unfortunately, the inherent variability of biological systems and knowledge gaps in physiological data limit confidence in model predictions for special populations. Sourcing data to reflect the desired physiologies can be resource intensive, particularly for a larger model. Thus, a critical step in model development for special populations involves an in-depth analysis of model inputs, which can be guided by Monte Carlo simulations. Such an approach enables the generation of parameter values by stochastic sampling that are subsequently restricted to the combinations that describe biologically plausible or target model output. Our approach utilized a published pharmacokinetic compartmental model to demonstrate how sampling in conjunction with global sensitivity analysis can be used to explore sexual dimorphism and inter-individual variability in multivariate parameter spaces for differentiation of model input and behavior across phenotypes. Despite limiting the model output to relatively narrow ranges, male and female phenotypes were associated with wide variability in both individual parameter values and combinations of parameters. Through an integrated approach using a support vector machine, principal component analysis and global sensitivity analysis, our approach revealed that specific combinations of parameters gave rise to a certain phenotype, while individual parameters influenced the shape of plasma concentration profile. Augmenting analysis of the model input with global sensitivity analysis enabled an understanding of both sexual dimorphism and inter-individual variability in pharmacokinetics. While the current study revealed how model input could be separated by sex for a simple compartment model, the approach could be extended to other patient factors, such as age or disease, and to a more complex physiologically-based model that describes absorption, distribution, metabolism, and elimination with more detail.

The use of PBPK modeling across the pediatric age range using propofol as a case

The project SAFEPEDRUG aims to provide guidelines for drug research in children, based on bottom-up and top-down approaches. Propofol, one of the studied model compounds, was selected because it is extensively metabolized in liver and kidney, with an important role for the glucuronidation pathway. Besides, being a lipophilic molecule, it is distributed into fat tissues, from where it redistributes into the systemic circulation. In the past, both bottom-up (Physiologically based pharmacokinetic, PBPK) and top-down approaches (population pharmacokinetic, popPK) were applied to describe its pharmacokinetics (PK). In this work, a combination of the two was used to check their performance to describe PK in children and neonates (both term and preterm) using propofol as a case compound. First, in vitro data was generated in human liver microsomes and recombinant enzymes and used to develop an adult PBPK model in Simcyp^®. Activity adjustment factors (AAFs) were calculated to account for differences between in vitro and in vivo enzyme activity. Clinical data were analyzed using a 3-compartment model in NONMEM. These data were used to construct a retrograde PBPK model and for qualification of the PBPK models. Once an accurate in vivo clearance was obtained accounting for the contribution of the different metabolic pathways, the resulting PBPK models were challenged with new data for qualification. After that, the constructed adult PPBK model for propofol was extrapolated to the pediatric population. Both the default built-in and in vivo derived ontogeny functions were used to do so. The models were qualified by comparing their predicted PK parameters to published values, and by comparison of predicted concentration-time profiles to available clinical data. Clearance values were predicted well, especially when compared with values obtained from trials where long-term sampling was applied, whereas volume of distribution was lower compared to the most common popPK model predictions. Concentration-time profiles were predicted well up until and including the preterm neonatal population. In this work, it was thus shown that PBPK can be used to predict the PK up to and including the preterm neonatal population without the use of pediatric in vivo data. This work adds weight to the need for further development of PBPK models, especially regarding distribution modeling and the use of in vivo derived ontogeny functions.

Model reduction in mathematical pharmacology : Integration, reduction and linking of PBPK and systems biology models

In this paper we present a framework for the reduction and linking of physiologically based pharmacokinetic (PBPK) models with models of systems biology to describe the effects of drug administration across multiple scales. To address the issue of model complexity, we propose the reduction of each type of model separately prior to being linked. We highlight the use of balanced truncation in reducing the linear components of PBPK models, whilst proper lumping is shown to be efficient in reducing typically nonlinear systems biology type models. The overall methodology is demonstrated via two example systems; a model of bacterial chemotactic signalling in Escherichia coli and a model of extracellular regulatory kinase activation mediated via the extracellular growth factor and nerve growth factor receptor pathways. Each system is tested under the simulated administration of three hypothetical compounds; a strong base, a weak base, and an acid, mirroring the parameterisation of pindolol, midazolam, and thiopental, respectively. Our method can produce up to an 80% decrease in simulation time, allowing substantial speed-up for computationally intensive applications including parameter fitting or agent based modelling. The approach provides a straightforward means to construct simplified Quantitative Systems Pharmacology models that still provide significant insight into the mechanisms of drug action. Such a framework can potentially bridge pre-clinical and clinical modelling - providing an intermediate level of model granularity between classical, empirical approaches and mechanistic systems describing the molecular scale.

In Silico Dose Prediction for Long-Acting Rilpivirine and Cabotegravir Administration to Children and Adolescents

BACKGROUND AND OBJECTIVES

Long-acting injectable antiretrovirals represent a pharmacological alternative to oral formulations and an innovative clinical option to address adherence and reduce drug costs. Clinical studies in children and adolescents are characterised by ethical and logistic barriers complicating the identification of dose optimisation. Physiologically-based pharmacokinetic modelling represents a valuable tool to inform dose finding prior to clinical trials. The objective of this study was to simulate potential dosing strategies for existing long-acting injectable depot formulations of cabotegravir and rilpivirine in children and adolescents (aged 3-18 years) using physiologically-based pharmacokinetic modelling.

METHODS

Whole-body physiologically-based pharmacokinetic models were developed to represent the anatomical, physiological and molecular processes and age-related changes in children and adolescents through allometric equations. Models were validated for long-acting injectable intramuscular cabotegravir and rilpivirine in adults. Subsequently, the anatomy and physiology of children and adolescents were validated against available literature. The optimal doses of monthly administration of cabotegravir and rilpivirine were identified in children and adolescents, to achieve trough concentrations over the target concentrations derived in a recent efficacy trial of the same formulations.

RESULTS

Pharmacokinetic data generated through the physiologically-based pharmacokinetic simulations were similar to observed clinical data in adults. Optimal doses of long-acting injectable antiretrovirals cabotegravir and rilpivirine were predicted using the release rate observed for existing clinical formulations, for different weight groups of children and adolescents. The intramuscular loading dose and maintenance dose of cabotegravir ranged from 200 to 600 mg and from 100 to 250 mg, respectively, and for rilpivirine it ranged from 250 to 550 mg and from 150 to 500 mg, respectively, across various weight groups of children ranging from 15 to 70 kg.

CONCLUSIONS

The reported findings represent a rational platform for the identification of suitable dosing strategies and can inform prospective clinical investigation of long-acting injectable formulations in children and adolescents.