Physiologically Based Pharmacokinetic Model Research Articles

Physiologically-based pharmacokinetic modelling guided dose evaluations of nirmatrelvir/ritonavir in renal impairment for the management of COVID-19.

We aimed to address factors contributing to the pharmacokinetic changes of nirmatrelvir/ritonavir in renal impaired (RI) patients and recommend dosing adjustment via a physiologically-based pharmacokinetic (PBPK) modelling approach. A PBPK model of nirmatrelvir/ritonavir was developed via Simcyp® Simulator. Sensitivity analysis of the influence of hepatic CYP3A4 intrinsic clearance and abundance, as well as hepatic non-CYP3A4 metabolism (other human liver microsomes [HLM] CLint) was performed to evaluate the effects of RI on oral clearance of nirmatrelvir. Other HLM CLint, the most sensitive parameter, was adjusted, and the simulated plasma concentration profiles of nirmatrelvir in severe RI subjects were within the therapeutic index of 292-10 000 ng/mL for dosing regimens of loading doses of 300/100 mg followed by 150/100 mg or 75/100 mg twice daily of nirmatrelvir/ritonavir. Considering that nirmatrelvir is available as a 150 mg tablet, we recommend 300/100 mg followed by 150/100 mg twice daily as the dosing regimen to be investigated in severe RI.

Use of quantitative in vitro to in vivo extrapolation (QIVIVE) for the assessment of non-combustible next-generation product aerosols

With the use of in vitro new approach methodologies (NAMs) for the assessment of non-combustible next-generation nicotine delivery products, new extrapolation methods will also be required to interpret and contextualize the physiological relevance of these results. Quantitative in vitro to in vivo extrapolation (QIVIVE) can translate in vitro concentrations into in-life exposures with physiologically-based pharmacokinetic (PBPK) modelling and provide estimates of the likelihood of harmful effects from expected exposures. A major challenge for evaluating inhalation toxicology is an accurate assessment of the delivered dose to the surface of the cells and the internalized dose. To estimate this, we ran the multiple-path particle dosimetry (MPPD) model to characterize particle deposition in the respiratory tract and developed a PBPK model for nicotine that was validated with human clinical trial data for cigarettes. Finally, we estimated a Human Equivalent Concentration (HEC) and predicted plasma concentrations based on the minimum effective concentration (MEC) derived after acute exposure of BEAS-2B cells to cigarette smoke (1R6F), or heated tobacco product (HTP) aerosol at the air liquid interface (ALI). The MPPD-PBPK model predicted the in vivo data from clinical studies within a factor of two, indicating good agreement as noted by WHO International Programme on Chemical Safety (2010) guidance. We then used QIVIVE to derive the exposure concentration (HEC) that matched the estimated in vitro deposition point of departure (POD) (MEC cigarette = 0.38 puffs or 11.6 µg nicotine, HTP = 22.9 puffs or 125.6 µg nicotine) and subsequently derived the equivalent human plasma concentrations. Results indicate that for the 1R6F cigarette, inhaling 1/6th of a stick would be required to induce the same effects observed in vitro, in vivo. Whereas, for HTP it would be necessary to consume 3 sticks simultaneously to induce in vivo the effects observed in vitro. This data further demonstrates the reduced physiological potency potential of HTP aerosol compared to cigarette smoke. The QIVIVE approach demonstrates great promise in assisting human health risk assessments, however, further optimization and standardization are required for the substantiation of a meaningful contribution to tobacco harm reduction by alternative nicotine delivery products.

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.

Physiologically Based Pharmacokinetic Modelling of UGT Substrate Drugs Lamotrigine and Raltegravir during Pregnancy

Pregnancy is associated with various physiological changes that can significantly impact the disposition of drugs. To further the insight into how pregnancy affects the pharmacokinetics of drugs at different stages, clinical studies can be simulated using Physiologically Based Pharmacokinetic modelling. PBPK modelling of drugs metabolised by Phase I enzymes (CYPs) in pregnant population models had been reported in the past, while its use in Phase II (UGTs) is not known. In this study, based on the results of a recent meta-analysis, lamotrigine (UGT1A4) and raltegravir (UGT1A1) were selected as candidate drugs, and pregnancy-specific models were developed for both using the Simcyp v.21 simulator. A middle-out strategy was used where previously published drug parameters were adapted from a minimal to a full PBPK model to allow their application for the pregnancy population models using Simcyp PBPK software. Adapted models were successfully validated against observed clinical data both qualitatively (visual overlay of plasma concentrations on graphs) and quantitatively (calculating the predicted/observed ratios for AUC, Cmax and CL as well as statistical analysis using model prediction power metrics). They were then applied to predict the PKs of both drugs in pregnancy population models. The temporal changes in maternal enzymatic activities during gestation were modelled based on in vitro data reported in literature and default relationships encoded in the Simcyp platform for UGT1A1 and UGT1A4, respectively. Our study demonstrates the successful development and validation of a PBPK model for LTG and RTG in pregnancy population models. Future work with additional UGT1A4 substrate drugs using the proposed changes in UGT1A4 activity may enable validating the pregnancy population model and its subsequent use for the prospective prediction of PK.

Predictions of tissue concentrations of myclobutanil, oxyfluorfen, and pronamide in rat and human after oral exposures via GastroPlusTM physiologically based pharmacokinetic modelling

ABSTRACT Heritage agrochemicals like myclobutanil, oxyfluorfen, and pronamide, are extensively used in agriculture, with well-established studies on their animal toxicity. Yet, human toxicity assessment relies on conventional human risk assessment approaches including the utilization of animal-based ADME (Absorption, Distribution, Metabolism, and Excretion) data. In recent years, Physiologically Based Pharmacokinetic (PBPK) modelling approaches have played an increasing role in human risk assessment of many chemicals including agrochemicals. This study addresses the absence of PBPK-type data for myclobutanil, oxyfluorfen, and pronamide by generating in vitro data for key input PBPK parameters (Caco-2 permeability, rat plasma binding, rat blood to plasma ratio, and rat liver microsomal half-life), followed by generation of PBPK models for these three chemicals via the GastroPlusTM software. Incorporating these experimental input parameters into PBPK models, the prediction accuracy of plasma AUC (area under curve) was significantly improved. Validation against rat oral administration data demonstrated substantial enhancement. Steady-state plasma concentrations (Css) of pronamide aligned well with published data using measured PBPK parameters. Following validation, parent-based tissue concentrations for these agrochemicals were predicted in humans and rats after single or 30-day repeat exposure of 10 mg/kg/day. These predicted concentrations contribute valuable information for future human toxicity risk assessments of these agrochemicals.

Extrapolation of lung pharmacokinetics of antitubercular drugs from preclinical species to humans using PBPK modelling.

To develop physiologically based pharmacokinetic (PBPK) models for widely used anti-TB drugs, namely rifampicin, pyrazinamide, isoniazid, ethambutol and moxifloxacin lung pharmacokinetics (PK)-regarding both healthy and TB-infected tissue (cellular lesion and caseum)-in preclinical species and to extrapolate to humans. Empirical models were used for the plasma PK of each species, which were connected to multicompartment permeability-limited lung models within a middle-out PBPK approach with an appropriate physiological parameterization that was scalable across species. Lung's extracellular water (EW) was assumed to be the linking component between healthy and infected tissue, while passive diffusion was assumed for the drug transferring between cellular lesion and caseum. In rabbits, optimized unbound fractions in intracellular water of rifampicin, moxifloxacin and ethambutol were 0.015, 0.056 and 0.08, respectively, while the optimized unbound fractions in EW of pyrazinamide and isoniazid in mice were 0.25 and 0.17, respectively. In humans, all mean extrapolated daily AUC and Cmax values of various lung regions were within 2-fold of the observed ones. Unbound concentrations in the caseum were lower than unbound plasma concentrations for both rifampicin and moxifloxacin. For rifampicin, unbound concentrations in cellular rim are slightly lower, while for moxifloxacin they are significantly higher than unbound plasma concentrations. The developed PBPK approach was able to extrapolate lung PK from preclinical species to humans and to predict unbound concentrations in the various TB-infected regions, unlike empirical lung models. We found that plasma free drug PK is not always a good surrogate for TB-infected tissue unbound PK.

Physiologically-based Pharmacokinetic Modeling for Assessment of the Drug-drug Interaction Potential of Zavegepant (P9-12.006)

Physiologically-based Pharmacokinetic Modeling for Assessment of the Drug-drug Interaction Potential of Zavegepant (P9-12.006)

Physiologically based pharmacokinetics modeling and transporter proteomics to predict systemic and local liver and muscle disposition of statins.

Statins are used to reduce liver cholesterol levels but also carry a dose-related risk of skeletal muscle toxicity. Concentrations of statins in plasma are often used to assess efficacy and safety, but because statins are substrates of membrane transporters that are present in diverse tissues, local differences in intracellular tissue concentrations cannot be ruled out. Thus, plasma concentration may not be an adequate indicator of efficacy and toxicity. To bridge this gap, we used physiologically based pharmacokinetic (PBPK) modeling to predict intracellular concentrations of statins. Quantitative data on transporter clearance were scaled from invitro to invivo conditions by integrating targeted proteomics and transporter kinetics data. The developed PBPK models, informed by proteomics, suggested that organic anion-transporting polypeptide 2B1 (OATP2B1) and multidrug resistance-associated protein 1 (MRP1) play a pivotal role in the distribution of statins in muscle. Using these PBPK models, we were able to predict the impact of alterations in transporter function due to genotype or drug-drug interactions on statin systemic concentrations and exposure in liver and muscle. These results underscore the potential of proteomics-guided PBPK modeling to scale transporter clearance from invitro data to real-world implications. It is important to evaluate the role of drug transporters when predicting tissue exposure associated with on- and off-target effects.

Mechanistic Modeling of In Vitro Biopharmaceutic Data for a Weak Acid Drug: A Pathway Towards Deriving Fundamental Parameters for Physiologically Based Biopharmaceutic Modeling.

Mechanistic modeling of in vitro experiments using metabolic enzyme systems enables the extrapolation of metabolic clearance for in vitro-in vivo predictions. This is particularly important for successful clearance predictions using physiologically based pharmacokinetic (PBPK) modeling. The concept of mechanistic modeling can also be extended to biopharmaceutics, where in vitro data is used to predict the in vivo pharmacokinetic profile of the drug. This approach further allows for the identification of parameters that are critical for oral drug absorption in vivo. However, the routine use of this analysis approach has been hindered by the lack of an integrated analysis workflow. The objective of this tutorial is to (1) review processes and parameters contributing to oral drug absorption in increasing levels of complexity, (2) outline a general physiologically based biopharmaceutic modeling workflow for weak acids, and (3) illustrate the outlined concepts via an ibuprofen (i.e., a weak, poorly soluble acid) case example in order to provide practical guidance on how to integrate biopharmaceutic and physiological data to better understand oral drug absorption. In the future, we plan to explore the usefulness of this tutorial/roadmap to inform the development of PBPK models for BCS 2 weak bases, by expanding the stepwise modeling approach to accommodate more intricate scenarios, including the presence of diprotic basic compounds and acidifying agents within the formulation.

Estimating the dynamic early life exposure to PFOA and PFOS of the HELIX children: Emerging profiles via prenatal exposure, breastfeeding, and diet

In utero and children’s exposure to per- and polyfluoroalkyl substances (PFAS) is a major concern in health risk assessment as early life exposures are suspected to induce adverse health effects. Our work aims to estimate children’s exposure (from birth to 12 years old) to PFOA and PFOS, using a Physiologically-Based Pharmacokinetic (PBPK) modelling approach.A model for PFAS was updated to simulate the internal PFAS exposures during the in utero life and childhood, and including individual characteristics and exposure scenarios (e.g., duration of breastfeeding, weight at birth, etc.). Our approach was applied to the HELIX cohort, involving 1,239 mother–child pairs with measured PFOA and PFOS plasma concentrations at two sampling times: maternal and child plasma concentrations (6 to 12 y.o).Our model predicted an increase in plasma concentrations during fetal development and childhood until 2 y.o when the maximum concentrations were reached. Higher plasma concentrations of PFOA than PFOS were predicted until 2 y.o, and then PFOS concentrations gradually became higher than PFOA concentrations. From 2 to 8 y.o, mean concentrations decreased from 3.1 to 1.88 µg/L or ng/mL (PFOA) and from 4.77 to 3.56 µg/L (PFOS). The concentration–time profiles vary with the age and were mostly influenced by in utero exposure (on the first 4 months after birth), breastfeeding (from 5 months to 2 (PFOA) or 5 (PFOS) y.o of the children), and food intake (after 3 (PFOA) or 6 (PFOS) y.o of the children). Similar measured biomarker levels can correspond to large differences in the simulated internal exposures, highlighting the importance to investigate the children’s exposure over the early life to improve exposure classification.Our approach demonstrates the possibility to simulate individual internal exposures using PBPK models when measured biomarkers are scarce, helping risk assessors in gaining insight into internal exposure during critical windows, such as early life.

Docetaxel, cyclophosphamide, and epirubicin: application of PBPK modeling to gain new insights for drug-drug interactions.

The new adjuvant chemotherapy of docetaxel, epirubicin, and cyclophosphamide has been recommended for treating breast cancer. It is necessary to investigate the potential drug-drug Interactions (DDIs) since they have a narrow therapeutic window in which slight differences in exposure might result in significant differences in treatment efficacy and tolerability. To guide clinical rational drug use, this study aimed to evaluate the DDI potentials of docetaxel, cyclophosphamide, and epirubicin in cancer patients using physiologically based pharmacokinetic (PBPK) models. The GastroPlus™ was used to develop the PBPK models, which were refined and validated with observed data. The established PBPK models accurately described the pharmacokinetics (PKs) of three drugs in cancer patients, and the predicted-to-observed ratios of all the PK parameters met the acceptance criterion. The PBPK model predicted no significant changes in plasma concentrations of these drugs during co-administration, which was consistent with the observed clinical phenomenon. Besides, the verified PBPK models were then used to predict the effect of other Cytochrome P450 3A4 (CYP3A4) inhibitors/inducers on these drug exposures. In the DDI simulation, strong CYP3A4 modulators changed the exposure of three drugs by 0.71-1.61 fold. Therefore, patients receiving these drugs in combination with strong CYP3A4 inhibitors should be monitored regularly to prevent adverse reactions. Furthermore, co-administration of docetaxel, cyclophosphamide, or epirubicin with strong CYP3A4 inducers should be avoided. In conclusion, the PBPK models can be used to further investigate the DDI potential of each drug and to develop dosage recommendations for concurrent usage by additional perpetrators or victims.

Modeling the complexity of drug-drug interactions: A physiologically-based pharmacokinetic study of Lenvatinib with Schisantherin A/Schisandrin A

BackgroundLenvatinib's efficacy as a frontline targeted therapy for radioactive iodine-refractory thyroid carcinoma and advanced hepatocellular carcinoma owes to its inhibition of multiple tyrosine kinases. However, as a CYP3A4 substrate, lenvatinib bears susceptibility to pharmacokinetic modulation by co-administered agents. Schisantherin A (STA) and schisandrin A (SIA) — bioactive lignans abundant in the traditional Chinese medicinal Wuzhi Capsule — act as CYP3A4 inhibitors, engendering the potential for drug-drug interactions (DDIs) with lenvatinib. MethodsTo explore potential DDIs between lenvatinib and STA/SIA, we developed a physiologically-based pharmacokinetic (PBPK) model for lenvatinib and used it to construct a DDI model for lenvatinib and STA/SIA. The model was validated with clinical trial data and used to predict changes in lenvatinib exposure with combined treatment. ResultsFollowing single-dose administration, the predicted area under the plasma concentration-time curve (AUC) and maximum plasma concentrations (Cmax) of lenvatinib increased 1.00- to 1.03-fold and 1.00- to 1.01-fold, respectively, in the presence of STA/SIA. Simulations of multiple-dose regimens revealed slightly greater interactions, with lenvatinib AUC0-t and Cmax increasing up to 1.09-fold and 1.02-fold, respectively. ConclusionOur study developed the first PBPK and DDI models for lenvatinib as a victim drug. STA and SIA slightly increased lenvatinib exposure in simulations, providing clinically valuable information on the safety of concurrent use. Given the minimal pharmacokinetic changes, STA/SIA are unlikely to interact with lenvatinib through pharmacokinetic alterations synergistically but rather may enhance efficacy through inherent anti-cancer efficacy of STA/ SIA.

Characterization of Pediatric Rectal Absorption, Drug Disposition, and Sedation Level for Midazolam Gel Using Physiologically Based Pharmacokinetic/Pharmacodynamic Modeling.

This study aims to explore and characterize the role of pediatric sedation via rectal route. A pediatric physiologically based pharmacokinetic-pharmacodynamic (PBPK/PD) model of midazolam gel was built and validated to support dose selection for pediatric clinical trials. Before developing the rectal PBPK model, an intravenous PBPK model was developed to determine drug disposition, specifically by describing the ontogeny model of the metabolic enzyme. Pediatric rectal absorption was developed based on the rectal PBPK model of adults. The improved Weibull function with permeability, surface area, and fluid volume parameters was used to extrapolate pediatric rectal absorption. A logistic regression model was used to characterize the relationship between the free concentrations of midazolam and the probability of sedation. All models successfully described the PK profiles with absolute average fold error (AAFE) < 2, especially our intravenous PBPK model that extended the predicted age to preterm. The simulation results of the PD model showed that when the free concentrations of midazolam ranged from 3.9 to 18.4 ng/mL, the probability of "Sedation" was greater than that of "Not-sedation" states. Combined with the rectal PBPK model, the recommended sedation doses were in the ranges of 0.44-2.08 mg/kg for children aged 2-3 years, 0.35-1.65 mg/kg for children aged 4-7 years, 0.24-1.27 mg/kg for children aged 8-12 years, and 0.20-1.10 mg/kg for adolescents aged 13-18 years. Overall, this model mechanistically quantified drug disposition and effect of midazolam gel in the pediatric population, accurately predicted the observed clinical data, and simulated the drug exposure for sedation that will inform dose selection for following pediatric clinical trials.

Clinical pharmacokinetics of leriglitazone and a translational approach using PBPK modeling to guide the selection of the starting dose in children.

Leriglitazone is a unique peroxisome proliferator-activated receptor-gamma (PPARγ) agonist that crosses the blood-brain barrier in humans and clinical trials have shown evidence of efficacy in neurodegenerative diseases. At clinical doses which are well-tolerated, leriglitazone reaches the target central nervous system (CNS) concentrations that are needed for PPARγ engagement and efficacy; PPARγ engagement is also supported by clinical and anti-inflammatory biomarker changes in the Cerebrospinal fluid in the CNS. Plasma pharmacokinetics (PK) of leriglitazone were determined in a phase 1 study in male healthy volunteers comprising a single ascending dose (SAD) and a multiple ascending dose (MAD) at oral doses of 30, 90, and 270 mg and 135 and 270 mg, respectively. Leriglitazone was rapidly absorbed with no food effect on overall exposure and showed a linear PK profile with dose-exposure correlation. A physiologically based pharmacokinetic (PBPK) model was developed for leriglitazone based on phase 1 data (SAD part) and incorporated CYP3A4 (fmCYP3A4 = 24%) and CYP2C8-mediated (fmCYP2C8 = 45%) metabolism, as well as biliary clearance (feBIL = 19.5%) derived from invitro data, and was verified by comparing the observed versus predicted concentration-time profiles from the MAD part. The PBPK model was prospectively applied to predict the starting pediatric doses and was preliminarily verified with data from five pediatric patients.

An Application of a Physiologically Based Pharmacokinetic Approach to Predict Ceftazidime Pharmacokinetics in a Pregnant Population

Physiological changes during pregnancy can alter maternal and fetal drug exposure. The objective of this work was to predict maternal and umbilical ceftazidime pharmacokinetics during pregnancy. Ceftazidime transplacental permeability was predicted from its physicochemical properties and incorporated into the model. Predicted concentrations and parameters from the PBPK model were compared to the observed data. PBPK predicted ceftazidime concentrations in non-pregnant and pregnant subjects of different gestational weeks were within 2-fold of the observations, and the observed concentrations fell within the 5th–95th prediction interval from the PBPK simulations. The calculated transplacental clearance (0.00137 L/h/mL of placenta volume) predicted an average umbilical cord-to-maternal plasma ratio of 0.7 after the first dose, increasing to about 1.0 at a steady state, which also agrees well with clinical observations. The developed maternal PBPK model adequately predicted the observed exposure and kinetics of ceftazidime in the pregnant population. Using a verified population-based PBPK model provides valuable insights into the disposition of drug concentrations in special individuals that are otherwise difficult to study and, in addition, offers the possibility of supplementing sparse samples obtained in vulnerable populations with additional knowledge, informing the dosing adjustment and study design, and improving the efficacy and safety of drugs in target populations.

Predictions of Bedaquiline Central Nervous System Exposure in Patients with Tuberculosis Meningitis Using Physiologically based Pharmacokinetic Modeling.

The use of bedaquiline as a treatment option for drug-resistant tuberculosis meningitis (TBM) is of interest to address the increased prevalence of resistance to first-line antibiotics. To this end, we describe a whole-body physiologically based pharmacokinetic (PBPK) model for bedaquiline to predict central nervous system (CNS) exposure. A whole-body PBPK model was developed for bedaquiline and its metabolite, M2. The model included compartments for brain and cerebrospinal fluid (CSF). Model predictions were evaluated by comparison to plasma PK time profiles following different dosing regimens and sparse CSF concentrations data from patients. Simulations were then conducted to compare CNS and lung exposures to plasma exposure at clinically relevant dosing schedules. The model appropriately described the observed plasma and CSF bedaquiline and M2 concentrations from patients with pulmonary tuberculosis (TB). The model predicted a high impact of tissue binding on target site drug concentrations in CNS. Predicted unbound exposures within brain interstitial exposures were comparable with unbound vascular plasma and unbound lung exposures. However, unbound brain intracellular exposures were predicted to be 7% of unbound vascular plasma and unbound lung intracellular exposures. The whole-body PBPK model for bedaquiline and M2 predicted unbound concentrations in brain to be significantly lower than the unbound concentrations in the lung at clinically relevant doses. Our findings suggest that bedaquiline may result in relatively inferior efficacy against drug-resistant TBM when compared with efficacy against drug-resistant pulmonary TB.

A Predictive Toxicokinetic Model for Nickel Leaching from Vascular Stents.

In vitro testing methods offer valuable insights into the corrosion vulnerability of metal implants and enable prompt comparison between devices. However, they fall short in predicting the extent of leaching and the biodistribution of implant byproducts under in vivo conditions. Physiologically based toxicokinetic (PBTK) models are capable of quantitatively establishing such correlations and therefore provide a powerful tool in advancing nonclinical methods to test medical implants and assess patient exposure to implant debris. In this study, we present a multicompartment PBTK model and a simulation engine for toxicological risk assessment of vascular stents. The mathematical model consists of a detailed set of constitutive equations that describe the transfer of nickel ions from the device to peri-implant tissue and circulation and the nickel mass exchange between blood and the various tissues/organs and excreta. Model parameterization was performed using (1) in-house-produced data from immersion testing to compute the device-specific diffusion parameters and (2) full-scale animal in situ implantation studies to extract the mammalian-specific biokinetic functions that characterize the time-dependent biodistribution of the released ions. The PBTK model was put to the test using a simulation engine to estimate the concentration-time profiles, along with confidence intervals through probabilistic Monte Carlo, of nickel ions leaching from the implanted devices and determine if permissible exposure limits are exceeded. The model-derived output demonstrated prognostic conformity with reported experimental data, indicating that it may provide the basis for the broader use of modeling and simulation tools to guide the optimal design of implantable devices in compliance with exposure limits and other regulatory requirements.

PBTK model-based analysis of CYP3A4 induction and the toxicokinetics of the pyrrolizidine alkaloid retrorsine in man.

Cytochrome P450 (CYP)3A4 induction by drugs and pesticides plays a critical role in the enhancement of pyrrolizidine alkaloid (PA) toxicity as it leads to increased formation of hepatotoxic dehydro-PA metabolites. Addressing the need for a quantitative analysis of this interaction, we developed a physiologically-based toxicokinetic (PBTK) model. Specifically, the model describes the impact of the well-characterized CYP3A4 inducer rifampicin on the kinetics of retrorsine, which is a prototypic PA and contaminant in herbal teas. Based on consumption data, the kinetics after daily intake of retrorsine were simulated with concomitant rifampicin treatment. Strongest impact on retrorsine kinetics (plasma AUC and reduced to 67% and 74% compared to the rifampicin-free reference) was predicted directly after withdrawal of rifampicin. At this time point, the competitive inhibitory effect of rifampicin stopped, while CYP3A4 induction was still near its maximum. Due to the impacted metabolism kinetics, the cumulative formation of intestinal retrorsine CYP3A4 metabolites increased to 254% (from 10 to 25 nmol), while the cumulative formation of hepatic CYP3A4 metabolites was not affected (57 nmol). Return to baseline PA toxicokinetics was predicted 14 days after stop of a 14-day rifampicin treatment. In conclusion, the PBTK model showed to be a promising tool to assess the dynamic interplay of enzyme induction and toxification pathways.

Abstract 875: Quantitative systems pharmacology modeling of loncastuximab tesirine combined with mosunetuzumab and glofitamab helps guide dosing for patients with DLBCL

Abstract Introduction: CD19 and CD20, B-lymphocyte surface antigens, are clinically validated therapeutic targets for the treatment of B-cell malignancies. Loncastuximab tesirine (loncastuximab tesirine-lpyl [Lonca]) is an antibody-drug conjugate comprising an anti-CD19 antibody conjugated to a pyrrolobenzodiazepine dimer cytotoxin. Mosunetuzumab (Mosun) and glofitamab (Glofit) are CD20 × CD3 T-cell-engaging bispecific antibodies (bsAbs) that redirect T cells to eliminate malignant B cells. These agents target a different B-cell surface antigen than Lonca; hence, combining Lonca with either one of these bsAbs is expected to have additive or even synergistic efficacy. Previously, a novel physiologically-based pharmacokinetic (PBPK)-quantitative systems pharmacology (QSP) model was developed (Utsey K, et al. Clin Pharmacol Drug Dev. 2023;12:123-125) and validated with Lonca monotherapy clinical observations in R/R DLBCL (Caimi P, et al. Blood. 2022;140:9548-9550). Modeling results of Lonca in combination with Mosun or Glofit in R/R DLBCL are described herein. Methods: The model was based on the previously validated and published Lonca PBPK-QSP model. This model was reduced in physiological complexity to be compatible with a published minimal PBPK model of Mosun (Hosseini I, et al. NPJ Syst Biol Appl. 2020;6:28) while maintaining the core functionality of predicting tumor dynamics during Lonca monotherapy. The following assumptions were made: (1) Lonca or Mosun/Glofit induce healthy and malignant B-cell killing; (2) the tumor comprises malignant B cells and T cells; (3) tumor volume is calculated using malignant B-cell number; and (4) T cells can enter or leave the tumor as they enter or leave other tissues. Results: Improved tumor growth inhibition (TGI) should result from Lonca + bsAb combination therapy compared with any of the agents as monotherapy. While TGI was predicted to plateau by Cycle (C) 2 with Mosun and Glofit monotherapies, maximum activity of Lonca + bsAb combination therapies may not be observed until C4 or later. Additionally, predicted TGI increased with additional treatment cycles. Conclusions: Results indicate that treatment by the end of C3 for Lonca + Mosun and by the end of C4 for Lonca + Glofit would promote substantially more TGI compared with any of these agents alone. Increased doses of Lonca in combinations were predicted to have limited additional therapeutic benefit; however, additional cycles of combination treatment were predicted to increase TGI. This model suggests that Lonca doses could be reduced to allow for improved tolerability for longer periods of time, up to the point of maximal benefit, although clinical testing is needed to explore these results. The ongoing LOTIS-7 clinical trial (NCT04970901) evaluates the safety and anticancer activity of Lonca in combination with both Glofit and Mosun. Citation Format: Yuezhe Li, A Katharina Wilkins, Tim Knab, Joseph P. Boni. Quantitative systems pharmacology modeling of loncastuximab tesirine combined with mosunetuzumab and glofitamab helps guide dosing for patients with DLBCL [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 875.

Abstract 7171: Non-linear IV pharmacokinetics of the ATR inhibitor berzosertib (M6620) in mice

Abstract Introduction: The ataxia-telangiectasia and Rad3-related (ATR) protein kinase is an attractive target for cancer therapies as it is an apical initiator of DNA damage response (DDR) pathways that facilitate cell cycle arrest and DNA repair, thus promoting survival of cancer cells. Several ATR inhibitors (ATRi) are in clinical development including berzosertib (M6620, VX-970), which is currently being evaluated in combination with cytotoxic chemotherapies and radiation. Although clinical studies have examined plasma pharmacokinetics (PK) in humans, little is known regarding dose/exposure relationships and tissue distribution which may influence berzosertib’s therapeutic window. To understand these concepts, we extensively described the PK of berzosertib in mouse plasma and tissue. Methods: Dose proportionality was assessed using single IV doses of berzosertib (2, 6, 20 and 60 mg/kg) administered to female BALB/c mice. Mice were euthanized from 5 min to 24 h post-dose. Using a highly sensitive LC-MS/MS method, berzosertib was quantitated in red blood cells, plasma, and tissues. Non-compartmental analysis (NCA) was performed using Phoenix WinNonlin to determine relevant PK parameters. Dose linearity was assessed by statistical comparison of dose-normalized plasma Cmax and dose-normalized tissue AUC across the four dose levels. Statistical analyses were performed using ANOVA with Tukey’s multiple comparisons test. Berzosertib plasma protein binding was assessed in vitro from 300 ng/mL to 100 μg/mL using rapid equilibrium dialysis. An extensive PK study was also conducted in tumor-bearing mice to assess distribution by quantitating drug in tissues. Results: The dose proportionality study indicated that both plasma Cmax and AUC increase with dose, and dose-normalized concentration vs time profiles suggested nonlinear increases in exposure based on different terminal slopes. At the two highest doses, dose-normalized AUC in each tissue were greater than the exposure generated at the two lowest doses. However, log transformations of AUC0-360 plotted vs dose generated a slope of 0.886 (95% CI: 0.718 - 1.054), suggesting a linear relationship. The fraction of unbound berzosertib in mouse plasma increased at concentrations above 10 μg/mL, which is a concentration exceeded at the two highest doses and where nonlinearity in exposure is observed. Tissue analysis revealed the following tissues had a tissue/plasma coefficient &amp;gt;1: bone marrow, kidney, liver, spleen, lung, tumor, lymph nodes, thymus, and heart. Conclusion: IV berzosertib displayed nonlinear PK in mice. Increased doses of berzosertib were associated with greater than proportional increases in plasma and tissue exposure most likely due to saturation of plasma protein binding. Our results will help to understand preclinical pharmacodynamic and toxicity data and subsequent PBPK modeling to inform optimal dosing and clinical deployment of berzosertib. Citation Format: Joshua J. Deppas, Brian F. Kiesel, Jianxia Guo, Robert A. Parise, Christopher J. Bakkenist, Jan H. Beumer. Non-linear IV pharmacokinetics of the ATR inhibitor berzosertib (M6620) in mice [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7171.