Characterization of the modes of action and dose-response relationship for thiocyanate on the thyroid hormone levels in rats using a computational approach.

Current practices for evaluating the cumulative risk of thyroid-active chemical mixtures (perchlorate, thiocyanate, nitrate) focus on the inhibition of thyroidal iodide uptake via the sodium iodide symporter (NIS) as the mode of action for potency equivalence calculations. However, unlike perchlorate, thiocyanate presents additional modes of action within the thyroid that could contribute to the overall thyroid perturbation. We tested the hypothesis of whether assuming a single mode of action of thyroidal iodide uptake inhibition is sufficient for describing the observed dose-response relationship for thiocyanate and its effects on serum thyroxine levels. An interaction model was developed by linking a biologically based dose-response model for iodide and thyroid hormones to a thiocyanate physiologically based pharmacokinetic model. Each model, adapted from the literature, was restructured and recalibrated in a Bayesian framework for the current mode of actions study. For a chronic exposure scenario, NIS inhibition alone was found not to be sufficient to describe the dose-response relationship for thiocyanate. Inclusion of additional modes of action involving iodide flux across the thyroid membrane and inhibition of iodide organification via thyroid peroxidase showed only moderate improvements in capturing the dose-response at environmental thiocyanate doses of exposure and failed to capture trends at very high doses. Our findings emphasize the need for more mechanistic data for chronic exposure scenarios to characterize better the overall dose-response relationship for thiocyanate. Risk assessment approaches for thyroid-active chemical mixtures that rely on NIS inhibition as the single mode of action may over-predict the contribution of thiocyanate to thyroid disruption.

Thiocyanate: a review and evaluation of the kinetics and the modes of action for thyroid hormone perturbations.

Exposure of the population to thiocyanate is predominantly through the diet and cigarette smoke. Thiocyanate is a potential thyroid disruptor due to its capacity to inhibit the uptake of iodide by the thyroid. Thiocyanate also interacts with the enzymatic reactions associated with iodide organification and thyroid hormone synthesis. Quantification of the dose-response relationships of thiocyanate and alteration in thyroid hormone levels is important for evaluating the risk of exposure to thiocyanate in humans. In this review, we highlight the key whole-body and intra-thyroidal aspects of thiocyanate kinetics in rats and its various modes of action for perturbing thyroid function. The inter-play between the various transporter- and enzyme-mediated modes of action contributes to the complexity in the dose-response relationship determinations for thiocyanate. We map the available modes of action in a mechanistic and quantitative manner. Findings summarized in this study can help support the development of a quantitative model to study the interaction effects of thiocyanate on the thyroid function. Additionally, the data gaps identified can help guide future experimental designs to characterize further thiocyanate dose-response. Finally, the strengths and weaknesses in current risk assessment considerations used for thiocyanate as a component of thyroid-active chemical mixtures are discussed.

Development of a PBPK model of thiocyanate in rats with an extrapolation to humans: A computational study to quantify the mechanism of action of thiocyanate kinetics in thyroid.

Thyroid homeostasis can be disturbed due to thiocyanate exposure from the diet or tobacco smoke. Thiocyanate inhibits both thyroidal uptake of iodide, via the sodium-iodide symporter (NIS), and thyroid hormone (TH) synthesis in the thyroid, via thyroid peroxidase (TPO), but the mode of action of thiocyanate is poorly quantified in the literature. The characterization of the link between intra-thyroidal thiocyanate concentrations and dose of exposure is crucial for assessing the risk of thyroid perturbations due to thiocyanate exposure. We developed a PBPK model for thiocyanate that describes its kinetics in the whole-body up to daily doses of 0.15mmol/kg, with a mechanistic description of the thyroidal kinetics including NIS, passive diffusion, and TPO. The model was calibrated in a Bayesian framework using published studies in rats. Goodness-of-fit was satisfactory, especially for intra-thyroidal thiocyanate concentrations. Thiocyanate kinetic processes were quantified in vivo, including the metabolic clearance by TPO. The passive diffusion rate was found to be greater than NIS-mediated uptake rate. The model captured the dose-dependent kinetics of thiocyanate after acute and chronic exposures. Model behavior was evaluated using a Morris screening test. The distribution of thiocyanate into the thyroid was found to be determined primarily by the partition coefficient, followed by NIS and passive diffusion; the impact of the latter two mechanisms appears to increase at very low doses. Extrapolation to humans resulted in good predictions of thiocyanate kinetics during chronic exposure. The developed PBPK model can be used in risk assessment to quantify dose-response effects of thiocyanate on TH.

PBPK modeling of the cis- and trans-permethrin isomers and their major urinary metabolites in rats.

Permethrin, a pyrethroid insecticide, is suspected to induce neuronal and hormonal disturbances in humans. The widespread exposure of the populations has been confirmed by the detection of the urinary metabolites of permethrin in biomonitoring studies. Permethrin is a chiral molecule presenting two forms, the cis and the trans isomers. Because in vitro studies indicated a metabolic interaction between the trans and cis isomers of permethrin, we adapted and calibrated a PBPK model for trans- and cis-permethrin separately in rats. The model also describes the toxicokinetics of three urinary metabolites, cis- and trans-3-(2,2 dichlorovinyl)-2,2-dimethyl-(1-cyclopropane) carboxylic acid (cis- and trans-DCCA), 3-phenoxybenzoic acid (3-PBA) and 4'OH-phenoxybenzoic acid (4'-OH-PBA). In vivo experiments performed in Sprague-Dawley rats were used to calibrate the PBPK model in a Bayesian framework. The model captured well the toxicokinetics of permethrin isomers and their metabolites including the rapid absorption, the accumulation in fat, the extensive metabolism of the parent compounds, and the rapid elimination of metabolites in urine. Average hepatic clearances in rats were estimated to be 2.4 and 5.7 L/h/kg for cis- and trans-permethrin, respectively. High concentrations of the metabolite 4'-OH-PBA were measured in urine compared to cis- and trans-DCCA and 3-PBA. The confidence in the extended PBPK model was then confirmed by good predictions of published experimental data obtained using the isomers mixture. The extended PBPK model could be extrapolated to humans to predict the internal dose of exposure to permethrin from biomonitoring data in urine.

Evaluating drug interaction potential from cytokine release syndrome using a physiologically based pharmacokinetic model: A case study of teclistamab.

Cytokine release syndrome (CRS) was associated with teclistamab treatment in the phase I/II MajesTEC-1 study. Cytokines, especially interleukin (IL)-6, are known suppressors of cytochrome P450 (CYP) enzymes' activity. A physiologically based pharmacokinetic model evaluated the impact of IL-6 serum levels on exposure of substrates of various CYP enzymes (1A2, 2C9, 2C19, 3A4, 3A5). Two IL-6 kinetics profiles were assessed, the mean IL-6 profile with a maximum concentration (Cmax) of IL-6 (21 pg/mL) and the IL-6 profile of the patient presenting the highest IL-6 Cmax (288 pg/mL) among patients receiving the recommended phase II dose of teclistamab in MajesTEC-1. For the mean IL-6 kinetics profile, teclistamab was predicted to result in a limited change in exposure of CYP substrates (area under the curve [AUC] mean ratio 0.87-1.20). For the maximum IL-6 kinetics profile, the impact on omeprazole, simvastatin, midazolam, and cyclosporine exposure was weak to moderate (mean AUC ratios 1.90-2.23), and minimal for caffeine and s-warfarin (mean AUC ratios 0.82-1.25). Maximum change in exposure for these substrates occurred 3-4 days after step-up dosing in cycle 1. These results suggest that after cycle 1, drug interaction from IL-6 effect has no meaningful impact on CYP activities, with minimal or moderate impact on CYP substrates. The highest risk of drug interaction is expected to occur during step-up dosing up to 7 days after the first treatment dose (1.5 mg/kg subcutaneously) and during and after CRS.

Evaluation of Drug-Drug Interaction Potential of Talquetamab, a T-Cell-Redirecting GPRC5D × CD3 Bispecific Antibody, as a Result of Cytokine Release Syndrome in Patients with Relapsed/Refractory Multiple Myeloma in MonumenTAL-1, Using a Physiologically Based Pharmacokinetic Model.

BACKGROUND: Cytokine release syndrome, commonly associated with T-cell immunotherapies, was observed with talquetamab, a T-cell-redirecting bispecific antibody, in the phase I/II MonumenTAL-1 study, leading to elevated interleukin (IL)-6, which can suppress cytochrome P450 (CYP) enzyme activity. OBJECTIVE: We aimed to evaluate the potential impact of elevated IL-6 on the exposure of co-administered CYP450 substrates for two scenarios: (1) the observed median IL-6 profile and (2) a profile with the highest IL-6 maximum concentration following talquetamab treatment. METHODS: A physiologically based pharmacokinetic model was developed based on the literature and simulations performed using observed IL-6 profiles from patients in MonumenTAL-1 who received the subcutaneous recommended phase 2 doses (RP2Ds) of talquetamab: 0.4 mg/kg weekly (QW) and 0.8 mg/kg every other week (Q2W). RESULTS: Median IL-6 maximum concentration was 18.4 and 7.1 pg/mL, and maximum IL-6 maximum concentration was 213 and 3503 pg/mL for talquetamab QW and Q2W RP2Ds, respectively. For the median IL-6 profile, no interaction between IL-6 and studied CYP substrates was predicted at either RP2D. The maximum IL-6 profile predicted weak-to-moderate impact on exposure of CYP2C19, CYP3A4, and CYP3A5 substrates and minimal impact on exposure of CYP1A2 substrates at both RP2Ds. Impact on exposure of CYP2C9 substrates was predicted as minimal at QW and minimal-to-weak at Q2W RP2Ds. Time to return to 20% difference from baseline enzymatic activity was predicted as 7 and 9 days after start of cycle 1 for QW and Q2W RP2Ds, respectively. CONCLUSIONS: These modeling results suggest that IL-6 release due to talquetamab-induced cytokine release syndrome has limited impact on potential drug-drug interactions, with the highest likelihood of impact occurring from initiation of talquetamab step-up dosing up to 7 (QW) or 9 (Q2W) days after first treatment dose in cycle 1 and during and after cytokine release syndrome. Multiple myeloma can be treated with immunotherapies such as the bispecific antibody, talquetamab, which binds the novel antigen G protein-coupled receptor family C group 5 member D on multiple myeloma cells and CD3 on T cells and induces T-cell-mediated lysis of multiple myeloma cells. Following talquetamab treatment, many patients experience cytokine release syndrome, an inflammatory immune response where levels of proinflammatory cytokines, including interleukin (IL)-6, are increased. Interleukin-6 can suppress the activity of important enzymes in the body (cytochrome [CYP] P450s) that are involved in drug clearance. This study used a physiologically based pharmacokinetic computer model to investigate the potential impact of increased IL-6 levels on CYP450 enzymes to determine subsequent impact on drugs that are metabolized by CYP450 enzymes. The results showed no predicted interaction between median levels of IL-6 observed in patients and CYP substrates (such as caffeine and omeprazole) with talquetamab. In a simulation that assessed higher (maximum) IL-6 levels observed in patients, the predicted impact of IL-6 was minimal to weak for most of the CYP substrates assessed. The effect on CYP450 enzymatic activity was highest from initiation of talquetamab step-up dosing up to 7-9 days after the first treatment dose of talquetamab. These results suggest that, in this treatment time period, elevated IL-6 levels due to talquetamab-induced cytokine release syndrome have limited impact on drugs that are CYP substrates that may be used in conjunction with talquetamab, but that the concentration and toxicity of these drugs should be monitored and the dose of CYP substrate adjusted as required.

Development of 4-Pyridoxic Acid PBPK Model to Support Biomarker-Informed Evaluation of OAT1/3 Inhibition and Effect of Chronic Kidney Disease.

Monitoring endogenous biomarkers is increasingly used to evaluate transporter-mediated drug-drug interactions (DDIs) in early drug development and may be applied to elucidate changes in transporter activity in disease. 4-pyridoxic acid (PDA) has been identified as the most sensitive plasma endogenous biomarker of renal organic anion transporters (OAT1/3). Increase in PDA baseline concentrations was observed after administration of probenecid, a strong clinical inhibitor of OAT1/3 and also in patients with chronic kidney disease (CKD). The aim of this study was to develop and verify a physiologically-based pharmacokinetic (PBPK) model of PDA, to predict the magnitude of probenecid DDI and predict the CKD-related changes in PDA baseline. The PBPK model for PDA was first developed in healthy population, building on from previous population pharmacokinetic modeling, and incorporating a mechanistic kidney model to consider OAT1/3-mediated renal secretion. Probenecid PBPK model was adapted from the Simcyp database and re-verified to capture its dose-dependent pharmacokinetics (n = 9 studies). The PBPK model successfully predicted the PDA plasma concentrations, area under the curve, and renal clearance in healthy subjects at baseline and after single/multiple probenecid doses. Prospective simulations in severe CKD predicted successfully the increase in PDA plasma concentration relative to healthy (within 2-fold of observed data) after accounting for 60% increase in fraction unbound in plasma and additional 50% decline in OAT1/3 activity beyond the decrease in glomerular filtration rate. The verified PDA PBPK model supports future robust evaluation of OAT1/3 DDI in drug development and increases our confidence in predicting exposure and renal secretion in patients with CKD.

Utilization of OATP1B Biomarker Coproporphyrin-I to Guide Drug-Drug Interaction Risk Assessment: Evaluation by the Pharmaceutical Industry.

Drug-drug interactions (DDIs) involving hepatic organic anion transporting polypeptides 1B1/1B3 (OATP1B) can be substantial, however, challenges remain for predicting interaction risk. Emerging evidence suggests that endogenous biomarkers, particularly coproporphyrin-I (CP-I), can be used to assess in vivo OATP1B activity. The present work under the International Consortium for Innovation and Quality in Pharmaceutical Development was aimed primarily at assessing CP-I as a biomarker for informing OATP1B DDI risk. Literature and unpublished CP-I data along with pertinent in vitro and clinical DDI information were collected to identify DDIs primarily involving OATP1B inhibition and assess the relationship between OATP1B substrate drug and CP-I exposure changes. Static models to predict changes in exposure of CP-I, as a selective OATP1B substrate, were also evaluated. Significant correlations were observed between CP-I area under the curve ratio (AUCR) or maximum concentration ratio (Cmax R) and AUCR of substrate drugs. In general, the CP-I Cmax R was equal to or greater than the CP-I AUCR. CP-I Cmax R < 1.25 was associated with absence of OATP1B-mediated DDIs (AUCR < 1.25) with no false negative predictions. CP-I Cmax R < 2 was associated with weak OATP1B-mediated DDIs (AUCR < 2). A correlation was identified between CP-I exposure changes and OATP1B1 static DDI predictions. Recommendations for collecting and interpreting CP-I data are discussed, including a decision tree for guiding DDI risk assessment. In conclusion, measurement of CP-I is recommended to inform OATP1B inhibition potential. The current analysis identified changes in CP-I exposure that may be used to prioritize, delay, or replace clinical DDI studies.

Prediction of Drug-Drug Interactions After Esketamine Intranasal Administration Using a Physiologically Based Pharmacokinetic Model.

BACKGROUND AND OBJECTIVE: A physiologically based pharmacokinetic (PBPK) modeling approach for esketamine and its metabolite noresketamine after esketamine intranasal administration was developed to aid the prediction of drug-drug interactions (DDIs) during the clinical development of esketamine nasal spray (SPRAVATO®). This article describes the development of the PBPK model to predict esketamine and noresketamine kinetics after intranasal administration of esketamine and its verification and application in the prediction of prospective DDIs with esketamine using models of index perpetrator and victim drugs. METHODS: The intranasal PBPK (IN-PBPK) models for esketamine/noresketamine were constructed in Simcyp® v14.1 by combining the oral and intravenous esketamine PBPK models, with the dose divided in the ratio 57.7/42.3. Verification of the model was based on comparing the pharmacokinetics and DDI simulations with observed data in healthy volunteers. RESULTS: The simulated and observed (171 healthy volunteers) plasma pharmacokinetic profiles of intranasal esketamine/noresketamine showed a good match. The relative contributions of different cytochromes P450 (CYPs), mainly CYP3A4 and CYP2B6, involved in esketamine/noresketamine clearance was captured correctly in the IN-PBPK model using the DDI clinical studies of intranasal esketamine with clarithromycin and rifampicin and a published DDI study of oral esketamine with ticlopidine. The induction potential of esketamine toward CYP3A4 was also well captured. Inhibition of intranasal esketamine in the presence of ticlopidine was predicted to be not clinically relevant. Different scenarios tested with esketamine as a CYP3A4 perpetrator of midazolam also predicted the absence of clinically relevant CYP3A4 interactions. CONCLUSION: This PBPK model of the intranasal route adequately described the pharmacokinetics and DDI of intranasal esketamine/noresketamine with potential perpetrator and victim drugs. This work was used to support regulatory submissions of SPRAVATO®.

Clinical Investigation on Endogenous Biomarkers to Predict Strong OAT-Mediated Drug-Drug Interactions.

BACKGROUND: Endogenous biomarkers are promising tools to assess transporter-mediated drug-drug interactions early in humans. METHODS: We evaluated on a common and validated in vitro system the selectivity of 4-pyridoxic acid (PDA), homovanillic acid (HVA), glycochenodeoxycholate-3-sulphate (GCDCA-S) and taurine towards different renal transporters, including multidrug resistance-associated protein, and assessed the in vivo biomarker sensitivity towards the strong organic anion transporter (OAT) inhibitor probenecid at 500 mg every 6 h to reach close to complete OAT inhibition. RESULTS: PDA and HVA were substrates of the OAT1/2/3, OAT4 (PDA only) and multidrug resistance-associated protein 4; GCDCA-S was more selective, having affinity only towards OAT3 and multidrug resistance-associated protein 2. Taurine was not a substrate of any of the investigated transporters under the in vitro conditions tested. Plasma exposure of PDA and HVA significantly increased and the renal clearance of GCDCA-S, PDA and HVA decreased; the magnitude of these changes was comparable to those of known clinical OAT probe substrates. PDA and GCDCA-S were the most promising endogenous biomarkers of the OAT pathway activity: PDA plasma exposure was the most sensitive to probenecid inhibition, and, in contrast, GCDCA-S was the most sensitive OAT biomarker based on renal clearance, with higher selectivity towards the OAT3 transporter. CONCLUSIONS: The current findings illustrate a clear benefit of measuring PDA plasma exposure during phase I studies when a clinical drug candidate is suspected to be an OAT inhibitor based on in vitro data. Subsequently, combined monitoring of PDA and GCDCA-S in both urine and plasma is recommended to tease out the involvement of OAT1/3 in the inhibition interaction. CLINICAL TRIAL REGISTRATION: EudraCT number: 2016-003923-49.