Race-specific coregulatory and transcriptomic profiles associated with DNA methylation and androgen receptor in prostate cancer.

BACKGROUND: Prostate cancer is a significant health concern, particularly among African American (AA) men who exhibit higher incidence and mortality compared to European American (EA) men. Understanding the molecular mechanisms underlying these disparities is imperative for enhancing clinical management and achieving better outcomes. METHODS: Employing a multi-omics approach, we analyzed prostate cancer in both AA and EA men. Using Illumina methylation arrays and RNA sequencing, we investigated DNA methylation and gene expression in tumor and non-tumor prostate tissues. Additionally, Boolean analysis was utilized to unravel complex networks contributing to racial disparities in prostate cancer. RESULTS: When comparing tumor and adjacent non-tumor prostate tissues, we found that DNA hypermethylated regions are enriched for PRC2/H3K27me3 pathways and EZH2/SUZ12 cofactors. Olfactory/ribosomal pathways and distinct cofactors, including CTCF and KMT2A, were enriched in DNA hypomethylated regions in prostate tumors from AA men. We identified race-specific inverse associations of DNA methylation with expression of several androgen receptor (AR) associated genes, including the GATA family of transcription factors and TRIM63. This suggests that race-specific dysregulation of the AR signaling pathway exists in prostate cancer. To investigate the effect of AR inhibition on race-specific gene expression changes, we generated in-silico patient-specific prostate cancer Boolean networks. Our simulations revealed prolonged AR inhibition causes significant dysregulation of TGF-ß, IDH1, and cell cycle pathways specifically in AA prostate cancer. We further quantified global gene expression changes, which revealed differential expression of genes related to microtubules, immune function, and TMPRSS2-fusion pathways, specifically in prostate tumors of AA men. Enrichment of these pathways significantly correlated with an altered risk of disease progression in a race-specific manner. CONCLUSIONS: Our study reveals unique signaling networks underlying prostate cancer biology in AA and EA men, offering potential insights for clinical management strategies tailored to specific racial groups. Targeting AR and associated pathways could be particularly beneficial in addressing the disparities observed in prostate cancer outcomes in the context of AA and EA men. Further investigation into these identified pathways may lead to the development of personalized therapeutic approaches to improve outcomes for prostate cancer patients across different racial backgrounds.

Target Reserve and Turnover Parameters Determine Rightward Shift of Enalaprilat Potency From its Binding Affinity to the Angiotensin Converting Enzyme.

Drug effects are often assumed to be directly proportional to the fraction of occupied targets. However, for a number of antagonists that exhibit target-mediated drug disposition (TMDD), such as angiotensin-converting enzyme (ACE) inhibitors, drug binding to the target at low concentrations may be significant enough to influence pharmacokinetics but insufficient to elicit a drug response (i.e., differences in drug-target binding affinity and potency). In this study, a pharmacokinetic/pharmacodynamic model for enalaprilat was developed in humans to provide a theoretical framework for assessing the relationship between ex vivo drug potency (IC50) and in vivo target-binding affinity (KD). The model includes competitive binding of angiotensin I and enalaprilat to ACE and accounts for the circulating target pool. Data were obtained from the literature, and model fitting and parameter estimation were conducted using maximum likelihood in ADAPT5. The model adequately characterized time-courses of enalaprilat concentrations and four biomarkers in the renin-angiotensin system and provided estimates for in vivo KD (0.646 nM) and system-specific parameters, such as total target density (32.0 nM) and fraction of circulating target (19.8%), which were in agreement with previous reports. Model simulations were used to predict the concentration-effect curve of enalaprilat, revealing a 6.3-fold increase in IC50 from KD. Additional simulations demonstrated that target reserve and degradation parameters are key factors determining the extent of shift of enalaprilat ex vivo potency from its in vivo binding affinity. This may be a common phenomenon for drugs exhibiting TMDD and has implications for translating binding affinity to potency in drug development.

Integrated PK/PD Modeling Relates Smoothened Inhibitor Biomarkers to The Heterogeneous Intratumor Disposition of Cetuximab in Pancreatic Cancer Tumor Models.

Therapeutic antibodies have shown little efficacy in the treatment of pancreatic ductal adenocarcinomas (PDAC). Tumor desmoplasia, hypovascularity, and poor perfusion result in insufficient tumor cell exposure, contributing to treatment failure. Smoothened inhibitors of hedgehog signaling (sHHi) increase PDAC tumor permeability, perfusion, and drug delivery, and provide a tool to develop a quantitative, mechanistic understanding as to how the temporal dynamics of tumor priming can impact intratumor distribution of monoclonal antibodies (mAb). A linked pharmacokinetic (PK)/pharmacodynamic (PD) model was developed to integrate the plasma and tumor PK of a sHHi priming agent with its effects upon downstream stromal biomarkers Gli1, hyaluronic acid, and interstitial fluid pressure in PDAC patient-derived xenograft (PDX) tumors. In parallel, in situ tumor concentrations of cetuximab (CTX: anti-epidermal growth factor receptor; EGFR) were quantified as a marker for tumor delivery of mAb or antibody-drug conjugates. A minimal, physiologically-based pharmacokinetic (mPBPK) model was constructed to link sHHi effects upon mechanistic effectors of tumor barrier compromise with the intratumor distribution of CTX, and CTX occupancy of EGFR in tumors. Integration of the mPBPK model of mAb deposition and intratumor distribution with the PK/PD model of tumor responses to priming not only identified physiological parameters that are critical for tumor antibody distribution, but also provides insight into dosing regimens that could achieve maximal tumor disposition of therapeutic antibodies under conditions of transient PDAC tumor permeability barrier compromise that mechanistically-diverse tumor priming strategies may achieve.

Population Pharmacokinetics and Pharmacodynamics of Carfilzomib in Combination with Rituximab, Ifosfamide, Carboplatin, and Etoposide in Adult Patients with Relapsed/Refractory Diffuse Large B Cell Lymphoma.

BACKGROUND: In patients with relapsed/refractory (R/R) diffuse large B-cell lymphoma (DLBCL), salvage chemotherapy regimens (e.g., rituximab, ifosfamide, carboplatin, and etoposide, R-ICE) yield poor outcomes. Carfilzomib, an irreversible proteasome inhibitor, can overcome acquired rituximab-chemotherapy resistance and, when combined with R-ICE, improves outcomes in patients with R/R DLBCL. OBJECTIVE: This analysis aimed to develop a population pharmacokinetic/pharmacodynamic (PK/PD) model for carfilzomib in R/R DLBCL patients. PATIENTS AND METHODS: In a single-center, open-label, prospective phase 1 study, patients received carfilzomib (10, 15, or 20 mg/m2) on days 1, 2, 8, and 9, and standard doses of R-ICE on days 3-6 every 21 days (maximum of three cycles). Carfilzomib plasma concentrations up to 24 h postinfusion were measured by liquid chromatography coupled with tandem mass spectrometry. Proteasome activity (PD biomarker) in peripheral blood mononuclear cells was assessed on days 1-2 with sparse sampling. PK/PD models were developed using NONMEM v7.4.1 interfaced with Finch Studio v1.1.0 and PsN v4.7.0. Model selection was guided by objective function value, goodness-of-fit, and visual predictive checks. Stepwise covariate modeling was used for covariate selection. RESULTS: Twenty-eight patients were enrolled in the PK/PD analysis, from whom 217 PK samples and 127 PD samples were included. Carfilzomib PK was best described by a two-compartment model with linear disposition (typical total clearance of 133 L/h). Proteasome activity was best characterized using a turnover model with irreversible inactivation. All parameters were estimated with good precision. No statistically significant covariates were identified. CONCLUSIONS: A validated population-based PK/PD model of carfilzomib was developed successfully. Further research is needed to identify sources of variability in response to treatment with carfilzomib in combination with R-ICE. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov identifier number NCT01959698.

Immunosuppressant adherence in adult outpatient hematopoietic cell transplant recipients.

INTRODUCTION: Medication nonadherence continues to be challenging for allogeneic hematopoietic cell transplant (HCT) recipients. The risk and severity of chronic graft-versus-host disease (GVHD) are associated with low immunosuppressant concentrations (which can be improved with model-informed precision dosing (MIPD)) and with immunosuppressant nonadherence (which can be improved with acceptable interventions). METHODS: With the goals of improving adherence and achieving therapeutic concentrations of immunosuppressants to eliminate GVHD, we characterized the feasibility of using the Medication Event Monitoring (MEMS®) Cap in adult HCT recipients. RESULTS: Of the 27 participants offered the MEMS® Cap at the time of hospital discharge, 7 (25.9%) used it, which is below our a priori threshold of 70%. These data suggest the MEMS® Cap is not feasible for HCT recipients. The MEMS® Cap data were available for a median of 35 days per participant per medication (range: 7-109 days). The average daily adherence per participant ranged from 0 to 100%; four participants had an average daily adherence of over 80%. CONCLUSIONS: MIPD may be supported by MEMS® technology to provide the precise time of immunosuppressant self-administration. The MEMS® Cap was used by only a small percentage (25.9%) of HCT recipients in this pilot study. In accordance with larger studies using less accurate tools to evaluate adherence, immunosuppressant adherence varied from 0% to 100%. Future studies should establish the feasibility and clinical benefit of combining MIPD with newer technology, specifically the MEMS® Button, which can inform the oncology pharmacist of the time of immunosuppressant self-administration.

Comparison of sequential and joint nonlinear mixed effects modeling of tumor kinetics and survival following Durvalumab treatment in patients with metastatic urothelial carcinoma.

Standard endpoints such as objective response rate are usually poorly correlated with overall survival (OS) for treatment with immune checkpoint inhibitors. Longitudinal tumor size may serve as a more useful predictor of OS, and establishing a quantitative relationship between tumor kinetics (TK) and OS is a crucial step for successfully predicting OS based on limited tumor size measurements. This study aims to develop a population TK model in combination with a parametric survival model by sequential and joint modeling approaches to characterize durvalumab phase I/II data from patients with metastatic urothelial cancer, and to evaluate and compare the performance of the two modeling approaches in terms of parameter estimates, TK and survival predictions, and covariate identification. The tumor growth rate constant was estimated to be greater for patients with OS ≤ 16 weeks as compared to that for patients with OS > 16 weeks with the joint modeling approach (kg= 0.130 vs. 0.0551 week-1, p-value < 0.0001), but similar for both groups (kg = 0.0624 vs.0.0563 week-1, p-value = 0.37) with the sequential modeling approach. The predicted TK profiles by joint modeling appeared better aligned with clinical observations. Joint modeling also predicted OS more accurately than the sequential approach according to concordance index and Brier score. The sequential and joint modeling approaches were also compared using additional simulated datasets, and survival was predicted better by joint modeling in the case of a strong association between TK and OS. In conclusion, joint modeling enabled the establishment of a robust association between TK and OS and may represent a better choice for parametric survival analyses over the sequential approach.

Accelerating robust plausible virtual patient cohort generation by substituting ODE simulations with parameter space mapping.

The generation of plausible virtual patients (VPs) is an important step in most quantitative systems pharmacology (QSP) workflows, which requires time-intensive solving of ordinary differential equations (ODEs). However, non-physiological profiles of outputs of interest (OoI) are frequently produced, and additional acceptance/rejection steps are needed for comparing and removing VPs with predicted values outside a pre-defined range. Here, a new approach is developed to accelerate the acceptance/rejection steps by leveraging patterns of parameter associations with OoI. In most models, some parameters are monotonic with respect to OoI, such that an increase in a parameter value always induces an increase or decrease in the OoI. This monotonic property can be used to replace some ODE-solving steps with appropriate monotonic parameter value comparisons to extrapolate the rejection or interpolate the acceptance of some VPs (after simulation) to others. Two algorithms were built that directly extract plausible VPs from a pre-defined initial cohort. These algorithms were first tested using a simple tumor growth inhibition model. Analyzing 200,000 VPs took 50 s with a reference method and 3 to 41 s (depending on the initial set-up) with the first algorithm. The method was then applied to an apoptosis QSP model, in which the clinical phenotypes (i.e., treatment sensitive or resistant) of 200,000 VPs were fully characterized for four different drug regimens in 12 min as compared to over 80 min with the reference approach. Extraction of each phenotype can also be performed individually in 34 s to 8 min, demonstrating the time benefit and flexibility of this approach.

FOLFIRINOX Pharmacodynamic Interactions in 2D and 3D Pancreatic Cancer Cell Cultures.

The multi-drug combination regime, FOLFIRINOX, is a standard of care chemotherapeutic therapy for pancreatic cancer patients. However, systematic evaluation of potential pharmacodynamic interactions among multi-drug therapy has not been reported previously. Here, pharmacodynamic interactions of the FOLFIRINOX agents (5-fluorouracil (5-FU), oxaliplatin (Oxa) and SN-38, the active metabolite of irinotecan) were assessed across a panel of primary and established pancreatic cancer cells. Inhibition of cell proliferation was quantified for each drug, alone and in combination, to obtain quantitative, drug-specific interaction parameters and assess the nature of drug interactions. The experimental data were analysed assuming Bliss independent interactions, and nonlinear regression model fitting was conducted in SAS. Estimates of the drug interaction term, psi (ψ), revealed that the Oxa/SN-38 combination appeared synergistic in PANC-1 (ψ = 0.6, 95% CI = 0.4, 0.9) and modestly synergistic, close to additive, in MIAPaCa-2 (ψ = 0.8, 95% CI = 0.6, 1.0) in 2D assays. The triple combination was strongly synergistic in MIAPaCa-2 (ψ = 0.2, 95% CI = 0.1, 0.3) and modestly synergistic/borderline additive in PANC-1 2D (ψ = 0.8, 95% CI = 0.6, 1.0). The triple combination showed antagonistic interactions in the primary PIN-127 and 3D PANC-1 model (ψ > 1). Quantitative pharmacodynamic interactions have not been described for the FOLFIRINOX regimen; this analysis suggests a complex interplay among the three chemotherapeutic agents. Extension of this pharmacodynamic analysis approach to clinical/translational studies of the FOLFIRINOX combination could reveal additional pharmacodynamic interactions and guide further refinement of this regimen to achieve optimal clinical responses.

Metabolic adaptation of ovarian tumors in patients treated with an IDO1 inhibitor constrains antitumor immune responses.

To uncover underlying mechanisms associated with failure of indoleamine 2,3-dioxygenase 1 (IDO1) blockade in clinical trials, we conducted a pilot, window-of-opportunity clinical study in 17 patients with newly diagnosed advanced high-grade serous ovarian cancer before their standard tumor debulking surgery. Patients were treated with the IDO1 inhibitor epacadostat, and immunologic, transcriptomic, and metabolomic characterization of the tumor microenvironment was undertaken in baseline and posttreatment tumor biopsies. IDO1 inhibition resulted in efficient blockade of the kynurenine pathway of tryptophan degradation and was accompanied by a metabolic adaptation that shunted tryptophan catabolism toward the serotonin pathway. This resulted in elevated nicotinamide adenine dinucleotide (NAD+), which reduced T cell proliferation and function. Because NAD+ metabolites could be ligands for purinergic receptors, we investigated the impact of blocking purinergic receptors in the presence or absence of NAD+ on T cell proliferation and function in our mouse model. We demonstrated that A2a and A2b purinergic receptor antagonists, SCH58261 or PSB1115, respectively, rescued NAD+-mediated suppression of T cell proliferation and function. Combining IDO1 inhibition and A2a/A2b receptor blockade improved survival and boosted the antitumor immune signature in mice with IDO1 overexpressing ovarian cancer. These findings elucidate the downstream adaptive metabolic consequences of IDO1 blockade in ovarian cancers that may undermine antitumor T cell responses in the tumor microenvironment.

Systems model identifies baseline cytokine concentrations as potential predictors of rheumatoid arthritis inflammatory response to biologics.

BACKGROUND AND PURPOSE: Circulating cytokines are central pathological mediators of inflammatory autoimmune diseases like rheumatoid arthritis. Immunological diversity in patients might contribute to inadequate responses to biological drugs. To address this therapeutic challenge, we developed a mathematical model that simultaneously describes temporal patterns of drug disposition for several biologics and their corresponding targeted cytokines, which were linked to triggering inflammatory responses. EXPERIMENTAL APPROACH: A modelling framework was applied to rheumatoid arthritis-relevant cytokines regulating C-reactive protein (CRP) as an inflammatory marker. Clinical data were extracted from the literature for anakinra, canakinumab, infliximab, secukinumab and tocilizumab, along with their corresponding cytokines, interleukin-1 receptor antagonist, IL-1ß, tumour necrosis factor α (TNFα), IL-17A and IL-6 receptor (IL-6R). Based on prior knowledge of regulatory mechanisms, cytokines were integrated with CRP profiles. KEY RESULTS: The model well captured all serum concentration-time profiles of cytokines and CRP ratios to respective baselines following drug treatment with good precision. On external validation, reasonable model performance on CRP dynamics, including rebound effects, was confirmed with clinical data not used in model development. Model-based simulations demonstrated that serum infliximab concentrations were accurately recapitulated in both a dose- and baseline TNFα-dependent manner. Furthermore, high baseline profiles of both IL-1ß and/or targeted cytokines could be predictors of poor responses to biologics targeting TNFα and IL-6R, although the impact of IL-1ß must be carefully interpreted. CONCLUSIONS AND IMPLICATION: Our model provides a quantitative platform to guide targeting and dosing strategies, including combination and/or sequential therapy, according to distinct baseline cytokine patterns in rheumatoid arthritis patients.

Mechanistic Modeling of the Interplay Between Host Immune System, IL-7 and UCART19 Allogeneic CAR-T Cells in Adult B-cell Acute Lymphoblastic Leukemia.

Chimeric antigen receptor (CAR)-T cell therapies have shown tremendous results against various hematologic cancers. Prior to cell infusion, a host preconditioning regimen is required to achieve lymphodepletion and improve CAR-T cell pharmacokinetic exposure, leading to greater chances of therapeutic success. To better understand and quantify the impact of the preconditioning regimen, we built a population-based mechanistic pharmacokinetic-pharmacodynamic model describing the complex interplay between lymphodepletion, host immune system, homeostatic cytokines, and pharmacokinetics of UCART19, an allogeneic product developed against CD19+ B cells. Data were collected from a phase I clinical trial in adult relapsed/refractory B-cell acute lymphoblastic leukemia and revealed three different UCART19 temporal patterns: (i) expansion and persistence, (ii) transient expansion with subsequent rapid decline, and (iii) absence of observed expansion. On the basis of translational assumptions, the final model was able to capture this variability through the incorporation of IL-7 kinetics, which are thought to be increased owing to lymphodepletion, and through an elimination of UCART19 by host T cells, which is specific to the allogeneic context. Simulations from the final model recapitulated UCART19 expansion rates in the clinical trial, confirmed the need for alemtuzumab to observe UCART19 expansion (along with fludarabine cyclophosphamide), quantified the importance of allogeneic elimination, and suggested a high impact of multipotent memory T-cell subpopulations on UCART19 expansion and persistence. In addition to supporting the role of host cytokines and lymphocytes in CAR-T cell therapy, such a model could help optimizing the preconditioning regimens in future clinical trials. Significance: A mathematical mechanistic pharmacokinetic/pharmacodynamic model supports and captures quantitatively the beneficial impact of lymphodepleting patients before the infusion of an allogeneic CAR-T cell product. Mediation through IL-7 increase and host T lymphocytes decrease is underlined, and the model can be further used to optimize CAR-T cell therapies lymphodepletion regimen.

Cluster Gauss-Newton and CellNOpt Parameter Estimation in a Small Protein Signaling Network of Vorinostat and Bortezomib Pharmacodynamics.

Ordinary differential equation (ODE)-based models of signal transduction pathways often contain parameters that are unidentifiable or unmeasurable by experimental data, and calibrating such models to data remains challenging. Here, two efficient parameter estimation methods, cluster Gauss-Newton (CGN) and CellNOpt (CNO), were applied to fit a signaling network model of U266 multiple myeloma cells to the activity dynamics of key proteins in response to vorinostat and/or bortezomib. A logic-based network model was constructed and transformed to 17 ODEs with 79 parameters estimated within broad ranges of biologically plausible values. The top 10% best-fit parameters by both methods had high uncertainties with CV > 50% for the majority of parameters. The root mean square and prediction errors were comparable without statistically significant differences between the two methods. Despite uncertain parameter estimation, protein dynamics after the sequential combination of bortezomib and vorinostat was predicted with reasonable accuracy and precision. Global sensitivity analyses of partial rank correlation coefficients and Sobol sensitivity demonstrated that apoptosis induction was most sensitive to parameters governing the activity of the proteasome-JNK-caspase-8 axis. Simulations revealed that the greatest magnitude of pharmacodynamic drug interactions between bortezomib and vorinostat occurred at caspase-9, AKT, and Bcl-2. Two sequential combinations were explored in silico, and the outcome matched qualitatively with an empirical evaluation of the pharmacodynamic interaction based on cell viability. Overall, the CGN and CNO algorithms performed similarly for this ODE-based network model calibration, and the calibrated model provided meaningful insights into cellular signaling mechanisms in response to pharmacological perturbations.

Network-Based Systems Analysis Explains Sequence-Dependent Synergism of Bortezomib and Vorinostat in Multiple Myeloma.

Bortezomib and vorinostat exhibit synergistic effects in multiple myeloma (MM) cells when given in sequence, and the purpose of this study was to evaluate the molecular determinants of the interaction using a systems pharmacology approach. A Boolean network model consisting of 79 proteins and 225 connections was developed using literature information characterizing mechanisms of drug action and intracellular signal transduction. Network visualization and structural analysis were conducted, and model simulations were compared with experimental data. Critical biomarkers, such as pNFκB, p53, cellular stress, and p21, were identified using measures of network centrality and model reduction. U266 cells were then exposed to bortezomib (3 nM) and vorinostat (2 µM) as single agents or in simultaneous and sequential (bortezomib for first 24 h, followed by addition of vorinostat for another 24 h) combinations. Temporal changes for nine of the critical proteins in the reduced Boolean model were measured over 48 h, and cellular proliferation was measured over 96 h. A mechanism-based systems model was developed that captured the biological basis of a bortezomib and vorinostat sequence-dependent pharmacodynamic interaction. The model was further extended in vivo by linking in vitro parameter values and dynamics of p21, caspase-3, and pAKT biomarkers to tumor growth in xenograft mice reported in the literature. Network-based methodologies and pharmacodynamic principles were integrated successfully to evaluate bortezomib and vorinostat interactions in a mechanistic and quantitative manner. The model can be potentially applied to evaluate their combination regimens and explore in vivo dosing regimens.

Safety and pharmacokinetics of docetaxel in combination with pegvorhyaluronidase alfa in patients with non-small cell lung cancer.

This open-label, phase Ib study (NCT02346370) assessed the effect of pegvorhyaluronidase alfa (PVHA; PEGPH20) on the plasma pharmacokinetics (PKs) and safety of docetaxel in 15 patients with stage IIIB/IV non-small cell lung cancer (NSCLC). The docetaxel PK profile from this study was consistent with simulations from a published docetaxel population PK model, and did not demonstrate an effect of PVHA on docetaxel PK. A maximum a posteriori Bayesian fit of the literature PK model to the docetaxel PK appeared unbiased. Adverse events (AEs) were generally consistent with previous reports for docetaxel monotherapy in NSCLC, except for higher incidence of musculoskeletal events, including myalgias, with PVHA plus docetaxel. The most common AEs were fatigue (87%), muscle spasms (60%), and myalgia (53%). Four patients experienced thromboembolic events (27%), three leading to treatment discontinuation. PVHA appeared to demonstrate an acceptable safety profile when given with docetaxel without significantly changing the plasma PK of docetaxel in patients with stage IIIB/IV NSCLC.

Population Pharmacokinetic Model of N-acetylmannosamine (ManNAc) and N-acetylneuraminic acid (Neu5Ac) in Subjects with GNE Myopathy.

BACKGROUND: GNE myopathy is a rare genetic muscle disease resulting from deficiency in an enzyme critical for the biosynthesis of N-acetylneuraminic acid (Neu5Ac, sialic acid). The uncharged Neu5Ac precursor, N-acetylmannosamine (ManNAc), is under development as an orphan drug for treating GNE myopathy. METHODS: A semi-mechanistic population pharmacokinetic model was developed to simultaneously characterize plasma ManNAc and its metabolite Neu5Ac following oral administration of ManNAc to subjects with GNE myopathy. Plasma ManNAc and Neu5Ac pharmacokinetic data were obtained from two clinical studies (ClinicalTrials.gov identifiers NCT01634750, NCT02346461) and were simultaneously modeled using NONMEM. RESULTS: ManNAc and Neu5Ac plasma concentrations were obtained from 34 subjects with GNE myopathy (16 male, 18 female, median age 39.5 years). The model parameter estimates included oral absorption rate (ka) = 0.256 h-1, relative bioavailability relationship with dose (F-Dose) slope = -0.405 (where F = 1 for 6-g dose), apparent clearance (CLM/F) = 631 L/h, volume of distribution (VM/F) = 506 L, Neu5Ac elimination rate constant (kout) = 0.283 h-1, initial ManNAc to Neu5Ac conversion (SLP0) = 0.000619 (ng/mL)-1 and at steady-state (SLPSS) = 0.00334 (ng/mL)-1, with a rate-constant of increase (kinc) = 0.0287 h-1. Goodness-of-fit plots demonstrated an acceptable and unbiased fit to the plasma ManNAc and Neu5Ac concentration data. Visual predictive checks demonstrated reasonable agreement between the 5th, 50th, and 95th percentiles of the observed and simulated data. CONCLUSIONS: This population pharmacokinetic model can be used to evaluate ManNAc dosing regimens and to calculate Neu5Ac production and exposure following oral administration of ManNAc in subjects with GNE myopathy.

Synergistic Pharmacodynamic Effects of Gemcitabine and Fibroblast Growth Factor Receptor Inhibitors on Pancreatic Cancer Cell Cycle Kinetics and Proliferation.

Median survival of pancreatic ductal adenocarcinoma cancer (PDAC) is 6 months, with 9% 5-year survival. Standard-of-care gemcitabine (Gem) provides only modest survival benefits, and combination therapies integrating novel targeted agents could improve outcomes. Fibroblast growth factor (FGF) receptors (FGFRs) play important roles in PDAC growth and invasion. Therefore, FGFR inhibitors (FGFRi) merit further investigation. Efficacy of Gem combined with NVP-BGJ398, a pan-FGFRi, was investigated in multiple PDAC cell lines exposed to the drugs alone and combined. Cell cycle distribution and cell numbers were quantified over time. Two pharmacodynamic models were developed to investigate Gem/BGJ398 interactions quantitatively: a drug-mediated cell proliferation/death model, and a drug-perturbed cell cycle progression model. The models captured temporal changes in cell numbers, cell cycle progression, and cell death during drug exposure. Simultaneous fitting of all data provided reasonable parameter estimates. Therapeutic efficacy was then evaluated in a PDAC mouse model. Compared with Gem alone, combined Gem + FGFRi significantly downregulated ribonucleotide-diphosphate reductase large subunit 1 (RRM1), a gemcitabine resistance (GemR) biomarker, suggesting the FGFRi inhibited GemR emergence. The cell proliferation/death pharmacodynamic model estimated the drug interaction coefficient ψ death = 0.798, suggesting synergistic effects. The mechanism-based cell cycle progression model estimated drug interaction coefficient ψ cycle = 0.647, also suggesting synergy. Thus, FGFR inhibition appears to synergize with Gem in PDAC cells and tumors by sensitizing cells to Gem-mediated inhibition of proliferation and cell cycle progression. SIGNIFICANCE STATEMENT: An integrated approach of quantitative modeling and experimentation was employed to investigate the nature of fibroblast growth factor receptor inhibitor (FGFRi)/gemcitabine (Gem) interaction, and to identify mechanisms by which FGFRi exposure reverses Gem resistance in pancreatic cancer cells. The results show that FGFRi interacts synergistically with Gem to sensitize pancreatic cancer cells and tumors to Gem-mediated inhibition of proliferation and cell cycle progression. Thus, addition of FGFRi to standard-of-care Gem treatment could be a clinically deployable approach to enhance therapeutic benefit to pancreatic cancer patients.

Evaluation of the performance of a prior tacrolimus population pharmacokinetic kidney transplant model among adult allogeneic hematopoietic stem cell transplant patients.

Tacrolimus is a calcineurin inhibitor used to prevent acute graft versus host disease in adult patients receiving allogeneic hematopoietic stem cell transplantation (HCT). Previous population pharmacokinetic (PK) models have been developed in solid organ transplant, yet none exists for patients receiving HCT. The primary objectives of this study were to (1) use a previously published population PK model in adult patients who underwent kidney transplant and apply it to allogeneic HCT; (2) evaluate model-predicted tacrolimus steady-state trough concentrations and simulations in patients receiving HCT; and (3) evaluate covariates that affect tacrolimus PK in allogeneic HCT. A total of 252 adult patients receiving allogeneic HCT were included in the study. They received oral tacrolimus twice daily (0.03 mg/kg) starting 3 days prior to transplant. Data for these analyses included baseline clinical and demographic data, genotype data for single nucleotide polymorphisms in CYP3A4/5 and ABCB1, and the first tacrolimus steady-state trough concentration. A dosing simulation strategy based on observed trough concentrations (rather than model-based predictions) resulted in 12% more patients successfully achieving tacrolimus trough concentrations within the institutional target range (5-10 ng/ml). Stepwise covariate analyses identified HLA match and conditioning regimen (myeloablative vs. reduced intensity) as significant covariates. Ultimately, a previously published tacrolimus population PK model in kidney transplant provided a platform to help establish a model-based dose adjustment strategy in patients receiving allogenic HCT, and identified HCT-specific covariates to be considered for future prospective studies. Study Highlights WHAT IS THE CURRENT KNOWLEDGE ON THE TOPIC? Tacrolimus is a cornerstone immunosuppressant used in patients who undergo organ transplantations. However, because of its narrow therapeutic index and wide interpatient pharmacokinetic (PK) variability, optimizing its dose is crucial to maximize efficacy and minimize tacrolimus-induced toxicities. Prior to this study, no tacrolimus population PK models have been developed for adult patients receiving allogeneic hematopoietic stem cell transplantation (HCT). Therefore, research effort was warranted to develop a population PK model that begins to propose more precision tacrolimus dosing and begins to address both a clinical and scientific gap in this patient population. WHAT QUESTION DID THIS STUDY ADDRESS? The study addressed whether there is value in utilizing the observed tacrolimus steady-state trough concentrations from patients receiving allogeneic HCT within the context of a pre-existing population PK model developed for kidney transplant. The study also addressed whether there are clinically relevant covariates specific to adult patients receiving allogeneic HCT. WHAT DOES THIS STUDY ADD TO OUR KNOWLEDGE? Inclusion of a single steady-state tacrolimus trough concentration is beneficial to model predictions. The dosing simulation strategy based on observed tacrolimus concentration, rather than the model-predicted concentration, resulted in more patients achieving the target range at first steady-state collection. Future studies should evaluate HLA matching and myeloablative conditioning versus reduced intensity conditioning regimens as covariates. These data and model-informed dose adjustments should be included in future prospective studies. This research could also serve as a template as to how to assess the utility of prior information for other disease settings. HOW MIGHT THIS CHANGE CLINICAL PHARMACOLOGY OR TRANSLATIONAL SCIENCE? The M2 model fitting method and D2 dosing simulation method can be applied to other clinical pharmacology studies where only a single steady-state trough concentration is available per patient in the presence of a previously published population PK model.

Systems Pharmacology Modeling Identifies a Novel Treatment Strategy for Bortezomib-Induced Neuropathic Pain.

Chemotherapy-induced peripheral neurotoxicity is a common dose-limiting side effect of several cancer chemotherapeutic agents, and no effective therapies exist. Here we constructed a systems pharmacology model of intracellular signaling in peripheral neurons to identify novel drug targets for preventing peripheral neuropathy associated with proteasome inhibitors. Model predictions suggested the combinatorial inhibition of TNFα, NMDA receptors, and reactive oxygen species should prevent proteasome inhibitor-induced neuronal apoptosis. Dexanabinol, an inhibitor of all three targets, partially restored bortezomib-induced reduction of proximal action potential amplitude and distal nerve conduction velocity in vitro and prevented bortezomib-induced mechanical allodynia and thermal hyperalgesia in rats, including a partial recovery of intraepidermal nerve fiber density. Dexanabinol failed to restore bortezomib-induced decreases in electrophysiological endpoints in rats, and it did not compromise bortezomib anti-cancer effects in U266 multiple myeloma cells and a murine xenograft model. Owing to its favorable safety profile in humans and preclinical efficacy, dexanabinol might represent a treatment option for bortezomib-induced neuropathic pain.