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Vestibular migraine (VM) has conventionally been treated through acute migraine-aborting therapeutic interventions and prevention to reduce migraines’ occurrence, length and intensity. There is growing attention to the development of non-pharmaceutical prophylactic interventions for migraines in the search for effective treatments, such as through mineral, vitamin and other supplementation. This research aims to examine the effectiveness of magnesium, vitamin B2 and Co-enzyme 10 supplementation to decrease vestibular migraines’ frequency, duration and severity. Method: This retrospective cohort study was conducted in a Jordanian context over 57- VM patients, each patient attending the outpatient dizziness clinic between August 2022 and February 2023. Patients were treated for six months with a combined supplementation of magnesium, vitamin B2, and Co-enzyme Q10. Assessments were made of three measures of VM attack, namely frequency, duration and severity, both before and after intervention. Result: Supplements administration demonstrated a significant reduction (by 81.1%) in VM-symptoms frequency (p < 0.001). Moreover, reductions in symptom duration in minutes occurred progressively as the treatment period continued and showed statistical significance, with impacts upon over 80% of the sample and a reduction from 763.9 minutes to 122.5 minutes (p < 0.001). The mean of pre-intervention severity was 7.2/10, with a significant decrease shown following treatment, at 2.1/10, and very positive results for over 71% of the sample (p < 0.001). Conclusion: The preliminary findings of this study showed a promising potential for such supplements in the treatment and prevention of VM; however, more research and a prospective trial are recommended.

Abstract A population pharmacokinetic (PopPK) model was previously developed for cemiplimab in patients with solid tumors, including advanced cutaneous squamous cell carcinoma (CSCC). Here, we update the existing PopPK model and characterize exposure‐response relationships using efficacy and safety data obtained in patients with recurrent or metastatic cervical cancer (R/M CC). To improve model stability and robustness of the existing PopPK model in 1062 patients, the random‐effect error model was revised, and structural covariates were removed from the base model to be tested in the covariate analysis. The updated model was used for external validation of cemiplimab pharmacokinetics (PK) in patients with R/M CC on cemiplimab monotherapy (350 mg every 3 weeks intravenously) from a phase III study (NCT03257267). Exposure‐response relationships for cemiplimab efficacy (overall survival [OS], progression‐free survival [PFS], duration of response [DOR], objective response rate [ORR]), and safety (immune‐related adverse events [irAEs]) were analyzed in 295 patients with R/M CC from the aforementioned study. The updated PopPK model showed improved stability with 94.8% successful bootstrap runs vs. 47.6% in the prior model. Cemiplimab exposure was similar across tumor types, including basal cell carcinoma, CSCC, and non‐small cell lung cancer. External validation showed the updated model adequately described cemiplimab PK in patients with R/M CC. In exposure‐response efficacy analyses, Cox proportional hazard modeling (CPHM) showed no trend between exposure and OS, Kaplan–Meier plots showed no trend between exposure and PFS or DOR, and logistic regression analyses conducted on ORR showed no exposure‐response relationship. In exposure‐response safety analyses, CPHM showed no trend between exposure and irAEs.

Abstract REGN‐EB3 (Inmazeb) is a cocktail of three human monoclonal antibodies approved for treatment of Ebola infection. This paper describes development of a mathematical model linking REGN‐EB3’s inhibition of Ebola virus to survival in a non‐human primate (NHP) model, and translational scaling to predict survival in humans. Pharmacokinetic/pharmacodynamic data from single‐ and multiple‐dose REGN‐EB3 studies in infected rhesus macaques were incorporated. Using discrete indirect response models, the antiviral mechanism of action was used as a forcing function to drive the reversal of key Ebola disease hallmarks over time, for example, liver and kidney damage (elevated alanine [ALT] and aspartate aminotransferases [AST], blood urea nitrogen [BUN], and creatinine), and hemorrhage (decreased platelet count). A composite disease characteristic function was introduced to describe disease severity and integrated with the ordinary differential equations estimating the time course of clinical biomarkers. Model simulation results appropriately represented the concentration‐dependence of the magnitude and time course of Ebola infection (viral and pathophysiological), including time course of viral load, ALT and AST elevations, platelet count, creatinine, and BUN. The model estimated the observed survival rate in rhesus macaques and the dose of REGN‐EB3 required for saturation of the pharmacodynamic effects of viral inhibition, reversal of Ebola pathophysiology, and survival. The model also predicted survival in clinical trials with appropriate scaling to humans. This mathematical investigation demonstrates that drug‐disease modeling can be an important translational tool to integrate preclinical data from an NHP model recapitulating disease progression to guide future translation of preclinical data to clinical study design.

Abstract A model to quantitatively characterize the effect of evinacumab, an investigational monoclonal antibody against angiopoietin‐like protein 3 (ANGPTL3) on lipid trafficking is needed. A quantitative systems pharmacology (QSP) approach was developed to predict the transient responses of different triglyceride (TG)‐rich lipoprotein particles in response to evinacumab administration. A previously published hepatic lipid model was modified to address specific queries relevant to the mechanism of evinacumab and its effect on lipid metabolism. Modifications included the addition of intermediate‐density lipoprotein and low‐density lipoprotein compartments to address the modulation of lipoprotein lipase (LPL) activity by evinacumab, ANGPTL3 biosynthesis and clearance, and a target‐mediated drug disposition model. A sensitivity analysis guided the creation of virtual patients (VPs). The drug‐free QSP model was found to agree well with clinical data published with the initial hepatic liver model over simulations ranging from 20 to 365 days in duration. The QSP model, including the interaction between LPL and ANGPTL3, was validated against clinical data for total evinacumab, total ANGPTL3, and TG concentrations as well as inhibition of apolipoprotein CIII. Free ANGPTL3 concentration and LPL activity were also modeled. In total, seven VPs were created; the lipid levels of the VPs were found to match the range of responses observed in evinacumab clinical trial data. The QSP model results agreed with clinical data for various subjects and was shown to characterize known TG physiology and drug effects in a range of patient populations with varying levels of TGs, enabling hypothesis testing of evinacumab effects on lipid metabolism.

Abstract Evinacumab, an angiopoietin‐like protein 3 (ANGPTL3) inhibitor, has been shown to significantly reduce low‐density lipoprotein cholesterol (LDL‐C) in patients with homozygous familial hypercholesterolemia (HoFH). This work characterized the population pharmacokinetics (PK)/pharmacodynamics (PD) of evinacumab using pooled phase III clinical data. Total evinacumab PK were described by a two‐compartment model with combined linear and saturable (Michaelis–Menten) elimination, and first‐order absorption. At clinically relevant concentrations, plasma drug concentrations were mainly influenced by the linear clearance pathway. Although the maximum target‐mediated rate of elimination (Vmax) parameter for the saturable pathway was found to be positively related to baseline ANGPLTL3, variability in body weight contributed more to the variability in evinacumab exposure than variability in ANGPTL3. An effect of HoFH versus healthy volunteers on Vmax was also identified. Weight‐based dosing regimens resulted in consistent evinacumab exposure across weight ranges. An indirect exposure–response model adequately described the relationship between evinacumab and LDL‐C, where drug concentration is assumed to inhibit LDL‐C production. The final population PK/PD model included two nonclinically significant covariates (race and baseline body weight) on the maximum drug‐induced inhibitory effect (Imax) and one (baseline LDL‐C) on the evinacumab concentration inducing 50% of Imax (IC50). A smaller IC50 was observed in patients with higher baseline LDL‐C, suggesting greater sensitivity to treatment. Population exposure–response analysis permitted estimation of derived PD parameters and individual LDL‐C levels over time for patients with HoFH. The model accurately predicted the proportion of patients with HoFH achieving prespecified LDL‐C goals with evinacumab during the ELIPSE HoFH study, further supporting a dosing strategy.

This article represents the first in a series of tutorials on model evaluation in nonlinear mixed effect models (NLMEMs), from the International Society of Pharmacometrics (ISoP) Model Evaluation Group. Numerous tools are available for evaluation of NLMEM, with a particular emphasis on visual assessment. This first basic tutorial focuses on presenting graphical evaluation tools of NLMEM for continuous data. It illustrates graphs for correct or misspecified models, discusses their pros and cons, and recalls the definition of metrics used.

Model‐informed drug development (MIDD) approaches have rapidly advanced in drug development in recent years. Additionally, the Prescription Drug User Fee Act (PDUFA) VI has specific commitments to further enhance MIDD. Tumor growth dynamic (TGD) modeling, as one of the commonly utilized MIDD approaches in oncology, fulfills the purposes to accelerate the drug development, to support new drug and biologics license applications, and to guide the market access. Increasing knowledge of TGD modeling methodologies, encouraging applications in clinical setting for patients’ survival, and complementing assessment of regulatory review for submissions, together fueled promising potentials for imminent enhancement of TGD in oncology. This review is to comprehensively summarize the history of TGD, and present case examples of the recent advance of TGD modeling (mixture model and joint model), as well as the TGD impact on regulatory decisions, thus illustrating challenges and opportunities. Additionally, this review presents the future perspectives for TGD approach.

Differences in the effect of gefitinib and chemotherapy on tumor burden in non‐small cell lung cancer remain to be fully understood. Using a Bayesian hierarchical model of tumor size dynamics, we estimated the rates of tumor growth and treatment resistance for patients in the Iressa Pan‐Asia Study study (NCT00322452). The following relationships characterize greater efficacy of gefitinib in epidermal growth factor receptor (EGFR) positive tumors: Maximum drug effect is, in decreasing order, gefitinib in EGFR‐positive, chemotherapy in EGFR‐positive, chemotherapy in EGFR‐negative, and gefitinib in EGFR‐negative tumors; the rate of resistance emergence is, in increasing order: gefitinib in EGFR positive, chemotherapy in EGFR positive, while each is plausibly similar to the rate in EGFR negative tumors, which are estimated with less certainty. The rate of growth is smaller in EGFR‐positive than in EGFR‐negative fully resistant tumors, regardless of treatment. The model can be used to compare treatment effects and resistance dynamics among different drugs.

Background: Ε‐Aminocaproic acid oral solution (EACA OS) is the only commercially available antifibrinolytic for patients who cannot swallow tablets. Insurance denials and high costs remain barriers to its use. Objectives: To determine the safety and efficacy of crushed tranexamic acid tablets in water (cTXAw) for children with bleeding disorders. Methods: We retrospectively reviewed records of children (<10 years) with bleeding disorders who received cTXAw or EACA OS from 1 December 2018, through 31 July 2022, at Mayo Clinic (Rochester, Minnesota). Bleeding outcomes were defined according to ISTH criteria. Results: Thirty‐two patients were included (median age, 3 years; male, n = 23). Diagnoses were VWD (n = 17), haemophilia (n = 5), FVII deficiency (n = 3), inherited platelet disorder (n = 4), ITP (n = 2), and combined FV and FVII deficiencies (n = 1). Thirty‐two courses of cTXAw (monotherapy 24/32; mean duration 6 days) and fifteen courses of EACA (monotherapy 12/15; mean duration 5 days) were administered. No surgical procedures (n = 28) were complicated by bleeding. Of the 19 bleeding events, 16 had effective haemostasis, two had no reported outcome, and one had no response. cTXAw and EACA were equally effective in preventing and treating bleeding (p value >.1). No patients had adverse effects. Eight of 19 patients (42%) who were initially prescribed EACA OS did not receive it because of cost or insurance denial. The estimated average wholesale price of one treatment was $94 for cTXAw and $905 for EACA OS. Conclusions: CTXAw appears to be an effective, safe, and low‐cost alternative option to EACA OS for young children with bleeding disorders. [ABSTRACT FROM AUTHOR]

Acalabrutinib, a selective, covalent Bruton tyrosine kinase inhibitor, is a CYP3A substrate and weak CYP3A/CYP2C8 inhibitor. A physiologically‐based pharmacokinetic (PBPK) model was developed for acalabrutinib and its active metabolite ACP‐5862 to predict potential drug–drug interactions (DDIs). The model indicated acalabrutinib would not perpetrate a CYP2C8 or CYP3A DDI with the sensitive CYP substrates rosiglitazone or midazolam, respectively. The model reasonably predicted clinically observed acalabrutinib DDI with the CYP3A perpetrators itraconazole (4.80‐fold vs. 5.21‐fold observed) and rifampicin (0.21‐fold vs. 0.23‐fold observed). An increase of two to threefold acalabrutinib area under the curve was predicted for coadministration with moderate CYP3A inhibitors. When both the parent drug and active metabolite (total active components) were considered, the magnitude of the CYP3A DDI was much less significant. PBPK dosing recommendations for DDIs should consider the magnitude of the parent drug excursion, relative to safe parent drug exposures, along with the excursion of total active components to best enable safe and adequate pharmacodynamic coverage.

Abstract Background Numerous oncology combination therapies involving modulators of the cancer immune cycle are being developed, yet quantitative simulation models predictive of outcome are lacking. We here present a model-based analysis of tumor size dynamics and immune markers, which integrates experimental data from multiple studies and provides a validated simulation framework predictive of biomarkers and anti-tumor response rates, for untested dosing sequences and schedules of combined radiation (RT) and anti PD-(L)1 therapies. Methods A quantitative systems pharmacology model, which includes key elements of the cancer immunity cycle and the tumor microenvironment, tumor growth, as well as dose-exposure-target modulation features, was developed to reproduce experimental data of CT26 tumor size dynamics upon administration of RT and/or a pharmacological IO treatment such as an anti-PD-L1 agent. Variability in individual tumor size dynamics was taken into account using a mixed-effects model at the level of tumor-infiltrating T cell influx. Results The model allowed for a detailed quantitative understanding of the synergistic kinetic effects underlying immune cell interactions as linked to tumor size modulation, under these treatments. The model showed that the ability of T cells to infiltrate tumor tissue is a primary determinant of variability in individual tumor size dynamics and tumor response. The model was further used as an in silico evaluation tool to quantitatively predict, prospectively, untested treatment combination schedules and sequences. We demonstrate that anti-PD-L1 administration prior to, or concurrently with RT reveal further synergistic effects, which, according to the model, may materialize due to more favorable dynamics between RT-induced immuno-modulation and reduced immuno-suppression of T cells through anti-PD-L1. Conclusions This study provides quantitative mechanistic explanations of the links between RT and anti-tumor immune responses, and describes how optimized combinations and schedules of immunomodulation and radiation may tip the immune balance in favor of the host, sufficiently to lead to tumor shrinkage or rejection.

Detailed formulation and coding of exact finite element is carried out to study the static behavior of a layered beam structure. The beam element is modelled based on the first-order shear deformation theory and it is assumed to be composed of three layers whereas the middle layer is made of functionally graded material (FGM), i.e. with variable elastic properties in the thickness direction. The shape of the FGM mechanical properties variation in the thickness direction takes the form of exponential or power-law. The governing equations and boundary conditions are derived by applying the virtual work principle. Variations of displacements along the beam and stresses across the depth due to mechanical loadings are investigated. Comparative examples are carried out to highlight the static behavior difference between FGM layered beams and pure metal-ceramic beams.

Dienoic acids and pentadienyl alcohols are coupled in a decarboxylative and dehydrative manner at ambient temperature using Pd(0) catalysis to generate 1,3,6,8-tetraenes. Contrary to related decarboxylative coupling reactions, an anion-stabilizing group is not required adjacent to the carboxyl group. Of mechanistic importance, it appears that both the diene of the acid and the diene of the alcohol are required for this reaction. To further understand this reaction, substitutions at every unique position of both coupling partners was examined and two potential mechanisms are presented.

Naloxegol is a polyethylene glycol derivative of naloxone approved in the US as a once‐daily oral treatment for opioid‐induced constipation (OIC) in adults with chronic noncancer pain. Population exposure–response models were constructed based on data from two phase III studies comprising 1,331 adults with noncancer pain and OIC. In order to characterize the protocol‐defined naloxegol responder rate, the number of daily spontaneous bowel movements (SBMs) was characterized by a longitudinal ordinal nonlinear mixed‐effects logistic regression dose–response model, and the incidence of diary entry discontinuation was described by a time‐to‐event model. The mean number of SBMs per week increased with increasing naloxegol dose. The predicted placebo‐adjusted responder rates (90% confidence interval) were 10.4% (4.6–13.4%) and 11.1% (4.8–14.4%) for naloxegol 12.5 and 25 mg/day, respectively. Model‐predicted response to naloxegol was influenced by the baseline SBM frequency and characteristics of the opioid treatment.