Emicizumab (ACE910) is a bispecific antibody mimicking the cofactor function of activated coagulation factor VIII. In phase I–I/II studies, emicizumab reduced the bleeding frequency in patients with severe hemophilia A, regardless of the presence of factor VIII inhibitors, at once-weekly subcutaneous doses of 0.3, 1, and 3 mg/kg. Using the phase I–I/II study data, population pharmacokinetic and repeated time-to-event (RTTE) modeling were performed to quantitatively characterize the relationship between the pharmacokinetics of emicizumab and reduction in bleeding frequency. Simulations were then performed to identify the minimal exposure expected to achieve zero bleeding events for 1 year in at least 50% of patients and to select the dosing regimens to be tested in phase III studies. The RTTE model adequately predicted the bleeding onset over time as a function of plasma emicizumab concentration. Simulations suggested that plasma emicizumab concentrations of ≥  45 μg/mL should result in zero bleeding events for 1 year in at least 50% of patients. This efficacious exposure provided the basis for selecting previously untested dosing regimens of 1.5 mg/kg once weekly, 3 mg/kg every 2 weeks, and 6 mg/kg every 4 weeks for phase III studies. A pharmacometric approach guided the phase III dose selection of emicizumab in hemophilia A, without conducting a conventional dose-finding study. Phase III studies with the selected dosing regimens are currently ongoing. This case study indicates that a pharmacometric approach can substitute for a conventional dose-finding study in rare diseases and will streamline the drug development process.

/pdf/a-pharmacometric-approach-to-substitute-for-a-conventional-108zgygqk9.pdf

A Pharmacometric Approach to Substitute for a Conventional Dose-Finding Study in Rare Diseases: Example of Phase III Dose Selection for Emicizumab in Hemophilia A

development of the three Rs concept

The predictive value of longitudinal biomarker data (vascular endothelial growth factor (VEGF), soluble VEGF receptor (sVEGFR)-2, sVEGFR-3, and soluble stem cell factor receptor (sKIT)) for tumor response and survival was assessed based on data from 303 patients with imatinib-resistant gastrointestinal stromal tumors (GIST) receiving sunitinib and/or placebo treatment. The longitudinal tumor size data were well characterized by a tumor growth inhibition model, which included, as significant descriptors of tumor size change, the model-predicted relative changes from baseline over time for sKIT (most significant) and sVEGFR-3, in addition to sunitinib exposure. Survival time was best described by a parametric time-to-event model with baseline tumor size and relative change in sVEGFR-3 over time as predictive factors. Based on the proposed modeling framework to link longitudinal biomarker data with overall survival using pharmacokinetic-pharmacodynamic models, sVEGFR-3 demonstrated the greatest predictive potential for overall survival following sunitinib treatment in GIST.CPT: Pharmacometrics & Systems Pharmacology (2013) 2, e84; doi:10.1038/psp.2013.61; advance online publication 20 November 2013.

https://ascpt.onlinelibrary.wiley.com/doi/pdf/10.1038/psp.2013.61

PKPD Modeling of VEGF, sVEGFR-2, sVEGFR-3, and sKIT as Predictors of Tumor Dynamics and Overall Survival Following Sunitinib Treatment in GIST

The aim of this investigation was to develop a mechanism-based pharmacokinetic-pharmacodynamic (PK-PD) model for the biological system prolactin response following a dopamine inhibition challenge using remoxipride as a paradigm compound. After assessment of baseline variation in prolactin concentrations, the prolactin response of remoxipride was measured following (1) single intravenous doses of 4, 8 and 16 mg/kg and (2) following double dosing of 3.8 mg/kg with different time intervals. The mechanistic PK-PD model consisted of: (i) a PK model for remoxipride concentrations in brain extracellular fluid; (ii) a pool model incorporating prolactin synthesis, storage in lactotrophs, release into- and elimination from plasma; (iii) a positive feedback component interconnecting prolactin plasma concentrations and prolactin synthesis; and (iv) a dopamine antagonism component interconnecting remoxipride brain extracellular fluid concentrations and stimulation of prolactin release. The most important findings were that the free brain concentration drives the prolactin release into plasma and that the positive feedback on prolactin synthesis in the lactotrophs, in contrast to the negative feedback in the previous models on the PK-PD correlation of remoxipride. An external validation was performed using a dataset obtained in rats following intranasal administration of 4, 8, or 16 mg/kg remoxipride. Following simulation of human remoxipride brain extracellular fluid concentrations, pharmacodynamic extrapolation from rat to humans was performed, using allometric scaling in combination with independent information on the values of biological system specific parameters as prior knowledge. The PK-PD model successfully predicted the system prolactin response in humans, indicating that positive feedback on prolactin synthesis and allometric scaling thereof could be a new feature in describing complex homeostatic mechanisms.

Mechanism-based PK-PD model for the prolactin biological system response following an acute dopamine inhibition challenge: quantitative extrapolation to humans

Background:Reduction in consumption of opioid rescue medication is often used as an endpoint when investigating analgesic efficacy of drugs by adjunct treatment, but appropriate methods are needed to analyze analgesic consumption in time. Repeated time-to-event (RTTE) modeling is proposed as a way t

/pdf/repeated-time-to-event-analysis-of-consecutive-analgesic-29ll0mb71b.pdf

Repeated Time-to-event Analysis of Consecutive Analgesic Events in Postoperative Pain.

Transient lower esophageal sphincter relaxation (TLESR) is the major mechanism for gastroesophageal reflux. Characterizations of candidate compounds for reduction of TLESRs are traditionally done through summary exposure and response measures and would benefit from model-based analyses of exposure-TLESR events relationships. Pharmacokinetic (PK)-pharmacodynamic (PD) modeling approaches treating TLESRs either as count data or repeated time-to-event (RTTE) data were developed and compared in terms of their ability to characterize system and drug characteristics. Vehicle data comprising 294 TLESR events were collected from nine dogs. Compound [(R)-(+)-[2,3-dihydro-5-methyl-3-(4-morpholinylmethyl)pyrrolo[1,2,3-de]-1,4-benzoxazin-6-yl]-1-naphthalenylmethanone mesylate (WIN55212-2)] data containing 66 TLESR events, as well as plasma concentrations, were obtained from four dogs. Each experiment lasted for 45 min and was initiated with a meal. Counts in equispaced 5- and 1-min intervals were modeled based on a Poisson probability distribution model. TLESR events were analyzed with the RTTE model. The PK was connected to the PD with a one-compartment model. Vehicle data were described by a baseline and a surge function; the surge peak was determined to be approximately 9.69 min by all approaches, and its width in time at half-maximal intensity was 5 min (1-min count and RTTE) or 10 min (5-min count). TLESR inhibition by WIN55212-2 was described by an I(max) model, with an IC(50) of on average 2.39 nmol · l(-1). Modeling approaches using count or RTTE data linked to a dynamic PK-PD representation of exposure are superior to using summary PK and PD measures and are associated with a higher power for detecting a statistically significant drug effect.

http://jpet.aspetjournals.org/content/jpet/early/2011/09/02/jpet.111.181636.full.pdf

Transient lower esophageal sphincter relaxation pharmacokinetic-pharmacodynamic modeling: count model and repeated time-to-event model.

AIMS The tolerance to the prolactin response following administration of antipsychotic drugs has been modelled as a depletion of a prolactin pool (pool model) and a model where the tolerance is explained by a feedback loop including the dopamine interaction of prolactin release (agonist-antagonist interaction model, (AAI model)). The AAI model was superior to the pool model when analyzing data from clinical trials of risperidone and paliperidone. Here we evaluated the two models using the remoxipride data, designed to challenge the short-term prolactin response, from which the original pool model was built. METHODS The remoxipride data were collected from a study where eight healthy male subjects received two remoxipride infusions on five occasions. The intervals between the first and second dose on each occasion were 2, 8, 12, 24 and 48 h, respectively. The pool and AAI models were fitted using NONMEM. RESULTS According to the objective function values the pool model with a circadian rhythm function fitted the data slightly better, while the AAI model was better in describing the circadian rhythm of prolactin. Visual predictive checks revealed that the models predicted the prolactin profiles equally well. CONCLUSIONS According to the analysis performed here, a previous analysis of several clinical studies and literature reports on prolactin concentrations, it appears that the dopamine feedback mechanism included in the AAI model is better than the storage depletion mechanism in the pool model to estimate the bio-rhythm of prolactin time-course and the tolerance development across different populations, drugs, treatment schedules and time.

https://bpspubs.onlinelibrary.wiley.com/doi/pdf/10.1111/j.1365-2125.2010.03758.x

Comparison of the agonist‐antagonist interaction model and the pool model for the effect of remoxipride on prolactin

New approaches quantifying the effect of treatment are needed in oncology to improve the drug development process and to enable treatment optimization for existing therapies This thesis focuses on the development of pharmacometric models for biomarkers, side effects and efficacy in order to identify predictors of clinical response in anti-cancer drug therapy The variability in myelosuppression was characterized in six different cytotoxic anticancer treatments to evaluate a model-based dose individualization approach utilizing neutrophil counts as a biomarker The estimated impact of inter-occasion variability was relatively low in relation to the inter-individual variability, indicating that myelosuppression is predictable from one treatment course to another The approach may thereby be useful for dose optimization within an individual To further study and to identify predictors for the severe side effect febrile neutropenia (FN), the relationship between the shape of the myelosuppression time-course and the probability of FN was characterized Patients with a rapid decline in neutrophil counts and high drug sensitivity were identified to have a higher probability of developing FN compared with other patients who experience grade 4 neutropenia Predictors of clinical response in patients receiving sunitinib for the treatment of gastro-intestinal stromal tumor (GIST) were identified by the development of an integrated modeling framework Drug exposure, biomarkers, tumor dynamics, side effects and overall survival (OS) were linked in a unified structure, and univariate and multivariate exposure variables were tested for their predictive capacities The soluble biomarker, sVEGFR-3 and tumor size at start of treatment were found to be promising predictors of overall survival, with decreased sVEGFR-3 levels and smaller baseline tumor size being predictive of longer OS Also hypertension and neutropenia was identified as predictors of OS The developed modeling framework may be useful to monitor clinical response, optimize dosing in sunitinib and to facilitate dose individualization

PKPD Modeling of Predictors for Side Effects and Overall Survival in Sunitinb Treated Patients with Gastro Intestinal Stromal Tumor

Transient Lower Esophageal Sphincter Relaxations PKPD Modeling: Count

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Papers

Transient lower esophageal sphincter relaxation pharmacokinetic-pharmacodynamic modeling: count model and repeated time-to-event model.

Comparison of the agonist‐antagonist interaction model and the pool model for the effect of remoxipride on prolactin

PKPD Modeling of Predictors for Side Effects and Overall Survival in Sunitinb Treated Patients with Gastro Intestinal Stromal Tumor

Transient Lower Esophageal Sphincter Relaxations PKPD Modeling: Count