Individualized Absorption Models in Population Pharmacokinetic Analyses

Model based assessment of food and acid reducing agent effects on oral absorption of mezigdomide (CC-92480), a novel cereblon E3 ligase modulator

Mezigdomide is a novel cereblon E3 ligase modulator (CELMoD) agent with enhanced autonomous cell-killing activity in multiple myeloma (MM) cells, and promising immunomodulatory and antitumor activity in patients with MM. We developed a population pharmacokinetics (PKs) model for mezigdomide in healthy subjects (HSs), and quantified effects of high-fat meal and proton pump inhibitor (PPI) on human disposition parameters. Plasma concentrations from 64 HS in two phase I clinical studies (NCT03803644 and NCT04211545) were used to develop a population PK model. The HSs received single oral doses of 0.4-3.2 mg mezigdomide with full PK profiles collected. A two-compartment linear PK model with first-order absorption and lag time best described mezigdomide PK profiles in HSs. The population PK parameters of absorption rate constant, lag time, central volume of distribution, clearance, peripheral volume of distribution, and intercompartmental clearance were estimated to be 1.18 h-1 (interoccasion variability [IOV]: 65%), 0.423 h (IOV: 31%), 440 L (interindividual variability [IIV]: 63%), 35.1 L/h (IIV: 40%), 243 L (IIV: 26%), and 36.8 L/h (IIV: 26%), respectively. High-fat meal increased oral bioavailability by ~30% and PPI co-administration decreased oral bioavailability by ~64%. Mezigdomide demonstrated a linear dose-exposure relationship in HSs. The PK model suggests a modest effect of high-fat meal, and a substantial effect of PPIs on mezigdomide oral bioavailability. This population PK model enables data integration across studies to identify important covariate effects and is being used to guide dose selection in clinical study designs for mezigdomide in patients with MM.

A framework for precision "dosing" of mental healthcare services: algorithm development and clinical pilot

BACKGROUND

One in five adults in the US experience mental illness and over half of these adults do not receive treatment. In addition to the access gap, few innovations have been reported for ensuring the right level of mental healthcare service is available at the right time for individual patients.

METHODS

Historical observational clinical data was leveraged from a virtual healthcare system. We conceptualize mental healthcare services themselves as therapeutic interventions and develop a prototype computational framework to estimate their potential longitudinal impacts on depressive symptom severity, which is then used to assess new treatment schedules and delivered to clinicians via a dashboard. We operationally define this process as "session dosing": 497 patients who started treatment with severe symptoms of depression between November 2020 and October 2021 were used for modeling. Subsequently, 22 mental health providers participated in a 5-week clinical quality improvement (QI) pilot, where they utilized the prototype dashboard in treatment planning with 126 patients.

RESULTS

The developed framework was able to resolve patient symptom fluctuations from their treatment schedules: 77% of the modeling dataset fit criteria for using the individual fits for subsequent clinical planning where five anecdotal profile types were identified that presented different clinical opportunities. Based on initial quality thresholds for model fits, 88% of those individuals were identified as adequate for session optimization planning using the developed dashboard, while 12% supported more thorough treatment planning (e.g. different treatment modalities). In the clinical pilot, 90% of clinicians reported using the dashboard a few times or more per member. Although most clinicians (67.5%) either rarely or never used the dashboard to change session types, numerous other discussions were enabled, and opportunities for automating session recommendations were identified.

CONCLUSIONS

It is possible to model and identify the extent to which mental healthcare services can resolve depressive symptom severity fluctuations. Implementation of one such prototype framework in a real-world clinic represents an advancement in mental healthcare treatment planning; however, investigations to assess which clinical endpoints are impacted by this technology, and the best way to incorporate such frameworks into clinical workflows, are needed and are actively being pursued.

Population Pharmacokinetic Modelling of the Complex Release Kinetics of Octreotide LAR: Defining Sub-Populations by Cluster Analysis

The aim of the study is to develop a population pharmacokinetic (PPK) model, of Octreotide long acting repeatable (LAR) formulation in healthy volunteers, which describes the highly variable, multiple peak absorption pattern of the pharmacokinetics of the drug, in individual and population levels. An empirical absorption model, coupled with a one-compartment distribution model with linear elimination was found to describe the data well. Absorption was modelled as a weighted sum of a first order and three transit compartment absorption processes, with delays and appropriately constrained model parameters. Identifiability analysis verified that all twelve parameters of the structural model are identifiable. A machine learning method, i.e., cluster analysis, was performed as pre-processing of the PK profiles, to define subpopulations, before PPK modelling. It revealed that 13% of the patients deviated considerably from the typical absorption pattern and allowed better characterization of the observed heterogeneity and variability of the study, while the approach may have wider applicability in building PPK models. The final model was evaluated by goodness of fit plots, Visual Predictive Check plots and bootstrap. The present model is the first to describe the multiple-peak absorption pattern observed after octreotide LAR administration and may be useful to provide insights and validate hypotheses regarding release from PLGA-based formulations.

Application of Deep Neural Networks as a Prescreening Tool to Assign Individualized Absorption Models in Pharmacokinetic Analysis

A specific model for drug absorption is necessarily assumed in pharmacokinetic (PK) analyses following extravascular dosing. Unfortunately, an inappropriate absorption model may force other model parameters to be poorly estimated. An added complexity arises in population PK analyses when different individuals appear to have different absorption patterns. The aim of this study is to demonstrate that a deep neural network (DNN) can be used to prescreen data and assign an individualized absorption model consistent with either a first-order, Erlang, or split-peak process. Ten thousand profiles were simulated for each of the three aforementioned shapes and used for training the DNN algorithm with a 30% hold-out validation set. During the training phase, a 99.7% accuracy was attained, with 99.4% accuracy during in the validation process. In testing the algorithm classification performance with external patient data, a 93.7% accuracy was reached. This algorithm was developed to prescreen individual data and assign a particular absorption model prior to a population PK analysis. We envision it being used as an efficient prescreening tool in other situations that involve a model component that appears to be variable across subjects. It has the potential to reduce the time needed to perform a manual visual assignment and eliminate inter-assessor variability and bias in assigning a sub-model.