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Multiple myeloma (MM) treatment guidelines recommend waiting for formal progression criteria (FPC) to be met before proceeding to the next line of therapy. As predicting progression may allow early switching to next‐line therapy while the disease burden is relatively low, we evaluated the predictive accuracy of a mathematical model to anticipate relapse 180 days before the FPC is met. A subset of 470/1143 patients from the IA16 dataset who were initially treated with VRd (Velcade (bortezomib), Revlimid (lenalidomide), and dexamethasone) in the CoMMpass study (NCT01454297) were randomly split 2:1 into training and testing sets. A model of M‐protein dynamics was developed using the training set and used to predict relapse probability in patients in the testing set given their response histories up to 12 or more months of treatment. The predictive accuracy of this model and M‐protein "velocity" were assessed via receiver operating characteristics (ROC) analysis. The final model was a two‐population tumor growth inhibition model with additive drug effect and transit delay compartments for cell killing. The ROC area under the curve value of relapse prediction 180 days ahead of observed relapse by FPC was 0.828 using at least 360 days of response data, which was superior to the M‐protein velocity ROC score of 0.706 under the same conditions. The model can predict future relapse from early M‐protein responses and can be used in a future clinical trial to test whether early switching to second‐line therapy results in better outcomes in MM. [ABSTRACT FROM AUTHOR]

Soticlestat is a first‐in‐class, selective inhibitor of cholesterol 24‐hydroxylase (CH24H), which catabolizes cholesterol to 24S‐hydroxycholesterol (24HC) in the brain, in phase III development for Dravet syndrome and Lennox–Gastaut syndrome treatment. This study aimed to develop a model of soticlestat pharmacokinetics (PKs) and pharmacodynamics (PDs) using 24HC plasma concentrations and CH24H enzyme occupancy (EO) time profiles. Subsequently, model‐based simulations were conducted to identify dosing strategies for phase II trials in children and adults with developmental and epileptic encephalopathies (DEEs). Four phase I trials of healthy adults involving oral administration of soticlestat 15–1350 mg were used to develop the mixed‐effect population PK/EO/PD model. The population PK analysis utilized 1727 observations (104 individuals), PK/EO analysis utilized 20 observations (11 individuals), and PK/PD analysis utilized 2270 observations (99 individuals). Optimal dosing strategies were identified from model‐based PK, EO, and PD simulations. The PK/EO/PD model described the observed data well and comprised a two‐compartment model with dose as a covariate on peripheral volume, linear elimination, and intercompartmental clearance. Transit and effect‐site compartments were included to accommodate different dosage forms and the delay between plasma drug concentrations and EO. Model‐based simulations indicated that soticlestat 100–300 mg twice daily may be an optimal adult dosing regimen with weight‐adjusted pediatric dosing strategies identified for evaluation in phase II trials. The population PK/EO/PD model provided understanding of the soticlestat PK/PD relationship with partial delineation of sources of variability, and identified dosing strategies for phase II trials of children and adults with DEEs. [ABSTRACT FROM AUTHOR]

Intrathecal administration is an important mode for delivering biological agents targeting central nervous system (CNS) diseases. However, current clinical practices lack a sound theorical basis for a quantitative understanding of the variables and conditions that govern the delivery efficiency and specific tissue targeting especially in the brain. This work presents a distributed mechanistic pharmacokinetic model (DMPK) for predictive analysis of intrathecal drug delivery to CNS. The proposed DMPK model captures the spatiotemporal dispersion of antisense oligonucleotides (ASO) along the neuraxis over clinically relevant time scales of days and weeks as a function of infusion, physiological and molecular properties. We demonstrate its prediction capability using biodistribution data of antisense oligonucleotide (ASO) administration in non-human primates. The results are in close agreement with the observed ASO pharmacokinetics in all key compartments of the central nervous system. The model enables determination of optimal injection parameters such as intrathecal infusion volume and duration for maximum ASO delivery to the brain. Our quantitative model-guided analysis is suitable for identifying optimal parameter settings to target specific brain regions with therapeutic drugs such as ASOs. [ABSTRACT FROM AUTHOR]

A model‐based meta‐analysis was performed with reported data from obese subjects and patients with type 2 diabetes (T2DM) to characterize the effects of dipeptidyl peptidase 4 (DPP4) inhibitors, gastric inhibitory polypeptides (GIPs), glucagon‐like peptide‐1 (GLP1), and dual GIP/GLP1 agonists, or a combination of these antidiabetic drugs (ADs) on heart rate (HR), diastolic blood pressure (DBP), and systolic blood pressure (SBP). A systematic literature search and review after the Cochrane method identified sources for investigational and approved ADs resulted in a comprehensive database with data from 178 clinical studies in obese subjects and patients with T2DM. Results indicated that there were AD class‐dependent effects on HR and SBP, whereas no clear AD‐related effects on DBP were found. All AD classes, except for DPP4 inhibitors, increased HR. The largest increase of 12 bpm was seen with GLP1 receptor agonists. All AD classes appeared to decrease SBP. DPP4 inhibitors were associated with a marginal decrease of ~ 1 mmHg, whereas GLP1 and GIP/GLP1 dual agonists exhibited the largest decrease of ~ 3 mmHg in SBP. AD‐related effects were similar in obese subjects and patients with T2DM. In conclusion, there are clinically relevant AD‐related effects on both HR and SBP, but not on DBP. DPP4 inhibitors are associated with the smallest (if at all) effects on HR and SBP, whereas GLP1 inhibitors exhibited the largest effects on these two cardiovascular end points. Additional studies are warranted to further investigate how AD‐related SBP decreases combined with HR increases affect long‐term cardiovascular mortality. [ABSTRACT FROM AUTHOR]