Developing Parametric and Nonparametric Models for Model-Informed Precision Dosing: A Quality Improvement Effort in Vancomycin for Patients With Obesity.

Background: Both parametric and nonparametric methods have been proposed to support model-informed precision dosing (MIPD). However, which approach leads to better models remains uncertain. Using open-source software, these 2 statistical approaches for model development were compared using the pharmacokinetics of vancomycin in a challenging subpopulation of class 3 obesity.

Maximum a posteriori Bayesian methods out-perform non-compartmental analysis for busulfan precision dosing.

Dose personalization improves patient outcomes for many drugs with a narrow therapeutic index and high inter-individuality variability, including busulfan. Non-compartmental analysis (NCA) and model-based methods like maximum a posteriori Bayesian (MAP) approaches are two methods routinely used for dose optimization. These approaches vary in how they estimate patient-specific pharmacokinetic parameters to inform a dose and the impact of these differences is not well-understood.

Clinical decision support for chemotherapy-induced neutropenia using a hybrid pharmacodynamic/machine learning model.

Consensus guidelines recommend use of granulocyte colony stimulating factor in patients deemed at risk of chemotherapy-induced neutropenia, however, these risk models are limited in the factors they consider and miss some cases of neutropenia. Clinical decision making could be supported using models that better tailor their predictions to the individual patient using the wealth of data available in electronic health records (EHRs). Here, we present a hybrid pharmacokinetic/pharmacodynamic (PKPD)/machine learning (ML) approach that uses predictions and individual Bayesian parameter estimates from a PKPD model to enrich an ML model built on her data.

Clinical Decision Support for Precision Dosing: Opportunities for Enhanced Equity and Inclusion in Health Care.

Precision dosing aims to tailor doses to individual patients with the goal of improving treatment efficacy and avoiding toxicity. Clinical decision support software (CDSS) plays a crucial role in mediating this process, translating knowledge derived from clinical trials and real-world data (RWD) into actionable insights for clinicians to use at the point of care. However, not all patient populations are proportionally represented in clinical trials and other data sources that inform CDSS tools, limiting the applicability of these tools for underrepresented populations.

Evaluation of Neonatal and Paediatric Vancomycin Pharmacokinetic Models and the Impact of Maturation and Serum Creatinine Covariates in a Large Multicentre Data Set.

Background And Objective: Infants and neonates present a clinical challenge for dosing drugs with high interindividual variability due to these patients' rapid growth and the interplay between maturation and organ function. Model-informed precision dosing (MIPD), which can account for interindividual variability via patient characteristics and Bayesian forecasting, promises to improve individualized dosing strategies in this complex population. Here, we assess the predictive performance of published population pharmacokinetic models describing vancomycin in neonates and infants, and analyze the robustness of these models in the face of clinical uncertainty surrounding covariate values.

Evaluating and Improving Neonatal Gentamicin Pharmacokinetic Models Using Aggregated Routine Clinical Care Data.

Model-informed precision dosing (MIPD) can aid dose decision-making for drugs such as gentamicin that have high inter-individual variability, a narrow therapeutic window, and a high risk of exposure-related adverse events. However, MIPD in neonates is challenging due to their dynamic development and maturation and by the need to minimize blood sampling due to low blood volume. Here, we investigate the ability of six published neonatal gentamicin population pharmacokinetic models to predict gentamicin concentrations in routine therapeutic drug monitoring from nine sites in the United State ( = 475 patients).

A hybrid machine learning/pharmacokinetic approach outperforms maximum a posteriori Bayesian estimation by selectively flattening model priors.

Model-informed precision dosing (MIPD) approaches typically apply maximum a posteriori (MAP) Bayesian estimation to determine individual pharmacokinetic (PK) parameters with the goal of optimizing future dosing regimens. This process combines knowledge about the individual, in the form of drug levels or pharmacodynamic biomarkers, with prior knowledge of the drug PK in the general population. Use of "flattened priors" (FPs), in which the weight of the model priors is reduced relative to observations about the patient, has been previously proposed to estimate individual PK parameters in instances where the patient is poorly described by the PK model.

Continuous Learning in Model-Informed Precision Dosing: A Case Study in Pediatric Dosing of Vancomycin.

Model-informed precision dosing (MIPD) leverages pharmacokinetic (PK) models to tailor dosing to an individual patient's needs, improving attainment of therapeutic drug exposure targets and thus potentially improving drug efficacy or reducing adverse events. However, selection of an appropriate model for supporting clinical decision making is not trivial. Error or bias in dose selection may arise if the selected model was developed in a population not fully representative of the intended MIPD population.

Transcriptomic analysis reveals that BMP4 sensitizes glioblastoma tumor-initiating cells to mechanical cues.

The poor prognosis of glioblastoma (GBM) is associated with a highly invasive stem-like subpopulation of tumor-initiating cells (TICs), which drive recurrence and contribute to intra-tumoral heterogeneity through differentiation. These TICs are better able to escape extracellular matrix-imposed mechanical restrictions on invasion than their more differentiated progeny, and sensitization of TICs to extracellular matrix mechanics extends survival in preclinical models of GBM. However, little is known about the molecular basis of the relationship between TIC differentiation and mechanotransduction.

Activation of ROCK and MLCK tunes regional stress fiber formation and mechanics via preferential myosin light chain phosphorylation.

The assembly and mechanics of actomyosin stress fibers (SFs) depend on myosin regulatory light chain (RLC) phosphorylation, which is driven by myosin light chain kinase (MLCK) and Rho-associated kinase (ROCK). Although previous work suggests that MLCK and ROCK control distinct pools of cellular SFs, it remains unclear how these kinases differ in their regulation of RLC phosphorylation or how phosphorylation influences individual SF mechanics. Here, we combine genetic approaches with biophysical tools to explore relationships between kinase activity, RLC phosphorylation, SF localization, and SF mechanics.

Synthetic mechanobiology: engineering cellular force generation and signaling.

Mechanobiology seeks to understand and control mechanical and related biophysical communication between cells and their surroundings. While experimental efforts in this field have traditionally emphasized manipulation of the extracellular force environment, a new suite of approaches has recently emerged in which cell phenotype and signaling are controlled by directly engineering the cell itself. One route is to control cell behavior by modulating gene expression using conditional promoters.

Time-lapse microscopy of macrophages during embryonic vascular development.

Background: Macrophages are present before the onset of blood flow, but very little is known about their function in vascular development. We have developed a technique to concurrently label both endothelial cells and macrophages for time-lapse microscopy using co-injection of fluorescently conjugated acetylated low-density lipoprotein (AcLDL) and phagocytic dye PKH26-PCL.

Results: We characterize double-labeled cells to confirm specific labeling of macrophages.