MBoC | ARTICLE Activation of ROCK and MLCK tunes regional stress fiber formation and mechanics via preferential myosin light chain phosphorylation ABSTRACT 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. We show that graded MLCK overexpression increases RLC monophosphorylation (p-RLC) in a graded manner and that this p-RLC localizes to peripheral SFs. Conversely, graded ROCK overexpression preferentially increases RLC diphosphorylation (pp-RLC), with pp-RLC localizing to central SFs. Interrogation of single SFs with subcellular laser ablation reveals that MLCK and ROCK quantitatively regulate the viscoelastic properties of peripheral and central SFs, respectively. The effects of MLCK and ROCK on single-SF mechanics may be correspondingly phenocopied by overexpression of mono-and diphosphomimetic RLC mutants. Our results point to a model in which MLCK and ROCK regulate peripheral and central SF viscoelastic properties through mono-and diphosphorylation of RLC, offering new quantitative connections between kinase activity, RLC phosphorylation, and SF viscoelasticity.

Precision dosing is expected to improve patient outcomes, however, models developed in one patient population may perform poorly when translated to a new patient population. Continuous learning has been proposed as a strategy to improve model-informed precision dosing (MIPD) by tailoring a model to the intended use population as more data become available.

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" (FP), 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. However, little is known about the predictive performance of FP and when to apply FP in MIPD. Here, FP is evaluated in a data set of 4679 adult patients treated with vancomycin.Depending on the PK model, prediction error could be reduced by applying FP in 42-55% of PK parameter estimations. Machine learning (ML) models could identify instances where FP would outperform MAP with a specificity of 81-86%, reducing overall root mean squared error (RMSE) of PK model predictions by 12-22% (0.5-1.2 mg/L) relative to MAP alone. The factors most indicative of the use of FP were past prediction residuals and bias in past PK predictions. A more clinically practical minimal model was developed using only these two features, reducing RMSE by 5-18% (0.20-0.93 mg/L) relative to MAP. This hybrid ML/PK approach advances the precision dosing toolkit by leveraging the power of ML while maintaining the mechanistic insight and interpretability of pharmacokinetic models.

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