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Sheiner LB : No suspect or victim name is mentioned in the title.

Sheiner LB, Beal S, Rosenberg B, and Marathe VV (1979) developed a method for forecasting individual pharmacokinetics. They further evaluated methods for estimating population pharmacokinetic parameters using routine clinical data, including the Michaelis-Menten model and the monoexponential model. McArthur GA et al. (2014) assessed the safety and efficacy of vemurafenib in melanoma patients with BRAF mutations. Henricks LM et al. (2017) called for a drug label update to implement DPYD genotype-guided dose individualization for fluoropyrimidine therapy to improve patient safety. Holford N et al. (2022) discussed the shift from TDM to TCI. Sheiner LB (1969) and Sheiner LB et al. (1972) developed computer-aided methods for long-term anticoagulation therapy and drug dosage regimens. Jelliffe RW et al. (1972) reduced digitalis toxicity using computer-assisted glycoside dosage regimens. Darwich AS et al. (2017) discussed the challenges of implementing model-informed precision dosing in clinical practice. Wright DFB et al. (2019) emphasized the importance of demonstrating improved patient outcomes with model-informed precision dosing.

Neely MN et al. (2018) conducted a prospective trial on the use of trough concentration versus area under the curve to determine therapeutic vancomycin dosing. Fuchs A et al. (2013) reviewed available therapeutic drug monitoring software, while Kantasiripitak W et al. (2020) evaluated the suitability of software tools for model-informed precision dosing. Keizer RJ et al. (2018) discussed the scientific challenges and opportunities of implementing model-informed precision dosing at the bedside. Cheng Y et al. (2021) assessed the extrapolation of population pharmacokinetics of antibiotics. Broeker A et al. (2019), Hughes JH et al. (2021), Uster DW et al. (2021), and others have conducted research on model-informed precision dosing for vancomycin and other drugs.

Abrantes JA et al. (2019) discussed handling interoccasion variability in model-based dose individualization using therapeutic drug monitoring data. Hamberg A-K et al. (2010, 2007, 2013, 2015) developed pharmacometric models to predict the impact of age, genotype, and drug exposure on individualized warfarin therapy. Friberg LE et al. (2002) developed a model of chemotherapy-induced myelosuppression, while Wallin JE et al. (2009) developed a tool for neutrophil-guided dose adaptation in chemotherapy. Netterberg I et al. (2017) developed a model to predict my

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