Pharmacometrics encapsulates using biological principles, statistics, computer programming, and quantitative methods to improve and optimize healthcare by creating useful solutions. The goals of this research are use pharmacometrics to (i) develop a product screening tool to facilitate medical counter measure (MCM) development for the treatment of hematopoietic acute radiation syndrome (H-ARS) (ii) identify the risk factors of acute kidney injury (AKI) in pediatric patients undergoing vancomycin therapy to inform a patient-centric monitoring by risk stratifying patients. For developing the product screening tool, mortality due to H-ARS was first predicted by developing machine learning (ML) models using pooled data from three good laboratory practice (GLP) non-human primate (NHP) trials. Application of the product screening tool was then demonstrated by developing a semi-mechanistic neutropenia model and performing simulations with hypothetical treatment effects. Risk factors of AKI were identified using real world data (RWD) obtained from a large therapeutic drug monitoring (TDM) database of pediatrics patients that underwent vancomycin therapy. There are only 4 MCMs that have been approved by the United States Food and Drug Administration (US-FDA) for the treatment of H-ARS even though MCM development has been underway for more than 60 years. The 4 FDA approved drugs – filgrastim, PEGfilgrastim, sargramostim, romiplostim – were repurposed for HARS from their oncologic indications. Building upon the achievements of already approved products, drug repurposing has been suggested as an MCM development strategy. A product screening tool that predicts effectiveness of potential treatments in reducing mortality due to HARS was therefore developed. Data from three GLP NHP studies where vehicle and sargramostim articles were administered was pooled (N=501) and an ML model was developed to predict mortality due to HARS with blood cell measures as inputs. The model was both internally and externally validated for its accuracy in predicting mortality. A semi-mechanistic disease progression model was then developed to characterize the kinetics of absolute neutrophil count post irradiation. After model qualification, application of the product screening tool was demonstrated in screening new treatments by performing simulations with hypothetical treatment effects on physiological parameters such as proliferation rate of myeloid progenitors. Vancomycin is a broad-spectrum antibiotic that it is widely administered for serious gram-positive infections. Along with broad usage, lack of understanding of risk factors causing this AKI has led to a 11.8% incidence of this serious adverse event in pediatric patients. AKI increases probability of progressing to chronic kidney disease and End Stage Renal Disease that can result in a 50% chance of mortality. Electronic health records data was queried from Texas Children’s Hospital’s for pediatric patients that underwent vancomycin TDM (N=2318). Information on patient demographics, vancomycin exposures, concomitant aminoglycosides, vasopressors, vasodilators, beta lactams, and clinical factors were evaluated as potential risk factors. 18.9% patients in the dataset met the definition of AKI. The following factors were identified to be related to AKI: low age, ICU residence, concomitant piperacillin/tazobactam, amphotericin, vasopressor agents, and vancomycin troughs (Ctrough). All risk factors identified in this work were consistent with literature. In patients receiving concomitant piperacillin/tazobactam and/or amphotericin, we recommended either increased monitoring or alternative treatments. Although high Ctrough was associated to AKI, contribution to AKI prediction was 9% among other findings which suggest a modest relationship. Therefore, owing to the experimental design (maintain Ctr below 20 mg/L), whether or not high trough concentrations cause AKI was deemed inconclusive.