About

Natalia V De Moraes, Ph.D., is an assistant professor in the University of Florida College of Pharmacy and the associate director of the Center for Pharmacometrics & Systems Pharmacology. She received her B.S. in Pharmaceutical Sciences from the University of Sao Paulo, Brazil in 2006, and her Ph.D. from the same university in 2011 under the supervision of Dr. Vera Lanchote. Her research activities include an internship at the Center for Applied Pharmacokinetic Research at the University of Manchester. Dr. De Moraes was an Assistant Professor at Sao Paulo State University (UNESP) for nine years, where she was involved in graduate program advisory boards in Pharmaceutical Sciences and Toxicology. She has served as a consultant for setting health-based exposure limits for Brazilian pharmaceutical companies and was the local coordinator of the Iberoamerican Pharmacometrics Network in Brazil. Her research interests focus on quantitative pharmacology approaches in chronic diseases, special populations, pharmacogenetics, and drug-drug interactions, with a particular emphasis on diabetes, obesity, and bariatric surgery effects on drug metabolism and elimination. She applies population pharmacokinetic modeling, simulation, and physiologically-based pharmacokinetic approaches to design rational dosing strategies.

Research topics

• Medicine
• Internal medicine
• Biochemistry
• Biology
• Oncology
• Pharmacology
• Microbiology
• Chemistry
• Botany
• Cell biology

Selected publications

• Enhancing bioavailability of a weakly basic drug through cocrystallization: PBPK modeling of ketoconazole–succinic acid

  European Journal of Pharmaceutics and Biopharmaceutics · 2026-01-08 · 2 citations

  articleSenior authorCorresponding
  PublisherDOI

• Enhanced Sensitivity to Tramadol in Diabetic Neuropathic Pain Compared to Nerve Compression Neuropathies: A Population <scp>PK</scp>/<scp>PD</scp> Model Analysis

  CPT Pharmacometrics & Systems Pharmacology · 2025-02-17

  articleOpen accessSenior authorCorresponding

  ABSTRACT Neuropathic pain, often associated with diabetic neuropathy or nerve compression injuries, arises from damage or dysfunction in the somatosensory nervous system. Tramadol, frequently prescribed for this pain, has its fraction unbound and that of its active metabolite (M1) significantly altered by diabetes. Yet, dosing adjustments for diabetic neuropathic pain remain underexplored. This study developed a comprehensive population pharmacokinetics/pharmacodynamics (PK/PD) model for tramadol and its major metabolites, focusing on diabetes's impact on PK and PK‐PD relationship to identify optimal dosing regimens. Data from patients with chronic neuropathic pain on oral tramadol were used to develop enantiomer‐specific population models, considering both total and unbound concentrations. Tramadol's PK was best described by a two‐compartment model with Weibull absorption and linear elimination and a one‐compartment model with enterohepatic circulation and first‐pass metabolism for the active M1. Simulations showed higher unbound fractions of the active M1 in patients with type 1 and type 2 diabetes. Despite a 67% and 14% reduction in the AUC of total ( 1R,2R )‐M1 in patients with type 1 and type 2 diabetes, respectively, the AUC of unbound ( 1R,2R )‐M1 remained consistent. The unbound concentration of the active M1 required to achieve 50% of the maximum pain reduction (IC 50 ) was lower in patients with diabetes, indicating increased sensitivity to the drug. This model‐based approach provides valuable dosing guidance, suggesting once‐daily dosing treatments in patients with diabetes and twice‐daily dosing for patients with neuropathic pain secondary to nerve compression mechanisms.

  PublisherOA PDFDOI

• Amikacin Dosing Adjustment in Critically Ill Oncologic Patients: A Study with Real-World Patients, PBPK Analysis, and Digital Twins

  Pharmaceutics · 2025-02-24 · 2 citations

  articleOpen access

  Background/Objectives: Guidelines recommend adjusting amikacin dosing based on patients’ renal function. Nevertheless, for critically ill cancer patients, the renal function equations based on serum creatinine levels have low or no correlation with amikacin clearance. Considering this, using real-world data, we built an amikacin PBPK model to predict amikacin plasma concentrations in critically ill oncologic patients stratified by renal impairment levels. Further, the model was applied for dose stratification and individualization (digital twin strategy) in this population. Methods: In the Therapeutic Drug Monitoring (TDM) study, 368 amikacin pharmacokinetic analyses from 184 critically ill cancer patients were enrolled in three cohorts. A full-body PBPK model was developed using PK-Sim v. 11.3. Results: The final PBPK model accounted for two groups of critically ill cancer patients with mild (creatinine clearance; CLcr ≥ 60 mL/min) or severe (CLcr &lt; 60 mL/min) renal dysfunction. In the dose stratification strategy, at the 7th dose, cancer patients with CLcr ≥ 60 mL/min under regimens 20 mg/kg (q24h); 25 mg/kg (q24h); 25 mg/kg (q48h); and 30 mg/kg (q72h) have probability of ≥69% of the patients achieving the efficacy target (AUC/MIC &gt; 80, MIC of 4 mg/L), while cancer patients with CLcr &lt; 60 mL/min under regimens 7.5 mg/kg (q24h); 15 mg/kg (q24h); 15 mg/kg (q48h); and 20 mg/kg (q36h) have ≥90% probability of achieving the same efficacy target. Conclusions: Our MIPD approach demonstrates potential in optimizing amikacin dosing for critically ill cancer patients. However, it does not eliminate the need for TDM due to unexplained variability still not accounted for by the PBPK model.

  PublisherOA PDFDOI

• Unravelling sources of variability on rocuronium pharmacokinetics: Implications for prolonged recovery in older patients

  British Journal of Clinical Pharmacology · 2025-01-09 · 1 citations

  articleOpen accessSenior authorCorresponding

  AIMS: Residual neuromuscular blockade (RNB) commonly occurs when using neuromuscular blockers and increases the risk for pulmonary complications, such as airway obstruction and severe hypoxemia, in extubated patients. Rocuronium exhibits a high variability in recovery time, contributing to an increased risk for RNB. This study aimed to identify and characterize the sources of variability in rocuronium exposure and response via a population pharmacokinetic/pharmacodynamic (PK/PD) analysis and to apply the developed PK/PD model to investigate clinical implications. METHODS: A nonlinear mixed-effect model was developed for rocuronium in patients undergoing general anaesthesia, using doses of 0.3-1.2 mg/kg. Plasma concentrations and the neuromuscular block (train of four ratio) were assessed up to 6 h after dosing. The influence of age, body mass index, renal function and sex on PK and PD was explored. Simulations were performed to predict the recovery time. RESULTS: A two-compartment model with linear elimination and an indirect sigmoid I-max model was used to describe PK and PD. The transfer rate into the periphery increases with age. The predicted recovery time was significantly longer in older subjects aged 85 years (median: 2.8 h; interquartile range [IQR]: 2.18-4.0) compared to young adults aged 25 years (median: 2.5 h; IQR: 2.0-3.1) following single bolus administrations of doses ≥ 0.7 mg/kg. CONCLUSIONS: Our findings suggest that older patients take slightly longer to recover than younger adults due to an age-dependent increase in tissue uptake. However, a priori dose adjustments for rocuronium in older patients are not feasible, since age contribution is overshadowed by the overall variability in the recovery time.

  PublisherOA PDFDOI

• Optimal dosing of amoxicillin in obese and post-gastric bypass patients using a population pharmacokinetics-pharmacodynamics model approach

  Journal of Antimicrobial Chemotherapy · 2025-05-13

  article

  AIM: To characterize the impact of obesity and Roux-en-Y gastric bypass (RYGB) on systemic exposure to amoxicillin using population modeling approach. We also performed simulations to provide insights into optimising the dosing of amoxicillin against infectious bacteria in the respiratory tract. METHODS: Non-obese, obese, and post-RYGB patients, aged between 24 and 50 years, from two clinical studies, were evaluated. Sex, age, body size descriptors, history of bariatric surgery and renal function were assessed as potential covariates. The percentage of time of unbound amoxicillin plasma concentration above the minimum inhibitory concentration (%fT > MIC) of >40%, representing bactericidal activity, was used as a PK/PD target to calculate the probability of target attainment (PTA). The PTA threshold was defined as 90% of treated individuals achieving fT > MIC ≥ 40%. RESULTS: Amoxicillin PK was best characterized by a one-compartment model including a zero-order absorption with lag time followed by a first-order absorption and linear elimination. The relative oral bioavailability in post-RYGB patients was nearly halved compared with non-obese subjects. Age exhibited a negative correlation with clearance, consistent with amoxicillin being a hydrophilic drug primarily eliminated through the kidneys. For MIC ≤ 2 mg/L, the oral dosing regimen of 1000 mg q6h reached the therapeutic target for non-obese. For MIC ≤ 1 mg/L, 1000 mg q6h is needed in obese and post-RYGB subjects. CONCLUSION: Amoxicillin doses of 1000 mg q6h were found to maximize the probability of attaining the PK/PD target with MIC ≤ 1 mg/L in obese and post-RYGB patients.

  PublisherDOI

• Leveraging Omeprazole PBPK/PD Modeling to Inform Drug–Drug Interactions and Specific Recommendations for Pediatric Labeling

  Pharmaceutics · 2025-03-14 · 7 citations

  articleOpen access

  Background/Objectives: Omeprazole is widely used for managing gastrointestinal disorders like GERD, ulcers, and H. pylori infections. However, its use in pediatrics presents challenges due to drug interactions (DDIs), metabolic variability, and safety concerns. Omeprazole’s pharmacokinetics (PK), primarily influenced by CYP2C19 metabolism, is affected by ontogenetic changes in enzyme expression, complicating dosing in children. Methods: This study aimed to develop and validate a physiologically based pharmacokinetic (PBPK) model for omeprazole and its metabolites to predict age-related variations in metabolism and response. Results: The PBPK model successfully predicted exposure to parent and metabolites in adults and pediatrics, incorporating competitive and mechanism-based inhibition of CYP2C19 and CYP3A4 by omeprazole and its metabolites. By accounting for age-dependent metabolic pathways, the model enabled priori predictions of omeprazole exposure in different age groups. Linking PK to the pharmacodynamics (PD) model, we described the impact of age-related physiological changes on intragastric pH, the primary outcome for proton pump inhibitors efficacy. Conclusions: The PBPK-PD model allowed for the virtual testing of dosing scenarios, providing an alternative to clinical studies in pediatrics where traditional DDI studies are challenging. This approach offers valuable insights for accurate dosing recommendations in pediatrics, accounting for age-dependent variability in metabolism, and underscores the potential of PBPK modeling in guiding pediatric drug development.

  PublisherOA PDFDOI

• Model-Informed Dose Optimization of Spironolactone in Neonates and Infants

  Pharmaceuticals · 2025-03-01 · 1 citations

  articleOpen accessSenior authorCorresponding

  Background/Objectives: Spironolactone (SP) has been used off-label in pediatrics since its approval, but its use is challenged by limited pharmacokinetic (PK) data in adults and especially in children. Methods: Physiologically based pharmacokinetic (PBPK) models for SP and its active metabolites, canrenone (CAN) and 7α thio-methyl spironolactone (TMS), in adults were developed. These models aim to enhance understanding of SP’s PK and provide a basis for predicting PK and optimizing SP dosing in infants and neonates. Given SP’s complex metabolism, we assumed complete conversion to CAN and TMS by CES1 enzymes, fitting CES1-mediated metabolism to the parent-metabolite model using PK data. We incorporated ontogeny for CES1 and CYP3A4 and other age-related physiological changes into the model to anticipate PK in the pediatric population. Results: The PBPK models for SP, CAN, and TMS accurately captured the observed PK data in healthy adults across various dosing regimens, including the impact of food on drug exposure. The pediatric PBPK model was evaluated using PK data from infants and neonates. Simulations indicate that 2.5 mg/kg in 6-month to 2-year infants and 2 mg/kg in 1–6-months infants matched the total unbound systemic exposure equivalent to the standard recommended daily maintenance dose of 100 mg in adults for treating edema. Conclusions: The developed PBPK model provides valuable insights for dosing decisions and optimizing therapeutic outcomes, especially in populations where clinical studies are challenging.

  PublisherOA PDFDOI

• A Bayesian Framework for Optimizing Amikacin Therapy in Critically Ill Patients With Cancer

  Therapeutic Drug Monitoring · 2025-04-10 · 1 citations

  articleSenior author

  BACKGROUND: Amikacin (AMK) is used to treat gram-negative bacterial infections in intensive care unit (ICU) patients. However, its narrow therapeutic range and high interindividual variability can lead to toxicity and ineffectiveness. This study aimed to establish a roadmap for AMK therapeutic drug monitoring in critically ill patients with cancer to provide a Bayesian estimator of bedside applicability. METHODS: An observational retrospective study was conducted on oncological patients admitted to the ICU, treated with AMK as a 30-min intravenous infusion at 5.8-39.2 mg/kg. The plasma concentrations were analyzed using a nonlinear mixed-effects modeling approach. Covariate analyses were performed using anthropometric and laboratory data, concomitant drugs, and comorbidities. The model predictive performance was compared with previous AMK dosing approaches using the Bland-Altman method. RESULTS: The concentration-time profiles were best described using a one-compartment model with linear elimination. The estimated glomerular filtration rate was a significant covariate of clearance (CL), explaining 16% of the interpatient variability. Body weight was positively correlated with the volume of distribution, accounting for 4% of the variability. Our model reduced the bias in the estimates of individual CL values compared with that of other available methods and was further implemented in DoseMeRx for real-time application at the bedside. CONCLUSIONS: This study provides an effective example of a Bayesian estimation method for individualizing AMK doses in critically ill patients with cancer. Collecting more comprehensive patient information, including additional biomarkers for renal function, could further refine the model and improve its predictive performance in this special population.

  PublisherDOI

• Physiologically-based pharmacokinetic modeling of enantioselective hydroxychloroquine kinetics and impact of genetic polymorphisms

  Brazilian Journal of Pharmaceutical Sciences · 2025-01-01

  articleOpen accessSenior author

  Abstract Hydroxychloroquine (HCQ) is a chiral drug used to treat malaria and inflammatory diseases, available as a racemic mixture of R-and S-HCQ. This work aimed to build physiologically-based pharmacokinetic (PBPK) models to predict the pharmacokinetics (PK) of R-and S-HCQ and assess the impact of major genetic polymorphisms on PK. Whole-body PBPK models accounting for first-order absorption, Rodgers and Rowland distribution method, and enzyme kinetics data were built for R-and S-HCQ. The models were verified by comparing predicted PK parameters with observed ones, with a mean error within the acceptable range (0.5-2 fold). Simulations covered CYP2D6 normal metabolizer (NM), poor metabolizer (PM), and ultra-rapid metabolizer (UM) phenotypes, as well as CYP2C8 NM and PM phenotypes. The results revealed a 1.1-fold increase in area under the curve blood concentration versus time (AUC) for CYP2D6 PM individuals and a 0.9-fold reduction for UM individuals compared to NM individuals. In addition, simulations with CYP2D6 and CYP2C8 PM phenotype individuals combined with the CYP3A4 inhibitor clarithromycin showed increased AUC for R-and S-HCQ of 2.34 and 2.68, respectively. These PBPK models offer reliable predictions for R-and S-HCQ enantioselective kinetics and shed light on previously unexplored scenarios.

  PublisherDOI

• Unveiling CYP450 inhibition by the pesticide prothioconazole through integrated in vitro studies and PBPK modeling

  Archives of Toxicology · 2025-04-20 · 4 citations

  article
  PublisherDOI

Frequent coauthors

• Priscila Akemi Yamamoto

  University of Florida

  35 shared

• Gabriela Rocha Lauretti

  Universidade de São Paulo

  20 shared

• Jhohann Richard de Lima Benzi

  University of Washington

  19 shared

• Vera Lúcia Lanchote

  Universidade de São Paulo

  16 shared

• A. Costa

  University of Florida

  11 shared

• Wilson Salgado

  Universidade de São Paulo

  11 shared

• Rafael Kemp

  Universidade de Ribeirão Preto

  9 shared

• Ajith Kumar Sankarankutty

  Universidade de São Paulo

  9 shared

Labs

• Department of PharmaceuticsPI

Education

• Ph.D.

  University of Florida

Awards & honors

• Best poster at the V Congress of the Brazilian Association o…
• Best oral presentation at the 12th International Congress of…