References

1. Mizuno T, et al. Clinical implementation of pharmacogenetics and model-informed precision dosing to improve patient care. Br J Clin Pharmacol. 2022.
2. Colman R, et al. Model-informed Precision Dosing for Biologics Is Now Available at the Bedside for Patients With Inflammatory Bowel Disease. Inflamm Bowel Dis. 2023.
3. Mizuno T, et al. Developmental Pharmacokinetics of Sirolimus: Implications for Precision Dosing in Neonates and Infants with Complicated Vascular Anomalies. Pediatr Blood Cancer. 2017.

Physiologically-Based Pharmacokinetics (PBPK) Modeling to Predict Drug Exposure in Pediatric and Maternal-Fetal Populations

PBPK is a mathematical modeling technique to describe drug behavior in the body (i.e. oral absorption, distribution to tissues, metabolisms, and eliminations) based on drug-specific physicochemical properties and patient’s physiology. Our lab utilizes PBPK technologies to predict drug disposition and exposure in support of optimal dose selections in special populations such as neonates and infants, children, pregnant women, and their fetuses.

References

1. van Hoogdalem M, et al. A review of pregnancy-induced changes in opioid pharmacokinetics, placental transfer, and fetal exposure: Towards fetomaternal physiologically-based pharmacokinetic modeling to improve the treatment of neonatal opioid withdrawal syndrome. Pharmacol Ther, 2022.
2. van Hoogdalem M, et al. Forecasting Fetal Buprenorphine Exposure through Maternal-Fetal Physiologically Based Pharmacokinetic Modeling. Pharmaceutics, 2024.

Clinical Pharmacokinetics Consultation

We offer model-informed clinical pharmacokinetics (PK) consultation service providing real-time PK assessment and dose recommendations to achieve the pre-defined goal exposure as part of routine clinical care or prospective concentration-control clinical trials. We provide consultation for various drugs including mTOR inhibitors, anti-cancer drugs, immunosuppressive agents, anti-inflammatory biologics, psychiatric drugs, and anti-infective drugs.

References

1. Mizuno T, et al. Model-based precision dosing of sirolimus in pediatric patients with vascular anomalies. Eur J Pharm Sci, 2017.
2. Mizuno T, et al. Significant effect of infection and food intake on sirolimus pharmacokinetics and exposure in pediatric patients with acute lymphoblastic leukemia. Eur J Pharm Sci, 2019.
3. Miyagawa B, et al. Model-Informed Estimation of Acutely Decreased Tacrolimus Clearance and Subsequent Dose Individualization in a Pediatric Renal Transplant Patient With Posterior Reversible Encephalopathy Syndrome. Ther Drug Mont, 2023.
4. Capal JK, et al. Preventative treatment of tuberous sclerosis complex with sirolimus: Phase I safety and efficacy results. Annals of CNS, 2024.

Model-Informed Pediatric Drug Development (MIPDD)

As part of the Pharmacometrics Services, we provide strategic clinical pharmacology consulting, pediatric study design, and data analysis support by using our pharmacometrics and systems pharmacology research expertise. We utilize quantitative modeling and clinical trial simulations to evaluate potential clinical trial designs, extrapolate from adult clinical data to pediatric patients, identify age-appropriate pediatric dosing regimens, determine optimal sampling design, and estimate the required sample size. We have a track record of a portfolio of MIPDD projects in collaboration with bio-pharmaceutical industries and academic collaborators. Learn more about our Pharmacometrics Services at Cincinnati Children's.

References

1. Vinks AA, et al. Model-Informed Pediatric Drug Development: Application of Pharmacometrics to Define the Right Dose for Children. J Clin Pharmacol. 2021.

Development of EHR-Integrated Clinical Decision Support Dashboards

The process of gathering clinical information, performing model-informed analyses and predictions, and translating simulated data to decision-support platforms is complex and time-consuming. User-friendly software integrated into electronic health record (EHR) systems is essential to widely disseminate the implementation of model-informed precision dosing at the bedside. As part of multidisciplinary teams at Cincinnati Children’s, we work together to develop EHR-integrated clinical decision support (CDS) platforms to help clinicians implement MIPD in the clinic.

References

1. Vinks AA, et al. Electronic Health Record (EHR)-embedded Decision Support Platform for Morphine Precision Dosing in Neonates. Clin Pharmacol Ther. 2020.
2. Xiong Y, Mizuno T, et al. Real-world Infliximab Pharmacokinetic Study Informs an Electronic Health Record-Embedded Dashboard to Guide Precision Dosing in Children with Crohn's Disease. Clin Pharmacol Ther. 2021.
3. Minar PP, et al. Precise infliximab exposure and pharmacodynamic control to achieve deep remission in paediatric Crohn's disease (REMODEL-CD): study protocol for a multicentre, open-label, pragmatic clinical trial in the USA. BMJ Open. 2024.
   
   

Leveraging Artificial Intelligence (AI) and Machine Learning (ML) Technologies to Facilitate Precision Dosing in Children

Artificial intelligence (AI) is a discipline of computer science that focuses on creating systems that can perform tasks typically requiring human intelligence, such as understanding natural language, recognizing patterns, making decisions, and solving complex problems. Machine Learning (ML) is a subset of AI that focuses on the development of algorithms and statistical models that enable computer systems to learn from and make predictions or decisions based on data. Our lab is interested in applying AI and ML approaches to support pediatric dose selection and therapeutic optimization based on the prediction of drug exposure, adverse drug reactions, and clinical outcomes by integrating real-world clinical data. We are also interested in utilizing AI/ML approaches to streamline the process of a model-informed precision dosing system.

References

Poweleit E, Vinks A, Mizuno T. Artificial Intelligence and Machine Learning Approaches to Facilitate Therapeutic Drug Management and Model-Informed Precision Dosing. Ther Drug Monit, 2023.

Irie K, et al. MACHINE LEARNING MODELING TO PREDICT INFLIXIMAB PHARMACOKINETICS IN PEDIATRIC PATIENTS WITH CROHN'S DISEASE. American Society for Clinical Pharmacology and Therapeutics (ASCPT), 2024 Annual Meeting, Meeting abstract.