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Prof. Dr. med. Marc Pfister

Department of Clinical Research
Profiles & Affiliations

Pediatric Pharmacometrics (PMX)-AI Research Group

The Pediatric PMX-AI Research Group at UKBB, DKF, University of Basel is inventing, translating, evaluating, and implementing intelligent digital health solutions and child-friendly formulations to improve and save lives of pediatric patients worldwide. 

UKBB level – As a result of a strong partnership with pediatric endocrinology (Prof. Gabor Szinnai) the digital health project OptiThyDose was granted ~1.0M by BRCCH to develop and clinically evaluate clinical decision support tools for children with a thyroid disease (https://brc.ch/research/optithydose/).

University of Basel level – While mentoring several master students and six PhD students we have published more than 100 peer-reviewed papers in 2-3 years. We have strengthened our partnerships with the Department of Pharmaceutical Sciences (Prof. Joerg Huwyler, Prof. Christoph Meier) and Swiss TPH (Prof. Jennifer Keiser). The latest joint research project with Swiss TPH was granted ~0.8M by the Bill & Melina Gates Foundation. 

National level – We lead SwissPedPha, serve in the SwissPedNet board and contribute to expert teams of SwissPedDose and PEDeDose. We partner with several Swiss research centers such as the SwissPedNet hub in Lucerne (Dr. Michael Buettcher). 

International level – A joint project with the University of Oxford/MORU Tropical Health Network was launched to evaluate the novel, child-friendly formulation of ivermectin CHILD-IVITAB in children <15 kg (EPIC-15). EPIC-15 was granted ~0.5M by the Thrasher Research Fund.

Start-up companies – NeoPrediX AG was established to facilitate implementation of PMX-AI based digital health solutions in perinatal medicine (www.neopredix.com). Galvita AG was founded to bring child-friendly formulations such as CHILD-IVITAB to pediatric patients not just in Switzerland but worldwide, including low- and middle-income countries (www.galvita.com). Further, EqVita AG was recently founded to develop PMX-AI based decision support tools to facilitate personalized care of pediatric and adult patients worldwide.

Selected Publications

Bräm, D. S., Koch, G., Allegaert, K., van den Anker, J., & Pfister, M. (2024). Applying Neural ODEs to Derive a Mechanism-Based Model for Characterizing Maturation-Related Serum Creatinine Dynamics in Preterm Newborns. Journal of Clinical Pharmacology, 64(9), 1141–1149. https://doi.org/10.1002/jcph.2460

URLs
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Golhen K, Buettcher M, Kost J, Huwyler J, & Pfister M. (2023). Meeting Challenges of Pediatric Drug Delivery: The Potential of Orally Fast Disintegrating Tablets for Infants and Children. Pharmaceutics, 15(4). https://doi.org/10.3390/pharmaceutics15041033

URLs
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Steffens, Britta, Koch, Gilbert, Gächter, Pascal, Claude, Fabien, Gotta, Verena, Bachmann, Freya, Schropp, Johannes, Janner, Marco, l’Allemand, Dagmar, Konrad, Daniel, Welzel, Tatjana, Szinnai, Gabor, & Pfister, Marc. (2023). Clinically practical pharmacometrics computer model to evaluate and personalize pharmacotherapy in pediatric rare diseases: application to Graves’ disease. Frontiers in Medicine, 10. https://doi.org/10.3389/fmed.2023.1099470

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Nahum U, Refardt J, Chifu I, Fenske WK, Fassnacht M, Szinnai G, Christ-Crain M, & Pfister M. (2022). Machine learning-based algorithm as an innovative approach for the differentiation between diabetes insipidus and primary polydipsia in clinical practice. European Journal of Endocrinology, 187(6), 777–786. https://doi.org/10.1530/EJE-22-0368

URLs
URLs

Koch, Gilbert, Steffens, Britta, Leroux, Stephanie, Gotta, Verena, Schropp, Johannes, Gächter, Pascal, Bachmann, Freya, Welzel, Tatjana, Janner, Marco, L’Allemand, Dagmar, Konrad, Daniel, Szinnai, Gabor, & Pfister, Marc. (2021). Modeling of levothyroxine in newborns and infants with congenital hypothyroidism: challenges and opportunities of a rare disease multi-center study. Journal of Pharmacokinetics and Pharmacodynamics, 48(5), 711–723. https://doi.org/10.1007/s10928-021-09765-w

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URLs

Selected Projects & Collaborations

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OptiThyDose: Intelligent Digital Decision Support Tool to Personalise Dosing for Children with Thyroid Diseases

Research Project  | 8 Project Members

Thyroid hormones are critical for normal brain development, growth, and puberty. Hypo-/hyperthyroidism manifests at birth or during childhood. Thus, prompt, adequate medical treatment is crucial to protect cognitive and physiological development in affected children. Current guidelines recommend standard dosing regimens, despite a wide spectrum of disease variability in terms of severity, activity and receptivity. Over- and under-dosing is frequent, making a decision support tool for optimal and fine-tuned personalised dosing essential for adequate medical care.

The aim of this project is to provide personalised treatment plans for children with a thyroid disease. The consortium will build upon their existing work on an intelligent decision support tool that optimises and computes personalised treatment dosing. The researchers will use computer models that consider individual disease characteristics obtained from observational studies in combination with optimal dosing algorithms to develop an intelligent clinical decision support tool “OptiThyDose” to personalise and optimize dosing regimens. OptiThyDose iteratively computes individual dose regimens based on the patient's age, weight and disease severity to restore and maintain thyroid hormone balance.

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The THY-MOD Study - Personalized Dosing in Children with Hyper- or Hypothyroidism Computed by Mathematical Modeling

Research Project  | 6 Project Members

Thyroid hormones are essential for normal brain development in the foetus and the infant, and for normal cognitive functions, normal growth and puberty in children and adolescents. However, treatment of thyroid diseases (congenital and acquired hyper- or hypothyroidism) is difficult in neonate, infants and children. First, a carefully selected initial dose based on clinical experience is necessary. Second, after reaching a physiological balance of thyroid hormones, a continuous adjustment of the individual dose depending on age and severity of thyroid disease is required to maintain euthyroidism during childhood and adolescence. To mitigate the risk of negative neurological and developmental outcome such as cognitive impairment, it is essential to establish an optimal, personalized dosing strategy that is continuously fine-tuned to account for specific needs in neonates, infants and children with hyper- or hypothyroidism. Dynamical mathematical models based on population pharmacokinetic / pharmacodynamic (PKPD) principles with individual covariate effects together with algorithms from optimal control theory will be developed and applied to retrospective longitudinal disease measurements from 5 Swiss paediatric hospitals. Developed models and algorithms will be validated based on prospective collected data in a large international paediatric thyroid centre. This will allow the prediction of an optimal personalized dosing strategy that maintains thyroid hormones in the normal reference range. Models and algorithms to compute optimal personalized dosing in neonates, infants and children are a medical necessity to optimize long-term neurological outcome and consequently enhance performance in school and professional education, and to increase life quality of paediatric patients and their parents.