Predictive Pharmacokinetics and Pharmacodynamics
Our research in Predictive Pharmacokinetics and Pharmacodynamics focuses on characterizing and quantifying dose-concentration-effect/toxicity relationships for new and available drugs against bacterial infections and various types of cancer.
Our goal is to develop models and methods that can predict which dosing strategies that provide most information in experimental and clinical studies and the best effect – with tolerable side effects – in clinical practice. We improve the interpretation of data by integrating in vitro, in vivo and clinical information via pharmacokinetic-pharmacodynamic (PKPD) modeling.
The developed methodology is expected to facilitate drug development and to identify optimal treatment therapy using existing drugs.
Antibiotic resistance is rapidly rising globally. In order to overcome and counteract the emergence of resistance, we contribute to the rational development of new therapies, for example with strategies where several antibiotics are administered in combination. The overall goal is to develop robust methods, based on preclinical data, that can predict clinical outcome variables for therapies against serious infections caused by difficult-to-treat pathogens.
We develop mechanism-based PKPD models that describe bacterial growth, killing and resistance development. In these analyses, in vitro and in vivo data are integrated to predict effects over time of different dosing regimens that can then be linked to different types of clinical data.
We are also investigating how the immune system and biomarkers are linked to the bactericidal effect and can be used for scaling between different animal species.
- Lena E Friberg. Pivotal Role of Translation in Anti-Infective Development Clinical Pharmacology & Therapeutics, 2021.
- Iris K Minichmayr, Vincent Aranzana-Climent, Lena E Friberg. Pharmacokinetic/pharmacodynamic models for time courses of antibiotic effects. International Journal of Antimicrobial Agents, September 2022.
- Vincent Aranzana-Climent, Diarmaid Hughes, Sha Cao, Magdalena Tomczak, Malgorzata Urbas, Dorota Zabicka, Carina Vingsbo Lundberg, Jon Hansen, Johan Lindberg, Sven N Hobbie, Lena E Friberg. Translational in vitro and in vivo PKPD modelling for apramycin against Gram-negative lung pathogens to facilitate prediction of human efficacious dose in pneumonia. Clinical Microbiology and Infection, October 2022.
- Anders Thorsted, Elisabet I Nielsen, Lena E Friberg. Pharmacodynamics of immune response biomarkers of interest for evaluation of treatment effects in bacterial infections. International Journal of Antimicrobial Agents, September 2020.
Researchers Bacterial infections: Lena Friberg, Iris Minichmayr, Diego Vera, Irene Hernández-Lozano, Viktor Rognås, Chenyan Zhao, Amaury O'Jeanson, Raphael Saporta, Haini Wen.
The number of cancer therapies under development has rapidly increased in recent decades. This places high demands on being able to predict the clinical potential of a new therapy at an early stage where desired and unwanted effects must be weighed against each other in a complex biological system.
We develop PKPD models that describe time courses for a number of different variables related to treatment with chemotherapeutic drugs as well as targeted and immune therapies. We characterize relationships between dose, concentration, biomarkers, side effects, tumor size (diameters, volumes), tumor and immune response activity, patient-reported evaluation of the therapy (PRO) and survival. These models are integrated to define a dosing strategy that best balances adverse effects, quality of life and survival.
We also develop methods that aim to improve drug therapy in children with cancer by minimizing the risk of serious side effects without simultaneously increasing the risk of relapse. We are also investigating which time-varying measures of tumor size and biomarkers that can best predict survival.
To optimize translation (scaling) from preclinical to clinical settings, we build mechanistic models for advanced treatments such as therapeutic vaccines and combination therapies of bispecific antibodies. The models are intended to be of value in supporting the development of new and existing drug treatments, and to be a tool for dose adjustments for an individual patient.
- Sreenath M Krishnan, Lena E Friberg. Bayesian forecasting of tumor size metrics and overall survival. CPT: Pharmacometrics & Systems Pharmacology, December 2022.
- Ida Netterberg, Mats O Karlsson, Leon W M M Terstappen, Miriam Koopman, Cornelis J A Punt, Lena E Friberg. Comparing Circulating Tumor Cell Counts with Dynamic Tumor Size Changes as Predictor of Overall Survival: A Quantitative Modeling Framework. Clinical Cancer Research, September 2020.
- Maddalena Centanni, Sreenath M Krishnan, Lena E Friberg. Model-based Dose Individualization of Sunitinib in Gastrointestinal Stromal Tumors. Clinical Cancer Research, September 2020.
Researchers Oncology: Lena Friberg, Maddalena Centanni, Eman Ibrahim, Han Liu, Daniel Centanni, Javier Sanchez Fernandez.
Professor of Pharmacokinetics and Pharmacodynamics
Department of Pharmacy
- Unfortunately there are no upcoming events at this time
Yin, Anyue; van Hasselt, Johan G. C.; Guchelaar, Henk-Jan; Friberg, Lena; Moes, Dirk Jan A. R.
Anti-cancer treatment schedule optimization based on tumor dynamics modelling incorporating evolving resistance
Krishnan, Sreenath M.; Friberg, Lena E.
Bayesian forecasting of tumor size metrics and overall survival
Arrazuria, Rakel; Kerscher, Bernhard; Huber, Karen E.; Hoover, Jennifer L.; Lundberg, Carina Vingsbo; Hansen, Jon Ulf; Sordello, Sylvie; Renard, Stephane; Aranzana-Climent, Vincent; Hughes, Diarmaid; Gribbon, Philip; Friberg, Lena; Bekeredjian-Ding, Isabelle
Expert workshop summary: Advancing toward a standardized murine model to evaluate treatments for antimicrobial resistance lung infections