Predictive Pharmacokinetics and Pharmacodynamics


Our Research

Our research in Predictive Pharmacokinetics and Pharmacodynamics focuses on characterizing and quantifying dose-concentration-effect/toxicity relationships for new and existing drugs against bacterial infections and various types of cancer.

Our goal is to develop models and methods that can predict which dosing strategy provides the most information in experimental and clinical studies and the best effect—with tolerable side effects—in clinical practice. We improve data interpretation by integrating in vitro, in vivo, and clinical information through pharmacokinetic-pharmacodynamic (PKPD) modeling.

The developed methodology is expected to facilitate drug development and the identification of optimal treatment therapy using existing drugs.

Current Research

Bacterial Infections

Antibiotic resistance is a rapidly growing global problem. To overcome and counteract the emergence of resistance, we contribute to the rational development of new therapies, such as strategies where multiple 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 severe 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 the effects over time of different dosing regimens, which can then be linked to various types of clinical data.

We also investigate how the immune system and biomarkers are linked to the bactericidal effect and can be used for scaling between different animal species.

Selected Publications

Researchers Bacterial Infections: Lena Friberg, Romain Aubry, Diego Vera, Irene Hernández-Lozano, Viktor Rognås, Chenyan Zhao, Amaury O’Jeanson, Raphael Saporta, Haini Wen.

Oncology

The number of cancer therapies under development has increased significantly 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 undesired effects must be weighed against each other in a complex biological system.

We create PKPD models that describe time profiles for a range of variables related to treatment with cytostatics and so-called targeted and immunotherapies. We characterize the 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 side 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 severe side effects without simultaneously increasing the risk of relapse. We also investigate which time-varying measures of tumor size and biomarkers can best predict survival.

To optimize translation (scaling) from preclinical to clinical, we build mechanistic models for advanced treatments such as therapeutic vaccines and combination therapies with bispecific antibodies. The models aim to be valuable for supporting the development of new and existing drug treatments and to be a tool for dose adjustments for an individual patient.

Selected Publications:

Researchers Oncology: Lena Friberg, Maddalena Centanni, Eman Ibrahim, Han Liu, Daniel Centanni, Javier Sanchez Fernandez.

Research Group Leader

Lena Friberg
Professor in Pharmacokinetics and Pharmacodynamics
Department of Pharmacy
Uppsala University
Lena.Friberg@uu.se
+46 (0)70-4250203
Mer information

Research Group Predictive Pharmacokinetics and Pharmacodynamics

Research Group Predictive Pharmacokinetics and Pharmacodynamics

Contact

  • Visiting Address: BMC, Husargatan 3, A1:2, A2:2, A3:3, B3:3, B3:4, C2:2
  • Letter and Postal Address: Box 580, SE-751 23 Uppsala

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