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Training towards Personalized Antibiotic Treatment

Periodic Reporting for period 1 - TIPAT (Training towards Personalized Antibiotic Treatment)

Reporting period: 2020-03-01 to 2022-02-28

Antimicrobial resistance (AMR) is an increasing threat to effectively treat serious bacterial infections. To this end, tailoring antibiotic treatments to individual patients is urgently needed to maximize efficacy while minimizing the risk of promoting further resistance. To enable development of innovative personalized antibiotic treatment strategies, quantitative understanding of the interplay between drug, pathogen and host is crucial. The Training towards Innovative Personalized Antibiotic Therapy (TIPAT) network will train inter-disciplinary specialists optimally equipped with a skill set to address this challenge.

TIPAT offers a cutting-edge training-by-research programme for 15 Early Stage Researchers (ESRs) who will investigate translation and integration of biological and pharmacological data related to drug-pathogen-host interactions towards personalized therapies. Central to the TIPAT research and training program is the combination of state-of-the art quantitative modelling with approaches in clinical pharmacology, immunology and microbiology, which enables development of innovative methodology to personalized antibiotic therapies.

The consortium unites a cross-disciplinary team of leading academic experts and inter-sectoral partners including SMEs, pharmaceutical companies and hospitals. Secondments complementary to ESR research projects will promote inter-professional and cross-disciplinary research and communication. The TIPAT integrated program is enriched with transferable, regulatory and entrepreneurial skills training delivered through themed Summer and Winter schools.

The TIPAT network will deliver inter-disciplinary specialists in personalized antibiotic treatment strategies. The TIPAT training will strongly expand career perspectives of ESRs in academia, hospitals, industry or regulatory agencies, and develop scientific innovations to enable optimal therapy for individual patients to address current and future challenges of AMR-infections.
The scientific goals of TIPAT are to 1) characterize the effect of host (patient)-associated factors impacting antibiotic (target-site) exposure; 2) quantify the effect of antibiotic exposure on pathogen-specific killing, resistance and tolerance; and 3) define relationships between host immune response, bacterial infection and antibiotic therapy.

The ESRs started their research projects between the end of 2020 and beginning of 2021. Since then, multiple training activities have taken place, including a Winter & Summer school and multiple themed webinars. At the end of 2021, when many COVID-19 restrictions were lifted, the secondments could also start to take place. In May 2022 we will have our first on site meet-up, during which a combination of scientific, training and social activities will be organized. Our network grew since the start of the project, adding multiple additional partner organizations.

Most ESRs are now in the second year of their PhD, meaning we have come to the end of the scoping phase of the research and move on to the data generation/collection phase. A few ESRs already published some of their results.

With the first results being published, we also started up our dissemination and outreach activities. We place special focus on open access and public outreach. Results of the research are now being shared in oral and poster presentations during scientific congresses. In addition, we launched an introductory TIPAT video and will publish short introductory videos on all individual ESR projects soon. In the upcoming period, we are planning on participating in local science events.
The research performed within the TIPAT network will have a broad application in the field of personalized antibiotic therapy. We aim for our research to:

1) Contribute to studies on the prevention of development of antibiotic resistance and personalized antibiotic strategies;
2) Promote awareness of the importance of exposure/effect relationships and thus optimal dosing for efficacy, clinical outcome and resistance prevention;
3) Further the understanding of the dynamic process of protein binding and characterize relationships between the unbound fraction and total drug concentration.
4) Benefit patients, by better dosing adjustment, and society, by fighting against resistant bacteria selection.
5) Support dose adjustment and decision making in special populations that lack golden treatment outcome.
6) Give clinicians confidence in biomarkers-guided dose individualization and avoid inappropriate use of antibiotics.
7) Emphasize the importance of considering target site PK in establishing dosing strategies, and create awareness of this within scientific and medical communities.
8) Increase understanding of the impact of the immune system on bacterial killing in the presence of antibiotics, which will be of value in future drug development and for treatment of patients that have variable degrees of immunosuppression.
9) Increase the ability to translate results on drug-pathogen interaction from in vitro to animals and humans.
10) Characterize immune signatures in different scenarios of infection and treatments, which will result in putative biomarkers which are predictive of therapeutic success or failure at an early timepoint.
11) Contribute to an overall better understanding of the drug-pathogen-host interactions, which are crucial to establish a foundation for the development of innovative personalized antibiotic treatment strategies.
12) Develop a model that can be a starting point for future translational research on the immune response in sepsis and potential treatment targets. This model could be the basis for potential mechanism-based models predicting other experimental scenarios than those the original model was developed from, and describe the temporal interplays between immune response biomarkers. Furthermore, it could be expanded to include antimicrobial agents and study the host-pathogen-drug interaction which could help optimize antimicrobial therapy is sepsis.
13) Develop models by innovatively integrating PK/PD-relationships with eco-evolutionary dynamics and apply state-of-the-art mathematical modelling approaches. Optimized treatment strategies which account for eco-evolutionary dynamics can improve treatment outcomes of poly- and monomicrobial infections and minimize the risk of antimicrobial resistance.

Ultimately, our research will contribute to better-informed dosing of antibiotics to improve patient outcomes and limit resistance development.
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