Ahmad Rami Ayoun Alsoud
PhD student at Institutionen för farmaceutisk biovetenskap; Forskning; Translationell läkemedelsutveckling; Farmakokinetik och kvantitativ farmakologi
- Telephone:
- +46 18 471 42 56
- Mobile phone:
- +46 72 999 93 26
- E-mail:
- rami.alsoud@uu.se
- Visiting address:
- BMC, Husargatan 3
75124 Uppsala - Postal address:
- Box 591
75124 Uppsala
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Short presentation
I am a PhD student at the Pharmacokinetics and Quantitative Pharmacology Research Group under the supervision of Professor Ulrika Simonsson, specializing the building pharmacometric models in the field of tuberculosis (TB).
My research interest lie in building biomarker frameworks for the prediction of TB biomarkers in the clinical phases of anti-TB drug development. I am also interested in the translational work from the preclinical to the clinical stage.
Keywords
- pharmacodynamics
- pharmacokinetics
- pharmacometrics
- quantitative pharmacology
- tuberculosis
Research
Tuberculosis (TB), a pulmonary bacterial infection caused by Mycobacterium tuberculosis, is
among the top ten causes of death worldwide and is the leading killer of HIV-positive patients. A main reason behind this is the emergence of multi-drug resistant (MDR-TB) and
extensively drug resistant (XDR-TB) TB. This puts emphasis on the need for not only more effective anti-TB drug regimens but also for shorter regimens to increase patient’s compliance and reduce the incidence of further drug resistance development.
To investigate this, pharmacometric models are used to quantify the pharmacological effects of anti-TB drugs including both their efficacy and safety. Using non-linear mixed effects modelling and simulation allows the application of individualized treatment dosing for TB patients. Through pharmacometric models, study designs can be optimized to reduced costs and number of patients involved in the trial. In addition, these models allow the evaluation of combination therapies and the translation of pre-clinical data to contribute of the selection process for the dose to be given in the clinical phases.
Through collaboration with many research groups and consortia, data on the efficacy and safety of a number of investigational drugs for TB are provided. Through this data, along with data and models from previous research, new PK and PKPD models utilizing non-linear mixed effects modelling are built.
Publications
Selection of publications
- Combined quantitative tuberculosis biomarker model for time-to-positivity and colony forming unit to support tuberculosis drug development (2023)
- Predictive Modeling to Study the Treatment-Shortening Potential of Novel Tuberculosis Drug Regimens, Toward Bundling of Preclinical Data (2022)
- Model-informed drug discovery and development strategy for the rapid development of anti-tuberculosis drug combinations (2020)
Recent publications
- Pharmacometric tools to support translational drug development (2024)
- Combined quantitative tuberculosis biomarker model for time-to-positivity and colony forming unit to support tuberculosis drug development (2023)
- Predictive Modeling to Study the Treatment-Shortening Potential of Novel Tuberculosis Drug Regimens, Toward Bundling of Preclinical Data (2022)
- Model-informed drug discovery and development strategy for the rapid development of anti-tuberculosis drug combinations (2020)
- Model-based effect evaluation of a novel Mmpl3 inhibitor in C3HeB/FeJ compared to BALB/c mouse models and translation to humans
All publications
Articles
- Combined quantitative tuberculosis biomarker model for time-to-positivity and colony forming unit to support tuberculosis drug development (2023)
- Predictive Modeling to Study the Treatment-Shortening Potential of Novel Tuberculosis Drug Regimens, Toward Bundling of Preclinical Data (2022)
- Model-informed drug discovery and development strategy for the rapid development of anti-tuberculosis drug combinations (2020)
- Model-based effect evaluation of a novel Mmpl3 inhibitor in C3HeB/FeJ compared to BALB/c mouse models and translation to humans
- Model-based interspecies scaling for predicting human pharmacokinetics of CB 4332, a complement factor I protein
- Model-based approaches to prospectively power pediatric pharmacokinetic trials with limited sample size