DC 16: Muskan Madan
Muskan Madan (India) is a budding researcher in pharmacometrics and systems pharmacology, with a strong foundation in PK/PD modelling, simulation, and translational drug development. She holds a master’s degree in drug discovery and development from Uppsala University, Sweden, and a bachelor’s in pharmacy from DIT University, India. She has applied tools such as NONMEM and various R packages, including mrgsolve, to optimize preclinical study designs in infectious disease pharmacology.
Her research includes semi-mechanistic modelling of antibiotic therapies, optimization of dose fractionation designs, and stochastic simulations to evaluate the impact of study design on PK/PD indices and parameter estimation across different antibiotic classes. This work resulted in a first-author publication in JAC-Antimicrobial Resistance and laid the groundwork for her continued interest in pharmacometrics.
In 2025, Muskan joined the European doctoral network as DC16 in Work Package 3, focusing on signalling network modeling of the mTOR pathway and PK/PD modeling of drug candidates. Her work bridges systems biology and clinical pharmacology to support model-informed drug development strategies. Muskan also co-founded Anko Bun Buddies, a student mental health initiative at Uppsala University.
Planned secondments: 2 months at EKUT for data evaluation and initiation of PK-PD modelling of S6K2 inhibitors. 2 months at UniBas for ata evaluation and initiation of PK-PD modelling of mTOR/PI3Kα inhibitors, 2 months at ICM for modelling of the mTOR pathway based on new gene discovery.
Publication
ORCID
University of Tübingen
2 monthsTübingen, Germany
University of Basel
2 months Basel, Switzerland
Institut du Cerveau
2 monthsParis, France
My research project
DC16 will predict the metabolic stability and blood brain barrier penetration of compounds from existing S6K2 and PI3Kα inhibitor libraries (EKUT-Gehringer and UNIBAS-Wymann). Since most of these compounds are targeted covalent inhibitors with a certain intrinsic reactivity, DC16 will place particular emphasis on the generation of suitable models for prediction of extrahepatic clearance. The simulations and models will support strategic decision making by prioritizing compounds for further evaluation. Moreover, DC16 will pursue predictive PK/PD modeling of promising virtual hit structures from structure-based design to select synthesis candidates. DC16 will also perform toxicity prediction and prediction of BBB penetration. Since PROTAC degraders (synthesized by EKUT-Gehringer) are particularly difficult to optimize in terms of ADMET properties due to their high molecular weight and (so far) limited training data, DC16 will use the specific PD Value expertise to establish tailor-made models enabling the simulation of the in vivo properties of such compounds. Jointly with Ukessen-Thedieck, PD value has linked their PK-PD models to ODE based dynamic models of the mTOR network to predict altered mTOR network dynamics upon physiological inhibitor concentrations. DC16 will link PK-PD outputs on S6K2 and PI3Kα inhibitors to ODE mTOR network models parameterized on dynamic data from TSC1 versus TSC2 deficient cell models and patients. Hence, DC16 will simulate the physiological drug response upon TSC1/2 deficiency.
PhD enrolment : 
DC 15: Sveva Pidello