Clinical Pharmacology Intern – Oncology

Internship Overview:
We are seeking a highly motivated graduate level intern to support translational PK-PD modeling efforts focused on antibody, drug conjugates (ADCs) containing the vedotin payload.
This internship will investigate the mechanistic and translational drivers of variability in preclinical tumor static concentration (TSC) estimates across vedotin ADCs and evaluate modeling strategies to improve interpretation of preclinical efficacy signals.
The intern will work closely with clinical pharmacology and translational modeling scientists to inform early ADC candidate selection and development decisions.

Project Description:
Vedotin containing ADCs often demonstrate broadly similar systemic pharmacokinetics and safety profiles across targets; however, xenograft PK/PD modeling has revealed substantial variability in preclinical tumor static concentration (TSC) estimates across different vedotin ADCs.
Emerging internal and limited clinical observations suggest that ADCs with lower preclinical TSC ranges may demonstrate greater translational promise than those with higher TSC estimates, raising questions about the mechanistic drivers and translational relevance of TSC as a preclinical efficacy metric.

This project will evaluate translational modeling strategies to explain variability in preclinical TSC estimates using exclusively in vitro and in vivo preclinical data.
The intern will integrate xenograft efficacy models with mechanistically relevant ADC specific attributes and explore alternative or augmented modeling frameworks to better contextualize TSC across vedotin ADCs.

Key Responsibilities:
Curate, harmonize, and quality check in vitro and in vivo preclinical datasets across multiple vedotin ADCs.
Standardize xenograft PK/PD model structures and tumor static concentration (TSC) estimation approaches across compounds.
Perform comparative multivariate and model based analyses to identify key drivers of TSC variability.
Evaluate existing translational PK/PD models and their assumptions across comparable experimental conditions.
Explore alternative mechanistic, semi mechanistic, or systems informed translational modeling strategies to rationalize observed TSC differences.
Interpret findings in the context of preclinical translational relevance and early ADC decision making.
Prepare a final technical summary and presentation outlining modeling results and recommendations.

Required Qualifications:
Enrollment in a graduate program (MS, PhD, PharmD, or equivalent) in pharmacometrics, clinical pharmacology, biomedical engineering, biostatistics, quantitative biology, or a related field.
Strong quantitative and analytical skills with demonstrated interest in translational PK/PD modeling
Experience programming and analyzing data in R (or equivalent statistical software)
Familiarity with PK/PD concepts, multivariate analysis, and model based data interpretation
Ability to synthesize complex quantitative results and communicate findings clearly to scientific audiences.

Preferred Qualifications:
Prior experience with PK/PD or mechanistic modeling, particularly in oncology or biologics.
Familiarity with xenograft efficacy modeling and preclinical tumor growth models (PK-TGI).
Exposure to antibody, drug conjugates (ADCs), payload pharmacology, or target mediated drug disposition.
Experience integrating in vitro and in vivo data to support translational modeling.
Interest in model informed drug development (MIDD) and early portfolio decision making.

Learning Outcomes:
By the end of this 3 month internship, the intern will gain hands on experience in translational modeling of ADCs, understand key drivers of preclinical efficacy metrics such as TSC, and contribute to best practice recommendations for interpreting preclinical PK/PD data to support early oncology development decisions.