Internship in Pharmacometrics

391079BR

**Internship in Pharmacometrics**:
Switzerland

**About the role**

At Novartis, we are reimagining medicine to improve and extend people's lives. We have one of the most diverse product pipelines in the pharmaceutical industry and performance-driven professionals are bringing innovative medicines to life.
The department of Pharmacometrics at Novartis will have an internship position available.
The internship will focus on using pharmacometric PKPD modeling and simulation for causal inference.
Situation: Causal questions, such as what treatment effect should we expect if we administer a drug to a patient population are ideally addressed with randomized clinical trials (RCTs). Sometimes, the (causal) clinical question of interest cannot be answered by relying on randomization alone, and randomization must be complemented by causal assumptions and analysis methods. Over the last few decades, causal inference has been established as a formal framework to answer causal questions mostly in non-randomized setting. Similarly, pharmacometrics has been established over the last few decades to characterize the causal relationship between treatment, PK exposure, and response. Despite the apparent important overlap of pharmacometrics and causal inference, it is only recently that the two approaches have been discussed together.
What has already been done: PMX modeling and simulation has been positioned as a particular implementation of g-computation (Rogers et al., 2023, CPT: Pharmacometrics & Systems Pharmacology, 12, 27-40). Non-linear mixed effects modeling as commonly used for PK and PKPD modeling has been discussed as a particular variant of g-computation that conditions on random effects (manuscript in preparation).
Your responsibilities:

- This internship will focus on further establishing pharmacometric PKPD modeling and simulation for causal inference. The potential should be evaluated to correct for confounding by conditioning on random effects as implemented in commonly used PKPD modeling. The evaluation should include assessments of exchangeability using causal diagrams and simulation estimation experiments.
- Work on this evaluation under the guidance of senior-level quantitative scientists
- Setup simulation estimation experiment using R, execute it, and analyze results.
- Contribute to assessments of exchangeability using causal diagrams.
- Attend seminars and other activities to enhance the understanding of the drug development process.
- Summarize results in a document and present your project results to quantitative scientists and other stakeholders.

Start : flexible from April to September
Duration : 3 months
LI-Hybrid

**Commitment to Diversity & Inclusion**:

- We are committed to building an outstanding, inclusive work environment and diverse teams representative of the patients and communities we serve._

**Role Requirements**

What you’ll bring to the role:

- Current students enrolled in a Bachelor, Masters or PhD degree
- Good knowledge of R for statistical data analysis is required, some knowledge of non-linear mixed effects modeling is an advantage.

This internship will provide motivated students an insight into the pharmaceutical industry, along with the opportunity to work in an exciting, multi-disciplinary and multi-cultural environment with senior-level scientists.

Commitment to Diversity and Inclusion / EEO:
Novartis is committed to building an outstanding, inclusive work environment and diverse teams repre-sentative of the patients and communities we serve.
Accessibility and accommodation:

Join our Novartis Network:
**Division**

Development

**Business Unit**

CD&A GDD

**Work Location**

Basel

**Company/Legal Entity**

Novartis Pharma AG

**Functional Area**

Interns/Students on Novartis Payroll

**Job Type**

Full Time

**Employment Type**

Internship

**Shift Work**

No

**Early Talent**

Yes