QSP Modeling Intern — Oncology Therapeutics

Nova In Silico company; Lyon; Ibrido; EUR 10.000 - 15.000; Tempo pieno

Vantaggi offerti da questo lavoro

Competitive salary

Flexible start date

Descrizione del lavoro

Nova In Silico is a health tech company in Lyon developing an in silico clinical trial platform, jinkō, to simulate drug efficacy and optimize clinical development using virtual patients and disease modeling. We offer a dynamic environment with significant responsibility for interns and a steep learning curve.

As part of a multidisciplinary team, you will conduct literature reviews, implement model components, calibrate to data, and evaluate model performance, gaining hands-on experience in QSP

Competenze

• Strong foundations in biology and pharmacology relevant to QSP modelling.
• Ability to perform literature reviews and extract actionable data.
• Experience with mathematical modeling and data analysis is a plus.

Mansioni

• Conduct a focused literature review to identify frameworks and parameters.
• Implement the new model component within the jinkō platform.
• Extract and curate publicly available data for model informing.
• Calibrate the model by fitting to curated data using optimization.
• Evaluate model performance via sensitivity analyses.

Conoscenze

Team player

Curious and proactive

Autonomous and self-motivated

Eager to learn

Willing to explore

English proficiency

Formazione

Biology/biochemistry/pharmaceutical sciences background

Strumenti

R

Python

Descrizione del lavoro

Nova In Silico is a health tech company that develops an in silico clinical trial platform jinkō to simulate drug efficacy and optimize clinical development using virtual patients and disease modeling. As an innovative company, we offer a dynamic work environment distinct from larger, established organizations. Interns will gain significant responsibilities and benefit from a steep learning curve, supported by a highly motivated team.

KEYWORDS

Quantitative Systems Pharmacology, Biomodelling, Drug R&D, QSP Modeling, Oncology

BACKGROUND

Quantitative Systems Pharmacology (QSP) is a cornerstone of modern, data-driven drug development. It utilizes mechanistic mathematical models to simulate the complex, dynamic interactions between a drug, the human body, and the disease process. At Nova In Silico, we specialize in building these high-fidelity QSP models to help de-risk and accelerate the path of new medicines to the clinic.

The field of oncology, a primary focus of our work, is currently experiencing a profound revolution. The therapeutic landscape has expanded far beyond traditional small-molecule chemotherapy. We are now seeing the rise of novel therapeutic modalities, each with its own unique and highly complex mechanism of action. These include:

• Immunotherapies (e.g., checkpoint inhibitors, bispecific T-cell engagers)
• Antibody-drug conjugates (ADCs)
• Cell-based therapies (e.g., CAR-T)

These therapies do not operate on simple "target-binding-effect" principles. Their efficacy and safety are governed by intricate, multi-scale biological processes, such as immune cell trafficking and activation, competition for target binding, tumor microenvironment interactions, and complex intracellular dynamics.

To accurately predict the clinical behavior of these innovative drugs, our QSP models must evolve. Standard pharmacokinetic/pharmacodynamic (PK/PD) models are often insufficient to capture this new biology. Therefore, a critical R&D objective for our team is to develop, validate, and internalize a robust library of model components specifically designed for these novel modalities. Building this internal library will enhance our platform's capabilities, allowing us to more rapidly and accurately build next-generation QSP models for our internal and client-facing projects.

OBJECTIVE

Contribute directly to the strategic expansion of our internal QSP model library for novel oncology therapeutics. The intern will be responsible for the end-to-end development and/or the improvement of a mechanistic model for a specific, high-priority drug class.

YOU ARE

• team player, a good listener, and an effective communicator: Join a growing multidisciplinary team of enthusiastic innovators
• Curious and proactive with a solid grounding in biology: Tackle real-life clinical challenges using knowledge in cellular and molecular biology
• Autonomous and self-motivated with strong analytical and problem-solving skills: Find innovative solutions to science and engineering problems
• Eager to learn and use mathematical methods for the modeling of biological systems: Simulate virtual diseases and treatments with ODEs, DDEs, Monte-Carlo simulations
• Willing to explore and exploit large datasets and virtual populations: Apply machine learning and statistical methods

YOU WILL

• Conduct a focused literature review to identify and synthesize established mathematical frameworks, key biological mechanisms, and relevant physiological parameters
• Implement the new model component within our in-house QSP modeling platform jinkō
• Identify, extract, and curate publicly available data (e.g., from preclinical in vitro/in vivo studies or early-phase clinical trial publications) suitable for informing the model
• Calibrate the model by fitting it to this curated dataset, using optimization algorithms to estimate key unknown parameters
• Perform a thorough model evaluation and validation (e.g., perform parameter sensitivity analyses to identify key model drivers)

METHODOLOGY AND TECHNICAL SKILLS

We mainly use internal tools (jinkō platform) for creating the models, and R for result analysis. We are looking for people who know some of the following fields or are eager to learn and work with them:

• Mechanistic modeling, ODEs, PK-PD
• Scientific computing and statistics (R, Python)
• Knowledge in biology, biochemistry and/or pharmaceutical sciences
• Knowledge in machine learning and optimization techniques (e.g. SAEM, gradient descent)
• Knowledge in clinical trials and drug development

A professional English level (written and oral) is required for this role.

PRACTICAL INFORMATION

• Apply directly on our careers page
• Contact: [e-mail]
• Salary: Competitive
• Start date: Flexible