About the Journal
Quantitative Medicine (QM) is a pioneering, fully open-access online journal devoted to advancing the development and application of quantitative methods in medicine. We have adopted the term “Quantitative Medicine” as a more inclusive reflection of the discipline that brings together the scientists, practitioners, and decision-makers engaged in the application of quantitative methods in the fields of clinical pharmacology, pharmacy, pharmacology and toxicology, translational sciences, medicine, drug discovery and development, regulatory assessment, health economics and outcomes research.
As the first official journal of ISoP, QM is grounded in our commitment to fostering scientific excellence and global collaboration in our field. This journal will serve as a platform to disseminate rigorous, innovative, and impactful research to inform drug discovery, development, regulatory decision-making, and pharmacotherapy. The journal will feature a multitude of formats for communicating novel quantitative methods, drug development and regulatory applications, perspectives, reviews, and tutorials throughout the life-cycle of drug development and clinical practice globally. The inaugural issue will provide a flavor of what this flagship journal of ISoP is intended to be.
Our Commitment to Scientific Excellence
ISoP champions a scientific culture rooted in transparency, rigor, and collaboration. QM reflects our commitment to data-driven insights and impact. Publishing with QM means joining a movement toward more transparent, impactful, and data-driven science. We stand for:
- Data Driven Insights
- Peer Reviewed Rigor
- Global Collaboration
- Therapeutic Impact
Aims & Scope
This cross-disciplinary journal is devoted to advancing the development and application of quantitative methods in medicine. It fosters global innovation and collaboration among scientists focused on the development, regulatory assessment, and therapeutic use of medicines.
QM showcases a diverse range of scientific content, including but not limited to:
- Pharmacometrics: Pharmaco-statistical, mechanistic, and systems biology/pharmacology modeling.
- Simulation-Based Approaches: Clinical trial simulations and digital twins.
- AI/ML and Informatics: Applications of artificial intelligence, machine learning, and data science.
- Integrated Evidence Synthesis: Model-based meta-analyses, causal inference, real-world evidence, network analyses, and health economics and outcomes research.
It highlights contributions such as:
- Development of novel methods and tools
- Standalone or integrated applications
- Insights from applied quantitative approaches for decision and information analysis
- Best practices and tutorials
- Regulatory perspectives and considerations