Alfa Cytology has launched its Next-Gen PrimePDX™ platform to strengthen preclinical cancer modeling from in vitro to in vivo stages, addressing a persistent challenge in immuno-oncology research where traditional tumor models often fail to fully reproduce immune responses. This limitation has made it difficult for researchers to predict how treatments will behave during preclinical testing, particularly as immunotherapy continues to grow as a cancer treatment approach.
The platform provides a system that shows tumor and immune interactions more clearly and allows studies that better reflect immune-related treatment effects in preclinical settings. Traditional PDX models frequently fall short when immune activity is important, limiting their usefulness in immuno-oncology research. PrimePDX™ solves this by adding human peripheral blood mononuclear cells to create a functional immune system in mice.
Small tumor fragments or early-passage tissues are used to keep tumor structures intact and maintain diversity. Human cancer-associated fibroblasts can also be added to reproduce key features of the tumor microenvironment when needed. Tumor growth and immune reconstitution are monitored throughout the study, and optional IVIS imaging allows non-invasive tracking of tumor progression.
PrimePDX™ is suitable for testing checkpoint inhibitors, antibody therapies, cell therapies, cancer vaccines, and other immune-based treatments. When used together with Alfa Cytology's in vitro platforms, researchers can first screen treatments in the lab, then confirm effects in animal models, and finally evaluate whether effects observed in lab studies are consistent in animal models. This integrated approach helps teams track tumor-immune interactions and assess the potential of therapeutic candidates and combination strategies in preclinical research.
"In immuno-oncology research, having models that accurately represent immune responses is crucial," said a project lead at Alfa Cytology. "PrimePDX™ provides a controlled setting to observe tumor growth, immune cell behavior, and treatment effects. This helps scientists to improve study designs, evaluate combination approaches, and make more confident decisions before further preclinical testing."
The company has been developing in vitro and in vivo models of cancer for years, including cell line-derived models, 3D cultures, cancer type-specific panels, and multiple animal models. These tools are used in drug testing, studying how treatments work, validating targets, assessing drug distribution, and tracking resistance. Combined, they assist research teams in starting with lab experiments and then confirming results in animal models, giving a clearer view of treatment effects at each stage before moving toward further preclinical studies.
This development matters because it addresses a fundamental gap in cancer research methodology. As immunotherapies become increasingly central to cancer treatment, the ability to accurately model immune responses in preclinical settings directly impacts drug development efficiency and success rates. Pharmaceutical companies and research institutions can potentially reduce costly late-stage failures by identifying promising candidates earlier and with greater confidence. For patients, this could translate to more effective treatments reaching clinical trials and eventually the market, potentially improving outcomes for various cancer types. The platform's ability to model combination strategies is particularly significant as combination therapies often show greater efficacy than single-agent treatments in oncology.
