Traditional approaches to clinical drug development have created a critical gap between preclinical research and patient applications. Only the drugs that pass big clinical trials reach to the clinic, and a potentially heterogeneous group of patients end up receiving the same treatment. With personalized medicine receiving increasing attention in the life sciences industry, pharmaceutical companies are reconsidering their principles based on data-driven approaches. This enables them to better adapt drug development to patient variability.

Co-clinical development describes the process of extrapolating disease outcomes based on preclinical models to patients and back. At the basis of co-clinical development are preclinical models such as patient-derived xenografts (PDX) and patient-derived organoids (PDO) in which patient material, e.g. from a biopsy, is either implanted in mice or brought into culture to create stable models for preclinical testing. The main goal here is to preserve and represent the scope of patient variability across them. To bridge the gap between preclinical research, using patient-derived models, and co-clinical development a continuous data flow needs to be established. By collecting the same dataset at patient, PDX and PDO stage, similarities and divergences can be reviewed. This includes treatment responses, model/patient history, imaging, histopathology, biomarkers, as well as -omics.

Guiding personalized medicine treatments with patient-derived models can be achieved using three different approaches. In a proactive method the molecular profiles of patients are characterized and matched to those profiles found within specific patient-derived models. This way a more informed decision can be made on individual treatment applicability. Secondly, a parallel approach may be taken. Here, treatment regimens are steered in real-time based on feedback that is acquired in a patient-derived model that is run simultaneously. Lastly, a retrospective approach may be taken in which data from patient trials and patient derived models may be used in translational research to improve future treatments.

Advancements in high-throughput screening capabilities, including high-content imaging-, biomarkers-, and omics-data means that the evidence pack available to researchers becomes more diverse and much deeper. This also means that the amount of data that needs to be processed is greatly increasing, requiring new approaches to streamlining the generation and processing of data. By using big data approaches to standardize and connect, e.g. using smart-linking and pattern analysis approaches, InnoSer aims to create a bridge between its PDX/O platform and clinical data to ultimately benefit the patient.

InnoSer is developing its solid-tumor PDX/O platform in collaboration with the Hasselt University, Jessa Hospital, VUB and LUMC. In addition, InnoSer is working on expanding this platform towards material including liquid tumors (non-Hodgkin lymphoma). InnoSer focuses on cancers with a high-unmet medical need; tumors exhibiting high tumor mutational load/burden (TMB) and associated with poor patient survival. We’re seeking early adopters of the PDX/O platform to validate it collaboratively.

Ready to advance this innovative approach by collaborating with us?