In this blog on bridging studies, we provide general best-practices relevant to oncology bridging studies, including basic study design considerations and insights that are the culmination of many years of hands-on experience by Inotiv experts.
In general, attention to the design, execution, analysis, and reporting of preclinical studies has been recognized as an area for improvement by funding agencies and scientists. For instance, the National Institute of Neurological Disorders and Stroke (NINDS) and scientists (e.gs. Ioannidis, 2014; Hooijmans, 2010; Kilkenny, 2010) have outlined some best-practices to help researchers with their preclinical study design. While a comprehensive discussion of this topic is beyond the scope of this article, readers are encouraged to consult one or more of the many publicly available resources prior to beginning preclinical studies to ensure the best information is being leveraged to maximize the success of their studies.
In the context of oncology studies, the primary objective of bridging studies often is to generate the necessary data required to validate (or not) the in vivo anti-cancer efficacy of the NME under investigation. During this process, it is crucial to minimize the possibility of obtaining false positives or false negatives. Inaccurate data can lead to invalid conclusions and erroneously influence downstream decisions.
This is certainly not intended to be an exhaustive list of study design considerations; it is a conceptual guide as to some best practices that should be taken into consideration when bridging a new strain in a preclinical oncology program.
Learn more about the importance of bridging studies when switching strains.
8 best practices for consideration
1. A match for as many general attributes as possible should be achieved between previous study designs that used the existing strain and the bridging studies that rely on the new strain.
Some of the important general attributes to match include method of blinding (if any), gender, strain, age, species, diet, bedding, enrichment, cage density, dose range, surgical protocols (including anesthesia/analgesia), dosing regimen, mode and site of administration of the NME, vehicle, disease state, cell lines, sample times, downstream analyses (including biomarkers), mode of euthanasia, vendor source (if possible), controls, and minimizing inter-variability across technicians and scientists by having the same individuals conduct the bridging studies.
2. Drug pharmacokinetics and pharmacodynamics assessment in the new strain is ideal, especially if these same datasets are available in the existing strain. While this can be a costly and time-consuming undertaking, these data help provide a better understanding of the efficacy and tolerability of the NME in the new strain versus the existing strain. Information such as exposure-response relationships can help assess whether peak plasma levels or area under the curve are required for efficacy in the new strain. Furthermore, these data can be helpful if major differences are observed in efficacy or safety parameters in the new versus existing strain. Overall, these studies provide additional confidence that the existing and new strains demonstrate similar drug behavior.
3. Inclusion of a vehicle-treated control group in addition to the drug treated group(s) is important when conducting PK/PD studies. Evaluation of PD behavior in vehicle-treated control animals can be very important; for instance, this is relevant in cases where biomarkers display circadian variation or when formulation vehicles might influence the pharmacological effect. Additionally, changes in biomarker response after drug treatment are often observed by comparison with control groups.
4. Tumor growth data of cell lines or patient-derived xenograft tumor model (PDX) should be well characterized prior to evaluating the in vivo anti-cancer efficacy of the drug in tumor-bearing animals.
5. Efficacy of the drug in the new strain should be closely monitored by assessing tumor size at regular intervals as well as noting any changes in weight, survival, and other relevant data points, including serological or immunological profiles.
6. The number of animals used for bridging studies should be minimized to adhere to the principle of the 3Rs (replacement, reduction, and refinement). However, the statistical power required to generate robust data that will be able to stand up to scientific and regulatory scrutiny must be taken into consideration. The number of animals required for a bridging study can vary based on the endpoints/biomarkers that are being evaluated, as well as how far along the research or development path the NME has progressed. Based on these variables, bridging studies may require as few as five animals per dose group, but it would not be uncommon to have twenty or more animals per dose group. Early consultation with a biostatistician with expertise in preclinical study design is highly recommended. Resources such as the Experimental Design Assistant (EDA) by NC3R can also be useful when planning a bridging study.
7. Demonstrating that known clinically effective drugs acting on the same target or pathway work in the new strain can provide high predictive validity. These types of assessments can be challenging for several reasons, but in some situations, this may contribute to the predictive validity of the new strain.
8. Early discussions – prior to initiating a study - with experienced partners or collaborators can help set your bridging studies on a trajectory that maximizes success. Experienced partners have likely already encountered many of the common pitfalls associated with bridging studies, and thus they can help facilitate study designs that can circumvent these pitfalls.
Conclusions
The available array of rodent strain options is constantly expanding, and the selection of the right strain for your oncology model is not always straightforward. Researchers may decide to switch rodent strains for various reasons, including the possibility of a more optimal oncology model for specific study purposes. In some cases, researchers may evaluate multiple strains to ultimately arrive at the ideal one for their oncology model.
Bridging studies play an important role in preclinical drug development, since they provide important information that can prevent the need to repeat entire study programs. The basis of these studies is that the existing strain serves as the positive control and its data is bridged to the data generated for the new strain.
By adhering to best-practices for general study design considerations and engaging in early discussions with experienced partners or collaborators, it is possible to enhance the potential success of the oncology preclinical program. From our comprehensive online resources through to our model selection consultants - talk to us about your oncology portfolio needs from start to finish.
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