Developing medicines involves overcoming many hurdles - from designing preclinical tests to fulfilling regulatory demands as the understanding evolves. Biotech companies must thoughtfully plan each step to navigate this complex, changing process. However, as industry experts Steven Zelenkofske, Benit Maru and Adriana Valenciano discussed in SSI's webinar on optimizing preclinical programs, taking a strategic approach rooted in comprehensive yet adaptable planning can help address obstacles.

Accurately predicting human outcomes from animal models is difficult as they often do not fully recapitulate disease. While valuable for early target validation and safety assessments, they may fall short in capturing the full complexity of human conditions.

Selecting the most appropriate model and identifying endpoints that reliably reflect human responses is challenging. Therefore, a deep understanding of the target patient population is crucial, especially for rare diseases with diverse manifestations among patients.

The panel emphasized the importance of cross-functional collaboration between research and clinical teams from the beginning to address translation challenges proactively. However, many companies do not engage the clinical perspectives until later in development and find themselves unprepared when assembling IND-enabling. Integrating clinical viewpoints helps shape research programs in a way that better positions them for success in the transition to clinical testing.

Natural history studies play an important role in derisking this transition by providing insights into human disease progression, phenotypes, and optimal endpoints. As Benit explained, natural history data can help identify homogeneous features within heterogeneous patient populations to design more translatable preclinical models. It also informs understanding of disease variability which regulators will question when evaluating patient eligibility criteria.

Steven highlighted using natural history to evaluate currently used animal models and endpoints. If these don't align well with human disease manifestations, alternative models better recapitulating the clinical course may need to be investigated. Considering the available natural history upfront prevents investing in preclinical work that may not translate.

• A thorough characterisation of the product, especially for potency, using multiple analytical tools
• Robust correlation of potency with clinical efficacy
• Early engagement with regulatory authorities to discuss potency testing

There are several key factors impacting translatability between preclinical models and humans. Animal age at dosing must reflect disease manifestation timing in humans to justify arguments made from animal data to regulators. Route of administration also requires consideration as this may differ dramatically between the animal model and humans. Standardizing assays, methods, and equipment across studies helps ensure data continuity from early research through late-stage trials.

It's important to acknowledge where animal models differ from human disease and consider supporting mechanistic data. While helpful for target validation, disease models often only recapitulate certain aspects, for example seizures without cognitive components. Understanding both similarities and limitations is key when designing integrated development plans reliant on preclinical data. Strategies such as using multiple models in parallel, or 3D tissue models can compensate for individual model shortcomings.

Benit and Steven offered several approaches to proactively de-risk transition-stage programs. Standardizing across studies ensures continuity of results as programs mature. Establishing integrated development plans with clear go/no-go milestones provides framework for the decision making. Pursuing early human data opportunities via alternative regions can validate models quicker. Perfection isn't necessary but envisioning potential pitfalls through "pre-mortems" allows planning contingencies. Keeping strategies simple and focused on translatable endpoints and patient populations will set the best foundation.

As companies expand into clinical development, investments in medical and clinical leadership are important, alongside the research expertise. Integrated development plans provide clear decision points for resource allocation over time. Considering the complexity of biotech development, no internal team possesses all the necessary expertise alone. Involving regulators and external advisors is helpful for specialized functions and shaping guidance. Integrating internal and external insights through stakeholder feedback loops enhances strategic decision-making.

In closing, the experts emphasized that while non-clinical research generates excitement, the ultimate goal is clinical impact. Considering the intended patient populations and clinical endpoint needs from program inception leads to optimal preclinical model selection and study design. Integrated development strategies incorporating cross-functional expertise promote efficient transitions by anticipating the challenges. With clear target profiles and intentional planning informed by diverse data sources including natural history, opportunities arise to streamline clinical translation and maximize the chances of successfully developing therapies for patients.