Risk assessment and management are complex processes intended to protect human health by identifying product-related toxicities (i.e., hazards); determining dose-response relations for a product within relevant biological systems; and assessing exposure levels to characterize potential risks. These assessments may be conducted via traditional frameworks using data from in vitro and in vivo studies, as well as novel frameworks using pharmacokinetic and pharmacodynamic model-based predictions.
Data used to assess risks should be leveraged to understand both local (e.g., at site of injection) and systemic toxicities. These data may be collected under conditions that mimic contexts in which a given product is intended for use, as well as those that are not. Across the mentioned frameworks, risk assessment must be based on clinically relevant information and include exposure data to help ensure the proper management of potential local and systemic toxicities.
In traditional frameworks, risks may be characterized by evaluating the mechanism/mode of action, onset and offset, duration, and incidence of clinically relevant hazards. These frameworks put risks into perspective based on the target organs of toxicity identified and the exposure levels at which hazards occurred under conditions that mimic, as closely as possible, the intended use of the product under development.
For example, general toxicology studies should be designed to use routes of administration that are the same as those planned for the clinic, as well as concentrations, doses (i.e., human equivalent doses), and treatment durations that are equal to or greater than those intended for the clinic. This approach helps to ensure that the local and systemic toxicities identified are clinically relevant.
The local and systemic exposure levels at which toxicities occur are used to identify the most sensitive species, which is typically deemed the most relevant species unless justified otherwise. Failure to follow these approaches may result in the collection of data that are not adequate to support the safety of the proposed clinical protocol, which could lead to a regulatory agency not allowing a clinical study to proceed.
Modeling approaches may be used within a framework that enables the extrapolation of data from preclinical species to humans using established physiological differences. These approaches also allow the prediction of hazard and exposure in scenarios not explicitly measured in preclinical studies.
Modeling frameworks can be incorporated at a very early stage in the development pipeline and can also assist in determining which set of preclinical studies would be most clinically relevant. These frameworks can also use in vitro and in silico data to support risk assessment when nonclinical data are not available to guide the selection of safe clinical dose levels.
There are various scenarios in which these frameworks may be highly valuable. These include occasions where the available nonclinical data have limited relevance to clinical outcomes or when test products required to be used in nonclinical studies are not equivalent to the final product to which humans may be exposed.
Manage Clinical Risks to Ensure Patient Safety
Although traditional and modeling frameworks are intended for characterizing and putting potential risks into perspective, each is critical in managing potential clinical risks within relevant contexts to ensure patient safety. Importantly, these frameworks help to manage clinical risks by leveraging nonclinical, invitro, and in silico data to inform the selection of safe clinical doses, as well as adequate inclusion/exclusion criteria.
Assessing and managing risks can be a complicated process requiring various forms of data and different perspectives from experts with experience evaluating new molecular and established chemical entities while simultaneously collaborating with other experts to manage them in various clinical contexts.
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