In Silico Approaches for Addressing Challenges in CNS Radiopharmaceutical Design

Abstract

Positron emission tomography (PET) is a highly sensitive and versatile molecular imaging modality that leverages radiolabeled molecules, known as radiotracers, to interrogate biochemical processes such as metabolism, enzymatic activity, and receptor expression. The ability to probe specific molecular and cellular events longitudinally in a noninvasive manner makes PET imaging a particularly powerful technique for studying the central nervous system (CNS) in both health and disease. Unfortunately, developing and translating a single CNS PET tracer for clinical use is typically an extremely resource-intensive endeavor, often requiring synthesis and evaluation of numerous candidate molecules. While existing in vitro methods are beginning to address the challenge of derisking molecules prior to costly in vivo PET studies, most require a significant investment of resources and possess substantial limitations. In the context of CNS drug development, significant time and resources have been invested into the development and optimization of computational methods, particularly involving machine learning, to streamline the design of better CNS therapeutics. However, analogous efforts developed and validated for CNS radiotracer design are conspicuously limited. In this Perspective, we overview the requirements and challenges of CNS PET tracer design, survey the most promising computational methods for in silico CNS drug design, and bridge these two areas by discussing the potential applications and impact of computational design tools in CNS radiotracer design.

Keywords: Positron emission tomography; in silico; machine learning; methodology; radiochemistry; radiotracer design.

Figure 1:

The need for in vivo preclinical imaging studies, low throughput (screening 1–5 molecules at a time), and the iterative nature of tracer design pose hurdles to the clinical translation process. Graphic created in part with Biorender.com.

Figure 2:

The BBB is comprised of the endothelial cells making up the brain microvasculature which are interconnected via tight junctions, decorated with many influx and efflux transporters, and surrounded by a combination of basal laminae, pericytes, and extended astrocyte foot processes. This complex system presents multiple challenges in CNS tracer design. Graphic created with Biorender.com.

Figure 3:

Current in silico approaches in CNS tracer design categorize molecules as either successful or unsuccessful, leading to either termination of development or radiosynthesis and in vivo evaluation. Reinforcement learning approaches will allow for iterative in silico optimization of compounds categorized as unsuccessful, generating novel candidates with high likelihood of success.