SapiensBio Inc. is a research-oriented company with a vision to advance the understanding of biological complexity through its SAPIENTIA platform, which facilitates the development of innovative treatments to address unmet medical needs. SAPIENTIA provides an effective approach for identifying and validating new target genes through the comprehensive analysis of genomic data from normal and diseased tissue samples. It evaluates context-dependent biological interactions between genes and their gene regulatory networks (GRNs) under both normal and pathological conditions, generating a list of potential candidate genes. These candidates are prioritized based on their novelty, druggability, and feasibility for drug discovery and development. The selected candidates are then experimentally validated using appropriate biological methods and models to confirm their involvement in the pathology of interest.

Using SAPIENTIA 1.0, we have identified ARPC2 as a novel target in idiopathic pulmonary fibrosis (IPF) through single-cell RNA genomic datasets obtained from the lung tissues of IPF patients. We have experimentally explored its novel mechanism of action (MOA) in lung fibrosis and generated the novel ARPC2 modulator for IPF treatment. In vitro, in vivo, and ex vivo studies have demonstrated the robust therapeutic efficacy of this modulator in human lung fibroblasts, murine bleomycin models, and human precision-cut lung slices, respectively. The ARPC2 program, currently under IND-enabling studies, has been successfully conducted since April 2024 and is scheduled for completion in April 2025.

During the development of the ARPC2 modulator, we identified areas for improvement in the SAPIENTIA 1.0 platform for several reasons. First, single-cell RNA sequencing (scRNA-seq) is a useful tool as it allows us to understand the biological complexity of diverse cell populations, as well as the dynamics of development and differentiation at the single-cell level. However, dissociating tissues into single cells inevitably results in the loss of spatial context, such as tissue structure and local gene regulation, which are critical for a deeper understanding of cell-cell communications (CCCs). Second, despite the successful identification of ARPC2 as a novel IPF target, SAPIENTIA 1.0 does not incorporate clinical information about donors in its single-cell genomic analyses. This is because it primarily uses single-cell datasets from public databases, which often lack such information. As a result, it is difficult to determine how an individual’s genetic makeup influences their response to drug treatments.

To overcome these limitations, SAPIENTIA 2.0, which is currently under development, will consist of two systems:

Single-cell tissue map (ScMap) with spatial transcriptomics (ST) [ScMap-ST].

Single-cell genomics using precision-cut tissue slices (PCTSes) [SCPCTS].

ScMap-ST utilizes heterogeneous graph methods to identify diverse cellular states and their associated gene modules by integrating multimodal data and known gene-gene

interactions/relations. This approach infers intracellular gene regulatory networks (GRNs), intercellular CCCs, and ligand-receptor pairs within the context of disease. It then generates multiple layers based on regional pathological progressions and suggests a set of causative or peripheral genes associated with distinct spatial units. These genes, after internal review and refinement, are considered as potential drug targets. This approach helps us better understand the spatial and local dynamics of tissue composition and structure during disease progression, offering insights that are not easily accessible with conventional single-cell methods. Ultimately, it enables us to pinpoint drug targets more accurately than before.

ScPCTS is a drug development module that integrates personalized genomics and PCTS to predict potential clinical outcomes of a drug candidate in future applications. While developing the ARPC2 modulator, we have utilized lung PCTSes from healthy and lung-fibrotic donors as ex vivo models to experimentally and indirectly evaluate its therapeutic efficacy in humans. PCTSes generally offer improved predictive power for human applications because they bridge the gap between in vitro and in vivo models. Since PCTSes are derived from genetically diverse individuals, they can also serve as powerful tools for understanding individual variations in drug responses when combined with genomic approaches.

First, a drug of interest is subjected to biological evaluations using PCTSes from individual donors, employing experimental methods such as quantitative real-time PCR, ELISA, and immunohistology, among others. Some PCTSes are further analyzed using single-cell techniques to capture personal genomic signatures at single-nucleotide resolution. Next, the experimental results and genomic signatures are integrated and statistically modeled to identify genomic features that contribute to variations in drug responses. In this way, our ScPCTS platform can suggest promising biomarkers for drug responses. This provides a valuable opportunity to predict the likelihood of success in clinical trials by enabling us to assess the genetic differences of future trial participants in advance.