Targets

In brief: I use diverse data to collaboratively select drug targets and build out concept sheets.

Discovering interesting animal biology is only the first step toward an actionable drug target. In my work at Fauna Bio, I collaborated to contextualize internal bioinformatic data with a broad range of supporting information crucial for translation, incorporating facets such as human genomics, druggability, homology, safety concerns, intellectual property constraints, and proofs-of-concept in model organisms. I personally pitched targets to a top 5 pharma company and contributed to drug development concept sheets.

As part of the target selection process, I developed a comprehensive foundation in the biology of metabolically active tissues and systems that govern the development of obesity and its comorbidities. To fully characterize target prospects, I deeply explored and reanalyzed public data. In addition, I learned and leveraged new tools tailored to the target modalities: in one case study, I developed a workflow to predict peptide hormones, their metabolic activity, and their potential targets across the human proteome.

To facilitate target selection by the rest of my team, I improved and extended the internal platform. I developed reusable pipelines to integrate the results of bioinformatic analyses into existing dashboards, introduced new tools to substantially speed up the visualization of transcriptomic data, and collaborated with quantitative and experimental scientists to simplify data presentation and interpretation. This feedback and responsive, rapid prototyping helped speed up target selection and ensure that non-experts could contribute to discovery and interpretation.

The selection of plausible drug targets involves the aggregation and judgment of diverse, qualitative data, and presents an intriguing use case for automation. In addition to user-centered improvements, I collaborated with the developers of the Fauna Brain AI platform to refine target scoring and develop analyses adapted to large language model workflows.

Finally, the strategy for target selection begins with data collection: the experiment needs to capture the cell populations of highest therapeutic relevance, and provide enough resolution to detect differences across conditions. Subtle technical choices can improve or preclude biological insights. I have experience designing experiments based on existing pilot experiments, biological constraints, and cost, and I have developed simulations and user-friendly tools for experiment power analysis.

Rigorous experimental design, including formal power analysis, is a cornerstone of reproducible RNA sequencing (RNA-seq) research. The design of RNA-seq experiments requires computing the minimum sample number required to identify an effect of a particular size at a predefined significance level. Ideally, the statistical test used for the analysis of experimental data should match the test used for sample size determination; however, few tools use the assumptions of the popular differential expression testing framework DESeq2, and most opt for simulation-based rather than analytical approaches. Grounded in the DESeq2 model framework, we derive sample size requirements for both single-cell and bulk RNA-seq experiments delivered as a web-based tool for power analysis, DEPower, available at https://poweranalysis-fb.streamlit.app/ that makes rigorous RNA-seq study design accessible to all researchers.

References

2026