From Manual Analysis to Scalable Biomarker Workflows
As biomarker studies increase in size and analytical complexity, high-performing analytical methods must also be executed
consistently, reproducibly and efficiently across increasing numbers of samples and studies.
Traditional workflows based on manually coordinated Bash, R or Python scripts can be effective for small or isolated projects.
At production scale, however, operators must coordinate software dependencies, parallel execution, intermediate files, failures
and computational resources. This increases hands-on effort and the risk of inconsistent processing, limited traceability and
unnecessary recalculation
Aliri optimizes workflow scalability using Nextflow to automate and standardize key steps of analytical pipelines. The results?
- Less Manual Intervention
- Consistent Execution
- Faster Cohort Processing
- Improved Traceability
- Scalable Biomarker Analysis
Download the case study to learn how to spend less time coordinating execution and troubleshooting individual runs and more time on data quality, biological interpretation and scientific decision-making.
Case Study: From Divergent Calibration Curves to 100% Sample Acceptance - ASO Quantitation
A sponsor developing a 17-mer 2’-MOE antisense oligonucleotide (ASO) needed a qualified bioanalytical method to quantify drug levels not only in cerebrospinal fluid, but in brain and spinal cord tissue homogenate matrices that are notoriously difficult for oligonucleotide LCMS work.
Rather than treating the divergence as an instrument or suppression problem by default, Aliri scientists systematically ruled out the
more common explanations and evaluated two extraction chemistries in parallel:
- Liquid-liquid extraction (LLE)
- Phenyl SPE with TEAA/DIPEA chemistry
Download the case study to view the results.
Case Study: Actionable, Qualified Spatial Profiling of Pharmacodynamic-Relevant Tumor–Immune Biology in NSCLC
The next evolution of spatial biology is the transition from exploratory imaging toward controlled generation of quantitative spatial metrics associated with pharmacodynamic-relevant tumor biology.
In non-small cell lung cancer (NSCLC) immuno-oncology studies, the challenge is not simply identifying immune cells within the tissue, but understanding whether their spatial organization reflects biologically meaningful mechanisms associated with therapeutic activity, immune exclusion, suppression, or cytotoxic engagement.
This case study illustrates how a qualified multiplex spatial panel can be deployed on FFPE NSCLC tissue to generate reproducible and biologically interpretable spatial metrics associated with pharmacodynamic (PD)-relevant tumor–immune biology. The objective: implementation of a controlled multiplex framework capable of moving beyond descriptive multiplex imaging toward more quantitative spatial characterization of immune biology.
Download the case study below.